03/05 02:22:07 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.5 (default, Sep 4 2020, 07:30:14) [GCC 7.3.0] CUDA available: False numpy_random_seed: 428316466 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.8.0a0+56b43f4 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - Build settings: BLAS_INFO=generic, BUILD_TYPE=Release, CXX_COMPILER=/opt/buildtools/gcc-7.3.0/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=generic, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.8.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_EIGEN_FOR_BLAS=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=OFF, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, OpenCV: 4.7.0 MMEngine: 0.5.0 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None deterministic: False Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 03/05 02:22:07 - mmengine - INFO - Config: model = dict( type='ImageClassifier', backbone=dict( type='SEResNet', depth=50, num_stages=4, out_indices=(3, ), style='pytorch'), neck=dict(type='GlobalAveragePooling'), head=dict( type='LinearClsHead', num_classes=1000, in_channels=2048, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=(1, 5))) dataset_type = 'ImageNet' data_preprocessor = dict( num_classes=1000, mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', scale=224, backend='pillow'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='PackClsInputs') ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'), dict(type='CenterCrop', crop_size=224), dict(type='PackClsInputs') ] train_dataloader = dict( pin_memory=True, persistent_workers=False, collate_fn=dict(type='default_collate'), batch_size=32, num_workers=64, dataset=dict( type='ImageNet', data_root='data/imagenet', data_prefix='train', pipeline=[ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', scale=224, backend='pillow'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='PackClsInputs') ]), sampler=dict(type='DefaultSampler', shuffle=True)) val_dataloader = dict( pin_memory=True, persistent_workers=False, collate_fn=dict(type='default_collate'), batch_size=32, num_workers=64, dataset=dict( type='ImageNet', data_root='data/imagenet', ann_file='meta/val.txt', data_prefix='val', pipeline=[ dict(type='LoadImageFromFile'), dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'), dict(type='CenterCrop', crop_size=224), dict(type='PackClsInputs') ]), sampler=dict(type='DefaultSampler', shuffle=False)) val_evaluator = dict(type='Accuracy', topk=(1, 5)) test_dataloader = dict( pin_memory=True, persistent_workers=False, collate_fn=dict(type='default_collate'), batch_size=32, num_workers=64, dataset=dict( type='ImageNet', data_root='data/imagenet', ann_file='meta/val.txt', data_prefix='val', pipeline=[ dict(type='LoadImageFromFile'), dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'), dict(type='CenterCrop', crop_size=224), dict(type='PackClsInputs') ]), sampler=dict(type='DefaultSampler', shuffle=False)) test_evaluator = dict(type='Accuracy', topk=(1, 5)) optim_wrapper = dict( optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)) param_scheduler = dict( type='MultiStepLR', by_epoch=True, milestones=[40, 80, 120], gamma=0.1) train_cfg = dict(by_epoch=True, max_epochs=140, val_interval=1) val_cfg = dict() test_cfg = dict() auto_scale_lr = dict(base_batch_size=256) default_scope = 'mmcls' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=100), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='VisualizationHook', enable=False)) env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='ClsVisualizer', vis_backends=[dict(type='LocalVisBackend')]) log_level = 'INFO' load_from = None resume = False randomness = dict(seed=None, deterministic=False) launcher = 'pytorch' work_dir = './work_dirs/seresnet50_8xb32_in1k' 03/05 02:22:07 - mmengine - WARNING - The "visualizer" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/05 02:22:07 - mmengine - WARNING - The "vis_backend" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/05 02:22:07 - mmengine - WARNING - The "model" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/05 02:22:12 - mmengine - WARNING - The "hook" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/05 02:22:12 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (BELOW_NORMAL) LoggerHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val_epoch: (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) VisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_test_epoch: (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) VisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 03/05 02:22:12 - mmengine - WARNING - The "dataset" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/05 02:22:12 - mmengine - WARNING - The "transform" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. GradScaler options are: init_scale : 65536.0 growth_factor : 2.0 backoff_factor : 0.5 growth_interval : 2000 dynamic : True enabled : True GradScaler options are: init_scale : 65536.0 growth_factor : 2.0 backoff_factor : 0.5 growth_interval : 2000 dynamic : True enabled : True GradScaler options are: init_scale : 65536.0 growth_factor : 2.0 backoff_factor : 0.5 growth_interval : 2000 dynamic : True enabled : True GradScaler options are: init_scale : 65536.0 growth_factor : 2.0 backoff_factor : 0.5 growth_interval : 2000 dynamic : True enabled : True GradScaler options are: init_scale : 65536.0 growth_factor : 2.0 backoff_factor : 0.5 growth_interval : 2000 dynamic : True enabled : True GradScaler options are: init_scale : 65536.0 growth_factor : 2.0 backoff_factor : 0.5 growth_interval : 2000 dynamic : True enabled : True 03/05 02:22:40 - mmengine - WARNING - The "data sampler" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/05 02:22:40 - mmengine - WARNING - The "optimizer wrapper constructor" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/05 02:22:40 - mmengine - WARNING - The "optimizer" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/05 02:22:40 - mmengine - WARNING - The "optimizer_wrapper" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. GradScaler options are: init_scale : 65536.0 growth_factor : 2.0 backoff_factor : 0.5 growth_interval : 2000 dynamic : True enabled : True 03/05 02:22:40 - mmengine - WARNING - The "parameter scheduler" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. GradScaler options are: init_scale : 65536.0 growth_factor : 2.0 backoff_factor : 0.5 growth_interval : 2000 dynamic : True enabled : True 03/05 02:22:40 - mmengine - WARNING - The "metric" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/05 02:22:50 - mmengine - WARNING - The "weight initializer" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/05 02:23:03 - mmengine - INFO - Checkpoints will be saved to /ubuntu-lv2/lml/mmcv_2.x/mmclassification/work_dirs/seresnet50_8xb32_in1k. [W reducer.cpp:401] Warning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance. grad.sizes() = [1000, 2048], strides() = [2048, 1] bucket_view.sizes() = [2048000], strides() = [1] (function operator()) [W reducer.cpp:401] Warning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance. grad.sizes() = [1000, 2048], strides() = [2048, 1] bucket_view.sizes() = [2048000], strides() = [1] (function operator()) [W reducer.cpp:401] Warning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance. grad.sizes() = [1000, 2048], strides() = [2048, 1] bucket_view.sizes() = [2048000], strides() = [1] (function operator()) [W reducer.cpp:401] Warning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance. grad.sizes() = [1000, 2048], strides() = [2048, 1] bucket_view.sizes() = [2048000], strides() = [1] (function operator()) [W reducer.cpp:401] Warning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance. grad.sizes() = [1000, 2048], strides() = [2048, 1] bucket_view.sizes() = [2048000], strides() = [1] (function operator()) [W reducer.cpp:401] Warning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance. grad.sizes() = [1000, 2048], strides() = [2048, 1] bucket_view.sizes() = [2048000], strides() = [1] (function operator()) [W reducer.cpp:401] Warning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance. grad.sizes() = [1000, 2048], strides() = [2048, 1] bucket_view.sizes() = [2048000], strides() = [1] (function operator()) [W reducer.cpp:401] Warning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance. grad.sizes() = [1000, 2048], strides() = [2048, 1] bucket_view.sizes() = [2048000], strides() = [1] (function operator()) 03/05 02:24:58 - mmengine - INFO - Epoch(train) [1][ 100/5005] lr: 1.0000e-01 eta: 9 days, 7:39:51 time: 0.2187 data_time: 0.0011 loss: 6.7140 03/05 02:25:20 - mmengine - INFO - Epoch(train) [1][ 200/5005] lr: 1.0000e-01 eta: 5 days, 13:15:30 time: 0.2235 data_time: 0.0016 loss: 6.5231 03/05 02:25:42 - mmengine - INFO - Epoch(train) [1][ 300/5005] lr: 1.0000e-01 eta: 4 days, 7:02:50 time: 0.2167 data_time: 0.0013 loss: 6.4704 03/05 02:26:04 - mmengine - INFO - Epoch(train) [1][ 400/5005] lr: 1.0000e-01 eta: 3 days, 15:57:33 time: 0.2194 data_time: 0.0012 loss: 6.1741 03/05 02:26:27 - mmengine - INFO - Epoch(train) [1][ 500/5005] lr: 1.0000e-01 eta: 3 days, 7:19:49 time: 0.2523 data_time: 0.0013 loss: 6.1579 03/05 02:26:49 - mmengine - INFO - Epoch(train) [1][ 600/5005] lr: 1.0000e-01 eta: 3 days, 1:25:08 time: 0.2195 data_time: 0.0013 loss: 6.0910 03/05 02:27:11 - mmengine - INFO - Epoch(train) [1][ 700/5005] lr: 1.0000e-01 eta: 2 days, 21:01:11 time: 0.2186 data_time: 0.0012 loss: 6.1374 03/05 02:27:33 - mmengine - INFO - Epoch(train) [1][ 800/5005] lr: 1.0000e-01 eta: 2 days, 17:42:04 time: 0.2185 data_time: 0.0013 loss: 5.9489 03/05 02:27:56 - mmengine - INFO - Epoch(train) [1][ 900/5005] lr: 1.0000e-01 eta: 2 days, 15:14:10 time: 0.2181 data_time: 0.0013 loss: 5.9889 03/05 02:28:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 02:28:18 - mmengine - INFO - Epoch(train) [1][1000/5005] lr: 1.0000e-01 eta: 2 days, 13:17:34 time: 0.2369 data_time: 0.0014 loss: 5.7500 03/05 02:28:40 - mmengine - INFO - Epoch(train) [1][1100/5005] lr: 1.0000e-01 eta: 2 days, 11:34:54 time: 0.2164 data_time: 0.0012 loss: 5.8649 03/05 02:29:02 - mmengine - INFO - Epoch(train) [1][1200/5005] lr: 1.0000e-01 eta: 2 days, 10:09:27 time: 0.2178 data_time: 0.0014 loss: 5.7443 03/05 02:29:24 - mmengine - INFO - Epoch(train) [1][1300/5005] lr: 1.0000e-01 eta: 2 days, 8:56:32 time: 0.2182 data_time: 0.0016 loss: 5.6610 03/05 02:29:47 - mmengine - INFO - Epoch(train) [1][1400/5005] lr: 1.0000e-01 eta: 2 days, 8:01:15 time: 0.2190 data_time: 0.0015 loss: 5.7088 03/05 02:30:09 - mmengine - INFO - Epoch(train) [1][1500/5005] lr: 1.0000e-01 eta: 2 days, 7:07:57 time: 0.2190 data_time: 0.0013 loss: 5.6327 03/05 02:30:31 - mmengine - INFO - Epoch(train) [1][1600/5005] lr: 1.0000e-01 eta: 2 days, 6:21:19 time: 0.2227 data_time: 0.0012 loss: 5.6350 03/05 02:30:53 - mmengine - INFO - Epoch(train) [1][1700/5005] lr: 1.0000e-01 eta: 2 days, 5:40:34 time: 0.2195 data_time: 0.0013 loss: 5.3930 03/05 02:31:15 - mmengine - INFO - Epoch(train) [1][1800/5005] lr: 1.0000e-01 eta: 2 days, 5:05:54 time: 0.2241 data_time: 0.0013 loss: 5.4715 03/05 02:31:38 - mmengine - INFO - Epoch(train) [1][1900/5005] lr: 1.0000e-01 eta: 2 days, 4:36:14 time: 0.2267 data_time: 0.0013 loss: 5.2702 03/05 02:32:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 02:32:00 - mmengine - INFO - Epoch(train) [1][2000/5005] lr: 1.0000e-01 eta: 2 days, 4:06:40 time: 0.2273 data_time: 0.0012 loss: 5.5003 03/05 02:32:22 - mmengine - INFO - Epoch(train) [1][2100/5005] lr: 1.0000e-01 eta: 2 days, 3:39:19 time: 0.2208 data_time: 0.0013 loss: 5.2280 03/05 02:32:44 - mmengine - INFO - Epoch(train) [1][2200/5005] lr: 1.0000e-01 eta: 2 days, 3:15:19 time: 0.2168 data_time: 0.0013 loss: 5.2349 03/05 02:33:07 - mmengine - INFO - Epoch(train) [1][2300/5005] lr: 1.0000e-01 eta: 2 days, 2:55:04 time: 0.2200 data_time: 0.0013 loss: 5.2188 03/05 02:33:29 - mmengine - INFO - Epoch(train) [1][2400/5005] lr: 1.0000e-01 eta: 2 days, 2:34:31 time: 0.2208 data_time: 0.0014 loss: 5.1600 03/05 02:33:51 - mmengine - INFO - Epoch(train) [1][2500/5005] lr: 1.0000e-01 eta: 2 days, 2:15:02 time: 0.2170 data_time: 0.0013 loss: 5.0983 03/05 02:34:13 - mmengine - INFO - Epoch(train) [1][2600/5005] lr: 1.0000e-01 eta: 2 days, 1:58:37 time: 0.2214 data_time: 0.0013 loss: 5.0382 03/05 02:34:35 - mmengine - INFO - Epoch(train) [1][2700/5005] lr: 1.0000e-01 eta: 2 days, 1:43:38 time: 0.2180 data_time: 0.0014 loss: 5.0133 03/05 02:34:58 - mmengine - INFO - Epoch(train) [1][2800/5005] lr: 1.0000e-01 eta: 2 days, 1:29:09 time: 0.2191 data_time: 0.0014 loss: 5.0403 03/05 02:35:20 - mmengine - INFO - Epoch(train) [1][2900/5005] lr: 1.0000e-01 eta: 2 days, 1:14:19 time: 0.2218 data_time: 0.0014 loss: 5.1012 03/05 02:35:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 02:35:42 - mmengine - INFO - Epoch(train) [1][3000/5005] lr: 1.0000e-01 eta: 2 days, 1:01:33 time: 0.2201 data_time: 0.0013 loss: 4.9289 03/05 02:36:04 - mmengine - INFO - Epoch(train) [1][3100/5005] lr: 1.0000e-01 eta: 2 days, 0:49:55 time: 0.2394 data_time: 0.0014 loss: 4.7407 03/05 02:36:27 - mmengine - INFO - Epoch(train) [1][3200/5005] lr: 1.0000e-01 eta: 2 days, 0:39:32 time: 0.2213 data_time: 0.0015 loss: 4.7201 03/05 02:36:49 - mmengine - INFO - Epoch(train) [1][3300/5005] lr: 1.0000e-01 eta: 2 days, 0:28:36 time: 0.2170 data_time: 0.0014 loss: 4.6712 03/05 02:37:11 - mmengine - INFO - Epoch(train) [1][3400/5005] lr: 1.0000e-01 eta: 2 days, 0:18:52 time: 0.2236 data_time: 0.0015 loss: 4.7851 03/05 02:37:33 - mmengine - INFO - Epoch(train) [1][3500/5005] lr: 1.0000e-01 eta: 2 days, 0:09:50 time: 0.2236 data_time: 0.0014 loss: 4.7370 03/05 02:37:56 - mmengine - INFO - Epoch(train) [1][3600/5005] lr: 1.0000e-01 eta: 2 days, 0:02:00 time: 0.2212 data_time: 0.0015 loss: 4.7229 03/05 02:38:18 - mmengine - INFO - Epoch(train) [1][3700/5005] lr: 1.0000e-01 eta: 1 day, 23:53:02 time: 0.2181 data_time: 0.0014 loss: 4.7812 03/05 02:38:40 - mmengine - INFO - Epoch(train) [1][3800/5005] lr: 1.0000e-01 eta: 1 day, 23:44:30 time: 0.2218 data_time: 0.0014 loss: 4.7173 03/05 02:39:02 - mmengine - INFO - Epoch(train) [1][3900/5005] lr: 1.0000e-01 eta: 1 day, 23:37:14 time: 0.2218 data_time: 0.0014 loss: 4.6630 03/05 02:39:25 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 02:39:25 - mmengine - INFO - Epoch(train) [1][4000/5005] lr: 1.0000e-01 eta: 1 day, 23:29:59 time: 0.2189 data_time: 0.0013 loss: 4.5074 03/05 02:39:47 - mmengine - INFO - Epoch(train) [1][4100/5005] lr: 1.0000e-01 eta: 1 day, 23:23:46 time: 0.2181 data_time: 0.0014 loss: 4.6875 03/05 02:40:10 - mmengine - INFO - Epoch(train) [1][4200/5005] lr: 1.0000e-01 eta: 1 day, 23:17:14 time: 0.2367 data_time: 0.0014 loss: 4.3943 03/05 02:40:32 - mmengine - INFO - Epoch(train) [1][4300/5005] lr: 1.0000e-01 eta: 1 day, 23:10:22 time: 0.2165 data_time: 0.0014 loss: 4.5779 03/05 02:40:54 - mmengine - INFO - Epoch(train) [1][4400/5005] lr: 1.0000e-01 eta: 1 day, 23:05:07 time: 0.2363 data_time: 0.0013 loss: 4.6246 03/05 02:41:17 - mmengine - INFO - Epoch(train) [1][4500/5005] lr: 1.0000e-01 eta: 1 day, 22:59:52 time: 0.2172 data_time: 0.0013 loss: 4.2998 03/05 02:41:39 - mmengine - INFO - Epoch(train) [1][4600/5005] lr: 1.0000e-01 eta: 1 day, 22:54:33 time: 0.2230 data_time: 0.0014 loss: 4.3816 03/05 02:42:01 - mmengine - INFO - Epoch(train) [1][4700/5005] lr: 1.0000e-01 eta: 1 day, 22:49:15 time: 0.2283 data_time: 0.0015 loss: 4.3432 03/05 02:42:23 - mmengine - INFO - Epoch(train) [1][4800/5005] lr: 1.0000e-01 eta: 1 day, 22:44:04 time: 0.2182 data_time: 0.0013 loss: 4.2198 03/05 02:42:48 - mmengine - INFO - Epoch(train) [1][4900/5005] lr: 1.0000e-01 eta: 1 day, 22:43:17 time: 0.2989 data_time: 0.0011 loss: 4.1790 03/05 02:43:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 02:43:18 - mmengine - INFO - Epoch(train) [1][5000/5005] lr: 1.0000e-01 eta: 1 day, 22:56:57 time: 0.3014 data_time: 0.0010 loss: 4.1511 03/05 02:43:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 02:43:58 - mmengine - INFO - Saving checkpoint at 1 epochs 03/05 02:44:21 - mmengine - INFO - Epoch(val) [1][100/196] eta: 0:00:21 time: 0.0178 data_time: 0.0002 03/05 02:44:48 - mmengine - INFO - Epoch(val) [1][196/196] accuracy/top1: 16.9240 accuracy/top5: 37.4880 03/05 02:45:20 - mmengine - INFO - Epoch(train) [2][ 100/5005] lr: 1.0000e-01 eta: 2 days, 0:37:38 time: 0.2193 data_time: 0.0013 loss: 4.2515 03/05 02:45:42 - mmengine - INFO - Epoch(train) [2][ 200/5005] lr: 1.0000e-01 eta: 2 days, 0:30:07 time: 0.2184 data_time: 0.0012 loss: 4.2579 03/05 02:46:05 - mmengine - INFO - Epoch(train) [2][ 300/5005] lr: 1.0000e-01 eta: 2 days, 0:24:02 time: 0.2200 data_time: 0.0014 loss: 4.3276 03/05 02:46:27 - mmengine - INFO - Epoch(train) [2][ 400/5005] lr: 1.0000e-01 eta: 2 days, 0:17:11 time: 0.2199 data_time: 0.0014 loss: 4.3100 03/05 02:46:49 - mmengine - INFO - Epoch(train) [2][ 500/5005] lr: 1.0000e-01 eta: 2 days, 0:12:08 time: 0.2211 data_time: 0.0013 loss: 4.2338 03/05 02:47:12 - mmengine - INFO - Epoch(train) [2][ 600/5005] lr: 1.0000e-01 eta: 2 days, 0:06:02 time: 0.2202 data_time: 0.0014 loss: 4.0728 03/05 02:47:34 - mmengine - INFO - Epoch(train) [2][ 700/5005] lr: 1.0000e-01 eta: 2 days, 0:00:20 time: 0.2184 data_time: 0.0013 loss: 4.0595 03/05 02:47:56 - mmengine - INFO - Epoch(train) [2][ 800/5005] lr: 1.0000e-01 eta: 1 day, 23:54:06 time: 0.2221 data_time: 0.0015 loss: 4.0873 03/05 02:48:19 - mmengine - INFO - Epoch(train) [2][ 900/5005] lr: 1.0000e-01 eta: 1 day, 23:49:51 time: 0.2357 data_time: 0.0015 loss: 3.9767 03/05 02:48:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 02:48:41 - mmengine - INFO - Epoch(train) [2][1000/5005] lr: 1.0000e-01 eta: 1 day, 23:44:08 time: 0.2188 data_time: 0.0016 loss: 4.1253 03/05 02:49:03 - mmengine - INFO - Epoch(train) [2][1100/5005] lr: 1.0000e-01 eta: 1 day, 23:38:59 time: 0.2182 data_time: 0.0015 loss: 4.0857 03/05 02:49:25 - mmengine - INFO - Epoch(train) [2][1200/5005] lr: 1.0000e-01 eta: 1 day, 23:33:40 time: 0.2212 data_time: 0.0015 loss: 4.3163 03/05 02:49:47 - mmengine - INFO - Epoch(train) [2][1300/5005] lr: 1.0000e-01 eta: 1 day, 23:28:43 time: 0.2251 data_time: 0.0015 loss: 4.1084 03/05 02:50:10 - mmengine - INFO - Epoch(train) [2][1400/5005] lr: 1.0000e-01 eta: 1 day, 23:24:09 time: 0.2176 data_time: 0.0013 loss: 3.9953 03/05 02:50:32 - mmengine - INFO - Epoch(train) [2][1500/5005] lr: 1.0000e-01 eta: 1 day, 23:19:16 time: 0.2180 data_time: 0.0017 loss: 3.8468 03/05 02:50:54 - mmengine - INFO - Epoch(train) [2][1600/5005] lr: 1.0000e-01 eta: 1 day, 23:14:32 time: 0.2192 data_time: 0.0016 loss: 3.9134 03/05 02:51:16 - mmengine - INFO - Epoch(train) [2][1700/5005] lr: 1.0000e-01 eta: 1 day, 23:10:11 time: 0.2171 data_time: 0.0014 loss: 3.7631 03/05 02:51:38 - mmengine - INFO - Epoch(train) [2][1800/5005] lr: 1.0000e-01 eta: 1 day, 23:06:20 time: 0.2322 data_time: 0.0014 loss: 4.0044 03/05 02:52:01 - mmengine - INFO - Epoch(train) [2][1900/5005] lr: 1.0000e-01 eta: 1 day, 23:02:12 time: 0.2218 data_time: 0.0014 loss: 3.8208 03/05 02:52:22 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 02:52:23 - mmengine - INFO - Epoch(train) [2][2000/5005] lr: 1.0000e-01 eta: 1 day, 22:57:58 time: 0.2184 data_time: 0.0015 loss: 3.8772 03/05 02:52:45 - mmengine - INFO - Epoch(train) [2][2100/5005] lr: 1.0000e-01 eta: 1 day, 22:53:35 time: 0.2259 data_time: 0.0015 loss: 4.0193 03/05 02:53:07 - mmengine - INFO - Epoch(train) [2][2200/5005] lr: 1.0000e-01 eta: 1 day, 22:49:55 time: 0.2156 data_time: 0.0014 loss: 4.0559 03/05 02:53:29 - mmengine - INFO - Epoch(train) [2][2300/5005] lr: 1.0000e-01 eta: 1 day, 22:46:11 time: 0.2157 data_time: 0.0014 loss: 3.8118 03/05 02:53:51 - mmengine - INFO - Epoch(train) [2][2400/5005] lr: 1.0000e-01 eta: 1 day, 22:42:19 time: 0.2192 data_time: 0.0013 loss: 3.6472 03/05 02:54:13 - mmengine - INFO - Epoch(train) [2][2500/5005] lr: 1.0000e-01 eta: 1 day, 22:38:22 time: 0.2251 data_time: 0.0014 loss: 3.7490 03/05 02:54:35 - mmengine - INFO - Epoch(train) [2][2600/5005] lr: 1.0000e-01 eta: 1 day, 22:34:49 time: 0.2244 data_time: 0.0016 loss: 3.7474 03/05 02:54:58 - mmengine - INFO - Epoch(train) [2][2700/5005] lr: 1.0000e-01 eta: 1 day, 22:31:42 time: 0.2166 data_time: 0.0015 loss: 3.6581 03/05 02:55:20 - mmengine - INFO - Epoch(train) [2][2800/5005] lr: 1.0000e-01 eta: 1 day, 22:28:06 time: 0.2183 data_time: 0.0015 loss: 3.8089 03/05 02:55:42 - mmengine - INFO - Epoch(train) [2][2900/5005] lr: 1.0000e-01 eta: 1 day, 22:24:38 time: 0.2246 data_time: 0.0014 loss: 3.6424 03/05 02:56:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 02:56:04 - mmengine - INFO - Epoch(train) [2][3000/5005] lr: 1.0000e-01 eta: 1 day, 22:21:33 time: 0.2183 data_time: 0.0014 loss: 3.6124 03/05 02:56:27 - mmengine - INFO - Epoch(train) [2][3100/5005] lr: 1.0000e-01 eta: 1 day, 22:18:50 time: 0.2326 data_time: 0.0014 loss: 3.6496 03/05 02:56:49 - mmengine - INFO - Epoch(train) [2][3200/5005] lr: 1.0000e-01 eta: 1 day, 22:15:47 time: 0.2217 data_time: 0.0014 loss: 3.7050 03/05 02:57:11 - mmengine - INFO - Epoch(train) [2][3300/5005] lr: 1.0000e-01 eta: 1 day, 22:12:41 time: 0.2371 data_time: 0.0013 loss: 3.6160 03/05 02:57:33 - mmengine - INFO - Epoch(train) [2][3400/5005] lr: 1.0000e-01 eta: 1 day, 22:09:49 time: 0.2218 data_time: 0.0015 loss: 3.8366 03/05 02:57:55 - mmengine - INFO - Epoch(train) [2][3500/5005] lr: 1.0000e-01 eta: 1 day, 22:06:52 time: 0.2300 data_time: 0.0015 loss: 3.7904 03/05 02:58:17 - mmengine - INFO - Epoch(train) [2][3600/5005] lr: 1.0000e-01 eta: 1 day, 22:04:08 time: 0.2203 data_time: 0.0014 loss: 3.7208 03/05 02:58:40 - mmengine - INFO - Epoch(train) [2][3700/5005] lr: 1.0000e-01 eta: 1 day, 22:01:30 time: 0.2300 data_time: 0.0013 loss: 3.9161 03/05 02:59:02 - mmengine - INFO - Epoch(train) [2][3800/5005] lr: 1.0000e-01 eta: 1 day, 21:58:51 time: 0.2199 data_time: 0.0016 loss: 3.5382 03/05 02:59:24 - mmengine - INFO - Epoch(train) [2][3900/5005] lr: 1.0000e-01 eta: 1 day, 21:55:56 time: 0.2246 data_time: 0.0014 loss: 3.3568 03/05 02:59:45 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 02:59:46 - mmengine - INFO - Epoch(train) [2][4000/5005] lr: 1.0000e-01 eta: 1 day, 21:53:41 time: 0.2179 data_time: 0.0016 loss: 3.5949 03/05 03:00:08 - mmengine - INFO - Epoch(train) [2][4100/5005] lr: 1.0000e-01 eta: 1 day, 21:50:57 time: 0.2194 data_time: 0.0016 loss: 3.4716 03/05 03:00:31 - mmengine - INFO - Epoch(train) [2][4200/5005] lr: 1.0000e-01 eta: 1 day, 21:48:25 time: 0.2214 data_time: 0.0015 loss: 3.3886 03/05 03:00:53 - mmengine - INFO - Epoch(train) [2][4300/5005] lr: 1.0000e-01 eta: 1 day, 21:45:55 time: 0.2179 data_time: 0.0014 loss: 3.6234 03/05 03:01:15 - mmengine - INFO - Epoch(train) [2][4400/5005] lr: 1.0000e-01 eta: 1 day, 21:43:47 time: 0.2163 data_time: 0.0013 loss: 3.6186 03/05 03:01:37 - mmengine - INFO - Epoch(train) [2][4500/5005] lr: 1.0000e-01 eta: 1 day, 21:41:25 time: 0.2313 data_time: 0.0014 loss: 3.6631 03/05 03:01:59 - mmengine - INFO - Epoch(train) [2][4600/5005] lr: 1.0000e-01 eta: 1 day, 21:38:49 time: 0.2210 data_time: 0.0014 loss: 3.3326 03/05 03:02:22 - mmengine - INFO - Epoch(train) [2][4700/5005] lr: 1.0000e-01 eta: 1 day, 21:36:39 time: 0.2212 data_time: 0.0014 loss: 3.6261 03/05 03:02:44 - mmengine - INFO - Epoch(train) [2][4800/5005] lr: 1.0000e-01 eta: 1 day, 21:34:27 time: 0.2226 data_time: 0.0014 loss: 3.3750 03/05 03:03:07 - mmengine - INFO - Epoch(train) [2][4900/5005] lr: 1.0000e-01 eta: 1 day, 21:33:42 time: 0.2991 data_time: 0.0011 loss: 3.3378 03/05 03:03:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:03:38 - mmengine - INFO - Epoch(train) [2][5000/5005] lr: 1.0000e-01 eta: 1 day, 21:40:52 time: 0.2932 data_time: 0.0012 loss: 3.6138 03/05 03:03:39 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:03:42 - mmengine - INFO - Saving checkpoint at 2 epochs 03/05 03:03:55 - mmengine - INFO - Epoch(val) [2][100/196] eta: 0:00:11 time: 0.0190 data_time: 0.0002 03/05 03:04:09 - mmengine - INFO - Epoch(val) [2][196/196] accuracy/top1: 29.7940 accuracy/top5: 54.9980 03/05 03:04:40 - mmengine - INFO - Epoch(train) [3][ 100/5005] lr: 1.0000e-01 eta: 1 day, 21:49:23 time: 0.2180 data_time: 0.0014 loss: 3.3257 03/05 03:05:02 - mmengine - INFO - Epoch(train) [3][ 200/5005] lr: 1.0000e-01 eta: 1 day, 21:47:01 time: 0.2163 data_time: 0.0014 loss: 3.6500 03/05 03:05:25 - mmengine - INFO - Epoch(train) [3][ 300/5005] lr: 1.0000e-01 eta: 1 day, 21:44:48 time: 0.2166 data_time: 0.0014 loss: 3.2451 03/05 03:05:47 - mmengine - INFO - Epoch(train) [3][ 400/5005] lr: 1.0000e-01 eta: 1 day, 21:42:14 time: 0.2201 data_time: 0.0015 loss: 3.4081 03/05 03:06:09 - mmengine - INFO - Epoch(train) [3][ 500/5005] lr: 1.0000e-01 eta: 1 day, 21:40:23 time: 0.2183 data_time: 0.0014 loss: 3.5042 03/05 03:06:31 - mmengine - INFO - Epoch(train) [3][ 600/5005] lr: 1.0000e-01 eta: 1 day, 21:38:02 time: 0.2201 data_time: 0.0014 loss: 3.4739 03/05 03:06:54 - mmengine - INFO - Epoch(train) [3][ 700/5005] lr: 1.0000e-01 eta: 1 day, 21:36:27 time: 0.2144 data_time: 0.0014 loss: 3.3055 03/05 03:07:16 - mmengine - INFO - Epoch(train) [3][ 800/5005] lr: 1.0000e-01 eta: 1 day, 21:34:07 time: 0.2198 data_time: 0.0014 loss: 3.4653 03/05 03:07:38 - mmengine - INFO - Epoch(train) [3][ 900/5005] lr: 1.0000e-01 eta: 1 day, 21:32:25 time: 0.2205 data_time: 0.0016 loss: 3.4427 03/05 03:07:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:08:00 - mmengine - INFO - Epoch(train) [3][1000/5005] lr: 1.0000e-01 eta: 1 day, 21:30:15 time: 0.2185 data_time: 0.0014 loss: 3.5920 03/05 03:08:23 - mmengine - INFO - Epoch(train) [3][1100/5005] lr: 1.0000e-01 eta: 1 day, 21:28:30 time: 0.2202 data_time: 0.0015 loss: 3.2827 03/05 03:08:45 - mmengine - INFO - Epoch(train) [3][1200/5005] lr: 1.0000e-01 eta: 1 day, 21:26:19 time: 0.2277 data_time: 0.0014 loss: 3.3035 03/05 03:09:07 - mmengine - INFO - Epoch(train) [3][1300/5005] lr: 1.0000e-01 eta: 1 day, 21:24:12 time: 0.2174 data_time: 0.0014 loss: 3.3149 03/05 03:09:29 - mmengine - INFO - Epoch(train) [3][1400/5005] lr: 1.0000e-01 eta: 1 day, 21:22:04 time: 0.2207 data_time: 0.0013 loss: 3.4044 03/05 03:09:51 - mmengine - INFO - Epoch(train) [3][1500/5005] lr: 1.0000e-01 eta: 1 day, 21:20:18 time: 0.2295 data_time: 0.0014 loss: 3.2919 03/05 03:10:13 - mmengine - INFO - Epoch(train) [3][1600/5005] lr: 1.0000e-01 eta: 1 day, 21:18:17 time: 0.2170 data_time: 0.0015 loss: 3.1811 03/05 03:10:35 - mmengine - INFO - Epoch(train) [3][1700/5005] lr: 1.0000e-01 eta: 1 day, 21:16:29 time: 0.2233 data_time: 0.0014 loss: 3.4159 03/05 03:10:57 - mmengine - INFO - Epoch(train) [3][1800/5005] lr: 1.0000e-01 eta: 1 day, 21:14:28 time: 0.2202 data_time: 0.0014 loss: 3.5931 03/05 03:11:20 - mmengine - INFO - Epoch(train) [3][1900/5005] lr: 1.0000e-01 eta: 1 day, 21:12:38 time: 0.2181 data_time: 0.0013 loss: 3.2447 03/05 03:11:39 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:11:42 - mmengine - INFO - Epoch(train) [3][2000/5005] lr: 1.0000e-01 eta: 1 day, 21:10:41 time: 0.2253 data_time: 0.0015 loss: 3.4290 03/05 03:12:04 - mmengine - INFO - Epoch(train) [3][2100/5005] lr: 1.0000e-01 eta: 1 day, 21:08:56 time: 0.2192 data_time: 0.0016 loss: 3.4678 03/05 03:12:26 - mmengine - INFO - Epoch(train) [3][2200/5005] lr: 1.0000e-01 eta: 1 day, 21:07:03 time: 0.2153 data_time: 0.0014 loss: 3.2748 03/05 03:12:48 - mmengine - INFO - Epoch(train) [3][2300/5005] lr: 1.0000e-01 eta: 1 day, 21:05:25 time: 0.2170 data_time: 0.0013 loss: 3.3176 03/05 03:13:10 - mmengine - INFO - Epoch(train) [3][2400/5005] lr: 1.0000e-01 eta: 1 day, 21:03:35 time: 0.2196 data_time: 0.0013 loss: 3.0385 03/05 03:13:32 - mmengine - INFO - Epoch(train) [3][2500/5005] lr: 1.0000e-01 eta: 1 day, 21:01:58 time: 0.2232 data_time: 0.0014 loss: 3.1451 03/05 03:13:54 - mmengine - INFO - Epoch(train) [3][2600/5005] lr: 1.0000e-01 eta: 1 day, 21:00:10 time: 0.2285 data_time: 0.0015 loss: 3.1069 03/05 03:14:17 - mmengine - INFO - Epoch(train) [3][2700/5005] lr: 1.0000e-01 eta: 1 day, 20:58:32 time: 0.2186 data_time: 0.0015 loss: 3.0455 03/05 03:14:39 - mmengine - INFO - Epoch(train) [3][2800/5005] lr: 1.0000e-01 eta: 1 day, 20:57:11 time: 0.2188 data_time: 0.0017 loss: 3.1686 03/05 03:15:01 - mmengine - INFO - Epoch(train) [3][2900/5005] lr: 1.0000e-01 eta: 1 day, 20:55:38 time: 0.2174 data_time: 0.0015 loss: 3.3092 03/05 03:15:21 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:15:23 - mmengine - INFO - Epoch(train) [3][3000/5005] lr: 1.0000e-01 eta: 1 day, 20:53:47 time: 0.2200 data_time: 0.0019 loss: 3.2465 03/05 03:15:46 - mmengine - INFO - Epoch(train) [3][3100/5005] lr: 1.0000e-01 eta: 1 day, 20:52:27 time: 0.2186 data_time: 0.0015 loss: 3.0172 03/05 03:16:08 - mmengine - INFO - Epoch(train) [3][3200/5005] lr: 1.0000e-01 eta: 1 day, 20:51:04 time: 0.2200 data_time: 0.0013 loss: 3.3224 03/05 03:16:30 - mmengine - INFO - Epoch(train) [3][3300/5005] lr: 1.0000e-01 eta: 1 day, 20:49:31 time: 0.2190 data_time: 0.0014 loss: 2.9525 03/05 03:16:52 - mmengine - INFO - Epoch(train) [3][3400/5005] lr: 1.0000e-01 eta: 1 day, 20:47:56 time: 0.2224 data_time: 0.0014 loss: 3.4039 03/05 03:17:14 - mmengine - INFO - Epoch(train) [3][3500/5005] lr: 1.0000e-01 eta: 1 day, 20:46:17 time: 0.2186 data_time: 0.0015 loss: 3.0883 03/05 03:17:36 - mmengine - INFO - Epoch(train) [3][3600/5005] lr: 1.0000e-01 eta: 1 day, 20:44:50 time: 0.2164 data_time: 0.0014 loss: 2.9957 03/05 03:17:58 - mmengine - INFO - Epoch(train) [3][3700/5005] lr: 1.0000e-01 eta: 1 day, 20:43:12 time: 0.2188 data_time: 0.0014 loss: 3.0365 03/05 03:18:20 - mmengine - INFO - Epoch(train) [3][3800/5005] lr: 1.0000e-01 eta: 1 day, 20:41:37 time: 0.2200 data_time: 0.0014 loss: 3.1746 03/05 03:18:42 - mmengine - INFO - Epoch(train) [3][3900/5005] lr: 1.0000e-01 eta: 1 day, 20:40:10 time: 0.2208 data_time: 0.0014 loss: 3.1430 03/05 03:19:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:19:05 - mmengine - INFO - Epoch(train) [3][4000/5005] lr: 1.0000e-01 eta: 1 day, 20:38:55 time: 0.2192 data_time: 0.0014 loss: 3.2341 03/05 03:19:27 - mmengine - INFO - Epoch(train) [3][4100/5005] lr: 1.0000e-01 eta: 1 day, 20:37:38 time: 0.2199 data_time: 0.0015 loss: 3.1878 03/05 03:19:49 - mmengine - INFO - Epoch(train) [3][4200/5005] lr: 1.0000e-01 eta: 1 day, 20:36:05 time: 0.2203 data_time: 0.0014 loss: 3.2604 03/05 03:20:11 - mmengine - INFO - Epoch(train) [3][4300/5005] lr: 1.0000e-01 eta: 1 day, 20:34:43 time: 0.2311 data_time: 0.0015 loss: 2.9054 03/05 03:20:34 - mmengine - INFO - Epoch(train) [3][4400/5005] lr: 1.0000e-01 eta: 1 day, 20:33:35 time: 0.2241 data_time: 0.0014 loss: 2.9791 03/05 03:20:56 - mmengine - INFO - Epoch(train) [3][4500/5005] lr: 1.0000e-01 eta: 1 day, 20:32:11 time: 0.2179 data_time: 0.0013 loss: 3.1293 03/05 03:21:18 - mmengine - INFO - Epoch(train) [3][4600/5005] lr: 1.0000e-01 eta: 1 day, 20:31:02 time: 0.2198 data_time: 0.0014 loss: 3.2010 03/05 03:21:40 - mmengine - INFO - Epoch(train) [3][4700/5005] lr: 1.0000e-01 eta: 1 day, 20:29:34 time: 0.2191 data_time: 0.0014 loss: 3.2372 03/05 03:22:03 - mmengine - INFO - Epoch(train) [3][4800/5005] lr: 1.0000e-01 eta: 1 day, 20:28:29 time: 0.2206 data_time: 0.0014 loss: 3.1436 03/05 03:22:26 - mmengine - INFO - Epoch(train) [3][4900/5005] lr: 1.0000e-01 eta: 1 day, 20:28:22 time: 0.3091 data_time: 0.0012 loss: 2.9432 03/05 03:22:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:22:57 - mmengine - INFO - Epoch(train) [3][5000/5005] lr: 1.0000e-01 eta: 1 day, 20:33:14 time: 0.2919 data_time: 0.0011 loss: 3.0717 03/05 03:22:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:23:01 - mmengine - INFO - Saving checkpoint at 3 epochs 03/05 03:23:14 - mmengine - INFO - Epoch(val) [3][100/196] eta: 0:00:12 time: 0.0172 data_time: 0.0003 03/05 03:23:28 - mmengine - INFO - Epoch(val) [3][196/196] accuracy/top1: 35.6680 accuracy/top5: 61.9240 03/05 03:23:59 - mmengine - INFO - Epoch(train) [4][ 100/5005] lr: 1.0000e-01 eta: 1 day, 20:39:07 time: 0.2434 data_time: 0.0014 loss: 3.1546 03/05 03:24:21 - mmengine - INFO - Epoch(train) [4][ 200/5005] lr: 1.0000e-01 eta: 1 day, 20:37:29 time: 0.2175 data_time: 0.0015 loss: 2.9655 03/05 03:24:43 - mmengine - INFO - Epoch(train) [4][ 300/5005] lr: 1.0000e-01 eta: 1 day, 20:36:07 time: 0.2404 data_time: 0.0015 loss: 3.1128 03/05 03:25:06 - mmengine - INFO - Epoch(train) [4][ 400/5005] lr: 1.0000e-01 eta: 1 day, 20:34:52 time: 0.2199 data_time: 0.0014 loss: 2.9624 03/05 03:25:29 - mmengine - INFO - Epoch(train) [4][ 500/5005] lr: 1.0000e-01 eta: 1 day, 20:34:03 time: 0.2214 data_time: 0.0013 loss: 3.0430 03/05 03:25:50 - mmengine - INFO - Epoch(train) [4][ 600/5005] lr: 1.0000e-01 eta: 1 day, 20:32:34 time: 0.2176 data_time: 0.0015 loss: 2.9712 03/05 03:26:13 - mmengine - INFO - Epoch(train) [4][ 700/5005] lr: 1.0000e-01 eta: 1 day, 20:31:12 time: 0.2209 data_time: 0.0016 loss: 2.9017 03/05 03:26:35 - mmengine - INFO - Epoch(train) [4][ 800/5005] lr: 1.0000e-01 eta: 1 day, 20:29:53 time: 0.2205 data_time: 0.0013 loss: 2.9167 03/05 03:26:58 - mmengine - INFO - Epoch(train) [4][ 900/5005] lr: 1.0000e-01 eta: 1 day, 20:29:09 time: 0.2186 data_time: 0.0015 loss: 2.7360 03/05 03:27:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:27:20 - mmengine - INFO - Epoch(train) [4][1000/5005] lr: 1.0000e-01 eta: 1 day, 20:27:48 time: 0.2193 data_time: 0.0014 loss: 3.2046 03/05 03:27:42 - mmengine - INFO - Epoch(train) [4][1100/5005] lr: 1.0000e-01 eta: 1 day, 20:26:27 time: 0.2195 data_time: 0.0015 loss: 2.8673 03/05 03:28:04 - mmengine - INFO - Epoch(train) [4][1200/5005] lr: 1.0000e-01 eta: 1 day, 20:25:09 time: 0.2209 data_time: 0.0014 loss: 2.7514 03/05 03:28:26 - mmengine - INFO - Epoch(train) [4][1300/5005] lr: 1.0000e-01 eta: 1 day, 20:24:01 time: 0.2244 data_time: 0.0015 loss: 2.8599 03/05 03:28:48 - mmengine - INFO - Epoch(train) [4][1400/5005] lr: 1.0000e-01 eta: 1 day, 20:22:48 time: 0.2178 data_time: 0.0014 loss: 3.3106 03/05 03:29:10 - mmengine - INFO - Epoch(train) [4][1500/5005] lr: 1.0000e-01 eta: 1 day, 20:21:25 time: 0.2182 data_time: 0.0014 loss: 2.6896 03/05 03:29:32 - mmengine - INFO - Epoch(train) [4][1600/5005] lr: 1.0000e-01 eta: 1 day, 20:20:12 time: 0.2173 data_time: 0.0014 loss: 2.7625 03/05 03:29:55 - mmengine - INFO - Epoch(train) [4][1700/5005] lr: 1.0000e-01 eta: 1 day, 20:19:06 time: 0.2178 data_time: 0.0015 loss: 2.8248 03/05 03:30:17 - mmengine - INFO - Epoch(train) [4][1800/5005] lr: 1.0000e-01 eta: 1 day, 20:18:06 time: 0.2194 data_time: 0.0015 loss: 2.8852 03/05 03:30:39 - mmengine - INFO - Epoch(train) [4][1900/5005] lr: 1.0000e-01 eta: 1 day, 20:16:49 time: 0.2181 data_time: 0.0014 loss: 2.8229 03/05 03:30:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:31:01 - mmengine - INFO - Epoch(train) [4][2000/5005] lr: 1.0000e-01 eta: 1 day, 20:15:36 time: 0.2228 data_time: 0.0016 loss: 2.9638 03/05 03:31:23 - mmengine - INFO - Epoch(train) [4][2100/5005] lr: 1.0000e-01 eta: 1 day, 20:14:25 time: 0.2182 data_time: 0.0013 loss: 3.0752 03/05 03:31:46 - mmengine - INFO - Epoch(train) [4][2200/5005] lr: 1.0000e-01 eta: 1 day, 20:13:24 time: 0.2391 data_time: 0.0015 loss: 2.8010 03/05 03:32:08 - mmengine - INFO - Epoch(train) [4][2300/5005] lr: 1.0000e-01 eta: 1 day, 20:12:14 time: 0.2215 data_time: 0.0014 loss: 2.7404 03/05 03:32:30 - mmengine - INFO - Epoch(train) [4][2400/5005] lr: 1.0000e-01 eta: 1 day, 20:11:00 time: 0.2178 data_time: 0.0013 loss: 2.7527 03/05 03:32:52 - mmengine - INFO - Epoch(train) [4][2500/5005] lr: 1.0000e-01 eta: 1 day, 20:09:56 time: 0.2188 data_time: 0.0015 loss: 2.9792 03/05 03:33:14 - mmengine - INFO - Epoch(train) [4][2600/5005] lr: 1.0000e-01 eta: 1 day, 20:08:37 time: 0.2201 data_time: 0.0016 loss: 3.0240 03/05 03:33:36 - mmengine - INFO - Epoch(train) [4][2700/5005] lr: 1.0000e-01 eta: 1 day, 20:07:44 time: 0.2203 data_time: 0.0015 loss: 2.6685 03/05 03:33:58 - mmengine - INFO - Epoch(train) [4][2800/5005] lr: 1.0000e-01 eta: 1 day, 20:06:39 time: 0.2193 data_time: 0.0014 loss: 2.8659 03/05 03:34:21 - mmengine - INFO - Epoch(train) [4][2900/5005] lr: 1.0000e-01 eta: 1 day, 20:05:37 time: 0.2199 data_time: 0.0013 loss: 2.9350 03/05 03:34:39 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:34:43 - mmengine - INFO - Epoch(train) [4][3000/5005] lr: 1.0000e-01 eta: 1 day, 20:04:25 time: 0.2217 data_time: 0.0016 loss: 2.7282 03/05 03:35:05 - mmengine - INFO - Epoch(train) [4][3100/5005] lr: 1.0000e-01 eta: 1 day, 20:03:39 time: 0.2392 data_time: 0.0015 loss: 2.8556 03/05 03:35:28 - mmengine - INFO - Epoch(train) [4][3200/5005] lr: 1.0000e-01 eta: 1 day, 20:02:44 time: 0.2234 data_time: 0.0015 loss: 2.9068 03/05 03:35:50 - mmengine - INFO - Epoch(train) [4][3300/5005] lr: 1.0000e-01 eta: 1 day, 20:01:44 time: 0.2184 data_time: 0.0016 loss: 2.9258 03/05 03:36:12 - mmengine - INFO - Epoch(train) [4][3400/5005] lr: 1.0000e-01 eta: 1 day, 20:00:36 time: 0.2242 data_time: 0.0014 loss: 3.0133 03/05 03:36:34 - mmengine - INFO - Epoch(train) [4][3500/5005] lr: 1.0000e-01 eta: 1 day, 19:59:29 time: 0.2200 data_time: 0.0015 loss: 2.9832 03/05 03:36:57 - mmengine - INFO - Epoch(train) [4][3600/5005] lr: 1.0000e-01 eta: 1 day, 19:58:40 time: 0.2433 data_time: 0.0015 loss: 2.8392 03/05 03:37:19 - mmengine - INFO - Epoch(train) [4][3700/5005] lr: 1.0000e-01 eta: 1 day, 19:57:37 time: 0.2188 data_time: 0.0016 loss: 3.2118 03/05 03:37:41 - mmengine - INFO - Epoch(train) [4][3800/5005] lr: 1.0000e-01 eta: 1 day, 19:56:29 time: 0.2189 data_time: 0.0014 loss: 2.6747 03/05 03:38:03 - mmengine - INFO - Epoch(train) [4][3900/5005] lr: 1.0000e-01 eta: 1 day, 19:55:28 time: 0.2214 data_time: 0.0015 loss: 2.8159 03/05 03:38:22 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:38:25 - mmengine - INFO - Epoch(train) [4][4000/5005] lr: 1.0000e-01 eta: 1 day, 19:54:42 time: 0.2222 data_time: 0.0015 loss: 2.7871 03/05 03:38:48 - mmengine - INFO - Epoch(train) [4][4100/5005] lr: 1.0000e-01 eta: 1 day, 19:53:54 time: 0.2197 data_time: 0.0014 loss: 2.7698 03/05 03:39:10 - mmengine - INFO - Epoch(train) [4][4200/5005] lr: 1.0000e-01 eta: 1 day, 19:52:50 time: 0.2203 data_time: 0.0015 loss: 2.6057 03/05 03:39:32 - mmengine - INFO - Epoch(train) [4][4300/5005] lr: 1.0000e-01 eta: 1 day, 19:51:49 time: 0.2165 data_time: 0.0015 loss: 2.8427 03/05 03:39:54 - mmengine - INFO - Epoch(train) [4][4400/5005] lr: 1.0000e-01 eta: 1 day, 19:50:54 time: 0.2372 data_time: 0.0014 loss: 2.7064 03/05 03:40:17 - mmengine - INFO - Epoch(train) [4][4500/5005] lr: 1.0000e-01 eta: 1 day, 19:50:07 time: 0.2212 data_time: 0.0014 loss: 2.6141 03/05 03:40:39 - mmengine - INFO - Epoch(train) [4][4600/5005] lr: 1.0000e-01 eta: 1 day, 19:48:59 time: 0.2151 data_time: 0.0014 loss: 2.8503 03/05 03:41:01 - mmengine - INFO - Epoch(train) [4][4700/5005] lr: 1.0000e-01 eta: 1 day, 19:47:59 time: 0.2189 data_time: 0.0015 loss: 2.7897 03/05 03:41:23 - mmengine - INFO - Epoch(train) [4][4800/5005] lr: 1.0000e-01 eta: 1 day, 19:47:02 time: 0.2234 data_time: 0.0014 loss: 2.8317 03/05 03:41:47 - mmengine - INFO - Epoch(train) [4][4900/5005] lr: 1.0000e-01 eta: 1 day, 19:47:02 time: 0.2976 data_time: 0.0012 loss: 2.7975 03/05 03:42:13 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:42:17 - mmengine - INFO - Epoch(train) [4][5000/5005] lr: 1.0000e-01 eta: 1 day, 19:50:51 time: 0.3069 data_time: 0.0013 loss: 2.8350 03/05 03:42:19 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:42:21 - mmengine - INFO - Saving checkpoint at 4 epochs 03/05 03:42:35 - mmengine - INFO - Epoch(val) [4][100/196] eta: 0:00:12 time: 0.0176 data_time: 0.0002 03/05 03:42:48 - mmengine - INFO - Epoch(val) [4][196/196] accuracy/top1: 42.6740 accuracy/top5: 69.2000 03/05 03:43:19 - mmengine - INFO - Epoch(train) [5][ 100/5005] lr: 1.0000e-01 eta: 1 day, 19:54:42 time: 0.2328 data_time: 0.0013 loss: 2.7401 03/05 03:43:41 - mmengine - INFO - Epoch(train) [5][ 200/5005] lr: 1.0000e-01 eta: 1 day, 19:53:42 time: 0.2192 data_time: 0.0015 loss: 3.0869 03/05 03:44:03 - mmengine - INFO - Epoch(train) [5][ 300/5005] lr: 1.0000e-01 eta: 1 day, 19:52:40 time: 0.2212 data_time: 0.0014 loss: 2.9252 03/05 03:44:25 - mmengine - INFO - Epoch(train) [5][ 400/5005] lr: 1.0000e-01 eta: 1 day, 19:51:47 time: 0.2182 data_time: 0.0014 loss: 2.6845 03/05 03:44:48 - mmengine - INFO - Epoch(train) [5][ 500/5005] lr: 1.0000e-01 eta: 1 day, 19:50:58 time: 0.2194 data_time: 0.0014 loss: 2.6910 03/05 03:45:10 - mmengine - INFO - Epoch(train) [5][ 600/5005] lr: 1.0000e-01 eta: 1 day, 19:49:57 time: 0.2191 data_time: 0.0014 loss: 2.8150 03/05 03:45:32 - mmengine - INFO - Epoch(train) [5][ 700/5005] lr: 1.0000e-01 eta: 1 day, 19:49:02 time: 0.2201 data_time: 0.0015 loss: 2.8923 03/05 03:45:54 - mmengine - INFO - Epoch(train) [5][ 800/5005] lr: 1.0000e-01 eta: 1 day, 19:47:59 time: 0.2208 data_time: 0.0015 loss: 2.7720 03/05 03:46:17 - mmengine - INFO - Epoch(train) [5][ 900/5005] lr: 1.0000e-01 eta: 1 day, 19:47:11 time: 0.2367 data_time: 0.0014 loss: 2.7238 03/05 03:46:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:46:39 - mmengine - INFO - Epoch(train) [5][1000/5005] lr: 1.0000e-01 eta: 1 day, 19:46:17 time: 0.2186 data_time: 0.0015 loss: 2.5236 03/05 03:47:01 - mmengine - INFO - Epoch(train) [5][1100/5005] lr: 1.0000e-01 eta: 1 day, 19:45:29 time: 0.2399 data_time: 0.0015 loss: 2.7569 03/05 03:47:23 - mmengine - INFO - Epoch(train) [5][1200/5005] lr: 1.0000e-01 eta: 1 day, 19:44:29 time: 0.2386 data_time: 0.0015 loss: 2.8243 03/05 03:47:45 - mmengine - INFO - Epoch(train) [5][1300/5005] lr: 1.0000e-01 eta: 1 day, 19:43:35 time: 0.2163 data_time: 0.0015 loss: 2.6883 03/05 03:48:08 - mmengine - INFO - Epoch(train) [5][1400/5005] lr: 1.0000e-01 eta: 1 day, 19:42:43 time: 0.2212 data_time: 0.0015 loss: 2.5304 03/05 03:48:30 - mmengine - INFO - Epoch(train) [5][1500/5005] lr: 1.0000e-01 eta: 1 day, 19:41:46 time: 0.2223 data_time: 0.0015 loss: 2.8285 03/05 03:48:52 - mmengine - INFO - Epoch(train) [5][1600/5005] lr: 1.0000e-01 eta: 1 day, 19:40:45 time: 0.2222 data_time: 0.0014 loss: 2.7327 03/05 03:49:14 - mmengine - INFO - Epoch(train) [5][1700/5005] lr: 1.0000e-01 eta: 1 day, 19:39:50 time: 0.2389 data_time: 0.0015 loss: 2.7938 03/05 03:49:36 - mmengine - INFO - Epoch(train) [5][1800/5005] lr: 1.0000e-01 eta: 1 day, 19:38:52 time: 0.2160 data_time: 0.0015 loss: 2.6881 03/05 03:49:58 - mmengine - INFO - Epoch(train) [5][1900/5005] lr: 1.0000e-01 eta: 1 day, 19:38:00 time: 0.2394 data_time: 0.0014 loss: 2.7908 03/05 03:50:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:50:20 - mmengine - INFO - Epoch(train) [5][2000/5005] lr: 1.0000e-01 eta: 1 day, 19:37:10 time: 0.2362 data_time: 0.0015 loss: 2.5487 03/05 03:50:43 - mmengine - INFO - Epoch(train) [5][2100/5005] lr: 1.0000e-01 eta: 1 day, 19:36:14 time: 0.2189 data_time: 0.0014 loss: 2.5841 03/05 03:51:05 - mmengine - INFO - Epoch(train) [5][2200/5005] lr: 1.0000e-01 eta: 1 day, 19:35:30 time: 0.2214 data_time: 0.0017 loss: 2.6025 03/05 03:51:27 - mmengine - INFO - Epoch(train) [5][2300/5005] lr: 1.0000e-01 eta: 1 day, 19:34:33 time: 0.2195 data_time: 0.0016 loss: 2.7720 03/05 03:51:49 - mmengine - INFO - Epoch(train) [5][2400/5005] lr: 1.0000e-01 eta: 1 day, 19:33:45 time: 0.2262 data_time: 0.0014 loss: 2.7384 03/05 03:52:11 - mmengine - INFO - Epoch(train) [5][2500/5005] lr: 1.0000e-01 eta: 1 day, 19:32:50 time: 0.2195 data_time: 0.0015 loss: 2.5300 03/05 03:52:34 - mmengine - INFO - Epoch(train) [5][2600/5005] lr: 1.0000e-01 eta: 1 day, 19:32:03 time: 0.2422 data_time: 0.0014 loss: 2.6895 03/05 03:52:56 - mmengine - INFO - Epoch(train) [5][2700/5005] lr: 1.0000e-01 eta: 1 day, 19:31:06 time: 0.2214 data_time: 0.0014 loss: 2.7462 03/05 03:53:18 - mmengine - INFO - Epoch(train) [5][2800/5005] lr: 1.0000e-01 eta: 1 day, 19:30:29 time: 0.2221 data_time: 0.0013 loss: 2.8541 03/05 03:53:40 - mmengine - INFO - Epoch(train) [5][2900/5005] lr: 1.0000e-01 eta: 1 day, 19:29:36 time: 0.2194 data_time: 0.0014 loss: 2.8617 03/05 03:53:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:54:02 - mmengine - INFO - Epoch(train) [5][3000/5005] lr: 1.0000e-01 eta: 1 day, 19:28:41 time: 0.2188 data_time: 0.0015 loss: 2.5868 03/05 03:54:25 - mmengine - INFO - Epoch(train) [5][3100/5005] lr: 1.0000e-01 eta: 1 day, 19:27:57 time: 0.2214 data_time: 0.0017 loss: 2.6446 03/05 03:54:47 - mmengine - INFO - Epoch(train) [5][3200/5005] lr: 1.0000e-01 eta: 1 day, 19:27:17 time: 0.2208 data_time: 0.0015 loss: 2.5275 03/05 03:55:09 - mmengine - INFO - Epoch(train) [5][3300/5005] lr: 1.0000e-01 eta: 1 day, 19:26:20 time: 0.2158 data_time: 0.0015 loss: 2.4617 03/05 03:55:31 - mmengine - INFO - Epoch(train) [5][3400/5005] lr: 1.0000e-01 eta: 1 day, 19:25:22 time: 0.2220 data_time: 0.0013 loss: 2.6992 03/05 03:55:54 - mmengine - INFO - Epoch(train) [5][3500/5005] lr: 1.0000e-01 eta: 1 day, 19:24:37 time: 0.2214 data_time: 0.0014 loss: 2.5039 03/05 03:56:16 - mmengine - INFO - Epoch(train) [5][3600/5005] lr: 1.0000e-01 eta: 1 day, 19:24:01 time: 0.2383 data_time: 0.0015 loss: 2.8617 03/05 03:56:38 - mmengine - INFO - Epoch(train) [5][3700/5005] lr: 1.0000e-01 eta: 1 day, 19:23:10 time: 0.2221 data_time: 0.0016 loss: 2.5819 03/05 03:57:00 - mmengine - INFO - Epoch(train) [5][3800/5005] lr: 1.0000e-01 eta: 1 day, 19:22:16 time: 0.2186 data_time: 0.0016 loss: 2.6524 03/05 03:57:22 - mmengine - INFO - Epoch(train) [5][3900/5005] lr: 1.0000e-01 eta: 1 day, 19:21:25 time: 0.2194 data_time: 0.0013 loss: 2.8321 03/05 03:57:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 03:57:45 - mmengine - INFO - Epoch(train) [5][4000/5005] lr: 1.0000e-01 eta: 1 day, 19:20:45 time: 0.2477 data_time: 0.0017 loss: 2.7276 03/05 03:58:07 - mmengine - INFO - Epoch(train) [5][4100/5005] lr: 1.0000e-01 eta: 1 day, 19:20:01 time: 0.2186 data_time: 0.0014 loss: 2.9152 03/05 03:58:29 - mmengine - INFO - Epoch(train) [5][4200/5005] lr: 1.0000e-01 eta: 1 day, 19:19:08 time: 0.2172 data_time: 0.0014 loss: 2.7098 03/05 03:58:51 - mmengine - INFO - Epoch(train) [5][4300/5005] lr: 1.0000e-01 eta: 1 day, 19:18:23 time: 0.2384 data_time: 0.0017 loss: 2.7052 03/05 03:59:14 - mmengine - INFO - Epoch(train) [5][4400/5005] lr: 1.0000e-01 eta: 1 day, 19:17:43 time: 0.2186 data_time: 0.0014 loss: 2.9906 03/05 03:59:36 - mmengine - INFO - Epoch(train) [5][4500/5005] lr: 1.0000e-01 eta: 1 day, 19:16:59 time: 0.2174 data_time: 0.0015 loss: 2.7695 03/05 03:59:58 - mmengine - INFO - Epoch(train) [5][4600/5005] lr: 1.0000e-01 eta: 1 day, 19:16:07 time: 0.2186 data_time: 0.0015 loss: 2.6434 03/05 04:00:20 - mmengine - INFO - Epoch(train) [5][4700/5005] lr: 1.0000e-01 eta: 1 day, 19:15:15 time: 0.2194 data_time: 0.0015 loss: 2.5921 03/05 04:00:43 - mmengine - INFO - Epoch(train) [5][4800/5005] lr: 1.0000e-01 eta: 1 day, 19:14:35 time: 0.2233 data_time: 0.0014 loss: 2.6307 03/05 04:01:07 - mmengine - INFO - Epoch(train) [5][4900/5005] lr: 1.0000e-01 eta: 1 day, 19:14:39 time: 0.3011 data_time: 0.0011 loss: 2.4566 03/05 04:01:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:01:36 - mmengine - INFO - Epoch(train) [5][5000/5005] lr: 1.0000e-01 eta: 1 day, 19:17:19 time: 0.3036 data_time: 0.0011 loss: 2.6012 03/05 04:01:38 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:01:41 - mmengine - INFO - Saving checkpoint at 5 epochs 03/05 04:01:54 - mmengine - INFO - Epoch(val) [5][100/196] eta: 0:00:11 time: 0.0186 data_time: 0.0002 03/05 04:02:07 - mmengine - INFO - Epoch(val) [5][196/196] accuracy/top1: 45.0080 accuracy/top5: 71.4160 03/05 04:02:38 - mmengine - INFO - Epoch(train) [6][ 100/5005] lr: 1.0000e-01 eta: 1 day, 19:20:16 time: 0.2271 data_time: 0.0015 loss: 2.7662 03/05 04:02:59 - mmengine - INFO - Epoch(train) [6][ 200/5005] lr: 1.0000e-01 eta: 1 day, 19:19:20 time: 0.2226 data_time: 0.0014 loss: 2.7130 03/05 04:03:21 - mmengine - INFO - Epoch(train) [6][ 300/5005] lr: 1.0000e-01 eta: 1 day, 19:18:28 time: 0.2181 data_time: 0.0016 loss: 2.5864 03/05 04:03:44 - mmengine - INFO - Epoch(train) [6][ 400/5005] lr: 1.0000e-01 eta: 1 day, 19:17:48 time: 0.2194 data_time: 0.0014 loss: 2.8001 03/05 04:04:07 - mmengine - INFO - Epoch(train) [6][ 500/5005] lr: 1.0000e-01 eta: 1 day, 19:17:14 time: 0.2384 data_time: 0.0013 loss: 2.3439 03/05 04:04:29 - mmengine - INFO - Epoch(train) [6][ 600/5005] lr: 1.0000e-01 eta: 1 day, 19:16:20 time: 0.2177 data_time: 0.0017 loss: 2.4703 03/05 04:04:51 - mmengine - INFO - Epoch(train) [6][ 700/5005] lr: 1.0000e-01 eta: 1 day, 19:15:32 time: 0.2195 data_time: 0.0014 loss: 2.4710 03/05 04:05:13 - mmengine - INFO - Epoch(train) [6][ 800/5005] lr: 1.0000e-01 eta: 1 day, 19:14:53 time: 0.2166 data_time: 0.0014 loss: 2.7887 03/05 04:05:35 - mmengine - INFO - Epoch(train) [6][ 900/5005] lr: 1.0000e-01 eta: 1 day, 19:14:11 time: 0.2218 data_time: 0.0013 loss: 2.4503 03/05 04:05:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:05:58 - mmengine - INFO - Epoch(train) [6][1000/5005] lr: 1.0000e-01 eta: 1 day, 19:13:22 time: 0.2227 data_time: 0.0015 loss: 2.5918 03/05 04:06:19 - mmengine - INFO - Epoch(train) [6][1100/5005] lr: 1.0000e-01 eta: 1 day, 19:12:26 time: 0.2169 data_time: 0.0013 loss: 2.5727 03/05 04:06:42 - mmengine - INFO - Epoch(train) [6][1200/5005] lr: 1.0000e-01 eta: 1 day, 19:11:46 time: 0.2219 data_time: 0.0014 loss: 2.6184 03/05 04:07:04 - mmengine - INFO - Epoch(train) [6][1300/5005] lr: 1.0000e-01 eta: 1 day, 19:11:00 time: 0.2274 data_time: 0.0014 loss: 2.6923 03/05 04:07:26 - mmengine - INFO - Epoch(train) [6][1400/5005] lr: 1.0000e-01 eta: 1 day, 19:10:12 time: 0.2219 data_time: 0.0015 loss: 2.6966 03/05 04:07:48 - mmengine - INFO - Epoch(train) [6][1500/5005] lr: 1.0000e-01 eta: 1 day, 19:09:18 time: 0.2177 data_time: 0.0015 loss: 2.6729 03/05 04:08:10 - mmengine - INFO - Epoch(train) [6][1600/5005] lr: 1.0000e-01 eta: 1 day, 19:08:41 time: 0.2351 data_time: 0.0014 loss: 2.6616 03/05 04:08:33 - mmengine - INFO - Epoch(train) [6][1700/5005] lr: 1.0000e-01 eta: 1 day, 19:08:02 time: 0.2299 data_time: 0.0014 loss: 2.7384 03/05 04:08:55 - mmengine - INFO - Epoch(train) [6][1800/5005] lr: 1.0000e-01 eta: 1 day, 19:07:16 time: 0.2169 data_time: 0.0014 loss: 2.5041 03/05 04:09:17 - mmengine - INFO - Epoch(train) [6][1900/5005] lr: 1.0000e-01 eta: 1 day, 19:06:25 time: 0.2218 data_time: 0.0014 loss: 2.4818 03/05 04:09:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:09:39 - mmengine - INFO - Epoch(train) [6][2000/5005] lr: 1.0000e-01 eta: 1 day, 19:05:42 time: 0.2186 data_time: 0.0015 loss: 2.5495 03/05 04:10:02 - mmengine - INFO - Epoch(train) [6][2100/5005] lr: 1.0000e-01 eta: 1 day, 19:05:07 time: 0.2191 data_time: 0.0014 loss: 2.3804 03/05 04:10:24 - mmengine - INFO - Epoch(train) [6][2200/5005] lr: 1.0000e-01 eta: 1 day, 19:04:24 time: 0.2196 data_time: 0.0013 loss: 2.5961 03/05 04:10:46 - mmengine - INFO - Epoch(train) [6][2300/5005] lr: 1.0000e-01 eta: 1 day, 19:03:38 time: 0.2173 data_time: 0.0013 loss: 2.7028 03/05 04:11:08 - mmengine - INFO - Epoch(train) [6][2400/5005] lr: 1.0000e-01 eta: 1 day, 19:02:55 time: 0.2202 data_time: 0.0014 loss: 2.6425 03/05 04:11:30 - mmengine - INFO - Epoch(train) [6][2500/5005] lr: 1.0000e-01 eta: 1 day, 19:02:15 time: 0.2188 data_time: 0.0015 loss: 2.5559 03/05 04:11:53 - mmengine - INFO - Epoch(train) [6][2600/5005] lr: 1.0000e-01 eta: 1 day, 19:01:33 time: 0.2190 data_time: 0.0015 loss: 2.6644 03/05 04:12:15 - mmengine - INFO - Epoch(train) [6][2700/5005] lr: 1.0000e-01 eta: 1 day, 19:00:45 time: 0.2178 data_time: 0.0015 loss: 2.8062 03/05 04:12:37 - mmengine - INFO - Epoch(train) [6][2800/5005] lr: 1.0000e-01 eta: 1 day, 18:59:58 time: 0.2225 data_time: 0.0014 loss: 2.5735 03/05 04:12:59 - mmengine - INFO - Epoch(train) [6][2900/5005] lr: 1.0000e-01 eta: 1 day, 18:59:28 time: 0.2369 data_time: 0.0015 loss: 2.8472 03/05 04:13:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:13:21 - mmengine - INFO - Epoch(train) [6][3000/5005] lr: 1.0000e-01 eta: 1 day, 18:58:39 time: 0.2194 data_time: 0.0014 loss: 2.7729 03/05 04:13:44 - mmengine - INFO - Epoch(train) [6][3100/5005] lr: 1.0000e-01 eta: 1 day, 18:57:57 time: 0.2236 data_time: 0.0014 loss: 2.3250 03/05 04:14:06 - mmengine - INFO - Epoch(train) [6][3200/5005] lr: 1.0000e-01 eta: 1 day, 18:57:14 time: 0.2419 data_time: 0.0014 loss: 2.4677 03/05 04:14:28 - mmengine - INFO - Epoch(train) [6][3300/5005] lr: 1.0000e-01 eta: 1 day, 18:56:39 time: 0.2374 data_time: 0.0014 loss: 2.5545 03/05 04:14:50 - mmengine - INFO - Epoch(train) [6][3400/5005] lr: 1.0000e-01 eta: 1 day, 18:55:52 time: 0.2183 data_time: 0.0014 loss: 2.6082 03/05 04:15:12 - mmengine - INFO - Epoch(train) [6][3500/5005] lr: 1.0000e-01 eta: 1 day, 18:55:08 time: 0.2162 data_time: 0.0013 loss: 2.4452 03/05 04:15:35 - mmengine - INFO - Epoch(train) [6][3600/5005] lr: 1.0000e-01 eta: 1 day, 18:54:30 time: 0.2362 data_time: 0.0015 loss: 2.5807 03/05 04:15:57 - mmengine - INFO - Epoch(train) [6][3700/5005] lr: 1.0000e-01 eta: 1 day, 18:53:50 time: 0.2373 data_time: 0.0014 loss: 2.6926 03/05 04:16:19 - mmengine - INFO - Epoch(train) [6][3800/5005] lr: 1.0000e-01 eta: 1 day, 18:53:02 time: 0.2185 data_time: 0.0014 loss: 2.5414 03/05 04:16:41 - mmengine - INFO - Epoch(train) [6][3900/5005] lr: 1.0000e-01 eta: 1 day, 18:52:18 time: 0.2259 data_time: 0.0015 loss: 2.6573 03/05 04:16:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:17:03 - mmengine - INFO - Epoch(train) [6][4000/5005] lr: 1.0000e-01 eta: 1 day, 18:51:38 time: 0.2210 data_time: 0.0014 loss: 2.6799 03/05 04:17:26 - mmengine - INFO - Epoch(train) [6][4100/5005] lr: 1.0000e-01 eta: 1 day, 18:50:59 time: 0.2191 data_time: 0.0015 loss: 2.4546 03/05 04:17:48 - mmengine - INFO - Epoch(train) [6][4200/5005] lr: 1.0000e-01 eta: 1 day, 18:50:17 time: 0.2212 data_time: 0.0014 loss: 2.4597 03/05 04:18:10 - mmengine - INFO - Epoch(train) [6][4300/5005] lr: 1.0000e-01 eta: 1 day, 18:49:37 time: 0.2211 data_time: 0.0015 loss: 2.3735 03/05 04:18:32 - mmengine - INFO - Epoch(train) [6][4400/5005] lr: 1.0000e-01 eta: 1 day, 18:48:54 time: 0.2267 data_time: 0.0013 loss: 2.6494 03/05 04:18:54 - mmengine - INFO - Epoch(train) [6][4500/5005] lr: 1.0000e-01 eta: 1 day, 18:48:11 time: 0.2171 data_time: 0.0014 loss: 2.5526 03/05 04:19:16 - mmengine - INFO - Epoch(train) [6][4600/5005] lr: 1.0000e-01 eta: 1 day, 18:47:31 time: 0.2179 data_time: 0.0014 loss: 2.5150 03/05 04:19:38 - mmengine - INFO - Epoch(train) [6][4700/5005] lr: 1.0000e-01 eta: 1 day, 18:46:49 time: 0.2187 data_time: 0.0014 loss: 2.6547 03/05 04:20:00 - mmengine - INFO - Epoch(train) [6][4800/5005] lr: 1.0000e-01 eta: 1 day, 18:46:06 time: 0.2180 data_time: 0.0014 loss: 2.5281 03/05 04:20:24 - mmengine - INFO - Epoch(train) [6][4900/5005] lr: 1.0000e-01 eta: 1 day, 18:46:03 time: 0.3090 data_time: 0.0012 loss: 2.3980 03/05 04:20:47 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:20:54 - mmengine - INFO - Epoch(train) [6][5000/5005] lr: 1.0000e-01 eta: 1 day, 18:48:20 time: 0.3051 data_time: 0.0013 loss: 2.4327 03/05 04:20:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:20:59 - mmengine - INFO - Saving checkpoint at 6 epochs 03/05 04:21:13 - mmengine - INFO - Epoch(val) [6][100/196] eta: 0:00:12 time: 0.0190 data_time: 0.0003 03/05 04:21:27 - mmengine - INFO - Epoch(val) [6][196/196] accuracy/top1: 45.1300 accuracy/top5: 72.0200 03/05 04:21:57 - mmengine - INFO - Epoch(train) [7][ 100/5005] lr: 1.0000e-01 eta: 1 day, 18:51:02 time: 0.2447 data_time: 0.0018 loss: 2.3908 03/05 04:22:19 - mmengine - INFO - Epoch(train) [7][ 200/5005] lr: 1.0000e-01 eta: 1 day, 18:50:15 time: 0.2168 data_time: 0.0016 loss: 2.6220 03/05 04:22:42 - mmengine - INFO - Epoch(train) [7][ 300/5005] lr: 1.0000e-01 eta: 1 day, 18:49:36 time: 0.2179 data_time: 0.0013 loss: 2.3458 03/05 04:23:04 - mmengine - INFO - Epoch(train) [7][ 400/5005] lr: 1.0000e-01 eta: 1 day, 18:48:49 time: 0.2179 data_time: 0.0015 loss: 2.6819 03/05 04:23:26 - mmengine - INFO - Epoch(train) [7][ 500/5005] lr: 1.0000e-01 eta: 1 day, 18:48:18 time: 0.2397 data_time: 0.0014 loss: 2.3383 03/05 04:23:48 - mmengine - INFO - Epoch(train) [7][ 600/5005] lr: 1.0000e-01 eta: 1 day, 18:47:31 time: 0.2199 data_time: 0.0015 loss: 2.4134 03/05 04:24:10 - mmengine - INFO - Epoch(train) [7][ 700/5005] lr: 1.0000e-01 eta: 1 day, 18:46:52 time: 0.2195 data_time: 0.0015 loss: 2.5222 03/05 04:24:32 - mmengine - INFO - Epoch(train) [7][ 800/5005] lr: 1.0000e-01 eta: 1 day, 18:46:06 time: 0.2235 data_time: 0.0015 loss: 2.5632 03/05 04:24:55 - mmengine - INFO - Epoch(train) [7][ 900/5005] lr: 1.0000e-01 eta: 1 day, 18:45:35 time: 0.2539 data_time: 0.0014 loss: 2.5334 03/05 04:25:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:25:17 - mmengine - INFO - Epoch(train) [7][1000/5005] lr: 1.0000e-01 eta: 1 day, 18:44:55 time: 0.2177 data_time: 0.0015 loss: 2.2943 03/05 04:25:39 - mmengine - INFO - Epoch(train) [7][1100/5005] lr: 1.0000e-01 eta: 1 day, 18:44:14 time: 0.2213 data_time: 0.0015 loss: 2.5703 03/05 04:26:01 - mmengine - INFO - Epoch(train) [7][1200/5005] lr: 1.0000e-01 eta: 1 day, 18:43:29 time: 0.2185 data_time: 0.0016 loss: 2.5799 03/05 04:26:23 - mmengine - INFO - Epoch(train) [7][1300/5005] lr: 1.0000e-01 eta: 1 day, 18:42:48 time: 0.2207 data_time: 0.0015 loss: 2.4322 03/05 04:26:46 - mmengine - INFO - Epoch(train) [7][1400/5005] lr: 1.0000e-01 eta: 1 day, 18:42:14 time: 0.2201 data_time: 0.0015 loss: 2.4373 03/05 04:27:08 - mmengine - INFO - Epoch(train) [7][1500/5005] lr: 1.0000e-01 eta: 1 day, 18:41:40 time: 0.2206 data_time: 0.0016 loss: 2.4836 03/05 04:27:30 - mmengine - INFO - Epoch(train) [7][1600/5005] lr: 1.0000e-01 eta: 1 day, 18:40:56 time: 0.2199 data_time: 0.0016 loss: 2.7554 03/05 04:27:52 - mmengine - INFO - Epoch(train) [7][1700/5005] lr: 1.0000e-01 eta: 1 day, 18:40:14 time: 0.2272 data_time: 0.0014 loss: 2.5303 03/05 04:28:15 - mmengine - INFO - Epoch(train) [7][1800/5005] lr: 1.0000e-01 eta: 1 day, 18:39:40 time: 0.2322 data_time: 0.0015 loss: 2.4432 03/05 04:28:37 - mmengine - INFO - Epoch(train) [7][1900/5005] lr: 1.0000e-01 eta: 1 day, 18:38:59 time: 0.2201 data_time: 0.0013 loss: 2.4507 03/05 04:28:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:28:59 - mmengine - INFO - Epoch(train) [7][2000/5005] lr: 1.0000e-01 eta: 1 day, 18:38:18 time: 0.2221 data_time: 0.0015 loss: 2.4994 03/05 04:29:21 - mmengine - INFO - Epoch(train) [7][2100/5005] lr: 1.0000e-01 eta: 1 day, 18:37:35 time: 0.2237 data_time: 0.0016 loss: 2.5488 03/05 04:29:44 - mmengine - INFO - Epoch(train) [7][2200/5005] lr: 1.0000e-01 eta: 1 day, 18:37:02 time: 0.2195 data_time: 0.0014 loss: 2.5524 03/05 04:30:06 - mmengine - INFO - Epoch(train) [7][2300/5005] lr: 1.0000e-01 eta: 1 day, 18:36:25 time: 0.2176 data_time: 0.0015 loss: 2.3866 03/05 04:30:28 - mmengine - INFO - Epoch(train) [7][2400/5005] lr: 1.0000e-01 eta: 1 day, 18:35:51 time: 0.2192 data_time: 0.0015 loss: 2.3433 03/05 04:30:50 - mmengine - INFO - Epoch(train) [7][2500/5005] lr: 1.0000e-01 eta: 1 day, 18:35:07 time: 0.2250 data_time: 0.0016 loss: 2.4975 03/05 04:31:13 - mmengine - INFO - Epoch(train) [7][2600/5005] lr: 1.0000e-01 eta: 1 day, 18:34:37 time: 0.2180 data_time: 0.0015 loss: 2.4645 03/05 04:31:35 - mmengine - INFO - Epoch(train) [7][2700/5005] lr: 1.0000e-01 eta: 1 day, 18:33:57 time: 0.2195 data_time: 0.0015 loss: 2.4835 03/05 04:31:57 - mmengine - INFO - Epoch(train) [7][2800/5005] lr: 1.0000e-01 eta: 1 day, 18:33:21 time: 0.2183 data_time: 0.0016 loss: 2.4614 03/05 04:32:19 - mmengine - INFO - Epoch(train) [7][2900/5005] lr: 1.0000e-01 eta: 1 day, 18:32:37 time: 0.2208 data_time: 0.0016 loss: 2.5124 03/05 04:32:35 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:32:41 - mmengine - INFO - Epoch(train) [7][3000/5005] lr: 1.0000e-01 eta: 1 day, 18:32:01 time: 0.2192 data_time: 0.0014 loss: 2.4938 03/05 04:33:04 - mmengine - INFO - Epoch(train) [7][3100/5005] lr: 1.0000e-01 eta: 1 day, 18:31:26 time: 0.2219 data_time: 0.0015 loss: 2.6095 03/05 04:33:26 - mmengine - INFO - Epoch(train) [7][3200/5005] lr: 1.0000e-01 eta: 1 day, 18:30:54 time: 0.2210 data_time: 0.0015 loss: 2.7905 03/05 04:33:48 - mmengine - INFO - Epoch(train) [7][3300/5005] lr: 1.0000e-01 eta: 1 day, 18:30:15 time: 0.2399 data_time: 0.0014 loss: 2.4472 03/05 04:34:11 - mmengine - INFO - Epoch(train) [7][3400/5005] lr: 1.0000e-01 eta: 1 day, 18:29:38 time: 0.2183 data_time: 0.0014 loss: 2.5359 03/05 04:34:33 - mmengine - INFO - Epoch(train) [7][3500/5005] lr: 1.0000e-01 eta: 1 day, 18:28:59 time: 0.2220 data_time: 0.0015 loss: 2.4949 03/05 04:34:55 - mmengine - INFO - Epoch(train) [7][3600/5005] lr: 1.0000e-01 eta: 1 day, 18:28:26 time: 0.2199 data_time: 0.0015 loss: 2.6220 03/05 04:35:17 - mmengine - INFO - Epoch(train) [7][3700/5005] lr: 1.0000e-01 eta: 1 day, 18:27:50 time: 0.2223 data_time: 0.0014 loss: 2.3583 03/05 04:35:40 - mmengine - INFO - Epoch(train) [7][3800/5005] lr: 1.0000e-01 eta: 1 day, 18:27:11 time: 0.2220 data_time: 0.0015 loss: 2.5829 03/05 04:36:02 - mmengine - INFO - Epoch(train) [7][3900/5005] lr: 1.0000e-01 eta: 1 day, 18:26:32 time: 0.2173 data_time: 0.0014 loss: 2.5510 03/05 04:36:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:36:24 - mmengine - INFO - Epoch(train) [7][4000/5005] lr: 1.0000e-01 eta: 1 day, 18:25:59 time: 0.2407 data_time: 0.0015 loss: 2.6099 03/05 04:36:46 - mmengine - INFO - Epoch(train) [7][4100/5005] lr: 1.0000e-01 eta: 1 day, 18:25:26 time: 0.2200 data_time: 0.0017 loss: 2.7129 03/05 04:37:09 - mmengine - INFO - Epoch(train) [7][4200/5005] lr: 1.0000e-01 eta: 1 day, 18:24:48 time: 0.2193 data_time: 0.0014 loss: 2.3643 03/05 04:37:31 - mmengine - INFO - Epoch(train) [7][4300/5005] lr: 1.0000e-01 eta: 1 day, 18:24:12 time: 0.2233 data_time: 0.0016 loss: 2.5484 03/05 04:37:53 - mmengine - INFO - Epoch(train) [7][4400/5005] lr: 1.0000e-01 eta: 1 day, 18:23:32 time: 0.2197 data_time: 0.0015 loss: 2.5564 03/05 04:38:15 - mmengine - INFO - Epoch(train) [7][4500/5005] lr: 1.0000e-01 eta: 1 day, 18:23:00 time: 0.2378 data_time: 0.0016 loss: 2.5625 03/05 04:38:38 - mmengine - INFO - Epoch(train) [7][4600/5005] lr: 1.0000e-01 eta: 1 day, 18:22:24 time: 0.2185 data_time: 0.0016 loss: 2.5468 03/05 04:39:00 - mmengine - INFO - Epoch(train) [7][4700/5005] lr: 1.0000e-01 eta: 1 day, 18:21:55 time: 0.2373 data_time: 0.0016 loss: 2.5253 03/05 04:39:22 - mmengine - INFO - Epoch(train) [7][4800/5005] lr: 1.0000e-01 eta: 1 day, 18:21:15 time: 0.2250 data_time: 0.0015 loss: 2.2288 03/05 04:39:46 - mmengine - INFO - Epoch(train) [7][4900/5005] lr: 1.0000e-01 eta: 1 day, 18:21:03 time: 0.3071 data_time: 0.0013 loss: 2.5184 03/05 04:40:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:40:16 - mmengine - INFO - Epoch(train) [7][5000/5005] lr: 1.0000e-01 eta: 1 day, 18:22:55 time: 0.3005 data_time: 0.0013 loss: 2.4387 03/05 04:40:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:40:20 - mmengine - INFO - Saving checkpoint at 7 epochs 03/05 04:40:34 - mmengine - INFO - Epoch(val) [7][100/196] eta: 0:00:12 time: 0.0182 data_time: 0.0003 03/05 04:40:47 - mmengine - INFO - Epoch(val) [7][196/196] accuracy/top1: 46.8760 accuracy/top5: 72.9960 03/05 04:41:18 - mmengine - INFO - Epoch(train) [8][ 100/5005] lr: 1.0000e-01 eta: 1 day, 18:25:07 time: 0.2461 data_time: 0.0019 loss: 2.5317 03/05 04:41:40 - mmengine - INFO - Epoch(train) [8][ 200/5005] lr: 1.0000e-01 eta: 1 day, 18:24:27 time: 0.2198 data_time: 0.0016 loss: 2.6138 03/05 04:42:03 - mmengine - INFO - Epoch(train) [8][ 300/5005] lr: 1.0000e-01 eta: 1 day, 18:24:05 time: 0.2179 data_time: 0.0015 loss: 2.3480 03/05 04:42:25 - mmengine - INFO - Epoch(train) [8][ 400/5005] lr: 1.0000e-01 eta: 1 day, 18:23:23 time: 0.2194 data_time: 0.0015 loss: 2.5206 03/05 04:42:48 - mmengine - INFO - Epoch(train) [8][ 500/5005] lr: 1.0000e-01 eta: 1 day, 18:22:55 time: 0.2364 data_time: 0.0018 loss: 2.2514 03/05 04:43:10 - mmengine - INFO - Epoch(train) [8][ 600/5005] lr: 1.0000e-01 eta: 1 day, 18:22:13 time: 0.2199 data_time: 0.0016 loss: 2.4089 03/05 04:43:33 - mmengine - INFO - Epoch(train) [8][ 700/5005] lr: 1.0000e-01 eta: 1 day, 18:21:54 time: 0.2250 data_time: 0.0016 loss: 2.5512 03/05 04:43:55 - mmengine - INFO - Epoch(train) [8][ 800/5005] lr: 1.0000e-01 eta: 1 day, 18:21:14 time: 0.2211 data_time: 0.0017 loss: 2.6252 03/05 04:44:17 - mmengine - INFO - Epoch(train) [8][ 900/5005] lr: 1.0000e-01 eta: 1 day, 18:20:39 time: 0.2234 data_time: 0.0015 loss: 2.3414 03/05 04:44:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:44:40 - mmengine - INFO - Epoch(train) [8][1000/5005] lr: 1.0000e-01 eta: 1 day, 18:20:05 time: 0.2171 data_time: 0.0015 loss: 2.5947 03/05 04:45:02 - mmengine - INFO - Epoch(train) [8][1100/5005] lr: 1.0000e-01 eta: 1 day, 18:19:37 time: 0.2200 data_time: 0.0015 loss: 2.3927 03/05 04:45:24 - mmengine - INFO - Epoch(train) [8][1200/5005] lr: 1.0000e-01 eta: 1 day, 18:18:58 time: 0.2237 data_time: 0.0015 loss: 2.2821 03/05 04:45:46 - mmengine - INFO - Epoch(train) [8][1300/5005] lr: 1.0000e-01 eta: 1 day, 18:18:20 time: 0.2187 data_time: 0.0019 loss: 2.4632 03/05 04:46:09 - mmengine - INFO - Epoch(train) [8][1400/5005] lr: 1.0000e-01 eta: 1 day, 18:17:47 time: 0.2166 data_time: 0.0015 loss: 2.1466 03/05 04:46:32 - mmengine - INFO - Epoch(train) [8][1500/5005] lr: 1.0000e-01 eta: 1 day, 18:17:22 time: 0.2176 data_time: 0.0016 loss: 2.3674 03/05 04:46:53 - mmengine - INFO - Epoch(train) [8][1600/5005] lr: 1.0000e-01 eta: 1 day, 18:16:40 time: 0.2182 data_time: 0.0016 loss: 2.2907 03/05 04:47:16 - mmengine - INFO - Epoch(train) [8][1700/5005] lr: 1.0000e-01 eta: 1 day, 18:16:04 time: 0.2189 data_time: 0.0014 loss: 2.4623 03/05 04:47:38 - mmengine - INFO - Epoch(train) [8][1800/5005] lr: 1.0000e-01 eta: 1 day, 18:15:33 time: 0.2409 data_time: 0.0016 loss: 2.3565 03/05 04:48:01 - mmengine - INFO - Epoch(train) [8][1900/5005] lr: 1.0000e-01 eta: 1 day, 18:15:02 time: 0.2412 data_time: 0.0014 loss: 2.2734 03/05 04:48:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:48:23 - mmengine - INFO - Epoch(train) [8][2000/5005] lr: 1.0000e-01 eta: 1 day, 18:14:23 time: 0.2237 data_time: 0.0015 loss: 2.1965 03/05 04:48:45 - mmengine - INFO - Epoch(train) [8][2100/5005] lr: 1.0000e-01 eta: 1 day, 18:13:47 time: 0.2211 data_time: 0.0015 loss: 2.4690 03/05 04:49:07 - mmengine - INFO - Epoch(train) [8][2200/5005] lr: 1.0000e-01 eta: 1 day, 18:13:15 time: 0.2223 data_time: 0.0015 loss: 2.2786 03/05 04:49:30 - mmengine - INFO - Epoch(train) [8][2300/5005] lr: 1.0000e-01 eta: 1 day, 18:12:42 time: 0.2459 data_time: 0.0016 loss: 2.1885 03/05 04:49:52 - mmengine - INFO - Epoch(train) [8][2400/5005] lr: 1.0000e-01 eta: 1 day, 18:12:05 time: 0.2191 data_time: 0.0014 loss: 2.4130 03/05 04:50:14 - mmengine - INFO - Epoch(train) [8][2500/5005] lr: 1.0000e-01 eta: 1 day, 18:11:28 time: 0.2194 data_time: 0.0015 loss: 2.3979 03/05 04:50:36 - mmengine - INFO - Epoch(train) [8][2600/5005] lr: 1.0000e-01 eta: 1 day, 18:10:51 time: 0.2191 data_time: 0.0014 loss: 2.2901 03/05 04:50:59 - mmengine - INFO - Epoch(train) [8][2700/5005] lr: 1.0000e-01 eta: 1 day, 18:10:24 time: 0.2411 data_time: 0.0014 loss: 2.4738 03/05 04:51:21 - mmengine - INFO - Epoch(train) [8][2800/5005] lr: 1.0000e-01 eta: 1 day, 18:09:51 time: 0.2188 data_time: 0.0014 loss: 2.4513 03/05 04:51:43 - mmengine - INFO - Epoch(train) [8][2900/5005] lr: 1.0000e-01 eta: 1 day, 18:09:16 time: 0.2173 data_time: 0.0015 loss: 2.4099 03/05 04:51:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:52:05 - mmengine - INFO - Epoch(train) [8][3000/5005] lr: 1.0000e-01 eta: 1 day, 18:08:39 time: 0.2165 data_time: 0.0016 loss: 2.4205 03/05 04:52:28 - mmengine - INFO - Epoch(train) [8][3100/5005] lr: 1.0000e-01 eta: 1 day, 18:08:07 time: 0.2190 data_time: 0.0015 loss: 2.3135 03/05 04:52:50 - mmengine - INFO - Epoch(train) [8][3200/5005] lr: 1.0000e-01 eta: 1 day, 18:07:29 time: 0.2204 data_time: 0.0014 loss: 2.4594 03/05 04:53:12 - mmengine - INFO - Epoch(train) [8][3300/5005] lr: 1.0000e-01 eta: 1 day, 18:06:58 time: 0.2311 data_time: 0.0016 loss: 2.6355 03/05 04:53:34 - mmengine - INFO - Epoch(train) [8][3400/5005] lr: 1.0000e-01 eta: 1 day, 18:06:21 time: 0.2202 data_time: 0.0015 loss: 2.2246 03/05 04:53:56 - mmengine - INFO - Epoch(train) [8][3500/5005] lr: 1.0000e-01 eta: 1 day, 18:05:44 time: 0.2172 data_time: 0.0015 loss: 2.4281 03/05 04:54:18 - mmengine - INFO - Epoch(train) [8][3600/5005] lr: 1.0000e-01 eta: 1 day, 18:05:08 time: 0.2242 data_time: 0.0016 loss: 2.3839 03/05 04:54:41 - mmengine - INFO - Epoch(train) [8][3700/5005] lr: 1.0000e-01 eta: 1 day, 18:04:41 time: 0.2224 data_time: 0.0015 loss: 2.2957 03/05 04:55:04 - mmengine - INFO - Epoch(train) [8][3800/5005] lr: 1.0000e-01 eta: 1 day, 18:04:10 time: 0.2389 data_time: 0.0016 loss: 2.5031 03/05 04:55:26 - mmengine - INFO - Epoch(train) [8][3900/5005] lr: 1.0000e-01 eta: 1 day, 18:03:34 time: 0.2348 data_time: 0.0015 loss: 2.4371 03/05 04:55:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:55:48 - mmengine - INFO - Epoch(train) [8][4000/5005] lr: 1.0000e-01 eta: 1 day, 18:02:58 time: 0.2373 data_time: 0.0015 loss: 2.3062 03/05 04:56:10 - mmengine - INFO - Epoch(train) [8][4100/5005] lr: 1.0000e-01 eta: 1 day, 18:02:24 time: 0.2204 data_time: 0.0014 loss: 2.5841 03/05 04:56:32 - mmengine - INFO - Epoch(train) [8][4200/5005] lr: 1.0000e-01 eta: 1 day, 18:01:52 time: 0.2386 data_time: 0.0015 loss: 2.4567 03/05 04:56:54 - mmengine - INFO - Epoch(train) [8][4300/5005] lr: 1.0000e-01 eta: 1 day, 18:01:14 time: 0.2192 data_time: 0.0015 loss: 2.5614 03/05 04:57:17 - mmengine - INFO - Epoch(train) [8][4400/5005] lr: 1.0000e-01 eta: 1 day, 18:00:40 time: 0.2252 data_time: 0.0016 loss: 2.3754 03/05 04:57:39 - mmengine - INFO - Epoch(train) [8][4500/5005] lr: 1.0000e-01 eta: 1 day, 18:00:08 time: 0.2345 data_time: 0.0014 loss: 2.3701 03/05 04:58:01 - mmengine - INFO - Epoch(train) [8][4600/5005] lr: 1.0000e-01 eta: 1 day, 17:59:34 time: 0.2179 data_time: 0.0015 loss: 2.3676 03/05 04:58:23 - mmengine - INFO - Epoch(train) [8][4700/5005] lr: 1.0000e-01 eta: 1 day, 17:58:59 time: 0.2203 data_time: 0.0015 loss: 2.2832 03/05 04:58:45 - mmengine - INFO - Epoch(train) [8][4800/5005] lr: 1.0000e-01 eta: 1 day, 17:58:24 time: 0.2209 data_time: 0.0015 loss: 2.6494 03/05 04:59:09 - mmengine - INFO - Epoch(train) [8][4900/5005] lr: 1.0000e-01 eta: 1 day, 17:58:10 time: 0.2938 data_time: 0.0012 loss: 2.4073 03/05 04:59:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:59:38 - mmengine - INFO - Epoch(train) [8][5000/5005] lr: 1.0000e-01 eta: 1 day, 17:59:34 time: 0.2897 data_time: 0.0012 loss: 2.4212 03/05 04:59:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 04:59:42 - mmengine - INFO - Saving checkpoint at 8 epochs 03/05 04:59:56 - mmengine - INFO - Epoch(val) [8][100/196] eta: 0:00:11 time: 0.0185 data_time: 0.0003 03/05 05:00:09 - mmengine - INFO - Epoch(val) [8][196/196] accuracy/top1: 49.7620 accuracy/top5: 75.8980 03/05 05:00:40 - mmengine - INFO - Epoch(train) [9][ 100/5005] lr: 1.0000e-01 eta: 1 day, 18:01:18 time: 0.2196 data_time: 0.0014 loss: 2.4717 03/05 05:01:02 - mmengine - INFO - Epoch(train) [9][ 200/5005] lr: 1.0000e-01 eta: 1 day, 18:00:50 time: 0.2199 data_time: 0.0016 loss: 2.2105 03/05 05:01:24 - mmengine - INFO - Epoch(train) [9][ 300/5005] lr: 1.0000e-01 eta: 1 day, 18:00:11 time: 0.2189 data_time: 0.0019 loss: 2.4041 03/05 05:01:46 - mmengine - INFO - Epoch(train) [9][ 400/5005] lr: 1.0000e-01 eta: 1 day, 17:59:32 time: 0.2210 data_time: 0.0016 loss: 2.3599 03/05 05:02:09 - mmengine - INFO - Epoch(train) [9][ 500/5005] lr: 1.0000e-01 eta: 1 day, 17:59:01 time: 0.2170 data_time: 0.0016 loss: 2.2021 03/05 05:02:31 - mmengine - INFO - Epoch(train) [9][ 600/5005] lr: 1.0000e-01 eta: 1 day, 17:58:29 time: 0.2195 data_time: 0.0014 loss: 2.2759 03/05 05:02:53 - mmengine - INFO - Epoch(train) [9][ 700/5005] lr: 1.0000e-01 eta: 1 day, 17:57:51 time: 0.2189 data_time: 0.0016 loss: 2.2797 03/05 05:03:15 - mmengine - INFO - Epoch(train) [9][ 800/5005] lr: 1.0000e-01 eta: 1 day, 17:57:12 time: 0.2192 data_time: 0.0016 loss: 2.4165 03/05 05:03:38 - mmengine - INFO - Epoch(train) [9][ 900/5005] lr: 1.0000e-01 eta: 1 day, 17:56:46 time: 0.2207 data_time: 0.0014 loss: 2.5129 03/05 05:03:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:04:01 - mmengine - INFO - Epoch(train) [9][1000/5005] lr: 1.0000e-01 eta: 1 day, 17:56:29 time: 0.2190 data_time: 0.0015 loss: 2.3565 03/05 05:04:23 - mmengine - INFO - Epoch(train) [9][1100/5005] lr: 1.0000e-01 eta: 1 day, 17:55:51 time: 0.2204 data_time: 0.0015 loss: 2.4165 03/05 05:04:45 - mmengine - INFO - Epoch(train) [9][1200/5005] lr: 1.0000e-01 eta: 1 day, 17:55:14 time: 0.2210 data_time: 0.0014 loss: 2.5288 03/05 05:05:07 - mmengine - INFO - Epoch(train) [9][1300/5005] lr: 1.0000e-01 eta: 1 day, 17:54:42 time: 0.2224 data_time: 0.0016 loss: 2.4557 03/05 05:05:29 - mmengine - INFO - Epoch(train) [9][1400/5005] lr: 1.0000e-01 eta: 1 day, 17:54:07 time: 0.2204 data_time: 0.0016 loss: 2.4255 03/05 05:05:52 - mmengine - INFO - Epoch(train) [9][1500/5005] lr: 1.0000e-01 eta: 1 day, 17:53:37 time: 0.2194 data_time: 0.0014 loss: 2.3051 03/05 05:06:14 - mmengine - INFO - Epoch(train) [9][1600/5005] lr: 1.0000e-01 eta: 1 day, 17:53:00 time: 0.2209 data_time: 0.0017 loss: 2.3200 03/05 05:06:36 - mmengine - INFO - Epoch(train) [9][1700/5005] lr: 1.0000e-01 eta: 1 day, 17:52:29 time: 0.2198 data_time: 0.0016 loss: 2.5885 03/05 05:06:58 - mmengine - INFO - Epoch(train) [9][1800/5005] lr: 1.0000e-01 eta: 1 day, 17:51:57 time: 0.2190 data_time: 0.0014 loss: 2.4723 03/05 05:07:21 - mmengine - INFO - Epoch(train) [9][1900/5005] lr: 1.0000e-01 eta: 1 day, 17:51:24 time: 0.2204 data_time: 0.0015 loss: 2.5994 03/05 05:07:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:07:43 - mmengine - INFO - Epoch(train) [9][2000/5005] lr: 1.0000e-01 eta: 1 day, 17:50:46 time: 0.2165 data_time: 0.0015 loss: 2.6108 03/05 05:08:05 - mmengine - INFO - Epoch(train) [9][2100/5005] lr: 1.0000e-01 eta: 1 day, 17:50:15 time: 0.2399 data_time: 0.0015 loss: 2.5105 03/05 05:08:27 - mmengine - INFO - Epoch(train) [9][2200/5005] lr: 1.0000e-01 eta: 1 day, 17:49:46 time: 0.2346 data_time: 0.0015 loss: 2.1226 03/05 05:08:50 - mmengine - INFO - Epoch(train) [9][2300/5005] lr: 1.0000e-01 eta: 1 day, 17:49:11 time: 0.2170 data_time: 0.0017 loss: 2.2490 03/05 05:09:11 - mmengine - INFO - Epoch(train) [9][2400/5005] lr: 1.0000e-01 eta: 1 day, 17:48:34 time: 0.2247 data_time: 0.0014 loss: 2.4190 03/05 05:09:34 - mmengine - INFO - Epoch(train) [9][2500/5005] lr: 1.0000e-01 eta: 1 day, 17:47:59 time: 0.2206 data_time: 0.0016 loss: 2.4333 03/05 05:09:56 - mmengine - INFO - Epoch(train) [9][2600/5005] lr: 1.0000e-01 eta: 1 day, 17:47:27 time: 0.2221 data_time: 0.0016 loss: 2.1988 03/05 05:10:18 - mmengine - INFO - Epoch(train) [9][2700/5005] lr: 1.0000e-01 eta: 1 day, 17:46:55 time: 0.2183 data_time: 0.0016 loss: 2.3896 03/05 05:10:40 - mmengine - INFO - Epoch(train) [9][2800/5005] lr: 1.0000e-01 eta: 1 day, 17:46:21 time: 0.2202 data_time: 0.0015 loss: 2.5522 03/05 05:11:02 - mmengine - INFO - Epoch(train) [9][2900/5005] lr: 1.0000e-01 eta: 1 day, 17:45:46 time: 0.2191 data_time: 0.0015 loss: 2.6217 03/05 05:11:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:11:25 - mmengine - INFO - Epoch(train) [9][3000/5005] lr: 1.0000e-01 eta: 1 day, 17:45:15 time: 0.2463 data_time: 0.0017 loss: 2.4392 03/05 05:11:47 - mmengine - INFO - Epoch(train) [9][3100/5005] lr: 1.0000e-01 eta: 1 day, 17:44:42 time: 0.2228 data_time: 0.0015 loss: 2.4719 03/05 05:12:09 - mmengine - INFO - Epoch(train) [9][3200/5005] lr: 1.0000e-01 eta: 1 day, 17:44:11 time: 0.2223 data_time: 0.0016 loss: 2.2891 03/05 05:12:31 - mmengine - INFO - Epoch(train) [9][3300/5005] lr: 1.0000e-01 eta: 1 day, 17:43:38 time: 0.2200 data_time: 0.0016 loss: 2.3561 03/05 05:12:54 - mmengine - INFO - Epoch(train) [9][3400/5005] lr: 1.0000e-01 eta: 1 day, 17:43:03 time: 0.2184 data_time: 0.0015 loss: 2.4528 03/05 05:13:16 - mmengine - INFO - Epoch(train) [9][3500/5005] lr: 1.0000e-01 eta: 1 day, 17:42:32 time: 0.2159 data_time: 0.0014 loss: 2.2992 03/05 05:13:38 - mmengine - INFO - Epoch(train) [9][3600/5005] lr: 1.0000e-01 eta: 1 day, 17:42:02 time: 0.2183 data_time: 0.0017 loss: 2.5151 03/05 05:14:00 - mmengine - INFO - Epoch(train) [9][3700/5005] lr: 1.0000e-01 eta: 1 day, 17:41:30 time: 0.2198 data_time: 0.0015 loss: 2.4252 03/05 05:14:23 - mmengine - INFO - Epoch(train) [9][3800/5005] lr: 1.0000e-01 eta: 1 day, 17:40:55 time: 0.2209 data_time: 0.0015 loss: 2.3806 03/05 05:14:45 - mmengine - INFO - Epoch(train) [9][3900/5005] lr: 1.0000e-01 eta: 1 day, 17:40:24 time: 0.2191 data_time: 0.0016 loss: 2.5781 03/05 05:14:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:15:07 - mmengine - INFO - Epoch(train) [9][4000/5005] lr: 1.0000e-01 eta: 1 day, 17:39:58 time: 0.2415 data_time: 0.0014 loss: 2.5779 03/05 05:15:30 - mmengine - INFO - Epoch(train) [9][4100/5005] lr: 1.0000e-01 eta: 1 day, 17:39:23 time: 0.2232 data_time: 0.0015 loss: 2.2630 03/05 05:15:52 - mmengine - INFO - Epoch(train) [9][4200/5005] lr: 1.0000e-01 eta: 1 day, 17:38:51 time: 0.2217 data_time: 0.0020 loss: 2.3418 03/05 05:16:14 - mmengine - INFO - Epoch(train) [9][4300/5005] lr: 1.0000e-01 eta: 1 day, 17:38:20 time: 0.2199 data_time: 0.0015 loss: 2.4327 03/05 05:16:36 - mmengine - INFO - Epoch(train) [9][4400/5005] lr: 1.0000e-01 eta: 1 day, 17:37:47 time: 0.2281 data_time: 0.0015 loss: 2.4721 03/05 05:16:59 - mmengine - INFO - Epoch(train) [9][4500/5005] lr: 1.0000e-01 eta: 1 day, 17:37:19 time: 0.2271 data_time: 0.0017 loss: 2.5389 03/05 05:17:21 - mmengine - INFO - Epoch(train) [9][4600/5005] lr: 1.0000e-01 eta: 1 day, 17:36:44 time: 0.2196 data_time: 0.0015 loss: 2.1594 03/05 05:17:43 - mmengine - INFO - Epoch(train) [9][4700/5005] lr: 1.0000e-01 eta: 1 day, 17:36:11 time: 0.2242 data_time: 0.0015 loss: 2.6023 03/05 05:18:05 - mmengine - INFO - Epoch(train) [9][4800/5005] lr: 1.0000e-01 eta: 1 day, 17:35:39 time: 0.2211 data_time: 0.0014 loss: 2.1965 03/05 05:18:29 - mmengine - INFO - Epoch(train) [9][4900/5005] lr: 1.0000e-01 eta: 1 day, 17:35:33 time: 0.2919 data_time: 0.0012 loss: 2.4089 03/05 05:18:47 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:18:59 - mmengine - INFO - Epoch(train) [9][5000/5005] lr: 1.0000e-01 eta: 1 day, 17:36:57 time: 0.3124 data_time: 0.0012 loss: 2.4264 03/05 05:19:01 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:19:04 - mmengine - INFO - Saving checkpoint at 9 epochs 03/05 05:19:17 - mmengine - INFO - Epoch(val) [9][100/196] eta: 0:00:12 time: 0.0188 data_time: 0.0003 03/05 05:19:31 - mmengine - INFO - Epoch(val) [9][196/196] accuracy/top1: 50.7200 accuracy/top5: 76.5020 03/05 05:20:01 - mmengine - INFO - Epoch(train) [10][ 100/5005] lr: 1.0000e-01 eta: 1 day, 17:38:29 time: 0.2205 data_time: 0.0015 loss: 2.5384 03/05 05:20:24 - mmengine - INFO - Epoch(train) [10][ 200/5005] lr: 1.0000e-01 eta: 1 day, 17:37:57 time: 0.2215 data_time: 0.0015 loss: 2.1820 03/05 05:20:46 - mmengine - INFO - Epoch(train) [10][ 300/5005] lr: 1.0000e-01 eta: 1 day, 17:37:27 time: 0.2232 data_time: 0.0017 loss: 2.3523 03/05 05:21:08 - mmengine - INFO - Epoch(train) [10][ 400/5005] lr: 1.0000e-01 eta: 1 day, 17:36:52 time: 0.2193 data_time: 0.0017 loss: 2.5268 03/05 05:21:30 - mmengine - INFO - Epoch(train) [10][ 500/5005] lr: 1.0000e-01 eta: 1 day, 17:36:21 time: 0.2208 data_time: 0.0017 loss: 2.3469 03/05 05:21:53 - mmengine - INFO - Epoch(train) [10][ 600/5005] lr: 1.0000e-01 eta: 1 day, 17:35:51 time: 0.2184 data_time: 0.0016 loss: 2.4347 03/05 05:22:15 - mmengine - INFO - Epoch(train) [10][ 700/5005] lr: 1.0000e-01 eta: 1 day, 17:35:19 time: 0.2200 data_time: 0.0015 loss: 2.3715 03/05 05:22:37 - mmengine - INFO - Epoch(train) [10][ 800/5005] lr: 1.0000e-01 eta: 1 day, 17:34:42 time: 0.2199 data_time: 0.0016 loss: 2.3324 03/05 05:22:59 - mmengine - INFO - Epoch(train) [10][ 900/5005] lr: 1.0000e-01 eta: 1 day, 17:34:14 time: 0.2262 data_time: 0.0015 loss: 2.3404 03/05 05:23:12 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:23:22 - mmengine - INFO - Epoch(train) [10][1000/5005] lr: 1.0000e-01 eta: 1 day, 17:33:46 time: 0.2212 data_time: 0.0015 loss: 2.3339 03/05 05:23:44 - mmengine - INFO - Epoch(train) [10][1100/5005] lr: 1.0000e-01 eta: 1 day, 17:33:11 time: 0.2254 data_time: 0.0016 loss: 2.0203 03/05 05:24:06 - mmengine - INFO - Epoch(train) [10][1200/5005] lr: 1.0000e-01 eta: 1 day, 17:32:33 time: 0.2207 data_time: 0.0017 loss: 2.1275 03/05 05:24:28 - mmengine - INFO - Epoch(train) [10][1300/5005] lr: 1.0000e-01 eta: 1 day, 17:32:06 time: 0.2229 data_time: 0.0015 loss: 2.0679 03/05 05:24:51 - mmengine - INFO - Epoch(train) [10][1400/5005] lr: 1.0000e-01 eta: 1 day, 17:31:39 time: 0.2219 data_time: 0.0016 loss: 2.4620 03/05 05:25:13 - mmengine - INFO - Epoch(train) [10][1500/5005] lr: 1.0000e-01 eta: 1 day, 17:31:03 time: 0.2159 data_time: 0.0016 loss: 2.5024 03/05 05:25:35 - mmengine - INFO - Epoch(train) [10][1600/5005] lr: 1.0000e-01 eta: 1 day, 17:30:30 time: 0.2243 data_time: 0.0014 loss: 2.4898 03/05 05:25:57 - mmengine - INFO - Epoch(train) [10][1700/5005] lr: 1.0000e-01 eta: 1 day, 17:29:56 time: 0.2187 data_time: 0.0015 loss: 2.4544 03/05 05:26:20 - mmengine - INFO - Epoch(train) [10][1800/5005] lr: 1.0000e-01 eta: 1 day, 17:29:31 time: 0.2381 data_time: 0.0018 loss: 2.5038 03/05 05:26:42 - mmengine - INFO - Epoch(train) [10][1900/5005] lr: 1.0000e-01 eta: 1 day, 17:28:56 time: 0.2183 data_time: 0.0016 loss: 2.2478 03/05 05:26:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:27:04 - mmengine - INFO - Epoch(train) [10][2000/5005] lr: 1.0000e-01 eta: 1 day, 17:28:22 time: 0.2190 data_time: 0.0016 loss: 2.3262 03/05 05:27:26 - mmengine - INFO - Epoch(train) [10][2100/5005] lr: 1.0000e-01 eta: 1 day, 17:27:50 time: 0.2184 data_time: 0.0015 loss: 2.3495 03/05 05:27:48 - mmengine - INFO - Epoch(train) [10][2200/5005] lr: 1.0000e-01 eta: 1 day, 17:27:22 time: 0.2186 data_time: 0.0015 loss: 2.4443 03/05 05:28:11 - mmengine - INFO - Epoch(train) [10][2300/5005] lr: 1.0000e-01 eta: 1 day, 17:26:51 time: 0.2182 data_time: 0.0016 loss: 2.3588 03/05 05:28:33 - mmengine - INFO - Epoch(train) [10][2400/5005] lr: 1.0000e-01 eta: 1 day, 17:26:20 time: 0.2187 data_time: 0.0017 loss: 2.1367 03/05 05:28:55 - mmengine - INFO - Epoch(train) [10][2500/5005] lr: 1.0000e-01 eta: 1 day, 17:25:45 time: 0.2186 data_time: 0.0016 loss: 2.1156 03/05 05:29:17 - mmengine - INFO - Epoch(train) [10][2600/5005] lr: 1.0000e-01 eta: 1 day, 17:25:15 time: 0.2197 data_time: 0.0016 loss: 2.2658 03/05 05:29:39 - mmengine - INFO - Epoch(train) [10][2700/5005] lr: 1.0000e-01 eta: 1 day, 17:24:42 time: 0.2169 data_time: 0.0015 loss: 2.5538 03/05 05:30:02 - mmengine - INFO - Epoch(train) [10][2800/5005] lr: 1.0000e-01 eta: 1 day, 17:24:11 time: 0.2183 data_time: 0.0015 loss: 2.3754 03/05 05:30:24 - mmengine - INFO - Epoch(train) [10][2900/5005] lr: 1.0000e-01 eta: 1 day, 17:23:37 time: 0.2190 data_time: 0.0017 loss: 2.3221 03/05 05:30:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:30:46 - mmengine - INFO - Epoch(train) [10][3000/5005] lr: 1.0000e-01 eta: 1 day, 17:23:10 time: 0.2474 data_time: 0.0015 loss: 2.5290 03/05 05:31:08 - mmengine - INFO - Epoch(train) [10][3100/5005] lr: 1.0000e-01 eta: 1 day, 17:22:38 time: 0.2369 data_time: 0.0015 loss: 2.1685 03/05 05:31:30 - mmengine - INFO - Epoch(train) [10][3200/5005] lr: 1.0000e-01 eta: 1 day, 17:22:05 time: 0.2259 data_time: 0.0015 loss: 2.2372 03/05 05:31:53 - mmengine - INFO - Epoch(train) [10][3300/5005] lr: 1.0000e-01 eta: 1 day, 17:21:33 time: 0.2220 data_time: 0.0014 loss: 2.3781 03/05 05:32:15 - mmengine - INFO - Epoch(train) [10][3400/5005] lr: 1.0000e-01 eta: 1 day, 17:21:00 time: 0.2191 data_time: 0.0014 loss: 2.3588 03/05 05:32:37 - mmengine - INFO - Epoch(train) [10][3500/5005] lr: 1.0000e-01 eta: 1 day, 17:20:27 time: 0.2204 data_time: 0.0018 loss: 2.4329 03/05 05:32:59 - mmengine - INFO - Epoch(train) [10][3600/5005] lr: 1.0000e-01 eta: 1 day, 17:19:57 time: 0.2370 data_time: 0.0015 loss: 2.4949 03/05 05:33:21 - mmengine - INFO - Epoch(train) [10][3700/5005] lr: 1.0000e-01 eta: 1 day, 17:19:28 time: 0.2200 data_time: 0.0016 loss: 2.4943 03/05 05:33:43 - mmengine - INFO - Epoch(train) [10][3800/5005] lr: 1.0000e-01 eta: 1 day, 17:18:55 time: 0.2205 data_time: 0.0015 loss: 2.2457 03/05 05:34:06 - mmengine - INFO - Epoch(train) [10][3900/5005] lr: 1.0000e-01 eta: 1 day, 17:18:25 time: 0.2184 data_time: 0.0015 loss: 2.3199 03/05 05:34:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:34:28 - mmengine - INFO - Epoch(train) [10][4000/5005] lr: 1.0000e-01 eta: 1 day, 17:17:56 time: 0.2371 data_time: 0.0017 loss: 2.3338 03/05 05:34:50 - mmengine - INFO - Epoch(train) [10][4100/5005] lr: 1.0000e-01 eta: 1 day, 17:17:24 time: 0.2200 data_time: 0.0016 loss: 2.3533 03/05 05:35:12 - mmengine - INFO - Epoch(train) [10][4200/5005] lr: 1.0000e-01 eta: 1 day, 17:16:52 time: 0.2188 data_time: 0.0015 loss: 2.4708 03/05 05:35:35 - mmengine - INFO - Epoch(train) [10][4300/5005] lr: 1.0000e-01 eta: 1 day, 17:16:22 time: 0.2169 data_time: 0.0016 loss: 2.4517 03/05 05:35:57 - mmengine - INFO - Epoch(train) [10][4400/5005] lr: 1.0000e-01 eta: 1 day, 17:15:50 time: 0.2185 data_time: 0.0015 loss: 2.2457 03/05 05:36:19 - mmengine - INFO - Epoch(train) [10][4500/5005] lr: 1.0000e-01 eta: 1 day, 17:15:22 time: 0.2167 data_time: 0.0015 loss: 2.2903 03/05 05:36:41 - mmengine - INFO - Epoch(train) [10][4600/5005] lr: 1.0000e-01 eta: 1 day, 17:14:51 time: 0.2207 data_time: 0.0015 loss: 2.3190 03/05 05:37:04 - mmengine - INFO - Epoch(train) [10][4700/5005] lr: 1.0000e-01 eta: 1 day, 17:14:20 time: 0.2319 data_time: 0.0015 loss: 2.2894 03/05 05:37:26 - mmengine - INFO - Epoch(train) [10][4800/5005] lr: 1.0000e-01 eta: 1 day, 17:13:52 time: 0.2219 data_time: 0.0016 loss: 2.3810 03/05 05:37:49 - mmengine - INFO - Epoch(train) [10][4900/5005] lr: 1.0000e-01 eta: 1 day, 17:13:37 time: 0.3062 data_time: 0.0012 loss: 2.1984 03/05 05:38:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:38:19 - mmengine - INFO - Epoch(train) [10][5000/5005] lr: 1.0000e-01 eta: 1 day, 17:14:44 time: 0.3003 data_time: 0.0014 loss: 2.3287 03/05 05:38:21 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:38:23 - mmengine - INFO - Saving checkpoint at 10 epochs 03/05 05:38:37 - mmengine - INFO - Epoch(val) [10][100/196] eta: 0:00:12 time: 0.0197 data_time: 0.0003 03/05 05:38:51 - mmengine - INFO - Epoch(val) [10][196/196] accuracy/top1: 51.7580 accuracy/top5: 77.2620 03/05 05:39:21 - mmengine - INFO - Epoch(train) [11][ 100/5005] lr: 1.0000e-01 eta: 1 day, 17:16:06 time: 0.2188 data_time: 0.0016 loss: 2.3802 03/05 05:39:43 - mmengine - INFO - Epoch(train) [11][ 200/5005] lr: 1.0000e-01 eta: 1 day, 17:15:31 time: 0.2197 data_time: 0.0014 loss: 2.3800 03/05 05:40:05 - mmengine - INFO - Epoch(train) [11][ 300/5005] lr: 1.0000e-01 eta: 1 day, 17:14:59 time: 0.2196 data_time: 0.0016 loss: 2.3020 03/05 05:40:28 - mmengine - INFO - Epoch(train) [11][ 400/5005] lr: 1.0000e-01 eta: 1 day, 17:14:33 time: 0.2193 data_time: 0.0016 loss: 2.3500 03/05 05:40:51 - mmengine - INFO - Epoch(train) [11][ 500/5005] lr: 1.0000e-01 eta: 1 day, 17:14:07 time: 0.2187 data_time: 0.0016 loss: 2.1635 03/05 05:41:13 - mmengine - INFO - Epoch(train) [11][ 600/5005] lr: 1.0000e-01 eta: 1 day, 17:13:32 time: 0.2165 data_time: 0.0017 loss: 2.1284 03/05 05:41:35 - mmengine - INFO - Epoch(train) [11][ 700/5005] lr: 1.0000e-01 eta: 1 day, 17:13:00 time: 0.2223 data_time: 0.0016 loss: 2.2133 03/05 05:41:57 - mmengine - INFO - Epoch(train) [11][ 800/5005] lr: 1.0000e-01 eta: 1 day, 17:12:30 time: 0.2222 data_time: 0.0017 loss: 2.1855 03/05 05:42:19 - mmengine - INFO - Epoch(train) [11][ 900/5005] lr: 1.0000e-01 eta: 1 day, 17:12:00 time: 0.2247 data_time: 0.0015 loss: 2.5431 03/05 05:42:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:42:41 - mmengine - INFO - Epoch(train) [11][1000/5005] lr: 1.0000e-01 eta: 1 day, 17:11:28 time: 0.2202 data_time: 0.0015 loss: 2.1349 03/05 05:43:04 - mmengine - INFO - Epoch(train) [11][1100/5005] lr: 1.0000e-01 eta: 1 day, 17:10:57 time: 0.2393 data_time: 0.0017 loss: 2.2210 03/05 05:43:26 - mmengine - INFO - Epoch(train) [11][1200/5005] lr: 1.0000e-01 eta: 1 day, 17:10:25 time: 0.2208 data_time: 0.0014 loss: 2.1557 03/05 05:43:48 - mmengine - INFO - Epoch(train) [11][1300/5005] lr: 1.0000e-01 eta: 1 day, 17:09:52 time: 0.2161 data_time: 0.0016 loss: 2.3460 03/05 05:44:10 - mmengine - INFO - Epoch(train) [11][1400/5005] lr: 1.0000e-01 eta: 1 day, 17:09:22 time: 0.2200 data_time: 0.0016 loss: 2.3793 03/05 05:44:32 - mmengine - INFO - Epoch(train) [11][1500/5005] lr: 1.0000e-01 eta: 1 day, 17:08:48 time: 0.2225 data_time: 0.0015 loss: 2.0859 03/05 05:44:54 - mmengine - INFO - Epoch(train) [11][1600/5005] lr: 1.0000e-01 eta: 1 day, 17:08:18 time: 0.2197 data_time: 0.0015 loss: 2.0517 03/05 05:45:17 - mmengine - INFO - Epoch(train) [11][1700/5005] lr: 1.0000e-01 eta: 1 day, 17:07:50 time: 0.2226 data_time: 0.0015 loss: 2.4300 03/05 05:45:39 - mmengine - INFO - Epoch(train) [11][1800/5005] lr: 1.0000e-01 eta: 1 day, 17:07:18 time: 0.2225 data_time: 0.0015 loss: 2.4836 03/05 05:46:01 - mmengine - INFO - Epoch(train) [11][1900/5005] lr: 1.0000e-01 eta: 1 day, 17:06:46 time: 0.2180 data_time: 0.0017 loss: 2.3869 03/05 05:46:12 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:46:23 - mmengine - INFO - Epoch(train) [11][2000/5005] lr: 1.0000e-01 eta: 1 day, 17:06:17 time: 0.2185 data_time: 0.0015 loss: 2.4069 03/05 05:46:46 - mmengine - INFO - Epoch(train) [11][2100/5005] lr: 1.0000e-01 eta: 1 day, 17:05:49 time: 0.2259 data_time: 0.0014 loss: 2.3609 03/05 05:47:08 - mmengine - INFO - Epoch(train) [11][2200/5005] lr: 1.0000e-01 eta: 1 day, 17:05:17 time: 0.2144 data_time: 0.0017 loss: 2.2140 03/05 05:47:30 - mmengine - INFO - Epoch(train) [11][2300/5005] lr: 1.0000e-01 eta: 1 day, 17:04:44 time: 0.2194 data_time: 0.0015 loss: 2.4736 03/05 05:47:52 - mmengine - INFO - Epoch(train) [11][2400/5005] lr: 1.0000e-01 eta: 1 day, 17:04:14 time: 0.2181 data_time: 0.0015 loss: 2.3029 03/05 05:48:14 - mmengine - INFO - Epoch(train) [11][2500/5005] lr: 1.0000e-01 eta: 1 day, 17:03:46 time: 0.2199 data_time: 0.0016 loss: 2.3913 03/05 05:48:37 - mmengine - INFO - Epoch(train) [11][2600/5005] lr: 1.0000e-01 eta: 1 day, 17:03:17 time: 0.2394 data_time: 0.0016 loss: 2.3211 03/05 05:48:59 - mmengine - INFO - Epoch(train) [11][2700/5005] lr: 1.0000e-01 eta: 1 day, 17:02:45 time: 0.2170 data_time: 0.0016 loss: 2.2621 03/05 05:49:21 - mmengine - INFO - Epoch(train) [11][2800/5005] lr: 1.0000e-01 eta: 1 day, 17:02:21 time: 0.2201 data_time: 0.0015 loss: 2.2357 03/05 05:49:44 - mmengine - INFO - Epoch(train) [11][2900/5005] lr: 1.0000e-01 eta: 1 day, 17:01:50 time: 0.2330 data_time: 0.0017 loss: 2.1948 03/05 05:49:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:50:06 - mmengine - INFO - Epoch(train) [11][3000/5005] lr: 1.0000e-01 eta: 1 day, 17:01:18 time: 0.2242 data_time: 0.0016 loss: 2.4462 03/05 05:50:28 - mmengine - INFO - Epoch(train) [11][3100/5005] lr: 1.0000e-01 eta: 1 day, 17:00:46 time: 0.2177 data_time: 0.0017 loss: 2.1828 03/05 05:50:50 - mmengine - INFO - Epoch(train) [11][3200/5005] lr: 1.0000e-01 eta: 1 day, 17:00:17 time: 0.2196 data_time: 0.0018 loss: 2.5965 03/05 05:51:12 - mmengine - INFO - Epoch(train) [11][3300/5005] lr: 1.0000e-01 eta: 1 day, 16:59:45 time: 0.2228 data_time: 0.0016 loss: 2.2021 03/05 05:51:34 - mmengine - INFO - Epoch(train) [11][3400/5005] lr: 1.0000e-01 eta: 1 day, 16:59:15 time: 0.2248 data_time: 0.0017 loss: 2.4047 03/05 05:51:56 - mmengine - INFO - Epoch(train) [11][3500/5005] lr: 1.0000e-01 eta: 1 day, 16:58:43 time: 0.2204 data_time: 0.0017 loss: 2.6457 03/05 05:52:19 - mmengine - INFO - Epoch(train) [11][3600/5005] lr: 1.0000e-01 eta: 1 day, 16:58:15 time: 0.2264 data_time: 0.0017 loss: 2.2363 03/05 05:52:41 - mmengine - INFO - Epoch(train) [11][3700/5005] lr: 1.0000e-01 eta: 1 day, 16:57:43 time: 0.2170 data_time: 0.0017 loss: 2.0802 03/05 05:53:03 - mmengine - INFO - Epoch(train) [11][3800/5005] lr: 1.0000e-01 eta: 1 day, 16:57:13 time: 0.2202 data_time: 0.0017 loss: 2.4028 03/05 05:53:25 - mmengine - INFO - Epoch(train) [11][3900/5005] lr: 1.0000e-01 eta: 1 day, 16:56:44 time: 0.2169 data_time: 0.0014 loss: 2.2500 03/05 05:53:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:53:48 - mmengine - INFO - Epoch(train) [11][4000/5005] lr: 1.0000e-01 eta: 1 day, 16:56:17 time: 0.2204 data_time: 0.0017 loss: 2.2866 03/05 05:54:10 - mmengine - INFO - Epoch(train) [11][4100/5005] lr: 1.0000e-01 eta: 1 day, 16:55:49 time: 0.2239 data_time: 0.0016 loss: 2.4058 03/05 05:54:32 - mmengine - INFO - Epoch(train) [11][4200/5005] lr: 1.0000e-01 eta: 1 day, 16:55:17 time: 0.2204 data_time: 0.0015 loss: 2.4399 03/05 05:54:55 - mmengine - INFO - Epoch(train) [11][4300/5005] lr: 1.0000e-01 eta: 1 day, 16:54:50 time: 0.2172 data_time: 0.0016 loss: 2.4483 03/05 05:55:17 - mmengine - INFO - Epoch(train) [11][4400/5005] lr: 1.0000e-01 eta: 1 day, 16:54:24 time: 0.2194 data_time: 0.0015 loss: 2.2190 03/05 05:55:39 - mmengine - INFO - Epoch(train) [11][4500/5005] lr: 1.0000e-01 eta: 1 day, 16:53:54 time: 0.2191 data_time: 0.0016 loss: 2.2417 03/05 05:56:01 - mmengine - INFO - Epoch(train) [11][4600/5005] lr: 1.0000e-01 eta: 1 day, 16:53:21 time: 0.2206 data_time: 0.0016 loss: 2.4078 03/05 05:56:24 - mmengine - INFO - Epoch(train) [11][4700/5005] lr: 1.0000e-01 eta: 1 day, 16:52:54 time: 0.2209 data_time: 0.0018 loss: 2.1377 03/05 05:56:46 - mmengine - INFO - Epoch(train) [11][4800/5005] lr: 1.0000e-01 eta: 1 day, 16:52:25 time: 0.2246 data_time: 0.0016 loss: 2.2228 03/05 05:57:10 - mmengine - INFO - Epoch(train) [11][4900/5005] lr: 1.0000e-01 eta: 1 day, 16:52:14 time: 0.2962 data_time: 0.0014 loss: 2.2376 03/05 05:57:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:57:40 - mmengine - INFO - Epoch(train) [11][5000/5005] lr: 1.0000e-01 eta: 1 day, 16:53:13 time: 0.2947 data_time: 0.0013 loss: 2.3839 03/05 05:57:41 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 05:57:44 - mmengine - INFO - Saving checkpoint at 11 epochs 03/05 05:57:57 - mmengine - INFO - Epoch(val) [11][100/196] eta: 0:00:12 time: 0.0184 data_time: 0.0003 03/05 05:58:11 - mmengine - INFO - Epoch(val) [11][196/196] accuracy/top1: 51.3360 accuracy/top5: 76.9000 03/05 05:58:42 - mmengine - INFO - Epoch(train) [12][ 100/5005] lr: 1.0000e-01 eta: 1 day, 16:54:26 time: 0.2238 data_time: 0.0016 loss: 2.1419 03/05 05:59:04 - mmengine - INFO - Epoch(train) [12][ 200/5005] lr: 1.0000e-01 eta: 1 day, 16:54:00 time: 0.2332 data_time: 0.0016 loss: 2.3475 03/05 05:59:26 - mmengine - INFO - Epoch(train) [12][ 300/5005] lr: 1.0000e-01 eta: 1 day, 16:53:32 time: 0.2194 data_time: 0.0021 loss: 2.0955 03/05 05:59:49 - mmengine - INFO - Epoch(train) [12][ 400/5005] lr: 1.0000e-01 eta: 1 day, 16:53:04 time: 0.2206 data_time: 0.0022 loss: 2.2429 03/05 06:00:11 - mmengine - INFO - Epoch(train) [12][ 500/5005] lr: 1.0000e-01 eta: 1 day, 16:52:39 time: 0.2208 data_time: 0.0017 loss: 2.2026 03/05 06:00:34 - mmengine - INFO - Epoch(train) [12][ 600/5005] lr: 1.0000e-01 eta: 1 day, 16:52:13 time: 0.2396 data_time: 0.0017 loss: 2.2857 03/05 06:00:56 - mmengine - INFO - Epoch(train) [12][ 700/5005] lr: 1.0000e-01 eta: 1 day, 16:51:42 time: 0.2187 data_time: 0.0016 loss: 2.2553 03/05 06:01:18 - mmengine - INFO - Epoch(train) [12][ 800/5005] lr: 1.0000e-01 eta: 1 day, 16:51:13 time: 0.2203 data_time: 0.0016 loss: 2.3481 03/05 06:01:41 - mmengine - INFO - Epoch(train) [12][ 900/5005] lr: 1.0000e-01 eta: 1 day, 16:50:42 time: 0.2186 data_time: 0.0015 loss: 2.1869 03/05 06:01:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:02:03 - mmengine - INFO - Epoch(train) [12][1000/5005] lr: 1.0000e-01 eta: 1 day, 16:50:16 time: 0.2187 data_time: 0.0017 loss: 2.2870 03/05 06:02:25 - mmengine - INFO - Epoch(train) [12][1100/5005] lr: 1.0000e-01 eta: 1 day, 16:49:46 time: 0.2201 data_time: 0.0018 loss: 2.3215 03/05 06:02:47 - mmengine - INFO - Epoch(train) [12][1200/5005] lr: 1.0000e-01 eta: 1 day, 16:49:14 time: 0.2173 data_time: 0.0018 loss: 2.0601 03/05 06:03:10 - mmengine - INFO - Epoch(train) [12][1300/5005] lr: 1.0000e-01 eta: 1 day, 16:48:48 time: 0.2219 data_time: 0.0016 loss: 2.3717 03/05 06:03:32 - mmengine - INFO - Epoch(train) [12][1400/5005] lr: 1.0000e-01 eta: 1 day, 16:48:22 time: 0.2198 data_time: 0.0016 loss: 2.3145 03/05 06:03:54 - mmengine - INFO - Epoch(train) [12][1500/5005] lr: 1.0000e-01 eta: 1 day, 16:47:50 time: 0.2181 data_time: 0.0015 loss: 2.3852 03/05 06:04:16 - mmengine - INFO - Epoch(train) [12][1600/5005] lr: 1.0000e-01 eta: 1 day, 16:47:17 time: 0.2205 data_time: 0.0015 loss: 2.4606 03/05 06:04:39 - mmengine - INFO - Epoch(train) [12][1700/5005] lr: 1.0000e-01 eta: 1 day, 16:46:50 time: 0.2308 data_time: 0.0016 loss: 2.2392 03/05 06:05:01 - mmengine - INFO - Epoch(train) [12][1800/5005] lr: 1.0000e-01 eta: 1 day, 16:46:23 time: 0.2187 data_time: 0.0015 loss: 2.3446 03/05 06:05:23 - mmengine - INFO - Epoch(train) [12][1900/5005] lr: 1.0000e-01 eta: 1 day, 16:45:50 time: 0.2226 data_time: 0.0015 loss: 2.4243 03/05 06:05:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:05:45 - mmengine - INFO - Epoch(train) [12][2000/5005] lr: 1.0000e-01 eta: 1 day, 16:45:19 time: 0.2187 data_time: 0.0016 loss: 2.3217 03/05 06:06:08 - mmengine - INFO - Epoch(train) [12][2100/5005] lr: 1.0000e-01 eta: 1 day, 16:44:52 time: 0.2211 data_time: 0.0016 loss: 2.2861 03/05 06:06:30 - mmengine - INFO - Epoch(train) [12][2200/5005] lr: 1.0000e-01 eta: 1 day, 16:44:27 time: 0.2226 data_time: 0.0015 loss: 2.2561 03/05 06:06:52 - mmengine - INFO - Epoch(train) [12][2300/5005] lr: 1.0000e-01 eta: 1 day, 16:43:54 time: 0.2174 data_time: 0.0016 loss: 2.3207 03/05 06:07:14 - mmengine - INFO - Epoch(train) [12][2400/5005] lr: 1.0000e-01 eta: 1 day, 16:43:24 time: 0.2187 data_time: 0.0016 loss: 2.3307 03/05 06:07:37 - mmengine - INFO - Epoch(train) [12][2500/5005] lr: 1.0000e-01 eta: 1 day, 16:42:54 time: 0.2428 data_time: 0.0016 loss: 2.4045 03/05 06:07:59 - mmengine - INFO - Epoch(train) [12][2600/5005] lr: 1.0000e-01 eta: 1 day, 16:42:29 time: 0.2192 data_time: 0.0014 loss: 2.1549 03/05 06:08:21 - mmengine - INFO - Epoch(train) [12][2700/5005] lr: 1.0000e-01 eta: 1 day, 16:42:00 time: 0.2201 data_time: 0.0015 loss: 2.2450 03/05 06:08:44 - mmengine - INFO - Epoch(train) [12][2800/5005] lr: 1.0000e-01 eta: 1 day, 16:41:30 time: 0.2459 data_time: 0.0017 loss: 2.2950 03/05 06:09:06 - mmengine - INFO - Epoch(train) [12][2900/5005] lr: 1.0000e-01 eta: 1 day, 16:41:00 time: 0.2181 data_time: 0.0016 loss: 2.4704 03/05 06:09:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:09:28 - mmengine - INFO - Epoch(train) [12][3000/5005] lr: 1.0000e-01 eta: 1 day, 16:40:29 time: 0.2201 data_time: 0.0016 loss: 2.2463 03/05 06:09:50 - mmengine - INFO - Epoch(train) [12][3100/5005] lr: 1.0000e-01 eta: 1 day, 16:40:02 time: 0.2214 data_time: 0.0019 loss: 2.2043 03/05 06:10:12 - mmengine - INFO - Epoch(train) [12][3200/5005] lr: 1.0000e-01 eta: 1 day, 16:39:31 time: 0.2173 data_time: 0.0015 loss: 2.2168 03/05 06:10:35 - mmengine - INFO - Epoch(train) [12][3300/5005] lr: 1.0000e-01 eta: 1 day, 16:39:03 time: 0.2369 data_time: 0.0017 loss: 2.3872 03/05 06:10:57 - mmengine - INFO - Epoch(train) [12][3400/5005] lr: 1.0000e-01 eta: 1 day, 16:38:37 time: 0.2196 data_time: 0.0015 loss: 2.2297 03/05 06:11:20 - mmengine - INFO - Epoch(train) [12][3500/5005] lr: 1.0000e-01 eta: 1 day, 16:38:11 time: 0.2199 data_time: 0.0017 loss: 2.3112 03/05 06:11:42 - mmengine - INFO - Epoch(train) [12][3600/5005] lr: 1.0000e-01 eta: 1 day, 16:37:39 time: 0.2176 data_time: 0.0017 loss: 2.3084 03/05 06:12:04 - mmengine - INFO - Epoch(train) [12][3700/5005] lr: 1.0000e-01 eta: 1 day, 16:37:09 time: 0.2157 data_time: 0.0017 loss: 2.0878 03/05 06:12:26 - mmengine - INFO - Epoch(train) [12][3800/5005] lr: 1.0000e-01 eta: 1 day, 16:36:43 time: 0.2177 data_time: 0.0017 loss: 2.5307 03/05 06:12:48 - mmengine - INFO - Epoch(train) [12][3900/5005] lr: 1.0000e-01 eta: 1 day, 16:36:14 time: 0.2299 data_time: 0.0016 loss: 2.3009 03/05 06:12:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:13:10 - mmengine - INFO - Epoch(train) [12][4000/5005] lr: 1.0000e-01 eta: 1 day, 16:35:43 time: 0.2187 data_time: 0.0014 loss: 2.2186 03/05 06:13:33 - mmengine - INFO - Epoch(train) [12][4100/5005] lr: 1.0000e-01 eta: 1 day, 16:35:14 time: 0.2196 data_time: 0.0015 loss: 2.2913 03/05 06:13:55 - mmengine - INFO - Epoch(train) [12][4200/5005] lr: 1.0000e-01 eta: 1 day, 16:34:47 time: 0.2318 data_time: 0.0015 loss: 2.4182 03/05 06:14:18 - mmengine - INFO - Epoch(train) [12][4300/5005] lr: 1.0000e-01 eta: 1 day, 16:34:21 time: 0.2345 data_time: 0.0015 loss: 2.3066 03/05 06:14:40 - mmengine - INFO - Epoch(train) [12][4400/5005] lr: 1.0000e-01 eta: 1 day, 16:33:51 time: 0.2221 data_time: 0.0017 loss: 2.2994 03/05 06:15:02 - mmengine - INFO - Epoch(train) [12][4500/5005] lr: 1.0000e-01 eta: 1 day, 16:33:22 time: 0.2206 data_time: 0.0021 loss: 2.1282 03/05 06:15:24 - mmengine - INFO - Epoch(train) [12][4600/5005] lr: 1.0000e-01 eta: 1 day, 16:32:52 time: 0.2167 data_time: 0.0016 loss: 2.4136 03/05 06:15:46 - mmengine - INFO - Epoch(train) [12][4700/5005] lr: 1.0000e-01 eta: 1 day, 16:32:23 time: 0.2305 data_time: 0.0016 loss: 2.3767 03/05 06:16:09 - mmengine - INFO - Epoch(train) [12][4800/5005] lr: 1.0000e-01 eta: 1 day, 16:31:57 time: 0.2192 data_time: 0.0016 loss: 2.2039 03/05 06:16:32 - mmengine - INFO - Epoch(train) [12][4900/5005] lr: 1.0000e-01 eta: 1 day, 16:31:44 time: 0.3075 data_time: 0.0014 loss: 2.3400 03/05 06:16:46 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:17:02 - mmengine - INFO - Epoch(train) [12][5000/5005] lr: 1.0000e-01 eta: 1 day, 16:32:37 time: 0.3065 data_time: 0.0015 loss: 2.3970 03/05 06:17:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:17:07 - mmengine - INFO - Saving checkpoint at 12 epochs 03/05 06:17:20 - mmengine - INFO - Epoch(val) [12][100/196] eta: 0:00:12 time: 0.0186 data_time: 0.0003 03/05 06:17:34 - mmengine - INFO - Epoch(val) [12][196/196] accuracy/top1: 52.6980 accuracy/top5: 78.0460 03/05 06:18:06 - mmengine - INFO - Epoch(train) [13][ 100/5005] lr: 1.0000e-01 eta: 1 day, 16:33:51 time: 0.2185 data_time: 0.0015 loss: 2.2257 03/05 06:18:28 - mmengine - INFO - Epoch(train) [13][ 200/5005] lr: 1.0000e-01 eta: 1 day, 16:33:23 time: 0.2234 data_time: 0.0020 loss: 2.2743 03/05 06:18:50 - mmengine - INFO - Epoch(train) [13][ 300/5005] lr: 1.0000e-01 eta: 1 day, 16:32:52 time: 0.2189 data_time: 0.0016 loss: 2.2967 03/05 06:19:13 - mmengine - INFO - Epoch(train) [13][ 400/5005] lr: 1.0000e-01 eta: 1 day, 16:32:26 time: 0.2180 data_time: 0.0017 loss: 2.3337 03/05 06:19:35 - mmengine - INFO - Epoch(train) [13][ 500/5005] lr: 1.0000e-01 eta: 1 day, 16:31:58 time: 0.2221 data_time: 0.0017 loss: 2.3184 03/05 06:19:58 - mmengine - INFO - Epoch(train) [13][ 600/5005] lr: 1.0000e-01 eta: 1 day, 16:31:31 time: 0.2166 data_time: 0.0016 loss: 2.1974 03/05 06:20:20 - mmengine - INFO - Epoch(train) [13][ 700/5005] lr: 1.0000e-01 eta: 1 day, 16:31:01 time: 0.2165 data_time: 0.0016 loss: 2.1446 03/05 06:20:42 - mmengine - INFO - Epoch(train) [13][ 800/5005] lr: 1.0000e-01 eta: 1 day, 16:30:33 time: 0.2258 data_time: 0.0017 loss: 2.2771 03/05 06:21:04 - mmengine - INFO - Epoch(train) [13][ 900/5005] lr: 1.0000e-01 eta: 1 day, 16:30:04 time: 0.2204 data_time: 0.0017 loss: 2.1413 03/05 06:21:13 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:21:27 - mmengine - INFO - Epoch(train) [13][1000/5005] lr: 1.0000e-01 eta: 1 day, 16:29:36 time: 0.2176 data_time: 0.0019 loss: 2.1475 03/05 06:21:49 - mmengine - INFO - Epoch(train) [13][1100/5005] lr: 1.0000e-01 eta: 1 day, 16:29:07 time: 0.2198 data_time: 0.0016 loss: 2.2634 03/05 06:22:11 - mmengine - INFO - Epoch(train) [13][1200/5005] lr: 1.0000e-01 eta: 1 day, 16:28:37 time: 0.2219 data_time: 0.0017 loss: 2.0489 03/05 06:22:33 - mmengine - INFO - Epoch(train) [13][1300/5005] lr: 1.0000e-01 eta: 1 day, 16:28:09 time: 0.2229 data_time: 0.0016 loss: 2.5021 03/05 06:22:56 - mmengine - INFO - Epoch(train) [13][1400/5005] lr: 1.0000e-01 eta: 1 day, 16:27:42 time: 0.2198 data_time: 0.0015 loss: 2.2510 03/05 06:23:18 - mmengine - INFO - Epoch(train) [13][1500/5005] lr: 1.0000e-01 eta: 1 day, 16:27:12 time: 0.2194 data_time: 0.0016 loss: 2.3781 03/05 06:23:40 - mmengine - INFO - Epoch(train) [13][1600/5005] lr: 1.0000e-01 eta: 1 day, 16:26:43 time: 0.2371 data_time: 0.0015 loss: 2.1920 03/05 06:24:02 - mmengine - INFO - Epoch(train) [13][1700/5005] lr: 1.0000e-01 eta: 1 day, 16:26:12 time: 0.2221 data_time: 0.0016 loss: 2.4694 03/05 06:24:24 - mmengine - INFO - Epoch(train) [13][1800/5005] lr: 1.0000e-01 eta: 1 day, 16:25:45 time: 0.2194 data_time: 0.0015 loss: 2.0762 03/05 06:24:47 - mmengine - INFO - Epoch(train) [13][1900/5005] lr: 1.0000e-01 eta: 1 day, 16:25:16 time: 0.2202 data_time: 0.0017 loss: 2.2323 03/05 06:24:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:25:09 - mmengine - INFO - Epoch(train) [13][2000/5005] lr: 1.0000e-01 eta: 1 day, 16:24:45 time: 0.2231 data_time: 0.0017 loss: 2.2247 03/05 06:25:31 - mmengine - INFO - Epoch(train) [13][2100/5005] lr: 1.0000e-01 eta: 1 day, 16:24:16 time: 0.2216 data_time: 0.0016 loss: 2.3706 03/05 06:25:53 - mmengine - INFO - Epoch(train) [13][2200/5005] lr: 1.0000e-01 eta: 1 day, 16:23:50 time: 0.2385 data_time: 0.0017 loss: 2.1437 03/05 06:26:16 - mmengine - INFO - Epoch(train) [13][2300/5005] lr: 1.0000e-01 eta: 1 day, 16:23:24 time: 0.2265 data_time: 0.0016 loss: 2.1093 03/05 06:26:38 - mmengine - INFO - Epoch(train) [13][2400/5005] lr: 1.0000e-01 eta: 1 day, 16:22:52 time: 0.2157 data_time: 0.0016 loss: 2.2091 03/05 06:27:00 - mmengine - INFO - Epoch(train) [13][2500/5005] lr: 1.0000e-01 eta: 1 day, 16:22:25 time: 0.2217 data_time: 0.0017 loss: 2.3611 03/05 06:27:23 - mmengine - INFO - Epoch(train) [13][2600/5005] lr: 1.0000e-01 eta: 1 day, 16:21:59 time: 0.2332 data_time: 0.0016 loss: 2.2728 03/05 06:27:45 - mmengine - INFO - Epoch(train) [13][2700/5005] lr: 1.0000e-01 eta: 1 day, 16:21:32 time: 0.2195 data_time: 0.0015 loss: 2.2978 03/05 06:28:07 - mmengine - INFO - Epoch(train) [13][2800/5005] lr: 1.0000e-01 eta: 1 day, 16:21:02 time: 0.2191 data_time: 0.0015 loss: 2.2946 03/05 06:28:29 - mmengine - INFO - Epoch(train) [13][2900/5005] lr: 1.0000e-01 eta: 1 day, 16:20:34 time: 0.2219 data_time: 0.0016 loss: 2.3135 03/05 06:28:38 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:28:52 - mmengine - INFO - Epoch(train) [13][3000/5005] lr: 1.0000e-01 eta: 1 day, 16:20:06 time: 0.2343 data_time: 0.0017 loss: 2.3551 03/05 06:29:14 - mmengine - INFO - Epoch(train) [13][3100/5005] lr: 1.0000e-01 eta: 1 day, 16:19:42 time: 0.2187 data_time: 0.0018 loss: 2.4483 03/05 06:29:36 - mmengine - INFO - Epoch(train) [13][3200/5005] lr: 1.0000e-01 eta: 1 day, 16:19:12 time: 0.2166 data_time: 0.0016 loss: 2.1539 03/05 06:29:59 - mmengine - INFO - Epoch(train) [13][3300/5005] lr: 1.0000e-01 eta: 1 day, 16:18:44 time: 0.2210 data_time: 0.0015 loss: 2.1855 03/05 06:30:21 - mmengine - INFO - Epoch(train) [13][3400/5005] lr: 1.0000e-01 eta: 1 day, 16:18:15 time: 0.2205 data_time: 0.0016 loss: 2.2774 03/05 06:30:44 - mmengine - INFO - Epoch(train) [13][3500/5005] lr: 1.0000e-01 eta: 1 day, 16:17:51 time: 0.2283 data_time: 0.0017 loss: 2.3859 03/05 06:31:06 - mmengine - INFO - Epoch(train) [13][3600/5005] lr: 1.0000e-01 eta: 1 day, 16:17:22 time: 0.2217 data_time: 0.0016 loss: 2.0739 03/05 06:31:28 - mmengine - INFO - Epoch(train) [13][3700/5005] lr: 1.0000e-01 eta: 1 day, 16:16:54 time: 0.2192 data_time: 0.0016 loss: 2.2260 03/05 06:31:50 - mmengine - INFO - Epoch(train) [13][3800/5005] lr: 1.0000e-01 eta: 1 day, 16:16:24 time: 0.2218 data_time: 0.0015 loss: 2.4752 03/05 06:32:12 - mmengine - INFO - Epoch(train) [13][3900/5005] lr: 1.0000e-01 eta: 1 day, 16:15:58 time: 0.2373 data_time: 0.0015 loss: 2.3307 03/05 06:32:22 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:32:35 - mmengine - INFO - Epoch(train) [13][4000/5005] lr: 1.0000e-01 eta: 1 day, 16:15:32 time: 0.2177 data_time: 0.0016 loss: 2.4034 03/05 06:32:57 - mmengine - INFO - Epoch(train) [13][4100/5005] lr: 1.0000e-01 eta: 1 day, 16:15:03 time: 0.2222 data_time: 0.0018 loss: 2.1898 03/05 06:33:19 - mmengine - INFO - Epoch(train) [13][4200/5005] lr: 1.0000e-01 eta: 1 day, 16:14:35 time: 0.2206 data_time: 0.0016 loss: 2.0998 03/05 06:33:41 - mmengine - INFO - Epoch(train) [13][4300/5005] lr: 1.0000e-01 eta: 1 day, 16:14:04 time: 0.2196 data_time: 0.0018 loss: 2.5042 03/05 06:34:04 - mmengine - INFO - Epoch(train) [13][4400/5005] lr: 1.0000e-01 eta: 1 day, 16:13:43 time: 0.2258 data_time: 0.0016 loss: 2.3851 03/05 06:34:27 - mmengine - INFO - Epoch(train) [13][4500/5005] lr: 1.0000e-01 eta: 1 day, 16:13:16 time: 0.2230 data_time: 0.0018 loss: 2.5454 03/05 06:34:49 - mmengine - INFO - Epoch(train) [13][4600/5005] lr: 1.0000e-01 eta: 1 day, 16:12:50 time: 0.2190 data_time: 0.0019 loss: 2.1197 03/05 06:35:11 - mmengine - INFO - Epoch(train) [13][4700/5005] lr: 1.0000e-01 eta: 1 day, 16:12:20 time: 0.2206 data_time: 0.0016 loss: 2.3260 03/05 06:35:34 - mmengine - INFO - Epoch(train) [13][4800/5005] lr: 1.0000e-01 eta: 1 day, 16:11:56 time: 0.2207 data_time: 0.0017 loss: 2.3653 03/05 06:35:57 - mmengine - INFO - Epoch(train) [13][4900/5005] lr: 1.0000e-01 eta: 1 day, 16:11:40 time: 0.2952 data_time: 0.0014 loss: 2.4139 03/05 06:36:09 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:36:27 - mmengine - INFO - Epoch(train) [13][5000/5005] lr: 1.0000e-01 eta: 1 day, 16:12:30 time: 0.2903 data_time: 0.0014 loss: 2.1525 03/05 06:36:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:36:32 - mmengine - INFO - Saving checkpoint at 13 epochs 03/05 06:36:45 - mmengine - INFO - Epoch(val) [13][100/196] eta: 0:00:12 time: 0.0206 data_time: 0.0004 03/05 06:36:59 - mmengine - INFO - Epoch(val) [13][196/196] accuracy/top1: 53.0880 accuracy/top5: 78.3720 03/05 06:37:30 - mmengine - INFO - Epoch(train) [14][ 100/5005] lr: 1.0000e-01 eta: 1 day, 16:13:27 time: 0.2193 data_time: 0.0016 loss: 2.3274 03/05 06:37:52 - mmengine - INFO - Epoch(train) [14][ 200/5005] lr: 1.0000e-01 eta: 1 day, 16:13:02 time: 0.2195 data_time: 0.0018 loss: 2.2875 03/05 06:38:14 - mmengine - INFO - Epoch(train) [14][ 300/5005] lr: 1.0000e-01 eta: 1 day, 16:12:32 time: 0.2226 data_time: 0.0017 loss: 2.2186 03/05 06:38:36 - mmengine - INFO - Epoch(train) [14][ 400/5005] lr: 1.0000e-01 eta: 1 day, 16:12:04 time: 0.2196 data_time: 0.0020 loss: 2.1889 03/05 06:38:59 - mmengine - INFO - Epoch(train) [14][ 500/5005] lr: 1.0000e-01 eta: 1 day, 16:11:38 time: 0.2202 data_time: 0.0017 loss: 2.1290 03/05 06:39:21 - mmengine - INFO - Epoch(train) [14][ 600/5005] lr: 1.0000e-01 eta: 1 day, 16:11:09 time: 0.2198 data_time: 0.0018 loss: 2.2016 03/05 06:39:43 - mmengine - INFO - Epoch(train) [14][ 700/5005] lr: 1.0000e-01 eta: 1 day, 16:10:38 time: 0.2192 data_time: 0.0018 loss: 2.0741 03/05 06:40:06 - mmengine - INFO - Epoch(train) [14][ 800/5005] lr: 1.0000e-01 eta: 1 day, 16:10:18 time: 0.2900 data_time: 0.0019 loss: 2.2371 03/05 06:40:28 - mmengine - INFO - Epoch(train) [14][ 900/5005] lr: 1.0000e-01 eta: 1 day, 16:09:49 time: 0.2181 data_time: 0.0016 loss: 2.0238 03/05 06:40:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:40:51 - mmengine - INFO - Epoch(train) [14][1000/5005] lr: 1.0000e-01 eta: 1 day, 16:09:23 time: 0.2283 data_time: 0.0017 loss: 2.2577 03/05 06:41:13 - mmengine - INFO - Epoch(train) [14][1100/5005] lr: 1.0000e-01 eta: 1 day, 16:08:53 time: 0.2205 data_time: 0.0016 loss: 2.1972 03/05 06:41:35 - mmengine - INFO - Epoch(train) [14][1200/5005] lr: 1.0000e-01 eta: 1 day, 16:08:25 time: 0.2187 data_time: 0.0018 loss: 2.2032 03/05 06:41:57 - mmengine - INFO - Epoch(train) [14][1300/5005] lr: 1.0000e-01 eta: 1 day, 16:07:57 time: 0.2219 data_time: 0.0017 loss: 2.2648 03/05 06:42:20 - mmengine - INFO - Epoch(train) [14][1400/5005] lr: 1.0000e-01 eta: 1 day, 16:07:29 time: 0.2189 data_time: 0.0015 loss: 2.4207 03/05 06:42:42 - mmengine - INFO - Epoch(train) [14][1500/5005] lr: 1.0000e-01 eta: 1 day, 16:07:02 time: 0.2196 data_time: 0.0017 loss: 2.2068 03/05 06:43:04 - mmengine - INFO - Epoch(train) [14][1600/5005] lr: 1.0000e-01 eta: 1 day, 16:06:33 time: 0.2231 data_time: 0.0016 loss: 2.3102 03/05 06:43:26 - mmengine - INFO - Epoch(train) [14][1700/5005] lr: 1.0000e-01 eta: 1 day, 16:06:06 time: 0.2188 data_time: 0.0018 loss: 2.0596 03/05 06:43:49 - mmengine - INFO - Epoch(train) [14][1800/5005] lr: 1.0000e-01 eta: 1 day, 16:05:38 time: 0.2153 data_time: 0.0017 loss: 2.4543 03/05 06:44:11 - mmengine - INFO - Epoch(train) [14][1900/5005] lr: 1.0000e-01 eta: 1 day, 16:05:12 time: 0.2300 data_time: 0.0017 loss: 2.1878 03/05 06:44:19 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:44:33 - mmengine - INFO - Epoch(train) [14][2000/5005] lr: 1.0000e-01 eta: 1 day, 16:04:42 time: 0.2195 data_time: 0.0016 loss: 2.0189 03/05 06:44:55 - mmengine - INFO - Epoch(train) [14][2100/5005] lr: 1.0000e-01 eta: 1 day, 16:04:13 time: 0.2185 data_time: 0.0019 loss: 2.2387 03/05 06:45:18 - mmengine - INFO - Epoch(train) [14][2200/5005] lr: 1.0000e-01 eta: 1 day, 16:03:46 time: 0.2214 data_time: 0.0016 loss: 2.0192 03/05 06:45:40 - mmengine - INFO - Epoch(train) [14][2300/5005] lr: 1.0000e-01 eta: 1 day, 16:03:19 time: 0.2204 data_time: 0.0018 loss: 2.2102 03/05 06:46:02 - mmengine - INFO - Epoch(train) [14][2400/5005] lr: 1.0000e-01 eta: 1 day, 16:02:53 time: 0.2188 data_time: 0.0017 loss: 2.2464 03/05 06:46:24 - mmengine - INFO - Epoch(train) [14][2500/5005] lr: 1.0000e-01 eta: 1 day, 16:02:24 time: 0.2182 data_time: 0.0017 loss: 2.3353 03/05 06:46:47 - mmengine - INFO - Epoch(train) [14][2600/5005] lr: 1.0000e-01 eta: 1 day, 16:01:59 time: 0.2199 data_time: 0.0017 loss: 2.3147 03/05 06:47:09 - mmengine - INFO - Epoch(train) [14][2700/5005] lr: 1.0000e-01 eta: 1 day, 16:01:30 time: 0.2242 data_time: 0.0016 loss: 2.2118 03/05 06:47:32 - mmengine - INFO - Epoch(train) [14][2800/5005] lr: 1.0000e-01 eta: 1 day, 16:01:05 time: 0.2211 data_time: 0.0018 loss: 2.5316 03/05 06:47:54 - mmengine - INFO - Epoch(train) [14][2900/5005] lr: 1.0000e-01 eta: 1 day, 16:00:34 time: 0.2189 data_time: 0.0019 loss: 2.1214 03/05 06:48:01 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:48:16 - mmengine - INFO - Epoch(train) [14][3000/5005] lr: 1.0000e-01 eta: 1 day, 16:00:05 time: 0.2192 data_time: 0.0021 loss: 2.1006 03/05 06:48:38 - mmengine - INFO - Epoch(train) [14][3100/5005] lr: 1.0000e-01 eta: 1 day, 15:59:37 time: 0.2172 data_time: 0.0016 loss: 2.1510 03/05 06:49:00 - mmengine - INFO - Epoch(train) [14][3200/5005] lr: 1.0000e-01 eta: 1 day, 15:59:12 time: 0.2168 data_time: 0.0015 loss: 2.4081 03/05 06:49:22 - mmengine - INFO - Epoch(train) [14][3300/5005] lr: 1.0000e-01 eta: 1 day, 15:58:42 time: 0.2189 data_time: 0.0017 loss: 2.3260 03/05 06:49:45 - mmengine - INFO - Epoch(train) [14][3400/5005] lr: 1.0000e-01 eta: 1 day, 15:58:13 time: 0.2205 data_time: 0.0016 loss: 2.3638 03/05 06:50:07 - mmengine - INFO - Epoch(train) [14][3500/5005] lr: 1.0000e-01 eta: 1 day, 15:57:49 time: 0.2216 data_time: 0.0016 loss: 2.1623 03/05 06:50:30 - mmengine - INFO - Epoch(train) [14][3600/5005] lr: 1.0000e-01 eta: 1 day, 15:57:22 time: 0.2389 data_time: 0.0017 loss: 2.0316 03/05 06:50:52 - mmengine - INFO - Epoch(train) [14][3700/5005] lr: 1.0000e-01 eta: 1 day, 15:56:54 time: 0.2218 data_time: 0.0017 loss: 2.2998 03/05 06:51:14 - mmengine - INFO - Epoch(train) [14][3800/5005] lr: 1.0000e-01 eta: 1 day, 15:56:26 time: 0.2191 data_time: 0.0015 loss: 2.2682 03/05 06:51:36 - mmengine - INFO - Epoch(train) [14][3900/5005] lr: 1.0000e-01 eta: 1 day, 15:56:00 time: 0.2212 data_time: 0.0016 loss: 2.2160 03/05 06:51:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:51:58 - mmengine - INFO - Epoch(train) [14][4000/5005] lr: 1.0000e-01 eta: 1 day, 15:55:29 time: 0.2183 data_time: 0.0015 loss: 2.3466 03/05 06:52:21 - mmengine - INFO - Epoch(train) [14][4100/5005] lr: 1.0000e-01 eta: 1 day, 15:55:03 time: 0.2197 data_time: 0.0018 loss: 2.3040 03/05 06:52:43 - mmengine - INFO - Epoch(train) [14][4200/5005] lr: 1.0000e-01 eta: 1 day, 15:54:35 time: 0.2235 data_time: 0.0017 loss: 2.3096 03/05 06:53:05 - mmengine - INFO - Epoch(train) [14][4300/5005] lr: 1.0000e-01 eta: 1 day, 15:54:06 time: 0.2218 data_time: 0.0016 loss: 2.2086 03/05 06:53:27 - mmengine - INFO - Epoch(train) [14][4400/5005] lr: 1.0000e-01 eta: 1 day, 15:53:40 time: 0.2359 data_time: 0.0016 loss: 2.2393 03/05 06:53:50 - mmengine - INFO - Epoch(train) [14][4500/5005] lr: 1.0000e-01 eta: 1 day, 15:53:14 time: 0.2199 data_time: 0.0016 loss: 2.2278 03/05 06:54:12 - mmengine - INFO - Epoch(train) [14][4600/5005] lr: 1.0000e-01 eta: 1 day, 15:52:48 time: 0.2220 data_time: 0.0016 loss: 1.9709 03/05 06:54:35 - mmengine - INFO - Epoch(train) [14][4700/5005] lr: 1.0000e-01 eta: 1 day, 15:52:21 time: 0.2393 data_time: 0.0018 loss: 2.4613 03/05 06:54:57 - mmengine - INFO - Epoch(train) [14][4800/5005] lr: 1.0000e-01 eta: 1 day, 15:51:53 time: 0.2343 data_time: 0.0017 loss: 2.4485 03/05 06:55:20 - mmengine - INFO - Epoch(train) [14][4900/5005] lr: 1.0000e-01 eta: 1 day, 15:51:35 time: 0.2989 data_time: 0.0015 loss: 1.9255 03/05 06:55:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:55:50 - mmengine - INFO - Epoch(train) [14][5000/5005] lr: 1.0000e-01 eta: 1 day, 15:52:16 time: 0.2993 data_time: 0.0014 loss: 2.3195 03/05 06:55:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 06:55:54 - mmengine - INFO - Saving checkpoint at 14 epochs 03/05 06:56:08 - mmengine - INFO - Epoch(val) [14][100/196] eta: 0:00:12 time: 0.0183 data_time: 0.0003 03/05 06:56:22 - mmengine - INFO - Epoch(val) [14][196/196] accuracy/top1: 51.8400 accuracy/top5: 77.3320 03/05 06:56:53 - mmengine - INFO - Epoch(train) [15][ 100/5005] lr: 1.0000e-01 eta: 1 day, 15:53:09 time: 0.2202 data_time: 0.0017 loss: 2.1977 03/05 06:57:15 - mmengine - INFO - Epoch(train) [15][ 200/5005] lr: 1.0000e-01 eta: 1 day, 15:52:43 time: 0.2188 data_time: 0.0016 loss: 2.2042 03/05 06:57:37 - mmengine - INFO - Epoch(train) [15][ 300/5005] lr: 1.0000e-01 eta: 1 day, 15:52:16 time: 0.2184 data_time: 0.0017 loss: 2.3461 03/05 06:57:59 - mmengine - INFO - Epoch(train) [15][ 400/5005] lr: 1.0000e-01 eta: 1 day, 15:51:46 time: 0.2209 data_time: 0.0019 loss: 2.2481 03/05 06:58:22 - mmengine - INFO - Epoch(train) [15][ 500/5005] lr: 1.0000e-01 eta: 1 day, 15:51:23 time: 0.2205 data_time: 0.0016 loss: 2.1199 03/05 06:58:45 - mmengine - INFO - Epoch(train) [15][ 600/5005] lr: 1.0000e-01 eta: 1 day, 15:50:57 time: 0.2203 data_time: 0.0017 loss: 2.0964 03/05 06:59:07 - mmengine - INFO - Epoch(train) [15][ 700/5005] lr: 1.0000e-01 eta: 1 day, 15:50:28 time: 0.2191 data_time: 0.0016 loss: 1.9699 03/05 06:59:29 - mmengine - INFO - Epoch(train) [15][ 800/5005] lr: 1.0000e-01 eta: 1 day, 15:49:58 time: 0.2211 data_time: 0.0015 loss: 2.0554 03/05 06:59:51 - mmengine - INFO - Epoch(train) [15][ 900/5005] lr: 1.0000e-01 eta: 1 day, 15:49:35 time: 0.2188 data_time: 0.0018 loss: 2.2807 03/05 06:59:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:00:14 - mmengine - INFO - Epoch(train) [15][1000/5005] lr: 1.0000e-01 eta: 1 day, 15:49:09 time: 0.2205 data_time: 0.0017 loss: 2.1672 03/05 07:00:36 - mmengine - INFO - Epoch(train) [15][1100/5005] lr: 1.0000e-01 eta: 1 day, 15:48:40 time: 0.2280 data_time: 0.0017 loss: 2.1060 03/05 07:00:58 - mmengine - INFO - Epoch(train) [15][1200/5005] lr: 1.0000e-01 eta: 1 day, 15:48:10 time: 0.2177 data_time: 0.0016 loss: 2.3639 03/05 07:01:20 - mmengine - INFO - Epoch(train) [15][1300/5005] lr: 1.0000e-01 eta: 1 day, 15:47:44 time: 0.2214 data_time: 0.0016 loss: 2.2098 03/05 07:01:43 - mmengine - INFO - Epoch(train) [15][1400/5005] lr: 1.0000e-01 eta: 1 day, 15:47:22 time: 0.2201 data_time: 0.0018 loss: 2.3433 03/05 07:02:05 - mmengine - INFO - Epoch(train) [15][1500/5005] lr: 1.0000e-01 eta: 1 day, 15:46:52 time: 0.2217 data_time: 0.0017 loss: 2.1900 03/05 07:02:27 - mmengine - INFO - Epoch(train) [15][1600/5005] lr: 1.0000e-01 eta: 1 day, 15:46:23 time: 0.2180 data_time: 0.0015 loss: 2.0342 03/05 07:02:50 - mmengine - INFO - Epoch(train) [15][1700/5005] lr: 1.0000e-01 eta: 1 day, 15:45:58 time: 0.2210 data_time: 0.0018 loss: 2.3656 03/05 07:03:12 - mmengine - INFO - Epoch(train) [15][1800/5005] lr: 1.0000e-01 eta: 1 day, 15:45:32 time: 0.2231 data_time: 0.0017 loss: 2.2874 03/05 07:03:34 - mmengine - INFO - Epoch(train) [15][1900/5005] lr: 1.0000e-01 eta: 1 day, 15:45:03 time: 0.2172 data_time: 0.0018 loss: 1.9827 03/05 07:03:41 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:03:56 - mmengine - INFO - Epoch(train) [15][2000/5005] lr: 1.0000e-01 eta: 1 day, 15:44:35 time: 0.2183 data_time: 0.0017 loss: 2.1724 03/05 07:04:19 - mmengine - INFO - Epoch(train) [15][2100/5005] lr: 1.0000e-01 eta: 1 day, 15:44:10 time: 0.2157 data_time: 0.0017 loss: 2.4899 03/05 07:04:41 - mmengine - INFO - Epoch(train) [15][2200/5005] lr: 1.0000e-01 eta: 1 day, 15:43:44 time: 0.2279 data_time: 0.0017 loss: 2.2481 03/05 07:05:03 - mmengine - INFO - Epoch(train) [15][2300/5005] lr: 1.0000e-01 eta: 1 day, 15:43:15 time: 0.2201 data_time: 0.0017 loss: 2.1935 03/05 07:05:25 - mmengine - INFO - Epoch(train) [15][2400/5005] lr: 1.0000e-01 eta: 1 day, 15:42:46 time: 0.2184 data_time: 0.0017 loss: 2.0705 03/05 07:05:48 - mmengine - INFO - Epoch(train) [15][2500/5005] lr: 1.0000e-01 eta: 1 day, 15:42:19 time: 0.2292 data_time: 0.0016 loss: 2.2124 03/05 07:06:10 - mmengine - INFO - Epoch(train) [15][2600/5005] lr: 1.0000e-01 eta: 1 day, 15:41:55 time: 0.2204 data_time: 0.0015 loss: 1.9509 03/05 07:06:33 - mmengine - INFO - Epoch(train) [15][2700/5005] lr: 1.0000e-01 eta: 1 day, 15:41:27 time: 0.2203 data_time: 0.0017 loss: 2.3461 03/05 07:06:55 - mmengine - INFO - Epoch(train) [15][2800/5005] lr: 1.0000e-01 eta: 1 day, 15:40:58 time: 0.2191 data_time: 0.0016 loss: 2.1884 03/05 07:07:17 - mmengine - INFO - Epoch(train) [15][2900/5005] lr: 1.0000e-01 eta: 1 day, 15:40:29 time: 0.2192 data_time: 0.0017 loss: 2.2572 03/05 07:07:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:07:39 - mmengine - INFO - Epoch(train) [15][3000/5005] lr: 1.0000e-01 eta: 1 day, 15:40:04 time: 0.2207 data_time: 0.0018 loss: 2.3329 03/05 07:08:01 - mmengine - INFO - Epoch(train) [15][3100/5005] lr: 1.0000e-01 eta: 1 day, 15:39:37 time: 0.2391 data_time: 0.0015 loss: 2.1737 03/05 07:08:23 - mmengine - INFO - Epoch(train) [15][3200/5005] lr: 1.0000e-01 eta: 1 day, 15:39:06 time: 0.2185 data_time: 0.0017 loss: 2.1293 03/05 07:08:46 - mmengine - INFO - Epoch(train) [15][3300/5005] lr: 1.0000e-01 eta: 1 day, 15:38:40 time: 0.2242 data_time: 0.0016 loss: 2.1201 03/05 07:09:08 - mmengine - INFO - Epoch(train) [15][3400/5005] lr: 1.0000e-01 eta: 1 day, 15:38:14 time: 0.2489 data_time: 0.0016 loss: 2.1206 03/05 07:09:30 - mmengine - INFO - Epoch(train) [15][3500/5005] lr: 1.0000e-01 eta: 1 day, 15:37:46 time: 0.2201 data_time: 0.0016 loss: 2.0230 03/05 07:09:52 - mmengine - INFO - Epoch(train) [15][3600/5005] lr: 1.0000e-01 eta: 1 day, 15:37:18 time: 0.2209 data_time: 0.0019 loss: 2.2972 03/05 07:10:15 - mmengine - INFO - Epoch(train) [15][3700/5005] lr: 1.0000e-01 eta: 1 day, 15:36:51 time: 0.2215 data_time: 0.0016 loss: 2.3816 03/05 07:10:37 - mmengine - INFO - Epoch(train) [15][3800/5005] lr: 1.0000e-01 eta: 1 day, 15:36:23 time: 0.2270 data_time: 0.0016 loss: 2.0930 03/05 07:10:59 - mmengine - INFO - Epoch(train) [15][3900/5005] lr: 1.0000e-01 eta: 1 day, 15:35:58 time: 0.2236 data_time: 0.0017 loss: 2.2968 03/05 07:11:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:11:22 - mmengine - INFO - Epoch(train) [15][4000/5005] lr: 1.0000e-01 eta: 1 day, 15:35:32 time: 0.2220 data_time: 0.0018 loss: 2.4104 03/05 07:11:44 - mmengine - INFO - Epoch(train) [15][4100/5005] lr: 1.0000e-01 eta: 1 day, 15:35:05 time: 0.2208 data_time: 0.0017 loss: 2.2844 03/05 07:12:06 - mmengine - INFO - Epoch(train) [15][4200/5005] lr: 1.0000e-01 eta: 1 day, 15:34:38 time: 0.2246 data_time: 0.0016 loss: 2.1047 03/05 07:12:29 - mmengine - INFO - Epoch(train) [15][4300/5005] lr: 1.0000e-01 eta: 1 day, 15:34:13 time: 0.2219 data_time: 0.0017 loss: 2.3682 03/05 07:12:51 - mmengine - INFO - Epoch(train) [15][4400/5005] lr: 1.0000e-01 eta: 1 day, 15:33:44 time: 0.2189 data_time: 0.0017 loss: 2.1449 03/05 07:13:14 - mmengine - INFO - Epoch(train) [15][4500/5005] lr: 1.0000e-01 eta: 1 day, 15:33:22 time: 0.2358 data_time: 0.0016 loss: 2.2510 03/05 07:13:36 - mmengine - INFO - Epoch(train) [15][4600/5005] lr: 1.0000e-01 eta: 1 day, 15:32:53 time: 0.2209 data_time: 0.0016 loss: 2.1994 03/05 07:13:58 - mmengine - INFO - Epoch(train) [15][4700/5005] lr: 1.0000e-01 eta: 1 day, 15:32:27 time: 0.2236 data_time: 0.0017 loss: 2.1940 03/05 07:14:20 - mmengine - INFO - Epoch(train) [15][4800/5005] lr: 1.0000e-01 eta: 1 day, 15:32:01 time: 0.2200 data_time: 0.0017 loss: 2.1539 03/05 07:14:44 - mmengine - INFO - Epoch(train) [15][4900/5005] lr: 1.0000e-01 eta: 1 day, 15:31:44 time: 0.3055 data_time: 0.0014 loss: 2.1495 03/05 07:14:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:15:14 - mmengine - INFO - Epoch(train) [15][5000/5005] lr: 1.0000e-01 eta: 1 day, 15:32:23 time: 0.3082 data_time: 0.0014 loss: 2.1553 03/05 07:15:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:15:18 - mmengine - INFO - Saving checkpoint at 15 epochs 03/05 07:15:32 - mmengine - INFO - Epoch(val) [15][100/196] eta: 0:00:11 time: 0.0196 data_time: 0.0003 03/05 07:15:45 - mmengine - INFO - Epoch(val) [15][196/196] accuracy/top1: 53.7040 accuracy/top5: 79.2980 03/05 07:16:16 - mmengine - INFO - Epoch(train) [16][ 100/5005] lr: 1.0000e-01 eta: 1 day, 15:33:08 time: 0.2180 data_time: 0.0017 loss: 2.2800 03/05 07:16:38 - mmengine - INFO - Epoch(train) [16][ 200/5005] lr: 1.0000e-01 eta: 1 day, 15:32:39 time: 0.2209 data_time: 0.0019 loss: 2.1891 03/05 07:17:01 - mmengine - INFO - Epoch(train) [16][ 300/5005] lr: 1.0000e-01 eta: 1 day, 15:32:18 time: 0.2211 data_time: 0.0017 loss: 2.2141 03/05 07:17:23 - mmengine - INFO - Epoch(train) [16][ 400/5005] lr: 1.0000e-01 eta: 1 day, 15:31:50 time: 0.2224 data_time: 0.0019 loss: 2.1444 03/05 07:17:46 - mmengine - INFO - Epoch(train) [16][ 500/5005] lr: 1.0000e-01 eta: 1 day, 15:31:25 time: 0.2385 data_time: 0.0016 loss: 2.1068 03/05 07:18:08 - mmengine - INFO - Epoch(train) [16][ 600/5005] lr: 1.0000e-01 eta: 1 day, 15:30:57 time: 0.2221 data_time: 0.0016 loss: 2.4906 03/05 07:18:30 - mmengine - INFO - Epoch(train) [16][ 700/5005] lr: 1.0000e-01 eta: 1 day, 15:30:28 time: 0.2178 data_time: 0.0016 loss: 2.3610 03/05 07:18:52 - mmengine - INFO - Epoch(train) [16][ 800/5005] lr: 1.0000e-01 eta: 1 day, 15:30:02 time: 0.2292 data_time: 0.0016 loss: 2.4446 03/05 07:19:15 - mmengine - INFO - Epoch(train) [16][ 900/5005] lr: 1.0000e-01 eta: 1 day, 15:29:35 time: 0.2358 data_time: 0.0016 loss: 2.2183 03/05 07:19:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:19:37 - mmengine - INFO - Epoch(train) [16][1000/5005] lr: 1.0000e-01 eta: 1 day, 15:29:10 time: 0.2258 data_time: 0.0016 loss: 2.3213 03/05 07:19:59 - mmengine - INFO - Epoch(train) [16][1100/5005] lr: 1.0000e-01 eta: 1 day, 15:28:42 time: 0.2180 data_time: 0.0017 loss: 2.2276 03/05 07:20:21 - mmengine - INFO - Epoch(train) [16][1200/5005] lr: 1.0000e-01 eta: 1 day, 15:28:15 time: 0.2434 data_time: 0.0016 loss: 2.1345 03/05 07:20:44 - mmengine - INFO - Epoch(train) [16][1300/5005] lr: 1.0000e-01 eta: 1 day, 15:27:47 time: 0.2370 data_time: 0.0016 loss: 2.1425 03/05 07:21:06 - mmengine - INFO - Epoch(train) [16][1400/5005] lr: 1.0000e-01 eta: 1 day, 15:27:21 time: 0.2223 data_time: 0.0017 loss: 2.2087 03/05 07:21:29 - mmengine - INFO - Epoch(train) [16][1500/5005] lr: 1.0000e-01 eta: 1 day, 15:26:59 time: 0.2212 data_time: 0.0017 loss: 2.3206 03/05 07:21:51 - mmengine - INFO - Epoch(train) [16][1600/5005] lr: 1.0000e-01 eta: 1 day, 15:26:34 time: 0.2166 data_time: 0.0016 loss: 2.2507 03/05 07:22:13 - mmengine - INFO - Epoch(train) [16][1700/5005] lr: 1.0000e-01 eta: 1 day, 15:26:05 time: 0.2221 data_time: 0.0019 loss: 2.2324 03/05 07:22:36 - mmengine - INFO - Epoch(train) [16][1800/5005] lr: 1.0000e-01 eta: 1 day, 15:25:39 time: 0.2182 data_time: 0.0016 loss: 2.1737 03/05 07:22:58 - mmengine - INFO - Epoch(train) [16][1900/5005] lr: 1.0000e-01 eta: 1 day, 15:25:14 time: 0.2202 data_time: 0.0016 loss: 2.0635 03/05 07:23:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:23:20 - mmengine - INFO - Epoch(train) [16][2000/5005] lr: 1.0000e-01 eta: 1 day, 15:24:47 time: 0.2185 data_time: 0.0018 loss: 2.5868 03/05 07:23:43 - mmengine - INFO - Epoch(train) [16][2100/5005] lr: 1.0000e-01 eta: 1 day, 15:24:20 time: 0.2361 data_time: 0.0016 loss: 2.3279 03/05 07:24:05 - mmengine - INFO - Epoch(train) [16][2200/5005] lr: 1.0000e-01 eta: 1 day, 15:23:52 time: 0.2225 data_time: 0.0018 loss: 2.2562 03/05 07:24:27 - mmengine - INFO - Epoch(train) [16][2300/5005] lr: 1.0000e-01 eta: 1 day, 15:23:26 time: 0.2409 data_time: 0.0017 loss: 2.2134 03/05 07:24:50 - mmengine - INFO - Epoch(train) [16][2400/5005] lr: 1.0000e-01 eta: 1 day, 15:23:00 time: 0.2228 data_time: 0.0017 loss: 2.0222 03/05 07:25:12 - mmengine - INFO - Epoch(train) [16][2500/5005] lr: 1.0000e-01 eta: 1 day, 15:22:33 time: 0.2220 data_time: 0.0016 loss: 2.1804 03/05 07:25:34 - mmengine - INFO - Epoch(train) [16][2600/5005] lr: 1.0000e-01 eta: 1 day, 15:22:05 time: 0.2224 data_time: 0.0018 loss: 2.2627 03/05 07:25:56 - mmengine - INFO - Epoch(train) [16][2700/5005] lr: 1.0000e-01 eta: 1 day, 15:21:39 time: 0.2199 data_time: 0.0016 loss: 2.4600 03/05 07:26:19 - mmengine - INFO - Epoch(train) [16][2800/5005] lr: 1.0000e-01 eta: 1 day, 15:21:13 time: 0.2205 data_time: 0.0015 loss: 2.2010 03/05 07:26:41 - mmengine - INFO - Epoch(train) [16][2900/5005] lr: 1.0000e-01 eta: 1 day, 15:20:50 time: 0.2202 data_time: 0.0018 loss: 2.3824 03/05 07:26:47 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:27:04 - mmengine - INFO - Epoch(train) [16][3000/5005] lr: 1.0000e-01 eta: 1 day, 15:20:22 time: 0.2197 data_time: 0.0016 loss: 2.0809 03/05 07:27:26 - mmengine - INFO - Epoch(train) [16][3100/5005] lr: 1.0000e-01 eta: 1 day, 15:19:56 time: 0.2227 data_time: 0.0018 loss: 2.3056 03/05 07:27:49 - mmengine - INFO - Epoch(train) [16][3200/5005] lr: 1.0000e-01 eta: 1 day, 15:19:32 time: 0.2370 data_time: 0.0016 loss: 2.2874 03/05 07:28:11 - mmengine - INFO - Epoch(train) [16][3300/5005] lr: 1.0000e-01 eta: 1 day, 15:19:05 time: 0.2186 data_time: 0.0018 loss: 2.1234 03/05 07:28:33 - mmengine - INFO - Epoch(train) [16][3400/5005] lr: 1.0000e-01 eta: 1 day, 15:18:39 time: 0.2193 data_time: 0.0016 loss: 2.3308 03/05 07:28:56 - mmengine - INFO - Epoch(train) [16][3500/5005] lr: 1.0000e-01 eta: 1 day, 15:18:13 time: 0.2210 data_time: 0.0017 loss: 2.3113 03/05 07:29:18 - mmengine - INFO - Epoch(train) [16][3600/5005] lr: 1.0000e-01 eta: 1 day, 15:17:46 time: 0.2173 data_time: 0.0018 loss: 2.3688 03/05 07:29:40 - mmengine - INFO - Epoch(train) [16][3700/5005] lr: 1.0000e-01 eta: 1 day, 15:17:19 time: 0.2217 data_time: 0.0017 loss: 2.3563 03/05 07:30:02 - mmengine - INFO - Epoch(train) [16][3800/5005] lr: 1.0000e-01 eta: 1 day, 15:16:53 time: 0.2169 data_time: 0.0018 loss: 2.1790 03/05 07:30:25 - mmengine - INFO - Epoch(train) [16][3900/5005] lr: 1.0000e-01 eta: 1 day, 15:16:28 time: 0.2454 data_time: 0.0016 loss: 2.4904 03/05 07:30:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:30:47 - mmengine - INFO - Epoch(train) [16][4000/5005] lr: 1.0000e-01 eta: 1 day, 15:16:00 time: 0.2203 data_time: 0.0017 loss: 2.0738 03/05 07:31:09 - mmengine - INFO - Epoch(train) [16][4100/5005] lr: 1.0000e-01 eta: 1 day, 15:15:34 time: 0.2204 data_time: 0.0016 loss: 2.1258 03/05 07:31:32 - mmengine - INFO - Epoch(train) [16][4200/5005] lr: 1.0000e-01 eta: 1 day, 15:15:08 time: 0.2203 data_time: 0.0016 loss: 2.2193 03/05 07:31:54 - mmengine - INFO - Epoch(train) [16][4300/5005] lr: 1.0000e-01 eta: 1 day, 15:14:42 time: 0.2220 data_time: 0.0016 loss: 2.2664 03/05 07:32:16 - mmengine - INFO - Epoch(train) [16][4400/5005] lr: 1.0000e-01 eta: 1 day, 15:14:17 time: 0.2195 data_time: 0.0016 loss: 2.1324 03/05 07:32:39 - mmengine - INFO - Epoch(train) [16][4500/5005] lr: 1.0000e-01 eta: 1 day, 15:13:50 time: 0.2240 data_time: 0.0015 loss: 2.2410 03/05 07:33:01 - mmengine - INFO - Epoch(train) [16][4600/5005] lr: 1.0000e-01 eta: 1 day, 15:13:25 time: 0.2190 data_time: 0.0017 loss: 2.0192 03/05 07:33:24 - mmengine - INFO - Epoch(train) [16][4700/5005] lr: 1.0000e-01 eta: 1 day, 15:12:59 time: 0.2202 data_time: 0.0017 loss: 2.3168 03/05 07:33:46 - mmengine - INFO - Epoch(train) [16][4800/5005] lr: 1.0000e-01 eta: 1 day, 15:12:34 time: 0.2237 data_time: 0.0016 loss: 2.2537 03/05 07:34:10 - mmengine - INFO - Epoch(train) [16][4900/5005] lr: 1.0000e-01 eta: 1 day, 15:12:19 time: 0.3040 data_time: 0.0015 loss: 2.0948 03/05 07:34:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:34:40 - mmengine - INFO - Epoch(train) [16][5000/5005] lr: 1.0000e-01 eta: 1 day, 15:12:51 time: 0.3054 data_time: 0.0015 loss: 2.1816 03/05 07:34:41 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:34:44 - mmengine - INFO - Saving checkpoint at 16 epochs 03/05 07:34:57 - mmengine - INFO - Epoch(val) [16][100/196] eta: 0:00:12 time: 0.0182 data_time: 0.0003 03/05 07:35:11 - mmengine - INFO - Epoch(val) [16][196/196] accuracy/top1: 50.4940 accuracy/top5: 76.3280 03/05 07:35:42 - mmengine - INFO - Epoch(train) [17][ 100/5005] lr: 1.0000e-01 eta: 1 day, 15:13:29 time: 0.2599 data_time: 0.0018 loss: 2.1945 03/05 07:36:04 - mmengine - INFO - Epoch(train) [17][ 200/5005] lr: 1.0000e-01 eta: 1 day, 15:13:05 time: 0.2709 data_time: 0.0021 loss: 1.9763 03/05 07:36:27 - mmengine - INFO - Epoch(train) [17][ 300/5005] lr: 1.0000e-01 eta: 1 day, 15:12:38 time: 0.2202 data_time: 0.0019 loss: 2.2344 03/05 07:36:49 - mmengine - INFO - Epoch(train) [17][ 400/5005] lr: 1.0000e-01 eta: 1 day, 15:12:10 time: 0.2181 data_time: 0.0017 loss: 2.4969 03/05 07:37:11 - mmengine - INFO - Epoch(train) [17][ 500/5005] lr: 1.0000e-01 eta: 1 day, 15:11:46 time: 0.2573 data_time: 0.0017 loss: 2.1395 03/05 07:37:34 - mmengine - INFO - Epoch(train) [17][ 600/5005] lr: 1.0000e-01 eta: 1 day, 15:11:21 time: 0.2169 data_time: 0.0015 loss: 1.9107 03/05 07:37:56 - mmengine - INFO - Epoch(train) [17][ 700/5005] lr: 1.0000e-01 eta: 1 day, 15:10:54 time: 0.2216 data_time: 0.0016 loss: 1.9505 03/05 07:38:18 - mmengine - INFO - Epoch(train) [17][ 800/5005] lr: 1.0000e-01 eta: 1 day, 15:10:26 time: 0.2194 data_time: 0.0018 loss: 2.2147 03/05 07:38:40 - mmengine - INFO - Epoch(train) [17][ 900/5005] lr: 1.0000e-01 eta: 1 day, 15:09:59 time: 0.2195 data_time: 