03/10 05:20:35 - 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: 171536495 GCC: gcc (GCC) 7.3.0 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: 0.6.0 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 ------------------------------------------------------------ 03/10 05:20:35 - mmengine - INFO - Config: model = dict( type='ImageClassifier', backbone=dict(type='DenseNet', arch='121'), neck=dict(type='GlobalAveragePooling'), head=dict( type='LinearClsHead', num_classes=1000, in_channels=1024, loss=dict(type='CrossEntropyLoss', loss_weight=1.0))) 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), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='PackClsInputs') ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='ResizeEdge', scale=256, edge='short'), 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=256, num_workers=5, dataset=dict( type='ImageNet', data_root='data/imagenet', ann_file='meta/train.txt', data_prefix='train', pipeline=[ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', scale=224), 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=64, num_workers=5, 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'), 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=64, num_workers=5, 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'), 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=[30, 60, 90], gamma=0.1) train_cfg = dict(by_epoch=True, max_epochs=90, val_interval=1) val_cfg = dict() test_cfg = dict() auto_scale_lr = dict(base_batch_size=1024) 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/densenet121_4xb256_in1k' 03/10 05:20:35 - mmengine - WARNING - The "visualizer" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/10 05:20:35 - mmengine - WARNING - The "vis_backend" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/10 05:20:35 - mmengine - WARNING - The "model" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/10 05:22:28 - mmengine - WARNING - The "hook" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/10 05:22:28 - 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 -------------------- 03/10 05:22:28 - mmengine - WARNING - The "dataset" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/10 05:22:28 - 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 GradScaler options are: init_scale : 65536.0 growth_factor : 2.0 backoff_factor : 0.5 growth_interval : 2000 dynamic : True enabled : True 03/10 05:22:37 - mmengine - WARNING - The "data sampler" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/10 05:22:37 - mmengine - WARNING - The "optimizer wrapper constructor" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/10 05:22:37 - mmengine - WARNING - The "optimizer" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/10 05:22:37 - 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 03/10 05:22:37 - mmengine - WARNING - The "parameter scheduler" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/10 05:22:38 - mmengine - WARNING - The "metric" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/10 05:22:53 - mmengine - WARNING - The "weight initializer" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 03/10 05:22:59 - mmengine - INFO - Checkpoints will be saved to /home/lml/mmcv_2.x/mmclassification/work_dirs/densenet121_4xb256_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, 1024], strides() = [1024, 1] bucket_view.sizes() = [1024000], 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, 1024], strides() = [1024, 1] bucket_view.sizes() = [1024000], 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, 1024], strides() = [1024, 1] bucket_view.sizes() = [1024000], 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, 1024], strides() = [1024, 1] bucket_view.sizes() = [1024000], 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, 1024], strides() = [1024, 1] bucket_view.sizes() = [1024000], 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, 1024], strides() = [1024, 1] bucket_view.sizes() = [1024000], 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, 1024], strides() = [1024, 1] bucket_view.sizes() = [1024000], 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, 1024], strides() = [1024, 1] bucket_view.sizes() = [1024000], strides() = [1] (function operator()) 03/10 05:34:42 - mmengine - INFO - Epoch(train) [1][100/626] lr: 1.0000e-01 eta: 4 days, 13:53:57 time: 0.3031 data_time: 0.0033 loss: 6.0957 03/10 05:35:14 - mmengine - INFO - Epoch(train) [1][200/626] lr: 1.0000e-01 eta: 2 days, 9:18:37 time: 0.3930 data_time: 0.0492 loss: 5.6359 03/10 05:35:44 - mmengine - INFO - Epoch(train) [1][300/626] lr: 1.0000e-01 eta: 1 day, 15:42:35 time: 0.2683 data_time: 0.0029 loss: 5.3393 03/10 05:36:15 - mmengine - INFO - Epoch(train) [1][400/626] lr: 1.0000e-01 eta: 1 day, 6:55:41 time: 0.3081 data_time: 0.0033 loss: 5.0151 03/10 05:36:46 - mmengine - INFO - Epoch(train) [1][500/626] lr: 1.0000e-01 eta: 1 day, 1:39:15 time: 0.2908 data_time: 0.0031 loss: 4.7278 03/10 05:37:17 - mmengine - INFO - Epoch(train) [1][600/626] lr: 1.0000e-01 eta: 22:08:35 time: 0.2894 data_time: 0.0029 loss: 4.4775 03/10 05:49:37 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 05:49:37 - mmengine - INFO - Saving checkpoint at 1 epochs 03/10 06:04:19 - mmengine - INFO - Epoch(val) [1][98/98] accuracy/top1: 13.8760 accuracy/top5: 32.8320 03/10 06:04:53 - mmengine - INFO - Epoch(train) [2][100/626] lr: 1.0000e-01 eta: 1 day, 10:43:23 time: 0.2658 data_time: 0.0031 loss: 4.2006 03/10 06:05:24 - mmengine - INFO - Epoch(train) [2][200/626] lr: 1.0000e-01 eta: 1 day, 7:02:39 time: 0.2897 data_time: 0.0034 loss: 4.0716 03/10 06:05:56 - mmengine - INFO - Epoch(train) [2][300/626] lr: 1.0000e-01 eta: 1 day, 4:09:57 time: 0.3252 data_time: 0.0200 loss: 3.9212 03/10 06:06:19 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:06:27 - mmengine - INFO - Epoch(train) [2][400/626] lr: 1.0000e-01 eta: 1 day, 1:50:37 time: 0.3335 data_time: 0.0030 loss: 3.7435 03/10 06:06:59 - mmengine - INFO - Epoch(train) [2][500/626] lr: 1.0000e-01 eta: 23:56:11 time: 0.2834 data_time: 0.0042 loss: 3.6502 03/10 06:07:30 - mmengine - INFO - Epoch(train) [2][600/626] lr: 1.0000e-01 eta: 22:20:14 time: 0.3404 data_time: 0.0040 loss: 3.4989 03/10 06:07:37 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:07:38 - mmengine - INFO - Saving checkpoint at 2 epochs 03/10 06:07:59 - mmengine - INFO - Epoch(val) [2][98/98] accuracy/top1: 26.1340 accuracy/top5: 51.4520 03/10 06:08:33 - mmengine - INFO - Epoch(train) [3][100/626] lr: 1.0000e-01 eta: 20:40:45 time: 0.3163 data_time: 0.0033 loss: 3.3209 03/10 06:09:04 - mmengine - INFO - Epoch(train) [3][200/626] lr: 1.0000e-01 eta: 19:32:30 time: 0.2787 data_time: 0.0127 loss: 3.2830 03/10 06:09:35 - mmengine - INFO - Epoch(train) [3][300/626] lr: 1.0000e-01 eta: 18:33:25 time: 0.3167 data_time: 0.0032 loss: 3.1927 03/10 06:10:07 - mmengine - INFO - Epoch(train) [3][400/626] lr: 1.0000e-01 eta: 17:41:37 time: 0.3123 data_time: 0.0040 loss: 3.2002 03/10 06:10:38 - mmengine - INFO - Epoch(train) [3][500/626] lr: 1.0000e-01 eta: 16:55:21 time: 0.2838 data_time: 0.0028 loss: 3.1011 03/10 06:11:08 - mmengine - INFO - Epoch(train) [3][600/626] lr: 1.0000e-01 eta: 16:13:48 time: 0.2941 data_time: 0.0034 loss: 3.0449 03/10 06:11:16 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:11:16 - mmengine - INFO - Saving checkpoint at 3 epochs 03/10 06:11:37 - mmengine - INFO - Epoch(val) [3][98/98] accuracy/top1: 33.8220 accuracy/top5: 60.3520 03/10 06:12:11 - mmengine - INFO - Epoch(train) [4][100/626] lr: 1.0000e-01 eta: 15:28:44 time: 0.3166 data_time: 0.0030 loss: 2.9553 03/10 06:12:19 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:12:43 - mmengine - INFO - Epoch(train) [4][200/626] lr: 1.0000e-01 eta: 14:56:07 time: 0.3323 data_time: 0.0082 loss: 2.8502 03/10 06:13:14 - mmengine - INFO - Epoch(train) [4][300/626] lr: 1.0000e-01 eta: 14:26:14 time: 0.3199 data_time: 0.0032 loss: 2.8634 03/10 06:13:45 - mmengine - INFO - Epoch(train) [4][400/626] lr: 1.0000e-01 eta: 13:59:10 time: 0.3254 data_time: 0.0031 loss: 2.8429 03/10 06:14:16 - mmengine - INFO - Epoch(train) [4][500/626] lr: 1.0000e-01 eta: 13:34:11 time: 0.3030 data_time: 0.0030 loss: 2.8676 03/10 06:14:48 - mmengine - INFO - Epoch(train) [4][600/626] lr: 1.0000e-01 eta: 13:11:15 time: 0.3166 data_time: 0.0032 loss: 2.7795 03/10 06:14:56 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:14:56 - mmengine - INFO - Saving checkpoint at 4 epochs 03/10 06:15:16 - mmengine - INFO - Epoch(val) [4][98/98] accuracy/top1: 38.7480 accuracy/top5: 65.9780 03/10 06:15:51 - mmengine - INFO - Epoch(train) [5][100/626] lr: 1.0000e-01 eta: 12:45:43 time: 0.3685 data_time: 0.0032 loss: 2.6650 03/10 06:16:22 - mmengine - INFO - Epoch(train) [5][200/626] lr: 1.0000e-01 eta: 12:26:14 time: 0.3253 data_time: 0.0031 loss: 2.6726 03/10 06:16:53 - mmengine - INFO - Epoch(train) [5][300/626] lr: 1.0000e-01 eta: 12:08:12 time: 0.3130 data_time: 0.0035 loss: 2.6655 03/10 06:17:24 - mmengine - INFO - Epoch(train) [5][400/626] lr: 1.0000e-01 eta: 11:51:20 time: 0.2906 data_time: 0.0032 loss: 2.6424 03/10 06:17:54 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:17:55 - mmengine - INFO - Epoch(train) [5][500/626] lr: 1.0000e-01 eta: 11:35:43 time: 0.3215 data_time: 0.0034 loss: 2.5185 03/10 06:18:26 - mmengine - INFO - Epoch(train) [5][600/626] lr: 1.0000e-01 eta: 11:20:51 time: 0.3196 data_time: 0.0029 loss: 2.4977 03/10 06:18:34 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:18:34 - mmengine - INFO - Saving checkpoint at 5 epochs 03/10 06:18:55 - mmengine - INFO - Epoch(val) [5][98/98] accuracy/top1: 44.5380 accuracy/top5: 71.3140 03/10 06:19:29 - mmengine - INFO - Epoch(train) [6][100/626] lr: 1.0000e-01 eta: 11:04:04 time: 0.2809 data_time: 0.0030 loss: 2.4447 03/10 06:20:00 - mmengine - INFO - Epoch(train) [6][200/626] lr: 1.0000e-01 eta: 10:51:14 time: 0.3069 data_time: 0.0030 loss: 2.5232 03/10 06:20:32 - mmengine - INFO - Epoch(train) [6][300/626] lr: 1.0000e-01 eta: 10:39:05 time: 0.3284 data_time: 0.0038 loss: 2.5886 03/10 06:21:02 - mmengine - INFO - Epoch(train) [6][400/626] lr: 1.0000e-01 eta: 10:27:27 time: 0.2929 data_time: 0.0032 loss: 2.4825 03/10 06:21:33 - mmengine - INFO - Epoch(train) [6][500/626] lr: 1.0000e-01 eta: 10:16:26 time: 0.3012 data_time: 0.0029 loss: 2.5015 03/10 06:22:04 - mmengine - INFO - Epoch(train) [6][600/626] lr: 1.0000e-01 eta: 10:06:01 time: 0.3131 data_time: 0.0033 loss: 2.4313 03/10 06:22:11 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:22:12 - mmengine - INFO - Saving checkpoint at 6 epochs 03/10 06:22:32 - mmengine - INFO - Epoch(val) [6][98/98] accuracy/top1: 47.6400 accuracy/top5: 73.9400 03/10 06:23:05 - mmengine - INFO - Epoch(train) [7][100/626] lr: 1.0000e-01 eta: 9:54:05 time: 0.2922 data_time: 0.0033 loss: 2.4289 03/10 06:23:36 - mmengine - INFO - Epoch(train) [7][200/626] lr: 1.0000e-01 eta: 9:44:49 time: 0.3086 data_time: 0.0030 loss: 2.3268 03/10 06:23:50 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:24:08 - mmengine - INFO - Epoch(train) [7][300/626] lr: 1.0000e-01 eta: 9:36:01 time: 0.3050 data_time: 0.0031 loss: 2.3689 03/10 06:24:38 - mmengine - INFO - Epoch(train) [7][400/626] lr: 1.0000e-01 eta: 9:27:31 time: 0.3031 data_time: 0.0030 loss: 2.3188 03/10 06:25:09 - mmengine - INFO - Epoch(train) [7][500/626] lr: 1.0000e-01 eta: 9:19:24 time: 0.2959 data_time: 0.0030 loss: 2.3734 03/10 06:25:40 - mmengine - INFO - Epoch(train) [7][600/626] lr: 1.0000e-01 eta: 9:11:39 time: 0.3382 data_time: 0.0032 loss: 2.2878 03/10 06:25:48 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:25:48 - mmengine - INFO - Saving checkpoint at 7 epochs 03/10 06:26:08 - mmengine - INFO - Epoch(val) [7][98/98] accuracy/top1: 50.0740 accuracy/top5: 76.0800 03/10 06:26:41 - mmengine - INFO - Epoch(train) [8][100/626] lr: 1.0000e-01 eta: 9:02:40 time: 0.2809 data_time: 0.0032 loss: 2.2585 03/10 06:27:12 - mmengine - INFO - Epoch(train) [8][200/626] lr: 1.0000e-01 eta: 8:55:28 time: 0.3137 data_time: 0.0032 loss: 2.2778 03/10 06:27:42 - mmengine - INFO - Epoch(train) [8][300/626] lr: 1.0000e-01 eta: 8:48:42 time: 0.3041 data_time: 0.0029 loss: 2.2283 03/10 06:28:13 - mmengine - INFO - Epoch(train) [8][400/626] lr: 1.0000e-01 eta: 8:42:07 time: 0.2950 data_time: 0.0032 loss: 2.2188 03/10 06:28:44 - mmengine - INFO - Epoch(train) [8][500/626] lr: 1.0000e-01 eta: 8:35:50 time: 0.3207 data_time: 0.0029 loss: 2.2105 03/10 06:29:14 - mmengine - INFO - Epoch(train) [8][600/626] lr: 1.0000e-01 eta: 8:29:46 time: 0.2998 data_time: 0.0030 loss: 2.1439 03/10 06:29:20 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:29:22 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:29:22 - mmengine - INFO - Saving checkpoint at 8 epochs 03/10 06:29:42 - mmengine - INFO - Epoch(val) [8][98/98] accuracy/top1: 51.4840 accuracy/top5: 77.1600 03/10 06:30:15 - mmengine - INFO - Epoch(train) [9][100/626] lr: 1.0000e-01 eta: 8:22:47 time: 0.3069 data_time: 0.0030 loss: 2.2524 03/10 06:30:46 - mmengine - INFO - Epoch(train) [9][200/626] lr: 1.0000e-01 eta: 8:17:12 time: 0.2779 data_time: 0.0032 loss: 2.1915 03/10 06:31:17 - mmengine - INFO - Epoch(train) [9][300/626] lr: 1.0000e-01 eta: 8:11:44 time: 0.2910 data_time: 0.0026 loss: 2.2422 03/10 06:31:47 - mmengine - INFO - Epoch(train) [9][400/626] lr: 1.0000e-01 eta: 8:06:32 time: 0.3094 data_time: 0.0034 loss: 2.1242 03/10 06:32:18 - mmengine - INFO - Epoch(train) [9][500/626] lr: 1.0000e-01 eta: 8:01:31 time: 0.2889 data_time: 0.0038 loss: 2.2957 03/10 06:32:49 - mmengine - INFO - Epoch(train) [9][600/626] lr: 1.0000e-01 eta: 7:56:42 time: 0.2995 data_time: 0.0038 loss: 2.1786 03/10 06:32:57 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:32:57 - mmengine - INFO - Saving checkpoint at 9 epochs 03/10 06:33:18 - mmengine - INFO - Epoch(val) [9][98/98] accuracy/top1: 53.5920 accuracy/top5: 78.8520 03/10 06:33:51 - mmengine - INFO - Epoch(train) [10][100/626] lr: 1.0000e-01 eta: 7:51:04 time: 0.2708 data_time: 0.0032 loss: 2.1910 03/10 06:34:22 - mmengine - INFO - Epoch(train) [10][200/626] lr: 1.0000e-01 eta: 7:46:31 time: 0.3088 data_time: 0.0031 loss: 2.0702 03/10 06:34:53 - mmengine - INFO - Epoch(train) [10][300/626] lr: 1.0000e-01 eta: 7:42:08 time: 0.3353 data_time: 0.0063 loss: 2.0742 03/10 06:35:13 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:35:24 - mmengine - INFO - Epoch(train) [10][400/626] lr: 1.0000e-01 eta: 7:37:50 time: 0.2842 data_time: 0.0029 loss: 2.1499 03/10 06:35:54 - mmengine - INFO - Epoch(train) [10][500/626] lr: 1.0000e-01 eta: 7:33:39 time: 0.2972 data_time: 0.0030 loss: 2.0878 03/10 06:36:25 - mmengine - INFO - Epoch(train) [10][600/626] lr: 1.0000e-01 eta: 7:29:36 time: 0.2969 data_time: 0.0030 loss: 2.0502 03/10 06:36:33 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:36:33 - mmengine - INFO - Saving checkpoint at 10 epochs 03/10 06:36:53 - mmengine - INFO - Epoch(val) [10][98/98] accuracy/top1: 53.9240 accuracy/top5: 79.1800 03/10 06:37:27 - mmengine - INFO - Epoch(train) [11][100/626] lr: 1.0000e-01 eta: 7:24:58 time: 0.3333 data_time: 0.0029 loss: 2.0754 03/10 06:37:57 - mmengine - INFO - Epoch(train) [11][200/626] lr: 1.0000e-01 eta: 7:21:08 time: 0.2956 data_time: 0.0030 loss: 2.0143 03/10 06:38:29 - mmengine - INFO - Epoch(train) [11][300/626] lr: 1.0000e-01 eta: 7:17:29 time: 0.3313 data_time: 0.0033 loss: 1.9695 03/10 06:38:59 - mmengine - INFO - Epoch(train) [11][400/626] lr: 1.0000e-01 eta: 7:13:53 time: 0.2886 data_time: 0.0030 loss: 2.0402 03/10 06:39:30 - mmengine - INFO - Epoch(train) [11][500/626] lr: 1.0000e-01 eta: 7:10:20 time: 0.2909 data_time: 0.0030 loss: 2.0715 03/10 06:40:01 - mmengine - INFO - Epoch(train) [11][600/626] lr: 1.0000e-01 eta: 7:06:55 time: 0.3172 data_time: 0.0031 loss: 2.0094 03/10 06:40:08 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:40:09 - mmengine - INFO - Saving checkpoint at 11 epochs 03/10 06:40:29 - mmengine - INFO - Epoch(val) [11][98/98] accuracy/top1: 52.8460 accuracy/top5: 77.8920 03/10 06:41:02 - mmengine - INFO - Epoch(train) [12][100/626] lr: 1.0000e-01 eta: 7:03:00 time: 0.3647 data_time: 0.0033 loss: 2.0051 03/10 06:41:07 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:41:33 - mmengine - INFO - Epoch(train) [12][200/626] lr: 1.0000e-01 eta: 6:59:45 time: 0.2889 data_time: 0.0029 loss: 2.0483 03/10 06:42:04 - mmengine - INFO - Epoch(train) [12][300/626] lr: 1.0000e-01 eta: 6:56:33 time: 0.3042 data_time: 0.0031 loss: 1.9590 03/10 06:42:34 - mmengine - INFO - Epoch(train) [12][400/626] lr: 1.0000e-01 eta: 6:53:26 time: 0.3054 data_time: 0.0032 loss: 1.8886 03/10 06:43:05 - mmengine - INFO - Epoch(train) [12][500/626] lr: 1.0000e-01 eta: 6:50:25 time: 0.3355 data_time: 0.0549 loss: 2.0043 03/10 06:43:36 - mmengine - INFO - Epoch(train) [12][600/626] lr: 1.0000e-01 eta: 6:47:27 time: 0.3095 data_time: 0.0031 loss: 1.9334 03/10 06:43:44 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:43:44 - mmengine - INFO - Saving checkpoint at 12 epochs 03/10 06:44:05 - mmengine - INFO - Epoch(val) [12][98/98] accuracy/top1: 56.9960 accuracy/top5: 81.2520 03/10 06:44:38 - mmengine - INFO - Epoch(train) [13][100/626] lr: 1.0000e-01 eta: 6:44:03 time: 0.3288 data_time: 0.0034 loss: 1.9838 03/10 06:45:10 - mmengine - INFO - Epoch(train) [13][200/626] lr: 1.0000e-01 eta: 6:41:17 time: 0.3370 data_time: 0.0031 loss: 1.9533 03/10 06:45:40 - mmengine - INFO - Epoch(train) [13][300/626] lr: 1.0000e-01 eta: 6:38:30 time: 0.3138 data_time: 0.0038 loss: 1.9615 03/10 06:46:11 - mmengine - INFO - Epoch(train) [13][400/626] lr: 1.0000e-01 eta: 6:35:49 time: 0.3174 data_time: 0.0032 loss: 1.8645 03/10 06:46:39 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:46:42 - mmengine - INFO - Epoch(train) [13][500/626] lr: 1.0000e-01 eta: 6:33:11 time: 0.3029 data_time: 0.0034 loss: 1.9146 03/10 06:47:13 - mmengine - INFO - Epoch(train) [13][600/626] lr: 1.0000e-01 eta: 6:30:35 time: 0.2838 data_time: 0.0032 loss: 1.9256 03/10 06:47:21 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:47:21 - mmengine - INFO - Saving checkpoint at 13 epochs 03/10 06:47:42 - mmengine - INFO - Epoch(val) [13][98/98] accuracy/top1: 57.9900 accuracy/top5: 81.7340 03/10 06:48:15 - mmengine - INFO - Epoch(train) [14][100/626] lr: 1.0000e-01 eta: 6:27:38 time: 0.3167 data_time: 0.0040 loss: 1.9530 03/10 06:48:46 - mmengine - INFO - Epoch(train) [14][200/626] lr: 1.0000e-01 eta: 6:25:07 time: 0.2860 data_time: 0.0030 loss: 1.9424 03/10 06:49:17 - mmengine - INFO - Epoch(train) [14][300/626] lr: 1.0000e-01 eta: 6:22:41 time: 0.3283 data_time: 0.0028 loss: 1.8984 03/10 06:49:47 - mmengine - INFO - Epoch(train) [14][400/626] lr: 1.0000e-01 eta: 6:20:15 time: 0.2965 data_time: 0.0032 loss: 1.9208 03/10 06:50:18 - mmengine - INFO - Epoch(train) [14][500/626] lr: 1.0000e-01 eta: 6:17:54 time: 0.3024 data_time: 0.0030 loss: 1.9843 03/10 06:50:49 - mmengine - INFO - Epoch(train) [14][600/626] lr: 1.0000e-01 eta: 6:15:35 time: 0.2803 data_time: 0.0031 loss: 1.9271 03/10 06:50:56 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:50:57 - mmengine - INFO - Saving checkpoint at 14 epochs 03/10 06:51:17 - mmengine - INFO - Epoch(val) [14][98/98] accuracy/top1: 57.0200 accuracy/top5: 81.2800 03/10 06:51:52 - mmengine - INFO - Epoch(train) [15][100/626] lr: 1.0000e-01 eta: 6:13:01 time: 0.3169 data_time: 0.0038 loss: 1.8366 03/10 06:52:23 - mmengine - INFO - Epoch(train) [15][200/626] lr: 1.0000e-01 eta: 6:10:48 time: 0.3165 data_time: 0.0035 loss: 1.8943 03/10 06:52:34 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:52:54 - mmengine - INFO - Epoch(train) [15][300/626] lr: 1.0000e-01 eta: 6:08:39 time: 0.2975 data_time: 0.0034 loss: 1.8803 03/10 06:53:24 - mmengine - INFO - Epoch(train) [15][400/626] lr: 1.0000e-01 eta: 6:06:27 time: 0.3148 data_time: 0.0041 loss: 1.8865 03/10 06:53:55 - mmengine - INFO - Epoch(train) [15][500/626] lr: 1.0000e-01 eta: 6:04:20 time: 0.2896 data_time: 0.0030 loss: 1.9544 03/10 06:54:26 - mmengine - INFO - Epoch(train) [15][600/626] lr: 1.0000e-01 eta: 6:02:14 time: 0.3283 data_time: 0.0031 loss: 1.8329 03/10 06:54:33 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:54:34 - mmengine - INFO - Saving checkpoint at 15 epochs 03/10 06:54:53 - mmengine - INFO - Epoch(val) [15][98/98] accuracy/top1: 59.5140 accuracy/top5: 83.2500 03/10 06:55:27 - mmengine - INFO - Epoch(train) [16][100/626] lr: 1.0000e-01 eta: 5:59:50 time: 0.2983 data_time: 0.0034 loss: 1.7883 03/10 06:55:57 - mmengine - INFO - Epoch(train) [16][200/626] lr: 1.0000e-01 eta: 5:57:48 time: 0.2933 data_time: 0.0031 loss: 1.8796 03/10 06:56:28 - mmengine - INFO - Epoch(train) [16][300/626] lr: 1.0000e-01 eta: 5:55:50 time: 0.3046 data_time: 0.0029 loss: 1.9105 03/10 06:56:59 - mmengine - INFO - Epoch(train) [16][400/626] lr: 1.0000e-01 eta: 5:53:52 time: 0.2893 data_time: 0.0031 loss: 1.9168 03/10 06:57:29 - mmengine - INFO - Epoch(train) [16][500/626] lr: 1.0000e-01 eta: 5:51:57 time: 0.3061 data_time: 0.0039 loss: 1.8993 03/10 06:58:01 - mmengine - INFO - Epoch(train) [16][600/626] lr: 1.0000e-01 eta: 5:50:04 time: 0.3213 data_time: 0.0031 loss: 1.8304 03/10 06:58:03 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:58:08 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 06:58:08 - mmengine - INFO - Saving checkpoint at 16 epochs 03/10 06:58:28 - mmengine - INFO - Epoch(val) [16][98/98] accuracy/top1: 59.5480 accuracy/top5: 83.2440 03/10 06:59:02 - mmengine - INFO - Epoch(train) [17][100/626] lr: 1.0000e-01 eta: 5:47:54 time: 0.2963 data_time: 0.0029 loss: 1.8909 03/10 06:59:33 - mmengine - INFO - Epoch(train) [17][200/626] lr: 1.0000e-01 eta: 5:46:04 time: 0.3204 data_time: 0.0031 loss: 1.8260 03/10 07:00:03 - mmengine - INFO - Epoch(train) [17][300/626] lr: 1.0000e-01 eta: 5:44:14 time: 0.2942 data_time: 0.0033 loss: 1.8577 03/10 07:00:34 - mmengine - INFO - Epoch(train) [17][400/626] lr: 1.0000e-01 eta: 5:42:26 time: 0.3044 data_time: 0.0031 loss: 1.8815 03/10 07:01:05 - mmengine - INFO - Epoch(train) [17][500/626] lr: 1.0000e-01 eta: 5:40:41 time: 0.3043 data_time: 0.0032 loss: 1.8726 03/10 07:01:36 - mmengine - INFO - Epoch(train) [17][600/626] lr: 1.0000e-01 eta: 5:38:58 time: 0.3445 data_time: 0.0031 loss: 1.8091 03/10 07:01:43 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:01:43 - mmengine - INFO - Saving checkpoint at 17 epochs 03/10 07:02:03 - mmengine - INFO - Epoch(val) [17][98/98] accuracy/top1: 60.4660 accuracy/top5: 83.7000 03/10 07:02:37 - mmengine - INFO - Epoch(train) [18][100/626] lr: 1.0000e-01 eta: 5:36:58 time: 0.3268 data_time: 0.0029 loss: 1.7434 03/10 07:03:07 - mmengine - INFO - Epoch(train) [18][200/626] lr: 1.0000e-01 eta: 5:35:15 time: 0.3100 data_time: 0.0031 loss: 1.8605 03/10 07:03:38 - mmengine - INFO - Epoch(train) [18][300/626] lr: 1.0000e-01 eta: 5:33:35 time: 0.3223 data_time: 0.0030 loss: 1.7153 03/10 07:03:56 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:04:09 - mmengine - INFO - Epoch(train) [18][400/626] lr: 1.0000e-01 eta: 5:31:56 time: 0.3064 data_time: 0.0044 loss: 1.8258 03/10 07:04:39 - mmengine - INFO - Epoch(train) [18][500/626] lr: 1.0000e-01 eta: 5:30:19 time: 0.2761 data_time: 0.0028 loss: 1.7719 03/10 07:05:10 - mmengine - INFO - Epoch(train) [18][600/626] lr: 1.0000e-01 eta: 5:28:44 time: 0.3075 data_time: 0.0033 loss: 1.8383 03/10 07:05:18 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:05:18 - mmengine - INFO - Saving checkpoint at 18 epochs 03/10 07:05:39 - mmengine - INFO - Epoch(val) [18][98/98] accuracy/top1: 60.5420 accuracy/top5: 83.8140 03/10 07:06:13 - mmengine - INFO - Epoch(train) [19][100/626] lr: 1.0000e-01 eta: 5:26:53 time: 0.3027 data_time: 0.0032 loss: 1.7547 03/10 07:06:44 - mmengine - INFO - Epoch(train) [19][200/626] lr: 1.0000e-01 eta: 5:25:22 time: 0.3503 data_time: 0.0035 loss: 1.7830 03/10 07:07:14 - mmengine - INFO - Epoch(train) [19][300/626] lr: 1.0000e-01 eta: 5:23:47 time: 0.3159 data_time: 0.0040 