0.0017 loss: 2.1927 03/05 07:38:45 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:39:03 - mmengine - INFO - Epoch(train) [17][1000/5005] lr: 1.0000e-01 eta: 1 day, 15:09:35 time: 0.2196 data_time: 0.0018 loss: 2.1525 03/05 07:39:25 - mmengine - INFO - Epoch(train) [17][1100/5005] lr: 1.0000e-01 eta: 1 day, 15:09:09 time: 0.2415 data_time: 0.0016 loss: 2.4220 03/05 07:39:47 - mmengine - INFO - Epoch(train) [17][1200/5005] lr: 1.0000e-01 eta: 1 day, 15:08:40 time: 0.2184 data_time: 0.0017 loss: 2.0788 03/05 07:40:09 - mmengine - INFO - Epoch(train) [17][1300/5005] lr: 1.0000e-01 eta: 1 day, 15:08:12 time: 0.2289 data_time: 0.0017 loss: 2.3045 03/05 07:40:32 - mmengine - INFO - Epoch(train) [17][1400/5005] lr: 1.0000e-01 eta: 1 day, 15:07:46 time: 0.2192 data_time: 0.0017 loss: 2.4200 03/05 07:40:54 - mmengine - INFO - Epoch(train) [17][1500/5005] lr: 1.0000e-01 eta: 1 day, 15:07:21 time: 0.2205 data_time: 0.0016 loss: 2.2056 03/05 07:41:16 - mmengine - INFO - Epoch(train) [17][1600/5005] lr: 1.0000e-01 eta: 1 day, 15:06:55 time: 0.2185 data_time: 0.0018 loss: 2.2944 03/05 07:41:39 - mmengine - INFO - Epoch(train) [17][1700/5005] lr: 1.0000e-01 eta: 1 day, 15:06:26 time: 0.2188 data_time: 0.0019 loss: 2.3398 03/05 07:42:01 - mmengine - INFO - Epoch(train) [17][1800/5005] lr: 1.0000e-01 eta: 1 day, 15:06:02 time: 0.2175 data_time: 0.0021 loss: 2.4001 03/05 07:42:23 - mmengine - INFO - Epoch(train) [17][1900/5005] lr: 1.0000e-01 eta: 1 day, 15:05:34 time: 0.2188 data_time: 0.0017 loss: 2.1549 03/05 07:42:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:42:45 - mmengine - INFO - Epoch(train) [17][2000/5005] lr: 1.0000e-01 eta: 1 day, 15:05:07 time: 0.2184 data_time: 0.0019 loss: 2.1078 03/05 07:43:07 - mmengine - INFO - Epoch(train) [17][2100/5005] lr: 1.0000e-01 eta: 1 day, 15:04:38 time: 0.2184 data_time: 0.0017 loss: 1.9753 03/05 07:43:30 - mmengine - INFO - Epoch(train) [17][2200/5005] lr: 1.0000e-01 eta: 1 day, 15:04:14 time: 0.2186 data_time: 0.0018 loss: 2.1539 03/05 07:43:52 - mmengine - INFO - Epoch(train) [17][2300/5005] lr: 1.0000e-01 eta: 1 day, 15:03:49 time: 0.2200 data_time: 0.0017 loss: 2.1413 03/05 07:44:15 - mmengine - INFO - Epoch(train) [17][2400/5005] lr: 1.0000e-01 eta: 1 day, 15:03:21 time: 0.2234 data_time: 0.0018 loss: 2.3440 03/05 07:44:37 - mmengine - INFO - Epoch(train) [17][2500/5005] lr: 1.0000e-01 eta: 1 day, 15:02:53 time: 0.2196 data_time: 0.0016 loss: 2.4048 03/05 07:44:59 - mmengine - INFO - Epoch(train) [17][2600/5005] lr: 1.0000e-01 eta: 1 day, 15:02:28 time: 0.2268 data_time: 0.0016 loss: 2.0137 03/05 07:45:21 - mmengine - INFO - Epoch(train) [17][2700/5005] lr: 1.0000e-01 eta: 1 day, 15:02:03 time: 0.2221 data_time: 0.0017 loss: 2.1349 03/05 07:45:44 - mmengine - INFO - Epoch(train) [17][2800/5005] lr: 1.0000e-01 eta: 1 day, 15:01:35 time: 0.2178 data_time: 0.0019 loss: 2.3516 03/05 07:46:05 - mmengine - INFO - Epoch(train) [17][2900/5005] lr: 1.0000e-01 eta: 1 day, 15:01:06 time: 0.2193 data_time: 0.0018 loss: 2.3505 03/05 07:46:10 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:46:28 - mmengine - INFO - Epoch(train) [17][3000/5005] lr: 1.0000e-01 eta: 1 day, 15:00:41 time: 0.2412 data_time: 0.0017 loss: 2.1769 03/05 07:46:51 - mmengine - INFO - Epoch(train) [17][3100/5005] lr: 1.0000e-01 eta: 1 day, 15:00:18 time: 0.2440 data_time: 0.0017 loss: 1.9615 03/05 07:47:13 - mmengine - INFO - Epoch(train) [17][3200/5005] lr: 1.0000e-01 eta: 1 day, 14:59:51 time: 0.2181 data_time: 0.0018 loss: 2.2166 03/05 07:47:35 - mmengine - INFO - Epoch(train) [17][3300/5005] lr: 1.0000e-01 eta: 1 day, 14:59:24 time: 0.2240 data_time: 0.0016 loss: 2.4486 03/05 07:47:57 - mmengine - INFO - Epoch(train) [17][3400/5005] lr: 1.0000e-01 eta: 1 day, 14:58:57 time: 0.2189 data_time: 0.0017 loss: 2.1462 03/05 07:48:20 - mmengine - INFO - Epoch(train) [17][3500/5005] lr: 1.0000e-01 eta: 1 day, 14:58:33 time: 0.2363 data_time: 0.0018 loss: 2.2836 03/05 07:48:42 - mmengine - INFO - Epoch(train) [17][3600/5005] lr: 1.0000e-01 eta: 1 day, 14:58:06 time: 0.2230 data_time: 0.0021 loss: 2.0540 03/05 07:49:04 - mmengine - INFO - Epoch(train) [17][3700/5005] lr: 1.0000e-01 eta: 1 day, 14:57:39 time: 0.2176 data_time: 0.0017 loss: 2.1672 03/05 07:49:26 - mmengine - INFO - Epoch(train) [17][3800/5005] lr: 1.0000e-01 eta: 1 day, 14:57:12 time: 0.2273 data_time: 0.0017 loss: 2.2197 03/05 07:49:49 - mmengine - INFO - Epoch(train) [17][3900/5005] lr: 1.0000e-01 eta: 1 day, 14:56:46 time: 0.2196 data_time: 0.0018 loss: 2.1517 03/05 07:49:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:50:11 - mmengine - INFO - Epoch(train) [17][4000/5005] lr: 1.0000e-01 eta: 1 day, 14:56:22 time: 0.2209 data_time: 0.0018 loss: 2.1887 03/05 07:50:33 - mmengine - INFO - Epoch(train) [17][4100/5005] lr: 1.0000e-01 eta: 1 day, 14:55:53 time: 0.2187 data_time: 0.0018 loss: 2.2556 03/05 07:50:55 - mmengine - INFO - Epoch(train) [17][4200/5005] lr: 1.0000e-01 eta: 1 day, 14:55:26 time: 0.2187 data_time: 0.0017 loss: 2.0907 03/05 07:51:18 - mmengine - INFO - Epoch(train) [17][4300/5005] lr: 1.0000e-01 eta: 1 day, 14:55:01 time: 0.2209 data_time: 0.0017 loss: 2.5028 03/05 07:51:40 - mmengine - INFO - Epoch(train) [17][4400/5005] lr: 1.0000e-01 eta: 1 day, 14:54:34 time: 0.2201 data_time: 0.0018 loss: 2.2319 03/05 07:52:02 - mmengine - INFO - Epoch(train) [17][4500/5005] lr: 1.0000e-01 eta: 1 day, 14:54:08 time: 0.2180 data_time: 0.0016 loss: 2.1359 03/05 07:52:25 - mmengine - INFO - Epoch(train) [17][4600/5005] lr: 1.0000e-01 eta: 1 day, 14:53:42 time: 0.2187 data_time: 0.0017 loss: 2.4369 03/05 07:52:47 - mmengine - INFO - Epoch(train) [17][4700/5005] lr: 1.0000e-01 eta: 1 day, 14:53:15 time: 0.2201 data_time: 0.0017 loss: 2.2804 03/05 07:53:09 - mmengine - INFO - Epoch(train) [17][4800/5005] lr: 1.0000e-01 eta: 1 day, 14:52:48 time: 0.2349 data_time: 0.0017 loss: 2.2200 03/05 07:53:33 - mmengine - INFO - Epoch(train) [17][4900/5005] lr: 1.0000e-01 eta: 1 day, 14:52:31 time: 0.2938 data_time: 0.0013 loss: 2.2024 03/05 07:53:39 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:54:03 - mmengine - INFO - Epoch(train) [17][5000/5005] lr: 1.0000e-01 eta: 1 day, 14:53:04 time: 0.3012 data_time: 0.0015 loss: 2.1627 03/05 07:54:05 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:54:07 - mmengine - INFO - Saving checkpoint at 17 epochs 03/05 07:54:21 - mmengine - INFO - Epoch(val) [17][100/196] eta: 0:00:11 time: 0.0208 data_time: 0.0004 03/05 07:54:34 - mmengine - INFO - Epoch(val) [17][196/196] accuracy/top1: 54.9100 accuracy/top5: 79.9940 03/05 07:55:06 - mmengine - INFO - Epoch(train) [18][ 100/5005] lr: 1.0000e-01 eta: 1 day, 14:53:45 time: 0.2226 data_time: 0.0016 loss: 2.0252 03/05 07:55:28 - mmengine - INFO - Epoch(train) [18][ 200/5005] lr: 1.0000e-01 eta: 1 day, 14:53:17 time: 0.2201 data_time: 0.0018 loss: 2.2273 03/05 07:55:50 - mmengine - INFO - Epoch(train) [18][ 300/5005] lr: 1.0000e-01 eta: 1 day, 14:52:50 time: 0.2181 data_time: 0.0017 loss: 2.1383 03/05 07:56:12 - mmengine - INFO - Epoch(train) [18][ 400/5005] lr: 1.0000e-01 eta: 1 day, 14:52:23 time: 0.2200 data_time: 0.0017 loss: 2.2356 03/05 07:56:35 - mmengine - INFO - Epoch(train) [18][ 500/5005] lr: 1.0000e-01 eta: 1 day, 14:51:58 time: 0.2284 data_time: 0.0018 loss: 2.4459 03/05 07:56:57 - mmengine - INFO - Epoch(train) [18][ 600/5005] lr: 1.0000e-01 eta: 1 day, 14:51:33 time: 0.2194 data_time: 0.0018 loss: 2.2269 03/05 07:57:19 - mmengine - INFO - Epoch(train) [18][ 700/5005] lr: 1.0000e-01 eta: 1 day, 14:51:07 time: 0.2190 data_time: 0.0016 loss: 2.0740 03/05 07:57:42 - mmengine - INFO - Epoch(train) [18][ 800/5005] lr: 1.0000e-01 eta: 1 day, 14:50:41 time: 0.2202 data_time: 0.0017 loss: 2.1062 03/05 07:58:04 - mmengine - INFO - Epoch(train) [18][ 900/5005] lr: 1.0000e-01 eta: 1 day, 14:50:13 time: 0.2232 data_time: 0.0016 loss: 2.1391 03/05 07:58:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 07:58:27 - mmengine - INFO - Epoch(train) [18][1000/5005] lr: 1.0000e-01 eta: 1 day, 14:49:50 time: 0.2231 data_time: 0.0017 loss: 2.2216 03/05 07:58:49 - mmengine - INFO - Epoch(train) [18][1100/5005] lr: 1.0000e-01 eta: 1 day, 14:49:24 time: 0.2184 data_time: 0.0019 loss: 2.0365 03/05 07:59:11 - mmengine - INFO - Epoch(train) [18][1200/5005] lr: 1.0000e-01 eta: 1 day, 14:48:57 time: 0.2184 data_time: 0.0017 loss: 1.7387 03/05 07:59:33 - mmengine - INFO - Epoch(train) [18][1300/5005] lr: 1.0000e-01 eta: 1 day, 14:48:29 time: 0.2174 data_time: 0.0017 loss: 2.2151 03/05 07:59:55 - mmengine - INFO - Epoch(train) [18][1400/5005] lr: 1.0000e-01 eta: 1 day, 14:48:04 time: 0.2201 data_time: 0.0016 loss: 2.4830 03/05 08:00:18 - mmengine - INFO - Epoch(train) [18][1500/5005] lr: 1.0000e-01 eta: 1 day, 14:47:37 time: 0.2188 data_time: 0.0017 loss: 2.1251 03/05 08:00:40 - mmengine - INFO - Epoch(train) [18][1600/5005] lr: 1.0000e-01 eta: 1 day, 14:47:11 time: 0.2184 data_time: 0.0017 loss: 2.4860 03/05 08:01:02 - mmengine - INFO - Epoch(train) [18][1700/5005] lr: 1.0000e-01 eta: 1 day, 14:46:43 time: 0.2201 data_time: 0.0018 loss: 2.1483 03/05 08:01:24 - mmengine - INFO - Epoch(train) [18][1800/5005] lr: 1.0000e-01 eta: 1 day, 14:46:18 time: 0.2206 data_time: 0.0018 loss: 2.1402 03/05 08:01:47 - mmengine - INFO - Epoch(train) [18][1900/5005] lr: 1.0000e-01 eta: 1 day, 14:45:53 time: 0.2216 data_time: 0.0017 loss: 2.3153 03/05 08:01:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:02:09 - mmengine - INFO - Epoch(train) [18][2000/5005] lr: 1.0000e-01 eta: 1 day, 14:45:27 time: 0.2193 data_time: 0.0015 loss: 2.1890 03/05 08:02:31 - mmengine - INFO - Epoch(train) [18][2100/5005] lr: 1.0000e-01 eta: 1 day, 14:45:00 time: 0.2221 data_time: 0.0017 loss: 2.1358 03/05 08:02:54 - mmengine - INFO - Epoch(train) [18][2200/5005] lr: 1.0000e-01 eta: 1 day, 14:44:33 time: 0.2265 data_time: 0.0016 loss: 1.9548 03/05 08:03:16 - mmengine - INFO - Epoch(train) [18][2300/5005] lr: 1.0000e-01 eta: 1 day, 14:44:10 time: 0.2307 data_time: 0.0017 loss: 2.0985 03/05 08:03:38 - mmengine - INFO - Epoch(train) [18][2400/5005] lr: 1.0000e-01 eta: 1 day, 14:43:43 time: 0.2189 data_time: 0.0018 loss: 2.0866 03/05 08:04:00 - mmengine - INFO - Epoch(train) [18][2500/5005] lr: 1.0000e-01 eta: 1 day, 14:43:15 time: 0.2198 data_time: 0.0017 loss: 2.2360 03/05 08:04:23 - mmengine - INFO - Epoch(train) [18][2600/5005] lr: 1.0000e-01 eta: 1 day, 14:42:50 time: 0.2381 data_time: 0.0019 loss: 2.1121 03/05 08:04:45 - mmengine - INFO - Epoch(train) [18][2700/5005] lr: 1.0000e-01 eta: 1 day, 14:42:25 time: 0.2392 data_time: 0.0017 loss: 2.4004 03/05 08:05:08 - mmengine - INFO - Epoch(train) [18][2800/5005] lr: 1.0000e-01 eta: 1 day, 14:42:01 time: 0.2194 data_time: 0.0017 loss: 2.1082 03/05 08:05:30 - mmengine - INFO - Epoch(train) [18][2900/5005] lr: 1.0000e-01 eta: 1 day, 14:41:33 time: 0.2206 data_time: 0.0016 loss: 2.1529 03/05 08:05:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:05:52 - mmengine - INFO - Epoch(train) [18][3000/5005] lr: 1.0000e-01 eta: 1 day, 14:41:07 time: 0.2424 data_time: 0.0018 loss: 2.2614 03/05 08:06:15 - mmengine - INFO - Epoch(train) [18][3100/5005] lr: 1.0000e-01 eta: 1 day, 14:40:44 time: 0.2583 data_time: 0.0018 loss: 2.2127 03/05 08:06:37 - mmengine - INFO - Epoch(train) [18][3200/5005] lr: 1.0000e-01 eta: 1 day, 14:40:18 time: 0.2168 data_time: 0.0018 loss: 2.1800 03/05 08:06:59 - mmengine - INFO - Epoch(train) [18][3300/5005] lr: 1.0000e-01 eta: 1 day, 14:39:51 time: 0.2209 data_time: 0.0016 loss: 2.1637 03/05 08:07:21 - mmengine - INFO - Epoch(train) [18][3400/5005] lr: 1.0000e-01 eta: 1 day, 14:39:24 time: 0.2200 data_time: 0.0016 loss: 1.8696 03/05 08:07:44 - mmengine - INFO - Epoch(train) [18][3500/5005] lr: 1.0000e-01 eta: 1 day, 14:38:59 time: 0.2380 data_time: 0.0018 loss: 2.2680 03/05 08:08:06 - mmengine - INFO - Epoch(train) [18][3600/5005] lr: 1.0000e-01 eta: 1 day, 14:38:34 time: 0.2191 data_time: 0.0018 loss: 2.1793 03/05 08:08:28 - mmengine - INFO - Epoch(train) [18][3700/5005] lr: 1.0000e-01 eta: 1 day, 14:38:07 time: 0.2192 data_time: 0.0016 loss: 2.4168 03/05 08:08:51 - mmengine - INFO - Epoch(train) [18][3800/5005] lr: 1.0000e-01 eta: 1 day, 14:37:39 time: 0.2199 data_time: 0.0018 loss: 2.1241 03/05 08:09:13 - mmengine - INFO - Epoch(train) [18][3900/5005] lr: 1.0000e-01 eta: 1 day, 14:37:13 time: 0.2200 data_time: 0.0017 loss: 2.3055 03/05 08:09:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:09:36 - mmengine - INFO - Epoch(train) [18][4000/5005] lr: 1.0000e-01 eta: 1 day, 14:36:51 time: 0.2194 data_time: 0.0016 loss: 2.1125 03/05 08:09:58 - mmengine - INFO - Epoch(train) [18][4100/5005] lr: 1.0000e-01 eta: 1 day, 14:36:24 time: 0.2206 data_time: 0.0016 loss: 2.4491 03/05 08:10:20 - mmengine - INFO - Epoch(train) [18][4200/5005] lr: 1.0000e-01 eta: 1 day, 14:35:58 time: 0.2178 data_time: 0.0018 loss: 2.1642 03/05 08:10:42 - mmengine - INFO - Epoch(train) [18][4300/5005] lr: 1.0000e-01 eta: 1 day, 14:35:32 time: 0.2194 data_time: 0.0018 loss: 2.1380 03/05 08:11:05 - mmengine - INFO - Epoch(train) [18][4400/5005] lr: 1.0000e-01 eta: 1 day, 14:35:08 time: 0.2386 data_time: 0.0018 loss: 1.9859 03/05 08:11:27 - mmengine - INFO - Epoch(train) [18][4500/5005] lr: 1.0000e-01 eta: 1 day, 14:34:42 time: 0.2219 data_time: 0.0017 loss: 2.0033 03/05 08:11:49 - mmengine - INFO - Epoch(train) [18][4600/5005] lr: 1.0000e-01 eta: 1 day, 14:34:15 time: 0.2228 data_time: 0.0018 loss: 1.9762 03/05 08:12:12 - mmengine - INFO - Epoch(train) [18][4700/5005] lr: 1.0000e-01 eta: 1 day, 14:33:49 time: 0.2178 data_time: 0.0017 loss: 2.1658 03/05 08:12:34 - mmengine - INFO - Epoch(train) [18][4800/5005] lr: 1.0000e-01 eta: 1 day, 14:33:24 time: 0.2168 data_time: 0.0017 loss: 2.1533 03/05 08:12:58 - mmengine - INFO - Epoch(train) [18][4900/5005] lr: 1.0000e-01 eta: 1 day, 14:33:09 time: 0.2899 data_time: 0.0018 loss: 2.3921 03/05 08:13:02 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:13:28 - mmengine - INFO - Epoch(train) [18][5000/5005] lr: 1.0000e-01 eta: 1 day, 14:33:35 time: 0.2967 data_time: 0.0015 loss: 2.0268 03/05 08:13:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:13:32 - mmengine - INFO - Saving checkpoint at 18 epochs 03/05 08:13:47 - mmengine - INFO - Epoch(val) [18][100/196] eta: 0:00:13 time: 0.0193 data_time: 0.0004 03/05 08:14:01 - mmengine - INFO - Epoch(val) [18][196/196] accuracy/top1: 55.4740 accuracy/top5: 80.3880 03/05 08:14:31 - mmengine - INFO - Epoch(train) [19][ 100/5005] lr: 1.0000e-01 eta: 1 day, 14:34:08 time: 0.2187 data_time: 0.0020 loss: 2.1266 03/05 08:14:54 - mmengine - INFO - Epoch(train) [19][ 200/5005] lr: 1.0000e-01 eta: 1 day, 14:33:42 time: 0.2222 data_time: 0.0021 loss: 2.2360 03/05 08:15:16 - mmengine - INFO - Epoch(train) [19][ 300/5005] lr: 1.0000e-01 eta: 1 day, 14:33:19 time: 0.2417 data_time: 0.0018 loss: 2.4037 03/05 08:15:39 - mmengine - INFO - Epoch(train) [19][ 400/5005] lr: 1.0000e-01 eta: 1 day, 14:32:52 time: 0.2231 data_time: 0.0018 loss: 2.3097 03/05 08:16:01 - mmengine - INFO - Epoch(train) [19][ 500/5005] lr: 1.0000e-01 eta: 1 day, 14:32:26 time: 0.2201 data_time: 0.0020 loss: 2.3201 03/05 08:16:23 - mmengine - INFO - Epoch(train) [19][ 600/5005] lr: 1.0000e-01 eta: 1 day, 14:32:01 time: 0.2211 data_time: 0.0015 loss: 1.9860 03/05 08:16:45 - mmengine - INFO - Epoch(train) [19][ 700/5005] lr: 1.0000e-01 eta: 1 day, 14:31:34 time: 0.2197 data_time: 0.0018 loss: 2.2572 03/05 08:17:08 - mmengine - INFO - Epoch(train) [19][ 800/5005] lr: 1.0000e-01 eta: 1 day, 14:31:07 time: 0.2184 data_time: 0.0016 loss: 2.1464 03/05 08:17:30 - mmengine - INFO - Epoch(train) [19][ 900/5005] lr: 1.0000e-01 eta: 1 day, 14:30:40 time: 0.2203 data_time: 0.0016 loss: 2.3208 03/05 08:17:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:17:52 - mmengine - INFO - Epoch(train) [19][1000/5005] lr: 1.0000e-01 eta: 1 day, 14:30:15 time: 0.2196 data_time: 0.0017 loss: 2.1152 03/05 08:18:14 - mmengine - INFO - Epoch(train) [19][1100/5005] lr: 1.0000e-01 eta: 1 day, 14:29:49 time: 0.2216 data_time: 0.0019 loss: 2.2662 03/05 08:18:37 - mmengine - INFO - Epoch(train) [19][1200/5005] lr: 1.0000e-01 eta: 1 day, 14:29:24 time: 0.2202 data_time: 0.0018 loss: 2.4064 03/05 08:18:59 - mmengine - INFO - Epoch(train) [19][1300/5005] lr: 1.0000e-01 eta: 1 day, 14:28:57 time: 0.2216 data_time: 0.0017 loss: 2.2678 03/05 08:19:21 - mmengine - INFO - Epoch(train) [19][1400/5005] lr: 1.0000e-01 eta: 1 day, 14:28:32 time: 0.2191 data_time: 0.0016 loss: 2.2051 03/05 08:19:44 - mmengine - INFO - Epoch(train) [19][1500/5005] lr: 1.0000e-01 eta: 1 day, 14:28:07 time: 0.2246 data_time: 0.0019 loss: 2.0878 03/05 08:20:06 - mmengine - INFO - Epoch(train) [19][1600/5005] lr: 1.0000e-01 eta: 1 day, 14:27:42 time: 0.2206 data_time: 0.0017 loss: 2.1807 03/05 08:20:28 - mmengine - INFO - Epoch(train) [19][1700/5005] lr: 1.0000e-01 eta: 1 day, 14:27:15 time: 0.2201 data_time: 0.0018 loss: 2.2350 03/05 08:20:50 - mmengine - INFO - Epoch(train) [19][1800/5005] lr: 1.0000e-01 eta: 1 day, 14:26:48 time: 0.2232 data_time: 0.0018 loss: 2.0690 03/05 08:21:13 - mmengine - INFO - Epoch(train) [19][1900/5005] lr: 1.0000e-01 eta: 1 day, 14:26:24 time: 0.2194 data_time: 0.0016 loss: 1.9841 03/05 08:21:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:21:35 - mmengine - INFO - Epoch(train) [19][2000/5005] lr: 1.0000e-01 eta: 1 day, 14:25:59 time: 0.2193 data_time: 0.0017 loss: 2.1586 03/05 08:21:58 - mmengine - INFO - Epoch(train) [19][2100/5005] lr: 1.0000e-01 eta: 1 day, 14:25:31 time: 0.2177 data_time: 0.0018 loss: 2.2276 03/05 08:22:20 - mmengine - INFO - Epoch(train) [19][2200/5005] lr: 1.0000e-01 eta: 1 day, 14:25:05 time: 0.2186 data_time: 0.0019 loss: 2.3165 03/05 08:22:42 - mmengine - INFO - Epoch(train) [19][2300/5005] lr: 1.0000e-01 eta: 1 day, 14:24:40 time: 0.2207 data_time: 0.0019 loss: 2.2105 03/05 08:23:04 - mmengine - INFO - Epoch(train) [19][2400/5005] lr: 1.0000e-01 eta: 1 day, 14:24:14 time: 0.2188 data_time: 0.0018 loss: 1.9246 03/05 08:23:27 - mmengine - INFO - Epoch(train) [19][2500/5005] lr: 1.0000e-01 eta: 1 day, 14:23:48 time: 0.2235 data_time: 0.0019 loss: 2.3844 03/05 08:23:49 - mmengine - INFO - Epoch(train) [19][2600/5005] lr: 1.0000e-01 eta: 1 day, 14:23:21 time: 0.2202 data_time: 0.0018 loss: 2.2117 03/05 08:24:11 - mmengine - INFO - Epoch(train) [19][2700/5005] lr: 1.0000e-01 eta: 1 day, 14:22:56 time: 0.2373 data_time: 0.0016 loss: 2.2067 03/05 08:24:34 - mmengine - INFO - Epoch(train) [19][2800/5005] lr: 1.0000e-01 eta: 1 day, 14:22:32 time: 0.2337 data_time: 0.0019 loss: 1.7648 03/05 08:24:56 - mmengine - INFO - Epoch(train) [19][2900/5005] lr: 1.0000e-01 eta: 1 day, 14:22:06 time: 0.2206 data_time: 0.0017 loss: 2.0594 03/05 08:24:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:25:18 - mmengine - INFO - Epoch(train) [19][3000/5005] lr: 1.0000e-01 eta: 1 day, 14:21:39 time: 0.2225 data_time: 0.0017 loss: 2.1585 03/05 08:25:41 - mmengine - INFO - Epoch(train) [19][3100/5005] lr: 1.0000e-01 eta: 1 day, 14:21:14 time: 0.2214 data_time: 0.0017 loss: 2.0808 03/05 08:26:03 - mmengine - INFO - Epoch(train) [19][3200/5005] lr: 1.0000e-01 eta: 1 day, 14:20:50 time: 0.2229 data_time: 0.0018 loss: 2.2582 03/05 08:26:25 - mmengine - INFO - Epoch(train) [19][3300/5005] lr: 1.0000e-01 eta: 1 day, 14:20:24 time: 0.2192 data_time: 0.0017 loss: 2.1994 03/05 08:26:48 - mmengine - INFO - Epoch(train) [19][3400/5005] lr: 1.0000e-01 eta: 1 day, 14:19:58 time: 0.2297 data_time: 0.0017 loss: 1.9740 03/05 08:27:10 - mmengine - INFO - Epoch(train) [19][3500/5005] lr: 1.0000e-01 eta: 1 day, 14:19:31 time: 0.2199 data_time: 0.0018 loss: 2.1304 03/05 08:27:33 - mmengine - INFO - Epoch(train) [19][3600/5005] lr: 1.0000e-01 eta: 1 day, 14:19:10 time: 0.2189 data_time: 0.0016 loss: 2.0637 03/05 08:27:55 - mmengine - INFO - Epoch(train) [19][3700/5005] lr: 1.0000e-01 eta: 1 day, 14:18:44 time: 0.2202 data_time: 0.0017 loss: 2.1121 03/05 08:28:17 - mmengine - INFO - Epoch(train) [19][3800/5005] lr: 1.0000e-01 eta: 1 day, 14:18:17 time: 0.2182 data_time: 0.0017 loss: 2.0371 03/05 08:28:39 - mmengine - INFO - Epoch(train) [19][3900/5005] lr: 1.0000e-01 eta: 1 day, 14:17:51 time: 0.2202 data_time: 0.0016 loss: 2.2181 03/05 08:28:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:29:02 - mmengine - INFO - Epoch(train) [19][4000/5005] lr: 1.0000e-01 eta: 1 day, 14:17:27 time: 0.2193 data_time: 0.0019 loss: 1.9141 03/05 08:29:24 - mmengine - INFO - Epoch(train) [19][4100/5005] lr: 1.0000e-01 eta: 1 day, 14:17:01 time: 0.2274 data_time: 0.0016 loss: 2.2838 03/05 08:29:46 - mmengine - INFO - Epoch(train) [19][4200/5005] lr: 1.0000e-01 eta: 1 day, 14:16:35 time: 0.2201 data_time: 0.0016 loss: 1.8804 03/05 08:30:09 - mmengine - INFO - Epoch(train) [19][4300/5005] lr: 1.0000e-01 eta: 1 day, 14:16:09 time: 0.2245 data_time: 0.0018 loss: 2.1816 03/05 08:30:31 - mmengine - INFO - Epoch(train) [19][4400/5005] lr: 1.0000e-01 eta: 1 day, 14:15:46 time: 0.2193 data_time: 0.0017 loss: 2.2240 03/05 08:30:53 - mmengine - INFO - Epoch(train) [19][4500/5005] lr: 1.0000e-01 eta: 1 day, 14:15:18 time: 0.2210 data_time: 0.0017 loss: 2.1505 03/05 08:31:16 - mmengine - INFO - Epoch(train) [19][4600/5005] lr: 1.0000e-01 eta: 1 day, 14:14:52 time: 0.2218 data_time: 0.0016 loss: 2.3031 03/05 08:31:38 - mmengine - INFO - Epoch(train) [19][4700/5005] lr: 1.0000e-01 eta: 1 day, 14:14:28 time: 0.2200 data_time: 0.0018 loss: 2.3893 03/05 08:32:00 - mmengine - INFO - Epoch(train) [19][4800/5005] lr: 1.0000e-01 eta: 1 day, 14:14:02 time: 0.2195 data_time: 0.0018 loss: 2.3057 03/05 08:32:24 - mmengine - INFO - Epoch(train) [19][4900/5005] lr: 1.0000e-01 eta: 1 day, 14:13:45 time: 0.2951 data_time: 0.0015 loss: 1.9798 03/05 08:32:27 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:32:54 - mmengine - INFO - Epoch(train) [19][5000/5005] lr: 1.0000e-01 eta: 1 day, 14:14:08 time: 0.2911 data_time: 0.0016 loss: 2.1077 03/05 08:32:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:32:58 - mmengine - INFO - Saving checkpoint at 19 epochs 03/05 08:33:12 - mmengine - INFO - Epoch(val) [19][100/196] eta: 0:00:12 time: 0.0181 data_time: 0.0003 03/05 08:33:26 - mmengine - INFO - Epoch(val) [19][196/196] accuracy/top1: 55.2700 accuracy/top5: 80.3380 03/05 08:33:57 - mmengine - INFO - Epoch(train) [20][ 100/5005] lr: 1.0000e-01 eta: 1 day, 14:14:36 time: 0.2232 data_time: 0.0020 loss: 2.3863 03/05 08:34:19 - mmengine - INFO - Epoch(train) [20][ 200/5005] lr: 1.0000e-01 eta: 1 day, 14:14:11 time: 0.2187 data_time: 0.0019 loss: 2.2242 03/05 08:34:42 - mmengine - INFO - Epoch(train) [20][ 300/5005] lr: 1.0000e-01 eta: 1 day, 14:13:49 time: 0.2186 data_time: 0.0019 loss: 2.1859 03/05 08:35:04 - mmengine - INFO - Epoch(train) [20][ 400/5005] lr: 1.0000e-01 eta: 1 day, 14:13:24 time: 0.2202 data_time: 0.0020 loss: 1.9253 03/05 08:35:27 - mmengine - INFO - Epoch(train) [20][ 500/5005] lr: 1.0000e-01 eta: 1 day, 14:13:03 time: 0.2183 data_time: 0.0017 loss: 1.9916 03/05 08:35:50 - mmengine - INFO - Epoch(train) [20][ 600/5005] lr: 1.0000e-01 eta: 1 day, 14:12:37 time: 0.2205 data_time: 0.0021 loss: 2.1658 03/05 08:36:12 - mmengine - INFO - Epoch(train) [20][ 700/5005] lr: 1.0000e-01 eta: 1 day, 14:12:12 time: 0.2201 data_time: 0.0022 loss: 2.0751 03/05 08:36:34 - mmengine - INFO - Epoch(train) [20][ 800/5005] lr: 1.0000e-01 eta: 1 day, 14:11:46 time: 0.2382 data_time: 0.0017 loss: 2.3237 03/05 08:36:57 - mmengine - INFO - Epoch(train) [20][ 900/5005] lr: 1.0000e-01 eta: 1 day, 14:11:21 time: 0.2220 data_time: 0.0017 loss: 2.2045 03/05 08:36:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:37:19 - mmengine - INFO - Epoch(train) [20][1000/5005] lr: 1.0000e-01 eta: 1 day, 14:10:54 time: 0.2194 data_time: 0.0022 loss: 2.2009 03/05 08:37:41 - mmengine - INFO - Epoch(train) [20][1100/5005] lr: 1.0000e-01 eta: 1 day, 14:10:30 time: 0.2197 data_time: 0.0018 loss: 2.0460 03/05 08:38:03 - mmengine - INFO - Epoch(train) [20][1200/5005] lr: 1.0000e-01 eta: 1 day, 14:10:03 time: 0.2216 data_time: 0.0021 loss: 2.0185 03/05 08:38:26 - mmengine - INFO - Epoch(train) [20][1300/5005] lr: 1.0000e-01 eta: 1 day, 14:09:37 time: 0.2212 data_time: 0.0019 loss: 2.2505 03/05 08:38:48 - mmengine - INFO - Epoch(train) [20][1400/5005] lr: 1.0000e-01 eta: 1 day, 14:09:11 time: 0.2178 data_time: 0.0018 loss: 2.1746 03/05 08:39:10 - mmengine - INFO - Epoch(train) [20][1500/5005] lr: 1.0000e-01 eta: 1 day, 14:08:46 time: 0.2211 data_time: 0.0020 loss: 2.2586 03/05 08:39:32 - mmengine - INFO - Epoch(train) [20][1600/5005] lr: 1.0000e-01 eta: 1 day, 14:08:19 time: 0.2226 data_time: 0.0018 loss: 2.2484 03/05 08:39:55 - mmengine - INFO - Epoch(train) [20][1700/5005] lr: 1.0000e-01 eta: 1 day, 14:07:53 time: 0.2219 data_time: 0.0018 loss: 2.1448 03/05 08:40:17 - mmengine - INFO - Epoch(train) [20][1800/5005] lr: 1.0000e-01 eta: 1 day, 14:07:28 time: 0.2172 data_time: 0.0017 loss: 2.2607 03/05 08:40:40 - mmengine - INFO - Epoch(train) [20][1900/5005] lr: 1.0000e-01 eta: 1 day, 14:07:04 time: 0.2226 data_time: 0.0017 loss: 2.2936 03/05 08:40:41 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:41:02 - mmengine - INFO - Epoch(train) [20][2000/5005] lr: 1.0000e-01 eta: 1 day, 14:06:38 time: 0.2190 data_time: 0.0018 loss: 2.2914 03/05 08:41:24 - mmengine - INFO - Epoch(train) [20][2100/5005] lr: 1.0000e-01 eta: 1 day, 14:06:12 time: 0.2227 data_time: 0.0020 loss: 2.3134 03/05 08:41:47 - mmengine - INFO - Epoch(train) [20][2200/5005] lr: 1.0000e-01 eta: 1 day, 14:05:48 time: 0.2186 data_time: 0.0018 loss: 1.9716 03/05 08:42:09 - mmengine - INFO - Epoch(train) [20][2300/5005] lr: 1.0000e-01 eta: 1 day, 14:05:24 time: 0.2398 data_time: 0.0018 loss: 2.0808 03/05 08:42:31 - mmengine - INFO - Epoch(train) [20][2400/5005] lr: 1.0000e-01 eta: 1 day, 14:04:57 time: 0.2177 data_time: 0.0019 loss: 2.0317 03/05 08:42:53 - mmengine - INFO - Epoch(train) [20][2500/5005] lr: 1.0000e-01 eta: 1 day, 14:04:30 time: 0.2236 data_time: 0.0020 loss: 2.1744 03/05 08:43:16 - mmengine - INFO - Epoch(train) [20][2600/5005] lr: 1.0000e-01 eta: 1 day, 14:04:06 time: 0.2442 data_time: 0.0017 loss: 2.1432 03/05 08:43:38 - mmengine - INFO - Epoch(train) [20][2700/5005] lr: 1.0000e-01 eta: 1 day, 14:03:39 time: 0.2202 data_time: 0.0018 loss: 2.2724 03/05 08:44:01 - mmengine - INFO - Epoch(train) [20][2800/5005] lr: 1.0000e-01 eta: 1 day, 14:03:16 time: 0.2342 data_time: 0.0019 loss: 2.2479 03/05 08:44:23 - mmengine - INFO - Epoch(train) [20][2900/5005] lr: 1.0000e-01 eta: 1 day, 14:02:49 time: 0.2165 data_time: 0.0019 loss: 2.0272 03/05 08:44:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:44:45 - mmengine - INFO - Epoch(train) [20][3000/5005] lr: 1.0000e-01 eta: 1 day, 14:02:23 time: 0.2208 data_time: 0.0018 loss: 2.2625 03/05 08:45:08 - mmengine - INFO - Epoch(train) [20][3100/5005] lr: 1.0000e-01 eta: 1 day, 14:01:59 time: 0.2233 data_time: 0.0018 loss: 2.2468 03/05 08:45:31 - mmengine - INFO - Epoch(train) [20][3200/5005] lr: 1.0000e-01 eta: 1 day, 14:01:38 time: 0.2372 data_time: 0.0019 loss: 2.2741 03/05 08:45:53 - mmengine - INFO - Epoch(train) [20][3300/5005] lr: 1.0000e-01 eta: 1 day, 14:01:12 time: 0.2211 data_time: 0.0018 loss: 2.0258 03/05 08:46:15 - mmengine - INFO - Epoch(train) [20][3400/5005] lr: 1.0000e-01 eta: 1 day, 14:00:46 time: 0.2194 data_time: 0.0019 loss: 2.4064 03/05 08:46:37 - mmengine - INFO - Epoch(train) [20][3500/5005] lr: 1.0000e-01 eta: 1 day, 14:00:20 time: 0.2202 data_time: 0.0018 loss: 2.0056 03/05 08:47:00 - mmengine - INFO - Epoch(train) [20][3600/5005] lr: 1.0000e-01 eta: 1 day, 13:59:58 time: 0.2283 data_time: 0.0018 loss: 2.0363 03/05 08:47:22 - mmengine - INFO - Epoch(train) [20][3700/5005] lr: 1.0000e-01 eta: 1 day, 13:59:32 time: 0.2223 data_time: 0.0019 loss: 1.9687 03/05 08:47:45 - mmengine - INFO - Epoch(train) [20][3800/5005] lr: 1.0000e-01 eta: 1 day, 13:59:06 time: 0.2217 data_time: 0.0019 loss: 1.8369 03/05 08:48:07 - mmengine - INFO - Epoch(train) [20][3900/5005] lr: 1.0000e-01 eta: 1 day, 13:58:39 time: 0.2190 data_time: 0.0018 loss: 2.2413 03/05 08:48:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:48:29 - mmengine - INFO - Epoch(train) [20][4000/5005] lr: 1.0000e-01 eta: 1 day, 13:58:16 time: 0.2337 data_time: 0.0018 loss: 2.1175 03/05 08:48:52 - mmengine - INFO - Epoch(train) [20][4100/5005] lr: 1.0000e-01 eta: 1 day, 13:57:50 time: 0.2189 data_time: 0.0020 loss: 2.0714 03/05 08:49:14 - mmengine - INFO - Epoch(train) [20][4200/5005] lr: 1.0000e-01 eta: 1 day, 13:57:24 time: 0.2221 data_time: 0.0020 loss: 2.2854 03/05 08:49:36 - mmengine - INFO - Epoch(train) [20][4300/5005] lr: 1.0000e-01 eta: 1 day, 13:56:57 time: 0.2171 data_time: 0.0016 loss: 2.0280 03/05 08:49:58 - mmengine - INFO - Epoch(train) [20][4400/5005] lr: 1.0000e-01 eta: 1 day, 13:56:32 time: 0.2255 data_time: 0.0017 loss: 2.2255 03/05 08:50:21 - mmengine - INFO - Epoch(train) [20][4500/5005] lr: 1.0000e-01 eta: 1 day, 13:56:08 time: 0.2221 data_time: 0.0019 loss: 2.0576 03/05 08:50:43 - mmengine - INFO - Epoch(train) [20][4600/5005] lr: 1.0000e-01 eta: 1 day, 13:55:42 time: 0.2189 data_time: 0.0016 loss: 2.1711 03/05 08:51:05 - mmengine - INFO - Epoch(train) [20][4700/5005] lr: 1.0000e-01 eta: 1 day, 13:55:16 time: 0.2282 data_time: 0.0018 loss: 2.2355 03/05 08:51:28 - mmengine - INFO - Epoch(train) [20][4800/5005] lr: 1.0000e-01 eta: 1 day, 13:54:52 time: 0.2202 data_time: 0.0017 loss: 2.1289 03/05 08:51:52 - mmengine - INFO - Epoch(train) [20][4900/5005] lr: 1.0000e-01 eta: 1 day, 13:54:35 time: 0.3024 data_time: 0.0015 loss: 2.0911 03/05 08:51:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:52:22 - mmengine - INFO - Epoch(train) [20][5000/5005] lr: 1.0000e-01 eta: 1 day, 13:54:57 time: 0.2977 data_time: 0.0017 loss: 2.0926 03/05 08:52:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:52:26 - mmengine - INFO - Saving checkpoint at 20 epochs 03/05 08:52:40 - mmengine - INFO - Epoch(val) [20][100/196] eta: 0:00:12 time: 0.0202 data_time: 0.0004 03/05 08:52:54 - mmengine - INFO - Epoch(val) [20][196/196] accuracy/top1: 53.7280 accuracy/top5: 78.8720 03/05 08:53:25 - mmengine - INFO - Epoch(train) [21][ 100/5005] lr: 1.0000e-01 eta: 1 day, 13:55:22 time: 0.2346 data_time: 0.0021 loss: 2.2969 03/05 08:53:47 - mmengine - INFO - Epoch(train) [21][ 200/5005] lr: 1.0000e-01 eta: 1 day, 13:54:57 time: 0.2241 data_time: 0.0021 loss: 2.1134 03/05 08:54:09 - mmengine - INFO - Epoch(train) [21][ 300/5005] lr: 1.0000e-01 eta: 1 day, 13:54:32 time: 0.2354 data_time: 0.0020 loss: 2.0327 03/05 08:54:31 - mmengine - INFO - Epoch(train) [21][ 400/5005] lr: 1.0000e-01 eta: 1 day, 13:54:05 time: 0.2205 data_time: 0.0023 loss: 2.0771 03/05 08:54:54 - mmengine - INFO - Epoch(train) [21][ 500/5005] lr: 1.0000e-01 eta: 1 day, 13:53:41 time: 0.2239 data_time: 0.0024 loss: 2.1369 03/05 08:55:17 - mmengine - INFO - Epoch(train) [21][ 600/5005] lr: 1.0000e-01 eta: 1 day, 13:53:18 time: 0.2204 data_time: 0.0021 loss: 1.9681 03/05 08:55:39 - mmengine - INFO - Epoch(train) [21][ 700/5005] lr: 1.0000e-01 eta: 1 day, 13:52:54 time: 0.2433 data_time: 0.0018 loss: 2.0782 03/05 08:56:01 - mmengine - INFO - Epoch(train) [21][ 800/5005] lr: 1.0000e-01 eta: 1 day, 13:52:27 time: 0.2213 data_time: 0.0018 loss: 1.9996 03/05 08:56:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 08:56:24 - mmengine - INFO - Epoch(train) [21][ 900/5005] lr: 1.0000e-01 eta: 1 day, 13:52:03 time: 0.2335 data_time: 0.0017 loss: 2.0813 03/05 08:56:46 - mmengine - INFO - Epoch(train) [21][1000/5005] lr: 1.0000e-01 eta: 1 day, 13:51:37 time: 0.2165 data_time: 0.0019 loss: 2.2188 03/05 08:57:08 - mmengine - INFO - Epoch(train) [21][1100/5005] lr: 1.0000e-01 eta: 1 day, 13:51:11 time: 0.2429 data_time: 0.0018 loss: 2.1826 03/05 08:57:30 - mmengine - INFO - Epoch(train) [21][1200/5005] lr: 1.0000e-01 eta: 1 day, 13:50:45 time: 0.2236 data_time: 0.0018 loss: 2.1503 03/05 08:57:53 - mmengine - INFO - Epoch(train) [21][1300/5005] lr: 1.0000e-01 eta: 1 day, 13:50:19 time: 0.2248 data_time: 0.0019 loss: 2.0802 03/05 08:58:15 - mmengine - INFO - Epoch(train) [21][1400/5005] lr: 1.0000e-01 eta: 1 day, 13:49:54 time: 0.2208 data_time: 0.0019 loss: 2.1067 03/05 08:58:37 - mmengine - INFO - Epoch(train) [21][1500/5005] lr: 1.0000e-01 eta: 1 day, 13:49:28 time: 0.2204 data_time: 0.0018 loss: 2.1700 03/05 08:59:00 - mmengine - INFO - Epoch(train) [21][1600/5005] lr: 1.0000e-01 eta: 1 day, 13:49:03 time: 0.2190 data_time: 0.0017 loss: 1.9685 03/05 08:59:22 - mmengine - INFO - Epoch(train) [21][1700/5005] lr: 1.0000e-01 eta: 1 day, 13:48:37 time: 0.2179 data_time: 0.0022 loss: 2.2777 03/05 08:59:45 - mmengine - INFO - Epoch(train) [21][1800/5005] lr: 1.0000e-01 eta: 1 day, 13:48:14 time: 0.2180 data_time: 0.0018 loss: 2.2290 03/05 09:00:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:00:07 - mmengine - INFO - Epoch(train) [21][1900/5005] lr: 1.0000e-01 eta: 1 day, 13:47:48 time: 0.2207 data_time: 0.0019 loss: 2.0721 03/05 09:00:29 - mmengine - INFO - Epoch(train) [21][2000/5005] lr: 1.0000e-01 eta: 1 day, 13:47:23 time: 0.2202 data_time: 0.0017 loss: 2.0752 03/05 09:00:52 - mmengine - INFO - Epoch(train) [21][2100/5005] lr: 1.0000e-01 eta: 1 day, 13:46:58 time: 0.2199 data_time: 0.0018 loss: 2.0821 03/05 09:01:14 - mmengine - INFO - Epoch(train) [21][2200/5005] lr: 1.0000e-01 eta: 1 day, 13:46:35 time: 0.2182 data_time: 0.0019 loss: 2.2101 03/05 09:01:36 - mmengine - INFO - Epoch(train) [21][2300/5005] lr: 1.0000e-01 eta: 1 day, 13:46:08 time: 0.2290 data_time: 0.0018 loss: 1.9934 03/05 09:01:59 - mmengine - INFO - Epoch(train) [21][2400/5005] lr: 1.0000e-01 eta: 1 day, 13:45:41 time: 0.2182 data_time: 0.0019 loss: 2.0885 03/05 09:02:21 - mmengine - INFO - Epoch(train) [21][2500/5005] lr: 1.0000e-01 eta: 1 day, 13:45:18 time: 0.2213 data_time: 0.0019 loss: 2.4643 03/05 09:02:44 - mmengine - INFO - Epoch(train) [21][2600/5005] lr: 1.0000e-01 eta: 1 day, 13:44:54 time: 0.2385 data_time: 0.0019 loss: 2.2223 03/05 09:03:06 - mmengine - INFO - Epoch(train) [21][2700/5005] lr: 1.0000e-01 eta: 1 day, 13:44:28 time: 0.2221 data_time: 0.0017 loss: 2.1417 03/05 09:03:28 - mmengine - INFO - Epoch(train) [21][2800/5005] lr: 1.0000e-01 eta: 1 day, 13:44:01 time: 0.2230 data_time: 0.0019 loss: 2.1413 03/05 09:03:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:03:50 - mmengine - INFO - Epoch(train) [21][2900/5005] lr: 1.0000e-01 eta: 1 day, 13:43:35 time: 0.2176 data_time: 0.0019 loss: 2.0061 03/05 09:04:13 - mmengine - INFO - Epoch(train) [21][3000/5005] lr: 1.0000e-01 eta: 1 day, 13:43:10 time: 0.2206 data_time: 0.0021 loss: 2.1013 03/05 09:04:35 - mmengine - INFO - Epoch(train) [21][3100/5005] lr: 1.0000e-01 eta: 1 day, 13:42:46 time: 0.2207 data_time: 0.0019 loss: 2.1981 03/05 09:04:58 - mmengine - INFO - Epoch(train) [21][3200/5005] lr: 1.0000e-01 eta: 1 day, 13:42:22 time: 0.2329 data_time: 0.0019 loss: 2.1833 03/05 09:05:20 - mmengine - INFO - Epoch(train) [21][3300/5005] lr: 1.0000e-01 eta: 1 day, 13:41:56 time: 0.2212 data_time: 0.0020 loss: 2.0980 03/05 09:05:42 - mmengine - INFO - Epoch(train) [21][3400/5005] lr: 1.0000e-01 eta: 1 day, 13:41:30 time: 0.2197 data_time: 0.0021 loss: 2.1573 03/05 09:06:05 - mmengine - INFO - Epoch(train) [21][3500/5005] lr: 1.0000e-01 eta: 1 day, 13:41:07 time: 0.2210 data_time: 0.0020 loss: 2.0129 03/05 09:06:27 - mmengine - INFO - Epoch(train) [21][3600/5005] lr: 1.0000e-01 eta: 1 day, 13:40:41 time: 0.2207 data_time: 0.0020 loss: 1.9959 03/05 09:06:49 - mmengine - INFO - Epoch(train) [21][3700/5005] lr: 1.0000e-01 eta: 1 day, 13:40:16 time: 0.2218 data_time: 0.0019 loss: 2.1434 03/05 09:07:12 - mmengine - INFO - Epoch(train) [21][3800/5005] lr: 1.0000e-01 eta: 1 day, 13:39:50 time: 0.2287 data_time: 0.0019 loss: 2.1312 03/05 09:07:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:07:34 - mmengine - INFO - Epoch(train) [21][3900/5005] lr: 1.0000e-01 eta: 1 day, 13:39:27 time: 0.2386 data_time: 0.0019 loss: 2.0789 03/05 09:07:56 - mmengine - INFO - Epoch(train) [21][4000/5005] lr: 1.0000e-01 eta: 1 day, 13:39:01 time: 0.2196 data_time: 0.0018 loss: 2.3429 03/05 09:08:19 - mmengine - INFO - Epoch(train) [21][4100/5005] lr: 1.0000e-01 eta: 1 day, 13:38:36 time: 0.2263 data_time: 0.0019 loss: 2.2399 03/05 09:08:41 - mmengine - INFO - Epoch(train) [21][4200/5005] lr: 1.0000e-01 eta: 1 day, 13:38:10 time: 0.2211 data_time: 0.0019 loss: 2.1128 03/05 09:09:04 - mmengine - INFO - Epoch(train) [21][4300/5005] lr: 1.0000e-01 eta: 1 day, 13:37:47 time: 0.2406 data_time: 0.0017 loss: 1.9820 03/05 09:09:26 - mmengine - INFO - Epoch(train) [21][4400/5005] lr: 1.0000e-01 eta: 1 day, 13:37:22 time: 0.2197 data_time: 0.0020 loss: 2.1598 03/05 09:09:48 - mmengine - INFO - Epoch(train) [21][4500/5005] lr: 1.0000e-01 eta: 1 day, 13:36:56 time: 0.2393 data_time: 0.0017 loss: 2.1338 03/05 09:10:10 - mmengine - INFO - Epoch(train) [21][4600/5005] lr: 1.0000e-01 eta: 1 day, 13:36:30 time: 0.2199 data_time: 0.0017 loss: 2.1958 03/05 09:10:33 - mmengine - INFO - Epoch(train) [21][4700/5005] lr: 1.0000e-01 eta: 1 day, 13:36:06 time: 0.2525 data_time: 0.0018 loss: 2.3141 03/05 09:10:55 - mmengine - INFO - Epoch(train) [21][4800/5005] lr: 1.0000e-01 eta: 1 day, 13:35:42 time: 0.2210 data_time: 0.0019 loss: 1.9758 03/05 09:11:19 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:11:19 - mmengine - INFO - Epoch(train) [21][4900/5005] lr: 1.0000e-01 eta: 1 day, 13:35:24 time: 0.3048 data_time: 0.0016 loss: 2.0481 03/05 09:11:49 - mmengine - INFO - Epoch(train) [21][5000/5005] lr: 1.0000e-01 eta: 1 day, 13:35:42 time: 0.2949 data_time: 0.0015 loss: 2.4066 03/05 09:11:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:11:53 - mmengine - INFO - Saving checkpoint at 21 epochs 03/05 09:12:07 - mmengine - INFO - Epoch(val) [21][100/196] eta: 0:00:12 time: 0.0190 data_time: 0.0004 03/05 09:12:21 - mmengine - INFO - Epoch(val) [21][196/196] accuracy/top1: 54.5220 accuracy/top5: 79.8960 03/05 09:12:52 - mmengine - INFO - Epoch(train) [22][ 100/5005] lr: 1.0000e-01 eta: 1 day, 13:36:07 time: 0.2406 data_time: 0.0019 loss: 2.3181 03/05 09:13:14 - mmengine - INFO - Epoch(train) [22][ 200/5005] lr: 1.0000e-01 eta: 1 day, 13:35:41 time: 0.2196 data_time: 0.0022 loss: 2.2316 03/05 09:13:37 - mmengine - INFO - Epoch(train) [22][ 300/5005] lr: 1.0000e-01 eta: 1 day, 13:35:17 time: 0.2213 data_time: 0.0019 loss: 2.1025 03/05 09:13:59 - mmengine - INFO - Epoch(train) [22][ 400/5005] lr: 1.0000e-01 eta: 1 day, 13:34:53 time: 0.2194 data_time: 0.0018 loss: 2.0693 03/05 09:14:22 - mmengine - INFO - Epoch(train) [22][ 500/5005] lr: 1.0000e-01 eta: 1 day, 13:34:27 time: 0.2186 data_time: 0.0018 loss: 2.2678 03/05 09:14:44 - mmengine - INFO - Epoch(train) [22][ 600/5005] lr: 1.0000e-01 eta: 1 day, 13:34:01 time: 0.2200 data_time: 0.0018 loss: 2.3012 03/05 09:15:07 - mmengine - INFO - Epoch(train) [22][ 700/5005] lr: 1.0000e-01 eta: 1 day, 13:33:38 time: 0.2204 data_time: 0.0018 loss: 2.2088 03/05 09:15:29 - mmengine - INFO - Epoch(train) [22][ 800/5005] lr: 1.0000e-01 eta: 1 day, 13:33:15 time: 0.2215 data_time: 0.0017 loss: 2.1875 03/05 09:15:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:15:51 - mmengine - INFO - Epoch(train) [22][ 900/5005] lr: 1.0000e-01 eta: 1 day, 13:32:48 time: 0.2199 data_time: 0.0018 loss: 2.0306 03/05 09:16:14 - mmengine - INFO - Epoch(train) [22][1000/5005] lr: 1.0000e-01 eta: 1 day, 13:32:25 time: 0.2280 data_time: 0.0020 loss: 2.0220 03/05 09:16:36 - mmengine - INFO - Epoch(train) [22][1100/5005] lr: 1.0000e-01 eta: 1 day, 13:31:59 time: 0.2255 data_time: 0.0017 loss: 2.1642 03/05 09:16:59 - mmengine - INFO - Epoch(train) [22][1200/5005] lr: 1.0000e-01 eta: 1 day, 13:31:35 time: 0.2179 data_time: 0.0019 loss: 2.0373 03/05 09:17:21 - mmengine - INFO - Epoch(train) [22][1300/5005] lr: 1.0000e-01 eta: 1 day, 13:31:09 time: 0.2170 data_time: 0.0017 loss: 2.1814 03/05 09:17:43 - mmengine - INFO - Epoch(train) [22][1400/5005] lr: 1.0000e-01 eta: 1 day, 13:30:43 time: 0.2215 data_time: 0.0019 loss: 2.0218 03/05 09:18:05 - mmengine - INFO - Epoch(train) [22][1500/5005] lr: 1.0000e-01 eta: 1 day, 13:30:18 time: 0.2374 data_time: 0.0018 loss: 2.1795 03/05 09:18:28 - mmengine - INFO - Epoch(train) [22][1600/5005] lr: 1.0000e-01 eta: 1 day, 13:29:54 time: 0.2403 data_time: 0.0018 loss: 2.1450 03/05 09:18:50 - mmengine - INFO - Epoch(train) [22][1700/5005] lr: 1.0000e-01 eta: 1 day, 13:29:28 time: 0.2215 data_time: 0.0019 loss: 2.0481 03/05 09:19:12 - mmengine - INFO - Epoch(train) [22][1800/5005] lr: 1.0000e-01 eta: 1 day, 13:29:02 time: 0.2192 data_time: 0.0019 loss: 2.1805 03/05 09:19:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:19:35 - mmengine - INFO - Epoch(train) [22][1900/5005] lr: 1.0000e-01 eta: 1 day, 13:28:36 time: 0.2214 data_time: 0.0020 loss: 2.1482 03/05 09:19:57 - mmengine - INFO - Epoch(train) [22][2000/5005] lr: 1.0000e-01 eta: 1 day, 13:28:12 time: 0.2178 data_time: 0.0017 loss: 1.9801 03/05 09:20:19 - mmengine - INFO - Epoch(train) [22][2100/5005] lr: 1.0000e-01 eta: 1 day, 13:27:46 time: 0.2185 data_time: 0.0018 loss: 1.9661 03/05 09:20:42 - mmengine - INFO - Epoch(train) [22][2200/5005] lr: 1.0000e-01 eta: 1 day, 13:27:21 time: 0.2187 data_time: 0.0020 loss: 2.1108 03/05 09:21:04 - mmengine - INFO - Epoch(train) [22][2300/5005] lr: 1.0000e-01 eta: 1 day, 13:26:54 time: 0.2205 data_time: 0.0019 loss: 2.0494 03/05 09:21:26 - mmengine - INFO - Epoch(train) [22][2400/5005] lr: 1.0000e-01 eta: 1 day, 13:26:31 time: 0.2177 data_time: 0.0018 loss: 2.2141 03/05 09:21:49 - mmengine - INFO - Epoch(train) [22][2500/5005] lr: 1.0000e-01 eta: 1 day, 13:26:05 time: 0.2207 data_time: 0.0018 loss: 2.0909 03/05 09:22:11 - mmengine - INFO - Epoch(train) [22][2600/5005] lr: 1.0000e-01 eta: 1 day, 13:25:41 time: 0.2198 data_time: 0.0020 loss: 2.2890 03/05 09:22:33 - mmengine - INFO - Epoch(train) [22][2700/5005] lr: 1.0000e-01 eta: 1 day, 13:25:14 time: 0.2198 data_time: 0.0019 loss: 2.2759 03/05 09:22:56 - mmengine - INFO - Epoch(train) [22][2800/5005] lr: 1.0000e-01 eta: 1 day, 13:24:50 time: 0.2203 data_time: 0.0018 loss: 2.2101 03/05 09:23:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:23:18 - mmengine - INFO - Epoch(train) [22][2900/5005] lr: 1.0000e-01 eta: 1 day, 13:24:25 time: 0.2183 data_time: 0.0019 loss: 2.2720 03/05 09:23:40 - mmengine - INFO - Epoch(train) [22][3000/5005] lr: 1.0000e-01 eta: 1 day, 13:24:00 time: 0.2182 data_time: 0.0019 loss: 2.1658 03/05 09:24:02 - mmengine - INFO - Epoch(train) [22][3100/5005] lr: 1.0000e-01 eta: 1 day, 13:23:34 time: 0.2194 data_time: 0.0019 loss: 2.0420 03/05 09:24:25 - mmengine - INFO - Epoch(train) [22][3200/5005] lr: 1.0000e-01 eta: 1 day, 13:23:12 time: 0.2374 data_time: 0.0019 loss: 2.2835 03/05 09:24:47 - mmengine - INFO - Epoch(train) [22][3300/5005] lr: 1.0000e-01 eta: 1 day, 13:22:46 time: 0.2210 data_time: 0.0020 loss: 2.2991 03/05 09:25:09 - mmengine - INFO - Epoch(train) [22][3400/5005] lr: 1.0000e-01 eta: 1 day, 13:22:19 time: 0.2193 data_time: 0.0018 loss: 2.0227 03/05 09:25:32 - mmengine - INFO - Epoch(train) [22][3500/5005] lr: 1.0000e-01 eta: 1 day, 13:21:55 time: 0.2183 data_time: 0.0020 loss: 2.3708 03/05 09:25:54 - mmengine - INFO - Epoch(train) [22][3600/5005] lr: 1.0000e-01 eta: 1 day, 13:21:31 time: 0.2182 data_time: 0.0018 loss: 2.0390 03/05 09:26:17 - mmengine - INFO - Epoch(train) [22][3700/5005] lr: 1.0000e-01 eta: 1 day, 13:21:05 time: 0.2183 data_time: 0.0020 loss: 1.9704 03/05 09:26:39 - mmengine - INFO - Epoch(train) [22][3800/5005] lr: 1.0000e-01 eta: 1 day, 13:20:39 time: 0.2172 data_time: 0.0018 loss: 2.1247 03/05 09:27:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:27:01 - mmengine - INFO - Epoch(train) [22][3900/5005] lr: 1.0000e-01 eta: 1 day, 13:20:13 time: 0.2204 data_time: 0.0020 loss: 2.3585 03/05 09:27:23 - mmengine - INFO - Epoch(train) [22][4000/5005] lr: 1.0000e-01 eta: 1 day, 13:19:49 time: 0.2350 data_time: 0.0019 loss: 2.2118 03/05 09:27:46 - mmengine - INFO - Epoch(train) [22][4100/5005] lr: 1.0000e-01 eta: 1 day, 13:19:23 time: 0.2218 data_time: 0.0020 loss: 2.2503 03/05 09:28:08 - mmengine - INFO - Epoch(train) [22][4200/5005] lr: 1.0000e-01 eta: 1 day, 13:18:58 time: 0.2200 data_time: 0.0019 loss: 2.2135 03/05 09:28:30 - mmengine - INFO - Epoch(train) [22][4300/5005] lr: 1.0000e-01 eta: 1 day, 13:18:34 time: 0.2172 data_time: 0.0021 loss: 2.2731 03/05 09:28:53 - mmengine - INFO - Epoch(train) [22][4400/5005] lr: 1.0000e-01 eta: 1 day, 13:18:09 time: 0.2207 data_time: 0.0018 loss: 2.1056 03/05 09:29:15 - mmengine - INFO - Epoch(train) [22][4500/5005] lr: 1.0000e-01 eta: 1 day, 13:17:46 time: 0.2360 data_time: 0.0018 loss: 2.2093 03/05 09:29:38 - mmengine - INFO - Epoch(train) [22][4600/5005] lr: 1.0000e-01 eta: 1 day, 13:17:20 time: 0.2239 data_time: 0.0020 loss: 2.3205 03/05 09:30:00 - mmengine - INFO - Epoch(train) [22][4700/5005] lr: 1.0000e-01 eta: 1 day, 13:16:55 time: 0.2188 data_time: 0.0019 loss: 2.0729 03/05 09:30:22 - mmengine - INFO - Epoch(train) [22][4800/5005] lr: 1.0000e-01 eta: 1 day, 13:16:30 time: 0.2200 data_time: 0.0021 loss: 2.1993 03/05 09:30:45 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:30:46 - mmengine - INFO - Epoch(train) [22][4900/5005] lr: 1.0000e-01 eta: 1 day, 13:16:13 time: 0.2853 data_time: 0.0020 loss: 2.0507 03/05 09:31:16 - mmengine - INFO - Epoch(train) [22][5000/5005] lr: 1.0000e-01 eta: 1 day, 13:16:27 time: 0.2872 data_time: 0.0018 loss: 2.3101 03/05 09:31:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:31:20 - mmengine - INFO - Saving checkpoint at 22 epochs 03/05 09:31:34 - mmengine - INFO - Epoch(val) [22][100/196] eta: 0:00:12 time: 0.0199 data_time: 0.0004 03/05 09:31:48 - mmengine - INFO - Epoch(val) [22][196/196] accuracy/top1: 55.1740 accuracy/top5: 80.3260 03/05 09:32:18 - mmengine - INFO - Epoch(train) [23][ 100/5005] lr: 1.0000e-01 eta: 1 day, 13:16:46 time: 0.2215 data_time: 0.0019 loss: 1.6683 03/05 09:32:41 - mmengine - INFO - Epoch(train) [23][ 200/5005] lr: 1.0000e-01 eta: 1 day, 13:16:22 time: 0.2405 data_time: 0.0019 loss: 2.1067 03/05 09:33:03 - mmengine - INFO - Epoch(train) [23][ 300/5005] lr: 1.0000e-01 eta: 1 day, 13:15:58 time: 0.2222 data_time: 0.0019 loss: 1.9951 03/05 09:33:25 - mmengine - INFO - Epoch(train) [23][ 400/5005] lr: 1.0000e-01 eta: 1 day, 13:15:32 time: 0.2186 data_time: 0.0018 loss: 2.0603 03/05 09:33:48 - mmengine - INFO - Epoch(train) [23][ 500/5005] lr: 1.0000e-01 eta: 1 day, 13:15:09 time: 0.2188 data_time: 0.0018 loss: 2.3007 03/05 09:34:11 - mmengine - INFO - Epoch(train) [23][ 600/5005] lr: 1.0000e-01 eta: 1 day, 13:14:45 time: 0.2204 data_time: 0.0019 loss: 2.2899 03/05 09:34:33 - mmengine - INFO - Epoch(train) [23][ 700/5005] lr: 1.0000e-01 eta: 1 day, 13:14:19 time: 0.2201 data_time: 0.0018 loss: 2.3324 03/05 09:34:55 - mmengine - INFO - Epoch(train) [23][ 800/5005] lr: 1.0000e-01 eta: 1 day, 13:13:53 time: 0.2184 data_time: 0.0020 loss: 2.1770 03/05 09:35:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:35:17 - mmengine - INFO - Epoch(train) [23][ 900/5005] lr: 1.0000e-01 eta: 1 day, 13:13:29 time: 0.2196 data_time: 0.0018 loss: 2.1981 03/05 09:35:40 - mmengine - INFO - Epoch(train) [23][1000/5005] lr: 1.0000e-01 eta: 1 day, 13:13:05 time: 0.2187 data_time: 0.0021 loss: 1.9528 03/05 09:36:02 - mmengine - INFO - Epoch(train) [23][1100/5005] lr: 1.0000e-01 eta: 1 day, 13:12:41 time: 0.2214 data_time: 0.0021 loss: 2.0539 03/05 09:36:25 - mmengine - INFO - Epoch(train) [23][1200/5005] lr: 1.0000e-01 eta: 1 day, 13:12:15 time: 0.2312 data_time: 0.0018 loss: 2.1619 03/05 09:36:47 - mmengine - INFO - Epoch(train) [23][1300/5005] lr: 1.0000e-01 eta: 1 day, 13:11:50 time: 0.2229 data_time: 0.0018 loss: 2.2041 03/05 09:37:09 - mmengine - INFO - Epoch(train) [23][1400/5005] lr: 1.0000e-01 eta: 1 day, 13:11:25 time: 0.2184 data_time: 0.0018 loss: 2.1860 03/05 09:37:32 - mmengine - INFO - Epoch(train) [23][1500/5005] lr: 1.0000e-01 eta: 1 day, 13:11:02 time: 0.2185 data_time: 0.0021 loss: 2.1328 03/05 09:37:54 - mmengine - INFO - Epoch(train) [23][1600/5005] lr: 1.0000e-01 eta: 1 day, 13:10:36 time: 0.2211 data_time: 0.0018 loss: 2.3367 03/05 09:38:17 - mmengine - INFO - Epoch(train) [23][1700/5005] lr: 1.0000e-01 eta: 1 day, 13:10:13 time: 0.2239 data_time: 0.0020 loss: 2.0363 03/05 09:38:39 - mmengine - INFO - Epoch(train) [23][1800/5005] lr: 1.0000e-01 eta: 1 day, 13:09:48 time: 0.2213 data_time: 0.0019 loss: 2.1688 03/05 09:39:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:39:02 - mmengine - INFO - Epoch(train) [23][1900/5005] lr: 1.0000e-01 eta: 1 day, 13:09:25 time: 0.2193 data_time: 0.0019 loss: 2.1089 03/05 09:39:24 - mmengine - INFO - Epoch(train) [23][2000/5005] lr: 1.0000e-01 eta: 1 day, 13:09:00 time: 0.2205 data_time: 0.0018 loss: 2.3230 03/05 09:39:47 - mmengine - INFO - Epoch(train) [23][2100/5005] lr: 1.0000e-01 eta: 1 day, 13:08:35 time: 0.2215 data_time: 0.0018 loss: 1.9519 03/05 09:40:09 - mmengine - INFO - Epoch(train) [23][2200/5005] lr: 1.0000e-01 eta: 1 day, 13:08:10 time: 0.2183 data_time: 0.0022 loss: 2.0042 03/05 09:40:31 - mmengine - INFO - Epoch(train) [23][2300/5005] lr: 1.0000e-01 eta: 1 day, 13:07:46 time: 0.2224 data_time: 0.0018 loss: 1.9849 03/05 09:40:54 - mmengine - INFO - Epoch(train) [23][2400/5005] lr: 1.0000e-01 eta: 1 day, 13:07:21 time: 0.2221 data_time: 0.0019 loss: 2.0676 03/05 09:41:16 - mmengine - INFO - Epoch(train) [23][2500/5005] lr: 1.0000e-01 eta: 1 day, 13:06:57 time: 0.2159 data_time: 0.0019 loss: 2.4785 03/05 09:41:38 - mmengine - INFO - Epoch(train) [23][2600/5005] lr: 1.0000e-01 eta: 1 day, 13:06:32 time: 0.2190 data_time: 0.0019 loss: 2.1404 03/05 09:42:01 - mmengine - INFO - Epoch(train) [23][2700/5005] lr: 1.0000e-01 eta: 1 day, 13:06:07 time: 0.2172 data_time: 0.0019 loss: 2.3147 03/05 09:42:23 - mmengine - INFO - Epoch(train) [23][2800/5005] lr: 1.0000e-01 eta: 1 day, 13:05:42 time: 0.2240 data_time: 0.0019 loss: 2.0882 03/05 09:42:43 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:42:46 - mmengine - INFO - Epoch(train) [23][2900/5005] lr: 1.0000e-01 eta: 1 day, 13:05:18 time: 0.2569 data_time: 0.0019 loss: 2.1905 03/05 09:43:08 - mmengine - INFO - Epoch(train) [23][3000/5005] lr: 1.0000e-01 eta: 1 day, 13:04:54 time: 0.2176 data_time: 0.0019 loss: 2.1582 03/05 09:43:30 - mmengine - INFO - Epoch(train) [23][3100/5005] lr: 1.0000e-01 eta: 1 day, 13:04:28 time: 0.2198 data_time: 0.0021 loss: 2.0419 03/05 09:43:53 - mmengine - INFO - Epoch(train) [23][3200/5005] lr: 1.0000e-01 eta: 1 day, 13:04:04 time: 0.2370 data_time: 0.0017 loss: 2.3588 03/05 09:44:15 - mmengine - INFO - Epoch(train) [23][3300/5005] lr: 1.0000e-01 eta: 1 day, 13:03:37 time: 0.2220 data_time: 0.0019 loss: 2.1824 03/05 09:44:37 - mmengine - INFO - Epoch(train) [23][3400/5005] lr: 1.0000e-01 eta: 1 day, 13:03:14 time: 0.2208 data_time: 0.0018 loss: 2.2935 03/05 09:45:00 - mmengine - INFO - Epoch(train) [23][3500/5005] lr: 1.0000e-01 eta: 1 day, 13:02:49 time: 0.2198 data_time: 0.0017 loss: 2.0810 03/05 09:45:22 - mmengine - INFO - Epoch(train) [23][3600/5005] lr: 1.0000e-01 eta: 1 day, 13:02:22 time: 0.2169 data_time: 0.0018 loss: 2.2329 03/05 09:45:44 - mmengine - INFO - Epoch(train) [23][3700/5005] lr: 1.0000e-01 eta: 1 day, 13:01:57 time: 0.2204 data_time: 0.0020 loss: 2.2087 03/05 09:46:07 - mmengine - INFO - Epoch(train) [23][3800/5005] lr: 1.0000e-01 eta: 1 day, 13:01:33 time: 0.2186 data_time: 0.0018 loss: 2.1963 03/05 09:46:27 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:46:29 - mmengine - INFO - Epoch(train) [23][3900/5005] lr: 1.0000e-01 eta: 1 day, 13:01:09 time: 0.2257 data_time: 0.0018 loss: 2.1147 03/05 09:46:51 - mmengine - INFO - Epoch(train) [23][4000/5005] lr: 1.0000e-01 eta: 1 day, 13:00:43 time: 0.2187 data_time: 0.0018 loss: 2.1804 03/05 09:47:13 - mmengine - INFO - Epoch(train) [23][4100/5005] lr: 1.0000e-01 eta: 1 day, 13:00:18 time: 0.2198 data_time: 0.0019 loss: 2.1998 03/05 09:47:36 - mmengine - INFO - Epoch(train) [23][4200/5005] lr: 1.0000e-01 eta: 1 day, 12:59:53 time: 0.2172 data_time: 0.0020 loss: 2.0870 03/05 09:47:58 - mmengine - INFO - Epoch(train) [23][4300/5005] lr: 1.0000e-01 eta: 1 day, 12:59:28 time: 0.2199 data_time: 0.0020 loss: 2.1938 03/05 09:48:20 - mmengine - INFO - Epoch(train) [23][4400/5005] lr: 1.0000e-01 eta: 1 day, 12:59:03 time: 0.2199 data_time: 0.0017 loss: 2.3262 03/05 09:48:43 - mmengine - INFO - Epoch(train) [23][4500/5005] lr: 1.0000e-01 eta: 1 day, 12:58:38 time: 0.2237 data_time: 0.0019 loss: 2.1562 03/05 09:49:05 - mmengine - INFO - Epoch(train) [23][4600/5005] lr: 1.0000e-01 eta: 1 day, 12:58:13 time: 0.2204 data_time: 0.0018 loss: 2.1107 03/05 09:49:27 - mmengine - INFO - Epoch(train) [23][4700/5005] lr: 1.0000e-01 eta: 1 day, 12:57:47 time: 0.2266 data_time: 0.0019 loss: 1.9705 03/05 09:49:50 - mmengine - INFO - Epoch(train) [23][4800/5005] lr: 1.0000e-01 eta: 1 day, 12:57:23 time: 0.2231 data_time: 0.0020 loss: 1.9805 03/05 09:50:10 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:50:13 - mmengine - INFO - Epoch(train) [23][4900/5005] lr: 1.0000e-01 eta: 1 day, 12:57:04 time: 0.2964 data_time: 0.0018 loss: 2.1830 03/05 09:50:43 - mmengine - INFO - Epoch(train) [23][5000/5005] lr: 1.0000e-01 eta: 1 day, 12:57:15 time: 0.2946 data_time: 0.0019 loss: 2.1627 03/05 09:50:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:50:47 - mmengine - INFO - Saving checkpoint at 23 epochs 03/05 09:51:01 - mmengine - INFO - Epoch(val) [23][100/196] eta: 0:00:12 time: 0.0176 data_time: 0.0003 03/05 09:51:15 - mmengine - INFO - Epoch(val) [23][196/196] accuracy/top1: 55.6700 accuracy/top5: 80.9860 03/05 09:51:46 - mmengine - INFO - Epoch(train) [24][ 100/5005] lr: 1.0000e-01 eta: 1 day, 12:57:35 time: 0.2191 data_time: 0.0019 loss: 2.3047 03/05 09:52:09 - mmengine - INFO - Epoch(train) [24][ 200/5005] lr: 1.0000e-01 eta: 1 day, 12:57:13 time: 0.2155 data_time: 0.0020 loss: 2.1016 03/05 09:52:31 - mmengine - INFO - Epoch(train) [24][ 300/5005] lr: 1.0000e-01 eta: 1 day, 12:56:51 time: 0.2213 data_time: 0.0018 loss: 2.3276 03/05 09:52:54 - mmengine - INFO - Epoch(train) [24][ 400/5005] lr: 1.0000e-01 eta: 1 day, 12:56:24 time: 0.2174 data_time: 0.0021 loss: 2.0713 03/05 09:53:16 - mmengine - INFO - Epoch(train) [24][ 500/5005] lr: 1.0000e-01 eta: 1 day, 12:55:58 time: 0.2211 data_time: 0.0018 loss: 2.1233 03/05 09:53:39 - mmengine - INFO - Epoch(train) [24][ 600/5005] lr: 1.0000e-01 eta: 1 day, 12:55:37 time: 0.2182 data_time: 0.0020 loss: 2.1643 03/05 09:54:01 - mmengine - INFO - Epoch(train) [24][ 700/5005] lr: 1.0000e-01 eta: 1 day, 12:55:11 time: 0.2384 data_time: 0.0019 loss: 2.1386 03/05 09:54:23 - mmengine - INFO - Epoch(train) [24][ 800/5005] lr: 1.0000e-01 eta: 1 day, 12:54:46 time: 0.2198 data_time: 0.0019 loss: 2.0627 03/05 09:54:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:54:45 - mmengine - INFO - Epoch(train) [24][ 900/5005] lr: 1.0000e-01 eta: 1 day, 12:54:20 time: 0.2188 data_time: 0.0019 loss: 2.2527 03/05 09:55:08 - mmengine - INFO - Epoch(train) [24][1000/5005] lr: 1.0000e-01 eta: 1 day, 12:53:57 time: 0.2218 data_time: 0.0021 loss: 1.9608 03/05 09:55:30 - mmengine - INFO - Epoch(train) [24][1100/5005] lr: 1.0000e-01 eta: 1 day, 12:53:31 time: 0.2376 data_time: 0.0017 loss: 2.1080 03/05 09:55:52 - mmengine - INFO - Epoch(train) [24][1200/5005] lr: 1.0000e-01 eta: 1 day, 12:53:05 time: 0.2216 data_time: 0.0019 loss: 2.1069 03/05 09:56:14 - mmengine - INFO - Epoch(train) [24][1300/5005] lr: 1.0000e-01 eta: 1 day, 12:52:40 time: 0.2378 data_time: 0.0020 loss: 2.2877 03/05 09:56:37 - mmengine - INFO - Epoch(train) [24][1400/5005] lr: 1.0000e-01 eta: 1 day, 12:52:17 time: 0.2187 data_time: 0.0018 loss: 2.3304 03/05 09:56:59 - mmengine - INFO - Epoch(train) [24][1500/5005] lr: 1.0000e-01 eta: 1 day, 12:51:51 time: 0.2181 data_time: 0.0022 loss: 2.2729 03/05 09:57:22 - mmengine - INFO - Epoch(train) [24][1600/5005] lr: 1.0000e-01 eta: 1 day, 12:51:26 time: 0.2236 data_time: 0.0020 loss: 2.1703 03/05 09:57:44 - mmengine - INFO - Epoch(train) [24][1700/5005] lr: 1.0000e-01 eta: 1 day, 12:51:00 time: 0.2194 data_time: 0.0020 loss: 2.0928 03/05 09:58:07 - mmengine - INFO - Epoch(train) [24][1800/5005] lr: 1.0000e-01 eta: 1 day, 12:50:38 time: 0.2423 data_time: 0.0020 loss: 2.0303 03/05 09:58:25 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 09:58:29 - mmengine - INFO - Epoch(train) [24][1900/5005] lr: 1.0000e-01 eta: 1 day, 12:50:12 time: 0.2195 data_time: 0.0017 loss: 2.3039 03/05 09:58:51 - mmengine - INFO - Epoch(train) [24][2000/5005] lr: 1.0000e-01 eta: 1 day, 12:49:47 time: 0.2196 data_time: 0.0019 loss: 2.0001 03/05 09:59:13 - mmengine - INFO - Epoch(train) [24][2100/5005] lr: 1.0000e-01 eta: 1 day, 12:49:21 time: 0.2199 data_time: 0.0019 loss: 2.2266 03/05 09:59:36 - mmengine - INFO - Epoch(train) [24][2200/5005] lr: 1.0000e-01 eta: 1 day, 12:48:58 time: 0.2203 data_time: 0.0020 loss: 2.2801 03/05 09:59:59 - mmengine - INFO - Epoch(train) [24][2300/5005] lr: 1.0000e-01 eta: 1 day, 12:48:35 time: 0.2367 data_time: 0.0020 loss: 2.1620 03/05 10:00:21 - mmengine - INFO - Epoch(train) [24][2400/5005] lr: 1.0000e-01 eta: 1 day, 12:48:09 time: 0.2227 data_time: 0.0019 loss: 1.9620 03/05 10:00:43 - mmengine - INFO - Epoch(train) [24][2500/5005] lr: 1.0000e-01 eta: 1 day, 12:47:43 time: 0.2200 data_time: 0.0021 loss: 2.0727 03/05 10:01:05 - mmengine - INFO - Epoch(train) [24][2600/5005] lr: 1.0000e-01 eta: 1 day, 12:47:18 time: 0.2335 data_time: 0.0021 loss: 2.2534 03/05 10:01:28 - mmengine - INFO - Epoch(train) [24][2700/5005] lr: 1.0000e-01 eta: 1 day, 12:46:55 time: 0.2454 data_time: 0.0019 loss: 2.1291 03/05 10:01:50 - mmengine - INFO - Epoch(train) [24][2800/5005] lr: 1.0000e-01 eta: 1 day, 12:46:30 time: 0.2185 data_time: 0.0020 loss: 2.1488 03/05 10:02:09 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:02:12 - mmengine - INFO - Epoch(train) [24][2900/5005] lr: 1.0000e-01 eta: 1 day, 12:46:04 time: 0.2222 data_time: 0.0021 loss: 2.4362 03/05 10:02:34 - mmengine - INFO - Epoch(train) [24][3000/5005] lr: 1.0000e-01 eta: 1 day, 12:45:39 time: 0.2186 data_time: 0.0019 loss: 2.3068 03/05 10:02:57 - mmengine - INFO - Epoch(train) [24][3100/5005] lr: 1.0000e-01 eta: 1 day, 12:45:17 time: 0.2174 data_time: 0.0018 loss: 2.0434 03/05 10:03:20 - mmengine - INFO - Epoch(train) [24][3200/5005] lr: 1.0000e-01 eta: 1 day, 12:44:51 time: 0.2178 data_time: 0.0019 loss: 2.2050 03/05 10:03:42 - mmengine - INFO - Epoch(train) [24][3300/5005] lr: 1.0000e-01 eta: 1 day, 12:44:26 time: 0.2205 data_time: 0.0021 loss: 2.2732 03/05 10:04:04 - mmengine - INFO - Epoch(train) [24][3400/5005] lr: 1.0000e-01 eta: 1 day, 12:44:00 time: 0.2192 data_time: 0.0019 loss: 2.0556 03/05 10:04:26 - mmengine - INFO - Epoch(train) [24][3500/5005] lr: 1.0000e-01 eta: 1 day, 12:43:36 time: 0.2164 data_time: 0.0020 loss: 1.9846 03/05 10:04:49 - mmengine - INFO - Epoch(train) [24][3600/5005] lr: 1.0000e-01 eta: 1 day, 12:43:11 time: 0.2210 data_time: 0.0019 loss: 2.2025 03/05 10:05:11 - mmengine - INFO - Epoch(train) [24][3700/5005] lr: 1.0000e-01 eta: 1 day, 12:42:47 time: 0.2202 data_time: 0.0021 loss: 2.3421 03/05 10:05:33 - mmengine - INFO - Epoch(train) [24][3800/5005] lr: 1.0000e-01 eta: 1 day, 12:42:21 time: 0.2207 data_time: 0.0017 loss: 2.2130 03/05 10:05:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:05:56 - mmengine - INFO - Epoch(train) [24][3900/5005] lr: 1.0000e-01 eta: 1 day, 12:41:57 time: 0.2361 data_time: 0.0020 loss: 2.2546 03/05 10:06:18 - mmengine - INFO - Epoch(train) [24][4000/5005] lr: 1.0000e-01 eta: 1 day, 12:41:32 time: 0.2197 data_time: 0.0021 loss: 2.2510 03/05 10:06:40 - mmengine - INFO - Epoch(train) [24][4100/5005] lr: 1.0000e-01 eta: 1 day, 12:41:06 time: 0.2231 data_time: 0.0019 loss: 2.2023 03/05 10:07:03 - mmengine - INFO - Epoch(train) [24][4200/5005] lr: 1.0000e-01 eta: 1 day, 12:40:42 time: 0.2217 data_time: 0.0021 loss: 2.2214 03/05 10:07:25 - mmengine - INFO - Epoch(train) [24][4300/5005] lr: 1.0000e-01 eta: 1 day, 12:40:18 time: 0.2271 data_time: 0.0018 loss: 2.1915 03/05 10:07:48 - mmengine - INFO - Epoch(train) [24][4400/5005] lr: 1.0000e-01 eta: 1 day, 12:39:55 time: 0.2227 data_time: 0.0019 loss: 2.1656 03/05 10:08:10 - mmengine - INFO - Epoch(train) [24][4500/5005] lr: 1.0000e-01 eta: 1 day, 12:39:30 time: 0.2176 data_time: 0.0024 loss: 2.0054 03/05 10:08:33 - mmengine - INFO - Epoch(train) [24][4600/5005] lr: 1.0000e-01 eta: 1 day, 12:39:06 time: 0.2199 data_time: 0.0018 loss: 2.1463 03/05 10:08:55 - mmengine - INFO - Epoch(train) [24][4700/5005] lr: 1.0000e-01 eta: 1 day, 12:38:41 time: 0.2180 data_time: 0.0019 loss: 2.1169 03/05 10:09:18 - mmengine - INFO - Epoch(train) [24][4800/5005] lr: 1.0000e-01 eta: 1 day, 12:38:18 time: 0.2208 data_time: 0.0017 loss: 2.2550 03/05 10:09:37 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:09:41 - mmengine - INFO - Epoch(train) [24][4900/5005] lr: 1.0000e-01 eta: 1 day, 12:37:59 time: 0.2985 data_time: 0.0017 loss: 2.0640 03/05 10:10:11 - mmengine - INFO - Epoch(train) [24][5000/5005] lr: 1.0000e-01 eta: 1 day, 12:38:10 time: 0.2989 data_time: 0.0017 loss: 2.2150 03/05 10:10:12 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:10:15 - mmengine - INFO - Saving checkpoint at 24 epochs 03/05 10:10:29 - mmengine - INFO - Epoch(val) [24][100/196] eta: 0:00:12 time: 0.0180 data_time: 0.0003 03/05 10:10:43 - mmengine - INFO - Epoch(val) [24][196/196] accuracy/top1: 54.7400 accuracy/top5: 80.4600 03/05 10:11:13 - mmengine - INFO - Epoch(train) [25][ 100/5005] lr: 1.0000e-01 eta: 1 day, 12:38:24 time: 0.2181 data_time: 0.0019 loss: 2.0547 03/05 10:11:36 - mmengine - INFO - Epoch(train) [25][ 200/5005] lr: 1.0000e-01 eta: 1 day, 12:38:00 time: 0.2162 data_time: 0.0019 loss: 1.9617 03/05 10:11:58 - mmengine - INFO - Epoch(train) [25][ 300/5005] lr: 1.0000e-01 eta: 1 day, 12:37:37 time: 0.2563 data_time: 0.0021 loss: 2.1497 03/05 10:12:21 - mmengine - INFO - Epoch(train) [25][ 400/5005] lr: 1.0000e-01 eta: 1 day, 12:37:13 time: 0.2205 data_time: 0.0020 loss: 2.1415 03/05 10:12:43 - mmengine - INFO - Epoch(train) [25][ 500/5005] lr: 1.0000e-01 eta: 1 day, 12:36:49 time: 0.2168 data_time: 0.0019 loss: 2.2879 03/05 10:13:06 - mmengine - INFO - Epoch(train) [25][ 600/5005] lr: 1.0000e-01 eta: 1 day, 12:36:24 time: 0.2215 data_time: 0.0019 loss: 1.9987 03/05 10:13:28 - mmengine - INFO - Epoch(train) [25][ 700/5005] lr: 1.0000e-01 eta: 1 day, 12:36:00 time: 0.2405 data_time: 0.0020 loss: 2.1619 03/05 10:13:51 - mmengine - INFO - Epoch(train) [25][ 800/5005] lr: 1.0000e-01 eta: 1 day, 12:35:36 time: 0.2186 data_time: 0.0019 loss: 2.1048 03/05 10:14:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:14:13 - mmengine - INFO - Epoch(train) [25][ 900/5005] lr: 1.0000e-01 eta: 1 day, 12:35:10 time: 0.2215 data_time: 0.0020 loss: 2.0928 03/05 10:14:35 - mmengine - INFO - Epoch(train) [25][1000/5005] lr: 1.0000e-01 eta: 1 day, 12:34:45 time: 0.2398 data_time: 0.0022 loss: 2.3957 03/05 10:14:58 - mmengine - INFO - Epoch(train) [25][1100/5005] lr: 1.0000e-01 eta: 1 day, 12:34:22 time: 0.2239 data_time: 0.0020 loss: 2.2824 03/05 10:15:20 - mmengine - INFO - Epoch(train) [25][1200/5005] lr: 1.0000e-01 eta: 1 day, 12:33:58 time: 0.2263 data_time: 0.0019 loss: 1.9945 03/05 10:15:42 - mmengine - INFO - Epoch(train) [25][1300/5005] lr: 1.0000e-01 eta: 1 day, 12:33:32 time: 0.2168 data_time: 0.0020 loss: 2.2940 03/05 10:16:05 - mmengine - INFO - Epoch(train) [25][1400/5005] lr: 1.0000e-01 eta: 1 day, 12:33:07 time: 0.2208 data_time: 0.0019 loss: 2.2522 03/05 10:16:27 - mmengine - INFO - Epoch(train) [25][1500/5005] lr: 1.0000e-01 eta: 1 day, 12:32:45 time: 0.2364 data_time: 0.0020 loss: 2.0456 03/05 10:16:50 - mmengine - INFO - Epoch(train) [25][1600/5005] lr: 1.0000e-01 eta: 1 day, 12:32:20 time: 0.2219 data_time: 0.0021 loss: 2.0411 03/05 10:17:12 - mmengine - INFO - Epoch(train) [25][1700/5005] lr: 1.0000e-01 eta: 1 day, 12:31:54 time: 0.2186 data_time: 0.0018 loss: 2.3097 03/05 10:17:34 - mmengine - INFO - Epoch(train) [25][1800/5005] lr: 1.0000e-01 eta: 1 day, 12:31:29 time: 0.2158 data_time: 0.0021 loss: 2.1880 03/05 10:17:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:17:57 - mmengine - INFO - Epoch(train) [25][1900/5005] lr: 1.0000e-01 eta: 1 day, 12:31:04 time: 0.2212 data_time: 0.0018 loss: 2.1737 03/05 10:18:19 - mmengine - INFO - Epoch(train) [25][2000/5005] lr: 1.0000e-01 eta: 1 day, 12:30:41 time: 0.2223 data_time: 0.0021 loss: 1.9672 03/05 10:18:41 - mmengine - INFO - Epoch(train) [25][2100/5005] lr: 1.0000e-01 eta: 1 day, 12:30:16 time: 0.2213 data_time: 0.0023 loss: 2.1236 03/05 10:19:03 - mmengine - INFO - Epoch(train) [25][2200/5005] lr: 1.0000e-01 eta: 1 day, 12:29:50 time: 0.2171 data_time: 0.0021 loss: 2.0820 03/05 10:19:26 - mmengine - INFO - Epoch(train) [25][2300/5005] lr: 1.0000e-01 eta: 1 day, 12:29:26 time: 0.2206 data_time: 0.0019 loss: 1.8939 03/05 10:19:48 - mmengine - INFO - Epoch(train) [25][2400/5005] lr: 1.0000e-01 eta: 1 day, 12:29:02 time: 0.2183 data_time: 0.0018 loss: 1.9835 03/05 10:20:11 - mmengine - INFO - Epoch(train) [25][2500/5005] lr: 1.0000e-01 eta: 1 day, 12:28:37 time: 0.2200 data_time: 0.0019 loss: 2.2460 03/05 10:20:33 - mmengine - INFO - Epoch(train) [25][2600/5005] lr: 1.0000e-01 eta: 1 day, 12:28:12 time: 0.2199 data_time: 0.0020 loss: 2.1239 03/05 10:20:56 - mmengine - INFO - Epoch(train) [25][2700/5005] lr: 1.0000e-01 eta: 1 day, 12:27:49 time: 0.2177 data_time: 0.0020 loss: 1.9771 03/05 10:21:18 - mmengine - INFO - Epoch(train) [25][2800/5005] lr: 1.0000e-01 eta: 1 day, 12:27:24 time: 0.2196 data_time: 0.0019 loss: 2.2631 03/05 10:21:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:21:41 - mmengine - INFO - Epoch(train) [25][2900/5005] lr: 1.0000e-01 eta: 1 day, 12:27:00 time: 0.2230 data_time: 0.0020 loss: 2.1881 03/05 10:22:03 - mmengine - INFO - Epoch(train) [25][3000/5005] lr: 1.0000e-01 eta: 1 day, 12:26:34 time: 0.2189 data_time: 0.0019 loss: 2.1679 03/05 10:22:25 - mmengine - INFO - Epoch(train) [25][3100/5005] lr: 1.0000e-01 eta: 1 day, 12:26:09 time: 0.2357 data_time: 0.0021 loss: 2.1749 03/05 10:22:48 - mmengine - INFO - Epoch(train) [25][3200/5005] lr: 1.0000e-01 eta: 1 day, 12:25:47 time: 0.2230 data_time: 0.0023 loss: 1.9186 03/05 10:23:10 - mmengine - INFO - Epoch(train) [25][3300/5005] lr: 1.0000e-01 eta: 1 day, 12:25:23 time: 0.2220 data_time: 0.0020 loss: 2.2036 03/05 10:23:32 - mmengine - INFO - Epoch(train) [25][3400/5005] lr: 1.0000e-01 eta: 1 day, 12:24:57 time: 0.2368 data_time: 0.0019 loss: 1.9670 03/05 10:23:55 - mmengine - INFO - Epoch(train) [25][3500/5005] lr: 1.0000e-01 eta: 1 day, 12:24:32 time: 0.2232 data_time: 0.0020 loss: 1.8989 03/05 10:24:17 - mmengine - INFO - Epoch(train) [25][3600/5005] lr: 1.0000e-01 eta: 1 day, 12:24:09 time: 0.2176 data_time: 0.0018 loss: 2.2111 03/05 10:24:39 - mmengine - INFO - Epoch(train) [25][3700/5005] lr: 1.0000e-01 eta: 1 day, 12:23:43 time: 0.2222 data_time: 0.0019 loss: 2.1449 03/05 10:25:02 - mmengine - INFO - Epoch(train) [25][3800/5005] lr: 1.0000e-01 eta: 1 day, 12:23:19 time: 0.2210 data_time: 0.0019 loss: 1.9192 03/05 10:25:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:25:24 - mmengine - INFO - Epoch(train) [25][3900/5005] lr: 1.0000e-01 eta: 1 day, 12:22:55 time: 0.2233 data_time: 0.0020 loss: 2.3143 03/05 10:25:47 - mmengine - INFO - Epoch(train) [25][4000/5005] lr: 1.0000e-01 eta: 1 day, 12:22:31 time: 0.2349 data_time: 0.0020 loss: 2.1339 03/05 10:26:09 - mmengine - INFO - Epoch(train) [25][4100/5005] lr: 1.0000e-01 eta: 1 day, 12:22:06 time: 0.2193 data_time: 0.0019 loss: 2.3188 03/05 10:26:31 - mmengine - INFO - Epoch(train) [25][4200/5005] lr: 1.0000e-01 eta: 1 day, 12:21:41 time: 0.2195 data_time: 0.0018 loss: 2.0908 03/05 10:26:54 - mmengine - INFO - Epoch(train) [25][4300/5005] lr: 1.0000e-01 eta: 1 day, 12:21:17 time: 0.2252 data_time: 0.0020 loss: 2.1816 03/05 10:27:16 - mmengine - INFO - Epoch(train) [25][4400/5005] lr: 1.0000e-01 eta: 1 day, 12:20:54 time: 0.2182 data_time: 0.0018 loss: 2.2519 03/05 10:27:39 - mmengine - INFO - Epoch(train) [25][4500/5005] lr: 1.0000e-01 eta: 1 day, 12:20:28 time: 0.2185 data_time: 0.0020 loss: 2.3223 03/05 10:28:01 - mmengine - INFO - Epoch(train) [25][4600/5005] lr: 1.0000e-01 eta: 1 day, 12:20:02 time: 0.2175 data_time: 0.0019 loss: 2.5049 03/05 10:28:23 - mmengine - INFO - Epoch(train) [25][4700/5005] lr: 1.0000e-01 eta: 1 day, 12:19:39 time: 0.2180 data_time: 0.0018 loss: 2.1206 03/05 10:28:46 - mmengine - INFO - Epoch(train) [25][4800/5005] lr: 1.0000e-01 eta: 1 day, 12:19:15 time: 0.2185 data_time: 0.0019 loss: 2.1957 03/05 10:29:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:29:10 - mmengine - INFO - Epoch(train) [25][4900/5005] lr: 1.0000e-01 eta: 1 day, 12:18:57 time: 0.3032 data_time: 0.0016 loss: 2.0657 03/05 10:29:39 - mmengine - INFO - Epoch(train) [25][5000/5005] lr: 1.0000e-01 eta: 1 day, 12:19:06 time: 0.2920 data_time: 0.0017 loss: 2.3062 03/05 10:29:41 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:29:43 - mmengine - INFO - Saving checkpoint at 25 epochs 03/05 10:29:57 - mmengine - INFO - Epoch(val) [25][100/196] eta: 0:00:12 time: 0.0188 data_time: 0.0003 03/05 10:30:11 - mmengine - INFO - Epoch(val) [25][196/196] accuracy/top1: 50.4120 accuracy/top5: 75.8300 03/05 10:30:41 - mmengine - INFO - Epoch(train) [26][ 100/5005] lr: 1.0000e-01 eta: 1 day, 12:19:18 time: 0.2227 data_time: 0.0018 loss: 2.2290 03/05 10:31:03 - mmengine - INFO - Epoch(train) [26][ 200/5005] lr: 1.0000e-01 eta: 1 day, 12:18:53 time: 0.2287 data_time: 0.0018 loss: 2.2176 03/05 10:31:26 - mmengine - INFO - Epoch(train) [26][ 300/5005] lr: 1.0000e-01 eta: 1 day, 12:18:30 time: 0.2190 data_time: 0.0023 loss: 2.1037 03/05 10:31:48 - mmengine - INFO - Epoch(train) [26][ 400/5005] lr: 1.0000e-01 eta: 1 day, 12:18:05 time: 0.2205 data_time: 0.0022 loss: 2.1629 03/05 10:32:11 - mmengine - INFO - Epoch(train) [26][ 500/5005] lr: 1.0000e-01 eta: 1 day, 12:17:40 time: 0.2388 data_time: 0.0020 loss: 2.0513 03/05 10:32:33 - mmengine - INFO - Epoch(train) [26][ 600/5005] lr: 1.0000e-01 eta: 1 day, 12:17:14 time: 0.2182 data_time: 0.0019 loss: 1.9418 03/05 10:32:55 - mmengine - INFO - Epoch(train) [26][ 700/5005] lr: 1.0000e-01 eta: 1 day, 12:16:51 time: 0.2241 data_time: 0.0019 loss: 2.1861 03/05 10:33:17 - mmengine - INFO - Epoch(train) [26][ 800/5005] lr: 1.0000e-01 eta: 1 day, 12:16:25 time: 0.2206 data_time: 0.0021 loss: 2.1968 03/05 10:33:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:33:39 - mmengine - INFO - Epoch(train) [26][ 900/5005] lr: 1.0000e-01 eta: 1 day, 12:15:59 time: 0.2184 data_time: 0.0019 loss: 1.8368 03/05 10:34:02 - mmengine - INFO - Epoch(train) [26][1000/5005] lr: 1.0000e-01 eta: 1 day, 12:15:33 time: 0.2187 data_time: 0.0019 loss: 2.1617 03/05 10:34:24 - mmengine - INFO - Epoch(train) [26][1100/5005] lr: 1.0000e-01 eta: 1 day, 12:15:10 time: 0.2256 data_time: 0.0019 loss: 2.3496 03/05 10:34:46 - mmengine - INFO - Epoch(train) [26][1200/5005] lr: 1.0000e-01 eta: 1 day, 12:14:45 time: 0.2189 data_time: 0.0020 loss: 2.1537 03/05 10:35:09 - mmengine - INFO - Epoch(train) [26][1300/5005] lr: 1.0000e-01 eta: 1 day, 12:14:19 time: 0.2173 data_time: 0.0022 loss: 2.2720 03/05 10:35:31 - mmengine - INFO - Epoch(train) [26][1400/5005] lr: 1.0000e-01 eta: 1 day, 12:13:54 time: 0.2197 data_time: 0.0020 loss: 2.1398 03/05 10:35:53 - mmengine - INFO - Epoch(train) [26][1500/5005] lr: 1.0000e-01 eta: 1 day, 12:13:30 time: 0.2213 data_time: 0.0019 loss: 2.2104 03/05 10:36:16 - mmengine - INFO - Epoch(train) [26][1600/5005] lr: 1.0000e-01 eta: 1 day, 12:13:05 time: 0.2232 data_time: 0.0019 loss: 2.2084 03/05 10:36:38 - mmengine - INFO - Epoch(train) [26][1700/5005] lr: 1.0000e-01 eta: 1 day, 12:12:39 time: 0.2215 data_time: 0.0018 loss: 2.1844 03/05 10:37:00 - mmengine - INFO - Epoch(train) [26][1800/5005] lr: 1.0000e-01 eta: 1 day, 12:12:14 time: 0.2189 data_time: 0.0020 loss: 2.1915 03/05 10:37:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:37:22 - mmengine - INFO - Epoch(train) [26][1900/5005] lr: 1.0000e-01 eta: 1 day, 12:11:49 time: 0.2194 data_time: 0.0018 loss: 2.3062 03/05 10:37:44 - mmengine - INFO - Epoch(train) [26][2000/5005] lr: 1.0000e-01 eta: 1 day, 12:11:24 time: 0.2162 data_time: 0.0019 loss: 2.2065 03/05 10:38:06 - mmengine - INFO - Epoch(train) [26][2100/5005] lr: 1.0000e-01 eta: 1 day, 12:10:58 time: 0.2180 data_time: 0.0021 loss: 2.1167 03/05 10:38:29 - mmengine - INFO - Epoch(train) [26][2200/5005] lr: 1.0000e-01 eta: 1 day, 12:10:33 time: 0.2227 data_time: 0.0018 loss: 2.2139 03/05 10:38:51 - mmengine - INFO - Epoch(train) [26][2300/5005] lr: 1.0000e-01 eta: 1 day, 12:10:09 time: 0.2200 data_time: 0.0020 loss: 2.2053 03/05 10:39:14 - mmengine - INFO - Epoch(train) [26][2400/5005] lr: 1.0000e-01 eta: 1 day, 12:09:45 time: 0.2184 data_time: 0.0019 loss: 2.1978 03/05 10:39:36 - mmengine - INFO - Epoch(train) [26][2500/5005] lr: 1.0000e-01 eta: 1 day, 12:09:20 time: 0.2222 data_time: 0.0020 loss: 2.0054 03/05 10:39:58 - mmengine - INFO - Epoch(train) [26][2600/5005] lr: 1.0000e-01 eta: 1 day, 12:08:53 time: 0.2176 data_time: 0.0019 loss: 1.9641 03/05 10:40:20 - mmengine - INFO - Epoch(train) [26][2700/5005] lr: 1.0000e-01 eta: 1 day, 12:08:28 time: 0.2190 data_time: 0.0021 loss: 2.2647 03/05 10:40:43 - mmengine - INFO - Epoch(train) [26][2800/5005] lr: 1.0000e-01 eta: 1 day, 12:08:06 time: 0.2444 data_time: 0.0019 loss: 2.3294 03/05 10:40:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:41:05 - mmengine - INFO - Epoch(train) [26][2900/5005] lr: 1.0000e-01 eta: 1 day, 12:07:41 time: 0.2202 data_time: 0.0020 loss: 2.1606 03/05 10:41:27 - mmengine - INFO - Epoch(train) [26][3000/5005] lr: 1.0000e-01 eta: 1 day, 12:07:15 time: 0.2197 data_time: 0.0021 loss: 2.1332 03/05 10:41:49 - mmengine - INFO - Epoch(train) [26][3100/5005] lr: 1.0000e-01 eta: 1 day, 12:06:50 time: 0.2219 data_time: 0.0020 loss: 2.3097 03/05 10:42:12 - mmengine - INFO - Epoch(train) [26][3200/5005] lr: 1.0000e-01 eta: 1 day, 12:06:26 time: 0.2200 data_time: 0.0020 loss: 2.1946 03/05 10:42:34 - mmengine - INFO - Epoch(train) [26][3300/5005] lr: 1.0000e-01 eta: 1 day, 12:06:01 time: 0.2189 data_time: 0.0020 loss: 2.0589 03/05 10:42:56 - mmengine - INFO - Epoch(train) [26][3400/5005] lr: 1.0000e-01 eta: 1 day, 12:05:36 time: 0.2203 data_time: 0.0018 loss: 2.1197 03/05 10:43:18 - mmengine - INFO - Epoch(train) [26][3500/5005] lr: 1.0000e-01 eta: 1 day, 12:05:10 time: 0.2204 data_time: 0.0018 loss: 1.9648 03/05 10:43:41 - mmengine - INFO - Epoch(train) [26][3600/5005] lr: 1.0000e-01 eta: 1 day, 12:04:49 time: 0.2215 data_time: 0.0020 loss: 2.3059 03/05 10:44:04 - mmengine - INFO - Epoch(train) [26][3700/5005] lr: 1.0000e-01 eta: 1 day, 12:04:24 time: 0.2226 data_time: 0.0019 loss: 2.2135 03/05 10:44:26 - mmengine - INFO - Epoch(train) [26][3800/5005] lr: 1.0000e-01 eta: 1 day, 12:03:59 time: 0.2212 data_time: 0.0019 loss: 2.0567 03/05 10:44:43 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:44:48 - mmengine - INFO - Epoch(train) [26][3900/5005] lr: 1.0000e-01 eta: 1 day, 12:03:33 time: 0.2172 data_time: 0.0019 loss: 2.1348 03/05 10:45:11 - mmengine - INFO - Epoch(train) [26][4000/5005] lr: 1.0000e-01 eta: 1 day, 12:03:11 time: 0.2185 data_time: 0.0019 loss: 2.0636 03/05 10:45:33 - mmengine - INFO - Epoch(train) [26][4100/5005] lr: 1.0000e-01 eta: 1 day, 12:02:46 time: 0.2199 data_time: 0.0023 loss: 1.9137 03/05 10:45:55 - mmengine - INFO - Epoch(train) [26][4200/5005] lr: 1.0000e-01 eta: 1 day, 12:02:21 time: 0.2211 data_time: 0.0020 loss: 2.1159 03/05 10:46:18 - mmengine - INFO - Epoch(train) [26][4300/5005] lr: 1.0000e-01 eta: 1 day, 12:01:56 time: 0.2198 data_time: 0.0018 loss: 2.1985 03/05 10:46:40 - mmengine - INFO - Epoch(train) [26][4400/5005] lr: 1.0000e-01 eta: 1 day, 12:01:32 time: 0.2205 data_time: 0.0019 loss: 2.2599 03/05 10:47:02 - mmengine - INFO - Epoch(train) [26][4500/5005] lr: 1.0000e-01 eta: 1 day, 12:01:08 time: 0.2228 data_time: 0.0020 loss: 2.1616 03/05 10:47:25 - mmengine - INFO - Epoch(train) [26][4600/5005] lr: 1.0000e-01 eta: 1 day, 12:00:42 time: 0.2193 data_time: 0.0021 loss: 2.0944 03/05 10:47:47 - mmengine - INFO - Epoch(train) [26][4700/5005] lr: 1.0000e-01 eta: 1 day, 12:00:17 time: 0.2199 data_time: 0.0021 loss: 2.1930 03/05 10:48:09 - mmengine - INFO - Epoch(train) [26][4800/5005] lr: 1.0000e-01 eta: 1 day, 11:59:54 time: 0.2235 data_time: 0.0020 loss: 2.1991 03/05 10:48:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:48:33 - mmengine - INFO - Epoch(train) [26][4900/5005] lr: 1.0000e-01 eta: 1 day, 11:59:33 time: 0.2811 data_time: 0.0016 loss: 1.9972 03/05 10:49:02 - mmengine - INFO - Epoch(train) [26][5000/5005] lr: 1.0000e-01 eta: 1 day, 11:59:41 time: 0.2902 data_time: 0.0021 loss: 2.1651 03/05 10:49:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:49:06 - mmengine - INFO - Saving checkpoint at 26 epochs 03/05 10:49:21 - mmengine - INFO - Epoch(val) [26][100/196] eta: 0:00:12 time: 0.0194 data_time: 0.0004 03/05 10:49:35 - mmengine - INFO - Epoch(val) [26][196/196] accuracy/top1: 54.2240 accuracy/top5: 79.2640 03/05 10:50:05 - mmengine - INFO - Epoch(train) [27][ 100/5005] lr: 1.0000e-01 eta: 1 day, 11:59:53 time: 0.2211 data_time: 0.0022 loss: 2.1707 03/05 10:50:28 - mmengine - INFO - Epoch(train) [27][ 200/5005] lr: 1.0000e-01 eta: 1 day, 11:59:29 time: 0.2406 data_time: 0.0028 loss: 2.0279 03/05 10:50:50 - mmengine - INFO - Epoch(train) [27][ 300/5005] lr: 1.0000e-01 eta: 1 day, 11:59:06 time: 0.2424 data_time: 0.0023 loss: 2.0894 03/05 10:51:13 - mmengine - INFO - Epoch(train) [27][ 400/5005] lr: 1.0000e-01 eta: 1 day, 11:58:43 time: 0.2193 data_time: 0.0025 loss: 2.0904 03/05 10:51:35 - mmengine - INFO - Epoch(train) [27][ 500/5005] lr: 1.0000e-01 eta: 1 day, 11:58:18 time: 0.2200 data_time: 0.0021 loss: 2.1392 03/05 10:51:57 - mmengine - INFO - Epoch(train) [27][ 600/5005] lr: 1.0000e-01 eta: 1 day, 11:57:52 time: 0.2192 data_time: 0.0020 loss: 2.0238 03/05 10:52:20 - mmengine - INFO - Epoch(train) [27][ 700/5005] lr: 1.0000e-01 eta: 1 day, 11:57:30 time: 0.2198 data_time: 0.0023 loss: 2.1527 03/05 10:52:43 - mmengine - INFO - Epoch(train) [27][ 800/5005] lr: 1.0000e-01 eta: 1 day, 11:57:05 time: 0.2388 data_time: 0.0025 loss: 1.8802 03/05 10:52:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:53:05 - mmengine - INFO - Epoch(train) [27][ 900/5005] lr: 1.0000e-01 eta: 1 day, 11:56:41 time: 0.2222 data_time: 0.0024 loss: 2.1257 03/05 10:53:27 - mmengine - INFO - Epoch(train) [27][1000/5005] lr: 1.0000e-01 eta: 1 day, 11:56:16 time: 0.2217 data_time: 0.0022 loss: 2.1964 03/05 10:53:50 - mmengine - INFO - Epoch(train) [27][1100/5005] lr: 1.0000e-01 eta: 1 day, 11:55:54 time: 0.2439 data_time: 0.0023 loss: 2.1344 03/05 10:54:12 - mmengine - INFO - Epoch(train) [27][1200/5005] lr: 1.0000e-01 eta: 1 day, 11:55:28 time: 0.2190 data_time: 0.0025 loss: 2.3944 03/05 10:54:35 - mmengine - INFO - Epoch(train) [27][1300/5005] lr: 1.0000e-01 eta: 1 day, 11:55:04 time: 0.2212 data_time: 0.0021 loss: 2.0345 03/05 10:54:57 - mmengine - INFO - Epoch(train) [27][1400/5005] lr: 1.0000e-01 eta: 1 day, 11:54:39 time: 0.2205 data_time: 0.0021 loss: 2.2087 03/05 10:55:19 - mmengine - INFO - Epoch(train) [27][1500/5005] lr: 1.0000e-01 eta: 1 day, 11:54:14 time: 0.2208 data_time: 0.0024 loss: 2.0045 03/05 10:55:42 - mmengine - INFO - Epoch(train) [27][1600/5005] lr: 1.0000e-01 eta: 1 day, 11:53:51 time: 0.2400 data_time: 0.0022 loss: 2.0659 03/05 10:56:04 - mmengine - INFO - Epoch(train) [27][1700/5005] lr: 1.0000e-01 eta: 1 day, 11:53:26 time: 0.2385 data_time: 0.0022 loss: 2.1698 03/05 10:56:26 - mmengine - INFO - Epoch(train) [27][1800/5005] lr: 1.0000e-01 eta: 1 day, 11:53:01 time: 0.2261 data_time: 0.0020 loss: 2.1085 03/05 10:56:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 10:56:49 - mmengine - INFO - Epoch(train) [27][1900/5005] lr: 1.0000e-01 eta: 1 day, 11:52:37 time: 0.2448 data_time: 0.0023 loss: 2.0921 03/05 10:57:11 - mmengine - INFO - Epoch(train) [27][2000/5005] lr: 1.0000e-01 eta: 1 day, 11:52:12 time: 0.2194 data_time: 0.0025 loss: 2.3144 03/05 10:57:33 - mmengine - INFO - Epoch(train) [27][2100/5005] lr: 1.0000e-01 eta: 1 day, 11:51:47 time: 0.2190 data_time: 0.0021 loss: 2.0499 03/05 10:57:56 - mmengine - INFO - Epoch(train) [27][2200/5005] lr: 1.0000e-01 eta: 1 day, 11:51:23 time: 0.2219 data_time: 0.0026 loss: 2.1329 03/05 10:58:18 - mmengine - INFO - Epoch(train) [27][2300/5005] lr: 1.0000e-01 eta: 1 day, 11:50:58 time: 0.2250 data_time: 0.0023 loss: 2.0879 03/05 10:58:40 - mmengine - INFO - Epoch(train) [27][2400/5005] lr: 1.0000e-01 eta: 1 day, 11:50:34 time: 0.2213 data_time: 0.0023 loss: 2.1694 03/05 10:59:03 - mmengine - INFO - Epoch(train) [27][2500/5005] lr: 1.0000e-01 eta: 1 day, 11:50:09 time: 0.2229 data_time: 0.0026 loss: 2.1469 03/05 10:59:25 - mmengine - INFO - Epoch(train) [27][2600/5005] lr: 1.0000e-01 eta: 1 day, 11:49:45 time: 0.2439 data_time: 0.0021 loss: 2.1532 03/05 10:59:47 - mmengine - INFO - Epoch(train) [27][2700/5005] lr: 1.0000e-01 eta: 1 day, 11:49:20 time: 0.2186 data_time: 0.0023 loss: 2.1910 03/05 11:00:10 - mmengine - INFO - Epoch(train) [27][2800/5005] lr: 1.0000e-01 eta: 1 day, 11:48:57 time: 0.2198 data_time: 0.0020 loss: 2.1175 03/05 11:00:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:00:32 - mmengine - INFO - Epoch(train) [27][2900/5005] lr: 1.0000e-01 eta: 1 day, 11:48:32 time: 0.2212 data_time: 0.0030 loss: 2.1654 03/05 11:00:55 - mmengine - INFO - Epoch(train) [27][3000/5005] lr: 1.0000e-01 eta: 1 day, 11:48:07 time: 0.2215 data_time: 0.0022 loss: 1.9665 03/05 11:01:17 - mmengine - INFO - Epoch(train) [27][3100/5005] lr: 1.0000e-01 eta: 1 day, 11:47:43 time: 0.2293 data_time: 0.0024 loss: 2.2421 03/05 11:01:40 - mmengine - INFO - Epoch(train) [27][3200/5005] lr: 1.0000e-01 eta: 1 day, 11:47:20 time: 0.2189 data_time: 0.0025 loss: 2.1537 03/05 11:02:02 - mmengine - INFO - Epoch(train) [27][3300/5005] lr: 1.0000e-01 eta: 1 day, 11:46:54 time: 0.2215 data_time: 0.0030 loss: 2.2416 03/05 11:02:24 - mmengine - INFO - Epoch(train) [27][3400/5005] lr: 1.0000e-01 eta: 1 day, 11:46:30 time: 0.2233 data_time: 0.0029 loss: 1.9037 03/05 11:02:46 - mmengine - INFO - Epoch(train) [27][3500/5005] lr: 1.0000e-01 eta: 1 day, 11:46:04 time: 0.2190 data_time: 0.0024 loss: 2.0975 03/05 11:03:09 - mmengine - INFO - Epoch(train) [27][3600/5005] lr: 1.0000e-01 eta: 1 day, 11:45:41 time: 0.2374 data_time: 0.0019 loss: 2.3053 03/05 11:03:31 - mmengine - INFO - Epoch(train) [27][3700/5005] lr: 1.0000e-01 eta: 1 day, 11:45:16 time: 0.2184 data_time: 0.0022 loss: 2.0618 03/05 11:03:53 - mmengine - INFO - Epoch(train) [27][3800/5005] lr: 1.0000e-01 eta: 1 day, 11:44:51 time: 0.2180 data_time: 0.0023 loss: 2.2792 03/05 11:04:09 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:04:15 - mmengine - INFO - Epoch(train) [27][3900/5005] lr: 1.0000e-01 eta: 1 day, 11:44:26 time: 0.2294 data_time: 0.0021 loss: 2.1446 03/05 11:04:38 - mmengine - INFO - Epoch(train) [27][4000/5005] lr: 1.0000e-01 eta: 1 day, 11:44:02 time: 0.2456 data_time: 0.0022 loss: 2.2851 03/05 11:05:00 - mmengine - INFO - Epoch(train) [27][4100/5005] lr: 1.0000e-01 eta: 1 day, 11:43:38 time: 0.2209 data_time: 0.0023 loss: 2.0513 03/05 11:05:23 - mmengine - INFO - Epoch(train) [27][4200/5005] lr: 1.0000e-01 eta: 1 day, 11:43:13 time: 0.2213 data_time: 0.0021 loss: 2.3236 03/05 11:05:45 - mmengine - INFO - Epoch(train) [27][4300/5005] lr: 1.0000e-01 eta: 1 day, 11:42:49 time: 0.2225 data_time: 0.0022 loss: 2.4742 03/05 11:06:08 - mmengine - INFO - Epoch(train) [27][4400/5005] lr: 1.0000e-01 eta: 1 day, 11:42:25 time: 0.2204 data_time: 0.0025 loss: 2.2797 03/05 11:06:30 - mmengine - INFO - Epoch(train) [27][4500/5005] lr: 1.0000e-01 eta: 1 day, 11:42:01 time: 0.2257 data_time: 0.0025 loss: 2.3287 03/05 11:06:52 - mmengine - INFO - Epoch(train) [27][4600/5005] lr: 1.0000e-01 eta: 1 day, 11:41:37 time: 0.2195 data_time: 0.0022 loss: 2.1343 03/05 11:07:14 - mmengine - INFO - Epoch(train) [27][4700/5005] lr: 1.0000e-01 eta: 1 day, 11:41:11 time: 0.2208 data_time: 0.0027 loss: 2.1793 03/05 11:07:37 - mmengine - INFO - Epoch(train) [27][4800/5005] lr: 1.0000e-01 eta: 1 day, 11:40:48 time: 0.2193 data_time: 0.0024 loss: 2.0690 03/05 11:07:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:08:01 - mmengine - INFO - Epoch(train) [27][4900/5005] lr: 1.0000e-01 eta: 1 day, 11:40:31 time: 0.3001 data_time: 0.0022 loss: 2.1942 03/05 11:08:31 - mmengine - INFO - Epoch(train) [27][5000/5005] lr: 1.0000e-01 eta: 1 day, 11:40:39 time: 0.3124 data_time: 0.0019 loss: 2.1647 03/05 11:08:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:08:36 - mmengine - INFO - Saving checkpoint at 27 epochs 03/05 11:08:51 - mmengine - INFO - Epoch(val) [27][100/196] eta: 0:00:13 time: 0.0170 data_time: 0.0002 03/05 11:09:05 - mmengine - INFO - Epoch(val) [27][196/196] accuracy/top1: 55.2740 accuracy/top5: 79.7840 03/05 11:09:35 - mmengine - INFO - Epoch(train) [28][ 100/5005] lr: 1.0000e-01 eta: 1 day, 11:40:51 time: 0.2417 data_time: 0.0028 loss: 2.2994 03/05 11:09:58 - mmengine - INFO - Epoch(train) [28][ 200/5005] lr: 1.0000e-01 eta: 1 day, 11:40:26 time: 0.2211 data_time: 0.0028 loss: 2.0954 03/05 11:10:20 - mmengine - INFO - Epoch(train) [28][ 300/5005] lr: 1.0000e-01 eta: 1 day, 11:40:01 time: 0.2189 data_time: 0.0026 loss: 2.3365 03/05 11:10:43 - mmengine - INFO - Epoch(train) [28][ 400/5005] lr: 1.0000e-01 eta: 1 day, 11:39:38 time: 0.2198 data_time: 0.0020 loss: 1.9578 03/05 11:11:05 - mmengine - INFO - Epoch(train) [28][ 500/5005] lr: 1.0000e-01 eta: 1 day, 11:39:14 time: 0.2202 data_time: 0.0026 loss: 2.2585 03/05 11:11:27 - mmengine - INFO - Epoch(train) [28][ 600/5005] lr: 1.0000e-01 eta: 1 day, 11:38:49 time: 0.2217 data_time: 0.0023 loss: 2.2008 03/05 11:11:50 - mmengine - INFO - Epoch(train) [28][ 700/5005] lr: 1.0000e-01 eta: 1 day, 11:38:24 time: 0.2221 data_time: 0.0024 loss: 1.8925 03/05 11:12:12 - mmengine - INFO - Epoch(train) [28][ 800/5005] lr: 1.0000e-01 eta: 1 day, 11:38:01 time: 0.2367 data_time: 0.0022 loss: 2.2334 03/05 11:12:27 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:12:35 - mmengine - INFO - Epoch(train) [28][ 900/5005] lr: 1.0000e-01 eta: 1 day, 11:37:37 time: 0.2215 data_time: 0.0027 loss: 2.0900 03/05 11:12:57 - mmengine - INFO - Epoch(train) [28][1000/5005] lr: 1.0000e-01 eta: 1 day, 11:37:13 time: 0.2193 data_time: 0.0023 loss: 1.9715 03/05 11:13:19 - mmengine - INFO - Epoch(train) [28][1100/5005] lr: 1.0000e-01 eta: 1 day, 11:36:47 time: 0.2192 data_time: 0.0027 loss: 2.1881 03/05 11:13:42 - mmengine - INFO - Epoch(train) [28][1200/5005] lr: 1.0000e-01 eta: 1 day, 11:36:24 time: 0.2824 data_time: 0.0024 loss: 2.0942 03/05 11:14:04 - mmengine - INFO - Epoch(train) [28][1300/5005] lr: 1.0000e-01 eta: 1 day, 11:36:01 time: 0.2216 data_time: 0.0026 loss: 2.1364 03/05 11:14:27 - mmengine - INFO - Epoch(train) [28][1400/5005] lr: 1.0000e-01 eta: 1 day, 11:35:36 time: 0.2228 data_time: 0.0021 loss: 2.0851 03/05 11:14:49 - mmengine - INFO - Epoch(train) [28][1500/5005] lr: 1.0000e-01 eta: 1 day, 11:35:11 time: 0.2220 data_time: 0.0025 loss: 2.0953 03/05 11:15:11 - mmengine - INFO - Epoch(train) [28][1600/5005] lr: 1.0000e-01 eta: 1 day, 11:34:47 time: 0.2197 data_time: 0.0026 loss: 1.9919 03/05 11:15:34 - mmengine - INFO - Epoch(train) [28][1700/5005] lr: 1.0000e-01 eta: 1 day, 11:34:24 time: 0.2366 data_time: 0.0021 loss: 1.8721 03/05 11:15:57 - mmengine - INFO - Epoch(train) [28][1800/5005] lr: 1.0000e-01 eta: 1 day, 11:34:01 time: 0.2219 data_time: 0.0023 loss: 2.2767 03/05 11:16:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:16:19 - mmengine - INFO - Epoch(train) [28][1900/5005] lr: 1.0000e-01 eta: 1 day, 11:33:36 time: 0.2179 data_time: 0.0020 loss: 2.2041 03/05 11:16:41 - mmengine - INFO - Epoch(train) [28][2000/5005] lr: 1.0000e-01 eta: 1 day, 11:33:12 time: 0.2219 data_time: 0.0021 loss: 2.2759 03/05 11:17:04 - mmengine - INFO - Epoch(train) [28][2100/5005] lr: 1.0000e-01 eta: 1 day, 11:32:47 time: 0.2185 data_time: 0.0021 loss: 2.1586 03/05 11:17:26 - mmengine - INFO - Epoch(train) [28][2200/5005] lr: 1.0000e-01 eta: 1 day, 11:32:23 time: 0.2218 data_time: 0.0023 loss: 2.2674 03/05 11:17:48 - mmengine - INFO - Epoch(train) [28][2300/5005] lr: 1.0000e-01 eta: 1 day, 11:31:58 time: 0.2197 data_time: 0.0021 loss: 2.2081 03/05 11:18:11 - mmengine - INFO - Epoch(train) [28][2400/5005] lr: 1.0000e-01 eta: 1 day, 11:31:34 time: 0.2194 data_time: 0.0022 loss: 1.8657 03/05 11:18:33 - mmengine - INFO - Epoch(train) [28][2500/5005] lr: 1.0000e-01 eta: 1 day, 11:31:09 time: 0.2205 data_time: 0.0024 loss: 2.0964 03/05 11:18:56 - mmengine - INFO - Epoch(train) [28][2600/5005] lr: 1.0000e-01 eta: 1 day, 11:30:47 time: 0.2224 data_time: 0.0023 loss: 2.0374 03/05 11:19:18 - mmengine - INFO - Epoch(train) [28][2700/5005] lr: 1.0000e-01 eta: 1 day, 11:30:22 time: 0.2226 data_time: 0.0023 loss: 2.1313 03/05 11:19:40 - mmengine - INFO - Epoch(train) [28][2800/5005] lr: 1.0000e-01 eta: 1 day, 11:29:57 time: 0.2223 data_time: 0.0021 loss: 2.1638 03/05 11:19:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:20:03 - mmengine - INFO - Epoch(train) [28][2900/5005] lr: 1.0000e-01 eta: 1 day, 11:29:32 time: 0.2192 data_time: 0.0027 loss: 2.0437 03/05 11:20:25 - mmengine - INFO - Epoch(train) [28][3000/5005] lr: 1.0000e-01 eta: 1 day, 11:29:08 time: 0.2181 data_time: 0.0027 loss: 2.0263 03/05 11:20:47 - mmengine - INFO - Epoch(train) [28][3100/5005] lr: 1.0000e-01 eta: 1 day, 11:28:42 time: 0.2182 data_time: 0.0023 loss: 2.4992 03/05 11:21:10 - mmengine - INFO - Epoch(train) [28][3200/5005] lr: 1.0000e-01 eta: 1 day, 11:28:19 time: 0.2208 data_time: 0.0024 loss: 2.2186 03/05 11:21:32 - mmengine - INFO - Epoch(train) [28][3300/5005] lr: 1.0000e-01 eta: 1 day, 11:27:55 time: 0.2397 data_time: 0.0019 loss: 2.0129 03/05 11:21:55 - mmengine - INFO - Epoch(train) [28][3400/5005] lr: 1.0000e-01 eta: 1 day, 11:27:32 time: 0.2549 data_time: 0.0022 loss: 2.3028 03/05 11:22:17 - mmengine - INFO - Epoch(train) [28][3500/5005] lr: 1.0000e-01 eta: 1 day, 11:27:08 time: 0.2212 data_time: 0.0021 loss: 2.2645 03/05 11:22:39 - mmengine - INFO - Epoch(train) [28][3600/5005] lr: 1.0000e-01 eta: 1 day, 11:26:43 time: 0.2405 data_time: 0.0021 loss: 2.2263 03/05 11:23:02 - mmengine - INFO - Epoch(train) [28][3700/5005] lr: 1.0000e-01 eta: 1 day, 11:26:18 time: 0.2167 data_time: 0.0021 loss: 2.0548 03/05 11:23:24 - mmengine - INFO - Epoch(train) [28][3800/5005] lr: 1.0000e-01 eta: 1 day, 11:25:54 time: 0.2200 data_time: 0.0024 loss: 2.2928 03/05 11:23:39 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:23:47 - mmengine - INFO - Epoch(train) [28][3900/5005] lr: 1.0000e-01 eta: 1 day, 11:25:30 time: 0.2207 data_time: 0.0020 loss: 2.1555 03/05 11:24:09 - mmengine - INFO - Epoch(train) [28][4000/5005] lr: 1.0000e-01 eta: 1 day, 11:25:05 time: 0.2234 data_time: 0.0024 loss: 2.2050 03/05 11:24:31 - mmengine - INFO - Epoch(train) [28][4100/5005] lr: 1.0000e-01 eta: 1 day, 11:24:41 time: 0.2174 data_time: 0.0020 loss: 2.2130 03/05 11:24:53 - mmengine - INFO - Epoch(train) [28][4200/5005] lr: 1.0000e-01 eta: 1 day, 11:24:16 time: 0.2188 data_time: 0.0023 loss: 2.4320 03/05 11:25:16 - mmengine - INFO - Epoch(train) [28][4300/5005] lr: 1.0000e-01 eta: 1 day, 11:23:52 time: 0.2410 data_time: 0.0024 loss: 2.0935 03/05 11:25:38 - mmengine - INFO - Epoch(train) [28][4400/5005] lr: 1.0000e-01 eta: 1 day, 11:23:27 time: 0.2221 data_time: 0.0027 loss: 2.1425 03/05 11:26:00 - mmengine - INFO - Epoch(train) [28][4500/5005] lr: 1.0000e-01 eta: 1 day, 11:23:03 time: 0.2208 data_time: 0.0023 loss: 2.2925 03/05 11:26:23 - mmengine - INFO - Epoch(train) [28][4600/5005] lr: 1.0000e-01 eta: 1 day, 11:22:41 time: 0.2199 data_time: 0.0022 loss: 2.2275 03/05 11:26:46 - mmengine - INFO - Epoch(train) [28][4700/5005] lr: 1.0000e-01 eta: 1 day, 11:22:16 time: 0.2226 data_time: 0.0026 loss: 2.0272 03/05 11:27:08 - mmengine - INFO - Epoch(train) [28][4800/5005] lr: 1.0000e-01 eta: 1 day, 11:21:53 time: 0.2217 data_time: 0.0025 loss: 2.0735 03/05 11:27:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:27:32 - mmengine - INFO - Epoch(train) [28][4900/5005] lr: 1.0000e-01 eta: 1 day, 11:21:34 time: 0.2890 data_time: 0.0023 loss: 2.1246 03/05 11:28:02 - mmengine - INFO - Epoch(train) [28][5000/5005] lr: 1.0000e-01 eta: 1 day, 11:21:40 time: 0.3046 data_time: 0.0023 loss: 1.9207 03/05 11:28:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:28:06 - mmengine - INFO - Saving checkpoint at 28 epochs 03/05 11:28:20 - mmengine - INFO - Epoch(val) [28][100/196] eta: 0:00:12 time: 0.0199 data_time: 0.0003 03/05 11:28:33 - mmengine - INFO - Epoch(val) [28][196/196] accuracy/top1: 55.4860 accuracy/top5: 80.7460 03/05 11:29:04 - mmengine - INFO - Epoch(train) [29][ 100/5005] lr: 1.0000e-01 eta: 1 day, 11:21:51 time: 0.2213 data_time: 0.0021 loss: 2.1706 03/05 11:29:27 - mmengine - INFO - Epoch(train) [29][ 200/5005] lr: 1.0000e-01 eta: 1 day, 11:21:29 time: 0.2220 data_time: 0.0020 loss: 1.9647 03/05 11:29:50 - mmengine - INFO - Epoch(train) [29][ 300/5005] lr: 1.0000e-01 eta: 1 day, 11:21:06 time: 0.2202 data_time: 0.0020 loss: 2.1063 03/05 11:30:12 - mmengine - INFO - Epoch(train) [29][ 400/5005] lr: 1.0000e-01 eta: 1 day, 11:20:41 time: 0.2186 data_time: 0.0019 loss: 1.8635 03/05 11:30:34 - mmengine - INFO - Epoch(train) [29][ 500/5005] lr: 1.0000e-01 eta: 1 day, 11:20:16 time: 0.2183 data_time: 0.0021 loss: 2.1463 03/05 11:30:57 - mmengine - INFO - Epoch(train) [29][ 600/5005] lr: 1.0000e-01 eta: 1 day, 11:19:54 time: 0.2384 data_time: 0.0024 loss: 1.8113 03/05 11:31:20 - mmengine - INFO - Epoch(train) [29][ 700/5005] lr: 1.0000e-01 eta: 1 day, 11:19:30 time: 0.2398 data_time: 0.0023 loss: 2.2493 03/05 11:31:42 - mmengine - INFO - Epoch(train) [29][ 800/5005] lr: 1.0000e-01 eta: 1 day, 11:19:05 time: 0.2194 data_time: 0.0024 loss: 2.1730 03/05 11:31:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:32:04 - mmengine - INFO - Epoch(train) [29][ 900/5005] lr: 1.0000e-01 eta: 1 day, 11:18:41 time: 0.2202 data_time: 0.0021 loss: 2.1156 03/05 11:32:27 - mmengine - INFO - Epoch(train) [29][1000/5005] lr: 1.0000e-01 eta: 1 day, 11:18:18 time: 0.2213 data_time: 0.0024 loss: 2.0239 03/05 11:32:50 - mmengine - INFO - Epoch(train) [29][1100/5005] lr: 1.0000e-01 eta: 1 day, 11:17:54 time: 0.2214 data_time: 0.0021 loss: 2.0876 03/05 11:33:12 - mmengine - INFO - Epoch(train) [29][1200/5005] lr: 1.0000e-01 eta: 1 day, 11:17:30 time: 0.2229 data_time: 0.0023 loss: 2.0021 03/05 11:33:34 - mmengine - INFO - Epoch(train) [29][1300/5005] lr: 1.0000e-01 eta: 1 day, 11:17:06 time: 0.2221 data_time: 0.0023 loss: 2.1508 03/05 11:33:57 - mmengine - INFO - Epoch(train) [29][1400/5005] lr: 1.0000e-01 eta: 1 day, 11:16:42 time: 0.2185 data_time: 0.0025 loss: 2.1039 03/05 11:34:19 - mmengine - INFO - Epoch(train) [29][1500/5005] lr: 1.0000e-01 eta: 1 day, 11:16:18 time: 0.2352 data_time: 0.0022 loss: 2.2904 03/05 11:34:42 - mmengine - INFO - Epoch(train) [29][1600/5005] lr: 1.0000e-01 eta: 1 day, 11:15:54 time: 0.2376 data_time: 0.0022 loss: 2.2449 03/05 11:35:04 - mmengine - INFO - Epoch(train) [29][1700/5005] lr: 1.0000e-01 eta: 1 day, 11:15:29 time: 0.2224 data_time: 0.0023 loss: 2.3136 03/05 11:35:26 - mmengine - INFO - Epoch(train) [29][1800/5005] lr: 1.0000e-01 eta: 1 day, 11:15:05 time: 0.2197 data_time: 0.0022 loss: 2.1507 03/05 11:35:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:35:49 - mmengine - INFO - Epoch(train) [29][1900/5005] lr: 1.0000e-01 eta: 1 day, 11:14:40 time: 0.2200 data_time: 0.0023 loss: 2.2084 03/05 11:36:11 - mmengine - INFO - Epoch(train) [29][2000/5005] lr: 1.0000e-01 eta: 1 day, 11:14:16 time: 0.2198 data_time: 0.0026 loss: 2.1179 03/05 11:36:33 - mmengine - INFO - Epoch(train) [29][2100/5005] lr: 1.0000e-01 eta: 1 day, 11:13:51 time: 0.2229 data_time: 0.0025 loss: 1.8648 03/05 11:36:56 - mmengine - INFO - Epoch(train) [29][2200/5005] lr: 1.0000e-01 eta: 1 day, 11:13:27 time: 0.2175 data_time: 0.0021 loss: 2.0837 03/05 11:37:18 - mmengine - INFO - Epoch(train) [29][2300/5005] lr: 1.0000e-01 eta: 1 day, 11:13:02 time: 0.2228 data_time: 0.0022 loss: 2.1434 03/05 11:37:40 - mmengine - INFO - Epoch(train) [29][2400/5005] lr: 1.0000e-01 eta: 1 day, 11:12:38 time: 0.2217 data_time: 0.0021 loss: 2.1972 03/05 11:38:03 - mmengine - INFO - Epoch(train) [29][2500/5005] lr: 1.0000e-01 eta: 1 day, 11:12:13 time: 0.2234 data_time: 0.0025 loss: 2.4580 03/05 11:38:25 - mmengine - INFO - Epoch(train) [29][2600/5005] lr: 1.0000e-01 eta: 1 day, 11:11:50 time: 0.2229 data_time: 0.0022 loss: 2.0663 03/05 11:38:48 - mmengine - INFO - Epoch(train) [29][2700/5005] lr: 1.0000e-01 eta: 1 day, 11:11:27 time: 0.2185 data_time: 0.0027 loss: 2.3185 03/05 11:39:10 - mmengine - INFO - Epoch(train) [29][2800/5005] lr: 1.0000e-01 eta: 1 day, 11:11:01 time: 0.2200 data_time: 0.0022 loss: 2.0622 03/05 11:39:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:39:32 - mmengine - INFO - Epoch(train) [29][2900/5005] lr: 1.0000e-01 eta: 1 day, 11:10:37 time: 0.2200 data_time: 0.0030 loss: 1.8832 03/05 11:39:55 - mmengine - INFO - Epoch(train) [29][3000/5005] lr: 1.0000e-01 eta: 1 day, 11:10:14 time: 0.2210 data_time: 0.0022 loss: 1.9674 03/05 11:40:17 - mmengine - INFO - Epoch(train) [29][3100/5005] lr: 1.0000e-01 eta: 1 day, 11:09:50 time: 0.2416 data_time: 0.0024 loss: 2.2001 03/05 11:40:40 - mmengine - INFO - Epoch(train) [29][3200/5005] lr: 1.0000e-01 eta: 1 day, 11:09:25 time: 0.2215 data_time: 0.0026 loss: 1.9299 03/05 11:41:02 - mmengine - INFO - Epoch(train) [29][3300/5005] lr: 1.0000e-01 eta: 1 day, 11:09:02 time: 0.2196 data_time: 0.0023 loss: 2.0586 03/05 11:41:25 - mmengine - INFO - Epoch(train) [29][3400/5005] lr: 1.0000e-01 eta: 1 day, 11:08:39 time: 0.2188 data_time: 0.0025 loss: 2.2512 03/05 11:41:47 - mmengine - INFO - Epoch(train) [29][3500/5005] lr: 1.0000e-01 eta: 1 day, 11:08:15 time: 0.2237 data_time: 0.0024 loss: 1.8824 03/05 11:42:09 - mmengine - INFO - Epoch(train) [29][3600/5005] lr: 1.0000e-01 eta: 1 day, 11:07:50 time: 0.2180 data_time: 0.0024 loss: 2.1474 03/05 11:42:32 - mmengine - INFO - Epoch(train) [29][3700/5005] lr: 1.0000e-01 eta: 1 day, 11:07:26 time: 0.2192 data_time: 0.0025 loss: 2.2767 03/05 11:42:55 - mmengine - INFO - Epoch(train) [29][3800/5005] lr: 1.0000e-01 eta: 1 day, 11:07:03 time: 0.2340 data_time: 0.0023 loss: 2.3208 03/05 11:43:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:43:17 - mmengine - INFO - Epoch(train) [29][3900/5005] lr: 1.0000e-01 eta: 1 day, 11:06:38 time: 0.2188 data_time: 0.0030 loss: 2.0639 03/05 11:43:39 - mmengine - INFO - Epoch(train) [29][4000/5005] lr: 1.0000e-01 eta: 1 day, 11:06:13 time: 0.2219 data_time: 0.0024 loss: 2.0845 03/05 11:44:01 - mmengine - INFO - Epoch(train) [29][4100/5005] lr: 1.0000e-01 eta: 1 day, 11:05:48 time: 0.2184 data_time: 0.0023 loss: 1.9313 03/05 11:44:24 - mmengine - INFO - Epoch(train) [29][4200/5005] lr: 1.0000e-01 eta: 1 day, 11:05:24 time: 0.2241 data_time: 0.0023 loss: 2.1171 03/05 11:44:46 - mmengine - INFO - Epoch(train) [29][4300/5005] lr: 1.0000e-01 eta: 1 day, 11:05:01 time: 0.2190 data_time: 0.0021 loss: 2.0822 03/05 11:45:08 - mmengine - INFO - Epoch(train) [29][4400/5005] lr: 1.0000e-01 eta: 1 day, 11:04:36 time: 0.2272 data_time: 0.0023 loss: 2.2229 03/05 11:45:31 - mmengine - INFO - Epoch(train) [29][4500/5005] lr: 1.0000e-01 eta: 1 day, 11:04:11 time: 0.2208 data_time: 0.0028 loss: 2.3796 03/05 11:45:53 - mmengine - INFO - Epoch(train) [29][4600/5005] lr: 1.0000e-01 eta: 1 day, 11:03:46 time: 0.2221 data_time: 0.0028 loss: 2.2468 03/05 11:46:16 - mmengine - INFO - Epoch(train) [29][4700/5005] lr: 1.0000e-01 eta: 1 day, 11:03:24 time: 0.2200 data_time: 0.0025 loss: 1.9679 03/05 11:46:38 - mmengine - INFO - Epoch(train) [29][4800/5005] lr: 1.0000e-01 eta: 1 day, 11:02:59 time: 0.2198 data_time: 0.0027 loss: 2.3403 03/05 11:46:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:47:01 - mmengine - INFO - Epoch(train) [29][4900/5005] lr: 1.0000e-01 eta: 1 day, 11:02:39 time: 0.3052 data_time: 0.0021 loss: 2.2321 03/05 11:47:31 - mmengine - INFO - Epoch(train) [29][5000/5005] lr: 1.0000e-01 eta: 1 day, 11:02:43 time: 0.2875 data_time: 0.0022 loss: 2.2577 03/05 11:47:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:47:35 - mmengine - INFO - Saving checkpoint at 29 epochs 03/05 11:47:49 - mmengine - INFO - Epoch(val) [29][100/196] eta: 0:00:12 time: 0.0210 data_time: 0.0004 03/05 11:48:03 - mmengine - INFO - Epoch(val) [29][196/196] accuracy/top1: 55.5660 accuracy/top5: 80.4780 03/05 11:48:34 - mmengine - INFO - Epoch(train) [30][ 100/5005] lr: 1.0000e-01 eta: 1 day, 11:02:50 time: 0.2197 data_time: 0.0031 loss: 2.1367 03/05 11:48:56 - mmengine - INFO - Epoch(train) [30][ 200/5005] lr: 1.0000e-01 eta: 1 day, 11:02:27 time: 0.2205 data_time: 0.0022 loss: 2.1358 03/05 11:49:19 - mmengine - INFO - Epoch(train) [30][ 300/5005] lr: 1.0000e-01 eta: 1 day, 11:02:04 time: 0.2207 data_time: 0.0026 loss: 2.3136 03/05 11:49:41 - mmengine - INFO - Epoch(train) [30][ 400/5005] lr: 1.0000e-01 eta: 1 day, 11:01:39 time: 0.2192 data_time: 0.0025 loss: 1.9075 03/05 11:50:04 - mmengine - INFO - Epoch(train) [30][ 500/5005] lr: 1.0000e-01 eta: 1 day, 11:01:15 time: 0.2189 data_time: 0.0025 loss: 2.0276 03/05 11:50:26 - mmengine - INFO - Epoch(train) [30][ 600/5005] lr: 1.0000e-01 eta: 1 day, 11:00:51 time: 0.2375 data_time: 0.0023 loss: 1.9589 03/05 11:50:49 - mmengine - INFO - Epoch(train) [30][ 700/5005] lr: 1.0000e-01 eta: 1 day, 11:00:28 time: 0.2235 data_time: 0.0022 loss: 2.1952 03/05 11:51:11 - mmengine - INFO - Epoch(train) [30][ 800/5005] lr: 1.0000e-01 eta: 1 day, 11:00:02 time: 0.2188 data_time: 0.0023 loss: 2.0984 03/05 11:51:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:51:33 - mmengine - INFO - Epoch(train) [30][ 900/5005] lr: 1.0000e-01 eta: 1 day, 10:59:40 time: 0.2346 data_time: 0.0020 loss: 2.1700 03/05 11:51:56 - mmengine - INFO - Epoch(train) [30][1000/5005] lr: 1.0000e-01 eta: 1 day, 10:59:16 time: 0.2193 data_time: 0.0022 loss: 1.9180 03/05 11:52:19 - mmengine - INFO - Epoch(train) [30][1100/5005] lr: 1.0000e-01 eta: 1 day, 10:58:53 time: 0.2203 data_time: 0.0025 loss: 2.0955 03/05 11:52:41 - mmengine - INFO - Epoch(train) [30][1200/5005] lr: 1.0000e-01 eta: 1 day, 10:58:28 time: 0.2264 data_time: 0.0023 loss: 2.3277 03/05 11:53:03 - mmengine - INFO - Epoch(train) [30][1300/5005] lr: 1.0000e-01 eta: 1 day, 10:58:04 time: 0.2205 data_time: 0.0021 loss: 2.1240 03/05 11:53:25 - mmengine - INFO - Epoch(train) [30][1400/5005] lr: 1.0000e-01 eta: 1 day, 10:57:39 time: 0.2207 data_time: 0.0022 loss: 1.8759 03/05 11:53:48 - mmengine - INFO - Epoch(train) [30][1500/5005] lr: 1.0000e-01 eta: 1 day, 10:57:17 time: 0.2179 data_time: 0.0021 loss: 1.9410 03/05 11:54:11 - mmengine - INFO - Epoch(train) [30][1600/5005] lr: 1.0000e-01 eta: 1 day, 10:56:52 time: 0.2247 data_time: 0.0025 loss: 2.1621 03/05 11:54:33 - mmengine - INFO - Epoch(train) [30][1700/5005] lr: 1.0000e-01 eta: 1 day, 10:56:26 time: 0.2186 data_time: 0.0023 loss: 2.5108 03/05 11:54:55 - mmengine - INFO - Epoch(train) [30][1800/5005] lr: 1.0000e-01 eta: 1 day, 10:56:02 time: 0.2214 data_time: 0.0019 loss: 2.2624 03/05 11:55:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:55:17 - mmengine - INFO - Epoch(train) [30][1900/5005] lr: 1.0000e-01 eta: 1 day, 10:55:38 time: 0.2220 data_time: 0.0021 loss: 1.9006 03/05 11:55:40 - mmengine - INFO - Epoch(train) [30][2000/5005] lr: 1.0000e-01 eta: 1 day, 10:55:14 time: 0.2207 data_time: 0.0023 loss: 2.0521 03/05 11:56:02 - mmengine - INFO - Epoch(train) [30][2100/5005] lr: 1.0000e-01 eta: 1 day, 10:54:50 time: 0.2231 data_time: 0.0023 loss: 2.0637 03/05 11:56:25 - mmengine - INFO - Epoch(train) [30][2200/5005] lr: 1.0000e-01 eta: 1 day, 10:54:26 time: 0.2200 data_time: 0.0023 loss: 2.0066 03/05 11:56:47 - mmengine - INFO - Epoch(train) [30][2300/5005] lr: 1.0000e-01 eta: 1 day, 10:54:02 time: 0.2200 data_time: 0.0022 loss: 2.1304 03/05 11:57:10 - mmengine - INFO - Epoch(train) [30][2400/5005] lr: 1.0000e-01 eta: 1 day, 10:53:40 time: 0.2220 data_time: 0.0024 loss: 2.0535 03/05 11:57:32 - mmengine - INFO - Epoch(train) [30][2500/5005] lr: 1.0000e-01 eta: 1 day, 10:53:15 time: 0.2162 data_time: 0.0021 loss: 1.9819 03/05 11:57:54 - mmengine - INFO - Epoch(train) [30][2600/5005] lr: 1.0000e-01 eta: 1 day, 10:52:50 time: 0.2171 data_time: 0.0025 loss: 2.0151 03/05 11:58:17 - mmengine - INFO - Epoch(train) [30][2700/5005] lr: 1.0000e-01 eta: 1 day, 10:52:26 time: 0.2186 data_time: 0.0024 loss: 2.1229 03/05 11:58:39 - mmengine - INFO - Epoch(train) [30][2800/5005] lr: 1.0000e-01 eta: 1 day, 10:52:02 time: 0.2191 data_time: 0.0025 loss: 2.3042 03/05 11:58:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 11:59:02 - mmengine - INFO - Epoch(train) [30][2900/5005] lr: 1.0000e-01 eta: 1 day, 10:51:39 time: 0.2395 data_time: 0.0021 loss: 2.1485 03/05 11:59:24 - mmengine - INFO - Epoch(train) [30][3000/5005] lr: 1.0000e-01 eta: 1 day, 10:51:14 time: 0.2239 data_time: 0.0024 loss: 2.0673 03/05 11:59:47 - mmengine - INFO - Epoch(train) [30][3100/5005] lr: 1.0000e-01 eta: 1 day, 10:50:50 time: 0.2187 data_time: 0.0027 loss: 2.1456 03/05 12:00:09 - mmengine - INFO - Epoch(train) [30][3200/5005] lr: 1.0000e-01 eta: 1 day, 10:50:26 time: 0.2454 data_time: 0.0026 loss: 2.1489 03/05 12:00:31 - mmengine - INFO - Epoch(train) [30][3300/5005] lr: 1.0000e-01 eta: 1 day, 10:50:02 time: 0.2232 data_time: 0.0019 loss: 2.0596 03/05 12:00:54 - mmengine - INFO - Epoch(train) [30][3400/5005] lr: 1.0000e-01 eta: 1 day, 10:49:38 time: 0.2218 data_time: 0.0028 loss: 2.2023 03/05 12:01:16 - mmengine - INFO - Epoch(train) [30][3500/5005] lr: 1.0000e-01 eta: 1 day, 10:49:15 time: 0.2183 data_time: 0.0024 loss: 2.2414 03/05 12:01:38 - mmengine - INFO - Epoch(train) [30][3600/5005] lr: 1.0000e-01 eta: 1 day, 10:48:49 time: 0.2203 data_time: 0.0023 loss: 2.1375 03/05 12:02:01 - mmengine - INFO - Epoch(train) [30][3700/5005] lr: 1.0000e-01 eta: 1 day, 10:48:25 time: 0.2211 data_time: 0.0023 loss: 2.0726 03/05 12:02:24 - mmengine - INFO - Epoch(train) [30][3800/5005] lr: 1.0000e-01 eta: 1 day, 10:48:03 time: 0.2217 data_time: 0.0025 loss: 2.1145 03/05 12:02:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:02:46 - mmengine - INFO - Epoch(train) [30][3900/5005] lr: 1.0000e-01 eta: 1 day, 10:47:39 time: 0.2448 data_time: 0.0025 loss: 2.0098 03/05 12:03:08 - mmengine - INFO - Epoch(train) [30][4000/5005] lr: 1.0000e-01 eta: 1 day, 10:47:14 time: 0.2213 data_time: 0.0021 loss: 1.7803 03/05 12:03:31 - mmengine - INFO - Epoch(train) [30][4100/5005] lr: 1.0000e-01 eta: 1 day, 10:46:51 time: 0.2203 data_time: 0.0022 loss: 2.1870 03/05 12:03:54 - mmengine - INFO - Epoch(train) [30][4200/5005] lr: 1.0000e-01 eta: 1 day, 10:46:28 time: 0.2196 data_time: 0.0029 loss: 2.2345 03/05 12:04:16 - mmengine - INFO - Epoch(train) [30][4300/5005] lr: 1.0000e-01 eta: 1 day, 10:46:05 time: 0.2191 data_time: 0.0021 loss: 2.3727 03/05 12:04:39 - mmengine - INFO - Epoch(train) [30][4400/5005] lr: 1.0000e-01 eta: 1 day, 10:45:41 time: 0.2199 data_time: 0.0021 loss: 2.1312 03/05 12:05:01 - mmengine - INFO - Epoch(train) [30][4500/5005] lr: 1.0000e-01 eta: 1 day, 10:45:16 time: 0.2177 data_time: 0.0026 loss: 2.1090 03/05 12:05:24 - mmengine - INFO - Epoch(train) [30][4600/5005] lr: 1.0000e-01 eta: 1 day, 10:44:53 time: 0.2215 data_time: 0.0024 loss: 2.2065 03/05 12:05:46 - mmengine - INFO - Epoch(train) [30][4700/5005] lr: 1.0000e-01 eta: 1 day, 10:44:29 time: 0.2186 data_time: 0.0022 loss: 1.8462 03/05 12:06:09 - mmengine - INFO - Epoch(train) [30][4800/5005] lr: 1.0000e-01 eta: 1 day, 10:44:05 time: 0.2203 data_time: 0.0020 loss: 2.0080 03/05 12:06:21 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:06:32 - mmengine - INFO - Epoch(train) [30][4900/5005] lr: 1.0000e-01 eta: 1 day, 10:43:44 time: 0.2992 data_time: 0.0022 loss: 2.1255 03/05 12:07:01 - mmengine - INFO - Epoch(train) [30][5000/5005] lr: 1.0000e-01 eta: 1 day, 10:43:46 time: 0.2859 data_time: 0.0025 loss: 1.9491 03/05 12:07:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:07:05 - mmengine - INFO - Saving checkpoint at 30 epochs 03/05 12:07:19 - mmengine - INFO - Epoch(val) [30][100/196] eta: 0:00:12 time: 0.0201 data_time: 0.0005 03/05 12:07:33 - mmengine - INFO - Epoch(val) [30][196/196] accuracy/top1: 54.9720 accuracy/top5: 80.0740 03/05 12:08:04 - mmengine - INFO - Epoch(train) [31][ 100/5005] lr: 1.0000e-01 eta: 1 day, 10:43:51 time: 0.2213 data_time: 0.0024 loss: 2.1383 03/05 12:08:26 - mmengine - INFO - Epoch(train) [31][ 200/5005] lr: 1.0000e-01 eta: 1 day, 10:43:28 time: 0.2204 data_time: 0.0025 loss: 2.0382 03/05 12:08:49 - mmengine - INFO - Epoch(train) [31][ 300/5005] lr: 1.0000e-01 eta: 1 day, 10:43:05 time: 0.2209 data_time: 0.0024 loss: 2.2956 03/05 12:09:12 - mmengine - INFO - Epoch(train) [31][ 400/5005] lr: 1.0000e-01 eta: 1 day, 10:42:42 time: 0.2206 data_time: 0.0024 loss: 2.0725 03/05 12:09:34 - mmengine - INFO - Epoch(train) [31][ 500/5005] lr: 1.0000e-01 eta: 1 day, 10:42:17 time: 0.2385 data_time: 0.0021 loss: 2.1395 03/05 12:09:57 - mmengine - INFO - Epoch(train) [31][ 600/5005] lr: 1.0000e-01 eta: 1 day, 10:41:54 time: 0.2221 data_time: 0.0024 loss: 2.2201 03/05 12:10:19 - mmengine - INFO - Epoch(train) [31][ 700/5005] lr: 1.0000e-01 eta: 1 day, 10:41:30 time: 0.2242 data_time: 0.0022 loss: 2.2508 03/05 12:10:41 - mmengine - INFO - Epoch(train) [31][ 800/5005] lr: 1.0000e-01 eta: 1 day, 10:41:06 time: 0.2248 data_time: 0.0026 loss: 2.2658 03/05 12:10:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:11:04 - mmengine - INFO - Epoch(train) [31][ 900/5005] lr: 1.0000e-01 eta: 1 day, 10:40:41 time: 0.2202 data_time: 0.0023 loss: 1.9064 03/05 12:11:26 - mmengine - INFO - Epoch(train) [31][1000/5005] lr: 1.0000e-01 eta: 1 day, 10:40:18 time: 0.2179 data_time: 0.0026 loss: 2.1271 03/05 12:11:49 - mmengine - INFO - Epoch(train) [31][1100/5005] lr: 1.0000e-01 eta: 1 day, 10:39:54 time: 0.2350 data_time: 0.0022 loss: 1.9304 03/05 12:12:11 - mmengine - INFO - Epoch(train) [31][1200/5005] lr: 1.0000e-01 eta: 1 day, 10:39:29 time: 0.2199 data_time: 0.0021 loss: 2.1838 03/05 12:12:33 - mmengine - INFO - Epoch(train) [31][1300/5005] lr: 1.0000e-01 eta: 1 day, 10:39:05 time: 0.2228 data_time: 0.0025 loss: 2.1058 03/05 12:12:56 - mmengine - INFO - Epoch(train) [31][1400/5005] lr: 1.0000e-01 eta: 1 day, 10:38:42 time: 0.2204 data_time: 0.0024 loss: 2.1030 03/05 12:13:18 - mmengine - INFO - Epoch(train) [31][1500/5005] lr: 1.0000e-01 eta: 1 day, 10:38:18 time: 0.2233 data_time: 0.0024 loss: 2.2749 03/05 12:13:41 - mmengine - INFO - Epoch(train) [31][1600/5005] lr: 1.0000e-01 eta: 1 day, 10:37:54 time: 0.2177 data_time: 0.0023 loss: 2.3590 03/05 12:14:03 - mmengine - INFO - Epoch(train) [31][1700/5005] lr: 1.0000e-01 eta: 1 day, 10:37:29 time: 0.2215 data_time: 0.0022 loss: 2.1023 03/05 12:14:26 - mmengine - INFO - Epoch(train) [31][1800/5005] lr: 1.0000e-01 eta: 1 day, 10:37:06 time: 0.2216 data_time: 0.0023 loss: 2.2376 03/05 12:14:37 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:14:48 - mmengine - INFO - Epoch(train) [31][1900/5005] lr: 1.0000e-01 eta: 1 day, 10:36:42 time: 0.2221 data_time: 0.0024 loss: 2.0870 03/05 12:15:11 - mmengine - INFO - Epoch(train) [31][2000/5005] lr: 1.0000e-01 eta: 1 day, 10:36:19 time: 0.2261 data_time: 0.0025 loss: 2.1904 03/05 12:15:33 - mmengine - INFO - Epoch(train) [31][2100/5005] lr: 1.0000e-01 eta: 1 day, 10:35:55 time: 0.2222 data_time: 0.0023 loss: 2.0705 03/05 12:15:55 - mmengine - INFO - Epoch(train) [31][2200/5005] lr: 1.0000e-01 eta: 1 day, 10:35:30 time: 0.2218 data_time: 0.0025 loss: 1.9428 03/05 12:16:18 - mmengine - INFO - Epoch(train) [31][2300/5005] lr: 1.0000e-01 eta: 1 day, 10:35:05 time: 0.2233 data_time: 0.0021 loss: 2.0839 03/05 12:16:40 - mmengine - INFO - Epoch(train) [31][2400/5005] lr: 1.0000e-01 eta: 1 day, 10:34:42 time: 0.2344 data_time: 0.0024 loss: 1.9384 03/05 12:17:03 - mmengine - INFO - Epoch(train) [31][2500/5005] lr: 1.0000e-01 eta: 1 day, 10:34:19 time: 0.2224 data_time: 0.0025 loss: 2.1353 03/05 12:17:25 - mmengine - INFO - Epoch(train) [31][2600/5005] lr: 1.0000e-01 eta: 1 day, 10:33:54 time: 0.2219 data_time: 0.0023 loss: 2.0085 03/05 12:17:48 - mmengine - INFO - Epoch(train) [31][2700/5005] lr: 1.0000e-01 eta: 1 day, 10:33:30 time: 0.2200 data_time: 0.0024 loss: 1.8614 03/05 12:18:10 - mmengine - INFO - Epoch(train) [31][2800/5005] lr: 1.0000e-01 eta: 1 day, 10:33:06 time: 0.2242 data_time: 0.0044 loss: 2.2911 03/05 12:18:21 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:18:33 - mmengine - INFO - Epoch(train) [31][2900/5005] lr: 1.0000e-01 eta: 1 day, 10:32:44 time: 0.2407 data_time: 0.0022 loss: 2.0663 03/05 12:18:55 - mmengine - INFO - Epoch(train) [31][3000/5005] lr: 1.0000e-01 eta: 1 day, 10:32:19 time: 0.2222 data_time: 0.0023 loss: 2.1154 03/05 12:19:17 - mmengine - INFO - Epoch(train) [31][3100/5005] lr: 1.0000e-01 eta: 1 day, 10:31:55 time: 0.2213 data_time: 0.0021 loss: 1.8951 03/05 12:19:40 - mmengine - INFO - Epoch(train) [31][3200/5005] lr: 1.0000e-01 eta: 1 day, 10:31:31 time: 0.2218 data_time: 0.0025 loss: 2.1398 03/05 12:20:03 - mmengine - INFO - Epoch(train) [31][3300/5005] lr: 1.0000e-01 eta: 1 day, 10:31:08 time: 0.2228 data_time: 0.0020 loss: 2.2525 03/05 12:20:25 - mmengine - INFO - Epoch(train) [31][3400/5005] lr: 1.0000e-01 eta: 1 day, 10:30:44 time: 0.2203 data_time: 0.0021 loss: 2.0349 03/05 12:20:48 - mmengine - INFO - Epoch(train) [31][3500/5005] lr: 1.0000e-01 eta: 1 day, 10:30:21 time: 0.2340 data_time: 0.0024 loss: 2.0284 03/05 12:21:10 - mmengine - INFO - Epoch(train) [31][3600/5005] lr: 1.0000e-01 eta: 1 day, 10:29:57 time: 0.2190 data_time: 0.0025 loss: 2.1559 03/05 12:21:32 - mmengine - INFO - Epoch(train) [31][3700/5005] lr: 1.0000e-01 eta: 1 day, 10:29:33 time: 0.2187 data_time: 0.0022 loss: 1.9968 03/05 12:21:55 - mmengine - INFO - Epoch(train) [31][3800/5005] lr: 1.0000e-01 eta: 1 day, 10:29:10 time: 0.2309 data_time: 0.0027 loss: 1.8287 03/05 12:22:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:22:17 - mmengine - INFO - Epoch(train) [31][3900/5005] lr: 1.0000e-01 eta: 1 day, 10:28:46 time: 0.2216 data_time: 0.0029 loss: 2.1502 03/05 12:22:40 - mmengine - INFO - Epoch(train) [31][4000/5005] lr: 1.0000e-01 eta: 1 day, 10:28:22 time: 0.2221 data_time: 0.0026 loss: 1.9579 03/05 12:23:02 - mmengine - INFO - Epoch(train) [31][4100/5005] lr: 1.0000e-01 eta: 1 day, 10:27:58 time: 0.2162 data_time: 0.0022 loss: 2.3415 03/05 12:23:25 - mmengine - INFO - Epoch(train) [31][4200/5005] lr: 1.0000e-01 eta: 1 day, 10:27:35 time: 0.2218 data_time: 0.0027 loss: 2.2126 03/05 12:23:47 - mmengine - INFO - Epoch(train) [31][4300/5005] lr: 1.0000e-01 eta: 1 day, 10:27:11 time: 0.2249 data_time: 0.0023 loss: 2.0985 03/05 12:24:10 - mmengine - INFO - Epoch(train) [31][4400/5005] lr: 1.0000e-01 eta: 1 day, 10:26:48 time: 0.2245 data_time: 0.0026 loss: 2.0577 03/05 12:24:32 - mmengine - INFO - Epoch(train) [31][4500/5005] lr: 1.0000e-01 eta: 1 day, 10:26:24 time: 0.2189 data_time: 0.0022 loss: 2.0184 03/05 12:24:55 - mmengine - INFO - Epoch(train) [31][4600/5005] lr: 1.0000e-01 eta: 1 day, 10:26:00 time: 0.2255 data_time: 0.0025 loss: 2.1271 03/05 12:25:17 - mmengine - INFO - Epoch(train) [31][4700/5005] lr: 1.0000e-01 eta: 1 day, 10:25:36 time: 0.2286 data_time: 0.0026 loss: 2.0405 03/05 12:25:40 - mmengine - INFO - Epoch(train) [31][4800/5005] lr: 1.0000e-01 eta: 1 day, 10:25:12 time: 0.2232 data_time: 0.0026 loss: 2.1958 03/05 12:25:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:26:03 - mmengine - INFO - Epoch(train) [31][4900/5005] lr: 1.0000e-01 eta: 1 day, 10:24:52 time: 0.2945 data_time: 0.0022 loss: 2.3636 03/05 12:26:32 - mmengine - INFO - Epoch(train) [31][5000/5005] lr: 1.0000e-01 eta: 1 day, 10:24:50 time: 0.2718 data_time: 0.0023 loss: 2.0062 03/05 12:26:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:26:36 - mmengine - INFO - Saving checkpoint at 31 epochs 03/05 12:26:51 - mmengine - INFO - Epoch(val) [31][100/196] eta: 0:00:12 time: 0.0192 data_time: 0.0004 03/05 12:27:04 - mmengine - INFO - Epoch(val) [31][196/196] accuracy/top1: 55.3220 accuracy/top5: 80.4260 03/05 12:27:35 - mmengine - INFO - Epoch(train) [32][ 100/5005] lr: 1.0000e-01 eta: 1 day, 10:24:56 time: 0.2428 data_time: 0.0023 loss: 2.0296 03/05 12:27:58 - mmengine - INFO - Epoch(train) [32][ 200/5005] lr: 1.0000e-01 eta: 1 day, 10:24:33 time: 0.2213 data_time: 0.0026 loss: 2.1456 03/05 12:28:20 - mmengine - INFO - Epoch(train) [32][ 300/5005] lr: 1.0000e-01 eta: 1 day, 10:24:08 time: 0.2211 data_time: 0.0025 loss: 2.1121 03/05 12:28:43 - mmengine - INFO - Epoch(train) [32][ 400/5005] lr: 1.0000e-01 eta: 1 day, 10:23:46 time: 0.2210 data_time: 0.0023 loss: 2.0567 03/05 12:29:06 - mmengine - INFO - Epoch(train) [32][ 500/5005] lr: 1.0000e-01 eta: 1 day, 10:23:23 time: 0.2203 data_time: 0.0027 loss: 1.9778 03/05 12:29:28 - mmengine - INFO - Epoch(train) [32][ 600/5005] lr: 1.0000e-01 eta: 1 day, 10:22:59 time: 0.2196 data_time: 0.0024 loss: 2.1516 03/05 12:29:50 - mmengine - INFO - Epoch(train) [32][ 700/5005] lr: 1.0000e-01 eta: 1 day, 10:22:35 time: 0.2189 data_time: 0.0022 loss: 2.0057 03/05 12:30:13 - mmengine - INFO - Epoch(train) [32][ 800/5005] lr: 1.0000e-01 eta: 1 day, 10:22:12 time: 0.2214 data_time: 0.0022 loss: 2.3750 03/05 12:30:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:30:36 - mmengine - INFO - Epoch(train) [32][ 900/5005] lr: 1.0000e-01 eta: 1 day, 10:21:48 time: 0.2248 data_time: 0.0022 loss: 2.1188 03/05 12:30:58 - mmengine - INFO - Epoch(train) [32][1000/5005] lr: 1.0000e-01 eta: 1 day, 10:21:24 time: 0.2182 data_time: 0.0022 loss: 2.0115 03/05 12:31:20 - mmengine - INFO - Epoch(train) [32][1100/5005] lr: 1.0000e-01 eta: 1 day, 10:21:00 time: 0.2223 data_time: 0.0022 loss: 1.9956 03/05 12:31:43 - mmengine - INFO - Epoch(train) [32][1200/5005] lr: 1.0000e-01 eta: 1 day, 10:20:36 time: 0.2209 data_time: 0.0019 loss: 2.2412 03/05 12:32:05 - mmengine - INFO - Epoch(train) [32][1300/5005] lr: 1.0000e-01 eta: 1 day, 10:20:13 time: 0.2197 data_time: 0.0023 loss: 2.3111 03/05 12:32:28 - mmengine - INFO - Epoch(train) [32][1400/5005] lr: 1.0000e-01 eta: 1 day, 10:19:49 time: 0.2217 data_time: 0.0023 loss: 2.1540 03/05 12:32:50 - mmengine - INFO - Epoch(train) [32][1500/5005] lr: 1.0000e-01 eta: 1 day, 10:19:25 time: 0.2240 data_time: 0.0023 loss: 2.1498 03/05 12:33:13 - mmengine - INFO - Epoch(train) [32][1600/5005] lr: 1.0000e-01 eta: 1 day, 10:19:00 time: 0.2197 data_time: 0.0023 loss: 2.2561 03/05 12:33:35 - mmengine - INFO - Epoch(train) [32][1700/5005] lr: 1.0000e-01 eta: 1 day, 10:18:37 time: 0.2225 data_time: 0.0022 loss: 1.9407 03/05 12:33:57 - mmengine - INFO - Epoch(train) [32][1800/5005] lr: 1.0000e-01 eta: 1 day, 10:18:12 time: 0.2217 data_time: 0.0021 loss: 2.1137 03/05 12:34:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:34:20 - mmengine - INFO - Epoch(train) [32][1900/5005] lr: 1.0000e-01 eta: 1 day, 10:17:49 time: 0.2349 data_time: 0.0023 loss: 2.2790 03/05 12:34:42 - mmengine - INFO - Epoch(train) [32][2000/5005] lr: 1.0000e-01 eta: 1 day, 10:17:24 time: 0.2192 data_time: 0.0024 loss: 2.3146 03/05 12:35:05 - mmengine - INFO - Epoch(train) [32][2100/5005] lr: 1.0000e-01 eta: 1 day, 10:17:01 time: 0.2575 data_time: 0.0023 loss: 2.2314 03/05 12:35:27 - mmengine - INFO - Epoch(train) [32][2200/5005] lr: 1.0000e-01 eta: 1 day, 10:16:37 time: 0.2201 data_time: 0.0024 loss: 2.2851 03/05 12:35:50 - mmengine - INFO - Epoch(train) [32][2300/5005] lr: 1.0000e-01 eta: 1 day, 10:16:13 time: 0.2207 data_time: 0.0027 loss: 2.0319 03/05 12:36:12 - mmengine - INFO - Epoch(train) [32][2400/5005] lr: 1.0000e-01 eta: 1 day, 10:15:49 time: 0.2194 data_time: 0.0026 loss: 2.1731 03/05 12:36:35 - mmengine - INFO - Epoch(train) [32][2500/5005] lr: 1.0000e-01 eta: 1 day, 10:15:26 time: 0.2402 data_time: 0.0021 loss: 2.1368 03/05 12:36:57 - mmengine - INFO - Epoch(train) [32][2600/5005] lr: 1.0000e-01 eta: 1 day, 10:15:02 time: 0.2196 data_time: 0.0022 loss: 2.0716 03/05 12:37:20 - mmengine - INFO - Epoch(train) [32][2700/5005] lr: 1.0000e-01 eta: 1 day, 10:14:39 time: 0.2394 data_time: 0.0023 loss: 2.1677 03/05 12:37:42 - mmengine - INFO - Epoch(train) [32][2800/5005] lr: 1.0000e-01 eta: 1 day, 10:14:15 time: 0.2209 data_time: 0.0021 loss: 2.2496 03/05 12:37:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:38:05 - mmengine - INFO - Epoch(train) [32][2900/5005] lr: 1.0000e-01 eta: 1 day, 10:13:52 time: 0.2182 data_time: 0.0021 loss: 2.0931 03/05 12:38:27 - mmengine - INFO - Epoch(train) [32][3000/5005] lr: 1.0000e-01 eta: 1 day, 10:13:28 time: 0.2200 data_time: 0.0022 loss: 2.0905 03/05 12:38:50 - mmengine - INFO - Epoch(train) [32][3100/5005] lr: 1.0000e-01 eta: 1 day, 10:13:04 time: 0.2208 data_time: 0.0024 loss: 2.1943 03/05 12:39:12 - mmengine - INFO - Epoch(train) [32][3200/5005] lr: 1.0000e-01 eta: 1 day, 10:12:40 time: 0.2233 data_time: 0.0024 loss: 2.1426 03/05 12:39:34 - mmengine - INFO - Epoch(train) [32][3300/5005] lr: 1.0000e-01 eta: 1 day, 10:12:16 time: 0.2187 data_time: 0.0024 loss: 2.1910 03/05 12:39:57 - mmengine - INFO - Epoch(train) [32][3400/5005] lr: 1.0000e-01 eta: 1 day, 10:11:52 time: 0.2199 data_time: 0.0025 loss: 2.1434 03/05 12:40:19 - mmengine - INFO - Epoch(train) [32][3500/5005] lr: 1.0000e-01 eta: 1 day, 10:11:28 time: 0.2200 data_time: 0.0027 loss: 2.0265 03/05 12:40:42 - mmengine - INFO - Epoch(train) [32][3600/5005] lr: 1.0000e-01 eta: 1 day, 10:11:05 time: 0.2323 data_time: 0.0022 loss: 2.1198 03/05 12:41:04 - mmengine - INFO - Epoch(train) [32][3700/5005] lr: 1.0000e-01 eta: 1 day, 10:10:40 time: 0.2218 data_time: 0.0020 loss: 2.1503 03/05 12:41:26 - mmengine - INFO - Epoch(train) [32][3800/5005] lr: 1.0000e-01 eta: 1 day, 10:10:16 time: 0.2186 data_time: 0.0023 loss: 2.1339 03/05 12:41:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:41:49 - mmengine - INFO - Epoch(train) [32][3900/5005] lr: 1.0000e-01 eta: 1 day, 10:09:52 time: 0.2237 data_time: 0.0026 loss: 1.9257 03/05 12:42:11 - mmengine - INFO - Epoch(train) [32][4000/5005] lr: 1.0000e-01 eta: 1 day, 10:09:28 time: 0.2209 data_time: 0.0025 loss: 2.3809 03/05 12:42:34 - mmengine - INFO - Epoch(train) [32][4100/5005] lr: 1.0000e-01 eta: 1 day, 10:09:05 time: 0.2206 data_time: 0.0025 loss: 2.0125 03/05 12:42:56 - mmengine - INFO - Epoch(train) [32][4200/5005] lr: 1.0000e-01 eta: 1 day, 10:08:41 time: 0.2381 data_time: 0.0025 loss: 2.1608 03/05 12:43:19 - mmengine - INFO - Epoch(train) [32][4300/5005] lr: 1.0000e-01 eta: 1 day, 10:08:17 time: 0.2222 data_time: 0.0022 loss: 2.1409 03/05 12:43:41 - mmengine - INFO - Epoch(train) [32][4400/5005] lr: 1.0000e-01 eta: 1 day, 10:07:53 time: 0.2276 data_time: 0.0025 loss: 2.2421 03/05 12:44:04 - mmengine - INFO - Epoch(train) [32][4500/5005] lr: 1.0000e-01 eta: 1 day, 10:07:30 time: 0.2233 data_time: 0.0022 loss: 2.1206 03/05 12:44:26 - mmengine - INFO - Epoch(train) [32][4600/5005] lr: 1.0000e-01 eta: 1 day, 10:07:06 time: 0.2228 data_time: 0.0022 loss: 1.9325 03/05 12:44:49 - mmengine - INFO - Epoch(train) [32][4700/5005] lr: 1.0000e-01 eta: 1 day, 10:06:43 time: 0.2234 data_time: 0.0023 loss: 2.1781 03/05 12:45:11 - mmengine - INFO - Epoch(train) [32][4800/5005] lr: 1.0000e-01 eta: 1 day, 10:06:20 time: 0.2199 data_time: 0.0026 loss: 1.9420 03/05 12:45:21 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:45:35 - mmengine - INFO - Epoch(train) [32][4900/5005] lr: 1.0000e-01 eta: 1 day, 10:06:00 time: 0.2954 data_time: 0.0019 loss: 2.2495 03/05 12:46:04 - mmengine - INFO - Epoch(train) [32][5000/5005] lr: 1.0000e-01 eta: 1 day, 10:05:57 time: 0.2989 data_time: 0.0021 loss: 2.1088 03/05 12:46:05 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:46:08 - mmengine - INFO - Saving checkpoint at 32 epochs 03/05 12:46:23 - mmengine - INFO - Epoch(val) [32][100/196] eta: 0:00:13 time: 0.0232 data_time: 0.0005 03/05 12:46:36 - mmengine - INFO - Epoch(val) [32][196/196] accuracy/top1: 55.6120 accuracy/top5: 81.0040 03/05 12:47:08 - mmengine - INFO - Epoch(train) [33][ 100/5005] lr: 1.0000e-01 eta: 1 day, 10:06:04 time: 0.2370 data_time: 0.0028 loss: 2.0007 03/05 12:47:30 - mmengine - INFO - Epoch(train) [33][ 200/5005] lr: 1.0000e-01 eta: 1 day, 10:05:40 time: 0.2221 data_time: 0.0026 loss: 2.0829 03/05 12:47:53 - mmengine - INFO - Epoch(train) [33][ 300/5005] lr: 1.0000e-01 eta: 1 day, 10:05:17 time: 0.2217 data_time: 0.0023 loss: 2.0714 03/05 12:48:15 - mmengine - INFO - Epoch(train) [33][ 400/5005] lr: 1.0000e-01 eta: 1 day, 10:04:53 time: 0.2324 data_time: 0.0028 loss: 2.1632 03/05 12:48:38 - mmengine - INFO - Epoch(train) [33][ 500/5005] lr: 1.0000e-01 eta: 1 day, 10:04:30 time: 0.2571 data_time: 0.0023 loss: 2.0622 03/05 12:49:01 - mmengine - INFO - Epoch(train) [33][ 600/5005] lr: 1.0000e-01 eta: 1 day, 10:04:08 time: 0.2227 data_time: 0.0023 loss: 2.0775 03/05 12:49:23 - mmengine - INFO - Epoch(train) [33][ 700/5005] lr: 1.0000e-01 eta: 1 day, 10:03:43 time: 0.2225 data_time: 0.0024 loss: 1.9773 03/05 12:49:46 - mmengine - INFO - Epoch(train) [33][ 800/5005] lr: 1.0000e-01 eta: 1 day, 10:03:21 time: 0.2239 data_time: 0.0024 loss: 1.9823 03/05 12:49:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:50:08 - mmengine - INFO - Epoch(train) [33][ 900/5005] lr: 1.0000e-01 eta: 1 day, 10:02:57 time: 0.2207 data_time: 0.0021 loss: 2.0720 03/05 12:50:31 - mmengine - INFO - Epoch(train) [33][1000/5005] lr: 1.0000e-01 eta: 1 day, 10:02:34 time: 0.2236 data_time: 0.0021 loss: 2.0748 03/05 12:50:53 - mmengine - INFO - Epoch(train) [33][1100/5005] lr: 1.0000e-01 eta: 1 day, 10:02:09 time: 0.2239 data_time: 0.0022 loss: 1.9267 03/05 12:51:16 - mmengine - INFO - Epoch(train) [33][1200/5005] lr: 1.0000e-01 eta: 1 day, 10:01:46 time: 0.2404 data_time: 0.0025 loss: 2.1504 03/05 12:51:38 - mmengine - INFO - Epoch(train) [33][1300/5005] lr: 1.0000e-01 eta: 1 day, 10:01:21 time: 0.2211 data_time: 0.0021 loss: 2.0860 03/05 12:52:01 - mmengine - INFO - Epoch(train) [33][1400/5005] lr: 1.0000e-01 eta: 1 day, 10:01:00 time: 0.2249 data_time: 0.0023 loss: 2.1376 03/05 12:52:24 - mmengine - INFO - Epoch(train) [33][1500/5005] lr: 1.0000e-01 eta: 1 day, 10:00:36 time: 0.2219 data_time: 0.0024 loss: 2.1071 03/05 12:52:46 - mmengine - INFO - Epoch(train) [33][1600/5005] lr: 1.0000e-01 eta: 1 day, 10:00:12 time: 0.2414 data_time: 0.0023 loss: 2.1522 03/05 12:53:08 - mmengine - INFO - Epoch(train) [33][1700/5005] lr: 1.0000e-01 eta: 1 day, 9:59:48 time: 0.2217 data_time: 0.0021 loss: 2.0797 03/05 12:53:31 - mmengine - INFO - Epoch(train) [33][1800/5005] lr: 1.0000e-01 eta: 1 day, 9:59:26 time: 0.2348 data_time: 0.0018 loss: 1.9611 03/05 12:53:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:53:54 - mmengine - INFO - Epoch(train) [33][1900/5005] lr: 1.0000e-01 eta: 1 day, 9:59:02 time: 0.2173 data_time: 0.0022 loss: 2.1095 03/05 12:54:16 - mmengine - INFO - Epoch(train) [33][2000/5005] lr: 1.0000e-01 eta: 1 day, 9:58:38 time: 0.2236 data_time: 0.0020 loss: 2.1799 03/05 12:54:39 - mmengine - INFO - Epoch(train) [33][2100/5005] lr: 1.0000e-01 eta: 1 day, 9:58:14 time: 0.2202 data_time: 0.0023 loss: 2.0882 03/05 12:55:01 - mmengine - INFO - Epoch(train) [33][2200/5005] lr: 1.0000e-01 eta: 1 day, 9:57:51 time: 0.2287 data_time: 0.0023 loss: 2.1189 03/05 12:55:24 - mmengine - INFO - Epoch(train) [33][2300/5005] lr: 1.0000e-01 eta: 1 day, 9:57:28 time: 0.2411 data_time: 0.0020 loss: 1.9386 03/05 12:55:46 - mmengine - INFO - Epoch(train) [33][2400/5005] lr: 1.0000e-01 eta: 1 day, 9:57:05 time: 0.2242 data_time: 0.0021 loss: 2.0774 03/05 12:56:09 - mmengine - INFO - Epoch(train) [33][2500/5005] lr: 1.0000e-01 eta: 1 day, 9:56:41 time: 0.2233 data_time: 0.0022 loss: 2.1246 03/05 12:56:32 - mmengine - INFO - Epoch(train) [33][2600/5005] lr: 1.0000e-01 eta: 1 day, 9:56:18 time: 0.2222 data_time: 0.0023 loss: 2.1938 03/05 12:56:54 - mmengine - INFO - Epoch(train) [33][2700/5005] lr: 1.0000e-01 eta: 1 day, 9:55:54 time: 0.2265 data_time: 0.0023 loss: 2.2229 03/05 12:57:17 - mmengine - INFO - Epoch(train) [33][2800/5005] lr: 1.0000e-01 eta: 1 day, 9:55:31 time: 0.2472 data_time: 0.0026 loss: 2.0316 03/05 12:57:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 12:57:39 - mmengine - INFO - Epoch(train) [33][2900/5005] lr: 1.0000e-01 eta: 1 day, 9:55:07 time: 0.2205 data_time: 0.0021 loss: 2.2543 03/05 12:58:02 - mmengine - INFO - Epoch(train) [33][3000/5005] lr: 1.0000e-01 eta: 1 day, 9:54:44 time: 0.2223 data_time: 0.0020 loss: 1.9907 03/05 12:58:24 - mmengine - INFO - Epoch(train) [33][3100/5005] lr: 1.0000e-01 eta: 1 day, 9:54:20 time: 0.2219 data_time: 0.0024 loss: 2.1606 03/05 12:58:47 - mmengine - INFO - Epoch(train) [33][3200/5005] lr: 1.0000e-01 eta: 1 day, 9:53:56 time: 0.2301 data_time: 0.0024 loss: 2.0053 03/05 12:59:09 - mmengine - INFO - Epoch(train) [33][3300/5005] lr: 1.0000e-01 eta: 1 day, 9:53:32 time: 0.2216 data_time: 0.0020 loss: 2.2794 03/05 12:59:32 - mmengine - INFO - Epoch(train) [33][3400/5005] lr: 1.0000e-01 eta: 1 day, 9:53:09 time: 0.2232 data_time: 0.0023 loss: 2.0806 03/05 12:59:54 - mmengine - INFO - Epoch(train) [33][3500/5005] lr: 1.0000e-01 eta: 1 day, 9:52:46 time: 0.2198 data_time: 0.0021 loss: 2.0178 03/05 13:00:17 - mmengine - INFO - Epoch(train) [33][3600/5005] lr: 1.0000e-01 eta: 1 day, 9:52:22 time: 0.2374 data_time: 0.0024 loss: 2.0229 03/05 13:00:40 - mmengine - INFO - Epoch(train) [33][3700/5005] lr: 1.0000e-01 eta: 1 day, 9:52:00 time: 0.2324 data_time: 0.0025 loss: 2.2458 03/05 13:01:02 - mmengine - INFO - Epoch(train) [33][3800/5005] lr: 1.0000e-01 eta: 1 day, 9:51:36 time: 0.2213 data_time: 0.0024 loss: 2.0536 03/05 13:01:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:01:25 - mmengine - INFO - Epoch(train) [33][3900/5005] lr: 1.0000e-01 eta: 1 day, 9:51:13 time: 0.2217 data_time: 0.0026 loss: 1.8674 03/05 13:01:47 - mmengine - INFO - Epoch(train) [33][4000/5005] lr: 1.0000e-01 eta: 1 day, 9:50:49 time: 0.2210 data_time: 0.0026 loss: 2.1350 03/05 13:02:10 - mmengine - INFO - Epoch(train) [33][4100/5005] lr: 1.0000e-01 eta: 1 day, 9:50:26 time: 0.2389 data_time: 0.0024 loss: 2.1135 03/05 13:02:33 - mmengine - INFO - Epoch(train) [33][4200/5005] lr: 1.0000e-01 eta: 1 day, 9:50:03 time: 0.2367 data_time: 0.0021 loss: 2.1301 03/05 13:02:55 - mmengine - INFO - Epoch(train) [33][4300/5005] lr: 1.0000e-01 eta: 1 day, 9:49:40 time: 0.2234 data_time: 0.0026 loss: 1.9313 03/05 13:03:18 - mmengine - INFO - Epoch(train) [33][4400/5005] lr: 1.0000e-01 eta: 1 day, 9:49:16 time: 0.2247 data_time: 0.0025 loss: 2.2610 03/05 13:03:40 - mmengine - INFO - Epoch(train) [33][4500/5005] lr: 1.0000e-01 eta: 1 day, 9:48:52 time: 0.2218 data_time: 0.0022 loss: 2.1075 03/05 13:04:03 - mmengine - INFO - Epoch(train) [33][4600/5005] lr: 1.0000e-01 eta: 1 day, 9:48:30 time: 0.2218 data_time: 0.0025 loss: 2.0792 03/05 13:04:26 - mmengine - INFO - Epoch(train) [33][4700/5005] lr: 1.0000e-01 eta: 1 day, 9:48:06 time: 0.2409 data_time: 0.0024 loss: 2.2685 03/05 13:04:48 - mmengine - INFO - Epoch(train) [33][4800/5005] lr: 1.0000e-01 eta: 1 day, 9:47:43 time: 0.2250 data_time: 0.0022 loss: 1.8697 03/05 13:04:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:05:12 - mmengine - INFO - Epoch(train) [33][4900/5005] lr: 1.0000e-01 eta: 1 day, 9:47:22 time: 0.2873 data_time: 0.0021 loss: 1.9871 03/05 13:05:40 - mmengine - INFO - Epoch(train) [33][5000/5005] lr: 1.0000e-01 eta: 1 day, 9:47:17 time: 0.2696 data_time: 0.0022 loss: 1.9418 03/05 13:05:41 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:05:44 - mmengine - INFO - Saving checkpoint at 33 epochs 03/05 13:05:58 - mmengine - INFO - Epoch(val) [33][100/196] eta: 0:00:12 time: 0.0196 data_time: 0.0004 03/05 13:06:11 - mmengine - INFO - Epoch(val) [33][196/196] accuracy/top1: 56.5520 accuracy/top5: 81.4880 03/05 13:06:42 - mmengine - INFO - Epoch(train) [34][ 100/5005] lr: 1.0000e-01 eta: 1 day, 9:47:19 time: 0.2209 data_time: 0.0024 loss: 2.2173 03/05 13:07:05 - mmengine - INFO - Epoch(train) [34][ 200/5005] lr: 1.0000e-01 eta: 1 day, 9:46:56 time: 0.2223 data_time: 0.0025 loss: 2.0686 03/05 13:07:27 - mmengine - INFO - Epoch(train) [34][ 300/5005] lr: 1.0000e-01 eta: 1 day, 9:46:34 time: 0.2265 data_time: 0.0023 loss: 2.3066 03/05 13:07:50 - mmengine - INFO - Epoch(train) [34][ 400/5005] lr: 1.0000e-01 eta: 1 day, 9:46:10 time: 0.2255 data_time: 0.0023 loss: 2.0438 03/05 13:08:13 - mmengine - INFO - Epoch(train) [34][ 500/5005] lr: 1.0000e-01 eta: 1 day, 9:45:47 time: 0.2412 data_time: 0.0022 loss: 2.1621 03/05 13:08:35 - mmengine - INFO - Epoch(train) [34][ 600/5005] lr: 1.0000e-01 eta: 1 day, 9:45:23 time: 0.2252 data_time: 0.0026 loss: 2.3767 03/05 13:08:58 - mmengine - INFO - Epoch(train) [34][ 700/5005] lr: 1.0000e-01 eta: 1 day, 9:45:01 time: 0.2250 data_time: 0.0024 loss: 2.2615 03/05 13:09:20 - mmengine - INFO - Epoch(train) [34][ 800/5005] lr: 1.0000e-01 eta: 1 day, 9:44:36 time: 0.2234 data_time: 0.0027 loss: 2.1348 03/05 13:09:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:09:43 - mmengine - INFO - Epoch(train) [34][ 900/5005] lr: 1.0000e-01 eta: 1 day, 9:44:13 time: 0.2332 data_time: 0.0026 loss: 2.1222 03/05 13:10:05 - mmengine - INFO - Epoch(train) [34][1000/5005] lr: 1.0000e-01 eta: 1 day, 9:43:49 time: 0.2199 data_time: 0.0023 loss: 2.2660 03/05 13:10:28 - mmengine - INFO - Epoch(train) [34][1100/5005] lr: 1.0000e-01 eta: 1 day, 9:43:26 time: 0.2236 data_time: 0.0022 loss: 2.0327 03/05 13:10:51 - mmengine - INFO - Epoch(train) [34][1200/5005] lr: 1.0000e-01 eta: 1 day, 9:43:04 time: 0.2193 data_time: 0.0027 loss: 2.3641 03/05 13:11:13 - mmengine - INFO - Epoch(train) [34][1300/5005] lr: 1.0000e-01 eta: 1 day, 9:42:40 time: 0.2266 data_time: 0.0028 loss: 2.0271 03/05 13:11:36 - mmengine - INFO - Epoch(train) [34][1400/5005] lr: 1.0000e-01 eta: 1 day, 9:42:17 time: 0.2245 data_time: 0.0021 loss: 2.2476 03/05 13:11:59 - mmengine - INFO - Epoch(train) [34][1500/5005] lr: 1.0000e-01 eta: 1 day, 9:41:53 time: 0.2223 data_time: 0.0021 loss: 2.0929 03/05 13:12:21 - mmengine - INFO - Epoch(train) [34][1600/5005] lr: 1.0000e-01 eta: 1 day, 9:41:30 time: 0.2224 data_time: 0.0022 loss: 2.3047 03/05 13:12:44 - mmengine - INFO - Epoch(train) [34][1700/5005] lr: 1.0000e-01 eta: 1 day, 9:41:07 time: 0.2260 data_time: 0.0025 loss: 2.0776 03/05 13:13:06 - mmengine - INFO - Epoch(train) [34][1800/5005] lr: 1.0000e-01 eta: 1 day, 9:40:43 time: 0.2295 data_time: 0.0026 loss: 2.3874 03/05 13:13:14 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:13:29 - mmengine - INFO - Epoch(train) [34][1900/5005] lr: 1.0000e-01 eta: 1 day, 9:40:19 time: 0.2395 data_time: 0.0026 loss: 