loss: 1.7802 03/10 07:07:45 - mmengine - INFO - Epoch(train) [19][400/626] lr: 1.0000e-01 eta: 5:22:15 time: 0.3038 data_time: 0.0031 loss: 1.7588 03/10 07:08:16 - mmengine - INFO - Epoch(train) [19][500/626] lr: 1.0000e-01 eta: 5:20:45 time: 0.3303 data_time: 0.0032 loss: 1.8002 03/10 07:08:47 - mmengine - INFO - Epoch(train) [19][600/626] lr: 1.0000e-01 eta: 5:19:16 time: 0.3162 data_time: 0.0032 loss: 1.8477 03/10 07:08:54 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:08:55 - mmengine - INFO - Saving checkpoint at 19 epochs 03/10 07:09:15 - mmengine - INFO - Epoch(val) [19][98/98] accuracy/top1: 60.0720 accuracy/top5: 83.7020 03/10 07:09:49 - mmengine - INFO - Epoch(train) [20][100/626] lr: 1.0000e-01 eta: 5:17:34 time: 0.3586 data_time: 0.0300 loss: 1.7784 03/10 07:09:51 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:10:20 - mmengine - INFO - Epoch(train) [20][200/626] lr: 1.0000e-01 eta: 5:16:08 time: 0.3122 data_time: 0.0037 loss: 1.7598 03/10 07:10:52 - mmengine - INFO - Epoch(train) [20][300/626] lr: 1.0000e-01 eta: 5:14:43 time: 0.2975 data_time: 0.0034 loss: 1.7399 03/10 07:11:23 - mmengine - INFO - Epoch(train) [20][400/626] lr: 1.0000e-01 eta: 5:13:19 time: 0.2939 data_time: 0.0152 loss: 1.7402 03/10 07:11:54 - mmengine - INFO - Epoch(train) [20][500/626] lr: 1.0000e-01 eta: 5:11:56 time: 0.2983 data_time: 0.0032 loss: 1.7377 03/10 07:12:25 - mmengine - INFO - Epoch(train) [20][600/626] lr: 1.0000e-01 eta: 5:10:32 time: 0.3123 data_time: 0.0035 loss: 1.8163 03/10 07:12:33 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:12:33 - mmengine - INFO - Saving checkpoint at 20 epochs 03/10 07:12:54 - mmengine - INFO - Epoch(val) [20][98/98] accuracy/top1: 61.6580 accuracy/top5: 84.5520 03/10 07:13:28 - mmengine - INFO - Epoch(train) [21][100/626] lr: 1.0000e-01 eta: 5:08:57 time: 0.3263 data_time: 0.0038 loss: 1.7793 03/10 07:13:58 - mmengine - INFO - Epoch(train) [21][200/626] lr: 1.0000e-01 eta: 5:07:33 time: 0.2874 data_time: 0.0033 loss: 1.6623 03/10 07:14:29 - mmengine - INFO - Epoch(train) [21][300/626] lr: 1.0000e-01 eta: 5:06:14 time: 0.3189 data_time: 0.0033 loss: 1.7181 03/10 07:15:00 - mmengine - INFO - Epoch(train) [21][400/626] lr: 1.0000e-01 eta: 5:04:54 time: 0.3555 data_time: 0.0540 loss: 1.6997 03/10 07:15:25 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:15:31 - mmengine - INFO - Epoch(train) [21][500/626] lr: 1.0000e-01 eta: 5:03:35 time: 0.2952 data_time: 0.0032 loss: 1.7665 03/10 07:16:03 - mmengine - INFO - Epoch(train) [21][600/626] lr: 1.0000e-01 eta: 5:02:18 time: 0.3262 data_time: 0.0038 loss: 1.7110 03/10 07:16:10 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:16:10 - mmengine - INFO - Saving checkpoint at 21 epochs 03/10 07:16:31 - mmengine - INFO - Epoch(val) [21][98/98] accuracy/top1: 61.5060 accuracy/top5: 84.1440 03/10 07:17:05 - mmengine - INFO - Epoch(train) [22][100/626] lr: 1.0000e-01 eta: 5:00:46 time: 0.3121 data_time: 0.0035 loss: 1.7182 03/10 07:17:36 - mmengine - INFO - Epoch(train) [22][200/626] lr: 1.0000e-01 eta: 4:59:29 time: 0.2761 data_time: 0.0034 loss: 1.7437 03/10 07:18:07 - mmengine - INFO - Epoch(train) [22][300/626] lr: 1.0000e-01 eta: 4:58:13 time: 0.3167 data_time: 0.0030 loss: 1.8196 03/10 07:18:38 - mmengine - INFO - Epoch(train) [22][400/626] lr: 1.0000e-01 eta: 4:56:57 time: 0.3178 data_time: 0.0487 loss: 1.7593 03/10 07:19:09 - mmengine - INFO - Epoch(train) [22][500/626] lr: 1.0000e-01 eta: 4:55:43 time: 0.3022 data_time: 0.0033 loss: 1.7702 03/10 07:19:41 - mmengine - INFO - Epoch(train) [22][600/626] lr: 1.0000e-01 eta: 4:54:30 time: 0.3056 data_time: 0.0032 loss: 1.7693 03/10 07:19:48 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:19:49 - mmengine - INFO - Saving checkpoint at 22 epochs 03/10 07:20:10 - mmengine - INFO - Epoch(val) [22][98/98] accuracy/top1: 60.4840 accuracy/top5: 83.8600 03/10 07:20:43 - mmengine - INFO - Epoch(train) [23][100/626] lr: 1.0000e-01 eta: 4:53:04 time: 0.3227 data_time: 0.0034 loss: 1.7689 03/10 07:21:14 - mmengine - INFO - Epoch(train) [23][200/626] lr: 1.0000e-01 eta: 4:51:51 time: 0.3121 data_time: 0.0033 loss: 1.7177 03/10 07:21:23 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:21:45 - mmengine - INFO - Epoch(train) [23][300/626] lr: 1.0000e-01 eta: 4:50:38 time: 0.2927 data_time: 0.0034 loss: 1.6944 03/10 07:22:16 - mmengine - INFO - Epoch(train) [23][400/626] lr: 1.0000e-01 eta: 4:49:26 time: 0.3149 data_time: 0.0033 loss: 1.6407 03/10 07:22:48 - mmengine - INFO - Epoch(train) [23][500/626] lr: 1.0000e-01 eta: 4:48:17 time: 0.3079 data_time: 0.0031 loss: 1.7870 03/10 07:23:19 - mmengine - INFO - Epoch(train) [23][600/626] lr: 1.0000e-01 eta: 4:47:06 time: 0.3082 data_time: 0.0031 loss: 1.7304 03/10 07:23:26 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:23:27 - mmengine - INFO - Saving checkpoint at 23 epochs 03/10 07:23:47 - mmengine - INFO - Epoch(val) [23][98/98] accuracy/top1: 62.5520 accuracy/top5: 84.9400 03/10 07:24:21 - mmengine - INFO - Epoch(train) [24][100/626] lr: 1.0000e-01 eta: 4:45:45 time: 0.2881 data_time: 0.0040 loss: 1.6606 03/10 07:24:52 - mmengine - INFO - Epoch(train) [24][200/626] lr: 1.0000e-01 eta: 4:44:36 time: 0.2967 data_time: 0.0028 loss: 1.5983 03/10 07:25:23 - mmengine - INFO - Epoch(train) [24][300/626] lr: 1.0000e-01 eta: 4:43:27 time: 0.3017 data_time: 0.0084 loss: 1.7977 03/10 07:25:55 - mmengine - INFO - Epoch(train) [24][400/626] lr: 1.0000e-01 eta: 4:42:20 time: 0.2853 data_time: 0.0031 loss: 1.7153 03/10 07:26:26 - mmengine - INFO - Epoch(train) [24][500/626] lr: 1.0000e-01 eta: 4:41:13 time: 0.3028 data_time: 0.0039 loss: 1.7100 03/10 07:26:57 - mmengine - INFO - Epoch(train) [24][600/626] lr: 1.0000e-01 eta: 4:40:06 time: 0.3159 data_time: 0.0041 loss: 1.6898 03/10 07:26:58 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:27:05 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:27:05 - mmengine - INFO - Saving checkpoint at 24 epochs 03/10 07:27:25 - mmengine - INFO - Epoch(val) [24][98/98] accuracy/top1: 61.9240 accuracy/top5: 84.6080 03/10 07:27:59 - mmengine - INFO - Epoch(train) [25][100/626] lr: 1.0000e-01 eta: 4:38:47 time: 0.2902 data_time: 0.0030 loss: 1.6519 03/10 07:28:29 - mmengine - INFO - Epoch(train) [25][200/626] lr: 1.0000e-01 eta: 4:37:39 time: 0.2912 data_time: 0.0032 loss: 1.6745 03/10 07:29:01 - mmengine - INFO - Epoch(train) [25][300/626] lr: 1.0000e-01 eta: 4:36:35 time: 0.3368 data_time: 0.0029 loss: 1.6583 03/10 07:29:32 - mmengine - INFO - Epoch(train) [25][400/626] lr: 1.0000e-01 eta: 4:35:28 time: 0.2940 data_time: 0.0033 loss: 1.6727 03/10 07:30:02 - mmengine - INFO - Epoch(train) [25][500/626] lr: 1.0000e-01 eta: 4:34:22 time: 0.3072 data_time: 0.0029 loss: 1.6482 03/10 07:30:33 - mmengine - INFO - Epoch(train) [25][600/626] lr: 1.0000e-01 eta: 4:33:17 time: 0.3258 data_time: 0.0031 loss: 1.7145 03/10 07:30:41 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:30:41 - mmengine - INFO - Saving checkpoint at 25 epochs 03/10 07:31:01 - mmengine - INFO - Epoch(val) [25][98/98] accuracy/top1: 61.6620 accuracy/top5: 84.1360 03/10 07:31:34 - mmengine - INFO - Epoch(train) [26][100/626] lr: 1.0000e-01 eta: 4:32:00 time: 0.3205 data_time: 0.0032 loss: 1.6164 03/10 07:32:05 - mmengine - INFO - Epoch(train) [26][200/626] lr: 1.0000e-01 eta: 4:30:56 time: 0.2810 data_time: 0.0029 loss: 1.6411 03/10 07:32:36 - mmengine - INFO - Epoch(train) [26][300/626] lr: 1.0000e-01 eta: 4:29:51 time: 0.3322 data_time: 0.0031 loss: 1.6134 03/10 07:32:51 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:33:07 - mmengine - INFO - Epoch(train) [26][400/626] lr: 1.0000e-01 eta: 4:28:49 time: 0.3146 data_time: 0.0161 loss: 1.6798 03/10 07:33:37 - mmengine - INFO - Epoch(train) [26][500/626] lr: 1.0000e-01 eta: 4:27:46 time: 0.2933 data_time: 0.0032 loss: 1.7112 03/10 07:34:08 - mmengine - INFO - Epoch(train) [26][600/626] lr: 1.0000e-01 eta: 4:26:43 time: 0.3057 data_time: 0.0038 loss: 1.6732 03/10 07:34:16 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:34:16 - mmengine - INFO - Saving checkpoint at 26 epochs 03/10 07:34:36 - mmengine - INFO - Epoch(val) [26][98/98] accuracy/top1: 62.3920 accuracy/top5: 84.9720 03/10 07:35:10 - mmengine - INFO - Epoch(train) [27][100/626] lr: 1.0000e-01 eta: 4:25:30 time: 0.2649 data_time: 0.0032 loss: 1.6971 03/10 07:35:41 - mmengine - INFO - Epoch(train) [27][200/626] lr: 1.0000e-01 eta: 4:24:29 time: 0.3616 data_time: 0.0133 loss: 1.6326 03/10 07:36:12 - mmengine - INFO - Epoch(train) [27][300/626] lr: 1.0000e-01 eta: 4:23:27 time: 0.3081 data_time: 0.0030 loss: 1.6442 03/10 07:36:42 - mmengine - INFO - Epoch(train) [27][400/626] lr: 1.0000e-01 eta: 4:22:26 time: 0.2826 data_time: 0.0030 loss: 1.6931 03/10 07:37:13 - mmengine - INFO - Epoch(train) [27][500/626] lr: 1.0000e-01 eta: 4:21:25 time: 0.3085 data_time: 0.0032 loss: 1.6253 03/10 07:37:44 - mmengine - INFO - Epoch(train) [27][600/626] lr: 1.0000e-01 eta: 4:20:25 time: 0.3099 data_time: 0.0032 loss: 1.6721 03/10 07:37:52 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:37:52 - mmengine - INFO - Saving checkpoint at 27 epochs 03/10 07:38:12 - mmengine - INFO - Epoch(val) [27][98/98] accuracy/top1: 61.6560 accuracy/top5: 84.9880 03/10 07:38:45 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:38:46 - mmengine - INFO - Epoch(train) [28][100/626] lr: 1.0000e-01 eta: 4:19:15 time: 0.3004 data_time: 0.0035 loss: 1.6081 03/10 07:39:17 - mmengine - INFO - Epoch(train) [28][200/626] lr: 1.0000e-01 eta: 4:18:16 time: 0.2824 data_time: 0.0045 loss: 1.6519 03/10 07:39:48 - mmengine - INFO - Epoch(train) [28][300/626] lr: 1.0000e-01 eta: 4:17:17 time: 0.3441 data_time: 0.0044 loss: 1.6581 03/10 07:40:18 - mmengine - INFO - Epoch(train) [28][400/626] lr: 1.0000e-01 eta: 4:16:17 time: 0.3007 data_time: 0.0031 loss: 1.6627 03/10 07:40:49 - mmengine - INFO - Epoch(train) [28][500/626] lr: 1.0000e-01 eta: 4:15:18 time: 0.2883 data_time: 0.0034 loss: 1.7166 03/10 07:41:19 - mmengine - INFO - Epoch(train) [28][600/626] lr: 1.0000e-01 eta: 4:14:19 time: 0.3038 data_time: 0.0045 loss: 1.6818 03/10 07:41:27 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:41:27 - mmengine - INFO - Saving checkpoint at 28 epochs 03/10 07:41:47 - mmengine - INFO - Epoch(val) [28][98/98] accuracy/top1: 61.9620 accuracy/top5: 84.6580 03/10 07:42:20 - mmengine - INFO - Epoch(train) [29][100/626] lr: 1.0000e-01 eta: 4:13:10 time: 0.2800 data_time: 0.0034 loss: 1.6104 03/10 07:42:51 - mmengine - INFO - Epoch(train) [29][200/626] lr: 1.0000e-01 eta: 4:12:12 time: 0.3100 data_time: 0.0032 loss: 1.6971 03/10 07:43:22 - mmengine - INFO - Epoch(train) [29][300/626] lr: 1.0000e-01 eta: 4:11:15 time: 0.3072 data_time: 0.0029 loss: 1.6079 03/10 07:43:53 - mmengine - INFO - Epoch(train) [29][400/626] lr: 1.0000e-01 eta: 4:10:20 time: 0.3140 data_time: 0.0031 loss: 1.6708 03/10 07:44:15 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:44:24 - mmengine - INFO - Epoch(train) [29][500/626] lr: 1.0000e-01 eta: 4:09:23 time: 0.2934 data_time: 0.0033 loss: 1.7074 03/10 07:44:55 - mmengine - INFO - Epoch(train) [29][600/626] lr: 1.0000e-01 eta: 4:08:26 time: 0.3193 data_time: 0.0034 loss: 1.6933 03/10 07:45:02 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:45:02 - mmengine - INFO - Saving checkpoint at 29 epochs 03/10 07:45:23 - mmengine - INFO - Epoch(val) [29][98/98] accuracy/top1: 62.4100 accuracy/top5: 85.0680 03/10 07:45:57 - mmengine - INFO - Epoch(train) [30][100/626] lr: 1.0000e-01 eta: 4:07:20 time: 0.3480 data_time: 0.0031 loss: 1.6094 03/10 07:46:27 - mmengine - INFO - Epoch(train) [30][200/626] lr: 1.0000e-01 eta: 4:06:24 time: 0.2941 data_time: 0.0033 loss: 1.6697 03/10 07:46:58 - mmengine - INFO - Epoch(train) [30][300/626] lr: 1.0000e-01 eta: 4:05:28 time: 0.2936 data_time: 0.0034 loss: 1.6126 03/10 07:47:28 - mmengine - INFO - Epoch(train) [30][400/626] lr: 1.0000e-01 eta: 4:04:32 time: 0.2772 data_time: 0.0029 loss: 1.5881 03/10 07:48:00 - mmengine - INFO - Epoch(train) [30][500/626] lr: 1.0000e-01 eta: 4:03:37 time: 0.2931 data_time: 0.0034 loss: 1.6088 03/10 07:48:30 - mmengine - INFO - Epoch(train) [30][600/626] lr: 1.0000e-01 eta: 4:02:42 time: 0.3109 data_time: 0.0031 loss: 1.6048 03/10 07:48:38 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:48:38 - mmengine - INFO - Saving checkpoint at 30 epochs 03/10 07:48:58 - mmengine - INFO - Epoch(val) [30][98/98] accuracy/top1: 62.4240 accuracy/top5: 85.0460 03/10 07:49:31 - mmengine - INFO - Epoch(train) [31][100/626] lr: 1.0000e-02 eta: 4:01:38 time: 0.3094 data_time: 0.0034 loss: 1.4481 03/10 07:50:02 - mmengine - INFO - Epoch(train) [31][200/626] lr: 1.0000e-02 eta: 4:00:43 time: 0.3033 data_time: 0.0030 loss: 1.3588 03/10 07:50:08 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:50:33 - mmengine - INFO - Epoch(train) [31][300/626] lr: 1.0000e-02 eta: 3:59:49 time: 0.3012 data_time: 0.0040 loss: 1.3364 03/10 07:51:04 - mmengine - INFO - Epoch(train) [31][400/626] lr: 1.0000e-02 eta: 3:58:56 time: 0.3179 data_time: 0.0030 loss: 1.3102 03/10 07:51:35 - mmengine - INFO - Epoch(train) [31][500/626] lr: 1.0000e-02 eta: 3:58:03 time: 0.3161 data_time: 0.0032 loss: 1.3883 03/10 07:52:06 - mmengine - INFO - Epoch(train) [31][600/626] lr: 1.0000e-02 eta: 3:57:09 time: 0.3036 data_time: 0.0033 loss: 1.3573 03/10 07:52:13 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:52:13 - mmengine - INFO - Saving checkpoint at 31 epochs 03/10 07:52:34 - mmengine - INFO - Epoch(val) [31][98/98] accuracy/top1: 70.4220 accuracy/top5: 89.7600 03/10 07:53:07 - mmengine - INFO - Epoch(train) [32][100/626] lr: 1.0000e-02 eta: 3:56:05 time: 0.3193 data_time: 0.0032 loss: 1.3286 03/10 07:53:38 - mmengine - INFO - Epoch(train) [32][200/626] lr: 1.0000e-02 eta: 3:55:12 time: 0.3330 data_time: 0.0522 loss: 1.2916 03/10 07:54:08 - mmengine - INFO - Epoch(train) [32][300/626] lr: 1.0000e-02 eta: 3:54:19 time: 0.3187 data_time: 0.0030 loss: 1.2933 03/10 07:54:39 - mmengine - INFO - Epoch(train) [32][400/626] lr: 1.0000e-02 eta: 3:53:27 time: 0.3308 data_time: 0.0034 loss: 1.2727 03/10 07:55:10 - mmengine - INFO - Epoch(train) [32][500/626] lr: 1.0000e-02 eta: 3:52:34 time: 0.3374 data_time: 0.0030 loss: 1.3037 03/10 07:55:38 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:55:40 - mmengine - INFO - Epoch(train) [32][600/626] lr: 1.0000e-02 eta: 3:51:41 time: 0.2898 data_time: 0.0030 loss: 1.3230 03/10 07:55:48 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:55:48 - mmengine - INFO - Saving checkpoint at 32 epochs 03/10 07:56:08 - mmengine - INFO - Epoch(val) [32][98/98] accuracy/top1: 70.5700 accuracy/top5: 89.6940 03/10 07:56:41 - mmengine - INFO - Epoch(train) [33][100/626] lr: 1.0000e-02 eta: 3:50:40 time: 0.2851 data_time: 0.0043 loss: 1.3172 03/10 07:57:12 - mmengine - INFO - Epoch(train) [33][200/626] lr: 1.0000e-02 eta: 3:49:49 time: 0.3477 data_time: 0.0038 loss: 1.2967 03/10 07:57:43 - mmengine - INFO - Epoch(train) [33][300/626] lr: 1.0000e-02 eta: 3:48:57 time: 0.3137 data_time: 0.0035 loss: 1.3078 03/10 07:58:13 - mmengine - INFO - Epoch(train) [33][400/626] lr: 1.0000e-02 eta: 3:48:06 time: 0.3118 data_time: 0.0032 loss: 1.2691 03/10 07:58:44 - mmengine - INFO - Epoch(train) [33][500/626] lr: 1.0000e-02 eta: 3:47:15 time: 0.2883 data_time: 0.0030 loss: 1.2985 03/10 07:59:15 - mmengine - INFO - Epoch(train) [33][600/626] lr: 1.0000e-02 eta: 3:46:23 time: 0.3027 data_time: 0.0034 loss: 1.3365 03/10 07:59:22 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 07:59:22 - mmengine - INFO - Saving checkpoint at 33 epochs 03/10 07:59:43 - mmengine - INFO - Epoch(val) [33][98/98] accuracy/top1: 70.8680 accuracy/top5: 89.9500 03/10 08:00:16 - mmengine - INFO - Epoch(train) [34][100/626] lr: 1.0000e-02 eta: 3:45:23 time: 0.3011 data_time: 0.0033 loss: 1.3632 03/10 08:00:47 - mmengine - INFO - Epoch(train) [34][200/626] lr: 1.0000e-02 eta: 3:44:33 time: 0.3133 data_time: 0.0032 loss: 1.2656 03/10 08:01:17 - mmengine - INFO - Epoch(train) [34][300/626] lr: 1.0000e-02 eta: 3:43:42 time: 0.3117 data_time: 0.0032 loss: 1.2595 03/10 08:01:30 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:01:48 - mmengine - INFO - Epoch(train) [34][400/626] lr: 1.0000e-02 eta: 3:42:52 time: 0.2807 data_time: 0.0040 loss: 1.2253 03/10 08:02:19 - mmengine - INFO - Epoch(train) [34][500/626] lr: 1.0000e-02 eta: 3:42:02 time: 0.3022 data_time: 0.0030 loss: 1.3450 03/10 08:02:49 - mmengine - INFO - Epoch(train) [34][600/626] lr: 1.0000e-02 eta: 3:41:12 time: 0.2871 data_time: 0.0032 loss: 1.2510 03/10 08:02:57 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:02:57 - mmengine - INFO - Saving checkpoint at 34 epochs 03/10 08:03:18 - mmengine - INFO - Epoch(val) [34][98/98] accuracy/top1: 70.9480 accuracy/top5: 90.0360 03/10 08:03:51 - mmengine - INFO - Epoch(train) [35][100/626] lr: 1.0000e-02 eta: 3:40:13 time: 0.3398 data_time: 0.0031 loss: 1.2740 03/10 08:04:22 - mmengine - INFO - Epoch(train) [35][200/626] lr: 1.0000e-02 eta: 3:39:24 time: 0.3063 data_time: 0.0030 loss: 1.2612 03/10 08:04:52 - mmengine - INFO - Epoch(train) [35][300/626] lr: 1.0000e-02 eta: 3:38:34 time: 0.3104 data_time: 0.0038 loss: 1.2591 03/10 08:05:23 - mmengine - INFO - Epoch(train) [35][400/626] lr: 1.0000e-02 eta: 3:37:46 time: 0.3666 data_time: 0.0286 loss: 1.1829 03/10 08:05:54 - mmengine - INFO - Epoch(train) [35][500/626] lr: 1.0000e-02 eta: 3:36:57 time: 0.3117 data_time: 0.0032 loss: 1.2604 03/10 08:06:24 - mmengine - INFO - Epoch(train) [35][600/626] lr: 1.0000e-02 eta: 3:36:07 time: 0.3145 data_time: 0.0030 loss: 1.2239 03/10 08:06:32 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:06:32 - mmengine - INFO - Saving checkpoint at 35 epochs 03/10 08:06:52 - mmengine - INFO - Epoch(val) [35][98/98] accuracy/top1: 71.0600 accuracy/top5: 90.0820 03/10 08:07:23 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:07:26 - mmengine - INFO - Epoch(train) [36][100/626] lr: 1.0000e-02 eta: 3:35:11 time: 0.2903 data_time: 0.0031 loss: 1.3133 03/10 08:07:57 - mmengine - INFO - Epoch(train) [36][200/626] lr: 1.0000e-02 eta: 3:34:23 time: 0.2848 data_time: 0.0031 loss: 1.2289 03/10 08:08:28 - mmengine - INFO - Epoch(train) [36][300/626] lr: 1.0000e-02 eta: 3:33:36 time: 0.2938 data_time: 0.0027 loss: 1.3409 03/10 08:08:58 - mmengine - INFO - Epoch(train) [36][400/626] lr: 1.0000e-02 eta: 3:32:47 time: 0.2723 data_time: 0.0033 loss: 1.1976 03/10 08:09:29 - mmengine - INFO - Epoch(train) [36][500/626] lr: 1.0000e-02 eta: 3:31:59 time: 0.2970 data_time: 0.0030 loss: 1.2498 03/10 08:10:00 - mmengine - INFO - Epoch(train) [36][600/626] lr: 1.0000e-02 eta: 3:31:12 time: 0.3167 data_time: 0.0032 loss: 1.2591 03/10 08:10:07 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:10:08 - mmengine - INFO - Saving checkpoint at 36 epochs 03/10 08:10:28 - mmengine - INFO - Epoch(val) [36][98/98] accuracy/top1: 71.1560 accuracy/top5: 90.1880 03/10 08:11:02 - mmengine - INFO - Epoch(train) [37][100/626] lr: 1.0000e-02 eta: 3:30:15 time: 0.2989 data_time: 0.0032 loss: 1.2817 03/10 08:11:33 - mmengine - INFO - Epoch(train) [37][200/626] lr: 1.0000e-02 eta: 3:29:28 time: 0.2883 data_time: 0.0034 loss: 1.2554 03/10 08:12:03 - mmengine - INFO - Epoch(train) [37][300/626] lr: 1.0000e-02 eta: 3:28:41 time: 0.3086 data_time: 0.0031 loss: 1.2174 03/10 08:12:34 - mmengine - INFO - Epoch(train) [37][400/626] lr: 1.0000e-02 eta: 3:27:54 time: 0.2882 data_time: 0.0030 loss: 1.2265 03/10 08:12:54 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:13:04 - mmengine - INFO - Epoch(train) [37][500/626] lr: 1.0000e-02 eta: 3:27:07 time: 0.2831 data_time: 0.0031 loss: 1.2294 03/10 08:13:36 - mmengine - INFO - Epoch(train) [37][600/626] lr: 1.0000e-02 eta: 3:26:20 time: 0.3082 data_time: 0.0028 loss: 1.2792 03/10 08:13:43 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:13:43 - mmengine - INFO - Saving checkpoint at 37 epochs 03/10 08:14:03 - mmengine - INFO - Epoch(val) [37][98/98] accuracy/top1: 71.1220 accuracy/top5: 90.0840 03/10 08:14:37 - mmengine - INFO - Epoch(train) [38][100/626] lr: 1.0000e-02 eta: 3:25:25 time: 0.3217 data_time: 0.0029 loss: 1.2322 03/10 08:15:08 - mmengine - INFO - Epoch(train) [38][200/626] lr: 1.0000e-02 eta: 3:24:38 time: 0.2970 data_time: 0.0036 loss: 1.2574 03/10 08:15:38 - mmengine - INFO - Epoch(train) [38][300/626] lr: 1.0000e-02 eta: 3:23:52 time: 0.3095 data_time: 0.0032 loss: 1.2260 03/10 08:16:09 - mmengine - INFO - Epoch(train) [38][400/626] lr: 1.0000e-02 eta: 3:23:05 time: 0.3219 data_time: 0.0032 loss: 1.2757 03/10 08:16:40 - mmengine - INFO - Epoch(train) [38][500/626] lr: 1.0000e-02 eta: 3:22:20 time: 0.2857 data_time: 0.0028 loss: 1.2549 03/10 08:17:10 - mmengine - INFO - Epoch(train) [38][600/626] lr: 1.0000e-02 eta: 3:21:33 time: 0.2803 data_time: 0.0035 loss: 1.2141 03/10 08:17:18 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:17:18 - mmengine - INFO - Saving checkpoint at 38 epochs 03/10 08:17:39 - mmengine - INFO - Epoch(val) [38][98/98] accuracy/top1: 71.1960 accuracy/top5: 90.2180 03/10 08:18:12 - mmengine - INFO - Epoch(train) [39][100/626] lr: 1.0000e-02 eta: 3:20:39 time: 0.2943 data_time: 0.0036 loss: 1.2323 03/10 08:18:43 - mmengine - INFO - Epoch(train) [39][200/626] lr: 1.0000e-02 eta: 3:19:53 time: 0.3479 data_time: 0.0029 loss: 1.2588 03/10 08:18:47 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:19:14 - mmengine - INFO - Epoch(train) [39][300/626] lr: 1.0000e-02 eta: 3:19:08 time: 0.3267 data_time: 0.0032 loss: 1.2475 03/10 08:19:44 - mmengine - INFO - Epoch(train) [39][400/626] lr: 1.0000e-02 eta: 3:18:22 time: 0.3013 data_time: 0.0036 loss: 1.2584 03/10 08:20:15 - mmengine - INFO - Epoch(train) [39][500/626] lr: 1.0000e-02 eta: 3:17:37 time: 0.3031 data_time: 0.0039 loss: 1.2080 03/10 08:20:46 - mmengine - INFO - Epoch(train) [39][600/626] lr: 1.0000e-02 eta: 3:16:52 time: 0.3181 data_time: 0.0030 loss: 1.2603 03/10 08:20:54 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:20:54 - mmengine - INFO - Saving checkpoint at 39 epochs 03/10 08:21:14 - mmengine - INFO - Epoch(val) [39][98/98] accuracy/top1: 71.2960 accuracy/top5: 90.2220 03/10 08:21:48 - mmengine - INFO - Epoch(train) [40][100/626] lr: 1.0000e-02 eta: 3:15:58 time: 0.3120 data_time: 0.0029 loss: 1.2634 03/10 08:22:19 - mmengine - INFO - Epoch(train) [40][200/626] lr: 1.0000e-02 eta: 3:15:13 time: 0.3219 data_time: 0.0031 loss: 1.2641 03/10 08:22:49 - mmengine - INFO - Epoch(train) [40][300/626] lr: 1.0000e-02 eta: 3:14:28 time: 0.2842 data_time: 0.0032 loss: 1.2348 03/10 08:23:20 - mmengine - INFO - Epoch(train) [40][400/626] lr: 1.0000e-02 eta: 3:13:44 time: 0.3339 data_time: 0.0036 loss: 1.2902 03/10 08:23:51 - mmengine - INFO - Epoch(train) [40][500/626] lr: 1.0000e-02 eta: 3:12:59 time: 0.2911 data_time: 0.0033 loss: 1.1976 03/10 08:24:17 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:24:22 - mmengine - INFO - Epoch(train) [40][600/626] lr: 1.0000e-02 eta: 3:12:15 time: 0.3296 data_time: 0.0041 loss: 1.2380 03/10 08:24:29 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:24:29 - mmengine - INFO - Saving checkpoint at 40 epochs 03/10 08:24:50 - mmengine - INFO - Epoch(val) [40][98/98] accuracy/top1: 71.4560 accuracy/top5: 90.2160 03/10 08:25:23 - mmengine - INFO - Epoch(train) [41][100/626] lr: 1.0000e-02 eta: 3:11:22 time: 0.3014 data_time: 0.0031 loss: 1.1951 03/10 08:25:55 - mmengine - INFO - Epoch(train) [41][200/626] lr: 1.0000e-02 eta: 3:10:38 time: 0.2996 data_time: 0.0032 loss: 1.2382 03/10 08:26:25 - mmengine - INFO - Epoch(train) [41][300/626] lr: 1.0000e-02 eta: 3:09:53 time: 0.3357 data_time: 0.0029 loss: 1.2491 03/10 08:26:56 - mmengine - INFO - Epoch(train) [41][400/626] lr: 1.0000e-02 eta: 3:09:09 time: 0.2896 data_time: 0.0030 loss: 1.1955 03/10 08:27:27 - mmengine - INFO - Epoch(train) [41][500/626] lr: 1.0000e-02 eta: 3:08:25 time: 0.2868 data_time: 0.0031 loss: 1.2337 03/10 08:27:57 - mmengine - INFO - Epoch(train) [41][600/626] lr: 1.0000e-02 eta: 3:07:41 time: 0.2880 data_time: 0.0029 loss: 1.1910 03/10 08:28:05 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:28:05 - mmengine - INFO - Saving checkpoint at 41 epochs 03/10 08:28:26 - mmengine - INFO - Epoch(val) [41][98/98] accuracy/top1: 71.4760 accuracy/top5: 90.3820 03/10 08:29:00 - mmengine - INFO - Epoch(train) [42][100/626] lr: 1.0000e-02 eta: 3:06:49 time: 0.3135 data_time: 0.0032 loss: 1.2141 03/10 08:29:30 - mmengine - INFO - Epoch(train) [42][200/626] lr: 1.0000e-02 eta: 3:06:05 time: 0.2758 data_time: 0.0038 loss: 1.2122 03/10 08:30:00 - mmengine - INFO - Epoch(train) [42][300/626] lr: 1.0000e-02 eta: 3:05:21 time: 0.2855 data_time: 0.0029 loss: 1.2236 03/10 08:30:11 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:30:31 - mmengine - INFO - Epoch(train) [42][400/626] lr: 1.0000e-02 eta: 3:04:38 time: 0.3472 data_time: 0.0029 loss: 1.1705 03/10 08:31:02 - mmengine - INFO - Epoch(train) [42][500/626] lr: 1.0000e-02 eta: 3:03:54 time: 0.2937 data_time: 0.0032 loss: 1.2036 03/10 08:31:32 - mmengine - INFO - Epoch(train) [42][600/626] lr: 1.0000e-02 eta: 3:03:10 time: 0.2965 data_time: 0.0034 loss: 1.1978 03/10 08:31:40 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:31:40 - mmengine - INFO - Saving checkpoint at 42 epochs 03/10 08:32:00 - mmengine - INFO - Epoch(val) [42][98/98] accuracy/top1: 71.2700 accuracy/top5: 90.1980 03/10 08:32:34 - mmengine - INFO - Epoch(train) [43][100/626] lr: 1.0000e-02 eta: 3:02:19 time: 0.3030 data_time: 0.0029 loss: 1.2016 03/10 08:33:04 - mmengine - INFO - Epoch(train) [43][200/626] lr: 1.0000e-02 eta: 3:01:36 time: 0.2931 data_time: 0.0030 loss: 1.2076 03/10 08:33:35 - mmengine - INFO - Epoch(train) [43][300/626] lr: 1.0000e-02 eta: 3:00:53 time: 0.3245 data_time: 0.0034 loss: 1.2134 03/10 08:34:06 - mmengine - INFO - Epoch(train) [43][400/626] lr: 1.0000e-02 eta: 3:00:10 time: 0.3532 data_time: 0.0525 loss: 1.1646 03/10 08:34:36 - mmengine - INFO - Epoch(train) [43][500/626] lr: 1.0000e-02 eta: 2:59:27 time: 0.3129 data_time: 0.0031 loss: 1.1881 03/10 08:35:07 - mmengine - INFO - Epoch(train) [43][600/626] lr: 1.0000e-02 eta: 2:58:44 time: 0.3062 data_time: 0.0033 loss: 1.1996 03/10 08:35:14 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:35:15 - mmengine - INFO - Saving checkpoint at 43 epochs 03/10 08:35:36 - mmengine - INFO - Epoch(val) [43][98/98] accuracy/top1: 71.3480 accuracy/top5: 90.2980 03/10 08:36:03 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:36:09 - mmengine - INFO - Epoch(train) [44][100/626] lr: 1.0000e-02 eta: 2:57:53 time: 0.3284 data_time: 0.0033 loss: 1.2224 03/10 08:36:40 - mmengine - INFO - Epoch(train) [44][200/626] lr: 1.0000e-02 eta: 2:57:10 time: 0.3153 data_time: 0.0029 loss: 1.2071 03/10 08:37:11 - mmengine - INFO - Epoch(train) [44][300/626] lr: 1.0000e-02 eta: 2:56:28 time: 0.3470 data_time: 0.0037 loss: 1.1740 03/10 08:37:41 - mmengine - INFO - Epoch(train) [44][400/626] lr: 1.0000e-02 eta: 2:55:46 time: 0.2982 data_time: 0.0032 loss: 1.2225 03/10 08:38:12 - mmengine - INFO - Epoch(train) [44][500/626] lr: 1.0000e-02 eta: 2:55:03 time: 0.2854 data_time: 0.0033 loss: 1.1659 03/10 08:38:43 - mmengine - INFO - Epoch(train) [44][600/626] lr: 1.0000e-02 eta: 2:54:21 time: 0.3289 data_time: 0.0033 loss: 1.1973 03/10 08:38:50 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:38:51 - mmengine - INFO - Saving checkpoint at 44 epochs 03/10 08:39:11 - mmengine - INFO - Epoch(val) [44][98/98] accuracy/top1: 71.3700 accuracy/top5: 90.1820 03/10 08:39:44 - mmengine - INFO - Epoch(train) [45][100/626] lr: 1.0000e-02 eta: 2:53:30 time: 0.3053 data_time: 0.0031 loss: 1.1845 03/10 08:40:15 - mmengine - INFO - Epoch(train) [45][200/626] lr: 1.0000e-02 eta: 2:52:48 time: 0.2799 data_time: 0.0033 loss: 1.1476 03/10 08:40:45 - mmengine - INFO - Epoch(train) [45][300/626] lr: 1.0000e-02 eta: 2:52:06 time: 0.3317 data_time: 0.0030 loss: 1.1614 03/10 08:41:16 - mmengine - INFO - Epoch(train) [45][400/626] lr: 1.0000e-02 eta: 2:51:24 time: 0.2853 data_time: 0.0029 loss: 1.1620 03/10 08:41:33 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:41:46 - mmengine - INFO - Epoch(train) [45][500/626] lr: 1.0000e-02 eta: 2:50:42 time: 0.3090 data_time: 0.0031 loss: 1.1515 03/10 08:42:17 - mmengine - INFO - Epoch(train) [45][600/626] lr: 1.0000e-02 eta: 2:50:01 time: 0.3376 data_time: 0.0042 loss: 1.2225 03/10 08:42:25 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:42:25 - mmengine - INFO - Saving checkpoint at 45 epochs 03/10 08:42:45 - mmengine - INFO - Epoch(val) [45][98/98] accuracy/top1: 71.4340 accuracy/top5: 90.2120 03/10 08:43:18 - mmengine - INFO - Epoch(train) [46][100/626] lr: 1.0000e-02 eta: 2:49:10 time: 0.2927 data_time: 0.0029 loss: 1.1553 03/10 08:43:49 - mmengine - INFO - Epoch(train) [46][200/626] lr: 1.0000e-02 eta: 2:48:28 time: 0.3023 data_time: 0.0030 loss: 1.1683 03/10 08:44:20 - mmengine - INFO - Epoch(train) [46][300/626] lr: 1.0000e-02 eta: 2:47:47 time: 0.3395 data_time: 0.0033 loss: 1.1944 03/10 08:44:51 - mmengine - INFO - Epoch(train) [46][400/626] lr: 1.0000e-02 eta: 2:47:06 time: 0.3041 data_time: 0.0031 loss: 1.1379 03/10 08:45:21 - mmengine - INFO - Epoch(train) [46][500/626] lr: 1.0000e-02 eta: 2:46:24 time: 0.2970 data_time: 0.0031 loss: 1.2255 03/10 08:45:52 - mmengine - INFO - Epoch(train) [46][600/626] lr: 1.0000e-02 eta: 2:45:43 time: 0.3292 data_time: 0.0036 loss: 1.1611 03/10 08:46:00 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:46:00 - mmengine - INFO - Saving checkpoint at 46 epochs 03/10 08:46:21 - mmengine - INFO - Epoch(val) [46][98/98] accuracy/top1: 71.5200 accuracy/top5: 90.4580 03/10 08:46:54 - mmengine - INFO - Epoch(train) [47][100/626] lr: 1.0000e-02 eta: 2:44:53 time: 0.2798 data_time: 0.0035 loss: 1.1824 03/10 08:47:25 - mmengine - INFO - Epoch(train) [47][200/626] lr: 1.0000e-02 eta: 2:44:13 time: 0.3048 data_time: 0.0047 loss: 1.2141 03/10 08:47:26 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:47:56 - mmengine - INFO - Epoch(train) [47][300/626] lr: 1.0000e-02 eta: 2:43:31 time: 0.2799 data_time: 0.0034 loss: 1.1526 03/10 08:48:27 - mmengine - INFO - Epoch(train) [47][400/626] lr: 1.0000e-02 eta: 2:42:51 time: 0.3099 data_time: 0.0034 loss: 1.2294 03/10 08:48:57 - mmengine - INFO - Epoch(train) [47][500/626] lr: 1.0000e-02 eta: 2:42:10 time: 0.2995 data_time: 0.0031 loss: 1.1928 03/10 08:49:28 - mmengine - INFO - Epoch(train) [47][600/626] lr: 1.0000e-02 eta: 2:41:29 time: 0.3032 data_time: 0.0030 loss: 1.2314 03/10 08:49:36 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:49:36 - mmengine - INFO - Saving checkpoint at 47 epochs 03/10 08:49:56 - mmengine - INFO - Epoch(val) [47][98/98] accuracy/top1: 71.4740 accuracy/top5: 90.2740 03/10 08:50:30 - mmengine - INFO - Epoch(train) [48][100/626] lr: 1.0000e-02 eta: 2:40:40 time: 0.2773 data_time: 0.0034 loss: 1.1667 03/10 08:51:00 - mmengine - INFO - Epoch(train) [48][200/626] lr: 1.0000e-02 eta: 2:39:59 time: 0.2965 data_time: 0.0033 loss: 1.1664 03/10 08:51:31 - mmengine - INFO - Epoch(train) [48][300/626] lr: 1.0000e-02 eta: 2:39:18 time: 0.3102 data_time: 0.0033 loss: 1.1980 03/10 08:52:02 - mmengine - INFO - Epoch(train) [48][400/626] lr: 1.0000e-02 eta: 2:38:38 time: 0.2909 data_time: 0.0031 loss: 1.1867 03/10 08:52:32 - mmengine - INFO - Epoch(train) [48][500/626] lr: 1.0000e-02 eta: 2:37:57 time: 0.3133 data_time: 0.0039 loss: 1.1942 03/10 08:52:56 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:53:03 - mmengine - INFO - Epoch(train) [48][600/626] lr: 1.0000e-02 eta: 2:37:17 time: 0.3537 data_time: 0.0034 loss: 1.2092 03/10 08:53:11 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:53:11 - mmengine - INFO - Saving checkpoint at 48 epochs 03/10 08:53:31 - mmengine - INFO - Epoch(val) [48][98/98] accuracy/top1: 71.3300 accuracy/top5: 90.3520 03/10 08:54:05 - mmengine - INFO - Epoch(train) [49][100/626] lr: 1.0000e-02 eta: 2:36:28 time: 0.3042 data_time: 0.0029 loss: 1.1684 03/10 08:54:35 - mmengine - INFO - Epoch(train) [49][200/626] lr: 1.0000e-02 eta: 2:35:48 time: 0.2735 data_time: 0.0032 loss: 1.1770 03/10 08:55:06 - mmengine - INFO - Epoch(train) [49][300/626] lr: 1.0000e-02 eta: 2:35:07 time: 0.2702 data_time: 0.0033 loss: 1.1966 03/10 08:55:36 - mmengine - INFO - Epoch(train) [49][400/626] lr: 1.0000e-02 eta: 2:34:27 time: 0.3182 data_time: 0.0033 loss: 1.1600 03/10 08:56:07 - mmengine - INFO - Epoch(train) [49][500/626] lr: 1.0000e-02 eta: 2:33:47 time: 0.2910 data_time: 0.0030 loss: 1.2495 03/10 08:56:38 - mmengine - INFO - Epoch(train) [49][600/626] lr: 1.0000e-02 eta: 2:33:07 time: 0.3031 data_time: 0.0032 loss: 1.1874 03/10 08:56:45 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:56:45 - mmengine - INFO - Saving checkpoint at 49 epochs 03/10 08:57:06 - mmengine - INFO - Epoch(val) [49][98/98] accuracy/top1: 71.4720 accuracy/top5: 90.3100 03/10 08:57:40 - mmengine - INFO - Epoch(train) [50][100/626] lr: 1.0000e-02 eta: 2:32:18 time: 0.3154 data_time: 0.0034 loss: 1.1969 03/10 08:58:11 - mmengine - INFO - Epoch(train) [50][200/626] lr: 1.0000e-02 eta: 2:31:39 time: 0.2849 data_time: 0.0032 loss: 1.2373 03/10 08:58:42 - mmengine - INFO - Epoch(train) [50][300/626] lr: 1.0000e-02 eta: 2:30:59 time: 0.3255 data_time: 0.0033 loss: 1.1843 03/10 08:58:50 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 08:59:12 - mmengine - INFO - Epoch(train) [50][400/626] lr: 1.0000e-02 eta: 2:30:19 time: 0.3221 data_time: 0.0041 loss: 1.1601 03/10 08:59:43 - mmengine - INFO - Epoch(train) [50][500/626] lr: 1.0000e-02 eta: 2:29:40 time: 0.3687 data_time: 0.0033 loss: 1.1572 03/10 09:00:14 - mmengine - INFO - Epoch(train) [50][600/626] lr: 1.0000e-02 eta: 2:29:00 time: 0.2995 data_time: 0.0033 loss: 1.1515 03/10 09:00:21 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:00:22 - mmengine - INFO - Saving checkpoint at 50 epochs 03/10 09:00:41 - mmengine - INFO - Epoch(val) [50][98/98] accuracy/top1: 71.5320 accuracy/top5: 90.4240 03/10 09:01:15 - mmengine - INFO - Epoch(train) [51][100/626] lr: 1.0000e-02 eta: 2:28:12 time: 0.3089 data_time: 0.0044 loss: 1.1871 03/10 09:01:46 - mmengine - INFO - Epoch(train) [51][200/626] lr: 1.0000e-02 eta: 2:27:33 time: 0.2972 data_time: 0.0034 loss: 1.1747 03/10 09:02:17 - mmengine - INFO - Epoch(train) [51][300/626] lr: 1.0000e-02 eta: 2:26:53 time: 0.3250 data_time: 0.0040 loss: 1.1996 03/10 09:02:48 - mmengine - INFO - Epoch(train) [51][400/626] lr: 1.0000e-02 eta: 2:26:14 time: 0.3487 data_time: 0.0030 loss: 1.1928 03/10 09:03:18 - mmengine - INFO - Epoch(train) [51][500/626] lr: 1.0000e-02 eta: 2:25:34 time: 0.3051 data_time: 0.0034 loss: 1.3209 03/10 09:03:49 - mmengine - INFO - Epoch(train) [51][600/626] lr: 1.0000e-02 eta: 2:24:55 time: 0.3370 data_time: 0.0039 loss: 1.2131 03/10 09:03:57 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:03:57 - mmengine - INFO - Saving checkpoint at 51 epochs 03/10 09:04:17 - mmengine - INFO - Epoch(val) [51][98/98] accuracy/top1: 71.4580 accuracy/top5: 90.3840 03/10 09:04:43 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:04:51 - mmengine - INFO - Epoch(train) [52][100/626] lr: 1.0000e-02 eta: 2:24:07 time: 0.3109 data_time: 0.0031 loss: 1.1589 03/10 09:05:21 - mmengine - INFO - Epoch(train) [52][200/626] lr: 1.0000e-02 eta: 2:23:28 time: 0.3101 data_time: 0.0133 loss: 1.1344 03/10 09:05:52 - mmengine - INFO - Epoch(train) [52][300/626] lr: 1.0000e-02 eta: 2:22:49 time: 0.2992 data_time: 0.0031 loss: 1.1741 03/10 09:06:23 - mmengine - INFO - Epoch(train) [52][400/626] lr: 1.0000e-02 eta: 2:22:10 time: 0.3083 data_time: 0.0031 loss: 1.1281 03/10 09:06:54 - mmengine - INFO - Epoch(train) [52][500/626] lr: 1.0000e-02 eta: 2:21:31 time: 0.3035 data_time: 0.0035 loss: 1.1043 03/10 09:07:24 - mmengine - INFO - Epoch(train) [52][600/626] lr: 1.0000e-02 eta: 2:20:52 time: 0.3144 data_time: 0.0039 loss: 1.2366 03/10 09:07:32 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:07:32 - mmengine - INFO - Saving checkpoint at 52 epochs 