2.0460 03/05 13:13:52 - mmengine - INFO - Epoch(train) [34][2000/5005] lr: 1.0000e-01 eta: 1 day, 9:39:57 time: 0.2436 data_time: 0.0024 loss: 2.1980 03/05 13:14:14 - mmengine - INFO - Epoch(train) [34][2100/5005] lr: 1.0000e-01 eta: 1 day, 9:39:33 time: 0.2214 data_time: 0.0022 loss: 2.3425 03/05 13:14:37 - mmengine - INFO - Epoch(train) [34][2200/5005] lr: 1.0000e-01 eta: 1 day, 9:39:10 time: 0.2198 data_time: 0.0023 loss: 2.1407 03/05 13:14:59 - mmengine - INFO - Epoch(train) [34][2300/5005] lr: 1.0000e-01 eta: 1 day, 9:38:47 time: 0.2228 data_time: 0.0024 loss: 2.2206 03/05 13:15:22 - mmengine - INFO - Epoch(train) [34][2400/5005] lr: 1.0000e-01 eta: 1 day, 9:38:24 time: 0.2265 data_time: 0.0021 loss: 2.1103 03/05 13:15:45 - mmengine - INFO - Epoch(train) [34][2500/5005] lr: 1.0000e-01 eta: 1 day, 9:38:01 time: 0.2392 data_time: 0.0024 loss: 2.0247 03/05 13:16:07 - mmengine - INFO - Epoch(train) [34][2600/5005] lr: 1.0000e-01 eta: 1 day, 9:37:37 time: 0.2228 data_time: 0.0023 loss: 1.9638 03/05 13:16:30 - mmengine - INFO - Epoch(train) [34][2700/5005] lr: 1.0000e-01 eta: 1 day, 9:37:13 time: 0.2214 data_time: 0.0021 loss: 2.1644 03/05 13:16:52 - mmengine - INFO - Epoch(train) [34][2800/5005] lr: 1.0000e-01 eta: 1 day, 9:36:50 time: 0.2211 data_time: 0.0026 loss: 1.9102 03/05 13:17:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:17:15 - mmengine - INFO - Epoch(train) [34][2900/5005] lr: 1.0000e-01 eta: 1 day, 9:36:27 time: 0.2437 data_time: 0.0020 loss: 2.0946 03/05 13:17:37 - mmengine - INFO - Epoch(train) [34][3000/5005] lr: 1.0000e-01 eta: 1 day, 9:36:03 time: 0.2470 data_time: 0.0029 loss: 1.9420 03/05 13:18:00 - mmengine - INFO - Epoch(train) [34][3100/5005] lr: 1.0000e-01 eta: 1 day, 9:35:39 time: 0.2210 data_time: 0.0024 loss: 2.2034 03/05 13:18:23 - mmengine - INFO - Epoch(train) [34][3200/5005] lr: 1.0000e-01 eta: 1 day, 9:35:17 time: 0.2316 data_time: 0.0023 loss: 2.2198 03/05 13:18:45 - mmengine - INFO - Epoch(train) [34][3300/5005] lr: 1.0000e-01 eta: 1 day, 9:34:53 time: 0.2227 data_time: 0.0022 loss: 2.2006 03/05 13:19:08 - mmengine - INFO - Epoch(train) [34][3400/5005] lr: 1.0000e-01 eta: 1 day, 9:34:30 time: 0.2219 data_time: 0.0024 loss: 2.0116 03/05 13:19:30 - mmengine - INFO - Epoch(train) [34][3500/5005] lr: 1.0000e-01 eta: 1 day, 9:34:07 time: 0.2267 data_time: 0.0024 loss: 2.0954 03/05 13:19:53 - mmengine - INFO - Epoch(train) [34][3600/5005] lr: 1.0000e-01 eta: 1 day, 9:33:45 time: 0.2397 data_time: 0.0023 loss: 2.1125 03/05 13:20:16 - mmengine - INFO - Epoch(train) [34][3700/5005] lr: 1.0000e-01 eta: 1 day, 9:33:21 time: 0.2209 data_time: 0.0025 loss: 2.3275 03/05 13:20:39 - mmengine - INFO - Epoch(train) [34][3800/5005] lr: 1.0000e-01 eta: 1 day, 9:32:58 time: 0.2262 data_time: 0.0024 loss: 2.2165 03/05 13:20:46 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:21:01 - mmengine - INFO - Epoch(train) [34][3900/5005] lr: 1.0000e-01 eta: 1 day, 9:32:34 time: 0.2233 data_time: 0.0023 loss: 2.1231 03/05 13:21:24 - mmengine - INFO - Epoch(train) [34][4000/5005] lr: 1.0000e-01 eta: 1 day, 9:32:12 time: 0.2310 data_time: 0.0023 loss: 1.8926 03/05 13:21:47 - mmengine - INFO - Epoch(train) [34][4100/5005] lr: 1.0000e-01 eta: 1 day, 9:31:49 time: 0.2251 data_time: 0.0024 loss: 2.1642 03/05 13:22:09 - mmengine - INFO - Epoch(train) [34][4200/5005] lr: 1.0000e-01 eta: 1 day, 9:31:25 time: 0.2272 data_time: 0.0027 loss: 1.9025 03/05 13:22:31 - mmengine - INFO - Epoch(train) [34][4300/5005] lr: 1.0000e-01 eta: 1 day, 9:31:01 time: 0.2214 data_time: 0.0024 loss: 2.0882 03/05 13:22:54 - mmengine - INFO - Epoch(train) [34][4400/5005] lr: 1.0000e-01 eta: 1 day, 9:30:38 time: 0.2310 data_time: 0.0022 loss: 2.0505 03/05 13:23:17 - mmengine - INFO - Epoch(train) [34][4500/5005] lr: 1.0000e-01 eta: 1 day, 9:30:15 time: 0.2250 data_time: 0.0024 loss: 2.1625 03/05 13:23:39 - mmengine - INFO - Epoch(train) [34][4600/5005] lr: 1.0000e-01 eta: 1 day, 9:29:51 time: 0.2212 data_time: 0.0022 loss: 2.1519 03/05 13:24:02 - mmengine - INFO - Epoch(train) [34][4700/5005] lr: 1.0000e-01 eta: 1 day, 9:29:27 time: 0.2222 data_time: 0.0022 loss: 1.9534 03/05 13:24:24 - mmengine - INFO - Epoch(train) [34][4800/5005] lr: 1.0000e-01 eta: 1 day, 9:29:04 time: 0.2213 data_time: 0.0026 loss: 2.0584 03/05 13:24:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:24:48 - mmengine - INFO - Epoch(train) [34][4900/5005] lr: 1.0000e-01 eta: 1 day, 9:28:44 time: 0.2847 data_time: 0.0019 loss: 2.0415 03/05 13:25:17 - mmengine - INFO - Epoch(train) [34][5000/5005] lr: 1.0000e-01 eta: 1 day, 9:28:42 time: 0.2907 data_time: 0.0020 loss: 2.2269 03/05 13:25:19 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:25:21 - mmengine - INFO - Saving checkpoint at 34 epochs 03/05 13:25:36 - mmengine - INFO - Epoch(val) [34][100/196] eta: 0:00:12 time: 0.0183 data_time: 0.0004 03/05 13:25:49 - mmengine - INFO - Epoch(val) [34][196/196] accuracy/top1: 56.4340 accuracy/top5: 81.2200 03/05 13:26:21 - mmengine - INFO - Epoch(train) [35][ 100/5005] lr: 1.0000e-01 eta: 1 day, 9:28:46 time: 0.2496 data_time: 0.0023 loss: 1.9177 03/05 13:26:43 - mmengine - INFO - Epoch(train) [35][ 200/5005] lr: 1.0000e-01 eta: 1 day, 9:28:22 time: 0.2218 data_time: 0.0025 loss: 2.0126 03/05 13:27:06 - mmengine - INFO - Epoch(train) [35][ 300/5005] lr: 1.0000e-01 eta: 1 day, 9:27:58 time: 0.2231 data_time: 0.0029 loss: 2.0231 03/05 13:27:28 - mmengine - INFO - Epoch(train) [35][ 400/5005] lr: 1.0000e-01 eta: 1 day, 9:27:35 time: 0.2202 data_time: 0.0023 loss: 2.1705 03/05 13:27:52 - mmengine - INFO - Epoch(train) [35][ 500/5005] lr: 1.0000e-01 eta: 1 day, 9:27:13 time: 0.2328 data_time: 0.0024 loss: 1.9814 03/05 13:28:14 - mmengine - INFO - Epoch(train) [35][ 600/5005] lr: 1.0000e-01 eta: 1 day, 9:26:49 time: 0.2218 data_time: 0.0025 loss: 2.0428 03/05 13:28:36 - mmengine - INFO - Epoch(train) [35][ 700/5005] lr: 1.0000e-01 eta: 1 day, 9:26:25 time: 0.2196 data_time: 0.0022 loss: 2.3804 03/05 13:28:59 - mmengine - INFO - Epoch(train) [35][ 800/5005] lr: 1.0000e-01 eta: 1 day, 9:26:02 time: 0.2174 data_time: 0.0022 loss: 2.1834 03/05 13:29:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:29:22 - mmengine - INFO - Epoch(train) [35][ 900/5005] lr: 1.0000e-01 eta: 1 day, 9:25:39 time: 0.2499 data_time: 0.0021 loss: 2.0556 03/05 13:29:44 - mmengine - INFO - Epoch(train) [35][1000/5005] lr: 1.0000e-01 eta: 1 day, 9:25:16 time: 0.2218 data_time: 0.0022 loss: 2.2189 03/05 13:30:06 - mmengine - INFO - Epoch(train) [35][1100/5005] lr: 1.0000e-01 eta: 1 day, 9:24:51 time: 0.2191 data_time: 0.0024 loss: 1.9620 03/05 13:30:29 - mmengine - INFO - Epoch(train) [35][1200/5005] lr: 1.0000e-01 eta: 1 day, 9:24:28 time: 0.2211 data_time: 0.0025 loss: 2.0259 03/05 13:30:52 - mmengine - INFO - Epoch(train) [35][1300/5005] lr: 1.0000e-01 eta: 1 day, 9:24:06 time: 0.2379 data_time: 0.0023 loss: 1.9930 03/05 13:31:14 - mmengine - INFO - Epoch(train) [35][1400/5005] lr: 1.0000e-01 eta: 1 day, 9:23:42 time: 0.2223 data_time: 0.0022 loss: 2.1684 03/05 13:31:37 - mmengine - INFO - Epoch(train) [35][1500/5005] lr: 1.0000e-01 eta: 1 day, 9:23:18 time: 0.2191 data_time: 0.0024 loss: 1.9251 03/05 13:31:59 - mmengine - INFO - Epoch(train) [35][1600/5005] lr: 1.0000e-01 eta: 1 day, 9:22:55 time: 0.2235 data_time: 0.0022 loss: 1.9997 03/05 13:32:22 - mmengine - INFO - Epoch(train) [35][1700/5005] lr: 1.0000e-01 eta: 1 day, 9:22:31 time: 0.2222 data_time: 0.0023 loss: 2.3115 03/05 13:32:45 - mmengine - INFO - Epoch(train) [35][1800/5005] lr: 1.0000e-01 eta: 1 day, 9:22:08 time: 0.2220 data_time: 0.0020 loss: 2.0413 03/05 13:32:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:33:07 - mmengine - INFO - Epoch(train) [35][1900/5005] lr: 1.0000e-01 eta: 1 day, 9:21:44 time: 0.2210 data_time: 0.0021 loss: 1.9306 03/05 13:33:30 - mmengine - INFO - Epoch(train) [35][2000/5005] lr: 1.0000e-01 eta: 1 day, 9:21:21 time: 0.2237 data_time: 0.0021 loss: 2.0515 03/05 13:33:52 - mmengine - INFO - Epoch(train) [35][2100/5005] lr: 1.0000e-01 eta: 1 day, 9:20:58 time: 0.2240 data_time: 0.0021 loss: 2.0157 03/05 13:34:15 - mmengine - INFO - Epoch(train) [35][2200/5005] lr: 1.0000e-01 eta: 1 day, 9:20:35 time: 0.2176 data_time: 0.0024 loss: 2.1440 03/05 13:34:38 - mmengine - INFO - Epoch(train) [35][2300/5005] lr: 1.0000e-01 eta: 1 day, 9:20:12 time: 0.2238 data_time: 0.0021 loss: 2.1648 03/05 13:35:00 - mmengine - INFO - Epoch(train) [35][2400/5005] lr: 1.0000e-01 eta: 1 day, 9:19:49 time: 0.2212 data_time: 0.0025 loss: 1.9773 03/05 13:35:23 - mmengine - INFO - Epoch(train) [35][2500/5005] lr: 1.0000e-01 eta: 1 day, 9:19:26 time: 0.2217 data_time: 0.0029 loss: 1.8477 03/05 13:35:46 - mmengine - INFO - Epoch(train) [35][2600/5005] lr: 1.0000e-01 eta: 1 day, 9:19:03 time: 0.2207 data_time: 0.0023 loss: 2.0431 03/05 13:36:08 - mmengine - INFO - Epoch(train) [35][2700/5005] lr: 1.0000e-01 eta: 1 day, 9:18:39 time: 0.2232 data_time: 0.0024 loss: 2.0080 03/05 13:36:31 - mmengine - INFO - Epoch(train) [35][2800/5005] lr: 1.0000e-01 eta: 1 day, 9:18:15 time: 0.2328 data_time: 0.0025 loss: 1.8190 03/05 13:36:38 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:36:53 - mmengine - INFO - Epoch(train) [35][2900/5005] lr: 1.0000e-01 eta: 1 day, 9:17:52 time: 0.2217 data_time: 0.0021 loss: 2.0542 03/05 13:37:16 - mmengine - INFO - Epoch(train) [35][3000/5005] lr: 1.0000e-01 eta: 1 day, 9:17:29 time: 0.2443 data_time: 0.0023 loss: 2.1108 03/05 13:37:38 - mmengine - INFO - Epoch(train) [35][3100/5005] lr: 1.0000e-01 eta: 1 day, 9:17:05 time: 0.2198 data_time: 0.0026 loss: 2.0653 03/05 13:38:01 - mmengine - INFO - Epoch(train) [35][3200/5005] lr: 1.0000e-01 eta: 1 day, 9:16:41 time: 0.2221 data_time: 0.0023 loss: 2.1439 03/05 13:38:24 - mmengine - INFO - Epoch(train) [35][3300/5005] lr: 1.0000e-01 eta: 1 day, 9:16:19 time: 0.2202 data_time: 0.0023 loss: 1.9239 03/05 13:38:46 - mmengine - INFO - Epoch(train) [35][3400/5005] lr: 1.0000e-01 eta: 1 day, 9:15:56 time: 0.2198 data_time: 0.0024 loss: 2.2367 03/05 13:39:09 - mmengine - INFO - Epoch(train) [35][3500/5005] lr: 1.0000e-01 eta: 1 day, 9:15:32 time: 0.2206 data_time: 0.0023 loss: 2.0992 03/05 13:39:31 - mmengine - INFO - Epoch(train) [35][3600/5005] lr: 1.0000e-01 eta: 1 day, 9:15:08 time: 0.2248 data_time: 0.0023 loss: 2.1208 03/05 13:39:54 - mmengine - INFO - Epoch(train) [35][3700/5005] lr: 1.0000e-01 eta: 1 day, 9:14:45 time: 0.2232 data_time: 0.0025 loss: 1.9138 03/05 13:40:17 - mmengine - INFO - Epoch(train) [35][3800/5005] lr: 1.0000e-01 eta: 1 day, 9:14:23 time: 0.2261 data_time: 0.0023 loss: 2.0606 03/05 13:40:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:40:39 - mmengine - INFO - Epoch(train) [35][3900/5005] lr: 1.0000e-01 eta: 1 day, 9:13:59 time: 0.2285 data_time: 0.0021 loss: 2.0379 03/05 13:41:02 - mmengine - INFO - Epoch(train) [35][4000/5005] lr: 1.0000e-01 eta: 1 day, 9:13:36 time: 0.2197 data_time: 0.0025 loss: 2.0534 03/05 13:41:25 - mmengine - INFO - Epoch(train) [35][4100/5005] lr: 1.0000e-01 eta: 1 day, 9:13:13 time: 0.2290 data_time: 0.0024 loss: 2.0801 03/05 13:41:48 - mmengine - INFO - Epoch(train) [35][4200/5005] lr: 1.0000e-01 eta: 1 day, 9:12:51 time: 0.2255 data_time: 0.0022 loss: 2.1189 03/05 13:42:10 - mmengine - INFO - Epoch(train) [35][4300/5005] lr: 1.0000e-01 eta: 1 day, 9:12:27 time: 0.2283 data_time: 0.0027 loss: 2.2652 03/05 13:42:33 - mmengine - INFO - Epoch(train) [35][4400/5005] lr: 1.0000e-01 eta: 1 day, 9:12:04 time: 0.2248 data_time: 0.0029 loss: 2.1587 03/05 13:42:55 - mmengine - INFO - Epoch(train) [35][4500/5005] lr: 1.0000e-01 eta: 1 day, 9:11:41 time: 0.2406 data_time: 0.0022 loss: 2.0010 03/05 13:43:18 - mmengine - INFO - Epoch(train) [35][4600/5005] lr: 1.0000e-01 eta: 1 day, 9:11:19 time: 0.2264 data_time: 0.0023 loss: 2.0010 03/05 13:43:41 - mmengine - INFO - Epoch(train) [35][4700/5005] lr: 1.0000e-01 eta: 1 day, 9:10:56 time: 0.2462 data_time: 0.0026 loss: 2.1655 03/05 13:44:04 - mmengine - INFO - Epoch(train) [35][4800/5005] lr: 1.0000e-01 eta: 1 day, 9:10:32 time: 0.2205 data_time: 0.0024 loss: 2.2179 03/05 13:44:10 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:44:27 - mmengine - INFO - Epoch(train) [35][4900/5005] lr: 1.0000e-01 eta: 1 day, 9:10:12 time: 0.2896 data_time: 0.0019 loss: 2.2943 03/05 13:44:56 - mmengine - INFO - Epoch(train) [35][5000/5005] lr: 1.0000e-01 eta: 1 day, 9:10:06 time: 0.2824 data_time: 0.0022 loss: 2.2147 03/05 13:44:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:45:00 - mmengine - INFO - Saving checkpoint at 35 epochs 03/05 13:45:15 - mmengine - INFO - Epoch(val) [35][100/196] eta: 0:00:13 time: 0.0207 data_time: 0.0003 03/05 13:45:29 - mmengine - INFO - Epoch(val) [35][196/196] accuracy/top1: 54.2100 accuracy/top5: 79.3340 03/05 13:46:00 - mmengine - INFO - Epoch(train) [36][ 100/5005] lr: 1.0000e-01 eta: 1 day, 9:10:10 time: 0.2913 data_time: 0.0024 loss: 1.9311 03/05 13:46:23 - mmengine - INFO - Epoch(train) [36][ 200/5005] lr: 1.0000e-01 eta: 1 day, 9:09:46 time: 0.2218 data_time: 0.0023 loss: 2.0551 03/05 13:46:45 - mmengine - INFO - Epoch(train) [36][ 300/5005] lr: 1.0000e-01 eta: 1 day, 9:09:23 time: 0.2206 data_time: 0.0025 loss: 1.8901 03/05 13:47:08 - mmengine - INFO - Epoch(train) [36][ 400/5005] lr: 1.0000e-01 eta: 1 day, 9:08:59 time: 0.2236 data_time: 0.0029 loss: 1.8035 03/05 13:47:30 - mmengine - INFO - Epoch(train) [36][ 500/5005] lr: 1.0000e-01 eta: 1 day, 9:08:35 time: 0.2281 data_time: 0.0024 loss: 1.8552 03/05 13:47:53 - mmengine - INFO - Epoch(train) [36][ 600/5005] lr: 1.0000e-01 eta: 1 day, 9:08:13 time: 0.2280 data_time: 0.0027 loss: 2.1435 03/05 13:48:16 - mmengine - INFO - Epoch(train) [36][ 700/5005] lr: 1.0000e-01 eta: 1 day, 9:07:50 time: 0.2218 data_time: 0.0026 loss: 2.0013 03/05 13:48:38 - mmengine - INFO - Epoch(train) [36][ 800/5005] lr: 1.0000e-01 eta: 1 day, 9:07:26 time: 0.2224 data_time: 0.0024 loss: 2.1754 03/05 13:48:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:49:01 - mmengine - INFO - Epoch(train) [36][ 900/5005] lr: 1.0000e-01 eta: 1 day, 9:07:02 time: 0.2206 data_time: 0.0024 loss: 2.0836 03/05 13:49:24 - mmengine - INFO - Epoch(train) [36][1000/5005] lr: 1.0000e-01 eta: 1 day, 9:06:41 time: 0.2291 data_time: 0.0023 loss: 1.8794 03/05 13:49:46 - mmengine - INFO - Epoch(train) [36][1100/5005] lr: 1.0000e-01 eta: 1 day, 9:06:17 time: 0.2240 data_time: 0.0024 loss: 2.1007 03/05 13:50:09 - mmengine - INFO - Epoch(train) [36][1200/5005] lr: 1.0000e-01 eta: 1 day, 9:05:54 time: 0.2249 data_time: 0.0024 loss: 2.2304 03/05 13:50:31 - mmengine - INFO - Epoch(train) [36][1300/5005] lr: 1.0000e-01 eta: 1 day, 9:05:29 time: 0.2224 data_time: 0.0026 loss: 2.1673 03/05 13:50:54 - mmengine - INFO - Epoch(train) [36][1400/5005] lr: 1.0000e-01 eta: 1 day, 9:05:07 time: 0.2246 data_time: 0.0022 loss: 1.8991 03/05 13:51:17 - mmengine - INFO - Epoch(train) [36][1500/5005] lr: 1.0000e-01 eta: 1 day, 9:04:44 time: 0.2203 data_time: 0.0027 loss: 2.0683 03/05 13:51:40 - mmengine - INFO - Epoch(train) [36][1600/5005] lr: 1.0000e-01 eta: 1 day, 9:04:21 time: 0.2206 data_time: 0.0025 loss: 1.7678 03/05 13:52:02 - mmengine - INFO - Epoch(train) [36][1700/5005] lr: 1.0000e-01 eta: 1 day, 9:03:57 time: 0.2197 data_time: 0.0025 loss: 2.0365 03/05 13:52:25 - mmengine - INFO - Epoch(train) [36][1800/5005] lr: 1.0000e-01 eta: 1 day, 9:03:34 time: 0.2274 data_time: 0.0022 loss: 2.0533 03/05 13:52:31 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:52:48 - mmengine - INFO - Epoch(train) [36][1900/5005] lr: 1.0000e-01 eta: 1 day, 9:03:11 time: 0.2353 data_time: 0.0025 loss: 2.3163 03/05 13:53:10 - mmengine - INFO - Epoch(train) [36][2000/5005] lr: 1.0000e-01 eta: 1 day, 9:02:48 time: 0.2264 data_time: 0.0024 loss: 2.0078 03/05 13:53:33 - mmengine - INFO - Epoch(train) [36][2100/5005] lr: 1.0000e-01 eta: 1 day, 9:02:24 time: 0.2230 data_time: 0.0023 loss: 2.0010 03/05 13:53:55 - mmengine - INFO - Epoch(train) [36][2200/5005] lr: 1.0000e-01 eta: 1 day, 9:02:01 time: 0.2318 data_time: 0.0022 loss: 2.2136 03/05 13:54:18 - mmengine - INFO - Epoch(train) [36][2300/5005] lr: 1.0000e-01 eta: 1 day, 9:01:38 time: 0.2413 data_time: 0.0022 loss: 2.1721 03/05 13:54:41 - mmengine - INFO - Epoch(train) [36][2400/5005] lr: 1.0000e-01 eta: 1 day, 9:01:15 time: 0.2195 data_time: 0.0021 loss: 2.1033 03/05 13:55:03 - mmengine - INFO - Epoch(train) [36][2500/5005] lr: 1.0000e-01 eta: 1 day, 9:00:51 time: 0.2328 data_time: 0.0028 loss: 2.0071 03/05 13:55:26 - mmengine - INFO - Epoch(train) [36][2600/5005] lr: 1.0000e-01 eta: 1 day, 9:00:28 time: 0.2315 data_time: 0.0023 loss: 1.8807 03/05 13:55:48 - mmengine - INFO - Epoch(train) [36][2700/5005] lr: 1.0000e-01 eta: 1 day, 9:00:04 time: 0.2211 data_time: 0.0021 loss: 2.0484 03/05 13:56:11 - mmengine - INFO - Epoch(train) [36][2800/5005] lr: 1.0000e-01 eta: 1 day, 8:59:41 time: 0.2244 data_time: 0.0024 loss: 2.2050 03/05 13:56:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 13:56:33 - mmengine - INFO - Epoch(train) [36][2900/5005] lr: 1.0000e-01 eta: 1 day, 8:59:18 time: 0.2229 data_time: 0.0022 loss: 2.1344 03/05 13:56:56 - mmengine - INFO - Epoch(train) [36][3000/5005] lr: 1.0000e-01 eta: 1 day, 8:58:55 time: 0.2402 data_time: 0.0021 loss: 2.2701 03/05 13:57:19 - mmengine - INFO - Epoch(train) [36][3100/5005] lr: 1.0000e-01 eta: 1 day, 8:58:31 time: 0.2205 data_time: 0.0025 loss: 2.2268 03/05 13:57:41 - mmengine - INFO - Epoch(train) [36][3200/5005] lr: 1.0000e-01 eta: 1 day, 8:58:09 time: 0.2380 data_time: 0.0025 loss: 1.9682 03/05 13:58:04 - mmengine - INFO - Epoch(train) [36][3300/5005] lr: 1.0000e-01 eta: 1 day, 8:57:45 time: 0.2410 data_time: 0.0025 loss: 1.9123 03/05 13:58:26 - mmengine - INFO - Epoch(train) [36][3400/5005] lr: 1.0000e-01 eta: 1 day, 8:57:22 time: 0.2316 data_time: 0.0022 loss: 2.0198 03/05 13:58:49 - mmengine - INFO - Epoch(train) [36][3500/5005] lr: 1.0000e-01 eta: 1 day, 8:56:59 time: 0.2218 data_time: 0.0023 loss: 2.2945 03/05 13:59:12 - mmengine - INFO - Epoch(train) [36][3600/5005] lr: 1.0000e-01 eta: 1 day, 8:56:35 time: 0.2256 data_time: 0.0023 loss: 2.2898 03/05 13:59:34 - mmengine - INFO - Epoch(train) [36][3700/5005] lr: 1.0000e-01 eta: 1 day, 8:56:12 time: 0.2219 data_time: 0.0024 loss: 2.0387 03/05 13:59:57 - mmengine - INFO - Epoch(train) [36][3800/5005] lr: 1.0000e-01 eta: 1 day, 8:55:50 time: 0.2213 data_time: 0.0023 loss: 2.0787 03/05 14:00:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:00:20 - mmengine - INFO - Epoch(train) [36][3900/5005] lr: 1.0000e-01 eta: 1 day, 8:55:26 time: 0.2231 data_time: 0.0024 loss: 2.1748 03/05 14:00:42 - mmengine - INFO - Epoch(train) [36][4000/5005] lr: 1.0000e-01 eta: 1 day, 8:55:03 time: 0.2249 data_time: 0.0023 loss: 1.8684 03/05 14:01:05 - mmengine - INFO - Epoch(train) [36][4100/5005] lr: 1.0000e-01 eta: 1 day, 8:54:39 time: 0.2244 data_time: 0.0024 loss: 2.0210 03/05 14:01:28 - mmengine - INFO - Epoch(train) [36][4200/5005] lr: 1.0000e-01 eta: 1 day, 8:54:16 time: 0.2424 data_time: 0.0022 loss: 2.1150 03/05 14:01:50 - mmengine - INFO - Epoch(train) [36][4300/5005] lr: 1.0000e-01 eta: 1 day, 8:53:53 time: 0.2208 data_time: 0.0022 loss: 2.1580 03/05 14:02:13 - mmengine - INFO - Epoch(train) [36][4400/5005] lr: 1.0000e-01 eta: 1 day, 8:53:29 time: 0.2277 data_time: 0.0027 loss: 2.1678 03/05 14:02:35 - mmengine - INFO - Epoch(train) [36][4500/5005] lr: 1.0000e-01 eta: 1 day, 8:53:06 time: 0.2403 data_time: 0.0021 loss: 1.9382 03/05 14:02:58 - mmengine - INFO - Epoch(train) [36][4600/5005] lr: 1.0000e-01 eta: 1 day, 8:52:43 time: 0.2256 data_time: 0.0022 loss: 2.1507 03/05 14:03:20 - mmengine - INFO - Epoch(train) [36][4700/5005] lr: 1.0000e-01 eta: 1 day, 8:52:19 time: 0.2256 data_time: 0.0022 loss: 1.8715 03/05 14:03:43 - mmengine - INFO - Epoch(train) [36][4800/5005] lr: 1.0000e-01 eta: 1 day, 8:51:55 time: 0.2239 data_time: 0.0025 loss: 1.9861 03/05 14:03:49 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:04:06 - mmengine - INFO - Epoch(train) [36][4900/5005] lr: 1.0000e-01 eta: 1 day, 8:51:35 time: 0.2854 data_time: 0.0022 loss: 2.2150 03/05 14:04:35 - mmengine - INFO - Epoch(train) [36][5000/5005] lr: 1.0000e-01 eta: 1 day, 8:51:28 time: 0.2801 data_time: 0.0020 loss: 2.2938 03/05 14:04:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:04:39 - mmengine - INFO - Saving checkpoint at 36 epochs 03/05 14:04:53 - mmengine - INFO - Epoch(val) [36][100/196] eta: 0:00:12 time: 0.0224 data_time: 0.0004 03/05 14:05:07 - mmengine - INFO - Epoch(val) [36][196/196] accuracy/top1: 55.8360 accuracy/top5: 80.7260 03/05 14:05:38 - mmengine - INFO - Epoch(train) [37][ 100/5005] lr: 1.0000e-01 eta: 1 day, 8:51:29 time: 0.2228 data_time: 0.0027 loss: 2.1315 03/05 14:06:00 - mmengine - INFO - Epoch(train) [37][ 200/5005] lr: 1.0000e-01 eta: 1 day, 8:51:05 time: 0.2273 data_time: 0.0023 loss: 1.8972 03/05 14:06:23 - mmengine - INFO - Epoch(train) [37][ 300/5005] lr: 1.0000e-01 eta: 1 day, 8:50:42 time: 0.2215 data_time: 0.0024 loss: 1.8755 03/05 14:06:45 - mmengine - INFO - Epoch(train) [37][ 400/5005] lr: 1.0000e-01 eta: 1 day, 8:50:18 time: 0.2212 data_time: 0.0023 loss: 2.0172 03/05 14:07:08 - mmengine - INFO - Epoch(train) [37][ 500/5005] lr: 1.0000e-01 eta: 1 day, 8:49:55 time: 0.2265 data_time: 0.0026 loss: 2.0906 03/05 14:07:31 - mmengine - INFO - Epoch(train) [37][ 600/5005] lr: 1.0000e-01 eta: 1 day, 8:49:31 time: 0.2249 data_time: 0.0024 loss: 1.8186 03/05 14:07:53 - mmengine - INFO - Epoch(train) [37][ 700/5005] lr: 1.0000e-01 eta: 1 day, 8:49:08 time: 0.2193 data_time: 0.0023 loss: 2.2442 03/05 14:08:16 - mmengine - INFO - Epoch(train) [37][ 800/5005] lr: 1.0000e-01 eta: 1 day, 8:48:45 time: 0.2433 data_time: 0.0024 loss: 1.9490 03/05 14:08:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:08:39 - mmengine - INFO - Epoch(train) [37][ 900/5005] lr: 1.0000e-01 eta: 1 day, 8:48:23 time: 0.2737 data_time: 0.0022 loss: 1.9709 03/05 14:09:01 - mmengine - INFO - Epoch(train) [37][1000/5005] lr: 1.0000e-01 eta: 1 day, 8:47:59 time: 0.2193 data_time: 0.0025 loss: 1.9073 03/05 14:09:24 - mmengine - INFO - Epoch(train) [37][1100/5005] lr: 1.0000e-01 eta: 1 day, 8:47:37 time: 0.2210 data_time: 0.0023 loss: 1.9637 03/05 14:09:47 - mmengine - INFO - Epoch(train) [37][1200/5005] lr: 1.0000e-01 eta: 1 day, 8:47:13 time: 0.2218 data_time: 0.0022 loss: 2.0238 03/05 14:10:09 - mmengine - INFO - Epoch(train) [37][1300/5005] lr: 1.0000e-01 eta: 1 day, 8:46:49 time: 0.2266 data_time: 0.0021 loss: 2.2400 03/05 14:10:32 - mmengine - INFO - Epoch(train) [37][1400/5005] lr: 1.0000e-01 eta: 1 day, 8:46:26 time: 0.2227 data_time: 0.0021 loss: 2.2637 03/05 14:10:54 - mmengine - INFO - Epoch(train) [37][1500/5005] lr: 1.0000e-01 eta: 1 day, 8:46:03 time: 0.2362 data_time: 0.0027 loss: 2.1716 03/05 14:11:17 - mmengine - INFO - Epoch(train) [37][1600/5005] lr: 1.0000e-01 eta: 1 day, 8:45:39 time: 0.2204 data_time: 0.0024 loss: 2.0833 03/05 14:11:40 - mmengine - INFO - Epoch(train) [37][1700/5005] lr: 1.0000e-01 eta: 1 day, 8:45:16 time: 0.2239 data_time: 0.0022 loss: 2.0579 03/05 14:12:02 - mmengine - INFO - Epoch(train) [37][1800/5005] lr: 1.0000e-01 eta: 1 day, 8:44:53 time: 0.2235 data_time: 0.0024 loss: 2.1078 03/05 14:12:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:12:25 - mmengine - INFO - Epoch(train) [37][1900/5005] lr: 1.0000e-01 eta: 1 day, 8:44:29 time: 0.2179 data_time: 0.0023 loss: 2.0805 03/05 14:12:47 - mmengine - INFO - Epoch(train) [37][2000/5005] lr: 1.0000e-01 eta: 1 day, 8:44:06 time: 0.2243 data_time: 0.0025 loss: 2.0820 03/05 14:13:10 - mmengine - INFO - Epoch(train) [37][2100/5005] lr: 1.0000e-01 eta: 1 day, 8:43:42 time: 0.2220 data_time: 0.0021 loss: 2.0395 03/05 14:13:32 - mmengine - INFO - Epoch(train) [37][2200/5005] lr: 1.0000e-01 eta: 1 day, 8:43:19 time: 0.2205 data_time: 0.0020 loss: 2.1202 03/05 14:13:55 - mmengine - INFO - Epoch(train) [37][2300/5005] lr: 1.0000e-01 eta: 1 day, 8:42:55 time: 0.2201 data_time: 0.0022 loss: 2.1570 03/05 14:14:18 - mmengine - INFO - Epoch(train) [37][2400/5005] lr: 1.0000e-01 eta: 1 day, 8:42:32 time: 0.2210 data_time: 0.0023 loss: 2.1937 03/05 14:14:40 - mmengine - INFO - Epoch(train) [37][2500/5005] lr: 1.0000e-01 eta: 1 day, 8:42:09 time: 0.2198 data_time: 0.0023 loss: 2.1488 03/05 14:15:03 - mmengine - INFO - Epoch(train) [37][2600/5005] lr: 1.0000e-01 eta: 1 day, 8:41:45 time: 0.2211 data_time: 0.0025 loss: 2.1301 03/05 14:15:25 - mmengine - INFO - Epoch(train) [37][2700/5005] lr: 1.0000e-01 eta: 1 day, 8:41:22 time: 0.2224 data_time: 0.0023 loss: 2.1148 03/05 14:15:48 - mmengine - INFO - Epoch(train) [37][2800/5005] lr: 1.0000e-01 eta: 1 day, 8:40:58 time: 0.2184 data_time: 0.0026 loss: 2.1902 03/05 14:15:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:16:10 - mmengine - INFO - Epoch(train) [37][2900/5005] lr: 1.0000e-01 eta: 1 day, 8:40:35 time: 0.2413 data_time: 0.0029 loss: 1.8504 03/05 14:16:33 - mmengine - INFO - Epoch(train) [37][3000/5005] lr: 1.0000e-01 eta: 1 day, 8:40:12 time: 0.2223 data_time: 0.0022 loss: 2.1669 03/05 14:16:55 - mmengine - INFO - Epoch(train) [37][3100/5005] lr: 1.0000e-01 eta: 1 day, 8:39:48 time: 0.2233 data_time: 0.0026 loss: 2.0433 03/05 14:17:18 - mmengine - INFO - Epoch(train) [37][3200/5005] lr: 1.0000e-01 eta: 1 day, 8:39:25 time: 0.2267 data_time: 0.0025 loss: 2.2837 03/05 14:17:41 - mmengine - INFO - Epoch(train) [37][3300/5005] lr: 1.0000e-01 eta: 1 day, 8:39:02 time: 0.2209 data_time: 0.0026 loss: 2.2809 03/05 14:18:03 - mmengine - INFO - Epoch(train) [37][3400/5005] lr: 1.0000e-01 eta: 1 day, 8:38:39 time: 0.2240 data_time: 0.0024 loss: 2.0849 03/05 14:18:26 - mmengine - INFO - Epoch(train) [37][3500/5005] lr: 1.0000e-01 eta: 1 day, 8:38:16 time: 0.2235 data_time: 0.0025 loss: 2.1009 03/05 14:18:49 - mmengine - INFO - Epoch(train) [37][3600/5005] lr: 1.0000e-01 eta: 1 day, 8:37:53 time: 0.2227 data_time: 0.0022 loss: 2.1165 03/05 14:19:11 - mmengine - INFO - Epoch(train) [37][3700/5005] lr: 1.0000e-01 eta: 1 day, 8:37:29 time: 0.2337 data_time: 0.0026 loss: 2.3564 03/05 14:19:34 - mmengine - INFO - Epoch(train) [37][3800/5005] lr: 1.0000e-01 eta: 1 day, 8:37:06 time: 0.2213 data_time: 0.0022 loss: 2.2529 03/05 14:19:38 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:19:57 - mmengine - INFO - Epoch(train) [37][3900/5005] lr: 1.0000e-01 eta: 1 day, 8:36:43 time: 0.2477 data_time: 0.0022 loss: 2.2588 03/05 14:20:19 - mmengine - INFO - Epoch(train) [37][4000/5005] lr: 1.0000e-01 eta: 1 day, 8:36:19 time: 0.2199 data_time: 0.0025 loss: 2.1173 03/05 14:20:42 - mmengine - INFO - Epoch(train) [37][4100/5005] lr: 1.0000e-01 eta: 1 day, 8:35:57 time: 0.2214 data_time: 0.0021 loss: 2.1047 03/05 14:21:05 - mmengine - INFO - Epoch(train) [37][4200/5005] lr: 1.0000e-01 eta: 1 day, 8:35:34 time: 0.2224 data_time: 0.0026 loss: 2.1133 03/05 14:21:27 - mmengine - INFO - Epoch(train) [37][4300/5005] lr: 1.0000e-01 eta: 1 day, 8:35:11 time: 0.2237 data_time: 0.0026 loss: 2.2926 03/05 14:21:50 - mmengine - INFO - Epoch(train) [37][4400/5005] lr: 1.0000e-01 eta: 1 day, 8:34:47 time: 0.2217 data_time: 0.0023 loss: 2.2657 03/05 14:22:12 - mmengine - INFO - Epoch(train) [37][4500/5005] lr: 1.0000e-01 eta: 1 day, 8:34:23 time: 0.2209 data_time: 0.0025 loss: 2.2707 03/05 14:22:35 - mmengine - INFO - Epoch(train) [37][4600/5005] lr: 1.0000e-01 eta: 1 day, 8:34:01 time: 0.2224 data_time: 0.0023 loss: 2.1499 03/05 14:22:58 - mmengine - INFO - Epoch(train) [37][4700/5005] lr: 1.0000e-01 eta: 1 day, 8:33:38 time: 0.2423 data_time: 0.0021 loss: 1.9819 03/05 14:23:20 - mmengine - INFO - Epoch(train) [37][4800/5005] lr: 1.0000e-01 eta: 1 day, 8:33:14 time: 0.2218 data_time: 0.0021 loss: 2.3012 03/05 14:23:25 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:23:44 - mmengine - INFO - Epoch(train) [37][4900/5005] lr: 1.0000e-01 eta: 1 day, 8:32:54 time: 0.2918 data_time: 0.0019 loss: 2.1495 03/05 14:24:12 - mmengine - INFO - Epoch(train) [37][5000/5005] lr: 1.0000e-01 eta: 1 day, 8:32:47 time: 0.2770 data_time: 0.0021 loss: 2.0838 03/05 14:24:14 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:24:17 - mmengine - INFO - Saving checkpoint at 37 epochs 03/05 14:24:30 - mmengine - INFO - Epoch(val) [37][100/196] eta: 0:00:11 time: 0.0188 data_time: 0.0003 03/05 14:24:43 - mmengine - INFO - Epoch(val) [37][196/196] accuracy/top1: 55.5360 accuracy/top5: 80.3100 03/05 14:25:14 - mmengine - INFO - Epoch(train) [38][ 100/5005] lr: 1.0000e-01 eta: 1 day, 8:32:46 time: 0.2331 data_time: 0.0025 loss: 2.1500 03/05 14:25:37 - mmengine - INFO - Epoch(train) [38][ 200/5005] lr: 1.0000e-01 eta: 1 day, 8:32:23 time: 0.2220 data_time: 0.0027 loss: 2.2064 03/05 14:25:59 - mmengine - INFO - Epoch(train) [38][ 300/5005] lr: 1.0000e-01 eta: 1 day, 8:31:59 time: 0.2242 data_time: 0.0026 loss: 2.0886 03/05 14:26:22 - mmengine - INFO - Epoch(train) [38][ 400/5005] lr: 1.0000e-01 eta: 1 day, 8:31:37 time: 0.2450 data_time: 0.0027 loss: 2.0533 03/05 14:26:45 - mmengine - INFO - Epoch(train) [38][ 500/5005] lr: 1.0000e-01 eta: 1 day, 8:31:13 time: 0.2217 data_time: 0.0024 loss: 2.1257 03/05 14:27:07 - mmengine - INFO - Epoch(train) [38][ 600/5005] lr: 1.0000e-01 eta: 1 day, 8:30:50 time: 0.2219 data_time: 0.0021 loss: 2.1717 03/05 14:27:30 - mmengine - INFO - Epoch(train) [38][ 700/5005] lr: 1.0000e-01 eta: 1 day, 8:30:26 time: 0.2188 data_time: 0.0022 loss: 2.0590 03/05 14:27:52 - mmengine - INFO - Epoch(train) [38][ 800/5005] lr: 1.0000e-01 eta: 1 day, 8:30:03 time: 0.2224 data_time: 0.0025 loss: 1.6708 03/05 14:27:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:28:15 - mmengine - INFO - Epoch(train) [38][ 900/5005] lr: 1.0000e-01 eta: 1 day, 8:29:40 time: 0.2226 data_time: 0.0024 loss: 2.1912 03/05 14:28:38 - mmengine - INFO - Epoch(train) [38][1000/5005] lr: 1.0000e-01 eta: 1 day, 8:29:16 time: 0.2214 data_time: 0.0023 loss: 2.1172 03/05 14:29:00 - mmengine - INFO - Epoch(train) [38][1100/5005] lr: 1.0000e-01 eta: 1 day, 8:28:53 time: 0.2216 data_time: 0.0022 loss: 2.1006 03/05 14:29:22 - mmengine - INFO - Epoch(train) [38][1200/5005] lr: 1.0000e-01 eta: 1 day, 8:28:29 time: 0.2218 data_time: 0.0025 loss: 2.1890 03/05 14:29:45 - mmengine - INFO - Epoch(train) [38][1300/5005] lr: 1.0000e-01 eta: 1 day, 8:28:06 time: 0.2288 data_time: 0.0025 loss: 2.0032 03/05 14:30:08 - mmengine - INFO - Epoch(train) [38][1400/5005] lr: 1.0000e-01 eta: 1 day, 8:27:43 time: 0.2479 data_time: 0.0024 loss: 1.9280 03/05 14:30:30 - mmengine - INFO - Epoch(train) [38][1500/5005] lr: 1.0000e-01 eta: 1 day, 8:27:19 time: 0.2207 data_time: 0.0025 loss: 1.8376 03/05 14:30:53 - mmengine - INFO - Epoch(train) [38][1600/5005] lr: 1.0000e-01 eta: 1 day, 8:26:55 time: 0.2266 data_time: 0.0025 loss: 1.9938 03/05 14:31:15 - mmengine - INFO - Epoch(train) [38][1700/5005] lr: 1.0000e-01 eta: 1 day, 8:26:32 time: 0.2223 data_time: 0.0022 loss: 2.2149 03/05 14:31:38 - mmengine - INFO - Epoch(train) [38][1800/5005] lr: 1.0000e-01 eta: 1 day, 8:26:10 time: 0.2381 data_time: 0.0025 loss: 2.0602 03/05 14:31:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:32:01 - mmengine - INFO - Epoch(train) [38][1900/5005] lr: 1.0000e-01 eta: 1 day, 8:25:46 time: 0.2260 data_time: 0.0022 loss: 2.0875 03/05 14:32:23 - mmengine - INFO - Epoch(train) [38][2000/5005] lr: 1.0000e-01 eta: 1 day, 8:25:22 time: 0.2228 data_time: 0.0024 loss: 2.0405 03/05 14:32:46 - mmengine - INFO - Epoch(train) [38][2100/5005] lr: 1.0000e-01 eta: 1 day, 8:24:59 time: 0.2252 data_time: 0.0025 loss: 2.1399 03/05 14:33:09 - mmengine - INFO - Epoch(train) [38][2200/5005] lr: 1.0000e-01 eta: 1 day, 8:24:36 time: 0.2209 data_time: 0.0021 loss: 2.3237 03/05 14:33:31 - mmengine - INFO - Epoch(train) [38][2300/5005] lr: 1.0000e-01 eta: 1 day, 8:24:13 time: 0.2250 data_time: 0.0022 loss: 2.1442 03/05 14:33:54 - mmengine - INFO - Epoch(train) [38][2400/5005] lr: 1.0000e-01 eta: 1 day, 8:23:49 time: 0.2247 data_time: 0.0023 loss: 2.1164 03/05 14:34:16 - mmengine - INFO - Epoch(train) [38][2500/5005] lr: 1.0000e-01 eta: 1 day, 8:23:27 time: 0.2383 data_time: 0.0024 loss: 2.0207 03/05 14:34:39 - mmengine - INFO - Epoch(train) [38][2600/5005] lr: 1.0000e-01 eta: 1 day, 8:23:04 time: 0.2341 data_time: 0.0024 loss: 2.1698 03/05 14:35:02 - mmengine - INFO - Epoch(train) [38][2700/5005] lr: 1.0000e-01 eta: 1 day, 8:22:41 time: 0.2221 data_time: 0.0025 loss: 1.9272 03/05 14:35:24 - mmengine - INFO - Epoch(train) [38][2800/5005] lr: 1.0000e-01 eta: 1 day, 8:22:17 time: 0.2254 data_time: 0.0023 loss: 2.1113 03/05 14:35:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:35:47 - mmengine - INFO - Epoch(train) [38][2900/5005] lr: 1.0000e-01 eta: 1 day, 8:21:54 time: 0.2238 data_time: 0.0021 loss: 2.0949 03/05 14:36:09 - mmengine - INFO - Epoch(train) [38][3000/5005] lr: 1.0000e-01 eta: 1 day, 8:21:30 time: 0.2237 data_time: 0.0024 loss: 2.0630 03/05 14:36:32 - mmengine - INFO - Epoch(train) [38][3100/5005] lr: 1.0000e-01 eta: 1 day, 8:21:07 time: 0.2185 data_time: 0.0023 loss: 2.0198 03/05 14:36:54 - mmengine - INFO - Epoch(train) [38][3200/5005] lr: 1.0000e-01 eta: 1 day, 8:20:43 time: 0.2225 data_time: 0.0022 loss: 2.1936 03/05 14:37:17 - mmengine - INFO - Epoch(train) [38][3300/5005] lr: 1.0000e-01 eta: 1 day, 8:20:20 time: 0.2249 data_time: 0.0021 loss: 1.9490 03/05 14:37:40 - mmengine - INFO - Epoch(train) [38][3400/5005] lr: 1.0000e-01 eta: 1 day, 8:19:57 time: 0.2262 data_time: 0.0025 loss: 2.0203 03/05 14:38:03 - mmengine - INFO - Epoch(train) [38][3500/5005] lr: 1.0000e-01 eta: 1 day, 8:19:34 time: 0.2393 data_time: 0.0023 loss: 2.1685 03/05 14:38:25 - mmengine - INFO - Epoch(train) [38][3600/5005] lr: 1.0000e-01 eta: 1 day, 8:19:10 time: 0.2241 data_time: 0.0026 loss: 2.2363 03/05 14:38:48 - mmengine - INFO - Epoch(train) [38][3700/5005] lr: 1.0000e-01 eta: 1 day, 8:18:47 time: 0.2348 data_time: 0.0023 loss: 2.1772 03/05 14:39:11 - mmengine - INFO - Epoch(train) [38][3800/5005] lr: 1.0000e-01 eta: 1 day, 8:18:25 time: 0.2221 data_time: 0.0024 loss: 2.0922 03/05 14:39:14 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:39:33 - mmengine - INFO - Epoch(train) [38][3900/5005] lr: 1.0000e-01 eta: 1 day, 8:18:01 time: 0.2213 data_time: 0.0021 loss: 2.1602 03/05 14:39:56 - mmengine - INFO - Epoch(train) [38][4000/5005] lr: 1.0000e-01 eta: 1 day, 8:17:38 time: 0.2232 data_time: 0.0024 loss: 2.0285 03/05 14:40:18 - mmengine - INFO - Epoch(train) [38][4100/5005] lr: 1.0000e-01 eta: 1 day, 8:17:14 time: 0.2206 data_time: 0.0021 loss: 1.9429 03/05 14:40:41 - mmengine - INFO - Epoch(train) [38][4200/5005] lr: 1.0000e-01 eta: 1 day, 8:16:51 time: 0.2258 data_time: 0.0023 loss: 2.1301 03/05 14:41:04 - mmengine - INFO - Epoch(train) [38][4300/5005] lr: 1.0000e-01 eta: 1 day, 8:16:28 time: 0.2245 data_time: 0.0022 loss: 2.0491 03/05 14:41:26 - mmengine - INFO - Epoch(train) [38][4400/5005] lr: 1.0000e-01 eta: 1 day, 8:16:05 time: 0.2251 data_time: 0.0026 loss: 1.9156 03/05 14:41:48 - mmengine - INFO - Epoch(train) [38][4500/5005] lr: 1.0000e-01 eta: 1 day, 8:15:41 time: 0.2268 data_time: 0.0024 loss: 2.0822 03/05 14:42:11 - mmengine - INFO - Epoch(train) [38][4600/5005] lr: 1.0000e-01 eta: 1 day, 8:15:18 time: 0.2423 data_time: 0.0022 loss: 2.1310 03/05 14:42:34 - mmengine - INFO - Epoch(train) [38][4700/5005] lr: 1.0000e-01 eta: 1 day, 8:14:55 time: 0.2437 data_time: 0.0022 loss: 2.0313 03/05 14:42:56 - mmengine - INFO - Epoch(train) [38][4800/5005] lr: 1.0000e-01 eta: 1 day, 8:14:31 time: 0.2251 data_time: 0.0022 loss: 2.1385 03/05 14:43:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:43:20 - mmengine - INFO - Epoch(train) [38][4900/5005] lr: 1.0000e-01 eta: 1 day, 8:14:11 time: 0.2817 data_time: 0.0022 loss: 1.8883 03/05 14:43:49 - mmengine - INFO - Epoch(train) [38][5000/5005] lr: 1.0000e-01 eta: 1 day, 8:14:04 time: 0.2842 data_time: 0.0020 loss: 1.9845 03/05 14:43:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:43:53 - mmengine - INFO - Saving checkpoint at 38 epochs 03/05 14:44:08 - mmengine - INFO - Epoch(val) [38][100/196] eta: 0:00:13 time: 0.0198 data_time: 0.0003 03/05 14:44:21 - mmengine - INFO - Epoch(val) [38][196/196] accuracy/top1: 57.3780 accuracy/top5: 82.1740 03/05 14:44:52 - mmengine - INFO - Epoch(train) [39][ 100/5005] lr: 1.0000e-01 eta: 1 day, 8:14:02 time: 0.2289 data_time: 0.0027 loss: 2.2294 03/05 14:45:15 - mmengine - INFO - Epoch(train) [39][ 200/5005] lr: 1.0000e-01 eta: 1 day, 8:13:41 time: 0.2242 data_time: 0.0027 loss: 2.1224 03/05 14:45:38 - mmengine - INFO - Epoch(train) [39][ 300/5005] lr: 1.0000e-01 eta: 1 day, 8:13:18 time: 0.2203 data_time: 0.0029 loss: 2.1042 03/05 14:46:00 - mmengine - INFO - Epoch(train) [39][ 400/5005] lr: 1.0000e-01 eta: 1 day, 8:12:53 time: 0.2199 data_time: 0.0027 loss: 1.9583 03/05 14:46:23 - mmengine - INFO - Epoch(train) [39][ 500/5005] lr: 1.0000e-01 eta: 1 day, 8:12:31 time: 0.2221 data_time: 0.0023 loss: 2.1667 03/05 14:46:46 - mmengine - INFO - Epoch(train) [39][ 600/5005] lr: 1.0000e-01 eta: 1 day, 8:12:08 time: 0.2253 data_time: 0.0025 loss: 1.9641 03/05 14:47:08 - mmengine - INFO - Epoch(train) [39][ 700/5005] lr: 1.0000e-01 eta: 1 day, 8:11:44 time: 0.2249 data_time: 0.0026 loss: 1.9575 03/05 14:47:31 - mmengine - INFO - Epoch(train) [39][ 800/5005] lr: 1.0000e-01 eta: 1 day, 8:11:22 time: 0.2243 data_time: 0.0026 loss: 2.0435 03/05 14:47:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:47:54 - mmengine - INFO - Epoch(train) [39][ 900/5005] lr: 1.0000e-01 eta: 1 day, 8:10:58 time: 0.2205 data_time: 0.0024 loss: 2.0713 03/05 14:48:16 - mmengine - INFO - Epoch(train) [39][1000/5005] lr: 1.0000e-01 eta: 1 day, 8:10:35 time: 0.2207 data_time: 0.0027 loss: 2.0546 03/05 14:48:39 - mmengine - INFO - Epoch(train) [39][1100/5005] lr: 1.0000e-01 eta: 1 day, 8:10:12 time: 0.2227 data_time: 0.0024 loss: 2.1135 03/05 14:49:02 - mmengine - INFO - Epoch(train) [39][1200/5005] lr: 1.0000e-01 eta: 1 day, 8:09:49 time: 0.2233 data_time: 0.0024 loss: 2.0626 03/05 14:49:24 - mmengine - INFO - Epoch(train) [39][1300/5005] lr: 1.0000e-01 eta: 1 day, 8:09:26 time: 0.2199 data_time: 0.0025 loss: 1.8814 03/05 14:49:47 - mmengine - INFO - Epoch(train) [39][1400/5005] lr: 1.0000e-01 eta: 1 day, 8:09:02 time: 0.2236 data_time: 0.0022 loss: 2.1784 03/05 14:50:10 - mmengine - INFO - Epoch(train) [39][1500/5005] lr: 1.0000e-01 eta: 1 day, 8:08:39 time: 0.2208 data_time: 0.0026 loss: 1.8608 03/05 14:50:32 - mmengine - INFO - Epoch(train) [39][1600/5005] lr: 1.0000e-01 eta: 1 day, 8:08:16 time: 0.2268 data_time: 0.0022 loss: 2.0809 03/05 14:50:55 - mmengine - INFO - Epoch(train) [39][1700/5005] lr: 1.0000e-01 eta: 1 day, 8:07:52 time: 0.2190 data_time: 0.0029 loss: 2.0126 03/05 14:51:18 - mmengine - INFO - Epoch(train) [39][1800/5005] lr: 1.0000e-01 eta: 1 day, 8:07:29 time: 0.2222 data_time: 0.0023 loss: 2.1071 03/05 14:51:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:51:40 - mmengine - INFO - Epoch(train) [39][1900/5005] lr: 1.0000e-01 eta: 1 day, 8:07:05 time: 0.2235 data_time: 0.0024 loss: 2.2744 03/05 14:52:03 - mmengine - INFO - Epoch(train) [39][2000/5005] lr: 1.0000e-01 eta: 1 day, 8:06:42 time: 0.2209 data_time: 0.0023 loss: 2.0303 03/05 14:52:25 - mmengine - INFO - Epoch(train) [39][2100/5005] lr: 1.0000e-01 eta: 1 day, 8:06:18 time: 0.2203 data_time: 0.0024 loss: 2.1251 03/05 14:52:48 - mmengine - INFO - Epoch(train) [39][2200/5005] lr: 1.0000e-01 eta: 1 day, 8:05:55 time: 0.2243 data_time: 0.0026 loss: 2.0244 03/05 14:53:10 - mmengine - INFO - Epoch(train) [39][2300/5005] lr: 1.0000e-01 eta: 1 day, 8:05:32 time: 0.2198 data_time: 0.0023 loss: 1.8348 03/05 14:53:33 - mmengine - INFO - Epoch(train) [39][2400/5005] lr: 1.0000e-01 eta: 1 day, 8:05:09 time: 0.2198 data_time: 0.0023 loss: 2.1405 03/05 14:53:56 - mmengine - INFO - Epoch(train) [39][2500/5005] lr: 1.0000e-01 eta: 1 day, 8:04:45 time: 0.2233 data_time: 0.0024 loss: 2.0818 03/05 14:54:18 - mmengine - INFO - Epoch(train) [39][2600/5005] lr: 1.0000e-01 eta: 1 day, 8:04:22 time: 0.2248 data_time: 0.0025 loss: 2.0180 03/05 14:54:41 - mmengine - INFO - Epoch(train) [39][2700/5005] lr: 1.0000e-01 eta: 1 day, 8:03:59 time: 0.2229 data_time: 0.0024 loss: 2.1369 03/05 14:55:04 - mmengine - INFO - Epoch(train) [39][2800/5005] lr: 1.0000e-01 eta: 1 day, 8:03:36 time: 0.2267 data_time: 0.0023 loss: 1.8802 03/05 14:55:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:55:26 - mmengine - INFO - Epoch(train) [39][2900/5005] lr: 1.0000e-01 eta: 1 day, 8:03:12 time: 0.2225 data_time: 0.0025 loss: 2.0095 03/05 14:55:49 - mmengine - INFO - Epoch(train) [39][3000/5005] lr: 1.0000e-01 eta: 1 day, 8:02:49 time: 0.2213 data_time: 0.0025 loss: 2.1201 03/05 14:56:11 - mmengine - INFO - Epoch(train) [39][3100/5005] lr: 1.0000e-01 eta: 1 day, 8:02:25 time: 0.2276 data_time: 0.0022 loss: 2.0382 03/05 14:56:34 - mmengine - INFO - Epoch(train) [39][3200/5005] lr: 1.0000e-01 eta: 1 day, 8:02:01 time: 0.2263 data_time: 0.0024 loss: 2.0293 03/05 14:56:56 - mmengine - INFO - Epoch(train) [39][3300/5005] lr: 1.0000e-01 eta: 1 day, 8:01:38 time: 0.2201 data_time: 0.0025 loss: 1.9857 03/05 14:57:19 - mmengine - INFO - Epoch(train) [39][3400/5005] lr: 1.0000e-01 eta: 1 day, 8:01:16 time: 0.2266 data_time: 0.0022 loss: 2.1732 03/05 14:57:41 - mmengine - INFO - Epoch(train) [39][3500/5005] lr: 1.0000e-01 eta: 1 day, 8:00:52 time: 0.2220 data_time: 0.0023 loss: 1.8961 03/05 14:58:04 - mmengine - INFO - Epoch(train) [39][3600/5005] lr: 1.0000e-01 eta: 1 day, 8:00:28 time: 0.2198 data_time: 0.0023 loss: 2.2450 03/05 14:58:26 - mmengine - INFO - Epoch(train) [39][3700/5005] lr: 1.0000e-01 eta: 1 day, 8:00:05 time: 0.2213 data_time: 0.0023 loss: 2.3611 03/05 14:58:49 - mmengine - INFO - Epoch(train) [39][3800/5005] lr: 1.0000e-01 eta: 1 day, 7:59:42 time: 0.2412 data_time: 0.0023 loss: 2.2086 03/05 14:58:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 14:59:12 - mmengine - INFO - Epoch(train) [39][3900/5005] lr: 1.0000e-01 eta: 1 day, 7:59:19 time: 0.2251 data_time: 0.0025 loss: 2.0688 03/05 14:59:35 - mmengine - INFO - Epoch(train) [39][4000/5005] lr: 1.0000e-01 eta: 1 day, 7:58:56 time: 0.2244 data_time: 0.0023 loss: 2.2281 03/05 14:59:57 - mmengine - INFO - Epoch(train) [39][4100/5005] lr: 1.0000e-01 eta: 1 day, 7:58:33 time: 0.2221 data_time: 0.0024 loss: 2.0382 03/05 15:00:20 - mmengine - INFO - Epoch(train) [39][4200/5005] lr: 1.0000e-01 eta: 1 day, 7:58:10 time: 0.2254 data_time: 0.0026 loss: 2.0713 03/05 15:00:43 - mmengine - INFO - Epoch(train) [39][4300/5005] lr: 1.0000e-01 eta: 1 day, 7:57:47 time: 0.2216 data_time: 0.0022 loss: 2.0551 03/05 15:01:05 - mmengine - INFO - Epoch(train) [39][4400/5005] lr: 1.0000e-01 eta: 1 day, 7:57:23 time: 0.2220 data_time: 0.0024 loss: 2.1765 03/05 15:01:28 - mmengine - INFO - Epoch(train) [39][4500/5005] lr: 1.0000e-01 eta: 1 day, 7:57:00 time: 0.2227 data_time: 0.0026 loss: 2.1475 03/05 15:01:50 - mmengine - INFO - Epoch(train) [39][4600/5005] lr: 1.0000e-01 eta: 1 day, 7:56:36 time: 0.2208 data_time: 0.0023 loss: 1.9259 03/05 15:02:13 - mmengine - INFO - Epoch(train) [39][4700/5005] lr: 1.0000e-01 eta: 1 day, 7:56:14 time: 0.2351 data_time: 0.0030 loss: 1.9655 03/05 15:02:36 - mmengine - INFO - Epoch(train) [39][4800/5005] lr: 1.0000e-01 eta: 1 day, 7:55:50 time: 0.2212 data_time: 0.0026 loss: 2.0301 03/05 15:02:38 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:03:00 - mmengine - INFO - Epoch(train) [39][4900/5005] lr: 1.0000e-01 eta: 1 day, 7:55:30 time: 0.2965 data_time: 0.0022 loss: 1.9413 03/05 15:03:28 - mmengine - INFO - Epoch(train) [39][5000/5005] lr: 1.0000e-01 eta: 1 day, 7:55:23 time: 0.2915 data_time: 0.0025 loss: 2.1197 03/05 15:03:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:03:33 - mmengine - INFO - Saving checkpoint at 39 epochs 03/05 15:03:47 - mmengine - INFO - Epoch(val) [39][100/196] eta: 0:00:12 time: 0.0211 data_time: 0.0003 03/05 15:04:00 - mmengine - INFO - Epoch(val) [39][196/196] accuracy/top1: 55.5220 accuracy/top5: 80.8320 03/05 15:04:32 - mmengine - INFO - Epoch(train) [40][ 100/5005] lr: 1.0000e-01 eta: 1 day, 7:55:22 time: 0.2457 data_time: 0.0027 loss: 2.1192 03/05 15:04:54 - mmengine - INFO - Epoch(train) [40][ 200/5005] lr: 1.0000e-01 eta: 1 day, 7:54:58 time: 0.2211 data_time: 0.0028 loss: 2.0807 03/05 15:05:18 - mmengine - INFO - Epoch(train) [40][ 300/5005] lr: 1.0000e-01 eta: 1 day, 7:54:36 time: 0.2528 data_time: 0.0027 loss: 2.0316 03/05 15:05:40 - mmengine - INFO - Epoch(train) [40][ 400/5005] lr: 1.0000e-01 eta: 1 day, 7:54:13 time: 0.2269 data_time: 0.0022 loss: 2.0423 03/05 15:06:03 - mmengine - INFO - Epoch(train) [40][ 500/5005] lr: 1.0000e-01 eta: 1 day, 7:53:50 time: 0.2199 data_time: 0.0023 loss: 2.1702 03/05 15:06:25 - mmengine - INFO - Epoch(train) [40][ 600/5005] lr: 1.0000e-01 eta: 1 day, 7:53:26 time: 0.2264 data_time: 0.0026 loss: 2.0512 03/05 15:06:48 - mmengine - INFO - Epoch(train) [40][ 700/5005] lr: 1.0000e-01 eta: 1 day, 7:53:03 time: 0.2202 data_time: 0.0022 loss: 1.8922 03/05 15:07:10 - mmengine - INFO - Epoch(train) [40][ 800/5005] lr: 1.0000e-01 eta: 1 day, 7:52:40 time: 0.2222 data_time: 0.0025 loss: 2.0372 03/05 15:07:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:07:33 - mmengine - INFO - Epoch(train) [40][ 900/5005] lr: 1.0000e-01 eta: 1 day, 7:52:16 time: 0.2208 data_time: 0.0023 loss: 1.8944 03/05 15:07:55 - mmengine - INFO - Epoch(train) [40][1000/5005] lr: 1.0000e-01 eta: 1 day, 7:51:53 time: 0.2214 data_time: 0.0022 loss: 2.0232 03/05 15:08:18 - mmengine - INFO - Epoch(train) [40][1100/5005] lr: 1.0000e-01 eta: 1 day, 7:51:29 time: 0.2238 data_time: 0.0026 loss: 2.1740 03/05 15:08:41 - mmengine - INFO - Epoch(train) [40][1200/5005] lr: 1.0000e-01 eta: 1 day, 7:51:06 time: 0.2223 data_time: 0.0025 loss: 1.9527 03/05 15:09:03 - mmengine - INFO - Epoch(train) [40][1300/5005] lr: 1.0000e-01 eta: 1 day, 7:50:42 time: 0.2276 data_time: 0.0025 loss: 2.1387 03/05 15:09:26 - mmengine - INFO - Epoch(train) [40][1400/5005] lr: 1.0000e-01 eta: 1 day, 7:50:19 time: 0.2216 data_time: 0.0024 loss: 2.2981 03/05 15:09:48 - mmengine - INFO - Epoch(train) [40][1500/5005] lr: 1.0000e-01 eta: 1 day, 7:49:56 time: 0.2220 data_time: 0.0023 loss: 1.8817 03/05 15:10:11 - mmengine - INFO - Epoch(train) [40][1600/5005] lr: 1.0000e-01 eta: 1 day, 7:49:32 time: 0.2225 data_time: 0.0023 loss: 2.1869 03/05 15:10:33 - mmengine - INFO - Epoch(train) [40][1700/5005] lr: 1.0000e-01 eta: 1 day, 7:49:09 time: 0.2230 data_time: 0.0024 loss: 2.0702 03/05 15:10:56 - mmengine - INFO - Epoch(train) [40][1800/5005] lr: 1.0000e-01 eta: 1 day, 7:48:45 time: 0.2249 data_time: 0.0021 loss: 2.0338 03/05 15:10:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:11:19 - mmengine - INFO - Epoch(train) [40][1900/5005] lr: 1.0000e-01 eta: 1 day, 7:48:23 time: 0.2245 data_time: 0.0023 loss: 2.1931 03/05 15:11:41 - mmengine - INFO - Epoch(train) [40][2000/5005] lr: 1.0000e-01 eta: 1 day, 7:47:59 time: 0.2235 data_time: 0.0023 loss: 2.0797 03/05 15:12:04 - mmengine - INFO - Epoch(train) [40][2100/5005] lr: 1.0000e-01 eta: 1 day, 7:47:36 time: 0.2240 data_time: 0.0025 loss: 2.0277 03/05 15:12:26 - mmengine - INFO - Epoch(train) [40][2200/5005] lr: 1.0000e-01 eta: 1 day, 7:47:12 time: 0.2219 data_time: 0.0024 loss: 2.0859 03/05 15:12:49 - mmengine - INFO - Epoch(train) [40][2300/5005] lr: 1.0000e-01 eta: 1 day, 7:46:49 time: 0.2200 data_time: 0.0023 loss: 1.9860 03/05 15:13:12 - mmengine - INFO - Epoch(train) [40][2400/5005] lr: 1.0000e-01 eta: 1 day, 7:46:26 time: 0.2251 data_time: 0.0024 loss: 2.0040 03/05 15:13:34 - mmengine - INFO - Epoch(train) [40][2500/5005] lr: 1.0000e-01 eta: 1 day, 7:46:03 time: 0.2413 data_time: 0.0024 loss: 2.0017 03/05 15:13:57 - mmengine - INFO - Epoch(train) [40][2600/5005] lr: 1.0000e-01 eta: 1 day, 7:45:40 time: 0.2429 data_time: 0.0026 loss: 2.0238 03/05 15:14:20 - mmengine - INFO - Epoch(train) [40][2700/5005] lr: 1.0000e-01 eta: 1 day, 7:45:17 time: 0.2393 data_time: 0.0023 loss: 1.9725 03/05 15:14:42 - mmengine - INFO - Epoch(train) [40][2800/5005] lr: 1.0000e-01 eta: 1 day, 7:44:54 time: 0.2220 data_time: 0.0025 loss: 2.0320 03/05 15:14:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:15:05 - mmengine - INFO - Epoch(train) [40][2900/5005] lr: 1.0000e-01 eta: 1 day, 7:44:30 time: 0.2209 data_time: 0.0024 loss: 2.2276 03/05 15:15:27 - mmengine - INFO - Epoch(train) [40][3000/5005] lr: 1.0000e-01 eta: 1 day, 7:44:07 time: 0.2245 data_time: 0.0025 loss: 1.8836 03/05 15:15:50 - mmengine - INFO - Epoch(train) [40][3100/5005] lr: 1.0000e-01 eta: 1 day, 7:43:45 time: 0.2253 data_time: 0.0027 loss: 2.2935 03/05 15:16:13 - mmengine - INFO - Epoch(train) [40][3200/5005] lr: 1.0000e-01 eta: 1 day, 7:43:21 time: 0.2217 data_time: 0.0023 loss: 2.0543 03/05 15:16:35 - mmengine - INFO - Epoch(train) [40][3300/5005] lr: 1.0000e-01 eta: 1 day, 7:42:57 time: 0.2264 data_time: 0.0029 loss: 2.0790 03/05 15:16:58 - mmengine - INFO - Epoch(train) [40][3400/5005] lr: 1.0000e-01 eta: 1 day, 7:42:33 time: 0.2216 data_time: 0.0026 loss: 2.0218 03/05 15:17:21 - mmengine - INFO - Epoch(train) [40][3500/5005] lr: 1.0000e-01 eta: 1 day, 7:42:12 time: 0.2506 data_time: 0.0023 loss: 2.1883 03/05 15:17:43 - mmengine - INFO - Epoch(train) [40][3600/5005] lr: 1.0000e-01 eta: 1 day, 7:41:48 time: 0.2247 data_time: 0.0025 loss: 1.9159 03/05 15:18:06 - mmengine - INFO - Epoch(train) [40][3700/5005] lr: 1.0000e-01 eta: 1 day, 7:41:24 time: 0.2226 data_time: 0.0026 loss: 1.9323 03/05 15:18:28 - mmengine - INFO - Epoch(train) [40][3800/5005] lr: 1.0000e-01 eta: 1 day, 7:41:01 time: 0.2233 data_time: 0.0025 loss: 2.0307 03/05 15:18:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:18:51 - mmengine - INFO - Epoch(train) [40][3900/5005] lr: 1.0000e-01 eta: 1 day, 7:40:38 time: 0.2206 data_time: 0.0026 loss: 2.0131 03/05 15:19:14 - mmengine - INFO - Epoch(train) [40][4000/5005] lr: 1.0000e-01 eta: 1 day, 7:40:14 time: 0.2225 data_time: 0.0024 loss: 2.1066 03/05 15:19:36 - mmengine - INFO - Epoch(train) [40][4100/5005] lr: 1.0000e-01 eta: 1 day, 7:39:51 time: 0.2274 data_time: 0.0023 loss: 2.0262 03/05 15:19:59 - mmengine - INFO - Epoch(train) [40][4200/5005] lr: 1.0000e-01 eta: 1 day, 7:39:27 time: 0.2188 data_time: 0.0023 loss: 2.0162 03/05 15:20:21 - mmengine - INFO - Epoch(train) [40][4300/5005] lr: 1.0000e-01 eta: 1 day, 7:39:04 time: 0.2214 data_time: 0.0025 loss: 2.0245 03/05 15:20:44 - mmengine - INFO - Epoch(train) [40][4400/5005] lr: 1.0000e-01 eta: 1 day, 7:38:41 time: 0.2231 data_time: 0.0021 loss: 2.0030 03/05 15:21:07 - mmengine - INFO - Epoch(train) [40][4500/5005] lr: 1.0000e-01 eta: 1 day, 7:38:18 time: 0.2421 data_time: 0.0022 loss: 2.3400 03/05 15:21:29 - mmengine - INFO - Epoch(train) [40][4600/5005] lr: 1.0000e-01 eta: 1 day, 7:37:54 time: 0.2324 data_time: 0.0025 loss: 1.9514 03/05 15:21:52 - mmengine - INFO - Epoch(train) [40][4700/5005] lr: 1.0000e-01 eta: 1 day, 7:37:32 time: 0.2337 data_time: 0.0025 loss: 2.1115 03/05 15:22:14 - mmengine - INFO - Epoch(train) [40][4800/5005] lr: 1.0000e-01 eta: 1 day, 7:37:08 time: 0.2194 data_time: 0.0023 loss: 2.2459 03/05 15:22:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:22:38 - mmengine - INFO - Epoch(train) [40][4900/5005] lr: 1.0000e-01 eta: 1 day, 7:36:47 time: 0.2835 data_time: 0.0020 loss: 1.9820 03/05 15:23:07 - mmengine - INFO - Epoch(train) [40][5000/5005] lr: 1.0000e-01 eta: 1 day, 7:36:40 time: 0.2892 data_time: 0.0023 loss: 2.0247 03/05 15:23:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:23:11 - mmengine - INFO - Saving checkpoint at 40 epochs 03/05 15:23:26 - mmengine - INFO - Epoch(val) [40][100/196] eta: 0:00:13 time: 0.0192 data_time: 0.0004 03/05 15:23:39 - mmengine - INFO - Epoch(val) [40][196/196] accuracy/top1: 55.5880 accuracy/top5: 80.6540 03/05 15:24:11 - mmengine - INFO - Epoch(train) [41][ 100/5005] lr: 1.0000e-02 eta: 1 day, 7:36:38 time: 0.2259 data_time: 0.0026 loss: 1.8452 03/05 15:24:33 - mmengine - INFO - Epoch(train) [41][ 200/5005] lr: 1.0000e-02 eta: 1 day, 7:36:14 time: 0.2230 data_time: 0.0024 loss: 1.8916 03/05 15:24:56 - mmengine - INFO - Epoch(train) [41][ 300/5005] lr: 1.0000e-02 eta: 1 day, 7:35:51 time: 0.2230 data_time: 0.0023 loss: 1.5877 03/05 15:25:18 - mmengine - INFO - Epoch(train) [41][ 400/5005] lr: 1.0000e-02 eta: 1 day, 7:35:28 time: 0.2199 data_time: 0.0026 loss: 1.6662 03/05 15:25:41 - mmengine - INFO - Epoch(train) [41][ 500/5005] lr: 1.0000e-02 eta: 1 day, 7:35:05 time: 0.2350 data_time: 0.0025 loss: 1.5712 03/05 15:26:04 - mmengine - INFO - Epoch(train) [41][ 600/5005] lr: 1.0000e-02 eta: 1 day, 7:34:42 time: 0.2220 data_time: 0.0024 loss: 1.5074 03/05 15:26:27 - mmengine - INFO - Epoch(train) [41][ 700/5005] lr: 1.0000e-02 eta: 1 day, 7:34:20 time: 0.2221 data_time: 0.0023 loss: 1.5416 03/05 15:26:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:26:50 - mmengine - INFO - Epoch(train) [41][ 800/5005] lr: 1.0000e-02 eta: 1 day, 7:33:57 time: 0.2193 data_time: 0.0024 loss: 1.4321 03/05 15:27:12 - mmengine - INFO - Epoch(train) [41][ 900/5005] lr: 1.0000e-02 eta: 1 day, 7:33:34 time: 0.2449 data_time: 0.0022 loss: 1.8442 03/05 15:27:35 - mmengine - INFO - Epoch(train) [41][1000/5005] lr: 1.0000e-02 eta: 1 day, 7:33:11 time: 0.2236 data_time: 0.0025 loss: 1.6663 03/05 15:27:58 - mmengine - INFO - Epoch(train) [41][1100/5005] lr: 1.0000e-02 eta: 1 day, 7:32:49 time: 0.2273 data_time: 0.0024 loss: 1.5938 03/05 15:28:21 - mmengine - INFO - Epoch(train) [41][1200/5005] lr: 1.0000e-02 eta: 1 day, 7:32:25 time: 0.2244 data_time: 0.0028 loss: 1.6799 03/05 15:28:43 - mmengine - INFO - Epoch(train) [41][1300/5005] lr: 1.0000e-02 eta: 1 day, 7:32:02 time: 0.2233 data_time: 0.0024 loss: 1.6900 03/05 15:29:06 - mmengine - INFO - Epoch(train) [41][1400/5005] lr: 1.0000e-02 eta: 1 day, 7:31:38 time: 0.2240 data_time: 0.0027 loss: 1.6087 03/05 15:29:28 - mmengine - INFO - Epoch(train) [41][1500/5005] lr: 1.0000e-02 eta: 1 day, 7:31:15 time: 0.2217 data_time: 0.0025 loss: 1.7306 03/05 15:29:51 - mmengine - INFO - Epoch(train) [41][1600/5005] lr: 1.0000e-02 eta: 1 day, 7:30:53 time: 0.2268 data_time: 0.0024 loss: 1.6926 03/05 15:30:14 - mmengine - INFO - Epoch(train) [41][1700/5005] lr: 1.0000e-02 eta: 1 day, 7:30:29 time: 0.2202 data_time: 0.0022 loss: 1.5602 03/05 15:30:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:30:36 - mmengine - INFO - Epoch(train) [41][1800/5005] lr: 1.0000e-02 eta: 1 day, 7:30:05 time: 0.2223 data_time: 0.0023 loss: 1.6505 03/05 15:30:59 - mmengine - INFO - Epoch(train) [41][1900/5005] lr: 1.0000e-02 eta: 1 day, 7:29:42 time: 0.2249 data_time: 0.0024 loss: 1.5367 03/05 15:31:22 - mmengine - INFO - Epoch(train) [41][2000/5005] lr: 1.0000e-02 eta: 1 day, 7:29:19 time: 0.2242 data_time: 0.0027 loss: 1.5868 03/05 15:31:44 - mmengine - INFO - Epoch(train) [41][2100/5005] lr: 1.0000e-02 eta: 1 day, 7:28:56 time: 0.2208 data_time: 0.0025 loss: 1.4233 03/05 15:32:07 - mmengine - INFO - Epoch(train) [41][2200/5005] lr: 1.0000e-02 eta: 1 day, 7:28:33 time: 0.2289 data_time: 0.0023 loss: 1.4379 03/05 15:32:30 - mmengine - INFO - Epoch(train) [41][2300/5005] lr: 1.0000e-02 eta: 1 day, 7:28:10 time: 0.2311 data_time: 0.0028 loss: 1.5357 03/05 15:32:52 - mmengine - INFO - Epoch(train) [41][2400/5005] lr: 1.0000e-02 eta: 1 day, 7:27:47 time: 0.2239 data_time: 0.0025 loss: 1.5681 03/05 15:33:15 - mmengine - INFO - Epoch(train) [41][2500/5005] lr: 1.0000e-02 eta: 1 day, 7:27:24 time: 0.2215 data_time: 0.0025 loss: 1.6101 03/05 15:33:38 - mmengine - INFO - Epoch(train) [41][2600/5005] lr: 1.0000e-02 eta: 1 day, 7:27:01 time: 0.2269 data_time: 0.0024 loss: 1.6615 03/05 15:34:00 - mmengine - INFO - Epoch(train) [41][2700/5005] lr: 1.0000e-02 eta: 1 day, 7:26:37 time: 0.2261 data_time: 0.0028 loss: 1.4753 03/05 15:34:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:34:23 - mmengine - INFO - Epoch(train) [41][2800/5005] lr: 1.0000e-02 eta: 1 day, 7:26:14 time: 0.2295 data_time: 0.0024 loss: 1.3853 03/05 15:34:45 - mmengine - INFO - Epoch(train) [41][2900/5005] lr: 1.0000e-02 eta: 1 day, 7:25:51 time: 0.2219 data_time: 0.0022 loss: 1.5901 03/05 15:35:09 - mmengine - INFO - Epoch(train) [41][3000/5005] lr: 1.0000e-02 eta: 1 day, 7:25:28 time: 0.2407 data_time: 0.0023 loss: 1.4596 03/05 15:35:31 - mmengine - INFO - Epoch(train) [41][3100/5005] lr: 1.0000e-02 eta: 1 day, 7:25:05 time: 0.2217 data_time: 0.0022 loss: 1.5002 03/05 15:35:53 - mmengine - INFO - Epoch(train) [41][3200/5005] lr: 1.0000e-02 eta: 1 day, 7:24:41 time: 0.2209 data_time: 0.0024 loss: 1.8087 03/05 15:36:16 - mmengine - INFO - Epoch(train) [41][3300/5005] lr: 1.0000e-02 eta: 1 day, 7:24:18 time: 0.2263 data_time: 0.0025 loss: 1.5220 03/05 15:36:39 - mmengine - INFO - Epoch(train) [41][3400/5005] lr: 1.0000e-02 eta: 1 day, 7:23:56 time: 0.2240 data_time: 0.0024 loss: 1.5498 03/05 15:37:02 - mmengine - INFO - Epoch(train) [41][3500/5005] lr: 1.0000e-02 eta: 1 day, 7:23:32 time: 0.2223 data_time: 0.0024 loss: 1.5924 03/05 15:37:24 - mmengine - INFO - Epoch(train) [41][3600/5005] lr: 1.0000e-02 eta: 1 day, 7:23:09 time: 0.2238 data_time: 0.0026 loss: 1.5709 03/05 15:37:47 - mmengine - INFO - Epoch(train) [41][3700/5005] lr: 1.0000e-02 eta: 1 day, 7:22:46 time: 0.2248 data_time: 0.0027 loss: 1.2483 03/05 15:38:10 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:38:10 - mmengine - INFO - Epoch(train) [41][3800/5005] lr: 1.0000e-02 eta: 1 day, 7:22:23 time: 0.2236 data_time: 0.0024 loss: 1.5938 03/05 15:38:32 - mmengine - INFO - Epoch(train) [41][3900/5005] lr: 1.0000e-02 eta: 1 day, 7:22:00 time: 0.2221 data_time: 0.0027 loss: 1.3909 03/05 15:38:55 - mmengine - INFO - Epoch(train) [41][4000/5005] lr: 1.0000e-02 eta: 1 day, 7:21:36 time: 0.2222 data_time: 0.0026 loss: 1.4772 03/05 15:39:17 - mmengine - INFO - Epoch(train) [41][4100/5005] lr: 1.0000e-02 eta: 1 day, 7:21:13 time: 0.2286 data_time: 0.0026 loss: 1.5756 03/05 15:39:40 - mmengine - INFO - Epoch(train) [41][4200/5005] lr: 1.0000e-02 eta: 1 day, 7:20:50 time: 0.2233 data_time: 0.0023 loss: 1.2966 03/05 15:40:03 - mmengine - INFO - Epoch(train) [41][4300/5005] lr: 1.0000e-02 eta: 1 day, 7:20:27 time: 0.2247 data_time: 0.0027 loss: 1.4686 03/05 15:40:25 - mmengine - INFO - Epoch(train) [41][4400/5005] lr: 1.0000e-02 eta: 1 day, 7:20:03 time: 0.2230 data_time: 0.0022 loss: 1.5033 03/05 15:40:48 - mmengine - INFO - Epoch(train) [41][4500/5005] lr: 1.0000e-02 eta: 1 day, 7:19:39 time: 0.2230 data_time: 0.0026 loss: 1.5187 03/05 15:41:10 - mmengine - INFO - Epoch(train) [41][4600/5005] lr: 1.0000e-02 eta: 1 day, 7:19:16 time: 0.2237 data_time: 0.0024 loss: 1.3854 03/05 15:41:33 - mmengine - INFO - Epoch(train) [41][4700/5005] lr: 1.0000e-02 eta: 1 day, 7:18:54 time: 0.2309 data_time: 0.0023 loss: 1.6213 03/05 15:41:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:41:56 - mmengine - INFO - Epoch(train) [41][4800/5005] lr: 1.0000e-02 eta: 1 day, 7:18:30 time: 0.2215 data_time: 0.0023 loss: 1.5238 03/05 15:42:19 - mmengine - INFO - Epoch(train) [41][4900/5005] lr: 1.0000e-02 eta: 1 day, 7:18:09 time: 0.2875 data_time: 0.0022 loss: 1.5035 03/05 15:42:48 - mmengine - INFO - Epoch(train) [41][5000/5005] lr: 1.0000e-02 eta: 1 day, 7:18:01 time: 0.2934 data_time: 0.0021 loss: 1.4994 03/05 15:42:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:42:52 - mmengine - INFO - Saving checkpoint at 41 epochs 03/05 15:43:07 - mmengine - INFO - Epoch(val) [41][100/196] eta: 0:00:12 time: 0.0209 data_time: 0.0005 03/05 15:43:20 - mmengine - INFO - Epoch(val) [41][196/196] accuracy/top1: 70.5360 accuracy/top5: 90.1440 03/05 15:43:51 - mmengine - INFO - Epoch(train) [42][ 100/5005] lr: 1.0000e-02 eta: 1 day, 7:17:58 time: 0.2209 data_time: 0.0031 loss: 1.5771 03/05 15:44:14 - mmengine - INFO - Epoch(train) [42][ 200/5005] lr: 1.0000e-02 eta: 1 day, 7:17:36 time: 0.2203 data_time: 0.0024 loss: 1.2558 03/05 15:44:37 - mmengine - INFO - Epoch(train) [42][ 300/5005] lr: 1.0000e-02 eta: 1 day, 7:17:14 time: 0.2232 data_time: 0.0024 loss: 1.5159 03/05 15:45:00 - mmengine - INFO - Epoch(train) [42][ 400/5005] lr: 1.0000e-02 eta: 1 day, 7:16:50 time: 0.2219 data_time: 0.0024 loss: 1.3753 03/05 15:45:22 - mmengine - INFO - Epoch(train) [42][ 500/5005] lr: 1.0000e-02 eta: 1 day, 7:16:26 time: 0.2192 data_time: 0.0022 loss: 1.5546 03/05 15:45:44 - mmengine - INFO - Epoch(train) [42][ 600/5005] lr: 1.0000e-02 eta: 1 day, 7:16:02 time: 0.2231 data_time: 0.0025 loss: 1.3564 03/05 15:46:07 - mmengine - INFO - Epoch(train) [42][ 700/5005] lr: 1.0000e-02 eta: 1 day, 7:15:40 time: 0.2287 data_time: 0.0026 loss: 1.4041 03/05 15:46:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:46:30 - mmengine - INFO - Epoch(train) [42][ 800/5005] lr: 1.0000e-02 eta: 1 day, 7:15:17 time: 0.2234 data_time: 0.0024 loss: 1.3959 03/05 15:46:52 - mmengine - INFO - Epoch(train) [42][ 900/5005] lr: 1.0000e-02 eta: 1 day, 7:14:53 time: 0.2240 data_time: 0.0022 loss: 1.5806 03/05 15:47:15 - mmengine - INFO - Epoch(train) [42][1000/5005] lr: 1.0000e-02 eta: 1 day, 7:14:30 time: 0.2203 data_time: 0.0022 loss: 1.5120 03/05 15:47:38 - mmengine - INFO - Epoch(train) [42][1100/5005] lr: 1.0000e-02 eta: 1 day, 7:14:07 time: 0.2308 data_time: 0.0026 loss: 1.3007 03/05 15:48:00 - mmengine - INFO - Epoch(train) [42][1200/5005] lr: 1.0000e-02 eta: 1 day, 7:13:44 time: 0.2353 data_time: 0.0027 loss: 1.4290 03/05 15:48:23 - mmengine - INFO - Epoch(train) [42][1300/5005] lr: 1.0000e-02 eta: 1 day, 7:13:20 time: 0.2246 data_time: 0.0027 loss: 1.5966 03/05 15:48:45 - mmengine - INFO - Epoch(train) [42][1400/5005] lr: 1.0000e-02 eta: 1 day, 7:12:57 time: 0.2214 data_time: 0.0023 loss: 1.5231 03/05 15:49:08 - mmengine - INFO - Epoch(train) [42][1500/5005] lr: 1.0000e-02 eta: 1 day, 7:12:34 time: 0.2640 data_time: 0.0023 loss: 1.2131 03/05 15:49:31 - mmengine - INFO - Epoch(train) [42][1600/5005] lr: 1.0000e-02 eta: 1 day, 7:12:10 time: 0.2198 data_time: 0.0025 loss: 1.3470 03/05 15:49:53 - mmengine - INFO - Epoch(train) [42][1700/5005] lr: 1.0000e-02 eta: 1 day, 7:11:47 time: 0.2221 data_time: 0.0025 loss: 1.3806 03/05 15:50:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:50:16 - mmengine - INFO - Epoch(train) [42][1800/5005] lr: 1.0000e-02 eta: 1 day, 7:11:23 time: 0.2212 data_time: 0.0023 loss: 1.4628 03/05 15:50:38 - mmengine - INFO - Epoch(train) [42][1900/5005] lr: 1.0000e-02 eta: 1 day, 7:11:00 time: 0.2238 data_time: 0.0024 loss: 1.3735 03/05 15:51:01 - mmengine - INFO - Epoch(train) [42][2000/5005] lr: 1.0000e-02 eta: 1 day, 7:10:38 time: 0.2344 data_time: 0.0026 loss: 1.5162 03/05 15:51:24 - mmengine - INFO - Epoch(train) [42][2100/5005] lr: 1.0000e-02 eta: 1 day, 7:10:14 time: 0.2214 data_time: 0.0026 loss: 1.5094 03/05 15:51:46 - mmengine - INFO - Epoch(train) [42][2200/5005] lr: 1.0000e-02 eta: 1 day, 7:09:51 time: 0.2223 data_time: 0.0024 loss: 1.4539 03/05 15:52:09 - mmengine - INFO - Epoch(train) [42][2300/5005] lr: 1.0000e-02 eta: 1 day, 7:09:28 time: 0.2228 data_time: 0.0027 loss: 1.3971 03/05 15:52:32 - mmengine - INFO - Epoch(train) [42][2400/5005] lr: 1.0000e-02 eta: 1 day, 7:09:05 time: 0.2233 data_time: 0.0027 loss: 1.3834 03/05 15:52:55 - mmengine - INFO - Epoch(train) [42][2500/5005] lr: 1.0000e-02 eta: 1 day, 7:08:42 time: 0.2222 data_time: 0.0025 loss: 1.4605 03/05 15:53:17 - mmengine - INFO - Epoch(train) [42][2600/5005] lr: 1.0000e-02 eta: 1 day, 7:08:19 time: 0.2230 data_time: 0.0021 loss: 1.3950 03/05 15:53:40 - mmengine - INFO - Epoch(train) [42][2700/5005] lr: 1.0000e-02 eta: 1 day, 7:07:56 time: 0.2288 data_time: 0.0026 loss: 1.2247 03/05 15:54:01 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:54:02 - mmengine - INFO - Epoch(train) [42][2800/5005] lr: 1.0000e-02 eta: 1 day, 7:07:32 time: 0.2226 data_time: 0.0028 loss: 1.3047 03/05 15:54:25 - mmengine - INFO - Epoch(train) [42][2900/5005] lr: 1.0000e-02 eta: 1 day, 7:07:09 time: 0.2456 data_time: 0.0025 loss: 1.4141 03/05 15:54:48 - mmengine - INFO - Epoch(train) [42][3000/5005] lr: 1.0000e-02 eta: 1 day, 7:06:46 time: 0.2267 data_time: 0.0026 loss: 1.4146 03/05 15:55:11 - mmengine - INFO - Epoch(train) [42][3100/5005] lr: 1.0000e-02 eta: 1 day, 7:06:23 time: 0.2248 data_time: 0.0022 loss: 1.5185 03/05 15:55:33 - mmengine - INFO - Epoch(train) [42][3200/5005] lr: 1.0000e-02 eta: 1 day, 7:06:00 time: 0.2232 data_time: 0.0024 loss: 1.3931 03/05 15:55:56 - mmengine - INFO - Epoch(train) [42][3300/5005] lr: 1.0000e-02 eta: 1 day, 7:05:36 time: 0.2401 data_time: 0.0022 loss: 1.4954 03/05 15:56:19 - mmengine - INFO - Epoch(train) [42][3400/5005] lr: 1.0000e-02 eta: 1 day, 7:05:14 time: 0.2359 data_time: 0.0022 loss: 1.5257 03/05 15:56:41 - mmengine - INFO - Epoch(train) [42][3500/5005] lr: 1.0000e-02 eta: 1 day, 7:04:51 time: 0.2261 data_time: 0.0024 loss: 1.4495 03/05 15:57:04 - mmengine - INFO - Epoch(train) [42][3600/5005] lr: 1.0000e-02 eta: 1 day, 7:04:28 time: 0.2263 data_time: 0.0024 loss: 1.4440 03/05 15:57:27 - mmengine - INFO - Epoch(train) [42][3700/5005] lr: 1.0000e-02 eta: 1 day, 7:04:05 time: 0.2221 data_time: 0.0024 loss: 1.2779 03/05 15:57:48 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 15:57:50 - mmengine - INFO - Epoch(train) [42][3800/5005] lr: 1.0000e-02 eta: 1 day, 7:03:42 time: 0.2411 data_time: 0.0024 loss: 1.4097 03/05 15:58:12 - mmengine - INFO - Epoch(train) [42][3900/5005] lr: 1.0000e-02 eta: 1 day, 7:03:19 time: 0.2218 data_time: 0.0024 loss: 1.2825 03/05 15:58:35 - mmengine - INFO - Epoch(train) [42][4000/5005] lr: 1.0000e-02 eta: 1 day, 7:02:57 time: 0.2226 data_time: 0.0023 loss: 1.2264 03/05 15:58:58 - mmengine - INFO - Epoch(train) [42][4100/5005] lr: 1.0000e-02 eta: 1 day, 7:02:33 time: 0.2236 data_time: 0.0025 loss: 1.4368 03/05 15:59:20 - mmengine - INFO - Epoch(train) [42][4200/5005] lr: 1.0000e-02 eta: 1 day, 7:02:09 time: 0.2224 data_time: 0.0026 loss: 1.5273 03/05 15:59:43 - mmengine - INFO - Epoch(train) [42][4300/5005] lr: 1.0000e-02 eta: 1 day, 7:01:47 time: 0.2226 data_time: 0.0027 loss: 1.4814 03/05 16:00:06 - mmengine - INFO - Epoch(train) [42][4400/5005] lr: 1.0000e-02 eta: 1 day, 7:01:24 time: 0.2626 data_time: 0.0027 loss: 1.3825 03/05 16:00:28 - mmengine - INFO - Epoch(train) [42][4500/5005] lr: 1.0000e-02 eta: 1 day, 7:01:01 time: 0.2242 data_time: 0.0025 loss: 1.4936 03/05 16:00:51 - mmengine - INFO - Epoch(train) [42][4600/5005] lr: 1.0000e-02 eta: 1 day, 7:00:38 time: 0.2245 data_time: 0.0026 loss: 1.4865 03/05 16:01:14 - mmengine - INFO - Epoch(train) [42][4700/5005] lr: 1.0000e-02 eta: 1 day, 7:00:15 time: 0.2636 data_time: 0.0027 loss: 1.3182 03/05 16:01:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:01:37 - mmengine - INFO - Epoch(train) [42][4800/5005] lr: 1.0000e-02 eta: 1 day, 6:59:52 time: 0.2380 data_time: 0.0025 loss: 1.6007 03/05 16:02:01 - mmengine - INFO - Epoch(train) [42][4900/5005] lr: 1.0000e-02 eta: 1 day, 6:59:32 time: 0.2888 data_time: 0.0020 loss: 1.3306 03/05 16:02:29 - mmengine - INFO - Epoch(train) [42][5000/5005] lr: 1.0000e-02 eta: 1 day, 6:59:23 time: 0.2567 data_time: 0.0020 loss: 1.5465 03/05 16:02:31 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:02:34 - mmengine - INFO - Saving checkpoint at 42 epochs 03/05 16:02:48 - mmengine - INFO - Epoch(val) [42][100/196] eta: 0:00:12 time: 0.0204 data_time: 0.0004 03/05 16:03:01 - mmengine - INFO - Epoch(val) [42][196/196] accuracy/top1: 70.9640 accuracy/top5: 90.5740 03/05 16:03:32 - mmengine - INFO - Epoch(train) [43][ 100/5005] lr: 1.0000e-02 eta: 1 day, 6:59:19 time: 0.2248 data_time: 0.0024 loss: 1.4186 03/05 16:03:55 - mmengine - INFO - Epoch(train) [43][ 200/5005] lr: 1.0000e-02 eta: 1 day, 6:58:56 time: 0.2240 data_time: 0.0023 loss: 1.2934 03/05 16:04:17 - mmengine - INFO - Epoch(train) [43][ 300/5005] lr: 1.0000e-02 eta: 1 day, 6:58:32 time: 0.2428 data_time: 0.0025 loss: 1.4396 03/05 16:04:40 - mmengine - INFO - Epoch(train) [43][ 400/5005] lr: 1.0000e-02 eta: 1 day, 6:58:10 time: 0.2237 data_time: 0.0026 loss: 1.4905 03/05 16:05:03 - mmengine - INFO - Epoch(train) [43][ 500/5005] lr: 1.0000e-02 eta: 1 day, 6:57:48 time: 0.2246 data_time: 0.0025 loss: 1.4481 03/05 16:05:26 - mmengine - INFO - Epoch(train) [43][ 600/5005] lr: 1.0000e-02 eta: 1 day, 6:57:24 time: 0.2229 data_time: 0.0025 loss: 1.2205 03/05 16:05:48 - mmengine - INFO - Epoch(train) [43][ 700/5005] lr: 1.0000e-02 eta: 1 day, 6:57:00 time: 0.2219 data_time: 0.0024 loss: 1.2884 03/05 16:06:09 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:06:11 - mmengine - INFO - Epoch(train) [43][ 800/5005] lr: 1.0000e-02 eta: 1 day, 6:56:38 time: 0.2422 data_time: 0.0022 loss: 1.3054 03/05 16:06:34 - mmengine - INFO - Epoch(train) [43][ 900/5005] lr: 1.0000e-02 eta: 1 day, 6:56:15 time: 0.2201 data_time: 0.0023 loss: 1.5587 03/05 16:06:56 - mmengine - INFO - Epoch(train) [43][1000/5005] lr: 1.0000e-02 eta: 1 day, 6:55:51 time: 0.2226 data_time: 0.0021 loss: 1.2222 03/05 16:07:19 - mmengine - INFO - Epoch(train) [43][1100/5005] lr: 1.0000e-02 eta: 1 day, 6:55:27 time: 0.2213 data_time: 0.0025 loss: 1.6230 03/05 16:07:41 - mmengine - INFO - Epoch(train) [43][1200/5005] lr: 1.0000e-02 eta: 1 day, 6:55:05 time: 0.2219 data_time: 0.0024 loss: 1.3754 03/05 16:08:04 - mmengine - INFO - Epoch(train) [43][1300/5005] lr: 1.0000e-02 eta: 1 day, 6:54:42 time: 0.2193 data_time: 0.0024 loss: 1.1753 03/05 16:08:27 - mmengine - INFO - Epoch(train) [43][1400/5005] lr: 1.0000e-02 eta: 1 day, 6:54:18 time: 0.2231 data_time: 0.0025 loss: 1.4594 03/05 16:08:49 - mmengine - INFO - Epoch(train) [43][1500/5005] lr: 1.0000e-02 eta: 1 day, 6:53:55 time: 0.2420 data_time: 0.0023 loss: 1.4914 03/05 16:09:12 - mmengine - INFO - Epoch(train) [43][1600/5005] lr: 1.0000e-02 eta: 1 day, 6:53:32 time: 0.2227 data_time: 0.0023 loss: 1.5922 03/05 16:09:35 - mmengine - INFO - Epoch(train) [43][1700/5005] lr: 1.0000e-02 eta: 1 day, 6:53:09 time: 0.2231 data_time: 0.0026 loss: 1.3340 03/05 16:09:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:09:58 - mmengine - INFO - Epoch(train) [43][1800/5005] lr: 1.0000e-02 eta: 1 day, 6:52:46 time: 0.2214 data_time: 0.0022 loss: 1.3191 03/05 16:10:20 - mmengine - INFO - Epoch(train) [43][1900/5005] lr: 1.0000e-02 eta: 1 day, 6:52:22 time: 0.2224 data_time: 0.0023 loss: 1.4055 03/05 16:10:43 - mmengine - INFO - Epoch(train) [43][2000/5005] lr: 1.0000e-02 eta: 1 day, 6:51:59 time: 0.2220 data_time: 0.0030 loss: 1.3258 03/05 16:11:05 - mmengine - INFO - Epoch(train) [43][2100/5005] lr: 1.0000e-02 eta: 1 day, 6:51:36 time: 0.2381 data_time: 0.0025 loss: 1.3816 03/05 16:11:28 - mmengine - INFO - Epoch(train) [43][2200/5005] lr: 1.0000e-02 eta: 1 day, 6:51:13 time: 0.2237 data_time: 0.0024 loss: 1.3318 03/05 16:11:51 - mmengine - INFO - Epoch(train) [43][2300/5005] lr: 1.0000e-02 eta: 1 day, 6:50:50 time: 0.2423 data_time: 0.0022 loss: 1.4551 03/05 16:12:13 - mmengine - INFO - Epoch(train) [43][2400/5005] lr: 1.0000e-02 eta: 1 day, 6:50:26 time: 0.2238 data_time: 0.0025 loss: 1.3737 03/05 16:12:36 - mmengine - INFO - Epoch(train) [43][2500/5005] lr: 1.0000e-02 eta: 1 day, 6:50:04 time: 0.2263 data_time: 0.0023 loss: 1.3450 03/05 16:12:59 - mmengine - INFO - Epoch(train) [43][2600/5005] lr: 1.0000e-02 eta: 1 day, 6:49:40 time: 0.2210 data_time: 0.0025 loss: 1.4015 03/05 16:13:21 - mmengine - INFO - Epoch(train) [43][2700/5005] lr: 1.0000e-02 eta: 1 day, 6:49:17 time: 0.2210 data_time: 0.0024 loss: 1.4700 03/05 16:13:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:13:44 - mmengine - INFO - Epoch(train) [43][2800/5005] lr: 1.0000e-02 eta: 1 day, 6:48:54 time: 0.2253 data_time: 0.0026 loss: 1.3843 03/05 16:14:07 - mmengine - INFO - Epoch(train) [43][2900/5005] lr: 1.0000e-02 eta: 1 day, 6:48:31 time: 0.2252 data_time: 0.0023 loss: 1.5568 03/05 16:14:29 - mmengine - INFO - Epoch(train) [43][3000/5005] lr: 1.0000e-02 eta: 1 day, 6:48:08 time: 0.2412 data_time: 0.0024 loss: 1.5024 03/05 16:14:52 - mmengine - INFO - Epoch(train) [43][3100/5005] lr: 1.0000e-02 eta: 1 day, 6:47:44 time: 0.2207 data_time: 0.0026 loss: 1.2376 03/05 16:15:15 - mmengine - INFO - Epoch(train) [43][3200/5005] lr: 1.0000e-02 eta: 1 day, 6:47:22 time: 0.2442 data_time: 0.0023 loss: 1.3553 03/05 16:15:38 - mmengine - INFO - Epoch(train) [43][3300/5005] lr: 1.0000e-02 eta: 1 day, 6:46:59 time: 0.2233 data_time: 0.0022 loss: 1.4533 03/05 16:16:00 - mmengine - INFO - Epoch(train) [43][3400/5005] lr: 1.0000e-02 eta: 1 day, 6:46:36 time: 0.2246 data_time: 0.0025 loss: 1.5432 03/05 16:16:23 - mmengine - INFO - Epoch(train) [43][3500/5005] lr: 1.0000e-02 eta: 1 day, 6:46:13 time: 0.2226 data_time: 0.0027 loss: 1.2790 03/05 16:16:46 - mmengine - INFO - Epoch(train) [43][3600/5005] lr: 1.0000e-02 eta: 1 day, 6:45:50 time: 0.2221 data_time: 0.0026 loss: 1.3874 03/05 16:17:08 - mmengine - INFO - Epoch(train) [43][3700/5005] lr: 1.0000e-02 eta: 1 day, 6:45:26 time: 0.2466 data_time: 0.0026 loss: 1.4582 03/05 16:17:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:17:31 - mmengine - INFO - Epoch(train) [43][3800/5005] lr: 1.0000e-02 eta: 1 day, 6:45:03 time: 0.2246 data_time: 0.0027 loss: 1.1538 03/05 16:17:53 - mmengine - INFO - Epoch(train) [43][3900/5005] lr: 1.0000e-02 eta: 1 day, 6:44:40 time: 0.2248 data_time: 0.0026 loss: 1.4991 03/05 16:18:16 - mmengine - INFO - Epoch(train) [43][4000/5005] lr: 1.0000e-02 eta: 1 day, 6:44:17 time: 0.2193 data_time: 0.0024 loss: 1.3341 03/05 16:18:39 - mmengine - INFO - Epoch(train) [43][4100/5005] lr: 1.0000e-02 eta: 1 day, 6:43:53 time: 0.2271 data_time: 0.0025 loss: 1.3364 03/05 16:19:02 - mmengine - INFO - Epoch(train) [43][4200/5005] lr: 1.0000e-02 eta: 1 day, 6:43:31 time: 0.2231 data_time: 0.0026 loss: 1.2386 03/05 16:19:24 - mmengine - INFO - Epoch(train) [43][4300/5005] lr: 1.0000e-02 eta: 1 day, 6:43:07 time: 0.2277 data_time: 0.0025 loss: 1.3885 03/05 16:19:47 - mmengine - INFO - Epoch(train) [43][4400/5005] lr: 1.0000e-02 eta: 1 day, 6:42:44 time: 0.2456 data_time: 0.0025 loss: 1.7196 03/05 16:20:10 - mmengine - INFO - Epoch(train) [43][4500/5005] lr: 1.0000e-02 eta: 1 day, 6:42:21 time: 0.2368 data_time: 0.0025 loss: 1.6696 03/05 16:20:32 - mmengine - INFO - Epoch(train) [43][4600/5005] lr: 1.0000e-02 eta: 1 day, 6:41:59 time: 0.2229 data_time: 0.0024 loss: 1.4455 03/05 16:20:55 - mmengine - INFO - Epoch(train) [43][4700/5005] lr: 1.0000e-02 eta: 1 day, 6:41:35 time: 0.2222 data_time: 0.0023 loss: 1.5041 03/05 16:21:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:21:17 - mmengine - INFO - Epoch(train) [43][4800/5005] lr: 1.0000e-02 eta: 1 day, 6:41:12 time: 0.2237 data_time: 0.0024 loss: 1.3574 03/05 16:21:41 - mmengine - INFO - Epoch(train) [43][4900/5005] lr: 1.0000e-02 eta: 1 day, 6:40:51 time: 0.2808 data_time: 0.0023 loss: 1.4798 03/05 16:22:10 - mmengine - INFO - Epoch(train) [43][5000/5005] lr: 1.0000e-02 eta: 1 day, 6:40:41 time: 0.2831 data_time: 0.0025 loss: 1.6207 03/05 16:22:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:22:14 - mmengine - INFO - Saving checkpoint at 43 epochs 03/05 16:22:28 - mmengine - INFO - Epoch(val) [43][100/196] eta: 0:00:12 time: 0.0186 data_time: 0.0004 03/05 16:22:42 - mmengine - INFO - Epoch(val) [43][196/196] accuracy/top1: 71.6960 accuracy/top5: 90.7400 03/05 16:23:14 - mmengine - INFO - Epoch(train) [44][ 100/5005] lr: 1.0000e-02 eta: 1 day, 6:40:39 time: 0.3178 data_time: 0.0024 loss: 1.3761 03/05 16:23:37 - mmengine - INFO - Epoch(train) [44][ 200/5005] lr: 1.0000e-02 eta: 1 day, 6:40:16 time: 0.2227 data_time: 0.0025 loss: 1.4599 03/05 16:23:59 - mmengine - INFO - Epoch(train) [44][ 300/5005] lr: 1.0000e-02 eta: 1 day, 6:39:53 time: 0.2203 data_time: 0.0025 loss: 1.3587 03/05 16:24:22 - mmengine - INFO - Epoch(train) [44][ 400/5005] lr: 1.0000e-02 eta: 1 day, 6:39:30 time: 0.2463 data_time: 0.0024 loss: 1.6634 03/05 16:24:45 - mmengine - INFO - Epoch(train) [44][ 500/5005] lr: 1.0000e-02 eta: 1 day, 6:39:06 time: 0.2243 data_time: 0.0028 loss: 1.5509 03/05 16:25:08 - mmengine - INFO - Epoch(train) [44][ 600/5005] lr: 1.0000e-02 eta: 1 day, 6:38:44 time: 0.2240 data_time: 0.0022 loss: 1.3308 03/05 16:25:30 - mmengine - INFO - Epoch(train) [44][ 700/5005] lr: 1.0000e-02 eta: 1 day, 6:38:20 time: 0.2238 data_time: 0.0025 loss: 1.5591 03/05 16:25:49 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:25:52 - mmengine - INFO - Epoch(train) [44][ 800/5005] lr: 1.0000e-02 eta: 1 day, 6:37:56 time: 0.2217 data_time: 0.0023 loss: 1.4641 03/05 16:26:16 - mmengine - INFO - Epoch(train) [44][ 900/5005] lr: 1.0000e-02 eta: 1 day, 6:37:34 time: 0.2955 data_time: 0.0023 loss: 1.4458 03/05 16:26:38 - mmengine - INFO - Epoch(train) [44][1000/5005] lr: 1.0000e-02 eta: 1 day, 6:37:12 time: 0.2497 data_time: 0.0022 loss: 1.3615 03/05 16:27:01 - mmengine - INFO - Epoch(train) [44][1100/5005] lr: 1.0000e-02 eta: 1 day, 6:36:48 time: 0.2244 data_time: 0.0024 loss: 1.3989 03/05 16:27:23 - mmengine - INFO - Epoch(train) [44][1200/5005] lr: 1.0000e-02 eta: 1 day, 6:36:25 time: 0.2239 data_time: 0.0024 loss: 1.2846 03/05 16:27:46 - mmengine - INFO - Epoch(train) [44][1300/5005] lr: 1.0000e-02 eta: 1 day, 6:36:01 time: 0.2223 data_time: 0.0025 loss: 1.4630 03/05 16:28:09 - mmengine - INFO - Epoch(train) [44][1400/5005] lr: 1.0000e-02 eta: 1 day, 6:35:39 time: 0.2495 data_time: 0.0024 loss: 1.4701 03/05 16:28:31 - mmengine - INFO - Epoch(train) [44][1500/5005] lr: 1.0000e-02 eta: 1 day, 6:35:15 time: 0.2266 data_time: 0.0025 loss: 1.4406 03/05 16:28:54 - mmengine - INFO - Epoch(train) [44][1600/5005] lr: 1.0000e-02 eta: 1 day, 6:34:52 time: 0.2242 data_time: 0.0026 loss: 1.4247 03/05 16:29:17 - mmengine - INFO - Epoch(train) [44][1700/5005] lr: 1.0000e-02 eta: 1 day, 6:34:29 time: 0.2248 data_time: 0.0024 loss: 1.4304 03/05 16:29:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:29:40 - mmengine - INFO - Epoch(train) [44][1800/5005] lr: 1.0000e-02 eta: 1 day, 6:34:06 time: 0.2456 data_time: 0.0026 loss: 1.2739 03/05 16:30:02 - mmengine - INFO - Epoch(train) [44][1900/5005] lr: 1.0000e-02 eta: 1 day, 6:33:43 time: 0.2226 data_time: 0.0025 loss: 1.1983 03/05 16:30:25 - mmengine - INFO - Epoch(train) [44][2000/5005] lr: 1.0000e-02 eta: 1 day, 6:33:20 time: 0.2239 data_time: 0.0025 loss: 1.4012 03/05 16:30:48 - mmengine - INFO - Epoch(train) [44][2100/5005] lr: 1.0000e-02 eta: 1 day, 6:32:57 time: 0.2226 data_time: 0.0029 loss: 1.3000 03/05 16:31:11 - mmengine - INFO - Epoch(train) [44][2200/5005] lr: 1.0000e-02 eta: 1 day, 6:32:35 time: 0.2269 data_time: 0.0025 loss: 1.3024 03/05 16:31:33 - mmengine - INFO - Epoch(train) [44][2300/5005] lr: 1.0000e-02 eta: 1 day, 6:32:11 time: 0.2250 data_time: 0.0024 loss: 1.3519 03/05 16:31:56 - mmengine - INFO - Epoch(train) [44][2400/5005] lr: 1.0000e-02 eta: 1 day, 6:31:48 time: 0.2248 data_time: 0.0026 loss: 1.2594 03/05 16:32:19 - mmengine - INFO - Epoch(train) [44][2500/5005] lr: 1.0000e-02 eta: 1 day, 6:31:25 time: 0.2299 data_time: 0.0027 loss: 1.5189 03/05 16:32:42 - mmengine - INFO - Epoch(train) [44][2600/5005] lr: 1.0000e-02 eta: 1 day, 6:31:02 time: 0.2197 data_time: 0.0026 loss: 1.4347 03/05 16:33:04 - mmengine - INFO - Epoch(train) [44][2700/5005] lr: 1.0000e-02 eta: 1 day, 6:30:39 time: 0.2210 data_time: 0.0023 loss: 1.4037 03/05 16:33:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:33:27 - mmengine - INFO - Epoch(train) [44][2800/5005] lr: 1.0000e-02 eta: 1 day, 6:30:16 time: 0.2257 data_time: 0.0028 loss: 1.3279 03/05 16:33:49 - mmengine - INFO - Epoch(train) [44][2900/5005] lr: 1.0000e-02 eta: 1 day, 6:29:52 time: 0.2501 data_time: 0.0026 loss: 1.3672 03/05 16:34:12 - mmengine - INFO - Epoch(train) [44][3000/5005] lr: 1.0000e-02 eta: 1 day, 6:29:30 time: 0.2209 data_time: 0.0024 loss: 1.5626 03/05 16:34:35 - mmengine - INFO - Epoch(train) [44][3100/5005] lr: 1.0000e-02 eta: 1 day, 6:29:07 time: 0.2229 data_time: 0.0029 loss: 1.3010 03/05 16:34:57 - mmengine - INFO - Epoch(train) [44][3200/5005] lr: 1.0000e-02 eta: 1 day, 6:28:43 time: 0.2248 data_time: 0.0025 loss: 1.4294 03/05 16:35:20 - mmengine - INFO - Epoch(train) [44][3300/5005] lr: 1.0000e-02 eta: 1 day, 6:28:20 time: 0.2227 data_time: 0.0029 loss: 1.5264 03/05 16:35:43 - mmengine - INFO - Epoch(train) [44][3400/5005] lr: 1.0000e-02 eta: 1 day, 6:27:57 time: 0.2218 data_time: 0.0028 loss: 1.2720 03/05 16:36:06 - mmengine - INFO - Epoch(train) [44][3500/5005] lr: 1.0000e-02 eta: 1 day, 6:27:34 time: 0.2236 data_time: 0.0023 loss: 1.2137 03/05 16:36:28 - mmengine - INFO - Epoch(train) [44][3600/5005] lr: 1.0000e-02 eta: 1 day, 6:27:11 time: 0.2257 data_time: 0.0026 loss: 1.5501 03/05 16:36:51 - mmengine - INFO - Epoch(train) [44][3700/5005] lr: 1.0000e-02 eta: 1 day, 6:26:47 time: 0.2206 data_time: 0.0030 loss: 1.5191 03/05 16:37:10 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:37:14 - mmengine - INFO - Epoch(train) [44][3800/5005] lr: 1.0000e-02 eta: 1 day, 6:26:25 time: 0.2456 data_time: 0.0025 loss: 1.3239 03/05 16:37:36 - mmengine - INFO - Epoch(train) [44][3900/5005] lr: 1.0000e-02 eta: 1 day, 6:26:01 time: 0.2250 data_time: 0.0024 loss: 1.2555 03/05 16:37:59 - mmengine - INFO - Epoch(train) [44][4000/5005] lr: 1.0000e-02 eta: 1 day, 6:25:38 time: 0.2212 data_time: 0.0027 loss: 1.2927 03/05 16:38:21 - mmengine - INFO - Epoch(train) [44][4100/5005] lr: 1.0000e-02 eta: 1 day, 6:25:15 time: 0.2225 data_time: 0.0027 loss: 1.4799 03/05 16:38:44 - mmengine - INFO - Epoch(train) [44][4200/5005] lr: 1.0000e-02 eta: 1 day, 6:24:51 time: 0.2224 data_time: 0.0028 loss: 1.4156 03/05 16:39:07 - mmengine - INFO - Epoch(train) [44][4300/5005] lr: 1.0000e-02 eta: 1 day, 6:24:28 time: 0.2286 data_time: 0.0024 loss: 1.3445 03/05 16:39:30 - mmengine - INFO - Epoch(train) [44][4400/5005] lr: 1.0000e-02 eta: 1 day, 6:24:06 time: 0.2297 data_time: 0.0024 loss: 1.5316 03/05 16:39:52 - mmengine - INFO - Epoch(train) [44][4500/5005] lr: 1.0000e-02 eta: 1 day, 6:23:42 time: 0.2248 data_time: 0.0026 loss: 1.4189 03/05 16:40:15 - mmengine - INFO - Epoch(train) [44][4600/5005] lr: 1.0000e-02 eta: 1 day, 6:23:19 time: 0.2225 data_time: 0.0025 loss: 1.4293 03/05 16:40:37 - mmengine - INFO - Epoch(train) [44][4700/5005] lr: 1.0000e-02 eta: 1 day, 6:22:56 time: 0.2224 data_time: 0.0026 loss: 1.3395 03/05 16:40:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:41:00 - mmengine - INFO - Epoch(train) [44][4800/5005] lr: 1.0000e-02 eta: 1 day, 6:22:32 time: 0.2335 data_time: 0.0024 loss: 1.2799 03/05 16:41:23 - mmengine - INFO - Epoch(train) [44][4900/5005] lr: 1.0000e-02 eta: 1 day, 6:22:11 time: 0.2903 data_time: 0.0022 loss: 1.3347 03/05 16:41:52 - mmengine - INFO - Epoch(train) [44][5000/5005] lr: 1.0000e-02 eta: 1 day, 6:22:01 time: 0.2883 data_time: 0.0025 loss: 1.6508 03/05 16:41:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:41:56 - mmengine - INFO - Saving checkpoint at 44 epochs 03/05 16:42:11 - mmengine - INFO - Epoch(val) [44][100/196] eta: 0:00:12 time: 0.0205 data_time: 0.0004 03/05 16:42:24 - mmengine - INFO - Epoch(val) [44][196/196] accuracy/top1: 71.3760 accuracy/top5: 90.7500 03/05 16:42:56 - mmengine - INFO - Epoch(train) [45][ 100/5005] lr: 1.0000e-02 eta: 1 day, 6:21:56 time: 0.2216 data_time: 0.0028 loss: 1.1755 03/05 16:43:18 - mmengine - INFO - Epoch(train) [45][ 200/5005] lr: 1.0000e-02 eta: 1 day, 6:21:33 time: 0.2206 data_time: 0.0032 loss: 1.3312 03/05 16:43:41 - mmengine - INFO - Epoch(train) [45][ 300/5005] lr: 1.0000e-02 eta: 1 day, 6:21:10 time: 0.2243 data_time: 0.0023 loss: 1.2169 03/05 16:44:04 - mmengine - INFO - Epoch(train) [45][ 400/5005] lr: 1.0000e-02 eta: 1 day, 6:20:47 time: 0.2202 data_time: 0.0024 loss: 1.3841 03/05 16:44:27 - mmengine - INFO - Epoch(train) [45][ 500/5005] lr: 1.0000e-02 eta: 1 day, 6:20:24 time: 0.2212 data_time: 0.0027 loss: 1.2638 03/05 16:44:49 - mmengine - INFO - Epoch(train) [45][ 600/5005] lr: 1.0000e-02 eta: 1 day, 6:20:01 time: 0.2203 data_time: 0.0024 loss: 1.2365 03/05 16:45:12 - mmengine - INFO - Epoch(train) [45][ 700/5005] lr: 1.0000e-02 eta: 1 day, 6:19:38 time: 0.2218 data_time: 0.0024 loss: 1.3782 03/05 16:45:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:45:34 - mmengine - INFO - Epoch(train) [45][ 800/5005] lr: 1.0000e-02 eta: 1 day, 6:19:14 time: 0.2236 data_time: 0.0027 loss: 1.4527 03/05 16:45:57 - mmengine - INFO - Epoch(train) [45][ 900/5005] lr: 1.0000e-02 eta: 1 day, 6:18:52 time: 0.2235 data_time: 0.0023 loss: 1.4709 03/05 16:46:20 - mmengine - INFO - Epoch(train) [45][1000/5005] lr: 1.0000e-02 eta: 1 day, 6:18:29 time: 0.2281 data_time: 0.0025 loss: 1.2531 03/05 16:46:42 - mmengine - INFO - Epoch(train) [45][1100/5005] lr: 1.0000e-02 eta: 1 day, 6:18:05 time: 0.2247 data_time: 0.0029 loss: 1.2448 03/05 16:47:05 - mmengine - INFO - Epoch(train) [45][1200/5005] lr: 1.0000e-02 eta: 1 day, 6:17:42 time: 0.2320 data_time: 0.0024 loss: 1.3249 03/05 16:47:28 - mmengine - INFO - Epoch(train) [45][1300/5005] lr: 1.0000e-02 eta: 1 day, 6:17:19 time: 0.2396 data_time: 0.0025 loss: 1.1868 03/05 16:47:51 - mmengine - INFO - Epoch(train) [45][1400/5005] lr: 1.0000e-02 eta: 1 day, 6:16:56 time: 0.2217 data_time: 0.0025 loss: 1.2829 03/05 16:48:13 - mmengine - INFO - Epoch(train) [45][1500/5005] lr: 1.0000e-02 eta: 1 day, 6:16:33 time: 0.2239 data_time: 0.0023 loss: 1.4528 03/05 16:48:36 - mmengine - INFO - Epoch(train) [45][1600/5005] lr: 1.0000e-02 eta: 1 day, 6:16:09 time: 0.2212 data_time: 0.0027 loss: 1.5281 03/05 16:48:59 - mmengine - INFO - Epoch(train) [45][1700/5005] lr: 1.0000e-02 eta: 1 day, 6:15:47 time: 0.2465 data_time: 0.0026 loss: 1.2559 03/05 16:49:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:49:21 - mmengine - INFO - Epoch(train) [45][1800/5005] lr: 1.0000e-02 eta: 1 day, 6:15:24 time: 0.2237 data_time: 0.0025 loss: 1.3834 03/05 16:49:44 - mmengine - INFO - Epoch(train) [45][1900/5005] lr: 1.0000e-02 eta: 1 day, 6:15:00 time: 0.2239 data_time: 0.0024 loss: 1.3444 03/05 16:50:06 - mmengine - INFO - Epoch(train) [45][2000/5005] lr: 1.0000e-02 eta: 1 day, 6:14:37 time: 0.2230 data_time: 0.0024 loss: 1.3110 03/05 16:50:29 - mmengine - INFO - Epoch(train) [45][2100/5005] lr: 1.0000e-02 eta: 1 day, 6:14:13 time: 0.2217 data_time: 0.0023 loss: 1.3764 03/05 16:50:52 - mmengine - INFO - Epoch(train) [45][2200/5005] lr: 1.0000e-02 eta: 1 day, 6:13:51 time: 0.2195 data_time: 0.0022 loss: 1.6558 03/05 16:51:15 - mmengine - INFO - Epoch(train) [45][2300/5005] lr: 1.0000e-02 eta: 1 day, 6:13:28 time: 0.2230 data_time: 0.0025 loss: 1.3188 03/05 16:51:37 - mmengine - INFO - Epoch(train) [45][2400/5005] lr: 1.0000e-02 eta: 1 day, 6:13:05 time: 0.2235 data_time: 0.0028 loss: 1.3160 03/05 16:52:00 - mmengine - INFO - Epoch(train) [45][2500/5005] lr: 1.0000e-02 eta: 1 day, 6:12:42 time: 0.2431 data_time: 0.0025 loss: 1.5122 03/05 16:52:23 - mmengine - INFO - Epoch(train) [45][2600/5005] lr: 1.0000e-02 eta: 1 day, 6:12:18 time: 0.2226 data_time: 0.0025 loss: 1.5068 03/05 16:52:45 - mmengine - INFO - Epoch(train) [45][2700/5005] lr: 1.0000e-02 eta: 1 day, 6:11:55 time: 0.2216 data_time: 0.0026 loss: 1.2482 03/05 16:53:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:53:08 - mmengine - INFO - Epoch(train) [45][2800/5005] lr: 1.0000e-02 eta: 1 day, 6:11:32 time: 0.2197 data_time: 0.0024 loss: 1.2728 03/05 16:53:30 - mmengine - INFO - Epoch(train) [45][2900/5005] lr: 1.0000e-02 eta: 1 day, 6:11:08 time: 0.2223 data_time: 0.0025 loss: 1.4509 03/05 16:53:53 - mmengine - INFO - Epoch(train) [45][3000/5005] lr: 1.0000e-02 eta: 1 day, 6:10:46 time: 0.2242 data_time: 0.0024 loss: 1.4063 03/05 16:54:16 - mmengine - INFO - Epoch(train) [45][3100/5005] lr: 1.0000e-02 eta: 1 day, 6:10:23 time: 0.2222 data_time: 0.0026 loss: 1.4992 03/05 16:54:39 - mmengine - INFO - Epoch(train) [45][3200/5005] lr: 1.0000e-02 eta: 1 day, 6:10:00 time: 0.2266 data_time: 0.0025 loss: 1.2580 03/05 16:55:01 - mmengine - INFO - Epoch(train) [45][3300/5005] lr: 1.0000e-02 eta: 1 day, 6:09:36 time: 0.2387 data_time: 0.0025 loss: 1.4749 03/05 16:55:24 - mmengine - INFO - Epoch(train) [45][3400/5005] lr: 1.0000e-02 eta: 1 day, 6:09:14 time: 0.2350 data_time: 0.0030 loss: 1.3152 03/05 16:55:47 - mmengine - INFO - Epoch(train) [45][3500/5005] lr: 1.0000e-02 eta: 1 day, 6:08:50 time: 0.2211 data_time: 0.0027 loss: 1.3635 03/05 16:56:09 - mmengine - INFO - Epoch(train) [45][3600/5005] lr: 1.0000e-02 eta: 1 day, 6:08:27 time: 0.2279 data_time: 0.0025 loss: 1.3888 03/05 16:56:32 - mmengine - INFO - Epoch(train) [45][3700/5005] lr: 1.0000e-02 eta: 1 day, 6:08:04 time: 0.2261 data_time: 0.0026 loss: 1.3048 03/05 16:56:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 16:56:55 - mmengine - INFO - Epoch(train) [45][3800/5005] lr: 1.0000e-02 eta: 1 day, 6:07:41 time: 0.2404 data_time: 0.0025 loss: 1.3358 03/05 16:57:18 - mmengine - INFO - Epoch(train) [45][3900/5005] lr: 1.0000e-02 eta: 1 day, 6:07:18 time: 0.2412 data_time: 0.0023 loss: 1.2851 03/05 16:57:40 - mmengine - INFO - Epoch(train) [45][4000/5005] lr: 1.0000e-02 eta: 1 day, 6:06:54 time: 0.2217 data_time: 0.0024 loss: 1.3977 03/05 16:58:02 - mmengine - INFO - Epoch(train) [45][4100/5005] lr: 1.0000e-02 eta: 1 day, 6:06:31 time: 0.2210 data_time: 0.0029 loss: 1.2727 03/05 16:58:25 - mmengine - INFO - Epoch(train) [45][4200/5005] lr: 1.0000e-02 eta: 1 day, 6:06:08 time: 0.2221 data_time: 0.0023 loss: 1.2480 03/05 16:58:48 - mmengine - INFO - Epoch(train) [45][4300/5005] lr: 1.0000e-02 eta: 1 day, 6:05:45 time: 0.2274 data_time: 0.0027 loss: 1.4595 03/05 16:59:11 - mmengine - INFO - Epoch(train) [45][4400/5005] lr: 1.0000e-02 eta: 1 day, 6:05:22 time: 0.2223 data_time: 0.0024 loss: 1.4274 03/05 16:59:33 - mmengine - INFO - Epoch(train) [45][4500/5005] lr: 1.0000e-02 eta: 1 day, 6:04:59 time: 0.2263 data_time: 0.0024 loss: 1.4501 03/05 16:59:56 - mmengine - INFO - Epoch(train) [45][4600/5005] lr: 1.0000e-02 eta: 1 day, 6:04:36 time: 0.2401 data_time: 0.0025 loss: 1.3860 03/05 17:00:19 - mmengine - INFO - Epoch(train) [45][4700/5005] lr: 1.0000e-02 eta: 1 day, 6:04:14 time: 0.2452 data_time: 0.0029 loss: 1.2764 03/05 17:00:37 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:00:42 - mmengine - INFO - Epoch(train) [45][4800/5005] lr: 1.0000e-02 eta: 1 day, 6:03:51 time: 0.2234 data_time: 0.0027 loss: 1.3999 03/05 17:01:05 - mmengine - INFO - Epoch(train) [45][4900/5005] lr: 1.0000e-02 eta: 1 day, 6:03:29 time: 0.2795 data_time: 0.0022 loss: 1.3372 03/05 17:01:33 - mmengine - INFO - Epoch(train) [45][5000/5005] lr: 1.0000e-02 eta: 1 day, 6:03:17 time: 0.2847 data_time: 0.0024 loss: 1.5972 03/05 17:01:35 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:01:37 - mmengine - INFO - Saving checkpoint at 45 epochs 03/05 17:01:52 - mmengine - INFO - Epoch(val) [45][100/196] eta: 0:00:13 time: 0.0198 data_time: 0.0003 03/05 17:02:06 - mmengine - INFO - Epoch(val) [45][196/196] accuracy/top1: 71.4180 accuracy/top5: 90.7020 03/05 17:02:37 - mmengine - INFO - Epoch(train) [46][ 100/5005] lr: 1.0000e-02 eta: 1 day, 6:03:11 time: 0.2223 data_time: 0.0026 loss: 1.1923 03/05 17:03:00 - mmengine - INFO - Epoch(train) [46][ 200/5005] lr: 1.0000e-02 eta: 1 day, 6:02:49 time: 0.2191 data_time: 0.0029 loss: 1.4633 03/05 17:03:23 - mmengine - INFO - Epoch(train) [46][ 300/5005] lr: 1.0000e-02 eta: 1 day, 6:02:26 time: 0.2243 data_time: 0.0027 loss: 1.2936 03/05 17:03:46 - mmengine - INFO - Epoch(train) [46][ 400/5005] lr: 1.0000e-02 eta: 1 day, 6:02:04 time: 0.2690 data_time: 0.0031 loss: 1.2915 03/05 17:04:08 - mmengine - INFO - Epoch(train) [46][ 500/5005] lr: 1.0000e-02 eta: 1 day, 6:01:40 time: 0.2254 data_time: 0.0026 loss: 1.2557 03/05 17:04:31 - mmengine - INFO - Epoch(train) [46][ 600/5005] lr: 1.0000e-02 eta: 1 day, 6:01:17 time: 0.2255 data_time: 0.0030 loss: 1.2715 03/05 17:04:54 - mmengine - INFO - Epoch(train) [46][ 700/5005] lr: 1.0000e-02 eta: 1 day, 6:00:54 time: 0.2289 data_time: 0.0026 loss: 1.4488 03/05 17:05:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:05:17 - mmengine - INFO - Epoch(train) [46][ 800/5005] lr: 1.0000e-02 eta: 1 day, 6:00:32 time: 0.2216 data_time: 0.0029 loss: 1.3058 03/05 17:05:40 - mmengine - INFO - Epoch(train) [46][ 900/5005] lr: 1.0000e-02 eta: 1 day, 6:00:09 time: 0.2253 data_time: 0.0027 loss: 1.3579 03/05 17:06:02 - mmengine - INFO - Epoch(train) [46][1000/5005] lr: 1.0000e-02 eta: 1 day, 5:59:45 time: 0.2214 data_time: 0.0028 loss: 1.2660 03/05 17:06:25 - mmengine - INFO - Epoch(train) [46][1100/5005] lr: 1.0000e-02 eta: 1 day, 5:59:23 time: 0.2241 data_time: 0.0029 loss: 1.3274 03/05 17:06:47 - mmengine - INFO - Epoch(train) [46][1200/5005] lr: 1.0000e-02 eta: 1 day, 5:58:59 time: 0.2237 data_time: 0.0024 loss: 1.2217 03/05 17:07:10 - mmengine - INFO - Epoch(train) [46][1300/5005] lr: 1.0000e-02 eta: 1 day, 5:58:36 time: 0.2231 data_time: 0.0025 loss: 1.5928 03/05 17:07:33 - mmengine - INFO - Epoch(train) [46][1400/5005] lr: 1.0000e-02 eta: 1 day, 5:58:13 time: 0.2220 data_time: 0.0024 loss: 1.3475 03/05 17:07:56 - mmengine - INFO - Epoch(train) [46][1500/5005] lr: 1.0000e-02 eta: 1 day, 5:57:51 time: 0.2228 data_time: 0.0026 loss: 1.0910 03/05 17:08:18 - mmengine - INFO - Epoch(train) [46][1600/5005] lr: 1.0000e-02 eta: 1 day, 5:57:27 time: 0.2228 data_time: 0.0025 loss: 1.4598 03/05 17:08:41 - mmengine - INFO - Epoch(train) [46][1700/5005] lr: 1.0000e-02 eta: 1 day, 5:57:04 time: 0.2227 data_time: 0.0025 loss: 1.2164 03/05 17:08:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:09:04 - mmengine - INFO - Epoch(train) [46][1800/5005] lr: 1.0000e-02 eta: 1 day, 5:56:41 time: 0.2302 data_time: 0.0023 loss: 1.3503 03/05 17:09:26 - mmengine - INFO - Epoch(train) [46][1900/5005] lr: 1.0000e-02 eta: 1 day, 5:56:18 time: 0.2243 data_time: 0.0027 loss: 1.1359 03/05 17:09:49 - mmengine - INFO - Epoch(train) [46][2000/5005] lr: 1.0000e-02 eta: 1 day, 5:55:55 time: 0.2239 data_time: 0.0023 loss: 1.3768 03/05 17:10:12 - mmengine - INFO - Epoch(train) [46][2100/5005] lr: 1.0000e-02 eta: 1 day, 5:55:31 time: 0.2217 data_time: 0.0023 loss: 1.2929 03/05 17:10:34 - mmengine - INFO - Epoch(train) [46][2200/5005] lr: 1.0000e-02 eta: 1 day, 5:55:08 time: 0.2207 data_time: 0.0027 loss: 1.3556 03/05 17:10:57 - mmengine - INFO - Epoch(train) [46][2300/5005] lr: 1.0000e-02 eta: 1 day, 5:54:45 time: 0.2234 data_time: 0.0027 loss: 1.2262 03/05 17:11:20 - mmengine - INFO - Epoch(train) [46][2400/5005] lr: 1.0000e-02 eta: 1 day, 5:54:22 time: 0.2405 data_time: 0.0024 loss: 1.2446 03/05 17:11:42 - mmengine - INFO - Epoch(train) [46][2500/5005] lr: 1.0000e-02 eta: 1 day, 5:53:58 time: 0.2227 data_time: 0.0027 loss: 1.5206 03/05 17:12:05 - mmengine - INFO - Epoch(train) [46][2600/5005] lr: 1.0000e-02 eta: 1 day, 5:53:36 time: 0.2204 data_time: 0.0026 loss: 1.2875 03/05 17:12:28 - mmengine - INFO - Epoch(train) [46][2700/5005] lr: 1.0000e-02 eta: 1 day, 5:53:12 time: 0.2243 data_time: 0.0027 loss: 1.4667 03/05 17:12:45 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:12:51 - mmengine - INFO - Epoch(train) [46][2800/5005] lr: 1.0000e-02 eta: 1 day, 5:52:50 time: 0.2399 data_time: 0.0028 loss: 1.1901 03/05 17:13:13 - mmengine - INFO - Epoch(train) [46][2900/5005] lr: 1.0000e-02 eta: 1 day, 5:52:26 time: 0.2219 data_time: 0.0027 loss: 1.2785 03/05 17:13:36 - mmengine - INFO - Epoch(train) [46][3000/5005] lr: 1.0000e-02 eta: 1 day, 5:52:03 time: 0.2229 data_time: 0.0026 loss: 1.2556 03/05 17:13:58 - mmengine - INFO - Epoch(train) [46][3100/5005] lr: 1.0000e-02 eta: 1 day, 5:51:40 time: 0.2215 data_time: 0.0026 loss: 1.2853 03/05 17:14:21 - mmengine - INFO - Epoch(train) [46][3200/5005] lr: 1.0000e-02 eta: 1 day, 5:51:17 time: 0.2417 data_time: 0.0026 loss: 1.4362 03/05 17:14:44 - mmengine - INFO - Epoch(train) [46][3300/5005] lr: 1.0000e-02 eta: 1 day, 5:50:54 time: 0.2241 data_time: 0.0028 loss: 1.3967 03/05 17:15:06 - mmengine - INFO - Epoch(train) [46][3400/5005] lr: 1.0000e-02 eta: 1 day, 5:50:31 time: 0.2239 data_time: 0.0026 loss: 1.3700 03/05 17:15:29 - mmengine - INFO - Epoch(train) [46][3500/5005] lr: 1.0000e-02 eta: 1 day, 5:50:08 time: 0.2267 data_time: 0.0028 loss: 1.4239 03/05 17:15:52 - mmengine - INFO - Epoch(train) [46][3600/5005] lr: 1.0000e-02 eta: 1 day, 5:49:45 time: 0.2214 data_time: 0.0025 loss: 1.3120 03/05 17:16:15 - mmengine - INFO - Epoch(train) [46][3700/5005] lr: 1.0000e-02 eta: 1 day, 5:49:21 time: 0.2226 data_time: 0.0027 loss: 1.3373 03/05 17:16:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:16:37 - mmengine - INFO - Epoch(train) [46][3800/5005] lr: 1.0000e-02 eta: 1 day, 5:48:58 time: 0.2229 data_time: 0.0025 loss: 1.4223 03/05 17:17:00 - mmengine - INFO - Epoch(train) [46][3900/5005] lr: 1.0000e-02 eta: 1 day, 5:48:36 time: 0.2273 data_time: 0.0027 loss: 1.4516 03/05 17:17:23 - mmengine - INFO - Epoch(train) [46][4000/5005] lr: 1.0000e-02 eta: 1 day, 5:48:13 time: 0.2230 data_time: 0.0027 loss: 1.2853 03/05 17:17:46 - mmengine - INFO - Epoch(train) [46][4100/5005] lr: 1.0000e-02 eta: 1 day, 5:47:49 time: 0.2262 data_time: 0.0027 loss: 1.2192 03/05 17:18:08 - mmengine - INFO - Epoch(train) [46][4200/5005] lr: 1.0000e-02 eta: 1 day, 5:47:27 time: 0.2245 data_time: 0.0026 loss: 1.4182 03/05 17:18:31 - mmengine - INFO - Epoch(train) [46][4300/5005] lr: 1.0000e-02 eta: 1 day, 5:47:04 time: 0.2202 data_time: 0.0024 loss: 1.3078 03/05 17:18:54 - mmengine - INFO - Epoch(train) [46][4400/5005] lr: 1.0000e-02 eta: 1 day, 5:46:41 time: 0.2239 data_time: 0.0026 loss: 1.3712 03/05 17:19:17 - mmengine - INFO - Epoch(train) [46][4500/5005] lr: 1.0000e-02 eta: 1 day, 5:46:18 time: 0.2260 data_time: 0.0026 loss: 1.4647 03/05 17:19:39 - mmengine - INFO - Epoch(train) [46][4600/5005] lr: 1.0000e-02 eta: 1 day, 5:45:55 time: 0.2230 data_time: 0.0025 loss: 1.3858 03/05 17:20:02 - mmengine - INFO - Epoch(train) [46][4700/5005] lr: 1.0000e-02 eta: 1 day, 5:45:32 time: 0.2224 data_time: 0.0026 loss: 1.2361 03/05 17:20:19 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:20:25 - mmengine - INFO - Epoch(train) [46][4800/5005] lr: 1.0000e-02 eta: 1 day, 5:45:09 time: 0.2218 data_time: 0.0027 loss: 1.2037 03/05 17:20:49 - mmengine - INFO - Epoch(train) [46][4900/5005] lr: 1.0000e-02 eta: 1 day, 5:44:48 time: 0.2836 data_time: 0.0024 loss: 1.6880 03/05 17:21:17 - mmengine - INFO - Epoch(train) [46][5000/5005] lr: 1.0000e-02 eta: 1 day, 5:44:37 time: 0.2863 data_time: 0.0023 loss: 1.2898 03/05 17:21:19 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:21:22 - mmengine - INFO - Saving checkpoint at 46 epochs 03/05 17:21:36 - mmengine - INFO - Epoch(val) [46][100/196] eta: 0:00:12 time: 0.0185 data_time: 0.0004 03/05 17:21:49 - mmengine - INFO - Epoch(val) [46][196/196] accuracy/top1: 71.8440 accuracy/top5: 91.0860 03/05 17:22:21 - mmengine - INFO - Epoch(train) [47][ 100/5005] lr: 1.0000e-02 eta: 1 day, 5:44:32 time: 0.2266 data_time: 0.0030 loss: 1.3671 03/05 17:22:44 - mmengine - INFO - Epoch(train) [47][ 200/5005] lr: 1.0000e-02 eta: 1 day, 5:44:09 time: 0.2229 data_time: 0.0027 loss: 1.2427 03/05 17:23:07 - mmengine - INFO - Epoch(train) [47][ 300/5005] lr: 1.0000e-02 eta: 1 day, 5:43:47 time: 0.2375 data_time: 0.0027 loss: 1.2649 03/05 17:23:29 - mmengine - INFO - Epoch(train) [47][ 400/5005] lr: 1.0000e-02 eta: 1 day, 5:43:23 time: 0.2222 data_time: 0.0028 loss: 1.3577 03/05 17:23:53 - mmengine - INFO - Epoch(train) [47][ 500/5005] lr: 1.0000e-02 eta: 1 day, 5:43:02 time: 0.2252 data_time: 0.0023 loss: 1.4083 03/05 17:24:15 - mmengine - INFO - Epoch(train) [47][ 600/5005] lr: 1.0000e-02 eta: 1 day, 5:42:38 time: 0.2218 data_time: 0.0024 loss: 1.1752 03/05 17:24:38 - mmengine - INFO - Epoch(train) [47][ 700/5005] lr: 1.0000e-02 eta: 1 day, 5:42:16 time: 0.2220 data_time: 0.0025 loss: 1.2788 03/05 17:24:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:25:01 - mmengine - INFO - Epoch(train) [47][ 800/5005] lr: 1.0000e-02 eta: 1 day, 5:41:52 time: 0.2237 data_time: 0.0024 loss: 1.2217 03/05 17:25:24 - mmengine - INFO - Epoch(train) [47][ 900/5005] lr: 1.0000e-02 eta: 1 day, 5:41:30 time: 0.2499 data_time: 0.0024 loss: 1.4948 03/05 17:25:47 - mmengine - INFO - Epoch(train) [47][1000/5005] lr: 1.0000e-02 eta: 1 day, 5:41:07 time: 0.2218 data_time: 0.0024 loss: 1.3720 03/05 17:26:10 - mmengine - INFO - Epoch(train) [47][1100/5005] lr: 1.0000e-02 eta: 1 day, 5:40:44 time: 0.2449 data_time: 0.0023 loss: 1.4594 03/05 17:26:32 - mmengine - INFO - Epoch(train) [47][1200/5005] lr: 1.0000e-02 eta: 1 day, 5:40:21 time: 0.2249 data_time: 0.0022 loss: 1.3863 03/05 17:26:55 - mmengine - INFO - Epoch(train) [47][1300/5005] lr: 1.0000e-02 eta: 1 day, 5:39:58 time: 0.2442 data_time: 0.0023 loss: 1.3676 03/05 17:27:18 - mmengine - INFO - Epoch(train) [47][1400/5005] lr: 1.0000e-02 eta: 1 day, 5:39:36 time: 0.2249 data_time: 0.0027 loss: 1.4307 03/05 17:27:41 - mmengine - INFO - Epoch(train) [47][1500/5005] lr: 1.0000e-02 eta: 1 day, 5:39:13 time: 0.2427 data_time: 0.0028 loss: 1.2602 03/05 17:28:03 - mmengine - INFO - Epoch(train) [47][1600/5005] lr: 1.0000e-02 eta: 1 day, 5:38:50 time: 0.2203 data_time: 0.0024 loss: 1.3539 03/05 17:28:26 - mmengine - INFO - Epoch(train) [47][1700/5005] lr: 1.0000e-02 eta: 1 day, 5:38:26 time: 0.2437 data_time: 0.0028 loss: 1.2783 03/05 17:28:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:28:49 - mmengine - INFO - Epoch(train) [47][1800/5005] lr: 1.0000e-02 eta: 1 day, 5:38:04 time: 0.2247 data_time: 0.0025 loss: 1.3247 03/05 17:29:12 - mmengine - INFO - Epoch(train) [47][1900/5005] lr: 1.0000e-02 eta: 1 day, 5:37:41 time: 0.2206 data_time: 0.0024 loss: 1.2615 03/05 17:29:34 - mmengine - INFO - Epoch(train) [47][2000/5005] lr: 1.0000e-02 eta: 1 day, 5:37:17 time: 0.2226 data_time: 0.0029 loss: 1.4038 03/05 17:29:57 - mmengine - INFO - Epoch(train) [47][2100/5005] lr: 1.0000e-02 eta: 1 day, 5:36:54 time: 0.2232 data_time: 0.0026 loss: 1.2496 03/05 17:30:19 - mmengine - INFO - Epoch(train) [47][2200/5005] lr: 1.0000e-02 eta: 1 day, 5:36:31 time: 0.2235 data_time: 0.0024 loss: 1.1256 03/05 17:30:42 - mmengine - INFO - Epoch(train) [47][2300/5005] lr: 1.0000e-02 eta: 1 day, 5:36:08 time: 0.2297 data_time: 0.0024 loss: 1.1360 03/05 17:31:05 - mmengine - INFO - Epoch(train) [47][2400/5005] lr: 1.0000e-02 eta: 1 day, 5:35:45 time: 0.2203 data_time: 0.0025 loss: 1.2840 03/05 17:31:28 - mmengine - INFO - Epoch(train) [47][2500/5005] lr: 1.0000e-02 eta: 1 day, 5:35:22 time: 0.2438 data_time: 0.0026 loss: 1.3621 03/05 17:31:51 - mmengine - INFO - Epoch(train) [47][2600/5005] lr: 1.0000e-02 eta: 1 day, 5:34:59 time: 0.2242 data_time: 0.0026 loss: 1.4156 03/05 17:32:13 - mmengine - INFO - Epoch(train) [47][2700/5005] lr: 1.0000e-02 eta: 1 day, 5:34:36 time: 0.2235 data_time: 0.0024 loss: 1.3485 03/05 17:32:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:32:36 - mmengine - INFO - Epoch(train) [47][2800/5005] lr: 1.0000e-02 eta: 1 day, 5:34:12 time: 0.2235 data_time: 0.0027 loss: 1.3499 03/05 17:32:59 - mmengine - INFO - Epoch(train) [47][2900/5005] lr: 1.0000e-02 eta: 1 day, 5:33:50 time: 0.2468 data_time: 0.0024 loss: 1.5192 03/05 17:33:21 - mmengine - INFO - Epoch(train) [47][3000/5005] lr: 1.0000e-02 eta: 1 day, 5:33:26 time: 0.2192 data_time: 0.0030 loss: 1.3631 03/05 17:33:44 - mmengine - INFO - Epoch(train) [47][3100/5005] lr: 1.0000e-02 eta: 1 day, 5:33:03 time: 0.2237 data_time: 0.0025 loss: 1.3737 03/05 17:34:06 - mmengine - INFO - Epoch(train) [47][3200/5005] lr: 1.0000e-02 eta: 1 day, 5:32:39 time: 0.2244 data_time: 0.0024 loss: 1.4527 03/05 17:34:29 - mmengine - INFO - Epoch(train) [47][3300/5005] lr: 1.0000e-02 eta: 1 day, 5:32:16 time: 0.2223 data_time: 0.0022 loss: 1.3167 03/05 17:34:51 - mmengine - INFO - Epoch(train) [47][3400/5005] lr: 1.0000e-02 eta: 1 day, 5:31:53 time: 0.2223 data_time: 0.0024 loss: 1.3119 03/05 17:35:14 - mmengine - INFO - Epoch(train) [47][3500/5005] lr: 1.0000e-02 eta: 1 day, 5:31:30 time: 0.2235 data_time: 0.0025 loss: 1.4230 03/05 17:35:36 - mmengine - INFO - Epoch(train) [47][3600/5005] lr: 1.0000e-02 eta: 1 day, 5:31:06 time: 0.2224 data_time: 0.0023 loss: 1.3002 03/05 17:35:59 - mmengine - INFO - Epoch(train) [47][3700/5005] lr: 1.0000e-02 eta: 1 day, 5:30:43 time: 0.2219 data_time: 0.0025 loss: 1.4870 03/05 17:36:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:36:22 - mmengine - INFO - Epoch(train) [47][3800/5005] lr: 1.0000e-02 eta: 1 day, 5:30:20 time: 0.2245 data_time: 0.0026 loss: 1.2760 03/05 17:36:45 - mmengine - INFO - Epoch(train) [47][3900/5005] lr: 1.0000e-02 eta: 1 day, 5:29:57 time: 0.2249 data_time: 0.0027 loss: 1.1890 03/05 17:37:07 - mmengine - INFO - Epoch(train) [47][4000/5005] lr: 1.0000e-02 eta: 1 day, 5:29:34 time: 0.2239 data_time: 0.0024 loss: 1.1537 03/05 17:37:30 - mmengine - INFO - Epoch(train) [47][4100/5005] lr: 1.0000e-02 eta: 1 day, 5:29:10 time: 0.2232 data_time: 0.0022 loss: 1.2731 03/05 17:37:53 - mmengine - INFO - Epoch(train) [47][4200/5005] lr: 1.0000e-02 eta: 1 day, 5:28:47 time: 0.2268 data_time: 0.0022 loss: 1.3089 03/05 17:38:16 - mmengine - INFO - Epoch(train) [47][4300/5005] lr: 1.0000e-02 eta: 1 day, 5:28:25 time: 0.2231 data_time: 0.0025 loss: 1.4573 03/05 17:38:38 - mmengine - INFO - Epoch(train) [47][4400/5005] lr: 1.0000e-02 eta: 1 day, 5:28:02 time: 0.2207 data_time: 0.0024 loss: 1.1813 03/05 17:39:01 - mmengine - INFO - Epoch(train) [47][4500/5005] lr: 1.0000e-02 eta: 1 day, 5:27:39 time: 0.2255 data_time: 0.0027 loss: 1.3052 03/05 17:39:24 - mmengine - INFO - Epoch(train) [47][4600/5005] lr: 1.0000e-02 eta: 1 day, 5:27:16 time: 0.2454 data_time: 0.0026 loss: 1.3161 03/05 17:39:47 - mmengine - INFO - Epoch(train) [47][4700/5005] lr: 1.0000e-02 eta: 1 day, 5:26:53 time: 0.2250 data_time: 0.0027 loss: 1.3497 03/05 17:40:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:40:09 - mmengine - INFO - Epoch(train) [47][4800/5005] lr: 1.0000e-02 eta: 1 day, 5:26:30 time: 0.2199 data_time: 0.0029 loss: 1.4263 03/05 17:40:33 - mmengine - INFO - Epoch(train) [47][4900/5005] lr: 1.0000e-02 eta: 1 day, 5:26:08 time: 0.2663 data_time: 0.0022 loss: 1.3248 03/05 17:41:01 - mmengine - INFO - Epoch(train) [47][5000/5005] lr: 1.0000e-02 eta: 1 day, 5:25:57 time: 0.2947 data_time: 0.0027 loss: 1.3844 03/05 17:41:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:41:05 - mmengine - INFO - Saving checkpoint at 47 epochs 03/05 17:41:20 - mmengine - INFO - Epoch(val) [47][100/196] eta: 0:00:13 time: 0.0194 data_time: 0.0003 03/05 17:41:34 - mmengine - INFO - Epoch(val) [47][196/196] accuracy/top1: 71.9560 accuracy/top5: 90.9320 03/05 17:42:05 - mmengine - INFO - Epoch(train) [48][ 100/5005] lr: 1.0000e-02 eta: 1 day, 5:25:49 time: 0.2237 data_time: 0.0030 loss: 1.4859 03/05 17:42:28 - mmengine - INFO - Epoch(train) [48][ 200/5005] lr: 1.0000e-02 eta: 1 day, 5:25:27 time: 0.2501 data_time: 0.0024 loss: 1.5042 03/05 17:42:51 - mmengine - INFO - Epoch(train) [48][ 300/5005] lr: 1.0000e-02 eta: 1 day, 5:25:04 time: 0.2380 data_time: 0.0032 loss: 1.1997 03/05 17:43:14 - mmengine - INFO - Epoch(train) [48][ 400/5005] lr: 1.0000e-02 eta: 1 day, 5:24:42 time: 0.2522 data_time: 0.0024 loss: 1.4062 03/05 17:43:36 - mmengine - INFO - Epoch(train) [48][ 500/5005] lr: 1.0000e-02 eta: 1 day, 5:24:18 time: 0.2193 data_time: 0.0025 loss: 1.2355 03/05 17:43:59 - mmengine - INFO - Epoch(train) [48][ 600/5005] lr: 1.0000e-02 eta: 1 day, 5:23:55 time: 0.2487 data_time: 0.0028 loss: 1.2137 03/05 17:44:21 - mmengine - INFO - Epoch(train) [48][ 700/5005] lr: 1.0000e-02 eta: 1 day, 5:23:32 time: 0.2245 data_time: 0.0028 loss: 1.3397 03/05 17:44:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:44:44 - mmengine - INFO - Epoch(train) [48][ 800/5005] lr: 1.0000e-02 eta: 1 day, 5:23:09 time: 0.2212 data_time: 0.0027 loss: 1.4130 03/05 17:45:07 - mmengine - INFO - Epoch(train) [48][ 900/5005] lr: 1.0000e-02 eta: 1 day, 5:22:45 time: 0.2217 data_time: 0.0028 loss: 1.2760 03/05 17:45:30 - mmengine - INFO - Epoch(train) [48][1000/5005] lr: 1.0000e-02 eta: 1 day, 5:22:22 time: 0.2254 data_time: 0.0025 loss: 1.3274 03/05 17:45:53 - mmengine - INFO - Epoch(train) [48][1100/5005] lr: 1.0000e-02 eta: 1 day, 5:21:59 time: 0.2247 data_time: 0.0025 loss: 1.1553 03/05 17:46:16 - mmengine - INFO - Epoch(train) [48][1200/5005] lr: 1.0000e-02 eta: 1 day, 5:21:37 time: 0.2262 data_time: 0.0027 loss: 1.3825 03/05 17:46:38 - mmengine - INFO - Epoch(train) [48][1300/5005] lr: 1.0000e-02 eta: 1 day, 5:21:14 time: 0.2219 data_time: 0.0028 loss: 1.3155 03/05 17:47:01 - mmengine - INFO - Epoch(train) [48][1400/5005] lr: 1.0000e-02 eta: 1 day, 5:20:50 time: 0.2218 data_time: 0.0025 loss: 1.4837 03/05 17:47:23 - mmengine - INFO - Epoch(train) [48][1500/5005] lr: 1.0000e-02 eta: 1 day, 5:20:27 time: 0.2186 data_time: 0.0028 loss: 1.3900 03/05 17:47:47 - mmengine - INFO - Epoch(train) [48][1600/5005] lr: 1.0000e-02 eta: 1 day, 5:20:05 time: 0.2518 data_time: 0.0025 loss: 1.3421 03/05 17:48:09 - mmengine - INFO - Epoch(train) [48][1700/5005] lr: 1.0000e-02 eta: 1 day, 5:19:42 time: 0.2199 data_time: 0.0024 loss: 1.4827 03/05 17:48:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:48:32 - mmengine - INFO - Epoch(train) [48][1800/5005] lr: 1.0000e-02 eta: 1 day, 5:19:18 time: 0.2241 data_time: 0.0025 loss: 1.2342 03/05 17:48:54 - mmengine - INFO - Epoch(train) [48][1900/5005] lr: 1.0000e-02 eta: 1 day, 5:18:55 time: 0.2230 data_time: 0.0027 loss: 1.3171 03/05 17:49:17 - mmengine - INFO - Epoch(train) [48][2000/5005] lr: 1.0000e-02 eta: 1 day, 5:18:32 time: 0.2197 data_time: 0.0027 loss: 1.4202 03/05 17:49:40 - mmengine - INFO - Epoch(train) [48][2100/5005] lr: 1.0000e-02 eta: 1 day, 5:18:10 time: 0.2222 data_time: 0.0026 loss: 1.3441 03/05 17:50:03 - mmengine - INFO - Epoch(train) [48][2200/5005] lr: 1.0000e-02 eta: 1 day, 5:17:46 time: 0.2239 data_time: 0.0028 loss: 1.3484 03/05 17:50:25 - mmengine - INFO - Epoch(train) [48][2300/5005] lr: 1.0000e-02 eta: 1 day, 5:17:23 time: 0.2258 data_time: 0.0024 loss: 1.2737 03/05 17:50:48 - mmengine - INFO - Epoch(train) [48][2400/5005] lr: 1.0000e-02 eta: 1 day, 5:17:00 time: 0.2226 data_time: 0.0026 loss: 1.2356 03/05 17:51:11 - mmengine - INFO - Epoch(train) [48][2500/5005] lr: 1.0000e-02 eta: 1 day, 5:16:37 time: 0.2438 data_time: 0.0026 loss: 1.3728 03/05 17:51:34 - mmengine - INFO - Epoch(train) [48][2600/5005] lr: 1.0000e-02 eta: 1 day, 5:16:14 time: 0.2206 data_time: 0.0027 loss: 1.3821 03/05 17:51:56 - mmengine - INFO - Epoch(train) [48][2700/5005] lr: 1.0000e-02 eta: 1 day, 5:15:51 time: 0.2264 data_time: 0.0030 loss: 1.2472 03/05 17:52:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:52:19 - mmengine - INFO - Epoch(train) [48][2800/5005] lr: 1.0000e-02 eta: 1 day, 5:15:28 time: 0.2274 data_time: 0.0025 loss: 1.2438 03/05 17:52:42 - mmengine - INFO - Epoch(train) [48][2900/5005] lr: 1.0000e-02 eta: 1 day, 5:15:05 time: 0.2223 data_time: 0.0026 loss: 1.2954 03/05 17:53:05 - mmengine - INFO - Epoch(train) [48][3000/5005] lr: 1.0000e-02 eta: 1 day, 5:14:42 time: 0.2207 data_time: 0.0027 loss: 1.3717 03/05 17:53:27 - mmengine - INFO - Epoch(train) [48][3100/5005] lr: 1.0000e-02 eta: 1 day, 5:14:19 time: 0.2262 data_time: 0.0031 loss: 1.4035 03/05 17:53:50 - mmengine - INFO - Epoch(train) [48][3200/5005] lr: 1.0000e-02 eta: 1 day, 5:13:56 time: 0.2257 data_time: 0.0027 loss: 1.4975 03/05 17:54:13 - mmengine - INFO - Epoch(train) [48][3300/5005] lr: 1.0000e-02 eta: 1 day, 5:13:33 time: 0.2211 data_time: 0.0023 loss: 1.3252 03/05 17:54:36 - mmengine - INFO - Epoch(train) [48][3400/5005] lr: 1.0000e-02 eta: 1 day, 5:13:10 time: 0.2369 data_time: 0.0026 loss: 1.2959 03/05 17:54:58 - mmengine - INFO - Epoch(train) [48][3500/5005] lr: 1.0000e-02 eta: 1 day, 5:12:46 time: 0.2234 data_time: 0.0028 loss: 1.1952 03/05 17:55:21 - mmengine - INFO - Epoch(train) [48][3600/5005] lr: 1.0000e-02 eta: 1 day, 5:12:24 time: 0.2230 data_time: 0.0025 loss: 1.4263 03/05 17:55:44 - mmengine - INFO - Epoch(train) [48][3700/5005] lr: 1.0000e-02 eta: 1 day, 5:12:00 time: 0.2308 data_time: 0.0027 loss: 1.2671 03/05 17:55:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:56:06 - mmengine - INFO - Epoch(train) [48][3800/5005] lr: 1.0000e-02 eta: 1 day, 5:11:37 time: 0.2214 data_time: 0.0029 loss: 1.3034 03/05 17:56:29 - mmengine - INFO - Epoch(train) [48][3900/5005] lr: 1.0000e-02 eta: 1 day, 5:11:14 time: 0.2209 data_time: 0.0024 loss: 1.3620 03/05 17:56:52 - mmengine - INFO - Epoch(train) [48][4000/5005] lr: 1.0000e-02 eta: 1 day, 5:10:51 time: 0.2241 data_time: 0.0026 loss: 1.3526 03/05 17:57:14 - mmengine - INFO - Epoch(train) [48][4100/5005] lr: 1.0000e-02 eta: 1 day, 5:10:28 time: 0.2435 data_time: 0.0027 loss: 1.3699 03/05 17:57:37 - mmengine - INFO - Epoch(train) [48][4200/5005] lr: 1.0000e-02 eta: 1 day, 5:10:05 time: 0.2232 data_time: 0.0029 loss: 1.4578 03/05 17:58:00 - mmengine - INFO - Epoch(train) [48][4300/5005] lr: 1.0000e-02 eta: 1 day, 5:09:42 time: 0.2237 data_time: 0.0026 loss: 1.4551 03/05 17:58:22 - mmengine - INFO - Epoch(train) [48][4400/5005] lr: 1.0000e-02 eta: 1 day, 5:09:18 time: 0.2214 data_time: 0.0026 loss: 1.3431 03/05 17:58:45 - mmengine - INFO - Epoch(train) [48][4500/5005] lr: 1.0000e-02 eta: 1 day, 5:08:55 time: 0.2235 data_time: 0.0029 loss: 1.4407 03/05 17:59:07 - mmengine - INFO - Epoch(train) [48][4600/5005] lr: 1.0000e-02 eta: 1 day, 5:08:32 time: 0.2208 data_time: 0.0023 loss: 1.3079 03/05 17:59:30 - mmengine - INFO - Epoch(train) [48][4700/5005] lr: 1.0000e-02 eta: 1 day, 5:08:08 time: 0.2242 data_time: 0.0027 loss: 1.3617 03/05 17:59:45 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 17:59:53 - mmengine - INFO - Epoch(train) [48][4800/5005] lr: 1.0000e-02 eta: 1 day, 5:07:46 time: 0.2415 data_time: 0.0025 loss: 1.2332 03/05 18:00:16 - mmengine - INFO - Epoch(train) [48][4900/5005] lr: 1.0000e-02 eta: 1 day, 5:07:24 time: 0.2865 data_time: 0.0025 loss: 1.3732 03/05 18:00:45 - mmengine - INFO - Epoch(train) [48][5000/5005] lr: 1.0000e-02 eta: 1 day, 5:07:12 time: 0.2837 data_time: 0.0024 loss: 1.3360 03/05 18:00:46 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:00:49 - mmengine - INFO - Saving checkpoint at 48 epochs 03/05 18:01:04 - mmengine - INFO - Epoch(val) [48][100/196] eta: 0:00:12 time: 0.0218 data_time: 0.0004 03/05 18:01:17 - mmengine - INFO - Epoch(val) [48][196/196] accuracy/top1: 71.7560 accuracy/top5: 90.9480 03/05 18:01:49 - mmengine - INFO - Epoch(train) [49][ 100/5005] lr: 1.0000e-02 eta: 1 day, 5:07:05 time: 0.2220 data_time: 0.0031 loss: 1.4130 03/05 18:02:11 - mmengine - INFO - Epoch(train) [49][ 200/5005] lr: 1.0000e-02 eta: 1 day, 5:06:42 time: 0.2230 data_time: 0.0032 loss: 1.1057 03/05 18:02:33 - mmengine - INFO - Epoch(train) [49][ 300/5005] lr: 1.0000e-02 eta: 1 day, 5:06:18 time: 0.2263 data_time: 0.0023 loss: 1.4763 03/05 18:02:56 - mmengine - INFO - Epoch(train) [49][ 400/5005] lr: 1.0000e-02 eta: 1 day, 5:05:55 time: 0.2230 data_time: 0.0023 loss: 1.3974 03/05 18:03:19 - mmengine - INFO - Epoch(train) [49][ 500/5005] lr: 1.0000e-02 eta: 1 day, 5:05:33 time: 0.2199 data_time: 0.0028 loss: 1.4224 03/05 18:03:41 - mmengine - INFO - Epoch(train) [49][ 600/5005] lr: 1.0000e-02 eta: 1 day, 5:05:09 time: 0.2201 data_time: 0.0029 loss: 1.4051 03/05 18:04:04 - mmengine - INFO - Epoch(train) [49][ 700/5005] lr: 1.0000e-02 eta: 1 day, 5:04:45 time: 0.2245 data_time: 0.0025 loss: 1.2473 03/05 18:04:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:04:27 - mmengine - INFO - Epoch(train) [49][ 800/5005] lr: 1.0000e-02 eta: 1 day, 5:04:22 time: 0.2483 data_time: 0.0024 loss: 1.2094 03/05 18:04:50 - mmengine - INFO - Epoch(train) [49][ 900/5005] lr: 1.0000e-02 eta: 1 day, 5:04:00 time: 0.2501 data_time: 0.0024 loss: 1.4015 03/05 18:05:12 - mmengine - INFO - Epoch(train) [49][1000/5005] lr: 1.0000e-02 eta: 1 day, 5:03:37 time: 0.2253 data_time: 0.0026 loss: 1.1970 03/05 18:05:35 - mmengine - INFO - Epoch(train) [49][1100/5005] lr: 1.0000e-02 eta: 1 day, 5:03:13 time: 0.2227 data_time: 0.0029 loss: 1.3860 03/05 18:05:58 - mmengine - INFO - Epoch(train) [49][1200/5005] lr: 1.0000e-02 eta: 1 day, 5:02:51 time: 0.2516 data_time: 0.0027 loss: 1.3743 03/05 18:06:21 - mmengine - INFO - Epoch(train) [49][1300/5005] lr: 1.0000e-02 eta: 1 day, 5:02:28 time: 0.2215 data_time: 0.0026 loss: 1.3688 03/05 18:06:43 - mmengine - INFO - Epoch(train) [49][1400/5005] lr: 1.0000e-02 eta: 1 day, 5:02:04 time: 0.2224 data_time: 0.0030 loss: 1.4055 03/05 18:07:06 - mmengine - INFO - Epoch(train) [49][1500/5005] lr: 1.0000e-02 eta: 1 day, 5:01:41 time: 0.2233 data_time: 0.0027 loss: 1.1639 03/05 18:07:29 - mmengine - INFO - Epoch(train) [49][1600/5005] lr: 1.0000e-02 eta: 1 day, 5:01:18 time: 0.2243 data_time: 0.0027 loss: 1.4368 03/05 18:07:52 - mmengine - INFO - Epoch(train) [49][1700/5005] lr: 1.0000e-02 eta: 1 day, 5:00:55 time: 0.2362 data_time: 0.0026 loss: 1.3629 03/05 18:08:05 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:08:14 - mmengine - INFO - Epoch(train) [49][1800/5005] lr: 1.0000e-02 eta: 1 day, 5:00:32 time: 0.2249 data_time: 0.0031 loss: 1.2076 03/05 18:08:37 - mmengine - INFO - Epoch(train) [49][1900/5005] lr: 1.0000e-02 eta: 1 day, 5:00:09 time: 0.2206 data_time: 0.0027 loss: 1.6352 03/05 18:08:59 - mmengine - INFO - Epoch(train) [49][2000/5005] lr: 1.0000e-02 eta: 1 day, 4:59:45 time: 0.2254 data_time: 0.0025 loss: 1.5822 03/05 18:09:22 - mmengine - INFO - Epoch(train) [49][2100/5005] lr: 1.0000e-02 eta: 1 day, 4:59:22 time: 0.2214 data_time: 0.0028 loss: 1.1996 03/05 18:09:45 - mmengine - INFO - Epoch(train) [49][2200/5005] lr: 1.0000e-02 eta: 1 day, 4:58:59 time: 0.2301 data_time: 0.0028 loss: 1.2946 03/05 18:10:07 - mmengine - INFO - Epoch(train) [49][2300/5005] lr: 1.0000e-02 eta: 1 day, 4:58:36 time: 0.2253 data_time: 0.0025 loss: 1.2139 03/05 18:10:30 - mmengine - INFO - Epoch(train) [49][2400/5005] lr: 1.0000e-02 eta: 1 day, 4:58:13 time: 0.2352 data_time: 0.0028 loss: 1.3153 03/05 18:10:53 - mmengine - INFO - Epoch(train) [49][2500/5005] lr: 1.0000e-02 eta: 1 day, 4:57:50 time: 0.2257 data_time: 0.0023 loss: 1.3681 03/05 18:11:16 - mmengine - INFO - Epoch(train) [49][2600/5005] lr: 1.0000e-02 eta: 1 day, 4:57:27 time: 0.2276 data_time: 0.0026 loss: 1.2373 03/05 18:11:38 - mmengine - INFO - Epoch(train) [49][2700/5005] lr: 1.0000e-02 eta: 1 day, 4:57:04 time: 0.2195 data_time: 0.0027 loss: 1.4333 03/05 18:11:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:12:01 - mmengine - INFO - Epoch(train) [49][2800/5005] lr: 1.0000e-02 eta: 1 day, 4:56:40 time: 0.2251 data_time: 0.0024 loss: 1.3184 03/05 18:12:24 - mmengine - INFO - Epoch(train) [49][2900/5005] lr: 1.0000e-02 eta: 1 day, 4:56:18 time: 0.2245 data_time: 0.0025 loss: 1.2568 03/05 18:12:46 - mmengine - INFO - Epoch(train) [49][3000/5005] lr: 1.0000e-02 eta: 1 day, 4:55:55 time: 0.2221 data_time: 0.0025 loss: 1.2491 03/05 18:13:09 - mmengine - INFO - Epoch(train) [49][3100/5005] lr: 1.0000e-02 eta: 1 day, 4:55:32 time: 0.2247 data_time: 0.0025 loss: 1.3209 03/05 18:13:32 - mmengine - INFO - Epoch(train) [49][3200/5005] lr: 1.0000e-02 eta: 1 day, 4:55:08 time: 0.2268 data_time: 0.0032 loss: 1.2841 03/05 18:13:55 - mmengine - INFO - Epoch(train) [49][3300/5005] lr: 1.0000e-02 eta: 1 day, 4:54:46 time: 0.2264 data_time: 0.0027 loss: 1.4138 03/05 18:14:18 - mmengine - INFO - Epoch(train) [49][3400/5005] lr: 1.0000e-02 eta: 1 day, 4:54:23 time: 0.2246 data_time: 0.0025 loss: 1.4119 03/05 18:14:40 - mmengine - INFO - Epoch(train) [49][3500/5005] lr: 1.0000e-02 eta: 1 day, 4:54:00 time: 0.2522 data_time: 0.0027 loss: 1.1793 03/05 18:15:03 - mmengine - INFO - Epoch(train) [49][3600/5005] lr: 1.0000e-02 eta: 1 day, 4:53:37 time: 0.2217 data_time: 0.0028 loss: 1.2829 03/05 18:15:26 - mmengine - INFO - Epoch(train) [49][3700/5005] lr: 1.0000e-02 eta: 1 day, 4:53:14 time: 0.2177 data_time: 0.0027 loss: 1.3107 03/05 18:15:39 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:15:49 - mmengine - INFO - Epoch(train) [49][3800/5005] lr: 1.0000e-02 eta: 1 day, 4:52:51 time: 0.2390 data_time: 0.0025 loss: 1.3116 03/05 18:16:11 - mmengine - INFO - Epoch(train) [49][3900/5005] lr: 1.0000e-02 eta: 1 day, 4:52:28 time: 0.2220 data_time: 0.0025 loss: 1.3168 03/05 18:16:34 - mmengine - INFO - Epoch(train) [49][4000/5005] lr: 1.0000e-02 eta: 1 day, 4:52:05 time: 0.2224 data_time: 0.0028 loss: 1.1696 03/05 18:16:57 - mmengine - INFO - Epoch(train) [49][4100/5005] lr: 1.0000e-02 eta: 1 day, 4:51:42 time: 0.2402 data_time: 0.0029 loss: 1.2726 03/05 18:17:20 - mmengine - INFO - Epoch(train) [49][4200/5005] lr: 1.0000e-02 eta: 1 day, 4:51:20 time: 0.2406 data_time: 0.0027 loss: 1.2132 03/05 18:17:43 - mmengine - INFO - Epoch(train) [49][4300/5005] lr: 1.0000e-02 eta: 1 day, 4:50:57 time: 0.2224 data_time: 0.0027 loss: 1.4338 03/05 18:18:05 - mmengine - INFO - Epoch(train) [49][4400/5005] lr: 1.0000e-02 eta: 1 day, 4:50:33 time: 0.2221 data_time: 0.0029 loss: 1.2963 03/05 18:18:28 - mmengine - INFO - Epoch(train) [49][4500/5005] lr: 1.0000e-02 eta: 1 day, 4:50:11 time: 0.2475 data_time: 0.0026 loss: 1.3575 03/05 18:18:51 - mmengine - INFO - Epoch(train) [49][4600/5005] lr: 1.0000e-02 eta: 1 day, 4:49:48 time: 0.2401 data_time: 0.0025 loss: 1.4145 03/05 18:19:14 - mmengine - INFO - Epoch(train) [49][4700/5005] lr: 1.0000e-02 eta: 1 day, 4:49:25 time: 0.2222 data_time: 0.0026 loss: 1.2485 03/05 18:19:27 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:19:36 - mmengine - INFO - Epoch(train) [49][4800/5005] lr: 1.0000e-02 eta: 1 day, 4:49:02 time: 0.2310 data_time: 0.0028 loss: 1.2847 03/05 18:20:00 - mmengine - INFO - Epoch(train) [49][4900/5005] lr: 1.0000e-02 eta: 1 day, 4:48:41 time: 0.2923 data_time: 0.0024 loss: 1.6765 03/05 18:20:29 - mmengine - INFO - Epoch(train) [49][5000/5005] lr: 1.0000e-02 eta: 1 day, 4:48:29 time: 0.2940 data_time: 0.0024 loss: 1.4559 03/05 18:20:31 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:20:33 - mmengine - INFO - Saving checkpoint at 49 epochs 03/05 18:20:48 - mmengine - INFO - Epoch(val) [49][100/196] eta: 0:00:12 time: 0.0235 data_time: 0.0004 03/05 18:21:01 - mmengine - INFO - Epoch(val) [49][196/196] accuracy/top1: 71.9420 accuracy/top5: 91.0160 03/05 18:21:34 - mmengine - INFO - Epoch(train) [50][ 100/5005] lr: 1.0000e-02 eta: 1 day, 4:48:23 time: 0.2226 data_time: 0.0028 loss: 1.3657 03/05 18:21:57 - mmengine - INFO - Epoch(train) [50][ 200/5005] lr: 1.0000e-02 eta: 1 day, 4:48:00 time: 0.2211 data_time: 0.0033 loss: 1.3157 03/05 18:22:19 - mmengine - INFO - Epoch(train) [50][ 300/5005] lr: 1.0000e-02 eta: 1 day, 4:47:37 time: 0.2257 data_time: 0.0029 loss: 1.2471 03/05 18:22:42 - mmengine - INFO - Epoch(train) [50][ 400/5005] lr: 1.0000e-02 eta: 1 day, 4:47:14 time: 0.2200 data_time: 0.0026 loss: 1.3411 03/05 18:23:05 - mmengine - INFO - Epoch(train) [50][ 500/5005] lr: 1.0000e-02 eta: 1 day, 4:46:51 time: 0.2340 data_time: 0.0026 loss: 1.2574 03/05 18:23:28 - mmengine - INFO - Epoch(train) [50][ 600/5005] lr: 1.0000e-02 eta: 1 day, 4:46:29 time: 0.2224 data_time: 0.0026 loss: 1.3953 03/05 18:23:51 - mmengine - INFO - Epoch(train) [50][ 700/5005] lr: 1.0000e-02 eta: 1 day, 4:46:06 time: 0.2265 data_time: 0.0026 loss: 1.3485 03/05 18:24:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:24:13 - mmengine - INFO - Epoch(train) [50][ 800/5005] lr: 1.0000e-02 eta: 1 day, 4:45:43 time: 0.2236 data_time: 0.0026 loss: 1.3266 03/05 18:24:36 - mmengine - INFO - Epoch(train) [50][ 900/5005] lr: 1.0000e-02 eta: 1 day, 4:45:20 time: 0.2199 data_time: 0.0026 loss: 1.4111 03/05 18:24:59 - mmengine - INFO - Epoch(train) [50][1000/5005] lr: 1.0000e-02 eta: 1 day, 4:44:57 time: 0.2223 data_time: 0.0025 loss: 1.2133 03/05 18:25:21 - mmengine - INFO - Epoch(train) [50][1100/5005] lr: 1.0000e-02 eta: 1 day, 4:44:33 time: 0.2225 data_time: 0.0025 loss: 1.2813 03/05 18:25:44 - mmengine - INFO - Epoch(train) [50][1200/5005] lr: 1.0000e-02 eta: 1 day, 4:44:11 time: 0.2239 data_time: 0.0027 loss: 1.4631 03/05 18:26:07 - mmengine - INFO - Epoch(train) [50][1300/5005] lr: 1.0000e-02 eta: 1 day, 4:43:48 time: 0.2402 data_time: 0.0030 loss: 1.4429 03/05 18:26:30 - mmengine - INFO - Epoch(train) [50][1400/5005] lr: 1.0000e-02 eta: 1 day, 4:43:25 time: 0.2447 data_time: 0.0024 loss: 1.4021 03/05 18:26:53 - mmengine - INFO - Epoch(train) [50][1500/5005] lr: 1.0000e-02 eta: 1 day, 4:43:02 time: 0.2193 data_time: 0.0027 loss: 1.3457 03/05 18:27:15 - mmengine - INFO - Epoch(train) [50][1600/5005] lr: 1.0000e-02 eta: 1 day, 4:42:39 time: 0.2249 data_time: 0.0024 loss: 1.1860 03/05 18:27:38 - mmengine - INFO - Epoch(train) [50][1700/5005] lr: 1.0000e-02 eta: 1 day, 4:42:15 time: 0.2219 data_time: 0.0027 loss: 1.3404 03/05 18:27:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:28:01 - mmengine - INFO - Epoch(train) [50][1800/5005] lr: 1.0000e-02 eta: 1 day, 4:41:54 time: 0.3062 data_time: 0.0030 loss: 1.3035 03/05 18:28:24 - mmengine - INFO - Epoch(train) [50][1900/5005] lr: 1.0000e-02 eta: 1 day, 4:41:31 time: 0.2252 data_time: 0.0031 loss: 1.2457 03/05 18:28:47 - mmengine - INFO - Epoch(train) [50][2000/5005] lr: 1.0000e-02 eta: 1 day, 4:41:07 time: 0.2232 data_time: 0.0028 loss: 1.4789 03/05 18:29:09 - mmengine - INFO - Epoch(train) [50][2100/5005] lr: 1.0000e-02 eta: 1 day, 4:40:44 time: 0.2241 data_time: 0.0026 loss: 1.3463 03/05 18:29:32 - mmengine - INFO - Epoch(train) [50][2200/5005] lr: 1.0000e-02 eta: 1 day, 4:40:21 time: 0.2217 data_time: 0.0026 loss: 1.2861 03/05 18:29:55 - mmengine - INFO - Epoch(train) [50][2300/5005] lr: 1.0000e-02 eta: 1 day, 4:39:58 time: 0.2207 data_time: 0.0025 loss: 1.2634 03/05 18:30:18 - mmengine - INFO - Epoch(train) [50][2400/5005] lr: 1.0000e-02 eta: 1 day, 4:39:35 time: 0.2377 data_time: 0.0024 loss: 1.0594 03/05 18:30:40 - mmengine - INFO - Epoch(train) [50][2500/5005] lr: 1.0000e-02 eta: 1 day, 4:39:12 time: 0.2254 data_time: 0.0027 loss: 1.4844 03/05 18:31:03 - mmengine - INFO - Epoch(train) [50][2600/5005] lr: 1.0000e-02 eta: 1 day, 4:38:48 time: 0.2250 data_time: 0.0028 loss: 1.4274 03/05 18:31:25 - mmengine - INFO - Epoch(train) [50][2700/5005] lr: 1.0000e-02 eta: 1 day, 4:38:25 time: 0.2262 data_time: 0.0027 loss: 1.2935 03/05 18:31:38 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:31:49 - mmengine - INFO - Epoch(train) [50][2800/5005] lr: 1.0000e-02 eta: 1 day, 4:38:03 time: 0.2293 data_time: 0.0030 loss: 1.3819 03/05 18:32:11 - mmengine - INFO - Epoch(train) [50][2900/5005] lr: 1.0000e-02 eta: 1 day, 4:37:40 time: 0.2246 data_time: 0.0030 loss: 1.3009 03/05 18:32:34 - mmengine - INFO - Epoch(train) [50][3000/5005] lr: 1.0000e-02 eta: 1 day, 4:37:17 time: 0.2222 data_time: 0.0026 loss: 1.4250 03/05 18:32:57 - mmengine - INFO - Epoch(train) [50][3100/5005] lr: 1.0000e-02 eta: 1 day, 4:36:53 time: 0.2256 data_time: 0.0024 loss: 1.3578 03/05 18:33:19 - mmengine - INFO - Epoch(train) [50][3200/5005] lr: 1.0000e-02 eta: 1 day, 4:36:30 time: 0.2435 data_time: 0.0028 loss: 1.3076 03/05 18:33:42 - mmengine - INFO - Epoch(train) [50][3300/5005] lr: 1.0000e-02 eta: 1 day, 4:36:08 time: 0.2225 data_time: 0.0031 loss: 1.3322 03/05 18:34:05 - mmengine - INFO - Epoch(train) [50][3400/5005] lr: 1.0000e-02 eta: 1 day, 4:35:44 time: 0.2203 data_time: 0.0026 loss: 1.4493 03/05 18:34:27 - mmengine - INFO - Epoch(train) [50][3500/5005] lr: 1.0000e-02 eta: 1 day, 4:35:21 time: 0.2246 data_time: 0.0030 loss: 1.2804 03/05 18:34:50 - mmengine - INFO - Epoch(train) [50][3600/5005] lr: 1.0000e-02 eta: 1 day, 4:34:58 time: 0.2214 data_time: 0.0027 loss: 1.3494 03/05 18:35:13 - mmengine - INFO - Epoch(train) [50][3700/5005] lr: 1.0000e-02 eta: 1 day, 4:34:35 time: 0.2338 data_time: 0.0026 loss: 1.3476 03/05 18:35:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:35:36 - mmengine - INFO - Epoch(train) [50][3800/5005] lr: 1.0000e-02 eta: 1 day, 4:34:13 time: 0.2225 data_time: 0.0024 loss: 1.3919 03/05 18:35:58 - mmengine - INFO - Epoch(train) [50][3900/5005] lr: 1.0000e-02 eta: 1 day, 4:33:49 time: 0.2240 data_time: 0.0026 loss: 1.3150 03/05 18:36:21 - mmengine - INFO - Epoch(train) [50][4000/5005] lr: 1.0000e-02 eta: 1 day, 4:33:26 time: 0.2225 data_time: 0.0032 loss: 1.2351 03/05 18:36:43 - mmengine - INFO - Epoch(train) [50][4100/5005] lr: 1.0000e-02 eta: 1 day, 4:33:02 time: 0.2273 data_time: 0.0027 loss: 1.2096 03/05 18:37:06 - mmengine - INFO - Epoch(train) [50][4200/5005] lr: 1.0000e-02 eta: 1 day, 4:32:40 time: 0.2206 data_time: 0.0034 loss: 1.3644 03/05 18:37:29 - mmengine - INFO - Epoch(train) [50][4300/5005] lr: 1.0000e-02 eta: 1 day, 4:32:16 time: 0.2206 data_time: 0.0025 loss: 1.3168 03/05 18:37:52 - mmengine - INFO - Epoch(train) [50][4400/5005] lr: 1.0000e-02 eta: 1 day, 4:31:53 time: 0.2224 data_time: 0.0024 loss: 1.4202 03/05 18:38:14 - mmengine - INFO - Epoch(train) [50][4500/5005] lr: 1.0000e-02 eta: 1 day, 4:31:29 time: 0.2245 data_time: 0.0025 loss: 1.3656 03/05 18:38:37 - mmengine - INFO - Epoch(train) [50][4600/5005] lr: 1.0000e-02 eta: 1 day, 4:31:07 time: 0.2200 data_time: 0.0026 loss: 1.4349 03/05 18:39:00 - mmengine - INFO - Epoch(train) [50][4700/5005] lr: 1.0000e-02 eta: 1 day, 4:30:44 time: 0.2261 data_time: 0.0026 loss: 1.0957 03/05 18:39:12 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:39:22 - mmengine - INFO - Epoch(train) [50][4800/5005] lr: 1.0000e-02 eta: 1 day, 4:30:21 time: 0.2248 data_time: 0.0025 loss: 1.2254 03/05 18:39:46 - mmengine - INFO - Epoch(train) [50][4900/5005] lr: 1.0000e-02 eta: 1 day, 4:29:59 time: 0.2907 data_time: 0.0023 loss: 1.2920 03/05 18:40:15 - mmengine - INFO - Epoch(train) [50][5000/5005] lr: 1.0000e-02 eta: 1 day, 4:29:48 time: 0.2957 data_time: 0.0024 loss: 1.3464 03/05 18:40:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:40:19 - mmengine - INFO - Saving checkpoint at 50 epochs 03/05 18:40:33 - mmengine - INFO - Epoch(val) [50][100/196] eta: 0:00:12 time: 0.0192 data_time: 0.0004 03/05 18:40:47 - mmengine - INFO - Epoch(val) [50][196/196] accuracy/top1: 71.7980 accuracy/top5: 91.0120 03/05 18:41:18 - mmengine - INFO - Epoch(train) [51][ 100/5005] lr: 1.0000e-02 eta: 1 day, 4:29:39 time: 0.2239 data_time: 0.0034 loss: 1.4352 03/05 18:41:41 - mmengine - INFO - Epoch(train) [51][ 200/5005] lr: 1.0000e-02 eta: 1 day, 4:29:17 time: 0.2254 data_time: 0.0029 loss: 1.3910 03/05 18:42:04 - mmengine - INFO - Epoch(train) [51][ 300/5005] lr: 1.0000e-02 eta: 1 day, 4:28:54 time: 0.2218 data_time: 0.0023 loss: 1.3526 03/05 18:42:27 - mmengine - INFO - Epoch(train) [51][ 400/5005] lr: 1.0000e-02 eta: 1 day, 4:28:31 time: 0.2465 data_time: 0.0023 loss: 1.1257 03/05 18:42:50 - mmengine - INFO - Epoch(train) [51][ 500/5005] lr: 1.0000e-02 eta: 1 day, 4:28:08 time: 0.2190 data_time: 0.0024 loss: 1.2835 03/05 18:43:12 - mmengine - INFO - Epoch(train) [51][ 600/5005] lr: 1.0000e-02 eta: 1 day, 4:27:45 time: 0.2228 data_time: 0.0024 loss: 1.3480 03/05 18:43:35 - mmengine - INFO - Epoch(train) [51][ 700/5005] lr: 1.0000e-02 eta: 1 day, 4:27:22 time: 0.2189 data_time: 0.0026 loss: 1.2779 03/05 18:43:46 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:43:57 - mmengine - INFO - Epoch(train) [51][ 800/5005] lr: 1.0000e-02 eta: 1 day, 4:26:58 time: 0.2372 data_time: 0.0027 loss: 1.2186 03/05 18:44:20 - mmengine - INFO - Epoch(train) [51][ 900/5005] lr: 1.0000e-02 eta: 1 day, 4:26:36 time: 0.2270 data_time: 0.0025 loss: 1.6226 03/05 18:44:43 - mmengine - INFO - Epoch(train) [51][1000/5005] lr: 1.0000e-02 eta: 1 day, 4:26:13 time: 0.2257 data_time: 0.0023 loss: 1.2588 03/05 18:45:06 - mmengine - INFO - Epoch(train) [51][1100/5005] lr: 1.0000e-02 eta: 1 day, 4:25:50 time: 0.2246 data_time: 0.0027 loss: 1.5274 03/05 18:45:29 - mmengine - INFO - Epoch(train) [51][1200/5005] lr: 1.0000e-02 eta: 1 day, 4:25:27 time: 0.2412 data_time: 0.0024 loss: 1.4323 03/05 18:45:52 - mmengine - INFO - Epoch(train) [51][1300/5005] lr: 1.0000e-02 eta: 1 day, 4:25:04 time: 0.2251 data_time: 0.0025 loss: 1.2725 03/05 18:46:14 - mmengine - INFO - Epoch(train) [51][1400/5005] lr: 1.0000e-02 eta: 1 day, 4:24:41 time: 0.2196 data_time: 0.0024 loss: 1.4563 03/05 18:46:37 - mmengine - INFO - Epoch(train) [51][1500/5005] lr: 1.0000e-02 eta: 1 day, 4:24:18 time: 0.2363 data_time: 0.0025 loss: 1.3304 03/05 18:47:00 - mmengine - INFO - Epoch(train) [51][1600/5005] lr: 1.0000e-02 eta: 1 day, 4:23:55 time: 0.2399 data_time: 0.0025 loss: 1.4839 03/05 18:47:23 - mmengine - INFO - Epoch(train) [51][1700/5005] lr: 1.0000e-02 eta: 1 day, 4:23:32 time: 0.2245 data_time: 0.0025 loss: 1.4190 03/05 18:47:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:47:45 - mmengine - INFO - Epoch(train) [51][1800/5005] lr: 1.0000e-02 eta: 1 day, 4:23:09 time: 0.2255 data_time: 0.0026 loss: 1.2465 03/05 18:48:08 - mmengine - INFO - Epoch(train) [51][1900/5005] lr: 1.0000e-02 eta: 1 day, 4:22:46 time: 0.2247 data_time: 0.0025 loss: 1.2622 03/05 18:48:31 - mmengine - INFO - Epoch(train) [51][2000/5005] lr: 1.0000e-02 eta: 1 day, 4:22:23 time: 0.2247 data_time: 0.0024 loss: 1.2956 03/05 18:48:54 - mmengine - INFO - Epoch(train) [51][2100/5005] lr: 1.0000e-02 eta: 1 day, 4:22:00 time: 0.2245 data_time: 0.0027 loss: 1.4438 03/05 18:49:16 - mmengine - INFO - Epoch(train) [51][2200/5005] lr: 1.0000e-02 eta: 1 day, 4:21:37 time: 0.2252 data_time: 0.0026 loss: 1.5605 03/05 18:49:39 - mmengine - INFO - Epoch(train) [51][2300/5005] lr: 1.0000e-02 eta: 1 day, 4:21:14 time: 0.2227 data_time: 0.0023 loss: 1.2313 03/05 18:50:02 - mmengine - INFO - Epoch(train) [51][2400/5005] lr: 1.0000e-02 eta: 1 day, 4:20:51 time: 0.2280 data_time: 0.0026 loss: 1.3480 03/05 18:50:24 - mmengine - INFO - Epoch(train) [51][2500/5005] lr: 1.0000e-02 eta: 1 day, 4:20:27 time: 0.2432 data_time: 0.0025 loss: 1.5930 03/05 18:50:47 - mmengine - INFO - Epoch(train) [51][2600/5005] lr: 1.0000e-02 eta: 1 day, 4:20:05 time: 0.2226 data_time: 0.0027 loss: 1.3030 03/05 18:51:10 - mmengine - INFO - Epoch(train) [51][2700/5005] lr: 1.0000e-02 eta: 1 day, 4:19:42 time: 0.2217 data_time: 0.0029 loss: 1.5046 03/05 18:51:21 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:51:32 - mmengine - INFO - Epoch(train) [51][2800/5005] lr: 1.0000e-02 eta: 1 day, 4:19:18 time: 0.2213 data_time: 0.0027 loss: 1.1522 03/05 18:51:55 - mmengine - INFO - Epoch(train) [51][2900/5005] lr: 1.0000e-02 eta: 1 day, 4:18:56 time: 0.2279 data_time: 0.0028 loss: 1.2641 03/05 18:52:19 - mmengine - INFO - Epoch(train) [51][3000/5005] lr: 1.0000e-02 eta: 1 day, 4:18:33 time: 0.2276 data_time: 0.0025 loss: 1.4430 03/05 18:52:41 - mmengine - INFO - Epoch(train) [51][3100/5005] lr: 1.0000e-02 eta: 1 day, 4:18:10 time: 0.2261 data_time: 0.0029 loss: 1.4161 03/05 18:53:04 - mmengine - INFO - Epoch(train) [51][3200/5005] lr: 1.0000e-02 eta: 1 day, 4:17:47 time: 0.2243 data_time: 0.0025 loss: 1.3184 03/05 18:53:26 - mmengine - INFO - Epoch(train) [51][3300/5005] lr: 1.0000e-02 eta: 1 day, 4:17:24 time: 0.2228 data_time: 0.0030 loss: 1.4671 03/05 18:53:49 - mmengine - INFO - Epoch(train) [51][3400/5005] lr: 1.0000e-02 eta: 1 day, 4:17:01 time: 0.2218 data_time: 0.0028 loss: 1.2574 03/05 18:54:12 - mmengine - INFO - Epoch(train) [51][3500/5005] lr: 1.0000e-02 eta: 1 day, 4:16:39 time: 0.2259 data_time: 0.0025 loss: 1.3156 03/05 