03/10 09:07:52 - mmengine - INFO - Epoch(val) [52][98/98] accuracy/top1: 71.4140 accuracy/top5: 90.2880 03/10 09:08:26 - mmengine - INFO - Epoch(train) [53][100/626] lr: 1.0000e-02 eta: 2:20:05 time: 0.2935 data_time: 0.0030 loss: 1.1332 03/10 09:08:58 - mmengine - INFO - Epoch(train) [53][200/626] lr: 1.0000e-02 eta: 2:19:26 time: 0.3333 data_time: 0.0032 loss: 1.1523 03/10 09:09:29 - mmengine - INFO - Epoch(train) [53][300/626] lr: 1.0000e-02 eta: 2:18:48 time: 0.3095 data_time: 0.0031 loss: 1.1720 03/10 09:09:59 - mmengine - INFO - Epoch(train) [53][400/626] lr: 1.0000e-02 eta: 2:18:09 time: 0.3102 data_time: 0.0031 loss: 1.2335 03/10 09:10:14 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:10:30 - mmengine - INFO - Epoch(train) [53][500/626] lr: 1.0000e-02 eta: 2:17:30 time: 0.3244 data_time: 0.0043 loss: 1.1429 03/10 09:11:01 - mmengine - INFO - Epoch(train) [53][600/626] lr: 1.0000e-02 eta: 2:16:51 time: 0.3385 data_time: 0.0032 loss: 1.1470 03/10 09:11:08 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:11:08 - mmengine - INFO - Saving checkpoint at 53 epochs 03/10 09:11:29 - mmengine - INFO - Epoch(val) [53][98/98] accuracy/top1: 71.4460 accuracy/top5: 90.2920 03/10 09:12:02 - mmengine - INFO - Epoch(train) [54][100/626] lr: 1.0000e-02 eta: 2:16:04 time: 0.2735 data_time: 0.0045 loss: 1.1382 03/10 09:12:33 - mmengine - INFO - Epoch(train) [54][200/626] lr: 1.0000e-02 eta: 2:15:25 time: 0.3370 data_time: 0.0036 loss: 1.1923 03/10 09:13:04 - mmengine - INFO - Epoch(train) [54][300/626] lr: 1.0000e-02 eta: 2:14:47 time: 0.3406 data_time: 0.0034 loss: 1.2217 03/10 09:13:34 - mmengine - INFO - Epoch(train) [54][400/626] lr: 1.0000e-02 eta: 2:14:08 time: 0.2899 data_time: 0.0033 loss: 1.1063 03/10 09:14:05 - mmengine - INFO - Epoch(train) [54][500/626] lr: 1.0000e-02 eta: 2:13:30 time: 0.2912 data_time: 0.0040 loss: 1.1858 03/10 09:14:36 - mmengine - INFO - Epoch(train) [54][600/626] lr: 1.0000e-02 eta: 2:12:51 time: 0.3473 data_time: 0.0031 loss: 1.1617 03/10 09:14:43 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:14:44 - mmengine - INFO - Saving checkpoint at 54 epochs 03/10 09:15:04 - mmengine - INFO - Epoch(val) [54][98/98] accuracy/top1: 71.5140 accuracy/top5: 90.2920 03/10 09:15:37 - mmengine - INFO - Epoch(train) [55][100/626] lr: 1.0000e-02 eta: 2:12:04 time: 0.2840 data_time: 0.0032 loss: 1.1215 03/10 09:16:07 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:16:08 - mmengine - INFO - Epoch(train) [55][200/626] lr: 1.0000e-02 eta: 2:11:26 time: 0.3232 data_time: 0.0030 loss: 1.1397 03/10 09:16:39 - mmengine - INFO - Epoch(train) [55][300/626] lr: 1.0000e-02 eta: 2:10:47 time: 0.3170 data_time: 0.0065 loss: 1.1533 03/10 09:17:09 - mmengine - INFO - Epoch(train) [55][400/626] lr: 1.0000e-02 eta: 2:10:09 time: 0.2958 data_time: 0.0031 loss: 1.1278 03/10 09:17:39 - mmengine - INFO - Epoch(train) [55][500/626] lr: 1.0000e-02 eta: 2:09:31 time: 0.2898 data_time: 0.0034 loss: 1.1605 03/10 09:18:10 - mmengine - INFO - Epoch(train) [55][600/626] lr: 1.0000e-02 eta: 2:08:52 time: 0.3209 data_time: 0.0040 loss: 1.1447 03/10 09:18:17 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:18:18 - mmengine - INFO - Saving checkpoint at 55 epochs 03/10 09:18:38 - mmengine - INFO - Epoch(val) [55][98/98] accuracy/top1: 71.5780 accuracy/top5: 90.3400 03/10 09:19:11 - mmengine - INFO - Epoch(train) [56][100/626] lr: 1.0000e-02 eta: 2:08:06 time: 0.3025 data_time: 0.0041 loss: 1.2350 03/10 09:19:42 - mmengine - INFO - Epoch(train) [56][200/626] lr: 1.0000e-02 eta: 2:07:27 time: 0.2760 data_time: 0.0033 loss: 1.1485 03/10 09:20:13 - mmengine - INFO - Epoch(train) [56][300/626] lr: 1.0000e-02 eta: 2:06:49 time: 0.3272 data_time: 0.0032 loss: 1.1514 03/10 09:20:44 - mmengine - INFO - Epoch(train) [56][400/626] lr: 1.0000e-02 eta: 2:06:12 time: 0.3194 data_time: 0.0031 loss: 1.0948 03/10 09:21:14 - mmengine - INFO - Epoch(train) [56][500/626] lr: 1.0000e-02 eta: 2:05:33 time: 0.2787 data_time: 0.0036 loss: 1.1579 03/10 09:21:36 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:21:45 - mmengine - INFO - Epoch(train) [56][600/626] lr: 1.0000e-02 eta: 2:04:56 time: 0.3556 data_time: 0.0033 loss: 1.1100 03/10 09:21:53 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:21:53 - mmengine - INFO - Saving checkpoint at 56 epochs 03/10 09:22:13 - mmengine - INFO - Epoch(val) [56][98/98] accuracy/top1: 71.5640 accuracy/top5: 90.3340 03/10 09:22:46 - mmengine - INFO - Epoch(train) [57][100/626] lr: 1.0000e-02 eta: 2:04:09 time: 0.2767 data_time: 0.0031 loss: 1.2110 03/10 09:23:17 - mmengine - INFO - Epoch(train) [57][200/626] lr: 1.0000e-02 eta: 2:03:31 time: 0.2812 data_time: 0.0039 loss: 1.1484 03/10 09:23:48 - mmengine - INFO - Epoch(train) [57][300/626] lr: 1.0000e-02 eta: 2:02:54 time: 0.3343 data_time: 0.0033 loss: 1.0858 03/10 09:24:18 - mmengine - INFO - Epoch(train) [57][400/626] lr: 1.0000e-02 eta: 2:02:16 time: 0.2997 data_time: 0.0031 loss: 1.1575 03/10 09:24:49 - mmengine - INFO - Epoch(train) [57][500/626] lr: 1.0000e-02 eta: 2:01:38 time: 0.3176 data_time: 0.0046 loss: 1.0958 03/10 09:25:20 - mmengine - INFO - Epoch(train) [57][600/626] lr: 1.0000e-02 eta: 2:01:01 time: 0.3445 data_time: 0.0034 loss: 1.1603 03/10 09:25:27 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:25:27 - mmengine - INFO - Saving checkpoint at 57 epochs 03/10 09:25:47 - mmengine - INFO - Epoch(val) [57][98/98] accuracy/top1: 71.2560 accuracy/top5: 90.1960 03/10 09:26:21 - mmengine - INFO - Epoch(train) [58][100/626] lr: 1.0000e-02 eta: 2:00:15 time: 0.3226 data_time: 0.0040 loss: 1.1355 03/10 09:26:52 - mmengine - INFO - Epoch(train) [58][200/626] lr: 1.0000e-02 eta: 1:59:37 time: 0.3045 data_time: 0.0032 loss: 1.2937 03/10 09:27:23 - mmengine - INFO - Epoch(train) [58][300/626] lr: 1.0000e-02 eta: 1:59:00 time: 0.3504 data_time: 0.0031 loss: 1.1647 03/10 09:27:28 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:27:53 - mmengine - INFO - Epoch(train) [58][400/626] lr: 1.0000e-02 eta: 1:58:22 time: 0.2753 data_time: 0.0032 loss: 1.1306 03/10 09:28:24 - mmengine - INFO - Epoch(train) [58][500/626] lr: 1.0000e-02 eta: 1:57:45 time: 0.3515 data_time: 0.0031 loss: 1.2263 03/10 09:28:55 - mmengine - INFO - Epoch(train) [58][600/626] lr: 1.0000e-02 eta: 1:57:07 time: 0.3390 data_time: 0.0031 loss: 1.1700 03/10 09:29:02 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:29:02 - mmengine - INFO - Saving checkpoint at 58 epochs 03/10 09:29:23 - mmengine - INFO - Epoch(val) [58][98/98] accuracy/top1: 71.3800 accuracy/top5: 90.3360 03/10 09:29:56 - mmengine - INFO - Epoch(train) [59][100/626] lr: 1.0000e-02 eta: 1:56:21 time: 0.2827 data_time: 0.0037 loss: 1.0985 03/10 09:30:27 - mmengine - INFO - Epoch(train) [59][200/626] lr: 1.0000e-02 eta: 1:55:43 time: 0.3243 data_time: 0.0040 loss: 1.0916 03/10 09:30:58 - mmengine - INFO - Epoch(train) [59][300/626] lr: 1.0000e-02 eta: 1:55:06 time: 0.2841 data_time: 0.0039 loss: 1.1596 03/10 09:31:29 - mmengine - INFO - Epoch(train) [59][400/626] lr: 1.0000e-02 eta: 1:54:29 time: 0.3140 data_time: 0.0032 loss: 1.1742 03/10 09:31:59 - mmengine - INFO - Epoch(train) [59][500/626] lr: 1.0000e-02 eta: 1:53:52 time: 0.3101 data_time: 0.0034 loss: 1.1320 03/10 09:32:30 - mmengine - INFO - Epoch(train) [59][600/626] lr: 1.0000e-02 eta: 1:53:14 time: 0.3110 data_time: 0.0030 loss: 1.1234 03/10 09:32:38 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:32:38 - mmengine - INFO - Saving checkpoint at 59 epochs 03/10 09:32:58 - mmengine - INFO - Epoch(val) [59][98/98] accuracy/top1: 71.5820 accuracy/top5: 90.3460 03/10 09:33:22 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:33:32 - mmengine - INFO - Epoch(train) [60][100/626] lr: 1.0000e-02 eta: 1:52:29 time: 0.2947 data_time: 0.0038 loss: 1.1197 03/10 09:34:03 - mmengine - INFO - Epoch(train) [60][200/626] lr: 1.0000e-02 eta: 1:51:51 time: 0.2971 data_time: 0.0033 loss: 1.1288 03/10 09:34:33 - mmengine - INFO - Epoch(train) [60][300/626] lr: 1.0000e-02 eta: 1:51:14 time: 0.3158 data_time: 0.0033 loss: 1.0678 03/10 09:35:04 - mmengine - INFO - Epoch(train) [60][400/626] lr: 1.0000e-02 eta: 1:50:37 time: 0.3120 data_time: 0.0034 loss: 1.1375 03/10 09:35:35 - mmengine - INFO - Epoch(train) [60][500/626] lr: 1.0000e-02 eta: 1:50:00 time: 0.3005 data_time: 0.0033 loss: 1.1827 03/10 09:36:05 - mmengine - INFO - Epoch(train) [60][600/626] lr: 1.0000e-02 eta: 1:49:23 time: 0.3157 data_time: 0.0032 loss: 1.1956 03/10 09:36:13 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:36:13 - mmengine - INFO - Saving checkpoint at 60 epochs 03/10 09:36:34 - mmengine - INFO - Epoch(val) [60][98/98] accuracy/top1: 71.5440 accuracy/top5: 90.2780 03/10 09:37:08 - mmengine - INFO - Epoch(train) [61][100/626] lr: 1.0000e-03 eta: 1:48:38 time: 0.3056 data_time: 0.0034 loss: 1.0977 03/10 09:37:38 - mmengine - INFO - Epoch(train) [61][200/626] lr: 1.0000e-03 eta: 1:48:01 time: 0.3302 data_time: 0.0033 loss: 1.0208 03/10 09:38:09 - mmengine - INFO - Epoch(train) [61][300/626] lr: 1.0000e-03 eta: 1:47:24 time: 0.3008 data_time: 0.0033 loss: 1.0393 03/10 09:38:40 - mmengine - INFO - Epoch(train) [61][400/626] lr: 1.0000e-03 eta: 1:46:47 time: 0.2798 data_time: 0.0032 loss: 1.1248 03/10 09:38:53 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:39:11 - mmengine - INFO - Epoch(train) [61][500/626] lr: 1.0000e-03 eta: 1:46:11 time: 0.3189 data_time: 0.0033 loss: 1.0909 03/10 09:39:41 - mmengine - INFO - Epoch(train) [61][600/626] lr: 1.0000e-03 eta: 1:45:34 time: 0.2843 data_time: 0.0029 loss: 1.1001 03/10 09:39:49 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:39:49 - mmengine - INFO - Saving checkpoint at 61 epochs 03/10 09:40:10 - mmengine - INFO - Epoch(val) [61][98/98] accuracy/top1: 72.3960 accuracy/top5: 90.7860 03/10 09:40:44 - mmengine - INFO - Epoch(train) [62][100/626] lr: 1.0000e-03 eta: 1:44:49 time: 0.3509 data_time: 0.0032 loss: 1.0798 03/10 09:41:14 - mmengine - INFO - Epoch(train) [62][200/626] lr: 1.0000e-03 eta: 1:44:12 time: 0.2955 data_time: 0.0033 loss: 1.0221 03/10 09:41:45 - mmengine - INFO - Epoch(train) [62][300/626] lr: 1.0000e-03 eta: 1:43:35 time: 0.3522 data_time: 0.0031 loss: 1.0650 03/10 09:42:16 - mmengine - INFO - Epoch(train) [62][400/626] lr: 1.0000e-03 eta: 1:42:58 time: 0.3019 data_time: 0.0033 loss: 1.0410 03/10 09:42:47 - mmengine - INFO - Epoch(train) [62][500/626] lr: 1.0000e-03 eta: 1:42:22 time: 0.3029 data_time: 0.0033 loss: 1.0959 03/10 09:43:17 - mmengine - INFO - Epoch(train) [62][600/626] lr: 1.0000e-03 eta: 1:41:45 time: 0.3310 data_time: 0.0033 loss: 1.1271 03/10 09:43:25 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:43:25 - mmengine - INFO - Saving checkpoint at 62 epochs 03/10 09:43:45 - mmengine - INFO - Epoch(val) [62][98/98] accuracy/top1: 72.4220 accuracy/top5: 90.8600 03/10 09:44:19 - mmengine - INFO - Epoch(train) [63][100/626] lr: 1.0000e-03 eta: 1:41:00 time: 0.2984 data_time: 0.0034 loss: 1.0621 03/10 09:44:46 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:44:49 - mmengine - INFO - Epoch(train) [63][200/626] lr: 1.0000e-03 eta: 1:40:23 time: 0.2712 data_time: 0.0031 loss: 1.0262 03/10 09:45:20 - mmengine - INFO - Epoch(train) [63][300/626] lr: 1.0000e-03 eta: 1:39:47 time: 0.3367 data_time: 0.0034 loss: 1.1013 03/10 09:45:50 - mmengine - INFO - Epoch(train) [63][400/626] lr: 1.0000e-03 eta: 1:39:10 time: 0.3488 data_time: 0.0030 loss: 1.0898 03/10 09:46:21 - mmengine - INFO - Epoch(train) [63][500/626] lr: 1.0000e-03 eta: 1:38:33 time: 0.2881 data_time: 0.0031 loss: 1.1125 03/10 09:46:51 - mmengine - INFO - Epoch(train) [63][600/626] lr: 1.0000e-03 eta: 1:37:57 time: 0.3189 data_time: 0.0207 loss: 1.1270 03/10 09:46:59 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:46:59 - mmengine - INFO - Saving checkpoint at 63 epochs 03/10 09:47:19 - mmengine - INFO - Epoch(val) [63][98/98] accuracy/top1: 72.4300 accuracy/top5: 90.9040 03/10 09:47:53 - mmengine - INFO - Epoch(train) [64][100/626] lr: 1.0000e-03 