18:54:35 - mmengine - INFO - Epoch(train) [51][3600/5005] lr: 1.0000e-02 eta: 1 day, 4:16:15 time: 0.2254 data_time: 0.0029 loss: 1.3871 03/05 18:54:57 - mmengine - INFO - Epoch(train) [51][3700/5005] lr: 1.0000e-02 eta: 1 day, 4:15:52 time: 0.2408 data_time: 0.0027 loss: 1.3662 03/05 18:55:09 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:55:21 - mmengine - INFO - Epoch(train) [51][3800/5005] lr: 1.0000e-02 eta: 1 day, 4:15:29 time: 0.2260 data_time: 0.0027 loss: 1.3933 03/05 18:55:43 - mmengine - INFO - Epoch(train) [51][3900/5005] lr: 1.0000e-02 eta: 1 day, 4:15:06 time: 0.2219 data_time: 0.0029 loss: 1.4344 03/05 18:56:06 - mmengine - INFO - Epoch(train) [51][4000/5005] lr: 1.0000e-02 eta: 1 day, 4:14:43 time: 0.2247 data_time: 0.0026 loss: 1.3032 03/05 18:56:29 - mmengine - INFO - Epoch(train) [51][4100/5005] lr: 1.0000e-02 eta: 1 day, 4:14:20 time: 0.2227 data_time: 0.0026 loss: 1.4104 03/05 18:56:52 - mmengine - INFO - Epoch(train) [51][4200/5005] lr: 1.0000e-02 eta: 1 day, 4:13:57 time: 0.2207 data_time: 0.0025 loss: 1.3164 03/05 18:57:14 - mmengine - INFO - Epoch(train) [51][4300/5005] lr: 1.0000e-02 eta: 1 day, 4:13:35 time: 0.2246 data_time: 0.0026 loss: 1.2688 03/05 18:57:37 - mmengine - INFO - Epoch(train) [51][4400/5005] lr: 1.0000e-02 eta: 1 day, 4:13:11 time: 0.2240 data_time: 0.0031 loss: 1.2976 03/05 18:58:00 - mmengine - INFO - Epoch(train) [51][4500/5005] lr: 1.0000e-02 eta: 1 day, 4:12:48 time: 0.2207 data_time: 0.0026 loss: 1.2994 03/05 18:58:23 - mmengine - INFO - Epoch(train) [51][4600/5005] lr: 1.0000e-02 eta: 1 day, 4:12:25 time: 0.2246 data_time: 0.0029 loss: 1.2411 03/05 18:58:46 - mmengine - INFO - Epoch(train) [51][4700/5005] lr: 1.0000e-02 eta: 1 day, 4:12:03 time: 0.2252 data_time: 0.0027 loss: 1.3862 03/05 18:58:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 18:59:08 - mmengine - INFO - Epoch(train) [51][4800/5005] lr: 1.0000e-02 eta: 1 day, 4:11:39 time: 0.2222 data_time: 0.0028 loss: 1.3716 03/05 18:59:32 - mmengine - INFO - Epoch(train) [51][4900/5005] lr: 1.0000e-02 eta: 1 day, 4:11:18 time: 0.2843 data_time: 0.0029 loss: 1.5953 03/05 19:00:00 - mmengine - INFO - Epoch(train) [51][5000/5005] lr: 1.0000e-02 eta: 1 day, 4:11:05 time: 0.2879 data_time: 0.0025 loss: 1.5276 03/05 19:00:02 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:00:04 - mmengine - INFO - Saving checkpoint at 51 epochs 03/05 19:00:18 - mmengine - INFO - Epoch(val) [51][100/196] eta: 0:00:12 time: 0.0245 data_time: 0.0004 03/05 19:00:32 - mmengine - INFO - Epoch(val) [51][196/196] accuracy/top1: 72.1160 accuracy/top5: 90.8220 03/05 19:01:04 - mmengine - INFO - Epoch(train) [52][ 100/5005] lr: 1.0000e-02 eta: 1 day, 4:10:57 time: 0.2206 data_time: 0.0026 loss: 1.4765 03/05 19:01:26 - mmengine - INFO - Epoch(train) [52][ 200/5005] lr: 1.0000e-02 eta: 1 day, 4:10:34 time: 0.2195 data_time: 0.0029 loss: 1.4107 03/05 19:01:49 - mmengine - INFO - Epoch(train) [52][ 300/5005] lr: 1.0000e-02 eta: 1 day, 4:10:11 time: 0.2243 data_time: 0.0026 loss: 1.3355 03/05 19:02:12 - mmengine - INFO - Epoch(train) [52][ 400/5005] lr: 1.0000e-02 eta: 1 day, 4:09:48 time: 0.2240 data_time: 0.0026 loss: 1.3540 03/05 19:02:34 - mmengine - INFO - Epoch(train) [52][ 500/5005] lr: 1.0000e-02 eta: 1 day, 4:09:25 time: 0.2250 data_time: 0.0031 loss: 1.3277 03/05 19:02:57 - mmengine - INFO - Epoch(train) [52][ 600/5005] lr: 1.0000e-02 eta: 1 day, 4:09:01 time: 0.2216 data_time: 0.0025 loss: 1.3240 03/05 19:03:20 - mmengine - INFO - Epoch(train) [52][ 700/5005] lr: 1.0000e-02 eta: 1 day, 4:08:39 time: 0.2193 data_time: 0.0026 loss: 1.0714 03/05 19:03:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:03:43 - mmengine - INFO - Epoch(train) [52][ 800/5005] lr: 1.0000e-02 eta: 1 day, 4:08:16 time: 0.2208 data_time: 0.0025 loss: 1.4458 03/05 19:04:05 - mmengine - INFO - Epoch(train) [52][ 900/5005] lr: 1.0000e-02 eta: 1 day, 4:07:53 time: 0.2327 data_time: 0.0026 loss: 1.1754 03/05 19:04:28 - mmengine - INFO - Epoch(train) [52][1000/5005] lr: 1.0000e-02 eta: 1 day, 4:07:30 time: 0.2262 data_time: 0.0028 loss: 1.3098 03/05 19:04:51 - mmengine - INFO - Epoch(train) [52][1100/5005] lr: 1.0000e-02 eta: 1 day, 4:07:07 time: 0.2264 data_time: 0.0028 loss: 1.3754 03/05 19:05:14 - mmengine - INFO - Epoch(train) [52][1200/5005] lr: 1.0000e-02 eta: 1 day, 4:06:44 time: 0.2252 data_time: 0.0023 loss: 1.3348 03/05 19:05:37 - mmengine - INFO - Epoch(train) [52][1300/5005] lr: 1.0000e-02 eta: 1 day, 4:06:22 time: 0.2247 data_time: 0.0028 loss: 1.3971 03/05 19:06:00 - mmengine - INFO - Epoch(train) [52][1400/5005] lr: 1.0000e-02 eta: 1 day, 4:05:58 time: 0.2222 data_time: 0.0032 loss: 1.5273 03/05 19:06:22 - mmengine - INFO - Epoch(train) [52][1500/5005] lr: 1.0000e-02 eta: 1 day, 4:05:35 time: 0.2222 data_time: 0.0026 loss: 1.1545 03/05 19:06:45 - mmengine - INFO - Epoch(train) [52][1600/5005] lr: 1.0000e-02 eta: 1 day, 4:05:12 time: 0.2225 data_time: 0.0028 loss: 1.2556 03/05 19:07:08 - mmengine - INFO - Epoch(train) [52][1700/5005] lr: 1.0000e-02 eta: 1 day, 4:04:49 time: 0.2235 data_time: 0.0029 loss: 1.1953 03/05 19:07:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:07:31 - mmengine - INFO - Epoch(train) [52][1800/5005] lr: 1.0000e-02 eta: 1 day, 4:04:26 time: 0.2237 data_time: 0.0026 loss: 1.2923 03/05 19:07:53 - mmengine - INFO - Epoch(train) [52][1900/5005] lr: 1.0000e-02 eta: 1 day, 4:04:03 time: 0.2263 data_time: 0.0028 loss: 1.4568 03/05 19:08:16 - mmengine - INFO - Epoch(train) [52][2000/5005] lr: 1.0000e-02 eta: 1 day, 4:03:40 time: 0.2272 data_time: 0.0030 loss: 1.2325 03/05 19:08:39 - mmengine - INFO - Epoch(train) [52][2100/5005] lr: 1.0000e-02 eta: 1 day, 4:03:17 time: 0.2288 data_time: 0.0029 loss: 1.1985 03/05 19:09:02 - mmengine - INFO - Epoch(train) [52][2200/5005] lr: 1.0000e-02 eta: 1 day, 4:02:54 time: 0.2233 data_time: 0.0026 loss: 1.3603 03/05 19:09:24 - mmengine - INFO - Epoch(train) [52][2300/5005] lr: 1.0000e-02 eta: 1 day, 4:02:31 time: 0.2402 data_time: 0.0029 loss: 1.2288 03/05 19:09:47 - mmengine - INFO - Epoch(train) [52][2400/5005] lr: 1.0000e-02 eta: 1 day, 4:02:08 time: 0.2258 data_time: 0.0026 loss: 1.3551 03/05 19:10:09 - mmengine - INFO - Epoch(train) [52][2500/5005] lr: 1.0000e-02 eta: 1 day, 4:01:44 time: 0.2269 data_time: 0.0030 loss: 1.2533 03/05 19:10:32 - mmengine - INFO - Epoch(train) [52][2600/5005] lr: 1.0000e-02 eta: 1 day, 4:01:22 time: 0.2317 data_time: 0.0027 loss: 1.2458 03/05 19:10:55 - mmengine - INFO - Epoch(train) [52][2700/5005] lr: 1.0000e-02 eta: 1 day, 4:00:58 time: 0.2238 data_time: 0.0028 loss: 1.3815 03/05 19:11:05 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:11:18 - mmengine - INFO - Epoch(train) [52][2800/5005] lr: 1.0000e-02 eta: 1 day, 4:00:35 time: 0.2233 data_time: 0.0028 loss: 1.3684 03/05 19:11:40 - mmengine - INFO - Epoch(train) [52][2900/5005] lr: 1.0000e-02 eta: 1 day, 4:00:12 time: 0.2311 data_time: 0.0025 loss: 1.2437 03/05 19:12:03 - mmengine - INFO - Epoch(train) [52][3000/5005] lr: 1.0000e-02 eta: 1 day, 3:59:49 time: 0.2227 data_time: 0.0025 loss: 1.2724 03/05 19:12:26 - mmengine - INFO - Epoch(train) [52][3100/5005] lr: 1.0000e-02 eta: 1 day, 3:59:26 time: 0.2232 data_time: 0.0027 loss: 1.3548 03/05 19:12:49 - mmengine - INFO - Epoch(train) [52][3200/5005] lr: 1.0000e-02 eta: 1 day, 3:59:04 time: 0.2260 data_time: 0.0026 loss: 1.2696 03/05 19:13:12 - mmengine - INFO - Epoch(train) [52][3300/5005] lr: 1.0000e-02 eta: 1 day, 3:58:41 time: 0.2213 data_time: 0.0029 loss: 1.3221 03/05 19:13:34 - mmengine - INFO - Epoch(train) [52][3400/5005] lr: 1.0000e-02 eta: 1 day, 3:58:18 time: 0.2203 data_time: 0.0029 loss: 1.2287 03/05 19:13:57 - mmengine - INFO - Epoch(train) [52][3500/5005] lr: 1.0000e-02 eta: 1 day, 3:57:55 time: 0.2266 data_time: 0.0026 loss: 1.2069 03/05 19:14:20 - mmengine - INFO - Epoch(train) [52][3600/5005] lr: 1.0000e-02 eta: 1 day, 3:57:32 time: 0.2243 data_time: 0.0031 loss: 1.2410 03/05 19:14:43 - mmengine - INFO - Epoch(train) [52][3700/5005] lr: 1.0000e-02 eta: 1 day, 3:57:08 time: 0.2259 data_time: 0.0030 loss: 1.3615 03/05 19:14:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:15:05 - mmengine - INFO - Epoch(train) [52][3800/5005] lr: 1.0000e-02 eta: 1 day, 3:56:46 time: 0.2251 data_time: 0.0028 loss: 1.4157 03/05 19:15:28 - mmengine - INFO - Epoch(train) [52][3900/5005] lr: 1.0000e-02 eta: 1 day, 3:56:22 time: 0.2373 data_time: 0.0027 loss: 1.3335 03/05 19:15:51 - mmengine - INFO - Epoch(train) [52][4000/5005] lr: 1.0000e-02 eta: 1 day, 3:55:59 time: 0.2322 data_time: 0.0026 loss: 1.2538 03/05 19:16:13 - mmengine - INFO - Epoch(train) [52][4100/5005] lr: 1.0000e-02 eta: 1 day, 3:55:36 time: 0.2187 data_time: 0.0027 loss: 1.2550 03/05 19:16:36 - mmengine - INFO - Epoch(train) [52][4200/5005] lr: 1.0000e-02 eta: 1 day, 3:55:13 time: 0.2217 data_time: 0.0030 loss: 1.2773 03/05 19:16:59 - mmengine - INFO - Epoch(train) [52][4300/5005] lr: 1.0000e-02 eta: 1 day, 3:54:50 time: 0.2239 data_time: 0.0027 loss: 1.3830 03/05 19:17:22 - mmengine - INFO - Epoch(train) [52][4400/5005] lr: 1.0000e-02 eta: 1 day, 3:54:27 time: 0.2240 data_time: 0.0025 loss: 1.3293 03/05 19:17:44 - mmengine - INFO - Epoch(train) [52][4500/5005] lr: 1.0000e-02 eta: 1 day, 3:54:04 time: 0.2321 data_time: 0.0028 loss: 1.2835 03/05 19:18:07 - mmengine - INFO - Epoch(train) [52][4600/5005] lr: 1.0000e-02 eta: 1 day, 3:53:41 time: 0.2194 data_time: 0.0028 loss: 1.3003 03/05 19:18:30 - mmengine - INFO - Epoch(train) [52][4700/5005] lr: 1.0000e-02 eta: 1 day, 3:53:18 time: 0.2316 data_time: 0.0030 loss: 1.1929 03/05 19:18:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:18:53 - mmengine - INFO - Epoch(train) [52][4800/5005] lr: 1.0000e-02 eta: 1 day, 3:52:55 time: 0.2237 data_time: 0.0028 loss: 1.4277 03/05 19:19:16 - mmengine - INFO - Epoch(train) [52][4900/5005] lr: 1.0000e-02 eta: 1 day, 3:52:33 time: 0.2859 data_time: 0.0024 loss: 1.4483 03/05 19:19:44 - mmengine - INFO - Epoch(train) [52][5000/5005] lr: 1.0000e-02 eta: 1 day, 3:52:20 time: 0.2841 data_time: 0.0024 loss: 1.2822 03/05 19:19:46 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:19:49 - mmengine - INFO - Saving checkpoint at 52 epochs 03/05 19:20:03 - mmengine - INFO - Epoch(val) [52][100/196] eta: 0:00:12 time: 0.0194 data_time: 0.0004 03/05 19:20:16 - mmengine - INFO - Epoch(val) [52][196/196] accuracy/top1: 71.4540 accuracy/top5: 90.7840 03/05 19:20:49 - mmengine - INFO - Epoch(train) [53][ 100/5005] lr: 1.0000e-02 eta: 1 day, 3:52:13 time: 0.2233 data_time: 0.0029 loss: 1.4112 03/05 19:21:12 - mmengine - INFO - Epoch(train) [53][ 200/5005] lr: 1.0000e-02 eta: 1 day, 3:51:50 time: 0.2266 data_time: 0.0027 loss: 1.1872 03/05 19:21:35 - mmengine - INFO - Epoch(train) [53][ 300/5005] lr: 1.0000e-02 eta: 1 day, 3:51:27 time: 0.2279 data_time: 0.0029 loss: 1.3817 03/05 19:21:57 - mmengine - INFO - Epoch(train) [53][ 400/5005] lr: 1.0000e-02 eta: 1 day, 3:51:03 time: 0.2241 data_time: 0.0029 loss: 1.3305 03/05 19:22:20 - mmengine - INFO - Epoch(train) [53][ 500/5005] lr: 1.0000e-02 eta: 1 day, 3:50:41 time: 0.2234 data_time: 0.0025 loss: 1.3385 03/05 19:22:43 - mmengine - INFO - Epoch(train) [53][ 600/5005] lr: 1.0000e-02 eta: 1 day, 3:50:18 time: 0.2247 data_time: 0.0028 loss: 1.4904 03/05 19:23:06 - mmengine - INFO - Epoch(train) [53][ 700/5005] lr: 1.0000e-02 eta: 1 day, 3:49:55 time: 0.2241 data_time: 0.0026 loss: 1.2864 03/05 19:23:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:23:28 - mmengine - INFO - Epoch(train) [53][ 800/5005] lr: 1.0000e-02 eta: 1 day, 3:49:31 time: 0.2232 data_time: 0.0028 loss: 1.2859 03/05 19:23:51 - mmengine - INFO - Epoch(train) [53][ 900/5005] lr: 1.0000e-02 eta: 1 day, 3:49:09 time: 0.2381 data_time: 0.0025 loss: 1.4994 03/05 19:24:14 - mmengine - INFO - Epoch(train) [53][1000/5005] lr: 1.0000e-02 eta: 1 day, 3:48:46 time: 0.2235 data_time: 0.0030 loss: 1.4183 03/05 19:24:37 - mmengine - INFO - Epoch(train) [53][1100/5005] lr: 1.0000e-02 eta: 1 day, 3:48:23 time: 0.2256 data_time: 0.0024 loss: 1.2071 03/05 19:24:59 - mmengine - INFO - Epoch(train) [53][1200/5005] lr: 1.0000e-02 eta: 1 day, 3:47:59 time: 0.2213 data_time: 0.0026 loss: 1.1659 03/05 19:25:22 - mmengine - INFO - Epoch(train) [53][1300/5005] lr: 1.0000e-02 eta: 1 day, 3:47:37 time: 0.2418 data_time: 0.0028 loss: 1.3233 03/05 19:25:45 - mmengine - INFO - Epoch(train) [53][1400/5005] lr: 1.0000e-02 eta: 1 day, 3:47:14 time: 0.2260 data_time: 0.0026 loss: 1.4340 03/05 19:26:08 - mmengine - INFO - Epoch(train) [53][1500/5005] lr: 1.0000e-02 eta: 1 day, 3:46:51 time: 0.2272 data_time: 0.0025 loss: 1.3293 03/05 19:26:31 - mmengine - INFO - Epoch(train) [53][1600/5005] lr: 1.0000e-02 eta: 1 day, 3:46:28 time: 0.2245 data_time: 0.0025 loss: 1.4332 03/05 19:26:53 - mmengine - INFO - Epoch(train) [53][1700/5005] lr: 1.0000e-02 eta: 1 day, 3:46:05 time: 0.2217 data_time: 0.0030 loss: 1.3302 03/05 19:27:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:27:16 - mmengine - INFO - Epoch(train) [53][1800/5005] lr: 1.0000e-02 eta: 1 day, 3:45:42 time: 0.2250 data_time: 0.0026 loss: 1.3046 03/05 19:27:39 - mmengine - INFO - Epoch(train) [53][1900/5005] lr: 1.0000e-02 eta: 1 day, 3:45:19 time: 0.2223 data_time: 0.0026 loss: 1.3886 03/05 19:28:01 - mmengine - INFO - Epoch(train) [53][2000/5005] lr: 1.0000e-02 eta: 1 day, 3:44:56 time: 0.2256 data_time: 0.0025 loss: 1.2657 03/05 19:28:24 - mmengine - INFO - Epoch(train) [53][2100/5005] lr: 1.0000e-02 eta: 1 day, 3:44:32 time: 0.2390 data_time: 0.0028 loss: 1.2965 03/05 19:28:47 - mmengine - INFO - Epoch(train) [53][2200/5005] lr: 1.0000e-02 eta: 1 day, 3:44:10 time: 0.2222 data_time: 0.0027 loss: 1.1858 03/05 19:29:10 - mmengine - INFO - Epoch(train) [53][2300/5005] lr: 1.0000e-02 eta: 1 day, 3:43:46 time: 0.2238 data_time: 0.0027 loss: 1.3087 03/05 19:29:32 - mmengine - INFO - Epoch(train) [53][2400/5005] lr: 1.0000e-02 eta: 1 day, 3:43:23 time: 0.2214 data_time: 0.0028 loss: 1.3144 03/05 19:29:55 - mmengine - INFO - Epoch(train) [53][2500/5005] lr: 1.0000e-02 eta: 1 day, 3:43:00 time: 0.2232 data_time: 0.0027 loss: 1.4639 03/05 19:30:18 - mmengine - INFO - Epoch(train) [53][2600/5005] lr: 1.0000e-02 eta: 1 day, 3:42:37 time: 0.2262 data_time: 0.0026 loss: 1.3465 03/05 19:30:40 - mmengine - INFO - Epoch(train) [53][2700/5005] lr: 1.0000e-02 eta: 1 day, 3:42:14 time: 0.2208 data_time: 0.0029 loss: 1.1848 03/05 19:30:49 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:31:03 - mmengine - INFO - Epoch(train) [53][2800/5005] lr: 1.0000e-02 eta: 1 day, 3:41:51 time: 0.2254 data_time: 0.0025 loss: 1.2441 03/05 19:31:26 - mmengine - INFO - Epoch(train) [53][2900/5005] lr: 1.0000e-02 eta: 1 day, 3:41:28 time: 0.2409 data_time: 0.0029 loss: 1.2490 03/05 19:31:49 - mmengine - INFO - Epoch(train) [53][3000/5005] lr: 1.0000e-02 eta: 1 day, 3:41:06 time: 0.2232 data_time: 0.0025 loss: 1.2946 03/05 19:32:12 - mmengine - INFO - Epoch(train) [53][3100/5005] lr: 1.0000e-02 eta: 1 day, 3:40:43 time: 0.2237 data_time: 0.0030 loss: 1.5002 03/05 19:32:35 - mmengine - INFO - Epoch(train) [53][3200/5005] lr: 1.0000e-02 eta: 1 day, 3:40:19 time: 0.2407 data_time: 0.0030 loss: 1.3174 03/05 19:32:57 - mmengine - INFO - Epoch(train) [53][3300/5005] lr: 1.0000e-02 eta: 1 day, 3:39:57 time: 0.2241 data_time: 0.0025 loss: 1.4075 03/05 19:33:20 - mmengine - INFO - Epoch(train) [53][3400/5005] lr: 1.0000e-02 eta: 1 day, 3:39:34 time: 0.2259 data_time: 0.0029 loss: 1.5535 03/05 19:33:43 - mmengine - INFO - Epoch(train) [53][3500/5005] lr: 1.0000e-02 eta: 1 day, 3:39:11 time: 0.2275 data_time: 0.0031 loss: 1.2530 03/05 19:34:06 - mmengine - INFO - Epoch(train) [53][3600/5005] lr: 1.0000e-02 eta: 1 day, 3:38:48 time: 0.2224 data_time: 0.0029 loss: 1.3698 03/05 19:34:29 - mmengine - INFO - Epoch(train) [53][3700/5005] lr: 1.0000e-02 eta: 1 day, 3:38:25 time: 0.2198 data_time: 0.0026 loss: 1.3434 03/05 19:34:38 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:34:52 - mmengine - INFO - Epoch(train) [53][3800/5005] lr: 1.0000e-02 eta: 1 day, 3:38:02 time: 0.2248 data_time: 0.0025 loss: 1.3293 03/05 19:35:15 - mmengine - INFO - Epoch(train) [53][3900/5005] lr: 1.0000e-02 eta: 1 day, 3:37:40 time: 0.2434 data_time: 0.0027 loss: 1.2233 03/05 19:35:37 - mmengine - INFO - Epoch(train) [53][4000/5005] lr: 1.0000e-02 eta: 1 day, 3:37:16 time: 0.2226 data_time: 0.0033 loss: 1.3101 03/05 19:36:00 - mmengine - INFO - Epoch(train) [53][4100/5005] lr: 1.0000e-02 eta: 1 day, 3:36:53 time: 0.2202 data_time: 0.0026 loss: 1.5513 03/05 19:36:23 - mmengine - INFO - Epoch(train) [53][4200/5005] lr: 1.0000e-02 eta: 1 day, 3:36:30 time: 0.2222 data_time: 0.0028 loss: 1.2069 03/05 19:36:46 - mmengine - INFO - Epoch(train) [53][4300/5005] lr: 1.0000e-02 eta: 1 day, 3:36:07 time: 0.2238 data_time: 0.0033 loss: 1.3750 03/05 19:37:08 - mmengine - INFO - Epoch(train) [53][4400/5005] lr: 1.0000e-02 eta: 1 day, 3:35:44 time: 0.2290 data_time: 0.0033 loss: 1.3174 03/05 19:37:31 - mmengine - INFO - Epoch(train) [53][4500/5005] lr: 1.0000e-02 eta: 1 day, 3:35:21 time: 0.2228 data_time: 0.0029 loss: 1.3989 03/05 19:37:54 - mmengine - INFO - Epoch(train) [53][4600/5005] lr: 1.0000e-02 eta: 1 day, 3:34:59 time: 0.2381 data_time: 0.0031 loss: 1.3298 03/05 19:38:17 - mmengine - INFO - Epoch(train) [53][4700/5005] lr: 1.0000e-02 eta: 1 day, 3:34:36 time: 0.2221 data_time: 0.0027 loss: 1.3798 03/05 19:38:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:38:39 - mmengine - INFO - Epoch(train) [53][4800/5005] lr: 1.0000e-02 eta: 1 day, 3:34:12 time: 0.2254 data_time: 0.0027 loss: 1.3849 03/05 19:39:03 - mmengine - INFO - Epoch(train) [53][4900/5005] lr: 1.0000e-02 eta: 1 day, 3:33:51 time: 0.2851 data_time: 0.0022 loss: 1.1073 03/05 19:39:33 - mmengine - INFO - Epoch(train) [53][5000/5005] lr: 1.0000e-02 eta: 1 day, 3:33:39 time: 0.2912 data_time: 0.0025 loss: 1.1933 03/05 19:39:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:39:37 - mmengine - INFO - Saving checkpoint at 53 epochs 03/05 19:39:51 - mmengine - INFO - Epoch(val) [53][100/196] eta: 0:00:12 time: 0.0196 data_time: 0.0004 03/05 19:40:05 - mmengine - INFO - Epoch(val) [53][196/196] accuracy/top1: 70.9440 accuracy/top5: 90.3940 03/05 19:40:36 - mmengine - INFO - Epoch(train) [54][ 100/5005] lr: 1.0000e-02 eta: 1 day, 3:33:30 time: 0.2238 data_time: 0.0032 loss: 1.2966 03/05 19:41:00 - mmengine - INFO - Epoch(train) [54][ 200/5005] lr: 1.0000e-02 eta: 1 day, 3:33:08 time: 0.2239 data_time: 0.0030 loss: 1.1554 03/05 19:41:22 - mmengine - INFO - Epoch(train) [54][ 300/5005] lr: 1.0000e-02 eta: 1 day, 3:32:44 time: 0.2200 data_time: 0.0023 loss: 1.1590 03/05 19:41:45 - mmengine - INFO - Epoch(train) [54][ 400/5005] lr: 1.0000e-02 eta: 1 day, 3:32:21 time: 0.2207 data_time: 0.0027 loss: 1.2674 03/05 19:42:08 - mmengine - INFO - Epoch(train) [54][ 500/5005] lr: 1.0000e-02 eta: 1 day, 3:31:58 time: 0.2249 data_time: 0.0027 loss: 1.3921 03/05 19:42:31 - mmengine - INFO - Epoch(train) [54][ 600/5005] lr: 1.0000e-02 eta: 1 day, 3:31:36 time: 0.2315 data_time: 0.0028 loss: 1.5098 03/05 19:42:53 - mmengine - INFO - Epoch(train) [54][ 700/5005] lr: 1.0000e-02 eta: 1 day, 3:31:12 time: 0.2217 data_time: 0.0029 loss: 1.2158 03/05 19:43:01 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:43:16 - mmengine - INFO - Epoch(train) [54][ 800/5005] lr: 1.0000e-02 eta: 1 day, 3:30:49 time: 0.2264 data_time: 0.0026 loss: 1.2616 03/05 19:43:38 - mmengine - INFO - Epoch(train) [54][ 900/5005] lr: 1.0000e-02 eta: 1 day, 3:30:26 time: 0.2232 data_time: 0.0025 loss: 1.4093 03/05 19:44:02 - mmengine - INFO - Epoch(train) [54][1000/5005] lr: 1.0000e-02 eta: 1 day, 3:30:04 time: 0.2192 data_time: 0.0025 loss: 1.2887 03/05 19:44:24 - mmengine - INFO - Epoch(train) [54][1100/5005] lr: 1.0000e-02 eta: 1 day, 3:29:41 time: 0.2267 data_time: 0.0026 loss: 1.3685 03/05 19:44:47 - mmengine - INFO - Epoch(train) [54][1200/5005] lr: 1.0000e-02 eta: 1 day, 3:29:17 time: 0.2195 data_time: 0.0026 loss: 1.2970 03/05 19:45:09 - mmengine - INFO - Epoch(train) [54][1300/5005] lr: 1.0000e-02 eta: 1 day, 3:28:54 time: 0.2221 data_time: 0.0027 loss: 1.4333 03/05 19:45:32 - mmengine - INFO - Epoch(train) [54][1400/5005] lr: 1.0000e-02 eta: 1 day, 3:28:31 time: 0.2299 data_time: 0.0028 loss: 1.3305 03/05 19:45:55 - mmengine - INFO - Epoch(train) [54][1500/5005] lr: 1.0000e-02 eta: 1 day, 3:28:08 time: 0.2250 data_time: 0.0032 loss: 1.4394 03/05 19:46:17 - mmengine - INFO - Epoch(train) [54][1600/5005] lr: 1.0000e-02 eta: 1 day, 3:27:44 time: 0.2249 data_time: 0.0026 loss: 1.3896 03/05 19:46:40 - mmengine - INFO - Epoch(train) [54][1700/5005] lr: 1.0000e-02 eta: 1 day, 3:27:21 time: 0.2280 data_time: 0.0029 loss: 1.5005 03/05 19:46:48 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:47:03 - mmengine - INFO - Epoch(train) [54][1800/5005] lr: 1.0000e-02 eta: 1 day, 3:26:58 time: 0.2254 data_time: 0.0025 loss: 1.3739 03/05 19:47:26 - mmengine - INFO - Epoch(train) [54][1900/5005] lr: 1.0000e-02 eta: 1 day, 3:26:35 time: 0.2239 data_time: 0.0027 loss: 1.4056 03/05 19:47:48 - mmengine - INFO - Epoch(train) [54][2000/5005] lr: 1.0000e-02 eta: 1 day, 3:26:12 time: 0.2210 data_time: 0.0027 loss: 1.3284 03/05 19:48:11 - mmengine - INFO - Epoch(train) [54][2100/5005] lr: 1.0000e-02 eta: 1 day, 3:25:48 time: 0.2226 data_time: 0.0025 loss: 1.3698 03/05 19:48:33 - mmengine - INFO - Epoch(train) [54][2200/5005] lr: 1.0000e-02 eta: 1 day, 3:25:25 time: 0.2232 data_time: 0.0029 loss: 1.2278 03/05 19:48:56 - mmengine - INFO - Epoch(train) [54][2300/5005] lr: 1.0000e-02 eta: 1 day, 3:25:02 time: 0.2218 data_time: 0.0025 loss: 1.4174 03/05 19:49:19 - mmengine - INFO - Epoch(train) [54][2400/5005] lr: 1.0000e-02 eta: 1 day, 3:24:39 time: 0.2220 data_time: 0.0029 loss: 1.2983 03/05 19:49:41 - mmengine - INFO - Epoch(train) [54][2500/5005] lr: 1.0000e-02 eta: 1 day, 3:24:16 time: 0.2209 data_time: 0.0027 loss: 1.4293 03/05 19:50:04 - mmengine - INFO - Epoch(train) [54][2600/5005] lr: 1.0000e-02 eta: 1 day, 3:23:53 time: 0.2216 data_time: 0.0032 loss: 1.1666 03/05 19:50:27 - mmengine - INFO - Epoch(train) [54][2700/5005] lr: 1.0000e-02 eta: 1 day, 3:23:30 time: 0.2243 data_time: 0.0028 loss: 1.4051 03/05 19:50:35 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:50:50 - mmengine - INFO - Epoch(train) [54][2800/5005] lr: 1.0000e-02 eta: 1 day, 3:23:07 time: 0.2208 data_time: 0.0026 loss: 1.1066 03/05 19:51:13 - mmengine - INFO - Epoch(train) [54][2900/5005] lr: 1.0000e-02 eta: 1 day, 3:22:44 time: 0.2215 data_time: 0.0027 loss: 1.5484 03/05 19:51:36 - mmengine - INFO - Epoch(train) [54][3000/5005] lr: 1.0000e-02 eta: 1 day, 3:22:21 time: 0.2493 data_time: 0.0027 loss: 1.1515 03/05 19:51:58 - mmengine - INFO - Epoch(train) [54][3100/5005] lr: 1.0000e-02 eta: 1 day, 3:21:58 time: 0.2266 data_time: 0.0027 loss: 1.4133 03/05 19:52:21 - mmengine - INFO - Epoch(train) [54][3200/5005] lr: 1.0000e-02 eta: 1 day, 3:21:35 time: 0.2298 data_time: 0.0026 loss: 1.2913 03/05 19:52:44 - mmengine - INFO - Epoch(train) [54][3300/5005] lr: 1.0000e-02 eta: 1 day, 3:21:13 time: 0.2277 data_time: 0.0028 loss: 1.2918 03/05 19:53:07 - mmengine - INFO - Epoch(train) [54][3400/5005] lr: 1.0000e-02 eta: 1 day, 3:20:49 time: 0.2242 data_time: 0.0028 loss: 1.3854 03/05 19:53:30 - mmengine - INFO - Epoch(train) [54][3500/5005] lr: 1.0000e-02 eta: 1 day, 3:20:27 time: 0.2252 data_time: 0.0029 loss: 1.2962 03/05 19:53:52 - mmengine - INFO - Epoch(train) [54][3600/5005] lr: 1.0000e-02 eta: 1 day, 3:20:03 time: 0.2236 data_time: 0.0028 loss: 1.3462 03/05 19:54:15 - mmengine - INFO - Epoch(train) [54][3700/5005] lr: 1.0000e-02 eta: 1 day, 3:19:41 time: 0.2257 data_time: 0.0028 loss: 1.2959 03/05 19:54:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:54:38 - mmengine - INFO - Epoch(train) [54][3800/5005] lr: 1.0000e-02 eta: 1 day, 3:19:18 time: 0.2223 data_time: 0.0026 loss: 1.5498 03/05 19:55:01 - mmengine - INFO - Epoch(train) [54][3900/5005] lr: 1.0000e-02 eta: 1 day, 3:18:55 time: 0.2256 data_time: 0.0029 loss: 1.2670 03/05 19:55:23 - mmengine - INFO - Epoch(train) [54][4000/5005] lr: 1.0000e-02 eta: 1 day, 3:18:32 time: 0.2283 data_time: 0.0031 loss: 1.3381 03/05 19:55:46 - mmengine - INFO - Epoch(train) [54][4100/5005] lr: 1.0000e-02 eta: 1 day, 3:18:08 time: 0.2233 data_time: 0.0028 loss: 1.2798 03/05 19:56:09 - mmengine - INFO - Epoch(train) [54][4200/5005] lr: 1.0000e-02 eta: 1 day, 3:17:46 time: 0.2262 data_time: 0.0026 loss: 1.3881 03/05 19:56:32 - mmengine - INFO - Epoch(train) [54][4300/5005] lr: 1.0000e-02 eta: 1 day, 3:17:23 time: 0.2265 data_time: 0.0026 loss: 1.2861 03/05 19:56:55 - mmengine - INFO - Epoch(train) [54][4400/5005] lr: 1.0000e-02 eta: 1 day, 3:17:00 time: 0.2278 data_time: 0.0036 loss: 1.2173 03/05 19:57:18 - mmengine - INFO - Epoch(train) [54][4500/5005] lr: 1.0000e-02 eta: 1 day, 3:16:37 time: 0.2207 data_time: 0.0027 loss: 1.3126 03/05 19:57:40 - mmengine - INFO - Epoch(train) [54][4600/5005] lr: 1.0000e-02 eta: 1 day, 3:16:14 time: 0.2261 data_time: 0.0025 loss: 1.3406 03/05 19:58:04 - mmengine - INFO - Epoch(train) [54][4700/5005] lr: 1.0000e-02 eta: 1 day, 3:15:52 time: 0.2340 data_time: 0.0026 loss: 1.2482 03/05 19:58:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:58:26 - mmengine - INFO - Epoch(train) [54][4800/5005] lr: 1.0000e-02 eta: 1 day, 3:15:28 time: 0.2254 data_time: 0.0027 loss: 1.2133 03/05 19:58:50 - mmengine - INFO - Epoch(train) [54][4900/5005] lr: 1.0000e-02 eta: 1 day, 3:15:07 time: 0.2937 data_time: 0.0024 loss: 1.4395 03/05 19:59:18 - mmengine - INFO - Epoch(train) [54][5000/5005] lr: 1.0000e-02 eta: 1 day, 3:14:53 time: 0.2851 data_time: 0.0025 loss: 1.3863 03/05 19:59:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 19:59:23 - mmengine - INFO - Saving checkpoint at 54 epochs 03/05 19:59:38 - mmengine - INFO - Epoch(val) [54][100/196] eta: 0:00:13 time: 0.0177 data_time: 0.0002 03/05 19:59:52 - mmengine - INFO - Epoch(val) [54][196/196] accuracy/top1: 71.5280 accuracy/top5: 90.7960 03/05 20:00:23 - mmengine - INFO - Epoch(train) [55][ 100/5005] lr: 1.0000e-02 eta: 1 day, 3:14:43 time: 0.2240 data_time: 0.0030 loss: 1.1688 03/05 20:00:45 - mmengine - INFO - Epoch(train) [55][ 200/5005] lr: 1.0000e-02 eta: 1 day, 3:14:20 time: 0.2313 data_time: 0.0032 loss: 1.2036 03/05 20:01:09 - mmengine - INFO - Epoch(train) [55][ 300/5005] lr: 1.0000e-02 eta: 1 day, 3:13:57 time: 0.2256 data_time: 0.0036 loss: 1.5041 03/05 20:01:31 - mmengine - INFO - Epoch(train) [55][ 400/5005] lr: 1.0000e-02 eta: 1 day, 3:13:34 time: 0.2246 data_time: 0.0028 loss: 1.4290 03/05 20:01:54 - mmengine - INFO - Epoch(train) [55][ 500/5005] lr: 1.0000e-02 eta: 1 day, 3:13:11 time: 0.2431 data_time: 0.0025 loss: 1.2671 03/05 20:02:17 - mmengine - INFO - Epoch(train) [55][ 600/5005] lr: 1.0000e-02 eta: 1 day, 3:12:48 time: 0.2232 data_time: 0.0029 loss: 1.2795 03/05 20:02:40 - mmengine - INFO - Epoch(train) [55][ 700/5005] lr: 1.0000e-02 eta: 1 day, 3:12:25 time: 0.2240 data_time: 0.0027 loss: 1.2551 03/05 20:02:46 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:03:02 - mmengine - INFO - Epoch(train) [55][ 800/5005] lr: 1.0000e-02 eta: 1 day, 3:12:02 time: 0.2226 data_time: 0.0032 loss: 1.4206 03/05 20:03:25 - mmengine - INFO - Epoch(train) [55][ 900/5005] lr: 1.0000e-02 eta: 1 day, 3:11:39 time: 0.2198 data_time: 0.0034 loss: 1.1638 03/05 20:03:48 - mmengine - INFO - Epoch(train) [55][1000/5005] lr: 1.0000e-02 eta: 1 day, 3:11:16 time: 0.2211 data_time: 0.0033 loss: 1.2135 03/05 20:04:10 - mmengine - INFO - Epoch(train) [55][1100/5005] lr: 1.0000e-02 eta: 1 day, 3:10:53 time: 0.2421 data_time: 0.0028 loss: 1.2818 03/05 20:04:33 - mmengine - INFO - Epoch(train) [55][1200/5005] lr: 1.0000e-02 eta: 1 day, 3:10:30 time: 0.2257 data_time: 0.0028 loss: 1.3682 03/05 20:04:56 - mmengine - INFO - Epoch(train) [55][1300/5005] lr: 1.0000e-02 eta: 1 day, 3:10:07 time: 0.2268 data_time: 0.0028 loss: 1.2311 03/05 20:05:19 - mmengine - INFO - Epoch(train) [55][1400/5005] lr: 1.0000e-02 eta: 1 day, 3:09:43 time: 0.2251 data_time: 0.0027 loss: 1.3513 03/05 20:05:41 - mmengine - INFO - Epoch(train) [55][1500/5005] lr: 1.0000e-02 eta: 1 day, 3:09:20 time: 0.2336 data_time: 0.0029 loss: 1.4819 03/05 20:06:04 - mmengine - INFO - Epoch(train) [55][1600/5005] lr: 1.0000e-02 eta: 1 day, 3:08:57 time: 0.2242 data_time: 0.0026 loss: 1.2561 03/05 20:06:27 - mmengine - INFO - Epoch(train) [55][1700/5005] lr: 1.0000e-02 eta: 1 day, 3:08:35 time: 0.2404 data_time: 0.0031 loss: 1.2172 03/05 20:06:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:06:50 - mmengine - INFO - Epoch(train) [55][1800/5005] lr: 1.0000e-02 eta: 1 day, 3:08:12 time: 0.2226 data_time: 0.0029 loss: 1.3541 03/05 20:07:12 - mmengine - INFO - Epoch(train) [55][1900/5005] lr: 1.0000e-02 eta: 1 day, 3:07:48 time: 0.2229 data_time: 0.0028 loss: 1.2755 03/05 20:07:35 - mmengine - INFO - Epoch(train) [55][2000/5005] lr: 1.0000e-02 eta: 1 day, 3:07:26 time: 0.2290 data_time: 0.0028 loss: 1.6482 03/05 20:07:58 - mmengine - INFO - Epoch(train) [55][2100/5005] lr: 1.0000e-02 eta: 1 day, 3:07:03 time: 0.2318 data_time: 0.0026 loss: 1.2586 03/05 20:08:21 - mmengine - INFO - Epoch(train) [55][2200/5005] lr: 1.0000e-02 eta: 1 day, 3:06:40 time: 0.2227 data_time: 0.0028 loss: 1.3828 03/05 20:08:43 - mmengine - INFO - Epoch(train) [55][2300/5005] lr: 1.0000e-02 eta: 1 day, 3:06:16 time: 0.2302 data_time: 0.0026 loss: 1.2892 03/05 20:09:06 - mmengine - INFO - Epoch(train) [55][2400/5005] lr: 1.0000e-02 eta: 1 day, 3:05:54 time: 0.2242 data_time: 0.0029 loss: 1.3507 03/05 20:09:29 - mmengine - INFO - Epoch(train) [55][2500/5005] lr: 1.0000e-02 eta: 1 day, 3:05:30 time: 0.2247 data_time: 0.0028 loss: 1.2721 03/05 20:09:52 - mmengine - INFO - Epoch(train) [55][2600/5005] lr: 1.0000e-02 eta: 1 day, 3:05:08 time: 0.2234 data_time: 0.0025 loss: 1.3933 03/05 20:10:15 - mmengine - INFO - Epoch(train) [55][2700/5005] lr: 1.0000e-02 eta: 1 day, 3:04:44 time: 0.2239 data_time: 0.0027 loss: 1.5243 03/05 20:10:21 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:10:37 - mmengine - INFO - Epoch(train) [55][2800/5005] lr: 1.0000e-02 eta: 1 day, 3:04:22 time: 0.2384 data_time: 0.0028 loss: 1.1785 03/05 20:11:00 - mmengine - INFO - Epoch(train) [55][2900/5005] lr: 1.0000e-02 eta: 1 day, 3:03:59 time: 0.2250 data_time: 0.0026 loss: 1.3891 03/05 20:11:23 - mmengine - INFO - Epoch(train) [55][3000/5005] lr: 1.0000e-02 eta: 1 day, 3:03:36 time: 0.2481 data_time: 0.0034 loss: 1.3161 03/05 20:11:46 - mmengine - INFO - Epoch(train) [55][3100/5005] lr: 1.0000e-02 eta: 1 day, 3:03:13 time: 0.2211 data_time: 0.0033 loss: 1.2782 03/05 20:12:09 - mmengine - INFO - Epoch(train) [55][3200/5005] lr: 1.0000e-02 eta: 1 day, 3:02:50 time: 0.2226 data_time: 0.0032 loss: 1.4309 03/05 20:12:31 - mmengine - INFO - Epoch(train) [55][3300/5005] lr: 1.0000e-02 eta: 1 day, 3:02:27 time: 0.2221 data_time: 0.0026 loss: 1.4231 03/05 20:12:54 - mmengine - INFO - Epoch(train) [55][3400/5005] lr: 1.0000e-02 eta: 1 day, 3:02:04 time: 0.2229 data_time: 0.0028 loss: 1.3458 03/05 20:13:17 - mmengine - INFO - Epoch(train) [55][3500/5005] lr: 1.0000e-02 eta: 1 day, 3:01:41 time: 0.2250 data_time: 0.0030 loss: 1.3592 03/05 20:13:40 - mmengine - INFO - Epoch(train) [55][3600/5005] lr: 1.0000e-02 eta: 1 day, 3:01:18 time: 0.2255 data_time: 0.0028 loss: 1.4165 03/05 20:14:03 - mmengine - INFO - Epoch(train) [55][3700/5005] lr: 1.0000e-02 eta: 1 day, 3:00:55 time: 0.2240 data_time: 0.0028 loss: 1.4113 03/05 20:14:10 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:14:26 - mmengine - INFO - Epoch(train) [55][3800/5005] lr: 1.0000e-02 eta: 1 day, 3:00:32 time: 0.2238 data_time: 0.0028 loss: 1.3105 03/05 20:14:48 - mmengine - INFO - Epoch(train) [55][3900/5005] lr: 1.0000e-02 eta: 1 day, 3:00:09 time: 0.2359 data_time: 0.0026 loss: 1.2199 03/05 20:15:11 - mmengine - INFO - Epoch(train) [55][4000/5005] lr: 1.0000e-02 eta: 1 day, 2:59:47 time: 0.2223 data_time: 0.0028 loss: 1.1688 03/05 20:15:34 - mmengine - INFO - Epoch(train) [55][4100/5005] lr: 1.0000e-02 eta: 1 day, 2:59:23 time: 0.2241 data_time: 0.0028 loss: 1.4511 03/05 20:15:57 - mmengine - INFO - Epoch(train) [55][4200/5005] lr: 1.0000e-02 eta: 1 day, 2:59:01 time: 0.2238 data_time: 0.0029 loss: 1.3288 03/05 20:16:20 - mmengine - INFO - Epoch(train) [55][4300/5005] lr: 1.0000e-02 eta: 1 day, 2:58:38 time: 0.2215 data_time: 0.0030 loss: 1.2293 03/05 20:16:43 - mmengine - INFO - Epoch(train) [55][4400/5005] lr: 1.0000e-02 eta: 1 day, 2:58:16 time: 0.2425 data_time: 0.0027 loss: 1.2732 03/05 20:17:06 - mmengine - INFO - Epoch(train) [55][4500/5005] lr: 1.0000e-02 eta: 1 day, 2:57:53 time: 0.2256 data_time: 0.0029 loss: 1.3173 03/05 20:17:29 - mmengine - INFO - Epoch(train) [55][4600/5005] lr: 1.0000e-02 eta: 1 day, 2:57:30 time: 0.2279 data_time: 0.0028 loss: 1.4081 03/05 20:17:51 - mmengine - INFO - Epoch(train) [55][4700/5005] lr: 1.0000e-02 eta: 1 day, 2:57:07 time: 0.2209 data_time: 0.0031 loss: 1.3214 03/05 20:17:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:18:14 - mmengine - INFO - Epoch(train) [55][4800/5005] lr: 1.0000e-02 eta: 1 day, 2:56:44 time: 0.2389 data_time: 0.0028 loss: 1.4511 03/05 20:18:38 - mmengine - INFO - Epoch(train) [55][4900/5005] lr: 1.0000e-02 eta: 1 day, 2:56:23 time: 0.3004 data_time: 0.0027 loss: 1.2823 03/05 20:19:07 - mmengine - INFO - Epoch(train) [55][5000/5005] lr: 1.0000e-02 eta: 1 day, 2:56:09 time: 0.2915 data_time: 0.0026 loss: 1.3730 03/05 20:19:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:19:11 - mmengine - INFO - Saving checkpoint at 55 epochs 03/05 20:19:26 - mmengine - INFO - Epoch(val) [55][100/196] eta: 0:00:13 time: 0.0198 data_time: 0.0004 03/05 20:19:40 - mmengine - INFO - Epoch(val) [55][196/196] accuracy/top1: 71.5680 accuracy/top5: 90.7700 03/05 20:20:11 - mmengine - INFO - Epoch(train) [56][ 100/5005] lr: 1.0000e-02 eta: 1 day, 2:55:59 time: 0.2217 data_time: 0.0030 loss: 1.4073 03/05 20:20:34 - mmengine - INFO - Epoch(train) [56][ 200/5005] lr: 1.0000e-02 eta: 1 day, 2:55:36 time: 0.2214 data_time: 0.0032 loss: 1.4092 03/05 20:20:57 - mmengine - INFO - Epoch(train) [56][ 300/5005] lr: 1.0000e-02 eta: 1 day, 2:55:12 time: 0.2267 data_time: 0.0032 loss: 1.3990 03/05 20:21:19 - mmengine - INFO - Epoch(train) [56][ 400/5005] lr: 1.0000e-02 eta: 1 day, 2:54:49 time: 0.2210 data_time: 0.0024 loss: 1.3440 03/05 20:21:42 - mmengine - INFO - Epoch(train) [56][ 500/5005] lr: 1.0000e-02 eta: 1 day, 2:54:27 time: 0.2346 data_time: 0.0028 loss: 1.3037 03/05 20:22:05 - mmengine - INFO - Epoch(train) [56][ 600/5005] lr: 1.0000e-02 eta: 1 day, 2:54:03 time: 0.2270 data_time: 0.0030 loss: 1.3347 03/05 20:22:27 - mmengine - INFO - Epoch(train) [56][ 700/5005] lr: 1.0000e-02 eta: 1 day, 2:53:40 time: 0.2232 data_time: 0.0028 loss: 1.3358 03/05 20:22:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:22:50 - mmengine - INFO - Epoch(train) [56][ 800/5005] lr: 1.0000e-02 eta: 1 day, 2:53:17 time: 0.2225 data_time: 0.0027 loss: 1.1957 03/05 20:23:13 - mmengine - INFO - Epoch(train) [56][ 900/5005] lr: 1.0000e-02 eta: 1 day, 2:52:55 time: 0.2237 data_time: 0.0027 loss: 1.3004 03/05 20:23:36 - mmengine - INFO - Epoch(train) [56][1000/5005] lr: 1.0000e-02 eta: 1 day, 2:52:32 time: 0.2226 data_time: 0.0028 loss: 1.2322 03/05 20:23:59 - mmengine - INFO - Epoch(train) [56][1100/5005] lr: 1.0000e-02 eta: 1 day, 2:52:08 time: 0.2207 data_time: 0.0033 loss: 1.4720 03/05 20:24:21 - mmengine - INFO - Epoch(train) [56][1200/5005] lr: 1.0000e-02 eta: 1 day, 2:51:45 time: 0.2257 data_time: 0.0032 loss: 1.4640 03/05 20:24:45 - mmengine - INFO - Epoch(train) [56][1300/5005] lr: 1.0000e-02 eta: 1 day, 2:51:23 time: 0.2316 data_time: 0.0029 loss: 1.3020 03/05 20:25:07 - mmengine - INFO - Epoch(train) [56][1400/5005] lr: 1.0000e-02 eta: 1 day, 2:51:00 time: 0.2235 data_time: 0.0030 loss: 1.3427 03/05 20:25:30 - mmengine - INFO - Epoch(train) [56][1500/5005] lr: 1.0000e-02 eta: 1 day, 2:50:37 time: 0.2246 data_time: 0.0030 loss: 1.1701 03/05 20:25:53 - mmengine - INFO - Epoch(train) [56][1600/5005] lr: 1.0000e-02 eta: 1 day, 2:50:13 time: 0.2228 data_time: 0.0028 loss: 1.3671 03/05 20:26:15 - mmengine - INFO - Epoch(train) [56][1700/5005] lr: 1.0000e-02 eta: 1 day, 2:49:50 time: 0.2247 data_time: 0.0029 loss: 1.2694 03/05 20:26:21 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:26:38 - mmengine - INFO - Epoch(train) [56][1800/5005] lr: 1.0000e-02 eta: 1 day, 2:49:27 time: 0.2240 data_time: 0.0030 loss: 1.4403 03/05 20:27:01 - mmengine - INFO - Epoch(train) [56][1900/5005] lr: 1.0000e-02 eta: 1 day, 2:49:04 time: 0.2248 data_time: 0.0029 loss: 1.3594 03/05 20:27:23 - mmengine - INFO - Epoch(train) [56][2000/5005] lr: 1.0000e-02 eta: 1 day, 2:48:41 time: 0.2230 data_time: 0.0026 loss: 1.4962 03/05 20:27:46 - mmengine - INFO - Epoch(train) [56][2100/5005] lr: 1.0000e-02 eta: 1 day, 2:48:18 time: 0.2254 data_time: 0.0030 loss: 1.0242 03/05 20:28:09 - mmengine - INFO - Epoch(train) [56][2200/5005] lr: 1.0000e-02 eta: 1 day, 2:47:55 time: 0.2267 data_time: 0.0028 loss: 1.2817 03/05 20:28:32 - mmengine - INFO - Epoch(train) [56][2300/5005] lr: 1.0000e-02 eta: 1 day, 2:47:32 time: 0.2217 data_time: 0.0026 loss: 1.2399 03/05 20:28:55 - mmengine - INFO - Epoch(train) [56][2400/5005] lr: 1.0000e-02 eta: 1 day, 2:47:09 time: 0.2250 data_time: 0.0025 loss: 1.3384 03/05 20:29:18 - mmengine - INFO - Epoch(train) [56][2500/5005] lr: 1.0000e-02 eta: 1 day, 2:46:46 time: 0.2557 data_time: 0.0028 loss: 1.2741 03/05 20:29:40 - mmengine - INFO - Epoch(train) [56][2600/5005] lr: 1.0000e-02 eta: 1 day, 2:46:23 time: 0.2234 data_time: 0.0029 loss: 1.1861 03/05 20:30:03 - mmengine - INFO - Epoch(train) [56][2700/5005] lr: 1.0000e-02 eta: 1 day, 2:45:59 time: 0.2229 data_time: 0.0028 loss: 1.2340 03/05 20:30:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:30:26 - mmengine - INFO - Epoch(train) [56][2800/5005] lr: 1.0000e-02 eta: 1 day, 2:45:37 time: 0.2206 data_time: 0.0030 loss: 1.4912 03/05 20:30:48 - mmengine - INFO - Epoch(train) [56][2900/5005] lr: 1.0000e-02 eta: 1 day, 2:45:13 time: 0.2245 data_time: 0.0029 loss: 1.2836 03/05 20:31:11 - mmengine - INFO - Epoch(train) [56][3000/5005] lr: 1.0000e-02 eta: 1 day, 2:44:50 time: 0.2314 data_time: 0.0029 loss: 1.3158 03/05 20:31:34 - mmengine - INFO - Epoch(train) [56][3100/5005] lr: 1.0000e-02 eta: 1 day, 2:44:27 time: 0.2436 data_time: 0.0026 loss: 1.2844 03/05 20:31:57 - mmengine - INFO - Epoch(train) [56][3200/5005] lr: 1.0000e-02 eta: 1 day, 2:44:05 time: 0.2340 data_time: 0.0031 loss: 1.3724 03/05 20:32:19 - mmengine - INFO - Epoch(train) [56][3300/5005] lr: 1.0000e-02 eta: 1 day, 2:43:41 time: 0.2217 data_time: 0.0027 loss: 1.4203 03/05 20:32:42 - mmengine - INFO - Epoch(train) [56][3400/5005] lr: 1.0000e-02 eta: 1 day, 2:43:19 time: 0.2242 data_time: 0.0029 loss: 1.2191 03/05 20:33:05 - mmengine - INFO - Epoch(train) [56][3500/5005] lr: 1.0000e-02 eta: 1 day, 2:42:55 time: 0.2260 data_time: 0.0028 loss: 1.4273 03/05 20:33:28 - mmengine - INFO - Epoch(train) [56][3600/5005] lr: 1.0000e-02 eta: 1 day, 2:42:33 time: 0.2242 data_time: 0.0026 loss: 1.2584 03/05 20:33:51 - mmengine - INFO - Epoch(train) [56][3700/5005] lr: 1.0000e-02 eta: 1 day, 2:42:10 time: 0.2273 data_time: 0.0026 loss: 1.3485 03/05 20:33:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:34:13 - mmengine - INFO - Epoch(train) [56][3800/5005] lr: 1.0000e-02 eta: 1 day, 2:41:47 time: 0.2234 data_time: 0.0027 loss: 1.3137 03/05 20:34:36 - mmengine - INFO - Epoch(train) [56][3900/5005] lr: 1.0000e-02 eta: 1 day, 2:41:23 time: 0.2219 data_time: 0.0029 loss: 1.2938 03/05 20:34:59 - mmengine - INFO - Epoch(train) [56][4000/5005] lr: 1.0000e-02 eta: 1 day, 2:41:00 time: 0.2223 data_time: 0.0029 loss: 1.3204 03/05 20:35:22 - mmengine - INFO - Epoch(train) [56][4100/5005] lr: 1.0000e-02 eta: 1 day, 2:40:38 time: 0.2210 data_time: 0.0030 loss: 1.4345 03/05 20:35:44 - mmengine - INFO - Epoch(train) [56][4200/5005] lr: 1.0000e-02 eta: 1 day, 2:40:15 time: 0.2230 data_time: 0.0030 loss: 1.2845 03/05 20:36:07 - mmengine - INFO - Epoch(train) [56][4300/5005] lr: 1.0000e-02 eta: 1 day, 2:39:52 time: 0.2324 data_time: 0.0026 loss: 1.2907 03/05 20:36:30 - mmengine - INFO - Epoch(train) [56][4400/5005] lr: 1.0000e-02 eta: 1 day, 2:39:29 time: 0.2266 data_time: 0.0027 loss: 1.4467 03/05 20:36:53 - mmengine - INFO - Epoch(train) [56][4500/5005] lr: 1.0000e-02 eta: 1 day, 2:39:06 time: 0.2291 data_time: 0.0031 loss: 1.3568 03/05 20:37:16 - mmengine - INFO - Epoch(train) [56][4600/5005] lr: 1.0000e-02 eta: 1 day, 2:38:44 time: 0.2263 data_time: 0.0027 loss: 1.3784 03/05 20:37:39 - mmengine - INFO - Epoch(train) [56][4700/5005] lr: 1.0000e-02 eta: 1 day, 2:38:20 time: 0.2233 data_time: 0.0031 loss: 1.1069 03/05 20:37:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:38:01 - mmengine - INFO - Epoch(train) [56][4800/5005] lr: 1.0000e-02 eta: 1 day, 2:37:57 time: 0.2228 data_time: 0.0030 loss: 1.5427 03/05 20:38:25 - mmengine - INFO - Epoch(train) [56][4900/5005] lr: 1.0000e-02 eta: 1 day, 2:37:36 time: 0.2907 data_time: 0.0026 loss: 1.3852 03/05 20:38:53 - mmengine - INFO - Epoch(train) [56][5000/5005] lr: 1.0000e-02 eta: 1 day, 2:37:20 time: 0.2746 data_time: 0.0025 loss: 1.4876 03/05 20:38:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:38:57 - mmengine - INFO - Saving checkpoint at 56 epochs 03/05 20:39:12 - mmengine - INFO - Epoch(val) [56][100/196] eta: 0:00:13 time: 0.0219 data_time: 0.0003 03/05 20:39:26 - mmengine - INFO - Epoch(val) [56][196/196] accuracy/top1: 71.1080 accuracy/top5: 90.6560 03/05 20:39:57 - mmengine - INFO - Epoch(train) [57][ 100/5005] lr: 1.0000e-02 eta: 1 day, 2:37:09 time: 0.2222 data_time: 0.0036 loss: 1.3504 03/05 20:40:21 - mmengine - INFO - Epoch(train) [57][ 200/5005] lr: 1.0000e-02 eta: 1 day, 2:36:47 time: 0.2218 data_time: 0.0027 loss: 1.2281 03/05 20:40:43 - mmengine - INFO - Epoch(train) [57][ 300/5005] lr: 1.0000e-02 eta: 1 day, 2:36:24 time: 0.2341 data_time: 0.0028 loss: 1.3877 03/05 20:41:06 - mmengine - INFO - Epoch(train) [57][ 400/5005] lr: 1.0000e-02 eta: 1 day, 2:36:01 time: 0.2468 data_time: 0.0034 loss: 1.2534 03/05 20:41:29 - mmengine - INFO - Epoch(train) [57][ 500/5005] lr: 1.0000e-02 eta: 1 day, 2:35:38 time: 0.2260 data_time: 0.0026 loss: 1.3008 03/05 20:41:52 - mmengine - INFO - Epoch(train) [57][ 600/5005] lr: 1.0000e-02 eta: 1 day, 2:35:15 time: 0.2228 data_time: 0.0030 loss: 1.2606 03/05 20:42:14 - mmengine - INFO - Epoch(train) [57][ 700/5005] lr: 1.0000e-02 eta: 1 day, 2:34:52 time: 0.2223 data_time: 0.0030 loss: 1.3056 03/05 20:42:19 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:42:37 - mmengine - INFO - Epoch(train) [57][ 800/5005] lr: 1.0000e-02 eta: 1 day, 2:34:29 time: 0.2580 data_time: 0.0026 loss: 1.1808 03/05 20:43:00 - mmengine - INFO - Epoch(train) [57][ 900/5005] lr: 1.0000e-02 eta: 1 day, 2:34:06 time: 0.2254 data_time: 0.0030 loss: 1.1242 03/05 20:43:23 - mmengine - INFO - Epoch(train) [57][1000/5005] lr: 1.0000e-02 eta: 1 day, 2:33:44 time: 0.2207 data_time: 0.0026 loss: 1.2732 03/05 20:43:46 - mmengine - INFO - Epoch(train) [57][1100/5005] lr: 1.0000e-02 eta: 1 day, 2:33:21 time: 0.2257 data_time: 0.0031 loss: 1.3420 03/05 20:44:09 - mmengine - INFO - Epoch(train) [57][1200/5005] lr: 1.0000e-02 eta: 1 day, 2:32:58 time: 0.2256 data_time: 0.0030 loss: 1.4343 03/05 20:44:32 - mmengine - INFO - Epoch(train) [57][1300/5005] lr: 1.0000e-02 eta: 1 day, 2:32:35 time: 0.2457 data_time: 0.0030 loss: 1.5424 03/05 20:44:55 - mmengine - INFO - Epoch(train) [57][1400/5005] lr: 1.0000e-02 eta: 1 day, 2:32:12 time: 0.2284 data_time: 0.0026 loss: 1.2903 03/05 20:45:17 - mmengine - INFO - Epoch(train) [57][1500/5005] lr: 1.0000e-02 eta: 1 day, 2:31:49 time: 0.2229 data_time: 0.0030 loss: 1.5015 03/05 20:45:40 - mmengine - INFO - Epoch(train) [57][1600/5005] lr: 1.0000e-02 eta: 1 day, 2:31:26 time: 0.2200 data_time: 0.0029 loss: 1.2542 03/05 20:46:03 - mmengine - INFO - Epoch(train) [57][1700/5005] lr: 1.0000e-02 eta: 1 day, 2:31:03 time: 0.2255 data_time: 0.0031 loss: 1.2803 03/05 20:46:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:46:26 - mmengine - INFO - Epoch(train) [57][1800/5005] lr: 1.0000e-02 eta: 1 day, 2:30:41 time: 0.2479 data_time: 0.0028 loss: 1.4606 03/05 20:46:49 - mmengine - INFO - Epoch(train) [57][1900/5005] lr: 1.0000e-02 eta: 1 day, 2:30:17 time: 0.2299 data_time: 0.0029 loss: 1.3817 03/05 20:47:12 - mmengine - INFO - Epoch(train) [57][2000/5005] lr: 1.0000e-02 eta: 1 day, 2:29:55 time: 0.2234 data_time: 0.0029 loss: 1.2974 03/05 20:47:34 - mmengine - INFO - Epoch(train) [57][2100/5005] lr: 1.0000e-02 eta: 1 day, 2:29:32 time: 0.2246 data_time: 0.0026 loss: 1.3261 03/05 20:47:57 - mmengine - INFO - Epoch(train) [57][2200/5005] lr: 1.0000e-02 eta: 1 day, 2:29:09 time: 0.2407 data_time: 0.0028 loss: 1.3104 03/05 20:48:20 - mmengine - INFO - Epoch(train) [57][2300/5005] lr: 1.0000e-02 eta: 1 day, 2:28:46 time: 0.2268 data_time: 0.0027 loss: 1.3652 03/05 20:48:43 - mmengine - INFO - Epoch(train) [57][2400/5005] lr: 1.0000e-02 eta: 1 day, 2:28:23 time: 0.2217 data_time: 0.0026 loss: 1.3334 03/05 20:49:06 - mmengine - INFO - Epoch(train) [57][2500/5005] lr: 1.0000e-02 eta: 1 day, 2:28:00 time: 0.2258 data_time: 0.0028 loss: 1.2508 03/05 20:49:28 - mmengine - INFO - Epoch(train) [57][2600/5005] lr: 1.0000e-02 eta: 1 day, 2:27:37 time: 0.2287 data_time: 0.0033 loss: 1.3598 03/05 20:49:51 - mmengine - INFO - Epoch(train) [57][2700/5005] lr: 1.0000e-02 eta: 1 day, 2:27:14 time: 0.2238 data_time: 0.0027 loss: 1.4231 03/05 20:49:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:50:14 - mmengine - INFO - Epoch(train) [57][2800/5005] lr: 1.0000e-02 eta: 1 day, 2:26:51 time: 0.2230 data_time: 0.0027 loss: 1.3399 03/05 20:50:37 - mmengine - INFO - Epoch(train) [57][2900/5005] lr: 1.0000e-02 eta: 1 day, 2:26:28 time: 0.2444 data_time: 0.0027 loss: 1.4405 03/05 20:51:00 - mmengine - INFO - Epoch(train) [57][3000/5005] lr: 1.0000e-02 eta: 1 day, 2:26:05 time: 0.2245 data_time: 0.0025 loss: 1.3033 03/05 20:51:22 - mmengine - INFO - Epoch(train) [57][3100/5005] lr: 1.0000e-02 eta: 1 day, 2:25:42 time: 0.2281 data_time: 0.0028 loss: 1.3293 03/05 20:51:45 - mmengine - INFO - Epoch(train) [57][3200/5005] lr: 1.0000e-02 eta: 1 day, 2:25:19 time: 0.2286 data_time: 0.0029 loss: 1.2170 03/05 20:52:08 - mmengine - INFO - Epoch(train) [57][3300/5005] lr: 1.0000e-02 eta: 1 day, 2:24:56 time: 0.2243 data_time: 0.0031 loss: 1.4958 03/05 20:52:31 - mmengine - INFO - Epoch(train) [57][3400/5005] lr: 1.0000e-02 eta: 1 day, 2:24:33 time: 0.2239 data_time: 0.0033 loss: 1.3801 03/05 20:52:54 - mmengine - INFO - Epoch(train) [57][3500/5005] lr: 1.0000e-02 eta: 1 day, 2:24:10 time: 0.2216 data_time: 0.0030 loss: 1.4353 03/05 20:53:16 - mmengine - INFO - Epoch(train) [57][3600/5005] lr: 1.0000e-02 eta: 1 day, 2:23:47 time: 0.2251 data_time: 0.0030 loss: 1.3689 03/05 20:53:39 - mmengine - INFO - Epoch(train) [57][3700/5005] lr: 1.0000e-02 eta: 1 day, 2:23:24 time: 0.2409 data_time: 0.0031 loss: 1.3727 03/05 20:53:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:54:02 - mmengine - INFO - Epoch(train) [57][3800/5005] lr: 1.0000e-02 eta: 1 day, 2:23:01 time: 0.2187 data_time: 0.0031 loss: 1.4924 03/05 20:54:25 - mmengine - INFO - Epoch(train) [57][3900/5005] lr: 1.0000e-02 eta: 1 day, 2:22:38 time: 0.2243 data_time: 0.0029 loss: 1.3966 03/05 20:54:47 - mmengine - INFO - Epoch(train) [57][4000/5005] lr: 1.0000e-02 eta: 1 day, 2:22:14 time: 0.2251 data_time: 0.0029 loss: 1.3103 03/05 20:55:10 - mmengine - INFO - Epoch(train) [57][4100/5005] lr: 1.0000e-02 eta: 1 day, 2:21:51 time: 0.2263 data_time: 0.0031 loss: 1.2000 03/05 20:55:33 - mmengine - INFO - Epoch(train) [57][4200/5005] lr: 1.0000e-02 eta: 1 day, 2:21:29 time: 0.2190 data_time: 0.0030 loss: 1.2261 03/05 20:55:56 - mmengine - INFO - Epoch(train) [57][4300/5005] lr: 1.0000e-02 eta: 1 day, 2:21:06 time: 0.2249 data_time: 0.0030 loss: 1.4125 03/05 20:56:19 - mmengine - INFO - Epoch(train) [57][4400/5005] lr: 1.0000e-02 eta: 1 day, 2:20:43 time: 0.2261 data_time: 0.0030 loss: 1.2058 03/05 20:56:41 - mmengine - INFO - Epoch(train) [57][4500/5005] lr: 1.0000e-02 eta: 1 day, 2:20:20 time: 0.2266 data_time: 0.0031 loss: 1.2564 03/05 20:57:05 - mmengine - INFO - Epoch(train) [57][4600/5005] lr: 1.0000e-02 eta: 1 day, 2:19:57 time: 0.2424 data_time: 0.0027 loss: 1.4649 03/05 20:57:28 - mmengine - INFO - Epoch(train) [57][4700/5005] lr: 1.0000e-02 eta: 1 day, 2:19:35 time: 0.2419 data_time: 0.0031 loss: 1.4670 03/05 20:57:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:57:50 - mmengine - INFO - Epoch(train) [57][4800/5005] lr: 1.0000e-02 eta: 1 day, 2:19:12 time: 0.2263 data_time: 0.0026 loss: 1.3806 03/05 20:58:14 - mmengine - INFO - Epoch(train) [57][4900/5005] lr: 1.0000e-02 eta: 1 day, 2:18:50 time: 0.2885 data_time: 0.0025 loss: 1.2382 03/05 20:58:43 - mmengine - INFO - Epoch(train) [57][5000/5005] lr: 1.0000e-02 eta: 1 day, 2:18:35 time: 0.2865 data_time: 0.0026 loss: 1.1695 03/05 20:58:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 20:58:47 - mmengine - INFO - Saving checkpoint at 57 epochs 03/05 20:59:01 - mmengine - INFO - Epoch(val) [57][100/196] eta: 0:00:12 time: 0.0194 data_time: 0.0004 03/05 20:59:14 - mmengine - INFO - Epoch(val) [57][196/196] accuracy/top1: 71.2700 accuracy/top5: 90.7900 03/05 20:59:46 - mmengine - INFO - Epoch(train) [58][ 100/5005] lr: 1.0000e-02 eta: 1 day, 2:18:24 time: 0.2252 data_time: 0.0033 loss: 1.4269 03/05 21:00:09 - mmengine - INFO - Epoch(train) [58][ 200/5005] lr: 1.0000e-02 eta: 1 day, 2:18:01 time: 0.2237 data_time: 0.0036 loss: 1.2770 03/05 21:00:32 - mmengine - INFO - Epoch(train) [58][ 300/5005] lr: 1.0000e-02 eta: 1 day, 2:17:39 time: 0.2240 data_time: 0.0037 loss: 1.4210 03/05 21:00:55 - mmengine - INFO - Epoch(train) [58][ 400/5005] lr: 1.0000e-02 eta: 1 day, 2:17:16 time: 0.2222 data_time: 0.0028 loss: 1.3880 03/05 21:01:18 - mmengine - INFO - Epoch(train) [58][ 500/5005] lr: 1.0000e-02 eta: 1 day, 2:16:53 time: 0.2318 data_time: 0.0028 loss: 1.1841 03/05 21:01:40 - mmengine - INFO - Epoch(train) [58][ 600/5005] lr: 1.0000e-02 eta: 1 day, 2:16:30 time: 0.2259 data_time: 0.0028 loss: 1.3283 03/05 21:02:03 - mmengine - INFO - Epoch(train) [58][ 700/5005] lr: 1.0000e-02 eta: 1 day, 2:16:07 time: 0.2419 data_time: 0.0026 loss: 1.3023 03/05 21:02:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:02:26 - mmengine - INFO - Epoch(train) [58][ 800/5005] lr: 1.0000e-02 eta: 1 day, 2:15:44 time: 0.2459 data_time: 0.0027 loss: 1.2358 03/05 21:02:49 - mmengine - INFO - Epoch(train) [58][ 900/5005] lr: 1.0000e-02 eta: 1 day, 2:15:21 time: 0.2208 data_time: 0.0031 loss: 1.3478 03/05 21:03:12 - mmengine - INFO - Epoch(train) [58][1000/5005] lr: 1.0000e-02 eta: 1 day, 2:14:58 time: 0.2224 data_time: 0.0027 loss: 1.2742 03/05 21:03:34 - mmengine - INFO - Epoch(train) [58][1100/5005] lr: 1.0000e-02 eta: 1 day, 2:14:35 time: 0.2228 data_time: 0.0030 loss: 1.1634 03/05 21:03:57 - mmengine - INFO - Epoch(train) [58][1200/5005] lr: 1.0000e-02 eta: 1 day, 2:14:11 time: 0.2262 data_time: 0.0028 loss: 1.4330 03/05 21:04:20 - mmengine - INFO - Epoch(train) [58][1300/5005] lr: 1.0000e-02 eta: 1 day, 2:13:49 time: 0.2375 data_time: 0.0029 loss: 1.2713 03/05 21:04:43 - mmengine - INFO - Epoch(train) [58][1400/5005] lr: 1.0000e-02 eta: 1 day, 2:13:26 time: 0.2457 data_time: 0.0028 loss: 1.4845 03/05 21:05:05 - mmengine - INFO - Epoch(train) [58][1500/5005] lr: 1.0000e-02 eta: 1 day, 2:13:03 time: 0.2226 data_time: 0.0030 loss: 1.2988 03/05 21:05:28 - mmengine - INFO - Epoch(train) [58][1600/5005] lr: 1.0000e-02 eta: 1 day, 2:12:40 time: 0.2254 data_time: 0.0029 loss: 1.3713 03/05 21:05:51 - mmengine - INFO - Epoch(train) [58][1700/5005] lr: 1.0000e-02 eta: 1 day, 2:12:16 time: 0.2267 data_time: 0.0033 loss: 1.3092 03/05 21:05:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:06:14 - mmengine - INFO - Epoch(train) [58][1800/5005] lr: 1.0000e-02 eta: 1 day, 2:11:54 time: 0.2265 data_time: 0.0032 loss: 1.2923 03/05 21:06:37 - mmengine - INFO - Epoch(train) [58][1900/5005] lr: 1.0000e-02 eta: 1 day, 2:11:31 time: 0.2251 data_time: 0.0029 loss: 1.3170 03/05 21:06:59 - mmengine - INFO - Epoch(train) [58][2000/5005] lr: 1.0000e-02 eta: 1 day, 2:11:07 time: 0.2239 data_time: 0.0026 loss: 1.2864 03/05 21:07:22 - mmengine - INFO - Epoch(train) [58][2100/5005] lr: 1.0000e-02 eta: 1 day, 2:10:44 time: 0.2232 data_time: 0.0027 loss: 1.3584 03/05 21:07:45 - mmengine - INFO - Epoch(train) [58][2200/5005] lr: 1.0000e-02 eta: 1 day, 2:10:22 time: 0.2234 data_time: 0.0027 loss: 1.2358 03/05 21:08:08 - mmengine - INFO - Epoch(train) [58][2300/5005] lr: 1.0000e-02 eta: 1 day, 2:09:59 time: 0.2251 data_time: 0.0027 loss: 1.3228 03/05 21:08:30 - mmengine - INFO - Epoch(train) [58][2400/5005] lr: 1.0000e-02 eta: 1 day, 2:09:35 time: 0.2242 data_time: 0.0031 loss: 1.3356 03/05 21:08:53 - mmengine - INFO - Epoch(train) [58][2500/5005] lr: 1.0000e-02 eta: 1 day, 2:09:12 time: 0.2227 data_time: 0.0025 loss: 1.3187 03/05 21:09:16 - mmengine - INFO - Epoch(train) [58][2600/5005] lr: 1.0000e-02 eta: 1 day, 2:08:50 time: 0.2283 data_time: 0.0029 loss: 1.3007 03/05 21:09:39 - mmengine - INFO - Epoch(train) [58][2700/5005] lr: 1.0000e-02 eta: 1 day, 2:08:27 time: 0.2217 data_time: 0.0024 loss: 1.4176 03/05 21:09:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:10:02 - mmengine - INFO - Epoch(train) [58][2800/5005] lr: 1.0000e-02 eta: 1 day, 2:08:04 time: 0.2287 data_time: 0.0028 loss: 1.2360 03/05 21:10:24 - mmengine - INFO - Epoch(train) [58][2900/5005] lr: 1.0000e-02 eta: 1 day, 2:07:41 time: 0.2228 data_time: 0.0032 loss: 1.4582 03/05 21:10:47 - mmengine - INFO - Epoch(train) [58][3000/5005] lr: 1.0000e-02 eta: 1 day, 2:07:18 time: 0.2259 data_time: 0.0030 loss: 1.2171 03/05 21:11:10 - mmengine - INFO - Epoch(train) [58][3100/5005] lr: 1.0000e-02 eta: 1 day, 2:06:55 time: 0.2251 data_time: 0.0033 loss: 1.3523 03/05 21:11:33 - mmengine - INFO - Epoch(train) [58][3200/5005] lr: 1.0000e-02 eta: 1 day, 2:06:32 time: 0.2238 data_time: 0.0026 loss: 1.2526 03/05 21:11:55 - mmengine - INFO - Epoch(train) [58][3300/5005] lr: 1.0000e-02 eta: 1 day, 2:06:08 time: 0.2220 data_time: 0.0024 loss: 1.3856 03/05 21:12:18 - mmengine - INFO - Epoch(train) [58][3400/5005] lr: 1.0000e-02 eta: 1 day, 2:05:46 time: 0.2273 data_time: 0.0031 loss: 1.2234 03/05 21:12:41 - mmengine - INFO - Epoch(train) [58][3500/5005] lr: 1.0000e-02 eta: 1 day, 2:05:23 time: 0.2245 data_time: 0.0033 loss: 1.4128 03/05 21:13:04 - mmengine - INFO - Epoch(train) [58][3600/5005] lr: 1.0000e-02 eta: 1 day, 2:05:00 time: 0.2243 data_time: 0.0034 loss: 1.3933 03/05 21:13:27 - mmengine - INFO - Epoch(train) [58][3700/5005] lr: 1.0000e-02 eta: 1 day, 2:04:36 time: 0.2238 data_time: 0.0033 loss: 1.3746 03/05 21:13:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:13:49 - mmengine - INFO - Epoch(train) [58][3800/5005] lr: 1.0000e-02 eta: 1 day, 2:04:13 time: 0.2234 data_time: 0.0030 loss: 1.3079 03/05 21:14:13 - mmengine - INFO - Epoch(train) [58][3900/5005] lr: 1.0000e-02 eta: 1 day, 2:03:51 time: 0.2246 data_time: 0.0030 loss: 1.3845 03/05 21:14:35 - mmengine - INFO - Epoch(train) [58][4000/5005] lr: 1.0000e-02 eta: 1 day, 2:03:28 time: 0.2214 data_time: 0.0029 loss: 1.2991 03/05 21:14:58 - mmengine - INFO - Epoch(train) [58][4100/5005] lr: 1.0000e-02 eta: 1 day, 2:03:05 time: 0.2245 data_time: 0.0030 loss: 1.3710 03/05 21:15:21 - mmengine - INFO - Epoch(train) [58][4200/5005] lr: 1.0000e-02 eta: 1 day, 2:02:42 time: 0.2229 data_time: 0.0027 loss: 1.3097 03/05 21:15:44 - mmengine - INFO - Epoch(train) [58][4300/5005] lr: 1.0000e-02 eta: 1 day, 2:02:19 time: 0.2253 data_time: 0.0026 loss: 1.3432 03/05 21:16:06 - mmengine - INFO - Epoch(train) [58][4400/5005] lr: 1.0000e-02 eta: 1 day, 2:01:56 time: 0.2391 data_time: 0.0030 loss: 1.3200 03/05 21:16:29 - mmengine - INFO - Epoch(train) [58][4500/5005] lr: 1.0000e-02 eta: 1 day, 2:01:33 time: 0.2253 data_time: 0.0029 loss: 1.3696 03/05 21:16:52 - mmengine - INFO - Epoch(train) [58][4600/5005] lr: 1.0000e-02 eta: 1 day, 2:01:10 time: 0.2405 data_time: 0.0026 loss: 1.4766 03/05 21:17:15 - mmengine - INFO - Epoch(train) [58][4700/5005] lr: 1.0000e-02 eta: 1 day, 2:00:47 time: 0.2222 data_time: 0.0027 loss: 1.2491 03/05 21:17:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:17:38 - mmengine - INFO - Epoch(train) [58][4800/5005] lr: 1.0000e-02 eta: 1 day, 2:00:24 time: 0.2270 data_time: 0.0027 loss: 1.2944 03/05 21:18:02 - mmengine - INFO - Epoch(train) [58][4900/5005] lr: 1.0000e-02 eta: 1 day, 2:00:03 time: 0.2883 data_time: 0.0030 loss: 1.2556 03/05 21:18:30 - mmengine - INFO - Epoch(train) [58][5000/5005] lr: 1.0000e-02 eta: 1 day, 1:59:48 time: 0.2968 data_time: 0.0024 loss: 1.4766 03/05 21:18:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:18:34 - mmengine - INFO - Saving checkpoint at 58 epochs 03/05 21:18:49 - mmengine - INFO - Epoch(val) [58][100/196] eta: 0:00:12 time: 0.0211 data_time: 0.0004 03/05 21:19:02 - mmengine - INFO - Epoch(val) [58][196/196] accuracy/top1: 71.3760 accuracy/top5: 90.7740 03/05 21:19:34 - mmengine - INFO - Epoch(train) [59][ 100/5005] lr: 1.0000e-02 eta: 1 day, 1:59:37 time: 0.2424 data_time: 0.0030 loss: 1.3166 03/05 21:19:58 - mmengine - INFO - Epoch(train) [59][ 200/5005] lr: 1.0000e-02 eta: 1 day, 1:59:14 time: 0.2252 data_time: 0.0026 loss: 1.4198 03/05 21:20:20 - mmengine - INFO - Epoch(train) [59][ 300/5005] lr: 1.0000e-02 eta: 1 day, 1:58:51 time: 0.2259 data_time: 0.0030 loss: 1.3837 03/05 21:20:43 - mmengine - INFO - Epoch(train) [59][ 400/5005] lr: 1.0000e-02 eta: 1 day, 1:58:27 time: 0.2237 data_time: 0.0029 loss: 1.2776 03/05 21:21:06 - mmengine - INFO - Epoch(train) [59][ 500/5005] lr: 1.0000e-02 eta: 1 day, 1:58:05 time: 0.2226 data_time: 0.0036 loss: 1.2804 03/05 21:21:29 - mmengine - INFO - Epoch(train) [59][ 600/5005] lr: 1.0000e-02 eta: 1 day, 1:57:43 time: 0.2221 data_time: 0.0030 loss: 1.2344 03/05 21:21:52 - mmengine - INFO - Epoch(train) [59][ 700/5005] lr: 1.0000e-02 eta: 1 day, 1:57:20 time: 0.2248 data_time: 0.0029 loss: 1.2987 03/05 21:21:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:22:15 - mmengine - INFO - Epoch(train) [59][ 800/5005] lr: 1.0000e-02 eta: 1 day, 1:56:57 time: 0.2270 data_time: 0.0034 loss: 1.3955 03/05 21:22:37 - mmengine - INFO - Epoch(train) [59][ 900/5005] lr: 1.0000e-02 eta: 1 day, 1:56:33 time: 0.2222 data_time: 0.0029 loss: 1.2812 03/05 21:23:01 - mmengine - INFO - Epoch(train) [59][1000/5005] lr: 1.0000e-02 eta: 1 day, 1:56:11 time: 0.2257 data_time: 0.0032 loss: 1.0741 03/05 21:23:24 - mmengine - INFO - Epoch(train) [59][1100/5005] lr: 1.0000e-02 eta: 1 day, 1:55:48 time: 0.2331 data_time: 0.0030 loss: 1.3306 03/05 21:23:46 - mmengine - INFO - Epoch(train) [59][1200/5005] lr: 1.0000e-02 eta: 1 day, 1:55:25 time: 0.2211 data_time: 0.0035 loss: 1.3959 03/05 21:24:09 - mmengine - INFO - Epoch(train) [59][1300/5005] lr: 1.0000e-02 eta: 1 day, 1:55:02 time: 0.2270 data_time: 0.0029 loss: 1.3491 03/05 21:24:32 - mmengine - INFO - Epoch(train) [59][1400/5005] lr: 1.0000e-02 eta: 1 day, 1:54:40 time: 0.2221 data_time: 0.0033 loss: 1.3507 03/05 21:24:56 - mmengine - INFO - Epoch(train) [59][1500/5005] lr: 1.0000e-02 eta: 1 day, 1:54:17 time: 0.2205 data_time: 0.0025 loss: 1.2145 03/05 21:25:18 - mmengine - INFO - Epoch(train) [59][1600/5005] lr: 1.0000e-02 eta: 1 day, 1:53:54 time: 0.2270 data_time: 0.0029 loss: 1.3320 03/05 21:25:41 - mmengine - INFO - Epoch(train) [59][1700/5005] lr: 1.0000e-02 eta: 1 day, 1:53:31 time: 0.2202 data_time: 0.0028 loss: 1.2958 03/05 21:25:43 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:26:04 - mmengine - INFO - Epoch(train) [59][1800/5005] lr: 1.0000e-02 eta: 1 day, 1:53:08 time: 0.2234 data_time: 0.0026 loss: 1.2554 03/05 21:26:27 - mmengine - INFO - Epoch(train) [59][1900/5005] lr: 1.0000e-02 eta: 1 day, 1:52:46 time: 0.2247 data_time: 0.0029 loss: 1.4764 03/05 21:26:50 - mmengine - INFO - Epoch(train) [59][2000/5005] lr: 1.0000e-02 eta: 1 day, 1:52:23 time: 0.2226 data_time: 0.0029 loss: 1.2561 03/05 21:27:12 - mmengine - INFO - Epoch(train) [59][2100/5005] lr: 1.0000e-02 eta: 1 day, 1:51:59 time: 0.2255 data_time: 0.0025 loss: 1.4542 03/05 21:27:35 - mmengine - INFO - Epoch(train) [59][2200/5005] lr: 1.0000e-02 eta: 1 day, 1:51:36 time: 0.2403 data_time: 0.0032 loss: 1.2865 03/05 21:27:58 - mmengine - INFO - Epoch(train) [59][2300/5005] lr: 1.0000e-02 eta: 1 day, 1:51:13 time: 0.2220 data_time: 0.0028 loss: 1.4111 03/05 21:28:21 - mmengine - INFO - Epoch(train) [59][2400/5005] lr: 1.0000e-02 eta: 1 day, 1:50:51 time: 0.2230 data_time: 0.0031 loss: 1.2703 03/05 21:28:43 - mmengine - INFO - Epoch(train) [59][2500/5005] lr: 1.0000e-02 eta: 1 day, 1:50:27 time: 0.2226 data_time: 0.0026 loss: 1.1871 03/05 21:29:06 - mmengine - INFO - Epoch(train) [59][2600/5005] lr: 1.0000e-02 eta: 1 day, 1:50:04 time: 0.2238 data_time: 0.0029 loss: 1.2676 03/05 21:29:28 - mmengine - INFO - Epoch(train) [59][2700/5005] lr: 1.0000e-02 eta: 1 day, 1:49:41 time: 0.2223 data_time: 0.0027 loss: 1.4805 03/05 21:29:31 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:29:52 - mmengine - INFO - Epoch(train) [59][2800/5005] lr: 1.0000e-02 eta: 1 day, 1:49:18 time: 0.2245 data_time: 0.0030 loss: 1.3230 03/05 21:30:14 - mmengine - INFO - Epoch(train) [59][2900/5005] lr: 1.0000e-02 eta: 1 day, 1:48:55 time: 0.2232 data_time: 0.0030 loss: 1.3615 03/05 21:30:37 - mmengine - INFO - Epoch(train) [59][3000/5005] lr: 1.0000e-02 eta: 1 day, 1:48:32 time: 0.2240 data_time: 0.0028 loss: 1.2035 03/05 21:31:00 - mmengine - INFO - Epoch(train) [59][3100/5005] lr: 1.0000e-02 eta: 1 day, 1:48:09 time: 0.2219 data_time: 0.0031 loss: 1.4828 03/05 21:31:23 - mmengine - INFO - Epoch(train) [59][3200/5005] lr: 1.0000e-02 eta: 1 day, 1:47:46 time: 0.2390 data_time: 0.0026 loss: 1.3956 03/05 21:31:45 - mmengine - INFO - Epoch(train) [59][3300/5005] lr: 1.0000e-02 eta: 1 day, 1:47:23 time: 0.2237 data_time: 0.0029 loss: 1.2384 03/05 21:32:08 - mmengine - INFO - Epoch(train) [59][3400/5005] lr: 1.0000e-02 eta: 1 day, 1:47:00 time: 0.2219 data_time: 0.0030 loss: 1.0491 03/05 21:32:31 - mmengine - INFO - Epoch(train) [59][3500/5005] lr: 1.0000e-02 eta: 1 day, 1:46:37 time: 0.2227 data_time: 0.0027 loss: 1.6103 03/05 21:32:54 - mmengine - INFO - Epoch(train) [59][3600/5005] lr: 1.0000e-02 eta: 1 day, 1:46:14 time: 0.2254 data_time: 0.0026 loss: 1.1536 03/05 21:33:17 - mmengine - INFO - Epoch(train) [59][3700/5005] lr: 1.0000e-02 eta: 1 day, 1:45:51 time: 0.2263 data_time: 0.0025 loss: 1.3423 03/05 21:33:19 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:33:39 - mmengine - INFO - Epoch(train) [59][3800/5005] lr: 1.0000e-02 eta: 1 day, 1:45:28 time: 0.2268 data_time: 0.0027 loss: 1.3539 03/05 21:34:02 - mmengine - INFO - Epoch(train) [59][3900/5005] lr: 1.0000e-02 eta: 1 day, 1:45:05 time: 0.2243 data_time: 0.0025 loss: 1.1786 03/05 21:34:25 - mmengine - INFO - Epoch(train) [59][4000/5005] lr: 1.0000e-02 eta: 1 day, 1:44:42 time: 0.2434 data_time: 0.0031 loss: 1.2162 03/05 21:34:48 - mmengine - INFO - Epoch(train) [59][4100/5005] lr: 1.0000e-02 eta: 1 day, 1:44:19 time: 0.2222 data_time: 0.0026 loss: 1.1653 03/05 21:35:11 - mmengine - INFO - Epoch(train) [59][4200/5005] lr: 1.0000e-02 eta: 1 day, 1:43:57 time: 0.2283 data_time: 0.0027 loss: 1.2765 03/05 21:35:34 - mmengine - INFO - Epoch(train) [59][4300/5005] lr: 1.0000e-02 eta: 1 day, 1:43:34 time: 0.2231 data_time: 0.0030 loss: 1.4911 03/05 21:35:57 - mmengine - INFO - Epoch(train) [59][4400/5005] lr: 1.0000e-02 eta: 1 day, 1:43:11 time: 0.2214 data_time: 0.0027 loss: 1.2387 03/05 21:36:19 - mmengine - INFO - Epoch(train) [59][4500/5005] lr: 1.0000e-02 eta: 1 day, 1:42:48 time: 0.2250 data_time: 0.0030 loss: 1.4081 03/05 21:36:42 - mmengine - INFO - Epoch(train) [59][4600/5005] lr: 1.0000e-02 eta: 1 day, 1:42:24 time: 0.2227 data_time: 0.0025 loss: 1.5639 03/05 21:37:05 - mmengine - INFO - Epoch(train) [59][4700/5005] lr: 1.0000e-02 eta: 1 day, 1:42:02 time: 0.2249 data_time: 0.0028 loss: 1.4245 03/05 21:37:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:37:28 - mmengine - INFO - Epoch(train) [59][4800/5005] lr: 1.0000e-02 eta: 1 day, 1:41:39 time: 0.2230 data_time: 0.0028 loss: 1.2493 03/05 21:37:52 - mmengine - INFO - Epoch(train) [59][4900/5005] lr: 1.0000e-02 eta: 1 day, 1:41:17 time: 0.2838 data_time: 0.0024 loss: 1.2609 03/05 21:38:20 - mmengine - INFO - Epoch(train) [59][5000/5005] lr: 1.0000e-02 eta: 1 day, 1:41:02 time: 0.2822 data_time: 0.0027 loss: 1.4189 03/05 21:38:22 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:38:24 - mmengine - INFO - Saving checkpoint at 59 epochs 03/05 21:38:39 - mmengine - INFO - Epoch(val) [59][100/196] eta: 0:00:13 time: 0.0194 data_time: 0.0004 03/05 21:38:53 - mmengine - INFO - Epoch(val) [59][196/196] accuracy/top1: 71.0900 accuracy/top5: 90.6060 03/05 21:39:24 - mmengine - INFO - Epoch(train) [60][ 100/5005] lr: 1.0000e-02 eta: 1 day, 1:40:50 time: 0.2275 data_time: 0.0032 loss: 1.3452 03/05 21:39:47 - mmengine - INFO - Epoch(train) [60][ 200/5005] lr: 1.0000e-02 eta: 1 day, 1:40:27 time: 0.2233 data_time: 0.0029 loss: 1.3190 03/05 21:40:10 - mmengine - INFO - Epoch(train) [60][ 300/5005] lr: 1.0000e-02 eta: 1 day, 1:40:05 time: 0.2534 data_time: 0.0029 loss: 1.2903 03/05 21:40:32 - mmengine - INFO - Epoch(train) [60][ 400/5005] lr: 1.0000e-02 eta: 1 day, 1:39:41 time: 0.2225 data_time: 0.0030 loss: 1.2614 03/05 21:40:55 - mmengine - INFO - Epoch(train) [60][ 500/5005] lr: 1.0000e-02 eta: 1 day, 1:39:18 time: 0.2272 data_time: 0.0032 loss: 1.4935 03/05 21:41:18 - mmengine - INFO - Epoch(train) [60][ 600/5005] lr: 1.0000e-02 eta: 1 day, 1:38:55 time: 0.2216 data_time: 0.0029 loss: 1.2171 03/05 21:41:41 - mmengine - INFO - Epoch(train) [60][ 700/5005] lr: 1.0000e-02 eta: 1 day, 1:38:32 time: 0.2360 data_time: 0.0028 loss: 1.1404 03/05 21:41:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:42:04 - mmengine - INFO - Epoch(train) [60][ 800/5005] lr: 1.0000e-02 eta: 1 day, 1:38:09 time: 0.2256 data_time: 0.0030 loss: 1.2495 03/05 21:42:26 - mmengine - INFO - Epoch(train) [60][ 900/5005] lr: 1.0000e-02 eta: 1 day, 1:37:46 time: 0.2236 data_time: 0.0026 loss: 1.4776 03/05 21:42:49 - mmengine - INFO - Epoch(train) [60][1000/5005] lr: 1.0000e-02 eta: 1 day, 1:37:23 time: 0.2459 data_time: 0.0030 loss: 1.1445 03/05 21:43:12 - mmengine - INFO - Epoch(train) [60][1100/5005] lr: 1.0000e-02 eta: 1 day, 1:37:00 time: 0.2240 data_time: 0.0027 loss: 1.1040 03/05 21:43:34 - mmengine - INFO - Epoch(train) [60][1200/5005] lr: 1.0000e-02 eta: 1 day, 1:36:37 time: 0.2227 data_time: 0.0031 loss: 1.2948 03/05 21:43:57 - mmengine - INFO - Epoch(train) [60][1300/5005] lr: 1.0000e-02 eta: 1 day, 1:36:14 time: 0.2483 data_time: 0.0028 loss: 1.3646 03/05 21:44:20 - mmengine - INFO - Epoch(train) [60][1400/5005] lr: 1.0000e-02 eta: 1 day, 1:35:51 time: 0.2233 data_time: 0.0027 loss: 1.3358 03/05 21:44:43 - mmengine - INFO - Epoch(train) [60][1500/5005] lr: 1.0000e-02 eta: 1 day, 1:35:28 time: 0.2245 data_time: 0.0029 loss: 1.1264 03/05 21:45:06 - mmengine - INFO - Epoch(train) [60][1600/5005] lr: 1.0000e-02 eta: 1 day, 1:35:05 time: 0.2262 data_time: 0.0028 loss: 1.2585 03/05 21:45:29 - mmengine - INFO - Epoch(train) [60][1700/5005] lr: 1.0000e-02 eta: 1 day, 1:34:42 time: 0.2411 data_time: 0.0031 loss: 1.2976 03/05 21:45:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:45:51 - mmengine - INFO - Epoch(train) [60][1800/5005] lr: 1.0000e-02 eta: 1 day, 1:34:19 time: 0.2279 data_time: 0.0030 loss: 1.2359 03/05 21:46:14 - mmengine - INFO - Epoch(train) [60][1900/5005] lr: 1.0000e-02 eta: 1 day, 1:33:56 time: 0.2401 data_time: 0.0028 loss: 1.3608 03/05 21:46:37 - mmengine - INFO - Epoch(train) [60][2000/5005] lr: 1.0000e-02 eta: 1 day, 1:33:33 time: 0.2227 data_time: 0.0029 loss: 1.3380 03/05 21:47:00 - mmengine - INFO - Epoch(train) [60][2100/5005] lr: 1.0000e-02 eta: 1 day, 1:33:10 time: 0.2241 data_time: 0.0029 loss: 1.3716 03/05 21:47:22 - mmengine - INFO - Epoch(train) [60][2200/5005] lr: 1.0000e-02 eta: 1 day, 1:32:47 time: 0.2248 data_time: 0.0033 loss: 1.3129 03/05 21:47:45 - mmengine - INFO - Epoch(train) [60][2300/5005] lr: 1.0000e-02 eta: 1 day, 1:32:23 time: 0.2237 data_time: 0.0027 loss: 1.3088 03/05 21:48:08 - mmengine - INFO - Epoch(train) [60][2400/5005] lr: 1.0000e-02 eta: 1 day, 1:32:01 time: 0.2230 data_time: 0.0028 loss: 1.4284 03/05 21:48:31 - mmengine - INFO - Epoch(train) [60][2500/5005] lr: 1.0000e-02 eta: 1 day, 1:31:37 time: 0.2215 data_time: 0.0029 loss: 1.4176 03/05 21:48:53 - mmengine - INFO - Epoch(train) [60][2600/5005] lr: 1.0000e-02 eta: 1 day, 1:31:14 time: 0.2264 data_time: 0.0027 loss: 1.3196 03/05 21:49:16 - mmengine - INFO - Epoch(train) [60][2700/5005] lr: 1.0000e-02 eta: 1 day, 1:30:51 time: 0.2277 data_time: 0.0031 loss: 1.2337 03/05 21:49:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:49:39 - mmengine - INFO - Epoch(train) [60][2800/5005] lr: 1.0000e-02 eta: 1 day, 1:30:28 time: 0.2269 data_time: 0.0031 loss: 1.1304 03/05 21:50:02 - mmengine - INFO - Epoch(train) [60][2900/5005] lr: 1.0000e-02 eta: 1 day, 1:30:06 time: 0.2237 data_time: 0.0026 loss: 1.3187 03/05 21:50:25 - mmengine - INFO - Epoch(train) [60][3000/5005] lr: 1.0000e-02 eta: 1 day, 1:29:43 time: 0.2224 data_time: 0.0029 loss: 1.4885 03/05 21:50:47 - mmengine - INFO - Epoch(train) [60][3100/5005] lr: 1.0000e-02 eta: 1 day, 1:29:19 time: 0.2267 data_time: 0.0029 loss: 1.2542 03/05 21:51:10 - mmengine - INFO - Epoch(train) [60][3200/5005] lr: 1.0000e-02 eta: 1 day, 1:28:56 time: 0.2405 data_time: 0.0029 loss: 1.4522 03/05 21:51:33 - mmengine - INFO - Epoch(train) [60][3300/5005] lr: 1.0000e-02 eta: 1 day, 1:28:34 time: 0.2595 data_time: 0.0032 loss: 1.2222 03/05 21:51:56 - mmengine - INFO - Epoch(train) [60][3400/5005] lr: 1.0000e-02 eta: 1 day, 1:28:11 time: 0.2227 data_time: 0.0029 loss: 1.2345 03/05 21:52:19 - mmengine - INFO - Epoch(train) [60][3500/5005] lr: 1.0000e-02 eta: 1 day, 1:27:48 time: 0.2270 data_time: 0.0027 loss: 1.2632 03/05 21:52:42 - mmengine - INFO - Epoch(train) [60][3600/5005] lr: 1.0000e-02 eta: 1 day, 1:27:25 time: 0.2414 data_time: 0.0030 loss: 1.2800 03/05 21:53:05 - mmengine - INFO - Epoch(train) [60][3700/5005] lr: 1.0000e-02 eta: 1 day, 1:27:02 time: 0.2230 data_time: 0.0032 loss: 1.2989 03/05 21:53:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:53:27 - mmengine - INFO - Epoch(train) [60][3800/5005] lr: 1.0000e-02 eta: 1 day, 1:26:39 time: 0.2226 data_time: 0.0029 loss: 1.3208 03/05 21:53:50 - mmengine - INFO - Epoch(train) [60][3900/5005] lr: 1.0000e-02 eta: 1 day, 1:26:16 time: 0.2274 data_time: 0.0029 loss: 1.4120 03/05 21:54:13 - mmengine - INFO - Epoch(train) [60][4000/5005] lr: 1.0000e-02 eta: 1 day, 1:25:53 time: 0.2261 data_time: 0.0029 loss: 1.1661 03/05 21:54:36 - mmengine - INFO - Epoch(train) [60][4100/5005] lr: 1.0000e-02 eta: 1 day, 1:25:30 time: 0.2238 data_time: 0.0031 loss: 1.2634 03/05 21:54:59 - mmengine - INFO - Epoch(train) [60][4200/5005] lr: 1.0000e-02 eta: 1 day, 1:25:07 time: 0.2231 data_time: 0.0027 loss: 1.3216 03/05 21:55:21 - mmengine - INFO - Epoch(train) [60][4300/5005] lr: 1.0000e-02 eta: 1 day, 1:24:44 time: 0.2392 data_time: 0.0030 loss: 1.1088 03/05 21:55:44 - mmengine - INFO - Epoch(train) [60][4400/5005] lr: 1.0000e-02 eta: 1 day, 1:24:21 time: 0.2208 data_time: 0.0030 loss: 1.2829 03/05 21:56:07 - mmengine - INFO - Epoch(train) [60][4500/5005] lr: 1.0000e-02 eta: 1 day, 1:23:58 time: 0.2409 data_time: 0.0031 loss: 1.3013 03/05 21:56:30 - mmengine - INFO - Epoch(train) [60][4600/5005] lr: 1.0000e-02 eta: 1 day, 1:23:35 time: 0.2213 data_time: 0.0027 loss: 1.3741 03/05 21:56:52 - mmengine - INFO - Epoch(train) [60][4700/5005] lr: 1.0000e-02 eta: 1 day, 1:23:12 time: 0.2224 data_time: 0.0031 loss: 1.4432 03/05 21:56:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:57:15 - mmengine - INFO - Epoch(train) [60][4800/5005] lr: 1.0000e-02 eta: 1 day, 1:22:49 time: 0.2245 data_time: 0.0030 loss: 1.1857 03/05 21:57:39 - mmengine - INFO - Epoch(train) [60][4900/5005] lr: 1.0000e-02 eta: 1 day, 1:22:27 time: 0.2843 data_time: 0.0026 loss: 1.2367 03/05 21:58:07 - mmengine - INFO - Epoch(train) [60][5000/5005] lr: 1.0000e-02 eta: 1 day, 1:22:11 time: 0.2675 data_time: 0.0028 loss: 1.1861 03/05 21:58:09 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 21:58:12 - mmengine - INFO - Saving checkpoint at 60 epochs 03/05 21:58:26 - mmengine - INFO - Epoch(val) [60][100/196] eta: 0:00:12 time: 0.0193 data_time: 0.0004 03/05 21:58:40 - mmengine - INFO - Epoch(val) [60][196/196] accuracy/top1: 70.9040 accuracy/top5: 90.6700 03/05 21:59:12 - mmengine - INFO - Epoch(train) [61][ 100/5005] lr: 1.0000e-02 eta: 1 day, 1:22:00 time: 0.2248 data_time: 0.0035 loss: 1.3840 03/05 21:59:34 - mmengine - INFO - Epoch(train) [61][ 200/5005] lr: 1.0000e-02 eta: 1 day, 1:21:37 time: 0.2260 data_time: 0.0033 loss: 1.5230 03/05 21:59:57 - mmengine - INFO - Epoch(train) [61][ 300/5005] lr: 1.0000e-02 eta: 1 day, 1:21:13 time: 0.2251 data_time: 0.0041 loss: 1.1962 03/05 22:00:20 - mmengine - INFO - Epoch(train) [61][ 400/5005] lr: 1.0000e-02 eta: 1 day, 1:20:51 time: 0.2629 data_time: 0.0038 loss: 1.4035 03/05 22:00:43 - mmengine - INFO - Epoch(train) [61][ 500/5005] lr: 1.0000e-02 eta: 1 day, 1:20:28 time: 0.2430 data_time: 0.0034 loss: 1.3022 03/05 22:01:06 - mmengine - INFO - Epoch(train) [61][ 600/5005] lr: 1.0000e-02 eta: 1 day, 1:20:05 time: 0.2244 data_time: 0.0031 loss: 1.3491 03/05 22:01:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:01:29 - mmengine - INFO - Epoch(train) [61][ 700/5005] lr: 1.0000e-02 eta: 1 day, 1:19:42 time: 0.2203 data_time: 0.0028 loss: 1.1829 03/05 22:01:51 - mmengine - INFO - Epoch(train) [61][ 800/5005] lr: 1.0000e-02 eta: 1 day, 1:19:19 time: 0.2401 data_time: 0.0031 loss: 1.4114 03/05 22:02:15 - mmengine - INFO - Epoch(train) [61][ 900/5005] lr: 1.0000e-02 eta: 1 day, 1:18:56 time: 0.2402 data_time: 0.0032 loss: 1.2304 03/05 22:02:37 - mmengine - INFO - Epoch(train) [61][1000/5005] lr: 1.0000e-02 eta: 1 day, 1:18:33 time: 0.2233 data_time: 0.0031 loss: 1.4151 03/05 22:03:00 - mmengine - INFO - Epoch(train) [61][1100/5005] lr: 1.0000e-02 eta: 1 day, 1:18:10 time: 0.2239 data_time: 0.0031 loss: 1.2326 03/05 22:03:22 - mmengine - INFO - Epoch(train) [61][1200/5005] lr: 1.0000e-02 eta: 1 day, 1:17:47 time: 0.2243 data_time: 0.0029 loss: 1.2125 03/05 22:03:46 - mmengine - INFO - Epoch(train) [61][1300/5005] lr: 1.0000e-02 eta: 1 day, 1:17:24 time: 0.2269 data_time: 0.0030 loss: 1.3790 03/05 22:04:08 - mmengine - INFO - Epoch(train) [61][1400/5005] lr: 1.0000e-02 eta: 1 day, 1:17:01 time: 0.2287 data_time: 0.0029 loss: 1.4065 03/05 22:04:31 - mmengine - INFO - Epoch(train) [61][1500/5005] lr: 1.0000e-02 eta: 1 day, 1:16:38 time: 0.2222 data_time: 0.0031 loss: 1.2583 03/05 22:04:54 - mmengine - INFO - Epoch(train) [61][1600/5005] lr: 1.0000e-02 eta: 1 day, 1:16:15 time: 0.2237 data_time: 0.0030 loss: 1.2659 03/05 22:05:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:05:17 - mmengine - INFO - Epoch(train) [61][1700/5005] lr: 1.0000e-02 eta: 1 day, 1:15:52 time: 0.2222 data_time: 0.0030 loss: 1.3053 03/05 22:05:40 - mmengine - INFO - Epoch(train) [61][1800/5005] lr: 1.0000e-02 eta: 1 day, 1:15:29 time: 0.2228 data_time: 0.0032 loss: 1.1570 03/05 22:06:03 - mmengine - INFO - Epoch(train) [61][1900/5005] lr: 1.0000e-02 eta: 1 day, 1:15:06 time: 0.2218 data_time: 0.0029 loss: 1.2510 03/05 22:06:25 - mmengine - INFO - Epoch(train) [61][2000/5005] lr: 1.0000e-02 eta: 1 day, 1:14:43 time: 0.2240 data_time: 0.0032 loss: 1.3523 03/05 22:06:49 - mmengine - INFO - Epoch(train) [61][2100/5005] lr: 1.0000e-02 eta: 1 day, 1:14:21 time: 0.2226 data_time: 0.0034 loss: 1.4532 03/05 22:07:11 - mmengine - INFO - Epoch(train) [61][2200/5005] lr: 1.0000e-02 eta: 1 day, 1:13:57 time: 0.2242 data_time: 0.0034 loss: 1.2381 03/05 22:07:34 - mmengine - INFO - Epoch(train) [61][2300/5005] lr: 1.0000e-02 eta: 1 day, 1:13:34 time: 0.2268 data_time: 0.0029 loss: 1.1803 03/05 22:07:56 - mmengine - INFO - Epoch(train) [61][2400/5005] lr: 1.0000e-02 eta: 1 day, 1:13:11 time: 0.2242 data_time: 0.0030 loss: 1.3886 03/05 22:08:20 - mmengine - INFO - Epoch(train) [61][2500/5005] lr: 1.0000e-02 eta: 1 day, 1:12:49 time: 0.2230 data_time: 0.0027 loss: 1.0710 03/05 22:08:43 - mmengine - INFO - Epoch(train) [61][2600/5005] lr: 1.0000e-02 eta: 1 day, 1:12:26 time: 0.2233 data_time: 0.0032 loss: 1.3569 03/05 22:09:05 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:09:05 - mmengine - INFO - Epoch(train) [61][2700/5005] lr: 1.0000e-02 eta: 1 day, 1:12:03 time: 0.2261 data_time: 0.0030 loss: 1.5130 03/05 22:09:28 - mmengine - INFO - Epoch(train) [61][2800/5005] lr: 1.0000e-02 eta: 1 day, 1:11:40 time: 0.2246 data_time: 0.0029 loss: 1.3464 03/05 22:09:51 - mmengine - INFO - Epoch(train) [61][2900/5005] lr: 1.0000e-02 eta: 1 day, 1:11:17 time: 0.2246 data_time: 0.0031 loss: 1.3958 03/05 22:10:14 - mmengine - INFO - Epoch(train) [61][3000/5005] lr: 1.0000e-02 eta: 1 day, 1:10:54 time: 0.2223 data_time: 0.0030 loss: 1.3071 03/05 22:10:36 - mmengine - INFO - Epoch(train) [61][3100/5005] lr: 1.0000e-02 eta: 1 day, 1:10:31 time: 0.2211 data_time: 0.0028 loss: 1.2546 03/05 22:10:59 - mmengine - INFO - Epoch(train) [61][3200/5005] lr: 1.0000e-02 eta: 1 day, 1:10:07 time: 0.2255 data_time: 0.0029 loss: 1.3213 03/05 22:11:22 - mmengine - INFO - Epoch(train) [61][3300/5005] lr: 1.0000e-02 eta: 1 day, 1:09:44 time: 0.2241 data_time: 0.0032 loss: 1.2780 03/05 22:11:45 - mmengine - INFO - Epoch(train) [61][3400/5005] lr: 1.0000e-02 eta: 1 day, 1:09:22 time: 0.2275 data_time: 0.0031 loss: 1.4801 03/05 22:12:08 - mmengine - INFO - Epoch(train) [61][3500/5005] lr: 1.0000e-02 eta: 1 day, 1:08:59 time: 0.2241 data_time: 0.0027 loss: 1.2016 03/05 22:12:30 - mmengine - INFO - Epoch(train) [61][3600/5005] lr: 1.0000e-02 eta: 1 day, 1:08:36 time: 0.2393 data_time: 0.0032 loss: 1.3229 03/05 22:12:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:12:53 - mmengine - INFO - Epoch(train) [61][3700/5005] lr: 1.0000e-02 eta: 1 day, 1:08:12 time: 0.2245 data_time: 0.0029 loss: 1.4922 03/05 22:13:16 - mmengine - INFO - Epoch(train) [61][3800/5005] lr: 1.0000e-02 eta: 1 day, 1:07:50 time: 0.2419 data_time: 0.0028 loss: 1.2725 03/05 22:13:39 - mmengine - INFO - Epoch(train) [61][3900/5005] lr: 1.0000e-02 eta: 1 day, 1:07:27 time: 0.2234 data_time: 0.0030 loss: 1.3444 03/05 22:14:01 - mmengine - INFO - Epoch(train) [61][4000/5005] lr: 1.0000e-02 eta: 1 day, 1:07:04 time: 0.2250 data_time: 0.0029 loss: 1.2630 03/05 22:14:24 - mmengine - INFO - Epoch(train) [61][4100/5005] lr: 1.0000e-02 eta: 1 day, 1:06:41 time: 0.2246 data_time: 0.0030 loss: 1.5276 03/05 22:14:47 - mmengine - INFO - Epoch(train) [61][4200/5005] lr: 1.0000e-02 eta: 1 day, 1:06:18 time: 0.2614 data_time: 0.0032 loss: 1.3240 03/05 22:15:10 - mmengine - INFO - Epoch(train) [61][4300/5005] lr: 1.0000e-02 eta: 1 day, 1:05:55 time: 0.2253 data_time: 0.0032 loss: 1.3604 03/05 22:15:33 - mmengine - INFO - Epoch(train) [61][4400/5005] lr: 1.0000e-02 eta: 1 day, 1:05:32 time: 0.2231 data_time: 0.0027 loss: 1.4838 03/05 22:15:56 - mmengine - INFO - Epoch(train) [61][4500/5005] lr: 1.0000e-02 eta: 1 day, 1:05:09 time: 0.2220 data_time: 0.0033 loss: 1.6291 03/05 22:16:18 - mmengine - INFO - Epoch(train) [61][4600/5005] lr: 1.0000e-02 eta: 1 day, 1:04:46 time: 0.2211 data_time: 0.0026 loss: 1.3437 03/05 22:16:41 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:16:41 - mmengine - INFO - Epoch(train) [61][4700/5005] lr: 1.0000e-02 eta: 1 day, 1:04:23 time: 0.2242 data_time: 0.0032 loss: 1.4661 03/05 22:17:04 - mmengine - INFO - Epoch(train) [61][4800/5005] lr: 1.0000e-02 eta: 1 day, 1:04:00 time: 0.2255 data_time: 0.0030 loss: 1.3292 03/05 22:17:27 - mmengine - INFO - Epoch(train) [61][4900/5005] lr: 1.0000e-02 eta: 1 day, 1:03:37 time: 0.2848 data_time: 0.0026 loss: 1.3966 03/05 22:17:56 - mmengine - INFO - Epoch(train) [61][5000/5005] lr: 1.0000e-02 eta: 1 day, 1:03:22 time: 0.2778 data_time: 0.0028 loss: 1.3983 03/05 22:17:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:18:00 - mmengine - INFO - Saving checkpoint at 61 epochs 03/05 22:18:14 - mmengine - INFO - Epoch(val) [61][100/196] eta: 0:00:13 time: 0.0221 data_time: 0.0004 03/05 22:18:28 - mmengine - INFO - Epoch(val) [61][196/196] accuracy/top1: 71.1340 accuracy/top5: 90.5780 03/05 22:18:59 - mmengine - INFO - Epoch(train) [62][ 100/5005] lr: 1.0000e-02 eta: 1 day, 1:03:08 time: 0.2246 data_time: 0.0029 loss: 1.2430 03/05 22:19:22 - mmengine - INFO - Epoch(train) [62][ 200/5005] lr: 1.0000e-02 eta: 1 day, 1:02:46 time: 0.2557 data_time: 0.0032 loss: 1.2424 03/05 22:19:45 - mmengine - INFO - Epoch(train) [62][ 300/5005] lr: 1.0000e-02 eta: 1 day, 1:02:23 time: 0.2252 data_time: 0.0035 loss: 1.2334 03/05 22:20:08 - mmengine - INFO - Epoch(train) [62][ 400/5005] lr: 1.0000e-02 eta: 1 day, 1:02:00 time: 0.2272 data_time: 0.0030 loss: 1.4199 03/05 22:20:30 - mmengine - INFO - Epoch(train) [62][ 500/5005] lr: 1.0000e-02 eta: 1 day, 1:01:37 time: 0.2238 data_time: 0.0029 loss: 1.1740 03/05 22:20:54 - mmengine - INFO - Epoch(train) [62][ 600/5005] lr: 1.0000e-02 eta: 1 day, 1:01:15 time: 0.2547 data_time: 0.0029 loss: 1.2791 03/05 22:21:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:21:16 - mmengine - INFO - Epoch(train) [62][ 700/5005] lr: 1.0000e-02 eta: 1 day, 1:00:51 time: 0.2255 data_time: 0.0031 loss: 1.3094 03/05 22:21:39 - mmengine - INFO - Epoch(train) [62][ 800/5005] lr: 1.0000e-02 eta: 1 day, 1:00:29 time: 0.2237 data_time: 0.0028 loss: 1.4814 03/05 22:22:02 - mmengine - INFO - Epoch(train) [62][ 900/5005] lr: 1.0000e-02 eta: 1 day, 1:00:06 time: 0.2362 data_time: 0.0030 loss: 1.4096 03/05 22:22:25 - mmengine - INFO - Epoch(train) [62][1000/5005] lr: 1.0000e-02 eta: 1 day, 0:59:43 time: 0.2265 data_time: 0.0030 loss: 1.2981 03/05 22:22:48 - mmengine - INFO - Epoch(train) [62][1100/5005] lr: 1.0000e-02 eta: 1 day, 0:59:20 time: 0.2254 data_time: 0.0030 loss: 1.2473 03/05 22:23:11 - mmengine - INFO - Epoch(train) [62][1200/5005] lr: 1.0000e-02 eta: 1 day, 0:58:57 time: 0.2220 data_time: 0.0025 loss: 1.3668 03/05 22:23:33 - mmengine - INFO - Epoch(train) [62][1300/5005] lr: 1.0000e-02 eta: 1 day, 0:58:33 time: 0.2264 data_time: 0.0028 loss: 1.2929 03/05 22:23:56 - mmengine - INFO - Epoch(train) [62][1400/5005] lr: 1.0000e-02 eta: 1 day, 0:58:10 time: 0.2244 data_time: 0.0028 loss: 1.5209 03/05 22:24:20 - mmengine - INFO - Epoch(train) [62][1500/5005] lr: 1.0000e-02 eta: 1 day, 0:57:48 time: 0.2234 data_time: 0.0027 loss: 1.2470 03/05 22:24:42 - mmengine - INFO - Epoch(train) [62][1600/5005] lr: 1.0000e-02 eta: 1 day, 0:57:25 time: 0.2241 data_time: 0.0027 loss: 1.4970 03/05 22:25:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:25:05 - mmengine - INFO - Epoch(train) [62][1700/5005] lr: 1.0000e-02 eta: 1 day, 0:57:02 time: 0.2273 data_time: 0.0026 loss: 1.3610 03/05 22:25:27 - mmengine - INFO - Epoch(train) [62][1800/5005] lr: 1.0000e-02 eta: 1 day, 0:56:39 time: 0.2247 data_time: 0.0030 loss: 1.4308 03/05 22:25:51 - mmengine - INFO - Epoch(train) [62][1900/5005] lr: 1.0000e-02 eta: 1 day, 0:56:16 time: 0.2236 data_time: 0.0026 loss: 1.2672 03/05 22:26:13 - mmengine - INFO - Epoch(train) [62][2000/5005] lr: 1.0000e-02 eta: 1 day, 0:55:53 time: 0.2270 data_time: 0.0030 loss: 1.3025 03/05 22:26:36 - mmengine - INFO - Epoch(train) [62][2100/5005] lr: 1.0000e-02 eta: 1 day, 0:55:30 time: 0.2271 data_time: 0.0031 loss: 1.3443 03/05 22:26:59 - mmengine - INFO - Epoch(train) [62][2200/5005] lr: 1.0000e-02 eta: 1 day, 0:55:07 time: 0.2220 data_time: 0.0026 loss: 1.5062 03/05 22:27:22 - mmengine - INFO - Epoch(train) [62][2300/5005] lr: 1.0000e-02 eta: 1 day, 0:54:44 time: 0.2235 data_time: 0.0029 loss: 1.4348 03/05 22:27:45 - mmengine - INFO - Epoch(train) [62][2400/5005] lr: 1.0000e-02 eta: 1 day, 0:54:21 time: 0.2220 data_time: 0.0030 loss: 1.5133 03/05 22:28:07 - mmengine - INFO - Epoch(train) [62][2500/5005] lr: 1.0000e-02 eta: 1 day, 0:53:58 time: 0.2225 data_time: 0.0031 loss: 1.3705 03/05 22:28:30 - mmengine - INFO - Epoch(train) [62][2600/5005] lr: 1.0000e-02 eta: 1 day, 0:53:35 time: 0.2213 data_time: 0.0029 loss: 1.3601 03/05 22:28:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:28:53 - mmengine - INFO - Epoch(train) [62][2700/5005] lr: 1.0000e-02 eta: 1 day, 0:53:12 time: 0.2247 data_time: 0.0037 loss: 1.3740 03/05 22:29:16 - mmengine - INFO - Epoch(train) [62][2800/5005] lr: 1.0000e-02 eta: 1 day, 0:52:50 time: 0.2219 data_time: 0.0026 loss: 1.3695 03/05 22:29:39 - mmengine - INFO - Epoch(train) [62][2900/5005] lr: 1.0000e-02 eta: 1 day, 0:52:26 time: 0.2203 data_time: 0.0027 loss: 1.1826 03/05 22:30:01 - mmengine - INFO - Epoch(train) [62][3000/5005] lr: 1.0000e-02 eta: 1 day, 0:52:03 time: 0.2250 data_time: 0.0029 loss: 1.4916 03/05 22:30:24 - mmengine - INFO - Epoch(train) [62][3100/5005] lr: 1.0000e-02 eta: 1 day, 0:51:40 time: 0.2320 data_time: 0.0031 loss: 1.3845 03/05 22:30:47 - mmengine - INFO - Epoch(train) [62][3200/5005] lr: 1.0000e-02 eta: 1 day, 0:51:17 time: 0.2412 data_time: 0.0031 loss: 1.3776 03/05 22:31:10 - mmengine - INFO - Epoch(train) [62][3300/5005] lr: 1.0000e-02 eta: 1 day, 0:50:54 time: 0.2248 data_time: 0.0030 loss: 1.3049 03/05 22:31:33 - mmengine - INFO - Epoch(train) [62][3400/5005] lr: 1.0000e-02 eta: 1 day, 0:50:31 time: 0.2258 data_time: 0.0028 loss: 1.3240 03/05 22:31:55 - mmengine - INFO - Epoch(train) [62][3500/5005] lr: 1.0000e-02 eta: 1 day, 0:50:08 time: 0.2261 data_time: 0.0032 loss: 1.4513 03/05 22:32:18 - mmengine - INFO - Epoch(train) [62][3600/5005] lr: 1.0000e-02 eta: 1 day, 0:49:45 time: 0.2267 data_time: 0.0030 loss: 1.2976 03/05 22:32:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:32:41 - mmengine - INFO - Epoch(train) [62][3700/5005] lr: 1.0000e-02 eta: 1 day, 0:49:22 time: 0.2210 data_time: 0.0027 loss: 1.3256 03/05 22:33:04 - mmengine - INFO - Epoch(train) [62][3800/5005] lr: 1.0000e-02 eta: 1 day, 0:49:00 time: 0.2236 data_time: 0.0028 loss: 1.4952 03/05 22:33:27 - mmengine - INFO - Epoch(train) [62][3900/5005] lr: 1.0000e-02 eta: 1 day, 0:48:37 time: 0.2411 data_time: 0.0027 loss: 1.2075 03/05 22:33:50 - mmengine - INFO - Epoch(train) [62][4000/5005] lr: 1.0000e-02 eta: 1 day, 0:48:14 time: 0.2260 data_time: 0.0029 loss: 1.0429 03/05 22:34:13 - mmengine - INFO - Epoch(train) [62][4100/5005] lr: 1.0000e-02 eta: 1 day, 0:47:51 time: 0.2242 data_time: 0.0031 loss: 1.2898 03/05 22:34:36 - mmengine - INFO - Epoch(train) [62][4200/5005] lr: 1.0000e-02 eta: 1 day, 0:47:28 time: 0.2292 data_time: 0.0030 loss: 1.2246 03/05 22:34:59 - mmengine - INFO - Epoch(train) [62][4300/5005] lr: 1.0000e-02 eta: 1 day, 0:47:05 time: 0.2220 data_time: 0.0028 loss: 1.3162 03/05 22:35:22 - mmengine - INFO - Epoch(train) [62][4400/5005] lr: 1.0000e-02 eta: 1 day, 0:46:43 time: 0.2254 data_time: 0.0029 loss: 1.3937 03/05 22:35:44 - mmengine - INFO - Epoch(train) [62][4500/5005] lr: 1.0000e-02 eta: 1 day, 0:46:19 time: 0.2207 data_time: 0.0030 loss: 1.3446 03/05 22:36:07 - mmengine - INFO - Epoch(train) [62][4600/5005] lr: 1.0000e-02 eta: 1 day, 0:45:56 time: 0.2212 data_time: 0.0033 loss: 1.2223 03/05 22:36:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:36:30 - mmengine - INFO - Epoch(train) [62][4700/5005] lr: 1.0000e-02 eta: 1 day, 0:45:33 time: 0.2216 data_time: 0.0029 loss: 1.3509 03/05 22:36:53 - mmengine - INFO - Epoch(train) [62][4800/5005] lr: 1.0000e-02 eta: 1 day, 0:45:11 time: 0.2445 data_time: 0.0027 loss: 1.3307 03/05 22:37:17 - mmengine - INFO - Epoch(train) [62][4900/5005] lr: 1.0000e-02 eta: 1 day, 0:44:49 time: 0.2914 data_time: 0.0030 loss: 1.4294 03/05 22:37:45 - mmengine - INFO - Epoch(train) [62][5000/5005] lr: 1.0000e-02 eta: 1 day, 0:44:33 time: 0.2846 data_time: 0.0028 loss: 1.3760 03/05 22:37:46 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:37:49 - mmengine - INFO - Saving checkpoint at 62 epochs 03/05 22:38:03 - mmengine - INFO - Epoch(val) [62][100/196] eta: 0:00:12 time: 0.0209 data_time: 0.0004 03/05 22:38:17 - mmengine - INFO - Epoch(val) [62][196/196] accuracy/top1: 71.6460 accuracy/top5: 90.9320 03/05 22:38:48 - mmengine - INFO - Epoch(train) [63][ 100/5005] lr: 1.0000e-02 eta: 1 day, 0:44:19 time: 0.2276 data_time: 0.0032 loss: 1.2938 03/05 22:39:11 - mmengine - INFO - Epoch(train) [63][ 200/5005] lr: 1.0000e-02 eta: 1 day, 0:43:56 time: 0.2266 data_time: 0.0032 loss: 1.3009 03/05 22:39:34 - mmengine - INFO - Epoch(train) [63][ 300/5005] lr: 1.0000e-02 eta: 1 day, 0:43:33 time: 0.2331 data_time: 0.0041 loss: 1.2648 03/05 22:39:57 - mmengine - INFO - Epoch(train) [63][ 400/5005] lr: 1.0000e-02 eta: 1 day, 0:43:10 time: 0.2208 data_time: 0.0027 loss: 1.3256 03/05 22:40:20 - mmengine - INFO - Epoch(train) [63][ 500/5005] lr: 1.0000e-02 eta: 1 day, 0:42:48 time: 0.2225 data_time: 0.0028 loss: 1.2680 03/05 22:40:43 - mmengine - INFO - Epoch(train) [63][ 600/5005] lr: 1.0000e-02 eta: 1 day, 0:42:25 time: 0.2238 data_time: 0.0030 loss: 1.2552 03/05 22:41:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:41:05 - mmengine - INFO - Epoch(train) [63][ 700/5005] lr: 1.0000e-02 eta: 1 day, 0:42:02 time: 0.2243 data_time: 0.0027 loss: 1.3829 03/05 22:41:28 - mmengine - INFO - Epoch(train) [63][ 800/5005] lr: 1.0000e-02 eta: 1 day, 0:41:39 time: 0.2255 data_time: 0.0029 loss: 1.3115 03/05 22:41:51 - mmengine - INFO - Epoch(train) [63][ 900/5005] lr: 1.0000e-02 eta: 1 day, 0:41:16 time: 0.2267 data_time: 0.0027 loss: 1.2451 03/05 22:42:14 - mmengine - INFO - Epoch(train) [63][1000/5005] lr: 1.0000e-02 eta: 1 day, 0:40:53 time: 0.2212 data_time: 0.0028 loss: 1.0930 03/05 22:42:37 - mmengine - INFO - Epoch(train) [63][1100/5005] lr: 1.0000e-02 eta: 1 day, 0:40:30 time: 0.2229 data_time: 0.0028 loss: 1.2437 03/05 22:43:00 - mmengine - INFO - Epoch(train) [63][1200/5005] lr: 1.0000e-02 eta: 1 day, 0:40:07 time: 0.2230 data_time: 0.0030 loss: 1.3657 03/05 22:43:23 - mmengine - INFO - Epoch(train) [63][1300/5005] lr: 1.0000e-02 eta: 1 day, 0:39:45 time: 0.2209 data_time: 0.0028 loss: 1.3401 03/05 22:43:46 - mmengine - INFO - Epoch(train) [63][1400/5005] lr: 1.0000e-02 eta: 1 day, 0:39:22 time: 0.2240 data_time: 0.0029 loss: 1.2834 03/05 22:44:08 - mmengine - INFO - Epoch(train) [63][1500/5005] lr: 1.0000e-02 eta: 1 day, 0:38:59 time: 0.2260 data_time: 0.0033 loss: 1.1711 03/05 22:44:31 - mmengine - INFO - Epoch(train) [63][1600/5005] lr: 1.0000e-02 eta: 1 day, 0:38:36 time: 0.2256 data_time: 0.0031 loss: 1.0432 03/05 22:44:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:44:54 - mmengine - INFO - Epoch(train) [63][1700/5005] lr: 1.0000e-02 eta: 1 day, 0:38:13 time: 0.2311 data_time: 0.0035 loss: 1.2158 03/05 22:45:17 - mmengine - INFO - Epoch(train) [63][1800/5005] lr: 1.0000e-02 eta: 1 day, 0:37:50 time: 0.2257 data_time: 0.0027 loss: 1.2768 03/05 22:45:40 - mmengine - INFO - Epoch(train) [63][1900/5005] lr: 1.0000e-02 eta: 1 day, 0:37:27 time: 0.2234 data_time: 0.0031 loss: 1.1870 03/05 22:46:03 - mmengine - INFO - Epoch(train) [63][2000/5005] lr: 1.0000e-02 eta: 1 day, 0:37:04 time: 0.2262 data_time: 0.0032 loss: 1.3996 03/05 22:46:25 - mmengine - INFO - Epoch(train) [63][2100/5005] lr: 1.0000e-02 eta: 1 day, 0:36:41 time: 0.2223 data_time: 0.0029 loss: 1.5226 03/05 22:46:48 - mmengine - INFO - Epoch(train) [63][2200/5005] lr: 1.0000e-02 eta: 1 day, 0:36:18 time: 0.2245 data_time: 0.0029 loss: 1.3927 03/05 22:47:11 - mmengine - INFO - Epoch(train) [63][2300/5005] lr: 1.0000e-02 eta: 1 day, 0:35:55 time: 0.2220 data_time: 0.0028 loss: 1.2115 03/05 22:47:34 - mmengine - INFO - Epoch(train) [63][2400/5005] lr: 1.0000e-02 eta: 1 day, 0:35:32 time: 0.2255 data_time: 0.0030 loss: 1.4400 03/05 22:47:56 - mmengine - INFO - Epoch(train) [63][2500/5005] lr: 1.0000e-02 eta: 1 day, 0:35:08 time: 0.2233 data_time: 0.0030 loss: 1.2049 03/05 22:48:19 - mmengine - INFO - Epoch(train) [63][2600/5005] lr: 1.0000e-02 eta: 1 day, 0:34:45 time: 0.2362 data_time: 0.0028 loss: 1.4372 03/05 22:48:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:48:42 - mmengine - INFO - Epoch(train) [63][2700/5005] lr: 1.0000e-02 eta: 1 day, 0:34:22 time: 0.2297 data_time: 0.0026 loss: 1.4796 03/05 22:49:05 - mmengine - INFO - Epoch(train) [63][2800/5005] lr: 1.0000e-02 eta: 1 day, 0:34:00 time: 0.2267 data_time: 0.0027 loss: 1.2441 03/05 22:49:27 - mmengine - INFO - Epoch(train) [63][2900/5005] lr: 1.0000e-02 eta: 1 day, 0:33:36 time: 0.2258 data_time: 0.0030 loss: 1.2452 03/05 22:49:50 - mmengine - INFO - Epoch(train) [63][3000/5005] lr: 1.0000e-02 eta: 1 day, 0:33:14 time: 0.2273 data_time: 0.0031 loss: 1.5005 03/05 22:50:13 - mmengine - INFO - Epoch(train) [63][3100/5005] lr: 1.0000e-02 eta: 1 day, 0:32:50 time: 0.2249 data_time: 0.0031 loss: 1.1419 03/05 22:50:36 - mmengine - INFO - Epoch(train) [63][3200/5005] lr: 1.0000e-02 eta: 1 day, 0:32:27 time: 0.2244 data_time: 0.0026 loss: 1.2806 03/05 22:50:59 - mmengine - INFO - Epoch(train) [63][3300/5005] lr: 1.0000e-02 eta: 1 day, 0:32:05 time: 0.2240 data_time: 0.0031 loss: 1.2203 03/05 22:51:22 - mmengine - INFO - Epoch(train) [63][3400/5005] lr: 1.0000e-02 eta: 1 day, 0:31:42 time: 0.2262 data_time: 0.0026 loss: 1.4495 03/05 22:51:44 - mmengine - INFO - Epoch(train) [63][3500/5005] lr: 1.0000e-02 eta: 1 day, 0:31:19 time: 0.2264 data_time: 0.0031 loss: 1.3448 03/05 22:52:07 - mmengine - INFO - Epoch(train) [63][3600/5005] lr: 1.0000e-02 eta: 1 day, 0:30:56 time: 0.2270 data_time: 0.0029 loss: 1.4086 03/05 22:52:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:52:30 - mmengine - INFO - Epoch(train) [63][3700/5005] lr: 1.0000e-02 eta: 1 day, 0:30:33 time: 0.2216 data_time: 0.0029 loss: 1.4694 03/05 22:52:53 - mmengine - INFO - Epoch(train) [63][3800/5005] lr: 1.0000e-02 eta: 1 day, 0:30:10 time: 0.2425 data_time: 0.0031 loss: 1.4134 03/05 22:53:16 - mmengine - INFO - Epoch(train) [63][3900/5005] lr: 1.0000e-02 eta: 1 day, 0:29:47 time: 0.2255 data_time: 0.0029 loss: 1.2972 03/05 22:53:39 - mmengine - INFO - Epoch(train) [63][4000/5005] lr: 1.0000e-02 eta: 1 day, 0:29:24 time: 0.2248 data_time: 0.0031 loss: 1.2750 03/05 22:54:02 - mmengine - INFO - Epoch(train) [63][4100/5005] lr: 1.0000e-02 eta: 1 day, 0:29:01 time: 0.2255 data_time: 0.0029 loss: 1.4747 03/05 22:54:25 - mmengine - INFO - Epoch(train) [63][4200/5005] lr: 1.0000e-02 eta: 1 day, 0:28:38 time: 0.2224 data_time: 0.0029 loss: 1.3618 03/05 22:54:47 - mmengine - INFO - Epoch(train) [63][4300/5005] lr: 1.0000e-02 eta: 1 day, 0:28:15 time: 0.2241 data_time: 0.0028 loss: 1.3282 03/05 22:55:10 - mmengine - INFO - Epoch(train) [63][4400/5005] lr: 1.0000e-02 eta: 1 day, 0:27:52 time: 0.2295 data_time: 0.0026 loss: 1.6530 03/05 22:55:33 - mmengine - INFO - Epoch(train) [63][4500/5005] lr: 1.0000e-02 eta: 1 day, 0:27:29 time: 0.2206 data_time: 0.0028 loss: 1.3283 03/05 22:55:55 - mmengine - INFO - Epoch(train) [63][4600/5005] lr: 1.0000e-02 eta: 1 day, 0:27:06 time: 0.2268 data_time: 0.0032 loss: 1.2592 03/05 22:56:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:56:18 - mmengine - INFO - Epoch(train) [63][4700/5005] lr: 1.0000e-02 eta: 1 day, 0:26:43 time: 0.2206 data_time: 0.0029 loss: 1.3596 03/05 22:56:41 - mmengine - INFO - Epoch(train) [63][4800/5005] lr: 1.0000e-02 eta: 1 day, 0:26:19 time: 0.2477 data_time: 0.0030 loss: 1.2095 03/05 22:57:04 - mmengine - INFO - Epoch(train) [63][4900/5005] lr: 1.0000e-02 eta: 1 day, 0:25:58 time: 0.2891 data_time: 0.0027 loss: 1.0858 03/05 22:57:34 - mmengine - INFO - Epoch(train) [63][5000/5005] lr: 1.0000e-02 eta: 1 day, 0:25:42 time: 0.2882 data_time: 0.0027 loss: 1.4125 03/05 22:57:35 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 22:57:38 - mmengine - INFO - Saving checkpoint at 63 epochs 03/05 22:57:52 - mmengine - INFO - Epoch(val) [63][100/196] eta: 0:00:12 time: 0.0197 data_time: 0.0004 03/05 22:58:06 - mmengine - INFO - Epoch(val) [63][196/196] accuracy/top1: 71.1880 accuracy/top5: 90.6800 03/05 22:58:38 - mmengine - INFO - Epoch(train) [64][ 100/5005] lr: 1.0000e-02 eta: 1 day, 0:25:29 time: 0.2258 data_time: 0.0027 loss: 1.2352 03/05 22:59:01 - mmengine - INFO - Epoch(train) [64][ 200/5005] lr: 1.0000e-02 eta: 1 day, 0:25:07 time: 0.2257 data_time: 0.0028 loss: 1.1065 03/05 22:59:23 - mmengine - INFO - Epoch(train) [64][ 300/5005] lr: 1.0000e-02 eta: 1 day, 0:24:44 time: 0.2243 data_time: 0.0031 loss: 1.1514 03/05 22:59:46 - mmengine - INFO - Epoch(train) [64][ 400/5005] lr: 1.0000e-02 eta: 1 day, 0:24:20 time: 0.2301 data_time: 0.0028 loss: 1.2260 03/05 23:00:09 - mmengine - INFO - Epoch(train) [64][ 500/5005] lr: 1.0000e-02 eta: 1 day, 0:23:58 time: 0.2216 data_time: 0.0028 loss: 1.2569 03/05 23:00:32 - mmengine - INFO - Epoch(train) [64][ 600/5005] lr: 1.0000e-02 eta: 1 day, 0:23:35 time: 0.2273 data_time: 0.0028 loss: 1.2950 03/05 23:00:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:00:55 - mmengine - INFO - Epoch(train) [64][ 700/5005] lr: 1.0000e-02 eta: 1 day, 0:23:12 time: 0.2204 data_time: 0.0028 loss: 1.3287 03/05 23:01:18 - mmengine - INFO - Epoch(train) [64][ 800/5005] lr: 1.0000e-02 eta: 1 day, 0:22:49 time: 0.2409 data_time: 0.0028 loss: 1.2417 03/05 23:01:41 - mmengine - INFO - Epoch(train) [64][ 900/5005] lr: 1.0000e-02 eta: 1 day, 0:22:26 time: 0.2243 data_time: 0.0029 loss: 1.6031 03/05 23:02:03 - mmengine - INFO - Epoch(train) [64][1000/5005] lr: 1.0000e-02 eta: 1 day, 0:22:03 time: 0.2229 data_time: 0.0034 loss: 1.4298 03/05 23:02:26 - mmengine - INFO - Epoch(train) [64][1100/5005] lr: 1.0000e-02 eta: 1 day, 0:21:40 time: 0.2337 data_time: 0.0029 loss: 1.4486 03/05 23:02:49 - mmengine - INFO - Epoch(train) [64][1200/5005] lr: 1.0000e-02 eta: 1 day, 0:21:17 time: 0.2259 data_time: 0.0030 loss: 1.1879 03/05 23:03:12 - mmengine - INFO - Epoch(train) [64][1300/5005] lr: 1.0000e-02 eta: 1 day, 0:20:54 time: 0.2271 data_time: 0.0030 loss: 1.2683 03/05 23:03:34 - mmengine - INFO - Epoch(train) [64][1400/5005] lr: 1.0000e-02 eta: 1 day, 0:20:31 time: 0.2228 data_time: 0.0030 loss: 1.1305 03/05 23:03:57 - mmengine - INFO - Epoch(train) [64][1500/5005] lr: 1.0000e-02 eta: 1 day, 0:20:08 time: 0.2231 data_time: 0.0028 loss: 1.3044 03/05 23:04:20 - mmengine - INFO - Epoch(train) [64][1600/5005] lr: 1.0000e-02 eta: 1 day, 0:19:45 time: 0.2245 data_time: 0.0031 loss: 1.3786 03/05 23:04:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:04:43 - mmengine - INFO - Epoch(train) [64][1700/5005] lr: 1.0000e-02 eta: 1 day, 0:19:22 time: 0.2241 data_time: 0.0029 loss: 1.4021 03/05 23:05:06 - mmengine - INFO - Epoch(train) [64][1800/5005] lr: 1.0000e-02 eta: 1 day, 0:18:59 time: 0.2244 data_time: 0.0026 loss: 1.2998 03/05 23:05:28 - mmengine - INFO - Epoch(train) [64][1900/5005] lr: 1.0000e-02 eta: 1 day, 0:18:36 time: 0.2221 data_time: 0.0029 loss: 1.4021 03/05 23:05:51 - mmengine - INFO - Epoch(train) [64][2000/5005] lr: 1.0000e-02 eta: 1 day, 0:18:13 time: 0.2229 data_time: 0.0035 loss: 1.3232 03/05 23:06:15 - mmengine - INFO - Epoch(train) [64][2100/5005] lr: 1.0000e-02 eta: 1 day, 0:17:51 time: 0.2219 data_time: 0.0025 loss: 1.1138 03/05 23:06:37 - mmengine - INFO - Epoch(train) [64][2200/5005] lr: 1.0000e-02 eta: 1 day, 0:17:27 time: 0.2199 data_time: 0.0028 loss: 1.1314 03/05 23:07:00 - mmengine - INFO - Epoch(train) [64][2300/5005] lr: 1.0000e-02 eta: 1 day, 0:17:04 time: 0.2250 data_time: 0.0028 loss: 1.3339 03/05 23:07:23 - mmengine - INFO - Epoch(train) [64][2400/5005] lr: 1.0000e-02 eta: 1 day, 0:16:42 time: 0.2409 data_time: 0.0028 loss: 1.3272 03/05 23:07:46 - mmengine - INFO - Epoch(train) [64][2500/5005] lr: 1.0000e-02 eta: 1 day, 0:16:19 time: 0.2570 data_time: 0.0029 loss: 1.3092 03/05 23:08:09 - mmengine - INFO - Epoch(train) [64][2600/5005] lr: 1.0000e-02 eta: 1 day, 0:15:56 time: 0.2232 data_time: 0.0031 loss: 1.2948 03/05 23:08:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:08:32 - mmengine - INFO - Epoch(train) [64][2700/5005] lr: 1.0000e-02 eta: 1 day, 0:15:33 time: 0.2227 data_time: 0.0031 loss: 1.4161 03/05 23:08:55 - mmengine - INFO - Epoch(train) [64][2800/5005] lr: 1.0000e-02 eta: 1 day, 0:15:10 time: 0.2254 data_time: 0.0026 loss: 1.4927 03/05 23:09:18 - mmengine - INFO - Epoch(train) [64][2900/5005] lr: 1.0000e-02 eta: 1 day, 0:14:47 time: 0.2235 data_time: 0.0029 loss: 1.3464 03/05 23:09:40 - mmengine - INFO - Epoch(train) [64][3000/5005] lr: 1.0000e-02 eta: 1 day, 0:14:24 time: 0.2221 data_time: 0.0031 loss: 1.3090 03/05 23:10:03 - mmengine - INFO - Epoch(train) [64][3100/5005] lr: 1.0000e-02 eta: 1 day, 0:14:01 time: 0.2269 data_time: 0.0030 loss: 1.1954 03/05 23:10:26 - mmengine - INFO - Epoch(train) [64][3200/5005] lr: 1.0000e-02 eta: 1 day, 0:13:38 time: 0.2253 data_time: 0.0029 loss: 1.4832 03/05 23:10:49 - mmengine - INFO - Epoch(train) [64][3300/5005] lr: 1.0000e-02 eta: 1 day, 0:13:15 time: 0.2263 data_time: 0.0030 loss: 1.3467 03/05 23:11:12 - mmengine - INFO - Epoch(train) [64][3400/5005] lr: 1.0000e-02 eta: 1 day, 0:12:52 time: 0.2230 data_time: 0.0029 loss: 1.5649 03/05 23:11:34 - mmengine - INFO - Epoch(train) [64][3500/5005] lr: 1.0000e-02 eta: 1 day, 0:12:29 time: 0.2248 data_time: 0.0032 loss: 1.3976 03/05 23:11:57 - mmengine - INFO - Epoch(train) [64][3600/5005] lr: 1.0000e-02 eta: 1 day, 0:12:06 time: 0.2228 data_time: 0.0030 loss: 1.4847 03/05 23:12:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:12:20 - mmengine - INFO - Epoch(train) [64][3700/5005] lr: 1.0000e-02 eta: 1 day, 0:11:44 time: 0.2432 data_time: 0.0028 loss: 1.4388 03/05 23:12:43 - mmengine - INFO - Epoch(train) [64][3800/5005] lr: 1.0000e-02 eta: 1 day, 0:11:21 time: 0.2229 data_time: 0.0030 loss: 1.0902 03/05 23:13:06 - mmengine - INFO - Epoch(train) [64][3900/5005] lr: 1.0000e-02 eta: 1 day, 0:10:58 time: 0.2218 data_time: 0.0027 loss: 1.3181 03/05 23:13:29 - mmengine - INFO - Epoch(train) [64][4000/5005] lr: 1.0000e-02 eta: 1 day, 0:10:35 time: 0.2257 data_time: 0.0030 loss: 1.3582 03/05 23:13:52 - mmengine - INFO - Epoch(train) [64][4100/5005] lr: 1.0000e-02 eta: 1 day, 0:10:12 time: 0.2236 data_time: 0.0030 loss: 1.2679 03/05 23:14:15 - mmengine - INFO - Epoch(train) [64][4200/5005] lr: 1.0000e-02 eta: 1 day, 0:09:49 time: 0.2286 data_time: 0.0034 loss: 1.3188 03/05 23:14:38 - mmengine - INFO - Epoch(train) [64][4300/5005] lr: 1.0000e-02 eta: 1 day, 0:09:26 time: 0.2246 data_time: 0.0029 loss: 1.0822 03/05 23:15:00 - mmengine - INFO - Epoch(train) [64][4400/5005] lr: 1.0000e-02 eta: 1 day, 0:09:03 time: 0.2226 data_time: 0.0031 loss: 1.4819 03/05 23:15:24 - mmengine - INFO - Epoch(train) [64][4500/5005] lr: 1.0000e-02 eta: 1 day, 0:08:41 time: 0.2236 data_time: 0.0032 loss: 1.3332 03/05 23:15:46 - mmengine - INFO - Epoch(train) [64][4600/5005] lr: 1.0000e-02 eta: 1 day, 0:08:18 time: 0.2291 data_time: 0.0030 loss: 1.2550 03/05 23:16:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:16:09 - mmengine - INFO - Epoch(train) [64][4700/5005] lr: 1.0000e-02 eta: 1 day, 0:07:54 time: 0.2248 data_time: 0.0030 loss: 1.3475 03/05 23:16:32 - mmengine - INFO - Epoch(train) [64][4800/5005] lr: 1.0000e-02 eta: 1 day, 0:07:31 time: 0.2238 data_time: 0.0029 loss: 1.5157 03/05 23:16:56 - mmengine - INFO - Epoch(train) [64][4900/5005] lr: 1.0000e-02 eta: 1 day, 0:07:09 time: 0.2851 data_time: 0.0026 loss: 1.4386 03/05 23:17:24 - mmengine - INFO - Epoch(train) [64][5000/5005] lr: 1.0000e-02 eta: 1 day, 0:06:54 time: 0.2943 data_time: 0.0026 loss: 1.2957 03/05 23:17:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:17:29 - mmengine - INFO - Saving checkpoint at 64 epochs 03/05 23:17:43 - mmengine - INFO - Epoch(val) [64][100/196] eta: 0:00:13 time: 0.0199 data_time: 0.0003 03/05 23:17:57 - mmengine - INFO - Epoch(val) [64][196/196] accuracy/top1: 71.1640 accuracy/top5: 90.4860 03/05 23:18:29 - mmengine - INFO - Epoch(train) [65][ 100/5005] lr: 1.0000e-02 eta: 1 day, 0:06:41 time: 0.2251 data_time: 0.0033 loss: 1.3960 03/05 23:18:52 - mmengine - INFO - Epoch(train) [65][ 200/5005] lr: 1.0000e-02 eta: 1 day, 0:06:18 time: 0.2294 data_time: 0.0046 loss: 1.2074 03/05 23:19:15 - mmengine - INFO - Epoch(train) [65][ 300/5005] lr: 1.0000e-02 eta: 1 day, 0:05:55 time: 0.2234 data_time: 0.0030 loss: 1.3092 03/05 23:19:38 - mmengine - INFO - Epoch(train) [65][ 400/5005] lr: 1.0000e-02 eta: 1 day, 0:05:32 time: 0.2275 data_time: 0.0031 loss: 1.2054 03/05 23:20:01 - mmengine - INFO - Epoch(train) [65][ 500/5005] lr: 1.0000e-02 eta: 1 day, 0:05:09 time: 0.2464 data_time: 0.0032 loss: 1.4865 03/05 23:20:24 - mmengine - INFO - Epoch(train) [65][ 600/5005] lr: 1.0000e-02 eta: 1 day, 0:04:46 time: 0.2367 data_time: 0.0032 loss: 1.2757 03/05 23:20:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:20:46 - mmengine - INFO - Epoch(train) [65][ 700/5005] lr: 1.0000e-02 eta: 1 day, 0:04:23 time: 0.2227 data_time: 0.0031 loss: 1.4405 03/05 23:21:09 - mmengine - INFO - Epoch(train) [65][ 800/5005] lr: 1.0000e-02 eta: 1 day, 0:04:00 time: 0.2209 data_time: 0.0027 loss: 1.1491 03/05 23:21:32 - mmengine - INFO - Epoch(train) [65][ 900/5005] lr: 1.0000e-02 eta: 1 day, 0:03:37 time: 0.2548 data_time: 0.0030 loss: 1.1836 03/05 23:21:55 - mmengine - INFO - Epoch(train) [65][1000/5005] lr: 1.0000e-02 eta: 1 day, 0:03:14 time: 0.2221 data_time: 0.0033 loss: 1.0940 03/05 23:22:17 - mmengine - INFO - Epoch(train) [65][1100/5005] lr: 1.0000e-02 eta: 1 day, 0:02:51 time: 0.2265 data_time: 0.0032 loss: 1.1610 03/05 23:22:40 - mmengine - INFO - Epoch(train) [65][1200/5005] lr: 1.0000e-02 eta: 1 day, 0:02:28 time: 0.2257 data_time: 0.0030 loss: 1.0869 03/05 23:23:03 - mmengine - INFO - Epoch(train) [65][1300/5005] lr: 1.0000e-02 eta: 1 day, 0:02:05 time: 0.2385 data_time: 0.0030 loss: 1.2976 03/05 23:23:26 - mmengine - INFO - Epoch(train) [65][1400/5005] lr: 1.0000e-02 eta: 1 day, 0:01:43 time: 0.2205 data_time: 0.0028 loss: 1.2776 03/05 23:23:49 - mmengine - INFO - Epoch(train) [65][1500/5005] lr: 1.0000e-02 eta: 1 day, 0:01:19 time: 0.2260 data_time: 0.0030 loss: 1.5437 03/05 23:24:12 - mmengine - INFO - Epoch(train) [65][1600/5005] lr: 1.0000e-02 eta: 1 day, 0:00:56 time: 0.2198 data_time: 0.0030 loss: 1.2127 03/05 23:24:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:24:35 - mmengine - INFO - Epoch(train) [65][1700/5005] lr: 1.0000e-02 eta: 1 day, 0:00:34 time: 0.2334 data_time: 0.0029 loss: 1.4103 03/05 23:24:57 - mmengine - INFO - Epoch(train) [65][1800/5005] lr: 1.0000e-02 eta: 1 day, 0:00:10 time: 0.2239 data_time: 0.0029 loss: 1.2710 03/05 23:25:20 - mmengine - INFO - Epoch(train) [65][1900/5005] lr: 1.0000e-02 eta: 23:59:47 time: 0.2252 data_time: 0.0030 loss: 1.0950 03/05 23:25:43 - mmengine - INFO - Epoch(train) [65][2000/5005] lr: 1.0000e-02 eta: 23:59:24 time: 0.2262 data_time: 0.0028 loss: 1.3944 03/05 23:26:06 - mmengine - INFO - Epoch(train) [65][2100/5005] lr: 1.0000e-02 eta: 23:59:01 time: 0.2395 data_time: 0.0029 loss: 1.4687 03/05 23:26:29 - mmengine - INFO - Epoch(train) [65][2200/5005] lr: 1.0000e-02 eta: 23:58:39 time: 0.2196 data_time: 0.0032 loss: 1.2304 03/05 23:26:52 - mmengine - INFO - Epoch(train) [65][2300/5005] lr: 1.0000e-02 eta: 23:58:16 time: 0.2199 data_time: 0.0032 loss: 1.4316 03/05 23:27:14 - mmengine - INFO - Epoch(train) [65][2400/5005] lr: 1.0000e-02 eta: 23:57:53 time: 0.2224 data_time: 0.0028 loss: 1.2785 03/05 23:27:37 - mmengine - INFO - Epoch(train) [65][2500/5005] lr: 1.0000e-02 eta: 23:57:30 time: 0.2446 data_time: 0.0029 loss: 1.4714 03/05 23:28:00 - mmengine - INFO - Epoch(train) [65][2600/5005] lr: 1.0000e-02 eta: 23:57:06 time: 0.2224 data_time: 0.0030 loss: 1.3334 03/05 23:28:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:28:23 - mmengine - INFO - Epoch(train) [65][2700/5005] lr: 1.0000e-02 eta: 23:56:43 time: 0.2334 data_time: 0.0031 loss: 1.3856 03/05 23:28:45 - mmengine - INFO - Epoch(train) [65][2800/5005] lr: 1.0000e-02 eta: 23:56:20 time: 0.2238 data_time: 0.0032 loss: 1.2820 03/05 23:29:08 - mmengine - INFO - Epoch(train) [65][2900/5005] lr: 1.0000e-02 eta: 23:55:57 time: 0.2343 data_time: 0.0027 loss: 1.2781 03/05 23:29:31 - mmengine - INFO - Epoch(train) [65][3000/5005] lr: 1.0000e-02 eta: 23:55:34 time: 0.2258 data_time: 0.0030 loss: 1.2552 03/05 23:29:53 - mmengine - INFO - Epoch(train) [65][3100/5005] lr: 1.0000e-02 eta: 23:55:11 time: 0.2247 data_time: 0.0029 loss: 1.4710 03/05 23:30:16 - mmengine - INFO - Epoch(train) [65][3200/5005] lr: 1.0000e-02 eta: 23:54:48 time: 0.2215 data_time: 0.0032 loss: 1.2708 03/05 23:30:39 - mmengine - INFO - Epoch(train) [65][3300/5005] lr: 1.0000e-02 eta: 23:54:25 time: 0.2224 data_time: 0.0030 loss: 1.4931 03/05 23:31:02 - mmengine - INFO - Epoch(train) [65][3400/5005] lr: 1.0000e-02 eta: 23:54:02 time: 0.2238 data_time: 0.0030 loss: 1.1840 03/05 23:31:25 - mmengine - INFO - Epoch(train) [65][3500/5005] lr: 1.0000e-02 eta: 23:53:39 time: 0.2265 data_time: 0.0029 loss: 1.1836 03/05 23:31:48 - mmengine - INFO - Epoch(train) [65][3600/5005] lr: 1.0000e-02 eta: 23:53:16 time: 0.2296 data_time: 0.0029 loss: 1.3001 03/05 23:32:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:32:11 - mmengine - INFO - Epoch(train) [65][3700/5005] lr: 1.0000e-02 eta: 23:52:53 time: 0.2243 data_time: 0.0035 loss: 1.2597 03/05 23:32:34 - mmengine - INFO - Epoch(train) [65][3800/5005] lr: 1.0000e-02 eta: 23:52:30 time: 0.2217 data_time: 0.0030 loss: 1.2067 03/05 23:32:57 - mmengine - INFO - Epoch(train) [65][3900/5005] lr: 1.0000e-02 eta: 23:52:08 time: 0.2227 data_time: 0.0031 loss: 1.3300 03/05 23:33:19 - mmengine - INFO - Epoch(train) [65][4000/5005] lr: 1.0000e-02 eta: 23:51:44 time: 0.2256 data_time: 0.0030 loss: 1.4591 03/05 23:33:42 - mmengine - INFO - Epoch(train) [65][4100/5005] lr: 1.0000e-02 eta: 23:51:21 time: 0.2228 data_time: 0.0033 loss: 1.2706 03/05 23:34:05 - mmengine - INFO - Epoch(train) [65][4200/5005] lr: 1.0000e-02 eta: 23:50:59 time: 0.2249 data_time: 0.0030 loss: 1.3286 03/05 23:34:28 - mmengine - INFO - Epoch(train) [65][4300/5005] lr: 1.0000e-02 eta: 23:50:36 time: 0.2243 data_time: 0.0030 loss: 1.4079 03/05 23:34:51 - mmengine - INFO - Epoch(train) [65][4400/5005] lr: 1.0000e-02 eta: 23:50:13 time: 0.2220 data_time: 0.0029 loss: 1.4383 03/05 23:35:13 - mmengine - INFO - Epoch(train) [65][4500/5005] lr: 1.0000e-02 eta: 23:49:50 time: 0.2262 data_time: 0.0030 loss: 1.2163 03/05 23:35:36 - mmengine - INFO - Epoch(train) [65][4600/5005] lr: 1.0000e-02 eta: 23:49:27 time: 0.2217 data_time: 0.0028 loss: 1.3567 03/05 23:35:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:35:59 - mmengine - INFO - Epoch(train) [65][4700/5005] lr: 1.0000e-02 eta: 23:49:04 time: 0.2259 data_time: 0.0030 loss: 1.2629 03/05 23:36:22 - mmengine - INFO - Epoch(train) [65][4800/5005] lr: 1.0000e-02 eta: 23:48:41 time: 0.2317 data_time: 0.0032 loss: 1.3728 03/05 23:36:46 - mmengine - INFO - Epoch(train) [65][4900/5005] lr: 1.0000e-02 eta: 23:48:19 time: 0.2938 data_time: 0.0026 loss: 1.3853 03/05 23:37:15 - mmengine - INFO - Epoch(train) [65][5000/5005] lr: 1.0000e-02 eta: 23:48:03 time: 0.2992 data_time: 0.0025 loss: 1.4890 03/05 23:37:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:37:19 - mmengine - INFO - Saving checkpoint at 65 epochs 03/05 23:37:34 - mmengine - INFO - Epoch(val) [65][100/196] eta: 0:00:12 time: 0.0297 data_time: 0.0005 03/05 23:37:47 - mmengine - INFO - Epoch(val) [65][196/196] accuracy/top1: 71.5400 accuracy/top5: 90.7320 03/05 23:38:19 - mmengine - INFO - Epoch(train) [66][ 100/5005] lr: 1.0000e-02 eta: 23:47:49 time: 0.2209 data_time: 0.0029 loss: 1.3594 03/05 23:38:42 - mmengine - INFO - Epoch(train) [66][ 200/5005] lr: 1.0000e-02 eta: 23:47:27 time: 0.2247 data_time: 0.0037 loss: 1.0836 03/05 23:39:05 - mmengine - INFO - Epoch(train) [66][ 300/5005] lr: 1.0000e-02 eta: 23:47:04 time: 0.2216 data_time: 0.0032 loss: 1.2753 03/05 23:39:28 - mmengine - INFO - Epoch(train) [66][ 400/5005] lr: 1.0000e-02 eta: 23:46:41 time: 0.2479 data_time: 0.0036 loss: 1.2810 03/05 23:39:50 - mmengine - INFO - Epoch(train) [66][ 500/5005] lr: 1.0000e-02 eta: 23:46:17 time: 0.2256 data_time: 0.0028 loss: 1.3799 03/05 23:40:13 - mmengine - INFO - Epoch(train) [66][ 600/5005] lr: 1.0000e-02 eta: 23:45:55 time: 0.2420 data_time: 0.0028 loss: 1.6278 03/05 23:40:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:40:36 - mmengine - INFO - Epoch(train) [66][ 700/5005] lr: 1.0000e-02 eta: 23:45:32 time: 0.2239 data_time: 0.0026 loss: 1.1582 03/05 23:40:59 - mmengine - INFO - Epoch(train) [66][ 800/5005] lr: 1.0000e-02 eta: 23:45:09 time: 0.2223 data_time: 0.0028 loss: 1.2039 03/05 23:41:21 - mmengine - INFO - Epoch(train) [66][ 900/5005] lr: 1.0000e-02 eta: 23:44:45 time: 0.2241 data_time: 0.0029 loss: 1.2809 03/05 23:41:44 - mmengine - INFO - Epoch(train) [66][1000/5005] lr: 1.0000e-02 eta: 23:44:22 time: 0.2221 data_time: 0.0026 loss: 1.1712 03/05 23:42:07 - mmengine - INFO - Epoch(train) [66][1100/5005] lr: 1.0000e-02 eta: 23:43:59 time: 0.2200 data_time: 0.0028 loss: 1.3056 03/05 23:42:30 - mmengine - INFO - Epoch(train) [66][1200/5005] lr: 1.0000e-02 eta: 23:43:37 time: 0.2486 data_time: 0.0028 loss: 1.2345 03/05 23:42:53 - mmengine - INFO - Epoch(train) [66][1300/5005] lr: 1.0000e-02 eta: 23:43:13 time: 0.2241 data_time: 0.0028 loss: 1.2965 03/05 23:43:15 - mmengine - INFO - Epoch(train) [66][1400/5005] lr: 1.0000e-02 eta: 23:42:50 time: 0.2230 data_time: 0.0026 loss: 1.3355 03/05 23:43:38 - mmengine - INFO - Epoch(train) [66][1500/5005] lr: 1.0000e-02 eta: 23:42:27 time: 0.2263 data_time: 0.0028 loss: 1.2039 03/05 23:44:01 - mmengine - INFO - Epoch(train) [66][1600/5005] lr: 1.0000e-02 eta: 23:42:05 time: 0.2446 data_time: 0.0030 loss: 1.1486 03/05 23:44:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:44:24 - mmengine - INFO - Epoch(train) [66][1700/5005] lr: 1.0000e-02 eta: 23:41:41 time: 0.2263 data_time: 0.0030 loss: 1.3124 03/05 23:44:47 - mmengine - INFO - Epoch(train) [66][1800/5005] lr: 1.0000e-02 eta: 23:41:18 time: 0.2249 data_time: 0.0033 loss: 1.3736 03/05 23:45:10 - mmengine - INFO - Epoch(train) [66][1900/5005] lr: 1.0000e-02 eta: 23:40:55 time: 0.2463 data_time: 0.0028 loss: 1.3094 03/05 23:45:33 - mmengine - INFO - Epoch(train) [66][2000/5005] lr: 1.0000e-02 eta: 23:40:33 time: 0.2229 data_time: 0.0029 loss: 1.3639 03/05 23:45:56 - mmengine - INFO - Epoch(train) [66][2100/5005] lr: 1.0000e-02 eta: 23:40:10 time: 0.2208 data_time: 0.0032 loss: 1.2622 03/05 23:46:19 - mmengine - INFO - Epoch(train) [66][2200/5005] lr: 1.0000e-02 eta: 23:39:47 time: 0.2286 data_time: 0.0027 loss: 1.2662 03/05 23:46:41 - mmengine - INFO - Epoch(train) [66][2300/5005] lr: 1.0000e-02 eta: 23:39:24 time: 0.2254 data_time: 0.0029 loss: 1.3828 03/05 23:47:04 - mmengine - INFO - Epoch(train) [66][2400/5005] lr: 1.0000e-02 eta: 23:39:00 time: 0.2278 data_time: 0.0029 loss: 1.4106 03/05 23:47:27 - mmengine - INFO - Epoch(train) [66][2500/5005] lr: 1.0000e-02 eta: 23:38:38 time: 0.2277 data_time: 0.0032 loss: 1.2948 03/05 23:47:50 - mmengine - INFO - Epoch(train) [66][2600/5005] lr: 1.0000e-02 eta: 23:38:15 time: 0.2214 data_time: 0.0029 loss: 1.2359 03/05 23:48:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:48:13 - mmengine - INFO - Epoch(train) [66][2700/5005] lr: 1.0000e-02 eta: 23:37:52 time: 0.2224 data_time: 0.0030 loss: 1.2072 03/05 23:48:36 - mmengine - INFO - Epoch(train) [66][2800/5005] lr: 1.0000e-02 eta: 23:37:29 time: 0.2246 data_time: 0.0030 loss: 1.2474 03/05 23:48:59 - mmengine - INFO - Epoch(train) [66][2900/5005] lr: 1.0000e-02 eta: 23:37:06 time: 0.2260 data_time: 0.0029 loss: 1.4881 03/05 23:49:22 - mmengine - INFO - Epoch(train) [66][3000/5005] lr: 1.0000e-02 eta: 23:36:44 time: 0.2241 data_time: 0.0031 loss: 1.2913 03/05 23:49:45 - mmengine - INFO - Epoch(train) [66][3100/5005] lr: 1.0000e-02 eta: 23:36:21 time: 0.2456 data_time: 0.0030 loss: 1.2196 03/05 23:50:07 - mmengine - INFO - Epoch(train) [66][3200/5005] lr: 1.0000e-02 eta: 23:35:58 time: 0.2303 data_time: 0.0029 loss: 1.2983 03/05 23:50:30 - mmengine - INFO - Epoch(train) [66][3300/5005] lr: 1.0000e-02 eta: 23:35:35 time: 0.2352 data_time: 0.0027 loss: 1.3544 03/05 23:50:53 - mmengine - INFO - Epoch(train) [66][3400/5005] lr: 1.0000e-02 eta: 23:35:12 time: 0.2236 data_time: 0.0033 loss: 1.2853 03/05 23:51:16 - mmengine - INFO - Epoch(train) [66][3500/5005] lr: 1.0000e-02 eta: 23:34:49 time: 0.2412 data_time: 0.0031 loss: 1.2476 03/05 23:51:39 - mmengine - INFO - Epoch(train) [66][3600/5005] lr: 1.0000e-02 eta: 23:34:26 time: 0.2273 data_time: 0.0030 loss: 1.4192 03/05 23:51:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:52:01 - mmengine - INFO - Epoch(train) [66][3700/5005] lr: 1.0000e-02 eta: 23:34:03 time: 0.2250 data_time: 0.0031 loss: 1.2908 03/05 23:52:25 - mmengine - INFO - Epoch(train) [66][3800/5005] lr: 1.0000e-02 eta: 23:33:40 time: 0.2226 data_time: 0.0029 loss: 1.2875 03/05 23:52:47 - mmengine - INFO - Epoch(train) [66][3900/5005] lr: 1.0000e-02 eta: 23:33:17 time: 0.2232 data_time: 0.0026 loss: 1.4317 03/05 23:53:10 - mmengine - INFO - Epoch(train) [66][4000/5005] lr: 1.0000e-02 eta: 23:32:54 time: 0.2272 data_time: 0.0032 loss: 1.0655 03/05 23:53:33 - mmengine - INFO - Epoch(train) [66][4100/5005] lr: 1.0000e-02 eta: 23:32:31 time: 0.2224 data_time: 0.0026 loss: 1.4590 03/05 23:53:56 - mmengine - INFO - Epoch(train) [66][4200/5005] lr: 1.0000e-02 eta: 23:32:08 time: 0.2411 data_time: 0.0030 loss: 1.3421 03/05 23:54:18 - mmengine - INFO - Epoch(train) [66][4300/5005] lr: 1.0000e-02 eta: 23:31:45 time: 0.2215 data_time: 0.0030 loss: 1.5373 03/05 23:54:41 - mmengine - INFO - Epoch(train) [66][4400/5005] lr: 1.0000e-02 eta: 23:31:22 time: 0.2267 data_time: 0.0041 loss: 1.2130 03/05 23:55:04 - mmengine - INFO - Epoch(train) [66][4500/5005] lr: 1.0000e-02 eta: 23:30:58 time: 0.2203 data_time: 0.0031 loss: 1.4236 03/05 23:55:27 - mmengine - INFO - Epoch(train) [66][4600/5005] lr: 1.0000e-02 eta: 23:30:36 time: 0.2434 data_time: 0.0032 loss: 1.4268 03/05 23:55:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:55:50 - mmengine - INFO - Epoch(train) [66][4700/5005] lr: 1.0000e-02 eta: 23:30:13 time: 0.2237 data_time: 0.0029 loss: 1.1961 03/05 23:56:12 - mmengine - INFO - Epoch(train) [66][4800/5005] lr: 1.0000e-02 eta: 23:29:50 time: 0.2247 data_time: 0.0031 loss: 1.4333 03/05 23:56:36 - mmengine - INFO - Epoch(train) [66][4900/5005] lr: 1.0000e-02 eta: 23:29:28 time: 0.2865 data_time: 0.0025 loss: 1.4040 03/05 23:57:05 - mmengine - INFO - Epoch(train) [66][5000/5005] lr: 1.0000e-02 eta: 23:29:12 time: 0.2914 data_time: 0.0031 loss: 1.3035 03/05 23:57:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/05 23:57:10 - mmengine - INFO - Saving checkpoint at 66 epochs 03/05 23:57:24 - mmengine - INFO - Epoch(val) [66][100/196] eta: 0:00:12 time: 0.0187 data_time: 0.0004 03/05 23:57:38 - mmengine - INFO - Epoch(val) [66][196/196] accuracy/top1: 71.3120 accuracy/top5: 90.7040 03/05 23:58:10 - mmengine - INFO - Epoch(train) [67][ 100/5005] lr: 1.0000e-02 eta: 23:28:58 time: 0.2233 data_time: 0.0033 loss: 1.2797 03/05 23:58:33 - mmengine - INFO - Epoch(train) [67][ 200/5005] lr: 1.0000e-02 eta: 23:28:36 time: 0.2245 data_time: 0.0032 loss: 1.2390 03/05 23:58:56 - mmengine - INFO - Epoch(train) [67][ 300/5005] lr: 1.0000e-02 eta: 23:28:13 time: 0.2263 data_time: 0.0038 loss: 1.3943 03/05 23:59:18 - mmengine - INFO - Epoch(train) [67][ 400/5005] lr: 1.0000e-02 eta: 23:27:50 time: 0.2234 data_time: 0.0031 loss: 1.1998 03/05 23:59:42 - mmengine - INFO - Epoch(train) [67][ 500/5005] lr: 1.0000e-02 eta: 23:27:27 time: 0.2681 data_time: 0.0033 loss: 1.2324 03/06 00:00:04 - mmengine - INFO - Epoch(train) [67][ 600/5005] lr: 1.0000e-02 eta: 23:27:04 time: 0.2434 data_time: 0.0033 loss: 1.2142 03/06 00:00:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:00:27 - mmengine - INFO - Epoch(train) [67][ 700/5005] lr: 1.0000e-02 eta: 23:26:41 time: 0.2273 data_time: 0.0029 loss: 1.2718 03/06 00:00:50 - mmengine - INFO - Epoch(train) [67][ 800/5005] lr: 1.0000e-02 eta: 23:26:18 time: 0.2210 data_time: 0.0027 loss: 1.1018 03/06 00:01:13 - mmengine - INFO - Epoch(train) [67][ 900/5005] lr: 1.0000e-02 eta: 23:25:55 time: 0.2205 data_time: 0.0030 loss: 1.0950 03/06 00:01:35 - mmengine - INFO - Epoch(train) [67][1000/5005] lr: 1.0000e-02 eta: 23:25:32 time: 0.2236 data_time: 0.0031 loss: 1.2694 03/06 00:01:58 - mmengine - INFO - Epoch(train) [67][1100/5005] lr: 1.0000e-02 eta: 23:25:09 time: 0.2249 data_time: 0.0033 loss: 1.2738 03/06 00:02:21 - mmengine - INFO - Epoch(train) [67][1200/5005] lr: 1.0000e-02 eta: 23:24:46 time: 0.2228 data_time: 0.0031 loss: 1.5127 03/06 00:02:44 - mmengine - INFO - Epoch(train) [67][1300/5005] lr: 1.0000e-02 eta: 23:24:23 time: 0.2233 data_time: 0.0026 loss: 1.2912 03/06 00:03:07 - mmengine - INFO - Epoch(train) [67][1400/5005] lr: 1.0000e-02 eta: 23:24:00 time: 0.2251 data_time: 0.0030 loss: 1.2150 03/06 00:03:30 - mmengine - INFO - Epoch(train) [67][1500/5005] lr: 1.0000e-02 eta: 23:23:37 time: 0.2437 data_time: 0.0030 loss: 1.3638 03/06 00:03:52 - mmengine - INFO - Epoch(train) [67][1600/5005] lr: 1.0000e-02 eta: 23:23:14 time: 0.2228 data_time: 0.0028 loss: 1.2003 03/06 00:04:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:04:15 - mmengine - INFO - Epoch(train) [67][1700/5005] lr: 1.0000e-02 eta: 23:22:51 time: 0.2268 data_time: 0.0031 loss: 1.2750 03/06 00:04:38 - mmengine - INFO - Epoch(train) [67][1800/5005] lr: 1.0000e-02 eta: 23:22:28 time: 0.2215 data_time: 0.0030 loss: 1.4068 03/06 00:05:01 - mmengine - INFO - Epoch(train) [67][1900/5005] lr: 1.0000e-02 eta: 23:22:05 time: 0.2248 data_time: 0.0035 loss: 1.1762 03/06 00:05:23 - mmengine - INFO - Epoch(train) [67][2000/5005] lr: 1.0000e-02 eta: 23:21:41 time: 0.2198 data_time: 0.0031 loss: 1.5295 03/06 00:05:46 - mmengine - INFO - Epoch(train) [67][2100/5005] lr: 1.0000e-02 eta: 23:21:18 time: 0.2211 data_time: 0.0028 loss: 1.2554 03/06 00:06:09 - mmengine - INFO - Epoch(train) [67][2200/5005] lr: 1.0000e-02 eta: 23:20:55 time: 0.2257 data_time: 0.0028 loss: 1.3057 03/06 00:06:32 - mmengine - INFO - Epoch(train) [67][2300/5005] lr: 1.0000e-02 eta: 23:20:32 time: 0.2233 data_time: 0.0030 loss: 1.3204 03/06 00:06:55 - mmengine - INFO - Epoch(train) [67][2400/5005] lr: 1.0000e-02 eta: 23:20:10 time: 0.2253 data_time: 0.0028 loss: 1.3256 03/06 00:07:18 - mmengine - INFO - Epoch(train) [67][2500/5005] lr: 1.0000e-02 eta: 23:19:47 time: 0.2271 data_time: 0.0030 loss: 1.2126 03/06 00:07:40 - mmengine - INFO - Epoch(train) [67][2600/5005] lr: 1.0000e-02 eta: 23:19:23 time: 0.2220 data_time: 0.0028 loss: 1.4090 03/06 00:07:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:08:03 - mmengine - INFO - Epoch(train) [67][2700/5005] lr: 1.0000e-02 eta: 23:19:01 time: 0.2280 data_time: 0.0030 loss: 1.3158 03/06 00:08:26 - mmengine - INFO - Epoch(train) [67][2800/5005] lr: 1.0000e-02 eta: 23:18:38 time: 0.2252 data_time: 0.0030 loss: 1.2787 03/06 00:08:49 - mmengine - INFO - Epoch(train) [67][2900/5005] lr: 1.0000e-02 eta: 23:18:15 time: 0.2413 data_time: 0.0028 loss: 1.3294 03/06 00:09:11 - mmengine - INFO - Epoch(train) [67][3000/5005] lr: 1.0000e-02 eta: 23:17:51 time: 0.2234 data_time: 0.0031 loss: 1.3024 03/06 00:09:34 - mmengine - INFO - Epoch(train) [67][3100/5005] lr: 1.0000e-02 eta: 23:17:28 time: 0.2231 data_time: 0.0030 loss: 1.3936 03/06 00:09:57 - mmengine - INFO - Epoch(train) [67][3200/5005] lr: 1.0000e-02 eta: 23:17:06 time: 0.2232 data_time: 0.0031 loss: 1.3703 03/06 00:10:20 - mmengine - INFO - Epoch(train) [67][3300/5005] lr: 1.0000e-02 eta: 23:16:43 time: 0.2452 data_time: 0.0029 loss: 1.1828 03/06 00:10:43 - mmengine - INFO - Epoch(train) [67][3400/5005] lr: 1.0000e-02 eta: 23:16:19 time: 0.2218 data_time: 0.0029 loss: 1.1784 03/06 00:11:06 - mmengine - INFO - Epoch(train) [67][3500/5005] lr: 1.0000e-02 eta: 23:15:57 time: 0.2248 data_time: 0.0031 loss: 1.4091 03/06 00:11:29 - mmengine - INFO - Epoch(train) [67][3600/5005] lr: 1.0000e-02 eta: 23:15:34 time: 0.2230 data_time: 0.0028 loss: 1.3494 03/06 00:11:45 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:11:52 - mmengine - INFO - Epoch(train) [67][3700/5005] lr: 1.0000e-02 eta: 23:15:11 time: 0.2257 data_time: 0.0027 loss: 1.2960 03/06 00:12:15 - mmengine - INFO - Epoch(train) [67][3800/5005] lr: 1.0000e-02 eta: 23:14:48 time: 0.2271 data_time: 0.0030 loss: 1.3257 03/06 00:12:37 - mmengine - INFO - Epoch(train) [67][3900/5005] lr: 1.0000e-02 eta: 23:14:25 time: 0.2251 data_time: 0.0030 loss: 1.1632 03/06 00:13:00 - mmengine - INFO - Epoch(train) [67][4000/5005] lr: 1.0000e-02 eta: 23:14:02 time: 0.2229 data_time: 0.0028 loss: 1.1422 03/06 00:13:23 - mmengine - INFO - Epoch(train) [67][4100/5005] lr: 1.0000e-02 eta: 23:13:39 time: 0.2275 data_time: 0.0031 loss: 1.3018 03/06 00:13:46 - mmengine - INFO - Epoch(train) [67][4200/5005] lr: 1.0000e-02 eta: 23:13:16 time: 0.2224 data_time: 0.0030 loss: 1.4318 03/06 00:14:09 - mmengine - INFO - Epoch(train) [67][4300/5005] lr: 1.0000e-02 eta: 23:12:53 time: 0.2221 data_time: 0.0032 loss: 1.4631 03/06 00:14:32 - mmengine - INFO - Epoch(train) [67][4400/5005] lr: 1.0000e-02 eta: 23:12:30 time: 0.2337 data_time: 0.0032 loss: 1.3735 03/06 00:14:54 - mmengine - INFO - Epoch(train) [67][4500/5005] lr: 1.0000e-02 eta: 23:12:07 time: 0.2244 data_time: 0.0033 loss: 1.6532 03/06 00:15:17 - mmengine - INFO - Epoch(train) [67][4600/5005] lr: 1.0000e-02 eta: 23:11:44 time: 0.2247 data_time: 0.0028 loss: 1.2287 03/06 00:15:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:15:40 - mmengine - INFO - Epoch(train) [67][4700/5005] lr: 1.0000e-02 eta: 23:11:21 time: 0.2409 data_time: 0.0028 loss: 1.2080 03/06 00:16:03 - mmengine - INFO - Epoch(train) [67][4800/5005] lr: 1.0000e-02 eta: 23:10:58 time: 0.2228 data_time: 0.0031 loss: 1.4220 03/06 00:16:27 - mmengine - INFO - Epoch(train) [67][4900/5005] lr: 1.0000e-02 eta: 23:10:37 time: 0.2993 data_time: 0.0029 loss: 1.3159 03/06 00:16:56 - mmengine - INFO - Epoch(train) [67][5000/5005] lr: 1.0000e-02 eta: 23:10:20 time: 0.2754 data_time: 0.0029 loss: 1.4357 03/06 00:16:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:17:00 - mmengine - INFO - Saving checkpoint at 67 epochs 03/06 00:17:16 - mmengine - INFO - Epoch(val) [67][100/196] eta: 0:00:14 time: 0.0177 data_time: 0.0002 03/06 00:17:30 - mmengine - INFO - Epoch(val) [67][196/196] accuracy/top1: 71.0800 accuracy/top5: 90.6180 03/06 00:18:02 - mmengine - INFO - Epoch(train) [68][ 100/5005] lr: 1.0000e-02 eta: 23:10:06 time: 0.2249 data_time: 0.0037 loss: 1.2481 03/06 00:18:24 - mmengine - INFO - Epoch(train) [68][ 200/5005] lr: 1.0000e-02 eta: 23:09:43 time: 0.2264 data_time: 0.0034 loss: 1.2528 03/06 00:18:47 - mmengine - INFO - Epoch(train) [68][ 300/5005] lr: 1.0000e-02 eta: 23:09:20 time: 0.2252 data_time: 0.0034 loss: 1.5232 03/06 00:19:10 - mmengine - INFO - Epoch(train) [68][ 400/5005] lr: 1.0000e-02 eta: 23:08:57 time: 0.2267 data_time: 0.0029 loss: 1.2992 03/06 00:19:34 - mmengine - INFO - Epoch(train) [68][ 500/5005] lr: 1.0000e-02 eta: 23:08:35 time: 0.2265 data_time: 0.0031 loss: 1.2576 03/06 00:19:56 - mmengine - INFO - Epoch(train) [68][ 600/5005] lr: 1.0000e-02 eta: 23:08:12 time: 0.2302 data_time: 0.0034 loss: 1.3117 03/06 00:20:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:20:19 - mmengine - INFO - Epoch(train) [68][ 700/5005] lr: 1.0000e-02 eta: 23:07:49 time: 0.2272 data_time: 0.0029 loss: 1.2571 03/06 00:20:43 - mmengine - INFO - Epoch(train) [68][ 800/5005] lr: 1.0000e-02 eta: 23:07:26 time: 0.2227 data_time: 0.0029 loss: 1.2674 03/06 00:21:05 - mmengine - INFO - Epoch(train) [68][ 900/5005] lr: 1.0000e-02 eta: 23:07:03 time: 0.2236 data_time: 0.0029 loss: 1.3162 03/06 00:21:28 - mmengine - INFO - Epoch(train) [68][1000/5005] lr: 1.0000e-02 eta: 23:06:40 time: 0.2218 data_time: 0.0031 loss: 1.3441 03/06 00:21:51 - mmengine - INFO - Epoch(train) [68][1100/5005] lr: 1.0000e-02 eta: 23:06:17 time: 0.2250 data_time: 0.0029 loss: 1.2835 03/06 00:22:14 - mmengine - INFO - Epoch(train) [68][1200/5005] lr: 1.0000e-02 eta: 23:05:55 time: 0.2442 data_time: 0.0032 loss: 1.2527 03/06 00:22:37 - mmengine - INFO - Epoch(train) [68][1300/5005] lr: 1.0000e-02 eta: 23:05:32 time: 0.2224 data_time: 0.0031 loss: 1.2461 03/06 00:23:00 - mmengine - INFO - Epoch(train) [68][1400/5005] lr: 1.0000e-02 eta: 23:05:09 time: 0.2286 data_time: 0.0030 loss: 1.3770 03/06 00:23:23 - mmengine - INFO - Epoch(train) [68][1500/5005] lr: 1.0000e-02 eta: 23:04:46 time: 0.2277 data_time: 0.0029 loss: 1.2393 03/06 00:23:46 - mmengine - INFO - Epoch(train) [68][1600/5005] lr: 1.0000e-02 eta: 23:04:23 time: 0.2218 data_time: 0.0031 loss: 1.2619 03/06 00:24:02 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:24:10 - mmengine - INFO - Epoch(train) [68][1700/5005] lr: 1.0000e-02 eta: 23:04:01 time: 0.2269 data_time: 0.0033 loss: 1.3996 03/06 00:24:32 - mmengine - INFO - Epoch(train) [68][1800/5005] lr: 1.0000e-02 eta: 23:03:38 time: 0.2243 data_time: 0.0031 loss: 1.3632 03/06 00:24:55 - mmengine - INFO - Epoch(train) [68][1900/5005] lr: 1.0000e-02 eta: 23:03:15 time: 0.2243 data_time: 0.0035 loss: 1.3452 03/06 00:25:18 - mmengine - INFO - Epoch(train) [68][2000/5005] lr: 1.0000e-02 eta: 23:02:52 time: 0.2449 data_time: 0.0030 loss: 1.3231 03/06 00:25:41 - mmengine - INFO - Epoch(train) [68][2100/5005] lr: 1.0000e-02 eta: 23:02:29 time: 0.2228 data_time: 0.0034 loss: 1.3095 03/06 00:26:04 - mmengine - INFO - Epoch(train) [68][2200/5005] lr: 1.0000e-02 eta: 23:02:06 time: 0.2252 data_time: 0.0032 loss: 1.2796 03/06 00:26:26 - mmengine - INFO - Epoch(train) [68][2300/5005] lr: 1.0000e-02 eta: 23:01:43 time: 0.2269 data_time: 0.0028 loss: 1.2668 03/06 00:26:50 - mmengine - INFO - Epoch(train) [68][2400/5005] lr: 1.0000e-02 eta: 23:01:20 time: 0.2259 data_time: 0.0027 loss: 1.3634 03/06 00:27:13 - mmengine - INFO - Epoch(train) [68][2500/5005] lr: 1.0000e-02 eta: 23:00:57 time: 0.2221 data_time: 0.0028 loss: 1.3388 03/06 00:27:35 - mmengine - INFO - Epoch(train) [68][2600/5005] lr: 1.0000e-02 eta: 23:00:34 time: 0.2220 data_time: 0.0029 loss: 1.3927 03/06 00:27:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:27:58 - mmengine - INFO - Epoch(train) [68][2700/5005] lr: 1.0000e-02 eta: 23:00:11 time: 0.2280 data_time: 0.0030 loss: 1.1045 03/06 00:28:21 - mmengine - INFO - Epoch(train) [68][2800/5005] lr: 1.0000e-02 eta: 22:59:48 time: 0.2455 data_time: 0.0031 loss: 1.3006 03/06 00:28:44 - mmengine - INFO - Epoch(train) [68][2900/5005] lr: 1.0000e-02 eta: 22:59:26 time: 0.2256 data_time: 0.0029 loss: 1.4255 03/06 00:29:07 - mmengine - INFO - Epoch(train) [68][3000/5005] lr: 1.0000e-02 eta: 22:59:03 time: 0.2260 data_time: 0.0031 loss: 1.1463 03/06 00:29:30 - mmengine - INFO - Epoch(train) [68][3100/5005] lr: 1.0000e-02 eta: 22:58:40 time: 0.2249 data_time: 0.0029 loss: 1.4123 03/06 00:29:53 - mmengine - INFO - Epoch(train) [68][3200/5005] lr: 1.0000e-02 eta: 22:58:17 time: 0.2419 data_time: 0.0030 loss: 1.4607 03/06 00:30:16 - mmengine - INFO - Epoch(train) [68][3300/5005] lr: 1.0000e-02 eta: 22:57:54 time: 0.2256 data_time: 0.0030 loss: 1.3275 03/06 00:30:39 - mmengine - INFO - Epoch(train) [68][3400/5005] lr: 1.0000e-02 eta: 22:57:31 time: 0.2230 data_time: 0.0029 loss: 1.2576 03/06 00:31:02 - mmengine - INFO - Epoch(train) [68][3500/5005] lr: 1.0000e-02 eta: 22:57:08 time: 0.2233 data_time: 0.0030 loss: 1.2636 03/06 00:31:25 - mmengine - INFO - Epoch(train) [68][3600/5005] lr: 1.0000e-02 eta: 22:56:46 time: 0.2449 data_time: 0.0028 loss: 1.2908 03/06 00:31:39 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:31:48 - mmengine - INFO - Epoch(train) [68][3700/5005] lr: 1.0000e-02 eta: 22:56:23 time: 0.2291 data_time: 0.0032 loss: 1.0864 03/06 00:32:10 - mmengine - INFO - Epoch(train) [68][3800/5005] lr: 1.0000e-02 eta: 22:56:00 time: 0.2247 data_time: 0.0032 loss: 1.2325 03/06 00:32:33 - mmengine - INFO - Epoch(train) [68][3900/5005] lr: 1.0000e-02 eta: 22:55:37 time: 0.2290 data_time: 0.0030 loss: 1.1111 03/06 00:32:56 - mmengine - INFO - Epoch(train) [68][4000/5005] lr: 1.0000e-02 eta: 22:55:14 time: 0.2229 data_time: 0.0029 loss: 1.1865 03/06 00:33:19 - mmengine - INFO - Epoch(train) [68][4100/5005] lr: 1.0000e-02 eta: 22:54:51 time: 0.2224 data_time: 0.0032 loss: 1.2790 03/06 00:33:42 - mmengine - INFO - Epoch(train) [68][4200/5005] lr: 1.0000e-02 eta: 22:54:28 time: 0.2248 data_time: 0.0032 loss: 1.1534 03/06 00:34:05 - mmengine - INFO - Epoch(train) [68][4300/5005] lr: 1.0000e-02 eta: 22:54:05 time: 0.2212 data_time: 0.0032 loss: 1.2448 03/06 00:34:28 - mmengine - INFO - Epoch(train) [68][4400/5005] lr: 1.0000e-02 eta: 22:53:42 time: 0.2227 data_time: 0.0029 loss: 1.3677 03/06 00:34:50 - mmengine - INFO - Epoch(train) [68][4500/5005] lr: 1.0000e-02 eta: 22:53:19 time: 0.2245 data_time: 0.0035 loss: 1.5134 03/06 00:35:13 - mmengine - INFO - Epoch(train) [68][4600/5005] lr: 1.0000e-02 eta: 22:52:56 time: 0.2259 data_time: 0.0031 loss: 1.2337 03/06 00:35:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:35:36 - mmengine - INFO - Epoch(train) [68][4700/5005] lr: 1.0000e-02 eta: 22:52:33 time: 0.2266 data_time: 0.0030 loss: 1.2417 03/06 00:35:59 - mmengine - INFO - Epoch(train) [68][4800/5005] lr: 1.0000e-02 eta: 22:52:10 time: 0.2267 data_time: 0.0029 loss: 1.5274 03/06 00:36:23 - mmengine - INFO - Epoch(train) [68][4900/5005] lr: 1.0000e-02 eta: 22:51:49 time: 0.2931 data_time: 0.0028 loss: 1.3937 03/06 00:36:51 - mmengine - INFO - Epoch(train) [68][5000/5005] lr: 1.0000e-02 eta: 22:51:31 time: 0.2851 data_time: 0.0030 loss: 1.3528 03/06 00:36:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:36:56 - mmengine - INFO - Saving checkpoint at 68 epochs 03/06 00:37:10 - mmengine - INFO - Epoch(val) [68][100/196] eta: 0:00:12 time: 0.0225 data_time: 0.0004 03/06 00:37:24 - mmengine - INFO - Epoch(val) [68][196/196] accuracy/top1: 71.6300 accuracy/top5: 90.9740 03/06 00:37:55 - mmengine - INFO - Epoch(train) [69][ 100/5005] lr: 1.0000e-02 eta: 22:51:16 time: 0.2307 data_time: 0.0035 loss: 1.4154 03/06 00:38:18 - mmengine - INFO - Epoch(train) [69][ 200/5005] lr: 1.0000e-02 eta: 22:50:54 time: 0.2479 data_time: 0.0040 loss: 1.4118 03/06 00:38:41 - mmengine - INFO - Epoch(train) [69][ 300/5005] lr: 1.0000e-02 eta: 22:50:31 time: 0.2478 data_time: 0.0047 loss: 1.3417 03/06 00:39:04 - mmengine - INFO - Epoch(train) [69][ 400/5005] lr: 1.0000e-02 eta: 22:50:08 time: 0.2202 data_time: 0.0034 loss: 1.2496 03/06 00:39:27 - mmengine - INFO - Epoch(train) [69][ 500/5005] lr: 1.0000e-02 eta: 22:49:45 time: 0.2235 data_time: 0.0033 loss: 1.2654 03/06 00:39:50 - mmengine - INFO - Epoch(train) [69][ 600/5005] lr: 1.0000e-02 eta: 22:49:22 time: 0.2235 data_time: 0.0034 loss: 1.3966 03/06 00:40:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:40:13 - mmengine - INFO - Epoch(train) [69][ 700/5005] lr: 1.0000e-02 eta: 22:48:59 time: 0.2276 data_time: 0.0028 loss: 1.3375 03/06 00:40:36 - mmengine - INFO - Epoch(train) [69][ 800/5005] lr: 1.0000e-02 eta: 22:48:36 time: 0.2255 data_time: 0.0029 loss: 1.0927 03/06 00:40:58 - mmengine - INFO - Epoch(train) [69][ 900/5005] lr: 1.0000e-02 eta: 22:48:13 time: 0.2234 data_time: 0.0028 loss: 1.4054 03/06 00:41:22 - mmengine - INFO - Epoch(train) [69][1000/5005] lr: 1.0000e-02 eta: 22:47:50 time: 0.2518 data_time: 0.0030 loss: 1.1997 03/06 00:41:45 - mmengine - INFO - Epoch(train) [69][1100/5005] lr: 1.0000e-02 eta: 22:47:28 time: 0.2217 data_time: 0.0031 loss: 1.3340 03/06 00:42:07 - mmengine - INFO - Epoch(train) [69][1200/5005] lr: 1.0000e-02 eta: 22:47:04 time: 0.2218 data_time: 0.0027 loss: 1.3662 03/06 00:42:30 - mmengine - INFO - Epoch(train) [69][1300/5005] lr: 1.0000e-02 eta: 22:46:41 time: 0.2259 data_time: 0.0033 loss: 1.3628 03/06 00:42:53 - mmengine - INFO - Epoch(train) [69][1400/5005] lr: 1.0000e-02 eta: 22:46:18 time: 0.2227 data_time: 0.0033 loss: 1.4871 03/06 00:43:16 - mmengine - INFO - Epoch(train) [69][1500/5005] lr: 1.0000e-02 eta: 22:45:56 time: 0.2239 data_time: 0.0030 loss: 1.1964 03/06 00:43:39 - mmengine - INFO - Epoch(train) [69][1600/5005] lr: 1.0000e-02 eta: 22:45:33 time: 0.2276 data_time: 0.0028 loss: 1.3338 03/06 00:43:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:44:02 - mmengine - INFO - Epoch(train) [69][1700/5005] lr: 1.0000e-02 eta: 22:45:10 time: 0.2280 data_time: 0.0032 loss: 1.3265 03/06 00:44:24 - mmengine - INFO - Epoch(train) [69][1800/5005] lr: 1.0000e-02 eta: 22:44:47 time: 0.2251 data_time: 0.0029 loss: 1.3472 03/06 00:44:47 - mmengine - INFO - Epoch(train) [69][1900/5005] lr: 1.0000e-02 eta: 22:44:24 time: 0.2263 data_time: 0.0030 loss: 1.2705 03/06 00:45:10 - mmengine - INFO - Epoch(train) [69][2000/5005] lr: 1.0000e-02 eta: 22:44:00 time: 0.2252 data_time: 0.0030 loss: 1.3917 03/06 00:45:33 - mmengine - INFO - Epoch(train) [69][2100/5005] lr: 1.0000e-02 eta: 22:43:38 time: 0.2240 data_time: 0.0031 loss: 1.3490 03/06 00:45:56 - mmengine - INFO - Epoch(train) [69][2200/5005] lr: 1.0000e-02 eta: 22:43:14 time: 0.2212 data_time: 0.0030 loss: 1.3004 03/06 00:46:18 - mmengine - INFO - Epoch(train) [69][2300/5005] lr: 1.0000e-02 eta: 22:42:51 time: 0.2250 data_time: 0.0030 loss: 1.1685 03/06 00:46:42 - mmengine - INFO - Epoch(train) [69][2400/5005] lr: 1.0000e-02 eta: 22:42:29 time: 0.2356 data_time: 0.0030 loss: 1.4021 03/06 00:47:05 - mmengine - INFO - Epoch(train) [69][2500/5005] lr: 1.0000e-02 eta: 22:42:06 time: 0.2276 data_time: 0.0030 loss: 1.2331 03/06 00:47:27 - mmengine - INFO - Epoch(train) [69][2600/5005] lr: 1.0000e-02 eta: 22:41:43 time: 0.2220 data_time: 0.0033 loss: 1.2849 03/06 00:47:41 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:47:50 - mmengine - INFO - Epoch(train) [69][2700/5005] lr: 1.0000e-02 eta: 22:41:20 time: 0.2250 data_time: 0.0029 loss: 1.5157 03/06 00:48:13 - mmengine - INFO - Epoch(train) [69][2800/5005] lr: 1.0000e-02 eta: 22:40:57 time: 0.2227 data_time: 0.0031 loss: 1.3450 03/06 00:48:36 - mmengine - INFO - Epoch(train) [69][2900/5005] lr: 1.0000e-02 eta: 22:40:34 time: 0.2302 data_time: 0.0030 loss: 1.3741 03/06 00:48:59 - mmengine - INFO - Epoch(train) [69][3000/5005] lr: 1.0000e-02 eta: 22:40:11 time: 0.2279 data_time: 0.0031 loss: 1.2184 03/06 00:49:22 - mmengine - INFO - Epoch(train) [69][3100/5005] lr: 1.0000e-02 eta: 22:39:48 time: 0.2296 data_time: 0.0029 loss: 1.2313 03/06 00:49:45 - mmengine - INFO - Epoch(train) [69][3200/5005] lr: 1.0000e-02 eta: 22:39:25 time: 0.2239 data_time: 0.0028 loss: 1.3947 03/06 00:50:08 - mmengine - INFO - Epoch(train) [69][3300/5005] lr: 1.0000e-02 eta: 22:39:02 time: 0.2353 data_time: 0.0028 loss: 1.2304 03/06 00:50:30 - mmengine - INFO - Epoch(train) [69][3400/5005] lr: 1.0000e-02 eta: 22:38:39 time: 0.2222 data_time: 0.0029 loss: 1.0113 03/06 00:50:53 - mmengine - INFO - Epoch(train) [69][3500/5005] lr: 1.0000e-02 eta: 22:38:16 time: 0.2267 data_time: 0.0032 loss: 1.4896 03/06 00:51:16 - mmengine - INFO - Epoch(train) [69][3600/5005] lr: 1.0000e-02 eta: 22:37:53 time: 0.2254 data_time: 0.0029 loss: 1.3808 03/06 00:51:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:51:39 - mmengine - INFO - Epoch(train) [69][3700/5005] lr: 1.0000e-02 eta: 22:37:30 time: 0.2245 data_time: 0.0030 loss: 1.2103 03/06 00:52:02 - mmengine - INFO - Epoch(train) [69][3800/5005] lr: 1.0000e-02 eta: 22:37:07 time: 0.2453 data_time: 0.0033 loss: 1.3561 03/06 00:52:25 - mmengine - INFO - Epoch(train) [69][3900/5005] lr: 1.0000e-02 eta: 22:36:45 time: 0.2274 data_time: 0.0032 loss: 1.4632 03/06 00:52:48 - mmengine - INFO - Epoch(train) [69][4000/5005] lr: 1.0000e-02 eta: 22:36:21 time: 0.2331 data_time: 0.0033 loss: 1.4124 03/06 00:53:10 - mmengine - INFO - Epoch(train) [69][4100/5005] lr: 1.0000e-02 eta: 22:35:58 time: 0.2204 data_time: 0.0029 loss: 1.4539 03/06 00:53:33 - mmengine - INFO - Epoch(train) [69][4200/5005] lr: 1.0000e-02 eta: 22:35:36 time: 0.2362 data_time: 0.0029 loss: 1.2829 03/06 00:53:56 - mmengine - INFO - Epoch(train) [69][4300/5005] lr: 1.0000e-02 eta: 22:35:13 time: 0.2230 data_time: 0.0029 loss: 1.4019 03/06 00:54:19 - mmengine - INFO - Epoch(train) [69][4400/5005] lr: 1.0000e-02 eta: 22:34:50 time: 0.2271 data_time: 0.0034 loss: 1.2462 03/06 00:54:42 - mmengine - INFO - Epoch(train) [69][4500/5005] lr: 1.0000e-02 eta: 22:34:27 time: 0.2244 data_time: 0.0028 loss: 1.4714 03/06 00:55:05 - mmengine - INFO - Epoch(train) [69][4600/5005] lr: 1.0000e-02 eta: 22:34:04 time: 0.2291 data_time: 0.0031 loss: 1.2433 03/06 00:55:19 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:55:28 - mmengine - INFO - Epoch(train) [69][4700/5005] lr: 1.0000e-02 eta: 22:33:41 time: 0.2341 data_time: 0.0027 loss: 1.1424 03/06 00:55:51 - mmengine - INFO - Epoch(train) [69][4800/5005] lr: 1.0000e-02 eta: 22:33:18 time: 0.2242 data_time: 0.0032 loss: 1.3590 03/06 00:56:15 - mmengine - INFO - Epoch(train) [69][4900/5005] lr: 1.0000e-02 eta: 22:32:56 time: 0.2819 data_time: 0.0028 loss: 1.4060 03/06 00:56:44 - mmengine - INFO - Epoch(train) [69][5000/5005] lr: 1.0000e-02 eta: 22:32:39 time: 0.2812 data_time: 0.0028 loss: 1.4189 03/06 00:56:45 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 00:56:48 - mmengine - INFO - Saving checkpoint at 69 epochs 03/06 00:57:02 - mmengine - INFO - Epoch(val) [69][100/196] eta: 0:00:12 time: 0.0191 data_time: 0.0003 03/06 00:57:16 - mmengine - INFO - Epoch(val) [69][196/196] accuracy/top1: 71.6600 accuracy/top5: 91.0180 03/06 00:57:48 - mmengine - INFO - Epoch(train) [70][ 100/5005] lr: 1.0000e-02 eta: 22:32:25 time: 0.2282 data_time: 0.0027 loss: 1.1952 03/06 00:58:10 - mmengine - INFO - Epoch(train) [70][ 200/5005] lr: 1.0000e-02 eta: 22:32:02 time: 0.2239 data_time: 0.0032 loss: 1.2836 03/06 00:58:33 - mmengine - INFO - Epoch(train) [70][ 300/5005] lr: 1.0000e-02 eta: 22:31:39 time: 0.2212 data_time: 0.0032 loss: 1.3080 03/06 00:58:56 - mmengine - INFO - Epoch(train) [70][ 400/5005] lr: 1.0000e-02 eta: 22:31:16 time: 0.2223 data_time: 0.0032 loss: 1.2445 03/06 00:59:19 - mmengine - INFO - Epoch(train) [70][ 500/5005] lr: 1.0000e-02 eta: 22:30:53 time: 0.2355 data_time: 0.0032 loss: 1.4575 03/06 00:59:42 - mmengine - INFO - Epoch(train) [70][ 600/5005] lr: 1.0000e-02 eta: 22:30:30 time: 0.2259 data_time: 0.0033 loss: 1.3590 03/06 00:59:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:00:05 - mmengine - INFO - Epoch(train) [70][ 700/5005] lr: 1.0000e-02 eta: 22:30:07 time: 0.2260 data_time: 0.0030 loss: 1.4029 03/06 01:00:27 - mmengine - INFO - Epoch(train) [70][ 800/5005] lr: 1.0000e-02 eta: 22:29:44 time: 0.2240 data_time: 0.0030 loss: 1.4505 03/06 01:00:50 - mmengine - INFO - Epoch(train) [70][ 900/5005] lr: 1.0000e-02 eta: 22:29:21 time: 0.2579 data_time: 0.0029 loss: 1.3877 03/06 01:01:13 - mmengine - INFO - Epoch(train) [70][1000/5005] lr: 1.0000e-02 eta: 22:28:58 time: 0.2242 data_time: 0.0034 loss: 1.1618 03/06 01:01:36 - mmengine - INFO - Epoch(train) [70][1100/5005] lr: 1.0000e-02 eta: 22:28:35 time: 0.2260 data_time: 0.0031 loss: 1.3441 03/06 01:01:59 - mmengine - INFO - Epoch(train) [70][1200/5005] lr: 1.0000e-02 eta: 22:28:12 time: 0.2253 data_time: 0.0031 loss: 1.3165 03/06 01:02:22 - mmengine - INFO - Epoch(train) [70][1300/5005] lr: 1.0000e-02 eta: 22:27:49 time: 0.2269 data_time: 0.0033 loss: 1.4772 03/06 01:02:45 - mmengine - INFO - Epoch(train) [70][1400/5005] lr: 1.0000e-02 eta: 22:27:26 time: 0.2208 data_time: 0.0029 loss: 1.1431 03/06 01:03:08 - mmengine - INFO - Epoch(train) [70][1500/5005] lr: 1.0000e-02 eta: 22:27:03 time: 0.2272 data_time: 0.0030 loss: 1.2927 03/06 01:03:30 - mmengine - INFO - Epoch(train) [70][1600/5005] lr: 1.0000e-02 eta: 22:26:40 time: 0.2269 data_time: 0.0034 loss: 1.4026 03/06 01:03:43 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:03:54 - mmengine - INFO - Epoch(train) [70][1700/5005] lr: 1.0000e-02 eta: 22:26:17 time: 0.2217 data_time: 0.0027 loss: 1.3438 03/06 01:04:16 - mmengine - INFO - Epoch(train) [70][1800/5005] lr: 1.0000e-02 eta: 22:25:54 time: 0.2265 data_time: 0.0031 loss: 1.2543 03/06 01:04:39 - mmengine - INFO - Epoch(train) [70][1900/5005] lr: 1.0000e-02 eta: 22:25:32 time: 0.2234 data_time: 0.0031 loss: 1.1649 03/06 01:05:02 - mmengine - INFO - Epoch(train) [70][2000/5005] lr: 1.0000e-02 eta: 22:25:08 time: 0.2252 data_time: 0.0031 loss: 1.4892 03/06 01:05:25 - mmengine - INFO - Epoch(train) [70][2100/5005] lr: 1.0000e-02 eta: 22:24:45 time: 0.2418 data_time: 0.0032 loss: 1.3783 03/06 01:05:48 - mmengine - INFO - Epoch(train) [70][2200/5005] lr: 1.0000e-02 eta: 22:24:22 time: 0.2223 data_time: 0.0032 loss: 1.4018 03/06 01:06:11 - mmengine - INFO - Epoch(train) [70][2300/5005] lr: 1.0000e-02 eta: 22:24:00 time: 0.2248 data_time: 0.0034 loss: 1.2868 03/06 01:06:34 - mmengine - INFO - Epoch(train) [70][2400/5005] lr: 1.0000e-02 eta: 22:23:37 time: 0.2262 data_time: 0.0029 loss: 1.2498 03/06 01:06:56 - mmengine - INFO - Epoch(train) [70][2500/5005] lr: 1.0000e-02 eta: 22:23:14 time: 0.2208 data_time: 0.0028 loss: 1.2683 03/06 01:07:19 - mmengine - INFO - Epoch(train) [70][2600/5005] lr: 1.0000e-02 eta: 22:22:51 time: 0.2298 data_time: 0.0032 loss: 1.3808 03/06 01:07:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:07:42 - mmengine - INFO - Epoch(train) [70][2700/5005] lr: 1.0000e-02 eta: 22:22:28 time: 0.2343 data_time: 0.0031 loss: 1.2993 03/06 01:08:05 - mmengine - INFO - Epoch(train) [70][2800/5005] lr: 1.0000e-02 eta: 22:22:05 time: 0.2392 data_time: 0.0029 loss: 1.3127 03/06 01:08:28 - mmengine - INFO - Epoch(train) [70][2900/5005] lr: 1.0000e-02 eta: 22:21:42 time: 0.2265 data_time: 0.0029 loss: 1.1436 03/06 01:08:51 - mmengine - INFO - Epoch(train) [70][3000/5005] lr: 1.0000e-02 eta: 22:21:19 time: 0.2275 data_time: 0.0032 loss: 1.5240 03/06 01:09:14 - mmengine - INFO - Epoch(train) [70][3100/5005] lr: 1.0000e-02 eta: 22:20:56 time: 0.2237 data_time: 0.0032 loss: 1.3929 03/06 01:09:36 - mmengine - INFO - Epoch(train) [70][3200/5005] lr: 1.0000e-02 eta: 22:20:33 time: 0.2274 data_time: 0.0028 loss: 1.2792 03/06 01:09:59 - mmengine - INFO - Epoch(train) [70][3300/5005] lr: 1.0000e-02 eta: 22:20:10 time: 0.2458 data_time: 0.0029 loss: 1.4138 03/06 01:10:22 - mmengine - INFO - Epoch(train) [70][3400/5005] lr: 1.0000e-02 eta: 22:19:47 time: 0.2238 data_time: 0.0029 loss: 1.2756 03/06 01:10:45 - mmengine - INFO - Epoch(train) [70][3500/5005] lr: 1.0000e-02 eta: 22:19:24 time: 0.2194 data_time: 0.0032 loss: 1.3110 03/06 01:11:08 - mmengine - INFO - Epoch(train) [70][3600/5005] lr: 1.0000e-02 eta: 22:19:01 time: 0.2234 data_time: 0.0029 loss: 1.3968 03/06 01:11:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:11:31 - mmengine - INFO - Epoch(train) [70][3700/5005] lr: 1.0000e-02 eta: 22:18:38 time: 0.2294 data_time: 0.0028 loss: 1.3629 03/06 01:11:54 - mmengine - INFO - Epoch(train) [70][3800/5005] lr: 1.0000e-02 eta: 22:18:15 time: 0.2256 data_time: 0.0032 loss: 1.3162 03/06 01:12:16 - mmengine - INFO - Epoch(train) [70][3900/5005] lr: 1.0000e-02 eta: 22:17:52 time: 0.2487 data_time: 0.0034 loss: 1.2230 03/06 01:12:40 - mmengine - INFO - Epoch(train) [70][4000/5005] lr: 1.0000e-02 eta: 22:17:29 time: 0.2213 data_time: 0.0029 loss: 1.3436 03/06 01:13:02 - mmengine - INFO - Epoch(train) [70][4100/5005] lr: 1.0000e-02 eta: 22:17:06 time: 0.2247 data_time: 0.0030 loss: 1.3352 03/06 01:13:25 - mmengine - INFO - Epoch(train) [70][4200/5005] lr: 1.0000e-02 eta: 22:16:43 time: 0.2206 data_time: 0.0029 loss: 1.2264 03/06 01:13:48 - mmengine - INFO - Epoch(train) [70][4300/5005] lr: 1.0000e-02 eta: 22:16:20 time: 0.2234 data_time: 0.0028 loss: 1.2680 03/06 01:14:12 - mmengine - INFO - Epoch(train) [70][4400/5005] lr: 1.0000e-02 eta: 22:15:58 time: 0.2249 data_time: 0.0028 loss: 1.4159 03/06 01:14:34 - mmengine - INFO - Epoch(train) [70][4500/5005] lr: 1.0000e-02 eta: 22:15:35 time: 0.2228 data_time: 0.0033 loss: 1.2926 03/06 01:14:57 - mmengine - INFO - Epoch(train) [70][4600/5005] lr: 1.0000e-02 eta: 22:15:12 time: 0.2307 data_time: 0.0033 loss: 1.3396 03/06 01:15:09 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:15:20 - mmengine - INFO - Epoch(train) [70][4700/5005] lr: 1.0000e-02 eta: 22:14:49 time: 0.2240 data_time: 0.0033 loss: 1.4728 03/06 01:15:43 - mmengine - INFO - Epoch(train) [70][4800/5005] lr: 1.0000e-02 eta: 22:14:26 time: 0.2297 data_time: 0.0029 loss: 1.3846 03/06 01:16:07 - mmengine - INFO - Epoch(train) [70][4900/5005] lr: 1.0000e-02 eta: 22:14:04 time: 0.2794 data_time: 0.0026 loss: 1.3354 03/06 01:16:36 - mmengine - INFO - Epoch(train) [70][5000/5005] lr: 1.0000e-02 eta: 22:13:47 time: 0.2918 data_time: 0.0028 loss: 1.4352 03/06 01:16:37 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:16:40 - mmengine - INFO - Saving checkpoint at 70 epochs 03/06 01:16:55 - mmengine - INFO - Epoch(val) [70][100/196] eta: 0:00:12 time: 0.0228 data_time: 0.0004 03/06 01:17:08 - mmengine - INFO - Epoch(val) [70][196/196] accuracy/top1: 71.5960 accuracy/top5: 90.7960 03/06 01:17:40 - mmengine - INFO - Epoch(train) [71][ 100/5005] lr: 1.0000e-02 eta: 22:13:33 time: 0.2277 data_time: 0.0035 loss: 1.4641 03/06 01:18:03 - mmengine - INFO - Epoch(train) [71][ 200/5005] lr: 1.0000e-02 eta: 22:13:10 time: 0.2248 data_time: 0.0029 loss: 1.4114 03/06 01:18:26 - mmengine - INFO - Epoch(train) [71][ 300/5005] lr: 1.0000e-02 eta: 22:12:46 time: 0.2183 data_time: 0.0032 loss: 1.3356 03/06 01:18:49 - mmengine - INFO - Epoch(train) [71][ 400/5005] lr: 1.0000e-02 eta: 22:12:23 time: 0.2280 data_time: 0.0028 loss: 1.1491 03/06 01:19:12 - mmengine - INFO - Epoch(train) [71][ 500/5005] lr: 1.0000e-02 eta: 22:12:01 time: 0.2433 data_time: 0.0029 loss: 1.5067 03/06 01:19:35 - mmengine - INFO - Epoch(train) [71][ 600/5005] lr: 1.0000e-02 eta: 22:11:38 time: 0.2260 data_time: 0.0032 loss: 1.4733 03/06 01:19:47 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:19:58 - mmengine - INFO - Epoch(train) [71][ 700/5005] lr: 1.0000e-02 eta: 22:11:15 time: 0.2243 data_time: 0.0035 loss: 1.1589 03/06 01:20:21 - mmengine - INFO - Epoch(train) [71][ 800/5005] lr: 1.0000e-02 eta: 22:10:52 time: 0.2262 data_time: 0.0035 loss: 1.4266 03/06 01:20:44 - mmengine - INFO - Epoch(train) [71][ 900/5005] lr: 1.0000e-02 eta: 22:10:29 time: 0.2242 data_time: 0.0030 loss: 1.4451 03/06 01:21:07 - mmengine - INFO - Epoch(train) [71][1000/5005] lr: 1.0000e-02 eta: 22:10:07 time: 0.2251 data_time: 0.0030 loss: 1.3175 03/06 01:21:30 - mmengine - INFO - Epoch(train) [71][1100/5005] lr: 1.0000e-02 eta: 22:09:44 time: 0.2263 data_time: 0.0030 loss: 1.0906 03/06 01:21:52 - mmengine - INFO - Epoch(train) [71][1200/5005] lr: 1.0000e-02 eta: 22:09:21 time: 0.2219 data_time: 0.0031 loss: 1.2839 03/06 01:22:16 - mmengine - INFO - Epoch(train) [71][1300/5005] lr: 1.0000e-02 eta: 22:08:58 time: 0.2259 data_time: 0.0034 loss: 1.2706 03/06 01:22:39 - mmengine - INFO - Epoch(train) [71][1400/5005] lr: 1.0000e-02 eta: 22:08:35 time: 0.2255 data_time: 0.0029 loss: 1.3454 03/06 01:23:01 - mmengine - INFO - Epoch(train) [71][1500/5005] lr: 1.0000e-02 eta: 22:08:12 time: 0.2270 data_time: 0.0030 loss: 1.0450 03/06 01:23:24 - mmengine - INFO - Epoch(train) [71][1600/5005] lr: 1.0000e-02 eta: 22:07:49 time: 0.2231 data_time: 0.0034 loss: 1.3006 03/06 01:23:35 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:23:47 - mmengine - INFO - Epoch(train) [71][1700/5005] lr: 1.0000e-02 eta: 22:07:26 time: 0.2220 data_time: 0.0032 loss: 1.2735 03/06 01:24:10 - mmengine - INFO - Epoch(train) [71][1800/5005] lr: 1.0000e-02 eta: 22:07:03 time: 0.2414 data_time: 0.0035 loss: 1.4625 03/06 01:24:33 - mmengine - INFO - Epoch(train) [71][1900/5005] lr: 1.0000e-02 eta: 22:06:40 time: 0.2296 data_time: 0.0031 loss: 1.4774 03/06 01:24:56 - mmengine - INFO - Epoch(train) [71][2000/5005] lr: 1.0000e-02 eta: 22:06:17 time: 0.2223 data_time: 0.0030 loss: 1.3094 03/06 01:25:19 - mmengine - INFO - Epoch(train) [71][2100/5005] lr: 1.0000e-02 eta: 22:05:54 time: 0.2270 data_time: 0.0031 loss: 1.3650 03/06 01:25:42 - mmengine - INFO - Epoch(train) [71][2200/5005] lr: 1.0000e-02 eta: 22:05:31 time: 0.2231 data_time: 0.0029 loss: 1.2347 03/06 01:26:04 - mmengine - INFO - Epoch(train) [71][2300/5005] lr: 1.0000e-02 eta: 22:05:08 time: 0.2258 data_time: 0.0032 loss: 1.3212 03/06 01:26:27 - mmengine - INFO - Epoch(train) [71][2400/5005] lr: 1.0000e-02 eta: 22:04:45 time: 0.2279 data_time: 0.0030 loss: 1.2292 03/06 01:26:51 - mmengine - INFO - Epoch(train) [71][2500/5005] lr: 1.0000e-02 eta: 22:04:23 time: 0.2475 data_time: 0.0033 loss: 1.2837 03/06 01:27:13 - mmengine - INFO - Epoch(train) [71][2600/5005] lr: 1.0000e-02 eta: 22:04:00 time: 0.2244 data_time: 0.0036 loss: 1.2700 03/06 01:27:25 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:27:36 - mmengine - INFO - Epoch(train) [71][2700/5005] lr: 1.0000e-02 eta: 22:03:37 time: 0.2386 data_time: 0.0033 loss: 1.4185 03/06 01:27:59 - mmengine - INFO - Epoch(train) [71][2800/5005] lr: 1.0000e-02 eta: 22:03:14 time: 0.2274 data_time: 0.0032 loss: 1.3608 03/06 01:28:22 - mmengine - INFO - Epoch(train) [71][2900/5005] lr: 1.0000e-02 eta: 22:02:51 time: 0.2231 data_time: 0.0031 loss: 1.2165 03/06 01:28:45 - mmengine - INFO - Epoch(train) [71][3000/5005] lr: 1.0000e-02 eta: 22:02:28 time: 0.2279 data_time: 0.0033 loss: 1.6031 03/06 01:29:08 - mmengine - INFO - Epoch(train) [71][3100/5005] lr: 1.0000e-02 eta: 22:02:05 time: 0.2236 data_time: 0.0030 loss: 1.2355 03/06 01:29:31 - mmengine - INFO - Epoch(train) [71][3200/5005] lr: 1.0000e-02 eta: 22:01:42 time: 0.2229 data_time: 0.0030 loss: 1.3932 03/06 01:29:54 - mmengine - INFO - Epoch(train) [71][3300/5005] lr: 1.0000e-02 eta: 22:01:19 time: 0.2247 data_time: 0.0030 loss: 1.4248 03/06 01:30:17 - mmengine - INFO - Epoch(train) [71][3400/5005] lr: 1.0000e-02 eta: 22:00:57 time: 0.2245 data_time: 0.0030 loss: 1.3179 03/06 01:30:40 - mmengine - INFO - Epoch(train) [71][3500/5005] lr: 1.0000e-02 eta: 22:00:34 time: 0.2263 data_time: 0.0034 loss: 1.2366 03/06 01:31:02 - mmengine - INFO - Epoch(train) [71][3600/5005] lr: 1.0000e-02 eta: 22:00:11 time: 0.2298 data_time: 0.0028 loss: 1.3429 03/06 01:31:14 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:31:26 - mmengine - INFO - Epoch(train) [71][3700/5005] lr: 1.0000e-02 eta: 21:59:48 time: 0.2259 data_time: 0.0030 loss: 1.2919 03/06 01:31:49 - mmengine - INFO - Epoch(train) [71][3800/5005] lr: 1.0000e-02 eta: 21:59:25 time: 0.2232 data_time: 0.0030 loss: 1.3733 03/06 01:32:12 - mmengine - INFO - Epoch(train) [71][3900/5005] lr: 1.0000e-02 eta: 21:59:02 time: 0.2247 data_time: 0.0029 loss: 1.3144 03/06 01:32:34 - mmengine - INFO - Epoch(train) [71][4000/5005] lr: 1.0000e-02 eta: 21:58:39 time: 0.2207 data_time: 0.0029 loss: 1.2918 03/06 01:32:57 - mmengine - INFO - Epoch(train) [71][4100/5005] lr: 1.0000e-02 eta: 21:58:16 time: 0.2270 data_time: 0.0033 loss: 1.5637 03/06 01:33:21 - mmengine - INFO - Epoch(train) [71][4200/5005] lr: 1.0000e-02 eta: 21:57:54 time: 0.2230 data_time: 0.0029 loss: 1.2599 03/06 01:33:43 - mmengine - INFO - Epoch(train) [71][4300/5005] lr: 1.0000e-02 eta: 21:57:31 time: 0.2250 data_time: 0.0029 loss: 1.3284 03/06 01:34:06 - mmengine - INFO - Epoch(train) [71][4400/5005] lr: 1.0000e-02 eta: 21:57:08 time: 0.2230 data_time: 0.0030 loss: 1.4086 03/06 01:34:29 - mmengine - INFO - Epoch(train) [71][4500/5005] lr: 1.0000e-02 eta: 21:56:45 time: 0.2258 data_time: 0.0031 loss: 1.3099 03/06 01:34:53 - mmengine - INFO - Epoch(train) [71][4600/5005] lr: 1.0000e-02 eta: 21:56:22 time: 0.2461 data_time: 0.0030 loss: 1.1931 03/06 01:35:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:35:15 - mmengine - INFO - Epoch(train) [71][4700/5005] lr: 1.0000e-02 eta: 21:55:59 time: 0.2281 data_time: 0.0031 loss: 1.4707 03/06 01:35:38 - mmengine - INFO - Epoch(train) [71][4800/5005] lr: 1.0000e-02 eta: 21:55:36 time: 0.2429 data_time: 0.0030 loss: 1.1527 03/06 01:36:02 - mmengine - INFO - Epoch(train) [71][4900/5005] lr: 1.0000e-02 eta: 21:55:14 time: 0.2819 data_time: 0.0028 loss: 1.2426 03/06 01:36:30 - mmengine - INFO - Epoch(train) [71][5000/5005] lr: 1.0000e-02 eta: 21:54:56 time: 0.2842 data_time: 0.0030 loss: 1.3737 03/06 01:36:31 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:36:34 - mmengine - INFO - Saving checkpoint at 71 epochs 03/06 01:36:49 - mmengine - INFO - Epoch(val) [71][100/196] eta: 0:00:12 time: 0.0188 data_time: 0.0003 03/06 01:37:02 - mmengine - INFO - Epoch(val) [71][196/196] accuracy/top1: 71.6880 accuracy/top5: 90.8340 03/06 01:37:35 - mmengine - INFO - Epoch(train) [72][ 100/5005] lr: 1.0000e-02 eta: 21:54:41 time: 0.2235 data_time: 0.0036 loss: 1.2519 03/06 01:37:58 - mmengine - INFO - Epoch(train) [72][ 200/5005] lr: 1.0000e-02 eta: 21:54:19 time: 0.2244 data_time: 0.0036 loss: 1.2678 03/06 01:38:21 - mmengine - INFO - Epoch(train) [72][ 300/5005] lr: 1.0000e-02 eta: 21:53:56 time: 0.2254 data_time: 0.0032 loss: 1.3288 03/06 01:38:43 - mmengine - INFO - Epoch(train) [72][ 400/5005] lr: 1.0000e-02 eta: 21:53:32 time: 0.2258 data_time: 0.0033 loss: 1.3858 03/06 01:39:06 - mmengine - INFO - Epoch(train) [72][ 500/5005] lr: 1.0000e-02 eta: 21:53:10 time: 0.2375 data_time: 0.0029 loss: 1.3337 03/06 01:39:29 - mmengine - INFO - Epoch(train) [72][ 600/5005] lr: 1.0000e-02 eta: 21:52:47 time: 0.2262 data_time: 0.0033 loss: 1.4982 03/06 01:39:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:39:52 - mmengine - INFO - Epoch(train) [72][ 700/5005] lr: 1.0000e-02 eta: 21:52:24 time: 0.2290 data_time: 0.0032 loss: 0.9708 03/06 01:40:15 - mmengine - INFO - Epoch(train) [72][ 800/5005] lr: 1.0000e-02 eta: 21:52:01 time: 0.2247 data_time: 0.0032 loss: 1.2357 03/06 01:40:38 - mmengine - INFO - Epoch(train) [72][ 900/5005] lr: 1.0000e-02 eta: 21:51:38 time: 0.2267 data_time: 0.0028 loss: 1.3642 03/06 01:41:01 - mmengine - INFO - Epoch(train) [72][1000/5005] lr: 1.0000e-02 eta: 21:51:15 time: 0.2238 data_time: 0.0035 loss: 1.2599 03/06 01:41:24 - mmengine - INFO - Epoch(train) [72][1100/5005] lr: 1.0000e-02 eta: 21:50:52 time: 0.2248 data_time: 0.0034 loss: 1.3239 03/06 01:41:47 - mmengine - INFO - Epoch(train) [72][1200/5005] lr: 1.0000e-02 eta: 21:50:29 time: 0.2248 data_time: 0.0033 loss: 1.3532 03/06 01:42:10 - mmengine - INFO - Epoch(train) [72][1300/5005] lr: 1.0000e-02 eta: 21:50:06 time: 0.2227 data_time: 0.0029 loss: 1.3972 03/06 01:42:33 - mmengine - INFO - Epoch(train) [72][1400/5005] lr: 1.0000e-02 eta: 21:49:44 time: 0.2253 data_time: 0.0029 loss: 1.2807 03/06 01:42:56 - mmengine - INFO - Epoch(train) [72][1500/5005] lr: 1.0000e-02 eta: 21:49:21 time: 0.2264 data_time: 0.0030 loss: 1.2982 03/06 01:43:19 - mmengine - INFO - Epoch(train) [72][1600/5005] lr: 1.0000e-02 eta: 21:48:58 time: 0.2233 data_time: 0.0034 loss: 1.2061 03/06 01:43:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:43:42 - mmengine - INFO - Epoch(train) [72][1700/5005] lr: 1.0000e-02 eta: 21:48:35 time: 0.2270 data_time: 0.0037 loss: 1.1885 03/06 01:44:05 - mmengine - INFO - Epoch(train) [72][1800/5005] lr: 1.0000e-02 eta: 21:48:12 time: 0.2314 data_time: 0.0048 loss: 1.2750 03/06 01:44:28 - mmengine - INFO - Epoch(train) [72][1900/5005] lr: 1.0000e-02 eta: 21:47:49 time: 0.2293 data_time: 0.0036 loss: 1.4520 03/06 01:44:51 - mmengine - INFO - Epoch(train) [72][2000/5005] lr: 1.0000e-02 eta: 21:47:27 time: 0.2256 data_time: 0.0038 loss: 1.0894 03/06 01:45:14 - mmengine - INFO - Epoch(train) [72][2100/5005] lr: 1.0000e-02 eta: 21:47:04 time: 0.2400 data_time: 0.0045 loss: 1.2379 03/06 01:45:37 - mmengine - INFO - Epoch(train) [72][2200/5005] lr: 1.0000e-02 eta: 21:46:41 time: 0.2207 data_time: 0.0032 loss: 1.3639 03/06 01:46:00 - mmengine - INFO - Epoch(train) [72][2300/5005] lr: 1.0000e-02 eta: 21:46:18 time: 0.2243 data_time: 0.0030 loss: 1.3128 03/06 01:46:23 - mmengine - INFO - Epoch(train) [72][2400/5005] lr: 1.0000e-02 eta: 21:45:55 time: 0.2244 data_time: 0.0034 loss: 1.2945 03/06 01:46:46 - mmengine - INFO - Epoch(train) [72][2500/5005] lr: 1.0000e-02 eta: 21:45:32 time: 0.2262 data_time: 0.0030 loss: 1.3079 03/06 01:47:09 - mmengine - INFO - Epoch(train) [72][2600/5005] lr: 1.0000e-02 eta: 21:45:09 time: 0.2291 data_time: 0.0032 loss: 1.3338 03/06 01:47:19 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:47:32 - mmengine - INFO - Epoch(train) [72][2700/5005] lr: 1.0000e-02 eta: 21:44:46 time: 0.2225 data_time: 0.0031 loss: 1.4246 03/06 01:47:55 - mmengine - INFO - Epoch(train) [72][2800/5005] lr: 1.0000e-02 eta: 21:44:23 time: 0.2245 data_time: 0.0031 loss: 1.2680 03/06 01:48:18 - mmengine - INFO - Epoch(train) [72][2900/5005] lr: 1.0000e-02 eta: 21:44:00 time: 0.2530 data_time: 0.0035 loss: 1.2801 03/06 01:48:41 - mmengine - INFO - Epoch(train) [72][3000/5005] lr: 1.0000e-02 eta: 21:43:38 time: 0.2235 data_time: 0.0028 loss: 1.2871 03/06 01:49:04 - mmengine - INFO - Epoch(train) [72][3100/5005] lr: 1.0000e-02 eta: 21:43:15 time: 0.2252 data_time: 0.0030 loss: 1.2841 03/06 01:49:26 - mmengine - INFO - Epoch(train) [72][3200/5005] lr: 1.0000e-02 eta: 21:42:52 time: 0.2264 data_time: 0.0031 loss: 1.3228 03/06 01:49:49 - mmengine - INFO - Epoch(train) [72][3300/5005] lr: 1.0000e-02 eta: 21:42:29 time: 0.2277 data_time: 0.0033 loss: 1.2277 03/06 01:50:12 - mmengine - INFO - Epoch(train) [72][3400/5005] lr: 1.0000e-02 eta: 21:42:06 time: 0.2230 data_time: 0.0030 loss: 1.2152 03/06 01:50:35 - mmengine - INFO - Epoch(train) [72][3500/5005] lr: 1.0000e-02 eta: 21:41:43 time: 0.2217 data_time: 0.0031 loss: 1.4920 03/06 01:50:58 - mmengine - INFO - Epoch(train) [72][3600/5005] lr: 1.0000e-02 eta: 21:41:20 time: 0.2240 data_time: 0.0032 loss: 1.4555 03/06 01:51:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:51:21 - mmengine - INFO - Epoch(train) [72][3700/5005] lr: 1.0000e-02 eta: 21:40:57 time: 0.2257 data_time: 0.0030 loss: 1.2604 03/06 01:51:43 - mmengine - INFO - Epoch(train) [72][3800/5005] lr: 1.0000e-02 eta: 21:40:34 time: 0.2259 data_time: 0.0031 loss: 1.4986 03/06 01:52:07 - mmengine - INFO - Epoch(train) [72][3900/5005] lr: 1.0000e-02 eta: 21:40:11 time: 0.2265 data_time: 0.0039 loss: 1.2839 03/06 01:52:30 - mmengine - INFO - Epoch(train) [72][4000/5005] lr: 1.0000e-02 eta: 21:39:48 time: 0.2239 data_time: 0.0034 loss: 1.3682 03/06 01:52:52 - mmengine - INFO - Epoch(train) [72][4100/5005] lr: 1.0000e-02 eta: 21:39:25 time: 0.2280 data_time: 0.0035 loss: 1.4693 03/06 01:53:16 - mmengine - INFO - Epoch(train) [72][4200/5005] lr: 1.0000e-02 eta: 21:39:03 time: 0.2308 data_time: 0.0031 loss: 1.3235 03/06 01:53:39 - mmengine - INFO - Epoch(train) [72][4300/5005] lr: 1.0000e-02 eta: 21:38:40 time: 0.2228 data_time: 0.0031 loss: 1.1512 03/06 01:54:02 - mmengine - INFO - Epoch(train) [72][4400/5005] lr: 1.0000e-02 eta: 21:38:17 time: 0.2275 data_time: 0.0030 loss: 1.2533 03/06 01:54:24 - mmengine - INFO - Epoch(train) [72][4500/5005] lr: 1.0000e-02 eta: 21:37:54 time: 0.2240 data_time: 0.0032 loss: 1.4223 03/06 01:54:47 - mmengine - INFO - Epoch(train) [72][4600/5005] lr: 1.0000e-02 eta: 21:37:31 time: 0.2254 data_time: 0.0029 loss: 1.4504 03/06 01:54:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:55:11 - mmengine - INFO - Epoch(train) [72][4700/5005] lr: 1.0000e-02 eta: 21:37:08 time: 0.2252 data_time: 0.0029 loss: 1.0869 03/06 01:55:34 - mmengine - INFO - Epoch(train) [72][4800/5005] lr: 1.0000e-02 eta: 21:36:45 time: 0.2448 data_time: 0.0032 loss: 1.2984 03/06 01:55:57 - mmengine - INFO - Epoch(train) [72][4900/5005] lr: 1.0000e-02 eta: 21:36:23 time: 0.2790 data_time: 0.0029 loss: 1.2470 03/06 01:56:26 - mmengine - INFO - Epoch(train) [72][5000/5005] lr: 1.0000e-02 eta: 21:36:06 time: 0.2733 data_time: 0.0029 loss: 1.1977 03/06 01:56:27 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:56:30 - mmengine - INFO - Saving checkpoint at 72 epochs 03/06 01:56:45 - mmengine - INFO - Epoch(val) [72][100/196] eta: 0:00:13 time: 0.0190 data_time: 0.0003 03/06 01:56:59 - mmengine - INFO - Epoch(val) [72][196/196] accuracy/top1: 71.8940 accuracy/top5: 91.0120 03/06 01:57:30 - mmengine - INFO - Epoch(train) [73][ 100/5005] lr: 1.0000e-02 eta: 21:35:50 time: 0.2250 data_time: 0.0034 loss: 1.0414 03/06 01:57:54 - mmengine - INFO - Epoch(train) [73][ 200/5005] lr: 1.0000e-02 eta: 21:35:28 time: 0.2286 data_time: 0.0035 loss: 1.2637 03/06 01:58:17 - mmengine - INFO - Epoch(train) [73][ 300/5005] lr: 1.0000e-02 eta: 21:35:05 time: 0.2214 data_time: 0.0033 loss: 1.3452 03/06 01:58:40 - mmengine - INFO - Epoch(train) [73][ 400/5005] lr: 1.0000e-02 eta: 21:34:42 time: 0.2224 data_time: 0.0036 loss: 1.1538 03/06 01:59:03 - mmengine - INFO - Epoch(train) [73][ 500/5005] lr: 1.0000e-02 eta: 21:34:19 time: 0.2356 data_time: 0.0029 loss: 1.1862 03/06 01:59:26 - mmengine - INFO - Epoch(train) [73][ 600/5005] lr: 1.0000e-02 eta: 21:33:56 time: 0.2322 data_time: 0.0030 loss: 1.3221 03/06 01:59:35 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 01:59:49 - mmengine - INFO - Epoch(train) [73][ 700/5005] lr: 1.0000e-02 eta: 21:33:34 time: 0.2250 data_time: 0.0030 loss: 1.3545 03/06 02:00:12 - mmengine - INFO - Epoch(train) [73][ 800/5005] lr: 1.0000e-02 eta: 21:33:10 time: 0.2231 data_time: 0.0032 loss: 1.2071 03/06 02:00:34 - mmengine - INFO - Epoch(train) [73][ 900/5005] lr: 1.0000e-02 eta: 21:32:47 time: 0.2238 data_time: 0.0033 loss: 1.1851 03/06 02:00:58 - mmengine - INFO - Epoch(train) [73][1000/5005] lr: 1.0000e-02 eta: 21:32:25 time: 0.2244 data_time: 0.0035 loss: 1.2086 03/06 02:01:21 - mmengine - INFO - Epoch(train) [73][1100/5005] lr: 1.0000e-02 eta: 21:32:02 time: 0.2302 data_time: 0.0035 loss: 1.3602 03/06 02:01:43 - mmengine - INFO - Epoch(train) [73][1200/5005] lr: 1.0000e-02 eta: 21:31:38 time: 0.2257 data_time: 0.0033 loss: 1.3072 03/06 02:02:06 - mmengine - INFO - Epoch(train) [73][1300/5005] lr: 1.0000e-02 eta: 21:31:15 time: 0.2277 data_time: 0.0035 loss: 1.3667 03/06 02:02:29 - mmengine - INFO - Epoch(train) [73][1400/5005] lr: 1.0000e-02 eta: 21:30:53 time: 0.2219 data_time: 0.0035 loss: 1.2567 03/06 02:02:52 - mmengine - INFO - Epoch(train) [73][1500/5005] lr: 1.0000e-02 eta: 21:30:30 time: 0.2261 data_time: 0.0030 loss: 1.1867 03/06 02:03:15 - mmengine - INFO - Epoch(train) [73][1600/5005] lr: 1.0000e-02 eta: 21:30:07 time: 0.2243 data_time: 0.0031 loss: 1.4764 03/06 02:03:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:03:38 - mmengine - INFO - Epoch(train) [73][1700/5005] lr: 1.0000e-02 eta: 21:29:44 time: 0.2268 data_time: 0.0031 loss: 1.0932 03/06 02:04:01 - mmengine - INFO - Epoch(train) [73][1800/5005] lr: 1.0000e-02 eta: 21:29:21 time: 0.2270 data_time: 0.0031 loss: 1.3476 03/06 02:04:24 - mmengine - INFO - Epoch(train) [73][1900/5005] lr: 1.0000e-02 eta: 21:28:58 time: 0.2239 data_time: 0.0032 loss: 1.2333 03/06 02:04:47 - mmengine - INFO - Epoch(train) [73][2000/5005] lr: 1.0000e-02 eta: 21:28:36 time: 0.2263 data_time: 0.0037 loss: 1.4142 03/06 02:05:10 - mmengine - INFO - Epoch(train) [73][2100/5005] lr: 1.0000e-02 eta: 21:28:12 time: 0.2219 data_time: 0.0032 loss: 1.3840 03/06 02:05:33 - mmengine - INFO - Epoch(train) [73][2200/5005] lr: 1.0000e-02 eta: 21:27:50 time: 0.2439 data_time: 0.0033 loss: 1.2135 03/06 02:05:56 - mmengine - INFO - Epoch(train) [73][2300/5005] lr: 1.0000e-02 eta: 21:27:27 time: 0.2328 data_time: 0.0031 loss: 1.4086 03/06 02:06:19 - mmengine - INFO - Epoch(train) [73][2400/5005] lr: 1.0000e-02 eta: 21:27:04 time: 0.2240 data_time: 0.0030 loss: 1.3892 03/06 02:06:42 - mmengine - INFO - Epoch(train) [73][2500/5005] lr: 1.0000e-02 eta: 21:26:41 time: 0.2284 data_time: 0.0030 loss: 1.1163 03/06 02:07:04 - mmengine - INFO - Epoch(train) [73][2600/5005] lr: 1.0000e-02 eta: 21:26:18 time: 0.2229 data_time: 0.0032 loss: 1.3188 03/06 02:07:14 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:07:28 - mmengine - INFO - Epoch(train) [73][2700/5005] lr: 1.0000e-02 eta: 21:25:55 time: 0.2266 data_time: 0.0029 loss: 1.5045 03/06 02:07:51 - mmengine - INFO - Epoch(train) [73][2800/5005] lr: 1.0000e-02 eta: 21:25:32 time: 0.2289 data_time: 0.0030 loss: 1.1540 03/06 02:08:14 - mmengine - INFO - Epoch(train) [73][2900/5005] lr: 1.0000e-02 eta: 21:25:09 time: 0.2238 data_time: 0.0029 loss: 1.4110 03/06 02:08:37 - mmengine - INFO - Epoch(train) [73][3000/5005] lr: 1.0000e-02 eta: 21:24:46 time: 0.2243 data_time: 0.0031 loss: 1.1163 03/06 02:09:00 - mmengine - INFO - Epoch(train) [73][3100/5005] lr: 1.0000e-02 eta: 21:24:24 time: 0.2214 data_time: 0.0035 loss: 1.1232 03/06 02:09:23 - mmengine - INFO - Epoch(train) [73][3200/5005] lr: 1.0000e-02 eta: 21:24:01 time: 0.2222 data_time: 0.0032 loss: 1.2828 03/06 02:09:46 - mmengine - INFO - Epoch(train) [73][3300/5005] lr: 1.0000e-02 eta: 21:23:38 time: 0.2449 data_time: 0.0034 loss: 1.3823 03/06 02:10:09 - mmengine - INFO - Epoch(train) [73][3400/5005] lr: 1.0000e-02 eta: 21:23:15 time: 0.2230 data_time: 0.0034 loss: 1.4711 03/06 02:10:32 - mmengine - INFO - Epoch(train) [73][3500/5005] lr: 1.0000e-02 eta: 21:22:53 time: 0.2469 data_time: 0.0031 loss: 1.4235 03/06 02:10:55 - mmengine - INFO - Epoch(train) [73][3600/5005] lr: 1.0000e-02 eta: 21:22:30 time: 0.2292 data_time: 0.0030 loss: 1.2783 03/06 02:11:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:11:18 - mmengine - INFO - Epoch(train) [73][3700/5005] lr: 1.0000e-02 eta: 21:22:06 time: 0.2234 data_time: 0.0030 loss: 1.0672 03/06 02:11:41 - mmengine - INFO - Epoch(train) [73][3800/5005] lr: 1.0000e-02 eta: 21:21:44 time: 0.2491 data_time: 0.0030 loss: 1.2868 03/06 02:12:04 - mmengine - INFO - Epoch(train) [73][3900/5005] lr: 1.0000e-02 eta: 21:21:21 time: 0.2308 data_time: 0.0034 loss: 1.4820 03/06 02:12:27 - mmengine - INFO - Epoch(train) [73][4000/5005] lr: 1.0000e-02 eta: 21:20:58 time: 0.2309 data_time: 0.0030 loss: 1.3348 03/06 02:12:50 - mmengine - INFO - Epoch(train) [73][4100/5005] lr: 1.0000e-02 eta: 21:20:35 time: 0.2256 data_time: 0.0032 loss: 1.4528 03/06 02:13:12 - mmengine - INFO - Epoch(train) [73][4200/5005] lr: 1.0000e-02 eta: 21:20:12 time: 0.2272 data_time: 0.0034 loss: 1.1400 03/06 02:13:35 - mmengine - INFO - Epoch(train) [73][4300/5005] lr: 1.0000e-02 eta: 21:19:49 time: 0.2239 data_time: 0.0031 loss: 1.4375 03/06 02:13:59 - mmengine - INFO - Epoch(train) [73][4400/5005] lr: 1.0000e-02 eta: 21:19:26 time: 0.2297 data_time: 0.0038 loss: 1.3546 03/06 02:14:21 - mmengine - INFO - Epoch(train) [73][4500/5005] lr: 1.0000e-02 eta: 21:19:03 time: 0.2201 data_time: 0.0029 loss: 1.3115 03/06 02:14:44 - mmengine - INFO - Epoch(train) [73][4600/5005] lr: 1.0000e-02 eta: 21:18:40 time: 0.2247 data_time: 0.0030 loss: 1.3908 03/06 02:14:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:15:07 - mmengine - INFO - Epoch(train) [73][4700/5005] lr: 1.0000e-02 eta: 21:18:17 time: 0.2217 data_time: 0.0032 loss: 1.3694 03/06 02:15:30 - mmengine - INFO - Epoch(train) [73][4800/5005] lr: 1.0000e-02 eta: 21:17:54 time: 0.2230 data_time: 0.0033 loss: 1.3527 03/06 02:15:54 - mmengine - INFO - Epoch(train) [73][4900/5005] lr: 1.0000e-02 eta: 21:17:32 time: 0.2835 data_time: 0.0027 loss: 1.2913 03/06 02:16:23 - mmengine - INFO - Epoch(train) [73][5000/5005] lr: 1.0000e-02 eta: 21:17:15 time: 0.2964 data_time: 0.0028 loss: 1.3280 03/06 02:16:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:16:27 - mmengine - INFO - Saving checkpoint at 73 epochs 03/06 02:16:42 - mmengine - INFO - Epoch(val) [73][100/196] eta: 0:00:13 time: 0.0201 data_time: 0.0004 03/06 02:16:56 - mmengine - INFO - Epoch(val) [73][196/196] accuracy/top1: 71.0040 accuracy/top5: 90.6160 03/06 02:17:27 - mmengine - INFO - Epoch(train) [74][ 100/5005] lr: 1.0000e-02 eta: 21:16:59 time: 0.2243 data_time: 0.0032 loss: 1.3630 03/06 02:17:50 - mmengine - INFO - Epoch(train) [74][ 200/5005] lr: 1.0000e-02 eta: 21:16:36 time: 0.2312 data_time: 0.0037 loss: 1.2498 03/06 02:18:13 - mmengine - INFO - Epoch(train) [74][ 300/5005] lr: 1.0000e-02 eta: 21:16:13 time: 0.2246 data_time: 0.0029 loss: 1.3821 03/06 02:18:37 - mmengine - INFO - Epoch(train) [74][ 400/5005] lr: 1.0000e-02 eta: 21:15:51 time: 0.2215 data_time: 0.0030 loss: 1.1209 03/06 02:19:00 - mmengine - INFO - Epoch(train) [74][ 500/5005] lr: 1.0000e-02 eta: 21:15:28 time: 0.2291 data_time: 0.0032 loss: 1.2593 03/06 02:19:22 - mmengine - INFO - Epoch(train) [74][ 600/5005] lr: 1.0000e-02 eta: 21:15:05 time: 0.2238 data_time: 0.0031 loss: 1.2915 03/06 02:19:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:19:46 - mmengine - INFO - Epoch(train) [74][ 700/5005] lr: 1.0000e-02 eta: 21:14:42 time: 0.2618 data_time: 0.0032 loss: 1.1828 03/06 02:20:09 - mmengine - INFO - Epoch(train) [74][ 800/5005] lr: 1.0000e-02 eta: 21:14:20 time: 0.2244 data_time: 0.0030 loss: 1.3210 03/06 02:20:32 - mmengine - INFO - Epoch(train) [74][ 900/5005] lr: 1.0000e-02 eta: 21:13:57 time: 0.2253 data_time: 0.0029 loss: 1.3114 03/06 02:20:55 - mmengine - INFO - Epoch(train) [74][1000/5005] lr: 1.0000e-02 eta: 21:13:33 time: 0.2215 data_time: 0.0029 loss: 1.2426 03/06 02:21:17 - mmengine - INFO - Epoch(train) [74][1100/5005] lr: 1.0000e-02 eta: 21:13:11 time: 0.2269 data_time: 0.0030 loss: 1.2068 03/06 02:21:41 - mmengine - INFO - Epoch(train) [74][1200/5005] lr: 1.0000e-02 eta: 21:12:48 time: 0.2231 data_time: 0.0030 loss: 1.1647 03/06 02:22:03 - mmengine - INFO - Epoch(train) [74][1300/5005] lr: 1.0000e-02 eta: 21:12:25 time: 0.2217 data_time: 0.0030 loss: 1.3948 03/06 02:22:26 - mmengine - INFO - Epoch(train) [74][1400/5005] lr: 1.0000e-02 eta: 21:12:02 time: 0.2271 data_time: 0.0038 loss: 1.2998 03/06 02:22:49 - mmengine - INFO - Epoch(train) [74][1500/5005] lr: 1.0000e-02 eta: 21:11:38 time: 0.2268 data_time: 0.0034 loss: 1.4422 03/06 02:23:12 - mmengine - INFO - Epoch(train) [74][1600/5005] lr: 1.0000e-02 eta: 21:11:16 time: 0.2579 data_time: 0.0030 loss: 1.3703 03/06 02:23:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:23:35 - mmengine - INFO - Epoch(train) [74][1700/5005] lr: 1.0000e-02 eta: 21:10:53 time: 0.2231 data_time: 0.0034 loss: 1.3472 03/06 02:23:58 - mmengine - INFO - Epoch(train) [74][1800/5005] lr: 1.0000e-02 eta: 21:10:30 time: 0.2218 data_time: 0.0032 loss: 1.2657 03/06 02:24:21 - mmengine - INFO - Epoch(train) [74][1900/5005] lr: 1.0000e-02 eta: 21:10:07 time: 0.2436 data_time: 0.0031 loss: 1.2337 03/06 02:24:44 - mmengine - INFO - Epoch(train) [74][2000/5005] lr: 1.0000e-02 eta: 21:09:44 time: 0.2239 data_time: 0.0030 loss: 1.1582 03/06 02:25:07 - mmengine - INFO - Epoch(train) [74][2100/5005] lr: 1.0000e-02 eta: 21:09:22 time: 0.2287 data_time: 0.0035 loss: 1.5395 03/06 02:25:30 - mmengine - INFO - Epoch(train) [74][2200/5005] lr: 1.0000e-02 eta: 21:08:58 time: 0.2251 data_time: 0.0031 loss: 1.2370 03/06 02:25:52 - mmengine - INFO - Epoch(train) [74][2300/5005] lr: 1.0000e-02 eta: 21:08:35 time: 0.2286 data_time: 0.0032 loss: 1.2250 03/06 02:26:16 - mmengine - INFO - Epoch(train) [74][2400/5005] lr: 1.0000e-02 eta: 21:08:13 time: 0.2270 data_time: 0.0031 loss: 1.3615 03/06 02:26:39 - mmengine - INFO - Epoch(train) [74][2500/5005] lr: 1.0000e-02 eta: 21:07:50 time: 0.2445 data_time: 0.0032 loss: 1.4776 03/06 02:27:01 - mmengine - INFO - Epoch(train) [74][2600/5005] lr: 1.0000e-02 eta: 21:07:27 time: 0.2222 data_time: 0.0034 loss: 1.1377 03/06 02:27:09 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:27:24 - mmengine - INFO - Epoch(train) [74][2700/5005] lr: 1.0000e-02 eta: 21:07:04 time: 0.2415 data_time: 0.0031 loss: 1.2877 03/06 02:27:48 - mmengine - INFO - Epoch(train) [74][2800/5005] lr: 1.0000e-02 eta: 21:06:41 time: 0.2244 data_time: 0.0032 loss: 1.1821 03/06 02:28:11 - mmengine - INFO - Epoch(train) [74][2900/5005] lr: 1.0000e-02 eta: 21:06:18 time: 0.2268 data_time: 0.0035 loss: 1.2357 03/06 02:28:34 - mmengine - INFO - Epoch(train) [74][3000/5005] lr: 1.0000e-02 eta: 21:05:55 time: 0.2469 data_time: 0.0030 loss: 1.5112 03/06 02:28:57 - mmengine - INFO - Epoch(train) [74][3100/5005] lr: 1.0000e-02 eta: 21:05:33 time: 0.2265 data_time: 0.0032 loss: 1.2931 03/06 02:29:20 - mmengine - INFO - Epoch(train) [74][3200/5005] lr: 1.0000e-02 eta: 21:05:10 time: 0.2222 data_time: 0.0029 loss: 1.3634 03/06 02:29:43 - mmengine - INFO - Epoch(train) [74][3300/5005] lr: 1.0000e-02 eta: 21:04:47 time: 0.2245 data_time: 0.0034 loss: 1.2283 03/06 02:30:05 - mmengine - INFO - Epoch(train) [74][3400/5005] lr: 1.0000e-02 eta: 21:04:24 time: 0.2255 data_time: 0.0033 loss: 1.3783 03/06 02:30:28 - mmengine - INFO - Epoch(train) [74][3500/5005] lr: 1.0000e-02 eta: 21:04:01 time: 0.2205 data_time: 0.0034 loss: 1.2162 03/06 02:30:51 - mmengine - INFO - Epoch(train) [74][3600/5005] lr: 1.0000e-02 eta: 21:03:38 time: 0.2284 data_time: 0.0031 loss: 1.2110 03/06 02:31:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:31:14 - mmengine - INFO - Epoch(train) [74][3700/5005] lr: 1.0000e-02 eta: 21:03:15 time: 0.2257 data_time: 0.0034 loss: 1.3172 03/06 02:31:37 - mmengine - INFO - Epoch(train) [74][3800/5005] lr: 1.0000e-02 eta: 21:02:52 time: 0.2235 data_time: 0.0029 loss: 1.2446 03/06 02:32:00 - mmengine - INFO - Epoch(train) [74][3900/5005] lr: 1.0000e-02 eta: 21:02:29 time: 0.2209 data_time: 0.0031 loss: 1.2663 03/06 02:32:23 - mmengine - INFO - Epoch(train) [74][4000/5005] lr: 1.0000e-02 eta: 21:02:06 time: 0.2300 data_time: 0.0037 loss: 1.2649 03/06 02:32:46 - mmengine - INFO - Epoch(train) [74][4100/5005] lr: 1.0000e-02 eta: 21:01:43 time: 0.2271 data_time: 0.0032 loss: 1.1823 03/06 02:33:09 - mmengine - INFO - Epoch(train) [74][4200/5005] lr: 1.0000e-02 eta: 21:01:21 time: 0.2224 data_time: 0.0035 loss: 1.3546 03/06 02:33:32 - mmengine - INFO - Epoch(train) [74][4300/5005] lr: 1.0000e-02 eta: 21:00:57 time: 0.2224 data_time: 0.0031 loss: 1.3268 03/06 02:33:55 - mmengine - INFO - Epoch(train) [74][4400/5005] lr: 1.0000e-02 eta: 21:00:34 time: 0.2245 data_time: 0.0030 loss: 1.1731 03/06 02:34:18 - mmengine - INFO - Epoch(train) [74][4500/5005] lr: 1.0000e-02 eta: 21:00:12 time: 0.2209 data_time: 0.0031 loss: 1.4272 03/06 02:34:41 - mmengine - INFO - Epoch(train) [74][4600/5005] lr: 1.0000e-02 eta: 20:59:49 time: 0.2282 data_time: 0.0032 loss: 1.2806 03/06 02:34:49 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:35:04 - mmengine - INFO - Epoch(train) [74][4700/5005] lr: 1.0000e-02 eta: 20:59:26 time: 0.2245 data_time: 0.0031 loss: 1.2670 03/06 02:35:26 - mmengine - INFO - Epoch(train) [74][4800/5005] lr: 1.0000e-02 eta: 20:59:02 time: 0.2270 data_time: 0.0035 loss: 1.2873 03/06 02:35:50 - mmengine - INFO - Epoch(train) [74][4900/5005] lr: 1.0000e-02 eta: 20:58:41 time: 0.2924 data_time: 0.0029 loss: 1.3215 03/06 02:36:19 - mmengine - INFO - Epoch(train) [74][5000/5005] lr: 1.0000e-02 eta: 20:58:23 time: 0.2791 data_time: 0.0032 loss: 1.2402 03/06 02:36:21 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:36:24 - mmengine - INFO - Saving checkpoint at 74 epochs 03/06 02:36:38 - mmengine - INFO - Epoch(val) [74][100/196] eta: 0:00:12 time: 0.0247 data_time: 0.0004 03/06 02:36:52 - mmengine - INFO - Epoch(val) [74][196/196] accuracy/top1: 71.7180 accuracy/top5: 90.8720 03/06 02:37:25 - mmengine - INFO - Epoch(train) [75][ 100/5005] lr: 1.0000e-02 eta: 20:58:08 time: 0.2263 data_time: 0.0035 loss: 1.1323 03/06 02:37:48 - mmengine - INFO - Epoch(train) [75][ 200/5005] lr: 1.0000e-02 eta: 20:57:45 time: 0.2259 data_time: 0.0038 loss: 1.4826 03/06 02:38:10 - mmengine - INFO - Epoch(train) [75][ 300/5005] lr: 1.0000e-02 eta: 20:57:22 time: 0.2231 data_time: 0.0038 loss: 1.2374 03/06 02:38:33 - mmengine - INFO - Epoch(train) [75][ 400/5005] lr: 1.0000e-02 eta: 20:56:59 time: 0.2291 data_time: 0.0031 loss: 1.2523 03/06 02:38:57 - mmengine - INFO - Epoch(train) [75][ 500/5005] lr: 1.0000e-02 eta: 20:56:37 time: 0.2550 data_time: 0.0029 loss: 1.3837 03/06 02:39:19 - mmengine - INFO - Epoch(train) [75][ 600/5005] lr: 1.0000e-02 eta: 20:56:14 time: 0.2343 data_time: 0.0035 loss: 1.2435 03/06 02:39:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:39:42 - mmengine - INFO - Epoch(train) [75][ 700/5005] lr: 1.0000e-02 eta: 20:55:50 time: 0.2213 data_time: 0.0030 loss: 1.3871 03/06 02:40:05 - mmengine - INFO - Epoch(train) [75][ 800/5005] lr: 1.0000e-02 eta: 20:55:27 time: 0.2296 data_time: 0.0035 loss: 1.2071 03/06 02:40:28 - mmengine - INFO - Epoch(train) [75][ 900/5005] lr: 1.0000e-02 eta: 20:55:04 time: 0.2625 data_time: 0.0029 loss: 1.2034 03/06 02:40:51 - mmengine - INFO - Epoch(train) [75][1000/5005] lr: 1.0000e-02 eta: 20:54:41 time: 0.2228 data_time: 0.0033 loss: 1.4320 03/06 02:41:13 - mmengine - INFO - Epoch(train) [75][1100/5005] lr: 1.0000e-02 eta: 20:54:18 time: 0.2303 data_time: 0.0031 loss: 1.2329 03/06 02:41:36 - mmengine - INFO - Epoch(train) [75][1200/5005] lr: 1.0000e-02 eta: 20:53:55 time: 0.2223 data_time: 0.0031 loss: 1.2227 03/06 02:41:59 - mmengine - INFO - Epoch(train) [75][1300/5005] lr: 1.0000e-02 eta: 20:53:32 time: 0.2509 data_time: 0.0033 loss: 1.2316 03/06 02:42:23 - mmengine - INFO - Epoch(train) [75][1400/5005] lr: 1.0000e-02 eta: 20:53:10 time: 0.2266 data_time: 0.0031 loss: 1.1725 03/06 02:42:45 - mmengine - INFO - Epoch(train) [75][1500/5005] lr: 1.0000e-02 eta: 20:52:47 time: 0.2267 data_time: 0.0040 loss: 1.2646 03/06 02:43:08 - mmengine - INFO - Epoch(train) [75][1600/5005] lr: 1.0000e-02 eta: 20:52:24 time: 0.2236 data_time: 0.0036 loss: 1.4142 03/06 02:43:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:43:31 - mmengine - INFO - Epoch(train) [75][1700/5005] lr: 1.0000e-02 eta: 20:52:01 time: 0.2265 data_time: 0.0033 loss: 1.2057 03/06 02:43:54 - mmengine - INFO - Epoch(train) [75][1800/5005] lr: 1.0000e-02 eta: 20:51:38 time: 0.2235 data_time: 0.0030 loss: 1.4163 03/06 02:44:17 - mmengine - INFO - Epoch(train) [75][1900/5005] lr: 1.0000e-02 eta: 20:51:15 time: 0.2237 data_time: 0.0032 loss: 1.2104 03/06 02:44:40 - mmengine - INFO - Epoch(train) [75][2000/5005] lr: 1.0000e-02 eta: 20:50:52 time: 0.2285 data_time: 0.0030 loss: 1.2707 03/06 02:45:03 - mmengine - INFO - Epoch(train) [75][2100/5005] lr: 1.0000e-02 eta: 20:50:29 time: 0.2267 data_time: 0.0037 loss: 1.2190 03/06 02:45:26 - mmengine - INFO - Epoch(train) [75][2200/5005] lr: 1.0000e-02 eta: 20:50:06 time: 0.2267 data_time: 0.0032 loss: 1.3973 03/06 02:45:49 - mmengine - INFO - Epoch(train) [75][2300/5005] lr: 1.0000e-02 eta: 20:49:43 time: 0.2238 data_time: 0.0034 loss: 1.2045 03/06 02:46:11 - mmengine - INFO - Epoch(train) [75][2400/5005] lr: 1.0000e-02 eta: 20:49:20 time: 0.2301 data_time: 0.0038 loss: 1.3736 03/06 02:46:35 - mmengine - INFO - Epoch(train) [75][2500/5005] lr: 1.0000e-02 eta: 20:48:57 time: 0.2391 data_time: 0.0033 loss: 1.4066 03/06 02:46:58 - mmengine - INFO - Epoch(train) [75][2600/5005] lr: 1.0000e-02 eta: 20:48:35 time: 0.2254 data_time: 0.0030 loss: 1.1731 03/06 02:47:05 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:47:21 - mmengine - INFO - Epoch(train) [75][2700/5005] lr: 1.0000e-02 eta: 20:48:12 time: 0.2234 data_time: 0.0034 loss: 1.1812 03/06 02:47:43 - mmengine - INFO - Epoch(train) [75][2800/5005] lr: 1.0000e-02 eta: 20:47:48 time: 0.2192 data_time: 0.0030 loss: 1.3529 03/06 02:48:06 - mmengine - INFO - Epoch(train) [75][2900/5005] lr: 1.0000e-02 eta: 20:47:25 time: 0.2235 data_time: 0.0031 loss: 1.5825 03/06 02:48:29 - mmengine - INFO - Epoch(train) [75][3000/5005] lr: 1.0000e-02 eta: 20:47:03 time: 0.2232 data_time: 0.0030 loss: 1.1760 03/06 02:48:52 - mmengine - INFO - Epoch(train) [75][3100/5005] lr: 1.0000e-02 eta: 20:46:40 time: 0.2226 data_time: 0.0029 loss: 1.2732 03/06 02:49:15 - mmengine - INFO - Epoch(train) [75][3200/5005] lr: 1.0000e-02 eta: 20:46:17 time: 0.2245 data_time: 0.0033 loss: 1.2602 03/06 02:49:38 - mmengine - INFO - Epoch(train) [75][3300/5005] lr: 1.0000e-02 eta: 20:45:54 time: 0.2370 data_time: 0.0032 loss: 1.2772 03/06 02:50:01 - mmengine - INFO - Epoch(train) [75][3400/5005] lr: 1.0000e-02 eta: 20:45:31 time: 0.2262 data_time: 0.0030 loss: 1.2934 03/06 02:50:24 - mmengine - INFO - Epoch(train) [75][3500/5005] lr: 1.0000e-02 eta: 20:45:08 time: 0.2280 data_time: 0.0029 loss: 1.1202 03/06 02:50:46 - mmengine - INFO - Epoch(train) [75][3600/5005] lr: 1.0000e-02 eta: 20:44:45 time: 0.2217 data_time: 0.0035 loss: 1.5326 03/06 02:50:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:51:09 - mmengine - INFO - Epoch(train) [75][3700/5005] lr: 1.0000e-02 eta: 20:44:22 time: 0.2230 data_time: 0.0038 loss: 1.4499 03/06 02:51:32 - mmengine - INFO - Epoch(train) [75][3800/5005] lr: 1.0000e-02 eta: 20:43:59 time: 0.2268 data_time: 0.0031 loss: 1.2552 03/06 02:51:56 - mmengine - INFO - Epoch(train) [75][3900/5005] lr: 1.0000e-02 eta: 20:43:36 time: 0.2243 data_time: 0.0034 loss: 1.2039 03/06 02:52:18 - mmengine - INFO - Epoch(train) [75][4000/5005] lr: 1.0000e-02 eta: 20:43:13 time: 0.2256 data_time: 0.0035 loss: 1.3366 03/06 02:52:41 - mmengine - INFO - Epoch(train) [75][4100/5005] lr: 1.0000e-02 eta: 20:42:50 time: 0.2264 data_time: 0.0032 loss: 1.2671 03/06 02:53:04 - mmengine - INFO - Epoch(train) [75][4200/5005] lr: 1.0000e-02 eta: 20:42:27 time: 0.2287 data_time: 0.0032 loss: 1.3812 03/06 02:53:27 - mmengine - INFO - Epoch(train) [75][4300/5005] lr: 1.0000e-02 eta: 20:42:05 time: 0.2260 data_time: 0.0036 loss: 1.3317 03/06 02:53:50 - mmengine - INFO - Epoch(train) [75][4400/5005] lr: 1.0000e-02 eta: 20:41:42 time: 0.2262 data_time: 0.0031 loss: 1.1928 03/06 02:54:13 - mmengine - INFO - Epoch(train) [75][4500/5005] lr: 1.0000e-02 eta: 20:41:19 time: 0.2264 data_time: 0.0032 loss: 1.2108 03/06 02:54:36 - mmengine - INFO - Epoch(train) [75][4600/5005] lr: 1.0000e-02 eta: 20:40:56 time: 0.2243 data_time: 0.0029 loss: 1.3538 03/06 02:54:43 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:54:59 - mmengine - INFO - Epoch(train) [75][4700/5005] lr: 1.0000e-02 eta: 20:40:33 time: 0.2232 data_time: 0.0032 loss: 1.3682 03/06 02:55:22 - mmengine - INFO - Epoch(train) [75][4800/5005] lr: 1.0000e-02 eta: 20:40:10 time: 0.2207 data_time: 0.0032 loss: 1.2375 03/06 02:55:46 - mmengine - INFO - Epoch(train) [75][4900/5005] lr: 1.0000e-02 eta: 20:39:48 time: 0.3002 data_time: 0.0033 loss: 1.2370 03/06 02:56:15 - mmengine - INFO - Epoch(train) [75][5000/5005] lr: 1.0000e-02 eta: 20:39:30 time: 0.2970 data_time: 0.0030 loss: 1.1566 03/06 02:56:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:56:19 - mmengine - INFO - Saving checkpoint at 75 epochs 03/06 02:56:34 - mmengine - INFO - Epoch(val) [75][100/196] eta: 0:00:13 time: 0.0200 data_time: 0.0004 03/06 02:56:48 - mmengine - INFO - Epoch(val) [75][196/196] accuracy/top1: 71.6960 accuracy/top5: 90.7580 03/06 02:57:19 - mmengine - INFO - Epoch(train) [76][ 100/5005] lr: 1.0000e-02 eta: 20:39:14 time: 0.2228 data_time: 0.0033 loss: 1.2380 03/06 02:57:42 - mmengine - INFO - Epoch(train) [76][ 200/5005] lr: 1.0000e-02 eta: 20:38:51 time: 0.2259 data_time: 0.0035 loss: 1.2497 03/06 02:58:06 - mmengine - INFO - Epoch(train) [76][ 300/5005] lr: 1.0000e-02 eta: 20:38:28 time: 0.2225 data_time: 0.0031 loss: 1.1643 03/06 02:58:29 - mmengine - INFO - Epoch(train) [76][ 400/5005] lr: 1.0000e-02 eta: 20:38:06 time: 0.2209 data_time: 0.0035 loss: 1.3766 03/06 02:58:52 - mmengine - INFO - Epoch(train) [76][ 500/5005] lr: 1.0000e-02 eta: 20:37:43 time: 0.2244 data_time: 0.0034 loss: 1.1498 03/06 02:59:15 - mmengine - INFO - Epoch(train) [76][ 600/5005] lr: 1.0000e-02 eta: 20:37:20 time: 0.2274 data_time: 0.0034 loss: 1.2638 03/06 02:59:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 02:59:38 - mmengine - INFO - Epoch(train) [76][ 700/5005] lr: 1.0000e-02 eta: 20:36:57 time: 0.2263 data_time: 0.0032 loss: 1.2794 03/06 03:00:01 - mmengine - INFO - Epoch(train) [76][ 800/5005] lr: 1.0000e-02 eta: 20:36:34 time: 0.2246 data_time: 0.0030 loss: 1.1443 03/06 03:00:24 - mmengine - INFO - Epoch(train) [76][ 900/5005] lr: 1.0000e-02 eta: 20:36:11 time: 0.2279 data_time: 0.0031 loss: 1.1655 03/06 03:00:47 - mmengine - INFO - Epoch(train) [76][1000/5005] lr: 1.0000e-02 eta: 20:35:48 time: 0.2245 data_time: 0.0032 loss: 1.2384 03/06 03:01:10 - mmengine - INFO - Epoch(train) [76][1100/5005] lr: 1.0000e-02 eta: 20:35:25 time: 0.2240 data_time: 0.0036 loss: 1.1659 03/06 03:01:33 - mmengine - INFO - Epoch(train) [76][1200/5005] lr: 1.0000e-02 eta: 20:35:02 time: 0.2256 data_time: 0.0033 loss: 1.1796 03/06 03:01:55 - mmengine - INFO - Epoch(train) [76][1300/5005] lr: 1.0000e-02 eta: 20:34:39 time: 0.2236 data_time: 0.0034 loss: 1.1804 03/06 03:02:18 - mmengine - INFO - Epoch(train) [76][1400/5005] lr: 1.0000e-02 eta: 20:34:16 time: 0.2238 data_time: 0.0033 loss: 1.4700 03/06 03:02:42 - mmengine - INFO - Epoch(train) [76][1500/5005] lr: 1.0000e-02 eta: 20:33:54 time: 0.2237 data_time: 0.0031 loss: 1.2694 03/06 03:03:05 - mmengine - INFO - Epoch(train) [76][1600/5005] lr: 1.0000e-02 eta: 20:33:31 time: 0.2424 data_time: 0.0031 loss: 1.2843 03/06 03:03:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:03:28 - mmengine - INFO - Epoch(train) [76][1700/5005] lr: 1.0000e-02 eta: 20:33:08 time: 0.2239 data_time: 0.0032 loss: 1.1955 03/06 03:03:50 - mmengine - INFO - Epoch(train) [76][1800/5005] lr: 1.0000e-02 eta: 20:32:45 time: 0.2254 data_time: 0.0031 loss: 1.1951 03/06 03:04:14 - mmengine - INFO - Epoch(train) [76][1900/5005] lr: 1.0000e-02 eta: 20:32:22 time: 0.2231 data_time: 0.0032 loss: 1.4397 03/06 03:04:37 - mmengine - INFO - Epoch(train) [76][2000/5005] lr: 1.0000e-02 eta: 20:31:59 time: 0.2286 data_time: 0.0031 loss: 1.3026 03/06 03:04:59 - mmengine - INFO - Epoch(train) [76][2100/5005] lr: 1.0000e-02 eta: 20:31:36 time: 0.2252 data_time: 0.0033 loss: 1.3368 03/06 03:05:22 - mmengine - INFO - Epoch(train) [76][2200/5005] lr: 1.0000e-02 eta: 20:31:13 time: 0.2250 data_time: 0.0028 loss: 1.3016 03/06 03:05:45 - mmengine - INFO - Epoch(train) [76][2300/5005] lr: 1.0000e-02 eta: 20:30:50 time: 0.2279 data_time: 0.0031 loss: 1.4596 03/06 03:06:08 - mmengine - INFO - Epoch(train) [76][2400/5005] lr: 1.0000e-02 eta: 20:30:28 time: 0.2408 data_time: 0.0029 loss: 1.1254 03/06 03:06:31 - mmengine - INFO - Epoch(train) [76][2500/5005] lr: 1.0000e-02 eta: 20:30:04 time: 0.2270 data_time: 0.0028 loss: 1.4814 03/06 03:06:54 - mmengine - INFO - Epoch(train) [76][2600/5005] lr: 1.0000e-02 eta: 20:29:41 time: 0.2264 data_time: 0.0037 loss: 1.2135 03/06 03:07:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:07:17 - mmengine - INFO - Epoch(train) [76][2700/5005] lr: 1.0000e-02 eta: 20:29:18 time: 0.2238 data_time: 0.0031 loss: 1.3386 03/06 03:07:40 - mmengine - INFO - Epoch(train) [76][2800/5005] lr: 1.0000e-02 eta: 20:28:56 time: 0.2267 data_time: 0.0029 loss: 1.3255 03/06 03:08:03 - mmengine - INFO - Epoch(train) [76][2900/5005] lr: 1.0000e-02 eta: 20:28:33 time: 0.2289 data_time: 0.0031 loss: 1.2909 03/06 03:08:26 - mmengine - INFO - Epoch(train) [76][3000/5005] lr: 1.0000e-02 eta: 20:28:10 time: 0.2297 data_time: 0.0030 loss: 1.2040 03/06 03:08:49 - mmengine - INFO - Epoch(train) [76][3100/5005] lr: 1.0000e-02 eta: 20:27:47 time: 0.2290 data_time: 0.0034 loss: 1.3374 03/06 03:09:12 - mmengine - INFO - Epoch(train) [76][3200/5005] lr: 1.0000e-02 eta: 20:27:24 time: 0.2229 data_time: 0.0032 loss: 1.2852 03/06 03:09:35 - mmengine - INFO - Epoch(train) [76][3300/5005] lr: 1.0000e-02 eta: 20:27:02 time: 0.2471 data_time: 0.0034 loss: 1.3785 03/06 03:09:58 - mmengine - INFO - Epoch(train) [76][3400/5005] lr: 1.0000e-02 eta: 20:26:39 time: 0.2272 data_time: 0.0031 loss: 1.2726 03/06 03:10:21 - mmengine - INFO - Epoch(train) [76][3500/5005] lr: 1.0000e-02 eta: 20:26:16 time: 0.2279 data_time: 0.0034 loss: 1.3193 03/06 03:10:45 - mmengine - INFO - Epoch(train) [76][3600/5005] lr: 1.0000e-02 eta: 20:25:53 time: 0.2253 data_time: 0.0034 loss: 1.3073 03/06 03:10:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:11:08 - mmengine - INFO - Epoch(train) [76][3700/5005] lr: 1.0000e-02 eta: 20:25:30 time: 0.2224 data_time: 0.0031 loss: 1.4251 03/06 03:11:31 - mmengine - INFO - Epoch(train) [76][3800/5005] lr: 1.0000e-02 eta: 20:25:07 time: 0.2343 data_time: 0.0031 loss: 1.3262 03/06 03:11:53 - mmengine - INFO - Epoch(train) [76][3900/5005] lr: 1.0000e-02 eta: 20:24:44 time: 0.2271 data_time: 0.0029 loss: 1.0569 03/06 03:12:16 - mmengine - INFO - Epoch(train) [76][4000/5005] lr: 1.0000e-02 eta: 20:24:21 time: 0.2242 data_time: 0.0031 loss: 1.2083 03/06 03:12:39 - mmengine - INFO - Epoch(train) [76][4100/5005] lr: 1.0000e-02 eta: 20:23:59 time: 0.2273 data_time: 0.0029 loss: 1.2311 03/06 03:13:03 - mmengine - INFO - Epoch(train) [76][4200/5005] lr: 1.0000e-02 eta: 20:23:36 time: 0.2463 data_time: 0.0031 loss: 1.1914 03/06 03:13:26 - mmengine - INFO - Epoch(train) [76][4300/5005] lr: 1.0000e-02 eta: 20:23:13 time: 0.2242 data_time: 0.0030 loss: 1.2214 03/06 03:13:48 - mmengine - INFO - Epoch(train) [76][4400/5005] lr: 1.0000e-02 eta: 20:22:50 time: 0.2261 data_time: 0.0033 loss: 1.2552 03/06 03:14:11 - mmengine - INFO - Epoch(train) [76][4500/5005] lr: 1.0000e-02 eta: 20:22:27 time: 0.2244 data_time: 0.0033 loss: 1.3056 03/06 03:14:34 - mmengine - INFO - Epoch(train) [76][4600/5005] lr: 1.0000e-02 eta: 20:22:04 time: 0.2319 data_time: 0.0028 loss: 1.2508 03/06 03:14:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:14:57 - mmengine - INFO - Epoch(train) [76][4700/5005] lr: 1.0000e-02 eta: 20:21:41 time: 0.2233 data_time: 0.0030 loss: 1.2294 03/06 03:15:20 - mmengine - INFO - Epoch(train) [76][4800/5005] lr: 1.0000e-02 eta: 20:21:18 time: 0.2232 data_time: 0.0029 loss: 1.2069 03/06 03:15:44 - mmengine - INFO - Epoch(train) [76][4900/5005] lr: 1.0000e-02 eta: 20:20:56 time: 0.2910 data_time: 0.0029 loss: 1.2499 03/06 03:16:13 - mmengine - INFO - Epoch(train) [76][5000/5005] lr: 1.0000e-02 eta: 20:20:38 time: 0.2538 data_time: 0.0031 loss: 1.3571 03/06 03:16:14 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:16:17 - mmengine - INFO - Saving checkpoint at 76 epochs 03/06 03:16:32 - mmengine - INFO - Epoch(val) [76][100/196] eta: 0:00:13 time: 0.0194 data_time: 0.0004 03/06 03:16:46 - mmengine - INFO - Epoch(val) [76][196/196] accuracy/top1: 71.5520 accuracy/top5: 90.7580 03/06 03:17:17 - mmengine - INFO - Epoch(train) [77][ 100/5005] lr: 1.0000e-02 eta: 20:20:22 time: 0.2400 data_time: 0.0032 loss: 1.4109 03/06 03:17:41 - mmengine - INFO - Epoch(train) [77][ 200/5005] lr: 1.0000e-02 eta: 20:19:59 time: 0.2255 data_time: 0.0040 loss: 1.2240 03/06 03:18:04 - mmengine - INFO - Epoch(train) [77][ 300/5005] lr: 1.0000e-02 eta: 20:19:36 time: 0.2240 data_time: 0.0030 loss: 1.3440 03/06 03:18:27 - mmengine - INFO - Epoch(train) [77][ 400/5005] lr: 1.0000e-02 eta: 20:19:13 time: 0.2248 data_time: 0.0032 loss: 1.2531 03/06 03:18:49 - mmengine - INFO - Epoch(train) [77][ 500/5005] lr: 1.0000e-02 eta: 20:18:50 time: 0.2255 data_time: 0.0032 loss: 1.4061 03/06 03:19:12 - mmengine - INFO - Epoch(train) [77][ 600/5005] lr: 1.0000e-02 eta: 20:18:27 time: 0.2217 data_time: 0.0033 loss: 1.1832 03/06 03:19:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:19:35 - mmengine - INFO - Epoch(train) [77][ 700/5005] lr: 1.0000e-02 eta: 20:18:04 time: 0.2250 data_time: 0.0031 loss: 1.4001 03/06 03:19:58 - mmengine - INFO - Epoch(train) [77][ 800/5005] lr: 1.0000e-02 eta: 20:17:41 time: 0.2228 data_time: 0.0030 loss: 1.1999 03/06 03:20:21 - mmengine - INFO - Epoch(train) [77][ 900/5005] lr: 1.0000e-02 eta: 20:17:18 time: 0.2249 data_time: 0.0032 loss: 1.3720 03/06 03:20:44 - mmengine - INFO - Epoch(train) [77][1000/5005] lr: 1.0000e-02 eta: 20:16:55 time: 0.2261 data_time: 0.0029 loss: 1.3056 03/06 03:21:07 - mmengine - INFO - Epoch(train) [77][1100/5005] lr: 1.0000e-02 eta: 20:16:32 time: 0.2212 data_time: 0.0032 loss: 1.2777 03/06 03:21:29 - mmengine - INFO - Epoch(train) [77][1200/5005] lr: 1.0000e-02 eta: 20:16:09 time: 0.2249 data_time: 0.0031 loss: 1.4553 03/06 03:21:52 - mmengine - INFO - Epoch(train) [77][1300/5005] lr: 1.0000e-02 eta: 20:15:46 time: 0.2251 data_time: 0.0032 loss: 1.2833 03/06 03:22:15 - mmengine - INFO - Epoch(train) [77][1400/5005] lr: 1.0000e-02 eta: 20:15:23 time: 0.2243 data_time: 0.0031 loss: 1.6374 03/06 03:22:38 - mmengine - INFO - Epoch(train) [77][1500/5005] lr: 1.0000e-02 eta: 20:15:00 time: 0.2215 data_time: 0.0029 loss: 1.3315 03/06 03:23:01 - mmengine - INFO - Epoch(train) [77][1600/5005] lr: 1.0000e-02 eta: 20:14:37 time: 0.2258 data_time: 0.0028 loss: 1.3071 03/06 03:23:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:23:24 - mmengine - INFO - Epoch(train) [77][1700/5005] lr: 1.0000e-02 eta: 20:14:14 time: 0.2247 data_time: 0.0032 loss: 1.2759 03/06 03:23:47 - mmengine - INFO - Epoch(train) [77][1800/5005] lr: 1.0000e-02 eta: 20:13:51 time: 0.2239 data_time: 0.0032 loss: 1.2572 03/06 03:24:10 - mmengine - INFO - Epoch(train) [77][1900/5005] lr: 1.0000e-02 eta: 20:13:28 time: 0.2244 data_time: 0.0031 loss: 1.3912 03/06 03:24:33 - mmengine - INFO - Epoch(train) [77][2000/5005] lr: 1.0000e-02 eta: 20:13:05 time: 0.2319 data_time: 0.0029 loss: 1.3479 03/06 03:24:56 - mmengine - INFO - Epoch(train) [77][2100/5005] lr: 1.0000e-02 eta: 20:12:42 time: 0.2436 data_time: 0.0034 loss: 1.3255 03/06 03:25:19 - mmengine - INFO - Epoch(train) [77][2200/5005] lr: 1.0000e-02 eta: 20:12:19 time: 0.2235 data_time: 0.0029 loss: 1.2081 03/06 03:25:42 - mmengine - INFO - Epoch(train) [77][2300/5005] lr: 1.0000e-02 eta: 20:11:56 time: 0.2286 data_time: 0.0029 loss: 1.3959 03/06 03:26:04 - mmengine - INFO - Epoch(train) [77][2400/5005] lr: 1.0000e-02 eta: 20:11:33 time: 0.2244 data_time: 0.0030 loss: 1.3938 03/06 03:26:27 - mmengine - INFO - Epoch(train) [77][2500/5005] lr: 1.0000e-02 eta: 20:11:10 time: 0.2234 data_time: 0.0031 loss: 1.2850 03/06 03:26:50 - mmengine - INFO - Epoch(train) [77][2600/5005] lr: 1.0000e-02 eta: 20:10:47 time: 0.2249 data_time: 0.0029 loss: 1.4069 03/06 03:26:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:27:13 - mmengine - INFO - Epoch(train) [77][2700/5005] lr: 1.0000e-02 eta: 20:10:24 time: 0.2229 data_time: 0.0031 loss: 1.1798 03/06 03:27:36 - mmengine - INFO - Epoch(train) [77][2800/5005] lr: 1.0000e-02 eta: 20:10:02 time: 0.2238 data_time: 0.0033 loss: 1.4656 03/06 03:27:59 - mmengine - INFO - Epoch(train) [77][2900/5005] lr: 1.0000e-02 eta: 20:09:39 time: 0.2244 data_time: 0.0029 loss: 1.4691 03/06 03:28:22 - mmengine - INFO - Epoch(train) [77][3000/5005] lr: 1.0000e-02 eta: 20:09:16 time: 0.2242 data_time: 0.0033 loss: 1.4905 03/06 03:28:45 - mmengine - INFO - Epoch(train) [77][3100/5005] lr: 1.0000e-02 eta: 20:08:53 time: 0.2237 data_time: 0.0032 loss: 1.2449 03/06 03:29:08 - mmengine - INFO - Epoch(train) [77][3200/5005] lr: 1.0000e-02 eta: 20:08:30 time: 0.2235 data_time: 0.0030 loss: 1.3494 03/06 03:29:31 - mmengine - INFO - Epoch(train) [77][3300/5005] lr: 1.0000e-02 eta: 20:08:07 time: 0.2243 data_time: 0.0029 loss: 1.2211 03/06 03:29:54 - mmengine - INFO - Epoch(train) [77][3400/5005] lr: 1.0000e-02 eta: 20:07:44 time: 0.2632 data_time: 0.0032 loss: 1.2557 03/06 03:30:17 - mmengine - INFO - Epoch(train) [77][3500/5005] lr: 1.0000e-02 eta: 20:07:22 time: 0.2284 data_time: 0.0033 loss: 1.2347 03/06 03:30:40 - mmengine - INFO - Epoch(train) [77][3600/5005] lr: 1.0000e-02 eta: 20:06:58 time: 0.2239 data_time: 0.0035 loss: 1.3374 03/06 03:30:45 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:31:03 - mmengine - INFO - Epoch(train) [77][3700/5005] lr: 1.0000e-02 eta: 20:06:36 time: 0.2272 data_time: 0.0030 loss: 1.2010 03/06 03:31:26 - mmengine - INFO - Epoch(train) [77][3800/5005] lr: 1.0000e-02 eta: 20:06:13 time: 0.2262 data_time: 0.0034 loss: 1.2281 03/06 03:31:49 - mmengine - INFO - Epoch(train) [77][3900/5005] lr: 1.0000e-02 eta: 20:05:50 time: 0.2228 data_time: 0.0031 loss: 1.2988 03/06 03:32:12 - mmengine - INFO - Epoch(train) [77][4000/5005] lr: 1.0000e-02 eta: 20:05:27 time: 0.2247 data_time: 0.0031 loss: 1.3727 03/06 03:32:35 - mmengine - INFO - Epoch(train) [77][4100/5005] lr: 1.0000e-02 eta: 20:05:04 time: 0.2424 data_time: 0.0032 loss: 1.2221 03/06 03:32:58 - mmengine - INFO - Epoch(train) [77][4200/5005] lr: 1.0000e-02 eta: 20:04:41 time: 0.2486 data_time: 0.0037 loss: 1.5469 03/06 03:33:21 - mmengine - INFO - Epoch(train) [77][4300/5005] lr: 1.0000e-02 eta: 20:04:18 time: 0.2232 data_time: 0.0035 loss: 1.4522 03/06 03:33:44 - mmengine - INFO - Epoch(train) [77][4400/5005] lr: 1.0000e-02 eta: 20:03:55 time: 0.2254 data_time: 0.0041 loss: 1.0666 03/06 03:34:07 - mmengine - INFO - Epoch(train) [77][4500/5005] lr: 1.0000e-02 eta: 20:03:32 time: 0.2191 data_time: 0.0035 loss: 1.5308 03/06 03:34:30 - mmengine - INFO - Epoch(train) [77][4600/5005] lr: 1.0000e-02 eta: 20:03:09 time: 0.2308 data_time: 0.0029 loss: 1.3522 03/06 03:34:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:34:53 - mmengine - INFO - Epoch(train) [77][4700/5005] lr: 1.0000e-02 eta: 20:02:46 time: 0.2281 data_time: 0.0038 loss: 1.1997 03/06 03:35:16 - mmengine - INFO - Epoch(train) [77][4800/5005] lr: 1.0000e-02 eta: 20:02:24 time: 0.2329 data_time: 0.0033 loss: 1.2828 03/06 03:35:40 - mmengine - INFO - Epoch(train) [77][4900/5005] lr: 1.0000e-02 eta: 20:02:01 time: 0.2846 data_time: 0.0027 loss: 1.4535 03/06 03:36:09 - mmengine - INFO - Epoch(train) [77][5000/5005] lr: 1.0000e-02 eta: 20:01:44 time: 0.2874 data_time: 0.0028 loss: 1.1950 03/06 03:36:10 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:36:13 - mmengine - INFO - Saving checkpoint at 77 epochs 03/06 03:36:28 - mmengine - INFO - Epoch(val) [77][100/196] eta: 0:00:12 time: 0.0203 data_time: 0.0004 03/06 03:36:42 - mmengine - INFO - Epoch(val) [77][196/196] accuracy/top1: 71.1100 accuracy/top5: 90.4140 03/06 03:37:14 - mmengine - INFO - Epoch(train) [78][ 100/5005] lr: 1.0000e-02 eta: 20:01:27 time: 0.2243 data_time: 0.0033 loss: 1.3287 03/06 03:37:37 - mmengine - INFO - Epoch(train) [78][ 200/5005] lr: 1.0000e-02 eta: 20:01:04 time: 0.2246 data_time: 0.0034 loss: 1.2128 03/06 03:38:00 - mmengine - INFO - Epoch(train) [78][ 300/5005] lr: 1.0000e-02 eta: 20:00:41 time: 0.2210 data_time: 0.0039 loss: 1.2397 03/06 03:38:23 - mmengine - INFO - Epoch(train) [78][ 400/5005] lr: 1.0000e-02 eta: 20:00:18 time: 0.2218 data_time: 0.0033 loss: 1.1650 03/06 03:38:46 - mmengine - INFO - Epoch(train) [78][ 500/5005] lr: 1.0000e-02 eta: 19:59:55 time: 0.2247 data_time: 0.0036 loss: 1.3033 03/06 03:39:09 - mmengine - INFO - Epoch(train) [78][ 600/5005] lr: 1.0000e-02 eta: 19:59:32 time: 0.2259 data_time: 0.0032 loss: 1.1431 03/06 03:39:12 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:39:31 - mmengine - INFO - Epoch(train) [78][ 700/5005] lr: 1.0000e-02 eta: 19:59:09 time: 0.2460 data_time: 0.0032 loss: 1.1073 03/06 03:39:54 - mmengine - INFO - Epoch(train) [78][ 800/5005] lr: 1.0000e-02 eta: 19:58:46 time: 0.2351 data_time: 0.0032 loss: 1.2776 03/06 03:40:17 - mmengine - INFO - Epoch(train) [78][ 900/5005] lr: 1.0000e-02 eta: 19:58:24 time: 0.2273 data_time: 0.0033 loss: 1.2361 03/06 03:40:40 - mmengine - INFO - Epoch(train) [78][1000/5005] lr: 1.0000e-02 eta: 19:58:01 time: 0.2318 data_time: 0.0029 loss: 1.4502 03/06 03:41:03 - mmengine - INFO - Epoch(train) [78][1100/5005] lr: 1.0000e-02 eta: 19:57:38 time: 0.2297 data_time: 0.0031 loss: 1.2697 03/06 03:41:26 - mmengine - INFO - Epoch(train) [78][1200/5005] lr: 1.0000e-02 eta: 19:57:15 time: 0.2297 data_time: 0.0031 loss: 1.1984 03/06 03:41:49 - mmengine - INFO - Epoch(train) [78][1300/5005] lr: 1.0000e-02 eta: 19:56:52 time: 0.2240 data_time: 0.0036 loss: 1.0671 03/06 03:42:12 - mmengine - INFO - Epoch(train) [78][1400/5005] lr: 1.0000e-02 eta: 19:56:29 time: 0.2296 data_time: 0.0034 loss: 1.3284 03/06 03:42:35 - mmengine - INFO - Epoch(train) [78][1500/5005] lr: 1.0000e-02 eta: 19:56:06 time: 0.2243 data_time: 0.0034 loss: 1.2530 03/06 03:42:58 - mmengine - INFO - Epoch(train) [78][1600/5005] lr: 1.0000e-02 eta: 19:55:43 time: 0.2255 data_time: 0.0032 loss: 1.1725 03/06 03:43:01 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:43:21 - mmengine - INFO - Epoch(train) [78][1700/5005] lr: 1.0000e-02 eta: 19:55:20 time: 0.2230 data_time: 0.0036 loss: 1.0972 03/06 03:43:44 - mmengine - INFO - Epoch(train) [78][1800/5005] lr: 1.0000e-02 eta: 19:54:57 time: 0.2205 data_time: 0.0032 loss: 1.2953 03/06 03:44:07 - mmengine - INFO - Epoch(train) [78][1900/5005] lr: 1.0000e-02 eta: 19:54:34 time: 0.2309 data_time: 0.0031 loss: 1.2616 03/06 03:44:30 - mmengine - INFO - Epoch(train) [78][2000/5005] lr: 1.0000e-02 eta: 19:54:11 time: 0.2237 data_time: 0.0028 loss: 1.3095 03/06 03:44:53 - mmengine - INFO - Epoch(train) [78][2100/5005] lr: 1.0000e-02 eta: 19:53:48 time: 0.2247 data_time: 0.0031 loss: 1.1925 03/06 03:45:16 - mmengine - INFO - Epoch(train) [78][2200/5005] lr: 1.0000e-02 eta: 19:53:26 time: 0.2457 data_time: 0.0031 loss: 1.3216 03/06 03:45:38 - mmengine - INFO - Epoch(train) [78][2300/5005] lr: 1.0000e-02 eta: 19:53:02 time: 0.2255 data_time: 0.0035 loss: 1.1713 03/06 03:46:02 - mmengine - INFO - Epoch(train) [78][2400/5005] lr: 1.0000e-02 eta: 19:52:40 time: 0.2299 data_time: 0.0034 loss: 1.1742 03/06 03:46:24 - mmengine - INFO - Epoch(train) [78][2500/5005] lr: 1.0000e-02 eta: 19:52:16 time: 0.2243 data_time: 0.0034 loss: 1.3199 03/06 03:46:47 - mmengine - INFO - Epoch(train) [78][2600/5005] lr: 1.0000e-02 eta: 19:51:53 time: 0.2242 data_time: 0.0031 loss: 1.2441 03/06 03:46:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:47:10 - mmengine - INFO - Epoch(train) [78][2700/5005] lr: 1.0000e-02 eta: 19:51:30 time: 0.2240 data_time: 0.0033 loss: 1.4367 03/06 03:47:33 - mmengine - INFO - Epoch(train) [78][2800/5005] lr: 1.0000e-02 eta: 19:51:08 time: 0.2355 data_time: 0.0031 loss: 1.1900 03/06 03:47:56 - mmengine - INFO - Epoch(train) [78][2900/5005] lr: 1.0000e-02 eta: 19:50:44 time: 0.2257 data_time: 0.0031 loss: 1.3457 03/06 03:48:19 - mmengine - INFO - Epoch(train) [78][3000/5005] lr: 1.0000e-02 eta: 19:50:21 time: 0.2229 data_time: 0.0034 loss: 1.2920 03/06 03:48:41 - mmengine - INFO - Epoch(train) [78][3100/5005] lr: 1.0000e-02 eta: 19:49:58 time: 0.2321 data_time: 0.0033 loss: 1.3928 03/06 03:49:04 - mmengine - INFO - Epoch(train) [78][3200/5005] lr: 1.0000e-02 eta: 19:49:35 time: 0.2273 data_time: 0.0031 loss: 1.3637 03/06 03:49:27 - mmengine - INFO - Epoch(train) [78][3300/5005] lr: 1.0000e-02 eta: 19:49:13 time: 0.2268 data_time: 0.0034 loss: 1.2329 03/06 03:49:50 - mmengine - INFO - Epoch(train) [78][3400/5005] lr: 1.0000e-02 eta: 19:48:50 time: 0.2238 data_time: 0.0030 loss: 1.2836 03/06 03:50:13 - mmengine - INFO - Epoch(train) [78][3500/5005] lr: 1.0000e-02 eta: 19:48:27 time: 0.2280 data_time: 0.0032 loss: 1.2583 03/06 03:50:36 - mmengine - INFO - Epoch(train) [78][3600/5005] lr: 1.0000e-02 eta: 19:48:04 time: 0.2384 data_time: 0.0029 loss: 1.3027 03/06 03:50:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:50:59 - mmengine - INFO - Epoch(train) [78][3700/5005] lr: 1.0000e-02 eta: 19:47:41 time: 0.2218 data_time: 0.0034 loss: 1.3496 03/06 03:51:22 - mmengine - INFO - Epoch(train) [78][3800/5005] lr: 1.0000e-02 eta: 19:47:18 time: 0.2214 data_time: 0.0035 loss: 1.3341 03/06 03:51:45 - mmengine - INFO - Epoch(train) [78][3900/5005] lr: 1.0000e-02 eta: 19:46:55 time: 0.2223 data_time: 0.0031 loss: 1.3079 03/06 03:52:08 - mmengine - INFO - Epoch(train) [78][4000/5005] lr: 1.0000e-02 eta: 19:46:32 time: 0.2386 data_time: 0.0030 loss: 1.4092 03/06 03:52:31 - mmengine - INFO - Epoch(train) [78][4100/5005] lr: 1.0000e-02 eta: 19:46:09 time: 0.2291 data_time: 0.0029 loss: 1.2697 03/06 03:52:54 - mmengine - INFO - Epoch(train) [78][4200/5005] lr: 1.0000e-02 eta: 19:45:46 time: 0.2463 data_time: 0.0030 loss: 1.3105 03/06 03:53:17 - mmengine - INFO - Epoch(train) [78][4300/5005] lr: 1.0000e-02 eta: 19:45:23 time: 0.2234 data_time: 0.0032 loss: 1.2801 03/06 03:53:40 - mmengine - INFO - Epoch(train) [78][4400/5005] lr: 1.0000e-02 eta: 19:45:00 time: 0.2211 data_time: 0.0034 loss: 1.3427 03/06 03:54:02 - mmengine - INFO - Epoch(train) [78][4500/5005] lr: 1.0000e-02 eta: 19:44:37 time: 0.2249 data_time: 0.0033 loss: 1.3767 03/06 03:54:25 - mmengine - INFO - Epoch(train) [78][4600/5005] lr: 1.0000e-02 eta: 19:44:14 time: 0.2249 data_time: 0.0034 loss: 1.2964 03/06 03:54:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:54:48 - mmengine - INFO - Epoch(train) [78][4700/5005] lr: 1.0000e-02 eta: 19:43:51 time: 0.2220 data_time: 0.0030 loss: 1.3165 03/06 03:55:11 - mmengine - INFO - Epoch(train) [78][4800/5005] lr: 1.0000e-02 eta: 19:43:28 time: 0.2263 data_time: 0.0028 loss: 1.2437 03/06 03:55:35 - mmengine - INFO - Epoch(train) [78][4900/5005] lr: 1.0000e-02 eta: 19:43:06 time: 0.2907 data_time: 0.0030 loss: 1.4673 03/06 03:56:04 - mmengine - INFO - Epoch(train) [78][5000/5005] lr: 1.0000e-02 eta: 19:42:48 time: 0.2877 data_time: 0.0029 loss: 1.2987 03/06 03:56:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:56:08 - mmengine - INFO - Saving checkpoint at 78 epochs 03/06 03:56:23 - mmengine - INFO - Epoch(val) [78][100/196] eta: 0:00:13 time: 0.0189 data_time: 0.0003 03/06 03:56:37 - mmengine - INFO - Epoch(val) [78][196/196] accuracy/top1: 71.6860 accuracy/top5: 91.0780 03/06 03:57:09 - mmengine - INFO - Epoch(train) [79][ 100/5005] lr: 1.0000e-02 eta: 19:42:31 time: 0.2304 data_time: 0.0043 loss: 1.2993 03/06 03:57:32 - mmengine - INFO - Epoch(train) [79][ 200/5005] lr: 1.0000e-02 eta: 19:42:09 time: 0.2243 data_time: 0.0039 loss: 1.2843 03/06 03:57:55 - mmengine - INFO - Epoch(train) [79][ 300/5005] lr: 1.0000e-02 eta: 19:41:46 time: 0.2234 data_time: 0.0039 loss: 1.3693 03/06 03:58:18 - mmengine - INFO - Epoch(train) [79][ 400/5005] lr: 1.0000e-02 eta: 19:41:23 time: 0.2235 data_time: 0.0034 loss: 1.2057 03/06 03:58:41 - mmengine - INFO - Epoch(train) [79][ 500/5005] lr: 1.0000e-02 eta: 19:41:00 time: 0.2233 data_time: 0.0032 loss: 1.2772 03/06 03:59:04 - mmengine - INFO - Epoch(train) [79][ 600/5005] lr: 1.0000e-02 eta: 19:40:37 time: 0.2412 data_time: 0.0031 loss: 1.3030 03/06 03:59:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 03:59:27 - mmengine - INFO - Epoch(train) [79][ 700/5005] lr: 1.0000e-02 eta: 19:40:14 time: 0.2251 data_time: 0.0034 loss: 1.2762 03/06 03:59:51 - mmengine - INFO - Epoch(train) [79][ 800/5005] lr: 1.0000e-02 eta: 19:39:51 time: 0.2422 data_time: 0.0030 loss: 1.3123 03/06 04:00:13 - mmengine - INFO - Epoch(train) [79][ 900/5005] lr: 1.0000e-02 eta: 19:39:28 time: 0.2248 data_time: 0.0035 loss: 1.3525 03/06 04:00:37 - mmengine - INFO - Epoch(train) [79][1000/5005] lr: 1.0000e-02 eta: 19:39:06 time: 0.2211 data_time: 0.0032 loss: 1.1111 03/06 04:01:00 - mmengine - INFO - Epoch(train) [79][1100/5005] lr: 1.0000e-02 eta: 19:38:43 time: 0.2240 data_time: 0.0029 loss: 1.1673 03/06 04:01:23 - mmengine - INFO - Epoch(train) [79][1200/5005] lr: 1.0000e-02 eta: 19:38:20 time: 0.2269 data_time: 0.0033 loss: 1.2899 03/06 04:01:46 - mmengine - INFO - Epoch(train) [79][1300/5005] lr: 1.0000e-02 eta: 19:37:57 time: 0.2227 data_time: 0.0038 loss: 1.2203 03/06 04:02:08 - mmengine - INFO - Epoch(train) [79][1400/5005] lr: 1.0000e-02 eta: 19:37:34 time: 0.2224 data_time: 0.0031 loss: 1.1927 03/06 04:02:32 - mmengine - INFO - Epoch(train) [79][1500/5005] lr: 1.0000e-02 eta: 19:37:11 time: 0.2248 data_time: 0.0031 loss: 1.1696 03/06 04:02:54 - mmengine - INFO - Epoch(train) [79][1600/5005] lr: 1.0000e-02 eta: 19:36:48 time: 0.2436 data_time: 0.0039 loss: 1.2031 03/06 04:02:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:03:17 - mmengine - INFO - Epoch(train) [79][1700/5005] lr: 1.0000e-02 eta: 19:36:25 time: 0.2219 data_time: 0.0033 loss: 1.1847 03/06 04:03:40 - mmengine - INFO - Epoch(train) [79][1800/5005] lr: 1.0000e-02 eta: 19:36:02 time: 0.2257 data_time: 0.0029 loss: 1.2120 03/06 04:04:03 - mmengine - INFO - Epoch(train) [79][1900/5005] lr: 1.0000e-02 eta: 19:35:39 time: 0.2275 data_time: 0.0034 loss: 1.4833 03/06 04:04:26 - mmengine - INFO - Epoch(train) [79][2000/5005] lr: 1.0000e-02 eta: 19:35:16 time: 0.2253 data_time: 0.0031 loss: 1.2692 03/06 04:04:49 - mmengine - INFO - Epoch(train) [79][2100/5005] lr: 1.0000e-02 eta: 19:34:53 time: 0.2234 data_time: 0.0033 loss: 1.2904 03/06 04:05:12 - mmengine - INFO - Epoch(train) [79][2200/5005] lr: 1.0000e-02 eta: 19:34:30 time: 0.2217 data_time: 0.0034 loss: 1.1514 03/06 04:05:35 - mmengine - INFO - Epoch(train) [79][2300/5005] lr: 1.0000e-02 eta: 19:34:07 time: 0.2427 data_time: 0.0032 loss: 1.3622 03/06 04:05:57 - mmengine - INFO - Epoch(train) [79][2400/5005] lr: 1.0000e-02 eta: 19:33:44 time: 0.2250 data_time: 0.0032 loss: 1.3184 03/06 04:06:21 - mmengine - INFO - Epoch(train) [79][2500/5005] lr: 1.0000e-02 eta: 19:33:21 time: 0.2265 data_time: 0.0033 loss: 1.0838 03/06 04:06:43 - mmengine - INFO - Epoch(train) [79][2600/5005] lr: 1.0000e-02 eta: 19:32:58 time: 0.2245 data_time: 0.0028 loss: 1.2760 03/06 04:06:46 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:07:07 - mmengine - INFO - Epoch(train) [79][2700/5005] lr: 1.0000e-02 eta: 19:32:36 time: 0.2281 data_time: 0.0034 loss: 1.2018 03/06 04:07:29 - mmengine - INFO - Epoch(train) [79][2800/5005] lr: 1.0000e-02 eta: 19:32:13 time: 0.2244 data_time: 0.0033 loss: 1.2554 03/06 04:07:53 - mmengine - INFO - Epoch(train) [79][2900/5005] lr: 1.0000e-02 eta: 19:31:50 time: 0.2308 data_time: 0.0029 loss: 1.3148 03/06 04:08:15 - mmengine - INFO - Epoch(train) [79][3000/5005] lr: 1.0000e-02 eta: 19:31:27 time: 0.2254 data_time: 0.0033 loss: 1.2873 03/06 04:08:39 - mmengine - INFO - Epoch(train) [79][3100/5005] lr: 1.0000e-02 eta: 19:31:04 time: 0.2313 data_time: 0.0035 loss: 1.4017 03/06 04:09:01 - mmengine - INFO - Epoch(train) [79][3200/5005] lr: 1.0000e-02 eta: 19:30:41 time: 0.2269 data_time: 0.0030 loss: 1.3132 03/06 04:09:24 - mmengine - INFO - Epoch(train) [79][3300/5005] lr: 1.0000e-02 eta: 19:30:18 time: 0.2255 data_time: 0.0036 loss: 1.3004 03/06 04:09:47 - mmengine - INFO - Epoch(train) [79][3400/5005] lr: 1.0000e-02 eta: 19:29:55 time: 0.2254 data_time: 0.0034 loss: 1.4177 03/06 04:10:10 - mmengine - INFO - Epoch(train) [79][3500/5005] lr: 1.0000e-02 eta: 19:29:32 time: 0.2224 data_time: 0.0032 loss: 1.2739 03/06 04:10:33 - mmengine - INFO - Epoch(train) [79][3600/5005] lr: 1.0000e-02 eta: 19:29:09 time: 0.2289 data_time: 0.0031 loss: 1.2594 03/06 04:10:35 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:10:56 - mmengine - INFO - Epoch(train) [79][3700/5005] lr: 1.0000e-02 eta: 19:28:46 time: 0.2263 data_time: 0.0032 loss: 1.0621 03/06 04:11:19 - mmengine - INFO - Epoch(train) [79][3800/5005] lr: 1.0000e-02 eta: 19:28:23 time: 0.2307 data_time: 0.0036 loss: 1.3020 03/06 04:11:42 - mmengine - INFO - Epoch(train) [79][3900/5005] lr: 1.0000e-02 eta: 19:28:00 time: 0.2231 data_time: 0.0030 loss: 1.1599 03/06 04:12:05 - mmengine - INFO - Epoch(train) [79][4000/5005] lr: 1.0000e-02 eta: 19:27:37 time: 0.2251 data_time: 0.0032 loss: 1.2237 03/06 04:12:28 - mmengine - INFO - Epoch(train) [79][4100/5005] lr: 1.0000e-02 eta: 19:27:15 time: 0.2224 data_time: 0.0030 loss: 1.4436 03/06 04:12:51 - mmengine - INFO - Epoch(train) [79][4200/5005] lr: 1.0000e-02 eta: 19:26:51 time: 0.2250 data_time: 0.0032 loss: 1.3509 03/06 04:13:14 - mmengine - INFO - Epoch(train) [79][4300/5005] lr: 1.0000e-02 eta: 19:26:29 time: 0.2217 data_time: 0.0031 loss: 1.3158 03/06 04:13:37 - mmengine - INFO - Epoch(train) [79][4400/5005] lr: 1.0000e-02 eta: 19:26:06 time: 0.2254 data_time: 0.0034 loss: 1.4510 03/06 04:14:00 - mmengine - INFO - Epoch(train) [79][4500/5005] lr: 1.0000e-02 eta: 19:25:43 time: 0.2265 data_time: 0.0031 loss: 1.3534 03/06 04:14:23 - mmengine - INFO - Epoch(train) [79][4600/5005] lr: 1.0000e-02 eta: 19:25:20 time: 0.2233 data_time: 0.0036 loss: 1.5023 03/06 04:14:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:14:46 - mmengine - INFO - Epoch(train) [79][4700/5005] lr: 1.0000e-02 eta: 19:24:57 time: 0.2237 data_time: 0.0032 loss: 1.3275 03/06 04:15:09 - mmengine - INFO - Epoch(train) [79][4800/5005] lr: 1.0000e-02 eta: 19:24:34 time: 0.2205 data_time: 0.0030 loss: 1.3693 03/06 04:15:33 - mmengine - INFO - Epoch(train) [79][4900/5005] lr: 1.0000e-02 eta: 19:24:12 time: 0.2920 data_time: 0.0030 loss: 1.2242 03/06 04:16:02 - mmengine - INFO - Epoch(train) [79][5000/5005] lr: 1.0000e-02 eta: 19:23:54 time: 0.2970 data_time: 0.0029 loss: 1.3317 03/06 04:16:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:16:07 - mmengine - INFO - Saving checkpoint at 79 epochs 03/06 04:16:22 - mmengine - INFO - Epoch(val) [79][100/196] eta: 0:00:13 time: 0.0178 data_time: 0.0003 03/06 04:16:36 - mmengine - INFO - Epoch(val) [79][196/196] accuracy/top1: 72.1940 accuracy/top5: 91.1320 03/06 04:17:08 - mmengine - INFO - Epoch(train) [80][ 100/5005] lr: 1.0000e-02 eta: 19:23:37 time: 0.2294 data_time: 0.0040 loss: 1.2918 03/06 04:17:31 - mmengine - INFO - Epoch(train) [80][ 200/5005] lr: 1.0000e-02 eta: 19:23:15 time: 0.2255 data_time: 0.0033 loss: 1.1873 03/06 04:17:54 - mmengine - INFO - Epoch(train) [80][ 300/5005] lr: 1.0000e-02 eta: 19:22:52 time: 0.2265 data_time: 0.0033 loss: 1.1705 03/06 04:18:17 - mmengine - INFO - Epoch(train) [80][ 400/5005] lr: 1.0000e-02 eta: 19:22:29 time: 0.2264 data_time: 0.0034 loss: 1.1848 03/06 04:18:40 - mmengine - INFO - Epoch(train) [80][ 500/5005] lr: 1.0000e-02 eta: 19:22:06 time: 0.2291 data_time: 0.0032 loss: 1.4637 03/06 04:19:03 - mmengine - INFO - Epoch(train) [80][ 600/5005] lr: 1.0000e-02 eta: 19:21:43 time: 0.2230 data_time: 0.0036 loss: 1.1674 03/06 04:19:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:19:26 - mmengine - INFO - Epoch(train) [80][ 700/5005] lr: 1.0000e-02 eta: 19:21:20 time: 0.2267 data_time: 0.0036 loss: 1.3390 03/06 04:19:49 - mmengine - INFO - Epoch(train) [80][ 800/5005] lr: 1.0000e-02 eta: 19:20:57 time: 0.2223 data_time: 0.0035 loss: 1.1492 03/06 04:20:12 - mmengine - INFO - Epoch(train) [80][ 900/5005] lr: 1.0000e-02 eta: 19:20:34 time: 0.2249 data_time: 0.0037 loss: 1.3617 03/06 04:20:35 - mmengine - INFO - Epoch(train) [80][1000/5005] lr: 1.0000e-02 eta: 19:20:12 time: 0.2413 data_time: 0.0031 loss: 1.3180 03/06 04:20:58 - mmengine - INFO - Epoch(train) [80][1100/5005] lr: 1.0000e-02 eta: 19:19:48 time: 0.2263 data_time: 0.0033 loss: 1.2917 03/06 04:21:21 - mmengine - INFO - Epoch(train) [80][1200/5005] lr: 1.0000e-02 eta: 19:19:25 time: 0.2302 data_time: 0.0034 loss: 1.2473 03/06 04:21:44 - mmengine - INFO - Epoch(train) [80][1300/5005] lr: 1.0000e-02 eta: 19:19:02 time: 0.2274 data_time: 0.0032 loss: 1.0997 03/06 04:22:07 - mmengine - INFO - Epoch(train) [80][1400/5005] lr: 1.0000e-02 eta: 19:18:39 time: 0.2246 data_time: 0.0034 loss: 1.1967 03/06 04:22:29 - mmengine - INFO - Epoch(train) [80][1500/5005] lr: 1.0000e-02 eta: 19:18:16 time: 0.2230 data_time: 0.0028 loss: 1.4056 03/06 04:22:53 - mmengine - INFO - Epoch(train) [80][1600/5005] lr: 1.0000e-02 eta: 19:17:54 time: 0.2264 data_time: 0.0038 loss: 1.1873 03/06 04:22:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:23:15 - mmengine - INFO - Epoch(train) [80][1700/5005] lr: 1.0000e-02 eta: 19:17:31 time: 0.2272 data_time: 0.0035 loss: 1.5321 03/06 04:23:38 - mmengine - INFO - Epoch(train) [80][1800/5005] lr: 1.0000e-02 eta: 19:17:08 time: 0.2235 data_time: 0.0033 loss: 1.4850 03/06 04:24:01 - mmengine - INFO - Epoch(train) [80][1900/5005] lr: 1.0000e-02 eta: 19:16:45 time: 0.2244 data_time: 0.0033 loss: 1.3427 03/06 04:24:25 - mmengine - INFO - Epoch(train) [80][2000/5005] lr: 1.0000e-02 eta: 19:16:22 time: 0.2279 data_time: 0.0033 loss: 1.3740 03/06 04:24:47 - mmengine - INFO - Epoch(train) [80][2100/5005] lr: 1.0000e-02 eta: 19:15:59 time: 0.2338 data_time: 0.0034 loss: 1.2162 03/06 04:25:10 - mmengine - INFO - Epoch(train) [80][2200/5005] lr: 1.0000e-02 eta: 19:15:36 time: 0.2250 data_time: 0.0031 loss: 1.3405 03/06 04:25:33 - mmengine - INFO - Epoch(train) [80][2300/5005] lr: 1.0000e-02 eta: 19:15:13 time: 0.2247 data_time: 0.0033 loss: 1.4222 03/06 04:25:56 - mmengine - INFO - Epoch(train) [80][2400/5005] lr: 1.0000e-02 eta: 19:14:50 time: 0.2238 data_time: 0.0033 loss: 1.2283 03/06 04:26:19 - mmengine - INFO - Epoch(train) [80][2500/5005] lr: 1.0000e-02 eta: 19:14:27 time: 0.2229 data_time: 0.0033 loss: 1.3552 03/06 04:26:42 - mmengine - INFO - Epoch(train) [80][2600/5005] lr: 1.0000e-02 eta: 19:14:04 time: 0.2449 data_time: 0.0037 loss: 1.3248 03/06 04:26:43 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:27:05 - mmengine - INFO - Epoch(train) [80][2700/5005] lr: 1.0000e-02 eta: 19:13:41 time: 0.2291 data_time: 0.0030 loss: 1.1774 03/06 04:27:28 - mmengine - INFO - Epoch(train) [80][2800/5005] lr: 1.0000e-02 eta: 19:13:18 time: 0.2281 data_time: 0.0034 loss: 1.2970 03/06 04:27:51 - mmengine - INFO - Epoch(train) [80][2900/5005] lr: 1.0000e-02 eta: 19:12:55 time: 0.2276 data_time: 0.0033 loss: 1.4703 03/06 04:28:14 - mmengine - INFO - Epoch(train) [80][3000/5005] lr: 1.0000e-02 eta: 19:12:32 time: 0.2286 data_time: 0.0033 loss: 1.2784 03/06 04:28:37 - mmengine - INFO - Epoch(train) [80][3100/5005] lr: 1.0000e-02 eta: 19:12:10 time: 0.2245 data_time: 0.0035 loss: 1.1269 03/06 04:29:00 - mmengine - INFO - Epoch(train) [80][3200/5005] lr: 1.0000e-02 eta: 19:11:46 time: 0.2320 data_time: 0.0032 loss: 1.4955 03/06 04:29:23 - mmengine - INFO - Epoch(train) [80][3300/5005] lr: 1.0000e-02 eta: 19:11:24 time: 0.2513 data_time: 0.0033 loss: 1.2658 03/06 04:29:46 - mmengine - INFO - Epoch(train) [80][3400/5005] lr: 1.0000e-02 eta: 19:11:01 time: 0.2275 data_time: 0.0034 loss: 1.3400 03/06 04:30:09 - mmengine - INFO - Epoch(train) [80][3500/5005] lr: 1.0000e-02 eta: 19:10:38 time: 0.2258 data_time: 0.0036 loss: 1.1808 03/06 04:30:32 - mmengine - INFO - Epoch(train) [80][3600/5005] lr: 1.0000e-02 eta: 19:10:15 time: 0.2246 data_time: 0.0032 loss: 1.3508 03/06 04:30:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:30:55 - mmengine - INFO - Epoch(train) [80][3700/5005] lr: 1.0000e-02 eta: 19:09:52 time: 0.2264 data_time: 0.0032 loss: 1.5722 03/06 04:31:18 - mmengine - INFO - Epoch(train) [80][3800/5005] lr: 1.0000e-02 eta: 19:09:29 time: 0.2280 data_time: 0.0032 loss: 1.3197 03/06 04:31:41 - mmengine - INFO - Epoch(train) [80][3900/5005] lr: 1.0000e-02 eta: 19:09:06 time: 0.2259 data_time: 0.0029 loss: 1.3317 03/06 04:32:04 - mmengine - INFO - Epoch(train) [80][4000/5005] lr: 1.0000e-02 eta: 19:08:43 time: 0.2255 data_time: 0.0034 loss: 1.2647 03/06 04:32:27 - mmengine - INFO - Epoch(train) [80][4100/5005] lr: 1.0000e-02 eta: 19:08:20 time: 0.2223 data_time: 0.0033 loss: 1.2855 03/06 04:32:50 - mmengine - INFO - Epoch(train) [80][4200/5005] lr: 1.0000e-02 eta: 19:07:57 time: 0.2437 data_time: 0.0032 loss: 1.2505 03/06 04:33:13 - mmengine - INFO - Epoch(train) [80][4300/5005] lr: 1.0000e-02 eta: 19:07:35 time: 0.2217 data_time: 0.0035 loss: 1.3483 03/06 04:33:36 - mmengine - INFO - Epoch(train) [80][4400/5005] lr: 1.0000e-02 eta: 19:07:11 time: 0.2240 data_time: 0.0033 loss: 1.5132 03/06 04:33:58 - mmengine - INFO - Epoch(train) [80][4500/5005] lr: 1.0000e-02 eta: 19:06:48 time: 0.2233 data_time: 0.0032 loss: 1.4324 03/06 04:34:21 - mmengine - INFO - Epoch(train) [80][4600/5005] lr: 1.0000e-02 eta: 19:06:25 time: 0.2429 data_time: 0.0037 loss: 1.2671 03/06 04:34:22 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:34:45 - mmengine - INFO - Epoch(train) [80][4700/5005] lr: 1.0000e-02 eta: 19:06:03 time: 0.2217 data_time: 0.0032 loss: 1.2758 03/06 04:35:07 - mmengine - INFO - Epoch(train) [80][4800/5005] lr: 1.0000e-02 eta: 19:05:39 time: 0.2258 data_time: 0.0035 loss: 1.2629 03/06 04:35:31 - mmengine - INFO - Epoch(train) [80][4900/5005] lr: 1.0000e-02 eta: 19:05:17 time: 0.2880 data_time: 0.0029 loss: 1.3643 03/06 04:36:00 - mmengine - INFO - Epoch(train) [80][5000/5005] lr: 1.0000e-02 eta: 19:04:59 time: 0.2774 data_time: 0.0030 loss: 1.3207 03/06 04:36:01 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:36:04 - mmengine - INFO - Saving checkpoint at 80 epochs 03/06 04:36:19 - mmengine - INFO - Epoch(val) [80][100/196] eta: 0:00:12 time: 0.0194 data_time: 0.0004 03/06 04:36:33 - mmengine - INFO - Epoch(val) [80][196/196] accuracy/top1: 71.5340 accuracy/top5: 90.8880 03/06 04:37:04 - mmengine - INFO - Epoch(train) [81][ 100/5005] lr: 1.0000e-03 eta: 19:04:41 time: 0.2279 data_time: 0.0034 loss: 1.4554 03/06 04:37:28 - mmengine - INFO - Epoch(train) [81][ 200/5005] lr: 1.0000e-03 eta: 19:04:19 time: 0.2239 data_time: 0.0036 loss: 1.3048 03/06 04:37:51 - mmengine - INFO - Epoch(train) [81][ 300/5005] lr: 1.0000e-03 eta: 19:03:56 time: 0.2228 data_time: 0.0039 loss: 1.1887 03/06 04:38:14 - mmengine - INFO - Epoch(train) [81][ 400/5005] lr: 1.0000e-03 eta: 19:03:33 time: 0.2354 data_time: 0.0032 loss: 1.1205 03/06 04:38:37 - mmengine - INFO - Epoch(train) [81][ 500/5005] lr: 1.0000e-03 eta: 19:03:10 time: 0.2266 data_time: 0.0031 loss: 1.0501 03/06 04:39:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:39:00 - mmengine - INFO - Epoch(train) [81][ 600/5005] lr: 1.0000e-03 eta: 19:02:47 time: 0.2266 data_time: 0.0034 loss: 0.9874 03/06 04:39:22 - mmengine - INFO - Epoch(train) [81][ 700/5005] lr: 1.0000e-03 eta: 19:02:24 time: 0.2348 data_time: 0.0031 loss: 1.3231 03/06 04:39:45 - mmengine - INFO - Epoch(train) [81][ 800/5005] lr: 1.0000e-03 eta: 19:02:01 time: 0.2260 data_time: 0.0033 loss: 1.0461 03/06 04:40:08 - mmengine - INFO - Epoch(train) [81][ 900/5005] lr: 1.0000e-03 eta: 19:01:38 time: 0.2240 data_time: 0.0035 loss: 1.0076 03/06 04:40:31 - mmengine - INFO - Epoch(train) [81][1000/5005] lr: 1.0000e-03 eta: 19:01:15 time: 0.2242 data_time: 0.0032 loss: 1.0006 03/06 04:40:54 - mmengine - INFO - Epoch(train) [81][1100/5005] lr: 1.0000e-03 eta: 19:00:52 time: 0.2239 data_time: 0.0032 loss: 1.1883 03/06 04:41:17 - mmengine - INFO - Epoch(train) [81][1200/5005] lr: 1.0000e-03 eta: 19:00:29 time: 0.2319 data_time: 0.0035 loss: 1.0493 03/06 04:41:40 - mmengine - INFO - Epoch(train) [81][1300/5005] lr: 1.0000e-03 eta: 19:00:06 time: 0.2358 data_time: 0.0031 loss: 1.1054 03/06 04:42:03 - mmengine - INFO - Epoch(train) [81][1400/5005] lr: 1.0000e-03 eta: 18:59:43 time: 0.2316 data_time: 0.0032 loss: 0.9628 03/06 04:42:26 - mmengine - INFO - Epoch(train) [81][1500/5005] lr: 1.0000e-03 eta: 18:59:20 time: 0.2244 data_time: 0.0029 loss: 0.9444 03/06 04:42:49 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:42:49 - mmengine - INFO - Epoch(train) [81][1600/5005] lr: 1.0000e-03 eta: 18:58:57 time: 0.2257 data_time: 0.0033 loss: 1.2023 03/06 04:43:12 - mmengine - INFO - Epoch(train) [81][1700/5005] lr: 1.0000e-03 eta: 18:58:34 time: 0.2258 data_time: 0.0034 loss: 1.1190 03/06 04:43:35 - mmengine - INFO - Epoch(train) [81][1800/5005] lr: 1.0000e-03 eta: 18:58:11 time: 0.2258 data_time: 0.0033 loss: 1.1780 03/06 04:43:58 - mmengine - INFO - Epoch(train) [81][1900/5005] lr: 1.0000e-03 eta: 18:57:48 time: 0.2526 data_time: 0.0036 loss: 1.1774 03/06 04:44:20 - mmengine - INFO - Epoch(train) [81][2000/5005] lr: 1.0000e-03 eta: 18:57:25 time: 0.2215 data_time: 0.0034 loss: 1.2313 03/06 04:44:43 - mmengine - INFO - Epoch(train) [81][2100/5005] lr: 1.0000e-03 eta: 18:57:02 time: 0.2242 data_time: 0.0036 loss: 1.2268 03/06 04:45:06 - mmengine - INFO - Epoch(train) [81][2200/5005] lr: 1.0000e-03 eta: 18:56:39 time: 0.2438 data_time: 0.0030 loss: 1.1382 03/06 04:45:29 - mmengine - INFO - Epoch(train) [81][2300/5005] lr: 1.0000e-03 eta: 18:56:17 time: 0.2429 data_time: 0.0029 loss: 1.0886 03/06 04:45:52 - mmengine - INFO - Epoch(train) [81][2400/5005] lr: 1.0000e-03 eta: 18:55:54 time: 0.2246 data_time: 0.0035 loss: 1.0638 03/06 04:46:15 - mmengine - INFO - Epoch(train) [81][2500/5005] lr: 1.0000e-03 eta: 18:55:30 time: 0.2349 data_time: 0.0041 loss: 0.8834 03/06 04:46:38 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:46:38 - mmengine - INFO - Epoch(train) [81][2600/5005] lr: 1.0000e-03 eta: 18:55:08 time: 0.2262 data_time: 0.0030 loss: 1.1510 03/06 04:47:01 - mmengine - INFO - Epoch(train) [81][2700/5005] lr: 1.0000e-03 eta: 18:54:45 time: 0.2362 data_time: 0.0034 loss: 1.0425 03/06 04:47:24 - mmengine - INFO - Epoch(train) [81][2800/5005] lr: 1.0000e-03 eta: 18:54:22 time: 0.2246 data_time: 0.0033 loss: 1.0195 03/06 04:47:47 - mmengine - INFO - Epoch(train) [81][2900/5005] lr: 1.0000e-03 eta: 18:53:59 time: 0.2246 data_time: 0.0035 loss: 0.9906 03/06 04:48:10 - mmengine - INFO - Epoch(train) [81][3000/5005] lr: 1.0000e-03 eta: 18:53:36 time: 0.2485 data_time: 0.0035 loss: 1.0106 03/06 04:48:33 - mmengine - INFO - Epoch(train) [81][3100/5005] lr: 1.0000e-03 eta: 18:53:13 time: 0.2248 data_time: 0.0036 loss: 1.0831 03/06 04:48:56 - mmengine - INFO - Epoch(train) [81][3200/5005] lr: 1.0000e-03 eta: 18:52:50 time: 0.2262 data_time: 0.0033 loss: 1.1284 03/06 04:49:19 - mmengine - INFO - Epoch(train) [81][3300/5005] lr: 1.0000e-03 eta: 18:52:27 time: 0.2261 data_time: 0.0034 loss: 1.0897 03/06 04:49:42 - mmengine - INFO - Epoch(train) [81][3400/5005] lr: 1.0000e-03 eta: 18:52:04 time: 0.2291 data_time: 0.0035 loss: 0.9521 03/06 04:50:05 - mmengine - INFO - Epoch(train) [81][3500/5005] lr: 1.0000e-03 eta: 18:51:41 time: 0.2236 data_time: 0.0033 loss: 1.1570 03/06 04:50:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:50:28 - mmengine - INFO - Epoch(train) [81][3600/5005] lr: 1.0000e-03 eta: 18:51:18 time: 0.2229 data_time: 0.0032 loss: 1.2068 03/06 04:50:51 - mmengine - INFO - Epoch(train) [81][3700/5005] lr: 1.0000e-03 eta: 18:50:56 time: 0.2235 data_time: 0.0033 loss: 1.1278 03/06 04:51:14 - mmengine - INFO - Epoch(train) [81][3800/5005] lr: 1.0000e-03 eta: 18:50:32 time: 0.2247 data_time: 0.0031 loss: 1.0628 03/06 04:51:37 - mmengine - INFO - Epoch(train) [81][3900/5005] lr: 1.0000e-03 eta: 18:50:09 time: 0.2247 data_time: 0.0032 loss: 1.2111 03/06 04:52:00 - mmengine - INFO - Epoch(train) [81][4000/5005] lr: 1.0000e-03 eta: 18:49:47 time: 0.2277 data_time: 0.0031 loss: 0.9392 03/06 04:52:23 - mmengine - INFO - Epoch(train) [81][4100/5005] lr: 1.0000e-03 eta: 18:49:24 time: 0.2258 data_time: 0.0032 loss: 1.0225 03/06 04:52:46 - mmengine - INFO - Epoch(train) [81][4200/5005] lr: 1.0000e-03 eta: 18:49:01 time: 0.2277 data_time: 0.0031 loss: 1.0550 03/06 04:53:09 - mmengine - INFO - Epoch(train) [81][4300/5005] lr: 1.0000e-03 eta: 18:48:38 time: 0.2284 data_time: 0.0029 loss: 1.0441 03/06 04:53:32 - mmengine - INFO - Epoch(train) [81][4400/5005] lr: 1.0000e-03 eta: 18:48:15 time: 0.2238 data_time: 0.0033 loss: 1.2060 03/06 04:53:55 - mmengine - INFO - Epoch(train) [81][4500/5005] lr: 1.0000e-03 eta: 18:47:52 time: 0.2283 data_time: 0.0032 loss: 1.0872 03/06 04:54:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:54:18 - mmengine - INFO - Epoch(train) [81][4600/5005] lr: 1.0000e-03 eta: 18:47:29 time: 0.2243 data_time: 0.0034 loss: 0.9278 03/06 04:54:41 - mmengine - INFO - Epoch(train) [81][4700/5005] lr: 1.0000e-03 eta: 18:47:06 time: 0.2255 data_time: 0.0032 loss: 1.0021 03/06 04:55:04 - mmengine - INFO - Epoch(train) [81][4800/5005] lr: 1.0000e-03 eta: 18:46:43 time: 0.2230 data_time: 0.0036 loss: 1.1087 03/06 04:55:28 - mmengine - INFO - Epoch(train) [81][4900/5005] lr: 1.0000e-03 eta: 18:46:21 time: 0.2854 data_time: 0.0032 loss: 0.9976 03/06 04:55:57 - mmengine - INFO - Epoch(train) [81][5000/5005] lr: 1.0000e-03 eta: 18:46:03 time: 0.2892 data_time: 0.0028 loss: 1.0269 03/06 04:55:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:56:01 - mmengine - INFO - Saving checkpoint at 81 epochs 03/06 04:56:17 - mmengine - INFO - Epoch(val) [81][100/196] eta: 0:00:13 time: 0.0208 data_time: 0.0004 03/06 04:56:30 - mmengine - INFO - Epoch(val) [81][196/196] accuracy/top1: 75.8940 accuracy/top5: 93.0720 03/06 04:57:02 - mmengine - INFO - Epoch(train) [82][ 100/5005] lr: 1.0000e-03 eta: 18:45:45 time: 0.2323 data_time: 0.0037 loss: 1.0519 03/06 04:57:25 - mmengine - INFO - Epoch(train) [82][ 200/5005] lr: 1.0000e-03 eta: 18:45:22 time: 0.2264 data_time: 0.0036 loss: 0.9711 03/06 04:57:48 - mmengine - INFO - Epoch(train) [82][ 300/5005] lr: 1.0000e-03 eta: 18:44:59 time: 0.2274 data_time: 0.0047 loss: 1.0826 03/06 04:58:11 - mmengine - INFO - Epoch(train) [82][ 400/5005] lr: 1.0000e-03 eta: 18:44:37 time: 0.2249 data_time: 0.0044 loss: 1.0421 03/06 04:58:34 - mmengine - INFO - Epoch(train) [82][ 500/5005] lr: 1.0000e-03 eta: 18:44:14 time: 0.2243 data_time: 0.0042 loss: 1.0573 03/06 04:58:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 04:58:58 - mmengine - INFO - Epoch(train) [82][ 600/5005] lr: 1.0000e-03 eta: 18:43:51 time: 0.2293 data_time: 0.0037 loss: 1.0285 03/06 04:59:20 - mmengine - INFO - Epoch(train) [82][ 700/5005] lr: 1.0000e-03 eta: 18:43:28 time: 0.2268 data_time: 0.0035 loss: 0.9401 03/06 04:59:44 - mmengine - INFO - Epoch(train) [82][ 800/5005] lr: 1.0000e-03 eta: 18:43:05 time: 0.2233 data_time: 0.0033 loss: 1.0040 03/06 05:00:07 - mmengine - INFO - Epoch(train) [82][ 900/5005] lr: 1.0000e-03 eta: 18:42:42 time: 0.2231 data_time: 0.0032 loss: 0.9879 03/06 05:00:30 - mmengine - INFO - Epoch(train) [82][1000/5005] lr: 1.0000e-03 eta: 18:42:19 time: 0.2226 data_time: 0.0033 loss: 1.0008 03/06 05:00:52 - mmengine - INFO - Epoch(train) [82][1100/5005] lr: 1.0000e-03 eta: 18:41:56 time: 0.2257 data_time: 0.0036 loss: 1.1799 03/06 05:01:16 - mmengine - INFO - Epoch(train) [82][1200/5005] lr: 1.0000e-03 eta: 18:41:34 time: 0.2281 data_time: 0.0037 loss: 1.0144 03/06 05:01:39 - mmengine - INFO - Epoch(train) [82][1300/5005] lr: 1.0000e-03 eta: 18:41:11 time: 0.2236 data_time: 0.0032 loss: 1.0329 03/06 05:02:02 - mmengine - INFO - Epoch(train) [82][1400/5005] lr: 1.0000e-03 eta: 18:40:48 time: 0.2263 data_time: 0.0033 loss: 1.0220 03/06 05:02:25 - mmengine - INFO - Epoch(train) [82][1500/5005] lr: 1.0000e-03 eta: 18:40:25 time: 0.2298 data_time: 0.0029 loss: 1.1144 03/06 05:02:47 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:02:48 - mmengine - INFO - Epoch(train) [82][1600/5005] lr: 1.0000e-03 eta: 18:40:02 time: 0.2232 data_time: 0.0034 loss: 1.0722 03/06 05:03:10 - mmengine - INFO - Epoch(train) [82][1700/5005] lr: 1.0000e-03 eta: 18:39:39 time: 0.2244 data_time: 0.0030 loss: 1.0289 03/06 05:03:34 - mmengine - INFO - Epoch(train) [82][1800/5005] lr: 1.0000e-03 eta: 18:39:16 time: 0.2283 data_time: 0.0033 loss: 0.9132 03/06 05:03:57 - mmengine - INFO - Epoch(train) [82][1900/5005] lr: 1.0000e-03 eta: 18:38:53 time: 0.2488 data_time: 0.0035 loss: 1.0554 03/06 05:04:20 - mmengine - INFO - Epoch(train) [82][2000/5005] lr: 1.0000e-03 eta: 18:38:30 time: 0.2222 data_time: 0.0032 loss: 1.1127 03/06 05:04:43 - mmengine - INFO - Epoch(train) [82][2100/5005] lr: 1.0000e-03 eta: 18:38:07 time: 0.2259 data_time: 0.0036 loss: 0.8079 03/06 05:05:06 - mmengine - INFO - Epoch(train) [82][2200/5005] lr: 1.0000e-03 eta: 18:37:44 time: 0.2241 data_time: 0.0031 loss: 0.9429 03/06 05:05:28 - mmengine - INFO - Epoch(train) [82][2300/5005] lr: 1.0000e-03 eta: 18:37:21 time: 0.2282 data_time: 0.0036 loss: 0.9864 03/06 05:05:52 - mmengine - INFO - Epoch(train) [82][2400/5005] lr: 1.0000e-03 eta: 18:36:59 time: 0.2400 data_time: 0.0036 loss: 1.0957 03/06 05:06:15 - mmengine - INFO - Epoch(train) [82][2500/5005] lr: 1.0000e-03 eta: 18:36:36 time: 0.2254 data_time: 0.0033 loss: 1.0783 03/06 05:06:37 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:06:38 - mmengine - INFO - Epoch(train) [82][2600/5005] lr: 1.0000e-03 eta: 18:36:13 time: 0.2215 data_time: 0.0038 loss: 1.0695 03/06 05:07:01 - mmengine - INFO - Epoch(train) [82][2700/5005] lr: 1.0000e-03 eta: 18:35:50 time: 0.2225 data_time: 0.0032 loss: 1.0613 03/06 05:07:24 - mmengine - INFO - Epoch(train) [82][2800/5005] lr: 1.0000e-03 eta: 18:35:27 time: 0.2452 data_time: 0.0031 loss: 1.1407 03/06 05:07:47 - mmengine - INFO - Epoch(train) [82][2900/5005] lr: 1.0000e-03 eta: 18:35:04 time: 0.2252 data_time: 0.0034 loss: 1.1225 03/06 05:08:10 - mmengine - INFO - Epoch(train) [82][3000/5005] lr: 1.0000e-03 eta: 18:34:41 time: 0.2276 data_time: 0.0031 loss: 0.8249 03/06 05:08:33 - mmengine - INFO - Epoch(train) [82][3100/5005] lr: 1.0000e-03 eta: 18:34:18 time: 0.2287 data_time: 0.0031 loss: 0.8716 03/06 05:08:56 - mmengine - INFO - Epoch(train) [82][3200/5005] lr: 1.0000e-03 eta: 18:33:55 time: 0.2263 data_time: 0.0033 loss: 1.0951 03/06 05:09:18 - mmengine - INFO - Epoch(train) [82][3300/5005] lr: 1.0000e-03 eta: 18:33:32 time: 0.2252 data_time: 0.0032 loss: 0.9542 03/06 05:09:42 - mmengine - INFO - Epoch(train) [82][3400/5005] lr: 1.0000e-03 eta: 18:33:10 time: 0.2235 data_time: 0.0032 loss: 0.9886 03/06 05:10:05 - mmengine - INFO - Epoch(train) [82][3500/5005] lr: 1.0000e-03 eta: 18:32:47 time: 0.2263 data_time: 0.0034 loss: 1.0188 03/06 05:10:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:10:27 - mmengine - INFO - Epoch(train) [82][3600/5005] lr: 1.0000e-03 eta: 18:32:24 time: 0.2290 data_time: 0.0038 loss: 1.0543 03/06 05:10:50 - mmengine - INFO - Epoch(train) [82][3700/5005] lr: 1.0000e-03 eta: 18:32:01 time: 0.2252 data_time: 0.0034 loss: 1.0842 03/06 05:11:14 - mmengine - INFO - Epoch(train) [82][3800/5005] lr: 1.0000e-03 eta: 18:31:38 time: 0.2255 data_time: 0.0034 loss: 0.8537 03/06 05:11:37 - mmengine - INFO - Epoch(train) [82][3900/5005] lr: 1.0000e-03 eta: 18:31:15 time: 0.2242 data_time: 0.0030 loss: 0.9067 03/06 05:11:59 - mmengine - INFO - Epoch(train) [82][4000/5005] lr: 1.0000e-03 eta: 18:30:52 time: 0.2221 data_time: 0.0032 loss: 0.9022 03/06 05:12:22 - mmengine - INFO - Epoch(train) [82][4100/5005] lr: 1.0000e-03 eta: 18:30:29 time: 0.2281 data_time: 0.0033 loss: 1.0548 03/06 05:12:45 - mmengine - INFO - Epoch(train) [82][4200/5005] lr: 1.0000e-03 eta: 18:30:06 time: 0.2246 data_time: 0.0034 loss: 0.9754 03/06 05:13:09 - mmengine - INFO - Epoch(train) [82][4300/5005] lr: 1.0000e-03 eta: 18:29:43 time: 0.2293 data_time: 0.0037 loss: 1.0491 03/06 05:13:32 - mmengine - INFO - Epoch(train) [82][4400/5005] lr: 1.0000e-03 eta: 18:29:20 time: 0.2294 data_time: 0.0035 loss: 1.0734 03/06 05:13:55 - mmengine - INFO - Epoch(train) [82][4500/5005] lr: 1.0000e-03 eta: 18:28:57 time: 0.2237 data_time: 0.0035 loss: 1.2221 03/06 05:14:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:14:17 - mmengine - INFO - Epoch(train) [82][4600/5005] lr: 1.0000e-03 eta: 18:28:34 time: 0.2297 data_time: 0.0032 loss: 0.9281 03/06 05:14:41 - mmengine - INFO - Epoch(train) [82][4700/5005] lr: 1.0000e-03 eta: 18:28:11 time: 0.2272 data_time: 0.0030 loss: 1.0806 03/06 05:15:04 - mmengine - INFO - Epoch(train) [82][4800/5005] lr: 1.0000e-03 eta: 18:27:49 time: 0.2282 data_time: 0.0031 loss: 1.1469 03/06 05:15:28 - mmengine - INFO - Epoch(train) [82][4900/5005] lr: 1.0000e-03 eta: 18:27:27 time: 0.2882 data_time: 0.0032 loss: 1.0672 03/06 05:15:57 - mmengine - INFO - Epoch(train) [82][5000/5005] lr: 1.0000e-03 eta: 18:27:08 time: 0.2793 data_time: 0.0031 loss: 1.0872 03/06 05:15:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:16:01 - mmengine - INFO - Saving checkpoint at 82 epochs 03/06 05:16:16 - mmengine - INFO - Epoch(val) [82][100/196] eta: 0:00:13 time: 0.0200 data_time: 0.0004 03/06 05:16:30 - mmengine - INFO - Epoch(val) [82][196/196] accuracy/top1: 76.2500 accuracy/top5: 93.2520 03/06 05:17:02 - mmengine - INFO - Epoch(train) [83][ 100/5005] lr: 1.0000e-03 eta: 18:26:51 time: 0.2602 data_time: 0.0037 loss: 1.0677 03/06 05:17:25 - mmengine - INFO - Epoch(train) [83][ 200/5005] lr: 1.0000e-03 eta: 18:26:28 time: 0.2255 data_time: 0.0032 loss: 1.2275 03/06 05:17:48 - mmengine - INFO - Epoch(train) [83][ 300/5005] lr: 1.0000e-03 eta: 18:26:05 time: 0.2270 data_time: 0.0035 loss: 0.9790 03/06 05:18:11 - mmengine - INFO - Epoch(train) [83][ 400/5005] lr: 1.0000e-03 eta: 18:25:42 time: 0.2252 data_time: 0.0035 loss: 0.9383 03/06 05:18:34 - mmengine - INFO - Epoch(train) [83][ 500/5005] lr: 1.0000e-03 eta: 18:25:19 time: 0.2267 data_time: 0.0031 loss: 0.8795 03/06 05:18:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:18:57 - mmengine - INFO - Epoch(train) [83][ 600/5005] lr: 1.0000e-03 eta: 18:24:56 time: 0.2226 data_time: 0.0031 loss: 1.0500 03/06 05:19:20 - mmengine - INFO - Epoch(train) [83][ 700/5005] lr: 1.0000e-03 eta: 18:24:33 time: 0.2209 data_time: 0.0034 loss: 0.8768 03/06 05:19:43 - mmengine - INFO - Epoch(train) [83][ 800/5005] lr: 1.0000e-03 eta: 18:24:10 time: 0.2514 data_time: 0.0032 loss: 0.8935 03/06 05:20:07 - mmengine - INFO - Epoch(train) [83][ 900/5005] lr: 1.0000e-03 eta: 18:23:48 time: 0.2268 data_time: 0.0031 loss: 1.0344 03/06 05:20:30 - mmengine - INFO - Epoch(train) [83][1000/5005] lr: 1.0000e-03 eta: 18:23:25 time: 0.2279 data_time: 0.0031 loss: 0.8461 03/06 05:20:53 - mmengine - INFO - Epoch(train) [83][1100/5005] lr: 1.0000e-03 eta: 18:23:02 time: 0.2280 data_time: 0.0032 loss: 1.1123 03/06 05:21:15 - mmengine - INFO - Epoch(train) [83][1200/5005] lr: 1.0000e-03 eta: 18:22:39 time: 0.2241 data_time: 0.0032 loss: 0.9324 03/06 05:21:39 - mmengine - INFO - Epoch(train) [83][1300/5005] lr: 1.0000e-03 eta: 18:22:16 time: 0.2319 data_time: 0.0033 loss: 1.0396 03/06 05:22:02 - mmengine - INFO - Epoch(train) [83][1400/5005] lr: 1.0000e-03 eta: 18:21:53 time: 0.2260 data_time: 0.0031 loss: 1.1270 03/06 05:22:25 - mmengine - INFO - Epoch(train) [83][1500/5005] lr: 1.0000e-03 eta: 18:21:30 time: 0.2278 data_time: 0.0032 loss: 1.0292 03/06 05:22:45 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:22:47 - mmengine - INFO - Epoch(train) [83][1600/5005] lr: 1.0000e-03 eta: 18:21:07 time: 0.2249 data_time: 0.0035 loss: 0.9739 03/06 05:23:11 - mmengine - INFO - Epoch(train) [83][1700/5005] lr: 1.0000e-03 eta: 18:20:44 time: 0.2235 data_time: 0.0036 loss: 1.1161 03/06 05:23:34 - mmengine - INFO - Epoch(train) [83][1800/5005] lr: 1.0000e-03 eta: 18:20:22 time: 0.2238 data_time: 0.0033 loss: 0.9236 03/06 05:23:57 - mmengine - INFO - Epoch(train) [83][1900/5005] lr: 1.0000e-03 eta: 18:19:59 time: 0.2237 data_time: 0.0036 loss: 0.8830 03/06 05:24:20 - mmengine - INFO - Epoch(train) [83][2000/5005] lr: 1.0000e-03 eta: 18:19:36 time: 0.2226 data_time: 0.0032 loss: 1.0573 03/06 05:24:43 - mmengine - INFO - Epoch(train) [83][2100/5005] lr: 1.0000e-03 eta: 18:19:13 time: 0.2445 data_time: 0.0034 loss: 1.0424 03/06 05:25:06 - mmengine - INFO - Epoch(train) [83][2200/5005] lr: 1.0000e-03 eta: 18:18:50 time: 0.2419 data_time: 0.0035 loss: 1.0069 03/06 05:25:29 - mmengine - INFO - Epoch(train) [83][2300/5005] lr: 1.0000e-03 eta: 18:18:27 time: 0.2221 data_time: 0.0033 loss: 0.9238 03/06 05:25:52 - mmengine - INFO - Epoch(train) [83][2400/5005] lr: 1.0000e-03 eta: 18:18:04 time: 0.2281 data_time: 0.0033 loss: 1.1344 03/06 05:26:15 - mmengine - INFO - Epoch(train) [83][2500/5005] lr: 1.0000e-03 eta: 18:17:41 time: 0.2287 data_time: 0.0034 loss: 0.9017 03/06 05:26:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:26:38 - mmengine - INFO - Epoch(train) [83][2600/5005] lr: 1.0000e-03 eta: 18:17:18 time: 0.2494 data_time: 0.0035 loss: 1.1276 03/06 05:27:02 - mmengine - INFO - Epoch(train) [83][2700/5005] lr: 1.0000e-03 eta: 18:16:55 time: 0.2300 data_time: 0.0032 loss: 0.9807 03/06 05:27:24 - mmengine - INFO - Epoch(train) [83][2800/5005] lr: 1.0000e-03 eta: 18:16:32 time: 0.2251 data_time: 0.0031 loss: 1.0006 03/06 05:27:47 - mmengine - INFO - Epoch(train) [83][2900/5005] lr: 1.0000e-03 eta: 18:16:09 time: 0.2237 data_time: 0.0033 loss: 0.8940 03/06 05:28:10 - mmengine - INFO - Epoch(train) [83][3000/5005] lr: 1.0000e-03 eta: 18:15:47 time: 0.2303 data_time: 0.0032 loss: 1.1537 03/06 05:28:33 - mmengine - INFO - Epoch(train) [83][3100/5005] lr: 1.0000e-03 eta: 18:15:24 time: 0.2241 data_time: 0.0040 loss: 0.9228 03/06 05:28:56 - mmengine - INFO - Epoch(train) [83][3200/5005] lr: 1.0000e-03 eta: 18:15:01 time: 0.2243 data_time: 0.0033 loss: 1.1560 03/06 05:29:19 - mmengine - INFO - Epoch(train) [83][3300/5005] lr: 1.0000e-03 eta: 18:14:38 time: 0.2272 data_time: 0.0033 loss: 1.0744 03/06 05:29:42 - mmengine - INFO - Epoch(train) [83][3400/5005] lr: 1.0000e-03 eta: 18:14:15 time: 0.2275 data_time: 0.0035 loss: 1.1636 03/06 05:30:06 - mmengine - INFO - Epoch(train) [83][3500/5005] lr: 1.0000e-03 eta: 18:13:52 time: 0.2482 data_time: 0.0037 loss: 1.1057 03/06 05:30:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:30:29 - mmengine - INFO - Epoch(train) [83][3600/5005] lr: 1.0000e-03 eta: 18:13:29 time: 0.2239 data_time: 0.0032 loss: 1.0625 03/06 05:30:52 - mmengine - INFO - Epoch(train) [83][3700/5005] lr: 1.0000e-03 eta: 18:13:06 time: 0.2404 data_time: 0.0032 loss: 1.0416 03/06 05:31:14 - mmengine - INFO - Epoch(train) [83][3800/5005] lr: 1.0000e-03 eta: 18:12:43 time: 0.2312 data_time: 0.0034 loss: 0.8464 03/06 05:31:37 - mmengine - INFO - Epoch(train) [83][3900/5005] lr: 1.0000e-03 eta: 18:12:20 time: 0.2249 data_time: 0.0033 loss: 1.1308 03/06 05:32:00 - mmengine - INFO - Epoch(train) [83][4000/5005] lr: 1.0000e-03 eta: 18:11:57 time: 0.2219 data_time: 0.0036 loss: 1.1908 03/06 05:32:24 - mmengine - INFO - Epoch(train) [83][4100/5005] lr: 1.0000e-03 eta: 18:11:35 time: 0.2244 data_time: 0.0038 loss: 0.9790 03/06 05:32:46 - mmengine - INFO - Epoch(train) [83][4200/5005] lr: 1.0000e-03 eta: 18:11:11 time: 0.2260 data_time: 0.0032 loss: 1.1437 03/06 05:33:10 - mmengine - INFO - Epoch(train) [83][4300/5005] lr: 1.0000e-03 eta: 18:10:49 time: 0.2260 data_time: 0.0030 loss: 1.0473 03/06 05:33:33 - mmengine - INFO - Epoch(train) [83][4400/5005] lr: 1.0000e-03 eta: 18:10:26 time: 0.2272 data_time: 0.0035 loss: 1.0662 03/06 05:33:56 - mmengine - INFO - Epoch(train) [83][4500/5005] lr: 1.0000e-03 eta: 18:10:03 time: 0.2357 data_time: 0.0034 loss: 0.9354 03/06 05:34:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:34:19 - mmengine - INFO - Epoch(train) [83][4600/5005] lr: 1.0000e-03 eta: 18:09:40 time: 0.2244 data_time: 0.0030 loss: 0.8814 03/06 05:34:42 - mmengine - INFO - Epoch(train) [83][4700/5005] lr: 1.0000e-03 eta: 18:09:17 time: 0.2257 data_time: 0.0031 loss: 1.0096 03/06 05:35:05 - mmengine - INFO - Epoch(train) [83][4800/5005] lr: 1.0000e-03 eta: 18:08:54 time: 0.2411 data_time: 0.0032 loss: 0.9139 03/06 05:35:29 - mmengine - INFO - Epoch(train) [83][4900/5005] lr: 1.0000e-03 eta: 18:08:32 time: 0.2919 data_time: 0.0033 loss: 1.0465 03/06 05:35:58 - mmengine - INFO - Epoch(train) [83][5000/5005] lr: 1.0000e-03 eta: 18:08:13 time: 0.2551 data_time: 0.0031 loss: 0.9897 03/06 05:35:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:36:02 - mmengine - INFO - Saving checkpoint at 83 epochs 03/06 05:36:17 - mmengine - INFO - Epoch(val) [83][100/196] eta: 0:00:13 time: 0.0193 data_time: 0.0004 03/06 05:36:30 - mmengine - INFO - Epoch(val) [83][196/196] accuracy/top1: 76.5080 accuracy/top5: 93.2900 03/06 05:37:03 - mmengine - INFO - Epoch(train) [84][ 100/5005] lr: 1.0000e-03 eta: 18:07:56 time: 0.2255 data_time: 0.0039 loss: 0.9579 03/06 05:37:26 - mmengine - INFO - Epoch(train) [84][ 200/5005] lr: 1.0000e-03 eta: 18:07:33 time: 0.2219 data_time: 0.0039 loss: 0.9112 03/06 05:37:49 - mmengine - INFO - Epoch(train) [84][ 300/5005] lr: 1.0000e-03 eta: 18:07:10 time: 0.2243 data_time: 0.0036 loss: 0.9842 03/06 05:38:12 - mmengine - INFO - Epoch(train) [84][ 400/5005] lr: 1.0000e-03 eta: 18:06:47 time: 0.2228 data_time: 0.0034 loss: 1.1088 03/06 05:38:36 - mmengine - INFO - Epoch(train) [84][ 500/5005] lr: 1.0000e-03 eta: 18:06:24 time: 0.2230 data_time: 0.0033 loss: 0.9359 03/06 05:38:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:38:58 - mmengine - INFO - Epoch(train) [84][ 600/5005] lr: 1.0000e-03 eta: 18:06:01 time: 0.2240 data_time: 0.0032 loss: 1.0509 03/06 05:39:22 - mmengine - INFO - Epoch(train) [84][ 700/5005] lr: 1.0000e-03 eta: 18:05:39 time: 0.2258 data_time: 0.0037 loss: 1.0830 03/06 05:39:45 - mmengine - INFO - Epoch(train) [84][ 800/5005] lr: 1.0000e-03 eta: 18:05:16 time: 0.2260 data_time: 0.0032 loss: 0.9191 03/06 05:40:08 - mmengine - INFO - Epoch(train) [84][ 900/5005] lr: 1.0000e-03 eta: 18:04:53 time: 0.2242 data_time: 0.0041 loss: 0.9597 03/06 05:40:31 - mmengine - INFO - Epoch(train) [84][1000/5005] lr: 1.0000e-03 eta: 18:04:30 time: 0.2484 data_time: 0.0036 loss: 1.0494 03/06 05:40:54 - mmengine - INFO - Epoch(train) [84][1100/5005] lr: 1.0000e-03 eta: 18:04:07 time: 0.2470 data_time: 0.0036 loss: 1.1599 03/06 05:41:18 - mmengine - INFO - Epoch(train) [84][1200/5005] lr: 1.0000e-03 eta: 18:03:44 time: 0.2688 data_time: 0.0033 loss: 0.9718 03/06 05:41:40 - mmengine - INFO - Epoch(train) [84][1300/5005] lr: 1.0000e-03 eta: 18:03:21 time: 0.2258 data_time: 0.0033 loss: 1.0107 03/06 05:42:03 - mmengine - INFO - Epoch(train) [84][1400/5005] lr: 1.0000e-03 eta: 18:02:58 time: 0.2276 data_time: 0.0036 loss: 1.0862 03/06 05:42:26 - mmengine - INFO - Epoch(train) [84][1500/5005] lr: 1.0000e-03 eta: 18:02:35 time: 0.2267 data_time: 0.0037 loss: 0.8133 03/06 05:42:46 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:42:49 - mmengine - INFO - Epoch(train) [84][1600/5005] lr: 1.0000e-03 eta: 18:02:13 time: 0.2244 data_time: 0.0031 loss: 1.2232 03/06 05:43:12 - mmengine - INFO - Epoch(train) [84][1700/5005] lr: 1.0000e-03 eta: 18:01:50 time: 0.2237 data_time: 0.0032 loss: 0.9105 03/06 05:43:35 - mmengine - INFO - Epoch(train) [84][1800/5005] lr: 1.0000e-03 eta: 18:01:27 time: 0.2231 data_time: 0.0031 loss: 0.8553 03/06 05:43:58 - mmengine - INFO - Epoch(train) [84][1900/5005] lr: 1.0000e-03 eta: 18:01:04 time: 0.2241 data_time: 0.0036 loss: 1.0257 03/06 05:44:21 - mmengine - INFO - Epoch(train) [84][2000/5005] lr: 1.0000e-03 eta: 18:00:40 time: 0.2235 data_time: 0.0033 loss: 1.0011 03/06 05:44:44 - mmengine - INFO - Epoch(train) [84][2100/5005] lr: 1.0000e-03 eta: 18:00:18 time: 0.2248 data_time: 0.0034 loss: 1.1174 03/06 05:45:07 - mmengine - INFO - Epoch(train) [84][2200/5005] lr: 1.0000e-03 eta: 17:59:55 time: 0.2459 data_time: 0.0037 loss: 0.9565 03/06 05:45:30 - mmengine - INFO - Epoch(train) [84][2300/5005] lr: 1.0000e-03 eta: 17:59:32 time: 0.2267 data_time: 0.0033 loss: 1.0676 03/06 05:45:53 - mmengine - INFO - Epoch(train) [84][2400/5005] lr: 1.0000e-03 eta: 17:59:09 time: 0.2279 data_time: 0.0039 loss: 0.9835 03/06 05:46:16 - mmengine - INFO - Epoch(train) [84][2500/5005] lr: 1.0000e-03 eta: 17:58:46 time: 0.2404 data_time: 0.0037 loss: 0.9697 03/06 05:46:35 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:46:39 - mmengine - INFO - Epoch(train) [84][2600/5005] lr: 1.0000e-03 eta: 17:58:23 time: 0.2263 data_time: 0.0041 loss: 0.9474 03/06 05:47:02 - mmengine - INFO - Epoch(train) [84][2700/5005] lr: 1.0000e-03 eta: 17:58:00 time: 0.2435 data_time: 0.0034 loss: 0.9271 03/06 05:47:25 - mmengine - INFO - Epoch(train) [84][2800/5005] lr: 1.0000e-03 eta: 17:57:37 time: 0.2255 data_time: 0.0032 loss: 1.0439 03/06 05:47:48 - mmengine - INFO - Epoch(train) [84][2900/5005] lr: 1.0000e-03 eta: 17:57:14 time: 0.2213 data_time: 0.0037 loss: 1.0323 03/06 05:48:11 - mmengine - INFO - Epoch(train) [84][3000/5005] lr: 1.0000e-03 eta: 17:56:51 time: 0.2257 data_time: 0.0032 loss: 1.1571 03/06 05:48:34 - mmengine - INFO - Epoch(train) [84][3100/5005] lr: 1.0000e-03 eta: 17:56:28 time: 0.2264 data_time: 0.0034 loss: 1.0735 03/06 05:48:57 - mmengine - INFO - Epoch(train) [84][3200/5005] lr: 1.0000e-03 eta: 17:56:05 time: 0.2505 data_time: 0.0032 loss: 0.9870 03/06 05:49:20 - mmengine - INFO - Epoch(train) [84][3300/5005] lr: 1.0000e-03 eta: 17:55:43 time: 0.2299 data_time: 0.0035 loss: 1.0448 03/06 05:49:43 - mmengine - INFO - Epoch(train) [84][3400/5005] lr: 1.0000e-03 eta: 17:55:20 time: 0.2283 data_time: 0.0035 loss: 0.8876 03/06 05:50:06 - mmengine - INFO - Epoch(train) [84][3500/5005] lr: 1.0000e-03 eta: 17:54:57 time: 0.2454 data_time: 0.0040 loss: 1.1448 03/06 05:50:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:50:29 - mmengine - INFO - Epoch(train) [84][3600/5005] lr: 1.0000e-03 eta: 17:54:34 time: 0.2455 data_time: 0.0036 loss: 1.0096 03/06 05:50:52 - mmengine - INFO - Epoch(train) [84][3700/5005] lr: 1.0000e-03 eta: 17:54:11 time: 0.2298 data_time: 0.0031 loss: 0.8295 03/06 05:51:15 - mmengine - INFO - Epoch(train) [84][3800/5005] lr: 1.0000e-03 eta: 17:53:48 time: 0.2259 data_time: 0.0030 loss: 1.0636 03/06 05:51:38 - mmengine - INFO - Epoch(train) [84][3900/5005] lr: 1.0000e-03 eta: 17:53:25 time: 0.2232 data_time: 0.0033 loss: 0.9337 03/06 05:52:01 - mmengine - INFO - Epoch(train) [84][4000/5005] lr: 1.0000e-03 eta: 17:53:02 time: 0.2235 data_time: 0.0035 loss: 0.8704 03/06 05:52:24 - mmengine - INFO - Epoch(train) [84][4100/5005] lr: 1.0000e-03 eta: 17:52:39 time: 0.2244 data_time: 0.0034 loss: 0.8858 03/06 05:52:47 - mmengine - INFO - Epoch(train) [84][4200/5005] lr: 1.0000e-03 eta: 17:52:16 time: 0.2250 data_time: 0.0041 loss: 1.0010 03/06 05:53:10 - mmengine - INFO - Epoch(train) [84][4300/5005] lr: 1.0000e-03 eta: 17:51:53 time: 0.2331 data_time: 0.0037 loss: 1.1834 03/06 05:53:33 - mmengine - INFO - Epoch(train) [84][4400/5005] lr: 1.0000e-03 eta: 17:51:30 time: 0.2221 data_time: 0.0034 loss: 0.8851 03/06 05:53:56 - mmengine - INFO - Epoch(train) [84][4500/5005] lr: 1.0000e-03 eta: 17:51:07 time: 0.2258 data_time: 0.0034 loss: 1.0481 03/06 05:54:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:54:19 - mmengine - INFO - Epoch(train) [84][4600/5005] lr: 1.0000e-03 eta: 17:50:45 time: 0.2418 data_time: 0.0031 loss: 0.8946 03/06 05:54:42 - mmengine - INFO - Epoch(train) [84][4700/5005] lr: 1.0000e-03 eta: 17:50:22 time: 0.2312 data_time: 0.0036 loss: 1.1817 03/06 05:55:05 - mmengine - INFO - Epoch(train) [84][4800/5005] lr: 1.0000e-03 eta: 17:49:59 time: 0.2291 data_time: 0.0037 loss: 0.9050 03/06 05:55:29 - mmengine - INFO - Epoch(train) [84][4900/5005] lr: 1.0000e-03 eta: 17:49:36 time: 0.2882 data_time: 0.0030 loss: 0.9285 03/06 05:55:58 - mmengine - INFO - Epoch(train) [84][5000/5005] lr: 1.0000e-03 eta: 17:49:17 time: 0.2817 data_time: 0.0032 loss: 0.9878 03/06 05:55:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:56:02 - mmengine - INFO - Saving checkpoint at 84 epochs 03/06 05:56:17 - mmengine - INFO - Epoch(val) [84][100/196] eta: 0:00:13 time: 0.0172 data_time: 0.0003 03/06 05:56:31 - mmengine - INFO - Epoch(val) [84][196/196] accuracy/top1: 76.5520 accuracy/top5: 93.4160 03/06 05:57:03 - mmengine - INFO - Epoch(train) [85][ 100/5005] lr: 1.0000e-03 eta: 17:48:59 time: 0.2244 data_time: 0.0042 loss: 1.0408 03/06 05:57:25 - mmengine - INFO - Epoch(train) [85][ 200/5005] lr: 1.0000e-03 eta: 17:48:36 time: 0.2252 data_time: 0.0039 loss: 0.8706 03/06 05:57:49 - mmengine - INFO - Epoch(train) [85][ 300/5005] lr: 1.0000e-03 eta: 17:48:13 time: 0.2250 data_time: 0.0038 loss: 0.8205 03/06 05:58:12 - mmengine - INFO - Epoch(train) [85][ 400/5005] lr: 1.0000e-03 eta: 17:47:50 time: 0.2413 data_time: 0.0034 loss: 0.9746 03/06 05:58:35 - mmengine - INFO - Epoch(train) [85][ 500/5005] lr: 1.0000e-03 eta: 17:47:28 time: 0.2255 data_time: 0.0030 loss: 0.9223 03/06 05:58:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 05:58:58 - mmengine - INFO - Epoch(train) [85][ 600/5005] lr: 1.0000e-03 eta: 17:47:05 time: 0.2232 data_time: 0.0032 loss: 0.9199 03/06 05:59:21 - mmengine - INFO - Epoch(train) [85][ 700/5005] lr: 1.0000e-03 eta: 17:46:42 time: 0.2282 data_time: 0.0033 loss: 0.9611 03/06 05:59:44 - mmengine - INFO - Epoch(train) [85][ 800/5005] lr: 1.0000e-03 eta: 17:46:19 time: 0.2283 data_time: 0.0033 loss: 0.9062 03/06 06:00:07 - mmengine - INFO - Epoch(train) [85][ 900/5005] lr: 1.0000e-03 eta: 17:45:56 time: 0.2250 data_time: 0.0037 loss: 0.9746 03/06 06:00:30 - mmengine - INFO - Epoch(train) [85][1000/5005] lr: 1.0000e-03 eta: 17:45:33 time: 0.2212 data_time: 0.0034 loss: 1.0177 03/06 06:00:53 - mmengine - INFO - Epoch(train) [85][1100/5005] lr: 1.0000e-03 eta: 17:45:10 time: 0.2386 data_time: 0.0036 loss: 1.1309 03/06 06:01:16 - mmengine - INFO - Epoch(train) [85][1200/5005] lr: 1.0000e-03 eta: 17:44:47 time: 0.2265 data_time: 0.0038 loss: 1.1670 03/06 06:01:39 - mmengine - INFO - Epoch(train) [85][1300/5005] lr: 1.0000e-03 eta: 17:44:24 time: 0.2250 data_time: 0.0036 loss: 0.9728 03/06 06:02:02 - mmengine - INFO - Epoch(train) [85][1400/5005] lr: 1.0000e-03 eta: 17:44:02 time: 0.2582 data_time: 0.0033 loss: 1.0941 03/06 06:02:26 - mmengine - INFO - Epoch(train) [85][1500/5005] lr: 1.0000e-03 eta: 17:43:39 time: 0.2438 data_time: 0.0033 loss: 0.8293 03/06 06:02:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:02:49 - mmengine - INFO - Epoch(train) [85][1600/5005] lr: 1.0000e-03 eta: 17:43:16 time: 0.2343 data_time: 0.0033 loss: 0.9883 03/06 06:03:11 - mmengine - INFO - Epoch(train) [85][1700/5005] lr: 1.0000e-03 eta: 17:42:53 time: 0.2248 data_time: 0.0034 loss: 0.9009 03/06 06:03:34 - mmengine - INFO - Epoch(train) [85][1800/5005] lr: 1.0000e-03 eta: 17:42:30 time: 0.2352 data_time: 0.0032 loss: 0.9048 03/06 06:03:57 - mmengine - INFO - Epoch(train) [85][1900/5005] lr: 1.0000e-03 eta: 17:42:07 time: 0.2257 data_time: 0.0033 loss: 0.9733 03/06 06:04:20 - mmengine - INFO - Epoch(train) [85][2000/5005] lr: 1.0000e-03 eta: 17:41:44 time: 0.2221 data_time: 0.0030 loss: 1.1112 03/06 06:04:43 - mmengine - INFO - Epoch(train) [85][2100/5005] lr: 1.0000e-03 eta: 17:41:21 time: 0.2233 data_time: 0.0034 loss: 0.8946 03/06 06:05:06 - mmengine - INFO - Epoch(train) [85][2200/5005] lr: 1.0000e-03 eta: 17:40:58 time: 0.2262 data_time: 0.0035 loss: 1.0020 03/06 06:05:29 - mmengine - INFO - Epoch(train) [85][2300/5005] lr: 1.0000e-03 eta: 17:40:35 time: 0.2266 data_time: 0.0033 loss: 1.0907 03/06 06:05:52 - mmengine - INFO - Epoch(train) [85][2400/5005] lr: 1.0000e-03 eta: 17:40:12 time: 0.2247 data_time: 0.0036 loss: 1.0227 03/06 06:06:15 - mmengine - INFO - Epoch(train) [85][2500/5005] lr: 1.0000e-03 eta: 17:39:49 time: 0.2270 data_time: 0.0032 loss: 1.1033 03/06 06:06:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:06:38 - mmengine - INFO - Epoch(train) [85][2600/5005] lr: 1.0000e-03 eta: 17:39:26 time: 0.2254 data_time: 0.0036 loss: 0.8986 03/06 06:07:01 - mmengine - INFO - Epoch(train) [85][2700/5005] lr: 1.0000e-03 eta: 17:39:03 time: 0.2244 data_time: 0.0035 loss: 1.1254 03/06 06:07:24 - mmengine - INFO - Epoch(train) [85][2800/5005] lr: 1.0000e-03 eta: 17:38:40 time: 0.2335 data_time: 0.0034 loss: 0.9997 03/06 06:07:47 - mmengine - INFO - Epoch(train) [85][2900/5005] lr: 1.0000e-03 eta: 17:38:17 time: 0.2246 data_time: 0.0034 loss: 1.0822 03/06 06:08:10 - mmengine - INFO - Epoch(train) [85][3000/5005] lr: 1.0000e-03 eta: 17:37:54 time: 0.2267 data_time: 0.0031 loss: 0.9532 03/06 06:08:33 - mmengine - INFO - Epoch(train) [85][3100/5005] lr: 1.0000e-03 eta: 17:37:31 time: 0.2245 data_time: 0.0033 loss: 0.9824 03/06 06:08:56 - mmengine - INFO - Epoch(train) [85][3200/5005] lr: 1.0000e-03 eta: 17:37:08 time: 0.2254 data_time: 0.0033 loss: 0.7857 03/06 06:09:19 - mmengine - INFO - Epoch(train) [85][3300/5005] lr: 1.0000e-03 eta: 17:36:45 time: 0.2287 data_time: 0.0034 loss: 0.8497 03/06 06:09:42 - mmengine - INFO - Epoch(train) [85][3400/5005] lr: 1.0000e-03 eta: 17:36:22 time: 0.2226 data_time: 0.0034 loss: 0.9781 03/06 06:10:05 - mmengine - INFO - Epoch(train) [85][3500/5005] lr: 1.0000e-03 eta: 17:35:59 time: 0.2251 data_time: 0.0031 loss: 1.1315 03/06 06:10:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:10:28 - mmengine - INFO - Epoch(train) [85][3600/5005] lr: 1.0000e-03 eta: 17:35:37 time: 0.2241 data_time: 0.0034 loss: 0.8570 03/06 06:10:51 - mmengine - INFO - Epoch(train) [85][3700/5005] lr: 1.0000e-03 eta: 17:35:13 time: 0.2255 data_time: 0.0033 loss: 0.8850 03/06 06:11:14 - mmengine - INFO - Epoch(train) [85][3800/5005] lr: 1.0000e-03 eta: 17:34:51 time: 0.2297 data_time: 0.0034 loss: 0.9297 03/06 06:11:37 - mmengine - INFO - Epoch(train) [85][3900/5005] lr: 1.0000e-03 eta: 17:34:28 time: 0.2250 data_time: 0.0034 loss: 0.9239 03/06 06:12:00 - mmengine - INFO - Epoch(train) [85][4000/5005] lr: 1.0000e-03 eta: 17:34:05 time: 0.2245 data_time: 0.0034 loss: 0.9223 03/06 06:12:23 - mmengine - INFO - Epoch(train) [85][4100/5005] lr: 1.0000e-03 eta: 17:33:42 time: 0.2266 data_time: 0.0036 loss: 1.0339 03/06 06:12:46 - mmengine - INFO - Epoch(train) [85][4200/5005] lr: 1.0000e-03 eta: 17:33:19 time: 0.2404 data_time: 0.0032 loss: 0.8816 03/06 06:13:09 - mmengine - INFO - Epoch(train) [85][4300/5005] lr: 1.0000e-03 eta: 17:32:56 time: 0.2245 data_time: 0.0034 loss: 1.0561 03/06 06:13:32 - mmengine - INFO - Epoch(train) [85][4400/5005] lr: 1.0000e-03 eta: 17:32:33 time: 0.2271 data_time: 0.0032 loss: 1.0810 03/06 06:13:55 - mmengine - INFO - Epoch(train) [85][4500/5005] lr: 1.0000e-03 eta: 17:32:10 time: 0.2242 data_time: 0.0030 loss: 1.0378 03/06 06:14:13 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:14:18 - mmengine - INFO - Epoch(train) [85][4600/5005] lr: 1.0000e-03 eta: 17:31:47 time: 0.2432 data_time: 0.0031 loss: 0.9454 03/06 06:14:41 - mmengine - INFO - Epoch(train) [85][4700/5005] lr: 1.0000e-03 eta: 17:31:24 time: 0.2257 data_time: 0.0031 loss: 0.9604 03/06 06:15:04 - mmengine - INFO - Epoch(train) [85][4800/5005] lr: 1.0000e-03 eta: 17:31:01 time: 0.2243 data_time: 0.0030 loss: 0.8589 03/06 06:15:28 - mmengine - INFO - Epoch(train) [85][4900/5005] lr: 1.0000e-03 eta: 17:30:39 time: 0.2724 data_time: 0.0031 loss: 1.1272 03/06 06:15:57 - mmengine - INFO - Epoch(train) [85][5000/5005] lr: 1.0000e-03 eta: 17:30:20 time: 0.3026 data_time: 0.0032 loss: 1.0050 03/06 06:15:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:16:02 - mmengine - INFO - Saving checkpoint at 85 epochs 03/06 06:16:17 - mmengine - INFO - Epoch(val) [85][100/196] eta: 0:00:13 time: 0.0196 data_time: 0.0003 03/06 06:16:31 - mmengine - INFO - Epoch(val) [85][196/196] accuracy/top1: 76.6620 accuracy/top5: 93.3680 03/06 06:17:03 - mmengine - INFO - Epoch(train) [86][ 100/5005] lr: 1.0000e-03 eta: 17:30:03 time: 0.2268 data_time: 0.0040 loss: 1.0185 03/06 06:17:26 - mmengine - INFO - Epoch(train) [86][ 200/5005] lr: 1.0000e-03 eta: 17:29:40 time: 0.2271 data_time: 0.0042 loss: 0.9335 03/06 06:17:49 - mmengine - INFO - Epoch(train) [86][ 300/5005] lr: 1.0000e-03 eta: 17:29:17 time: 0.2251 data_time: 0.0036 loss: 0.8684 03/06 06:18:12 - mmengine - INFO - Epoch(train) [86][ 400/5005] lr: 1.0000e-03 eta: 17:28:54 time: 0.2242 data_time: 0.0031 loss: 0.8908 03/06 06:18:35 - mmengine - INFO - Epoch(train) [86][ 500/5005] lr: 1.0000e-03 eta: 17:28:31 time: 0.2248 data_time: 0.0035 loss: 0.8713 03/06 06:18:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:18:59 - mmengine - INFO - Epoch(train) [86][ 600/5005] lr: 1.0000e-03 eta: 17:28:08 time: 0.2239 data_time: 0.0034 loss: 1.0454 03/06 06:19:21 - mmengine - INFO - Epoch(train) [86][ 700/5005] lr: 1.0000e-03 eta: 17:27:45 time: 0.2262 data_time: 0.0035 loss: 1.0213 03/06 06:19:45 - mmengine - INFO - Epoch(train) [86][ 800/5005] lr: 1.0000e-03 eta: 17:27:22 time: 0.2756 data_time: 0.0032 loss: 0.9403 03/06 06:20:08 - mmengine - INFO - Epoch(train) [86][ 900/5005] lr: 1.0000e-03 eta: 17:26:59 time: 0.2221 data_time: 0.0037 loss: 1.1443 03/06 06:20:31 - mmengine - INFO - Epoch(train) [86][1000/5005] lr: 1.0000e-03 eta: 17:26:36 time: 0.2227 data_time: 0.0033 loss: 1.0231 03/06 06:20:53 - mmengine - INFO - Epoch(train) [86][1100/5005] lr: 1.0000e-03 eta: 17:26:13 time: 0.2236 data_time: 0.0035 loss: 1.0029 03/06 06:21:16 - mmengine - INFO - Epoch(train) [86][1200/5005] lr: 1.0000e-03 eta: 17:25:50 time: 0.2263 data_time: 0.0034 loss: 0.9590 03/06 06:21:39 - mmengine - INFO - Epoch(train) [86][1300/5005] lr: 1.0000e-03 eta: 17:25:27 time: 0.2254 data_time: 0.0035 loss: 1.0614 03/06 06:22:02 - mmengine - INFO - Epoch(train) [86][1400/5005] lr: 1.0000e-03 eta: 17:25:04 time: 0.2240 data_time: 0.0034 loss: 1.1471 03/06 06:22:25 - mmengine - INFO - Epoch(train) [86][1500/5005] lr: 1.0000e-03 eta: 17:24:41 time: 0.2232 data_time: 0.0039 loss: 0.8408 03/06 06:22:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:22:48 - mmengine - INFO - Epoch(train) [86][1600/5005] lr: 1.0000e-03 eta: 17:24:18 time: 0.2274 data_time: 0.0037 loss: 0.9735 03/06 06:23:11 - mmengine - INFO - Epoch(train) [86][1700/5005] lr: 1.0000e-03 eta: 17:23:55 time: 0.2269 data_time: 0.0034 loss: 0.9471 03/06 06:23:34 - mmengine - INFO - Epoch(train) [86][1800/5005] lr: 1.0000e-03 eta: 17:23:32 time: 0.2243 data_time: 0.0032 loss: 0.9983 03/06 06:23:57 - mmengine - INFO - Epoch(train) [86][1900/5005] lr: 1.0000e-03 eta: 17:23:09 time: 0.2236 data_time: 0.0034 loss: 0.9326 03/06 06:24:20 - mmengine - INFO - Epoch(train) [86][2000/5005] lr: 1.0000e-03 eta: 17:22:46 time: 0.2238 data_time: 0.0034 loss: 1.0057 03/06 06:24:43 - mmengine - INFO - Epoch(train) [86][2100/5005] lr: 1.0000e-03 eta: 17:22:24 time: 0.2334 data_time: 0.0037 loss: 1.0026 03/06 06:25:06 - mmengine - INFO - Epoch(train) [86][2200/5005] lr: 1.0000e-03 eta: 17:22:01 time: 0.2254 data_time: 0.0033 loss: 0.9709 03/06 06:25:29 - mmengine - INFO - Epoch(train) [86][2300/5005] lr: 1.0000e-03 eta: 17:21:37 time: 0.2235 data_time: 0.0032 loss: 0.9677 03/06 06:25:52 - mmengine - INFO - Epoch(train) [86][2400/5005] lr: 1.0000e-03 eta: 17:21:15 time: 0.2283 data_time: 0.0033 loss: 1.0712 03/06 06:26:15 - mmengine - INFO - Epoch(train) [86][2500/5005] lr: 1.0000e-03 eta: 17:20:52 time: 0.2349 data_time: 0.0036 loss: 0.8122 03/06 06:26:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:26:38 - mmengine - INFO - Epoch(train) [86][2600/5005] lr: 1.0000e-03 eta: 17:20:29 time: 0.2235 data_time: 0.0034 loss: 0.9621 03/06 06:27:01 - mmengine - INFO - Epoch(train) [86][2700/5005] lr: 1.0000e-03 eta: 17:20:06 time: 0.2255 data_time: 0.0034 loss: 0.9318 03/06 06:27:24 - mmengine - INFO - Epoch(train) [86][2800/5005] lr: 1.0000e-03 eta: 17:19:43 time: 0.2286 data_time: 0.0035 loss: 0.9401 03/06 06:27:47 - mmengine - INFO - Epoch(train) [86][2900/5005] lr: 1.0000e-03 eta: 17:19:20 time: 0.2304 data_time: 0.0035 loss: 0.9941 03/06 06:28:10 - mmengine - INFO - Epoch(train) [86][3000/5005] lr: 1.0000e-03 eta: 17:18:57 time: 0.2230 data_time: 0.0035 loss: 0.8398 03/06 06:28:33 - mmengine - INFO - Epoch(train) [86][3100/5005] lr: 1.0000e-03 eta: 17:18:34 time: 0.2272 data_time: 0.0032 loss: 0.9504 03/06 06:28:56 - mmengine - INFO - Epoch(train) [86][3200/5005] lr: 1.0000e-03 eta: 17:18:11 time: 0.2457 data_time: 0.0032 loss: 0.9184 03/06 06:29:19 - mmengine - INFO - Epoch(train) [86][3300/5005] lr: 1.0000e-03 eta: 17:17:48 time: 0.2436 data_time: 0.0038 loss: 0.8886 03/06 06:29:43 - mmengine - INFO - Epoch(train) [86][3400/5005] lr: 1.0000e-03 eta: 17:17:26 time: 0.2256 data_time: 0.0034 loss: 0.7577 03/06 06:30:06 - mmengine - INFO - Epoch(train) [86][3500/5005] lr: 1.0000e-03 eta: 17:17:03 time: 0.2276 data_time: 0.0037 loss: 0.8267 03/06 06:30:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:30:28 - mmengine - INFO - Epoch(train) [86][3600/5005] lr: 1.0000e-03 eta: 17:16:40 time: 0.2279 data_time: 0.0034 loss: 0.8933 03/06 06:30:51 - mmengine - INFO - Epoch(train) [86][3700/5005] lr: 1.0000e-03 eta: 17:16:17 time: 0.2263 data_time: 0.0035 loss: 1.0583 03/06 06:31:15 - mmengine - INFO - Epoch(train) [86][3800/5005] lr: 1.0000e-03 eta: 17:15:54 time: 0.2282 data_time: 0.0032 loss: 1.1249 03/06 06:31:38 - mmengine - INFO - Epoch(train) [86][3900/5005] lr: 1.0000e-03 eta: 17:15:31 time: 0.2257 data_time: 0.0033 loss: 0.8721 03/06 06:32:00 - mmengine - INFO - Epoch(train) [86][4000/5005] lr: 1.0000e-03 eta: 17:15:08 time: 0.2241 data_time: 0.0031 loss: 0.9967 03/06 06:32:23 - mmengine - INFO - Epoch(train) [86][4100/5005] lr: 1.0000e-03 eta: 17:14:45 time: 0.2264 data_time: 0.0031 loss: 1.0525 03/06 06:32:47 - mmengine - INFO - Epoch(train) [86][4200/5005] lr: 1.0000e-03 eta: 17:14:22 time: 0.2243 data_time: 0.0033 loss: 0.8964 03/06 06:33:10 - mmengine - INFO - Epoch(train) [86][4300/5005] lr: 1.0000e-03 eta: 17:13:59 time: 0.2474 data_time: 0.0034 loss: 0.8486 03/06 06:33:32 - mmengine - INFO - Epoch(train) [86][4400/5005] lr: 1.0000e-03 eta: 17:13:36 time: 0.2235 data_time: 0.0034 loss: 0.9134 03/06 06:33:55 - mmengine - INFO - Epoch(train) [86][4500/5005] lr: 1.0000e-03 eta: 17:13:13 time: 0.2245 data_time: 0.0034 loss: 0.9653 03/06 06:34:13 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:34:18 - mmengine - INFO - Epoch(train) [86][4600/5005] lr: 1.0000e-03 eta: 17:12:50 time: 0.2255 data_time: 0.0031 loss: 1.0047 03/06 06:34:42 - mmengine - INFO - Epoch(train) [86][4700/5005] lr: 1.0000e-03 eta: 17:12:28 time: 0.2227 data_time: 0.0034 loss: 0.9191 03/06 06:35:05 - mmengine - INFO - Epoch(train) [86][4800/5005] lr: 1.0000e-03 eta: 17:12:05 time: 0.2399 data_time: 0.0032 loss: 1.1775 03/06 06:35:28 - mmengine - INFO - Epoch(train) [86][4900/5005] lr: 1.0000e-03 eta: 17:11:42 time: 0.2817 data_time: 0.0030 loss: 1.0342 03/06 06:35:57 - mmengine - INFO - Epoch(train) [86][5000/5005] lr: 1.0000e-03 eta: 17:11:23 time: 0.2829 data_time: 0.0031 loss: 1.0766 03/06 06:35:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:36:02 - mmengine - INFO - Saving checkpoint at 86 epochs 03/06 06:36:17 - mmengine - INFO - Epoch(val) [86][100/196] eta: 0:00:13 time: 0.0194 data_time: 0.0002 03/06 06:36:30 - mmengine - INFO - Epoch(val) [86][196/196] accuracy/top1: 76.7140 accuracy/top5: 93.3720 03/06 06:37:03 - mmengine - INFO - Epoch(train) [87][ 100/5005] lr: 1.0000e-03 eta: 17:11:05 time: 0.2279 data_time: 0.0039 loss: 0.9202 03/06 06:37:26 - mmengine - INFO - Epoch(train) [87][ 200/5005] lr: 1.0000e-03 eta: 17:10:42 time: 0.2250 data_time: 0.0039 loss: 0.8606 03/06 06:37:49 - mmengine - INFO - Epoch(train) [87][ 300/5005] lr: 1.0000e-03 eta: 17:10:19 time: 0.2240 data_time: 0.0043 loss: 0.8865 03/06 06:38:12 - mmengine - INFO - Epoch(train) [87][ 400/5005] lr: 1.0000e-03 eta: 17:09:56 time: 0.2254 data_time: 0.0041 loss: 0.9773 03/06 06:38:35 - mmengine - INFO - Epoch(train) [87][ 500/5005] lr: 1.0000e-03 eta: 17:09:33 time: 0.2849 data_time: 0.0048 loss: 0.9260 03/06 06:38:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:38:58 - mmengine - INFO - Epoch(train) [87][ 600/5005] lr: 1.0000e-03 eta: 17:09:10 time: 