eta: 1:37:12 time: 0.2873 data_time: 0.0032 loss: 1.0334 03/10 09:48:23 - mmengine - INFO - Epoch(train) [64][200/626] lr: 1.0000e-03 eta: 1:36:35 time: 0.2755 data_time: 0.0035 loss: 1.0730 03/10 09:48:54 - mmengine - INFO - Epoch(train) [64][300/626] lr: 1.0000e-03 eta: 1:35:59 time: 0.2912 data_time: 0.0031 loss: 1.0516 03/10 09:49:25 - mmengine - INFO - Epoch(train) [64][400/626] lr: 1.0000e-03 eta: 1:35:23 time: 0.3094 data_time: 0.0030 loss: 1.0731 03/10 09:49:56 - mmengine - INFO - Epoch(train) [64][500/626] lr: 1.0000e-03 eta: 1:34:47 time: 0.3029 data_time: 0.0031 loss: 1.0997 03/10 09:50:14 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:50:26 - mmengine - INFO - Epoch(train) [64][600/626] lr: 1.0000e-03 eta: 1:34:10 time: 0.3211 data_time: 0.0031 loss: 1.0667 03/10 09:50:34 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:50:34 - mmengine - INFO - Saving checkpoint at 64 epochs 03/10 09:50:55 - mmengine - INFO - Epoch(val) [64][98/98] accuracy/top1: 72.4560 accuracy/top5: 90.8400 03/10 09:51:28 - mmengine - INFO - Epoch(train) [65][100/626] lr: 1.0000e-03 eta: 1:33:25 time: 0.3193 data_time: 0.0032 loss: 1.0789 03/10 09:51:59 - mmengine - INFO - Epoch(train) [65][200/626] lr: 1.0000e-03 eta: 1:32:49 time: 0.2930 data_time: 0.0035 loss: 1.1421 03/10 09:52:29 - mmengine - INFO - Epoch(train) [65][300/626] lr: 1.0000e-03 eta: 1:32:13 time: 0.2964 data_time: 0.0033 loss: 1.1197 03/10 09:53:00 - mmengine - INFO - Epoch(train) [65][400/626] lr: 1.0000e-03 eta: 1:31:37 time: 0.2982 data_time: 0.0032 loss: 1.0987 03/10 09:53:31 - mmengine - INFO - Epoch(train) [65][500/626] lr: 1.0000e-03 eta: 1:31:01 time: 0.2813 data_time: 0.0031 loss: 1.0096 03/10 09:54:02 - mmengine - INFO - Epoch(train) [65][600/626] lr: 1.0000e-03 eta: 1:30:25 time: 0.2919 data_time: 0.0031 loss: 1.1251 03/10 09:54:10 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:54:10 - mmengine - INFO - Saving checkpoint at 65 epochs 03/10 09:54:30 - mmengine - INFO - Epoch(val) [65][98/98] accuracy/top1: 72.3980 accuracy/top5: 90.8660 03/10 09:55:04 - mmengine - INFO - Epoch(train) [66][100/626] lr: 1.0000e-03 eta: 1:29:40 time: 0.3394 data_time: 0.0031 loss: 1.1409 03/10 09:55:35 - mmengine - INFO - Epoch(train) [66][200/626] lr: 1.0000e-03 eta: 1:29:04 time: 0.3086 data_time: 0.0038 loss: 1.0416 03/10 09:56:05 - mmengine - INFO - Epoch(train) [66][300/626] lr: 1.0000e-03 eta: 1:28:28 time: 0.2825 data_time: 0.0034 loss: 1.0286 03/10 09:56:08 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:56:36 - mmengine - INFO - Epoch(train) [66][400/626] lr: 1.0000e-03 eta: 1:27:52 time: 0.2959 data_time: 0.0032 loss: 1.0562 03/10 09:57:07 - mmengine - INFO - Epoch(train) [66][500/626] lr: 1.0000e-03 eta: 1:27:16 time: 0.3423 data_time: 0.0033 loss: 1.0699 03/10 09:57:38 - mmengine - INFO - Epoch(train) [66][600/626] lr: 1.0000e-03 eta: 1:26:40 time: 0.3016 data_time: 0.0032 loss: 1.0373 03/10 09:57:46 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 09:57:46 - mmengine - INFO - Saving checkpoint at 66 epochs 03/10 09:58:07 - mmengine - INFO - Epoch(val) [66][98/98] accuracy/top1: 72.5060 accuracy/top5: 90.8940 03/10 09:58:40 - mmengine - INFO - Epoch(train) [67][100/626] lr: 1.0000e-03 eta: 1:25:56 time: 0.2782 data_time: 0.0034 loss: 1.0200 03/10 09:59:11 - mmengine - INFO - Epoch(train) [67][200/626] lr: 1.0000e-03 eta: 1:25:20 time: 0.3759 data_time: 0.0032 loss: 1.0571 03/10 09:59:41 - mmengine - INFO - Epoch(train) [67][300/626] lr: 1.0000e-03 eta: 1:24:44 time: 0.2867 data_time: 0.0034 loss: 1.1084 03/10 10:00:12 - mmengine - INFO - Epoch(train) [67][400/626] lr: 1.0000e-03 eta: 1:24:08 time: 0.2790 data_time: 0.0033 loss: 1.1046 03/10 10:00:43 - mmengine - INFO - Epoch(train) [67][500/626] lr: 1.0000e-03 eta: 1:23:32 time: 0.3151 data_time: 0.0040 loss: 1.0358 03/10 10:01:14 - mmengine - INFO - Epoch(train) [67][600/626] lr: 1.0000e-03 eta: 1:22:56 time: 0.3243 data_time: 0.0031 loss: 0.9785 03/10 10:01:21 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:01:21 - mmengine - INFO - Saving checkpoint at 67 epochs 03/10 10:01:42 - mmengine - INFO - Epoch(val) [67][98/98] accuracy/top1: 72.5020 accuracy/top5: 90.9180 03/10 10:02:02 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:02:15 - mmengine - INFO - Epoch(train) [68][100/626] lr: 1.0000e-03 eta: 1:22:12 time: 0.3185 data_time: 0.0448 loss: 1.0415 03/10 10:02:46 - mmengine - INFO - Epoch(train) [68][200/626] lr: 1.0000e-03 eta: 1:21:36 time: 0.3248 data_time: 0.0034 loss: 1.0762 03/10 10:03:16 - mmengine - INFO - Epoch(train) [68][300/626] lr: 1.0000e-03 eta: 1:21:00 time: 0.2949 data_time: 0.0036 loss: 1.0117 03/10 10:03:47 - mmengine - INFO - Epoch(train) [68][400/626] lr: 1.0000e-03 eta: 1:20:25 time: 0.3207 data_time: 0.0034 loss: 1.0160 03/10 10:04:17 - mmengine - INFO - Epoch(train) [68][500/626] lr: 1.0000e-03 eta: 1:19:49 time: 0.2729 data_time: 0.0037 loss: 1.0697 03/10 10:04:48 - mmengine - INFO - Epoch(train) [68][600/626] lr: 1.0000e-03 eta: 1:19:13 time: 0.3124 data_time: 0.0052 loss: 1.0365 03/10 10:04:55 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:04:56 - mmengine - INFO - Saving checkpoint at 68 epochs 03/10 10:05:16 - mmengine - INFO - Epoch(val) [68][98/98] accuracy/top1: 72.4200 accuracy/top5: 90.8680 03/10 10:05:49 - mmengine - INFO - Epoch(train) [69][100/626] lr: 1.0000e-03 eta: 1:18:29 time: 0.3272 data_time: 0.0036 loss: 1.0892 03/10 10:06:20 - mmengine - INFO - Epoch(train) [69][200/626] lr: 1.0000e-03 eta: 1:17:53 time: 0.3116 data_time: 0.0032 loss: 1.0800 03/10 10:06:50 - mmengine - INFO - Epoch(train) [69][300/626] lr: 1.0000e-03 eta: 1:17:18 time: 0.2965 data_time: 0.0029 loss: 1.0422 03/10 10:07:21 - mmengine - INFO - Epoch(train) [69][400/626] lr: 1.0000e-03 eta: 1:16:42 time: 0.3201 data_time: 0.0033 loss: 1.0837 03/10 10:07:31 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:07:52 - mmengine - INFO - Epoch(train) [69][500/626] lr: 1.0000e-03 eta: 1:16:07 time: 0.2615 data_time: 0.0031 loss: 1.0556 03/10 10:08:22 - mmengine - INFO - Epoch(train) [69][600/626] lr: 1.0000e-03 eta: 1:15:31 time: 0.2872 data_time: 0.0031 loss: 1.0730 03/10 10:08:30 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:08:30 - mmengine - INFO - Saving checkpoint at 69 epochs 03/10 10:08:50 - mmengine - INFO - Epoch(val) [69][98/98] accuracy/top1: 72.4600 accuracy/top5: 90.8480 03/10 10:09:24 - mmengine - INFO - Epoch(train) [70][100/626] lr: 1.0000e-03 eta: 1:14:47 time: 0.2847 data_time: 0.0031 loss: 1.1024 03/10 10:09:55 - mmengine - INFO - Epoch(train) [70][200/626] lr: 1.0000e-03 eta: 1:14:12 time: 0.2911 data_time: 0.0031 loss: 1.1050 03/10 10:10:26 - mmengine - INFO - Epoch(train) [70][300/626] lr: 1.0000e-03 eta: 1:13:36 time: 0.3024 data_time: 0.0033 loss: 0.9795 03/10 10:10:57 - mmengine - INFO - Epoch(train) [70][400/626] lr: 1.0000e-03 eta: 1:13:01 time: 0.3005 data_time: 0.0032 loss: 1.0652 03/10 10:11:28 - mmengine - INFO - Epoch(train) [70][500/626] lr: 1.0000e-03 eta: 1:12:25 time: 0.3206 data_time: 0.0032 loss: 1.0509 03/10 10:11:59 - mmengine - INFO - Epoch(train) [70][600/626] lr: 1.0000e-03 eta: 1:11:50 time: 0.3206 data_time: 0.0033 loss: 1.0916 03/10 10:12:07 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:12:07 - mmengine - INFO - Saving checkpoint at 70 epochs 03/10 10:12:27 - mmengine - INFO - Epoch(val) [70][98/98] accuracy/top1: 72.5920 accuracy/top5: 90.8360 03/10 10:13:01 - mmengine - INFO - Epoch(train) [71][100/626] lr: 1.0000e-03 eta: 1:11:06 time: 0.2771 data_time: 0.0036 loss: 1.0216 03/10 10:13:25 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:13:31 - mmengine - INFO - Epoch(train) [71][200/626] lr: 1.0000e-03 eta: 1:10:31 time: 0.2886 data_time: 0.0034 loss: 1.0553 03/10 10:14:02 - mmengine - INFO - Epoch(train) [71][300/626] lr: 1.0000e-03 eta: 1:09:55 time: 0.3126 data_time: 0.0034 loss: 1.0751 03/10 10:14:33 - mmengine - INFO - Epoch(train) [71][400/626] lr: 1.0000e-03 eta: 1:09:20 time: 0.3324 data_time: 0.0032 loss: 1.0767 03/10 10:15:03 - mmengine - INFO - Epoch(train) [71][500/626] lr: 1.0000e-03 eta: 1:08:45 time: 0.2872 data_time: 0.0034 loss: 1.0721 03/10 10:15:34 - mmengine - INFO - Epoch(train) [71][600/626] lr: 1.0000e-03 eta: 1:08:10 time: 0.3116 data_time: 0.0033 loss: 1.0867 03/10 10:15:41 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:15:41 - mmengine - INFO - Saving checkpoint at 71 epochs 03/10 10:16:02 - mmengine - INFO - Epoch(val) [71][98/98] accuracy/top1: 72.5580 accuracy/top5: 90.9440 03/10 10:16:36 - mmengine - INFO - Epoch(train) [72][100/626] lr: 1.0000e-03 eta: 1:07:26 time: 0.3037 data_time: 0.0034 loss: 1.0413 03/10 10:17:07 - mmengine - INFO - Epoch(train) [72][200/626] lr: 1.0000e-03 eta: 1:06:51 time: 0.2977 data_time: 0.0040 loss: 1.0799 03/10 10:17:37 - mmengine - INFO - Epoch(train) [72][300/626] lr: 1.0000e-03 eta: 1:06:15 time: 0.2979 data_time: 0.0034 loss: 1.0591 03/10 10:18:08 - mmengine - INFO - Epoch(train) [72][400/626] lr: 1.0000e-03 eta: 1:05:40 time: 0.2958 data_time: 0.0033 loss: 1.0652 03/10 10:18:39 - mmengine - INFO - Epoch(train) [72][500/626] lr: 1.0000e-03 eta: 1:05:05 time: 0.2976 data_time: 0.0031 loss: 1.0418 03/10 10:18:56 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:19:09 - mmengine - INFO - Epoch(train) [72][600/626] lr: 1.0000e-03 eta: 1:04:30 time: 0.3087 data_time: 0.0030 loss: 1.0413 03/10 10:19:17 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:19:17 - mmengine - INFO - Saving checkpoint at 72 epochs 03/10 10:19:38 - mmengine - INFO - Epoch(val) [72][98/98] accuracy/top1: 72.4740 accuracy/top5: 90.8920 03/10 10:20:11 - mmengine - INFO - Epoch(train) [73][100/626] lr: 1.0000e-03 eta: 1:03:46 time: 0.2902 data_time: 0.0030 loss: 1.0536 03/10 10:20:42 - mmengine - INFO - Epoch(train) [73][200/626] lr: 1.0000e-03 eta: 1:03:11 time: 0.3123 data_time: 0.0033 loss: 1.0468 03/10 10:21:13 - mmengine - INFO - Epoch(train) [73][300/626] lr: 1.0000e-03 eta: 1:02:36 time: 0.3213 data_time: 0.0034 loss: 1.1455 03/10 10:21:43 - mmengine - INFO - Epoch(train) [73][400/626] lr: 1.0000e-03 eta: 1:02:01 time: 0.2960 data_time: 0.0038 loss: 1.0646 03/10 10:22:14 - mmengine - INFO - Epoch(train) [73][500/626] lr: 1.0000e-03 eta: 1:01:25 time: 0.3080 data_time: 0.0036 loss: 1.1057 03/10 10:22:44 - mmengine - INFO - Epoch(train) [73][600/626] lr: 1.0000e-03 eta: 1:00:50 time: 0.2951 data_time: 0.0031 loss: 1.0750 03/10 10:22:52 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:22:52 - mmengine - INFO - Saving checkpoint at 73 epochs 03/10 10:23:13 - mmengine - INFO - Epoch(val) [73][98/98] accuracy/top1: 72.4980 accuracy/top5: 90.8420 03/10 10:23:46 - mmengine - INFO - Epoch(train) [74][100/626] lr: 1.0000e-03 eta: 1:00:07 time: 0.3079 data_time: 0.0031 loss: 1.0669 03/10 10:24:17 - mmengine - INFO - Epoch(train) [74][200/626] lr: 1.0000e-03 eta: 0:59:32 time: 0.3549 data_time: 0.0040 loss: 1.0581 03/10 10:24:47 - mmengine - INFO - Epoch(train) [74][300/626] lr: 1.0000e-03 eta: 0:58:57 time: 0.3246 data_time: 0.0031 loss: 1.0919 03/10 10:24:48 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:25:18 - mmengine - INFO - Epoch(train) [74][400/626] lr: 1.0000e-03 eta: 0:58:22 time: 0.2791 data_time: 0.0031 loss: 1.0678 03/10 10:25:49 - mmengine - INFO - Epoch(train) [74][500/626] lr: 1.0000e-03 eta: 0:57:47 time: 0.2975 data_time: 0.0032 loss: 1.0824 03/10 10:26:19 - mmengine - INFO - Epoch(train) [74][600/626] lr: 1.0000e-03 eta: 0:57:12 time: 0.3194 data_time: 0.0032 loss: 1.0464 03/10 10:26:27 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:26:27 - mmengine - INFO - Saving checkpoint at 74 epochs 03/10 10:26:48 - mmengine - INFO - Epoch(val) [74][98/98] accuracy/top1: 72.4560 accuracy/top5: 90.8800 03/10 10:27:21 - mmengine - INFO - Epoch(train) [75][100/626] lr: 1.0000e-03 eta: 0:56:28 time: 0.3053 data_time: 0.0035 loss: 1.0599 03/10 10:27:52 - mmengine - INFO - Epoch(train) [75][200/626] lr: 1.0000e-03 eta: 0:55:53 time: 0.3189 data_time: 