0.2264 data_time: 0.0037 loss: 0.8819 03/06 06:39:21 - mmengine - INFO - Epoch(train) [87][ 700/5005] lr: 1.0000e-03 eta: 17:08:47 time: 0.2274 data_time: 0.0038 loss: 0.9832 03/06 06:39:44 - mmengine - INFO - Epoch(train) [87][ 800/5005] lr: 1.0000e-03 eta: 17:08:24 time: 0.2255 data_time: 0.0034 loss: 0.9663 03/06 06:40:07 - mmengine - INFO - Epoch(train) [87][ 900/5005] lr: 1.0000e-03 eta: 17:08:01 time: 0.2416 data_time: 0.0033 loss: 1.1319 03/06 06:40:30 - mmengine - INFO - Epoch(train) [87][1000/5005] lr: 1.0000e-03 eta: 17:07:39 time: 0.2279 data_time: 0.0035 loss: 0.9121 03/06 06:40:53 - mmengine - INFO - Epoch(train) [87][1100/5005] lr: 1.0000e-03 eta: 17:07:15 time: 0.2251 data_time: 0.0042 loss: 0.9353 03/06 06:41:16 - mmengine - INFO - Epoch(train) [87][1200/5005] lr: 1.0000e-03 eta: 17:06:52 time: 0.2249 data_time: 0.0032 loss: 0.9624 03/06 06:41:39 - mmengine - INFO - Epoch(train) [87][1300/5005] lr: 1.0000e-03 eta: 17:06:29 time: 0.2243 data_time: 0.0035 loss: 1.0141 03/06 06:42:03 - mmengine - INFO - Epoch(train) [87][1400/5005] lr: 1.0000e-03 eta: 17:06:07 time: 0.2277 data_time: 0.0034 loss: 0.9317 03/06 06:42:26 - mmengine - INFO - Epoch(train) [87][1500/5005] lr: 1.0000e-03 eta: 17:05:44 time: 0.2277 data_time: 0.0035 loss: 0.9426 03/06 06:42:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:42:49 - mmengine - INFO - Epoch(train) [87][1600/5005] lr: 1.0000e-03 eta: 17:05:21 time: 0.2445 data_time: 0.0032 loss: 0.9578 03/06 06:43:11 - mmengine - INFO - Epoch(train) [87][1700/5005] lr: 1.0000e-03 eta: 17:04:58 time: 0.2240 data_time: 0.0036 loss: 1.1298 03/06 06:43:35 - mmengine - INFO - Epoch(train) [87][1800/5005] lr: 1.0000e-03 eta: 17:04:35 time: 0.2260 data_time: 0.0034 loss: 0.7914 03/06 06:43:58 - mmengine - INFO - Epoch(train) [87][1900/5005] lr: 1.0000e-03 eta: 17:04:12 time: 0.2279 data_time: 0.0035 loss: 0.9337 03/06 06:44:21 - mmengine - INFO - Epoch(train) [87][2000/5005] lr: 1.0000e-03 eta: 17:03:49 time: 0.2257 data_time: 0.0035 loss: 1.0447 03/06 06:44:44 - mmengine - INFO - Epoch(train) [87][2100/5005] lr: 1.0000e-03 eta: 17:03:26 time: 0.2237 data_time: 0.0032 loss: 0.8827 03/06 06:45:07 - mmengine - INFO - Epoch(train) [87][2200/5005] lr: 1.0000e-03 eta: 17:03:03 time: 0.2254 data_time: 0.0032 loss: 0.9999 03/06 06:45:30 - mmengine - INFO - Epoch(train) [87][2300/5005] lr: 1.0000e-03 eta: 17:02:40 time: 0.2258 data_time: 0.0033 loss: 1.0413 03/06 06:45:53 - mmengine - INFO - Epoch(train) [87][2400/5005] lr: 1.0000e-03 eta: 17:02:17 time: 0.2254 data_time: 0.0033 loss: 1.1271 03/06 06:46:15 - mmengine - INFO - Epoch(train) [87][2500/5005] lr: 1.0000e-03 eta: 17:01:54 time: 0.2242 data_time: 0.0038 loss: 0.9525 03/06 06:46:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:46:39 - mmengine - INFO - Epoch(train) [87][2600/5005] lr: 1.0000e-03 eta: 17:01:32 time: 0.2286 data_time: 0.0036 loss: 0.8964 03/06 06:47:02 - mmengine - INFO - Epoch(train) [87][2700/5005] lr: 1.0000e-03 eta: 17:01:09 time: 0.2265 data_time: 0.0036 loss: 1.1069 03/06 06:47:25 - mmengine - INFO - Epoch(train) [87][2800/5005] lr: 1.0000e-03 eta: 17:00:46 time: 0.2290 data_time: 0.0035 loss: 0.9481 03/06 06:47:47 - mmengine - INFO - Epoch(train) [87][2900/5005] lr: 1.0000e-03 eta: 17:00:23 time: 0.2252 data_time: 0.0030 loss: 0.8769 03/06 06:48:11 - mmengine - INFO - Epoch(train) [87][3000/5005] lr: 1.0000e-03 eta: 17:00:00 time: 0.2269 data_time: 0.0034 loss: 0.9078 03/06 06:48:34 - mmengine - INFO - Epoch(train) [87][3100/5005] lr: 1.0000e-03 eta: 16:59:37 time: 0.2268 data_time: 0.0041 loss: 0.9985 03/06 06:48:57 - mmengine - INFO - Epoch(train) [87][3200/5005] lr: 1.0000e-03 eta: 16:59:14 time: 0.2234 data_time: 0.0039 loss: 1.0398 03/06 06:49:20 - mmengine - INFO - Epoch(train) [87][3300/5005] lr: 1.0000e-03 eta: 16:58:51 time: 0.2259 data_time: 0.0036 loss: 0.9145 03/06 06:49:43 - mmengine - INFO - Epoch(train) [87][3400/5005] lr: 1.0000e-03 eta: 16:58:28 time: 0.2453 data_time: 0.0035 loss: 0.8755 03/06 06:50:06 - mmengine - INFO - Epoch(train) [87][3500/5005] lr: 1.0000e-03 eta: 16:58:05 time: 0.2270 data_time: 0.0032 loss: 0.9003 03/06 06:50:22 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:50:29 - mmengine - INFO - Epoch(train) [87][3600/5005] lr: 1.0000e-03 eta: 16:57:42 time: 0.2270 data_time: 0.0036 loss: 0.9100 03/06 06:50:52 - mmengine - INFO - Epoch(train) [87][3700/5005] lr: 1.0000e-03 eta: 16:57:19 time: 0.2271 data_time: 0.0031 loss: 0.9648 03/06 06:51:15 - mmengine - INFO - Epoch(train) [87][3800/5005] lr: 1.0000e-03 eta: 16:56:57 time: 0.2306 data_time: 0.0034 loss: 0.9949 03/06 06:51:39 - mmengine - INFO - Epoch(train) [87][3900/5005] lr: 1.0000e-03 eta: 16:56:34 time: 0.2312 data_time: 0.0034 loss: 0.9023 03/06 06:52:02 - mmengine - INFO - Epoch(train) [87][4000/5005] lr: 1.0000e-03 eta: 16:56:11 time: 0.2289 data_time: 0.0032 loss: 0.9519 03/06 06:52:24 - mmengine - INFO - Epoch(train) [87][4100/5005] lr: 1.0000e-03 eta: 16:55:48 time: 0.2263 data_time: 0.0031 loss: 0.9219 03/06 06:52:47 - mmengine - INFO - Epoch(train) [87][4200/5005] lr: 1.0000e-03 eta: 16:55:25 time: 0.2252 data_time: 0.0031 loss: 0.9541 03/06 06:53:11 - mmengine - INFO - Epoch(train) [87][4300/5005] lr: 1.0000e-03 eta: 16:55:02 time: 0.2240 data_time: 0.0036 loss: 0.9388 03/06 06:53:34 - mmengine - INFO - Epoch(train) [87][4400/5005] lr: 1.0000e-03 eta: 16:54:39 time: 0.2241 data_time: 0.0032 loss: 1.1163 03/06 06:53:57 - mmengine - INFO - Epoch(train) [87][4500/5005] lr: 1.0000e-03 eta: 16:54:16 time: 0.2315 data_time: 0.0036 loss: 0.8480 03/06 06:54:13 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:54:20 - mmengine - INFO - Epoch(train) [87][4600/5005] lr: 1.0000e-03 eta: 16:53:53 time: 0.2288 data_time: 0.0034 loss: 1.0992 03/06 06:54:43 - mmengine - INFO - Epoch(train) [87][4700/5005] lr: 1.0000e-03 eta: 16:53:30 time: 0.2254 data_time: 0.0032 loss: 0.8665 03/06 06:55:06 - mmengine - INFO - Epoch(train) [87][4800/5005] lr: 1.0000e-03 eta: 16:53:08 time: 0.2299 data_time: 0.0036 loss: 0.9906 03/06 06:55:30 - mmengine - INFO - Epoch(train) [87][4900/5005] lr: 1.0000e-03 eta: 16:52:45 time: 0.2831 data_time: 0.0033 loss: 1.1502 03/06 06:55:59 - mmengine - INFO - Epoch(train) [87][5000/5005] lr: 1.0000e-03 eta: 16:52:26 time: 0.2924 data_time: 0.0033 loss: 1.0400 03/06 06:56:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:56:03 - mmengine - INFO - Saving checkpoint at 87 epochs 03/06 06:56:18 - mmengine - INFO - Epoch(val) [87][100/196] eta: 0:00:13 time: 0.0178 data_time: 0.0004 03/06 06:56:31 - mmengine - INFO - Epoch(val) [87][196/196] accuracy/top1: 76.8280 accuracy/top5: 93.4840 03/06 06:57:03 - mmengine - INFO - Epoch(train) [88][ 100/5005] lr: 1.0000e-03 eta: 16:52:07 time: 0.2271 data_time: 0.0039 loss: 1.0121 03/06 06:57:26 - mmengine - INFO - Epoch(train) [88][ 200/5005] lr: 1.0000e-03 eta: 16:51:44 time: 0.2303 data_time: 0.0043 loss: 0.9211 03/06 06:57:49 - mmengine - INFO - Epoch(train) [88][ 300/5005] lr: 1.0000e-03 eta: 16:51:21 time: 0.2238 data_time: 0.0043 loss: 0.8854 03/06 06:58:12 - mmengine - INFO - Epoch(train) [88][ 400/5005] lr: 1.0000e-03 eta: 16:50:58 time: 0.2253 data_time: 0.0034 loss: 0.8393 03/06 06:58:36 - mmengine - INFO - Epoch(train) [88][ 500/5005] lr: 1.0000e-03 eta: 16:50:36 time: 0.2308 data_time: 0.0042 loss: 0.8419 03/06 06:58:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 06:58:59 - mmengine - INFO - Epoch(train) [88][ 600/5005] lr: 1.0000e-03 eta: 16:50:13 time: 0.2424 data_time: 0.0032 loss: 0.9284 03/06 06:59:22 - mmengine - INFO - Epoch(train) [88][ 700/5005] lr: 1.0000e-03 eta: 16:49:50 time: 0.2250 data_time: 0.0033 loss: 0.9972 03/06 06:59:45 - mmengine - INFO - Epoch(train) [88][ 800/5005] lr: 1.0000e-03 eta: 16:49:27 time: 0.2269 data_time: 0.0032 loss: 1.0721 03/06 07:00:08 - mmengine - INFO - Epoch(train) [88][ 900/5005] lr: 1.0000e-03 eta: 16:49:04 time: 0.2257 data_time: 0.0031 loss: 0.8084 03/06 07:00:31 - mmengine - INFO - Epoch(train) [88][1000/5005] lr: 1.0000e-03 eta: 16:48:41 time: 0.2272 data_time: 0.0038 loss: 0.9336 03/06 07:00:54 - mmengine - INFO - Epoch(train) [88][1100/5005] lr: 1.0000e-03 eta: 16:48:18 time: 0.2258 data_time: 0.0033 loss: 0.9150 03/06 07:01:17 - mmengine - INFO - Epoch(train) [88][1200/5005] lr: 1.0000e-03 eta: 16:47:55 time: 0.2230 data_time: 0.0036 loss: 0.8877 03/06 07:01:40 - mmengine - INFO - Epoch(train) [88][1300/5005] lr: 1.0000e-03 eta: 16:47:32 time: 0.2248 data_time: 0.0032 loss: 0.8009 03/06 07:02:04 - mmengine - INFO - Epoch(train) [88][1400/5005] lr: 1.0000e-03 eta: 16:47:10 time: 0.2248 data_time: 0.0032 loss: 0.9759 03/06 07:02:27 - mmengine - INFO - Epoch(train) [88][1500/5005] lr: 1.0000e-03 eta: 16:46:47 time: 0.2243 data_time: 0.0033 loss: 0.9980 03/06 07:02:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:02:50 - mmengine - INFO - Epoch(train) [88][1600/5005] lr: 1.0000e-03 eta: 16:46:24 time: 0.2262 data_time: 0.0038 loss: 0.9489 03/06 07:03:12 - mmengine - INFO - Epoch(train) [88][1700/5005] lr: 1.0000e-03 eta: 16:46:01 time: 0.2207 data_time: 0.0038 loss: 0.9335 03/06 07:03:36 - mmengine - INFO - Epoch(train) [88][1800/5005] lr: 1.0000e-03 eta: 16:45:38 time: 0.2265 data_time: 0.0032 loss: 1.0447 03/06 07:03:59 - mmengine - INFO - Epoch(train) [88][1900/5005] lr: 1.0000e-03 eta: 16:45:15 time: 0.2240 data_time: 0.0034 loss: 1.0728 03/06 07:04:22 - mmengine - INFO - Epoch(train) [88][2000/5005] lr: 1.0000e-03 eta: 16:44:52 time: 0.2236 data_time: 0.0037 loss: 0.9590 03/06 07:04:45 - mmengine - INFO - Epoch(train) [88][2100/5005] lr: 1.0000e-03 eta: 16:44:29 time: 0.2271 data_time: 0.0035 loss: 0.9789 03/06 07:05:08 - mmengine - INFO - Epoch(train) [88][2200/5005] lr: 1.0000e-03 eta: 16:44:06 time: 0.2231 data_time: 0.0036 loss: 1.0048 03/06 07:05:31 - mmengine - INFO - Epoch(train) [88][2300/5005] lr: 1.0000e-03 eta: 16:43:43 time: 0.2255 data_time: 0.0033 loss: 0.8331 03/06 07:05:54 - mmengine - INFO - Epoch(train) [88][2400/5005] lr: 1.0000e-03 eta: 16:43:20 time: 0.2263 data_time: 0.0033 loss: 0.9655 03/06 07:06:17 - mmengine - INFO - Epoch(train) [88][2500/5005] lr: 1.0000e-03 eta: 16:42:57 time: 0.2252 data_time: 0.0039 loss: 0.9438 03/06 07:06:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:06:40 - mmengine - INFO - Epoch(train) [88][2600/5005] lr: 1.0000e-03 eta: 16:42:34 time: 0.2497 data_time: 0.0031 loss: 1.1818 03/06 07:07:03 - mmengine - INFO - Epoch(train) [88][2700/5005] lr: 1.0000e-03 eta: 16:42:12 time: 0.2292 data_time: 0.0039 loss: 1.0331 03/06 07:07:26 - mmengine - INFO - Epoch(train) [88][2800/5005] lr: 1.0000e-03 eta: 16:41:49 time: 0.2378 data_time: 0.0040 loss: 0.7777 03/06 07:07:49 - mmengine - INFO - Epoch(train) [88][2900/5005] lr: 1.0000e-03 eta: 16:41:26 time: 0.2294 data_time: 0.0035 loss: 1.0284 03/06 07:08:12 - mmengine - INFO - Epoch(train) [88][3000/5005] lr: 1.0000e-03 eta: 16:41:03 time: 0.2497 data_time: 0.0032 loss: 0.9685 03/06 07:08:36 - mmengine - INFO - Epoch(train) [88][3100/5005] lr: 1.0000e-03 eta: 16:40:40 time: 0.2238 data_time: 0.0034 loss: 0.8679 03/06 07:08:59 - mmengine - INFO - Epoch(train) [88][3200/5005] lr: 1.0000e-03 eta: 16:40:17 time: 0.2242 data_time: 0.0033 loss: 0.8061 03/06 07:09:22 - mmengine - INFO - Epoch(train) [88][3300/5005] lr: 1.0000e-03 eta: 16:39:54 time: 0.2269 data_time: 0.0039 loss: 0.9441 03/06 07:09:45 - mmengine - INFO - Epoch(train) [88][3400/5005] lr: 1.0000e-03 eta: 16:39:31 time: 0.2233 data_time: 0.0034 loss: 0.9346 03/06 07:10:08 - mmengine - INFO - Epoch(train) [88][3500/5005] lr: 1.0000e-03 eta: 16:39:08 time: 0.2253 data_time: 0.0035 loss: 0.8252 03/06 07:10:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:10:31 - mmengine - INFO - Epoch(train) [88][3600/5005] lr: 1.0000e-03 eta: 16:38:45 time: 0.2276 data_time: 0.0035 loss: 0.9826 03/06 07:10:54 - mmengine - INFO - Epoch(train) [88][3700/5005] lr: 1.0000e-03 eta: 16:38:22 time: 0.2228 data_time: 0.0038 loss: 0.8846 03/06 07:11:17 - mmengine - INFO - Epoch(train) [88][3800/5005] lr: 1.0000e-03 eta: 16:37:59 time: 0.2246 data_time: 0.0031 loss: 0.9501 03/06 07:11:40 - mmengine - INFO - Epoch(train) [88][3900/5005] lr: 1.0000e-03 eta: 16:37:37 time: 0.2283 data_time: 0.0035 loss: 1.0223 03/06 07:12:03 - mmengine - INFO - Epoch(train) [88][4000/5005] lr: 1.0000e-03 eta: 16:37:14 time: 0.2243 data_time: 0.0037 loss: 1.0019 03/06 07:12:26 - mmengine - INFO - Epoch(train) [88][4100/5005] lr: 1.0000e-03 eta: 16:36:51 time: 0.2230 data_time: 0.0036 loss: 1.0775 03/06 07:12:49 - mmengine - INFO - Epoch(train) [88][4200/5005] lr: 1.0000e-03 eta: 16:36:28 time: 0.2436 data_time: 0.0030 loss: 1.0247 03/06 07:13:13 - mmengine - INFO - Epoch(train) [88][4300/5005] lr: 1.0000e-03 eta: 16:36:05 time: 0.2403 data_time: 0.0037 loss: 0.9078 03/06 07:13:36 - mmengine - INFO - Epoch(train) [88][4400/5005] lr: 1.0000e-03 eta: 16:35:42 time: 0.2255 data_time: 0.0036 loss: 0.9159 03/06 07:13:59 - mmengine - INFO - Epoch(train) [88][4500/5005] lr: 1.0000e-03 eta: 16:35:19 time: 0.2277 data_time: 0.0038 loss: 0.7733 03/06 07:14:14 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:14:22 - mmengine - INFO - Epoch(train) [88][4600/5005] lr: 1.0000e-03 eta: 16:34:56 time: 0.2241 data_time: 0.0037 loss: 1.0582 03/06 07:14:46 - mmengine - INFO - Epoch(train) [88][4700/5005] lr: 1.0000e-03 eta: 16:34:34 time: 0.2300 data_time: 0.0035 loss: 0.9015 03/06 07:15:08 - mmengine - INFO - Epoch(train) [88][4800/5005] lr: 1.0000e-03 eta: 16:34:11 time: 0.2295 data_time: 0.0032 loss: 0.9098 03/06 07:15:33 - mmengine - INFO - Epoch(train) [88][4900/5005] lr: 1.0000e-03 eta: 16:33:49 time: 0.2932 data_time: 0.0031 loss: 0.8894 03/06 07:16:01 - mmengine - INFO - Epoch(train) [88][5000/5005] lr: 1.0000e-03 eta: 16:33:29 time: 0.2788 data_time: 0.0032 loss: 0.8624 03/06 07:16:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:16:06 - mmengine - INFO - Saving checkpoint at 88 epochs 03/06 07:16:21 - mmengine - INFO - Epoch(val) [88][100/196] eta: 0:00:13 time: 0.0214 data_time: 0.0003 03/06 07:16:35 - mmengine - INFO - Epoch(val) [88][196/196] accuracy/top1: 76.7640 accuracy/top5: 93.4520 03/06 07:17:06 - mmengine - INFO - Epoch(train) [89][ 100/5005] lr: 1.0000e-03 eta: 16:33:10 time: 0.2391 data_time: 0.0034 loss: 1.0595 03/06 07:17:29 - mmengine - INFO - Epoch(train) [89][ 200/5005] lr: 1.0000e-03 eta: 16:32:47 time: 0.2290 data_time: 0.0033 loss: 1.0158 03/06 07:17:53 - mmengine - INFO - Epoch(train) [89][ 300/5005] lr: 1.0000e-03 eta: 16:32:25 time: 0.2255 data_time: 0.0031 loss: 1.1071 03/06 07:18:16 - mmengine - INFO - Epoch(train) [89][ 400/5005] lr: 1.0000e-03 eta: 16:32:02 time: 0.2287 data_time: 0.0034 loss: 0.8779 03/06 07:18:39 - mmengine - INFO - Epoch(train) [89][ 500/5005] lr: 1.0000e-03 eta: 16:31:39 time: 0.2248 data_time: 0.0032 loss: 0.8269 03/06 07:18:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:19:02 - mmengine - INFO - Epoch(train) [89][ 600/5005] lr: 1.0000e-03 eta: 16:31:16 time: 0.2210 data_time: 0.0031 loss: 0.9509 03/06 07:19:25 - mmengine - INFO - Epoch(train) [89][ 700/5005] lr: 1.0000e-03 eta: 16:30:53 time: 0.2351 data_time: 0.0032 loss: 0.8959 03/06 07:19:48 - mmengine - INFO - Epoch(train) [89][ 800/5005] lr: 1.0000e-03 eta: 16:30:30 time: 0.2311 data_time: 0.0031 loss: 0.8119 03/06 07:20:11 - mmengine - INFO - Epoch(train) [89][ 900/5005] lr: 1.0000e-03 eta: 16:30:07 time: 0.2245 data_time: 0.0034 loss: 1.0234 03/06 07:20:34 - mmengine - INFO - Epoch(train) [89][1000/5005] lr: 1.0000e-03 eta: 16:29:44 time: 0.2279 data_time: 0.0033 loss: 1.0244 03/06 07:20:57 - mmengine - INFO - Epoch(train) [89][1100/5005] lr: 1.0000e-03 eta: 16:29:21 time: 0.2275 data_time: 0.0037 loss: 0.9492 03/06 07:21:20 - mmengine - INFO - Epoch(train) [89][1200/5005] lr: 1.0000e-03 eta: 16:28:58 time: 0.2279 data_time: 0.0034 loss: 1.0530 03/06 07:21:43 - mmengine - INFO - Epoch(train) [89][1300/5005] lr: 1.0000e-03 eta: 16:28:35 time: 0.2230 data_time: 0.0035 loss: 0.9970 03/06 07:22:06 - mmengine - INFO - Epoch(train) [89][1400/5005] lr: 1.0000e-03 eta: 16:28:12 time: 0.2262 data_time: 0.0032 loss: 0.9966 03/06 07:22:30 - mmengine - INFO - Epoch(train) [89][1500/5005] lr: 1.0000e-03 eta: 16:27:50 time: 0.2254 data_time: 0.0034 loss: 0.9121 03/06 07:22:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:22:53 - mmengine - INFO - Epoch(train) [89][1600/5005] lr: 1.0000e-03 eta: 16:27:27 time: 0.2324 data_time: 0.0033 loss: 1.1473 03/06 07:23:16 - mmengine - INFO - Epoch(train) [89][1700/5005] lr: 1.0000e-03 eta: 16:27:04 time: 0.2206 data_time: 0.0033 loss: 0.8433 03/06 07:23:39 - mmengine - INFO - Epoch(train) [89][1800/5005] lr: 1.0000e-03 eta: 16:26:41 time: 0.2245 data_time: 0.0034 loss: 0.8891 03/06 07:24:02 - mmengine - INFO - Epoch(train) [89][1900/5005] lr: 1.0000e-03 eta: 16:26:18 time: 0.2388 data_time: 0.0031 loss: 0.9055 03/06 07:24:25 - mmengine - INFO - Epoch(train) [89][2000/5005] lr: 1.0000e-03 eta: 16:25:55 time: 0.2261 data_time: 0.0032 loss: 0.8476 03/06 07:24:48 - mmengine - INFO - Epoch(train) [89][2100/5005] lr: 1.0000e-03 eta: 16:25:32 time: 0.2237 data_time: 0.0033 loss: 1.0680 03/06 07:25:11 - mmengine - INFO - Epoch(train) [89][2200/5005] lr: 1.0000e-03 eta: 16:25:09 time: 0.2247 data_time: 0.0037 loss: 0.8719 03/06 07:25:34 - mmengine - INFO - Epoch(train) [89][2300/5005] lr: 1.0000e-03 eta: 16:24:46 time: 0.2439 data_time: 0.0033 loss: 0.8614 03/06 07:25:57 - mmengine - INFO - Epoch(train) [89][2400/5005] lr: 1.0000e-03 eta: 16:24:23 time: 0.2329 data_time: 0.0031 loss: 0.9273 03/06 07:26:20 - mmengine - INFO - Epoch(train) [89][2500/5005] lr: 1.0000e-03 eta: 16:24:00 time: 0.2285 data_time: 0.0033 loss: 1.0418 03/06 07:26:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:26:43 - mmengine - INFO - Epoch(train) [89][2600/5005] lr: 1.0000e-03 eta: 16:23:37 time: 0.2267 data_time: 0.0033 loss: 1.1381 03/06 07:27:06 - mmengine - INFO - Epoch(train) [89][2700/5005] lr: 1.0000e-03 eta: 16:23:14 time: 0.2248 data_time: 0.0033 loss: 1.0106 03/06 07:27:29 - mmengine - INFO - Epoch(train) [89][2800/5005] lr: 1.0000e-03 eta: 16:22:51 time: 0.2260 data_time: 0.0039 loss: 0.8529 03/06 07:27:52 - mmengine - INFO - Epoch(train) [89][2900/5005] lr: 1.0000e-03 eta: 16:22:28 time: 0.2234 data_time: 0.0037 loss: 0.8684 03/06 07:28:15 - mmengine - INFO - Epoch(train) [89][3000/5005] lr: 1.0000e-03 eta: 16:22:06 time: 0.2271 data_time: 0.0032 loss: 0.8531 03/06 07:28:38 - mmengine - INFO - Epoch(train) [89][3100/5005] lr: 1.0000e-03 eta: 16:21:43 time: 0.2241 data_time: 0.0032 loss: 0.8645 03/06 07:29:01 - mmengine - INFO - Epoch(train) [89][3200/5005] lr: 1.0000e-03 eta: 16:21:20 time: 0.2251 data_time: 0.0032 loss: 1.0419 03/06 07:29:24 - mmengine - INFO - Epoch(train) [89][3300/5005] lr: 1.0000e-03 eta: 16:20:57 time: 0.2276 data_time: 0.0032 loss: 0.9566 03/06 07:29:47 - mmengine - INFO - Epoch(train) [89][3400/5005] lr: 1.0000e-03 eta: 16:20:34 time: 0.2257 data_time: 0.0034 loss: 1.1484 03/06 07:30:10 - mmengine - INFO - Epoch(train) [89][3500/5005] lr: 1.0000e-03 eta: 16:20:11 time: 0.2493 data_time: 0.0034 loss: 0.9272 03/06 07:30:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:30:33 - mmengine - INFO - Epoch(train) [89][3600/5005] lr: 1.0000e-03 eta: 16:19:48 time: 0.2378 data_time: 0.0032 loss: 0.9480 03/06 07:30:56 - mmengine - INFO - Epoch(train) [89][3700/5005] lr: 1.0000e-03 eta: 16:19:25 time: 0.2288 data_time: 0.0040 loss: 1.0976 03/06 07:31:19 - mmengine - INFO - Epoch(train) [89][3800/5005] lr: 1.0000e-03 eta: 16:19:02 time: 0.2243 data_time: 0.0032 loss: 0.9771 03/06 07:31:42 - mmengine - INFO - Epoch(train) [89][3900/5005] lr: 1.0000e-03 eta: 16:18:39 time: 0.2618 data_time: 0.0031 loss: 0.8772 03/06 07:32:05 - mmengine - INFO - Epoch(train) [89][4000/5005] lr: 1.0000e-03 eta: 16:18:16 time: 0.2310 data_time: 0.0038 loss: 1.0891 03/06 07:32:28 - mmengine - INFO - Epoch(train) [89][4100/5005] lr: 1.0000e-03 eta: 16:17:53 time: 0.2237 data_time: 0.0034 loss: 0.8658 03/06 07:32:51 - mmengine - INFO - Epoch(train) [89][4200/5005] lr: 1.0000e-03 eta: 16:17:30 time: 0.2247 data_time: 0.0034 loss: 1.1902 03/06 07:33:14 - mmengine - INFO - Epoch(train) [89][4300/5005] lr: 1.0000e-03 eta: 16:17:07 time: 0.2451 data_time: 0.0034 loss: 1.0341 03/06 07:33:38 - mmengine - INFO - Epoch(train) [89][4400/5005] lr: 1.0000e-03 eta: 16:16:45 time: 0.2403 data_time: 0.0034 loss: 1.1572 03/06 07:34:01 - mmengine - INFO - Epoch(train) [89][4500/5005] lr: 1.0000e-03 eta: 16:16:22 time: 0.2302 data_time: 0.0031 loss: 0.8910 03/06 07:34:14 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:34:24 - mmengine - INFO - Epoch(train) [89][4600/5005] lr: 1.0000e-03 eta: 16:15:59 time: 0.2300 data_time: 0.0037 loss: 0.9045 03/06 07:34:47 - mmengine - INFO - Epoch(train) [89][4700/5005] lr: 1.0000e-03 eta: 16:15:36 time: 0.2257 data_time: 0.0031 loss: 0.9768 03/06 07:35:09 - mmengine - INFO - Epoch(train) [89][4800/5005] lr: 1.0000e-03 eta: 16:15:13 time: 0.2258 data_time: 0.0035 loss: 0.8002 03/06 07:35:34 - mmengine - INFO - Epoch(train) [89][4900/5005] lr: 1.0000e-03 eta: 16:14:51 time: 0.2813 data_time: 0.0030 loss: 0.9337 03/06 07:36:03 - mmengine - INFO - Epoch(train) [89][5000/5005] lr: 1.0000e-03 eta: 16:14:31 time: 0.2920 data_time: 0.0032 loss: 1.0206 03/06 07:36:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:36:07 - mmengine - INFO - Saving checkpoint at 89 epochs 03/06 07:36:22 - mmengine - INFO - Epoch(val) [89][100/196] eta: 0:00:13 time: 0.0190 data_time: 0.0003 03/06 07:36:35 - mmengine - INFO - Epoch(val) [89][196/196] accuracy/top1: 76.7520 accuracy/top5: 93.3900 03/06 07:37:07 - mmengine - INFO - Epoch(train) [90][ 100/5005] lr: 1.0000e-03 eta: 16:14:12 time: 0.2260 data_time: 0.0038 loss: 1.0567 03/06 07:37:31 - mmengine - INFO - Epoch(train) [90][ 200/5005] lr: 1.0000e-03 eta: 16:13:49 time: 0.2244 data_time: 0.0036 loss: 0.8475 03/06 07:37:54 - mmengine - INFO - Epoch(train) [90][ 300/5005] lr: 1.0000e-03 eta: 16:13:27 time: 0.2244 data_time: 0.0041 loss: 1.1059 03/06 07:38:17 - mmengine - INFO - Epoch(train) [90][ 400/5005] lr: 1.0000e-03 eta: 16:13:03 time: 0.2261 data_time: 0.0033 loss: 0.9971 03/06 07:38:40 - mmengine - INFO - Epoch(train) [90][ 500/5005] lr: 1.0000e-03 eta: 16:12:41 time: 0.2359 data_time: 0.0040 loss: 0.8043 03/06 07:38:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:39:03 - mmengine - INFO - Epoch(train) [90][ 600/5005] lr: 1.0000e-03 eta: 16:12:18 time: 0.2261 data_time: 0.0031 loss: 0.8106 03/06 07:39:26 - mmengine - INFO - Epoch(train) [90][ 700/5005] lr: 1.0000e-03 eta: 16:11:55 time: 0.2294 data_time: 0.0038 loss: 0.8026 03/06 07:39:49 - mmengine - INFO - Epoch(train) [90][ 800/5005] lr: 1.0000e-03 eta: 16:11:32 time: 0.2277 data_time: 0.0035 loss: 0.9917 03/06 07:40:13 - mmengine - INFO - Epoch(train) [90][ 900/5005] lr: 1.0000e-03 eta: 16:11:09 time: 0.2269 data_time: 0.0034 loss: 0.8215 03/06 07:40:36 - mmengine - INFO - Epoch(train) [90][1000/5005] lr: 1.0000e-03 eta: 16:10:46 time: 0.2226 data_time: 0.0034 loss: 0.7854 03/06 07:40:59 - mmengine - INFO - Epoch(train) [90][1100/5005] lr: 1.0000e-03 eta: 16:10:23 time: 0.2254 data_time: 0.0032 loss: 1.0328 03/06 07:41:22 - mmengine - INFO - Epoch(train) [90][1200/5005] lr: 1.0000e-03 eta: 16:10:00 time: 0.2298 data_time: 0.0035 loss: 0.8501 03/06 07:41:45 - mmengine - INFO - Epoch(train) [90][1300/5005] lr: 1.0000e-03 eta: 16:09:37 time: 0.2429 data_time: 0.0034 loss: 0.9315 03/06 07:42:08 - mmengine - INFO - Epoch(train) [90][1400/5005] lr: 1.0000e-03 eta: 16:09:14 time: 0.2408 data_time: 0.0033 loss: 0.9694 03/06 07:42:31 - mmengine - INFO - Epoch(train) [90][1500/5005] lr: 1.0000e-03 eta: 16:08:52 time: 0.2285 data_time: 0.0034 loss: 1.0646 03/06 07:42:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:42:54 - mmengine - INFO - Epoch(train) [90][1600/5005] lr: 1.0000e-03 eta: 16:08:29 time: 0.2216 data_time: 0.0032 loss: 0.9733 03/06 07:43:17 - mmengine - INFO - Epoch(train) [90][1700/5005] lr: 1.0000e-03 eta: 16:08:06 time: 0.2269 data_time: 0.0037 loss: 0.8582 03/06 07:43:41 - mmengine - INFO - Epoch(train) [90][1800/5005] lr: 1.0000e-03 eta: 16:07:43 time: 0.2265 data_time: 0.0034 loss: 0.7966 03/06 07:44:04 - mmengine - INFO - Epoch(train) [90][1900/5005] lr: 1.0000e-03 eta: 16:07:20 time: 0.2272 data_time: 0.0035 loss: 1.2060 03/06 07:44:27 - mmengine - INFO - Epoch(train) [90][2000/5005] lr: 1.0000e-03 eta: 16:06:57 time: 0.2346 data_time: 0.0036 loss: 0.8919 03/06 07:44:50 - mmengine - INFO - Epoch(train) [90][2100/5005] lr: 1.0000e-03 eta: 16:06:34 time: 0.2252 data_time: 0.0035 loss: 0.9573 03/06 07:45:13 - mmengine - INFO - Epoch(train) [90][2200/5005] lr: 1.0000e-03 eta: 16:06:11 time: 0.2248 data_time: 0.0032 loss: 0.8698 03/06 07:45:36 - mmengine - INFO - Epoch(train) [90][2300/5005] lr: 1.0000e-03 eta: 16:05:48 time: 0.2358 data_time: 0.0034 loss: 0.7984 03/06 07:45:59 - mmengine - INFO - Epoch(train) [90][2400/5005] lr: 1.0000e-03 eta: 16:05:25 time: 0.2279 data_time: 0.0037 loss: 0.9768 03/06 07:46:22 - mmengine - INFO - Epoch(train) [90][2500/5005] lr: 1.0000e-03 eta: 16:05:03 time: 0.2290 data_time: 0.0037 loss: 1.0757 03/06 07:46:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:46:45 - mmengine - INFO - Epoch(train) [90][2600/5005] lr: 1.0000e-03 eta: 16:04:40 time: 0.2306 data_time: 0.0034 loss: 0.8542 03/06 07:47:08 - mmengine - INFO - Epoch(train) [90][2700/5005] lr: 1.0000e-03 eta: 16:04:17 time: 0.2241 data_time: 0.0034 loss: 0.7469 03/06 07:47:31 - mmengine - INFO - Epoch(train) [90][2800/5005] lr: 1.0000e-03 eta: 16:03:54 time: 0.2433 data_time: 0.0036 loss: 1.0063 03/06 07:47:54 - mmengine - INFO - Epoch(train) [90][2900/5005] lr: 1.0000e-03 eta: 16:03:31 time: 0.2281 data_time: 0.0035 loss: 1.0234 03/06 07:48:17 - mmengine - INFO - Epoch(train) [90][3000/5005] lr: 1.0000e-03 eta: 16:03:08 time: 0.2258 data_time: 0.0034 loss: 0.8873 03/06 07:48:40 - mmengine - INFO - Epoch(train) [90][3100/5005] lr: 1.0000e-03 eta: 16:02:45 time: 0.2452 data_time: 0.0038 loss: 1.0038 03/06 07:49:03 - mmengine - INFO - Epoch(train) [90][3200/5005] lr: 1.0000e-03 eta: 16:02:22 time: 0.2258 data_time: 0.0036 loss: 0.9232 03/06 07:49:27 - mmengine - INFO - Epoch(train) [90][3300/5005] lr: 1.0000e-03 eta: 16:01:59 time: 0.2466 data_time: 0.0034 loss: 0.9143 03/06 07:49:50 - mmengine - INFO - Epoch(train) [90][3400/5005] lr: 1.0000e-03 eta: 16:01:36 time: 0.2290 data_time: 0.0033 loss: 1.0052 03/06 07:50:13 - mmengine - INFO - Epoch(train) [90][3500/5005] lr: 1.0000e-03 eta: 16:01:14 time: 0.2516 data_time: 0.0036 loss: 0.8568 03/06 07:50:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:50:36 - mmengine - INFO - Epoch(train) [90][3600/5005] lr: 1.0000e-03 eta: 16:00:51 time: 0.2273 data_time: 0.0037 loss: 0.9729 03/06 07:50:59 - mmengine - INFO - Epoch(train) [90][3700/5005] lr: 1.0000e-03 eta: 16:00:28 time: 0.2464 data_time: 0.0034 loss: 0.8188 03/06 07:51:22 - mmengine - INFO - Epoch(train) [90][3800/5005] lr: 1.0000e-03 eta: 16:00:05 time: 0.2259 data_time: 0.0034 loss: 1.0007 03/06 07:51:45 - mmengine - INFO - Epoch(train) [90][3900/5005] lr: 1.0000e-03 eta: 15:59:42 time: 0.2295 data_time: 0.0034 loss: 0.8937 03/06 07:52:08 - mmengine - INFO - Epoch(train) [90][4000/5005] lr: 1.0000e-03 eta: 15:59:19 time: 0.2282 data_time: 0.0035 loss: 1.0027 03/06 07:52:31 - mmengine - INFO - Epoch(train) [90][4100/5005] lr: 1.0000e-03 eta: 15:58:56 time: 0.2241 data_time: 0.0037 loss: 0.9195 03/06 07:52:55 - mmengine - INFO - Epoch(train) [90][4200/5005] lr: 1.0000e-03 eta: 15:58:33 time: 0.2240 data_time: 0.0037 loss: 0.9119 03/06 07:53:18 - mmengine - INFO - Epoch(train) [90][4300/5005] lr: 1.0000e-03 eta: 15:58:10 time: 0.2269 data_time: 0.0040 loss: 1.0496 03/06 07:53:41 - mmengine - INFO - Epoch(train) [90][4400/5005] lr: 1.0000e-03 eta: 15:57:47 time: 0.2236 data_time: 0.0035 loss: 0.9032 03/06 07:54:04 - mmengine - INFO - Epoch(train) [90][4500/5005] lr: 1.0000e-03 eta: 15:57:24 time: 0.2465 data_time: 0.0033 loss: 0.8142 03/06 07:54:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:54:27 - mmengine - INFO - Epoch(train) [90][4600/5005] lr: 1.0000e-03 eta: 15:57:01 time: 0.2300 data_time: 0.0037 loss: 0.9689 03/06 07:54:51 - mmengine - INFO - Epoch(train) [90][4700/5005] lr: 1.0000e-03 eta: 15:56:39 time: 0.2387 data_time: 0.0037 loss: 0.9175 03/06 07:55:13 - mmengine - INFO - Epoch(train) [90][4800/5005] lr: 1.0000e-03 eta: 15:56:16 time: 0.2257 data_time: 0.0042 loss: 1.0084 03/06 07:55:37 - mmengine - INFO - Epoch(train) [90][4900/5005] lr: 1.0000e-03 eta: 15:55:53 time: 0.2820 data_time: 0.0031 loss: 0.9311 03/06 07:56:06 - mmengine - INFO - Epoch(train) [90][5000/5005] lr: 1.0000e-03 eta: 15:55:34 time: 0.2849 data_time: 0.0032 loss: 0.9789 03/06 07:56:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:56:10 - mmengine - INFO - Saving checkpoint at 90 epochs 03/06 07:56:25 - mmengine - INFO - Epoch(val) [90][100/196] eta: 0:00:13 time: 0.0209 data_time: 0.0003 03/06 07:56:39 - mmengine - INFO - Epoch(val) [90][196/196] accuracy/top1: 76.8820 accuracy/top5: 93.3760 03/06 07:57:11 - mmengine - INFO - Epoch(train) [91][ 100/5005] lr: 1.0000e-03 eta: 15:55:15 time: 0.2286 data_time: 0.0038 loss: 1.0160 03/06 07:57:35 - mmengine - INFO - Epoch(train) [91][ 200/5005] lr: 1.0000e-03 eta: 15:54:52 time: 0.2406 data_time: 0.0043 loss: 0.9688 03/06 07:57:58 - mmengine - INFO - Epoch(train) [91][ 300/5005] lr: 1.0000e-03 eta: 15:54:29 time: 0.2277 data_time: 0.0032 loss: 0.8990 03/06 07:58:21 - mmengine - INFO - Epoch(train) [91][ 400/5005] lr: 1.0000e-03 eta: 15:54:06 time: 0.2247 data_time: 0.0037 loss: 0.9872 03/06 07:58:45 - mmengine - INFO - Epoch(train) [91][ 500/5005] lr: 1.0000e-03 eta: 15:53:43 time: 0.2284 data_time: 0.0034 loss: 0.8641 03/06 07:58:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 07:59:07 - mmengine - INFO - Epoch(train) [91][ 600/5005] lr: 1.0000e-03 eta: 15:53:20 time: 0.2307 data_time: 0.0033 loss: 0.9185 03/06 07:59:31 - mmengine - INFO - Epoch(train) [91][ 700/5005] lr: 1.0000e-03 eta: 15:52:58 time: 0.2277 data_time: 0.0032 loss: 0.7234 03/06 07:59:54 - mmengine - INFO - Epoch(train) [91][ 800/5005] lr: 1.0000e-03 eta: 15:52:35 time: 0.2228 data_time: 0.0033 loss: 0.9859 03/06 08:00:17 - mmengine - INFO - Epoch(train) [91][ 900/5005] lr: 1.0000e-03 eta: 15:52:12 time: 0.2451 data_time: 0.0035 loss: 1.0273 03/06 08:00:40 - mmengine - INFO - Epoch(train) [91][1000/5005] lr: 1.0000e-03 eta: 15:51:49 time: 0.2270 data_time: 0.0032 loss: 0.9704 03/06 08:01:03 - mmengine - INFO - Epoch(train) [91][1100/5005] lr: 1.0000e-03 eta: 15:51:26 time: 0.2322 data_time: 0.0039 loss: 0.9555 03/06 08:01:26 - mmengine - INFO - Epoch(train) [91][1200/5005] lr: 1.0000e-03 eta: 15:51:03 time: 0.2417 data_time: 0.0032 loss: 0.9450 03/06 08:01:49 - mmengine - INFO - Epoch(train) [91][1300/5005] lr: 1.0000e-03 eta: 15:50:40 time: 0.2361 data_time: 0.0032 loss: 0.8724 03/06 08:02:12 - mmengine - INFO - Epoch(train) [91][1400/5005] lr: 1.0000e-03 eta: 15:50:17 time: 0.2289 data_time: 0.0034 loss: 1.0351 03/06 08:02:35 - mmengine - INFO - Epoch(train) [91][1500/5005] lr: 1.0000e-03 eta: 15:49:54 time: 0.2299 data_time: 0.0032 loss: 0.9999 03/06 08:02:47 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:02:58 - mmengine - INFO - Epoch(train) [91][1600/5005] lr: 1.0000e-03 eta: 15:49:31 time: 0.2229 data_time: 0.0036 loss: 0.8009 03/06 08:03:21 - mmengine - INFO - Epoch(train) [91][1700/5005] lr: 1.0000e-03 eta: 15:49:08 time: 0.2280 data_time: 0.0033 loss: 1.1403 03/06 08:03:44 - mmengine - INFO - Epoch(train) [91][1800/5005] lr: 1.0000e-03 eta: 15:48:45 time: 0.2254 data_time: 0.0035 loss: 0.8978 03/06 08:04:07 - mmengine - INFO - Epoch(train) [91][1900/5005] lr: 1.0000e-03 eta: 15:48:22 time: 0.2243 data_time: 0.0034 loss: 0.8849 03/06 08:04:30 - mmengine - INFO - Epoch(train) [91][2000/5005] lr: 1.0000e-03 eta: 15:47:59 time: 0.2261 data_time: 0.0031 loss: 0.9009 03/06 08:04:53 - mmengine - INFO - Epoch(train) [91][2100/5005] lr: 1.0000e-03 eta: 15:47:36 time: 0.2240 data_time: 0.0036 loss: 0.9038 03/06 08:05:17 - mmengine - INFO - Epoch(train) [91][2200/5005] lr: 1.0000e-03 eta: 15:47:14 time: 0.2245 data_time: 0.0038 loss: 0.8749 03/06 08:05:40 - mmengine - INFO - Epoch(train) [91][2300/5005] lr: 1.0000e-03 eta: 15:46:51 time: 0.2251 data_time: 0.0035 loss: 0.7481 03/06 08:06:02 - mmengine - INFO - Epoch(train) [91][2400/5005] lr: 1.0000e-03 eta: 15:46:28 time: 0.2293 data_time: 0.0033 loss: 0.9558 03/06 08:06:26 - mmengine - INFO - Epoch(train) [91][2500/5005] lr: 1.0000e-03 eta: 15:46:05 time: 0.2246 data_time: 0.0033 loss: 0.8737 03/06 08:06:37 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:06:49 - mmengine - INFO - Epoch(train) [91][2600/5005] lr: 1.0000e-03 eta: 15:45:42 time: 0.2358 data_time: 0.0036 loss: 0.9353 03/06 08:07:12 - mmengine - INFO - Epoch(train) [91][2700/5005] lr: 1.0000e-03 eta: 15:45:19 time: 0.2255 data_time: 0.0034 loss: 1.0135 03/06 08:07:35 - mmengine - INFO - Epoch(train) [91][2800/5005] lr: 1.0000e-03 eta: 15:44:56 time: 0.2249 data_time: 0.0036 loss: 0.8722 03/06 08:07:58 - mmengine - INFO - Epoch(train) [91][2900/5005] lr: 1.0000e-03 eta: 15:44:33 time: 0.2225 data_time: 0.0035 loss: 0.9702 03/06 08:08:21 - mmengine - INFO - Epoch(train) [91][3000/5005] lr: 1.0000e-03 eta: 15:44:10 time: 0.2424 data_time: 0.0034 loss: 0.9114 03/06 08:08:44 - mmengine - INFO - Epoch(train) [91][3100/5005] lr: 1.0000e-03 eta: 15:43:47 time: 0.2231 data_time: 0.0032 loss: 0.8573 03/06 08:09:08 - mmengine - INFO - Epoch(train) [91][3200/5005] lr: 1.0000e-03 eta: 15:43:24 time: 0.2312 data_time: 0.0033 loss: 1.0738 03/06 08:09:30 - mmengine - INFO - Epoch(train) [91][3300/5005] lr: 1.0000e-03 eta: 15:43:01 time: 0.2240 data_time: 0.0039 loss: 0.9718 03/06 08:09:53 - mmengine - INFO - Epoch(train) [91][3400/5005] lr: 1.0000e-03 eta: 15:42:38 time: 0.2289 data_time: 0.0035 loss: 0.9356 03/06 08:10:17 - mmengine - INFO - Epoch(train) [91][3500/5005] lr: 1.0000e-03 eta: 15:42:16 time: 0.2297 data_time: 0.0037 loss: 0.9730 03/06 08:10:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:10:40 - mmengine - INFO - Epoch(train) [91][3600/5005] lr: 1.0000e-03 eta: 15:41:53 time: 0.2260 data_time: 0.0039 loss: 0.9267 03/06 08:11:03 - mmengine - INFO - Epoch(train) [91][3700/5005] lr: 1.0000e-03 eta: 15:41:30 time: 0.2309 data_time: 0.0035 loss: 0.8877 03/06 08:11:26 - mmengine - INFO - Epoch(train) [91][3800/5005] lr: 1.0000e-03 eta: 15:41:07 time: 0.2312 data_time: 0.0031 loss: 0.8955 03/06 08:11:49 - mmengine - INFO - Epoch(train) [91][3900/5005] lr: 1.0000e-03 eta: 15:40:44 time: 0.2289 data_time: 0.0037 loss: 1.0896 03/06 08:12:12 - mmengine - INFO - Epoch(train) [91][4000/5005] lr: 1.0000e-03 eta: 15:40:21 time: 0.2326 data_time: 0.0041 loss: 0.9920 03/06 08:12:35 - mmengine - INFO - Epoch(train) [91][4100/5005] lr: 1.0000e-03 eta: 15:39:58 time: 0.2217 data_time: 0.0031 loss: 0.8832 03/06 08:12:58 - mmengine - INFO - Epoch(train) [91][4200/5005] lr: 1.0000e-03 eta: 15:39:35 time: 0.2267 data_time: 0.0033 loss: 0.8702 03/06 08:13:21 - mmengine - INFO - Epoch(train) [91][4300/5005] lr: 1.0000e-03 eta: 15:39:12 time: 0.2340 data_time: 0.0033 loss: 0.9447 03/06 08:13:44 - mmengine - INFO - Epoch(train) [91][4400/5005] lr: 1.0000e-03 eta: 15:38:49 time: 0.2254 data_time: 0.0033 loss: 0.9616 03/06 08:14:07 - mmengine - INFO - Epoch(train) [91][4500/5005] lr: 1.0000e-03 eta: 15:38:26 time: 0.2302 data_time: 0.0036 loss: 0.9265 03/06 08:14:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:14:30 - mmengine - INFO - Epoch(train) [91][4600/5005] lr: 1.0000e-03 eta: 15:38:03 time: 0.2253 data_time: 0.0037 loss: 1.0447 03/06 08:14:53 - mmengine - INFO - Epoch(train) [91][4700/5005] lr: 1.0000e-03 eta: 15:37:40 time: 0.2234 data_time: 0.0034 loss: 1.0288 03/06 08:15:16 - mmengine - INFO - Epoch(train) [91][4800/5005] lr: 1.0000e-03 eta: 15:37:17 time: 0.2247 data_time: 0.0034 loss: 0.8913 03/06 08:15:40 - mmengine - INFO - Epoch(train) [91][4900/5005] lr: 1.0000e-03 eta: 15:36:55 time: 0.2932 data_time: 0.0032 loss: 0.8902 03/06 08:16:09 - mmengine - INFO - Epoch(train) [91][5000/5005] lr: 1.0000e-03 eta: 15:36:35 time: 0.2958 data_time: 0.0033 loss: 0.7747 03/06 08:16:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:16:14 - mmengine - INFO - Saving checkpoint at 91 epochs 03/06 08:16:29 - mmengine - INFO - Epoch(val) [91][100/196] eta: 0:00:13 time: 0.0192 data_time: 0.0004 03/06 08:16:43 - mmengine - INFO - Epoch(val) [91][196/196] accuracy/top1: 76.8640 accuracy/top5: 93.4960 03/06 08:17:15 - mmengine - INFO - Epoch(train) [92][ 100/5005] lr: 1.0000e-03 eta: 15:36:16 time: 0.2271 data_time: 0.0044 loss: 1.0242 03/06 08:17:38 - mmengine - INFO - Epoch(train) [92][ 200/5005] lr: 1.0000e-03 eta: 15:35:53 time: 0.2249 data_time: 0.0032 loss: 0.9185 03/06 08:18:01 - mmengine - INFO - Epoch(train) [92][ 300/5005] lr: 1.0000e-03 eta: 15:35:30 time: 0.2312 data_time: 0.0034 loss: 0.8343 03/06 08:18:24 - mmengine - INFO - Epoch(train) [92][ 400/5005] lr: 1.0000e-03 eta: 15:35:07 time: 0.2254 data_time: 0.0039 loss: 0.9819 03/06 08:18:47 - mmengine - INFO - Epoch(train) [92][ 500/5005] lr: 1.0000e-03 eta: 15:34:45 time: 0.2450 data_time: 0.0031 loss: 0.7433 03/06 08:18:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:19:10 - mmengine - INFO - Epoch(train) [92][ 600/5005] lr: 1.0000e-03 eta: 15:34:22 time: 0.2216 data_time: 0.0039 loss: 1.1260 03/06 08:19:33 - mmengine - INFO - Epoch(train) [92][ 700/5005] lr: 1.0000e-03 eta: 15:33:59 time: 0.2270 data_time: 0.0035 loss: 1.0491 03/06 08:19:56 - mmengine - INFO - Epoch(train) [92][ 800/5005] lr: 1.0000e-03 eta: 15:33:36 time: 0.2270 data_time: 0.0034 loss: 0.8340 03/06 08:20:19 - mmengine - INFO - Epoch(train) [92][ 900/5005] lr: 1.0000e-03 eta: 15:33:13 time: 0.2212 data_time: 0.0033 loss: 0.9804 03/06 08:20:42 - mmengine - INFO - Epoch(train) [92][1000/5005] lr: 1.0000e-03 eta: 15:32:50 time: 0.2264 data_time: 0.0033 loss: 0.9543 03/06 08:21:05 - mmengine - INFO - Epoch(train) [92][1100/5005] lr: 1.0000e-03 eta: 15:32:27 time: 0.2260 data_time: 0.0032 loss: 1.0414 03/06 08:21:28 - mmengine - INFO - Epoch(train) [92][1200/5005] lr: 1.0000e-03 eta: 15:32:04 time: 0.2269 data_time: 0.0034 loss: 0.8226 03/06 08:21:51 - mmengine - INFO - Epoch(train) [92][1300/5005] lr: 1.0000e-03 eta: 15:31:41 time: 0.2476 data_time: 0.0035 loss: 0.8453 03/06 08:22:15 - mmengine - INFO - Epoch(train) [92][1400/5005] lr: 1.0000e-03 eta: 15:31:18 time: 0.2304 data_time: 0.0040 loss: 0.9999 03/06 08:22:38 - mmengine - INFO - Epoch(train) [92][1500/5005] lr: 1.0000e-03 eta: 15:30:55 time: 0.2263 data_time: 0.0036 loss: 0.9745 03/06 08:22:48 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:23:00 - mmengine - INFO - Epoch(train) [92][1600/5005] lr: 1.0000e-03 eta: 15:30:32 time: 0.2249 data_time: 0.0035 loss: 0.8970 03/06 08:23:24 - mmengine - INFO - Epoch(train) [92][1700/5005] lr: 1.0000e-03 eta: 15:30:09 time: 0.2279 data_time: 0.0036 loss: 0.9420 03/06 08:23:47 - mmengine - INFO - Epoch(train) [92][1800/5005] lr: 1.0000e-03 eta: 15:29:46 time: 0.2379 data_time: 0.0035 loss: 0.8970 03/06 08:24:10 - mmengine - INFO - Epoch(train) [92][1900/5005] lr: 1.0000e-03 eta: 15:29:24 time: 0.2229 data_time: 0.0034 loss: 0.9848 03/06 08:24:33 - mmengine - INFO - Epoch(train) [92][2000/5005] lr: 1.0000e-03 eta: 15:29:00 time: 0.2234 data_time: 0.0034 loss: 0.9561 03/06 08:24:56 - mmengine - INFO - Epoch(train) [92][2100/5005] lr: 1.0000e-03 eta: 15:28:38 time: 0.2265 data_time: 0.0036 loss: 0.9530 03/06 08:25:19 - mmengine - INFO - Epoch(train) [92][2200/5005] lr: 1.0000e-03 eta: 15:28:15 time: 0.2246 data_time: 0.0037 loss: 0.8189 03/06 08:25:43 - mmengine - INFO - Epoch(train) [92][2300/5005] lr: 1.0000e-03 eta: 15:27:52 time: 0.2274 data_time: 0.0036 loss: 0.9010 03/06 08:26:06 - mmengine - INFO - Epoch(train) [92][2400/5005] lr: 1.0000e-03 eta: 15:27:29 time: 0.2268 data_time: 0.0036 loss: 0.8672 03/06 08:26:28 - mmengine - INFO - Epoch(train) [92][2500/5005] lr: 1.0000e-03 eta: 15:27:06 time: 0.2230 data_time: 0.0036 loss: 0.8541 03/06 08:26:39 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:26:51 - mmengine - INFO - Epoch(train) [92][2600/5005] lr: 1.0000e-03 eta: 15:26:43 time: 0.2266 data_time: 0.0037 loss: 0.8990 03/06 08:27:15 - mmengine - INFO - Epoch(train) [92][2700/5005] lr: 1.0000e-03 eta: 15:26:20 time: 0.2230 data_time: 0.0038 loss: 0.9569 03/06 08:27:38 - mmengine - INFO - Epoch(train) [92][2800/5005] lr: 1.0000e-03 eta: 15:25:57 time: 0.2295 data_time: 0.0031 loss: 0.8743 03/06 08:28:01 - mmengine - INFO - Epoch(train) [92][2900/5005] lr: 1.0000e-03 eta: 15:25:34 time: 0.2266 data_time: 0.0035 loss: 0.9955 03/06 08:28:24 - mmengine - INFO - Epoch(train) [92][3000/5005] lr: 1.0000e-03 eta: 15:25:11 time: 0.2305 data_time: 0.0037 loss: 0.9294 03/06 08:28:47 - mmengine - INFO - Epoch(train) [92][3100/5005] lr: 1.0000e-03 eta: 15:24:48 time: 0.2274 data_time: 0.0035 loss: 0.8576 03/06 08:29:10 - mmengine - INFO - Epoch(train) [92][3200/5005] lr: 1.0000e-03 eta: 15:24:26 time: 0.2267 data_time: 0.0033 loss: 0.9413 03/06 08:29:33 - mmengine - INFO - Epoch(train) [92][3300/5005] lr: 1.0000e-03 eta: 15:24:03 time: 0.2257 data_time: 0.0032 loss: 0.8041 03/06 08:29:56 - mmengine - INFO - Epoch(train) [92][3400/5005] lr: 1.0000e-03 eta: 15:23:40 time: 0.2457 data_time: 0.0036 loss: 1.0524 03/06 08:30:20 - mmengine - INFO - Epoch(train) [92][3500/5005] lr: 1.0000e-03 eta: 15:23:17 time: 0.2444 data_time: 0.0039 loss: 0.9687 03/06 08:30:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:30:43 - mmengine - INFO - Epoch(train) [92][3600/5005] lr: 1.0000e-03 eta: 15:22:54 time: 0.2312 data_time: 0.0040 loss: 0.8727 03/06 08:31:06 - mmengine - INFO - Epoch(train) [92][3700/5005] lr: 1.0000e-03 eta: 15:22:31 time: 0.2256 data_time: 0.0033 loss: 0.8392 03/06 08:31:29 - mmengine - INFO - Epoch(train) [92][3800/5005] lr: 1.0000e-03 eta: 15:22:08 time: 0.2258 data_time: 0.0038 loss: 0.8980 03/06 08:31:52 - mmengine - INFO - Epoch(train) [92][3900/5005] lr: 1.0000e-03 eta: 15:21:45 time: 0.2446 data_time: 0.0039 loss: 0.8580 03/06 08:32:15 - mmengine - INFO - Epoch(train) [92][4000/5005] lr: 1.0000e-03 eta: 15:21:22 time: 0.2270 data_time: 0.0033 loss: 1.0073 03/06 08:32:38 - mmengine - INFO - Epoch(train) [92][4100/5005] lr: 1.0000e-03 eta: 15:20:59 time: 0.2262 data_time: 0.0035 loss: 0.8777 03/06 08:33:02 - mmengine - INFO - Epoch(train) [92][4200/5005] lr: 1.0000e-03 eta: 15:20:36 time: 0.2289 data_time: 0.0035 loss: 1.0581 03/06 08:33:25 - mmengine - INFO - Epoch(train) [92][4300/5005] lr: 1.0000e-03 eta: 15:20:14 time: 0.2504 data_time: 0.0034 loss: 0.8624 03/06 08:33:48 - mmengine - INFO - Epoch(train) [92][4400/5005] lr: 1.0000e-03 eta: 15:19:51 time: 0.2243 data_time: 0.0033 loss: 0.9610 03/06 08:34:11 - mmengine - INFO - Epoch(train) [92][4500/5005] lr: 1.0000e-03 eta: 15:19:28 time: 0.2254 data_time: 0.0035 loss: 0.9312 03/06 08:34:21 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:34:34 - mmengine - INFO - Epoch(train) [92][4600/5005] lr: 1.0000e-03 eta: 15:19:05 time: 0.2423 data_time: 0.0036 loss: 1.0170 03/06 08:34:58 - mmengine - INFO - Epoch(train) [92][4700/5005] lr: 1.0000e-03 eta: 15:18:42 time: 0.2467 data_time: 0.0035 loss: 0.7691 03/06 08:35:20 - mmengine - INFO - Epoch(train) [92][4800/5005] lr: 1.0000e-03 eta: 15:18:19 time: 0.2274 data_time: 0.0034 loss: 0.9422 03/06 08:35:44 - mmengine - INFO - Epoch(train) [92][4900/5005] lr: 1.0000e-03 eta: 15:17:56 time: 0.2830 data_time: 0.0031 loss: 1.0908 03/06 08:36:13 - mmengine - INFO - Epoch(train) [92][5000/5005] lr: 1.0000e-03 eta: 15:17:36 time: 0.2859 data_time: 0.0032 loss: 1.0167 03/06 08:36:14 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:36:17 - mmengine - INFO - Saving checkpoint at 92 epochs 03/06 08:36:33 - mmengine - INFO - Epoch(val) [92][100/196] eta: 0:00:13 time: 0.0204 data_time: 0.0003 03/06 08:36:46 - mmengine - INFO - Epoch(val) [92][196/196] accuracy/top1: 76.8720 accuracy/top5: 93.4420 03/06 08:37:19 - mmengine - INFO - Epoch(train) [93][ 100/5005] lr: 1.0000e-03 eta: 15:17:17 time: 0.2260 data_time: 0.0040 loss: 1.0119 03/06 08:37:42 - mmengine - INFO - Epoch(train) [93][ 200/5005] lr: 1.0000e-03 eta: 15:16:55 time: 0.2268 data_time: 0.0038 loss: 0.9492 03/06 08:38:06 - mmengine - INFO - Epoch(train) [93][ 300/5005] lr: 1.0000e-03 eta: 15:16:32 time: 0.2378 data_time: 0.0043 loss: 0.9959 03/06 08:38:29 - mmengine - INFO - Epoch(train) [93][ 400/5005] lr: 1.0000e-03 eta: 15:16:09 time: 0.2312 data_time: 0.0037 loss: 0.8803 03/06 08:38:52 - mmengine - INFO - Epoch(train) [93][ 500/5005] lr: 1.0000e-03 eta: 15:15:46 time: 0.2240 data_time: 0.0038 loss: 0.8308 03/06 08:39:02 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:39:15 - mmengine - INFO - Epoch(train) [93][ 600/5005] lr: 1.0000e-03 eta: 15:15:23 time: 0.2444 data_time: 0.0035 loss: 0.9419 03/06 08:39:39 - mmengine - INFO - Epoch(train) [93][ 700/5005] lr: 1.0000e-03 eta: 15:15:00 time: 0.2429 data_time: 0.0035 loss: 0.9857 03/06 08:40:02 - mmengine - INFO - Epoch(train) [93][ 800/5005] lr: 1.0000e-03 eta: 15:14:38 time: 0.2281 data_time: 0.0037 loss: 0.8524 03/06 08:40:25 - mmengine - INFO - Epoch(train) [93][ 900/5005] lr: 1.0000e-03 eta: 15:14:15 time: 0.2526 data_time: 0.0039 loss: 0.8051 03/06 08:40:48 - mmengine - INFO - Epoch(train) [93][1000/5005] lr: 1.0000e-03 eta: 15:13:52 time: 0.2473 data_time: 0.0038 loss: 0.8678 03/06 08:41:11 - mmengine - INFO - Epoch(train) [93][1100/5005] lr: 1.0000e-03 eta: 15:13:29 time: 0.2254 data_time: 0.0036 loss: 0.8537 03/06 08:41:34 - mmengine - INFO - Epoch(train) [93][1200/5005] lr: 1.0000e-03 eta: 15:13:06 time: 0.2282 data_time: 0.0034 loss: 0.8576 03/06 08:41:58 - mmengine - INFO - Epoch(train) [93][1300/5005] lr: 1.0000e-03 eta: 15:12:43 time: 0.2894 data_time: 0.0032 loss: 0.8178 03/06 08:42:21 - mmengine - INFO - Epoch(train) [93][1400/5005] lr: 1.0000e-03 eta: 15:12:20 time: 0.2251 data_time: 0.0035 loss: 0.8486 03/06 08:42:44 - mmengine - INFO - Epoch(train) [93][1500/5005] lr: 1.0000e-03 eta: 15:11:57 time: 0.2255 data_time: 0.0032 loss: 0.8934 03/06 08:42:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:43:07 - mmengine - INFO - Epoch(train) [93][1600/5005] lr: 1.0000e-03 eta: 15:11:35 time: 0.2287 data_time: 0.0032 loss: 0.7546 03/06 08:43:30 - mmengine - INFO - Epoch(train) [93][1700/5005] lr: 1.0000e-03 eta: 15:11:12 time: 0.2246 data_time: 0.0035 loss: 0.9310 03/06 08:43:53 - mmengine - INFO - Epoch(train) [93][1800/5005] lr: 1.0000e-03 eta: 15:10:49 time: 0.2285 data_time: 0.0038 loss: 1.0860 03/06 08:44:16 - mmengine - INFO - Epoch(train) [93][1900/5005] lr: 1.0000e-03 eta: 15:10:26 time: 0.2240 data_time: 0.0032 loss: 0.9860 03/06 08:44:40 - mmengine - INFO - Epoch(train) [93][2000/5005] lr: 1.0000e-03 eta: 15:10:03 time: 0.2261 data_time: 0.0032 loss: 1.0562 03/06 08:45:03 - mmengine - INFO - Epoch(train) [93][2100/5005] lr: 1.0000e-03 eta: 15:09:40 time: 0.2217 data_time: 0.0034 loss: 0.9648 03/06 08:45:26 - mmengine - INFO - Epoch(train) [93][2200/5005] lr: 1.0000e-03 eta: 15:09:17 time: 0.2264 data_time: 0.0036 loss: 0.9405 03/06 08:45:49 - mmengine - INFO - Epoch(train) [93][2300/5005] lr: 1.0000e-03 eta: 15:08:54 time: 0.2236 data_time: 0.0035 loss: 0.9124 03/06 08:46:12 - mmengine - INFO - Epoch(train) [93][2400/5005] lr: 1.0000e-03 eta: 15:08:31 time: 0.2449 data_time: 0.0035 loss: 0.8695 03/06 08:46:36 - mmengine - INFO - Epoch(train) [93][2500/5005] lr: 1.0000e-03 eta: 15:08:09 time: 0.2296 data_time: 0.0032 loss: 0.9202 03/06 08:46:45 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:46:59 - mmengine - INFO - Epoch(train) [93][2600/5005] lr: 1.0000e-03 eta: 15:07:46 time: 0.2265 data_time: 0.0037 loss: 0.8836 03/06 08:47:22 - mmengine - INFO - Epoch(train) [93][2700/5005] lr: 1.0000e-03 eta: 15:07:23 time: 0.2243 data_time: 0.0035 loss: 0.9641 03/06 08:47:45 - mmengine - INFO - Epoch(train) [93][2800/5005] lr: 1.0000e-03 eta: 15:07:00 time: 0.2236 data_time: 0.0033 loss: 0.9410 03/06 08:48:08 - mmengine - INFO - Epoch(train) [93][2900/5005] lr: 1.0000e-03 eta: 15:06:37 time: 0.2258 data_time: 0.0033 loss: 0.8824 03/06 08:48:31 - mmengine - INFO - Epoch(train) [93][3000/5005] lr: 1.0000e-03 eta: 15:06:14 time: 0.2276 data_time: 0.0032 loss: 0.9128 03/06 08:48:54 - mmengine - INFO - Epoch(train) [93][3100/5005] lr: 1.0000e-03 eta: 15:05:51 time: 0.2237 data_time: 0.0038 loss: 0.9028 03/06 08:49:18 - mmengine - INFO - Epoch(train) [93][3200/5005] lr: 1.0000e-03 eta: 15:05:28 time: 0.2264 data_time: 0.0036 loss: 0.8155 03/06 08:49:41 - mmengine - INFO - Epoch(train) [93][3300/5005] lr: 1.0000e-03 eta: 15:05:05 time: 0.2316 data_time: 0.0035 loss: 0.7776 03/06 08:50:04 - mmengine - INFO - Epoch(train) [93][3400/5005] lr: 1.0000e-03 eta: 15:04:42 time: 0.2232 data_time: 0.0036 loss: 0.9911 03/06 08:50:27 - mmengine - INFO - Epoch(train) [93][3500/5005] lr: 1.0000e-03 eta: 15:04:19 time: 0.2435 data_time: 0.0033 loss: 0.9597 03/06 08:50:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:50:50 - mmengine - INFO - Epoch(train) [93][3600/5005] lr: 1.0000e-03 eta: 15:03:56 time: 0.2254 data_time: 0.0034 loss: 0.9720 03/06 08:51:13 - mmengine - INFO - Epoch(train) [93][3700/5005] lr: 1.0000e-03 eta: 15:03:34 time: 0.2259 data_time: 0.0034 loss: 0.8429 03/06 08:51:36 - mmengine - INFO - Epoch(train) [93][3800/5005] lr: 1.0000e-03 eta: 15:03:11 time: 0.2240 data_time: 0.0036 loss: 1.0426 03/06 08:51:59 - mmengine - INFO - Epoch(train) [93][3900/5005] lr: 1.0000e-03 eta: 15:02:48 time: 0.2295 data_time: 0.0033 loss: 0.9872 03/06 08:52:23 - mmengine - INFO - Epoch(train) [93][4000/5005] lr: 1.0000e-03 eta: 15:02:25 time: 0.2270 data_time: 0.0033 loss: 1.0860 03/06 08:52:46 - mmengine - INFO - Epoch(train) [93][4100/5005] lr: 1.0000e-03 eta: 15:02:02 time: 0.2315 data_time: 0.0033 loss: 0.8901 03/06 08:53:09 - mmengine - INFO - Epoch(train) [93][4200/5005] lr: 1.0000e-03 eta: 15:01:39 time: 0.2284 data_time: 0.0037 loss: 0.9139 03/06 08:53:31 - mmengine - INFO - Epoch(train) [93][4300/5005] lr: 1.0000e-03 eta: 15:01:16 time: 0.2253 data_time: 0.0033 loss: 0.9653 03/06 08:53:55 - mmengine - INFO - Epoch(train) [93][4400/5005] lr: 1.0000e-03 eta: 15:00:53 time: 0.2283 data_time: 0.0037 loss: 0.8637 03/06 08:54:18 - mmengine - INFO - Epoch(train) [93][4500/5005] lr: 1.0000e-03 eta: 15:00:30 time: 0.2224 data_time: 0.0037 loss: 0.8735 03/06 08:54:27 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:54:41 - mmengine - INFO - Epoch(train) [93][4600/5005] lr: 1.0000e-03 eta: 15:00:07 time: 0.2266 data_time: 0.0033 loss: 0.9474 03/06 08:55:04 - mmengine - INFO - Epoch(train) [93][4700/5005] lr: 1.0000e-03 eta: 14:59:44 time: 0.2268 data_time: 0.0038 loss: 0.8875 03/06 08:55:27 - mmengine - INFO - Epoch(train) [93][4800/5005] lr: 1.0000e-03 eta: 14:59:22 time: 0.2283 data_time: 0.0037 loss: 0.9162 03/06 08:55:52 - mmengine - INFO - Epoch(train) [93][4900/5005] lr: 1.0000e-03 eta: 14:58:59 time: 0.2926 data_time: 0.0034 loss: 0.7889 03/06 08:56:21 - mmengine - INFO - Epoch(train) [93][5000/5005] lr: 1.0000e-03 eta: 14:58:39 time: 0.2932 data_time: 0.0032 loss: 1.0135 03/06 08:56:22 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:56:25 - mmengine - INFO - Saving checkpoint at 93 epochs 03/06 08:56:41 - mmengine - INFO - Epoch(val) [93][100/196] eta: 0:00:13 time: 0.0201 data_time: 0.0004 03/06 08:56:55 - mmengine - INFO - Epoch(val) [93][196/196] accuracy/top1: 76.9980 accuracy/top5: 93.5200 03/06 08:57:27 - mmengine - INFO - Epoch(train) [94][ 100/5005] lr: 1.0000e-03 eta: 14:58:20 time: 0.2250 data_time: 0.0045 loss: 0.8935 03/06 08:57:51 - mmengine - INFO - Epoch(train) [94][ 200/5005] lr: 1.0000e-03 eta: 14:57:57 time: 0.2284 data_time: 0.0039 loss: 0.9665 03/06 08:58:14 - mmengine - INFO - Epoch(train) [94][ 300/5005] lr: 1.0000e-03 eta: 14:57:34 time: 0.2431 data_time: 0.0034 loss: 0.8903 03/06 08:58:37 - mmengine - INFO - Epoch(train) [94][ 400/5005] lr: 1.0000e-03 eta: 14:57:12 time: 0.2269 data_time: 0.0037 loss: 0.9052 03/06 08:59:00 - mmengine - INFO - Epoch(train) [94][ 500/5005] lr: 1.0000e-03 eta: 14:56:49 time: 0.2327 data_time: 0.0033 loss: 0.8524 03/06 08:59:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 08:59:23 - mmengine - INFO - Epoch(train) [94][ 600/5005] lr: 1.0000e-03 eta: 14:56:26 time: 0.2220 data_time: 0.0032 loss: 0.8668 03/06 08:59:46 - mmengine - INFO - Epoch(train) [94][ 700/5005] lr: 1.0000e-03 eta: 14:56:03 time: 0.2364 data_time: 0.0031 loss: 0.7820 03/06 09:00:10 - mmengine - INFO - Epoch(train) [94][ 800/5005] lr: 1.0000e-03 eta: 14:55:40 time: 0.2222 data_time: 0.0034 loss: 0.8648 03/06 09:00:33 - mmengine - INFO - Epoch(train) [94][ 900/5005] lr: 1.0000e-03 eta: 14:55:17 time: 0.2264 data_time: 0.0040 loss: 0.8555 03/06 09:00:56 - mmengine - INFO - Epoch(train) [94][1000/5005] lr: 1.0000e-03 eta: 14:54:54 time: 0.2252 data_time: 0.0033 loss: 0.9676 03/06 09:01:19 - mmengine - INFO - Epoch(train) [94][1100/5005] lr: 1.0000e-03 eta: 14:54:31 time: 0.2234 data_time: 0.0035 loss: 0.9858 03/06 09:01:42 - mmengine - INFO - Epoch(train) [94][1200/5005] lr: 1.0000e-03 eta: 14:54:08 time: 0.2245 data_time: 0.0034 loss: 0.8385 03/06 09:02:05 - mmengine - INFO - Epoch(train) [94][1300/5005] lr: 1.0000e-03 eta: 14:53:45 time: 0.2240 data_time: 0.0034 loss: 0.8856 03/06 09:02:28 - mmengine - INFO - Epoch(train) [94][1400/5005] lr: 1.0000e-03 eta: 14:53:22 time: 0.2272 data_time: 0.0034 loss: 0.8590 03/06 09:02:51 - mmengine - INFO - Epoch(train) [94][1500/5005] lr: 1.0000e-03 eta: 14:52:59 time: 0.2217 data_time: 0.0033 loss: 0.8855 03/06 09:02:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:03:14 - mmengine - INFO - Epoch(train) [94][1600/5005] lr: 1.0000e-03 eta: 14:52:37 time: 0.2298 data_time: 0.0034 loss: 0.9079 03/06 09:03:37 - mmengine - INFO - Epoch(train) [94][1700/5005] lr: 1.0000e-03 eta: 14:52:14 time: 0.2263 data_time: 0.0038 loss: 0.9340 03/06 09:04:00 - mmengine - INFO - Epoch(train) [94][1800/5005] lr: 1.0000e-03 eta: 14:51:51 time: 0.2294 data_time: 0.0037 loss: 0.8168 03/06 09:04:23 - mmengine - INFO - Epoch(train) [94][1900/5005] lr: 1.0000e-03 eta: 14:51:28 time: 0.2230 data_time: 0.0035 loss: 1.1536 03/06 09:04:47 - mmengine - INFO - Epoch(train) [94][2000/5005] lr: 1.0000e-03 eta: 14:51:05 time: 0.2284 data_time: 0.0039 loss: 1.0370 03/06 09:05:10 - mmengine - INFO - Epoch(train) [94][2100/5005] lr: 1.0000e-03 eta: 14:50:42 time: 0.2298 data_time: 0.0039 loss: 0.9782 03/06 09:05:33 - mmengine - INFO - Epoch(train) [94][2200/5005] lr: 1.0000e-03 eta: 14:50:19 time: 0.2258 data_time: 0.0035 loss: 1.0408 03/06 09:05:56 - mmengine - INFO - Epoch(train) [94][2300/5005] lr: 1.0000e-03 eta: 14:49:56 time: 0.2250 data_time: 0.0034 loss: 0.8514 03/06 09:06:19 - mmengine - INFO - Epoch(train) [94][2400/5005] lr: 1.0000e-03 eta: 14:49:33 time: 0.2280 data_time: 0.0032 loss: 1.0056 03/06 09:06:43 - mmengine - INFO - Epoch(train) [94][2500/5005] lr: 1.0000e-03 eta: 14:49:10 time: 0.2283 data_time: 0.0034 loss: 1.0716 03/06 09:06:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:07:05 - mmengine - INFO - Epoch(train) [94][2600/5005] lr: 1.0000e-03 eta: 14:48:47 time: 0.2255 data_time: 0.0039 loss: 0.8047 03/06 09:07:28 - mmengine - INFO - Epoch(train) [94][2700/5005] lr: 1.0000e-03 eta: 14:48:24 time: 0.2299 data_time: 0.0038 loss: 0.9632 03/06 09:07:52 - mmengine - INFO - Epoch(train) [94][2800/5005] lr: 1.0000e-03 eta: 14:48:02 time: 0.2281 data_time: 0.0034 loss: 0.8393 03/06 09:08:15 - mmengine - INFO - Epoch(train) [94][2900/5005] lr: 1.0000e-03 eta: 14:47:39 time: 0.2452 data_time: 0.0040 loss: 0.9939 03/06 09:08:38 - mmengine - INFO - Epoch(train) [94][3000/5005] lr: 1.0000e-03 eta: 14:47:16 time: 0.2269 data_time: 0.0037 loss: 1.0208 03/06 09:09:01 - mmengine - INFO - Epoch(train) [94][3100/5005] lr: 1.0000e-03 eta: 14:46:53 time: 0.2300 data_time: 0.0040 loss: 1.0453 03/06 09:09:24 - mmengine - INFO - Epoch(train) [94][3200/5005] lr: 1.0000e-03 eta: 14:46:30 time: 0.2254 data_time: 0.0035 loss: 0.8048 03/06 09:09:47 - mmengine - INFO - Epoch(train) [94][3300/5005] lr: 1.0000e-03 eta: 14:46:07 time: 0.2266 data_time: 0.0033 loss: 0.9960 03/06 09:10:11 - mmengine - INFO - Epoch(train) [94][3400/5005] lr: 1.0000e-03 eta: 14:45:44 time: 0.2273 data_time: 0.0035 loss: 1.0056 03/06 09:10:34 - mmengine - INFO - Epoch(train) [94][3500/5005] lr: 1.0000e-03 eta: 14:45:21 time: 0.2269 data_time: 0.0034 loss: 1.0798 03/06 09:10:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:10:57 - mmengine - INFO - Epoch(train) [94][3600/5005] lr: 1.0000e-03 eta: 14:44:58 time: 0.2267 data_time: 0.0036 loss: 0.8513 03/06 09:11:20 - mmengine - INFO - Epoch(train) [94][3700/5005] lr: 1.0000e-03 eta: 14:44:35 time: 0.2264 data_time: 0.0040 loss: 0.8697 03/06 09:11:43 - mmengine - INFO - Epoch(train) [94][3800/5005] lr: 1.0000e-03 eta: 14:44:12 time: 0.2322 data_time: 0.0034 loss: 0.9659 03/06 09:12:06 - mmengine - INFO - Epoch(train) [94][3900/5005] lr: 1.0000e-03 eta: 14:43:49 time: 0.2257 data_time: 0.0042 loss: 0.8688 03/06 09:12:30 - mmengine - INFO - Epoch(train) [94][4000/5005] lr: 1.0000e-03 eta: 14:43:27 time: 0.2260 data_time: 0.0034 loss: 0.9798 03/06 09:12:53 - mmengine - INFO - Epoch(train) [94][4100/5005] lr: 1.0000e-03 eta: 14:43:04 time: 0.2423 data_time: 0.0035 loss: 0.9485 03/06 09:13:16 - mmengine - INFO - Epoch(train) [94][4200/5005] lr: 1.0000e-03 eta: 14:42:41 time: 0.2363 data_time: 0.0035 loss: 0.8387 03/06 09:13:39 - mmengine - INFO - Epoch(train) [94][4300/5005] lr: 1.0000e-03 eta: 14:42:18 time: 0.2303 data_time: 0.0037 loss: 1.0559 03/06 09:14:02 - mmengine - INFO - Epoch(train) [94][4400/5005] lr: 1.0000e-03 eta: 14:41:55 time: 0.2450 data_time: 0.0036 loss: 1.0208 03/06 09:14:25 - mmengine - INFO - Epoch(train) [94][4500/5005] lr: 1.0000e-03 eta: 14:41:32 time: 0.2264 data_time: 0.0037 loss: 0.8983 03/06 09:14:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:14:48 - mmengine - INFO - Epoch(train) [94][4600/5005] lr: 1.0000e-03 eta: 14:41:09 time: 0.2263 data_time: 0.0034 loss: 0.9475 03/06 09:15:11 - mmengine - INFO - Epoch(train) [94][4700/5005] lr: 1.0000e-03 eta: 14:40:46 time: 0.2256 data_time: 0.0037 loss: 0.9384 03/06 09:15:35 - mmengine - INFO - Epoch(train) [94][4800/5005] lr: 1.0000e-03 eta: 14:40:23 time: 0.2513 data_time: 0.0036 loss: 0.8432 03/06 09:15:59 - mmengine - INFO - Epoch(train) [94][4900/5005] lr: 1.0000e-03 eta: 14:40:01 time: 0.2856 data_time: 0.0033 loss: 1.0028 03/06 09:16:27 - mmengine - INFO - Epoch(train) [94][5000/5005] lr: 1.0000e-03 eta: 14:39:41 time: 0.2466 data_time: 0.0033 loss: 0.8229 03/06 09:16:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:16:31 - mmengine - INFO - Saving checkpoint at 94 epochs 03/06 09:16:47 - mmengine - INFO - Epoch(val) [94][100/196] eta: 0:00:13 time: 0.0178 data_time: 0.0003 03/06 09:17:00 - mmengine - INFO - Epoch(val) [94][196/196] accuracy/top1: 76.9440 accuracy/top5: 93.5420 03/06 09:17:33 - mmengine - INFO - Epoch(train) [95][ 100/5005] lr: 1.0000e-03 eta: 14:39:21 time: 0.2284 data_time: 0.0038 loss: 0.7882 03/06 09:17:56 - mmengine - INFO - Epoch(train) [95][ 200/5005] lr: 1.0000e-03 eta: 14:38:58 time: 0.2273 data_time: 0.0044 loss: 0.8052 03/06 09:18:19 - mmengine - INFO - Epoch(train) [95][ 300/5005] lr: 1.0000e-03 eta: 14:38:35 time: 0.2271 data_time: 0.0044 loss: 1.0890 03/06 09:18:42 - mmengine - INFO - Epoch(train) [95][ 400/5005] lr: 1.0000e-03 eta: 14:38:12 time: 0.2244 data_time: 0.0034 loss: 0.9369 03/06 09:19:06 - mmengine - INFO - Epoch(train) [95][ 500/5005] lr: 1.0000e-03 eta: 14:37:50 time: 0.2252 data_time: 0.0037 loss: 0.8033 03/06 09:19:12 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:19:28 - mmengine - INFO - Epoch(train) [95][ 600/5005] lr: 1.0000e-03 eta: 14:37:27 time: 0.2263 data_time: 0.0036 loss: 0.9709 03/06 09:19:51 - mmengine - INFO - Epoch(train) [95][ 700/5005] lr: 1.0000e-03 eta: 14:37:04 time: 0.2252 data_time: 0.0033 loss: 0.7888 03/06 09:20:14 - mmengine - INFO - Epoch(train) [95][ 800/5005] lr: 1.0000e-03 eta: 14:36:41 time: 0.2269 data_time: 0.0036 loss: 0.8472 03/06 09:20:38 - mmengine - INFO - Epoch(train) [95][ 900/5005] lr: 1.0000e-03 eta: 14:36:18 time: 0.2550 data_time: 0.0037 loss: 0.9543 03/06 09:21:01 - mmengine - INFO - Epoch(train) [95][1000/5005] lr: 1.0000e-03 eta: 14:35:55 time: 0.2250 data_time: 0.0036 loss: 0.9008 03/06 09:21:24 - mmengine - INFO - Epoch(train) [95][1100/5005] lr: 1.0000e-03 eta: 14:35:32 time: 0.2245 data_time: 0.0035 loss: 1.0335 03/06 09:21:47 - mmengine - INFO - Epoch(train) [95][1200/5005] lr: 1.0000e-03 eta: 14:35:09 time: 0.2276 data_time: 0.0039 loss: 0.9130 03/06 09:22:11 - mmengine - INFO - Epoch(train) [95][1300/5005] lr: 1.0000e-03 eta: 14:34:46 time: 0.2294 data_time: 0.0041 loss: 0.7702 03/06 09:22:34 - mmengine - INFO - Epoch(train) [95][1400/5005] lr: 1.0000e-03 eta: 14:34:23 time: 0.2248 data_time: 0.0036 loss: 0.9594 03/06 09:22:57 - mmengine - INFO - Epoch(train) [95][1500/5005] lr: 1.0000e-03 eta: 14:34:00 time: 0.2273 data_time: 0.0033 loss: 0.8681 03/06 09:23:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:23:20 - mmengine - INFO - Epoch(train) [95][1600/5005] lr: 1.0000e-03 eta: 14:33:37 time: 0.2323 data_time: 0.0038 loss: 0.9168 03/06 09:23:43 - mmengine - INFO - Epoch(train) [95][1700/5005] lr: 1.0000e-03 eta: 14:33:14 time: 0.2272 data_time: 0.0033 loss: 0.7055 03/06 09:24:06 - mmengine - INFO - Epoch(train) [95][1800/5005] lr: 1.0000e-03 eta: 14:32:51 time: 0.2356 data_time: 0.0034 loss: 0.8464 03/06 09:24:29 - mmengine - INFO - Epoch(train) [95][1900/5005] lr: 1.0000e-03 eta: 14:32:28 time: 0.2248 data_time: 0.0036 loss: 1.0746 03/06 09:24:52 - mmengine - INFO - Epoch(train) [95][2000/5005] lr: 1.0000e-03 eta: 14:32:06 time: 0.2253 data_time: 0.0041 loss: 0.8699 03/06 09:25:15 - mmengine - INFO - Epoch(train) [95][2100/5005] lr: 1.0000e-03 eta: 14:31:43 time: 0.2251 data_time: 0.0038 loss: 0.9165 03/06 09:25:38 - mmengine - INFO - Epoch(train) [95][2200/5005] lr: 1.0000e-03 eta: 14:31:20 time: 0.2239 data_time: 0.0033 loss: 0.8444 03/06 09:26:01 - mmengine - INFO - Epoch(train) [95][2300/5005] lr: 1.0000e-03 eta: 14:30:57 time: 0.2269 data_time: 0.0034 loss: 1.0025 03/06 09:26:24 - mmengine - INFO - Epoch(train) [95][2400/5005] lr: 1.0000e-03 eta: 14:30:34 time: 0.2364 data_time: 0.0035 loss: 0.8202 03/06 09:26:48 - mmengine - INFO - Epoch(train) [95][2500/5005] lr: 1.0000e-03 eta: 14:30:11 time: 0.2265 data_time: 0.0035 loss: 0.8720 03/06 09:26:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:27:11 - mmengine - INFO - Epoch(train) [95][2600/5005] lr: 1.0000e-03 eta: 14:29:48 time: 0.2333 data_time: 0.0037 loss: 1.0589 03/06 09:27:33 - mmengine - INFO - Epoch(train) [95][2700/5005] lr: 1.0000e-03 eta: 14:29:25 time: 0.2235 data_time: 0.0032 loss: 0.7785 03/06 09:27:56 - mmengine - INFO - Epoch(train) [95][2800/5005] lr: 1.0000e-03 eta: 14:29:02 time: 0.2251 data_time: 0.0037 loss: 0.9282 03/06 09:28:20 - mmengine - INFO - Epoch(train) [95][2900/5005] lr: 1.0000e-03 eta: 14:28:39 time: 0.2249 data_time: 0.0036 loss: 1.0530 03/06 09:28:43 - mmengine - INFO - Epoch(train) [95][3000/5005] lr: 1.0000e-03 eta: 14:28:16 time: 0.2259 data_time: 0.0037 loss: 0.9004 03/06 09:29:06 - mmengine - INFO - Epoch(train) [95][3100/5005] lr: 1.0000e-03 eta: 14:27:53 time: 0.2306 data_time: 0.0038 loss: 1.0345 03/06 09:29:29 - mmengine - INFO - Epoch(train) [95][3200/5005] lr: 1.0000e-03 eta: 14:27:30 time: 0.2292 data_time: 0.0041 loss: 1.1851 03/06 09:29:52 - mmengine - INFO - Epoch(train) [95][3300/5005] lr: 1.0000e-03 eta: 14:27:07 time: 0.2277 data_time: 0.0041 loss: 0.8391 03/06 09:30:16 - mmengine - INFO - Epoch(train) [95][3400/5005] lr: 1.0000e-03 eta: 14:26:45 time: 0.2286 data_time: 0.0033 loss: 1.0990 03/06 09:30:39 - mmengine - INFO - Epoch(train) [95][3500/5005] lr: 1.0000e-03 eta: 14:26:22 time: 0.2255 data_time: 0.0034 loss: 0.9121 03/06 09:30:46 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:31:02 - mmengine - INFO - Epoch(train) [95][3600/5005] lr: 1.0000e-03 eta: 14:25:59 time: 0.2262 data_time: 0.0034 loss: 1.0490 03/06 09:31:25 - mmengine - INFO - Epoch(train) [95][3700/5005] lr: 1.0000e-03 eta: 14:25:36 time: 0.2452 data_time: 0.0035 loss: 0.8562 03/06 09:31:49 - mmengine - INFO - Epoch(train) [95][3800/5005] lr: 1.0000e-03 eta: 14:25:13 time: 0.2222 data_time: 0.0030 loss: 0.8793 03/06 09:32:11 - mmengine - INFO - Epoch(train) [95][3900/5005] lr: 1.0000e-03 eta: 14:24:50 time: 0.2249 data_time: 0.0038 loss: 1.0521 03/06 09:32:34 - mmengine - INFO - Epoch(train) [95][4000/5005] lr: 1.0000e-03 eta: 14:24:27 time: 0.2355 data_time: 0.0034 loss: 0.8290 03/06 09:32:58 - mmengine - INFO - Epoch(train) [95][4100/5005] lr: 1.0000e-03 eta: 14:24:04 time: 0.2232 data_time: 0.0041 loss: 0.8990 03/06 09:33:21 - mmengine - INFO - Epoch(train) [95][4200/5005] lr: 1.0000e-03 eta: 14:23:41 time: 0.2268 data_time: 0.0036 loss: 0.9367 03/06 09:33:44 - mmengine - INFO - Epoch(train) [95][4300/5005] lr: 1.0000e-03 eta: 14:23:18 time: 0.2266 data_time: 0.0039 loss: 0.8766 03/06 09:34:07 - mmengine - INFO - Epoch(train) [95][4400/5005] lr: 1.0000e-03 eta: 14:22:55 time: 0.2326 data_time: 0.0041 loss: 0.9313 03/06 09:34:30 - mmengine - INFO - Epoch(train) [95][4500/5005] lr: 1.0000e-03 eta: 14:22:32 time: 0.2292 data_time: 0.0031 loss: 0.7915 03/06 09:34:37 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:34:53 - mmengine - INFO - Epoch(train) [95][4600/5005] lr: 1.0000e-03 eta: 14:22:10 time: 0.2261 data_time: 0.0035 loss: 0.9300 03/06 09:35:17 - mmengine - INFO - Epoch(train) [95][4700/5005] lr: 1.0000e-03 eta: 14:21:47 time: 0.2282 data_time: 0.0033 loss: 0.9073 03/06 09:35:40 - mmengine - INFO - Epoch(train) [95][4800/5005] lr: 1.0000e-03 eta: 14:21:24 time: 0.2240 data_time: 0.0035 loss: 0.8620 03/06 09:36:03 - mmengine - INFO - Epoch(train) [95][4900/5005] lr: 1.0000e-03 eta: 14:21:01 time: 0.2915 data_time: 0.0032 loss: 0.9585 03/06 09:36:32 - mmengine - INFO - Epoch(train) [95][5000/5005] lr: 1.0000e-03 eta: 14:20:41 time: 0.2824 data_time: 0.0032 loss: 0.9632 03/06 09:36:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:36:36 - mmengine - INFO - Saving checkpoint at 95 epochs 03/06 09:36:52 - mmengine - INFO - Epoch(val) [95][100/196] eta: 0:00:13 time: 0.0184 data_time: 0.0004 03/06 09:37:05 - mmengine - INFO - Epoch(val) [95][196/196] accuracy/top1: 77.0740 accuracy/top5: 93.4800 03/06 09:37:39 - mmengine - INFO - Epoch(train) [96][ 100/5005] lr: 1.0000e-03 eta: 14:20:21 time: 0.2623 data_time: 0.0057 loss: 1.0371 03/06 09:38:02 - mmengine - INFO - Epoch(train) [96][ 200/5005] lr: 1.0000e-03 eta: 14:19:59 time: 0.2489 data_time: 0.0044 loss: 0.9386 03/06 09:38:25 - mmengine - INFO - Epoch(train) [96][ 300/5005] lr: 1.0000e-03 eta: 14:19:36 time: 0.2311 data_time: 0.0037 loss: 1.1556 03/06 09:38:48 - mmengine - INFO - Epoch(train) [96][ 400/5005] lr: 1.0000e-03 eta: 14:19:13 time: 0.2271 data_time: 0.0035 loss: 0.7664 03/06 09:39:12 - mmengine - INFO - Epoch(train) [96][ 500/5005] lr: 1.0000e-03 eta: 14:18:50 time: 0.2696 data_time: 0.0033 loss: 0.9269 03/06 09:39:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:39:35 - mmengine - INFO - Epoch(train) [96][ 600/5005] lr: 1.0000e-03 eta: 14:18:27 time: 0.2276 data_time: 0.0037 loss: 0.8568 03/06 09:39:58 - mmengine - INFO - Epoch(train) [96][ 700/5005] lr: 1.0000e-03 eta: 14:18:04 time: 0.2238 data_time: 0.0035 loss: 0.8707 03/06 09:40:21 - mmengine - INFO - Epoch(train) [96][ 800/5005] lr: 1.0000e-03 eta: 14:17:41 time: 0.2233 data_time: 0.0032 loss: 0.9134 03/06 09:40:44 - mmengine - INFO - Epoch(train) [96][ 900/5005] lr: 1.0000e-03 eta: 14:17:18 time: 0.2564 data_time: 0.0033 loss: 0.8699 03/06 09:41:07 - mmengine - INFO - Epoch(train) [96][1000/5005] lr: 1.0000e-03 eta: 14:16:55 time: 0.2238 data_time: 0.0039 loss: 1.0181 03/06 09:41:30 - mmengine - INFO - Epoch(train) [96][1100/5005] lr: 1.0000e-03 eta: 14:16:32 time: 0.2251 data_time: 0.0034 loss: 0.9796 03/06 09:41:53 - mmengine - INFO - Epoch(train) [96][1200/5005] lr: 1.0000e-03 eta: 14:16:09 time: 0.2268 data_time: 0.0033 loss: 1.0491 03/06 09:42:16 - mmengine - INFO - Epoch(train) [96][1300/5005] lr: 1.0000e-03 eta: 14:15:46 time: 0.2272 data_time: 0.0034 loss: 0.9895 03/06 09:42:39 - mmengine - INFO - Epoch(train) [96][1400/5005] lr: 1.0000e-03 eta: 14:15:23 time: 0.2251 data_time: 0.0033 loss: 0.9883 03/06 09:43:02 - mmengine - INFO - Epoch(train) [96][1500/5005] lr: 1.0000e-03 eta: 14:15:01 time: 0.2254 data_time: 0.0036 loss: 0.9582 03/06 09:43:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:43:25 - mmengine - INFO - Epoch(train) [96][1600/5005] lr: 1.0000e-03 eta: 14:14:37 time: 0.2268 data_time: 0.0033 loss: 0.8707 03/06 09:43:48 - mmengine - INFO - Epoch(train) [96][1700/5005] lr: 1.0000e-03 eta: 14:14:14 time: 0.2295 data_time: 0.0033 loss: 1.1336 03/06 09:44:12 - mmengine - INFO - Epoch(train) [96][1800/5005] lr: 1.0000e-03 eta: 14:13:52 time: 0.2497 data_time: 0.0033 loss: 1.0115 03/06 09:44:35 - mmengine - INFO - Epoch(train) [96][1900/5005] lr: 1.0000e-03 eta: 14:13:29 time: 0.2283 data_time: 0.0035 loss: 1.0212 03/06 09:44:57 - mmengine - INFO - Epoch(train) [96][2000/5005] lr: 1.0000e-03 eta: 14:13:06 time: 0.2295 data_time: 0.0033 loss: 1.1091 03/06 09:45:20 - mmengine - INFO - Epoch(train) [96][2100/5005] lr: 1.0000e-03 eta: 14:12:43 time: 0.2252 data_time: 0.0034 loss: 0.9647 03/06 09:45:44 - mmengine - INFO - Epoch(train) [96][2200/5005] lr: 1.0000e-03 eta: 14:12:20 time: 0.2246 data_time: 0.0035 loss: 0.7314 03/06 09:46:07 - mmengine - INFO - Epoch(train) [96][2300/5005] lr: 1.0000e-03 eta: 14:11:57 time: 0.2270 data_time: 0.0033 loss: 1.0172 03/06 09:46:30 - mmengine - INFO - Epoch(train) [96][2400/5005] lr: 1.0000e-03 eta: 14:11:34 time: 0.2264 data_time: 0.0033 loss: 0.9813 03/06 09:46:53 - mmengine - INFO - Epoch(train) [96][2500/5005] lr: 1.0000e-03 eta: 14:11:11 time: 0.2479 data_time: 0.0037 loss: 0.9239 03/06 09:46:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:47:16 - mmengine - INFO - Epoch(train) [96][2600/5005] lr: 1.0000e-03 eta: 14:10:48 time: 0.2254 data_time: 0.0034 loss: 0.9284 03/06 09:47:39 - mmengine - INFO - Epoch(train) [96][2700/5005] lr: 1.0000e-03 eta: 14:10:25 time: 0.2288 data_time: 0.0037 loss: 0.9938 03/06 09:48:02 - mmengine - INFO - Epoch(train) [96][2800/5005] lr: 1.0000e-03 eta: 14:10:02 time: 0.2251 data_time: 0.0034 loss: 0.8107 03/06 09:48:25 - mmengine - INFO - Epoch(train) [96][2900/5005] lr: 1.0000e-03 eta: 14:09:39 time: 0.2249 data_time: 0.0035 loss: 1.0687 03/06 09:48:49 - mmengine - INFO - Epoch(train) [96][3000/5005] lr: 1.0000e-03 eta: 14:09:16 time: 0.2264 data_time: 0.0033 loss: 0.8768 03/06 09:49:12 - mmengine - INFO - Epoch(train) [96][3100/5005] lr: 1.0000e-03 eta: 14:08:53 time: 0.2260 data_time: 0.0033 loss: 0.9250 03/06 09:49:35 - mmengine - INFO - Epoch(train) [96][3200/5005] lr: 1.0000e-03 eta: 14:08:30 time: 0.2414 data_time: 0.0037 loss: 0.9401 03/06 09:49:58 - mmengine - INFO - Epoch(train) [96][3300/5005] lr: 1.0000e-03 eta: 14:08:07 time: 0.2258 data_time: 0.0032 loss: 0.8875 03/06 09:50:21 - mmengine - INFO - Epoch(train) [96][3400/5005] lr: 1.0000e-03 eta: 14:07:45 time: 0.2267 data_time: 0.0033 loss: 0.8751 03/06 09:50:44 - mmengine - INFO - Epoch(train) [96][3500/5005] lr: 1.0000e-03 eta: 14:07:22 time: 0.2238 data_time: 0.0034 loss: 0.8308 03/06 09:50:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:51:07 - mmengine - INFO - Epoch(train) [96][3600/5005] lr: 1.0000e-03 eta: 14:06:59 time: 0.2262 data_time: 0.0035 loss: 1.0169 03/06 09:51:30 - mmengine - INFO - Epoch(train) [96][3700/5005] lr: 1.0000e-03 eta: 14:06:36 time: 0.2247 data_time: 0.0035 loss: 0.8897 03/06 09:51:54 - mmengine - INFO - Epoch(train) [96][3800/5005] lr: 1.0000e-03 eta: 14:06:13 time: 0.2451 data_time: 0.0033 loss: 1.0982 03/06 09:52:17 - mmengine - INFO - Epoch(train) [96][3900/5005] lr: 1.0000e-03 eta: 14:05:50 time: 0.2343 data_time: 0.0034 loss: 0.8806 03/06 09:52:40 - mmengine - INFO - Epoch(train) [96][4000/5005] lr: 1.0000e-03 eta: 14:05:27 time: 0.2250 data_time: 0.0035 loss: 0.9927 03/06 09:53:03 - mmengine - INFO - Epoch(train) [96][4100/5005] lr: 1.0000e-03 eta: 14:05:04 time: 0.2233 data_time: 0.0034 loss: 1.1882 03/06 09:53:26 - mmengine - INFO - Epoch(train) [96][4200/5005] lr: 1.0000e-03 eta: 14:04:41 time: 0.2482 data_time: 0.0035 loss: 0.7743 03/06 09:53:50 - mmengine - INFO - Epoch(train) [96][4300/5005] lr: 1.0000e-03 eta: 14:04:18 time: 0.2461 data_time: 0.0032 loss: 0.8019 03/06 09:54:13 - mmengine - INFO - Epoch(train) [96][4400/5005] lr: 1.0000e-03 eta: 14:03:55 time: 0.2241 data_time: 0.0034 loss: 1.0060 03/06 09:54:35 - mmengine - INFO - Epoch(train) [96][4500/5005] lr: 1.0000e-03 eta: 14:03:32 time: 0.2291 data_time: 0.0040 loss: 0.9608 03/06 09:54:41 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:54:59 - mmengine - INFO - Epoch(train) [96][4600/5005] lr: 1.0000e-03 eta: 14:03:09 time: 0.2360 data_time: 0.0038 loss: 0.9198 03/06 09:55:22 - mmengine - INFO - Epoch(train) [96][4700/5005] lr: 1.0000e-03 eta: 14:02:47 time: 0.2399 data_time: 0.0038 loss: 0.9306 03/06 09:55:45 - mmengine - INFO - Epoch(train) [96][4800/5005] lr: 1.0000e-03 eta: 14:02:24 time: 0.2253 data_time: 0.0036 loss: 0.7855 03/06 09:56:09 - mmengine - INFO - Epoch(train) [96][4900/5005] lr: 1.0000e-03 eta: 14:02:01 time: 0.2958 data_time: 0.0034 loss: 0.8926 03/06 09:56:38 - mmengine - INFO - Epoch(train) [96][5000/5005] lr: 1.0000e-03 eta: 14:01:41 time: 0.2793 data_time: 0.0032 loss: 0.8936 03/06 09:56:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:56:43 - mmengine - INFO - Saving checkpoint at 96 epochs 03/06 09:56:58 - mmengine - INFO - Epoch(val) [96][100/196] eta: 0:00:13 time: 0.0194 data_time: 0.0003 03/06 09:57:12 - mmengine - INFO - Epoch(val) [96][196/196] accuracy/top1: 76.7900 accuracy/top5: 93.5080 03/06 09:57:44 - mmengine - INFO - Epoch(train) [97][ 100/5005] lr: 1.0000e-03 eta: 14:01:21 time: 0.2275 data_time: 0.0043 loss: 0.8902 03/06 09:58:07 - mmengine - INFO - Epoch(train) [97][ 200/5005] lr: 1.0000e-03 eta: 14:00:58 time: 0.2261 data_time: 0.0036 loss: 1.0685 03/06 09:58:31 - mmengine - INFO - Epoch(train) [97][ 300/5005] lr: 1.0000e-03 eta: 14:00:36 time: 0.2455 data_time: 0.0041 loss: 0.9899 03/06 09:58:54 - mmengine - INFO - Epoch(train) [97][ 400/5005] lr: 1.0000e-03 eta: 14:00:13 time: 0.2278 data_time: 0.0038 loss: 0.9779 03/06 09:59:17 - mmengine - INFO - Epoch(train) [97][ 500/5005] lr: 1.0000e-03 eta: 13:59:50 time: 0.2281 data_time: 0.0035 loss: 0.8828 03/06 09:59:22 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 09:59:40 - mmengine - INFO - Epoch(train) [97][ 600/5005] lr: 1.0000e-03 eta: 13:59:27 time: 0.2243 data_time: 0.0039 loss: 1.0263 03/06 10:00:03 - mmengine - INFO - Epoch(train) [97][ 700/5005] lr: 1.0000e-03 eta: 13:59:04 time: 0.2319 data_time: 0.0032 loss: 0.9022 03/06 10:00:26 - mmengine - INFO - Epoch(train) [97][ 800/5005] lr: 1.0000e-03 eta: 13:58:41 time: 0.2250 data_time: 0.0035 loss: 0.9512 03/06 10:00:49 - mmengine - INFO - Epoch(train) [97][ 900/5005] lr: 1.0000e-03 eta: 13:58:18 time: 0.2625 data_time: 0.0035 loss: 0.9394 03/06 10:01:12 - mmengine - INFO - Epoch(train) [97][1000/5005] lr: 1.0000e-03 eta: 13:57:55 time: 0.2266 data_time: 0.0031 loss: 0.9998 03/06 10:01:35 - mmengine - INFO - Epoch(train) [97][1100/5005] lr: 1.0000e-03 eta: 13:57:32 time: 0.2243 data_time: 0.0034 loss: 0.9177 03/06 10:01:59 - mmengine - INFO - Epoch(train) [97][1200/5005] lr: 1.0000e-03 eta: 13:57:09 time: 0.2259 data_time: 0.0046 loss: 0.9885 03/06 10:02:21 - mmengine - INFO - Epoch(train) [97][1300/5005] lr: 1.0000e-03 eta: 13:56:46 time: 0.2287 data_time: 0.0042 loss: 0.7461 03/06 10:02:45 - mmengine - INFO - Epoch(train) [97][1400/5005] lr: 1.0000e-03 eta: 13:56:23 time: 0.2283 data_time: 0.0036 loss: 0.9472 03/06 10:03:08 - mmengine - INFO - Epoch(train) [97][1500/5005] lr: 1.0000e-03 eta: 13:56:00 time: 0.2269 data_time: 0.0036 loss: 0.8929 03/06 10:03:12 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:03:31 - mmengine - INFO - Epoch(train) [97][1600/5005] lr: 1.0000e-03 eta: 13:55:37 time: 0.2221 data_time: 0.0034 loss: 0.8746 03/06 10:03:54 - mmengine - INFO - Epoch(train) [97][1700/5005] lr: 1.0000e-03 eta: 13:55:14 time: 0.2281 data_time: 0.0035 loss: 0.9259 03/06 10:04:17 - mmengine - INFO - Epoch(train) [97][1800/5005] lr: 1.0000e-03 eta: 13:54:51 time: 0.2238 data_time: 0.0034 loss: 0.8801 03/06 10:04:40 - mmengine - INFO - Epoch(train) [97][1900/5005] lr: 1.0000e-03 eta: 13:54:28 time: 0.2276 data_time: 0.0035 loss: 0.8939 03/06 10:05:03 - mmengine - INFO - Epoch(train) [97][2000/5005] lr: 1.0000e-03 eta: 13:54:05 time: 0.2256 data_time: 0.0035 loss: 0.9357 03/06 10:05:26 - mmengine - INFO - Epoch(train) [97][2100/5005] lr: 1.0000e-03 eta: 13:53:42 time: 0.2310 data_time: 0.0039 loss: 0.8332 03/06 10:05:50 - mmengine - INFO - Epoch(train) [97][2200/5005] lr: 1.0000e-03 eta: 13:53:19 time: 0.2377 data_time: 0.0037 loss: 1.0256 03/06 10:06:13 - mmengine - INFO - Epoch(train) [97][2300/5005] lr: 1.0000e-03 eta: 13:52:57 time: 0.2263 data_time: 0.0037 loss: 0.8097 03/06 10:06:36 - mmengine - INFO - Epoch(train) [97][2400/5005] lr: 1.0000e-03 eta: 13:52:34 time: 0.2266 data_time: 0.0035 loss: 0.8906 03/06 10:06:59 - mmengine - INFO - Epoch(train) [97][2500/5005] lr: 1.0000e-03 eta: 13:52:11 time: 0.2259 data_time: 0.0036 loss: 0.9937 03/06 10:07:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:07:22 - mmengine - INFO - Epoch(train) [97][2600/5005] lr: 1.0000e-03 eta: 13:51:48 time: 0.2224 data_time: 0.0039 loss: 1.0094 03/06 10:07:45 - mmengine - INFO - Epoch(train) [97][2700/5005] lr: 1.0000e-03 eta: 13:51:25 time: 0.2304 data_time: 0.0032 loss: 0.9111 03/06 10:08:08 - mmengine - INFO - Epoch(train) [97][2800/5005] lr: 1.0000e-03 eta: 13:51:02 time: 0.2250 data_time: 0.0036 loss: 0.9406 03/06 10:08:32 - mmengine - INFO - Epoch(train) [97][2900/5005] lr: 1.0000e-03 eta: 13:50:39 time: 0.2268 data_time: 0.0037 loss: 0.7785 03/06 10:08:55 - mmengine - INFO - Epoch(train) [97][3000/5005] lr: 1.0000e-03 eta: 13:50:16 time: 0.2283 data_time: 0.0036 loss: 1.0077 03/06 10:09:18 - mmengine - INFO - Epoch(train) [97][3100/5005] lr: 1.0000e-03 eta: 13:49:53 time: 0.2340 data_time: 0.0038 loss: 0.7698 03/06 10:09:41 - mmengine - INFO - Epoch(train) [97][3200/5005] lr: 1.0000e-03 eta: 13:49:30 time: 0.2268 data_time: 0.0040 loss: 0.7116 03/06 10:10:05 - mmengine - INFO - Epoch(train) [97][3300/5005] lr: 1.0000e-03 eta: 13:49:07 time: 0.2277 data_time: 0.0041 loss: 0.8034 03/06 10:10:27 - mmengine - INFO - Epoch(train) [97][3400/5005] lr: 1.0000e-03 eta: 13:48:44 time: 0.2272 data_time: 0.0036 loss: 0.7114 03/06 10:10:51 - mmengine - INFO - Epoch(train) [97][3500/5005] lr: 1.0000e-03 eta: 13:48:22 time: 0.2249 data_time: 0.0037 loss: 0.7455 03/06 10:10:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:11:14 - mmengine - INFO - Epoch(train) [97][3600/5005] lr: 1.0000e-03 eta: 13:47:59 time: 0.2475 data_time: 0.0037 loss: 0.9765 03/06 10:11:37 - mmengine - INFO - Epoch(train) [97][3700/5005] lr: 1.0000e-03 eta: 13:47:36 time: 0.2255 data_time: 0.0034 loss: 0.9686 03/06 10:12:00 - mmengine - INFO - Epoch(train) [97][3800/5005] lr: 1.0000e-03 eta: 13:47:13 time: 0.2285 data_time: 0.0037 loss: 0.9148 03/06 10:12:23 - mmengine - INFO - Epoch(train) [97][3900/5005] lr: 1.0000e-03 eta: 13:46:50 time: 0.2478 data_time: 0.0036 loss: 0.9207 03/06 10:12:47 - mmengine - INFO - Epoch(train) [97][4000/5005] lr: 1.0000e-03 eta: 13:46:27 time: 0.2259 data_time: 0.0035 loss: 0.9798 03/06 10:13:10 - mmengine - INFO - Epoch(train) [97][4100/5005] lr: 1.0000e-03 eta: 13:46:04 time: 0.2443 data_time: 0.0034 loss: 0.9423 03/06 10:13:33 - mmengine - INFO - Epoch(train) [97][4200/5005] lr: 1.0000e-03 eta: 13:45:41 time: 0.2292 data_time: 0.0034 loss: 0.9570 03/06 10:13:56 - mmengine - INFO - Epoch(train) [97][4300/5005] lr: 1.0000e-03 eta: 13:45:18 time: 0.2448 data_time: 0.0041 loss: 0.8882 03/06 10:14:19 - mmengine - INFO - Epoch(train) [97][4400/5005] lr: 1.0000e-03 eta: 13:44:55 time: 0.2321 data_time: 0.0031 loss: 0.8178 03/06 10:14:43 - mmengine - INFO - Epoch(train) [97][4500/5005] lr: 1.0000e-03 eta: 13:44:32 time: 0.2310 data_time: 0.0040 loss: 0.8827 03/06 10:14:47 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:15:06 - mmengine - INFO - Epoch(train) [97][4600/5005] lr: 1.0000e-03 eta: 13:44:10 time: 0.2284 data_time: 0.0040 loss: 1.0080 03/06 10:15:29 - mmengine - INFO - Epoch(train) [97][4700/5005] lr: 1.0000e-03 eta: 13:43:47 time: 0.2365 data_time: 0.0039 loss: 0.8971 03/06 10:15:52 - mmengine - INFO - Epoch(train) [97][4800/5005] lr: 1.0000e-03 eta: 13:43:24 time: 0.2268 data_time: 0.0037 loss: 1.0511 03/06 10:16:16 - mmengine - INFO - Epoch(train) [97][4900/5005] lr: 1.0000e-03 eta: 13:43:01 time: 0.2859 data_time: 0.0032 loss: 0.8188 03/06 10:16:45 - mmengine - INFO - Epoch(train) [97][5000/5005] lr: 1.0000e-03 eta: 13:42:41 time: 0.2854 data_time: 0.0032 loss: 0.8847 03/06 10:16:47 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:16:49 - mmengine - INFO - Saving checkpoint at 97 epochs 03/06 10:17:04 - mmengine - INFO - Epoch(val) [97][100/196] eta: 0:00:13 time: 0.0196 data_time: 0.0004 03/06 10:17:18 - mmengine - INFO - Epoch(val) [97][196/196] accuracy/top1: 76.9580 accuracy/top5: 93.4240 03/06 10:17:51 - mmengine - INFO - Epoch(train) [98][ 100/5005] lr: 1.0000e-03 eta: 13:42:21 time: 0.2476 data_time: 0.0042 loss: 0.9166 03/06 10:18:14 - mmengine - INFO - Epoch(train) [98][ 200/5005] lr: 1.0000e-03 eta: 13:41:58 time: 0.2270 data_time: 0.0043 loss: 0.8112 03/06 10:18:37 - mmengine - INFO - Epoch(train) [98][ 300/5005] lr: 1.0000e-03 eta: 13:41:35 time: 0.2341 data_time: 0.0036 loss: 1.0423 03/06 10:19:00 - mmengine - INFO - Epoch(train) [98][ 400/5005] lr: 1.0000e-03 eta: 13:41:12 time: 0.2272 data_time: 0.0033 loss: 0.9311 03/06 10:19:23 - mmengine - INFO - Epoch(train) [98][ 500/5005] lr: 1.0000e-03 eta: 13:40:49 time: 0.2257 data_time: 0.0040 loss: 0.8083 03/06 10:19:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:19:46 - mmengine - INFO - Epoch(train) [98][ 600/5005] lr: 1.0000e-03 eta: 13:40:26 time: 0.2226 data_time: 0.0037 loss: 0.9436 03/06 10:20:09 - mmengine - INFO - Epoch(train) [98][ 700/5005] lr: 1.0000e-03 eta: 13:40:03 time: 0.2261 data_time: 0.0035 loss: 0.8738 03/06 10:20:33 - mmengine - INFO - Epoch(train) [98][ 800/5005] lr: 1.0000e-03 eta: 13:39:40 time: 0.2254 data_time: 0.0033 loss: 0.7417 03/06 10:20:56 - mmengine - INFO - Epoch(train) [98][ 900/5005] lr: 1.0000e-03 eta: 13:39:18 time: 0.2269 data_time: 0.0034 loss: 0.7934 03/06 10:21:19 - mmengine - INFO - Epoch(train) [98][1000/5005] lr: 1.0000e-03 eta: 13:38:54 time: 0.2257 data_time: 0.0038 loss: 0.9809 03/06 10:21:42 - mmengine - INFO - Epoch(train) [98][1100/5005] lr: 1.0000e-03 eta: 13:38:32 time: 0.2264 data_time: 0.0035 loss: 1.0187 03/06 10:22:05 - mmengine - INFO - Epoch(train) [98][1200/5005] lr: 1.0000e-03 eta: 13:38:09 time: 0.2261 data_time: 0.0035 loss: 0.9467 03/06 10:22:28 - mmengine - INFO - Epoch(train) [98][1300/5005] lr: 1.0000e-03 eta: 13:37:46 time: 0.2284 data_time: 0.0039 loss: 0.9408 03/06 10:22:51 - mmengine - INFO - Epoch(train) [98][1400/5005] lr: 1.0000e-03 eta: 13:37:23 time: 0.2276 data_time: 0.0034 loss: 1.0729 03/06 10:23:15 - mmengine - INFO - Epoch(train) [98][1500/5005] lr: 1.0000e-03 eta: 13:37:00 time: 0.2235 data_time: 0.0037 loss: 0.9309 03/06 10:23:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:23:38 - mmengine - INFO - Epoch(train) [98][1600/5005] lr: 1.0000e-03 eta: 13:36:37 time: 0.2285 data_time: 0.0036 loss: 0.8611 03/06 10:24:01 - mmengine - INFO - Epoch(train) [98][1700/5005] lr: 1.0000e-03 eta: 13:36:14 time: 0.2290 data_time: 0.0034 loss: 1.0708 03/06 10:24:24 - mmengine - INFO - Epoch(train) [98][1800/5005] lr: 1.0000e-03 eta: 13:35:51 time: 0.2226 data_time: 0.0035 loss: 0.9746 03/06 10:24:47 - mmengine - INFO - Epoch(train) [98][1900/5005] lr: 1.0000e-03 eta: 13:35:28 time: 0.2290 data_time: 0.0036 loss: 1.0278 03/06 10:25:10 - mmengine - INFO - Epoch(train) [98][2000/5005] lr: 1.0000e-03 eta: 13:35:05 time: 0.2232 data_time: 0.0037 loss: 0.9856 03/06 10:25:33 - mmengine - INFO - Epoch(train) [98][2100/5005] lr: 1.0000e-03 eta: 13:34:42 time: 0.2239 data_time: 0.0034 loss: 1.0031 03/06 10:25:56 - mmengine - INFO - Epoch(train) [98][2200/5005] lr: 1.0000e-03 eta: 13:34:19 time: 0.2245 data_time: 0.0037 loss: 0.7739 03/06 10:26:19 - mmengine - INFO - Epoch(train) [98][2300/5005] lr: 1.0000e-03 eta: 13:33:56 time: 0.2260 data_time: 0.0033 loss: 1.0933 03/06 10:26:42 - mmengine - INFO - Epoch(train) [98][2400/5005] lr: 1.0000e-03 eta: 13:33:33 time: 0.2251 data_time: 0.0032 loss: 1.0370 03/06 10:27:05 - mmengine - INFO - Epoch(train) [98][2500/5005] lr: 1.0000e-03 eta: 13:33:10 time: 0.2248 data_time: 0.0038 loss: 0.9433 03/06 10:27:09 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:27:28 - mmengine - INFO - Epoch(train) [98][2600/5005] lr: 1.0000e-03 eta: 13:32:47 time: 0.2247 data_time: 0.0036 loss: 0.9146 03/06 10:27:51 - mmengine - INFO - Epoch(train) [98][2700/5005] lr: 1.0000e-03 eta: 13:32:24 time: 0.2347 data_time: 0.0034 loss: 1.2021 03/06 10:28:14 - mmengine - INFO - Epoch(train) [98][2800/5005] lr: 1.0000e-03 eta: 13:32:01 time: 0.2356 data_time: 0.0041 loss: 0.8724 03/06 10:28:37 - mmengine - INFO - Epoch(train) [98][2900/5005] lr: 1.0000e-03 eta: 13:31:38 time: 0.2241 data_time: 0.0037 loss: 0.9174 03/06 10:29:00 - mmengine - INFO - Epoch(train) [98][3000/5005] lr: 1.0000e-03 eta: 13:31:15 time: 0.2245 data_time: 0.0036 loss: 0.9306 03/06 10:29:24 - mmengine - INFO - Epoch(train) [98][3100/5005] lr: 1.0000e-03 eta: 13:30:52 time: 0.2644 data_time: 0.0037 loss: 0.9740 03/06 10:29:47 - mmengine - INFO - Epoch(train) [98][3200/5005] lr: 1.0000e-03 eta: 13:30:29 time: 0.2250 data_time: 0.0034 loss: 0.8725 03/06 10:30:10 - mmengine - INFO - Epoch(train) [98][3300/5005] lr: 1.0000e-03 eta: 13:30:06 time: 0.2254 data_time: 0.0034 loss: 0.8456 03/06 10:30:33 - mmengine - INFO - Epoch(train) [98][3400/5005] lr: 1.0000e-03 eta: 13:29:43 time: 0.2250 data_time: 0.0036 loss: 0.9611 03/06 10:30:56 - mmengine - INFO - Epoch(train) [98][3500/5005] lr: 1.0000e-03 eta: 13:29:21 time: 0.2263 data_time: 0.0035 loss: 0.9820 03/06 10:30:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:31:19 - mmengine - INFO - Epoch(train) [98][3600/5005] lr: 1.0000e-03 eta: 13:28:58 time: 0.2230 data_time: 0.0033 loss: 0.7782 03/06 10:31:42 - mmengine - INFO - Epoch(train) [98][3700/5005] lr: 1.0000e-03 eta: 13:28:35 time: 0.2455 data_time: 0.0037 loss: 0.9506 03/06 10:32:05 - mmengine - INFO - Epoch(train) [98][3800/5005] lr: 1.0000e-03 eta: 13:28:12 time: 0.2267 data_time: 0.0039 loss: 0.8204 03/06 10:32:28 - mmengine - INFO - Epoch(train) [98][3900/5005] lr: 1.0000e-03 eta: 13:27:49 time: 0.2418 data_time: 0.0035 loss: 1.0576 03/06 10:32:52 - mmengine - INFO - Epoch(train) [98][4000/5005] lr: 1.0000e-03 eta: 13:27:26 time: 0.2260 data_time: 0.0036 loss: 0.9153 03/06 10:33:14 - mmengine - INFO - Epoch(train) [98][4100/5005] lr: 1.0000e-03 eta: 13:27:03 time: 0.2278 data_time: 0.0036 loss: 0.9665 03/06 10:33:38 - mmengine - INFO - Epoch(train) [98][4200/5005] lr: 1.0000e-03 eta: 13:26:40 time: 0.2245 data_time: 0.0038 loss: 0.9511 03/06 10:34:01 - mmengine - INFO - Epoch(train) [98][4300/5005] lr: 1.0000e-03 eta: 13:26:17 time: 0.2507 data_time: 0.0035 loss: 0.9693 03/06 10:34:24 - mmengine - INFO - Epoch(train) [98][4400/5005] lr: 1.0000e-03 eta: 13:25:54 time: 0.2225 data_time: 0.0040 loss: 1.0117 03/06 10:34:47 - mmengine - INFO - Epoch(train) [98][4500/5005] lr: 1.0000e-03 eta: 13:25:31 time: 0.2269 data_time: 0.0032 loss: 1.2082 03/06 10:34:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:35:10 - mmengine - INFO - Epoch(train) [98][4600/5005] lr: 1.0000e-03 eta: 13:25:08 time: 0.2293 data_time: 0.0038 loss: 0.8346 03/06 10:35:33 - mmengine - INFO - Epoch(train) [98][4700/5005] lr: 1.0000e-03 eta: 13:24:45 time: 0.2273 data_time: 0.0037 loss: 1.1840 03/06 10:35:57 - mmengine - INFO - Epoch(train) [98][4800/5005] lr: 1.0000e-03 eta: 13:24:22 time: 0.2435 data_time: 0.0037 loss: 0.8911 03/06 10:36:21 - mmengine - INFO - Epoch(train) [98][4900/5005] lr: 1.0000e-03 eta: 13:24:00 time: 0.2749 data_time: 0.0034 loss: 1.0284 03/06 10:36:50 - mmengine - INFO - Epoch(train) [98][5000/5005] lr: 1.0000e-03 eta: 13:23:39 time: 0.2884 data_time: 0.0034 loss: 0.8543 03/06 10:36:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:36:54 - mmengine - INFO - Saving checkpoint at 98 epochs 03/06 10:37:09 - mmengine - INFO - Epoch(val) [98][100/196] eta: 0:00:13 time: 0.0208 data_time: 0.0004 03/06 10:37:23 - mmengine - INFO - Epoch(val) [98][196/196] accuracy/top1: 76.8080 accuracy/top5: 93.4760 03/06 10:37:55 - mmengine - INFO - Epoch(train) [99][ 100/5005] lr: 1.0000e-03 eta: 13:23:19 time: 0.2274 data_time: 0.0045 loss: 1.0251 03/06 10:38:18 - mmengine - INFO - Epoch(train) [99][ 200/5005] lr: 1.0000e-03 eta: 13:22:56 time: 0.2258 data_time: 0.0042 loss: 0.9307 03/06 10:38:41 - mmengine - INFO - Epoch(train) [99][ 300/5005] lr: 1.0000e-03 eta: 13:22:33 time: 0.2276 data_time: 0.0041 loss: 0.8464 03/06 10:39:04 - mmengine - INFO - Epoch(train) [99][ 400/5005] lr: 1.0000e-03 eta: 13:22:10 time: 0.2260 data_time: 0.0038 loss: 0.8495 03/06 10:39:28 - mmengine - INFO - Epoch(train) [99][ 500/5005] lr: 1.0000e-03 eta: 13:21:47 time: 0.2256 data_time: 0.0036 loss: 0.8484 03/06 10:39:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:39:50 - mmengine - INFO - Epoch(train) [99][ 600/5005] lr: 1.0000e-03 eta: 13:21:24 time: 0.2330 data_time: 0.0036 loss: 0.8650 03/06 10:40:13 - mmengine - INFO - Epoch(train) [99][ 700/5005] lr: 1.0000e-03 eta: 13:21:01 time: 0.2536 data_time: 0.0034 loss: 0.7525 03/06 10:40:36 - mmengine - INFO - Epoch(train) [99][ 800/5005] lr: 1.0000e-03 eta: 13:20:38 time: 0.2452 data_time: 0.0038 loss: 0.7893 03/06 10:41:00 - mmengine - INFO - Epoch(train) [99][ 900/5005] lr: 1.0000e-03 eta: 13:20:16 time: 0.2236 data_time: 0.0034 loss: 0.8733 03/06 10:41:23 - mmengine - INFO - Epoch(train) [99][1000/5005] lr: 1.0000e-03 eta: 13:19:53 time: 0.2298 data_time: 0.0033 loss: 0.8721 03/06 10:41:46 - mmengine - INFO - Epoch(train) [99][1100/5005] lr: 1.0000e-03 eta: 13:19:30 time: 0.2237 data_time: 0.0039 loss: 0.8155 03/06 10:42:09 - mmengine - INFO - Epoch(train) [99][1200/5005] lr: 1.0000e-03 eta: 13:19:07 time: 0.2278 data_time: 0.0033 loss: 0.7893 03/06 10:42:33 - mmengine - INFO - Epoch(train) [99][1300/5005] lr: 1.0000e-03 eta: 13:18:44 time: 0.2264 data_time: 0.0035 loss: 1.0083 03/06 10:42:56 - mmengine - INFO - Epoch(train) [99][1400/5005] lr: 1.0000e-03 eta: 13:18:21 time: 0.2247 data_time: 0.0032 loss: 0.9639 03/06 10:43:19 - mmengine - INFO - Epoch(train) [99][1500/5005] lr: 1.0000e-03 eta: 13:17:58 time: 0.2259 data_time: 0.0032 loss: 0.7925 03/06 10:43:21 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:43:42 - mmengine - INFO - Epoch(train) [99][1600/5005] lr: 1.0000e-03 eta: 13:17:35 time: 0.2269 data_time: 0.0037 loss: 1.1188 03/06 10:44:05 - mmengine - INFO - Epoch(train) [99][1700/5005] lr: 1.0000e-03 eta: 13:17:12 time: 0.2416 data_time: 0.0037 loss: 0.7788 03/06 10:44:28 - mmengine - INFO - Epoch(train) [99][1800/5005] lr: 1.0000e-03 eta: 13:16:49 time: 0.2252 data_time: 0.0037 loss: 0.9364 03/06 10:44:51 - mmengine - INFO - Epoch(train) [99][1900/5005] lr: 1.0000e-03 eta: 13:16:26 time: 0.2283 data_time: 0.0037 loss: 0.9738 03/06 10:45:14 - mmengine - INFO - Epoch(train) [99][2000/5005] lr: 1.0000e-03 eta: 13:16:03 time: 0.2280 data_time: 0.0034 loss: 1.0192 03/06 10:45:38 - mmengine - INFO - Epoch(train) [99][2100/5005] lr: 1.0000e-03 eta: 13:15:40 time: 0.2238 data_time: 0.0036 loss: 0.9575 03/06 10:46:00 - mmengine - INFO - Epoch(train) [99][2200/5005] lr: 1.0000e-03 eta: 13:15:17 time: 0.2255 data_time: 0.0041 loss: 0.8688 03/06 10:46:23 - mmengine - INFO - Epoch(train) [99][2300/5005] lr: 1.0000e-03 eta: 13:14:54 time: 0.2278 data_time: 0.0033 loss: 0.9536 03/06 10:46:46 - mmengine - INFO - Epoch(train) [99][2400/5005] lr: 1.0000e-03 eta: 13:14:31 time: 0.2259 data_time: 0.0038 loss: 0.8385 03/06 10:47:10 - mmengine - INFO - Epoch(train) [99][2500/5005] lr: 1.0000e-03 eta: 13:14:08 time: 0.2237 data_time: 0.0037 loss: 0.9449 03/06 10:47:12 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:47:33 - mmengine - INFO - Epoch(train) [99][2600/5005] lr: 1.0000e-03 eta: 13:13:45 time: 0.2278 data_time: 0.0034 loss: 1.0085 03/06 10:47:56 - mmengine - INFO - Epoch(train) [99][2700/5005] lr: 1.0000e-03 eta: 13:13:23 time: 0.2273 data_time: 0.0037 loss: 0.9474 03/06 10:48:19 - mmengine - INFO - Epoch(train) [99][2800/5005] lr: 1.0000e-03 eta: 13:13:00 time: 0.2269 data_time: 0.0038 loss: 0.9199 03/06 10:48:42 - mmengine - INFO - Epoch(train) [99][2900/5005] lr: 1.0000e-03 eta: 13:12:37 time: 0.2267 data_time: 0.0033 loss: 1.0376 03/06 10:49:05 - mmengine - INFO - Epoch(train) [99][3000/5005] lr: 1.0000e-03 eta: 13:12:14 time: 0.2306 data_time: 0.0037 loss: 0.8150 03/06 10:49:28 - mmengine - INFO - Epoch(train) [99][3100/5005] lr: 1.0000e-03 eta: 13:11:51 time: 0.2384 data_time: 0.0036 loss: 0.8642 03/06 10:49:51 - mmengine - INFO - Epoch(train) [99][3200/5005] lr: 1.0000e-03 eta: 13:11:28 time: 0.2247 data_time: 0.0037 loss: 1.0646 03/06 10:50:14 - mmengine - INFO - Epoch(train) [99][3300/5005] lr: 1.0000e-03 eta: 13:11:05 time: 0.2250 data_time: 0.0033 loss: 0.8097 03/06 10:50:37 - mmengine - INFO - Epoch(train) [99][3400/5005] lr: 1.0000e-03 eta: 13:10:42 time: 0.2300 data_time: 0.0034 loss: 0.8708 03/06 10:51:00 - mmengine - INFO - Epoch(train) [99][3500/5005] lr: 1.0000e-03 eta: 13:10:19 time: 0.2240 data_time: 0.0040 loss: 1.0182 03/06 10:51:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:51:24 - mmengine - INFO - Epoch(train) [99][3600/5005] lr: 1.0000e-03 eta: 13:09:56 time: 0.2264 data_time: 0.0033 loss: 0.8700 03/06 10:51:47 - mmengine - INFO - Epoch(train) [99][3700/5005] lr: 1.0000e-03 eta: 13:09:33 time: 0.2268 data_time: 0.0035 loss: 0.9448 03/06 10:52:10 - mmengine - INFO - Epoch(train) [99][3800/5005] lr: 1.0000e-03 eta: 13:09:10 time: 0.2275 data_time: 0.0036 loss: 1.1195 03/06 10:52:33 - mmengine - INFO - Epoch(train) [99][3900/5005] lr: 1.0000e-03 eta: 13:08:47 time: 0.2260 data_time: 0.0034 loss: 0.9304 03/06 10:52:56 - mmengine - INFO - Epoch(train) [99][4000/5005] lr: 1.0000e-03 eta: 13:08:24 time: 0.2333 data_time: 0.0037 loss: 0.7988 03/06 10:53:19 - mmengine - INFO - Epoch(train) [99][4100/5005] lr: 1.0000e-03 eta: 13:08:01 time: 0.2233 data_time: 0.0037 loss: 0.8341 03/06 10:53:42 - mmengine - INFO - Epoch(train) [99][4200/5005] lr: 1.0000e-03 eta: 13:07:38 time: 0.2240 data_time: 0.0036 loss: 0.9740 03/06 10:54:06 - mmengine - INFO - Epoch(train) [99][4300/5005] lr: 1.0000e-03 eta: 13:07:15 time: 0.2277 data_time: 0.0037 loss: 0.9466 03/06 10:54:29 - mmengine - INFO - Epoch(train) [99][4400/5005] lr: 1.0000e-03 eta: 13:06:52 time: 0.2258 data_time: 0.0034 loss: 0.8838 03/06 10:54:52 - mmengine - INFO - Epoch(train) [99][4500/5005] lr: 1.0000e-03 eta: 13:06:30 time: 0.2276 data_time: 0.0033 loss: 1.0223 03/06 10:54:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:55:15 - mmengine - INFO - Epoch(train) [99][4600/5005] lr: 1.0000e-03 eta: 13:06:07 time: 0.2242 data_time: 0.0036 loss: 0.9505 03/06 10:55:39 - mmengine - INFO - Epoch(train) [99][4700/5005] lr: 1.0000e-03 eta: 13:05:44 time: 0.2283 data_time: 0.0036 loss: 0.8732 03/06 10:56:01 - mmengine - INFO - Epoch(train) [99][4800/5005] lr: 1.0000e-03 eta: 13:05:21 time: 0.2273 data_time: 0.0040 loss: 0.8570 03/06 10:56:26 - mmengine - INFO - Epoch(train) [99][4900/5005] lr: 1.0000e-03 eta: 13:04:58 time: 0.2915 data_time: 0.0036 loss: 1.0030 03/06 10:56:55 - mmengine - INFO - Epoch(train) [99][5000/5005] lr: 1.0000e-03 eta: 13:04:38 time: 0.2926 data_time: 0.0034 loss: 0.8800 03/06 10:56:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:56:59 - mmengine - INFO - Saving checkpoint at 99 epochs 03/06 10:57:14 - mmengine - INFO - Epoch(val) [99][100/196] eta: 0:00:13 time: 0.0192 data_time: 0.0003 03/06 10:57:28 - mmengine - INFO - Epoch(val) [99][196/196] accuracy/top1: 76.9220 accuracy/top5: 93.5100 03/06 10:58:01 - mmengine - INFO - Epoch(train) [100][ 100/5005] lr: 1.0000e-03 eta: 13:04:18 time: 0.2984 data_time: 0.0034 loss: 0.8288 03/06 10:58:25 - mmengine - INFO - Epoch(train) [100][ 200/5005] lr: 1.0000e-03 eta: 13:03:55 time: 0.2298 data_time: 0.0037 loss: 0.9713 03/06 10:58:48 - mmengine - INFO - Epoch(train) [100][ 300/5005] lr: 1.0000e-03 eta: 13:03:32 time: 0.2278 data_time: 0.0039 loss: 0.9236 03/06 10:59:11 - mmengine - INFO - Epoch(train) [100][ 400/5005] lr: 1.0000e-03 eta: 13:03:09 time: 0.2263 data_time: 0.0042 loss: 0.9611 03/06 10:59:34 - mmengine - INFO - Epoch(train) [100][ 500/5005] lr: 1.0000e-03 eta: 13:02:46 time: 0.2512 data_time: 0.0036 loss: 0.8135 03/06 10:59:35 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 10:59:57 - mmengine - INFO - Epoch(train) [100][ 600/5005] lr: 1.0000e-03 eta: 13:02:23 time: 0.2247 data_time: 0.0034 loss: 0.7961 03/06 11:00:20 - mmengine - INFO - Epoch(train) [100][ 700/5005] lr: 1.0000e-03 eta: 13:02:00 time: 0.2255 data_time: 0.0039 loss: 0.9896 03/06 11:00:44 - mmengine - INFO - Epoch(train) [100][ 800/5005] lr: 1.0000e-03 eta: 13:01:37 time: 0.2289 data_time: 0.0039 loss: 0.8094 03/06 11:01:07 - mmengine - INFO - Epoch(train) [100][ 900/5005] lr: 1.0000e-03 eta: 13:01:14 time: 0.2488 data_time: 0.0036 loss: 0.9393 03/06 11:01:30 - mmengine - INFO - Epoch(train) [100][1000/5005] lr: 1.0000e-03 eta: 13:00:52 time: 0.2703 data_time: 0.0034 loss: 0.8748 03/06 11:01:53 - mmengine - INFO - Epoch(train) [100][1100/5005] lr: 1.0000e-03 eta: 13:00:29 time: 0.2274 data_time: 0.0037 loss: 0.8236 03/06 11:02:16 - mmengine - INFO - Epoch(train) [100][1200/5005] lr: 1.0000e-03 eta: 13:00:06 time: 0.2250 data_time: 0.0038 loss: 0.9483 03/06 11:02:39 - mmengine - INFO - Epoch(train) [100][1300/5005] lr: 1.0000e-03 eta: 12:59:43 time: 0.2269 data_time: 0.0034 loss: 0.9958 03/06 11:03:03 - mmengine - INFO - Epoch(train) [100][1400/5005] lr: 1.0000e-03 eta: 12:59:20 time: 0.2300 data_time: 0.0035 loss: 1.0428 03/06 11:03:26 - mmengine - INFO - Epoch(train) [100][1500/5005] lr: 1.0000e-03 eta: 12:58:57 time: 0.2289 data_time: 0.0039 loss: 0.9647 03/06 11:03:27 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:03:49 - mmengine - INFO - Epoch(train) [100][1600/5005] lr: 1.0000e-03 eta: 12:58:34 time: 0.2271 data_time: 0.0035 loss: 1.1341 03/06 11:04:12 - mmengine - INFO - Epoch(train) [100][1700/5005] lr: 1.0000e-03 eta: 12:58:11 time: 0.2449 data_time: 0.0035 loss: 0.8910 03/06 11:04:35 - mmengine - INFO - Epoch(train) [100][1800/5005] lr: 1.0000e-03 eta: 12:57:48 time: 0.2279 data_time: 0.0036 loss: 0.8364 03/06 11:04:59 - mmengine - INFO - Epoch(train) [100][1900/5005] lr: 1.0000e-03 eta: 12:57:25 time: 0.2264 data_time: 0.0035 loss: 0.8131 03/06 11:05:22 - mmengine - INFO - Epoch(train) [100][2000/5005] lr: 1.0000e-03 eta: 12:57:02 time: 0.2281 data_time: 0.0041 loss: 0.9015 03/06 11:05:45 - mmengine - INFO - Epoch(train) [100][2100/5005] lr: 1.0000e-03 eta: 12:56:39 time: 0.2341 data_time: 0.0038 loss: 0.9394 03/06 11:06:08 - mmengine - INFO - Epoch(train) [100][2200/5005] lr: 1.0000e-03 eta: 12:56:16 time: 0.2297 data_time: 0.0036 loss: 0.9128 03/06 11:06:31 - mmengine - INFO - Epoch(train) [100][2300/5005] lr: 1.0000e-03 eta: 12:55:53 time: 0.2293 data_time: 0.0035 loss: 0.7496 03/06 11:06:54 - mmengine - INFO - Epoch(train) [100][2400/5005] lr: 1.0000e-03 eta: 12:55:30 time: 0.2329 data_time: 0.0035 loss: 0.9595 03/06 11:07:17 - mmengine - INFO - Epoch(train) [100][2500/5005] lr: 1.0000e-03 eta: 12:55:07 time: 0.2258 data_time: 0.0040 loss: 0.9715 03/06 11:07:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:07:40 - mmengine - INFO - Epoch(train) [100][2600/5005] lr: 1.0000e-03 eta: 12:54:45 time: 0.2276 data_time: 0.0040 loss: 0.7097 03/06 11:08:04 - mmengine - INFO - Epoch(train) [100][2700/5005] lr: 1.0000e-03 eta: 12:54:22 time: 0.2266 data_time: 0.0036 loss: 0.8021 03/06 11:08:27 - mmengine - INFO - Epoch(train) [100][2800/5005] lr: 1.0000e-03 eta: 12:53:59 time: 0.2306 data_time: 0.0035 loss: 0.9053 03/06 11:08:50 - mmengine - INFO - Epoch(train) [100][2900/5005] lr: 1.0000e-03 eta: 12:53:36 time: 0.2273 data_time: 0.0040 loss: 0.9752 03/06 11:09:13 - mmengine - INFO - Epoch(train) [100][3000/5005] lr: 1.0000e-03 eta: 12:53:13 time: 0.2264 data_time: 0.0035 loss: 1.0718 03/06 11:09:36 - mmengine - INFO - Epoch(train) [100][3100/5005] lr: 1.0000e-03 eta: 12:52:50 time: 0.2282 data_time: 0.0036 loss: 0.8217 03/06 11:09:59 - mmengine - INFO - Epoch(train) [100][3200/5005] lr: 1.0000e-03 eta: 12:52:27 time: 0.2317 data_time: 0.0037 loss: 0.9184 03/06 11:10:22 - mmengine - INFO - Epoch(train) [100][3300/5005] lr: 1.0000e-03 eta: 12:52:04 time: 0.2299 data_time: 0.0036 loss: 0.8763 03/06 11:10:46 - mmengine - INFO - Epoch(train) [100][3400/5005] lr: 1.0000e-03 eta: 12:51:41 time: 0.2291 data_time: 0.0037 loss: 0.9631 03/06 11:11:09 - mmengine - INFO - Epoch(train) [100][3500/5005] lr: 1.0000e-03 eta: 12:51:18 time: 0.2273 data_time: 0.0044 loss: 1.0036 03/06 11:11:10 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:11:32 - mmengine - INFO - Epoch(train) [100][3600/5005] lr: 1.0000e-03 eta: 12:50:55 time: 0.2267 data_time: 0.0033 loss: 0.9208 03/06 11:11:55 - mmengine - INFO - Epoch(train) [100][3700/5005] lr: 1.0000e-03 eta: 12:50:32 time: 0.2262 data_time: 0.0036 loss: 0.9072 03/06 11:12:18 - mmengine - INFO - Epoch(train) [100][3800/5005] lr: 1.0000e-03 eta: 12:50:09 time: 0.2315 data_time: 0.0054 loss: 0.8974 03/06 11:12:41 - mmengine - INFO - Epoch(train) [100][3900/5005] lr: 1.0000e-03 eta: 12:49:46 time: 0.2304 data_time: 0.0036 loss: 0.9675 03/06 11:13:05 - mmengine - INFO - Epoch(train) [100][4000/5005] lr: 1.0000e-03 eta: 12:49:23 time: 0.2287 data_time: 0.0040 loss: 0.9672 03/06 11:13:28 - mmengine - INFO - Epoch(train) [100][4100/5005] lr: 1.0000e-03 eta: 12:49:01 time: 0.2249 data_time: 0.0037 loss: 1.0617 03/06 11:13:51 - mmengine - INFO - Epoch(train) [100][4200/5005] lr: 1.0000e-03 eta: 12:48:38 time: 0.2292 data_time: 0.0038 loss: 0.9125 03/06 11:14:14 - mmengine - INFO - Epoch(train) [100][4300/5005] lr: 1.0000e-03 eta: 12:48:15 time: 0.2287 data_time: 0.0040 loss: 0.8428 03/06 11:14:37 - mmengine - INFO - Epoch(train) [100][4400/5005] lr: 1.0000e-03 eta: 12:47:52 time: 0.2266 data_time: 0.0035 loss: 0.8924 03/06 11:15:01 - mmengine - INFO - Epoch(train) [100][4500/5005] lr: 1.0000e-03 eta: 12:47:29 time: 0.2261 data_time: 0.0035 loss: 0.8501 03/06 11:15:02 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:15:24 - mmengine - INFO - Epoch(train) [100][4600/5005] lr: 1.0000e-03 eta: 12:47:06 time: 0.2281 data_time: 0.0038 loss: 0.8900 03/06 11:15:47 - mmengine - INFO - Epoch(train) [100][4700/5005] lr: 1.0000e-03 eta: 12:46:43 time: 0.2294 data_time: 0.0035 loss: 0.9046 03/06 11:16:10 - mmengine - INFO - Epoch(train) [100][4800/5005] lr: 1.0000e-03 eta: 12:46:20 time: 0.2285 data_time: 0.0034 loss: 0.9682 03/06 11:16:34 - mmengine - INFO - Epoch(train) [100][4900/5005] lr: 1.0000e-03 eta: 12:45:57 time: 0.2659 data_time: 0.0034 loss: 1.0242 03/06 11:17:03 - mmengine - INFO - Epoch(train) [100][5000/5005] lr: 1.0000e-03 eta: 12:45:37 time: 0.2945 data_time: 0.0033 loss: 0.9396 03/06 11:17:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:17:07 - mmengine - INFO - Saving checkpoint at 100 epochs 03/06 11:17:22 - mmengine - INFO - Epoch(val) [100][100/196] eta: 0:00:13 time: 0.0193 data_time: 0.0003 03/06 11:17:36 - mmengine - INFO - Epoch(val) [100][196/196] accuracy/top1: 76.9280 accuracy/top5: 93.4620 03/06 11:18:08 - mmengine - INFO - Epoch(train) [101][ 100/5005] lr: 1.0000e-03 eta: 12:45:16 time: 0.2521 data_time: 0.0045 loss: 0.9426 03/06 11:18:32 - mmengine - INFO - Epoch(train) [101][ 200/5005] lr: 1.0000e-03 eta: 12:44:53 time: 0.2268 data_time: 0.0040 loss: 0.8691 03/06 11:18:55 - mmengine - INFO - Epoch(train) [101][ 300/5005] lr: 1.0000e-03 eta: 12:44:30 time: 0.2274 data_time: 0.0037 loss: 1.0458 03/06 11:19:18 - mmengine - INFO - Epoch(train) [101][ 400/5005] lr: 1.0000e-03 eta: 12:44:08 time: 0.2257 data_time: 0.0037 loss: 0.8573 03/06 11:19:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:19:42 - mmengine - INFO - Epoch(train) [101][ 500/5005] lr: 1.0000e-03 eta: 12:43:45 time: 0.2303 data_time: 0.0038 loss: 0.9178 03/06 11:20:05 - mmengine - INFO - Epoch(train) [101][ 600/5005] lr: 1.0000e-03 eta: 12:43:22 time: 0.2262 data_time: 0.0033 loss: 0.7444 03/06 11:20:28 - mmengine - INFO - Epoch(train) [101][ 700/5005] lr: 1.0000e-03 eta: 12:42:59 time: 0.2267 data_time: 0.0036 loss: 0.9201 03/06 11:20:51 - mmengine - INFO - Epoch(train) [101][ 800/5005] lr: 1.0000e-03 eta: 12:42:36 time: 0.2270 data_time: 0.0039 loss: 0.8930 03/06 11:21:14 - mmengine - INFO - Epoch(train) [101][ 900/5005] lr: 1.0000e-03 eta: 12:42:13 time: 0.2720 data_time: 0.0035 loss: 0.8497 03/06 11:21:37 - mmengine - INFO - Epoch(train) [101][1000/5005] lr: 1.0000e-03 eta: 12:41:50 time: 0.2287 data_time: 0.0039 loss: 0.7900 03/06 11:22:00 - mmengine - INFO - Epoch(train) [101][1100/5005] lr: 1.0000e-03 eta: 12:41:27 time: 0.2327 data_time: 0.0036 loss: 0.9131 03/06 11:22:23 - mmengine - INFO - Epoch(train) [101][1200/5005] lr: 1.0000e-03 eta: 12:41:04 time: 0.2476 data_time: 0.0037 loss: 1.1745 03/06 11:22:47 - mmengine - INFO - Epoch(train) [101][1300/5005] lr: 1.0000e-03 eta: 12:40:41 time: 0.2250 data_time: 0.0034 loss: 0.9531 03/06 11:23:10 - mmengine - INFO - Epoch(train) [101][1400/5005] lr: 1.0000e-03 eta: 12:40:18 time: 0.2294 data_time: 0.0039 loss: 0.9314 03/06 11:23:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:23:33 - mmengine - INFO - Epoch(train) [101][1500/5005] lr: 1.0000e-03 eta: 12:39:55 time: 0.2298 data_time: 0.0038 loss: 0.9097 03/06 11:23:56 - mmengine - INFO - Epoch(train) [101][1600/5005] lr: 1.0000e-03 eta: 12:39:32 time: 0.2262 data_time: 0.0037 loss: 0.7297 03/06 11:24:19 - mmengine - INFO - Epoch(train) [101][1700/5005] lr: 1.0000e-03 eta: 12:39:09 time: 0.2262 data_time: 0.0040 loss: 0.9525 03/06 11:24:43 - mmengine - INFO - Epoch(train) [101][1800/5005] lr: 1.0000e-03 eta: 12:38:47 time: 0.2261 data_time: 0.0040 loss: 0.8124 03/06 11:25:06 - mmengine - INFO - Epoch(train) [101][1900/5005] lr: 1.0000e-03 eta: 12:38:24 time: 0.2258 data_time: 0.0039 loss: 0.8160 03/06 11:25:29 - mmengine - INFO - Epoch(train) [101][2000/5005] lr: 1.0000e-03 eta: 12:38:01 time: 0.2251 data_time: 0.0036 loss: 0.8240 03/06 11:25:52 - mmengine - INFO - Epoch(train) [101][2100/5005] lr: 1.0000e-03 eta: 12:37:38 time: 0.2277 data_time: 0.0037 loss: 0.6940 03/06 11:26:16 - mmengine - INFO - Epoch(train) [101][2200/5005] lr: 1.0000e-03 eta: 12:37:15 time: 0.2267 data_time: 0.0032 loss: 0.9085 03/06 11:26:39 - mmengine - INFO - Epoch(train) [101][2300/5005] lr: 1.0000e-03 eta: 12:36:52 time: 0.2265 data_time: 0.0034 loss: 0.8921 03/06 11:27:02 - mmengine - INFO - Epoch(train) [101][2400/5005] lr: 1.0000e-03 eta: 12:36:29 time: 0.2349 data_time: 0.0032 loss: 0.8511 03/06 11:27:25 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:27:25 - mmengine - INFO - Epoch(train) [101][2500/5005] lr: 1.0000e-03 eta: 12:36:06 time: 0.2249 data_time: 0.0036 loss: 1.0344 03/06 11:27:48 - mmengine - INFO - Epoch(train) [101][2600/5005] lr: 1.0000e-03 eta: 12:35:43 time: 0.2273 data_time: 0.0033 loss: 0.7787 03/06 11:28:11 - mmengine - INFO - Epoch(train) [101][2700/5005] lr: 1.0000e-03 eta: 12:35:20 time: 0.2325 data_time: 0.0035 loss: 0.8943 03/06 11:28:35 - mmengine - INFO - Epoch(train) [101][2800/5005] lr: 1.0000e-03 eta: 12:34:57 time: 0.2469 data_time: 0.0040 loss: 0.9476 03/06 11:28:58 - mmengine - INFO - Epoch(train) [101][2900/5005] lr: 1.0000e-03 eta: 12:34:34 time: 0.2448 data_time: 0.0037 loss: 1.0997 03/06 11:29:21 - mmengine - INFO - Epoch(train) [101][3000/5005] lr: 1.0000e-03 eta: 12:34:11 time: 0.2294 data_time: 0.0036 loss: 1.0258 03/06 11:29:44 - mmengine - INFO - Epoch(train) [101][3100/5005] lr: 1.0000e-03 eta: 12:33:48 time: 0.2262 data_time: 0.0037 loss: 0.8157 03/06 11:30:07 - mmengine - INFO - Epoch(train) [101][3200/5005] lr: 1.0000e-03 eta: 12:33:25 time: 0.2231 data_time: 0.0036 loss: 0.9417 03/06 11:30:30 - mmengine - INFO - Epoch(train) [101][3300/5005] lr: 1.0000e-03 eta: 12:33:03 time: 0.2258 data_time: 0.0034 loss: 0.8046 03/06 11:30:53 - mmengine - INFO - Epoch(train) [101][3400/5005] lr: 1.0000e-03 eta: 12:32:40 time: 0.2254 data_time: 0.0039 loss: 0.9682 03/06 11:31:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:31:16 - mmengine - INFO - Epoch(train) [101][3500/5005] lr: 1.0000e-03 eta: 12:32:17 time: 0.2244 data_time: 0.0035 loss: 0.8064 03/06 11:31:40 - mmengine - INFO - Epoch(train) [101][3600/5005] lr: 1.0000e-03 eta: 12:31:54 time: 0.2470 data_time: 0.0041 loss: 0.9354 03/06 11:32:03 - mmengine - INFO - Epoch(train) [101][3700/5005] lr: 1.0000e-03 eta: 12:31:31 time: 0.2296 data_time: 0.0038 loss: 0.8715 03/06 11:32:26 - mmengine - INFO - Epoch(train) [101][3800/5005] lr: 1.0000e-03 eta: 12:31:08 time: 0.2278 data_time: 0.0034 loss: 0.8020 03/06 11:32:49 - mmengine - INFO - Epoch(train) [101][3900/5005] lr: 1.0000e-03 eta: 12:30:45 time: 0.2276 data_time: 0.0037 loss: 0.8390 03/06 11:33:12 - mmengine - INFO - Epoch(train) [101][4000/5005] lr: 1.0000e-03 eta: 12:30:22 time: 0.2298 data_time: 0.0037 loss: 0.9339 03/06 11:33:36 - mmengine - INFO - Epoch(train) [101][4100/5005] lr: 1.0000e-03 eta: 12:29:59 time: 0.2487 data_time: 0.0036 loss: 0.8417 03/06 11:33:59 - mmengine - INFO - Epoch(train) [101][4200/5005] lr: 1.0000e-03 eta: 12:29:36 time: 0.2335 data_time: 0.0033 loss: 1.0986 03/06 11:34:22 - mmengine - INFO - Epoch(train) [101][4300/5005] lr: 1.0000e-03 eta: 12:29:13 time: 0.2265 data_time: 0.0034 loss: 1.1656 03/06 11:34:45 - mmengine - INFO - Epoch(train) [101][4400/5005] lr: 1.0000e-03 eta: 12:28:50 time: 0.2276 data_time: 0.0033 loss: 0.9267 03/06 11:35:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:35:08 - mmengine - INFO - Epoch(train) [101][4500/5005] lr: 1.0000e-03 eta: 12:28:27 time: 0.2289 data_time: 0.0037 loss: 0.8361 03/06 11:35:32 - mmengine - INFO - Epoch(train) [101][4600/5005] lr: 1.0000e-03 eta: 12:28:04 time: 0.2230 data_time: 0.0034 loss: 1.0249 03/06 11:35:55 - mmengine - INFO - Epoch(train) [101][4700/5005] lr: 1.0000e-03 eta: 12:27:42 time: 0.2262 data_time: 0.0037 loss: 0.9547 03/06 11:36:18 - mmengine - INFO - Epoch(train) [101][4800/5005] lr: 1.0000e-03 eta: 12:27:19 time: 0.2424 data_time: 0.0035 loss: 0.8605 03/06 11:36:42 - mmengine - INFO - Epoch(train) [101][4900/5005] lr: 1.0000e-03 eta: 12:26:56 time: 0.2906 data_time: 0.0038 loss: 0.9074 03/06 11:37:11 - mmengine - INFO - Epoch(train) [101][5000/5005] lr: 1.0000e-03 eta: 12:26:35 time: 0.2798 data_time: 0.0034 loss: 0.8983 03/06 11:37:12 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:37:15 - mmengine - INFO - Saving checkpoint at 101 epochs 03/06 11:37:30 - mmengine - INFO - Epoch(val) [101][100/196] eta: 0:00:13 time: 0.0207 data_time: 0.0004 03/06 11:37:44 - mmengine - INFO - Epoch(val) [101][196/196] accuracy/top1: 76.9560 accuracy/top5: 93.5180 03/06 11:38:16 - mmengine - INFO - Epoch(train) [102][ 100/5005] lr: 1.0000e-03 eta: 12:26:15 time: 0.2291 data_time: 0.0039 loss: 0.9220 03/06 11:38:39 - mmengine - INFO - Epoch(train) [102][ 200/5005] lr: 1.0000e-03 eta: 12:25:52 time: 0.2284 data_time: 0.0039 loss: 0.7532 03/06 11:39:02 - mmengine - INFO - Epoch(train) [102][ 300/5005] lr: 1.0000e-03 eta: 12:25:29 time: 0.2255 data_time: 0.0039 loss: 0.9207 03/06 11:39:25 - mmengine - INFO - Epoch(train) [102][ 400/5005] lr: 1.0000e-03 eta: 12:25:06 time: 0.2285 data_time: 0.0034 loss: 1.0144 03/06 11:39:47 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:39:48 - mmengine - INFO - Epoch(train) [102][ 500/5005] lr: 1.0000e-03 eta: 12:24:43 time: 0.2260 data_time: 0.0037 loss: 0.8830 03/06 11:40:11 - mmengine - INFO - Epoch(train) [102][ 600/5005] lr: 1.0000e-03 eta: 12:24:20 time: 0.2254 data_time: 0.0035 loss: 0.8401 03/06 11:40:35 - mmengine - INFO - Epoch(train) [102][ 700/5005] lr: 1.0000e-03 eta: 12:23:57 time: 0.2292 data_time: 0.0035 loss: 0.9388 03/06 11:40:58 - mmengine - INFO - Epoch(train) [102][ 800/5005] lr: 1.0000e-03 eta: 12:23:34 time: 0.2362 data_time: 0.0036 loss: 0.9563 03/06 11:41:21 - mmengine - INFO - Epoch(train) [102][ 900/5005] lr: 1.0000e-03 eta: 12:23:11 time: 0.2253 data_time: 0.0035 loss: 1.2387 03/06 11:41:45 - mmengine - INFO - Epoch(train) [102][1000/5005] lr: 1.0000e-03 eta: 12:22:48 time: 0.2441 data_time: 0.0034 loss: 0.9132 03/06 11:42:08 - mmengine - INFO - Epoch(train) [102][1100/5005] lr: 1.0000e-03 eta: 12:22:25 time: 0.2252 data_time: 0.0033 loss: 0.9130 03/06 11:42:31 - mmengine - INFO - Epoch(train) [102][1200/5005] lr: 1.0000e-03 eta: 12:22:02 time: 0.2269 data_time: 0.0033 loss: 0.8493 03/06 11:42:54 - mmengine - INFO - Epoch(train) [102][1300/5005] lr: 1.0000e-03 eta: 12:21:40 time: 0.2273 data_time: 0.0037 loss: 0.8977 03/06 11:43:17 - mmengine - INFO - Epoch(train) [102][1400/5005] lr: 1.0000e-03 eta: 12:21:17 time: 0.2268 data_time: 0.0040 loss: 1.0056 03/06 11:43:39 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:43:41 - mmengine - INFO - Epoch(train) [102][1500/5005] lr: 1.0000e-03 eta: 12:20:54 time: 0.2296 data_time: 0.0042 loss: 0.9511 03/06 11:44:04 - mmengine - INFO - Epoch(train) [102][1600/5005] lr: 1.0000e-03 eta: 12:20:31 time: 0.2526 data_time: 0.0035 loss: 0.9160 03/06 11:44:27 - mmengine - INFO - Epoch(train) [102][1700/5005] lr: 1.0000e-03 eta: 12:20:08 time: 0.2271 data_time: 0.0039 loss: 0.9640 03/06 11:44:50 - mmengine - INFO - Epoch(train) [102][1800/5005] lr: 1.0000e-03 eta: 12:19:45 time: 0.2267 data_time: 0.0035 loss: 0.9833 03/06 11:45:14 - mmengine - INFO - Epoch(train) [102][1900/5005] lr: 1.0000e-03 eta: 12:19:22 time: 0.2269 data_time: 0.0036 loss: 1.0210 03/06 11:45:37 - mmengine - INFO - Epoch(train) [102][2000/5005] lr: 1.0000e-03 eta: 12:18:59 time: 0.2342 data_time: 0.0037 loss: 0.8530 03/06 11:46:00 - mmengine - INFO - Epoch(train) [102][2100/5005] lr: 1.0000e-03 eta: 12:18:36 time: 0.2245 data_time: 0.0035 loss: 0.8855 03/06 11:46:23 - mmengine - INFO - Epoch(train) [102][2200/5005] lr: 1.0000e-03 eta: 12:18:13 time: 0.2251 data_time: 0.0036 loss: 0.6943 03/06 11:46:46 - mmengine - INFO - Epoch(train) [102][2300/5005] lr: 1.0000e-03 eta: 12:17:50 time: 0.2240 data_time: 0.0036 loss: 0.8414 03/06 11:47:09 - mmengine - INFO - Epoch(train) [102][2400/5005] lr: 1.0000e-03 eta: 12:17:27 time: 0.2262 data_time: 0.0034 loss: 0.8980 03/06 11:47:31 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:47:32 - mmengine - INFO - Epoch(train) [102][2500/5005] lr: 1.0000e-03 eta: 12:17:04 time: 0.2270 data_time: 0.0037 loss: 0.8687 03/06 11:47:56 - mmengine - INFO - Epoch(train) [102][2600/5005] lr: 1.0000e-03 eta: 12:16:41 time: 0.2223 data_time: 0.0035 loss: 0.8809 03/06 11:48:19 - mmengine - INFO - Epoch(train) [102][2700/5005] lr: 1.0000e-03 eta: 12:16:18 time: 0.2283 data_time: 0.0033 loss: 0.9151 03/06 11:48:42 - mmengine - INFO - Epoch(train) [102][2800/5005] lr: 1.0000e-03 eta: 12:15:55 time: 0.2271 data_time: 0.0037 loss: 1.0725 03/06 11:49:06 - mmengine - INFO - Epoch(train) [102][2900/5005] lr: 1.0000e-03 eta: 12:15:33 time: 0.2421 data_time: 0.0039 loss: 0.9175 03/06 11:49:29 - mmengine - INFO - Epoch(train) [102][3000/5005] lr: 1.0000e-03 eta: 12:15:10 time: 0.2273 data_time: 0.0036 loss: 0.8612 03/06 11:49:52 - mmengine - INFO - Epoch(train) [102][3100/5005] lr: 1.0000e-03 eta: 12:14:47 time: 0.2309 data_time: 0.0034 loss: 0.7891 03/06 11:50:15 - mmengine - INFO - Epoch(train) [102][3200/5005] lr: 1.0000e-03 eta: 12:14:24 time: 0.2247 data_time: 0.0038 loss: 0.9215 03/06 11:50:39 - mmengine - INFO - Epoch(train) [102][3300/5005] lr: 1.0000e-03 eta: 12:14:01 time: 0.2286 data_time: 0.0035 loss: 0.9413 03/06 11:51:02 - mmengine - INFO - Epoch(train) [102][3400/5005] lr: 1.0000e-03 eta: 12:13:38 time: 0.2270 data_time: 0.0038 loss: 0.8664 03/06 11:51:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:51:25 - mmengine - INFO - Epoch(train) [102][3500/5005] lr: 1.0000e-03 eta: 12:13:15 time: 0.2258 data_time: 0.0040 loss: 0.9550 03/06 11:51:48 - mmengine - INFO - Epoch(train) [102][3600/5005] lr: 1.0000e-03 eta: 12:12:52 time: 0.2263 data_time: 0.0035 loss: 0.9293 03/06 11:52:11 - mmengine - INFO - Epoch(train) [102][3700/5005] lr: 1.0000e-03 eta: 12:12:29 time: 0.2442 data_time: 0.0037 loss: 0.9433 03/06 11:52:35 - mmengine - INFO - Epoch(train) [102][3800/5005] lr: 1.0000e-03 eta: 12:12:06 time: 0.2316 data_time: 0.0041 loss: 1.0655 03/06 11:52:58 - mmengine - INFO - Epoch(train) [102][3900/5005] lr: 1.0000e-03 eta: 12:11:44 time: 0.2266 data_time: 0.0035 loss: 0.8968 03/06 11:53:21 - mmengine - INFO - Epoch(train) [102][4000/5005] lr: 1.0000e-03 eta: 12:11:20 time: 0.2271 data_time: 0.0036 loss: 0.9159 03/06 11:53:44 - mmengine - INFO - Epoch(train) [102][4100/5005] lr: 1.0000e-03 eta: 12:10:58 time: 0.2461 data_time: 0.0035 loss: 0.9192 03/06 11:54:07 - mmengine - INFO - Epoch(train) [102][4200/5005] lr: 1.0000e-03 eta: 12:10:35 time: 0.2221 data_time: 0.0033 loss: 0.9517 03/06 11:54:31 - mmengine - INFO - Epoch(train) [102][4300/5005] lr: 1.0000e-03 eta: 12:10:12 time: 0.2347 data_time: 0.0034 loss: 0.8995 03/06 11:54:54 - mmengine - INFO - Epoch(train) [102][4400/5005] lr: 1.0000e-03 eta: 12:09:49 time: 0.2246 data_time: 0.0039 loss: 0.8396 03/06 11:55:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:55:17 - mmengine - INFO - Epoch(train) [102][4500/5005] lr: 1.0000e-03 eta: 12:09:26 time: 0.2296 data_time: 0.0034 loss: 0.7775 03/06 11:55:41 - mmengine - INFO - Epoch(train) [102][4600/5005] lr: 1.0000e-03 eta: 12:09:03 time: 0.2298 data_time: 0.0035 loss: 0.7627 03/06 11:56:04 - mmengine - INFO - Epoch(train) [102][4700/5005] lr: 1.0000e-03 eta: 12:08:40 time: 0.2258 data_time: 0.0036 loss: 0.7763 03/06 11:56:27 - mmengine - INFO - Epoch(train) [102][4800/5005] lr: 1.0000e-03 eta: 12:08:17 time: 0.2299 data_time: 0.0035 loss: 0.8558 03/06 11:56:51 - mmengine - INFO - Epoch(train) [102][4900/5005] lr: 1.0000e-03 eta: 12:07:55 time: 0.2858 data_time: 0.0033 loss: 0.9255 03/06 11:57:19 - mmengine - INFO - Epoch(train) [102][5000/5005] lr: 1.0000e-03 eta: 12:07:34 time: 0.2909 data_time: 0.0035 loss: 0.7375 03/06 11:57:21 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:57:24 - mmengine - INFO - Saving checkpoint at 102 epochs 03/06 11:57:40 - mmengine - INFO - Epoch(val) [102][100/196] eta: 0:00:14 time: 0.0172 data_time: 0.0002 03/06 11:57:54 - mmengine - INFO - Epoch(val) [102][196/196] accuracy/top1: 77.0080 accuracy/top5: 93.4720 03/06 11:58:26 - mmengine - INFO - Epoch(train) [103][ 100/5005] lr: 1.0000e-03 eta: 12:07:13 time: 0.2303 data_time: 0.0033 loss: 0.9649 03/06 11:58:49 - mmengine - INFO - Epoch(train) [103][ 200/5005] lr: 1.0000e-03 eta: 12:06:50 time: 0.2385 data_time: 0.0039 loss: 0.7784 03/06 11:59:12 - mmengine - INFO - Epoch(train) [103][ 300/5005] lr: 1.0000e-03 eta: 12:06:27 time: 0.2229 data_time: 0.0035 loss: 0.8129 03/06 11:59:35 - mmengine - INFO - Epoch(train) [103][ 400/5005] lr: 1.0000e-03 eta: 12:06:04 time: 0.2267 data_time: 0.0037 loss: 0.7206 03/06 11:59:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 11:59:59 - mmengine - INFO - Epoch(train) [103][ 500/5005] lr: 1.0000e-03 eta: 12:05:41 time: 0.2263 data_time: 0.0038 loss: 0.9843 03/06 12:00:22 - mmengine - INFO - Epoch(train) [103][ 600/5005] lr: 1.0000e-03 eta: 12:05:18 time: 0.2257 data_time: 0.0036 loss: 0.8527 03/06 12:00:45 - mmengine - INFO - Epoch(train) [103][ 700/5005] lr: 1.0000e-03 eta: 12:04:55 time: 0.2458 data_time: 0.0037 loss: 0.9229 03/06 12:01:09 - mmengine - INFO - Epoch(train) [103][ 800/5005] lr: 1.0000e-03 eta: 12:04:32 time: 0.2441 data_time: 0.0039 loss: 0.8215 03/06 12:01:32 - mmengine - INFO - Epoch(train) [103][ 900/5005] lr: 1.0000e-03 eta: 12:04:10 time: 0.2475 data_time: 0.0034 loss: 1.0520 03/06 12:01:55 - mmengine - INFO - Epoch(train) [103][1000/5005] lr: 1.0000e-03 eta: 12:03:47 time: 0.2267 data_time: 0.0034 loss: 1.0165 03/06 12:02:18 - mmengine - INFO - Epoch(train) [103][1100/5005] lr: 1.0000e-03 eta: 12:03:24 time: 0.2270 data_time: 0.0037 loss: 0.8551 03/06 12:02:41 - mmengine - INFO - Epoch(train) [103][1200/5005] lr: 1.0000e-03 eta: 12:03:01 time: 0.2252 data_time: 0.0036 loss: 0.8491 03/06 12:03:04 - mmengine - INFO - Epoch(train) [103][1300/5005] lr: 1.0000e-03 eta: 12:02:38 time: 0.2261 data_time: 0.0035 loss: 0.8227 03/06 12:03:28 - mmengine - INFO - Epoch(train) [103][1400/5005] lr: 1.0000e-03 eta: 12:02:15 time: 0.2292 data_time: 0.0037 loss: 0.9203 03/06 12:03:49 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:03:51 - mmengine - INFO - Epoch(train) [103][1500/5005] lr: 1.0000e-03 eta: 12:01:52 time: 0.2237 data_time: 0.0039 loss: 0.8917 03/06 12:04:14 - mmengine - INFO - Epoch(train) [103][1600/5005] lr: 1.0000e-03 eta: 12:01:29 time: 0.2267 data_time: 0.0037 loss: 0.8141 03/06 12:04:37 - mmengine - INFO - Epoch(train) [103][1700/5005] lr: 1.0000e-03 eta: 12:01:06 time: 0.2296 data_time: 0.0036 loss: 0.8351 03/06 12:05:00 - mmengine - INFO - Epoch(train) [103][1800/5005] lr: 1.0000e-03 eta: 12:00:43 time: 0.2301 data_time: 0.0038 loss: 0.9807 03/06 12:05:23 - mmengine - INFO - Epoch(train) [103][1900/5005] lr: 1.0000e-03 eta: 12:00:20 time: 0.2363 data_time: 0.0036 loss: 0.8797 03/06 12:05:46 - mmengine - INFO - Epoch(train) [103][2000/5005] lr: 1.0000e-03 eta: 11:59:57 time: 0.2369 data_time: 0.0032 loss: 1.1087 03/06 12:06:09 - mmengine - INFO - Epoch(train) [103][2100/5005] lr: 1.0000e-03 eta: 11:59:34 time: 0.2215 data_time: 0.0040 loss: 0.8688 03/06 12:06:33 - mmengine - INFO - Epoch(train) [103][2200/5005] lr: 1.0000e-03 eta: 11:59:11 time: 0.2463 data_time: 0.0038 loss: 0.8144 03/06 12:06:56 - mmengine - INFO - Epoch(train) [103][2300/5005] lr: 1.0000e-03 eta: 11:58:48 time: 0.2277 data_time: 0.0037 loss: 0.8116 03/06 12:07:19 - mmengine - INFO - Epoch(train) [103][2400/5005] lr: 1.0000e-03 eta: 11:58:25 time: 0.2254 data_time: 0.0034 loss: 1.0296 03/06 12:07:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:07:43 - mmengine - INFO - Epoch(train) [103][2500/5005] lr: 1.0000e-03 eta: 11:58:02 time: 0.2291 data_time: 0.0039 loss: 0.9762 03/06 12:08:06 - mmengine - INFO - Epoch(train) [103][2600/5005] lr: 1.0000e-03 eta: 11:57:39 time: 0.2279 data_time: 0.0034 loss: 0.8545 03/06 12:08:29 - mmengine - INFO - Epoch(train) [103][2700/5005] lr: 1.0000e-03 eta: 11:57:16 time: 0.2268 data_time: 0.0041 loss: 0.9192 03/06 12:08:52 - mmengine - INFO - Epoch(train) [103][2800/5005] lr: 1.0000e-03 eta: 11:56:53 time: 0.2309 data_time: 0.0037 loss: 1.0214 03/06 12:09:15 - mmengine - INFO - Epoch(train) [103][2900/5005] lr: 1.0000e-03 eta: 11:56:30 time: 0.2250 data_time: 0.0036 loss: 0.8761 03/06 12:09:39 - mmengine - INFO - Epoch(train) [103][3000/5005] lr: 1.0000e-03 eta: 11:56:07 time: 0.2262 data_time: 0.0037 loss: 1.0568 03/06 12:10:02 - mmengine - INFO - Epoch(train) [103][3100/5005] lr: 1.0000e-03 eta: 11:55:44 time: 0.2287 data_time: 0.0034 loss: 0.9855 03/06 12:10:25 - mmengine - INFO - Epoch(train) [103][3200/5005] lr: 1.0000e-03 eta: 11:55:22 time: 0.2302 data_time: 0.0035 loss: 0.8812 03/06 12:10:48 - mmengine - INFO - Epoch(train) [103][3300/5005] lr: 1.0000e-03 eta: 11:54:59 time: 0.2224 data_time: 0.0034 loss: 0.8383 03/06 12:11:11 - mmengine - INFO - Epoch(train) [103][3400/5005] lr: 1.0000e-03 eta: 11:54:36 time: 0.2249 data_time: 0.0038 loss: 0.8125 03/06 12:11:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:11:34 - mmengine - INFO - Epoch(train) [103][3500/5005] lr: 1.0000e-03 eta: 11:54:13 time: 0.2277 data_time: 0.0037 loss: 0.9236 03/06 12:11:58 - mmengine - INFO - Epoch(train) [103][3600/5005] lr: 1.0000e-03 eta: 11:53:50 time: 0.2247 data_time: 0.0036 loss: 0.9447 03/06 12:12:21 - mmengine - INFO - Epoch(train) [103][3700/5005] lr: 1.0000e-03 eta: 11:53:27 time: 0.2275 data_time: 0.0042 loss: 0.9027 03/06 12:12:44 - mmengine - INFO - Epoch(train) [103][3800/5005] lr: 1.0000e-03 eta: 11:53:04 time: 0.2254 data_time: 0.0035 loss: 0.7313 03/06 12:13:07 - mmengine - INFO - Epoch(train) [103][3900/5005] lr: 1.0000e-03 eta: 11:52:41 time: 0.2466 data_time: 0.0036 loss: 0.8628 03/06 12:13:31 - mmengine - INFO - Epoch(train) [103][4000/5005] lr: 1.0000e-03 eta: 11:52:18 time: 0.2268 data_time: 0.0035 loss: 0.8952 03/06 12:13:54 - mmengine - INFO - Epoch(train) [103][4100/5005] lr: 1.0000e-03 eta: 11:51:55 time: 0.2230 data_time: 0.0034 loss: 0.8163 03/06 12:14:17 - mmengine - INFO - Epoch(train) [103][4200/5005] lr: 1.0000e-03 eta: 11:51:32 time: 0.2274 data_time: 0.0035 loss: 0.9227 03/06 12:14:40 - mmengine - INFO - Epoch(train) [103][4300/5005] lr: 1.0000e-03 eta: 11:51:09 time: 0.2253 data_time: 0.0036 loss: 0.8146 03/06 12:15:03 - mmengine - INFO - Epoch(train) [103][4400/5005] lr: 1.0000e-03 eta: 11:50:46 time: 0.2273 data_time: 0.0036 loss: 1.0230 03/06 12:15:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:15:26 - mmengine - INFO - Epoch(train) [103][4500/5005] lr: 1.0000e-03 eta: 11:50:23 time: 0.2275 data_time: 0.0041 loss: 0.8807 03/06 12:15:50 - mmengine - INFO - Epoch(train) [103][4600/5005] lr: 1.0000e-03 eta: 11:50:00 time: 0.2262 data_time: 0.0037 loss: 0.8417 03/06 12:16:13 - mmengine - INFO - Epoch(train) [103][4700/5005] lr: 1.0000e-03 eta: 11:49:37 time: 0.2272 data_time: 0.0037 loss: 1.0227 03/06 12:16:36 - mmengine - INFO - Epoch(train) [103][4800/5005] lr: 1.0000e-03 eta: 11:49:15 time: 0.2243 data_time: 0.0035 loss: 0.8388 03/06 12:17:00 - mmengine - INFO - Epoch(train) [103][4900/5005] lr: 1.0000e-03 eta: 11:48:52 time: 0.2656 data_time: 0.0034 loss: 0.9343 03/06 12:17:29 - mmengine - INFO - Epoch(train) [103][5000/5005] lr: 1.0000e-03 eta: 11:48:31 time: 0.2820 data_time: 0.0034 loss: 0.9431 03/06 12:17:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:17:33 - mmengine - INFO - Saving checkpoint at 103 epochs 03/06 12:17:49 - mmengine - INFO - Epoch(val) [103][100/196] eta: 0:00:14 time: 0.0196 data_time: 0.0002 03/06 12:18:03 - mmengine - INFO - Epoch(val) [103][196/196] accuracy/top1: 76.9860 accuracy/top5: 93.5240 03/06 12:18:35 - mmengine - INFO - Epoch(train) [104][ 100/5005] lr: 1.0000e-03 eta: 11:48:10 time: 0.2274 data_time: 0.0046 loss: 1.0732 03/06 12:18:58 - mmengine - INFO - Epoch(train) [104][ 200/5005] lr: 1.0000e-03 eta: 11:47:47 time: 0.2271 data_time: 0.0038 loss: 0.9314 03/06 12:19:22 - mmengine - INFO - Epoch(train) [104][ 300/5005] lr: 1.0000e-03 eta: 11:47:25 time: 0.2248 data_time: 0.0041 loss: 0.8461 03/06 12:19:45 - mmengine - INFO - Epoch(train) [104][ 400/5005] lr: 1.0000e-03 eta: 11:47:02 time: 0.2234 data_time: 0.0043 loss: 0.8666 03/06 12:20:05 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:20:08 - mmengine - INFO - Epoch(train) [104][ 500/5005] lr: 1.0000e-03 eta: 11:46:39 time: 0.2276 data_time: 0.0035 loss: 0.9018 03/06 12:20:32 - mmengine - INFO - Epoch(train) [104][ 600/5005] lr: 1.0000e-03 eta: 11:46:16 time: 0.2319 data_time: 0.0038 loss: 0.9273 03/06 12:20:55 - mmengine - INFO - Epoch(train) [104][ 700/5005] lr: 1.0000e-03 eta: 11:45:53 time: 0.2460 data_time: 0.0039 loss: 0.8600 03/06 12:21:19 - mmengine - INFO - Epoch(train) [104][ 800/5005] lr: 1.0000e-03 eta: 11:45:30 time: 0.2301 data_time: 0.0036 loss: 0.8877 03/06 12:21:42 - mmengine - INFO - Epoch(train) [104][ 900/5005] lr: 1.0000e-03 eta: 11:45:07 time: 0.2266 data_time: 0.0034 loss: 0.8953 03/06 12:22:05 - mmengine - INFO - Epoch(train) [104][1000/5005] lr: 1.0000e-03 eta: 11:44:44 time: 0.2314 data_time: 0.0038 loss: 0.9019 03/06 12:22:28 - mmengine - INFO - Epoch(train) [104][1100/5005] lr: 1.0000e-03 eta: 11:44:21 time: 0.2389 data_time: 0.0040 loss: 0.9003 03/06 12:22:52 - mmengine - INFO - Epoch(train) [104][1200/5005] lr: 1.0000e-03 eta: 11:43:58 time: 0.2462 data_time: 0.0037 loss: 0.8938 03/06 12:23:15 - mmengine - INFO - Epoch(train) [104][1300/5005] lr: 1.0000e-03 eta: 11:43:36 time: 0.2277 data_time: 0.0035 loss: 0.8553 03/06 12:23:38 - mmengine - INFO - Epoch(train) [104][1400/5005] lr: 1.0000e-03 eta: 11:43:13 time: 0.2281 data_time: 0.0034 loss: 0.8203 03/06 12:23:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:24:01 - mmengine - INFO - Epoch(train) [104][1500/5005] lr: 1.0000e-03 eta: 11:42:50 time: 0.2259 data_time: 0.0036 loss: 1.1111 03/06 12:24:25 - mmengine - INFO - Epoch(train) [104][1600/5005] lr: 1.0000e-03 eta: 11:42:27 time: 0.2261 data_time: 0.0037 loss: 0.8845 03/06 12:24:48 - mmengine - INFO - Epoch(train) [104][1700/5005] lr: 1.0000e-03 eta: 11:42:04 time: 0.2260 data_time: 0.0035 loss: 0.8473 03/06 12:25:11 - mmengine - INFO - Epoch(train) [104][1800/5005] lr: 1.0000e-03 eta: 11:41:41 time: 0.2245 data_time: 0.0036 loss: 0.9150 03/06 12:25:34 - mmengine - INFO - Epoch(train) [104][1900/5005] lr: 1.0000e-03 eta: 11:41:18 time: 0.2263 data_time: 0.0035 loss: 0.8935 03/06 12:25:57 - mmengine - INFO - Epoch(train) [104][2000/5005] lr: 1.0000e-03 eta: 11:40:55 time: 0.2245 data_time: 0.0042 loss: 0.8548 03/06 12:26:21 - mmengine - INFO - Epoch(train) [104][2100/5005] lr: 1.0000e-03 eta: 11:40:32 time: 0.2413 data_time: 0.0034 loss: 0.9568 03/06 12:26:44 - mmengine - INFO - Epoch(train) [104][2200/5005] lr: 1.0000e-03 eta: 11:40:09 time: 0.2293 data_time: 0.0034 loss: 0.9114 03/06 12:27:07 - mmengine - INFO - Epoch(train) [104][2300/5005] lr: 1.0000e-03 eta: 11:39:46 time: 0.2254 data_time: 0.0040 loss: 0.8493 03/06 12:27:30 - mmengine - INFO - Epoch(train) [104][2400/5005] lr: 1.0000e-03 eta: 11:39:23 time: 0.2308 data_time: 0.0035 loss: 0.8373 03/06 12:27:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:27:54 - mmengine - INFO - Epoch(train) [104][2500/5005] lr: 1.0000e-03 eta: 11:39:00 time: 0.2239 data_time: 0.0036 loss: 0.9253 03/06 12:28:17 - mmengine - INFO - Epoch(train) [104][2600/5005] lr: 1.0000e-03 eta: 11:38:37 time: 0.2286 data_time: 0.0035 loss: 0.8101 03/06 12:28:40 - mmengine - INFO - Epoch(train) [104][2700/5005] lr: 1.0000e-03 eta: 11:38:14 time: 0.2359 data_time: 0.0038 loss: 0.9119 03/06 12:29:03 - mmengine - INFO - Epoch(train) [104][2800/5005] lr: 1.0000e-03 eta: 11:37:51 time: 0.2234 data_time: 0.0035 loss: 0.7821 03/06 12:29:27 - mmengine - INFO - Epoch(train) [104][2900/5005] lr: 1.0000e-03 eta: 11:37:29 time: 0.2252 data_time: 0.0038 loss: 0.8400 03/06 12:29:50 - mmengine - INFO - Epoch(train) [104][3000/5005] lr: 1.0000e-03 eta: 11:37:06 time: 0.2270 data_time: 0.0036 loss: 0.9627 03/06 12:30:13 - mmengine - INFO - Epoch(train) [104][3100/5005] lr: 1.0000e-03 eta: 11:36:43 time: 0.2335 data_time: 0.0035 loss: 1.0179 03/06 12:30:36 - mmengine - INFO - Epoch(train) [104][3200/5005] lr: 1.0000e-03 eta: 11:36:20 time: 0.2308 data_time: 0.0038 loss: 0.9792 03/06 12:31:00 - mmengine - INFO - Epoch(train) [104][3300/5005] lr: 1.0000e-03 eta: 11:35:57 time: 0.2278 data_time: 0.0035 loss: 0.8571 03/06 12:31:23 - mmengine - INFO - Epoch(train) [104][3400/5005] lr: 1.0000e-03 eta: 11:35:34 time: 0.2255 data_time: 0.0036 loss: 0.8437 03/06 12:31:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:31:46 - mmengine - INFO - Epoch(train) [104][3500/5005] lr: 1.0000e-03 eta: 11:35:11 time: 0.2252 data_time: 0.0038 loss: 0.8270 03/06 12:32:09 - mmengine - INFO - Epoch(train) [104][3600/5005] lr: 1.0000e-03 eta: 11:34:48 time: 0.2281 data_time: 0.0035 loss: 0.9354 03/06 12:32:33 - mmengine - INFO - Epoch(train) [104][3700/5005] lr: 1.0000e-03 eta: 11:34:25 time: 0.2277 data_time: 0.0037 loss: 0.8707 03/06 12:32:56 - mmengine - INFO - Epoch(train) [104][3800/5005] lr: 1.0000e-03 eta: 11:34:02 time: 0.2309 data_time: 0.0040 loss: 0.9364 03/06 12:33:19 - mmengine - INFO - Epoch(train) [104][3900/5005] lr: 1.0000e-03 eta: 11:33:39 time: 0.2299 data_time: 0.0038 loss: 0.9897 03/06 12:33:42 - mmengine - INFO - Epoch(train) [104][4000/5005] lr: 1.0000e-03 eta: 11:33:16 time: 0.2320 data_time: 0.0038 loss: 0.9220 03/06 12:34:06 - mmengine - INFO - Epoch(train) [104][4100/5005] lr: 1.0000e-03 eta: 11:32:54 time: 0.2507 data_time: 0.0036 loss: 0.8690 03/06 12:34:29 - mmengine - INFO - Epoch(train) [104][4200/5005] lr: 1.0000e-03 eta: 11:32:30 time: 0.2315 data_time: 0.0042 loss: 0.8263 03/06 12:34:52 - mmengine - INFO - Epoch(train) [104][4300/5005] lr: 1.0000e-03 eta: 11:32:07 time: 0.2264 data_time: 0.0039 loss: 0.8428 03/06 12:35:15 - mmengine - INFO - Epoch(train) [104][4400/5005] lr: 1.0000e-03 eta: 11:31:44 time: 0.2248 data_time: 0.0037 loss: 0.9199 03/06 12:35:35 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:35:39 - mmengine - INFO - Epoch(train) [104][4500/5005] lr: 1.0000e-03 eta: 11:31:22 time: 0.2442 data_time: 0.0035 loss: 0.8305 03/06 12:36:02 - mmengine - INFO - Epoch(train) [104][4600/5005] lr: 1.0000e-03 eta: 11:30:59 time: 0.2327 data_time: 0.0038 loss: 1.0207 03/06 12:36:25 - mmengine - INFO - Epoch(train) [104][4700/5005] lr: 1.0000e-03 eta: 11:30:36 time: 0.2479 data_time: 0.0042 loss: 0.9917 03/06 12:36:48 - mmengine - INFO - Epoch(train) [104][4800/5005] lr: 1.0000e-03 eta: 11:30:13 time: 0.2374 data_time: 0.0036 loss: 0.9658 03/06 12:37:12 - mmengine - INFO - Epoch(train) [104][4900/5005] lr: 1.0000e-03 eta: 11:29:50 time: 0.2864 data_time: 0.0033 loss: 0.8606 03/06 12:37:41 - mmengine - INFO - Epoch(train) [104][5000/5005] lr: 1.0000e-03 eta: 11:29:29 time: 0.2819 data_time: 0.0035 loss: 0.9029 03/06 12:37:43 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:37:45 - mmengine - INFO - Saving checkpoint at 104 epochs 03/06 12:38:01 - mmengine - INFO - Epoch(val) [104][100/196] eta: 0:00:13 time: 0.0191 data_time: 0.0006 03/06 12:38:14 - mmengine - INFO - Epoch(val) [104][196/196] accuracy/top1: 77.0400 accuracy/top5: 93.4920 03/06 12:38:47 - mmengine - INFO - Epoch(train) [105][ 100/5005] lr: 1.0000e-03 eta: 11:29:09 time: 0.2287 data_time: 0.0042 loss: 0.8171 03/06 12:39:11 - mmengine - INFO - Epoch(train) [105][ 200/5005] lr: 1.0000e-03 eta: 11:28:46 time: 0.2281 data_time: 0.0044 loss: 0.9212 03/06 12:39:34 - mmengine - INFO - Epoch(train) [105][ 300/5005] lr: 1.0000e-03 eta: 11:28:23 time: 0.2262 data_time: 0.0040 loss: 0.9443 03/06 12:39:57 - mmengine - INFO - Epoch(train) [105][ 400/5005] lr: 1.0000e-03 eta: 11:28:00 time: 0.2302 data_time: 0.0037 loss: 0.8035 03/06 12:40:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:40:20 - mmengine - INFO - Epoch(train) [105][ 500/5005] lr: 1.0000e-03 eta: 11:27:37 time: 0.2264 data_time: 0.0036 loss: 0.8678 03/06 12:40:44 - mmengine - INFO - Epoch(train) [105][ 600/5005] lr: 1.0000e-03 eta: 11:27:14 time: 0.2282 data_time: 0.0037 loss: 0.8883 03/06 12:41:07 - mmengine - INFO - Epoch(train) [105][ 700/5005] lr: 1.0000e-03 eta: 11:26:51 time: 0.2270 data_time: 0.0039 loss: 0.9596 03/06 12:41:30 - mmengine - INFO - Epoch(train) [105][ 800/5005] lr: 1.0000e-03 eta: 11:26:28 time: 0.2294 data_time: 0.0038 loss: 0.8400 03/06 12:41:54 - mmengine - INFO - Epoch(train) [105][ 900/5005] lr: 1.0000e-03 eta: 11:26:05 time: 0.2474 data_time: 0.0039 loss: 0.8704 03/06 12:42:17 - mmengine - INFO - Epoch(train) [105][1000/5005] lr: 1.0000e-03 eta: 11:25:42 time: 0.2292 data_time: 0.0035 loss: 0.8227 03/06 12:42:40 - mmengine - INFO - Epoch(train) [105][1100/5005] lr: 1.0000e-03 eta: 11:25:19 time: 0.2254 data_time: 0.0043 loss: 0.8858 03/06 12:43:03 - mmengine - INFO - Epoch(train) [105][1200/5005] lr: 1.0000e-03 eta: 11:24:56 time: 0.2250 data_time: 0.0041 loss: 0.9107 03/06 12:43:26 - mmengine - INFO - Epoch(train) [105][1300/5005] lr: 1.0000e-03 eta: 11:24:33 time: 0.2265 data_time: 0.0037 loss: 0.8330 03/06 12:43:49 - mmengine - INFO - Epoch(train) [105][1400/5005] lr: 1.0000e-03 eta: 11:24:10 time: 0.2331 data_time: 0.0037 loss: 0.9144 03/06 12:44:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:44:13 - mmengine - INFO - Epoch(train) [105][1500/5005] lr: 1.0000e-03 eta: 11:23:47 time: 0.2250 data_time: 0.0039 loss: 0.7425 03/06 12:44:36 - mmengine - INFO - Epoch(train) [105][1600/5005] lr: 1.0000e-03 eta: 11:23:24 time: 0.2272 data_time: 0.0039 loss: 0.8239 03/06 12:44:59 - mmengine - INFO - Epoch(train) [105][1700/5005] lr: 1.0000e-03 eta: 11:23:01 time: 0.2312 data_time: 0.0037 loss: 0.9361 03/06 12:45:22 - mmengine - INFO - Epoch(train) [105][1800/5005] lr: 1.0000e-03 eta: 11:22:38 time: 0.2267 data_time: 0.0037 loss: 0.8747 03/06 12:45:45 - mmengine - INFO - Epoch(train) [105][1900/5005] lr: 1.0000e-03 eta: 11:22:15 time: 0.2279 data_time: 0.0037 loss: 0.8387 03/06 12:46:09 - mmengine - INFO - Epoch(train) [105][2000/5005] lr: 1.0000e-03 eta: 11:21:53 time: 0.2288 data_time: 0.0043 loss: 0.9030 03/06 12:46:32 - mmengine - INFO - Epoch(train) [105][2100/5005] lr: 1.0000e-03 eta: 11:21:30 time: 0.2466 data_time: 0.0035 loss: 0.7932 03/06 12:46:56 - mmengine - INFO - Epoch(train) [105][2200/5005] lr: 1.0000e-03 eta: 11:21:07 time: 0.2272 data_time: 0.0039 loss: 0.7441 03/06 12:47:19 - mmengine - INFO - Epoch(train) [105][2300/5005] lr: 1.0000e-03 eta: 11:20:44 time: 0.2278 data_time: 0.0034 loss: 0.8968 03/06 12:47:42 - mmengine - INFO - Epoch(train) [105][2400/5005] lr: 1.0000e-03 eta: 11:20:21 time: 0.2287 data_time: 0.0037 loss: 0.9219 03/06 12:48:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:48:05 - mmengine - INFO - Epoch(train) [105][2500/5005] lr: 1.0000e-03 eta: 11:19:58 time: 0.2248 data_time: 0.0038 loss: 0.9574 03/06 12:48:28 - mmengine - INFO - Epoch(train) [105][2600/5005] lr: 1.0000e-03 eta: 11:19:35 time: 0.2289 data_time: 0.0043 loss: 0.8119 03/06 12:48:51 - mmengine - INFO - Epoch(train) [105][2700/5005] lr: 1.0000e-03 eta: 11:19:12 time: 0.2278 data_time: 0.0036 loss: 0.8335 03/06 12:49:14 - mmengine - INFO - Epoch(train) [105][2800/5005] lr: 1.0000e-03 eta: 11:18:49 time: 0.2246 data_time: 0.0040 loss: 0.8028 03/06 12:49:38 - mmengine - INFO - Epoch(train) [105][2900/5005] lr: 1.0000e-03 eta: 11:18:26 time: 0.2335 data_time: 0.0040 loss: 0.8484 03/06 12:50:02 - mmengine - INFO - Epoch(train) [105][3000/5005] lr: 1.0000e-03 eta: 11:18:03 time: 0.2240 data_time: 0.0038 loss: 0.9415 03/06 12:50:25 - mmengine - INFO - Epoch(train) [105][3100/5005] lr: 1.0000e-03 eta: 11:17:40 time: 0.2286 data_time: 0.0041 loss: 0.7802 03/06 12:50:47 - mmengine - INFO - Epoch(train) [105][3200/5005] lr: 1.0000e-03 eta: 11:17:17 time: 0.2258 data_time: 0.0043 loss: 0.9835 03/06 12:51:11 - mmengine - INFO - Epoch(train) [105][3300/5005] lr: 1.0000e-03 eta: 11:16:54 time: 0.2281 data_time: 0.0037 loss: 0.6903 03/06 12:51:35 - mmengine - INFO - Epoch(train) [105][3400/5005] lr: 1.0000e-03 eta: 11:16:32 time: 0.2255 data_time: 0.0043 loss: 1.1288 03/06 12:51:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:51:58 - mmengine - INFO - Epoch(train) [105][3500/5005] lr: 1.0000e-03 eta: 11:16:09 time: 0.2290 data_time: 0.0039 loss: 0.8865 03/06 12:52:21 - mmengine - INFO - Epoch(train) [105][3600/5005] lr: 1.0000e-03 eta: 11:15:46 time: 0.2270 data_time: 0.0035 loss: 0.7725 03/06 12:52:44 - mmengine - INFO - Epoch(train) [105][3700/5005] lr: 1.0000e-03 eta: 11:15:23 time: 0.2261 data_time: 0.0037 loss: 0.8185 03/06 12:53:07 - mmengine - INFO - Epoch(train) [105][3800/5005] lr: 1.0000e-03 eta: 11:15:00 time: 0.2440 data_time: 0.0036 loss: 0.8628 03/06 12:53:31 - mmengine - INFO - Epoch(train) [105][3900/5005] lr: 1.0000e-03 eta: 11:14:37 time: 0.2255 data_time: 0.0038 loss: 0.9596 03/06 12:53:54 - mmengine - INFO - Epoch(train) [105][4000/5005] lr: 1.0000e-03 eta: 11:14:14 time: 0.2296 data_time: 0.0037 loss: 0.9478 03/06 12:54:17 - mmengine - INFO - Epoch(train) [105][4100/5005] lr: 1.0000e-03 eta: 11:13:51 time: 0.2287 data_time: 0.0040 loss: 0.9119 03/06 12:54:41 - mmengine - INFO - Epoch(train) [105][4200/5005] lr: 1.0000e-03 eta: 11:13:28 time: 0.2441 data_time: 0.0039 loss: 0.9373 03/06 12:55:04 - mmengine - INFO - Epoch(train) [105][4300/5005] lr: 1.0000e-03 eta: 11:13:05 time: 0.2271 data_time: 0.0036 loss: 1.0342 03/06 12:55:27 - mmengine - INFO - Epoch(train) [105][4400/5005] lr: 1.0000e-03 eta: 11:12:42 time: 0.2250 data_time: 0.0039 loss: 0.7107 03/06 12:55:45 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:55:50 - mmengine - INFO - Epoch(train) [105][4500/5005] lr: 1.0000e-03 eta: 11:12:19 time: 0.2240 data_time: 0.0038 loss: 1.0249 03/06 12:56:13 - mmengine - INFO - Epoch(train) [105][4600/5005] lr: 1.0000e-03 eta: 11:11:56 time: 0.2427 data_time: 0.0040 loss: 0.7938 03/06 12:56:37 - mmengine - INFO - Epoch(train) [105][4700/5005] lr: 1.0000e-03 eta: 11:11:33 time: 0.2281 data_time: 0.0036 loss: 0.7860 03/06 12:57:00 - mmengine - INFO - Epoch(train) [105][4800/5005] lr: 1.0000e-03 eta: 11:11:10 time: 0.2300 data_time: 0.0036 loss: 0.8893 03/06 12:57:24 - mmengine - INFO - Epoch(train) [105][4900/5005] lr: 1.0000e-03 eta: 11:10:48 time: 0.2840 data_time: 0.0036 loss: 0.8345 03/06 12:57:53 - mmengine - INFO - Epoch(train) [105][5000/5005] lr: 1.0000e-03 eta: 11:10:27 time: 0.2717 data_time: 0.0036 loss: 0.8277 03/06 12:57:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 12:57:57 - mmengine - INFO - Saving checkpoint at 105 epochs 03/06 12:58:12 - mmengine - INFO - Epoch(val) [105][100/196] eta: 0:00:13 time: 0.0189 data_time: 0.0004 03/06 12:58:25 - mmengine - INFO - Epoch(val) [105][196/196] accuracy/top1: 76.9840 accuracy/top5: 93.4560 03/06 12:58:58 - mmengine - INFO - Epoch(train) [106][ 100/5005] lr: 1.0000e-03 eta: 11:10:06 time: 0.2280 data_time: 0.0039 loss: 0.8312 03/06 12:59:22 - mmengine - INFO - Epoch(train) [106][ 200/5005] lr: 1.0000e-03 eta: 11:09:43 time: 0.2308 data_time: 0.0038 loss: 0.8168 03/06 12:59:45 - mmengine - INFO - Epoch(train) [106][ 300/5005] lr: 1.0000e-03 eta: 11:09:20 time: 0.2266 data_time: 0.0040 loss: 0.8705 03/06 13:00:08 - mmengine - INFO - Epoch(train) [106][ 400/5005] lr: 1.0000e-03 eta: 11:08:57 time: 0.2284 data_time: 0.0038 loss: 1.0147 03/06 13:00:25 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:00:31 - mmengine - INFO - Epoch(train) [106][ 500/5005] lr: 1.0000e-03 eta: 11:08:34 time: 0.2310 data_time: 0.0040 loss: 0.9481 03/06 13:00:55 - mmengine - INFO - Epoch(train) [106][ 600/5005] lr: 1.0000e-03 eta: 11:08:11 time: 0.2441 data_time: 0.0042 loss: 0.9747 03/06 13:01:18 - mmengine - INFO - Epoch(train) [106][ 700/5005] lr: 1.0000e-03 eta: 11:07:48 time: 0.2304 data_time: 0.0044 loss: 0.9938 03/06 13:01:41 - mmengine - INFO - Epoch(train) [106][ 800/5005] lr: 1.0000e-03 eta: 11:07:25 time: 0.2334 data_time: 0.0036 loss: 0.8456 03/06 13:02:04 - mmengine - INFO - Epoch(train) [106][ 900/5005] lr: 1.0000e-03 eta: 11:07:02 time: 0.2268 data_time: 0.0037 loss: 0.8845 03/06 13:02:28 - mmengine - INFO - Epoch(train) [106][1000/5005] lr: 1.0000e-03 eta: 11:06:40 time: 0.2262 data_time: 0.0037 loss: 0.9359 03/06 13:02:51 - mmengine - INFO - Epoch(train) [106][1100/5005] lr: 1.0000e-03 eta: 11:06:17 time: 0.2252 data_time: 0.0039 loss: 1.1181 03/06 13:03:14 - mmengine - INFO - Epoch(train) [106][1200/5005] lr: 1.0000e-03 eta: 11:05:54 time: 0.2254 data_time: 0.0040 loss: 0.8102 03/06 13:03:37 - mmengine - INFO - Epoch(train) [106][1300/5005] lr: 1.0000e-03 eta: 11:05:31 time: 0.2219 data_time: 0.0038 loss: 1.0368 03/06 13:04:00 - mmengine - INFO - Epoch(train) [106][1400/5005] lr: 1.0000e-03 eta: 11:05:08 time: 0.2351 data_time: 0.0041 loss: 1.0818 03/06 13:04:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:04:24 - mmengine - INFO - Epoch(train) [106][1500/5005] lr: 1.0000e-03 eta: 11:04:45 time: 0.2288 data_time: 0.0038 loss: 0.9479 03/06 13:04:47 - mmengine - INFO - Epoch(train) [106][1600/5005] lr: 1.0000e-03 eta: 11:04:22 time: 0.2265 data_time: 0.0039 loss: 0.9031 03/06 13:05:10 - mmengine - INFO - Epoch(train) [106][1700/5005] lr: 1.0000e-03 eta: 11:03:59 time: 0.2253 data_time: 0.0039 loss: 0.7991 03/06 13:05:33 - mmengine - INFO - Epoch(train) [106][1800/5005] lr: 1.0000e-03 eta: 11:03:36 time: 0.2255 data_time: 0.0034 loss: 0.9370 03/06 13:05:57 - mmengine - INFO - Epoch(train) [106][1900/5005] lr: 1.0000e-03 eta: 11:03:13 time: 0.2264 data_time: 0.0037 loss: 0.9123 03/06 13:06:20 - mmengine - INFO - Epoch(train) [106][2000/5005] lr: 1.0000e-03 eta: 11:02:50 time: 0.2239 data_time: 0.0038 loss: 1.1293 03/06 13:06:43 - mmengine - INFO - Epoch(train) [106][2100/5005] lr: 1.0000e-03 eta: 11:02:27 time: 0.2303 data_time: 0.0041 loss: 0.8321 03/06 13:07:06 - mmengine - INFO - Epoch(train) [106][2200/5005] lr: 1.0000e-03 eta: 11:02:04 time: 0.2255 data_time: 0.0041 loss: 0.8192 03/06 13:07:29 - mmengine - INFO - Epoch(train) [106][2300/5005] lr: 1.0000e-03 eta: 11:01:41 time: 0.2251 data_time: 0.0034 loss: 0.8308 03/06 13:07:53 - mmengine - INFO - Epoch(train) [106][2400/5005] lr: 1.0000e-03 eta: 11:01:18 time: 0.2268 data_time: 0.0040 loss: 0.7780 03/06 13:08:10 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:08:16 - mmengine - INFO - Epoch(train) [106][2500/5005] lr: 1.0000e-03 eta: 11:00:55 time: 0.2255 data_time: 0.0037 loss: 0.8312 03/06 13:08:39 - mmengine - INFO - Epoch(train) [106][2600/5005] lr: 1.0000e-03 eta: 11:00:32 time: 0.2297 data_time: 0.0037 loss: 0.7842 03/06 13:09:03 - mmengine - INFO - Epoch(train) [106][2700/5005] lr: 1.0000e-03 eta: 11:00:09 time: 0.2439 data_time: 0.0039 loss: 0.9657 03/06 13:09:26 - mmengine - INFO - Epoch(train) [106][2800/5005] lr: 1.0000e-03 eta: 10:59:46 time: 0.2318 data_time: 0.0036 loss: 0.8820 03/06 13:09:49 - mmengine - INFO - Epoch(train) [106][2900/5005] lr: 1.0000e-03 eta: 10:59:23 time: 0.2313 data_time: 0.0039 loss: 0.9145 03/06 13:10:12 - mmengine - INFO - Epoch(train) [106][3000/5005] lr: 1.0000e-03 eta: 10:59:00 time: 0.2316 data_time: 0.0036 loss: 1.0777 03/06 13:10:35 - mmengine - INFO - Epoch(train) [106][3100/5005] lr: 1.0000e-03 eta: 10:58:38 time: 0.2304 data_time: 0.0039 loss: 0.9544 03/06 13:10:59 - mmengine - INFO - Epoch(train) [106][3200/5005] lr: 1.0000e-03 eta: 10:58:15 time: 0.2273 data_time: 0.0039 loss: 0.9981 03/06 13:11:22 - mmengine - INFO - Epoch(train) [106][3300/5005] lr: 1.0000e-03 eta: 10:57:52 time: 0.2206 data_time: 0.0036 loss: 0.8595 03/06 13:11:45 - mmengine - INFO - Epoch(train) [106][3400/5005] lr: 1.0000e-03 eta: 10:57:29 time: 0.2304 data_time: 0.0037 loss: 1.0039 03/06 13:12:02 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:12:08 - mmengine - INFO - Epoch(train) [106][3500/5005] lr: 1.0000e-03 eta: 10:57:06 time: 0.2380 data_time: 0.0039 loss: 0.8845 03/06 13:12:32 - mmengine - INFO - Epoch(train) [106][3600/5005] lr: 1.0000e-03 eta: 10:56:43 time: 0.2266 data_time: 0.0037 loss: 0.8715 03/06 13:12:55 - mmengine - INFO - Epoch(train) [106][3700/5005] lr: 1.0000e-03 eta: 10:56:20 time: 0.2255 data_time: 0.0040 loss: 0.9840 03/06 13:13:18 - mmengine - INFO - Epoch(train) [106][3800/5005] lr: 1.0000e-03 eta: 10:55:57 time: 0.2249 data_time: 0.0037 loss: 0.7494 03/06 13:13:41 - mmengine - INFO - Epoch(train) [106][3900/5005] lr: 1.0000e-03 eta: 10:55:34 time: 0.2240 data_time: 0.0038 loss: 0.8673 03/06 13:14:04 - mmengine - INFO - Epoch(train) [106][4000/5005] lr: 1.0000e-03 eta: 10:55:11 time: 0.2274 data_time: 0.0038 loss: 0.9082 03/06 13:14:28 - mmengine - INFO - Epoch(train) [106][4100/5005] lr: 1.0000e-03 eta: 10:54:48 time: 0.2275 data_time: 0.0041 loss: 0.8489 03/06 13:14:51 - mmengine - INFO - Epoch(train) [106][4200/5005] lr: 1.0000e-03 eta: 10:54:25 time: 0.2287 data_time: 0.0038 loss: 0.9902 03/06 13:15:15 - mmengine - INFO - Epoch(train) [106][4300/5005] lr: 1.0000e-03 eta: 10:54:02 time: 0.2298 data_time: 0.0039 loss: 0.8641 03/06 13:15:38 - mmengine - INFO - Epoch(train) [106][4400/5005] lr: 1.0000e-03 eta: 10:53:39 time: 0.2260 data_time: 0.0034 loss: 0.8805 03/06 13:15:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:16:01 - mmengine - INFO - Epoch(train) [106][4500/5005] lr: 1.0000e-03 eta: 10:53:16 time: 0.2255 data_time: 0.0037 loss: 0.8367 03/06 13:16:24 - mmengine - INFO - Epoch(train) [106][4600/5005] lr: 1.0000e-03 eta: 10:52:53 time: 0.2277 data_time: 0.0042 loss: 0.8446 03/06 13:16:48 - mmengine - INFO - Epoch(train) [106][4700/5005] lr: 1.0000e-03 eta: 10:52:30 time: 0.2290 data_time: 0.0035 loss: 1.0044 03/06 13:17:10 - mmengine - INFO - Epoch(train) [106][4800/5005] lr: 1.0000e-03 eta: 10:52:07 time: 0.2281 data_time: 0.0036 loss: 0.8260 03/06 13:17:35 - mmengine - INFO - Epoch(train) [106][4900/5005] lr: 1.0000e-03 eta: 10:51:45 time: 0.2918 data_time: 0.0035 loss: 1.0108 03/06 13:18:03 - mmengine - INFO - Epoch(train) [106][5000/5005] lr: 1.0000e-03 eta: 10:51:24 time: 0.2803 data_time: 0.0035 loss: 0.8562 03/06 13:18:05 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:18:07 - mmengine - INFO - Saving checkpoint at 106 epochs 03/06 13:18:22 - mmengine - INFO - Epoch(val) [106][100/196] eta: 0:00:13 time: 0.0201 data_time: 0.0005 03/06 13:18:36 - mmengine - INFO - Epoch(val) [106][196/196] accuracy/top1: 77.0320 accuracy/top5: 93.4620 03/06 13:19:09 - mmengine - INFO - Epoch(train) [107][ 100/5005] lr: 1.0000e-03 eta: 10:51:03 time: 0.3030 data_time: 0.0042 loss: 0.9255 03/06 13:19:34 - mmengine - INFO - Epoch(train) [107][ 200/5005] lr: 1.0000e-03 eta: 10:50:40 time: 0.2315 data_time: 0.0041 loss: 0.7850 03/06 13:19:57 - mmengine - INFO - Epoch(train) [107][ 300/5005] lr: 1.0000e-03 eta: 10:50:17 time: 0.2315 data_time: 0.0035 loss: 0.9027 03/06 13:20:20 - mmengine - INFO - Epoch(train) [107][ 400/5005] lr: 1.0000e-03 eta: 10:49:54 time: 0.2313 data_time: 0.0033 loss: 0.8288 03/06 13:20:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:20:44 - mmengine - INFO - Epoch(train) [107][ 500/5005] lr: 1.0000e-03 eta: 10:49:31 time: 0.2495 data_time: 0.0038 loss: 0.9239 03/06 13:21:07 - mmengine - INFO - Epoch(train) [107][ 600/5005] lr: 1.0000e-03 eta: 10:49:08 time: 0.2480 data_time: 0.0037 loss: 0.9415 03/06 13:21:31 - mmengine - INFO - Epoch(train) [107][ 700/5005] lr: 1.0000e-03 eta: 10:48:46 time: 0.2267 data_time: 0.0034 loss: 1.0865 03/06 13:21:54 - mmengine - INFO - Epoch(train) [107][ 800/5005] lr: 1.0000e-03 eta: 10:48:22 time: 0.2292 data_time: 0.0038 loss: 0.8208 03/06 13:22:17 - mmengine - INFO - Epoch(train) [107][ 900/5005] lr: 1.0000e-03 eta: 10:48:00 time: 0.2300 data_time: 0.0041 loss: 0.7899 03/06 13:22:41 - mmengine - INFO - Epoch(train) [107][1000/5005] lr: 1.0000e-03 eta: 10:47:37 time: 0.2603 data_time: 0.0034 loss: 0.7271 03/06 13:23:04 - mmengine - INFO - Epoch(train) [107][1100/5005] lr: 1.0000e-03 eta: 10:47:14 time: 0.2302 data_time: 0.0039 loss: 0.8015 03/06 13:23:27 - mmengine - INFO - Epoch(train) [107][1200/5005] lr: 1.0000e-03 eta: 10:46:51 time: 0.2260 data_time: 0.0036 loss: 0.7812 03/06 13:23:50 - mmengine - INFO - Epoch(train) [107][1300/5005] lr: 1.0000e-03 eta: 10:46:28 time: 0.2253 data_time: 0.0036 loss: 0.9316 03/06 13:24:14 - mmengine - INFO - Epoch(train) [107][1400/5005] lr: 1.0000e-03 eta: 10:46:05 time: 0.2296 data_time: 0.0036 loss: 0.9096 03/06 13:24:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:24:37 - mmengine - INFO - Epoch(train) [107][1500/5005] lr: 1.0000e-03 eta: 10:45:42 time: 0.2311 data_time: 0.0039 loss: 0.7731 03/06 13:25:00 - mmengine - INFO - Epoch(train) [107][1600/5005] lr: 1.0000e-03 eta: 10:45:19 time: 0.2251 data_time: 0.0039 loss: 0.8110 03/06 13:25:23 - mmengine - INFO - Epoch(train) [107][1700/5005] lr: 1.0000e-03 eta: 10:44:56 time: 0.2276 data_time: 0.0042 loss: 0.9345 03/06 13:25:47 - mmengine - INFO - Epoch(train) [107][1800/5005] lr: 1.0000e-03 eta: 10:44:33 time: 0.2290 data_time: 0.0039 loss: 0.7616 03/06 13:26:10 - mmengine - INFO - Epoch(train) [107][1900/5005] lr: 1.0000e-03 eta: 10:44:10 time: 0.2320 data_time: 0.0038 loss: 0.8667 03/06 13:26:33 - mmengine - INFO - Epoch(train) [107][2000/5005] lr: 1.0000e-03 eta: 10:43:47 time: 0.2276 data_time: 0.0036 loss: 0.9555 03/06 13:26:56 - mmengine - INFO - Epoch(train) [107][2100/5005] lr: 1.0000e-03 eta: 10:43:24 time: 0.2263 data_time: 0.0040 loss: 0.9361 03/06 13:27:19 - mmengine - INFO - Epoch(train) [107][2200/5005] lr: 1.0000e-03 eta: 10:43:01 time: 0.2464 data_time: 0.0036 loss: 1.0112 03/06 13:27:43 - mmengine - INFO - Epoch(train) [107][2300/5005] lr: 1.0000e-03 eta: 10:42:38 time: 0.2259 data_time: 0.0036 loss: 0.8794 03/06 13:28:06 - mmengine - INFO - Epoch(train) [107][2400/5005] lr: 1.0000e-03 eta: 10:42:15 time: 0.2263 data_time: 0.0036 loss: 0.8048 03/06 13:28:22 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:28:29 - mmengine - INFO - Epoch(train) [107][2500/5005] lr: 1.0000e-03 eta: 10:41:52 time: 0.2324 data_time: 0.0036 loss: 0.9650 03/06 13:28:52 - mmengine - INFO - Epoch(train) [107][2600/5005] lr: 1.0000e-03 eta: 10:41:29 time: 0.2250 data_time: 0.0035 loss: 0.9324 03/06 13:29:15 - mmengine - INFO - Epoch(train) [107][2700/5005] lr: 1.0000e-03 eta: 10:41:06 time: 0.2283 data_time: 0.0036 loss: 0.7851 03/06 13:29:38 - mmengine - INFO - Epoch(train) [107][2800/5005] lr: 1.0000e-03 eta: 10:40:43 time: 0.2259 data_time: 0.0041 loss: 0.8547 03/06 13:30:01 - mmengine - INFO - Epoch(train) [107][2900/5005] lr: 1.0000e-03 eta: 10:40:20 time: 0.2257 data_time: 0.0035 loss: 0.9003 03/06 13:30:25 - mmengine - INFO - Epoch(train) [107][3000/5005] lr: 1.0000e-03 eta: 10:39:57 time: 0.2262 data_time: 0.0037 loss: 0.8744 03/06 13:30:48 - mmengine - INFO - Epoch(train) [107][3100/5005] lr: 1.0000e-03 eta: 10:39:34 time: 0.2272 data_time: 0.0037 loss: 0.8162 03/06 13:31:11 - mmengine - INFO - Epoch(train) [107][3200/5005] lr: 1.0000e-03 eta: 10:39:12 time: 0.2284 data_time: 0.0037 loss: 0.9007 03/06 13:31:34 - mmengine - INFO - Epoch(train) [107][3300/5005] lr: 1.0000e-03 eta: 10:38:49 time: 0.2276 data_time: 0.0041 loss: 0.8069 03/06 13:31:58 - mmengine - INFO - Epoch(train) [107][3400/5005] lr: 1.0000e-03 eta: 10:38:26 time: 0.2451 data_time: 0.0037 loss: 0.9176 03/06 13:32:14 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:32:21 - mmengine - INFO - Epoch(train) [107][3500/5005] lr: 1.0000e-03 eta: 10:38:03 time: 0.2279 data_time: 0.0044 loss: 0.9171 03/06 13:32:44 - mmengine - INFO - Epoch(train) [107][3600/5005] lr: 1.0000e-03 eta: 10:37:40 time: 0.2298 data_time: 0.0039 loss: 0.9418 03/06 13:33:07 - mmengine - INFO - Epoch(train) [107][3700/5005] lr: 1.0000e-03 eta: 10:37:17 time: 0.2329 data_time: 0.0038 loss: 0.8348 03/06 13:33:30 - mmengine - INFO - Epoch(train) [107][3800/5005] lr: 1.0000e-03 eta: 10:36:54 time: 0.2250 data_time: 0.0041 loss: 1.0567 03/06 13:33:53 - mmengine - INFO - Epoch(train) [107][3900/5005] lr: 1.0000e-03 eta: 10:36:31 time: 0.2254 data_time: 0.0041 loss: 0.7286 03/06 13:34:17 - mmengine - INFO - Epoch(train) [107][4000/5005] lr: 1.0000e-03 eta: 10:36:08 time: 0.2308 data_time: 0.0035 loss: 1.0081 03/06 13:34:40 - mmengine - INFO - Epoch(train) [107][4100/5005] lr: 1.0000e-03 eta: 10:35:45 time: 0.2279 data_time: 0.0036 loss: 0.9332 03/06 13:35:03 - mmengine - INFO - Epoch(train) [107][4200/5005] lr: 1.0000e-03 eta: 10:35:22 time: 0.2289 data_time: 0.0037 loss: 0.8665 03/06 13:35:26 - mmengine - INFO - Epoch(train) [107][4300/5005] lr: 1.0000e-03 eta: 10:34:59 time: 0.2396 data_time: 0.0037 loss: 0.9816 03/06 13:35:50 - mmengine - INFO - Epoch(train) [107][4400/5005] lr: 1.0000e-03 eta: 10:34:36 time: 0.2234 data_time: 0.0041 loss: 0.9359 03/06 13:36:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:36:13 - mmengine - INFO - Epoch(train) [107][4500/5005] lr: 1.0000e-03 eta: 10:34:13 time: 0.2276 data_time: 0.0038 loss: 0.8937 03/06 13:36:36 - mmengine - INFO - Epoch(train) [107][4600/5005] lr: 1.0000e-03 eta: 10:33:50 time: 0.2287 data_time: 0.0039 loss: 1.0308 03/06 13:36:59 - mmengine - INFO - Epoch(train) [107][4700/5005] lr: 1.0000e-03 eta: 10:33:27 time: 0.2241 data_time: 0.0037 loss: 0.7133 03/06 13:37:22 - mmengine - INFO - Epoch(train) [107][4800/5005] lr: 1.0000e-03 eta: 10:33:04 time: 0.2247 data_time: 0.0037 loss: 0.9313 03/06 13:37:46 - mmengine - INFO - Epoch(train) [107][4900/5005] lr: 1.0000e-03 eta: 10:32:41 time: 0.2849 data_time: 0.0036 loss: 0.8701 03/06 13:38:15 - mmengine - INFO - Epoch(train) [107][5000/5005] lr: 1.0000e-03 eta: 10:32:20 time: 0.2856 data_time: 0.0035 loss: 1.0157 03/06 13:38:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:38:19 - mmengine - INFO - Saving checkpoint at 107 epochs 03/06 13:38:35 - mmengine - INFO - Epoch(val) [107][100/196] eta: 0:00:13 time: 0.0186 data_time: 0.0002 03/06 13:38:48 - mmengine - INFO - Epoch(val) [107][196/196] accuracy/top1: 77.0200 accuracy/top5: 93.4960 03/06 13:39:21 - mmengine - INFO - Epoch(train) [108][ 100/5005] lr: 1.0000e-03 eta: 10:31:59 time: 0.2302 data_time: 0.0046 loss: 0.9510 03/06 13:39:44 - mmengine - INFO - Epoch(train) [108][ 200/5005] lr: 1.0000e-03 eta: 10:31:36 time: 0.2244 data_time: 0.0050 loss: 0.8938 03/06 13:40:08 - mmengine - INFO - Epoch(train) [108][ 300/5005] lr: 1.0000e-03 eta: 10:31:13 time: 0.2305 data_time: 0.0047 loss: 1.0583 03/06 13:40:31 - mmengine - INFO - Epoch(train) [108][ 400/5005] lr: 1.0000e-03 eta: 10:30:50 time: 0.2261 data_time: 0.0045 loss: 0.8195 03/06 13:40:46 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:40:54 - mmengine - INFO - Epoch(train) [108][ 500/5005] lr: 1.0000e-03 eta: 10:30:27 time: 0.2487 data_time: 0.0041 loss: 0.7627 03/06 13:41:18 - mmengine - INFO - Epoch(train) [108][ 600/5005] lr: 1.0000e-03 eta: 10:30:04 time: 0.2260 data_time: 0.0038 loss: 0.8413 03/06 13:41:42 - mmengine - INFO - Epoch(train) [108][ 700/5005] lr: 1.0000e-03 eta: 10:29:42 time: 0.2280 data_time: 0.0041 loss: 0.7980 03/06 13:42:05 - mmengine - INFO - Epoch(train) [108][ 800/5005] lr: 1.0000e-03 eta: 10:29:19 time: 0.2498 data_time: 0.0041 loss: 0.8856 03/06 13:42:28 - mmengine - INFO - Epoch(train) [108][ 900/5005] lr: 1.0000e-03 eta: 10:28:56 time: 0.2308 data_time: 0.0040 loss: 0.8334 03/06 13:42:52 - mmengine - INFO - Epoch(train) [108][1000/5005] lr: 1.0000e-03 eta: 10:28:33 time: 0.2333 data_time: 0.0042 loss: 0.8737 03/06 13:43:15 - mmengine - INFO - Epoch(train) [108][1100/5005] lr: 1.0000e-03 eta: 10:28:10 time: 0.2273 data_time: 0.0040 loss: 0.8554 03/06 13:43:38 - mmengine - INFO - Epoch(train) [108][1200/5005] lr: 1.0000e-03 eta: 10:27:47 time: 0.2294 data_time: 0.0043 loss: 0.8785 03/06 13:44:02 - mmengine - INFO - Epoch(train) [108][1300/5005] lr: 1.0000e-03 eta: 10:27:24 time: 0.2278 data_time: 0.0041 loss: 1.1026 03/06 13:44:25 - mmengine - INFO - Epoch(train) [108][1400/5005] lr: 1.0000e-03 eta: 10:27:01 time: 0.2290 data_time: 0.0041 loss: 0.7764 03/06 13:44:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:44:48 - mmengine - INFO - Epoch(train) [108][1500/5005] lr: 1.0000e-03 eta: 10:26:38 time: 0.2272 data_time: 0.0038 loss: 0.8253 03/06 13:45:11 - mmengine - INFO - Epoch(train) [108][1600/5005] lr: 1.0000e-03 eta: 10:26:15 time: 0.2300 data_time: 0.0042 loss: 0.7999 03/06 13:45:35 - mmengine - INFO - Epoch(train) [108][1700/5005] lr: 1.0000e-03 eta: 10:25:52 time: 0.2274 data_time: 0.0043 loss: 0.8920 03/06 13:45:58 - mmengine - INFO - Epoch(train) [108][1800/5005] lr: 1.0000e-03 eta: 10:25:29 time: 0.2277 data_time: 0.0041 loss: 1.0210 03/06 13:46:21 - mmengine - INFO - Epoch(train) [108][1900/5005] lr: 1.0000e-03 eta: 10:25:06 time: 0.2290 data_time: 0.0042 loss: 0.9354 03/06 13:46:44 - mmengine - INFO - Epoch(train) [108][2000/5005] lr: 1.0000e-03 eta: 10:24:43 time: 0.2298 data_time: 0.0043 loss: 0.8242 03/06 13:47:08 - mmengine - INFO - Epoch(train) [108][2100/5005] lr: 1.0000e-03 eta: 10:24:20 time: 0.2417 data_time: 0.0046 loss: 0.8057 03/06 13:47:31 - mmengine - INFO - Epoch(train) [108][2200/5005] lr: 1.0000e-03 eta: 10:23:57 time: 0.2499 data_time: 0.0043 loss: 0.9074 03/06 13:47:55 - mmengine - INFO - Epoch(train) [108][2300/5005] lr: 1.0000e-03 eta: 10:23:34 time: 0.2321 data_time: 0.0039 loss: 0.8274 03/06 13:48:18 - mmengine - INFO - Epoch(train) [108][2400/5005] lr: 1.0000e-03 eta: 10:23:11 time: 0.2255 data_time: 0.0038 loss: 0.9046 03/06 13:48:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:48:41 - mmengine - INFO - Epoch(train) [108][2500/5005] lr: 1.0000e-03 eta: 10:22:48 time: 0.2269 data_time: 0.0038 loss: 0.8896 03/06 13:49:04 - mmengine - INFO - Epoch(train) [108][2600/5005] lr: 1.0000e-03 eta: 10:22:26 time: 0.2341 data_time: 0.0041 loss: 0.8919 03/06 13:49:27 - mmengine - INFO - Epoch(train) [108][2700/5005] lr: 1.0000e-03 eta: 10:22:03 time: 0.2289 data_time: 0.0039 loss: 0.9348 03/06 13:49:51 - mmengine - INFO - Epoch(train) [108][2800/5005] lr: 1.0000e-03 eta: 10:21:40 time: 0.2298 data_time: 0.0040 loss: 0.8080 03/06 13:50:14 - mmengine - INFO - Epoch(train) [108][2900/5005] lr: 1.0000e-03 eta: 10:21:17 time: 0.2289 data_time: 0.0040 loss: 0.7202 03/06 13:50:37 - mmengine - INFO - Epoch(train) [108][3000/5005] lr: 1.0000e-03 eta: 10:20:54 time: 0.2257 data_time: 0.0042 loss: 0.7876 03/06 13:51:01 - mmengine - INFO - Epoch(train) [108][3100/5005] lr: 1.0000e-03 eta: 10:20:31 time: 0.2256 data_time: 0.0048 loss: 0.7986 03/06 13:51:24 - mmengine - INFO - Epoch(train) [108][3200/5005] lr: 1.0000e-03 eta: 10:20:08 time: 0.2288 data_time: 0.0041 loss: 1.0074 03/06 13:51:47 - mmengine - INFO - Epoch(train) [108][3300/5005] lr: 1.0000e-03 eta: 10:19:45 time: 0.2333 data_time: 0.0044 loss: 0.7323 03/06 13:52:10 - mmengine - INFO - Epoch(train) [108][3400/5005] lr: 1.0000e-03 eta: 10:19:22 time: 0.2277 data_time: 0.0041 loss: 0.7982 03/06 13:52:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:52:34 - mmengine - INFO - Epoch(train) [108][3500/5005] lr: 1.0000e-03 eta: 10:18:59 time: 0.2303 data_time: 0.0043 loss: 0.9053 03/06 13:52:57 - mmengine - INFO - Epoch(train) [108][3600/5005] lr: 1.0000e-03 eta: 10:18:36 time: 0.2514 data_time: 0.0036 loss: 0.8171 03/06 13:53:21 - mmengine - INFO - Epoch(train) [108][3700/5005] lr: 1.0000e-03 eta: 10:18:13 time: 0.2266 data_time: 0.0041 loss: 0.7522 03/06 13:53:44 - mmengine - INFO - Epoch(train) [108][3800/5005] lr: 1.0000e-03 eta: 10:17:50 time: 0.2305 data_time: 0.0039 loss: 0.9015 03/06 13:54:07 - mmengine - INFO - Epoch(train) [108][3900/5005] lr: 1.0000e-03 eta: 10:17:27 time: 0.2252 data_time: 0.0043 loss: 1.0433 03/06 13:54:31 - mmengine - INFO - Epoch(train) [108][4000/5005] lr: 1.0000e-03 eta: 10:17:04 time: 0.2277 data_time: 0.0043 loss: 0.8957 03/06 13:54:54 - mmengine - INFO - Epoch(train) [108][4100/5005] lr: 1.0000e-03 eta: 10:16:41 time: 0.2305 data_time: 0.0042 loss: 0.9825 03/06 13:55:17 - mmengine - INFO - Epoch(train) [108][4200/5005] lr: 1.0000e-03 eta: 10:16:18 time: 0.2290 data_time: 0.0043 loss: 0.9930 03/06 13:55:41 - mmengine - INFO - Epoch(train) [108][4300/5005] lr: 1.0000e-03 eta: 10:15:56 time: 0.2273 data_time: 0.0040 loss: 0.9144 03/06 13:56:04 - mmengine - INFO - Epoch(train) [108][4400/5005] lr: 1.0000e-03 eta: 10:15:33 time: 0.2280 data_time: 0.0049 loss: 0.9492 03/06 13:56:19 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:56:27 - mmengine - INFO - Epoch(train) [108][4500/5005] lr: 1.0000e-03 eta: 10:15:10 time: 0.2348 data_time: 0.0039 loss: 0.8597 03/06 13:56:50 - mmengine - INFO - Epoch(train) [108][4600/5005] lr: 1.0000e-03 eta: 10:14:47 time: 0.2293 data_time: 0.0040 loss: 1.0503 03/06 13:57:14 - mmengine - INFO - Epoch(train) [108][4700/5005] lr: 1.0000e-03 eta: 10:14:24 time: 0.2320 data_time: 0.0040 loss: 0.9188 03/06 13:57:37 - mmengine - INFO - Epoch(train) [108][4800/5005] lr: 1.0000e-03 eta: 10:14:01 time: 0.2296 data_time: 0.0041 loss: 1.0965 03/06 13:58:01 - mmengine - INFO - Epoch(train) [108][4900/5005] lr: 1.0000e-03 eta: 10:13:38 time: 0.2918 data_time: 0.0041 loss: 0.9206 03/06 13:58:30 - mmengine - INFO - Epoch(train) [108][5000/5005] lr: 1.0000e-03 eta: 10:13:17 time: 0.2839 data_time: 0.0039 loss: 0.7281 03/06 13:58:31 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 13:58:34 - mmengine - INFO - Saving checkpoint at 108 epochs 03/06 13:58:49 - mmengine - INFO - Epoch(val) [108][100/196] eta: 0:00:13 time: 0.0170 data_time: 0.0002 03/06 13:59:03 - mmengine - INFO - Epoch(val) [108][196/196] accuracy/top1: 76.9840 accuracy/top5: 93.4920 03/06 13:59:36 - mmengine - INFO - Epoch(train) [109][ 100/5005] lr: 1.0000e-03 eta: 10:12:55 time: 0.2263 data_time: 0.0055 loss: 0.7337 03/06 13:59:59 - mmengine - INFO - Epoch(train) [109][ 200/5005] lr: 1.0000e-03 eta: 10:12:32 time: 0.2340 data_time: 0.0041 loss: 0.7827 03/06 14:00:22 - mmengine - INFO - Epoch(train) [109][ 300/5005] lr: 1.0000e-03 eta: 10:12:09 time: 0.2304 data_time: 0.0053 loss: 0.9382 03/06 14:00:45 - mmengine - INFO - Epoch(train) [109][ 400/5005] lr: 1.0000e-03 eta: 10:11:46 time: 0.2269 data_time: 0.0045 loss: 0.9224 03/06 14:00:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:01:09 - mmengine - INFO - Epoch(train) [109][ 500/5005] lr: 1.0000e-03 eta: 10:11:24 time: 0.2412 data_time: 0.0042 loss: 0.9329 03/06 14:01:32 - mmengine - INFO - Epoch(train) [109][ 600/5005] lr: 1.0000e-03 eta: 10:11:01 time: 0.2485 data_time: 0.0052 loss: 0.9455 03/06 14:01:56 - mmengine - INFO - Epoch(train) [109][ 700/5005] lr: 1.0000e-03 eta: 10:10:38 time: 0.2319 data_time: 0.0045 loss: 0.9676 03/06 14:02:19 - mmengine - INFO - Epoch(train) [109][ 800/5005] lr: 1.0000e-03 eta: 10:10:15 time: 0.2290 data_time: 0.0043 loss: 0.9697 03/06 14:02:42 - mmengine - INFO - Epoch(train) [109][ 900/5005] lr: 1.0000e-03 eta: 10:09:52 time: 0.2262 data_time: 0.0046 loss: 0.9683 03/06 14:03:05 - mmengine - INFO - Epoch(train) [109][1000/5005] lr: 1.0000e-03 eta: 10:09:29 time: 0.2246 data_time: 0.0053 loss: 0.7940 03/06 14:03:29 - mmengine - INFO - Epoch(train) [109][1100/5005] lr: 1.0000e-03 eta: 10:09:06 time: 0.2282 data_time: 0.0041 loss: 0.8798 03/06 14:03:52 - mmengine - INFO - Epoch(train) [109][1200/5005] lr: 1.0000e-03 eta: 10:08:43 time: 0.2293 data_time: 0.0044 loss: 0.9520 03/06 14:04:15 - mmengine - INFO - Epoch(train) [109][1300/5005] lr: 1.0000e-03 eta: 10:08:20 time: 0.2255 data_time: 0.0043 loss: 0.9885 03/06 14:04:38 - mmengine - INFO - Epoch(train) [109][1400/5005] lr: 1.0000e-03 eta: 10:07:57 time: 0.2321 data_time: 0.0042 loss: 0.9159 03/06 14:04:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:05:02 - mmengine - INFO - Epoch(train) [109][1500/5005] lr: 1.0000e-03 eta: 10:07:34 time: 0.2319 data_time: 0.0044 loss: 0.6970 03/06 14:05:25 - mmengine - INFO - Epoch(train) [109][1600/5005] lr: 1.0000e-03 eta: 10:07:11 time: 0.2286 data_time: 0.0049 loss: 0.9233 03/06 14:05:48 - mmengine - INFO - Epoch(train) [109][1700/5005] lr: 1.0000e-03 eta: 10:06:48 time: 0.2276 data_time: 0.0043 loss: 0.8496 03/06 14:06:11 - mmengine - INFO - Epoch(train) [109][1800/5005] lr: 1.0000e-03 eta: 10:06:25 time: 0.2287 data_time: 0.0041 loss: 0.8727 03/06 14:06:35 - mmengine - INFO - Epoch(train) [109][1900/5005] lr: 1.0000e-03 eta: 10:06:02 time: 0.2296 data_time: 0.0041 loss: 0.8973 03/06 14:06:58 - mmengine - INFO - Epoch(train) [109][2000/5005] lr: 1.0000e-03 eta: 10:05:39 time: 0.2276 data_time: 0.0042 loss: 0.8667 03/06 14:07:21 - mmengine - INFO - Epoch(train) [109][2100/5005] lr: 1.0000e-03 eta: 10:05:16 time: 0.2298 data_time: 0.0044 loss: 0.9015 03/06 14:07:44 - mmengine - INFO - Epoch(train) [109][2200/5005] lr: 1.0000e-03 eta: 10:04:53 time: 0.2253 data_time: 0.0042 loss: 0.7503 03/06 14:08:08 - mmengine - INFO - Epoch(train) [109][2300/5005] lr: 1.0000e-03 eta: 10:04:30 time: 0.2266 data_time: 0.0043 loss: 0.8712 03/06 14:08:31 - mmengine - INFO - Epoch(train) [109][2400/5005] lr: 1.0000e-03 eta: 10:04:07 time: 0.2277 data_time: 0.0040 loss: 0.8169 03/06 14:08:45 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:08:54 - mmengine - INFO - Epoch(train) [109][2500/5005] lr: 1.0000e-03 eta: 10:03:44 time: 0.2260 data_time: 0.0042 loss: 0.9976 03/06 14:09:17 - mmengine - INFO - Epoch(train) [109][2600/5005] lr: 1.0000e-03 eta: 10:03:21 time: 0.2253 data_time: 0.0044 loss: 1.0049 03/06 14:09:41 - mmengine - INFO - Epoch(train) [109][2700/5005] lr: 1.0000e-03 eta: 10:02:58 time: 0.2281 data_time: 0.0043 loss: 0.8799 03/06 14:10:04 - mmengine - INFO - Epoch(train) [109][2800/5005] lr: 1.0000e-03 eta: 10:02:35 time: 0.2283 data_time: 0.0042 loss: 0.7659 03/06 14:10:27 - mmengine - INFO - Epoch(train) [109][2900/5005] lr: 1.0000e-03 eta: 10:02:12 time: 0.2269 data_time: 0.0043 loss: 0.8879 03/06 14:10:51 - mmengine - INFO - Epoch(train) [109][3000/5005] lr: 1.0000e-03 eta: 10:01:49 time: 0.2322 data_time: 0.0044 loss: 0.7881 03/06 14:11:14 - mmengine - INFO - Epoch(train) [109][3100/5005] lr: 1.0000e-03 eta: 10:01:27 time: 0.2268 data_time: 0.0044 loss: 0.9222 03/06 14:11:37 - mmengine - INFO - Epoch(train) [109][3200/5005] lr: 1.0000e-03 eta: 10:01:04 time: 0.2258 data_time: 0.0045 loss: 1.0052 03/06 14:12:00 - mmengine - INFO - Epoch(train) [109][3300/5005] lr: 1.0000e-03 eta: 10:00:41 time: 0.2318 data_time: 0.0043 loss: 0.9662 03/06 14:12:24 - mmengine - INFO - Epoch(train) [109][3400/5005] lr: 1.0000e-03 eta: 10:00:18 time: 0.2308 data_time: 0.0042 loss: 0.8058 03/06 14:12:38 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:12:47 - mmengine - INFO - Epoch(train) [109][3500/5005] lr: 1.0000e-03 eta: 9:59:55 time: 0.2278 data_time: 0.0045 loss: 0.8410 03/06 14:13:10 - mmengine - INFO - Epoch(train) [109][3600/5005] lr: 1.0000e-03 eta: 9:59:32 time: 0.2290 data_time: 0.0048 loss: 0.8643 03/06 14:13:33 - mmengine - INFO - Epoch(train) [109][3700/5005] lr: 1.0000e-03 eta: 9:59:09 time: 0.2279 data_time: 0.0041 loss: 0.9062 03/06 14:13:57 - mmengine - INFO - Epoch(train) [109][3800/5005] lr: 1.0000e-03 eta: 9:58:46 time: 0.2541 data_time: 0.0042 loss: 0.7478 03/06 14:14:20 - mmengine - INFO - Epoch(train) [109][3900/5005] lr: 1.0000e-03 eta: 9:58:23 time: 0.2252 data_time: 0.0044 loss: 0.8019 03/06 14:14:44 - mmengine - INFO - Epoch(train) [109][4000/5005] lr: 1.0000e-03 eta: 9:58:00 time: 0.2272 data_time: 0.0047 loss: 0.8917 03/06 14:15:07 - mmengine - INFO - Epoch(train) [109][4100/5005] lr: 1.0000e-03 eta: 9:57:37 time: 0.2296 data_time: 0.0045 loss: 0.7428 03/06 14:15:30 - mmengine - INFO - Epoch(train) [109][4200/5005] lr: 1.0000e-03 eta: 9:57:14 time: 0.2462 data_time: 0.0042 loss: 0.9174 03/06 14:15:54 - mmengine - INFO - Epoch(train) [109][4300/5005] lr: 1.0000e-03 eta: 9:56:51 time: 0.2268 data_time: 0.0042 loss: 0.9142 03/06 14:16:17 - mmengine - INFO - Epoch(train) [109][4400/5005] lr: 1.0000e-03 eta: 9:56:28 time: 0.2279 data_time: 0.0044 loss: 0.7527 03/06 14:16:31 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:16:40 - mmengine - INFO - Epoch(train) [109][4500/5005] lr: 1.0000e-03 eta: 9:56:05 time: 0.2279 data_time: 0.0044 loss: 0.7504 03/06 14:17:03 - mmengine - INFO - Epoch(train) [109][4600/5005] lr: 1.0000e-03 eta: 9:55:42 time: 0.2340 data_time: 0.0039 loss: 0.7322 03/06 14:17:27 - mmengine - INFO - Epoch(train) [109][4700/5005] lr: 1.0000e-03 eta: 9:55:19 time: 0.2240 data_time: 0.0043 loss: 0.9210 03/06 14:17:50 - mmengine - INFO - Epoch(train) [109][4800/5005] lr: 1.0000e-03 eta: 9:54:56 time: 0.2245 data_time: 0.0044 loss: 0.9993 03/06 14:18:14 - mmengine - INFO - Epoch(train) [109][4900/5005] lr: 1.0000e-03 eta: 9:54:34 time: 0.2812 data_time: 0.0040 loss: 1.1099 03/06 14:18:43 - mmengine - INFO - Epoch(train) [109][5000/5005] lr: 1.0000e-03 eta: 9:54:12 time: 0.2870 data_time: 0.0050 loss: 0.9676 03/06 14:18:45 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:18:47 - mmengine - INFO - Saving checkpoint at 109 epochs 03/06 14:19:03 - mmengine - INFO - Epoch(val) [109][100/196] eta: 0:00:13 time: 0.0184 data_time: 0.0003 03/06 14:19:17 - mmengine - INFO - Epoch(val) [109][196/196] accuracy/top1: 77.0320 accuracy/top5: 93.5860 03/06 14:19:49 - mmengine - INFO - Epoch(train) [110][ 100/5005] lr: 1.0000e-03 eta: 9:53:51 time: 0.2514 data_time: 0.0047 loss: 0.8302 03/06 14:20:12 - mmengine - INFO - Epoch(train) [110][ 200/5005] lr: 1.0000e-03 eta: 9:53:28 time: 0.2291 data_time: 0.0052 loss: 0.8308 03/06 14:20:36 - mmengine - INFO - Epoch(train) [110][ 300/5005] lr: 1.0000e-03 eta: 9:53:05 time: 0.2280 data_time: 0.0047 loss: 0.9023 03/06 14:20:59 - mmengine - INFO - Epoch(train) [110][ 400/5005] lr: 1.0000e-03 eta: 9:52:42 time: 0.2265 data_time: 0.0047 loss: 0.9280 03/06 14:21:12 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:21:22 - mmengine - INFO - Epoch(train) [110][ 500/5005] lr: 1.0000e-03 eta: 9:52:19 time: 0.2264 data_time: 0.0041 loss: 0.8223 03/06 14:21:46 - mmengine - INFO - Epoch(train) [110][ 600/5005] lr: 1.0000e-03 eta: 9:51:56 time: 0.2285 data_time: 0.0046 loss: 0.7817 03/06 14:22:09 - mmengine - INFO - Epoch(train) [110][ 700/5005] lr: 1.0000e-03 eta: 9:51:33 time: 0.2246 data_time: 0.0041 loss: 0.8491 03/06 14:22:32 - mmengine - INFO - Epoch(train) [110][ 800/5005] lr: 1.0000e-03 eta: 9:51:10 time: 0.2262 data_time: 0.0042 loss: 0.7627 03/06 14:22:55 - mmengine - INFO - Epoch(train) [110][ 900/5005] lr: 1.0000e-03 eta: 9:50:47 time: 0.2271 data_time: 0.0037 loss: 0.9630 03/06 14:23:19 - mmengine - INFO - Epoch(train) [110][1000/5005] lr: 1.0000e-03 eta: 9:50:24 time: 0.2331 data_time: 0.0041 loss: 0.8452 03/06 14:23:42 - mmengine - INFO - Epoch(train) [110][1100/5005] lr: 1.0000e-03 eta: 9:50:01 time: 0.2466 data_time: 0.0043 loss: 0.9553 03/06 14:24:05 - mmengine - INFO - Epoch(train) [110][1200/5005] lr: 1.0000e-03 eta: 9:49:38 time: 0.2270 data_time: 0.0041 loss: 0.7605 03/06 14:24:29 - mmengine - INFO - Epoch(train) [110][1300/5005] lr: 1.0000e-03 eta: 9:49:15 time: 0.2429 data_time: 0.0040 loss: 0.8598 03/06 14:24:52 - mmengine - INFO - Epoch(train) [110][1400/5005] lr: 1.0000e-03 eta: 9:48:52 time: 0.2261 data_time: 0.0039 loss: 0.9881 03/06 14:25:05 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:25:15 - mmengine - INFO - Epoch(train) [110][1500/5005] lr: 1.0000e-03 eta: 9:48:29 time: 0.2271 data_time: 0.0040 loss: 0.9781 03/06 14:25:38 - mmengine - INFO - Epoch(train) [110][1600/5005] lr: 1.0000e-03 eta: 9:48:06 time: 0.2250 data_time: 0.0045 loss: 1.0472 03/06 14:26:01 - mmengine - INFO - Epoch(train) [110][1700/5005] lr: 1.0000e-03 eta: 9:47:43 time: 0.2276 data_time: 0.0040 loss: 0.9110 03/06 14:26:25 - mmengine - INFO - Epoch(train) [110][1800/5005] lr: 1.0000e-03 eta: 9:47:20 time: 0.2259 data_time: 0.0043 loss: 0.8616 03/06 14:26:48 - mmengine - INFO - Epoch(train) [110][1900/5005] lr: 1.0000e-03 eta: 9:46:57 time: 0.2314 data_time: 0.0040 loss: 0.9188 03/06 14:27:11 - mmengine - INFO - Epoch(train) [110][2000/5005] lr: 1.0000e-03 eta: 9:46:35 time: 0.2269 data_time: 0.0045 loss: 0.8885 03/06 14:27:34 - mmengine - INFO - Epoch(train) [110][2100/5005] lr: 1.0000e-03 eta: 9:46:12 time: 0.2272 data_time: 0.0041 loss: 0.8764 03/06 14:27:58 - mmengine - INFO - Epoch(train) [110][2200/5005] lr: 1.0000e-03 eta: 9:45:49 time: 0.2301 data_time: 0.0040 loss: 0.8167 03/06 14:28:21 - mmengine - INFO - Epoch(train) [110][2300/5005] lr: 1.0000e-03 eta: 9:45:26 time: 0.2276 data_time: 0.0042 loss: 0.8095 03/06 14:28:44 - mmengine - INFO - Epoch(train) [110][2400/5005] lr: 1.0000e-03 eta: 9:45:03 time: 0.2481 data_time: 0.0042 loss: 0.8106 03/06 14:28:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:29:08 - mmengine - INFO - Epoch(train) [110][2500/5005] lr: 1.0000e-03 eta: 9:44:40 time: 0.2264 data_time: 0.0038 loss: 0.8442 03/06 14:29:31 - mmengine - INFO - Epoch(train) [110][2600/5005] lr: 1.0000e-03 eta: 9:44:17 time: 0.2344 data_time: 0.0044 loss: 0.7044 03/06 14:29:54 - mmengine - INFO - Epoch(train) [110][2700/5005] lr: 1.0000e-03 eta: 9:43:54 time: 0.2257 data_time: 0.0043 loss: 1.0175 03/06 14:30:17 - mmengine - INFO - Epoch(train) [110][2800/5005] lr: 1.0000e-03 eta: 9:43:31 time: 0.2492 data_time: 0.0041 loss: 0.8842 03/06 14:30:41 - mmengine - INFO - Epoch(train) [110][2900/5005] lr: 1.0000e-03 eta: 9:43:08 time: 0.2294 data_time: 0.0038 loss: 0.8398 03/06 14:31:04 - mmengine - INFO - Epoch(train) [110][3000/5005] lr: 1.0000e-03 eta: 9:42:45 time: 0.2312 data_time: 0.0037 loss: 0.8841 03/06 14:31:27 - mmengine - INFO - Epoch(train) [110][3100/5005] lr: 1.0000e-03 eta: 9:42:22 time: 0.2241 data_time: 0.0041 loss: 0.9204 03/06 14:31:50 - mmengine - INFO - Epoch(train) [110][3200/5005] lr: 1.0000e-03 eta: 9:41:59 time: 0.2290 data_time: 0.0040 loss: 0.8984 03/06 14:32:14 - mmengine - INFO - Epoch(train) [110][3300/5005] lr: 1.0000e-03 eta: 9:41:36 time: 0.2263 data_time: 0.0043 loss: 0.7828 03/06 14:32:37 - mmengine - INFO - Epoch(train) [110][3400/5005] lr: 1.0000e-03 eta: 9:41:13 time: 0.2295 data_time: 0.0039 loss: 0.9336 03/06 14:32:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:33:00 - mmengine - INFO - Epoch(train) [110][3500/5005] lr: 1.0000e-03 eta: 9:40:50 time: 0.2290 data_time: 0.0040 loss: 0.8298 03/06 14:33:24 - mmengine - INFO - Epoch(train) [110][3600/5005] lr: 1.0000e-03 eta: 9:40:27 time: 0.2309 data_time: 0.0041 loss: 0.8862 03/06 14:33:47 - mmengine - INFO - Epoch(train) [110][3700/5005] lr: 1.0000e-03 eta: 9:40:04 time: 0.2276 data_time: 0.0037 loss: 0.7042 03/06 14:34:10 - mmengine - INFO - Epoch(train) [110][3800/5005] lr: 1.0000e-03 eta: 9:39:41 time: 0.2256 data_time: 0.0041 loss: 0.8160 03/06 14:34:33 - mmengine - INFO - Epoch(train) [110][3900/5005] lr: 1.0000e-03 eta: 9:39:18 time: 0.2248 data_time: 0.0042 loss: 1.1772 03/06 14:34:57 - mmengine - INFO - Epoch(train) [110][4000/5005] lr: 1.0000e-03 eta: 9:38:55 time: 0.2295 data_time: 0.0041 loss: 1.1041 03/06 14:35:20 - mmengine - INFO - Epoch(train) [110][4100/5005] lr: 1.0000e-03 eta: 9:38:32 time: 0.2244 data_time: 0.0041 loss: 0.9252 03/06 14:35:43 - mmengine - INFO - Epoch(train) [110][4200/5005] lr: 1.0000e-03 eta: 9:38:09 time: 0.2284 data_time: 0.0038 loss: 0.8439 03/06 14:36:07 - mmengine - INFO - Epoch(train) [110][4300/5005] lr: 1.0000e-03 eta: 9:37:46 time: 0.2249 data_time: 0.0041 loss: 0.9897 03/06 14:36:30 - mmengine - INFO - Epoch(train) [110][4400/5005] lr: 1.0000e-03 eta: 9:37:24 time: 0.2274 data_time: 0.0042 loss: 0.8593 03/06 14:36:43 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:36:54 - mmengine - INFO - Epoch(train) [110][4500/5005] lr: 1.0000e-03 eta: 9:37:01 time: 0.2310 data_time: 0.0039 loss: 1.0724 03/06 14:37:17 - mmengine - INFO - Epoch(train) [110][4600/5005] lr: 1.0000e-03 eta: 9:36:38 time: 0.2308 data_time: 0.0039 loss: 0.8826 03/06 14:37:40 - mmengine - INFO - Epoch(train) [110][4700/5005] lr: 1.0000e-03 eta: 9:36:15 time: 0.2254 data_time: 0.0039 loss: 0.8172 03/06 14:38:03 - mmengine - INFO - Epoch(train) [110][4800/5005] lr: 1.0000e-03 eta: 9:35:52 time: 0.2265 data_time: 0.0040 loss: 0.8136 03/06 14:38:27 - mmengine - INFO - Epoch(train) [110][4900/5005] lr: 1.0000e-03 eta: 9:35:29 time: 0.2836 data_time: 0.0037 loss: 0.9029 03/06 14:38:56 - mmengine - INFO - Epoch(train) [110][5000/5005] lr: 1.0000e-03 eta: 9:35:08 time: 0.2867 data_time: 0.0041 loss: 0.8484 03/06 14:38:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:39:00 - mmengine - INFO - Saving checkpoint at 110 epochs 03/06 14:39:16 - mmengine - INFO - Epoch(val) [110][100/196] eta: 0:00:13 time: 0.0172 data_time: 0.0002 03/06 14:39:29 - mmengine - INFO - Epoch(val) [110][196/196] accuracy/top1: 76.9480 accuracy/top5: 93.5360 03/06 14:40:02 - mmengine - INFO - Epoch(train) [111][ 100/5005] lr: 1.0000e-03 eta: 9:34:46 time: 0.2288 data_time: 0.0045 loss: 1.0021 03/06 14:40:26 - mmengine - INFO - Epoch(train) [111][ 200/5005] lr: 1.0000e-03 eta: 9:34:23 time: 0.2304 data_time: 0.0046 loss: 0.9903 03/06 14:40:49 - mmengine - INFO - Epoch(train) [111][ 300/5005] lr: 1.0000e-03 eta: 9:34:00 time: 0.2268 data_time: 0.0041 loss: 0.9258 03/06 14:41:12 - mmengine - INFO - Epoch(train) [111][ 400/5005] lr: 1.0000e-03 eta: 9:33:37 time: 0.2269 data_time: 0.0043 loss: 0.9256 03/06 14:41:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:41:36 - mmengine - INFO - Epoch(train) [111][ 500/5005] lr: 1.0000e-03 eta: 9:33:14 time: 0.2285 data_time: 0.0044 loss: 0.7802 03/06 14:41:59 - mmengine - INFO - Epoch(train) [111][ 600/5005] lr: 1.0000e-03 eta: 9:32:51 time: 0.2270 data_time: 0.0043 loss: 0.8645 03/06 14:42:22 - mmengine - INFO - Epoch(train) [111][ 700/5005] lr: 1.0000e-03 eta: 9:32:28 time: 0.2272 data_time: 0.0042 loss: 0.8298 03/06 14:42:45 - mmengine - INFO - Epoch(train) [111][ 800/5005] lr: 1.0000e-03 eta: 9:32:05 time: 0.2254 data_time: 0.0043 loss: 1.0144 03/06 14:43:09 - mmengine - INFO - Epoch(train) [111][ 900/5005] lr: 1.0000e-03 eta: 9:31:42 time: 0.2281 data_time: 0.0041 loss: 0.9146 03/06 14:43:33 - mmengine - INFO - Epoch(train) [111][1000/5005] lr: 1.0000e-03 eta: 9:31:19 time: 0.2261 data_time: 0.0043 loss: 0.8217 03/06 14:43:56 - mmengine - INFO - Epoch(train) [111][1100/5005] lr: 1.0000e-03 eta: 9:30:56 time: 0.2309 data_time: 0.0043 loss: 0.8981 03/06 14:44:19 - mmengine - INFO - Epoch(train) [111][1200/5005] lr: 1.0000e-03 eta: 9:30:33 time: 0.2274 data_time: 0.0042 loss: 0.9463 03/06 14:44:42 - mmengine - INFO - Epoch(train) [111][1300/5005] lr: 1.0000e-03 eta: 9:30:10 time: 0.2305 data_time: 0.0046 loss: 0.7816 03/06 14:45:06 - mmengine - INFO - Epoch(train) [111][1400/5005] lr: 1.0000e-03 eta: 9:29:48 time: 0.2267 data_time: 0.0042 loss: 0.7741 03/06 14:45:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:45:29 - mmengine - INFO - Epoch(train) [111][1500/5005] lr: 1.0000e-03 eta: 9:29:25 time: 0.2225 data_time: 0.0044 loss: 0.8634 03/06 14:45:52 - mmengine - INFO - Epoch(train) [111][1600/5005] lr: 1.0000e-03 eta: 9:29:02 time: 0.2289 data_time: 0.0042 loss: 1.0507 03/06 14:46:15 - mmengine - INFO - Epoch(train) [111][1700/5005] lr: 1.0000e-03 eta: 9:28:39 time: 0.2288 data_time: 0.0046 loss: 0.8722 03/06 14:46:39 - mmengine - INFO - Epoch(train) [111][1800/5005] lr: 1.0000e-03 eta: 9:28:16 time: 0.2475 data_time: 0.0043 loss: 0.8601 03/06 14:47:02 - mmengine - INFO - Epoch(train) [111][1900/5005] lr: 1.0000e-03 eta: 9:27:53 time: 0.2521 data_time: 0.0040 loss: 0.9446 03/06 14:47:25 - mmengine - INFO - Epoch(train) [111][2000/5005] lr: 1.0000e-03 eta: 9:27:30 time: 0.2292 data_time: 0.0042 loss: 0.8096 03/06 14:47:48 - mmengine - INFO - Epoch(train) [111][2100/5005] lr: 1.0000e-03 eta: 9:27:07 time: 0.2279 data_time: 0.0042 loss: 0.9512 03/06 14:48:12 - mmengine - INFO - Epoch(train) [111][2200/5005] lr: 1.0000e-03 eta: 9:26:44 time: 0.2282 data_time: 0.0044 loss: 0.8657 03/06 14:48:35 - mmengine - INFO - Epoch(train) [111][2300/5005] lr: 1.0000e-03 eta: 9:26:21 time: 0.2248 data_time: 0.0042 loss: 0.7681 03/06 14:48:58 - mmengine - INFO - Epoch(train) [111][2400/5005] lr: 1.0000e-03 eta: 9:25:58 time: 0.2288 data_time: 0.0044 loss: 0.7872 03/06 14:49:10 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:49:22 - mmengine - INFO - Epoch(train) [111][2500/5005] lr: 1.0000e-03 eta: 9:25:35 time: 0.2499 data_time: 0.0042 loss: 0.7659 03/06 14:49:45 - mmengine - INFO - Epoch(train) [111][2600/5005] lr: 1.0000e-03 eta: 9:25:12 time: 0.2483 data_time: 0.0047 loss: 0.9285 03/06 14:50:08 - mmengine - INFO - Epoch(train) [111][2700/5005] lr: 1.0000e-03 eta: 9:24:49 time: 0.2276 data_time: 0.0040 loss: 0.8453 03/06 14:50:32 - mmengine - INFO - Epoch(train) [111][2800/5005] lr: 1.0000e-03 eta: 9:24:26 time: 0.2264 data_time: 0.0044 loss: 0.7267 03/06 14:50:55 - mmengine - INFO - Epoch(train) [111][2900/5005] lr: 1.0000e-03 eta: 9:24:03 time: 0.2276 data_time: 0.0041 loss: 0.8738 03/06 14:51:18 - mmengine - INFO - Epoch(train) [111][3000/5005] lr: 1.0000e-03 eta: 9:23:40 time: 0.2278 data_time: 0.0045 loss: 0.9347 03/06 14:51:41 - mmengine - INFO - Epoch(train) [111][3100/5005] lr: 1.0000e-03 eta: 9:23:17 time: 0.2242 data_time: 0.0041 loss: 0.9066 03/06 14:52:05 - mmengine - INFO - Epoch(train) [111][3200/5005] lr: 1.0000e-03 eta: 9:22:54 time: 0.2286 data_time: 0.0044 loss: 0.9396 03/06 14:52:28 - mmengine - INFO - Epoch(train) [111][3300/5005] lr: 1.0000e-03 eta: 9:22:31 time: 0.2464 data_time: 0.0043 loss: 0.9422 03/06 14:52:51 - mmengine - INFO - Epoch(train) [111][3400/5005] lr: 1.0000e-03 eta: 9:22:08 time: 0.2312 data_time: 0.0048 loss: 0.9514 03/06 14:53:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:53:15 - mmengine - INFO - Epoch(train) [111][3500/5005] lr: 1.0000e-03 eta: 9:21:45 time: 0.2284 data_time: 0.0045 loss: 0.7991 03/06 14:53:38 - mmengine - INFO - Epoch(train) [111][3600/5005] lr: 1.0000e-03 eta: 9:21:22 time: 0.2257 data_time: 0.0041 loss: 0.7157 03/06 14:54:01 - mmengine - INFO - Epoch(train) [111][3700/5005] lr: 1.0000e-03 eta: 9:20:59 time: 0.2495 data_time: 0.0042 loss: 0.7844 03/06 14:54:24 - mmengine - INFO - Epoch(train) [111][3800/5005] lr: 1.0000e-03 eta: 9:20:36 time: 0.2293 data_time: 0.0046 loss: 0.8667 03/06 14:54:48 - mmengine - INFO - Epoch(train) [111][3900/5005] lr: 1.0000e-03 eta: 9:20:13 time: 0.2302 data_time: 0.0041 loss: 0.7919 03/06 14:55:11 - mmengine - INFO - Epoch(train) [111][4000/5005] lr: 1.0000e-03 eta: 9:19:50 time: 0.2247 data_time: 0.0047 loss: 0.8383 03/06 14:55:34 - mmengine - INFO - Epoch(train) [111][4100/5005] lr: 1.0000e-03 eta: 9:19:27 time: 0.2336 data_time: 0.0046 loss: 1.0127 03/06 14:55:57 - mmengine - INFO - Epoch(train) [111][4200/5005] lr: 1.0000e-03 eta: 9:19:04 time: 0.2261 data_time: 0.0046 loss: 0.8985 03/06 14:56:21 - mmengine - INFO - Epoch(train) [111][4300/5005] lr: 1.0000e-03 eta: 9:18:41 time: 0.2287 data_time: 0.0045 loss: 0.8221 03/06 14:56:44 - mmengine - INFO - Epoch(train) [111][4400/5005] lr: 1.0000e-03 eta: 9:18:18 time: 0.2272 data_time: 0.0042 loss: 0.7681 03/06 14:56:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:57:07 - mmengine - INFO - Epoch(train) [111][4500/5005] lr: 1.0000e-03 eta: 9:17:55 time: 0.2290 data_time: 0.0042 loss: 0.9296 03/06 14:57:30 - mmengine - INFO - Epoch(train) [111][4600/5005] lr: 1.0000e-03 eta: 9:17:32 time: 0.2287 data_time: 0.0043 loss: 0.8409 03/06 14:57:54 - mmengine - INFO - Epoch(train) [111][4700/5005] lr: 1.0000e-03 eta: 9:17:09 time: 0.2255 data_time: 0.0045 loss: 0.9204 03/06 14:58:17 - mmengine - INFO - Epoch(train) [111][4800/5005] lr: 1.0000e-03 eta: 9:16:46 time: 0.2295 data_time: 0.0047 loss: 0.8437 03/06 14:58:42 - mmengine - INFO - Epoch(train) [111][4900/5005] lr: 1.0000e-03 eta: 9:16:24 time: 0.2972 data_time: 0.0039 loss: 0.8430 03/06 14:59:10 - mmengine - INFO - Epoch(train) [111][5000/5005] lr: 1.0000e-03 eta: 9:16:02 time: 0.2922 data_time: 0.0039 loss: 0.8418 03/06 14:59:12 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 14:59:15 - mmengine - INFO - Saving checkpoint at 111 epochs 03/06 14:59:30 - mmengine - INFO - Epoch(val) [111][100/196] eta: 0:00:13 time: 0.0177 data_time: 0.0002 03/06 14:59:43 - mmengine - INFO - Epoch(val) [111][196/196] accuracy/top1: 76.9500 accuracy/top5: 93.4960 03/06 15:00:16 - mmengine - INFO - Epoch(train) [112][ 100/5005] lr: 1.0000e-03 eta: 9:15:41 time: 0.2296 data_time: 0.0053 loss: 0.9573 03/06 15:00:39 - mmengine - INFO - Epoch(train) [112][ 200/5005] lr: 1.0000e-03 eta: 9:15:18 time: 0.2278 data_time: 0.0044 loss: 0.9773 03/06 15:01:02 - mmengine - INFO - Epoch(train) [112][ 300/5005] lr: 1.0000e-03 eta: 9:14:55 time: 0.2275 data_time: 0.0046 loss: 0.8557 03/06 15:01:26 - mmengine - INFO - Epoch(train) [112][ 400/5005] lr: 1.0000e-03 eta: 9:14:32 time: 0.2585 data_time: 0.0047 loss: 0.8247 03/06 15:01:37 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:01:49 - mmengine - INFO - Epoch(train) [112][ 500/5005] lr: 1.0000e-03 eta: 9:14:09 time: 0.2244 data_time: 0.0045 loss: 1.0303 03/06 15:02:12 - mmengine - INFO - Epoch(train) [112][ 600/5005] lr: 1.0000e-03 eta: 9:13:46 time: 0.2292 data_time: 0.0043 loss: 0.9151 03/06 15:02:36 - mmengine - INFO - Epoch(train) [112][ 700/5005] lr: 1.0000e-03 eta: 9:13:23 time: 0.2227 data_time: 0.0045 loss: 1.0324 03/06 15:02:59 - mmengine - INFO - Epoch(train) [112][ 800/5005] lr: 1.0000e-03 eta: 9:13:00 time: 0.2508 data_time: 0.0046 loss: 0.7317 03/06 15:03:23 - mmengine - INFO - Epoch(train) [112][ 900/5005] lr: 1.0000e-03 eta: 9:12:37 time: 0.2327 data_time: 0.0043 loss: 0.7844 03/06 15:03:47 - mmengine - INFO - Epoch(train) [112][1000/5005] lr: 1.0000e-03 eta: 9:12:14 time: 0.2214 data_time: 0.0042 loss: 0.8638 03/06 15:04:10 - mmengine - INFO - Epoch(train) [112][1100/5005] lr: 1.0000e-03 eta: 9:11:51 time: 0.2281 data_time: 0.0042 loss: 0.8410 03/06 15:04:33 - mmengine - INFO - Epoch(train) [112][1200/5005] lr: 1.0000e-03 eta: 9:11:28 time: 0.2277 data_time: 0.0040 loss: 0.9642 03/06 15:04:57 - mmengine - INFO - Epoch(train) [112][1300/5005] lr: 1.0000e-03 eta: 9:11:05 time: 0.2302 data_time: 0.0043 loss: 0.8932 03/06 15:05:20 - mmengine - INFO - Epoch(train) [112][1400/5005] lr: 1.0000e-03 eta: 9:10:42 time: 0.2271 data_time: 0.0042 loss: 0.8656 03/06 15:05:31 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:05:43 - mmengine - INFO - Epoch(train) [112][1500/5005] lr: 1.0000e-03 eta: 9:10:19 time: 0.2236 data_time: 0.0044 loss: 0.8138 03/06 15:06:06 - mmengine - INFO - Epoch(train) [112][1600/5005] lr: 1.0000e-03 eta: 9:09:56 time: 0.2261 data_time: 0.0045 loss: 0.8924 03/06 15:06:30 - mmengine - INFO - Epoch(train) [112][1700/5005] lr: 1.0000e-03 eta: 9:09:33 time: 0.2288 data_time: 0.0044 loss: 0.9330 03/06 15:06:53 - mmengine - INFO - Epoch(train) [112][1800/5005] lr: 1.0000e-03 eta: 9:09:10 time: 0.2276 data_time: 0.0041 loss: 0.8647 03/06 15:07:17 - mmengine - INFO - Epoch(train) [112][1900/5005] lr: 1.0000e-03 eta: 9:08:47 time: 0.2276 data_time: 0.0046 loss: 0.7937 03/06 15:07:40 - mmengine - INFO - Epoch(train) [112][2000/5005] lr: 1.0000e-03 eta: 9:08:24 time: 0.2260 data_time: 0.0042 loss: 0.9132 03/06 15:08:03 - mmengine - INFO - Epoch(train) [112][2100/5005] lr: 1.0000e-03 eta: 9:08:01 time: 0.2479 data_time: 0.0045 loss: 0.8831 03/06 15:08:26 - mmengine - INFO - Epoch(train) [112][2200/5005] lr: 1.0000e-03 eta: 9:07:38 time: 0.2303 data_time: 0.0044 loss: 0.9026 03/06 15:08:50 - mmengine - INFO - Epoch(train) [112][2300/5005] lr: 1.0000e-03 eta: 9:07:15 time: 0.2297 data_time: 0.0043 loss: 0.9254 03/06 15:09:13 - mmengine - INFO - Epoch(train) [112][2400/5005] lr: 1.0000e-03 eta: 9:06:52 time: 0.2276 data_time: 0.0043 loss: 0.7527 03/06 15:09:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:09:36 - mmengine - INFO - Epoch(train) [112][2500/5005] lr: 1.0000e-03 eta: 9:06:29 time: 0.2446 data_time: 0.0046 loss: 0.8564 03/06 15:09:59 - mmengine - INFO - Epoch(train) [112][2600/5005] lr: 1.0000e-03 eta: 9:06:06 time: 0.2250 data_time: 0.0042 loss: 0.6922 03/06 15:10:22 - mmengine - INFO - Epoch(train) [112][2700/5005] lr: 1.0000e-03 eta: 9:05:43 time: 0.2233 data_time: 0.0047 loss: 0.8540 03/06 15:10:46 - mmengine - INFO - Epoch(train) [112][2800/5005] lr: 1.0000e-03 eta: 9:05:20 time: 0.2269 data_time: 0.0043 loss: 0.9433 03/06 15:11:09 - mmengine - INFO - Epoch(train) [112][2900/5005] lr: 1.0000e-03 eta: 9:04:57 time: 0.2257 data_time: 0.0047 loss: 0.8017 03/06 15:11:32 - mmengine - INFO - Epoch(train) [112][3000/5005] lr: 1.0000e-03 eta: 9:04:34 time: 0.2214 data_time: 0.0045 loss: 0.8609 03/06 15:11:55 - mmengine - INFO - Epoch(train) [112][3100/5005] lr: 1.0000e-03 eta: 9:04:11 time: 0.2267 data_time: 0.0044 loss: 0.7931 03/06 15:12:19 - mmengine - INFO - Epoch(train) [112][3200/5005] lr: 1.0000e-03 eta: 9:03:48 time: 0.2291 data_time: 0.0043 loss: 0.8008 03/06 15:12:42 - mmengine - INFO - Epoch(train) [112][3300/5005] lr: 1.0000e-03 eta: 9:03:25 time: 0.2305 data_time: 0.0043 loss: 0.9208 03/06 15:13:05 - mmengine - INFO - Epoch(train) [112][3400/5005] lr: 1.0000e-03 eta: 9:03:02 time: 0.2277 data_time: 0.0044 loss: 0.9410 03/06 15:13:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:13:28 - mmengine - INFO - Epoch(train) [112][3500/5005] lr: 1.0000e-03 eta: 9:02:40 time: 0.2253 data_time: 0.0039 loss: 0.8248 03/06 15:13:52 - mmengine - INFO - Epoch(train) [112][3600/5005] lr: 1.0000e-03 eta: 9:02:16 time: 0.2268 data_time: 0.0041 loss: 0.9963 03/06 15:14:15 - mmengine - INFO - Epoch(train) [112][3700/5005] lr: 1.0000e-03 eta: 9:01:53 time: 0.2314 data_time: 0.0043 loss: 0.9223 03/06 15:14:38 - mmengine - INFO - Epoch(train) [112][3800/5005] lr: 1.0000e-03 eta: 9:01:30 time: 0.2264 data_time: 0.0043 loss: 0.8692 03/06 15:15:01 - mmengine - INFO - Epoch(train) [112][3900/5005] lr: 1.0000e-03 eta: 9:01:08 time: 0.2290 data_time: 0.0044 loss: 0.7918 03/06 15:15:25 - mmengine - INFO - Epoch(train) [112][4000/5005] lr: 1.0000e-03 eta: 9:00:45 time: 0.2233 data_time: 0.0048 loss: 0.8517 03/06 15:15:48 - mmengine - INFO - Epoch(train) [112][4100/5005] lr: 1.0000e-03 eta: 9:00:22 time: 0.2311 data_time: 0.0044 loss: 0.8662 03/06 15:16:11 - mmengine - INFO - Epoch(train) [112][4200/5005] lr: 1.0000e-03 eta: 8:59:59 time: 0.2275 data_time: 0.0041 loss: 0.8042 03/06 15:16:34 - mmengine - INFO - Epoch(train) [112][4300/5005] lr: 1.0000e-03 eta: 8:59:36 time: 0.2248 data_time: 0.0044 loss: 0.9381 03/06 15:16:58 - mmengine - INFO - Epoch(train) [112][4400/5005] lr: 1.0000e-03 eta: 8:59:13 time: 0.2282 data_time: 0.0042 loss: 1.0965 03/06 15:17:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:17:21 - mmengine - INFO - Epoch(train) [112][4500/5005] lr: 1.0000e-03 eta: 8:58:50 time: 0.2243 data_time: 0.0043 loss: 0.7624 03/06 15:17:44 - mmengine - INFO - Epoch(train) [112][4600/5005] lr: 1.0000e-03 eta: 8:58:27 time: 0.2288 data_time: 0.0041 loss: 0.7919 03/06 15:18:08 - mmengine - INFO - Epoch(train) [112][4700/5005] lr: 1.0000e-03 eta: 8:58:04 time: 0.2306 data_time: 0.0042 loss: 0.7638 03/06 15:18:31 - mmengine - INFO - Epoch(train) [112][4800/5005] lr: 1.0000e-03 eta: 8:57:41 time: 0.2293 data_time: 0.0041 loss: 1.0131 03/06 15:18:55 - mmengine - INFO - Epoch(train) [112][4900/5005] lr: 1.0000e-03 eta: 8:57:18 time: 0.2919 data_time: 0.0038 loss: 1.0158 03/06 15:19:24 - mmengine - INFO - Epoch(train) [112][5000/5005] lr: 1.0000e-03 eta: 8:56:56 time: 0.2853 data_time: 0.0044 loss: 0.9054 03/06 15:19:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:19:28 - mmengine - INFO - Saving checkpoint at 112 epochs 03/06 15:19:44 - mmengine - INFO - Epoch(val) [112][100/196] eta: 0:00:13 time: 0.0190 data_time: 0.0002 03/06 15:19:57 - mmengine - INFO - Epoch(val) [112][196/196] accuracy/top1: 76.9960 accuracy/top5: 93.5120 03/06 15:20:30 - mmengine - INFO - Epoch(train) [113][ 100/5005] lr: 1.0000e-03 eta: 8:56:35 time: 0.2294 data_time: 0.0043 loss: 0.7953 03/06 15:20:53 - mmengine - INFO - Epoch(train) [113][ 200/5005] lr: 1.0000e-03 eta: 8:56:12 time: 0.2281 data_time: 0.0052 loss: 0.9305 03/06 15:21:17 - mmengine - INFO - Epoch(train) [113][ 300/5005] lr: 1.0000e-03 eta: 8:55:49 time: 0.2272 data_time: 0.0043 loss: 1.0919 03/06 15:21:40 - mmengine - INFO - Epoch(train) [113][ 400/5005] lr: 1.0000e-03 eta: 8:55:26 time: 0.2266 data_time: 0.0041 loss: 0.8759 03/06 15:21:49 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:22:03 - mmengine - INFO - Epoch(train) [113][ 500/5005] lr: 1.0000e-03 eta: 8:55:03 time: 0.2281 data_time: 0.0039 loss: 0.8706 03/06 15:22:26 - mmengine - INFO - Epoch(train) [113][ 600/5005] lr: 1.0000e-03 eta: 8:54:40 time: 0.2260 data_time: 0.0043 loss: 0.7603 03/06 15:22:50 - mmengine - INFO - Epoch(train) [113][ 700/5005] lr: 1.0000e-03 eta: 8:54:17 time: 0.2303 data_time: 0.0045 loss: 0.9627 03/06 15:23:13 - mmengine - INFO - Epoch(train) [113][ 800/5005] lr: 1.0000e-03 eta: 8:53:54 time: 0.2247 data_time: 0.0040 loss: 1.0251 03/06 15:23:37 - mmengine - INFO - Epoch(train) [113][ 900/5005] lr: 1.0000e-03 eta: 8:53:31 time: 0.2298 data_time: 0.0041 loss: 0.7595 03/06 15:24:00 - mmengine - INFO - Epoch(train) [113][1000/5005] lr: 1.0000e-03 eta: 8:53:08 time: 0.2259 data_time: 0.0046 loss: 0.8590 03/06 15:24:23 - mmengine - INFO - Epoch(train) [113][1100/5005] lr: 1.0000e-03 eta: 8:52:45 time: 0.2262 data_time: 0.0046 loss: 0.6872 03/06 15:24:47 - mmengine - INFO - Epoch(train) [113][1200/5005] lr: 1.0000e-03 eta: 8:52:22 time: 0.2280 data_time: 0.0045 loss: 0.7151 03/06 15:25:10 - mmengine - INFO - Epoch(train) [113][1300/5005] lr: 1.0000e-03 eta: 8:51:59 time: 0.2546 data_time: 0.0052 loss: 1.0330 03/06 15:25:33 - mmengine - INFO - Epoch(train) [113][1400/5005] lr: 1.0000e-03 eta: 8:51:36 time: 0.2242 data_time: 0.0043 loss: 0.9330 03/06 15:25:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:25:57 - mmengine - INFO - Epoch(train) [113][1500/5005] lr: 1.0000e-03 eta: 8:51:13 time: 0.2487 data_time: 0.0047 loss: 0.8294 03/06 15:26:20 - mmengine - INFO - Epoch(train) [113][1600/5005] lr: 1.0000e-03 eta: 8:50:50 time: 0.2533 data_time: 0.0041 loss: 0.7573 03/06 15:26:43 - mmengine - INFO - Epoch(train) [113][1700/5005] lr: 1.0000e-03 eta: 8:50:27 time: 0.2261 data_time: 0.0042 loss: 0.9870 03/06 15:27:07 - mmengine - INFO - Epoch(train) [113][1800/5005] lr: 1.0000e-03 eta: 8:50:04 time: 0.2278 data_time: 0.0040 loss: 0.9661 03/06 15:27:30 - mmengine - INFO - Epoch(train) [113][1900/5005] lr: 1.0000e-03 eta: 8:49:41 time: 0.2264 data_time: 0.0041 loss: 0.6740 03/06 15:27:53 - mmengine - INFO - Epoch(train) [113][2000/5005] lr: 1.0000e-03 eta: 8:49:18 time: 0.2250 data_time: 0.0044 loss: 0.8621 03/06 15:28:17 - mmengine - INFO - Epoch(train) [113][2100/5005] lr: 1.0000e-03 eta: 8:48:55 time: 0.2299 data_time: 0.0044 loss: 0.8767 03/06 15:28:40 - mmengine - INFO - Epoch(train) [113][2200/5005] lr: 1.0000e-03 eta: 8:48:32 time: 0.2288 data_time: 0.0045 loss: 0.7631 03/06 15:29:03 - mmengine - INFO - Epoch(train) [113][2300/5005] lr: 1.0000e-03 eta: 8:48:09 time: 0.2267 data_time: 0.0047 loss: 1.0173 03/06 15:29:27 - mmengine - INFO - Epoch(train) [113][2400/5005] lr: 1.0000e-03 eta: 8:47:46 time: 0.2302 data_time: 0.0042 loss: 1.0495 03/06 15:29:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:29:50 - mmengine - INFO - Epoch(train) [113][2500/5005] lr: 1.0000e-03 eta: 8:47:23 time: 0.2316 data_time: 0.0045 loss: 0.7805 03/06 15:30:13 - mmengine - INFO - Epoch(train) [113][2600/5005] lr: 1.0000e-03 eta: 8:47:01 time: 0.2257 data_time: 0.0045 loss: 0.9406 03/06 15:30:37 - mmengine - INFO - Epoch(train) [113][2700/5005] lr: 1.0000e-03 eta: 8:46:38 time: 0.2295 data_time: 0.0038 loss: 0.9454 03/06 15:31:00 - mmengine - INFO - Epoch(train) [113][2800/5005] lr: 1.0000e-03 eta: 8:46:15 time: 0.2287 data_time: 0.0042 loss: 0.7942 03/06 15:31:23 - mmengine - INFO - Epoch(train) [113][2900/5005] lr: 1.0000e-03 eta: 8:45:52 time: 0.2282 data_time: 0.0044 loss: 0.8580 03/06 15:31:47 - mmengine - INFO - Epoch(train) [113][3000/5005] lr: 1.0000e-03 eta: 8:45:29 time: 0.2288 data_time: 0.0043 loss: 0.8916 03/06 15:32:10 - mmengine - INFO - Epoch(train) [113][3100/5005] lr: 1.0000e-03 eta: 8:45:06 time: 0.2289 data_time: 0.0044 loss: 0.8189 03/06 15:32:34 - mmengine - INFO - Epoch(train) [113][3200/5005] lr: 1.0000e-03 eta: 8:44:43 time: 0.2333 data_time: 0.0043 loss: 0.9268 03/06 15:32:57 - mmengine - INFO - Epoch(train) [113][3300/5005] lr: 1.0000e-03 eta: 8:44:20 time: 0.2257 data_time: 0.0042 loss: 0.8336 03/06 15:33:20 - mmengine - INFO - Epoch(train) [113][3400/5005] lr: 1.0000e-03 eta: 8:43:57 time: 0.2265 data_time: 0.0038 loss: 0.9916 03/06 15:33:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:33:44 - mmengine - INFO - Epoch(train) [113][3500/5005] lr: 1.0000e-03 eta: 8:43:34 time: 0.2428 data_time: 0.0039 loss: 0.9447 03/06 15:34:07 - mmengine - INFO - Epoch(train) [113][3600/5005] lr: 1.0000e-03 eta: 8:43:11 time: 0.2471 data_time: 0.0047 loss: 0.7693 03/06 15:34:30 - mmengine - INFO - Epoch(train) [113][3700/5005] lr: 1.0000e-03 eta: 8:42:48 time: 0.2269 data_time: 0.0039 loss: 0.9608 03/06 15:34:53 - mmengine - INFO - Epoch(train) [113][3800/5005] lr: 1.0000e-03 eta: 8:42:25 time: 0.2281 data_time: 0.0039 loss: 0.9002 03/06 15:35:17 - mmengine - INFO - Epoch(train) [113][3900/5005] lr: 1.0000e-03 eta: 8:42:02 time: 0.2475 data_time: 0.0039 loss: 0.8697 03/06 15:35:40 - mmengine - INFO - Epoch(train) [113][4000/5005] lr: 1.0000e-03 eta: 8:41:39 time: 0.2304 data_time: 0.0039 loss: 0.7852 03/06 15:36:03 - mmengine - INFO - Epoch(train) [113][4100/5005] lr: 1.0000e-03 eta: 8:41:16 time: 0.2259 data_time: 0.0044 loss: 0.9238 03/06 15:36:26 - mmengine - INFO - Epoch(train) [113][4200/5005] lr: 1.0000e-03 eta: 8:40:53 time: 0.2266 data_time: 0.0041 loss: 0.9356 03/06 15:36:50 - mmengine - INFO - Epoch(train) [113][4300/5005] lr: 1.0000e-03 eta: 8:40:30 time: 0.2265 data_time: 0.0043 loss: 0.9495 03/06 15:37:13 - mmengine - INFO - Epoch(train) [113][4400/5005] lr: 1.0000e-03 eta: 8:40:07 time: 0.2476 data_time: 0.0039 loss: 0.6008 03/06 15:37:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:37:37 - mmengine - INFO - Epoch(train) [113][4500/5005] lr: 1.0000e-03 eta: 8:39:44 time: 0.2269 data_time: 0.0049 loss: 0.7696 03/06 15:38:00 - mmengine - INFO - Epoch(train) [113][4600/5005] lr: 1.0000e-03 eta: 8:39:21 time: 0.2280 data_time: 0.0047 loss: 1.1746 03/06 15:38:23 - mmengine - INFO - Epoch(train) [113][4700/5005] lr: 1.0000e-03 eta: 8:38:58 time: 0.2281 data_time: 0.0042 loss: 0.7502 03/06 15:38:46 - mmengine - INFO - Epoch(train) [113][4800/5005] lr: 1.0000e-03 eta: 8:38:35 time: 0.2290 data_time: 0.0043 loss: 0.9965 03/06 15:39:11 - mmengine - INFO - Epoch(train) [113][4900/5005] lr: 1.0000e-03 eta: 8:38:13 time: 0.2843 data_time: 0.0037 loss: 0.8570 03/06 15:39:40 - mmengine - INFO - Epoch(train) [113][5000/5005] lr: 1.0000e-03 eta: 8:37:51 time: 0.2812 data_time: 0.0039 loss: 0.9237 03/06 15:39:41 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:39:44 - mmengine - INFO - Saving checkpoint at 113 epochs 03/06 15:39:59 - mmengine - INFO - Epoch(val) [113][100/196] eta: 0:00:13 time: 0.0169 data_time: 0.0002 03/06 15:40:13 - mmengine - INFO - Epoch(val) [113][196/196] accuracy/top1: 76.8360 accuracy/top5: 93.4940 03/06 15:40:45 - mmengine - INFO - Epoch(train) [114][ 100/5005] lr: 1.0000e-03 eta: 8:37:29 time: 0.2305 data_time: 0.0050 loss: 0.9186 03/06 15:41:09 - mmengine - INFO - Epoch(train) [114][ 200/5005] lr: 1.0000e-03 eta: 8:37:06 time: 0.2293 data_time: 0.0049 loss: 0.7335 03/06 15:41:32 - mmengine - INFO - Epoch(train) [114][ 300/5005] lr: 1.0000e-03 eta: 8:36:43 time: 0.2283 data_time: 0.0043 loss: 0.7890 03/06 15:41:55 - mmengine - INFO - Epoch(train) [114][ 400/5005] lr: 1.0000e-03 eta: 8:36:20 time: 0.2262 data_time: 0.0043 loss: 0.7415 03/06 15:42:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:42:19 - mmengine - INFO - Epoch(train) [114][ 500/5005] lr: 1.0000e-03 eta: 8:35:57 time: 0.2814 data_time: 0.0046 loss: 0.8161 03/06 15:42:42 - mmengine - INFO - Epoch(train) [114][ 600/5005] lr: 1.0000e-03 eta: 8:35:34 time: 0.2295 data_time: 0.0045 loss: 0.8692 03/06 15:43:05 - mmengine - INFO - Epoch(train) [114][ 700/5005] lr: 1.0000e-03 eta: 8:35:11 time: 0.2259 data_time: 0.0043 loss: 0.9339 03/06 15:43:28 - mmengine - INFO - Epoch(train) [114][ 800/5005] lr: 1.0000e-03 eta: 8:34:48 time: 0.2294 data_time: 0.0045 loss: 0.8304 03/06 15:43:52 - mmengine - INFO - Epoch(train) [114][ 900/5005] lr: 1.0000e-03 eta: 8:34:25 time: 0.2466 data_time: 0.0041 loss: 0.9413 03/06 15:44:16 - mmengine - INFO - Epoch(train) [114][1000/5005] lr: 1.0000e-03 eta: 8:34:02 time: 0.2275 data_time: 0.0045 loss: 0.7513 03/06 15:44:39 - mmengine - INFO - Epoch(train) [114][1100/5005] lr: 1.0000e-03 eta: 8:33:39 time: 0.2284 data_time: 0.0044 loss: 0.8011 03/06 15:45:02 - mmengine - INFO - Epoch(train) [114][1200/5005] lr: 1.0000e-03 eta: 8:33:16 time: 0.2273 data_time: 0.0047 loss: 0.7480 03/06 15:45:25 - mmengine - INFO - Epoch(train) [114][1300/5005] lr: 1.0000e-03 eta: 8:32:53 time: 0.2269 data_time: 0.0044 loss: 0.9542 03/06 15:45:49 - mmengine - INFO - Epoch(train) [114][1400/5005] lr: 1.0000e-03 eta: 8:32:30 time: 0.2253 data_time: 0.0044 loss: 0.9729 03/06 15:45:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:46:12 - mmengine - INFO - Epoch(train) [114][1500/5005] lr: 1.0000e-03 eta: 8:32:07 time: 0.2415 data_time: 0.0044 loss: 1.0143 03/06 15:46:35 - mmengine - INFO - Epoch(train) [114][1600/5005] lr: 1.0000e-03 eta: 8:31:44 time: 0.2239 data_time: 0.0043 loss: 0.9161 03/06 15:46:59 - mmengine - INFO - Epoch(train) [114][1700/5005] lr: 1.0000e-03 eta: 8:31:21 time: 0.2493 data_time: 0.0041 loss: 0.8916 03/06 15:47:22 - mmengine - INFO - Epoch(train) [114][1800/5005] lr: 1.0000e-03 eta: 8:30:59 time: 0.2299 data_time: 0.0040 loss: 0.9322 03/06 15:47:46 - mmengine - INFO - Epoch(train) [114][1900/5005] lr: 1.0000e-03 eta: 8:30:36 time: 0.2270 data_time: 0.0043 loss: 0.7974 03/06 15:48:09 - mmengine - INFO - Epoch(train) [114][2000/5005] lr: 1.0000e-03 eta: 8:30:13 time: 0.2264 data_time: 0.0042 loss: 0.9111 03/06 15:48:32 - mmengine - INFO - Epoch(train) [114][2100/5005] lr: 1.0000e-03 eta: 8:29:50 time: 0.2470 data_time: 0.0044 loss: 0.8769 03/06 15:48:56 - mmengine - INFO - Epoch(train) [114][2200/5005] lr: 1.0000e-03 eta: 8:29:27 time: 0.2272 data_time: 0.0042 loss: 0.9637 03/06 15:49:19 - mmengine - INFO - Epoch(train) [114][2300/5005] lr: 1.0000e-03 eta: 8:29:04 time: 0.2254 data_time: 0.0042 loss: 0.9448 03/06 15:49:42 - mmengine - INFO - Epoch(train) [114][2400/5005] lr: 1.0000e-03 eta: 8:28:41 time: 0.2296 data_time: 0.0040 loss: 0.8110 03/06 15:49:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:50:05 - mmengine - INFO - Epoch(train) [114][2500/5005] lr: 1.0000e-03 eta: 8:28:18 time: 0.2285 data_time: 0.0045 loss: 0.7646 03/06 15:50:29 - mmengine - INFO - Epoch(train) [114][2600/5005] lr: 1.0000e-03 eta: 8:27:55 time: 0.2251 data_time: 0.0043 loss: 0.8729 03/06 15:50:52 - mmengine - INFO - Epoch(train) [114][2700/5005] lr: 1.0000e-03 eta: 8:27:32 time: 0.2262 data_time: 0.0040 loss: 0.9258 03/06 15:51:16 - mmengine - INFO - Epoch(train) [114][2800/5005] lr: 1.0000e-03 eta: 8:27:09 time: 0.2479 data_time: 0.0041 loss: 0.7611 03/06 15:51:39 - mmengine - INFO - Epoch(train) [114][2900/5005] lr: 1.0000e-03 eta: 8:26:46 time: 0.2309 data_time: 0.0041 loss: 0.9583 03/06 15:52:02 - mmengine - INFO - Epoch(train) [114][3000/5005] lr: 1.0000e-03 eta: 8:26:23 time: 0.2308 data_time: 0.0047 loss: 0.8771 03/06 15:52:25 - mmengine - INFO - Epoch(train) [114][3100/5005] lr: 1.0000e-03 eta: 8:26:00 time: 0.2270 data_time: 0.0045 loss: 0.9468 03/06 15:52:49 - mmengine - INFO - Epoch(train) [114][3200/5005] lr: 1.0000e-03 eta: 8:25:37 time: 0.2292 data_time: 0.0041 loss: 0.6994 03/06 15:53:12 - mmengine - INFO - Epoch(train) [114][3300/5005] lr: 1.0000e-03 eta: 8:25:14 time: 0.2281 data_time: 0.0042 loss: 0.9217 03/06 15:53:35 - mmengine - INFO - Epoch(train) [114][3400/5005] lr: 1.0000e-03 eta: 8:24:51 time: 0.2524 data_time: 0.0046 loss: 0.7886 03/06 15:53:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:53:59 - mmengine - INFO - Epoch(train) [114][3500/5005] lr: 1.0000e-03 eta: 8:24:28 time: 0.2299 data_time: 0.0041 loss: 0.8779 03/06 15:54:22 - mmengine - INFO - Epoch(train) [114][3600/5005] lr: 1.0000e-03 eta: 8:24:05 time: 0.2281 data_time: 0.0050 loss: 0.7807 03/06 15:54:45 - mmengine - INFO - Epoch(train) [114][3700/5005] lr: 1.0000e-03 eta: 8:23:42 time: 0.2275 data_time: 0.0047 loss: 1.0522 03/06 15:55:08 - mmengine - INFO - Epoch(train) [114][3800/5005] lr: 1.0000e-03 eta: 8:23:19 time: 0.2268 data_time: 0.0042 loss: 0.9631 03/06 15:55:32 - mmengine - INFO - Epoch(train) [114][3900/5005] lr: 1.0000e-03 eta: 8:22:56 time: 0.2273 data_time: 0.0046 loss: 0.6999 03/06 15:55:55 - mmengine - INFO - Epoch(train) [114][4000/5005] lr: 1.0000e-03 eta: 8:22:33 time: 0.2264 data_time: 0.0044 loss: 0.8966 03/06 15:56:18 - mmengine - INFO - Epoch(train) [114][4100/5005] lr: 1.0000e-03 eta: 8:22:10 time: 0.2294 data_time: 0.0043 loss: 0.9966 03/06 15:56:42 - mmengine - INFO - Epoch(train) [114][4200/5005] lr: 1.0000e-03 eta: 8:21:47 time: 0.2299 data_time: 0.0041 loss: 0.8895 03/06 15:57:05 - mmengine - INFO - Epoch(train) [114][4300/5005] lr: 1.0000e-03 eta: 8:21:24 time: 0.2470 data_time: 0.0044 loss: 1.0615 03/06 15:57:29 - mmengine - INFO - Epoch(train) [114][4400/5005] lr: 1.0000e-03 eta: 8:21:01 time: 0.2337 data_time: 0.0050 loss: 0.8361 03/06 15:57:37 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 15:57:52 - mmengine - INFO - Epoch(train) [114][4500/5005] lr: 1.0000e-03 eta: 8:20:38 time: 0.2280 data_time: 0.0044 loss: 0.9673 03/06 15:58:15 - mmengine - INFO - Epoch(train) [114][4600/5005] lr: 1.0000e-03 eta: 8:20:15 time: 0.2298 data_time: 0.0045 loss: 0.8921 03/06 15:58:39 - mmengine - INFO - Epoch(train) [114][4700/5005] lr: 1.0000e-03 eta: 8:19:52 time: 0.2268 data_time: 0.0046 loss: 0.9368 03/06 15:59:02 - mmengine - INFO - Epoch(train) [114][4800/5005] lr: 1.0000e-03 eta: 8:19:29 time: 0.2325 data_time: 0.0042 loss: 0.9136 03/06 15:59:27 - mmengine - INFO - Epoch(train) [114][4900/5005] lr: 1.0000e-03 eta: 8:19:06 time: 0.2973 data_time: 0.0043 loss: 0.7369 03/06 15:59:56 - mmengine - INFO - Epoch(train) [114][5000/5005] lr: 1.0000e-03 eta: 8:18:45 time: 0.2910 data_time: 0.0041 loss: 1.0174 03/06 15:59:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:00:00 - mmengine - INFO - Saving checkpoint at 114 epochs 03/06 16:00:15 - mmengine - INFO - Epoch(val) [114][100/196] eta: 0:00:13 time: 0.0209 data_time: 0.0003 03/06 16:00:29 - mmengine - INFO - Epoch(val) [114][196/196] accuracy/top1: 76.8320 accuracy/top5: 93.5140 03/06 16:01:02 - mmengine - INFO - Epoch(train) [115][ 100/5005] lr: 1.0000e-03 eta: 8:18:23 time: 0.2665 data_time: 0.0056 loss: 0.7763 03/06 16:01:26 - mmengine - INFO - Epoch(train) [115][ 200/5005] lr: 1.0000e-03 eta: 8:18:00 time: 0.2262 data_time: 0.0045 loss: 0.9190 03/06 16:01:49 - mmengine - INFO - Epoch(train) [115][ 300/5005] lr: 1.0000e-03 eta: 8:17:37 time: 0.2314 data_time: 0.0046 loss: 1.0537 03/06 16:02:12 - mmengine - INFO - Epoch(train) [115][ 400/5005] lr: 1.0000e-03 eta: 8:17:14 time: 0.2263 data_time: 0.0048 loss: 0.7374 03/06 16:02:19 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:02:36 - mmengine - INFO - Epoch(train) [115][ 500/5005] lr: 1.0000e-03 eta: 8:16:51 time: 0.2296 data_time: 0.0045 loss: 0.7497 03/06 16:02:59 - mmengine - INFO - Epoch(train) [115][ 600/5005] lr: 1.0000e-03 eta: 8:16:28 time: 0.2261 data_time: 0.0048 loss: 0.9995 03/06 16:03:22 - mmengine - INFO - Epoch(train) [115][ 700/5005] lr: 1.0000e-03 eta: 8:16:05 time: 0.2287 data_time: 0.0046 loss: 0.7573 03/06 16:03:46 - mmengine - INFO - Epoch(train) [115][ 800/5005] lr: 1.0000e-03 eta: 8:15:42 time: 0.2247 data_time: 0.0045 loss: 0.8001 03/06 16:04:10 - mmengine - INFO - Epoch(train) [115][ 900/5005] lr: 1.0000e-03 eta: 8:15:19 time: 0.2289 data_time: 0.0052 loss: 0.8141 03/06 16:04:33 - mmengine - INFO - Epoch(train) [115][1000/5005] lr: 1.0000e-03 eta: 8:14:56 time: 0.2286 data_time: 0.0047 loss: 0.9770 03/06 16:04:57 - mmengine - INFO - Epoch(train) [115][1100/5005] lr: 1.0000e-03 eta: 8:14:33 time: 0.2288 data_time: 0.0051 loss: 0.8214 03/06 16:05:20 - mmengine - INFO - Epoch(train) [115][1200/5005] lr: 1.0000e-03 eta: 8:14:10 time: 0.2378 data_time: 0.0045 loss: 0.7320 03/06 16:05:43 - mmengine - INFO - Epoch(train) [115][1300/5005] lr: 1.0000e-03 eta: 8:13:47 time: 0.2264 data_time: 0.0052 loss: 0.9278 03/06 16:06:07 - mmengine - INFO - Epoch(train) [115][1400/5005] lr: 1.0000e-03 eta: 8:13:24 time: 0.2290 data_time: 0.0051 loss: 0.8824 03/06 16:06:14 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:06:30 - mmengine - INFO - Epoch(train) [115][1500/5005] lr: 1.0000e-03 eta: 8:13:01 time: 0.2234 data_time: 0.0050 loss: 0.8767 03/06 16:06:54 - mmengine - INFO - Epoch(train) [115][1600/5005] lr: 1.0000e-03 eta: 8:12:38 time: 0.2536 data_time: 0.0045 loss: 0.7602 03/06 16:07:17 - mmengine - INFO - Epoch(train) [115][1700/5005] lr: 1.0000e-03 eta: 8:12:15 time: 0.2275 data_time: 0.0044 loss: 1.0835 03/06 16:07:40 - mmengine - INFO - Epoch(train) [115][1800/5005] lr: 1.0000e-03 eta: 8:11:52 time: 0.2287 data_time: 0.0045 loss: 0.8836 03/06 16:08:03 - mmengine - INFO - Epoch(train) [115][1900/5005] lr: 1.0000e-03 eta: 8:11:29 time: 0.2292 data_time: 0.0044 loss: 1.1062 03/06 16:08:26 - mmengine - INFO - Epoch(train) [115][2000/5005] lr: 1.0000e-03 eta: 8:11:06 time: 0.2464 data_time: 0.0046 loss: 1.0046 03/06 16:08:50 - mmengine - INFO - Epoch(train) [115][2100/5005] lr: 1.0000e-03 eta: 8:10:43 time: 0.2330 data_time: 0.0047 loss: 0.7273 03/06 16:09:13 - mmengine - INFO - Epoch(train) [115][2200/5005] lr: 1.0000e-03 eta: 8:10:20 time: 0.2240 data_time: 0.0049 loss: 1.0089 03/06 16:09:37 - mmengine - INFO - Epoch(train) [115][2300/5005] lr: 1.0000e-03 eta: 8:09:58 time: 0.2272 data_time: 0.0046 loss: 0.9266 03/06 16:10:00 - mmengine - INFO - Epoch(train) [115][2400/5005] lr: 1.0000e-03 eta: 8:09:34 time: 0.2287 data_time: 0.0048 loss: 0.8964 03/06 16:10:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:10:23 - mmengine - INFO - Epoch(train) [115][2500/5005] lr: 1.0000e-03 eta: 8:09:12 time: 0.2280 data_time: 0.0045 loss: 0.8241 03/06 16:10:47 - mmengine - INFO - Epoch(train) [115][2600/5005] lr: 1.0000e-03 eta: 8:08:49 time: 0.2307 data_time: 0.0049 loss: 0.7875 03/06 16:11:10 - mmengine - INFO - Epoch(train) [115][2700/5005] lr: 1.0000e-03 eta: 8:08:26 time: 0.2285 data_time: 0.0049 loss: 0.8460 03/06 16:11:33 - mmengine - INFO - Epoch(train) [115][2800/5005] lr: 1.0000e-03 eta: 8:08:03 time: 0.2268 data_time: 0.0043 loss: 0.7704 03/06 16:11:57 - mmengine - INFO - Epoch(train) [115][2900/5005] lr: 1.0000e-03 eta: 8:07:40 time: 0.2457 data_time: 0.0045 loss: 0.7615 03/06 16:12:20 - mmengine - INFO - Epoch(train) [115][3000/5005] lr: 1.0000e-03 eta: 8:07:17 time: 0.2309 data_time: 0.0048 loss: 0.9238 03/06 16:12:44 - mmengine - INFO - Epoch(train) [115][3100/5005] lr: 1.0000e-03 eta: 8:06:54 time: 0.2262 data_time: 0.0043 loss: 1.0197 03/06 16:13:07 - mmengine - INFO - Epoch(train) [115][3200/5005] lr: 1.0000e-03 eta: 8:06:31 time: 0.2297 data_time: 0.0053 loss: 0.9187 03/06 16:13:30 - mmengine - INFO - Epoch(train) [115][3300/5005] lr: 1.0000e-03 eta: 8:06:08 time: 0.2304 data_time: 0.0048 loss: 0.7977 03/06 16:13:53 - mmengine - INFO - Epoch(train) [115][3400/5005] lr: 1.0000e-03 eta: 8:05:45 time: 0.2277 data_time: 0.0043 loss: 0.8676 03/06 16:14:01 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:14:17 - mmengine - INFO - Epoch(train) [115][3500/5005] lr: 1.0000e-03 eta: 8:05:22 time: 0.2260 data_time: 0.0046 loss: 0.8746 03/06 16:14:41 - mmengine - INFO - Epoch(train) [115][3600/5005] lr: 1.0000e-03 eta: 8:04:59 time: 0.2304 data_time: 0.0045 loss: 0.9453 03/06 16:15:04 - mmengine - INFO - Epoch(train) [115][3700/5005] lr: 1.0000e-03 eta: 8:04:36 time: 0.2274 data_time: 0.0049 loss: 0.7258 03/06 16:15:28 - mmengine - INFO - Epoch(train) [115][3800/5005] lr: 1.0000e-03 eta: 8:04:13 time: 0.2310 data_time: 0.0044 loss: 0.9051 03/06 16:15:51 - mmengine - INFO - Epoch(train) [115][3900/5005] lr: 1.0000e-03 eta: 8:03:50 time: 0.2293 data_time: 0.0046 loss: 0.9057 03/06 16:16:14 - mmengine - INFO - Epoch(train) [115][4000/5005] lr: 1.0000e-03 eta: 8:03:27 time: 0.2286 data_time: 0.0048 loss: 0.6879 03/06 16:16:38 - mmengine - INFO - Epoch(train) [115][4100/5005] lr: 1.0000e-03 eta: 8:03:04 time: 0.2271 data_time: 0.0052 loss: 0.9741 03/06 16:17:01 - mmengine - INFO - Epoch(train) [115][4200/5005] lr: 1.0000e-03 eta: 8:02:41 time: 0.2278 data_time: 0.0046 loss: 0.7726 03/06 16:17:25 - mmengine - INFO - Epoch(train) [115][4300/5005] lr: 1.0000e-03 eta: 8:02:18 time: 0.2287 data_time: 0.0046 loss: 0.8497 03/06 16:17:48 - mmengine - INFO - Epoch(train) [115][4400/5005] lr: 1.0000e-03 eta: 8:01:55 time: 0.2266 data_time: 0.0049 loss: 0.7135 03/06 16:17:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:18:11 - mmengine - INFO - Epoch(train) [115][4500/5005] lr: 1.0000e-03 eta: 8:01:32 time: 0.2243 data_time: 0.0048 loss: 0.9154 03/06 16:18:35 - mmengine - INFO - Epoch(train) [115][4600/5005] lr: 1.0000e-03 eta: 8:01:09 time: 0.2523 data_time: 0.0046 loss: 0.8408 03/06 16:18:59 - mmengine - INFO - Epoch(train) [115][4700/5005] lr: 1.0000e-03 eta: 8:00:46 time: 0.2270 data_time: 0.0047 loss: 0.8106 03/06 16:19:22 - mmengine - INFO - Epoch(train) [115][4800/5005] lr: 1.0000e-03 eta: 8:00:23 time: 0.2264 data_time: 0.0044 loss: 0.7989 03/06 16:19:46 - mmengine - INFO - Epoch(train) [115][4900/5005] lr: 1.0000e-03 eta: 8:00:00 time: 0.2831 data_time: 0.0043 loss: 0.8560 03/06 16:20:14 - mmengine - INFO - Epoch(train) [115][5000/5005] lr: 1.0000e-03 eta: 7:59:39 time: 0.2884 data_time: 0.0043 loss: 0.8196 03/06 16:20:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:20:18 - mmengine - INFO - Saving checkpoint at 115 epochs 03/06 16:20:34 - mmengine - INFO - Epoch(val) [115][100/196] eta: 0:00:13 time: 0.0188 data_time: 0.0002 03/06 16:20:47 - mmengine - INFO - Epoch(val) [115][196/196] accuracy/top1: 76.8300 accuracy/top5: 93.4820 03/06 16:21:20 - mmengine - INFO - Epoch(train) [116][ 100/5005] lr: 1.0000e-03 eta: 7:59:17 time: 0.2326 data_time: 0.0046 loss: 0.6344 03/06 16:21:45 - mmengine - INFO - Epoch(train) [116][ 200/5005] lr: 1.0000e-03 eta: 7:58:54 time: 0.2322 data_time: 0.0049 loss: 0.8941 03/06 16:22:08 - mmengine - INFO - Epoch(train) [116][ 300/5005] lr: 1.0000e-03 eta: 7:58:31 time: 0.2276 data_time: 0.0043 loss: 0.9121 03/06 16:22:31 - mmengine - INFO - Epoch(train) [116][ 400/5005] lr: 1.0000e-03 eta: 7:58:08 time: 0.2299 data_time: 0.0045 loss: 0.8382 03/06 16:22:37 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:22:54 - mmengine - INFO - Epoch(train) [116][ 500/5005] lr: 1.0000e-03 eta: 7:57:45 time: 0.2252 data_time: 0.0041 loss: 0.8657 03/06 16:23:18 - mmengine - INFO - Epoch(train) [116][ 600/5005] lr: 1.0000e-03 eta: 7:57:22 time: 0.2278 data_time: 0.0037 loss: 0.7668 03/06 16:23:41 - mmengine - INFO - Epoch(train) [116][ 700/5005] lr: 1.0000e-03 eta: 7:56:59 time: 0.2265 data_time: 0.0043 loss: 0.8581 03/06 16:24:04 - mmengine - INFO - Epoch(train) [116][ 800/5005] lr: 1.0000e-03 eta: 7:56:36 time: 0.2344 data_time: 0.0035 loss: 0.8448 03/06 16:24:28 - mmengine - INFO - Epoch(train) [116][ 900/5005] lr: 1.0000e-03 eta: 7:56:13 time: 0.2265 data_time: 0.0037 loss: 0.8164 03/06 16:24:51 - mmengine - INFO - Epoch(train) [116][1000/5005] lr: 1.0000e-03 eta: 7:55:50 time: 0.2485 data_time: 0.0042 loss: 1.0197 03/06 16:25:14 - mmengine - INFO - Epoch(train) [116][1100/5005] lr: 1.0000e-03 eta: 7:55:27 time: 0.2326 data_time: 0.0049 loss: 0.7352 03/06 16:25:38 - mmengine - INFO - Epoch(train) [116][1200/5005] lr: 1.0000e-03 eta: 7:55:04 time: 0.2268 data_time: 0.0039 loss: 1.0142 03/06 16:26:01 - mmengine - INFO - Epoch(train) [116][1300/5005] lr: 1.0000e-03 eta: 7:54:41 time: 0.2544 data_time: 0.0036 loss: 0.7423 03/06 16:26:24 - mmengine - INFO - Epoch(train) [116][1400/5005] lr: 1.0000e-03 eta: 7:54:18 time: 0.2306 data_time: 0.0037 loss: 0.7515 03/06 16:26:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:26:48 - mmengine - INFO - Epoch(train) [116][1500/5005] lr: 1.0000e-03 eta: 7:53:55 time: 0.2296 data_time: 0.0038 loss: 0.8183 03/06 16:27:11 - mmengine - INFO - Epoch(train) [116][1600/5005] lr: 1.0000e-03 eta: 7:53:32 time: 0.2281 data_time: 0.0041 loss: 0.8803 03/06 16:27:34 - mmengine - INFO - Epoch(train) [116][1700/5005] lr: 1.0000e-03 eta: 7:53:09 time: 0.2292 data_time: 0.0038 loss: 0.8479 03/06 16:27:58 - mmengine - INFO - Epoch(train) [116][1800/5005] lr: 1.0000e-03 eta: 7:52:46 time: 0.2327 data_time: 0.0037 loss: 0.9254 03/06 16:28:21 - mmengine - INFO - Epoch(train) [116][1900/5005] lr: 1.0000e-03 eta: 7:52:23 time: 0.2278 data_time: 0.0035 loss: 1.0431 03/06 16:28:45 - mmengine - INFO - Epoch(train) [116][2000/5005] lr: 1.0000e-03 eta: 7:52:00 time: 0.2285 data_time: 0.0037 loss: 0.8622 03/06 16:29:08 - mmengine - INFO - Epoch(train) [116][2100/5005] lr: 1.0000e-03 eta: 7:51:37 time: 0.2296 data_time: 0.0044 loss: 0.8602 03/06 16:29:31 - mmengine - INFO - Epoch(train) [116][2200/5005] lr: 1.0000e-03 eta: 7:51:14 time: 0.2264 data_time: 0.0038 loss: 0.7467 03/06 16:29:54 - mmengine - INFO - Epoch(train) [116][2300/5005] lr: 1.0000e-03 eta: 7:50:51 time: 0.2266 data_time: 0.0037 loss: 0.8145 03/06 16:30:18 - mmengine - INFO - Epoch(train) [116][2400/5005] lr: 1.0000e-03 eta: 7:50:28 time: 0.2336 data_time: 0.0036 loss: 0.8369 03/06 16:30:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:30:41 - mmengine - INFO - Epoch(train) [116][2500/5005] lr: 1.0000e-03 eta: 7:50:05 time: 0.2287 data_time: 0.0037 loss: 0.8371 03/06 16:31:04 - mmengine - INFO - Epoch(train) [116][2600/5005] lr: 1.0000e-03 eta: 7:49:42 time: 0.2443 data_time: 0.0043 loss: 0.9554 03/06 16:31:28 - mmengine - INFO - Epoch(train) [116][2700/5005] lr: 1.0000e-03 eta: 7:49:19 time: 0.2520 data_time: 0.0039 loss: 1.0323 03/06 16:31:51 - mmengine - INFO - Epoch(train) [116][2800/5005] lr: 1.0000e-03 eta: 7:48:56 time: 0.2361 data_time: 0.0038 loss: 1.0336 03/06 16:32:15 - mmengine - INFO - Epoch(train) [116][2900/5005] lr: 1.0000e-03 eta: 7:48:33 time: 0.2458 data_time: 0.0040 loss: 0.9854 03/06 16:32:38 - mmengine - INFO - Epoch(train) [116][3000/5005] lr: 1.0000e-03 eta: 7:48:10 time: 0.2280 data_time: 0.0042 loss: 0.8787 03/06 16:33:01 - mmengine - INFO - Epoch(train) [116][3100/5005] lr: 1.0000e-03 eta: 7:47:47 time: 0.2271 data_time: 0.0039 loss: 0.6783 03/06 16:33:25 - mmengine - INFO - Epoch(train) [116][3200/5005] lr: 1.0000e-03 eta: 7:47:24 time: 0.2266 data_time: 0.0036 loss: 0.8103 03/06 16:33:48 - mmengine - INFO - Epoch(train) [116][3300/5005] lr: 1.0000e-03 eta: 7:47:01 time: 0.2298 data_time: 0.0040 loss: 0.8156 03/06 16:34:11 - mmengine - INFO - Epoch(train) [116][3400/5005] lr: 1.0000e-03 eta: 7:46:39 time: 0.2272 data_time: 0.0038 loss: 0.8247 03/06 16:34:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:34:35 - mmengine - INFO - Epoch(train) [116][3500/5005] lr: 1.0000e-03 eta: 7:46:16 time: 0.2310 data_time: 0.0037 loss: 0.8353 03/06 16:34:59 - mmengine - INFO - Epoch(train) [116][3600/5005] lr: 1.0000e-03 eta: 7:45:53 time: 0.2297 data_time: 0.0037 loss: 0.8905 03/06 16:35:22 - mmengine - INFO - Epoch(train) [116][3700/5005] lr: 1.0000e-03 eta: 7:45:30 time: 0.2295 data_time: 0.0043 loss: 0.6976 03/06 16:35:45 - mmengine - INFO - Epoch(train) [116][3800/5005] lr: 1.0000e-03 eta: 7:45:07 time: 0.2367 data_time: 0.0039 loss: 0.9229 03/06 16:36:08 - mmengine - INFO - Epoch(train) [116][3900/5005] lr: 1.0000e-03 eta: 7:44:44 time: 0.2283 data_time: 0.0043 loss: 0.8589 03/06 16:36:32 - mmengine - INFO - Epoch(train) [116][4000/5005] lr: 1.0000e-03 eta: 7:44:21 time: 0.2469 data_time: 0.0050 loss: 0.9014 03/06 16:36:55 - mmengine - INFO - Epoch(train) [116][4100/5005] lr: 1.0000e-03 eta: 7:43:58 time: 0.2268 data_time: 0.0038 loss: 0.8936 03/06 16:37:19 - mmengine - INFO - Epoch(train) [116][4200/5005] lr: 1.0000e-03 eta: 7:43:35 time: 0.2379 data_time: 0.0039 loss: 0.8973 03/06 16:37:42 - mmengine - INFO - Epoch(train) [116][4300/5005] lr: 1.0000e-03 eta: 7:43:12 time: 0.2448 data_time: 0.0036 loss: 0.9427 03/06 16:38:05 - mmengine - INFO - Epoch(train) [116][4400/5005] lr: 1.0000e-03 eta: 7:42:49 time: 0.2263 data_time: 0.0036 loss: 0.9403 03/06 16:38:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:38:29 - mmengine - INFO - Epoch(train) [116][4500/5005] lr: 1.0000e-03 eta: 7:42:26 time: 0.2274 data_time: 0.0037 loss: 0.8922 03/06 16:38:52 - mmengine - INFO - Epoch(train) [116][4600/5005] lr: 1.0000e-03 eta: 7:42:03 time: 0.2297 data_time: 0.0038 loss: 0.8768 03/06 16:39:15 - mmengine - INFO - Epoch(train) [116][4700/5005] lr: 1.0000e-03 eta: 7:41:40 time: 0.2428 data_time: 0.0035 loss: 0.9175 03/06 16:39:39 - mmengine - INFO - Epoch(train) [116][4800/5005] lr: 1.0000e-03 eta: 7:41:17 time: 0.2287 data_time: 0.0040 loss: 0.8142 03/06 16:40:03 - mmengine - INFO - Epoch(train) [116][4900/5005] lr: 1.0000e-03 eta: 7:40:54 time: 0.2928 data_time: 0.0041 loss: 0.7879 03/06 16:40:33 - mmengine - INFO - Epoch(train) [116][5000/5005] lr: 1.0000e-03 eta: 7:40:32 time: 0.2912 data_time: 0.0035 loss: 1.0820 03/06 16:40:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:40:37 - mmengine - INFO - Saving checkpoint at 116 epochs 03/06 16:40:52 - mmengine - INFO - Epoch(val) [116][100/196] eta: 0:00:13 time: 0.0204 data_time: 0.0004 03/06 16:41:05 - mmengine - INFO - Epoch(val) [116][196/196] accuracy/top1: 76.9300 accuracy/top5: 93.4960 03/06 16:41:38 - mmengine - INFO - Epoch(train) [117][ 100/5005] lr: 1.0000e-03 eta: 7:40:10 time: 0.2270 data_time: 0.0048 loss: 0.7847 03/06 16:42:01 - mmengine - INFO - Epoch(train) [117][ 200/5005] lr: 1.0000e-03 eta: 7:39:47 time: 0.2280 data_time: 0.0055 loss: 0.9455 03/06 16:42:25 - mmengine - INFO - Epoch(train) [117][ 300/5005] lr: 1.0000e-03 eta: 7:39:24 time: 0.2251 data_time: 0.0050 loss: 0.9475 03/06 16:42:48 - mmengine - INFO - Epoch(train) [117][ 400/5005] lr: 1.0000e-03 eta: 7:39:01 time: 0.2512 data_time: 0.0054 loss: 0.7980 03/06 16:42:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:43:12 - mmengine - INFO - Epoch(train) [117][ 500/5005] lr: 1.0000e-03 eta: 7:38:38 time: 0.2278 data_time: 0.0054 loss: 0.8646 03/06 16:43:36 - mmengine - INFO - Epoch(train) [117][ 600/5005] lr: 1.0000e-03 eta: 7:38:15 time: 0.2285 data_time: 0.0048 loss: 0.8906 03/06 16:43:59 - mmengine - INFO - Epoch(train) [117][ 700/5005] lr: 1.0000e-03 eta: 7:37:52 time: 0.2275 data_time: 0.0050 loss: 0.7485 03/06 16:44:22 - mmengine - INFO - Epoch(train) [117][ 800/5005] lr: 1.0000e-03 eta: 7:37:29 time: 0.2272 data_time: 0.0051 loss: 1.0188 03/06 16:44:46 - mmengine - INFO - Epoch(train) [117][ 900/5005] lr: 1.0000e-03 eta: 7:37:06 time: 0.2265 data_time: 0.0046 loss: 0.8622 03/06 16:45:09 - mmengine - INFO - Epoch(train) [117][1000/5005] lr: 1.0000e-03 eta: 7:36:43 time: 0.2460 data_time: 0.0051 loss: 0.8969 03/06 16:45:32 - mmengine - INFO - Epoch(train) [117][1100/5005] lr: 1.0000e-03 eta: 7:36:20 time: 0.2277 data_time: 0.0052 loss: 0.7524 03/06 16:45:56 - mmengine - INFO - Epoch(train) [117][1200/5005] lr: 1.0000e-03 eta: 7:35:57 time: 0.2265 data_time: 0.0048 loss: 0.8344 03/06 16:46:19 - mmengine - INFO - Epoch(train) [117][1300/5005] lr: 1.0000e-03 eta: 7:35:34 time: 0.2284 data_time: 0.0046 loss: 1.0453 03/06 16:46:43 - mmengine - INFO - Epoch(train) [117][1400/5005] lr: 1.0000e-03 eta: 7:35:11 time: 0.2304 data_time: 0.0051 loss: 0.7322 03/06 16:46:48 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:47:06 - mmengine - INFO - Epoch(train) [117][1500/5005] lr: 1.0000e-03 eta: 7:34:48 time: 0.2290 data_time: 0.0049 loss: 0.8678 03/06 16:47:30 - mmengine - INFO - Epoch(train) [117][1600/5005] lr: 1.0000e-03 eta: 7:34:26 time: 0.2264 data_time: 0.0051 loss: 1.0049 03/06 16:47:53 - mmengine - INFO - Epoch(train) [117][1700/5005] lr: 1.0000e-03 eta: 7:34:03 time: 0.2275 data_time: 0.0055 loss: 0.8179 03/06 16:48:17 - mmengine - INFO - Epoch(train) [117][1800/5005] lr: 1.0000e-03 eta: 7:33:40 time: 0.2276 data_time: 0.0049 loss: 0.7666 03/06 16:48:40 - mmengine - INFO - Epoch(train) [117][1900/5005] lr: 1.0000e-03 eta: 7:33:17 time: 0.2520 data_time: 0.0048 loss: 0.7893 03/06 16:49:03 - mmengine - INFO - Epoch(train) [117][2000/5005] lr: 1.0000e-03 eta: 7:32:54 time: 0.2286 data_time: 0.0047 loss: 0.9260 03/06 16:49:27 - mmengine - INFO - Epoch(train) [117][2100/5005] lr: 1.0000e-03 eta: 7:32:31 time: 0.2271 data_time: 0.0053 loss: 0.8404 03/06 16:49:50 - mmengine - INFO - Epoch(train) [117][2200/5005] lr: 1.0000e-03 eta: 7:32:08 time: 0.2284 data_time: 0.0048 loss: 0.9306 03/06 16:50:13 - mmengine - INFO - Epoch(train) [117][2300/5005] lr: 1.0000e-03 eta: 7:31:45 time: 0.2454 data_time: 0.0046 loss: 0.9439 03/06 16:50:37 - mmengine - INFO - Epoch(train) [117][2400/5005] lr: 1.0000e-03 eta: 7:31:22 time: 0.2290 data_time: 0.0049 loss: 0.8491 03/06 16:50:41 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:51:00 - mmengine - INFO - Epoch(train) [117][2500/5005] lr: 1.0000e-03 eta: 7:30:59 time: 0.2253 data_time: 0.0046 loss: 1.0024 03/06 16:51:23 - mmengine - INFO - Epoch(train) [117][2600/5005] lr: 1.0000e-03 eta: 7:30:36 time: 0.2345 data_time: 0.0050 loss: 0.8030 03/06 16:51:47 - mmengine - INFO - Epoch(train) [117][2700/5005] lr: 1.0000e-03 eta: 7:30:13 time: 0.2258 data_time: 0.0046 loss: 0.9422 03/06 16:52:10 - mmengine - INFO - Epoch(train) [117][2800/5005] lr: 1.0000e-03 eta: 7:29:50 time: 0.2276 data_time: 0.0049 loss: 0.7052 03/06 16:52:34 - mmengine - INFO - Epoch(train) [117][2900/5005] lr: 1.0000e-03 eta: 7:29:27 time: 0.2274 data_time: 0.0055 loss: 0.9600 03/06 16:52:57 - mmengine - INFO - Epoch(train) [117][3000/5005] lr: 1.0000e-03 eta: 7:29:04 time: 0.2444 data_time: 0.0048 loss: 0.9131 03/06 16:53:20 - mmengine - INFO - Epoch(train) [117][3100/5005] lr: 1.0000e-03 eta: 7:28:41 time: 0.2315 data_time: 0.0049 loss: 1.0112 03/06 16:53:44 - mmengine - INFO - Epoch(train) [117][3200/5005] lr: 1.0000e-03 eta: 7:28:18 time: 0.2260 data_time: 0.0048 loss: 0.9054 03/06 16:54:07 - mmengine - INFO - Epoch(train) [117][3300/5005] lr: 1.0000e-03 eta: 7:27:55 time: 0.2249 data_time: 0.0046 loss: 0.7794 03/06 16:54:30 - mmengine - INFO - Epoch(train) [117][3400/5005] lr: 1.0000e-03 eta: 7:27:32 time: 0.2285 data_time: 0.0048 loss: 0.9527 03/06 16:54:35 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:54:54 - mmengine - INFO - Epoch(train) [117][3500/5005] lr: 1.0000e-03 eta: 7:27:09 time: 0.2308 data_time: 0.0046 loss: 0.6269 03/06 16:55:17 - mmengine - INFO - Epoch(train) [117][3600/5005] lr: 1.0000e-03 eta: 7:26:46 time: 0.2279 data_time: 0.0049 loss: 0.9596 03/06 16:55:41 - mmengine - INFO - Epoch(train) [117][3700/5005] lr: 1.0000e-03 eta: 7:26:23 time: 0.2249 data_time: 0.0047 loss: 0.6956 03/06 16:56:04 - mmengine - INFO - Epoch(train) [117][3800/5005] lr: 1.0000e-03 eta: 7:26:00 time: 0.2319 data_time: 0.0048 loss: 0.7976 03/06 16:56:27 - mmengine - INFO - Epoch(train) [117][3900/5005] lr: 1.0000e-03 eta: 7:25:37 time: 0.2257 data_time: 0.0046 loss: 0.8374 03/06 16:56:51 - mmengine - INFO - Epoch(train) [117][4000/5005] lr: 1.0000e-03 eta: 7:25:14 time: 0.2300 data_time: 0.0050 loss: 0.7025 03/06 16:57:14 - mmengine - INFO - Epoch(train) [117][4100/5005] lr: 1.0000e-03 eta: 7:24:51 time: 0.2363 data_time: 0.0049 loss: 0.8593 03/06 16:57:38 - mmengine - INFO - Epoch(train) [117][4200/5005] lr: 1.0000e-03 eta: 7:24:28 time: 0.2306 data_time: 0.0049 loss: 0.8560 03/06 16:58:01 - mmengine - INFO - Epoch(train) [117][4300/5005] lr: 1.0000e-03 eta: 7:24:05 time: 0.2474 data_time: 0.0049 loss: 0.8800 03/06 16:58:24 - mmengine - INFO - Epoch(train) [117][4400/5005] lr: 1.0000e-03 eta: 7:23:42 time: 0.2304 data_time: 0.0050 loss: 0.7705 03/06 16:58:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 16:58:48 - mmengine - INFO - Epoch(train) [117][4500/5005] lr: 1.0000e-03 eta: 7:23:19 time: 0.2258 data_time: 0.0048 loss: 0.7661 03/06 16:59:11 - mmengine - INFO - Epoch(train) [117][4600/5005] lr: 1.0000e-03 eta: 7:22:56 time: 0.2304 data_time: 0.0045 loss: 0.8109 03/06 16:59:35 - mmengine - INFO - Epoch(train) [117][4700/5005] lr: 1.0000e-03 eta: 7:22:33 time: 0.2305 data_time: 0.0046 loss: 0.9171 03/06 16:59:58 - mmengine - INFO - Epoch(train) [117][4800/5005] lr: 1.0000e-03 eta: 7:22:10 time: 0.2459 data_time: 0.0048 loss: 0.9585 03/06 17:00:23 - mmengine - INFO - Epoch(train) [117][4900/5005] lr: 1.0000e-03 eta: 7:21:47 time: 0.2817 data_time: 0.0045 loss: 0.8141 03/06 17:00:52 - mmengine - INFO - Epoch(train) [117][5000/5005] lr: 1.0000e-03 eta: 7:21:25 time: 0.2996 data_time: 0.0046 loss: 0.8879 03/06 17:00:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:00:56 - mmengine - INFO - Saving checkpoint at 117 epochs 03/06 17:01:11 - mmengine - INFO - Epoch(val) [117][100/196] eta: 0:00:13 time: 0.0194 data_time: 0.0002 03/06 17:01:24 - mmengine - INFO - Epoch(val) [117][196/196] accuracy/top1: 76.7460 accuracy/top5: 93.4060 03/06 17:01:58 - mmengine - INFO - Epoch(train) [118][ 100/5005] lr: 1.0000e-03 eta: 7:21:03 time: 0.2290 data_time: 0.0051 loss: 0.9189 03/06 17:02:21 - mmengine - INFO - Epoch(train) [118][ 200/5005] lr: 1.0000e-03 eta: 7:20:40 time: 0.2303 data_time: 0.0057 loss: 0.7921 03/06 17:02:44 - mmengine - INFO - Epoch(train) [118][ 300/5005] lr: 1.0000e-03 eta: 7:20:17 time: 0.2313 data_time: 0.0052 loss: 0.9311 03/06 17:03:07 - mmengine - INFO - Epoch(train) [118][ 400/5005] lr: 1.0000e-03 eta: 7:19:54 time: 0.2283 data_time: 0.0049 loss: 0.8596 03/06 17:03:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:03:31 - mmengine - INFO - Epoch(train) [118][ 500/5005] lr: 1.0000e-03 eta: 7:19:31 time: 0.2271 data_time: 0.0049 loss: 0.7868 03/06 17:03:55 - mmengine - INFO - Epoch(train) [118][ 600/5005] lr: 1.0000e-03 eta: 7:19:08 time: 0.2350 data_time: 0.0054 loss: 0.7034 03/06 17:04:18 - mmengine - INFO - Epoch(train) [118][ 700/5005] lr: 1.0000e-03 eta: 7:18:45 time: 0.2270 data_time: 0.0051 loss: 0.8373 03/06 17:04:41 - mmengine - INFO - Epoch(train) [118][ 800/5005] lr: 1.0000e-03 eta: 7:18:22 time: 0.2258 data_time: 0.0047 loss: 0.7915 03/06 17:05:05 - mmengine - INFO - Epoch(train) [118][ 900/5005] lr: 1.0000e-03 eta: 7:17:59 time: 0.2464 data_time: 0.0050 loss: 0.8336 03/06 17:05:29 - mmengine - INFO - Epoch(train) [118][1000/5005] lr: 1.0000e-03 eta: 7:17:36 time: 0.2317 data_time: 0.0049 loss: 0.7073 03/06 17:05:52 - mmengine - INFO - Epoch(train) [118][1100/5005] lr: 1.0000e-03 eta: 7:17:13 time: 0.2316 data_time: 0.0054 loss: 0.9837 03/06 17:06:15 - mmengine - INFO - Epoch(train) [118][1200/5005] lr: 1.0000e-03 eta: 7:16:50 time: 0.2289 data_time: 0.0050 loss: 0.9813 03/06 17:06:39 - mmengine - INFO - Epoch(train) [118][1300/5005] lr: 1.0000e-03 eta: 7:16:27 time: 0.2527 data_time: 0.0045 loss: 0.8903 03/06 17:07:02 - mmengine - INFO - Epoch(train) [118][1400/5005] lr: 1.0000e-03 eta: 7:16:04 time: 0.2280 data_time: 0.0050 loss: 1.0464 03/06 17:07:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:07:25 - mmengine - INFO - Epoch(train) [118][1500/5005] lr: 1.0000e-03 eta: 7:15:41 time: 0.2276 data_time: 0.0050 loss: 0.7747 03/06 17:07:49 - mmengine - INFO - Epoch(train) [118][1600/5005] lr: 1.0000e-03 eta: 7:15:18 time: 0.2279 data_time: 0.0051 loss: 0.8480 03/06 17:08:13 - mmengine - INFO - Epoch(train) [118][1700/5005] lr: 1.0000e-03 eta: 7:14:55 time: 0.2638 data_time: 0.0047 loss: 0.8397 03/06 17:08:36 - mmengine - INFO - Epoch(train) [118][1800/5005] lr: 1.0000e-03 eta: 7:14:32 time: 0.2364 data_time: 0.0051 loss: 0.9288 03/06 17:09:00 - mmengine - INFO - Epoch(train) [118][1900/5005] lr: 1.0000e-03 eta: 7:14:09 time: 0.2485 data_time: 0.0052 loss: 0.9646 03/06 17:09:23 - mmengine - INFO - Epoch(train) [118][2000/5005] lr: 1.0000e-03 eta: 7:13:46 time: 0.2309 data_time: 0.0047 loss: 0.8249 03/06 17:09:47 - mmengine - INFO - Epoch(train) [118][2100/5005] lr: 1.0000e-03 eta: 7:13:23 time: 0.2528 data_time: 0.0048 loss: 0.8869 03/06 17:10:10 - mmengine - INFO - Epoch(train) [118][2200/5005] lr: 1.0000e-03 eta: 7:13:00 time: 0.2471 data_time: 0.0056 loss: 0.6851 03/06 17:10:33 - mmengine - INFO - Epoch(train) [118][2300/5005] lr: 1.0000e-03 eta: 7:12:37 time: 0.2277 data_time: 0.0052 loss: 0.9159 03/06 17:10:57 - mmengine - INFO - Epoch(train) [118][2400/5005] lr: 1.0000e-03 eta: 7:12:14 time: 0.2295 data_time: 0.0050 loss: 0.8447 03/06 17:11:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:11:20 - mmengine - INFO - Epoch(train) [118][2500/5005] lr: 1.0000e-03 eta: 7:11:51 time: 0.2262 data_time: 0.0047 loss: 0.8187 03/06 17:11:44 - mmengine - INFO - Epoch(train) [118][2600/5005] lr: 1.0000e-03 eta: 7:11:28 time: 0.2274 data_time: 0.0049 loss: 0.8608 03/06 17:12:07 - mmengine - INFO - Epoch(train) [118][2700/5005] lr: 1.0000e-03 eta: 7:11:05 time: 0.2304 data_time: 0.0054 loss: 0.9303 03/06 17:12:31 - mmengine - INFO - Epoch(train) [118][2800/5005] lr: 1.0000e-03 eta: 7:10:42 time: 0.2315 data_time: 0.0054 loss: 0.8946 03/06 17:12:54 - mmengine - INFO - Epoch(train) [118][2900/5005] lr: 1.0000e-03 eta: 7:10:19 time: 0.2502 data_time: 0.0049 loss: 0.8539 03/06 17:13:18 - mmengine - INFO - Epoch(train) [118][3000/5005] lr: 1.0000e-03 eta: 7:09:56 time: 0.2269 data_time: 0.0049 loss: 0.7609 03/06 17:13:41 - mmengine - INFO - Epoch(train) [118][3100/5005] lr: 1.0000e-03 eta: 7:09:33 time: 0.2267 data_time: 0.0049 loss: 0.9786 03/06 17:14:04 - mmengine - INFO - Epoch(train) [118][3200/5005] lr: 1.0000e-03 eta: 7:09:11 time: 0.2323 data_time: 0.0048 loss: 0.6316 03/06 17:14:28 - mmengine - INFO - Epoch(train) [118][3300/5005] lr: 1.0000e-03 eta: 7:08:48 time: 0.2450 data_time: 0.0049 loss: 0.9162 03/06 17:14:51 - mmengine - INFO - Epoch(train) [118][3400/5005] lr: 1.0000e-03 eta: 7:08:25 time: 0.2320 data_time: 0.0049 loss: 0.8969 03/06 17:14:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:15:15 - mmengine - INFO - Epoch(train) [118][3500/5005] lr: 1.0000e-03 eta: 7:08:02 time: 0.2316 data_time: 0.0047 loss: 0.8305 03/06 17:15:38 - mmengine - INFO - Epoch(train) [118][3600/5005] lr: 1.0000e-03 eta: 7:07:39 time: 0.2639 data_time: 0.0049 loss: 0.8311 03/06 17:16:01 - mmengine - INFO - Epoch(train) [118][3700/5005] lr: 1.0000e-03 eta: 7:07:16 time: 0.2304 data_time: 0.0052 loss: 0.7755 03/06 17:16:25 - mmengine - INFO - Epoch(train) [118][3800/5005] lr: 1.0000e-03 eta: 7:06:53 time: 0.2273 data_time: 0.0050 loss: 0.8960 03/06 17:16:48 - mmengine - INFO - Epoch(train) [118][3900/5005] lr: 1.0000e-03 eta: 7:06:30 time: 0.2298 data_time: 0.0047 loss: 0.8103 03/06 17:17:12 - mmengine - INFO - Epoch(train) [118][4000/5005] lr: 1.0000e-03 eta: 7:06:07 time: 0.2327 data_time: 0.0047 loss: 0.9388 03/06 17:17:35 - mmengine - INFO - Epoch(train) [118][4100/5005] lr: 1.0000e-03 eta: 7:05:44 time: 0.2291 data_time: 0.0051 loss: 0.8348 03/06 17:17:59 - mmengine - INFO - Epoch(train) [118][4200/5005] lr: 1.0000e-03 eta: 7:05:21 time: 0.2260 data_time: 0.0053 loss: 1.0866 03/06 17:18:23 - mmengine - INFO - Epoch(train) [118][4300/5005] lr: 1.0000e-03 eta: 7:04:58 time: 0.2285 data_time: 0.0050 loss: 1.0142 03/06 17:18:46 - mmengine - INFO - Epoch(train) [118][4400/5005] lr: 1.0000e-03 eta: 7:04:35 time: 0.2286 data_time: 0.0053 loss: 0.7571 03/06 17:18:49 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:19:09 - mmengine - INFO - Epoch(train) [118][4500/5005] lr: 1.0000e-03 eta: 7:04:12 time: 0.2243 data_time: 0.0050 loss: 0.8382 03/06 17:19:33 - mmengine - INFO - Epoch(train) [118][4600/5005] lr: 1.0000e-03 eta: 7:03:49 time: 0.2275 data_time: 0.0049 loss: 0.7545 03/06 17:19:56 - mmengine - INFO - Epoch(train) [118][4700/5005] lr: 1.0000e-03 eta: 7:03:26 time: 0.2283 data_time: 0.0055 loss: 0.8516 03/06 17:20:20 - mmengine - INFO - Epoch(train) [118][4800/5005] lr: 1.0000e-03 eta: 7:03:03 time: 0.2407 data_time: 0.0046 loss: 0.8689 03/06 17:20:44 - mmengine - INFO - Epoch(train) [118][4900/5005] lr: 1.0000e-03 eta: 7:02:40 time: 0.2874 data_time: 0.0045 loss: 0.9048 03/06 17:21:13 - mmengine - INFO - Epoch(train) [118][5000/5005] lr: 1.0000e-03 eta: 7:02:18 time: 0.2819 data_time: 0.0049 loss: 0.8932 03/06 17:21:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:21:17 - mmengine - INFO - Saving checkpoint at 118 epochs 03/06 17:21:33 - mmengine - INFO - Epoch(val) [118][100/196] eta: 0:00:14 time: 0.0175 data_time: 0.0003 03/06 17:21:47 - mmengine - INFO - Epoch(val) [118][196/196] accuracy/top1: 76.9740 accuracy/top5: 93.4700 03/06 17:22:19 - mmengine - INFO - Epoch(train) [119][ 100/5005] lr: 1.0000e-03 eta: 7:01:56 time: 0.2266 data_time: 0.0052 loss: 0.9867 03/06 17:22:44 - mmengine - INFO - Epoch(train) [119][ 200/5005] lr: 1.0000e-03 eta: 7:01:33 time: 0.2312 data_time: 0.0048 loss: 0.9493 03/06 17:23:07 - mmengine - INFO - Epoch(train) [119][ 300/5005] lr: 1.0000e-03 eta: 7:01:10 time: 0.2258 data_time: 0.0057 loss: 0.7822 03/06 17:23:30 - mmengine - INFO - Epoch(train) [119][ 400/5005] lr: 1.0000e-03 eta: 7:00:47 time: 0.2307 data_time: 0.0049 loss: 0.8087 03/06 17:23:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:23:54 - mmengine - INFO - Epoch(train) [119][ 500/5005] lr: 1.0000e-03 eta: 7:00:24 time: 0.2298 data_time: 0.0046 loss: 0.8733 03/06 17:24:17 - mmengine - INFO - Epoch(train) [119][ 600/5005] lr: 1.0000e-03 eta: 7:00:01 time: 0.2270 data_time: 0.0051 loss: 0.8465 03/06 17:24:41 - mmengine - INFO - Epoch(train) [119][ 700/5005] lr: 1.0000e-03 eta: 6:59:38 time: 0.2321 data_time: 0.0050 loss: 0.8144 03/06 17:25:04 - mmengine - INFO - Epoch(train) [119][ 800/5005] lr: 1.0000e-03 eta: 6:59:15 time: 0.2416 data_time: 0.0053 loss: 0.9259 03/06 17:25:28 - mmengine - INFO - Epoch(train) [119][ 900/5005] lr: 1.0000e-03 eta: 6:58:52 time: 0.2434 data_time: 0.0050 loss: 0.8075 03/06 17:25:52 - mmengine - INFO - Epoch(train) [119][1000/5005] lr: 1.0000e-03 eta: 6:58:29 time: 0.2328 data_time: 0.0049 loss: 0.6911 03/06 17:26:15 - mmengine - INFO - Epoch(train) [119][1100/5005] lr: 1.0000e-03 eta: 6:58:06 time: 0.2297 data_time: 0.0057 loss: 0.7744 03/06 17:26:38 - mmengine - INFO - Epoch(train) [119][1200/5005] lr: 1.0000e-03 eta: 6:57:43 time: 0.2291 data_time: 0.0048 loss: 0.7737 03/06 17:27:02 - mmengine - INFO - Epoch(train) [119][1300/5005] lr: 1.0000e-03 eta: 6:57:20 time: 0.2326 data_time: 0.0053 loss: 0.6979 03/06 17:27:25 - mmengine - INFO - Epoch(train) [119][1400/5005] lr: 1.0000e-03 eta: 6:56:57 time: 0.2275 data_time: 0.0045 loss: 0.9436 03/06 17:27:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:27:49 - mmengine - INFO - Epoch(train) [119][1500/5005] lr: 1.0000e-03 eta: 6:56:34 time: 0.2299 data_time: 0.0051 loss: 0.8150 03/06 17:28:12 - mmengine - INFO - Epoch(train) [119][1600/5005] lr: 1.0000e-03 eta: 6:56:11 time: 0.2288 data_time: 0.0046 loss: 0.7201 03/06 17:28:36 - mmengine - INFO - Epoch(train) [119][1700/5005] lr: 1.0000e-03 eta: 6:55:48 time: 0.2500 data_time: 0.0047 loss: 0.9132 03/06 17:28:59 - mmengine - INFO - Epoch(train) [119][1800/5005] lr: 1.0000e-03 eta: 6:55:25 time: 0.2288 data_time: 0.0050 loss: 0.9050 03/06 17:29:23 - mmengine - INFO - Epoch(train) [119][1900/5005] lr: 1.0000e-03 eta: 6:55:02 time: 0.2291 data_time: 0.0050 loss: 0.9956 03/06 17:29:46 - mmengine - INFO - Epoch(train) [119][2000/5005] lr: 1.0000e-03 eta: 6:54:39 time: 0.2272 data_time: 0.0048 loss: 0.7798 03/06 17:30:09 - mmengine - INFO - Epoch(train) [119][2100/5005] lr: 1.0000e-03 eta: 6:54:16 time: 0.2330 data_time: 0.0048 loss: 0.9093 03/06 17:30:33 - mmengine - INFO - Epoch(train) [119][2200/5005] lr: 1.0000e-03 eta: 6:53:53 time: 0.2292 data_time: 0.0051 loss: 0.7415 03/06 17:30:56 - mmengine - INFO - Epoch(train) [119][2300/5005] lr: 1.0000e-03 eta: 6:53:30 time: 0.2323 data_time: 0.0045 loss: 0.7405 03/06 17:31:20 - mmengine - INFO - Epoch(train) [119][2400/5005] lr: 1.0000e-03 eta: 6:53:07 time: 0.2276 data_time: 0.0048 loss: 0.7383 03/06 17:31:22 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:31:43 - mmengine - INFO - Epoch(train) [119][2500/5005] lr: 1.0000e-03 eta: 6:52:44 time: 0.2280 data_time: 0.0044 loss: 0.8092 03/06 17:32:07 - mmengine - INFO - Epoch(train) [119][2600/5005] lr: 1.0000e-03 eta: 6:52:21 time: 0.2250 data_time: 0.0049 loss: 0.9165 03/06 17:32:30 - mmengine - INFO - Epoch(train) [119][2700/5005] lr: 1.0000e-03 eta: 6:51:58 time: 0.2289 data_time: 0.0046 loss: 0.9427 03/06 17:32:53 - mmengine - INFO - Epoch(train) [119][2800/5005] lr: 1.0000e-03 eta: 6:51:35 time: 0.2305 data_time: 0.0049 loss: 0.9336 03/06 17:33:17 - mmengine - INFO - Epoch(train) [119][2900/5005] lr: 1.0000e-03 eta: 6:51:12 time: 0.2448 data_time: 0.0050 loss: 0.9041 03/06 17:33:40 - mmengine - INFO - Epoch(train) [119][3000/5005] lr: 1.0000e-03 eta: 6:50:49 time: 0.2490 data_time: 0.0049 loss: 0.8928 03/06 17:34:04 - mmengine - INFO - Epoch(train) [119][3100/5005] lr: 1.0000e-03 eta: 6:50:26 time: 0.2274 data_time: 0.0049 loss: 0.9467 03/06 17:34:27 - mmengine - INFO - Epoch(train) [119][3200/5005] lr: 1.0000e-03 eta: 6:50:03 time: 0.2313 data_time: 0.0045 loss: 0.9126 03/06 17:34:50 - mmengine - INFO - Epoch(train) [119][3300/5005] lr: 1.0000e-03 eta: 6:49:40 time: 0.2253 data_time: 0.0047 loss: 0.8346 03/06 17:35:14 - mmengine - INFO - Epoch(train) [119][3400/5005] lr: 1.0000e-03 eta: 6:49:17 time: 0.2261 data_time: 0.0050 loss: 0.7139 03/06 17:35:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:35:38 - mmengine - INFO - Epoch(train) [119][3500/5005] lr: 1.0000e-03 eta: 6:48:54 time: 0.2268 data_time: 0.0048 loss: 0.8330 03/06 17:36:01 - mmengine - INFO - Epoch(train) [119][3600/5005] lr: 1.0000e-03 eta: 6:48:31 time: 0.2310 data_time: 0.0054 loss: 0.9156 03/06 17:36:24 - mmengine - INFO - Epoch(train) [119][3700/5005] lr: 1.0000e-03 eta: 6:48:08 time: 0.2300 data_time: 0.0050 loss: 0.8132 03/06 17:36:48 - mmengine - INFO - Epoch(train) [119][3800/5005] lr: 1.0000e-03 eta: 6:47:45 time: 0.2267 data_time: 0.0049 loss: 0.7747 03/06 17:37:11 - mmengine - INFO - Epoch(train) [119][3900/5005] lr: 1.0000e-03 eta: 6:47:22 time: 0.2267 data_time: 0.0045 loss: 0.7266 03/06 17:37:34 - mmengine - INFO - Epoch(train) [119][4000/5005] lr: 1.0000e-03 eta: 6:46:59 time: 0.2282 data_time: 0.0051 loss: 0.9492 03/06 17:37:57 - mmengine - INFO - Epoch(train) [119][4100/5005] lr: 1.0000e-03 eta: 6:46:36 time: 0.2257 data_time: 0.0051 loss: 1.0355 03/06 17:38:21 - mmengine - INFO - Epoch(train) [119][4200/5005] lr: 1.0000e-03 eta: 6:46:13 time: 0.2348 data_time: 0.0046 loss: 0.8627 03/06 17:38:45 - mmengine - INFO - Epoch(train) [119][4300/5005] lr: 1.0000e-03 eta: 6:45:50 time: 0.2320 data_time: 0.0051 loss: 0.7208 03/06 17:39:08 - mmengine - INFO - Epoch(train) [119][4400/5005] lr: 1.0000e-03 eta: 6:45:27 time: 0.2337 data_time: 0.0052 loss: 0.8578 03/06 17:39:10 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:39:31 - mmengine - INFO - Epoch(train) [119][4500/5005] lr: 1.0000e-03 eta: 6:45:04 time: 0.2319 data_time: 0.0050 loss: 0.7787 03/06 17:39:55 - mmengine - INFO - Epoch(train) [119][4600/5005] lr: 1.0000e-03 eta: 6:44:41 time: 0.2294 data_time: 0.0047 loss: 0.9577 03/06 17:40:19 - mmengine - INFO - Epoch(train) [119][4700/5005] lr: 1.0000e-03 eta: 6:44:18 time: 0.2295 data_time: 0.0051 loss: 0.9566 03/06 17:40:42 - mmengine - INFO - Epoch(train) [119][4800/5005] lr: 1.0000e-03 eta: 6:43:55 time: 0.2323 data_time: 0.0047 loss: 0.8524 03/06 17:41:06 - mmengine - INFO - Epoch(train) [119][4900/5005] lr: 1.0000e-03 eta: 6:43:32 time: 0.2915 data_time: 0.0043 loss: 0.8419 03/06 17:41:35 - mmengine - INFO - Epoch(train) [119][5000/5005] lr: 1.0000e-03 eta: 6:43:10 time: 0.2885 data_time: 0.0045 loss: 0.8391 03/06 17:41:37 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:41:39 - mmengine - INFO - Saving checkpoint at 119 epochs 03/06 17:41:55 - mmengine - INFO - Epoch(val) [119][100/196] eta: 0:00:13 time: 0.0201 data_time: 0.0003 03/06 17:42:08 - mmengine - INFO - Epoch(val) [119][196/196] accuracy/top1: 76.9500 accuracy/top5: 93.5320 03/06 17:42:41 - mmengine - INFO - Epoch(train) [120][ 100/5005] lr: 1.0000e-03 eta: 6:42:48 time: 0.2497 data_time: 0.0048 loss: 0.7054 03/06 17:43:05 - mmengine - INFO - Epoch(train) [120][ 200/5005] lr: 1.0000e-03 eta: 6:42:25 time: 0.2513 data_time: 0.0057 loss: 0.8276 03/06 17:43:28 - mmengine - INFO - Epoch(train) [120][ 300/5005] lr: 1.0000e-03 eta: 6:42:02 time: 0.2290 data_time: 0.0053 loss: 0.7599 03/06 17:43:51 - mmengine - INFO - Epoch(train) [120][ 400/5005] lr: 1.0000e-03 eta: 6:41:39 time: 0.2278 data_time: 0.0051 loss: 0.7953 03/06 17:43:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:44:15 - mmengine - INFO - Epoch(train) [120][ 500/5005] lr: 1.0000e-03 eta: 6:41:16 time: 0.2290 data_time: 0.0053 loss: 0.7809 03/06 17:44:38 - mmengine - INFO - Epoch(train) [120][ 600/5005] lr: 1.0000e-03 eta: 6:40:53 time: 0.2282 data_time: 0.0053 loss: 0.9481 03/06 17:45:02 - mmengine - INFO - Epoch(train) [120][ 700/5005] lr: 1.0000e-03 eta: 6:40:30 time: 0.2462 data_time: 0.0053 loss: 0.8135 03/06 17:45:25 - mmengine - INFO - Epoch(train) [120][ 800/5005] lr: 1.0000e-03 eta: 6:40:07 time: 0.2344 data_time: 0.0048 loss: 0.8133 03/06 17:45:49 - mmengine - INFO - Epoch(train) [120][ 900/5005] lr: 1.0000e-03 eta: 6:39:44 time: 0.2253 data_time: 0.0049 loss: 0.9621 03/06 17:46:12 - mmengine - INFO - Epoch(train) [120][1000/5005] lr: 1.0000e-03 eta: 6:39:21 time: 0.2274 data_time: 0.0048 loss: 0.8028 03/06 17:46:36 - mmengine - INFO - Epoch(train) [120][1100/5005] lr: 1.0000e-03 eta: 6:38:58 time: 0.2280 data_time: 0.0053 loss: 0.9702 03/06 17:46:59 - mmengine - INFO - Epoch(train) [120][1200/5005] lr: 1.0000e-03 eta: 6:38:35 time: 0.2274 data_time: 0.0049 loss: 0.9406 03/06 17:47:23 - mmengine - INFO - Epoch(train) [120][1300/5005] lr: 1.0000e-03 eta: 6:38:12 time: 0.2289 data_time: 0.0050 loss: 0.7858 03/06 17:47:46 - mmengine - INFO - Epoch(train) [120][1400/5005] lr: 1.0000e-03 eta: 6:37:49 time: 0.2301 data_time: 0.0061 loss: 0.9195 03/06 17:47:47 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:48:10 - mmengine - INFO - Epoch(train) [120][1500/5005] lr: 1.0000e-03 eta: 6:37:26 time: 0.2267 data_time: 0.0052 loss: 0.8770 03/06 17:48:33 - mmengine - INFO - Epoch(train) [120][1600/5005] lr: 1.0000e-03 eta: 6:37:03 time: 0.2271 data_time: 0.0048 loss: 0.8816 03/06 17:48:56 - mmengine - INFO - Epoch(train) [120][1700/5005] lr: 1.0000e-03 eta: 6:36:40 time: 0.2271 data_time: 0.0053 loss: 0.8677 03/06 17:49:20 - mmengine - INFO - Epoch(train) [120][1800/5005] lr: 1.0000e-03 eta: 6:36:17 time: 0.2317 data_time: 0.0052 loss: 0.7323 03/06 17:49:43 - mmengine - INFO - Epoch(train) [120][1900/5005] lr: 1.0000e-03 eta: 6:35:54 time: 0.2266 data_time: 0.0050 loss: 0.9175 03/06 17:50:07 - mmengine - INFO - Epoch(train) [120][2000/5005] lr: 1.0000e-03 eta: 6:35:31 time: 0.2513 data_time: 0.0048 loss: 0.8969 03/06 17:50:30 - mmengine - INFO - Epoch(train) [120][2100/5005] lr: 1.0000e-03 eta: 6:35:08 time: 0.2265 data_time: 0.0051 loss: 0.8491 03/06 17:50:53 - mmengine - INFO - Epoch(train) [120][2200/5005] lr: 1.0000e-03 eta: 6:34:45 time: 0.2290 data_time: 0.0052 loss: 0.6915 03/06 17:51:17 - mmengine - INFO - Epoch(train) [120][2300/5005] lr: 1.0000e-03 eta: 6:34:22 time: 0.2255 data_time: 0.0047 loss: 0.8215 03/06 17:51:40 - mmengine - INFO - Epoch(train) [120][2400/5005] lr: 1.0000e-03 eta: 6:33:59 time: 0.2476 data_time: 0.0050 loss: 0.8364 03/06 17:51:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:52:04 - mmengine - INFO - Epoch(train) [120][2500/5005] lr: 1.0000e-03 eta: 6:33:36 time: 0.2301 data_time: 0.0052 loss: 0.8480 03/06 17:52:27 - mmengine - INFO - Epoch(train) [120][2600/5005] lr: 1.0000e-03 eta: 6:33:13 time: 0.2294 data_time: 0.0053 loss: 0.9683 03/06 17:52:51 - mmengine - INFO - Epoch(train) [120][2700/5005] lr: 1.0000e-03 eta: 6:32:50 time: 0.2294 data_time: 0.0048 loss: 0.8390 03/06 17:53:14 - mmengine - INFO - Epoch(train) [120][2800/5005] lr: 1.0000e-03 eta: 6:32:27 time: 0.2293 data_time: 0.0053 loss: 0.7963 03/06 17:53:38 - mmengine - INFO - Epoch(train) [120][2900/5005] lr: 1.0000e-03 eta: 6:32:04 time: 0.2286 data_time: 0.0048 loss: 0.9514 03/06 17:54:01 - mmengine - INFO - Epoch(train) [120][3000/5005] lr: 1.0000e-03 eta: 6:31:41 time: 0.2588 data_time: 0.0053 loss: 1.0575 03/06 17:54:25 - mmengine - INFO - Epoch(train) [120][3100/5005] lr: 1.0000e-03 eta: 6:31:18 time: 0.2304 data_time: 0.0051 loss: 0.8888 03/06 17:54:48 - mmengine - INFO - Epoch(train) [120][3200/5005] lr: 1.0000e-03 eta: 6:30:55 time: 0.2254 data_time: 0.0048 loss: 0.8447 03/06 17:55:12 - mmengine - INFO - Epoch(train) [120][3300/5005] lr: 1.0000e-03 eta: 6:30:32 time: 0.2345 data_time: 0.0056 loss: 0.9868 03/06 17:55:35 - mmengine - INFO - Epoch(train) [120][3400/5005] lr: 1.0000e-03 eta: 6:30:09 time: 0.2260 data_time: 0.0049 loss: 0.7281 03/06 17:55:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:55:59 - mmengine - INFO - Epoch(train) [120][3500/5005] lr: 1.0000e-03 eta: 6:29:46 time: 0.2253 data_time: 0.0051 loss: 0.9135 03/06 17:56:22 - mmengine - INFO - Epoch(train) [120][3600/5005] lr: 1.0000e-03 eta: 6:29:23 time: 0.2291 data_time: 0.0047 loss: 1.0679 03/06 17:56:46 - mmengine - INFO - Epoch(train) [120][3700/5005] lr: 1.0000e-03 eta: 6:29:00 time: 0.2264 data_time: 0.0048 loss: 0.8161 03/06 17:57:09 - mmengine - INFO - Epoch(train) [120][3800/5005] lr: 1.0000e-03 eta: 6:28:37 time: 0.2241 data_time: 0.0048 loss: 0.6542 03/06 17:57:33 - mmengine - INFO - Epoch(train) [120][3900/5005] lr: 1.0000e-03 eta: 6:28:14 time: 0.2284 data_time: 0.0053 loss: 0.8484 03/06 17:57:56 - mmengine - INFO - Epoch(train) [120][4000/5005] lr: 1.0000e-03 eta: 6:27:51 time: 0.2289 data_time: 0.0049 loss: 1.1520 03/06 17:58:20 - mmengine - INFO - Epoch(train) [120][4100/5005] lr: 1.0000e-03 eta: 6:27:28 time: 0.2312 data_time: 0.0050 loss: 0.8873 03/06 17:58:43 - mmengine - INFO - Epoch(train) [120][4200/5005] lr: 1.0000e-03 eta: 6:27:05 time: 0.2245 data_time: 0.0052 loss: 0.9630 03/06 17:59:06 - mmengine - INFO - Epoch(train) [120][4300/5005] lr: 1.0000e-03 eta: 6:26:42 time: 0.2269 data_time: 0.0054 loss: 0.8433 03/06 17:59:30 - mmengine - INFO - Epoch(train) [120][4400/5005] lr: 1.0000e-03 eta: 6:26:19 time: 0.2250 data_time: 0.0052 loss: 0.8634 03/06 17:59:31 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 17:59:53 - mmengine - INFO - Epoch(train) [120][4500/5005] lr: 1.0000e-03 eta: 6:25:56 time: 0.2508 data_time: 0.0053 loss: 0.8719 03/06 18:00:17 - mmengine - INFO - Epoch(train) [120][4600/5005] lr: 1.0000e-03 eta: 6:25:33 time: 0.2261 data_time: 0.0055 loss: 0.9023 03/06 18:00:40 - mmengine - INFO - Epoch(train) [120][4700/5005] lr: 1.0000e-03 eta: 6:25:10 time: 0.2276 data_time: 0.0050 loss: 0.6584 03/06 18:01:03 - mmengine - INFO - Epoch(train) [120][4800/5005] lr: 1.0000e-03 eta: 6:24:47 time: 0.2266 data_time: 0.0051 loss: 0.8501 03/06 18:01:28 - mmengine - INFO - Epoch(train) [120][4900/5005] lr: 1.0000e-03 eta: 6:24:24 time: 0.2849 data_time: 0.0046 loss: 0.8987 03/06 18:01:57 - mmengine - INFO - Epoch(train) [120][5000/5005] lr: 1.0000e-03 eta: 6:24:02 time: 0.2822 data_time: 0.0049 loss: 0.8168 03/06 18:01:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:02:00 - mmengine - INFO - Saving checkpoint at 120 epochs 03/06 18:02:16 - mmengine - INFO - Epoch(val) [120][100/196] eta: 0:00:13 time: 0.0186 data_time: 0.0002 03/06 18:02:29 - mmengine - INFO - Epoch(val) [120][196/196] accuracy/top1: 76.9160 accuracy/top5: 93.4080 03/06 18:03:02 - mmengine - INFO - Epoch(train) [121][ 100/5005] lr: 1.0000e-04 eta: 6:23:40 time: 0.2530 data_time: 0.0052 loss: 0.8965 03/06 18:03:25 - mmengine - INFO - Epoch(train) [121][ 200/5005] lr: 1.0000e-04 eta: 6:23:17 time: 0.2317 data_time: 0.0050 loss: 0.7806 03/06 18:03:49 - mmengine - INFO - Epoch(train) [121][ 300/5005] lr: 1.0000e-04 eta: 6:22:54 time: 0.2305 data_time: 0.0051 loss: 0.8055 03/06 18:04:12 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:04:12 - mmengine - INFO - Epoch(train) [121][ 400/5005] lr: 1.0000e-04 eta: 6:22:31 time: 0.2316 data_time: 0.0051 loss: 0.7863 03/06 18:04:35 - mmengine - INFO - Epoch(train) [121][ 500/5005] lr: 1.0000e-04 eta: 6:22:08 time: 0.2295 data_time: 0.0048 loss: 0.6992 03/06 18:04:59 - mmengine - INFO - Epoch(train) [121][ 600/5005] lr: 1.0000e-04 eta: 6:21:45 time: 0.2273 data_time: 0.0051 loss: 0.7447 03/06 18:05:22 - mmengine - INFO - Epoch(train) [121][ 700/5005] lr: 1.0000e-04 eta: 6:21:22 time: 0.2263 data_time: 0.0048 loss: 0.9231 03/06 18:05:46 - mmengine - INFO - Epoch(train) [121][ 800/5005] lr: 1.0000e-04 eta: 6:20:59 time: 0.2295 data_time: 0.0049 loss: 0.7700 03/06 18:06:09 - mmengine - INFO - Epoch(train) [121][ 900/5005] lr: 1.0000e-04 eta: 6:20:36 time: 0.2295 data_time: 0.0050 loss: 0.7390 03/06 18:06:33 - mmengine - INFO - Epoch(train) [121][1000/5005] lr: 1.0000e-04 eta: 6:20:13 time: 0.2309 data_time: 0.0048 loss: 0.8599 03/06 18:06:56 - mmengine - INFO - Epoch(train) [121][1100/5005] lr: 1.0000e-04 eta: 6:19:49 time: 0.2282 data_time: 0.0052 loss: 0.8711 03/06 18:07:19 - mmengine - INFO - Epoch(train) [121][1200/5005] lr: 1.0000e-04 eta: 6:19:26 time: 0.2270 data_time: 0.0055 loss: 0.8618 03/06 18:07:43 - mmengine - INFO - Epoch(train) [121][1300/5005] lr: 1.0000e-04 eta: 6:19:03 time: 0.2284 data_time: 0.0048 loss: 0.7826 03/06 18:08:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:08:06 - mmengine - INFO - Epoch(train) [121][1400/5005] lr: 1.0000e-04 eta: 6:18:40 time: 0.2516 data_time: 0.0049 loss: 0.9006 03/06 18:08:30 - mmengine - INFO - Epoch(train) [121][1500/5005] lr: 1.0000e-04 eta: 6:18:17 time: 0.2303 data_time: 0.0051 loss: 0.8099 03/06 18:08:53 - mmengine - INFO - Epoch(train) [121][1600/5005] lr: 1.0000e-04 eta: 6:17:54 time: 0.2261 data_time: 0.0049 loss: 0.8094 03/06 18:09:16 - mmengine - INFO - Epoch(train) [121][1700/5005] lr: 1.0000e-04 eta: 6:17:31 time: 0.2327 data_time: 0.0050 loss: 0.8643 03/06 18:09:40 - mmengine - INFO - Epoch(train) [121][1800/5005] lr: 1.0000e-04 eta: 6:17:08 time: 0.2290 data_time: 0.0048 loss: 0.6469 03/06 18:10:03 - mmengine - INFO - Epoch(train) [121][1900/5005] lr: 1.0000e-04 eta: 6:16:45 time: 0.2312 data_time: 0.0049 loss: 0.7599 03/06 18:10:26 - mmengine - INFO - Epoch(train) [121][2000/5005] lr: 1.0000e-04 eta: 6:16:22 time: 0.2300 data_time: 0.0049 loss: 0.9040 03/06 18:10:50 - mmengine - INFO - Epoch(train) [121][2100/5005] lr: 1.0000e-04 eta: 6:15:59 time: 0.2319 data_time: 0.0053 loss: 0.7935 03/06 18:11:14 - mmengine - INFO - Epoch(train) [121][2200/5005] lr: 1.0000e-04 eta: 6:15:36 time: 0.2283 data_time: 0.0049 loss: 0.6960 03/06 18:11:37 - mmengine - INFO - Epoch(train) [121][2300/5005] lr: 1.0000e-04 eta: 6:15:13 time: 0.2316 data_time: 0.0050 loss: 0.7490 03/06 18:12:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:12:00 - mmengine - INFO - Epoch(train) [121][2400/5005] lr: 1.0000e-04 eta: 6:14:50 time: 0.2303 data_time: 0.0051 loss: 0.7801 03/06 18:12:24 - mmengine - INFO - Epoch(train) [121][2500/5005] lr: 1.0000e-04 eta: 6:14:27 time: 0.2318 data_time: 0.0053 loss: 0.7339 03/06 18:12:47 - mmengine - INFO - Epoch(train) [121][2600/5005] lr: 1.0000e-04 eta: 6:14:04 time: 0.2600 data_time: 0.0050 loss: 0.9234 03/06 18:13:11 - mmengine - INFO - Epoch(train) [121][2700/5005] lr: 1.0000e-04 eta: 6:13:41 time: 0.2312 data_time: 0.0051 loss: 0.8648 03/06 18:13:34 - mmengine - INFO - Epoch(train) [121][2800/5005] lr: 1.0000e-04 eta: 6:13:18 time: 0.2265 data_time: 0.0048 loss: 0.8426 03/06 18:13:58 - mmengine - INFO - Epoch(train) [121][2900/5005] lr: 1.0000e-04 eta: 6:12:55 time: 0.2309 data_time: 0.0050 loss: 0.7583 03/06 18:14:21 - mmengine - INFO - Epoch(train) [121][3000/5005] lr: 1.0000e-04 eta: 6:12:32 time: 0.2458 data_time: 0.0053 loss: 0.9162 03/06 18:14:45 - mmengine - INFO - Epoch(train) [121][3100/5005] lr: 1.0000e-04 eta: 6:12:09 time: 0.2306 data_time: 0.0050 loss: 0.7979 03/06 18:15:08 - mmengine - INFO - Epoch(train) [121][3200/5005] lr: 1.0000e-04 eta: 6:11:46 time: 0.2252 data_time: 0.0051 loss: 0.7945 03/06 18:15:32 - mmengine - INFO - Epoch(train) [121][3300/5005] lr: 1.0000e-04 eta: 6:11:23 time: 0.2264 data_time: 0.0050 loss: 0.8917 03/06 18:15:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:15:55 - mmengine - INFO - Epoch(train) [121][3400/5005] lr: 1.0000e-04 eta: 6:11:00 time: 0.2456 data_time: 0.0048 loss: 0.7459 03/06 18:16:19 - mmengine - INFO - Epoch(train) [121][3500/5005] lr: 1.0000e-04 eta: 6:10:37 time: 0.2288 data_time: 0.0049 loss: 0.7239 03/06 18:16:42 - mmengine - INFO - Epoch(train) [121][3600/5005] lr: 1.0000e-04 eta: 6:10:14 time: 0.2268 data_time: 0.0046 loss: 0.8368 03/06 18:17:05 - mmengine - INFO - Epoch(train) [121][3700/5005] lr: 1.0000e-04 eta: 6:09:51 time: 0.2327 data_time: 0.0049 loss: 0.7032 03/06 18:17:29 - mmengine - INFO - Epoch(train) [121][3800/5005] lr: 1.0000e-04 eta: 6:09:28 time: 0.2298 data_time: 0.0050 loss: 0.7610 03/06 18:17:53 - mmengine - INFO - Epoch(train) [121][3900/5005] lr: 1.0000e-04 eta: 6:09:05 time: 0.2328 data_time: 0.0050 loss: 0.8746 03/06 18:18:16 - mmengine - INFO - Epoch(train) [121][4000/5005] lr: 1.0000e-04 eta: 6:08:42 time: 0.2304 data_time: 0.0055 loss: 0.9280 03/06 18:18:39 - mmengine - INFO - Epoch(train) [121][4100/5005] lr: 1.0000e-04 eta: 6:08:19 time: 0.2279 data_time: 0.0047 loss: 0.8191 03/06 18:19:03 - mmengine - INFO - Epoch(train) [121][4200/5005] lr: 1.0000e-04 eta: 6:07:56 time: 0.2281 data_time: 0.0047 loss: 0.7499 03/06 18:19:27 - mmengine - INFO - Epoch(train) [121][4300/5005] lr: 1.0000e-04 eta: 6:07:33 time: 0.2256 data_time: 0.0051 loss: 0.6591 03/06 18:19:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:19:50 - mmengine - INFO - Epoch(train) [121][4400/5005] lr: 1.0000e-04 eta: 6:07:10 time: 0.2295 data_time: 0.0051 loss: 0.6820 03/06 18:20:13 - mmengine - INFO - Epoch(train) [121][4500/5005] lr: 1.0000e-04 eta: 6:06:47 time: 0.2217 data_time: 0.0048 loss: 0.8066 03/06 18:20:37 - mmengine - INFO - Epoch(train) [121][4600/5005] lr: 1.0000e-04 eta: 6:06:24 time: 0.2294 data_time: 0.0051 loss: 0.8415 03/06 18:21:00 - mmengine - INFO - Epoch(train) [121][4700/5005] lr: 1.0000e-04 eta: 6:06:01 time: 0.2388 data_time: 0.0052 loss: 0.8794 03/06 18:21:24 - mmengine - INFO - Epoch(train) [121][4800/5005] lr: 1.0000e-04 eta: 6:05:38 time: 0.2296 data_time: 0.0047 loss: 0.7187 03/06 18:21:48 - mmengine - INFO - Epoch(train) [121][4900/5005] lr: 1.0000e-04 eta: 6:05:16 time: 0.2826 data_time: 0.0048 loss: 0.8831 03/06 18:22:17 - mmengine - INFO - Epoch(train) [121][5000/5005] lr: 1.0000e-04 eta: 6:04:53 time: 0.2886 data_time: 0.0050 loss: 0.8613 03/06 18:22:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:22:21 - mmengine - INFO - Saving checkpoint at 121 epochs 03/06 18:22:36 - mmengine - INFO - Epoch(val) [121][100/196] eta: 0:00:13 time: 0.0213 data_time: 0.0004 03/06 18:22:50 - mmengine - INFO - Epoch(val) [121][196/196] accuracy/top1: 77.6100 accuracy/top5: 93.7260 03/06 18:23:22 - mmengine - INFO - Epoch(train) [122][ 100/5005] lr: 1.0000e-04 eta: 6:04:31 time: 0.2250 data_time: 0.0051 loss: 0.9937 03/06 18:23:46 - mmengine - INFO - Epoch(train) [122][ 200/5005] lr: 1.0000e-04 eta: 6:04:08 time: 0.2306 data_time: 0.0058 loss: 0.7795 03/06 18:24:10 - mmengine - INFO - Epoch(train) [122][ 300/5005] lr: 1.0000e-04 eta: 6:03:45 time: 0.2480 data_time: 0.0050 loss: 0.9180 03/06 18:24:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:24:33 - mmengine - INFO - Epoch(train) [122][ 400/5005] lr: 1.0000e-04 eta: 6:03:22 time: 0.2256 data_time: 0.0049 loss: 0.6593 03/06 18:24:57 - mmengine - INFO - Epoch(train) [122][ 500/5005] lr: 1.0000e-04 eta: 6:02:59 time: 0.2264 data_time: 0.0049 loss: 0.9196 03/06 18:25:20 - mmengine - INFO - Epoch(train) [122][ 600/5005] lr: 1.0000e-04 eta: 6:02:36 time: 0.2314 data_time: 0.0050 loss: 0.7876 03/06 18:25:44 - mmengine - INFO - Epoch(train) [122][ 700/5005] lr: 1.0000e-04 eta: 6:02:13 time: 0.2420 data_time: 0.0047 loss: 0.8235 03/06 18:26:07 - mmengine - INFO - Epoch(train) [122][ 800/5005] lr: 1.0000e-04 eta: 6:01:50 time: 0.2241 data_time: 0.0048 loss: 1.0422 03/06 18:26:31 - mmengine - INFO - Epoch(train) [122][ 900/5005] lr: 1.0000e-04 eta: 6:01:27 time: 0.2489 data_time: 0.0047 loss: 0.8077 03/06 18:26:54 - mmengine - INFO - Epoch(train) [122][1000/5005] lr: 1.0000e-04 eta: 6:01:04 time: 0.2271 data_time: 0.0048 loss: 0.7802 03/06 18:27:17 - mmengine - INFO - Epoch(train) [122][1100/5005] lr: 1.0000e-04 eta: 6:00:41 time: 0.2268 data_time: 0.0048 loss: 0.8807 03/06 18:27:41 - mmengine - INFO - Epoch(train) [122][1200/5005] lr: 1.0000e-04 eta: 6:00:18 time: 0.2306 data_time: 0.0051 loss: 0.7644 03/06 18:28:04 - mmengine - INFO - Epoch(train) [122][1300/5005] lr: 1.0000e-04 eta: 5:59:55 time: 0.2296 data_time: 0.0046 loss: 0.9969 03/06 18:28:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:28:28 - mmengine - INFO - Epoch(train) [122][1400/5005] lr: 1.0000e-04 eta: 5:59:32 time: 0.2304 data_time: 0.0053 loss: 1.0039 03/06 18:28:51 - mmengine - INFO - Epoch(train) [122][1500/5005] lr: 1.0000e-04 eta: 5:59:09 time: 0.2300 data_time: 0.0050 loss: 0.8476 03/06 18:29:15 - mmengine - INFO - Epoch(train) [122][1600/5005] lr: 1.0000e-04 eta: 5:58:46 time: 0.2257 data_time: 0.0048 loss: 0.8652 03/06 18:29:38 - mmengine - INFO - Epoch(train) [122][1700/5005] lr: 1.0000e-04 eta: 5:58:23 time: 0.2293 data_time: 0.0045 loss: 0.8233 03/06 18:30:02 - mmengine - INFO - Epoch(train) [122][1800/5005] lr: 1.0000e-04 eta: 5:58:00 time: 0.2301 data_time: 0.0048 loss: 0.8724 03/06 18:30:25 - mmengine - INFO - Epoch(train) [122][1900/5005] lr: 1.0000e-04 eta: 5:57:37 time: 0.2262 data_time: 0.0047 loss: 0.7577 03/06 18:30:49 - mmengine - INFO - Epoch(train) [122][2000/5005] lr: 1.0000e-04 eta: 5:57:14 time: 0.2290 data_time: 0.0045 loss: 0.7094 03/06 18:31:12 - mmengine - INFO - Epoch(train) [122][2100/5005] lr: 1.0000e-04 eta: 5:56:51 time: 0.2285 data_time: 0.0051 loss: 0.9783 03/06 18:31:35 - mmengine - INFO - Epoch(train) [122][2200/5005] lr: 1.0000e-04 eta: 5:56:28 time: 0.2301 data_time: 0.0047 loss: 0.8517 03/06 18:31:59 - mmengine - INFO - Epoch(train) [122][2300/5005] lr: 1.0000e-04 eta: 5:56:05 time: 0.2284 data_time: 0.0045 loss: 0.8847 03/06 18:32:21 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:32:22 - mmengine - INFO - Epoch(train) [122][2400/5005] lr: 1.0000e-04 eta: 5:55:42 time: 0.2308 data_time: 0.0051 loss: 0.8309 03/06 18:32:46 - mmengine - INFO - Epoch(train) [122][2500/5005] lr: 1.0000e-04 eta: 5:55:19 time: 0.2300 data_time: 0.0047 loss: 0.8617 03/06 18:33:09 - mmengine - INFO - Epoch(train) [122][2600/5005] lr: 1.0000e-04 eta: 5:54:56 time: 0.2294 data_time: 0.0047 loss: 0.7354 03/06 18:33:32 - mmengine - INFO - Epoch(train) [122][2700/5005] lr: 1.0000e-04 eta: 5:54:33 time: 0.2327 data_time: 0.0046 loss: 0.7095 03/06 18:33:56 - mmengine - INFO - Epoch(train) [122][2800/5005] lr: 1.0000e-04 eta: 5:54:10 time: 0.2259 data_time: 0.0048 loss: 0.6978 03/06 18:34:19 - mmengine - INFO - Epoch(train) [122][2900/5005] lr: 1.0000e-04 eta: 5:53:47 time: 0.2277 data_time: 0.0050 loss: 0.8534 03/06 18:34:42 - mmengine - INFO - Epoch(train) [122][3000/5005] lr: 1.0000e-04 eta: 5:53:24 time: 0.2250 data_time: 0.0044 loss: 0.9174 03/06 18:35:06 - mmengine - INFO - Epoch(train) [122][3100/5005] lr: 1.0000e-04 eta: 5:53:01 time: 0.2470 data_time: 0.0044 loss: 0.8772 03/06 18:35:29 - mmengine - INFO - Epoch(train) [122][3200/5005] lr: 1.0000e-04 eta: 5:52:38 time: 0.2306 data_time: 0.0058 loss: 0.7715 03/06 18:35:53 - mmengine - INFO - Epoch(train) [122][3300/5005] lr: 1.0000e-04 eta: 5:52:15 time: 0.2267 data_time: 0.0045 loss: 0.8732 03/06 18:36:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:36:16 - mmengine - INFO - Epoch(train) [122][3400/5005] lr: 1.0000e-04 eta: 5:51:52 time: 0.2307 data_time: 0.0047 loss: 0.8101 03/06 18:36:39 - mmengine - INFO - Epoch(train) [122][3500/5005] lr: 1.0000e-04 eta: 5:51:28 time: 0.2299 data_time: 0.0047 loss: 0.8101 03/06 18:37:03 - mmengine - INFO - Epoch(train) [122][3600/5005] lr: 1.0000e-04 eta: 5:51:05 time: 0.2381 data_time: 0.0047 loss: 0.8431 03/06 18:37:26 - mmengine - INFO - Epoch(train) [122][3700/5005] lr: 1.0000e-04 eta: 5:50:43 time: 0.2284 data_time: 0.0052 loss: 0.7240 03/06 18:37:49 - mmengine - INFO - Epoch(train) [122][3800/5005] lr: 1.0000e-04 eta: 5:50:19 time: 0.2238 data_time: 0.0045 loss: 0.7432 03/06 18:38:13 - mmengine - INFO - Epoch(train) [122][3900/5005] lr: 1.0000e-04 eta: 5:49:56 time: 0.2265 data_time: 0.0047 loss: 0.9173 03/06 18:38:36 - mmengine - INFO - Epoch(train) [122][4000/5005] lr: 1.0000e-04 eta: 5:49:33 time: 0.2310 data_time: 0.0054 loss: 0.8794 03/06 18:39:00 - mmengine - INFO - Epoch(train) [122][4100/5005] lr: 1.0000e-04 eta: 5:49:10 time: 0.2480 data_time: 0.0046 loss: 0.7771 03/06 18:39:23 - mmengine - INFO - Epoch(train) [122][4200/5005] lr: 1.0000e-04 eta: 5:48:47 time: 0.2263 data_time: 0.0050 loss: 0.8635 03/06 18:39:47 - mmengine - INFO - Epoch(train) [122][4300/5005] lr: 1.0000e-04 eta: 5:48:24 time: 0.2294 data_time: 0.0044 loss: 0.7969 03/06 18:40:09 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:40:10 - mmengine - INFO - Epoch(train) [122][4400/5005] lr: 1.0000e-04 eta: 5:48:01 time: 0.2497 data_time: 0.0045 loss: 0.6837 03/06 18:40:34 - mmengine - INFO - Epoch(train) [122][4500/5005] lr: 1.0000e-04 eta: 5:47:38 time: 0.2418 data_time: 0.0048 loss: 0.7530 03/06 18:40:57 - mmengine - INFO - Epoch(train) [122][4600/5005] lr: 1.0000e-04 eta: 5:47:15 time: 0.2267 data_time: 0.0048 loss: 0.8102 03/06 18:41:20 - mmengine - INFO - Epoch(train) [122][4700/5005] lr: 1.0000e-04 eta: 5:46:52 time: 0.2268 data_time: 0.0048 loss: 0.8344 03/06 18:41:44 - mmengine - INFO - Epoch(train) [122][4800/5005] lr: 1.0000e-04 eta: 5:46:29 time: 0.2306 data_time: 0.0046 loss: 0.7751 03/06 18:42:09 - mmengine - INFO - Epoch(train) [122][4900/5005] lr: 1.0000e-04 eta: 5:46:07 time: 0.2948 data_time: 0.0046 loss: 0.8440 03/06 18:42:37 - mmengine - INFO - Epoch(train) [122][5000/5005] lr: 1.0000e-04 eta: 5:45:44 time: 0.2915 data_time: 0.0045 loss: 0.7830 03/06 18:42:39 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:42:41 - mmengine - INFO - Saving checkpoint at 122 epochs 03/06 18:42:57 - mmengine - INFO - Epoch(val) [122][100/196] eta: 0:00:13 time: 0.0202 data_time: 0.0003 03/06 18:43:10 - mmengine - INFO - Epoch(val) [122][196/196] accuracy/top1: 77.5340 accuracy/top5: 93.7860 03/06 18:43:44 - mmengine - INFO - Epoch(train) [123][ 100/5005] lr: 1.0000e-04 eta: 5:45:22 time: 0.2312 data_time: 0.0056 loss: 0.6789 03/06 18:44:07 - mmengine - INFO - Epoch(train) [123][ 200/5005] lr: 1.0000e-04 eta: 5:44:59 time: 0.2262 data_time: 0.0055 loss: 0.7949 03/06 18:44:31 - mmengine - INFO - Epoch(train) [123][ 300/5005] lr: 1.0000e-04 eta: 5:44:36 time: 0.2339 data_time: 0.0065 loss: 0.8232 03/06 18:44:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:44:54 - mmengine - INFO - Epoch(train) [123][ 400/5005] lr: 1.0000e-04 eta: 5:44:13 time: 0.2270 data_time: 0.0056 loss: 0.8727 03/06 18:45:18 - mmengine - INFO - Epoch(train) [123][ 500/5005] lr: 1.0000e-04 eta: 5:43:50 time: 0.2292 data_time: 0.0052 loss: 0.9021 03/06 18:45:41 - mmengine - INFO - Epoch(train) [123][ 600/5005] lr: 1.0000e-04 eta: 5:43:27 time: 0.2272 data_time: 0.0055 loss: 0.9197 03/06 18:46:05 - mmengine - INFO - Epoch(train) [123][ 700/5005] lr: 1.0000e-04 eta: 5:43:04 time: 0.2290 data_time: 0.0053 loss: 0.6324 03/06 18:46:29 - mmengine - INFO - Epoch(train) [123][ 800/5005] lr: 1.0000e-04 eta: 5:42:41 time: 0.2606 data_time: 0.0053 loss: 0.7155 03/06 18:46:53 - mmengine - INFO - Epoch(train) [123][ 900/5005] lr: 1.0000e-04 eta: 5:42:18 time: 0.2272 data_time: 0.0055 loss: 0.9001 03/06 18:47:16 - mmengine - INFO - Epoch(train) [123][1000/5005] lr: 1.0000e-04 eta: 5:41:55 time: 0.2252 data_time: 0.0051 loss: 0.7798 03/06 18:47:39 - mmengine - INFO - Epoch(train) [123][1100/5005] lr: 1.0000e-04 eta: 5:41:32 time: 0.2267 data_time: 0.0053 loss: 0.8825 03/06 18:48:03 - mmengine - INFO - Epoch(train) [123][1200/5005] lr: 1.0000e-04 eta: 5:41:09 time: 0.2288 data_time: 0.0053 loss: 0.6579 03/06 18:48:27 - mmengine - INFO - Epoch(train) [123][1300/5005] lr: 1.0000e-04 eta: 5:40:46 time: 0.2354 data_time: 0.0050 loss: 0.9048 03/06 18:48:47 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:48:50 - mmengine - INFO - Epoch(train) [123][1400/5005] lr: 1.0000e-04 eta: 5:40:23 time: 0.2268 data_time: 0.0052 loss: 0.7485 03/06 18:49:13 - mmengine - INFO - Epoch(train) [123][1500/5005] lr: 1.0000e-04 eta: 5:40:00 time: 0.2258 data_time: 0.0049 loss: 0.7174 03/06 18:49:37 - mmengine - INFO - Epoch(train) [123][1600/5005] lr: 1.0000e-04 eta: 5:39:37 time: 0.2248 data_time: 0.0050 loss: 0.7408 03/06 18:50:00 - mmengine - INFO - Epoch(train) [123][1700/5005] lr: 1.0000e-04 eta: 5:39:14 time: 0.2289 data_time: 0.0051 loss: 0.8222 03/06 18:50:24 - mmengine - INFO - Epoch(train) [123][1800/5005] lr: 1.0000e-04 eta: 5:38:51 time: 0.2293 data_time: 0.0051 loss: 0.7313 03/06 18:50:47 - mmengine - INFO - Epoch(train) [123][1900/5005] lr: 1.0000e-04 eta: 5:38:28 time: 0.2283 data_time: 0.0052 loss: 0.7069 03/06 18:51:11 - mmengine - INFO - Epoch(train) [123][2000/5005] lr: 1.0000e-04 eta: 5:38:05 time: 0.2521 data_time: 0.0050 loss: 0.7816 03/06 18:51:34 - mmengine - INFO - Epoch(train) [123][2100/5005] lr: 1.0000e-04 eta: 5:37:42 time: 0.2267 data_time: 0.0053 loss: 0.8865 03/06 18:51:58 - mmengine - INFO - Epoch(train) [123][2200/5005] lr: 1.0000e-04 eta: 5:37:19 time: 0.2303 data_time: 0.0052 loss: 0.7758 03/06 18:52:21 - mmengine - INFO - Epoch(train) [123][2300/5005] lr: 1.0000e-04 eta: 5:36:55 time: 0.2269 data_time: 0.0051 loss: 0.6391 03/06 18:52:42 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:52:44 - mmengine - INFO - Epoch(train) [123][2400/5005] lr: 1.0000e-04 eta: 5:36:32 time: 0.2313 data_time: 0.0053 loss: 0.8590 03/06 18:53:08 - mmengine - INFO - Epoch(train) [123][2500/5005] lr: 1.0000e-04 eta: 5:36:09 time: 0.2351 data_time: 0.0051 loss: 0.7388 03/06 18:53:32 - mmengine - INFO - Epoch(train) [123][2600/5005] lr: 1.0000e-04 eta: 5:35:47 time: 0.2285 data_time: 0.0053 loss: 0.8521 03/06 18:53:55 - mmengine - INFO - Epoch(train) [123][2700/5005] lr: 1.0000e-04 eta: 5:35:23 time: 0.2277 data_time: 0.0050 loss: 0.7852 03/06 18:54:19 - mmengine - INFO - Epoch(train) [123][2800/5005] lr: 1.0000e-04 eta: 5:35:00 time: 0.2304 data_time: 0.0052 loss: 0.7679 03/06 18:54:42 - mmengine - INFO - Epoch(train) [123][2900/5005] lr: 1.0000e-04 eta: 5:34:37 time: 0.2295 data_time: 0.0050 loss: 0.7676 03/06 18:55:06 - mmengine - INFO - Epoch(train) [123][3000/5005] lr: 1.0000e-04 eta: 5:34:15 time: 0.2267 data_time: 0.0057 loss: 0.7784 03/06 18:55:30 - mmengine - INFO - Epoch(train) [123][3100/5005] lr: 1.0000e-04 eta: 5:33:52 time: 0.2317 data_time: 0.0051 loss: 0.7101 03/06 18:55:53 - mmengine - INFO - Epoch(train) [123][3200/5005] lr: 1.0000e-04 eta: 5:33:28 time: 0.2319 data_time: 0.0053 loss: 0.6784 03/06 18:56:16 - mmengine - INFO - Epoch(train) [123][3300/5005] lr: 1.0000e-04 eta: 5:33:05 time: 0.2318 data_time: 0.0050 loss: 0.7593 03/06 18:56:38 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 18:56:40 - mmengine - INFO - Epoch(train) [123][3400/5005] lr: 1.0000e-04 eta: 5:32:43 time: 0.2279 data_time: 0.0056 loss: 0.8146 03/06 18:57:04 - mmengine - INFO - Epoch(train) [123][3500/5005] lr: 1.0000e-04 eta: 5:32:20 time: 0.2281 data_time: 0.0054 loss: 0.8831 03/06 18:57:27 - mmengine - INFO - Epoch(train) [123][3600/5005] lr: 1.0000e-04 eta: 5:31:56 time: 0.2292 data_time: 0.0055 loss: 0.9309 03/06 18:57:50 - mmengine - INFO - Epoch(train) [123][3700/5005] lr: 1.0000e-04 eta: 5:31:33 time: 0.2466 data_time: 0.0053 loss: 0.9084 03/06 18:58:14 - mmengine - INFO - Epoch(train) [123][3800/5005] lr: 1.0000e-04 eta: 5:31:10 time: 0.2271 data_time: 0.0059 loss: 0.8598 03/06 18:58:38 - mmengine - INFO - Epoch(train) [123][3900/5005] lr: 1.0000e-04 eta: 5:30:47 time: 0.2259 data_time: 0.0050 loss: 0.8674 03/06 18:59:01 - mmengine - INFO - Epoch(train) [123][4000/5005] lr: 1.0000e-04 eta: 5:30:24 time: 0.2287 data_time: 0.0050 loss: 0.9126 03/06 18:59:24 - mmengine - INFO - Epoch(train) [123][4100/5005] lr: 1.0000e-04 eta: 5:30:01 time: 0.2281 data_time: 0.0053 loss: 0.8251 03/06 18:59:48 - mmengine - INFO - Epoch(train) [123][4200/5005] lr: 1.0000e-04 eta: 5:29:38 time: 0.2293 data_time: 0.0053 loss: 0.9233 03/06 19:00:12 - mmengine - INFO - Epoch(train) [123][4300/5005] lr: 1.0000e-04 eta: 5:29:15 time: 0.2477 data_time: 0.0053 loss: 0.9447 03/06 19:00:33 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:00:35 - mmengine - INFO - Epoch(train) [123][4400/5005] lr: 1.0000e-04 eta: 5:28:52 time: 0.2278 data_time: 0.0051 loss: 0.7581 03/06 19:00:58 - mmengine - INFO - Epoch(train) [123][4500/5005] lr: 1.0000e-04 eta: 5:28:29 time: 0.2275 data_time: 0.0052 loss: 0.7053 03/06 19:01:22 - mmengine - INFO - Epoch(train) [123][4600/5005] lr: 1.0000e-04 eta: 5:28:06 time: 0.2274 data_time: 0.0052 loss: 0.7349 03/06 19:01:46 - mmengine - INFO - Epoch(train) [123][4700/5005] lr: 1.0000e-04 eta: 5:27:43 time: 0.2272 data_time: 0.0051 loss: 0.7326 03/06 19:02:09 - mmengine - INFO - Epoch(train) [123][4800/5005] lr: 1.0000e-04 eta: 5:27:20 time: 0.2267 data_time: 0.0052 loss: 0.7787 03/06 19:02:33 - mmengine - INFO - Epoch(train) [123][4900/5005] lr: 1.0000e-04 eta: 5:26:57 time: 0.2787 data_time: 0.0051 loss: 0.8234 03/06 19:03:02 - mmengine - INFO - Epoch(train) [123][5000/5005] lr: 1.0000e-04 eta: 5:26:35 time: 0.2871 data_time: 0.0051 loss: 0.8720 03/06 19:03:04 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:03:06 - mmengine - INFO - Saving checkpoint at 123 epochs 03/06 19:03:22 - mmengine - INFO - Epoch(val) [123][100/196] eta: 0:00:14 time: 0.0185 data_time: 0.0002 03/06 19:03:35 - mmengine - INFO - Epoch(val) [123][196/196] accuracy/top1: 77.6100 accuracy/top5: 93.8480 03/06 19:04:08 - mmengine - INFO - Epoch(train) [124][ 100/5005] lr: 1.0000e-04 eta: 5:26:12 time: 0.2326 data_time: 0.0050 loss: 0.8736 03/06 19:04:31 - mmengine - INFO - Epoch(train) [124][ 200/5005] lr: 1.0000e-04 eta: 5:25:49 time: 0.2386 data_time: 0.0058 loss: 0.8752 03/06 19:04:55 - mmengine - INFO - Epoch(train) [124][ 300/5005] lr: 1.0000e-04 eta: 5:25:26 time: 0.2290 data_time: 0.0051 loss: 0.7513 03/06 19:05:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:05:18 - mmengine - INFO - Epoch(train) [124][ 400/5005] lr: 1.0000e-04 eta: 5:25:03 time: 0.2434 data_time: 0.0046 loss: 0.8389 03/06 19:05:42 - mmengine - INFO - Epoch(train) [124][ 500/5005] lr: 1.0000e-04 eta: 5:24:40 time: 0.2297 data_time: 0.0048 loss: 0.9134 03/06 19:06:05 - mmengine - INFO - Epoch(train) [124][ 600/5005] lr: 1.0000e-04 eta: 5:24:17 time: 0.2313 data_time: 0.0047 loss: 0.7039 03/06 19:06:29 - mmengine - INFO - Epoch(train) [124][ 700/5005] lr: 1.0000e-04 eta: 5:23:54 time: 0.2310 data_time: 0.0050 loss: 0.8050 03/06 19:06:52 - mmengine - INFO - Epoch(train) [124][ 800/5005] lr: 1.0000e-04 eta: 5:23:31 time: 0.2267 data_time: 0.0050 loss: 0.7594 03/06 19:07:16 - mmengine - INFO - Epoch(train) [124][ 900/5005] lr: 1.0000e-04 eta: 5:23:08 time: 0.2259 data_time: 0.0046 loss: 0.7833 03/06 19:07:40 - mmengine - INFO - Epoch(train) [124][1000/5005] lr: 1.0000e-04 eta: 5:22:45 time: 0.2328 data_time: 0.0047 loss: 0.7211 03/06 19:08:03 - mmengine - INFO - Epoch(train) [124][1100/5005] lr: 1.0000e-04 eta: 5:22:22 time: 0.2291 data_time: 0.0051 loss: 0.8299 03/06 19:08:26 - mmengine - INFO - Epoch(train) [124][1200/5005] lr: 1.0000e-04 eta: 5:21:59 time: 0.2301 data_time: 0.0047 loss: 0.8830 03/06 19:08:50 - mmengine - INFO - Epoch(train) [124][1300/5005] lr: 1.0000e-04 eta: 5:21:36 time: 0.2492 data_time: 0.0047 loss: 0.9428 03/06 19:09:10 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:09:13 - mmengine - INFO - Epoch(train) [124][1400/5005] lr: 1.0000e-04 eta: 5:21:13 time: 0.2303 data_time: 0.0050 loss: 0.7499 03/06 19:09:37 - mmengine - INFO - Epoch(train) [124][1500/5005] lr: 1.0000e-04 eta: 5:20:50 time: 0.2257 data_time: 0.0047 loss: 0.7843 03/06 19:10:00 - mmengine - INFO - Epoch(train) [124][1600/5005] lr: 1.0000e-04 eta: 5:20:27 time: 0.2298 data_time: 0.0047 loss: 0.7231 03/06 19:10:24 - mmengine - INFO - Epoch(train) [124][1700/5005] lr: 1.0000e-04 eta: 5:20:04 time: 0.2323 data_time: 0.0050 loss: 0.8799 03/06 19:10:47 - mmengine - INFO - Epoch(train) [124][1800/5005] lr: 1.0000e-04 eta: 5:19:41 time: 0.2317 data_time: 0.0048 loss: 0.8969 03/06 19:11:11 - mmengine - INFO - Epoch(train) [124][1900/5005] lr: 1.0000e-04 eta: 5:19:18 time: 0.2298 data_time: 0.0047 loss: 0.7105 03/06 19:11:34 - mmengine - INFO - Epoch(train) [124][2000/5005] lr: 1.0000e-04 eta: 5:18:55 time: 0.2327 data_time: 0.0052 loss: 0.9185 03/06 19:11:58 - mmengine - INFO - Epoch(train) [124][2100/5005] lr: 1.0000e-04 eta: 5:18:32 time: 0.2316 data_time: 0.0051 loss: 0.8225 03/06 19:12:21 - mmengine - INFO - Epoch(train) [124][2200/5005] lr: 1.0000e-04 eta: 5:18:09 time: 0.2293 data_time: 0.0046 loss: 0.7335 03/06 19:12:45 - mmengine - INFO - Epoch(train) [124][2300/5005] lr: 1.0000e-04 eta: 5:17:46 time: 0.2274 data_time: 0.0046 loss: 0.7477 03/06 19:13:05 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:13:08 - mmengine - INFO - Epoch(train) [124][2400/5005] lr: 1.0000e-04 eta: 5:17:23 time: 0.2326 data_time: 0.0049 loss: 0.6770 03/06 19:13:32 - mmengine - INFO - Epoch(train) [124][2500/5005] lr: 1.0000e-04 eta: 5:17:00 time: 0.2288 data_time: 0.0047 loss: 0.8237 03/06 19:13:56 - mmengine - INFO - Epoch(train) [124][2600/5005] lr: 1.0000e-04 eta: 5:16:37 time: 0.2292 data_time: 0.0051 loss: 0.8144 03/06 19:14:19 - mmengine - INFO - Epoch(train) [124][2700/5005] lr: 1.0000e-04 eta: 5:16:14 time: 0.2304 data_time: 0.0047 loss: 0.9754 03/06 19:14:43 - mmengine - INFO - Epoch(train) [124][2800/5005] lr: 1.0000e-04 eta: 5:15:51 time: 0.2290 data_time: 0.0051 loss: 0.8659 03/06 19:15:06 - mmengine - INFO - Epoch(train) [124][2900/5005] lr: 1.0000e-04 eta: 5:15:28 time: 0.2282 data_time: 0.0048 loss: 0.6349 03/06 19:15:30 - mmengine - INFO - Epoch(train) [124][3000/5005] lr: 1.0000e-04 eta: 5:15:05 time: 0.2450 data_time: 0.0047 loss: 0.9965 03/06 19:15:53 - mmengine - INFO - Epoch(train) [124][3100/5005] lr: 1.0000e-04 eta: 5:14:42 time: 0.2276 data_time: 0.0049 loss: 0.9209 03/06 19:16:17 - mmengine - INFO - Epoch(train) [124][3200/5005] lr: 1.0000e-04 eta: 5:14:19 time: 0.2496 data_time: 0.0045 loss: 0.7547 03/06 19:16:40 - mmengine - INFO - Epoch(train) [124][3300/5005] lr: 1.0000e-04 eta: 5:13:56 time: 0.2283 data_time: 0.0046 loss: 0.8344 03/06 19:17:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:17:04 - mmengine - INFO - Epoch(train) [124][3400/5005] lr: 1.0000e-04 eta: 5:13:33 time: 0.2282 data_time: 0.0053 loss: 0.7462 03/06 19:17:28 - mmengine - INFO - Epoch(train) [124][3500/5005] lr: 1.0000e-04 eta: 5:13:10 time: 0.2282 data_time: 0.0053 loss: 0.7780 03/06 19:17:51 - mmengine - INFO - Epoch(train) [124][3600/5005] lr: 1.0000e-04 eta: 5:12:47 time: 0.2271 data_time: 0.0049 loss: 0.8321 03/06 19:18:14 - mmengine - INFO - Epoch(train) [124][3700/5005] lr: 1.0000e-04 eta: 5:12:24 time: 0.2312 data_time: 0.0049 loss: 0.8363 03/06 19:18:38 - mmengine - INFO - Epoch(train) [124][3800/5005] lr: 1.0000e-04 eta: 5:12:01 time: 0.2318 data_time: 0.0050 loss: 0.7843 03/06 19:19:02 - mmengine - INFO - Epoch(train) [124][3900/5005] lr: 1.0000e-04 eta: 5:11:38 time: 0.2296 data_time: 0.0050 loss: 0.7961 03/06 19:19:25 - mmengine - INFO - Epoch(train) [124][4000/5005] lr: 1.0000e-04 eta: 5:11:15 time: 0.2306 data_time: 0.0050 loss: 0.7499 03/06 19:19:48 - mmengine - INFO - Epoch(train) [124][4100/5005] lr: 1.0000e-04 eta: 5:10:52 time: 0.2326 data_time: 0.0046 loss: 0.6580 03/06 19:20:12 - mmengine - INFO - Epoch(train) [124][4200/5005] lr: 1.0000e-04 eta: 5:10:29 time: 0.2391 data_time: 0.0049 loss: 0.8617 03/06 19:20:35 - mmengine - INFO - Epoch(train) [124][4300/5005] lr: 1.0000e-04 eta: 5:10:06 time: 0.2295 data_time: 0.0047 loss: 0.8247 03/06 19:20:56 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:20:59 - mmengine - INFO - Epoch(train) [124][4400/5005] lr: 1.0000e-04 eta: 5:09:43 time: 0.2296 data_time: 0.0047 loss: 0.8182 03/06 19:21:23 - mmengine - INFO - Epoch(train) [124][4500/5005] lr: 1.0000e-04 eta: 5:09:20 time: 0.2282 data_time: 0.0049 loss: 0.8387 03/06 19:21:46 - mmengine - INFO - Epoch(train) [124][4600/5005] lr: 1.0000e-04 eta: 5:08:57 time: 0.2278 data_time: 0.0046 loss: 0.6344 03/06 19:22:10 - mmengine - INFO - Epoch(train) [124][4700/5005] lr: 1.0000e-04 eta: 5:08:34 time: 0.2272 data_time: 0.0047 loss: 0.7704 03/06 19:22:33 - mmengine - INFO - Epoch(train) [124][4800/5005] lr: 1.0000e-04 eta: 5:08:11 time: 0.2293 data_time: 0.0048 loss: 0.7647 03/06 19:22:58 - mmengine - INFO - Epoch(train) [124][4900/5005] lr: 1.0000e-04 eta: 5:07:48 time: 0.2832 data_time: 0.0044 loss: 0.6627 03/06 19:23:27 - mmengine - INFO - Epoch(train) [124][5000/5005] lr: 1.0000e-04 eta: 5:07:26 time: 0.2858 data_time: 0.0046 loss: 0.8938 03/06 19:23:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:23:31 - mmengine - INFO - Saving checkpoint at 124 epochs 03/06 19:23:46 - mmengine - INFO - Epoch(val) [124][100/196] eta: 0:00:13 time: 0.0187 data_time: 0.0003 03/06 19:24:00 - mmengine - INFO - Epoch(val) [124][196/196] accuracy/top1: 77.5560 accuracy/top5: 93.7420 03/06 19:24:33 - mmengine - INFO - Epoch(train) [125][ 100/5005] lr: 1.0000e-04 eta: 5:07:03 time: 0.2399 data_time: 0.0054 loss: 0.9976 03/06 19:24:56 - mmengine - INFO - Epoch(train) [125][ 200/5005] lr: 1.0000e-04 eta: 5:06:40 time: 0.2294 data_time: 0.0062 loss: 0.7214 03/06 19:25:20 - mmengine - INFO - Epoch(train) [125][ 300/5005] lr: 1.0000e-04 eta: 5:06:17 time: 0.2279 data_time: 0.0053 loss: 0.7424 03/06 19:25:39 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:25:43 - mmengine - INFO - Epoch(train) [125][ 400/5005] lr: 1.0000e-04 eta: 5:05:54 time: 0.2273 data_time: 0.0056 loss: 0.9349 03/06 19:26:07 - mmengine - INFO - Epoch(train) [125][ 500/5005] lr: 1.0000e-04 eta: 5:05:31 time: 0.2299 data_time: 0.0050 loss: 0.7493 03/06 19:26:31 - mmengine - INFO - Epoch(train) [125][ 600/5005] lr: 1.0000e-04 eta: 5:05:08 time: 0.2334 data_time: 0.0055 loss: 0.7695 03/06 19:26:54 - mmengine - INFO - Epoch(train) [125][ 700/5005] lr: 1.0000e-04 eta: 5:04:45 time: 0.2257 data_time: 0.0049 loss: 0.8901 03/06 19:27:18 - mmengine - INFO - Epoch(train) [125][ 800/5005] lr: 1.0000e-04 eta: 5:04:22 time: 0.2282 data_time: 0.0052 loss: 0.8340 03/06 19:27:41 - mmengine - INFO - Epoch(train) [125][ 900/5005] lr: 1.0000e-04 eta: 5:03:59 time: 0.2264 data_time: 0.0049 loss: 0.8942 03/06 19:28:05 - mmengine - INFO - Epoch(train) [125][1000/5005] lr: 1.0000e-04 eta: 5:03:36 time: 0.2306 data_time: 0.0055 loss: 0.7650 03/06 19:28:28 - mmengine - INFO - Epoch(train) [125][1100/5005] lr: 1.0000e-04 eta: 5:03:13 time: 0.2355 data_time: 0.0056 loss: 0.6808 03/06 19:28:52 - mmengine - INFO - Epoch(train) [125][1200/5005] lr: 1.0000e-04 eta: 5:02:50 time: 0.2271 data_time: 0.0050 loss: 0.8073 03/06 19:29:15 - mmengine - INFO - Epoch(train) [125][1300/5005] lr: 1.0000e-04 eta: 5:02:27 time: 0.2259 data_time: 0.0052 loss: 0.9766 03/06 19:29:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:29:39 - mmengine - INFO - Epoch(train) [125][1400/5005] lr: 1.0000e-04 eta: 5:02:04 time: 0.2289 data_time: 0.0053 loss: 0.7263 03/06 19:30:02 - mmengine - INFO - Epoch(train) [125][1500/5005] lr: 1.0000e-04 eta: 5:01:41 time: 0.2310 data_time: 0.0056 loss: 0.7410 03/06 19:30:26 - mmengine - INFO - Epoch(train) [125][1600/5005] lr: 1.0000e-04 eta: 5:01:18 time: 0.2282 data_time: 0.0057 loss: 0.9955 03/06 19:30:49 - mmengine - INFO - Epoch(train) [125][1700/5005] lr: 1.0000e-04 eta: 5:00:55 time: 0.2407 data_time: 0.0051 loss: 0.7637 03/06 19:31:13 - mmengine - INFO - Epoch(train) [125][1800/5005] lr: 1.0000e-04 eta: 5:00:32 time: 0.2281 data_time: 0.0054 loss: 0.9209 03/06 19:31:36 - mmengine - INFO - Epoch(train) [125][1900/5005] lr: 1.0000e-04 eta: 5:00:09 time: 0.2272 data_time: 0.0051 loss: 0.7910 03/06 19:32:00 - mmengine - INFO - Epoch(train) [125][2000/5005] lr: 1.0000e-04 eta: 4:59:46 time: 0.2259 data_time: 0.0053 loss: 0.9233 03/06 19:32:23 - mmengine - INFO - Epoch(train) [125][2100/5005] lr: 1.0000e-04 eta: 4:59:23 time: 0.2299 data_time: 0.0051 loss: 0.7158 03/06 19:32:47 - mmengine - INFO - Epoch(train) [125][2200/5005] lr: 1.0000e-04 eta: 4:59:00 time: 0.2244 data_time: 0.0051 loss: 0.7250 03/06 19:33:10 - mmengine - INFO - Epoch(train) [125][2300/5005] lr: 1.0000e-04 eta: 4:58:36 time: 0.2288 data_time: 0.0055 loss: 0.8182 03/06 19:33:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:33:34 - mmengine - INFO - Epoch(train) [125][2400/5005] lr: 1.0000e-04 eta: 4:58:14 time: 0.2308 data_time: 0.0050 loss: 0.8224 03/06 19:33:58 - mmengine - INFO - Epoch(train) [125][2500/5005] lr: 1.0000e-04 eta: 4:57:50 time: 0.2324 data_time: 0.0049 loss: 0.7923 03/06 19:34:21 - mmengine - INFO - Epoch(train) [125][2600/5005] lr: 1.0000e-04 eta: 4:57:27 time: 0.2277 data_time: 0.0049 loss: 0.7308 03/06 19:34:45 - mmengine - INFO - Epoch(train) [125][2700/5005] lr: 1.0000e-04 eta: 4:57:04 time: 0.2504 data_time: 0.0052 loss: 0.7988 03/06 19:35:08 - mmengine - INFO - Epoch(train) [125][2800/5005] lr: 1.0000e-04 eta: 4:56:41 time: 0.2262 data_time: 0.0049 loss: 0.5368 03/06 19:35:32 - mmengine - INFO - Epoch(train) [125][2900/5005] lr: 1.0000e-04 eta: 4:56:18 time: 0.2343 data_time: 0.0050 loss: 0.7532 03/06 19:35:55 - mmengine - INFO - Epoch(train) [125][3000/5005] lr: 1.0000e-04 eta: 4:55:55 time: 0.2280 data_time: 0.0052 loss: 0.8097 03/06 19:36:18 - mmengine - INFO - Epoch(train) [125][3100/5005] lr: 1.0000e-04 eta: 4:55:32 time: 0.2272 data_time: 0.0051 loss: 0.6525 03/06 19:36:42 - mmengine - INFO - Epoch(train) [125][3200/5005] lr: 1.0000e-04 eta: 4:55:09 time: 0.2480 data_time: 0.0052 loss: 0.6706 03/06 19:37:06 - mmengine - INFO - Epoch(train) [125][3300/5005] lr: 1.0000e-04 eta: 4:54:46 time: 0.2269 data_time: 0.0050 loss: 0.7586 03/06 19:37:25 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:37:29 - mmengine - INFO - Epoch(train) [125][3400/5005] lr: 1.0000e-04 eta: 4:54:23 time: 0.2254 data_time: 0.0052 loss: 0.7195 03/06 19:37:53 - mmengine - INFO - Epoch(train) [125][3500/5005] lr: 1.0000e-04 eta: 4:54:00 time: 0.2344 data_time: 0.0050 loss: 0.9002 03/06 19:38:16 - mmengine - INFO - Epoch(train) [125][3600/5005] lr: 1.0000e-04 eta: 4:53:37 time: 0.2324 data_time: 0.0053 loss: 0.8115 03/06 19:38:40 - mmengine - INFO - Epoch(train) [125][3700/5005] lr: 1.0000e-04 eta: 4:53:14 time: 0.2270 data_time: 0.0049 loss: 0.9020 03/06 19:39:03 - mmengine - INFO - Epoch(train) [125][3800/5005] lr: 1.0000e-04 eta: 4:52:51 time: 0.2266 data_time: 0.0049 loss: 0.6727 03/06 19:39:27 - mmengine - INFO - Epoch(train) [125][3900/5005] lr: 1.0000e-04 eta: 4:52:28 time: 0.2303 data_time: 0.0055 loss: 0.9276 03/06 19:39:50 - mmengine - INFO - Epoch(train) [125][4000/5005] lr: 1.0000e-04 eta: 4:52:05 time: 0.2282 data_time: 0.0058 loss: 0.7307 03/06 19:40:14 - mmengine - INFO - Epoch(train) [125][4100/5005] lr: 1.0000e-04 eta: 4:51:42 time: 0.2340 data_time: 0.0054 loss: 0.8087 03/06 19:40:37 - mmengine - INFO - Epoch(train) [125][4200/5005] lr: 1.0000e-04 eta: 4:51:19 time: 0.2419 data_time: 0.0053 loss: 0.9338 03/06 19:41:01 - mmengine - INFO - Epoch(train) [125][4300/5005] lr: 1.0000e-04 eta: 4:50:56 time: 0.2321 data_time: 0.0051 loss: 0.8588 03/06 19:41:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:41:24 - mmengine - INFO - Epoch(train) [125][4400/5005] lr: 1.0000e-04 eta: 4:50:33 time: 0.2293 data_time: 0.0049 loss: 0.7216 03/06 19:41:48 - mmengine - INFO - Epoch(train) [125][4500/5005] lr: 1.0000e-04 eta: 4:50:10 time: 0.2280 data_time: 0.0051 loss: 0.7182 03/06 19:42:11 - mmengine - INFO - Epoch(train) [125][4600/5005] lr: 1.0000e-04 eta: 4:49:47 time: 0.2299 data_time: 0.0050 loss: 0.7987 03/06 19:42:35 - mmengine - INFO - Epoch(train) [125][4700/5005] lr: 1.0000e-04 eta: 4:49:24 time: 0.2457 data_time: 0.0053 loss: 0.7448 03/06 19:42:58 - mmengine - INFO - Epoch(train) [125][4800/5005] lr: 1.0000e-04 eta: 4:49:01 time: 0.2315 data_time: 0.0050 loss: 0.7455 03/06 19:43:23 - mmengine - INFO - Epoch(train) [125][4900/5005] lr: 1.0000e-04 eta: 4:48:38 time: 0.2842 data_time: 0.0048 loss: 0.7759 03/06 19:43:52 - mmengine - INFO - Epoch(train) [125][5000/5005] lr: 1.0000e-04 eta: 4:48:16 time: 0.2927 data_time: 0.0051 loss: 0.8835 03/06 19:43:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:43:56 - mmengine - INFO - Saving checkpoint at 125 epochs 03/06 19:44:12 - mmengine - INFO - Epoch(val) [125][100/196] eta: 0:00:13 time: 0.0187 data_time: 0.0003 03/06 19:44:25 - mmengine - INFO - Epoch(val) [125][196/196] accuracy/top1: 77.5240 accuracy/top5: 93.7660 03/06 19:44:59 - mmengine - INFO - Epoch(train) [126][ 100/5005] lr: 1.0000e-04 eta: 4:47:53 time: 0.2317 data_time: 0.0063 loss: 0.7401 03/06 19:45:22 - mmengine - INFO - Epoch(train) [126][ 200/5005] lr: 1.0000e-04 eta: 4:47:30 time: 0.2331 data_time: 0.0053 loss: 0.7482 03/06 19:45:46 - mmengine - INFO - Epoch(train) [126][ 300/5005] lr: 1.0000e-04 eta: 4:47:07 time: 0.2286 data_time: 0.0052 loss: 0.8249 03/06 19:46:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:46:09 - mmengine - INFO - Epoch(train) [126][ 400/5005] lr: 1.0000e-04 eta: 4:46:44 time: 0.2285 data_time: 0.0052 loss: 0.7795 03/06 19:46:33 - mmengine - INFO - Epoch(train) [126][ 500/5005] lr: 1.0000e-04 eta: 4:46:21 time: 0.2363 data_time: 0.0051 loss: 0.9099 03/06 19:46:56 - mmengine - INFO - Epoch(train) [126][ 600/5005] lr: 1.0000e-04 eta: 4:45:58 time: 0.2268 data_time: 0.0052 loss: 0.7289 03/06 19:47:20 - mmengine - INFO - Epoch(train) [126][ 700/5005] lr: 1.0000e-04 eta: 4:45:35 time: 0.2391 data_time: 0.0050 loss: 0.7999 03/06 19:47:44 - mmengine - INFO - Epoch(train) [126][ 800/5005] lr: 1.0000e-04 eta: 4:45:12 time: 0.2318 data_time: 0.0050 loss: 0.9353 03/06 19:48:07 - mmengine - INFO - Epoch(train) [126][ 900/5005] lr: 1.0000e-04 eta: 4:44:49 time: 0.2351 data_time: 0.0052 loss: 0.8085 03/06 19:48:31 - mmengine - INFO - Epoch(train) [126][1000/5005] lr: 1.0000e-04 eta: 4:44:26 time: 0.2297 data_time: 0.0050 loss: 0.7562 03/06 19:48:54 - mmengine - INFO - Epoch(train) [126][1100/5005] lr: 1.0000e-04 eta: 4:44:03 time: 0.2289 data_time: 0.0051 loss: 0.7570 03/06 19:49:18 - mmengine - INFO - Epoch(train) [126][1200/5005] lr: 1.0000e-04 eta: 4:43:40 time: 0.2400 data_time: 0.0046 loss: 0.6976 03/06 19:49:42 - mmengine - INFO - Epoch(train) [126][1300/5005] lr: 1.0000e-04 eta: 4:43:17 time: 0.2462 data_time: 0.0056 loss: 0.8218 03/06 19:49:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:50:05 - mmengine - INFO - Epoch(train) [126][1400/5005] lr: 1.0000e-04 eta: 4:42:54 time: 0.2413 data_time: 0.0050 loss: 0.7216 03/06 19:50:28 - mmengine - INFO - Epoch(train) [126][1500/5005] lr: 1.0000e-04 eta: 4:42:31 time: 0.2292 data_time: 0.0053 loss: 0.6958 03/06 19:50:52 - mmengine - INFO - Epoch(train) [126][1600/5005] lr: 1.0000e-04 eta: 4:42:08 time: 0.2303 data_time: 0.0048 loss: 0.9434 03/06 19:51:16 - mmengine - INFO - Epoch(train) [126][1700/5005] lr: 1.0000e-04 eta: 4:41:45 time: 0.2568 data_time: 0.0051 loss: 0.6903 03/06 19:51:39 - mmengine - INFO - Epoch(train) [126][1800/5005] lr: 1.0000e-04 eta: 4:41:22 time: 0.2299 data_time: 0.0054 loss: 0.8785 03/06 19:52:03 - mmengine - INFO - Epoch(train) [126][1900/5005] lr: 1.0000e-04 eta: 4:40:59 time: 0.2307 data_time: 0.0052 loss: 0.7504 03/06 19:52:27 - mmengine - INFO - Epoch(train) [126][2000/5005] lr: 1.0000e-04 eta: 4:40:36 time: 0.2275 data_time: 0.0055 loss: 0.7520 03/06 19:52:50 - mmengine - INFO - Epoch(train) [126][2100/5005] lr: 1.0000e-04 eta: 4:40:13 time: 0.2291 data_time: 0.0049 loss: 0.7821 03/06 19:53:14 - mmengine - INFO - Epoch(train) [126][2200/5005] lr: 1.0000e-04 eta: 4:39:50 time: 0.2306 data_time: 0.0053 loss: 0.5849 03/06 19:53:37 - mmengine - INFO - Epoch(train) [126][2300/5005] lr: 1.0000e-04 eta: 4:39:27 time: 0.2302 data_time: 0.0051 loss: 0.8050 03/06 19:53:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:54:01 - mmengine - INFO - Epoch(train) [126][2400/5005] lr: 1.0000e-04 eta: 4:39:04 time: 0.2294 data_time: 0.0049 loss: 0.7143 03/06 19:54:24 - mmengine - INFO - Epoch(train) [126][2500/5005] lr: 1.0000e-04 eta: 4:38:41 time: 0.2517 data_time: 0.0050 loss: 0.8478 03/06 19:54:48 - mmengine - INFO - Epoch(train) [126][2600/5005] lr: 1.0000e-04 eta: 4:38:17 time: 0.2283 data_time: 0.0049 loss: 0.7932 03/06 19:55:11 - mmengine - INFO - Epoch(train) [126][2700/5005] lr: 1.0000e-04 eta: 4:37:54 time: 0.2296 data_time: 0.0052 loss: 0.7268 03/06 19:55:35 - mmengine - INFO - Epoch(train) [126][2800/5005] lr: 1.0000e-04 eta: 4:37:31 time: 0.2536 data_time: 0.0050 loss: 0.8438 03/06 19:55:59 - mmengine - INFO - Epoch(train) [126][2900/5005] lr: 1.0000e-04 eta: 4:37:08 time: 0.2304 data_time: 0.0050 loss: 1.0165 03/06 19:56:22 - mmengine - INFO - Epoch(train) [126][3000/5005] lr: 1.0000e-04 eta: 4:36:45 time: 0.2315 data_time: 0.0052 loss: 0.8400 03/06 19:56:46 - mmengine - INFO - Epoch(train) [126][3100/5005] lr: 1.0000e-04 eta: 4:36:22 time: 0.2272 data_time: 0.0052 loss: 0.7271 03/06 19:57:09 - mmengine - INFO - Epoch(train) [126][3200/5005] lr: 1.0000e-04 eta: 4:35:59 time: 0.2492 data_time: 0.0052 loss: 0.8950 03/06 19:57:33 - mmengine - INFO - Epoch(train) [126][3300/5005] lr: 1.0000e-04 eta: 4:35:36 time: 0.2478 data_time: 0.0052 loss: 0.6479 03/06 19:57:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 19:57:56 - mmengine - INFO - Epoch(train) [126][3400/5005] lr: 1.0000e-04 eta: 4:35:13 time: 0.2268 data_time: 0.0051 loss: 0.7510 03/06 19:58:20 - mmengine - INFO - Epoch(train) [126][3500/5005] lr: 1.0000e-04 eta: 4:34:50 time: 0.2276 data_time: 0.0049 loss: 0.7535 03/06 19:58:44 - mmengine - INFO - Epoch(train) [126][3600/5005] lr: 1.0000e-04 eta: 4:34:27 time: 0.2316 data_time: 0.0051 loss: 0.6359 03/06 19:59:08 - mmengine - INFO - Epoch(train) [126][3700/5005] lr: 1.0000e-04 eta: 4:34:04 time: 0.2477 data_time: 0.0052 loss: 0.8458 03/06 19:59:31 - mmengine - INFO - Epoch(train) [126][3800/5005] lr: 1.0000e-04 eta: 4:33:41 time: 0.2281 data_time: 0.0049 loss: 0.6884 03/06 19:59:55 - mmengine - INFO - Epoch(train) [126][3900/5005] lr: 1.0000e-04 eta: 4:33:18 time: 0.2322 data_time: 0.0049 loss: 0.7831 03/06 20:00:18 - mmengine - INFO - Epoch(train) [126][4000/5005] lr: 1.0000e-04 eta: 4:32:55 time: 0.2373 data_time: 0.0054 loss: 0.7029 03/06 20:00:42 - mmengine - INFO - Epoch(train) [126][4100/5005] lr: 1.0000e-04 eta: 4:32:32 time: 0.2268 data_time: 0.0048 loss: 0.8922 03/06 20:01:05 - mmengine - INFO - Epoch(train) [126][4200/5005] lr: 1.0000e-04 eta: 4:32:09 time: 0.2281 data_time: 0.0052 loss: 0.8429 03/06 20:01:29 - mmengine - INFO - Epoch(train) [126][4300/5005] lr: 1.0000e-04 eta: 4:31:46 time: 0.2342 data_time: 0.0051 loss: 0.7329 03/06 20:01:47 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:01:53 - mmengine - INFO - Epoch(train) [126][4400/5005] lr: 1.0000e-04 eta: 4:31:23 time: 0.2325 data_time: 0.0049 loss: 0.8787 03/06 20:02:16 - mmengine - INFO - Epoch(train) [126][4500/5005] lr: 1.0000e-04 eta: 4:31:00 time: 0.2288 data_time: 0.0052 loss: 0.7485 03/06 20:02:40 - mmengine - INFO - Epoch(train) [126][4600/5005] lr: 1.0000e-04 eta: 4:30:37 time: 0.2298 data_time: 0.0050 loss: 0.7209 03/06 20:03:04 - mmengine - INFO - Epoch(train) [126][4700/5005] lr: 1.0000e-04 eta: 4:30:14 time: 0.2480 data_time: 0.0054 loss: 0.8264 03/06 20:03:27 - mmengine - INFO - Epoch(train) [126][4800/5005] lr: 1.0000e-04 eta: 4:29:51 time: 0.2500 data_time: 0.0053 loss: 0.6400 03/06 20:03:52 - mmengine - INFO - Epoch(train) [126][4900/5005] lr: 1.0000e-04 eta: 4:29:28 time: 0.2922 data_time: 0.0048 loss: 0.7674 03/06 20:04:21 - mmengine - INFO - Epoch(train) [126][5000/5005] lr: 1.0000e-04 eta: 4:29:06 time: 0.2783 data_time: 0.0055 loss: 0.7163 03/06 20:04:22 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:04:25 - mmengine - INFO - Saving checkpoint at 126 epochs 03/06 20:04:40 - mmengine - INFO - Epoch(val) [126][100/196] eta: 0:00:13 time: 0.0192 data_time: 0.0002 03/06 20:04:54 - mmengine - INFO - Epoch(val) [126][196/196] accuracy/top1: 77.5980 accuracy/top5: 93.7220 03/06 20:05:27 - mmengine - INFO - Epoch(train) [127][ 100/5005] lr: 1.0000e-04 eta: 4:28:43 time: 0.2295 data_time: 0.0055 loss: 0.9280 03/06 20:05:51 - mmengine - INFO - Epoch(train) [127][ 200/5005] lr: 1.0000e-04 eta: 4:28:20 time: 0.2300 data_time: 0.0056 loss: 0.6684 03/06 20:06:14 - mmengine - INFO - Epoch(train) [127][ 300/5005] lr: 1.0000e-04 eta: 4:27:57 time: 0.2420 data_time: 0.0058 loss: 0.6774 03/06 20:06:31 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:06:38 - mmengine - INFO - Epoch(train) [127][ 400/5005] lr: 1.0000e-04 eta: 4:27:34 time: 0.2276 data_time: 0.0056 loss: 0.8229 03/06 20:07:02 - mmengine - INFO - Epoch(train) [127][ 500/5005] lr: 1.0000e-04 eta: 4:27:11 time: 0.2751 data_time: 0.0059 loss: 0.6704 03/06 20:07:26 - mmengine - INFO - Epoch(train) [127][ 600/5005] lr: 1.0000e-04 eta: 4:26:48 time: 0.2300 data_time: 0.0053 loss: 0.7462 03/06 20:07:49 - mmengine - INFO - Epoch(train) [127][ 700/5005] lr: 1.0000e-04 eta: 4:26:25 time: 0.2298 data_time: 0.0053 loss: 0.8672 03/06 20:08:12 - mmengine - INFO - Epoch(train) [127][ 800/5005] lr: 1.0000e-04 eta: 4:26:02 time: 0.2269 data_time: 0.0053 loss: 0.7920 03/06 20:08:36 - mmengine - INFO - Epoch(train) [127][ 900/5005] lr: 1.0000e-04 eta: 4:25:39 time: 0.2472 data_time: 0.0054 loss: 0.7249 03/06 20:09:00 - mmengine - INFO - Epoch(train) [127][1000/5005] lr: 1.0000e-04 eta: 4:25:16 time: 0.2285 data_time: 0.0059 loss: 0.7664 03/06 20:09:23 - mmengine - INFO - Epoch(train) [127][1100/5005] lr: 1.0000e-04 eta: 4:24:53 time: 0.2294 data_time: 0.0057 loss: 0.7100 03/06 20:09:47 - mmengine - INFO - Epoch(train) [127][1200/5005] lr: 1.0000e-04 eta: 4:24:30 time: 0.2324 data_time: 0.0052 loss: 0.8006 03/06 20:10:10 - mmengine - INFO - Epoch(train) [127][1300/5005] lr: 1.0000e-04 eta: 4:24:07 time: 0.2275 data_time: 0.0050 loss: 0.8168 03/06 20:10:27 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:10:34 - mmengine - INFO - Epoch(train) [127][1400/5005] lr: 1.0000e-04 eta: 4:23:44 time: 0.2323 data_time: 0.0056 loss: 0.8324 03/06 20:10:58 - mmengine - INFO - Epoch(train) [127][1500/5005] lr: 1.0000e-04 eta: 4:23:21 time: 0.2292 data_time: 0.0053 loss: 0.7069 03/06 20:11:21 - mmengine - INFO - Epoch(train) [127][1600/5005] lr: 1.0000e-04 eta: 4:22:58 time: 0.2275 data_time: 0.0052 loss: 0.8228 03/06 20:11:45 - mmengine - INFO - Epoch(train) [127][1700/5005] lr: 1.0000e-04 eta: 4:22:34 time: 0.2466 data_time: 0.0057 loss: 0.8147 03/06 20:12:09 - mmengine - INFO - Epoch(train) [127][1800/5005] lr: 1.0000e-04 eta: 4:22:11 time: 0.2295 data_time: 0.0053 loss: 0.7807 03/06 20:12:32 - mmengine - INFO - Epoch(train) [127][1900/5005] lr: 1.0000e-04 eta: 4:21:48 time: 0.2328 data_time: 0.0055 loss: 0.9532 03/06 20:12:55 - mmengine - INFO - Epoch(train) [127][2000/5005] lr: 1.0000e-04 eta: 4:21:25 time: 0.2287 data_time: 0.0054 loss: 0.9360 03/06 20:13:19 - mmengine - INFO - Epoch(train) [127][2100/5005] lr: 1.0000e-04 eta: 4:21:02 time: 0.2308 data_time: 0.0060 loss: 0.7466 03/06 20:13:43 - mmengine - INFO - Epoch(train) [127][2200/5005] lr: 1.0000e-04 eta: 4:20:39 time: 0.2298 data_time: 0.0055 loss: 0.9029 03/06 20:14:06 - mmengine - INFO - Epoch(train) [127][2300/5005] lr: 1.0000e-04 eta: 4:20:16 time: 0.2298 data_time: 0.0060 loss: 0.7711 03/06 20:14:23 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:14:30 - mmengine - INFO - Epoch(train) [127][2400/5005] lr: 1.0000e-04 eta: 4:19:53 time: 0.2291 data_time: 0.0056 loss: 0.8074 03/06 20:14:54 - mmengine - INFO - Epoch(train) [127][2500/5005] lr: 1.0000e-04 eta: 4:19:30 time: 0.2472 data_time: 0.0057 loss: 0.7541 03/06 20:15:17 - mmengine - INFO - Epoch(train) [127][2600/5005] lr: 1.0000e-04 eta: 4:19:07 time: 0.2307 data_time: 0.0052 loss: 0.8314 03/06 20:15:41 - mmengine - INFO - Epoch(train) [127][2700/5005] lr: 1.0000e-04 eta: 4:18:44 time: 0.2328 data_time: 0.0056 loss: 0.7663 03/06 20:16:04 - mmengine - INFO - Epoch(train) [127][2800/5005] lr: 1.0000e-04 eta: 4:18:21 time: 0.2297 data_time: 0.0053 loss: 0.8591 03/06 20:16:27 - mmengine - INFO - Epoch(train) [127][2900/5005] lr: 1.0000e-04 eta: 4:17:58 time: 0.2305 data_time: 0.0054 loss: 0.7202 03/06 20:16:51 - mmengine - INFO - Epoch(train) [127][3000/5005] lr: 1.0000e-04 eta: 4:17:35 time: 0.2269 data_time: 0.0053 loss: 0.9049 03/06 20:17:15 - mmengine - INFO - Epoch(train) [127][3100/5005] lr: 1.0000e-04 eta: 4:17:12 time: 0.2278 data_time: 0.0054 loss: 0.7003 03/06 20:17:38 - mmengine - INFO - Epoch(train) [127][3200/5005] lr: 1.0000e-04 eta: 4:16:49 time: 0.2294 data_time: 0.0053 loss: 0.7508 03/06 20:18:02 - mmengine - INFO - Epoch(train) [127][3300/5005] lr: 1.0000e-04 eta: 4:16:26 time: 0.2319 data_time: 0.0057 loss: 0.7127 03/06 20:18:19 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:18:26 - mmengine - INFO - Epoch(train) [127][3400/5005] lr: 1.0000e-04 eta: 4:16:03 time: 0.2302 data_time: 0.0055 loss: 0.7653 03/06 20:18:50 - mmengine - INFO - Epoch(train) [127][3500/5005] lr: 1.0000e-04 eta: 4:15:40 time: 0.2312 data_time: 0.0053 loss: 0.7830 03/06 20:19:13 - mmengine - INFO - Epoch(train) [127][3600/5005] lr: 1.0000e-04 eta: 4:15:17 time: 0.2278 data_time: 0.0054 loss: 0.8686 03/06 20:19:37 - mmengine - INFO - Epoch(train) [127][3700/5005] lr: 1.0000e-04 eta: 4:14:54 time: 0.2351 data_time: 0.0055 loss: 0.9379 03/06 20:20:00 - mmengine - INFO - Epoch(train) [127][3800/5005] lr: 1.0000e-04 eta: 4:14:31 time: 0.2291 data_time: 0.0064 loss: 0.7153 03/06 20:20:24 - mmengine - INFO - Epoch(train) [127][3900/5005] lr: 1.0000e-04 eta: 4:14:08 time: 0.2298 data_time: 0.0054 loss: 0.8342 03/06 20:20:47 - mmengine - INFO - Epoch(train) [127][4000/5005] lr: 1.0000e-04 eta: 4:13:45 time: 0.2310 data_time: 0.0056 loss: 0.7381 03/06 20:21:11 - mmengine - INFO - Epoch(train) [127][4100/5005] lr: 1.0000e-04 eta: 4:13:22 time: 0.2314 data_time: 0.0056 loss: 0.8893 03/06 20:21:34 - mmengine - INFO - Epoch(train) [127][4200/5005] lr: 1.0000e-04 eta: 4:12:59 time: 0.2294 data_time: 0.0052 loss: 0.8019 03/06 20:21:58 - mmengine - INFO - Epoch(train) [127][4300/5005] lr: 1.0000e-04 eta: 4:12:36 time: 0.2454 data_time: 0.0052 loss: 0.7328 03/06 20:22:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:22:21 - mmengine - INFO - Epoch(train) [127][4400/5005] lr: 1.0000e-04 eta: 4:12:13 time: 0.2266 data_time: 0.0052 loss: 0.8951 03/06 20:22:45 - mmengine - INFO - Epoch(train) [127][4500/5005] lr: 1.0000e-04 eta: 4:11:50 time: 0.2292 data_time: 0.0060 loss: 0.7612 03/06 20:23:09 - mmengine - INFO - Epoch(train) [127][4600/5005] lr: 1.0000e-04 eta: 4:11:27 time: 0.2493 data_time: 0.0054 loss: 0.9277 03/06 20:23:32 - mmengine - INFO - Epoch(train) [127][4700/5005] lr: 1.0000e-04 eta: 4:11:04 time: 0.2305 data_time: 0.0058 loss: 0.8969 03/06 20:23:56 - mmengine - INFO - Epoch(train) [127][4800/5005] lr: 1.0000e-04 eta: 4:10:41 time: 0.2291 data_time: 0.0053 loss: 0.8035 03/06 20:24:21 - mmengine - INFO - Epoch(train) [127][4900/5005] lr: 1.0000e-04 eta: 4:10:18 time: 0.2919 data_time: 0.0050 loss: 0.7433 03/06 20:24:50 - mmengine - INFO - Epoch(train) [127][5000/5005] lr: 1.0000e-04 eta: 4:09:55 time: 0.2883 data_time: 0.0054 loss: 0.6850 03/06 20:24:52 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:24:54 - mmengine - INFO - Saving checkpoint at 127 epochs 03/06 20:25:10 - mmengine - INFO - Epoch(val) [127][100/196] eta: 0:00:13 time: 0.0171 data_time: 0.0002 03/06 20:25:23 - mmengine - INFO - Epoch(val) [127][196/196] accuracy/top1: 77.6060 accuracy/top5: 93.7620 03/06 20:25:56 - mmengine - INFO - Epoch(train) [128][ 100/5005] lr: 1.0000e-04 eta: 4:09:32 time: 0.2289 data_time: 0.0057 loss: 0.8212 03/06 20:26:20 - mmengine - INFO - Epoch(train) [128][ 200/5005] lr: 1.0000e-04 eta: 4:09:09 time: 0.2314 data_time: 0.0066 loss: 0.7270 03/06 20:26:43 - mmengine - INFO - Epoch(train) [128][ 300/5005] lr: 1.0000e-04 eta: 4:08:46 time: 0.2302 data_time: 0.0058 loss: 0.7986 03/06 20:26:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:27:07 - mmengine - INFO - Epoch(train) [128][ 400/5005] lr: 1.0000e-04 eta: 4:08:23 time: 0.2295 data_time: 0.0056 loss: 0.7005 03/06 20:27:31 - mmengine - INFO - Epoch(train) [128][ 500/5005] lr: 1.0000e-04 eta: 4:08:00 time: 0.2271 data_time: 0.0056 loss: 0.9456 03/06 20:27:54 - mmengine - INFO - Epoch(train) [128][ 600/5005] lr: 1.0000e-04 eta: 4:07:37 time: 0.2289 data_time: 0.0058 loss: 0.7897 03/06 20:28:18 - mmengine - INFO - Epoch(train) [128][ 700/5005] lr: 1.0000e-04 eta: 4:07:14 time: 0.2300 data_time: 0.0056 loss: 0.7152 03/06 20:28:42 - mmengine - INFO - Epoch(train) [128][ 800/5005] lr: 1.0000e-04 eta: 4:06:51 time: 0.2274 data_time: 0.0052 loss: 0.7450 03/06 20:29:05 - mmengine - INFO - Epoch(train) [128][ 900/5005] lr: 1.0000e-04 eta: 4:06:28 time: 0.2280 data_time: 0.0055 loss: 0.7927 03/06 20:29:29 - mmengine - INFO - Epoch(train) [128][1000/5005] lr: 1.0000e-04 eta: 4:06:05 time: 0.2246 data_time: 0.0054 loss: 0.7850 03/06 20:29:52 - mmengine - INFO - Epoch(train) [128][1100/5005] lr: 1.0000e-04 eta: 4:05:42 time: 0.2287 data_time: 0.0058 loss: 0.8921 03/06 20:30:16 - mmengine - INFO - Epoch(train) [128][1200/5005] lr: 1.0000e-04 eta: 4:05:19 time: 0.2302 data_time: 0.0058 loss: 1.0224 03/06 20:30:39 - mmengine - INFO - Epoch(train) [128][1300/5005] lr: 1.0000e-04 eta: 4:04:56 time: 0.2335 data_time: 0.0052 loss: 0.6926 03/06 20:30:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:31:03 - mmengine - INFO - Epoch(train) [128][1400/5005] lr: 1.0000e-04 eta: 4:04:33 time: 0.2279 data_time: 0.0056 loss: 0.6957 03/06 20:31:27 - mmengine - INFO - Epoch(train) [128][1500/5005] lr: 1.0000e-04 eta: 4:04:10 time: 0.2263 data_time: 0.0056 loss: 0.8511 03/06 20:31:50 - mmengine - INFO - Epoch(train) [128][1600/5005] lr: 1.0000e-04 eta: 4:03:47 time: 0.2313 data_time: 0.0053 loss: 0.7632 03/06 20:32:13 - mmengine - INFO - Epoch(train) [128][1700/5005] lr: 1.0000e-04 eta: 4:03:24 time: 0.2283 data_time: 0.0053 loss: 0.7441 03/06 20:32:37 - mmengine - INFO - Epoch(train) [128][1800/5005] lr: 1.0000e-04 eta: 4:03:01 time: 0.2309 data_time: 0.0057 loss: 0.8381 03/06 20:33:01 - mmengine - INFO - Epoch(train) [128][1900/5005] lr: 1.0000e-04 eta: 4:02:38 time: 0.2290 data_time: 0.0056 loss: 0.8124 03/06 20:33:25 - mmengine - INFO - Epoch(train) [128][2000/5005] lr: 1.0000e-04 eta: 4:02:15 time: 0.2291 data_time: 0.0057 loss: 0.9113 03/06 20:33:48 - mmengine - INFO - Epoch(train) [128][2100/5005] lr: 1.0000e-04 eta: 4:01:52 time: 0.2455 data_time: 0.0058 loss: 0.6525 03/06 20:34:12 - mmengine - INFO - Epoch(train) [128][2200/5005] lr: 1.0000e-04 eta: 4:01:29 time: 0.2272 data_time: 0.0055 loss: 0.6864 03/06 20:34:35 - mmengine - INFO - Epoch(train) [128][2300/5005] lr: 1.0000e-04 eta: 4:01:06 time: 0.2297 data_time: 0.0059 loss: 0.7682 03/06 20:34:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:34:59 - mmengine - INFO - Epoch(train) [128][2400/5005] lr: 1.0000e-04 eta: 4:00:43 time: 0.2284 data_time: 0.0055 loss: 0.7548 03/06 20:35:23 - mmengine - INFO - Epoch(train) [128][2500/5005] lr: 1.0000e-04 eta: 4:00:19 time: 0.2301 data_time: 0.0058 loss: 0.9386 03/06 20:35:46 - mmengine - INFO - Epoch(train) [128][2600/5005] lr: 1.0000e-04 eta: 3:59:56 time: 0.2306 data_time: 0.0055 loss: 0.8204 03/06 20:36:10 - mmengine - INFO - Epoch(train) [128][2700/5005] lr: 1.0000e-04 eta: 3:59:33 time: 0.2304 data_time: 0.0056 loss: 0.8593 03/06 20:36:34 - mmengine - INFO - Epoch(train) [128][2800/5005] lr: 1.0000e-04 eta: 3:59:10 time: 0.2296 data_time: 0.0058 loss: 0.6992 03/06 20:36:57 - mmengine - INFO - Epoch(train) [128][2900/5005] lr: 1.0000e-04 eta: 3:58:47 time: 0.2321 data_time: 0.0059 loss: 0.8364 03/06 20:37:21 - mmengine - INFO - Epoch(train) [128][3000/5005] lr: 1.0000e-04 eta: 3:58:24 time: 0.2293 data_time: 0.0059 loss: 0.7459 03/06 20:37:45 - mmengine - INFO - Epoch(train) [128][3100/5005] lr: 1.0000e-04 eta: 3:58:01 time: 0.2294 data_time: 0.0055 loss: 0.7655 03/06 20:38:08 - mmengine - INFO - Epoch(train) [128][3200/5005] lr: 1.0000e-04 eta: 3:57:38 time: 0.2282 data_time: 0.0056 loss: 0.8279 03/06 20:38:32 - mmengine - INFO - Epoch(train) [128][3300/5005] lr: 1.0000e-04 eta: 3:57:15 time: 0.2329 data_time: 0.0055 loss: 0.8537 03/06 20:38:48 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:38:56 - mmengine - INFO - Epoch(train) [128][3400/5005] lr: 1.0000e-04 eta: 3:56:52 time: 0.2497 data_time: 0.0055 loss: 0.8327 03/06 20:39:19 - mmengine - INFO - Epoch(train) [128][3500/5005] lr: 1.0000e-04 eta: 3:56:29 time: 0.2292 data_time: 0.0059 loss: 0.7837 03/06 20:39:43 - mmengine - INFO - Epoch(train) [128][3600/5005] lr: 1.0000e-04 eta: 3:56:06 time: 0.2311 data_time: 0.0054 loss: 0.9354 03/06 20:40:06 - mmengine - INFO - Epoch(train) [128][3700/5005] lr: 1.0000e-04 eta: 3:55:43 time: 0.2286 data_time: 0.0057 loss: 0.9107 03/06 20:40:31 - mmengine - INFO - Epoch(train) [128][3800/5005] lr: 1.0000e-04 eta: 3:55:20 time: 0.2319 data_time: 0.0057 loss: 0.8405 03/06 20:40:54 - mmengine - INFO - Epoch(train) [128][3900/5005] lr: 1.0000e-04 eta: 3:54:57 time: 0.2274 data_time: 0.0057 loss: 0.9211 03/06 20:41:18 - mmengine - INFO - Epoch(train) [128][4000/5005] lr: 1.0000e-04 eta: 3:54:34 time: 0.2282 data_time: 0.0056 loss: 0.9005 03/06 20:41:41 - mmengine - INFO - Epoch(train) [128][4100/5005] lr: 1.0000e-04 eta: 3:54:11 time: 0.2306 data_time: 0.0056 loss: 0.8526 03/06 20:42:05 - mmengine - INFO - Epoch(train) [128][4200/5005] lr: 1.0000e-04 eta: 3:53:48 time: 0.2299 data_time: 0.0055 loss: 0.7328 03/06 20:42:28 - mmengine - INFO - Epoch(train) [128][4300/5005] lr: 1.0000e-04 eta: 3:53:25 time: 0.2303 data_time: 0.0057 loss: 0.6774 03/06 20:42:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:42:52 - mmengine - INFO - Epoch(train) [128][4400/5005] lr: 1.0000e-04 eta: 3:53:02 time: 0.2302 data_time: 0.0055 loss: 0.8684 03/06 20:43:16 - mmengine - INFO - Epoch(train) [128][4500/5005] lr: 1.0000e-04 eta: 3:52:39 time: 0.2298 data_time: 0.0058 loss: 0.7273 03/06 20:43:40 - mmengine - INFO - Epoch(train) [128][4600/5005] lr: 1.0000e-04 eta: 3:52:16 time: 0.2264 data_time: 0.0058 loss: 0.7598 03/06 20:44:03 - mmengine - INFO - Epoch(train) [128][4700/5005] lr: 1.0000e-04 eta: 3:51:53 time: 0.2285 data_time: 0.0057 loss: 0.9107 03/06 20:44:27 - mmengine - INFO - Epoch(train) [128][4800/5005] lr: 1.0000e-04 eta: 3:51:30 time: 0.2432 data_time: 0.0056 loss: 0.7129 03/06 20:44:51 - mmengine - INFO - Epoch(train) [128][4900/5005] lr: 1.0000e-04 eta: 3:51:07 time: 0.2921 data_time: 0.0051 loss: 0.7229 03/06 20:45:21 - mmengine - INFO - Epoch(train) [128][5000/5005] lr: 1.0000e-04 eta: 3:50:44 time: 0.2882 data_time: 0.0053 loss: 0.7226 03/06 20:45:22 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:45:24 - mmengine - INFO - Saving checkpoint at 128 epochs 03/06 20:45:40 - mmengine - INFO - Epoch(val) [128][100/196] eta: 0:00:13 time: 0.0206 data_time: 0.0003 03/06 20:45:53 - mmengine - INFO - Epoch(val) [128][196/196] accuracy/top1: 77.5940 accuracy/top5: 93.7900 03/06 20:46:26 - mmengine - INFO - Epoch(train) [129][ 100/5005] lr: 1.0000e-04 eta: 3:50:21 time: 0.2302 data_time: 0.0062 loss: 0.9234 03/06 20:46:51 - mmengine - INFO - Epoch(train) [129][ 200/5005] lr: 1.0000e-04 eta: 3:49:58 time: 0.2282 data_time: 0.0066 loss: 1.0153 03/06 20:47:14 - mmengine - INFO - Epoch(train) [129][ 300/5005] lr: 1.0000e-04 eta: 3:49:35 time: 0.2307 data_time: 0.0055 loss: 0.7751 03/06 20:47:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:47:38 - mmengine - INFO - Epoch(train) [129][ 400/5005] lr: 1.0000e-04 eta: 3:49:12 time: 0.2478 data_time: 0.0051 loss: 0.8918 03/06 20:48:01 - mmengine - INFO - Epoch(train) [129][ 500/5005] lr: 1.0000e-04 eta: 3:48:49 time: 0.2278 data_time: 0.0051 loss: 0.8230 03/06 20:48:25 - mmengine - INFO - Epoch(train) [129][ 600/5005] lr: 1.0000e-04 eta: 3:48:26 time: 0.2305 data_time: 0.0053 loss: 0.7108 03/06 20:48:49 - mmengine - INFO - Epoch(train) [129][ 700/5005] lr: 1.0000e-04 eta: 3:48:03 time: 0.2259 data_time: 0.0052 loss: 0.6851 03/06 20:49:12 - mmengine - INFO - Epoch(train) [129][ 800/5005] lr: 1.0000e-04 eta: 3:47:40 time: 0.2511 data_time: 0.0051 loss: 0.6869 03/06 20:49:36 - mmengine - INFO - Epoch(train) [129][ 900/5005] lr: 1.0000e-04 eta: 3:47:17 time: 0.2317 data_time: 0.0053 loss: 0.7822 03/06 20:50:00 - mmengine - INFO - Epoch(train) [129][1000/5005] lr: 1.0000e-04 eta: 3:46:54 time: 0.2249 data_time: 0.0049 loss: 0.8565 03/06 20:50:24 - mmengine - INFO - Epoch(train) [129][1100/5005] lr: 1.0000e-04 eta: 3:46:31 time: 0.2330 data_time: 0.0049 loss: 0.8455 03/06 20:50:47 - mmengine - INFO - Epoch(train) [129][1200/5005] lr: 1.0000e-04 eta: 3:46:08 time: 0.2270 data_time: 0.0052 loss: 0.8609 03/06 20:51:10 - mmengine - INFO - Epoch(train) [129][1300/5005] lr: 1.0000e-04 eta: 3:45:45 time: 0.2275 data_time: 0.0052 loss: 0.7729 03/06 20:51:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:51:34 - mmengine - INFO - Epoch(train) [129][1400/5005] lr: 1.0000e-04 eta: 3:45:22 time: 0.2274 data_time: 0.0050 loss: 0.7606 03/06 20:51:57 - mmengine - INFO - Epoch(train) [129][1500/5005] lr: 1.0000e-04 eta: 3:44:59 time: 0.2324 data_time: 0.0053 loss: 0.7569 03/06 20:52:21 - mmengine - INFO - Epoch(train) [129][1600/5005] lr: 1.0000e-04 eta: 3:44:36 time: 0.2314 data_time: 0.0053 loss: 0.8601 03/06 20:52:44 - mmengine - INFO - Epoch(train) [129][1700/5005] lr: 1.0000e-04 eta: 3:44:13 time: 0.2297 data_time: 0.0050 loss: 0.7142 03/06 20:53:08 - mmengine - INFO - Epoch(train) [129][1800/5005] lr: 1.0000e-04 eta: 3:43:50 time: 0.2453 data_time: 0.0050 loss: 0.7900 03/06 20:53:31 - mmengine - INFO - Epoch(train) [129][1900/5005] lr: 1.0000e-04 eta: 3:43:27 time: 0.2309 data_time: 0.0049 loss: 0.8053 03/06 20:53:55 - mmengine - INFO - Epoch(train) [129][2000/5005] lr: 1.0000e-04 eta: 3:43:03 time: 0.2305 data_time: 0.0049 loss: 0.9446 03/06 20:54:18 - mmengine - INFO - Epoch(train) [129][2100/5005] lr: 1.0000e-04 eta: 3:42:40 time: 0.2275 data_time: 0.0052 loss: 0.8126 03/06 20:54:42 - mmengine - INFO - Epoch(train) [129][2200/5005] lr: 1.0000e-04 eta: 3:42:17 time: 0.2265 data_time: 0.0052 loss: 0.7688 03/06 20:55:06 - mmengine - INFO - Epoch(train) [129][2300/5005] lr: 1.0000e-04 eta: 3:41:54 time: 0.2475 data_time: 0.0049 loss: 0.9227 03/06 20:55:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:55:29 - mmengine - INFO - Epoch(train) [129][2400/5005] lr: 1.0000e-04 eta: 3:41:31 time: 0.2296 data_time: 0.0056 loss: 0.8202 03/06 20:55:52 - mmengine - INFO - Epoch(train) [129][2500/5005] lr: 1.0000e-04 eta: 3:41:08 time: 0.2310 data_time: 0.0051 loss: 0.6794 03/06 20:56:15 - mmengine - INFO - Epoch(train) [129][2600/5005] lr: 1.0000e-04 eta: 3:40:45 time: 0.2280 data_time: 0.0054 loss: 0.6054 03/06 20:56:39 - mmengine - INFO - Epoch(train) [129][2700/5005] lr: 1.0000e-04 eta: 3:40:22 time: 0.2470 data_time: 0.0048 loss: 0.8914 03/06 20:57:03 - mmengine - INFO - Epoch(train) [129][2800/5005] lr: 1.0000e-04 eta: 3:39:59 time: 0.2312 data_time: 0.0051 loss: 0.8663 03/06 20:57:26 - mmengine - INFO - Epoch(train) [129][2900/5005] lr: 1.0000e-04 eta: 3:39:36 time: 0.2273 data_time: 0.0051 loss: 0.7369 03/06 20:57:50 - mmengine - INFO - Epoch(train) [129][3000/5005] lr: 1.0000e-04 eta: 3:39:13 time: 0.2289 data_time: 0.0052 loss: 0.6585 03/06 20:58:14 - mmengine - INFO - Epoch(train) [129][3100/5005] lr: 1.0000e-04 eta: 3:38:50 time: 0.2331 data_time: 0.0055 loss: 0.7159 03/06 20:58:37 - mmengine - INFO - Epoch(train) [129][3200/5005] lr: 1.0000e-04 eta: 3:38:27 time: 0.2298 data_time: 0.0052 loss: 0.8076 03/06 20:59:01 - mmengine - INFO - Epoch(train) [129][3300/5005] lr: 1.0000e-04 eta: 3:38:04 time: 0.2287 data_time: 0.0050 loss: 0.6916 03/06 20:59:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 20:59:24 - mmengine - INFO - Epoch(train) [129][3400/5005] lr: 1.0000e-04 eta: 3:37:41 time: 0.2315 data_time: 0.0054 loss: 0.8722 03/06 20:59:48 - mmengine - INFO - Epoch(train) [129][3500/5005] lr: 1.0000e-04 eta: 3:37:18 time: 0.2284 data_time: 0.0050 loss: 0.6703 03/06 21:00:12 - mmengine - INFO - Epoch(train) [129][3600/5005] lr: 1.0000e-04 eta: 3:36:55 time: 0.2293 data_time: 0.0049 loss: 0.7142 03/06 21:00:35 - mmengine - INFO - Epoch(train) [129][3700/5005] lr: 1.0000e-04 eta: 3:36:32 time: 0.2300 data_time: 0.0051 loss: 0.7399 03/06 21:00:59 - mmengine - INFO - Epoch(train) [129][3800/5005] lr: 1.0000e-04 eta: 3:36:09 time: 0.2306 data_time: 0.0050 loss: 0.7776 03/06 21:01:23 - mmengine - INFO - Epoch(train) [129][3900/5005] lr: 1.0000e-04 eta: 3:35:46 time: 0.2702 data_time: 0.0049 loss: 0.9057 03/06 21:01:46 - mmengine - INFO - Epoch(train) [129][4000/5005] lr: 1.0000e-04 eta: 3:35:23 time: 0.2295 data_time: 0.0051 loss: 0.5761 03/06 21:02:10 - mmengine - INFO - Epoch(train) [129][4100/5005] lr: 1.0000e-04 eta: 3:35:00 time: 0.2280 data_time: 0.0053 loss: 0.8055 03/06 21:02:33 - mmengine - INFO - Epoch(train) [129][4200/5005] lr: 1.0000e-04 eta: 3:34:37 time: 0.2292 data_time: 0.0051 loss: 0.8175 03/06 21:02:57 - mmengine - INFO - Epoch(train) [129][4300/5005] lr: 1.0000e-04 eta: 3:34:14 time: 0.2279 data_time: 0.0053 loss: 0.9603 03/06 21:03:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:03:20 - mmengine - INFO - Epoch(train) [129][4400/5005] lr: 1.0000e-04 eta: 3:33:51 time: 0.2262 data_time: 0.0056 loss: 0.7837 03/06 21:03:44 - mmengine - INFO - Epoch(train) [129][4500/5005] lr: 1.0000e-04 eta: 3:33:28 time: 0.2324 data_time: 0.0051 loss: 0.8373 03/06 21:04:07 - mmengine - INFO - Epoch(train) [129][4600/5005] lr: 1.0000e-04 eta: 3:33:05 time: 0.2272 data_time: 0.0049 loss: 0.7240 03/06 21:04:31 - mmengine - INFO - Epoch(train) [129][4700/5005] lr: 1.0000e-04 eta: 3:32:41 time: 0.2329 data_time: 0.0049 loss: 0.7985 03/06 21:04:55 - mmengine - INFO - Epoch(train) [129][4800/5005] lr: 1.0000e-04 eta: 3:32:18 time: 0.2314 data_time: 0.0054 loss: 0.8358 03/06 21:05:19 - mmengine - INFO - Epoch(train) [129][4900/5005] lr: 1.0000e-04 eta: 3:31:55 time: 0.2891 data_time: 0.0049 loss: 0.7856 03/06 21:05:48 - mmengine - INFO - Epoch(train) [129][5000/5005] lr: 1.0000e-04 eta: 3:31:33 time: 0.2926 data_time: 0.0050 loss: 0.6952 03/06 21:05:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:05:52 - mmengine - INFO - Saving checkpoint at 129 epochs 03/06 21:06:08 - mmengine - INFO - Epoch(val) [129][100/196] eta: 0:00:13 time: 0.0187 data_time: 0.0003 03/06 21:06:21 - mmengine - INFO - Epoch(val) [129][196/196] accuracy/top1: 77.5940 accuracy/top5: 93.7540 03/06 21:06:54 - mmengine - INFO - Epoch(train) [130][ 100/5005] lr: 1.0000e-04 eta: 3:31:10 time: 0.2309 data_time: 0.0060 loss: 0.7121 03/06 21:07:18 - mmengine - INFO - Epoch(train) [130][ 200/5005] lr: 1.0000e-04 eta: 3:30:47 time: 0.2270 data_time: 0.0056 loss: 0.7831 03/06 21:07:41 - mmengine - INFO - Epoch(train) [130][ 300/5005] lr: 1.0000e-04 eta: 3:30:24 time: 0.2391 data_time: 0.0054 loss: 0.7539 03/06 21:07:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:08:05 - mmengine - INFO - Epoch(train) [130][ 400/5005] lr: 1.0000e-04 eta: 3:30:00 time: 0.2485 data_time: 0.0050 loss: 0.7920 03/06 21:08:28 - mmengine - INFO - Epoch(train) [130][ 500/5005] lr: 1.0000e-04 eta: 3:29:37 time: 0.2274 data_time: 0.0051 loss: 0.8937 03/06 21:08:52 - mmengine - INFO - Epoch(train) [130][ 600/5005] lr: 1.0000e-04 eta: 3:29:14 time: 0.2293 data_time: 0.0050 loss: 0.7287 03/06 21:09:16 - mmengine - INFO - Epoch(train) [130][ 700/5005] lr: 1.0000e-04 eta: 3:28:51 time: 0.2269 data_time: 0.0052 loss: 0.7810 03/06 21:09:39 - mmengine - INFO - Epoch(train) [130][ 800/5005] lr: 1.0000e-04 eta: 3:28:28 time: 0.2452 data_time: 0.0053 loss: 0.7555 03/06 21:10:02 - mmengine - INFO - Epoch(train) [130][ 900/5005] lr: 1.0000e-04 eta: 3:28:05 time: 0.2264 data_time: 0.0053 loss: 0.9317 03/06 21:10:26 - mmengine - INFO - Epoch(train) [130][1000/5005] lr: 1.0000e-04 eta: 3:27:42 time: 0.2288 data_time: 0.0052 loss: 0.9422 03/06 21:10:50 - mmengine - INFO - Epoch(train) [130][1100/5005] lr: 1.0000e-04 eta: 3:27:19 time: 0.2317 data_time: 0.0051 loss: 0.8038 03/06 21:11:13 - mmengine - INFO - Epoch(train) [130][1200/5005] lr: 1.0000e-04 eta: 3:26:56 time: 0.2484 data_time: 0.0051 loss: 0.7138 03/06 21:11:37 - mmengine - INFO - Epoch(train) [130][1300/5005] lr: 1.0000e-04 eta: 3:26:33 time: 0.2288 data_time: 0.0053 loss: 0.7261 03/06 21:11:49 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:12:00 - mmengine - INFO - Epoch(train) [130][1400/5005] lr: 1.0000e-04 eta: 3:26:10 time: 0.2285 data_time: 0.0050 loss: 0.8380 03/06 21:12:24 - mmengine - INFO - Epoch(train) [130][1500/5005] lr: 1.0000e-04 eta: 3:25:47 time: 0.2300 data_time: 0.0054 loss: 0.7077 03/06 21:12:47 - mmengine - INFO - Epoch(train) [130][1600/5005] lr: 1.0000e-04 eta: 3:25:24 time: 0.2310 data_time: 0.0051 loss: 0.7293 03/06 21:13:11 - mmengine - INFO - Epoch(train) [130][1700/5005] lr: 1.0000e-04 eta: 3:25:01 time: 0.2316 data_time: 0.0051 loss: 0.7689 03/06 21:13:34 - mmengine - INFO - Epoch(train) [130][1800/5005] lr: 1.0000e-04 eta: 3:24:38 time: 0.2307 data_time: 0.0057 loss: 0.8189 03/06 21:13:57 - mmengine - INFO - Epoch(train) [130][1900/5005] lr: 1.0000e-04 eta: 3:24:15 time: 0.2295 data_time: 0.0050 loss: 0.7479 03/06 21:14:21 - mmengine - INFO - Epoch(train) [130][2000/5005] lr: 1.0000e-04 eta: 3:23:52 time: 0.2295 data_time: 0.0054 loss: 0.7948 03/06 21:14:45 - mmengine - INFO - Epoch(train) [130][2100/5005] lr: 1.0000e-04 eta: 3:23:29 time: 0.2269 data_time: 0.0054 loss: 0.8100 03/06 21:15:08 - mmengine - INFO - Epoch(train) [130][2200/5005] lr: 1.0000e-04 eta: 3:23:06 time: 0.2291 data_time: 0.0054 loss: 0.6617 03/06 21:15:31 - mmengine - INFO - Epoch(train) [130][2300/5005] lr: 1.0000e-04 eta: 3:22:43 time: 0.2229 data_time: 0.0052 loss: 0.8894 03/06 21:15:44 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:15:55 - mmengine - INFO - Epoch(train) [130][2400/5005] lr: 1.0000e-04 eta: 3:22:20 time: 0.2298 data_time: 0.0052 loss: 0.8111 03/06 21:16:19 - mmengine - INFO - Epoch(train) [130][2500/5005] lr: 1.0000e-04 eta: 3:21:56 time: 0.2460 data_time: 0.0049 loss: 0.7470 03/06 21:16:42 - mmengine - INFO - Epoch(train) [130][2600/5005] lr: 1.0000e-04 eta: 3:21:33 time: 0.2283 data_time: 0.0048 loss: 0.7392 03/06 21:17:06 - mmengine - INFO - Epoch(train) [130][2700/5005] lr: 1.0000e-04 eta: 3:21:10 time: 0.2277 data_time: 0.0053 loss: 0.8783 03/06 21:17:29 - mmengine - INFO - Epoch(train) [130][2800/5005] lr: 1.0000e-04 eta: 3:20:47 time: 0.2273 data_time: 0.0051 loss: 0.6941 03/06 21:17:53 - mmengine - INFO - Epoch(train) [130][2900/5005] lr: 1.0000e-04 eta: 3:20:24 time: 0.2315 data_time: 0.0054 loss: 0.8273 03/06 21:18:16 - mmengine - INFO - Epoch(train) [130][3000/5005] lr: 1.0000e-04 eta: 3:20:01 time: 0.2279 data_time: 0.0050 loss: 0.7060 03/06 21:18:40 - mmengine - INFO - Epoch(train) [130][3100/5005] lr: 1.0000e-04 eta: 3:19:38 time: 0.2294 data_time: 0.0053 loss: 0.7780 03/06 21:19:03 - mmengine - INFO - Epoch(train) [130][3200/5005] lr: 1.0000e-04 eta: 3:19:15 time: 0.2289 data_time: 0.0051 loss: 0.7095 03/06 21:19:27 - mmengine - INFO - Epoch(train) [130][3300/5005] lr: 1.0000e-04 eta: 3:18:52 time: 0.2322 data_time: 0.0052 loss: 0.9574 03/06 21:19:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:19:50 - mmengine - INFO - Epoch(train) [130][3400/5005] lr: 1.0000e-04 eta: 3:18:29 time: 0.2465 data_time: 0.0053 loss: 0.7906 03/06 21:20:14 - mmengine - INFO - Epoch(train) [130][3500/5005] lr: 1.0000e-04 eta: 3:18:06 time: 0.2278 data_time: 0.0058 loss: 0.7291 03/06 21:20:37 - mmengine - INFO - Epoch(train) [130][3600/5005] lr: 1.0000e-04 eta: 3:17:43 time: 0.2295 data_time: 0.0050 loss: 0.8049 03/06 21:21:01 - mmengine - INFO - Epoch(train) [130][3700/5005] lr: 1.0000e-04 eta: 3:17:20 time: 0.2262 data_time: 0.0051 loss: 0.8976 03/06 21:21:25 - mmengine - INFO - Epoch(train) [130][3800/5005] lr: 1.0000e-04 eta: 3:16:57 time: 0.2295 data_time: 0.0051 loss: 0.6912 03/06 21:21:48 - mmengine - INFO - Epoch(train) [130][3900/5005] lr: 1.0000e-04 eta: 3:16:34 time: 0.2322 data_time: 0.0054 loss: 0.5631 03/06 21:22:12 - mmengine - INFO - Epoch(train) [130][4000/5005] lr: 1.0000e-04 eta: 3:16:11 time: 0.2321 data_time: 0.0054 loss: 0.8962 03/06 21:22:36 - mmengine - INFO - Epoch(train) [130][4100/5005] lr: 1.0000e-04 eta: 3:15:48 time: 0.2656 data_time: 0.0053 loss: 0.8546 03/06 21:22:59 - mmengine - INFO - Epoch(train) [130][4200/5005] lr: 1.0000e-04 eta: 3:15:25 time: 0.2297 data_time: 0.0055 loss: 0.8263 03/06 21:23:22 - mmengine - INFO - Epoch(train) [130][4300/5005] lr: 1.0000e-04 eta: 3:15:02 time: 0.2298 data_time: 0.0054 loss: 0.8045 03/06 21:23:35 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:23:46 - mmengine - INFO - Epoch(train) [130][4400/5005] lr: 1.0000e-04 eta: 3:14:39 time: 0.2238 data_time: 0.0049 loss: 0.8333 03/06 21:24:09 - mmengine - INFO - Epoch(train) [130][4500/5005] lr: 1.0000e-04 eta: 3:14:16 time: 0.2272 data_time: 0.0056 loss: 0.7439 03/06 21:24:33 - mmengine - INFO - Epoch(train) [130][4600/5005] lr: 1.0000e-04 eta: 3:13:53 time: 0.2264 data_time: 0.0050 loss: 1.0216 03/06 21:24:56 - mmengine - INFO - Epoch(train) [130][4700/5005] lr: 1.0000e-04 eta: 3:13:29 time: 0.2283 data_time: 0.0050 loss: 0.7584 03/06 21:25:20 - mmengine - INFO - Epoch(train) [130][4800/5005] lr: 1.0000e-04 eta: 3:13:06 time: 0.2278 data_time: 0.0052 loss: 0.6048 03/06 21:25:44 - mmengine - INFO - Epoch(train) [130][4900/5005] lr: 1.0000e-04 eta: 3:12:43 time: 0.2793 data_time: 0.0051 loss: 0.7951 03/06 21:26:14 - mmengine - INFO - Epoch(train) [130][5000/5005] lr: 1.0000e-04 eta: 3:12:21 time: 0.2882 data_time: 0.0048 loss: 0.8944 03/06 21:26:15 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:26:17 - mmengine - INFO - Saving checkpoint at 130 epochs 03/06 21:26:33 - mmengine - INFO - Epoch(val) [130][100/196] eta: 0:00:13 time: 0.0207 data_time: 0.0003 03/06 21:26:47 - mmengine - INFO - Epoch(val) [130][196/196] accuracy/top1: 77.6500 accuracy/top5: 93.7360 03/06 21:27:20 - mmengine - INFO - Epoch(train) [131][ 100/5005] lr: 1.0000e-04 eta: 3:11:57 time: 0.2254 data_time: 0.0055 loss: 0.7034 03/06 21:27:44 - mmengine - INFO - Epoch(train) [131][ 200/5005] lr: 1.0000e-04 eta: 3:11:34 time: 0.2342 data_time: 0.0055 loss: 0.7420 03/06 21:28:07 - mmengine - INFO - Epoch(train) [131][ 300/5005] lr: 1.0000e-04 eta: 3:11:11 time: 0.2273 data_time: 0.0052 loss: 0.8690 03/06 21:28:18 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:28:30 - mmengine - INFO - Epoch(train) [131][ 400/5005] lr: 1.0000e-04 eta: 3:10:48 time: 0.2284 data_time: 0.0055 loss: 0.7337 03/06 21:28:55 - mmengine - INFO - Epoch(train) [131][ 500/5005] lr: 1.0000e-04 eta: 3:10:25 time: 0.2396 data_time: 0.0055 loss: 0.8713 03/06 21:29:18 - mmengine - INFO - Epoch(train) [131][ 600/5005] lr: 1.0000e-04 eta: 3:10:02 time: 0.2269 data_time: 0.0054 loss: 0.6911 03/06 21:29:41 - mmengine - INFO - Epoch(train) [131][ 700/5005] lr: 1.0000e-04 eta: 3:09:39 time: 0.2334 data_time: 0.0055 loss: 0.7293 03/06 21:30:04 - mmengine - INFO - Epoch(train) [131][ 800/5005] lr: 1.0000e-04 eta: 3:09:16 time: 0.2245 data_time: 0.0051 loss: 0.5675 03/06 21:30:28 - mmengine - INFO - Epoch(train) [131][ 900/5005] lr: 1.0000e-04 eta: 3:08:53 time: 0.2366 data_time: 0.0052 loss: 0.8122 03/06 21:30:52 - mmengine - INFO - Epoch(train) [131][1000/5005] lr: 1.0000e-04 eta: 3:08:30 time: 0.2292 data_time: 0.0051 loss: 0.7964 03/06 21:31:15 - mmengine - INFO - Epoch(train) [131][1100/5005] lr: 1.0000e-04 eta: 3:08:07 time: 0.2290 data_time: 0.0051 loss: 0.9036 03/06 21:31:38 - mmengine - INFO - Epoch(train) [131][1200/5005] lr: 1.0000e-04 eta: 3:07:44 time: 0.2262 data_time: 0.0055 loss: 0.7546 03/06 21:32:01 - mmengine - INFO - Epoch(train) [131][1300/5005] lr: 1.0000e-04 eta: 3:07:21 time: 0.2288 data_time: 0.0054 loss: 0.9213 03/06 21:32:14 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:32:26 - mmengine - INFO - Epoch(train) [131][1400/5005] lr: 1.0000e-04 eta: 3:06:58 time: 0.2472 data_time: 0.0049 loss: 0.7906 03/06 21:32:49 - mmengine - INFO - Epoch(train) [131][1500/5005] lr: 1.0000e-04 eta: 3:06:35 time: 0.2283 data_time: 0.0055 loss: 0.7354 03/06 21:33:12 - mmengine - INFO - Epoch(train) [131][1600/5005] lr: 1.0000e-04 eta: 3:06:12 time: 0.2303 data_time: 0.0052 loss: 0.7062 03/06 21:33:35 - mmengine - INFO - Epoch(train) [131][1700/5005] lr: 1.0000e-04 eta: 3:05:49 time: 0.2294 data_time: 0.0051 loss: 0.8614 03/06 21:33:59 - mmengine - INFO - Epoch(train) [131][1800/5005] lr: 1.0000e-04 eta: 3:05:26 time: 0.2312 data_time: 0.0055 loss: 0.9658 03/06 21:34:23 - mmengine - INFO - Epoch(train) [131][1900/5005] lr: 1.0000e-04 eta: 3:05:02 time: 0.2260 data_time: 0.0053 loss: 0.6739 03/06 21:34:46 - mmengine - INFO - Epoch(train) [131][2000/5005] lr: 1.0000e-04 eta: 3:04:39 time: 0.2293 data_time: 0.0052 loss: 0.8646 03/06 21:35:09 - mmengine - INFO - Epoch(train) [131][2100/5005] lr: 1.0000e-04 eta: 3:04:16 time: 0.2292 data_time: 0.0051 loss: 0.7214 03/06 21:35:33 - mmengine - INFO - Epoch(train) [131][2200/5005] lr: 1.0000e-04 eta: 3:03:53 time: 0.2323 data_time: 0.0056 loss: 0.8490 03/06 21:35:56 - mmengine - INFO - Epoch(train) [131][2300/5005] lr: 1.0000e-04 eta: 3:03:30 time: 0.2302 data_time: 0.0054 loss: 0.9129 03/06 21:36:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:36:20 - mmengine - INFO - Epoch(train) [131][2400/5005] lr: 1.0000e-04 eta: 3:03:07 time: 0.2310 data_time: 0.0059 loss: 0.8019 03/06 21:36:43 - mmengine - INFO - Epoch(train) [131][2500/5005] lr: 1.0000e-04 eta: 3:02:44 time: 0.2291 data_time: 0.0059 loss: 0.6806 03/06 21:37:07 - mmengine - INFO - Epoch(train) [131][2600/5005] lr: 1.0000e-04 eta: 3:02:21 time: 0.2465 data_time: 0.0052 loss: 0.8122 03/06 21:37:30 - mmengine - INFO - Epoch(train) [131][2700/5005] lr: 1.0000e-04 eta: 3:01:58 time: 0.2266 data_time: 0.0050 loss: 0.8907 03/06 21:37:54 - mmengine - INFO - Epoch(train) [131][2800/5005] lr: 1.0000e-04 eta: 3:01:35 time: 0.2314 data_time: 0.0057 loss: 0.7181 03/06 21:38:18 - mmengine - INFO - Epoch(train) [131][2900/5005] lr: 1.0000e-04 eta: 3:01:12 time: 0.2274 data_time: 0.0054 loss: 0.8807 03/06 21:38:41 - mmengine - INFO - Epoch(train) [131][3000/5005] lr: 1.0000e-04 eta: 3:00:49 time: 0.2291 data_time: 0.0057 loss: 0.8200 03/06 21:39:05 - mmengine - INFO - Epoch(train) [131][3100/5005] lr: 1.0000e-04 eta: 3:00:26 time: 0.2265 data_time: 0.0053 loss: 0.8563 03/06 21:39:28 - mmengine - INFO - Epoch(train) [131][3200/5005] lr: 1.0000e-04 eta: 3:00:03 time: 0.2262 data_time: 0.0050 loss: 0.8733 03/06 21:39:51 - mmengine - INFO - Epoch(train) [131][3300/5005] lr: 1.0000e-04 eta: 2:59:40 time: 0.2278 data_time: 0.0055 loss: 0.8323 03/06 21:40:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:40:15 - mmengine - INFO - Epoch(train) [131][3400/5005] lr: 1.0000e-04 eta: 2:59:17 time: 0.2274 data_time: 0.0055 loss: 0.8174 03/06 21:40:39 - mmengine - INFO - Epoch(train) [131][3500/5005] lr: 1.0000e-04 eta: 2:58:54 time: 0.2479 data_time: 0.0052 loss: 0.8836 03/06 21:41:02 - mmengine - INFO - Epoch(train) [131][3600/5005] lr: 1.0000e-04 eta: 2:58:31 time: 0.2281 data_time: 0.0050 loss: 0.7884 03/06 21:41:25 - mmengine - INFO - Epoch(train) [131][3700/5005] lr: 1.0000e-04 eta: 2:58:07 time: 0.2270 data_time: 0.0053 loss: 0.7635 03/06 21:41:49 - mmengine - INFO - Epoch(train) [131][3800/5005] lr: 1.0000e-04 eta: 2:57:44 time: 0.2305 data_time: 0.0055 loss: 0.6999 03/06 21:42:13 - mmengine - INFO - Epoch(train) [131][3900/5005] lr: 1.0000e-04 eta: 2:57:21 time: 0.2465 data_time: 0.0058 loss: 0.7810 03/06 21:42:36 - mmengine - INFO - Epoch(train) [131][4000/5005] lr: 1.0000e-04 eta: 2:56:58 time: 0.2282 data_time: 0.0052 loss: 0.8501 03/06 21:42:59 - mmengine - INFO - Epoch(train) [131][4100/5005] lr: 1.0000e-04 eta: 2:56:35 time: 0.2326 data_time: 0.0052 loss: 0.8569 03/06 21:43:23 - mmengine - INFO - Epoch(train) [131][4200/5005] lr: 1.0000e-04 eta: 2:56:12 time: 0.2308 data_time: 0.0053 loss: 0.7404 03/06 21:43:47 - mmengine - INFO - Epoch(train) [131][4300/5005] lr: 1.0000e-04 eta: 2:55:49 time: 0.2283 data_time: 0.0052 loss: 0.7150 03/06 21:43:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:44:10 - mmengine - INFO - Epoch(train) [131][4400/5005] lr: 1.0000e-04 eta: 2:55:26 time: 0.2264 data_time: 0.0053 loss: 0.7329 03/06 21:44:34 - mmengine - INFO - Epoch(train) [131][4500/5005] lr: 1.0000e-04 eta: 2:55:03 time: 0.2324 data_time: 0.0056 loss: 0.8710 03/06 21:44:57 - mmengine - INFO - Epoch(train) [131][4600/5005] lr: 1.0000e-04 eta: 2:54:40 time: 0.2302 data_time: 0.0053 loss: 0.9732 03/06 21:45:21 - mmengine - INFO - Epoch(train) [131][4700/5005] lr: 1.0000e-04 eta: 2:54:17 time: 0.2372 data_time: 0.0054 loss: 0.6735 03/06 21:45:45 - mmengine - INFO - Epoch(train) [131][4800/5005] lr: 1.0000e-04 eta: 2:53:54 time: 0.2260 data_time: 0.0054 loss: 0.8121 03/06 21:46:09 - mmengine - INFO - Epoch(train) [131][4900/5005] lr: 1.0000e-04 eta: 2:53:31 time: 0.2758 data_time: 0.0051 loss: 0.8694 03/06 21:46:38 - mmengine - INFO - Epoch(train) [131][5000/5005] lr: 1.0000e-04 eta: 2:53:08 time: 0.2901 data_time: 0.0053 loss: 0.8112 03/06 21:46:40 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:46:42 - mmengine - INFO - Saving checkpoint at 131 epochs 03/06 21:46:57 - mmengine - INFO - Epoch(val) [131][100/196] eta: 0:00:13 time: 0.0170 data_time: 0.0002 03/06 21:47:11 - mmengine - INFO - Epoch(val) [131][196/196] accuracy/top1: 77.6680 accuracy/top5: 93.7360 03/06 21:47:44 - mmengine - INFO - Epoch(train) [132][ 100/5005] lr: 1.0000e-04 eta: 2:52:45 time: 0.2291 data_time: 0.0055 loss: 0.8199 03/06 21:48:08 - mmengine - INFO - Epoch(train) [132][ 200/5005] lr: 1.0000e-04 eta: 2:52:22 time: 0.2297 data_time: 0.0060 loss: 0.7860 03/06 21:48:32 - mmengine - INFO - Epoch(train) [132][ 300/5005] lr: 1.0000e-04 eta: 2:51:59 time: 0.2313 data_time: 0.0068 loss: 0.8350 03/06 21:48:43 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:48:55 - mmengine - INFO - Epoch(train) [132][ 400/5005] lr: 1.0000e-04 eta: 2:51:36 time: 0.2264 data_time: 0.0056 loss: 0.7245 03/06 21:49:19 - mmengine - INFO - Epoch(train) [132][ 500/5005] lr: 1.0000e-04 eta: 2:51:13 time: 0.2372 data_time: 0.0057 loss: 0.8082 03/06 21:49:42 - mmengine - INFO - Epoch(train) [132][ 600/5005] lr: 1.0000e-04 eta: 2:50:49 time: 0.2286 data_time: 0.0058 loss: 0.7644 03/06 21:50:06 - mmengine - INFO - Epoch(train) [132][ 700/5005] lr: 1.0000e-04 eta: 2:50:26 time: 0.2258 data_time: 0.0052 loss: 0.5292 03/06 21:50:30 - mmengine - INFO - Epoch(train) [132][ 800/5005] lr: 1.0000e-04 eta: 2:50:03 time: 0.2306 data_time: 0.0052 loss: 0.8763 03/06 21:50:54 - mmengine - INFO - Epoch(train) [132][ 900/5005] lr: 1.0000e-04 eta: 2:49:40 time: 0.2273 data_time: 0.0052 loss: 0.8019 03/06 21:51:17 - mmengine - INFO - Epoch(train) [132][1000/5005] lr: 1.0000e-04 eta: 2:49:17 time: 0.2253 data_time: 0.0055 loss: 0.8736 03/06 21:51:41 - mmengine - INFO - Epoch(train) [132][1100/5005] lr: 1.0000e-04 eta: 2:48:54 time: 0.2342 data_time: 0.0055 loss: 0.8230 03/06 21:52:04 - mmengine - INFO - Epoch(train) [132][1200/5005] lr: 1.0000e-04 eta: 2:48:31 time: 0.2407 data_time: 0.0055 loss: 0.8547 03/06 21:52:28 - mmengine - INFO - Epoch(train) [132][1300/5005] lr: 1.0000e-04 eta: 2:48:08 time: 0.2307 data_time: 0.0054 loss: 0.7828 03/06 21:52:39 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:52:52 - mmengine - INFO - Epoch(train) [132][1400/5005] lr: 1.0000e-04 eta: 2:47:45 time: 0.2479 data_time: 0.0055 loss: 0.8573 03/06 21:53:15 - mmengine - INFO - Epoch(train) [132][1500/5005] lr: 1.0000e-04 eta: 2:47:22 time: 0.2298 data_time: 0.0055 loss: 0.6803 03/06 21:53:39 - mmengine - INFO - Epoch(train) [132][1600/5005] lr: 1.0000e-04 eta: 2:46:59 time: 0.2305 data_time: 0.0054 loss: 0.7589 03/06 21:54:03 - mmengine - INFO - Epoch(train) [132][1700/5005] lr: 1.0000e-04 eta: 2:46:36 time: 0.2268 data_time: 0.0053 loss: 0.5894 03/06 21:54:26 - mmengine - INFO - Epoch(train) [132][1800/5005] lr: 1.0000e-04 eta: 2:46:13 time: 0.2332 data_time: 0.0056 loss: 0.7842 03/06 21:54:50 - mmengine - INFO - Epoch(train) [132][1900/5005] lr: 1.0000e-04 eta: 2:45:50 time: 0.2283 data_time: 0.0057 loss: 0.8878 03/06 21:55:14 - mmengine - INFO - Epoch(train) [132][2000/5005] lr: 1.0000e-04 eta: 2:45:27 time: 0.2267 data_time: 0.0052 loss: 0.9151 03/06 21:55:37 - mmengine - INFO - Epoch(train) [132][2100/5005] lr: 1.0000e-04 eta: 2:45:04 time: 0.2469 data_time: 0.0054 loss: 0.7628 03/06 21:56:01 - mmengine - INFO - Epoch(train) [132][2200/5005] lr: 1.0000e-04 eta: 2:44:41 time: 0.2276 data_time: 0.0057 loss: 0.8701 03/06 21:56:24 - mmengine - INFO - Epoch(train) [132][2300/5005] lr: 1.0000e-04 eta: 2:44:18 time: 0.2289 data_time: 0.0053 loss: 0.8818 03/06 21:56:35 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 21:56:48 - mmengine - INFO - Epoch(train) [132][2400/5005] lr: 1.0000e-04 eta: 2:43:55 time: 0.2303 data_time: 0.0055 loss: 0.8031 03/06 21:57:11 - mmengine - INFO - Epoch(train) [132][2500/5005] lr: 1.0000e-04 eta: 2:43:32 time: 0.2363 data_time: 0.0052 loss: 0.8101 03/06 21:57:35 - mmengine - INFO - Epoch(train) [132][2600/5005] lr: 1.0000e-04 eta: 2:43:08 time: 0.2270 data_time: 0.0053 loss: 0.7136 03/06 21:57:59 - mmengine - INFO - Epoch(train) [132][2700/5005] lr: 1.0000e-04 eta: 2:42:45 time: 0.2302 data_time: 0.0053 loss: 0.6934 03/06 21:58:23 - mmengine - INFO - Epoch(train) [132][2800/5005] lr: 1.0000e-04 eta: 2:42:22 time: 0.2279 data_time: 0.0063 loss: 0.6437 03/06 21:58:46 - mmengine - INFO - Epoch(train) [132][2900/5005] lr: 1.0000e-04 eta: 2:41:59 time: 0.2325 data_time: 0.0056 loss: 0.8987 03/06 21:59:09 - mmengine - INFO - Epoch(train) [132][3000/5005] lr: 1.0000e-04 eta: 2:41:36 time: 0.2300 data_time: 0.0059 loss: 0.7645 03/06 21:59:33 - mmengine - INFO - Epoch(train) [132][3100/5005] lr: 1.0000e-04 eta: 2:41:13 time: 0.2491 data_time: 0.0053 loss: 0.8032 03/06 21:59:57 - mmengine - INFO - Epoch(train) [132][3200/5005] lr: 1.0000e-04 eta: 2:40:50 time: 0.2278 data_time: 0.0056 loss: 0.9243 03/06 22:00:20 - mmengine - INFO - Epoch(train) [132][3300/5005] lr: 1.0000e-04 eta: 2:40:27 time: 0.2354 data_time: 0.0055 loss: 0.7620 03/06 22:00:31 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:00:44 - mmengine - INFO - Epoch(train) [132][3400/5005] lr: 1.0000e-04 eta: 2:40:04 time: 0.2293 data_time: 0.0055 loss: 0.9306 03/06 22:01:07 - mmengine - INFO - Epoch(train) [132][3500/5005] lr: 1.0000e-04 eta: 2:39:41 time: 0.2278 data_time: 0.0055 loss: 0.9530 03/06 22:01:31 - mmengine - INFO - Epoch(train) [132][3600/5005] lr: 1.0000e-04 eta: 2:39:18 time: 0.2552 data_time: 0.0058 loss: 0.7789 03/06 22:01:55 - mmengine - INFO - Epoch(train) [132][3700/5005] lr: 1.0000e-04 eta: 2:38:55 time: 0.2503 data_time: 0.0060 loss: 0.8559 03/06 22:02:18 - mmengine - INFO - Epoch(train) [132][3800/5005] lr: 1.0000e-04 eta: 2:38:32 time: 0.2316 data_time: 0.0053 loss: 0.8039 03/06 22:02:42 - mmengine - INFO - Epoch(train) [132][3900/5005] lr: 1.0000e-04 eta: 2:38:09 time: 0.2295 data_time: 0.0055 loss: 0.8214 03/06 22:03:06 - mmengine - INFO - Epoch(train) [132][4000/5005] lr: 1.0000e-04 eta: 2:37:46 time: 0.2609 data_time: 0.0054 loss: 0.7605 03/06 22:03:29 - mmengine - INFO - Epoch(train) [132][4100/5005] lr: 1.0000e-04 eta: 2:37:23 time: 0.2278 data_time: 0.0055 loss: 0.8116 03/06 22:03:53 - mmengine - INFO - Epoch(train) [132][4200/5005] lr: 1.0000e-04 eta: 2:37:00 time: 0.2265 data_time: 0.0062 loss: 0.7459 03/06 22:04:16 - mmengine - INFO - Epoch(train) [132][4300/5005] lr: 1.0000e-04 eta: 2:36:37 time: 0.2312 data_time: 0.0053 loss: 0.6324 03/06 22:04:27 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:04:40 - mmengine - INFO - Epoch(train) [132][4400/5005] lr: 1.0000e-04 eta: 2:36:14 time: 0.2294 data_time: 0.0060 loss: 0.7451 03/06 22:05:04 - mmengine - INFO - Epoch(train) [132][4500/5005] lr: 1.0000e-04 eta: 2:35:50 time: 0.2299 data_time: 0.0057 loss: 0.7104 03/06 22:05:27 - mmengine - INFO - Epoch(train) [132][4600/5005] lr: 1.0000e-04 eta: 2:35:27 time: 0.2285 data_time: 0.0054 loss: 0.6900 03/06 22:05:50 - mmengine - INFO - Epoch(train) [132][4700/5005] lr: 1.0000e-04 eta: 2:35:04 time: 0.2258 data_time: 0.0056 loss: 0.7284 03/06 22:06:14 - mmengine - INFO - Epoch(train) [132][4800/5005] lr: 1.0000e-04 eta: 2:34:41 time: 0.2457 data_time: 0.0054 loss: 0.7181 03/06 22:06:39 - mmengine - INFO - Epoch(train) [132][4900/5005] lr: 1.0000e-04 eta: 2:34:18 time: 0.2919 data_time: 0.0055 loss: 0.8005 03/06 22:07:08 - mmengine - INFO - Epoch(train) [132][5000/5005] lr: 1.0000e-04 eta: 2:33:56 time: 0.2700 data_time: 0.0053 loss: 0.7787 03/06 22:07:09 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:07:11 - mmengine - INFO - Saving checkpoint at 132 epochs 03/06 22:07:27 - mmengine - INFO - Epoch(val) [132][100/196] eta: 0:00:13 time: 0.0194 data_time: 0.0004 03/06 22:07:41 - mmengine - INFO - Epoch(val) [132][196/196] accuracy/top1: 77.6060 accuracy/top5: 93.7420 03/06 22:08:14 - mmengine - INFO - Epoch(train) [133][ 100/5005] lr: 1.0000e-04 eta: 2:33:32 time: 0.2275 data_time: 0.0056 loss: 0.8869 03/06 22:08:38 - mmengine - INFO - Epoch(train) [133][ 200/5005] lr: 1.0000e-04 eta: 2:33:09 time: 0.2266 data_time: 0.0056 loss: 0.8772 03/06 22:09:02 - mmengine - INFO - Epoch(train) [133][ 300/5005] lr: 1.0000e-04 eta: 2:32:46 time: 0.2316 data_time: 0.0054 loss: 0.8831 03/06 22:09:11 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:09:25 - mmengine - INFO - Epoch(train) [133][ 400/5005] lr: 1.0000e-04 eta: 2:32:23 time: 0.2314 data_time: 0.0056 loss: 0.8684 03/06 22:09:49 - mmengine - INFO - Epoch(train) [133][ 500/5005] lr: 1.0000e-04 eta: 2:32:00 time: 0.2470 data_time: 0.0056 loss: 0.7673 03/06 22:10:13 - mmengine - INFO - Epoch(train) [133][ 600/5005] lr: 1.0000e-04 eta: 2:31:37 time: 0.2269 data_time: 0.0055 loss: 0.6511 03/06 22:10:36 - mmengine - INFO - Epoch(train) [133][ 700/5005] lr: 1.0000e-04 eta: 2:31:14 time: 0.2322 data_time: 0.0053 loss: 0.7172 03/06 22:11:00 - mmengine - INFO - Epoch(train) [133][ 800/5005] lr: 1.0000e-04 eta: 2:30:51 time: 0.2282 data_time: 0.0055 loss: 0.6853 03/06 22:11:23 - mmengine - INFO - Epoch(train) [133][ 900/5005] lr: 1.0000e-04 eta: 2:30:28 time: 0.2296 data_time: 0.0057 loss: 0.8981 03/06 22:11:47 - mmengine - INFO - Epoch(train) [133][1000/5005] lr: 1.0000e-04 eta: 2:30:04 time: 0.2312 data_time: 0.0053 loss: 0.7670 03/06 22:12:10 - mmengine - INFO - Epoch(train) [133][1100/5005] lr: 1.0000e-04 eta: 2:29:41 time: 0.2398 data_time: 0.0057 loss: 0.7118 03/06 22:12:34 - mmengine - INFO - Epoch(train) [133][1200/5005] lr: 1.0000e-04 eta: 2:29:18 time: 0.2285 data_time: 0.0051 loss: 0.8706 03/06 22:12:58 - mmengine - INFO - Epoch(train) [133][1300/5005] lr: 1.0000e-04 eta: 2:28:55 time: 0.2298 data_time: 0.0054 loss: 0.8471 03/06 22:13:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:13:21 - mmengine - INFO - Epoch(train) [133][1400/5005] lr: 1.0000e-04 eta: 2:28:32 time: 0.2275 data_time: 0.0055 loss: 0.8450 03/06 22:13:45 - mmengine - INFO - Epoch(train) [133][1500/5005] lr: 1.0000e-04 eta: 2:28:09 time: 0.2284 data_time: 0.0057 loss: 0.7489 03/06 22:14:08 - mmengine - INFO - Epoch(train) [133][1600/5005] lr: 1.0000e-04 eta: 2:27:46 time: 0.2316 data_time: 0.0056 loss: 0.8079 03/06 22:14:32 - mmengine - INFO - Epoch(train) [133][1700/5005] lr: 1.0000e-04 eta: 2:27:23 time: 0.2495 data_time: 0.0055 loss: 0.7938 03/06 22:14:55 - mmengine - INFO - Epoch(train) [133][1800/5005] lr: 1.0000e-04 eta: 2:27:00 time: 0.2286 data_time: 0.0055 loss: 0.8929 03/06 22:15:19 - mmengine - INFO - Epoch(train) [133][1900/5005] lr: 1.0000e-04 eta: 2:26:37 time: 0.2319 data_time: 0.0056 loss: 0.6688 03/06 22:15:43 - mmengine - INFO - Epoch(train) [133][2000/5005] lr: 1.0000e-04 eta: 2:26:14 time: 0.2482 data_time: 0.0057 loss: 0.7353 03/06 22:16:06 - mmengine - INFO - Epoch(train) [133][2100/5005] lr: 1.0000e-04 eta: 2:25:51 time: 0.2277 data_time: 0.0054 loss: 0.6605 03/06 22:16:30 - mmengine - INFO - Epoch(train) [133][2200/5005] lr: 1.0000e-04 eta: 2:25:28 time: 0.2257 data_time: 0.0052 loss: 0.7326 03/06 22:16:53 - mmengine - INFO - Epoch(train) [133][2300/5005] lr: 1.0000e-04 eta: 2:25:05 time: 0.2275 data_time: 0.0053 loss: 0.8161 03/06 22:17:03 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:17:17 - mmengine - INFO - Epoch(train) [133][2400/5005] lr: 1.0000e-04 eta: 2:24:42 time: 0.2284 data_time: 0.0052 loss: 0.7188 03/06 22:17:40 - mmengine - INFO - Epoch(train) [133][2500/5005] lr: 1.0000e-04 eta: 2:24:19 time: 0.2274 data_time: 0.0055 loss: 0.9076 03/06 22:18:04 - mmengine - INFO - Epoch(train) [133][2600/5005] lr: 1.0000e-04 eta: 2:23:56 time: 0.2484 data_time: 0.0056 loss: 0.9060 03/06 22:18:28 - mmengine - INFO - Epoch(train) [133][2700/5005] lr: 1.0000e-04 eta: 2:23:33 time: 0.2282 data_time: 0.0056 loss: 0.7614 03/06 22:18:51 - mmengine - INFO - Epoch(train) [133][2800/5005] lr: 1.0000e-04 eta: 2:23:09 time: 0.2311 data_time: 0.0054 loss: 0.6601 03/06 22:19:15 - mmengine - INFO - Epoch(train) [133][2900/5005] lr: 1.0000e-04 eta: 2:22:46 time: 0.2463 data_time: 0.0055 loss: 0.8058 03/06 22:19:39 - mmengine - INFO - Epoch(train) [133][3000/5005] lr: 1.0000e-04 eta: 2:22:23 time: 0.2284 data_time: 0.0057 loss: 0.6798 03/06 22:20:02 - mmengine - INFO - Epoch(train) [133][3100/5005] lr: 1.0000e-04 eta: 2:22:00 time: 0.2283 data_time: 0.0052 loss: 0.8777 03/06 22:20:26 - mmengine - INFO - Epoch(train) [133][3200/5005] lr: 1.0000e-04 eta: 2:21:37 time: 0.2260 data_time: 0.0054 loss: 0.8930 03/06 22:20:49 - mmengine - INFO - Epoch(train) [133][3300/5005] lr: 1.0000e-04 eta: 2:21:14 time: 0.2274 data_time: 0.0053 loss: 0.6375 03/06 22:20:59 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:21:13 - mmengine - INFO - Epoch(train) [133][3400/5005] lr: 1.0000e-04 eta: 2:20:51 time: 0.2255 data_time: 0.0053 loss: 0.8276 03/06 22:21:36 - mmengine - INFO - Epoch(train) [133][3500/5005] lr: 1.0000e-04 eta: 2:20:28 time: 0.2288 data_time: 0.0053 loss: 0.6844 03/06 22:22:00 - mmengine - INFO - Epoch(train) [133][3600/5005] lr: 1.0000e-04 eta: 2:20:05 time: 0.2290 data_time: 0.0057 loss: 0.8117 03/06 22:22:23 - mmengine - INFO - Epoch(train) [133][3700/5005] lr: 1.0000e-04 eta: 2:19:42 time: 0.2316 data_time: 0.0054 loss: 0.7469 03/06 22:22:47 - mmengine - INFO - Epoch(train) [133][3800/5005] lr: 1.0000e-04 eta: 2:19:19 time: 0.2281 data_time: 0.0052 loss: 0.8374 03/06 22:23:11 - mmengine - INFO - Epoch(train) [133][3900/5005] lr: 1.0000e-04 eta: 2:18:56 time: 0.2276 data_time: 0.0055 loss: 0.7033 03/06 22:23:34 - mmengine - INFO - Epoch(train) [133][4000/5005] lr: 1.0000e-04 eta: 2:18:33 time: 0.2245 data_time: 0.0054 loss: 0.8981 03/06 22:23:58 - mmengine - INFO - Epoch(train) [133][4100/5005] lr: 1.0000e-04 eta: 2:18:10 time: 0.2463 data_time: 0.0051 loss: 0.6867 03/06 22:24:21 - mmengine - INFO - Epoch(train) [133][4200/5005] lr: 1.0000e-04 eta: 2:17:47 time: 0.2313 data_time: 0.0056 loss: 0.8410 03/06 22:24:45 - mmengine - INFO - Epoch(train) [133][4300/5005] lr: 1.0000e-04 eta: 2:17:24 time: 0.2280 data_time: 0.0056 loss: 0.8905 03/06 22:24:54 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:25:08 - mmengine - INFO - Epoch(train) [133][4400/5005] lr: 1.0000e-04 eta: 2:17:00 time: 0.2326 data_time: 0.0053 loss: 0.8297 03/06 22:25:32 - mmengine - INFO - Epoch(train) [133][4500/5005] lr: 1.0000e-04 eta: 2:16:37 time: 0.2308 data_time: 0.0051 loss: 0.7920 03/06 22:25:56 - mmengine - INFO - Epoch(train) [133][4600/5005] lr: 1.0000e-04 eta: 2:16:14 time: 0.2318 data_time: 0.0055 loss: 0.7976 03/06 22:26:19 - mmengine - INFO - Epoch(train) [133][4700/5005] lr: 1.0000e-04 eta: 2:15:51 time: 0.2271 data_time: 0.0050 loss: 0.7623 03/06 22:26:43 - mmengine - INFO - Epoch(train) [133][4800/5005] lr: 1.0000e-04 eta: 2:15:28 time: 0.2373 data_time: 0.0055 loss: 0.7796 03/06 22:27:07 - mmengine - INFO - Epoch(train) [133][4900/5005] lr: 1.0000e-04 eta: 2:15:05 time: 0.2861 data_time: 0.0050 loss: 0.7747 03/06 22:27:36 - mmengine - INFO - Epoch(train) [133][5000/5005] lr: 1.0000e-04 eta: 2:14:42 time: 0.2853 data_time: 0.0054 loss: 0.8257 03/06 22:27:38 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:27:40 - mmengine - INFO - Saving checkpoint at 133 epochs 03/06 22:27:56 - mmengine - INFO - Epoch(val) [133][100/196] eta: 0:00:13 time: 0.0188 data_time: 0.0003 03/06 22:28:09 - mmengine - INFO - Epoch(val) [133][196/196] accuracy/top1: 77.6460 accuracy/top5: 93.7660 03/06 22:28:42 - mmengine - INFO - Epoch(train) [134][ 100/5005] lr: 1.0000e-04 eta: 2:14:19 time: 0.2299 data_time: 0.0060 loss: 0.8433 03/06 22:29:06 - mmengine - INFO - Epoch(train) [134][ 200/5005] lr: 1.0000e-04 eta: 2:13:56 time: 0.2283 data_time: 0.0059 loss: 0.8743 03/06 22:29:29 - mmengine - INFO - Epoch(train) [134][ 300/5005] lr: 1.0000e-04 eta: 2:13:33 time: 0.2278 data_time: 0.0067 loss: 0.8658 03/06 22:29:38 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:29:53 - mmengine - INFO - Epoch(train) [134][ 400/5005] lr: 1.0000e-04 eta: 2:13:10 time: 0.2301 data_time: 0.0052 loss: 0.5912 03/06 22:30:17 - mmengine - INFO - Epoch(train) [134][ 500/5005] lr: 1.0000e-04 eta: 2:12:47 time: 0.2276 data_time: 0.0052 loss: 0.7170 03/06 22:30:41 - mmengine - INFO - Epoch(train) [134][ 600/5005] lr: 1.0000e-04 eta: 2:12:24 time: 0.2296 data_time: 0.0055 loss: 0.8434 03/06 22:31:04 - mmengine - INFO - Epoch(train) [134][ 700/5005] lr: 1.0000e-04 eta: 2:12:00 time: 0.2285 data_time: 0.0052 loss: 0.6992 03/06 22:31:28 - mmengine - INFO - Epoch(train) [134][ 800/5005] lr: 1.0000e-04 eta: 2:11:37 time: 0.2302 data_time: 0.0053 loss: 0.8531 03/06 22:31:51 - mmengine - INFO - Epoch(train) [134][ 900/5005] lr: 1.0000e-04 eta: 2:11:14 time: 0.2347 data_time: 0.0050 loss: 0.7368 03/06 22:32:15 - mmengine - INFO - Epoch(train) [134][1000/5005] lr: 1.0000e-04 eta: 2:10:51 time: 0.2271 data_time: 0.0050 loss: 0.7498 03/06 22:32:38 - mmengine - INFO - Epoch(train) [134][1100/5005] lr: 1.0000e-04 eta: 2:10:28 time: 0.2298 data_time: 0.0052 loss: 0.6146 03/06 22:33:02 - mmengine - INFO - Epoch(train) [134][1200/5005] lr: 1.0000e-04 eta: 2:10:05 time: 0.2374 data_time: 0.0054 loss: 0.7776 03/06 22:33:26 - mmengine - INFO - Epoch(train) [134][1300/5005] lr: 1.0000e-04 eta: 2:09:42 time: 0.2389 data_time: 0.0053 loss: 1.1238 03/06 22:33:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:33:50 - mmengine - INFO - Epoch(train) [134][1400/5005] lr: 1.0000e-04 eta: 2:09:19 time: 0.2367 data_time: 0.0051 loss: 0.7537 03/06 22:34:13 - mmengine - INFO - Epoch(train) [134][1500/5005] lr: 1.0000e-04 eta: 2:08:56 time: 0.2318 data_time: 0.0049 loss: 0.9044 03/06 22:34:36 - mmengine - INFO - Epoch(train) [134][1600/5005] lr: 1.0000e-04 eta: 2:08:33 time: 0.2316 data_time: 0.0052 loss: 0.7905 03/06 22:35:00 - mmengine - INFO - Epoch(train) [134][1700/5005] lr: 1.0000e-04 eta: 2:08:10 time: 0.2362 data_time: 0.0050 loss: 0.7941 03/06 22:35:24 - mmengine - INFO - Epoch(train) [134][1800/5005] lr: 1.0000e-04 eta: 2:07:47 time: 0.2277 data_time: 0.0050 loss: 0.7527 03/06 22:35:47 - mmengine - INFO - Epoch(train) [134][1900/5005] lr: 1.0000e-04 eta: 2:07:24 time: 0.2274 data_time: 0.0048 loss: 0.8137 03/06 22:36:11 - mmengine - INFO - Epoch(train) [134][2000/5005] lr: 1.0000e-04 eta: 2:07:01 time: 0.2310 data_time: 0.0050 loss: 0.8226 03/06 22:36:34 - mmengine - INFO - Epoch(train) [134][2100/5005] lr: 1.0000e-04 eta: 2:06:38 time: 0.2310 data_time: 0.0053 loss: 0.7494 03/06 22:36:58 - mmengine - INFO - Epoch(train) [134][2200/5005] lr: 1.0000e-04 eta: 2:06:15 time: 0.2295 data_time: 0.0052 loss: 0.8513 03/06 22:37:22 - mmengine - INFO - Epoch(train) [134][2300/5005] lr: 1.0000e-04 eta: 2:05:51 time: 0.2250 data_time: 0.0054 loss: 0.8232 03/06 22:37:30 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:37:45 - mmengine - INFO - Epoch(train) [134][2400/5005] lr: 1.0000e-04 eta: 2:05:28 time: 0.2266 data_time: 0.0049 loss: 0.7398 03/06 22:38:09 - mmengine - INFO - Epoch(train) [134][2500/5005] lr: 1.0000e-04 eta: 2:05:05 time: 0.2479 data_time: 0.0050 loss: 0.8223 03/06 22:38:32 - mmengine - INFO - Epoch(train) [134][2600/5005] lr: 1.0000e-04 eta: 2:04:42 time: 0.2471 data_time: 0.0050 loss: 0.8393 03/06 22:38:56 - mmengine - INFO - Epoch(train) [134][2700/5005] lr: 1.0000e-04 eta: 2:04:19 time: 0.2397 data_time: 0.0055 loss: 0.7223 03/06 22:39:20 - mmengine - INFO - Epoch(train) [134][2800/5005] lr: 1.0000e-04 eta: 2:03:56 time: 0.2327 data_time: 0.0051 loss: 0.7528 03/06 22:39:43 - mmengine - INFO - Epoch(train) [134][2900/5005] lr: 1.0000e-04 eta: 2:03:33 time: 0.2328 data_time: 0.0050 loss: 0.5887 03/06 22:40:07 - mmengine - INFO - Epoch(train) [134][3000/5005] lr: 1.0000e-04 eta: 2:03:10 time: 0.2284 data_time: 0.0052 loss: 0.8706 03/06 22:40:31 - mmengine - INFO - Epoch(train) [134][3100/5005] lr: 1.0000e-04 eta: 2:02:47 time: 0.2497 data_time: 0.0052 loss: 0.9005 03/06 22:40:54 - mmengine - INFO - Epoch(train) [134][3200/5005] lr: 1.0000e-04 eta: 2:02:24 time: 0.2285 data_time: 0.0052 loss: 0.7369 03/06 22:41:18 - mmengine - INFO - Epoch(train) [134][3300/5005] lr: 1.0000e-04 eta: 2:02:01 time: 0.2276 data_time: 0.0051 loss: 0.6894 03/06 22:41:26 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:41:41 - mmengine - INFO - Epoch(train) [134][3400/5005] lr: 1.0000e-04 eta: 2:01:38 time: 0.2384 data_time: 0.0051 loss: 0.8254 03/06 22:42:05 - mmengine - INFO - Epoch(train) [134][3500/5005] lr: 1.0000e-04 eta: 2:01:15 time: 0.2276 data_time: 0.0049 loss: 0.8380 03/06 22:42:29 - mmengine - INFO - Epoch(train) [134][3600/5005] lr: 1.0000e-04 eta: 2:00:52 time: 0.2305 data_time: 0.0052 loss: 0.8282 03/06 22:42:52 - mmengine - INFO - Epoch(train) [134][3700/5005] lr: 1.0000e-04 eta: 2:00:29 time: 0.2492 data_time: 0.0048 loss: 0.6540 03/06 22:43:16 - mmengine - INFO - Epoch(train) [134][3800/5005] lr: 1.0000e-04 eta: 2:00:06 time: 0.2307 data_time: 0.0053 loss: 0.7867 03/06 22:43:39 - mmengine - INFO - Epoch(train) [134][3900/5005] lr: 1.0000e-04 eta: 1:59:42 time: 0.2300 data_time: 0.0048 loss: 0.7685 03/06 22:44:03 - mmengine - INFO - Epoch(train) [134][4000/5005] lr: 1.0000e-04 eta: 1:59:19 time: 0.2446 data_time: 0.0053 loss: 0.6841 03/06 22:44:27 - mmengine - INFO - Epoch(train) [134][4100/5005] lr: 1.0000e-04 eta: 1:58:56 time: 0.2463 data_time: 0.0051 loss: 0.8053 03/06 22:44:50 - mmengine - INFO - Epoch(train) [134][4200/5005] lr: 1.0000e-04 eta: 1:58:33 time: 0.2300 data_time: 0.0059 loss: 0.8877 03/06 22:45:14 - mmengine - INFO - Epoch(train) [134][4300/5005] lr: 1.0000e-04 eta: 1:58:10 time: 0.2324 data_time: 0.0052 loss: 0.8156 03/06 22:45:22 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:45:37 - mmengine - INFO - Epoch(train) [134][4400/5005] lr: 1.0000e-04 eta: 1:57:47 time: 0.2309 data_time: 0.0051 loss: 0.7690 03/06 22:46:01 - mmengine - INFO - Epoch(train) [134][4500/5005] lr: 1.0000e-04 eta: 1:57:24 time: 0.2297 data_time: 0.0053 loss: 0.8164 03/06 22:46:25 - mmengine - INFO - Epoch(train) [134][4600/5005] lr: 1.0000e-04 eta: 1:57:01 time: 0.2283 data_time: 0.0052 loss: 0.8129 03/06 22:46:49 - mmengine - INFO - Epoch(train) [134][4700/5005] lr: 1.0000e-04 eta: 1:56:38 time: 0.2310 data_time: 0.0059 loss: 0.8148 03/06 22:47:12 - mmengine - INFO - Epoch(train) [134][4800/5005] lr: 1.0000e-04 eta: 1:56:15 time: 0.2336 data_time: 0.0052 loss: 0.7154 03/06 22:47:37 - mmengine - INFO - Epoch(train) [134][4900/5005] lr: 1.0000e-04 eta: 1:55:52 time: 0.2897 data_time: 0.0048 loss: 0.7632 03/06 22:48:06 - mmengine - INFO - Epoch(train) [134][5000/5005] lr: 1.0000e-04 eta: 1:55:29 time: 0.2808 data_time: 0.0051 loss: 0.8889 03/06 22:48:07 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:48:09 - mmengine - INFO - Saving checkpoint at 134 epochs 03/06 22:48:25 - mmengine - INFO - Epoch(val) [134][100/196] eta: 0:00:13 time: 0.0192 data_time: 0.0003 03/06 22:48:39 - mmengine - INFO - Epoch(val) [134][196/196] accuracy/top1: 77.7420 accuracy/top5: 93.7260 03/06 22:49:11 - mmengine - INFO - Epoch(train) [135][ 100/5005] lr: 1.0000e-04 eta: 1:55:05 time: 0.2336 data_time: 0.0060 loss: 0.7577 03/06 22:49:35 - mmengine - INFO - Epoch(train) [135][ 200/5005] lr: 1.0000e-04 eta: 1:54:42 time: 0.2340 data_time: 0.0064 loss: 0.7835 03/06 22:49:59 - mmengine - INFO - Epoch(train) [135][ 300/5005] lr: 1.0000e-04 eta: 1:54:19 time: 0.2510 data_time: 0.0061 loss: 0.8269 03/06 22:50:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:50:22 - mmengine - INFO - Epoch(train) [135][ 400/5005] lr: 1.0000e-04 eta: 1:53:56 time: 0.2281 data_time: 0.0057 loss: 0.7300 03/06 22:50:46 - mmengine - INFO - Epoch(train) [135][ 500/5005] lr: 1.0000e-04 eta: 1:53:33 time: 0.2299 data_time: 0.0057 loss: 0.8710 03/06 22:51:09 - mmengine - INFO - Epoch(train) [135][ 600/5005] lr: 1.0000e-04 eta: 1:53:10 time: 0.2277 data_time: 0.0059 loss: 0.8478 03/06 22:51:33 - mmengine - INFO - Epoch(train) [135][ 700/5005] lr: 1.0000e-04 eta: 1:52:47 time: 0.2283 data_time: 0.0057 loss: 0.6758 03/06 22:51:57 - mmengine - INFO - Epoch(train) [135][ 800/5005] lr: 1.0000e-04 eta: 1:52:24 time: 0.2279 data_time: 0.0056 loss: 0.8993 03/06 22:52:20 - mmengine - INFO - Epoch(train) [135][ 900/5005] lr: 1.0000e-04 eta: 1:52:01 time: 0.2278 data_time: 0.0054 loss: 0.7666 03/06 22:52:45 - mmengine - INFO - Epoch(train) [135][1000/5005] lr: 1.0000e-04 eta: 1:51:38 time: 0.2318 data_time: 0.0060 loss: 0.7081 03/06 22:53:08 - mmengine - INFO - Epoch(train) [135][1100/5005] lr: 1.0000e-04 eta: 1:51:15 time: 0.2452 data_time: 0.0056 loss: 0.6813 03/06 22:53:32 - mmengine - INFO - Epoch(train) [135][1200/5005] lr: 1.0000e-04 eta: 1:50:52 time: 0.2269 data_time: 0.0057 loss: 0.8523 03/06 22:53:55 - mmengine - INFO - Epoch(train) [135][1300/5005] lr: 1.0000e-04 eta: 1:50:29 time: 0.2324 data_time: 0.0056 loss: 0.8146 03/06 22:54:02 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:54:19 - mmengine - INFO - Epoch(train) [135][1400/5005] lr: 1.0000e-04 eta: 1:50:05 time: 0.2469 data_time: 0.0057 loss: 0.9349 03/06 22:54:42 - mmengine - INFO - Epoch(train) [135][1500/5005] lr: 1.0000e-04 eta: 1:49:42 time: 0.2268 data_time: 0.0059 loss: 0.6893 03/06 22:55:06 - mmengine - INFO - Epoch(train) [135][1600/5005] lr: 1.0000e-04 eta: 1:49:19 time: 0.2313 data_time: 0.0056 loss: 0.5826 03/06 22:55:29 - mmengine - INFO - Epoch(train) [135][1700/5005] lr: 1.0000e-04 eta: 1:48:56 time: 0.2275 data_time: 0.0058 loss: 0.7401 03/06 22:55:53 - mmengine - INFO - Epoch(train) [135][1800/5005] lr: 1.0000e-04 eta: 1:48:33 time: 0.2313 data_time: 0.0056 loss: 0.8280 03/06 22:56:17 - mmengine - INFO - Epoch(train) [135][1900/5005] lr: 1.0000e-04 eta: 1:48:10 time: 0.2300 data_time: 0.0052 loss: 0.7129 03/06 22:56:40 - mmengine - INFO - Epoch(train) [135][2000/5005] lr: 1.0000e-04 eta: 1:47:47 time: 0.2289 data_time: 0.0060 loss: 0.8620 03/06 22:57:04 - mmengine - INFO - Epoch(train) [135][2100/5005] lr: 1.0000e-04 eta: 1:47:24 time: 0.2242 data_time: 0.0060 loss: 0.7024 03/06 22:57:27 - mmengine - INFO - Epoch(train) [135][2200/5005] lr: 1.0000e-04 eta: 1:47:01 time: 0.2402 data_time: 0.0057 loss: 0.8690 03/06 22:57:51 - mmengine - INFO - Epoch(train) [135][2300/5005] lr: 1.0000e-04 eta: 1:46:38 time: 0.2264 data_time: 0.0059 loss: 0.9266 03/06 22:57:58 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 22:58:15 - mmengine - INFO - Epoch(train) [135][2400/5005] lr: 1.0000e-04 eta: 1:46:15 time: 0.2291 data_time: 0.0058 loss: 0.8874 03/06 22:58:38 - mmengine - INFO - Epoch(train) [135][2500/5005] lr: 1.0000e-04 eta: 1:45:52 time: 0.2297 data_time: 0.0057 loss: 0.7666 03/06 22:59:02 - mmengine - INFO - Epoch(train) [135][2600/5005] lr: 1.0000e-04 eta: 1:45:29 time: 0.2250 data_time: 0.0055 loss: 0.8646 03/06 22:59:26 - mmengine - INFO - Epoch(train) [135][2700/5005] lr: 1.0000e-04 eta: 1:45:06 time: 0.2287 data_time: 0.0056 loss: 0.8438 03/06 22:59:49 - mmengine - INFO - Epoch(train) [135][2800/5005] lr: 1.0000e-04 eta: 1:44:43 time: 0.2460 data_time: 0.0057 loss: 0.5878 03/06 23:00:13 - mmengine - INFO - Epoch(train) [135][2900/5005] lr: 1.0000e-04 eta: 1:44:19 time: 0.2296 data_time: 0.0055 loss: 0.7987 03/06 23:00:36 - mmengine - INFO - Epoch(train) [135][3000/5005] lr: 1.0000e-04 eta: 1:43:56 time: 0.2311 data_time: 0.0056 loss: 0.8789 03/06 23:01:00 - mmengine - INFO - Epoch(train) [135][3100/5005] lr: 1.0000e-04 eta: 1:43:33 time: 0.2267 data_time: 0.0054 loss: 0.7184 03/06 23:01:24 - mmengine - INFO - Epoch(train) [135][3200/5005] lr: 1.0000e-04 eta: 1:43:10 time: 0.2307 data_time: 0.0063 loss: 0.8082 03/06 23:01:48 - mmengine - INFO - Epoch(train) [135][3300/5005] lr: 1.0000e-04 eta: 1:42:47 time: 0.2341 data_time: 0.0059 loss: 0.7072 03/06 23:01:55 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:02:11 - mmengine - INFO - Epoch(train) [135][3400/5005] lr: 1.0000e-04 eta: 1:42:24 time: 0.2260 data_time: 0.0056 loss: 0.8424 03/06 23:02:35 - mmengine - INFO - Epoch(train) [135][3500/5005] lr: 1.0000e-04 eta: 1:42:01 time: 0.2285 data_time: 0.0055 loss: 0.7475 03/06 23:02:58 - mmengine - INFO - Epoch(train) [135][3600/5005] lr: 1.0000e-04 eta: 1:41:38 time: 0.2502 data_time: 0.0062 loss: 0.8271 03/06 23:03:22 - mmengine - INFO - Epoch(train) [135][3700/5005] lr: 1.0000e-04 eta: 1:41:15 time: 0.2277 data_time: 0.0056 loss: 0.8809 03/06 23:03:46 - mmengine - INFO - Epoch(train) [135][3800/5005] lr: 1.0000e-04 eta: 1:40:52 time: 0.2272 data_time: 0.0060 loss: 0.7186 03/06 23:04:09 - mmengine - INFO - Epoch(train) [135][3900/5005] lr: 1.0000e-04 eta: 1:40:29 time: 0.2536 data_time: 0.0056 loss: 0.7432 03/06 23:04:33 - mmengine - INFO - Epoch(train) [135][4000/5005] lr: 1.0000e-04 eta: 1:40:06 time: 0.2276 data_time: 0.0055 loss: 0.6867 03/06 23:04:56 - mmengine - INFO - Epoch(train) [135][4100/5005] lr: 1.0000e-04 eta: 1:39:43 time: 0.2289 data_time: 0.0059 loss: 0.7850 03/06 23:05:20 - mmengine - INFO - Epoch(train) [135][4200/5005] lr: 1.0000e-04 eta: 1:39:20 time: 0.2484 data_time: 0.0057 loss: 0.6854 03/06 23:05:44 - mmengine - INFO - Epoch(train) [135][4300/5005] lr: 1.0000e-04 eta: 1:38:57 time: 0.2261 data_time: 0.0056 loss: 0.7693 03/06 23:05:51 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:06:08 - mmengine - INFO - Epoch(train) [135][4400/5005] lr: 1.0000e-04 eta: 1:38:33 time: 0.2323 data_time: 0.0054 loss: 0.9000 03/06 23:06:31 - mmengine - INFO - Epoch(train) [135][4500/5005] lr: 1.0000e-04 eta: 1:38:10 time: 0.2324 data_time: 0.0058 loss: 0.6754 03/06 23:06:55 - mmengine - INFO - Epoch(train) [135][4600/5005] lr: 1.0000e-04 eta: 1:37:47 time: 0.2283 data_time: 0.0057 loss: 0.6287 03/06 23:07:19 - mmengine - INFO - Epoch(train) [135][4700/5005] lr: 1.0000e-04 eta: 1:37:24 time: 0.2290 data_time: 0.0057 loss: 0.7843 03/06 23:07:42 - mmengine - INFO - Epoch(train) [135][4800/5005] lr: 1.0000e-04 eta: 1:37:01 time: 0.2284 data_time: 0.0057 loss: 0.8399 03/06 23:08:07 - mmengine - INFO - Epoch(train) [135][4900/5005] lr: 1.0000e-04 eta: 1:36:38 time: 0.2895 data_time: 0.0054 loss: 0.7917 03/06 23:08:36 - mmengine - INFO - Epoch(train) [135][5000/5005] lr: 1.0000e-04 eta: 1:36:15 time: 0.2784 data_time: 0.0055 loss: 0.7102 03/06 23:08:38 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:08:40 - mmengine - INFO - Saving checkpoint at 135 epochs 03/06 23:08:55 - mmengine - INFO - Epoch(val) [135][100/196] eta: 0:00:13 time: 0.0209 data_time: 0.0003 03/06 23:09:09 - mmengine - INFO - Epoch(val) [135][196/196] accuracy/top1: 77.6120 accuracy/top5: 93.7760 03/06 23:09:42 - mmengine - INFO - Epoch(train) [136][ 100/5005] lr: 1.0000e-04 eta: 1:35:51 time: 0.2295 data_time: 0.0060 loss: 0.6370 03/06 23:10:05 - mmengine - INFO - Epoch(train) [136][ 200/5005] lr: 1.0000e-04 eta: 1:35:28 time: 0.2597 data_time: 0.0062 loss: 0.7458 03/06 23:10:29 - mmengine - INFO - Epoch(train) [136][ 300/5005] lr: 1.0000e-04 eta: 1:35:05 time: 0.2369 data_time: 0.0062 loss: 0.7240 03/06 23:10:36 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:10:53 - mmengine - INFO - Epoch(train) [136][ 400/5005] lr: 1.0000e-04 eta: 1:34:42 time: 0.2326 data_time: 0.0054 loss: 0.8804 03/06 23:11:17 - mmengine - INFO - Epoch(train) [136][ 500/5005] lr: 1.0000e-04 eta: 1:34:19 time: 0.2323 data_time: 0.0059 loss: 0.8022 03/06 23:11:41 - mmengine - INFO - Epoch(train) [136][ 600/5005] lr: 1.0000e-04 eta: 1:33:56 time: 0.2314 data_time: 0.0059 loss: 0.7895 03/06 23:12:04 - mmengine - INFO - Epoch(train) [136][ 700/5005] lr: 1.0000e-04 eta: 1:33:33 time: 0.2417 data_time: 0.0055 loss: 0.7793 03/06 23:12:29 - mmengine - INFO - Epoch(train) [136][ 800/5005] lr: 1.0000e-04 eta: 1:33:10 time: 0.2303 data_time: 0.0058 loss: 0.7285 03/06 23:12:52 - mmengine - INFO - Epoch(train) [136][ 900/5005] lr: 1.0000e-04 eta: 1:32:47 time: 0.2452 data_time: 0.0062 loss: 0.7778 03/06 23:13:16 - mmengine - INFO - Epoch(train) [136][1000/5005] lr: 1.0000e-04 eta: 1:32:24 time: 0.2286 data_time: 0.0056 loss: 0.7039 03/06 23:13:39 - mmengine - INFO - Epoch(train) [136][1100/5005] lr: 1.0000e-04 eta: 1:32:01 time: 0.2293 data_time: 0.0055 loss: 0.8768 03/06 23:14:03 - mmengine - INFO - Epoch(train) [136][1200/5005] lr: 1.0000e-04 eta: 1:31:38 time: 0.2276 data_time: 0.0056 loss: 0.8791 03/06 23:14:26 - mmengine - INFO - Epoch(train) [136][1300/5005] lr: 1.0000e-04 eta: 1:31:15 time: 0.2318 data_time: 0.0055 loss: 0.8555 03/06 23:14:32 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:14:50 - mmengine - INFO - Epoch(train) [136][1400/5005] lr: 1.0000e-04 eta: 1:30:52 time: 0.2303 data_time: 0.0057 loss: 0.6910 03/06 23:15:13 - mmengine - INFO - Epoch(train) [136][1500/5005] lr: 1.0000e-04 eta: 1:30:28 time: 0.2291 data_time: 0.0053 loss: 0.7220 03/06 23:15:37 - mmengine - INFO - Epoch(train) [136][1600/5005] lr: 1.0000e-04 eta: 1:30:05 time: 0.2293 data_time: 0.0054 loss: 0.7183 03/06 23:16:00 - mmengine - INFO - Epoch(train) [136][1700/5005] lr: 1.0000e-04 eta: 1:29:42 time: 0.2293 data_time: 0.0057 loss: 0.8629 03/06 23:16:24 - mmengine - INFO - Epoch(train) [136][1800/5005] lr: 1.0000e-04 eta: 1:29:19 time: 0.2478 data_time: 0.0053 loss: 0.8781 03/06 23:16:48 - mmengine - INFO - Epoch(train) [136][1900/5005] lr: 1.0000e-04 eta: 1:28:56 time: 0.2305 data_time: 0.0053 loss: 0.8050 03/06 23:17:11 - mmengine - INFO - Epoch(train) [136][2000/5005] lr: 1.0000e-04 eta: 1:28:33 time: 0.2277 data_time: 0.0056 loss: 0.9720 03/06 23:17:35 - mmengine - INFO - Epoch(train) [136][2100/5005] lr: 1.0000e-04 eta: 1:28:10 time: 0.2306 data_time: 0.0053 loss: 0.7385 03/06 23:17:58 - mmengine - INFO - Epoch(train) [136][2200/5005] lr: 1.0000e-04 eta: 1:27:47 time: 0.2385 data_time: 0.0060 loss: 0.6851 03/06 23:18:22 - mmengine - INFO - Epoch(train) [136][2300/5005] lr: 1.0000e-04 eta: 1:27:24 time: 0.2306 data_time: 0.0056 loss: 0.8107 03/06 23:18:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:18:45 - mmengine - INFO - Epoch(train) [136][2400/5005] lr: 1.0000e-04 eta: 1:27:01 time: 0.2316 data_time: 0.0055 loss: 0.8823 03/06 23:19:09 - mmengine - INFO - Epoch(train) [136][2500/5005] lr: 1.0000e-04 eta: 1:26:38 time: 0.2263 data_time: 0.0052 loss: 0.7501 03/06 23:19:33 - mmengine - INFO - Epoch(train) [136][2600/5005] lr: 1.0000e-04 eta: 1:26:15 time: 0.2282 data_time: 0.0056 loss: 0.8802 03/06 23:19:56 - mmengine - INFO - Epoch(train) [136][2700/5005] lr: 1.0000e-04 eta: 1:25:52 time: 0.2366 data_time: 0.0054 loss: 0.8297 03/06 23:20:20 - mmengine - INFO - Epoch(train) [136][2800/5005] lr: 1.0000e-04 eta: 1:25:28 time: 0.2364 data_time: 0.0055 loss: 0.7684 03/06 23:20:43 - mmengine - INFO - Epoch(train) [136][2900/5005] lr: 1.0000e-04 eta: 1:25:05 time: 0.2263 data_time: 0.0056 loss: 0.6833 03/06 23:21:07 - mmengine - INFO - Epoch(train) [136][3000/5005] lr: 1.0000e-04 eta: 1:24:42 time: 0.2292 data_time: 0.0056 loss: 0.7157 03/06 23:21:30 - mmengine - INFO - Epoch(train) [136][3100/5005] lr: 1.0000e-04 eta: 1:24:19 time: 0.2295 data_time: 0.0055 loss: 0.8528 03/06 23:21:54 - mmengine - INFO - Epoch(train) [136][3200/5005] lr: 1.0000e-04 eta: 1:23:56 time: 0.2459 data_time: 0.0057 loss: 0.8301 03/06 23:22:18 - mmengine - INFO - Epoch(train) [136][3300/5005] lr: 1.0000e-04 eta: 1:23:33 time: 0.2277 data_time: 0.0054 loss: 0.7911 03/06 23:22:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:22:41 - mmengine - INFO - Epoch(train) [136][3400/5005] lr: 1.0000e-04 eta: 1:23:10 time: 0.2329 data_time: 0.0063 loss: 0.7570 03/06 23:23:05 - mmengine - INFO - Epoch(train) [136][3500/5005] lr: 1.0000e-04 eta: 1:22:47 time: 0.2265 data_time: 0.0057 loss: 0.8705 03/06 23:23:28 - mmengine - INFO - Epoch(train) [136][3600/5005] lr: 1.0000e-04 eta: 1:22:24 time: 0.2319 data_time: 0.0055 loss: 0.8443 03/06 23:23:52 - mmengine - INFO - Epoch(train) [136][3700/5005] lr: 1.0000e-04 eta: 1:22:01 time: 0.2291 data_time: 0.0062 loss: 0.7539 03/06 23:24:16 - mmengine - INFO - Epoch(train) [136][3800/5005] lr: 1.0000e-04 eta: 1:21:38 time: 0.2464 data_time: 0.0056 loss: 0.9378 03/06 23:24:40 - mmengine - INFO - Epoch(train) [136][3900/5005] lr: 1.0000e-04 eta: 1:21:15 time: 0.2335 data_time: 0.0057 loss: 0.7137 03/06 23:25:03 - mmengine - INFO - Epoch(train) [136][4000/5005] lr: 1.0000e-04 eta: 1:20:52 time: 0.2335 data_time: 0.0058 loss: 0.7580 03/06 23:25:27 - mmengine - INFO - Epoch(train) [136][4100/5005] lr: 1.0000e-04 eta: 1:20:29 time: 0.2300 data_time: 0.0056 loss: 0.7857 03/06 23:25:51 - mmengine - INFO - Epoch(train) [136][4200/5005] lr: 1.0000e-04 eta: 1:20:05 time: 0.2292 data_time: 0.0054 loss: 0.7644 03/06 23:26:14 - mmengine - INFO - Epoch(train) [136][4300/5005] lr: 1.0000e-04 eta: 1:19:42 time: 0.2263 data_time: 0.0055 loss: 0.7035 03/06 23:26:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:26:38 - mmengine - INFO - Epoch(train) [136][4400/5005] lr: 1.0000e-04 eta: 1:19:19 time: 0.2295 data_time: 0.0056 loss: 0.7247 03/06 23:27:01 - mmengine - INFO - Epoch(train) [136][4500/5005] lr: 1.0000e-04 eta: 1:18:56 time: 0.2324 data_time: 0.0053 loss: 0.9573 03/06 23:27:25 - mmengine - INFO - Epoch(train) [136][4600/5005] lr: 1.0000e-04 eta: 1:18:33 time: 0.2260 data_time: 0.0054 loss: 0.6516 03/06 23:27:49 - mmengine - INFO - Epoch(train) [136][4700/5005] lr: 1.0000e-04 eta: 1:18:10 time: 0.2253 data_time: 0.0054 loss: 0.8995 03/06 23:28:12 - mmengine - INFO - Epoch(train) [136][4800/5005] lr: 1.0000e-04 eta: 1:17:47 time: 0.2318 data_time: 0.0055 loss: 0.8231 03/06 23:28:37 - mmengine - INFO - Epoch(train) [136][4900/5005] lr: 1.0000e-04 eta: 1:17:24 time: 0.2988 data_time: 0.0054 loss: 0.6460 03/06 23:29:06 - mmengine - INFO - Epoch(train) [136][5000/5005] lr: 1.0000e-04 eta: 1:17:01 time: 0.2854 data_time: 0.0051 loss: 0.8085 03/06 23:29:08 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:29:10 - mmengine - INFO - Saving checkpoint at 136 epochs 03/06 23:29:25 - mmengine - INFO - Epoch(val) [136][100/196] eta: 0:00:13 time: 0.0191 data_time: 0.0003 03/06 23:29:38 - mmengine - INFO - Epoch(val) [136][196/196] accuracy/top1: 77.6680 accuracy/top5: 93.7360 03/06 23:30:12 - mmengine - INFO - Epoch(train) [137][ 100/5005] lr: 1.0000e-04 eta: 1:16:37 time: 0.2288 data_time: 0.0052 loss: 0.9918 03/06 23:30:36 - mmengine - INFO - Epoch(train) [137][ 200/5005] lr: 1.0000e-04 eta: 1:16:14 time: 0.2473 data_time: 0.0048 loss: 0.9712 03/06 23:31:00 - mmengine - INFO - Epoch(train) [137][ 300/5005] lr: 1.0000e-04 eta: 1:15:51 time: 0.2306 data_time: 0.0054 loss: 0.9363 03/06 23:31:05 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:31:24 - mmengine - INFO - Epoch(train) [137][ 400/5005] lr: 1.0000e-04 eta: 1:15:28 time: 0.2291 data_time: 0.0051 loss: 0.6733 03/06 23:31:47 - mmengine - INFO - Epoch(train) [137][ 500/5005] lr: 1.0000e-04 eta: 1:15:05 time: 0.2275 data_time: 0.0051 loss: 0.7703 03/06 23:32:11 - mmengine - INFO - Epoch(train) [137][ 600/5005] lr: 1.0000e-04 eta: 1:14:42 time: 0.2260 data_time: 0.0056 loss: 0.9428 03/06 23:32:35 - mmengine - INFO - Epoch(train) [137][ 700/5005] lr: 1.0000e-04 eta: 1:14:19 time: 0.2290 data_time: 0.0048 loss: 0.6390 03/06 23:32:58 - mmengine - INFO - Epoch(train) [137][ 800/5005] lr: 1.0000e-04 eta: 1:13:56 time: 0.2312 data_time: 0.0049 loss: 0.6510 03/06 23:33:22 - mmengine - INFO - Epoch(train) [137][ 900/5005] lr: 1.0000e-04 eta: 1:13:33 time: 0.2286 data_time: 0.0049 loss: 0.7061 03/06 23:33:46 - mmengine - INFO - Epoch(train) [137][1000/5005] lr: 1.0000e-04 eta: 1:13:10 time: 0.2345 data_time: 0.0051 loss: 0.7815 03/06 23:34:09 - mmengine - INFO - Epoch(train) [137][1100/5005] lr: 1.0000e-04 eta: 1:12:46 time: 0.2302 data_time: 0.0048 loss: 0.8752 03/06 23:34:33 - mmengine - INFO - Epoch(train) [137][1200/5005] lr: 1.0000e-04 eta: 1:12:23 time: 0.2639 data_time: 0.0049 loss: 0.9378 03/06 23:34:56 - mmengine - INFO - Epoch(train) [137][1300/5005] lr: 1.0000e-04 eta: 1:12:00 time: 0.2269 data_time: 0.0051 loss: 0.8315 03/06 23:35:01 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:35:20 - mmengine - INFO - Epoch(train) [137][1400/5005] lr: 1.0000e-04 eta: 1:11:37 time: 0.2301 data_time: 0.0051 loss: 0.7913 03/06 23:35:43 - mmengine - INFO - Epoch(train) [137][1500/5005] lr: 1.0000e-04 eta: 1:11:14 time: 0.2305 data_time: 0.0050 loss: 0.8295 03/06 23:36:07 - mmengine - INFO - Epoch(train) [137][1600/5005] lr: 1.0000e-04 eta: 1:10:51 time: 0.2299 data_time: 0.0048 loss: 0.8079 03/06 23:36:30 - mmengine - INFO - Epoch(train) [137][1700/5005] lr: 1.0000e-04 eta: 1:10:28 time: 0.2313 data_time: 0.0051 loss: 0.8693 03/06 23:36:54 - mmengine - INFO - Epoch(train) [137][1800/5005] lr: 1.0000e-04 eta: 1:10:05 time: 0.2308 data_time: 0.0054 loss: 0.8023 03/06 23:37:18 - mmengine - INFO - Epoch(train) [137][1900/5005] lr: 1.0000e-04 eta: 1:09:42 time: 0.2287 data_time: 0.0050 loss: 0.7152 03/06 23:37:41 - mmengine - INFO - Epoch(train) [137][2000/5005] lr: 1.0000e-04 eta: 1:09:19 time: 0.2323 data_time: 0.0049 loss: 0.8110 03/06 23:38:05 - mmengine - INFO - Epoch(train) [137][2100/5005] lr: 1.0000e-04 eta: 1:08:56 time: 0.2362 data_time: 0.0053 loss: 0.8688 03/06 23:38:29 - mmengine - INFO - Epoch(train) [137][2200/5005] lr: 1.0000e-04 eta: 1:08:33 time: 0.2303 data_time: 0.0053 loss: 0.7306 03/06 23:38:52 - mmengine - INFO - Epoch(train) [137][2300/5005] lr: 1.0000e-04 eta: 1:08:10 time: 0.2286 data_time: 0.0052 loss: 0.9407 03/06 23:38:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:39:16 - mmengine - INFO - Epoch(train) [137][2400/5005] lr: 1.0000e-04 eta: 1:07:46 time: 0.2303 data_time: 0.0052 loss: 0.7556 03/06 23:39:39 - mmengine - INFO - Epoch(train) [137][2500/5005] lr: 1.0000e-04 eta: 1:07:23 time: 0.2335 data_time: 0.0047 loss: 0.8363 03/06 23:40:03 - mmengine - INFO - Epoch(train) [137][2600/5005] lr: 1.0000e-04 eta: 1:07:00 time: 0.2590 data_time: 0.0048 loss: 0.8086 03/06 23:40:27 - mmengine - INFO - Epoch(train) [137][2700/5005] lr: 1.0000e-04 eta: 1:06:37 time: 0.2359 data_time: 0.0050 loss: 0.7784 03/06 23:40:50 - mmengine - INFO - Epoch(train) [137][2800/5005] lr: 1.0000e-04 eta: 1:06:14 time: 0.2333 data_time: 0.0055 loss: 0.7759 03/06 23:41:14 - mmengine - INFO - Epoch(train) [137][2900/5005] lr: 1.0000e-04 eta: 1:05:51 time: 0.2297 data_time: 0.0049 loss: 0.8516 03/06 23:41:38 - mmengine - INFO - Epoch(train) [137][3000/5005] lr: 1.0000e-04 eta: 1:05:28 time: 0.2509 data_time: 0.0052 loss: 0.7359 03/06 23:42:01 - mmengine - INFO - Epoch(train) [137][3100/5005] lr: 1.0000e-04 eta: 1:05:05 time: 0.2304 data_time: 0.0049 loss: 0.8346 03/06 23:42:25 - mmengine - INFO - Epoch(train) [137][3200/5005] lr: 1.0000e-04 eta: 1:04:42 time: 0.2280 data_time: 0.0049 loss: 0.7801 03/06 23:42:48 - mmengine - INFO - Epoch(train) [137][3300/5005] lr: 1.0000e-04 eta: 1:04:19 time: 0.2276 data_time: 0.0048 loss: 0.7638 03/06 23:42:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:43:12 - mmengine - INFO - Epoch(train) [137][3400/5005] lr: 1.0000e-04 eta: 1:03:56 time: 0.2501 data_time: 0.0053 loss: 0.7340 03/06 23:43:36 - mmengine - INFO - Epoch(train) [137][3500/5005] lr: 1.0000e-04 eta: 1:03:33 time: 0.2298 data_time: 0.0050 loss: 0.9111 03/06 23:43:59 - mmengine - INFO - Epoch(train) [137][3600/5005] lr: 1.0000e-04 eta: 1:03:10 time: 0.2269 data_time: 0.0051 loss: 0.7387 03/06 23:44:23 - mmengine - INFO - Epoch(train) [137][3700/5005] lr: 1.0000e-04 eta: 1:02:46 time: 0.2303 data_time: 0.0050 loss: 0.7121 03/06 23:44:46 - mmengine - INFO - Epoch(train) [137][3800/5005] lr: 1.0000e-04 eta: 1:02:23 time: 0.2517 data_time: 0.0050 loss: 0.9132 03/06 23:45:10 - mmengine - INFO - Epoch(train) [137][3900/5005] lr: 1.0000e-04 eta: 1:02:00 time: 0.2326 data_time: 0.0052 loss: 1.0160 03/06 23:45:34 - mmengine - INFO - Epoch(train) [137][4000/5005] lr: 1.0000e-04 eta: 1:01:37 time: 0.2285 data_time: 0.0048 loss: 0.7825 03/06 23:45:58 - mmengine - INFO - Epoch(train) [137][4100/5005] lr: 1.0000e-04 eta: 1:01:14 time: 0.2331 data_time: 0.0050 loss: 0.8776 03/06 23:46:21 - mmengine - INFO - Epoch(train) [137][4200/5005] lr: 1.0000e-04 eta: 1:00:51 time: 0.2300 data_time: 0.0049 loss: 0.7480 03/06 23:46:45 - mmengine - INFO - Epoch(train) [137][4300/5005] lr: 1.0000e-04 eta: 1:00:28 time: 0.2286 data_time: 0.0055 loss: 0.8374 03/06 23:46:50 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:47:09 - mmengine - INFO - Epoch(train) [137][4400/5005] lr: 1.0000e-04 eta: 1:00:05 time: 0.2300 data_time: 0.0050 loss: 0.7678 03/06 23:47:32 - mmengine - INFO - Epoch(train) [137][4500/5005] lr: 1.0000e-04 eta: 0:59:42 time: 0.2249 data_time: 0.0047 loss: 0.9663 03/06 23:47:56 - mmengine - INFO - Epoch(train) [137][4600/5005] lr: 1.0000e-04 eta: 0:59:19 time: 0.2297 data_time: 0.0049 loss: 0.7634 03/06 23:48:19 - mmengine - INFO - Epoch(train) [137][4700/5005] lr: 1.0000e-04 eta: 0:58:56 time: 0.2508 data_time: 0.0050 loss: 0.7769 03/06 23:48:43 - mmengine - INFO - Epoch(train) [137][4800/5005] lr: 1.0000e-04 eta: 0:58:33 time: 0.2299 data_time: 0.0052 loss: 0.8455 03/06 23:49:08 - mmengine - INFO - Epoch(train) [137][4900/5005] lr: 1.0000e-04 eta: 0:58:10 time: 0.2962 data_time: 0.0050 loss: 0.9268 03/06 23:49:37 - mmengine - INFO - Epoch(train) [137][5000/5005] lr: 1.0000e-04 eta: 0:57:47 time: 0.2831 data_time: 0.0048 loss: 0.8820 03/06 23:49:39 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:49:41 - mmengine - INFO - Saving checkpoint at 137 epochs 03/06 23:49:56 - mmengine - INFO - Epoch(val) [137][100/196] eta: 0:00:13 time: 0.0226 data_time: 0.0004 03/06 23:50:09 - mmengine - INFO - Epoch(val) [137][196/196] accuracy/top1: 77.5960 accuracy/top5: 93.7860 03/06 23:50:42 - mmengine - INFO - Epoch(train) [138][ 100/5005] lr: 1.0000e-04 eta: 0:57:23 time: 0.2290 data_time: 0.0059 loss: 0.7257 03/06 23:51:06 - mmengine - INFO - Epoch(train) [138][ 200/5005] lr: 1.0000e-04 eta: 0:56:59 time: 0.2278 data_time: 0.0058 loss: 0.8424 03/06 23:51:30 - mmengine - INFO - Epoch(train) [138][ 300/5005] lr: 1.0000e-04 eta: 0:56:36 time: 0.2312 data_time: 0.0055 loss: 0.7792 03/06 23:51:34 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:51:54 - mmengine - INFO - Epoch(train) [138][ 400/5005] lr: 1.0000e-04 eta: 0:56:13 time: 0.2284 data_time: 0.0055 loss: 0.6983 03/06 23:52:17 - mmengine - INFO - Epoch(train) [138][ 500/5005] lr: 1.0000e-04 eta: 0:55:50 time: 0.2315 data_time: 0.0052 loss: 0.7587 03/06 23:52:41 - mmengine - INFO - Epoch(train) [138][ 600/5005] lr: 1.0000e-04 eta: 0:55:27 time: 0.2319 data_time: 0.0060 loss: 0.6849 03/06 23:53:05 - mmengine - INFO - Epoch(train) [138][ 700/5005] lr: 1.0000e-04 eta: 0:55:04 time: 0.2293 data_time: 0.0056 loss: 0.6563 03/06 23:53:28 - mmengine - INFO - Epoch(train) [138][ 800/5005] lr: 1.0000e-04 eta: 0:54:41 time: 0.2310 data_time: 0.0054 loss: 0.7291 03/06 23:53:52 - mmengine - INFO - Epoch(train) [138][ 900/5005] lr: 1.0000e-04 eta: 0:54:18 time: 0.2293 data_time: 0.0055 loss: 0.7937 03/06 23:54:16 - mmengine - INFO - Epoch(train) [138][1000/5005] lr: 1.0000e-04 eta: 0:53:55 time: 0.2293 data_time: 0.0054 loss: 0.7207 03/06 23:54:40 - mmengine - INFO - Epoch(train) [138][1100/5005] lr: 1.0000e-04 eta: 0:53:32 time: 0.2542 data_time: 0.0056 loss: 0.7669 03/06 23:55:03 - mmengine - INFO - Epoch(train) [138][1200/5005] lr: 1.0000e-04 eta: 0:53:09 time: 0.2276 data_time: 0.0055 loss: 0.7801 03/06 23:55:26 - mmengine - INFO - Epoch(train) [138][1300/5005] lr: 1.0000e-04 eta: 0:52:46 time: 0.2300 data_time: 0.0054 loss: 0.8481 03/06 23:55:29 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:55:49 - mmengine - INFO - Epoch(train) [138][1400/5005] lr: 1.0000e-04 eta: 0:52:23 time: 0.2328 data_time: 0.0054 loss: 0.7858 03/06 23:56:13 - mmengine - INFO - Epoch(train) [138][1500/5005] lr: 1.0000e-04 eta: 0:51:59 time: 0.2281 data_time: 0.0054 loss: 0.6432 03/06 23:56:37 - mmengine - INFO - Epoch(train) [138][1600/5005] lr: 1.0000e-04 eta: 0:51:36 time: 0.2275 data_time: 0.0056 loss: 0.9057 03/06 23:57:00 - mmengine - INFO - Epoch(train) [138][1700/5005] lr: 1.0000e-04 eta: 0:51:13 time: 0.2287 data_time: 0.0052 loss: 0.6721 03/06 23:57:23 - mmengine - INFO - Epoch(train) [138][1800/5005] lr: 1.0000e-04 eta: 0:50:50 time: 0.2451 data_time: 0.0053 loss: 0.6387 03/06 23:57:47 - mmengine - INFO - Epoch(train) [138][1900/5005] lr: 1.0000e-04 eta: 0:50:27 time: 0.2293 data_time: 0.0053 loss: 0.7897 03/06 23:58:11 - mmengine - INFO - Epoch(train) [138][2000/5005] lr: 1.0000e-04 eta: 0:50:04 time: 0.2348 data_time: 0.0050 loss: 0.8242 03/06 23:58:34 - mmengine - INFO - Epoch(train) [138][2100/5005] lr: 1.0000e-04 eta: 0:49:41 time: 0.2299 data_time: 0.0058 loss: 0.7109 03/06 23:58:58 - mmengine - INFO - Epoch(train) [138][2200/5005] lr: 1.0000e-04 eta: 0:49:18 time: 0.2325 data_time: 0.0055 loss: 0.9348 03/06 23:59:21 - mmengine - INFO - Epoch(train) [138][2300/5005] lr: 1.0000e-04 eta: 0:48:55 time: 0.2303 data_time: 0.0057 loss: 0.7723 03/06 23:59:25 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/06 23:59:45 - mmengine - INFO - Epoch(train) [138][2400/5005] lr: 1.0000e-04 eta: 0:48:32 time: 0.2316 data_time: 0.0053 loss: 0.8653 03/07 00:00:08 - mmengine - INFO - Epoch(train) [138][2500/5005] lr: 1.0000e-04 eta: 0:48:09 time: 0.2285 data_time: 0.0054 loss: 0.9142 03/07 00:00:32 - mmengine - INFO - Epoch(train) [138][2600/5005] lr: 1.0000e-04 eta: 0:47:46 time: 0.2370 data_time: 0.0057 loss: 0.8495 03/07 00:00:56 - mmengine - INFO - Epoch(train) [138][2700/5005] lr: 1.0000e-04 eta: 0:47:22 time: 0.2284 data_time: 0.0054 loss: 0.8594 03/07 00:01:19 - mmengine - INFO - Epoch(train) [138][2800/5005] lr: 1.0000e-04 eta: 0:46:59 time: 0.2284 data_time: 0.0057 loss: 0.8292 03/07 00:01:43 - mmengine - INFO - Epoch(train) [138][2900/5005] lr: 1.0000e-04 eta: 0:46:36 time: 0.2294 data_time: 0.0051 loss: 0.9326 03/07 00:02:06 - mmengine - INFO - Epoch(train) [138][3000/5005] lr: 1.0000e-04 eta: 0:46:13 time: 0.2428 data_time: 0.0055 loss: 0.8217 03/07 00:02:30 - mmengine - INFO - Epoch(train) [138][3100/5005] lr: 1.0000e-04 eta: 0:45:50 time: 0.2338 data_time: 0.0055 loss: 0.8239 03/07 00:02:53 - mmengine - INFO - Epoch(train) [138][3200/5005] lr: 1.0000e-04 eta: 0:45:27 time: 0.2271 data_time: 0.0054 loss: 0.8674 03/07 00:03:17 - mmengine - INFO - Epoch(train) [138][3300/5005] lr: 1.0000e-04 eta: 0:45:04 time: 0.2338 data_time: 0.0057 loss: 0.7350 03/07 00:03:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:03:40 - mmengine - INFO - Epoch(train) [138][3400/5005] lr: 1.0000e-04 eta: 0:44:41 time: 0.2267 data_time: 0.0056 loss: 0.8399 03/07 00:04:03 - mmengine - INFO - Epoch(train) [138][3500/5005] lr: 1.0000e-04 eta: 0:44:18 time: 0.2320 data_time: 0.0054 loss: 0.6391 03/07 00:04:27 - mmengine - INFO - Epoch(train) [138][3600/5005] lr: 1.0000e-04 eta: 0:43:55 time: 0.2315 data_time: 0.0058 loss: 0.6209 03/07 00:04:51 - mmengine - INFO - Epoch(train) [138][3700/5005] lr: 1.0000e-04 eta: 0:43:32 time: 0.2331 data_time: 0.0055 loss: 0.7563 03/07 00:05:14 - mmengine - INFO - Epoch(train) [138][3800/5005] lr: 1.0000e-04 eta: 0:43:09 time: 0.2336 data_time: 0.0055 loss: 0.8725 03/07 00:05:38 - mmengine - INFO - Epoch(train) [138][3900/5005] lr: 1.0000e-04 eta: 0:42:45 time: 0.2283 data_time: 0.0056 loss: 0.7828 03/07 00:06:02 - mmengine - INFO - Epoch(train) [138][4000/5005] lr: 1.0000e-04 eta: 0:42:22 time: 0.2556 data_time: 0.0053 loss: 0.6297 03/07 00:06:26 - mmengine - INFO - Epoch(train) [138][4100/5005] lr: 1.0000e-04 eta: 0:41:59 time: 0.2288 data_time: 0.0056 loss: 0.8638 03/07 00:06:49 - mmengine - INFO - Epoch(train) [138][4200/5005] lr: 1.0000e-04 eta: 0:41:36 time: 0.2290 data_time: 0.0054 loss: 0.7211 03/07 00:07:13 - mmengine - INFO - Epoch(train) [138][4300/5005] lr: 1.0000e-04 eta: 0:41:13 time: 0.2488 data_time: 0.0057 loss: 0.8506 03/07 00:07:16 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:07:36 - mmengine - INFO - Epoch(train) [138][4400/5005] lr: 1.0000e-04 eta: 0:40:50 time: 0.2292 data_time: 0.0053 loss: 0.9321 03/07 00:08:00 - mmengine - INFO - Epoch(train) [138][4500/5005] lr: 1.0000e-04 eta: 0:40:27 time: 0.2283 data_time: 0.0055 loss: 0.8330 03/07 00:08:24 - mmengine - INFO - Epoch(train) [138][4600/5005] lr: 1.0000e-04 eta: 0:40:04 time: 0.2291 data_time: 0.0054 loss: 0.8903 03/07 00:08:47 - mmengine - INFO - Epoch(train) [138][4700/5005] lr: 1.0000e-04 eta: 0:39:41 time: 0.2323 data_time: 0.0053 loss: 0.7151 03/07 00:09:11 - mmengine - INFO - Epoch(train) [138][4800/5005] lr: 1.0000e-04 eta: 0:39:18 time: 0.2308 data_time: 0.0057 loss: 0.7746 03/07 00:09:35 - mmengine - INFO - Epoch(train) [138][4900/5005] lr: 1.0000e-04 eta: 0:38:55 time: 0.2854 data_time: 0.0054 loss: 0.7830 03/07 00:10:04 - mmengine - INFO - Epoch(train) [138][5000/5005] lr: 1.0000e-04 eta: 0:38:32 time: 0.2892 data_time: 0.0053 loss: 0.8383 03/07 00:10:06 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:10:08 - mmengine - INFO - Saving checkpoint at 138 epochs 03/07 00:10:24 - mmengine - INFO - Epoch(val) [138][100/196] eta: 0:00:14 time: 0.0180 data_time: 0.0002 03/07 00:10:37 - mmengine - INFO - Epoch(val) [138][196/196] accuracy/top1: 77.5300 accuracy/top5: 93.7360 03/07 00:11:11 - mmengine - INFO - Epoch(train) [139][ 100/5005] lr: 1.0000e-04 eta: 0:38:08 time: 0.2726 data_time: 0.0066 loss: 0.8248 03/07 00:11:35 - mmengine - INFO - Epoch(train) [139][ 200/5005] lr: 1.0000e-04 eta: 0:37:44 time: 0.2322 data_time: 0.0055 loss: 0.7838 03/07 00:11:58 - mmengine - INFO - Epoch(train) [139][ 300/5005] lr: 1.0000e-04 eta: 0:37:21 time: 0.2252 data_time: 0.0059 loss: 0.8921 03/07 00:12:00 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:12:22 - mmengine - INFO - Epoch(train) [139][ 400/5005] lr: 1.0000e-04 eta: 0:36:58 time: 0.2345 data_time: 0.0064 loss: 0.7426 03/07 00:12:46 - mmengine - INFO - Epoch(train) [139][ 500/5005] lr: 1.0000e-04 eta: 0:36:35 time: 0.2368 data_time: 0.0057 loss: 0.7699 03/07 00:13:10 - mmengine - INFO - Epoch(train) [139][ 600/5005] lr: 1.0000e-04 eta: 0:36:12 time: 0.2310 data_time: 0.0054 loss: 0.7418 03/07 00:13:33 - mmengine - INFO - Epoch(train) [139][ 700/5005] lr: 1.0000e-04 eta: 0:35:49 time: 0.2310 data_time: 0.0057 loss: 0.7009 03/07 00:13:57 - mmengine - INFO - Epoch(train) [139][ 800/5005] lr: 1.0000e-04 eta: 0:35:26 time: 0.2299 data_time: 0.0058 loss: 0.6159 03/07 00:14:21 - mmengine - INFO - Epoch(train) [139][ 900/5005] lr: 1.0000e-04 eta: 0:35:03 time: 0.2269 data_time: 0.0055 loss: 0.7978 03/07 00:14:44 - mmengine - INFO - Epoch(train) [139][1000/5005] lr: 1.0000e-04 eta: 0:34:40 time: 0.2266 data_time: 0.0054 loss: 0.7177 03/07 00:15:08 - mmengine - INFO - Epoch(train) [139][1100/5005] lr: 1.0000e-04 eta: 0:34:17 time: 0.2266 data_time: 0.0056 loss: 0.8167 03/07 00:15:31 - mmengine - INFO - Epoch(train) [139][1200/5005] lr: 1.0000e-04 eta: 0:33:54 time: 0.2281 data_time: 0.0052 loss: 0.7662 03/07 00:15:55 - mmengine - INFO - Epoch(train) [139][1300/5005] lr: 1.0000e-04 eta: 0:33:30 time: 0.2392 data_time: 0.0058 loss: 0.7994 03/07 00:15:57 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:16:19 - mmengine - INFO - Epoch(train) [139][1400/5005] lr: 1.0000e-04 eta: 0:33:07 time: 0.2298 data_time: 0.0058 loss: 0.8552 03/07 00:16:42 - mmengine - INFO - Epoch(train) [139][1500/5005] lr: 1.0000e-04 eta: 0:32:44 time: 0.2265 data_time: 0.0054 loss: 0.8089 03/07 00:17:06 - mmengine - INFO - Epoch(train) [139][1600/5005] lr: 1.0000e-04 eta: 0:32:21 time: 0.2279 data_time: 0.0055 loss: 0.7503 03/07 00:17:29 - mmengine - INFO - Epoch(train) [139][1700/5005] lr: 1.0000e-04 eta: 0:31:58 time: 0.2295 data_time: 0.0053 loss: 0.5935 03/07 00:17:53 - mmengine - INFO - Epoch(train) [139][1800/5005] lr: 1.0000e-04 eta: 0:31:35 time: 0.2278 data_time: 0.0053 loss: 0.7419 03/07 00:18:16 - mmengine - INFO - Epoch(train) [139][1900/5005] lr: 1.0000e-04 eta: 0:31:12 time: 0.2338 data_time: 0.0060 loss: 0.7967 03/07 00:18:40 - mmengine - INFO - Epoch(train) [139][2000/5005] lr: 1.0000e-04 eta: 0:30:49 time: 0.2540 data_time: 0.0054 loss: 0.7986 03/07 00:19:04 - mmengine - INFO - Epoch(train) [139][2100/5005] lr: 1.0000e-04 eta: 0:30:26 time: 0.2261 data_time: 0.0058 loss: 0.7825 03/07 00:19:28 - mmengine - INFO - Epoch(train) [139][2200/5005] lr: 1.0000e-04 eta: 0:30:03 time: 0.2300 data_time: 0.0058 loss: 0.7760 03/07 00:19:51 - mmengine - INFO - Epoch(train) [139][2300/5005] lr: 1.0000e-04 eta: 0:29:40 time: 0.2281 data_time: 0.0054 loss: 0.7626 03/07 00:19:53 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:20:14 - mmengine - INFO - Epoch(train) [139][2400/5005] lr: 1.0000e-04 eta: 0:29:17 time: 0.2294 data_time: 0.0055 loss: 0.7223 03/07 00:20:38 - mmengine - INFO - Epoch(train) [139][2500/5005] lr: 1.0000e-04 eta: 0:28:53 time: 0.2278 data_time: 0.0056 loss: 0.6085 03/07 00:21:02 - mmengine - INFO - Epoch(train) [139][2600/5005] lr: 1.0000e-04 eta: 0:28:30 time: 0.2325 data_time: 0.0058 loss: 0.7712 03/07 00:21:25 - mmengine - INFO - Epoch(train) [139][2700/5005] lr: 1.0000e-04 eta: 0:28:07 time: 0.2297 data_time: 0.0053 loss: 0.6757 03/07 00:21:49 - mmengine - INFO - Epoch(train) [139][2800/5005] lr: 1.0000e-04 eta: 0:27:44 time: 0.2495 data_time: 0.0053 loss: 0.7939 03/07 00:22:12 - mmengine - INFO - Epoch(train) [139][2900/5005] lr: 1.0000e-04 eta: 0:27:21 time: 0.2319 data_time: 0.0056 loss: 0.7853 03/07 00:22:36 - mmengine - INFO - Epoch(train) [139][3000/5005] lr: 1.0000e-04 eta: 0:26:58 time: 0.2335 data_time: 0.0058 loss: 0.7180 03/07 00:23:00 - mmengine - INFO - Epoch(train) [139][3100/5005] lr: 1.0000e-04 eta: 0:26:35 time: 0.2291 data_time: 0.0056 loss: 0.6555 03/07 00:23:23 - mmengine - INFO - Epoch(train) [139][3200/5005] lr: 1.0000e-04 eta: 0:26:12 time: 0.2305 data_time: 0.0057 loss: 0.9323 03/07 00:23:47 - mmengine - INFO - Epoch(train) [139][3300/5005] lr: 1.0000e-04 eta: 0:25:49 time: 0.2456 data_time: 0.0055 loss: 0.7398 03/07 00:23:49 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:24:11 - mmengine - INFO - Epoch(train) [139][3400/5005] lr: 1.0000e-04 eta: 0:25:26 time: 0.2291 data_time: 0.0054 loss: 0.7333 03/07 00:24:34 - mmengine - INFO - Epoch(train) [139][3500/5005] lr: 1.0000e-04 eta: 0:25:03 time: 0.2302 data_time: 0.0058 loss: 0.8251 03/07 00:24:58 - mmengine - INFO - Epoch(train) [139][3600/5005] lr: 1.0000e-04 eta: 0:24:39 time: 0.2285 data_time: 0.0052 loss: 1.0139 03/07 00:25:21 - mmengine - INFO - Epoch(train) [139][3700/5005] lr: 1.0000e-04 eta: 0:24:16 time: 0.2426 data_time: 0.0055 loss: 0.6058 03/07 00:25:45 - mmengine - INFO - Epoch(train) [139][3800/5005] lr: 1.0000e-04 eta: 0:23:53 time: 0.2261 data_time: 0.0055 loss: 0.8914 03/07 00:26:09 - mmengine - INFO - Epoch(train) [139][3900/5005] lr: 1.0000e-04 eta: 0:23:30 time: 0.2289 data_time: 0.0058 loss: 0.6579 03/07 00:26:32 - mmengine - INFO - Epoch(train) [139][4000/5005] lr: 1.0000e-04 eta: 0:23:07 time: 0.2279 data_time: 0.0054 loss: 0.8475 03/07 00:26:56 - mmengine - INFO - Epoch(train) [139][4100/5005] lr: 1.0000e-04 eta: 0:22:44 time: 0.2262 data_time: 0.0059 loss: 0.8836 03/07 00:27:20 - mmengine - INFO - Epoch(train) [139][4200/5005] lr: 1.0000e-04 eta: 0:22:21 time: 0.2372 data_time: 0.0054 loss: 0.7894 03/07 00:27:43 - mmengine - INFO - Epoch(train) [139][4300/5005] lr: 1.0000e-04 eta: 0:21:58 time: 0.2298 data_time: 0.0058 loss: 0.7929 03/07 00:27:46 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:28:07 - mmengine - INFO - Epoch(train) [139][4400/5005] lr: 1.0000e-04 eta: 0:21:35 time: 0.2293 data_time: 0.0056 loss: 0.8894 03/07 00:28:31 - mmengine - INFO - Epoch(train) [139][4500/5005] lr: 1.0000e-04 eta: 0:21:12 time: 0.2289 data_time: 0.0053 loss: 0.6503 03/07 00:28:54 - mmengine - INFO - Epoch(train) [139][4600/5005] lr: 1.0000e-04 eta: 0:20:49 time: 0.2277 data_time: 0.0064 loss: 0.7363 03/07 00:29:18 - mmengine - INFO - Epoch(train) [139][4700/5005] lr: 1.0000e-04 eta: 0:20:26 time: 0.2285 data_time: 0.0057 loss: 0.7131 03/07 00:29:42 - mmengine - INFO - Epoch(train) [139][4800/5005] lr: 1.0000e-04 eta: 0:20:02 time: 0.2320 data_time: 0.0055 loss: 0.6124 03/07 00:30:06 - mmengine - INFO - Epoch(train) [139][4900/5005] lr: 1.0000e-04 eta: 0:19:39 time: 0.2901 data_time: 0.0052 loss: 0.7551 03/07 00:30:36 - mmengine - INFO - Epoch(train) [139][5000/5005] lr: 1.0000e-04 eta: 0:19:16 time: 0.2946 data_time: 0.0052 loss: 0.7484 03/07 00:30:37 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:30:39 - mmengine - INFO - Saving checkpoint at 139 epochs 03/07 00:30:55 - mmengine - INFO - Epoch(val) [139][100/196] eta: 0:00:13 time: 0.0191 data_time: 0.0003 03/07 00:31:09 - mmengine - INFO - Epoch(val) [139][196/196] accuracy/top1: 77.5840 accuracy/top5: 93.7060 03/07 00:31:42 - mmengine - INFO - Epoch(train) [140][ 100/5005] lr: 1.0000e-04 eta: 0:18:52 time: 0.2346 data_time: 0.0062 loss: 0.8290 03/07 00:32:06 - mmengine - INFO - Epoch(train) [140][ 200/5005] lr: 1.0000e-04 eta: 0:18:29 time: 0.2292 data_time: 0.0062 loss: 0.8526 03/07 00:32:30 - mmengine - INFO - Epoch(train) [140][ 300/5005] lr: 1.0000e-04 eta: 0:18:06 time: 0.2919 data_time: 0.0057 loss: 0.8585 03/07 00:32:31 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:32:54 - mmengine - INFO - Epoch(train) [140][ 400/5005] lr: 1.0000e-04 eta: 0:17:43 time: 0.2279 data_time: 0.0061 loss: 0.8388 03/07 00:33:17 - mmengine - INFO - Epoch(train) [140][ 500/5005] lr: 1.0000e-04 eta: 0:17:20 time: 0.2322 data_time: 0.0058 loss: 0.7978 03/07 00:33:41 - mmengine - INFO - Epoch(train) [140][ 600/5005] lr: 1.0000e-04 eta: 0:16:57 time: 0.2302 data_time: 0.0063 loss: 0.6628 03/07 00:34:04 - mmengine - INFO - Epoch(train) [140][ 700/5005] lr: 1.0000e-04 eta: 0:16:34 time: 0.2271 data_time: 0.0062 loss: 0.6491 03/07 00:34:29 - mmengine - INFO - Epoch(train) [140][ 800/5005] lr: 1.0000e-04 eta: 0:16:11 time: 0.2307 data_time: 0.0059 loss: 0.7691 03/07 00:34:52 - mmengine - INFO - Epoch(train) [140][ 900/5005] lr: 1.0000e-04 eta: 0:15:47 time: 0.2296 data_time: 0.0058 loss: 0.7107 03/07 00:35:16 - mmengine - INFO - Epoch(train) [140][1000/5005] lr: 1.0000e-04 eta: 0:15:24 time: 0.2258 data_time: 0.0057 loss: 0.6631 03/07 00:35:39 - mmengine - INFO - Epoch(train) [140][1100/5005] lr: 1.0000e-04 eta: 0:15:01 time: 0.2320 data_time: 0.0055 loss: 0.8018 03/07 00:36:03 - mmengine - INFO - Epoch(train) [140][1200/5005] lr: 1.0000e-04 eta: 0:14:38 time: 0.2263 data_time: 0.0058 loss: 0.7919 03/07 00:36:27 - mmengine - INFO - Epoch(train) [140][1300/5005] lr: 1.0000e-04 eta: 0:14:15 time: 0.2326 data_time: 0.0055 loss: 0.9820 03/07 00:36:28 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:36:50 - mmengine - INFO - Epoch(train) [140][1400/5005] lr: 1.0000e-04 eta: 0:13:52 time: 0.2288 data_time: 0.0059 loss: 0.9754 03/07 00:37:14 - mmengine - INFO - Epoch(train) [140][1500/5005] lr: 1.0000e-04 eta: 0:13:29 time: 0.2273 data_time: 0.0058 loss: 0.6225 03/07 00:37:38 - mmengine - INFO - Epoch(train) [140][1600/5005] lr: 1.0000e-04 eta: 0:13:06 time: 0.2309 data_time: 0.0057 loss: 0.7631 03/07 00:38:01 - mmengine - INFO - Epoch(train) [140][1700/5005] lr: 1.0000e-04 eta: 0:12:43 time: 0.2479 data_time: 0.0058 loss: 0.7957 03/07 00:38:25 - mmengine - INFO - Epoch(train) [140][1800/5005] lr: 1.0000e-04 eta: 0:12:20 time: 0.2300 data_time: 0.0057 loss: 0.7466 03/07 00:38:48 - mmengine - INFO - Epoch(train) [140][1900/5005] lr: 1.0000e-04 eta: 0:11:57 time: 0.2278 data_time: 0.0059 loss: 0.7457 03/07 00:39:12 - mmengine - INFO - Epoch(train) [140][2000/5005] lr: 1.0000e-04 eta: 0:11:33 time: 0.2368 data_time: 0.0059 loss: 0.8532 03/07 00:39:36 - mmengine - INFO - Epoch(train) [140][2100/5005] lr: 1.0000e-04 eta: 0:11:10 time: 0.2291 data_time: 0.0068 loss: 0.6862 03/07 00:39:59 - mmengine - INFO - Epoch(train) [140][2200/5005] lr: 1.0000e-04 eta: 0:10:47 time: 0.2279 data_time: 0.0058 loss: 0.8522 03/07 00:40:23 - mmengine - INFO - Epoch(train) [140][2300/5005] lr: 1.0000e-04 eta: 0:10:24 time: 0.2311 data_time: 0.0058 loss: 0.9258 03/07 00:40:24 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:40:46 - mmengine - INFO - Epoch(train) [140][2400/5005] lr: 1.0000e-04 eta: 0:10:01 time: 0.2286 data_time: 0.0059 loss: 1.0033 03/07 00:41:10 - mmengine - INFO - Epoch(train) [140][2500/5005] lr: 1.0000e-04 eta: 0:09:38 time: 0.2268 data_time: 0.0056 loss: 0.8436 03/07 00:41:34 - mmengine - INFO - Epoch(train) [140][2600/5005] lr: 1.0000e-04 eta: 0:09:15 time: 0.2308 data_time: 0.0060 loss: 0.8129 03/07 00:41:57 - mmengine - INFO - Epoch(train) [140][2700/5005] lr: 1.0000e-04 eta: 0:08:52 time: 0.2275 data_time: 0.0058 loss: 0.7801 03/07 00:42:21 - mmengine - INFO - Epoch(train) [140][2800/5005] lr: 1.0000e-04 eta: 0:08:29 time: 0.2274 data_time: 0.0060 loss: 0.7577 03/07 00:42:44 - mmengine - INFO - Epoch(train) [140][2900/5005] lr: 1.0000e-04 eta: 0:08:06 time: 0.2284 data_time: 0.0059 loss: 0.6914 03/07 00:43:08 - mmengine - INFO - Epoch(train) [140][3000/5005] lr: 1.0000e-04 eta: 0:07:42 time: 0.2320 data_time: 0.0054 loss: 0.7692 03/07 00:43:32 - mmengine - INFO - Epoch(train) [140][3100/5005] lr: 1.0000e-04 eta: 0:07:19 time: 0.2309 data_time: 0.0056 loss: 0.7709 03/07 00:43:55 - mmengine - INFO - Epoch(train) [140][3200/5005] lr: 1.0000e-04 eta: 0:06:56 time: 0.2257 data_time: 0.0055 loss: 0.8956 03/07 00:44:19 - mmengine - INFO - Epoch(train) [140][3300/5005] lr: 1.0000e-04 eta: 0:06:33 time: 0.2305 data_time: 0.0062 loss: 0.7892 03/07 00:44:20 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:44:43 - mmengine - INFO - Epoch(train) [140][3400/5005] lr: 1.0000e-04 eta: 0:06:10 time: 0.2318 data_time: 0.0059 loss: 0.9170 03/07 00:45:06 - mmengine - INFO - Epoch(train) [140][3500/5005] lr: 1.0000e-04 eta: 0:05:47 time: 0.2280 data_time: 0.0060 loss: 0.5317 03/07 00:45:30 - mmengine - INFO - Epoch(train) [140][3600/5005] lr: 1.0000e-04 eta: 0:05:24 time: 0.2320 data_time: 0.0058 loss: 0.8148 03/07 00:45:53 - mmengine - INFO - Epoch(train) [140][3700/5005] lr: 1.0000e-04 eta: 0:05:01 time: 0.2268 data_time: 0.0056 loss: 0.8779 03/07 00:46:18 - mmengine - INFO - Epoch(train) [140][3800/5005] lr: 1.0000e-04 eta: 0:04:38 time: 0.2295 data_time: 0.0062 loss: 0.8252 03/07 00:46:41 - mmengine - INFO - Epoch(train) [140][3900/5005] lr: 1.0000e-04 eta: 0:04:15 time: 0.2323 data_time: 0.0059 loss: 0.9115 03/07 00:47:05 - mmengine - INFO - Epoch(train) [140][4000/5005] lr: 1.0000e-04 eta: 0:03:52 time: 0.2311 data_time: 0.0058 loss: 0.7956 03/07 00:47:29 - mmengine - INFO - Epoch(train) [140][4100/5005] lr: 1.0000e-04 eta: 0:03:28 time: 0.2319 data_time: 0.0060 loss: 0.8599 03/07 00:47:53 - mmengine - INFO - Epoch(train) [140][4200/5005] lr: 1.0000e-04 eta: 0:03:05 time: 0.2292 data_time: 0.0059 loss: 0.8757 03/07 00:48:16 - mmengine - INFO - Epoch(train) [140][4300/5005] lr: 1.0000e-04 eta: 0:02:42 time: 0.2335 data_time: 0.0056 loss: 0.9327 03/07 00:48:17 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:48:40 - mmengine - INFO - Epoch(train) [140][4400/5005] lr: 1.0000e-04 eta: 0:02:19 time: 0.2338 data_time: 0.0056 loss: 0.8311 03/07 00:49:03 - mmengine - INFO - Epoch(train) [140][4500/5005] lr: 1.0000e-04 eta: 0:01:56 time: 0.2304 data_time: 0.0060 loss: 0.6658 03/07 00:49:27 - mmengine - INFO - Epoch(train) [140][4600/5005] lr: 1.0000e-04 eta: 0:01:33 time: 0.2393 data_time: 0.0056 loss: 0.7925 03/07 00:49:51 - mmengine - INFO - Epoch(train) [140][4700/5005] lr: 1.0000e-04 eta: 0:01:10 time: 0.2296 data_time: 0.0056 loss: 0.7648 03/07 00:50:14 - mmengine - INFO - Epoch(train) [140][4800/5005] lr: 1.0000e-04 eta: 0:00:47 time: 0.2295 data_time: 0.0059 loss: 0.7890 03/07 00:50:39 - mmengine - INFO - Epoch(train) [140][4900/5005] lr: 1.0000e-04 eta: 0:00:24 time: 0.2400 data_time: 0.0054 loss: 0.8079 03/07 00:51:08 - mmengine - INFO - Epoch(train) [140][5000/5005] lr: 1.0000e-04 eta: 0:00:01 time: 0.2783 data_time: 0.0055 loss: 0.9053 03/07 00:51:09 - mmengine - INFO - Exp name: seresnet50_8xb32_in1k_20230305_022205 03/07 00:51:12 - mmengine - INFO - Saving checkpoint at 140 epochs 03/07 00:51:27 - mmengine - INFO - Epoch(val) [140][100/196] eta: 0:00:13 time: 0.0181 data_time: 0.0003 03/07 00:51:41 - mmengine - INFO - Epoch(val) [140][196/196] accuracy/top1: 77.6560 accuracy/top5: 93.7440