0.0032 loss: 1.0641 03/10 10:28:22 - mmengine - INFO - Epoch(train) [75][300/626] lr: 1.0000e-03 eta: 0:55:18 time: 0.3103 data_time: 0.0033 loss: 1.0747 03/10 10:28:53 - mmengine - INFO - Epoch(train) [75][400/626] lr: 1.0000e-03 eta: 0:54:44 time: 0.2766 data_time: 0.0032 loss: 1.1455 03/10 10:29:23 - mmengine - INFO - Epoch(train) [75][500/626] lr: 1.0000e-03 eta: 0:54:09 time: 0.3088 data_time: 0.0032 loss: 1.0843 03/10 10:29:54 - mmengine - INFO - Epoch(train) [75][600/626] lr: 1.0000e-03 eta: 0:53:34 time: 0.3629 data_time: 0.0033 loss: 1.0433 03/10 10:30:02 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:30:02 - mmengine - INFO - Saving checkpoint at 75 epochs 03/10 10:30:22 - mmengine - INFO - Epoch(val) [75][98/98] accuracy/top1: 72.6520 accuracy/top5: 90.8960 03/10 10:30:40 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:30:56 - mmengine - INFO - Epoch(train) [76][100/626] lr: 1.0000e-03 eta: 0:52:50 time: 0.2915 data_time: 0.0036 loss: 1.0200 03/10 10:31:26 - mmengine - INFO - Epoch(train) [76][200/626] lr: 1.0000e-03 eta: 0:52:16 time: 0.2996 data_time: 0.0036 loss: 1.0567 03/10 10:31:57 - mmengine - INFO - Epoch(train) [76][300/626] lr: 1.0000e-03 eta: 0:51:41 time: 0.3238 data_time: 0.0033 loss: 1.0511 03/10 10:32:28 - mmengine - INFO - Epoch(train) [76][400/626] lr: 1.0000e-03 eta: 0:51:06 time: 0.3062 data_time: 0.0047 loss: 1.0940 03/10 10:32:59 - mmengine - INFO - Epoch(train) [76][500/626] lr: 1.0000e-03 eta: 0:50:31 time: 0.2984 data_time: 0.0033 loss: 1.0903 03/10 10:33:30 - mmengine - INFO - Epoch(train) [76][600/626] lr: 1.0000e-03 eta: 0:49:57 time: 0.3060 data_time: 0.0033 loss: 1.0743 03/10 10:33:37 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:33:37 - mmengine - INFO - Saving checkpoint at 76 epochs 03/10 10:33:59 - mmengine - INFO - Epoch(val) [76][98/98] accuracy/top1: 72.4740 accuracy/top5: 90.8680 03/10 10:34:32 - mmengine - INFO - Epoch(train) [77][100/626] lr: 1.0000e-03 eta: 0:49:13 time: 0.2958 data_time: 0.0033 loss: 1.0606 03/10 10:35:03 - mmengine - INFO - Epoch(train) [77][200/626] lr: 1.0000e-03 eta: 0:48:39 time: 0.3536 data_time: 0.0030 loss: 1.0321 03/10 10:35:34 - mmengine - INFO - Epoch(train) [77][300/626] lr: 1.0000e-03 eta: 0:48:04 time: 0.3282 data_time: 0.0031 loss: 1.0401 03/10 10:36:04 - mmengine - INFO - Epoch(train) [77][400/626] lr: 1.0000e-03 eta: 0:47:29 time: 0.3009 data_time: 0.0033 loss: 1.0826 03/10 10:36:11 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:36:35 - mmengine - INFO - Epoch(train) [77][500/626] lr: 1.0000e-03 eta: 0:46:55 time: 0.2853 data_time: 0.0032 loss: 1.0393 03/10 10:37:06 - mmengine - INFO - Epoch(train) [77][600/626] lr: 1.0000e-03 eta: 0:46:20 time: 0.3400 data_time: 0.0031 loss: 0.9776 03/10 10:37:14 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:37:14 - mmengine - INFO - Saving checkpoint at 77 epochs 03/10 10:37:34 - mmengine - INFO - Epoch(val) [77][98/98] accuracy/top1: 72.6020 accuracy/top5: 90.9120 03/10 10:38:07 - mmengine - INFO - Epoch(train) [78][100/626] lr: 1.0000e-03 eta: 0:45:37 time: 0.3173 data_time: 0.0036 loss: 1.0050 03/10 10:38:38 - mmengine - INFO - Epoch(train) [78][200/626] lr: 1.0000e-03 eta: 0:45:02 time: 0.2972 data_time: 0.0032 loss: 1.1502 03/10 10:39:09 - mmengine - INFO - Epoch(train) [78][300/626] lr: 1.0000e-03 eta: 0:44:28 time: 0.3024 data_time: 0.0032 loss: 1.0675 03/10 10:39:39 - mmengine - INFO - Epoch(train) [78][400/626] lr: 1.0000e-03 eta: 0:43:53 time: 0.3172 data_time: 0.0034 loss: 1.0485 03/10 10:40:10 - mmengine - INFO - Epoch(train) [78][500/626] lr: 1.0000e-03 eta: 0:43:18 time: 0.3210 data_time: 0.0032 loss: 1.0889 03/10 10:40:41 - mmengine - INFO - Epoch(train) [78][600/626] lr: 1.0000e-03 eta: 0:42:44 time: 0.3361 data_time: 0.0031 loss: 1.0424 03/10 10:40:49 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:40:49 - mmengine - INFO - Saving checkpoint at 78 epochs 03/10 10:41:09 - mmengine - INFO - Epoch(val) [78][98/98] accuracy/top1: 72.5540 accuracy/top5: 90.8400 03/10 10:41:42 - mmengine - INFO - Epoch(train) [79][100/626] lr: 1.0000e-03 eta: 0:42:01 time: 0.3128 data_time: 0.0033 loss: 1.0279 03/10 10:42:04 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:42:13 - mmengine - INFO - Epoch(train) [79][200/626] lr: 1.0000e-03 eta: 0:41:26 time: 0.3530 data_time: 0.0029 loss: 1.0964 03/10 10:42:44 - mmengine - INFO - Epoch(train) [79][300/626] lr: 1.0000e-03 eta: 0:40:52 time: 0.3001 data_time: 0.0032 loss: 1.0219 03/10 10:43:14 - mmengine - INFO - Epoch(train) [79][400/626] lr: 1.0000e-03 eta: 0:40:17 time: 0.3082 data_time: 0.0034 loss: 1.0449 03/10 10:43:45 - mmengine - INFO - Epoch(train) [79][500/626] lr: 1.0000e-03 eta: 0:39:43 time: 0.3296 data_time: 0.0040 loss: 1.0289 03/10 10:44:16 - mmengine - INFO - Epoch(train) [79][600/626] lr: 1.0000e-03 eta: 0:39:08 time: 0.3110 data_time: 0.0032 loss: 1.0323 03/10 10:44:23 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:44:24 - mmengine - INFO - Saving checkpoint at 79 epochs 03/10 10:44:44 - mmengine - INFO - Epoch(val) [79][98/98] accuracy/top1: 72.5620 accuracy/top5: 90.9320 03/10 10:45:18 - mmengine - INFO - Epoch(train) [80][100/626] lr: 1.0000e-03 eta: 0:38:25 time: 0.3002 data_time: 0.0035 loss: 0.9798 03/10 10:45:48 - mmengine - INFO - Epoch(train) [80][200/626] lr: 1.0000e-03 eta: 0:37:51 time: 0.3000 data_time: 0.0035 loss: 1.0996 03/10 10:46:19 - mmengine - INFO - Epoch(train) [80][300/626] lr: 1.0000e-03 eta: 0:37:17 time: 0.2914 data_time: 0.0039 loss: 1.0331 03/10 10:46:50 - mmengine - INFO - Epoch(train) [80][400/626] lr: 1.0000e-03 eta: 0:36:42 time: 0.3115 data_time: 0.0031 loss: 1.0739 03/10 10:47:21 - mmengine - INFO - Epoch(train) [80][500/626] lr: 1.0000e-03 eta: 0:36:08 time: 0.3101 data_time: 0.0040 loss: 1.0206 03/10 10:47:35 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:47:51 - mmengine - INFO - Epoch(train) [80][600/626] lr: 1.0000e-03 eta: 0:35:33 time: 0.2899 data_time: 0.0033 loss: 1.0172 03/10 10:47:59 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:47:59 - mmengine - INFO - Saving checkpoint at 80 epochs 03/10 10:48:20 - mmengine - INFO - Epoch(val) [80][98/98] accuracy/top1: 72.4640 accuracy/top5: 90.8340 03/10 10:48:54 - mmengine - INFO - Epoch(train) [81][100/626] lr: 1.0000e-03 eta: 0:34:50 time: 0.3056 data_time: 0.0035 loss: 1.0379 03/10 10:49:25 - mmengine - INFO - Epoch(train) [81][200/626] lr: 1.0000e-03 eta: 0:34:16 time: 0.2946 data_time: 0.0034 loss: 1.0434 03/10 10:49:56 - mmengine - INFO - Epoch(train) [81][300/626] lr: 1.0000e-03 eta: 0:33:42 time: 0.3057 data_time: 0.0032 loss: 1.0399 03/10 10:50:26 - mmengine - INFO - Epoch(train) [81][400/626] lr: 1.0000e-03 eta: 0:33:07 time: 0.2684 data_time: 0.0031 loss: 1.0436 03/10 10:50:57 - mmengine - INFO - Epoch(train) [81][500/626] lr: 1.0000e-03 eta: 0:32:33 time: 0.3408 data_time: 0.0030 loss: 1.0092 03/10 10:51:28 - mmengine - INFO - Epoch(train) [81][600/626] lr: 1.0000e-03 eta: 0:31:59 time: 0.3220 data_time: 0.0040 loss: 1.0379 03/10 10:51:35 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:51:36 - mmengine - INFO - Saving checkpoint at 81 epochs 03/10 10:51:57 - mmengine - INFO - Epoch(val) [81][98/98] accuracy/top1: 72.4940 accuracy/top5: 90.9340 03/10 10:52:30 - mmengine - INFO - Epoch(train) [82][100/626] lr: 1.0000e-03 eta: 0:31:16 time: 0.2834 data_time: 0.0038 loss: 1.0365 03/10 10:53:01 - mmengine - INFO - Epoch(train) [82][200/626] lr: 1.0000e-03 eta: 0:30:42 time: 0.2889 data_time: 0.0034 loss: 1.0261 03/10 10:53:30 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:53:32 - mmengine - INFO - Epoch(train) [82][300/626] lr: 1.0000e-03 eta: 0:30:08 time: 0.3264 data_time: 0.0032 loss: 1.0520 03/10 10:54:02 - mmengine - INFO - Epoch(train) [82][400/626] lr: 1.0000e-03 eta: 0:29:33 time: 0.2859 data_time: 0.0033 loss: 1.0439 03/10 10:54:33 - mmengine - INFO - Epoch(train) [82][500/626] lr: 1.0000e-03 eta: 0:28:59 time: 0.2858 data_time: 0.0035 loss: 1.0577 03/10 10:55:04 - mmengine - INFO - Epoch(train) [82][600/626] lr: 1.0000e-03 eta: 0:28:25 time: 0.3815 data_time: 0.0034 loss: 1.0271 03/10 10:55:11 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:55:12 - mmengine - INFO - Saving checkpoint at 82 epochs 03/10 10:55:32 - mmengine - INFO - Epoch(val) [82][98/98] accuracy/top1: 72.5140 accuracy/top5: 90.9520 03/10 10:56:05 - mmengine - INFO - Epoch(train) [83][100/626] lr: 1.0000e-03 eta: 0:27:42 time: 0.3065 data_time: 0.0030 loss: 1.0734 03/10 10:56:36 - mmengine - INFO - Epoch(train) [83][200/626] lr: 1.0000e-03 eta: 0:27:08 time: 0.2820 data_time: 0.0034 loss: 1.0414 03/10 10:57:07 - mmengine - INFO - Epoch(train) [83][300/626] lr: 1.0000e-03 eta: 0:26:34 time: 0.3266 data_time: 0.0033 loss: 1.0254 03/10 10:57:37 - mmengine - INFO - Epoch(train) [83][400/626] lr: 1.0000e-03 eta: 0:26:00 time: 0.3066 data_time: 0.0036 loss: 1.0634 03/10 10:58:08 - mmengine - INFO - Epoch(train) [83][500/626] lr: 1.0000e-03 eta: 0:25:25 time: 0.3098 data_time: 0.0034 loss: 0.9787 03/10 10:58:39 - mmengine - INFO - Epoch(train) [83][600/626] lr: 1.0000e-03 eta: 0:24:51 time: 0.3274 data_time: 0.0033 loss: 1.0399 03/10 10:58:46 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:58:46 - mmengine - INFO - Saving checkpoint at 83 epochs 03/10 10:59:07 - mmengine - INFO - Epoch(val) [83][98/98] accuracy/top1: 72.6200 accuracy/top5: 90.9140 03/10 10:59:22 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 10:59:40 - mmengine - INFO - Epoch(train) [84][100/626] lr: 1.0000e-03 eta: 0:24:09 time: 0.3047 data_time: 0.0031 loss: 1.0661 03/10 11:00:11 - mmengine - INFO - Epoch(train) [84][200/626] lr: 1.0000e-03 eta: 0:23:34 time: 0.3444 data_time: 0.0030 loss: 1.0149 03/10 11:00:42 - mmengine - INFO - Epoch(train) [84][300/626] lr: 1.0000e-03 eta: 0:23:00 time: 0.2988 data_time: 0.0031 loss: 1.0693 03/10 11:01:13 - mmengine - INFO - Epoch(train) [84][400/626] lr: 1.0000e-03 eta: 0:22:26 time: 0.3000 data_time: 0.0035 loss: 1.0405 03/10 11:01:44 - mmengine - INFO - Epoch(train) [84][500/626] lr: 1.0000e-03 eta: 0:21:52 time: 0.3075 data_time: 0.0032 loss: 1.0281 03/10 11:02:14 - mmengine - INFO - Epoch(train) [84][600/626] lr: 1.0000e-03 eta: 0:21:18 time: 0.3068 data_time: 0.0034 loss: 1.0875 03/10 11:02:22 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 11:02:22 - mmengine - INFO - Saving checkpoint at 84 epochs 03/10 11:02:43 - mmengine - INFO - Epoch(val) [84][98/98] accuracy/top1: 72.5240 accuracy/top5: 90.8920 03/10 11:03:17 - mmengine - INFO - Epoch(train) [85][100/626] lr: 1.0000e-03 eta: 0:20:36 time: 0.3166 data_time: 0.0033 loss: 1.0538 03/10 11:03:47 - mmengine - INFO - Epoch(train) [85][200/626] lr: 1.0000e-03 eta: 0:20:02 time: 0.2892 data_time: 0.0034 loss: 0.9778 03/10 11:04:18 - mmengine - INFO - Epoch(train) [85][300/626] lr: 1.0000e-03 eta: 0:19:28 time: 0.3041 data_time: 0.0035 loss: 1.1063 03/10 11:04:49 - mmengine - INFO - Epoch(train) [85][400/626] lr: 1.0000e-03 eta: 0:18:54 time: 0.3043 data_time: 0.0034 loss: 1.0765 03/10 11:04:54 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 11:05:20 - mmengine - INFO - Epoch(train) [85][500/626] lr: 1.0000e-03 eta: 0:18:20 time: 0.3235 data_time: 0.0322 loss: 1.0291 03/10 11:05:52 - mmengine - INFO - Epoch(train) [85][600/626] lr: 1.0000e-03 eta: 0:17:46 time: 0.3348 data_time: 0.0034 loss: 1.0436 03/10 11:05:59 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 11:05:59 - mmengine - INFO - Saving checkpoint at 85 epochs 03/10 11:06:20 - mmengine - INFO - Epoch(val) [85][98/98] accuracy/top1: 72.5700 accuracy/top5: 90.8720 03/10 11:06:54 - mmengine - INFO - Epoch(train) [86][100/626] lr: 1.0000e-03 eta: 0:17:03 time: 0.2849 data_time: 0.0035 loss: 1.1108 03/10 11:07:25 - mmengine - INFO - Epoch(train) [86][200/626] lr: 1.0000e-03 eta: 0:16:29 time: 0.3175 data_time: 0.0033 loss: 1.0754 03/10 11:07:56 - mmengine - INFO - Epoch(train) [86][300/626] lr: 1.0000e-03 eta: 0:15:55 time: 0.3444 data_time: 0.0030 loss: 1.0871 03/10 11:08:27 - mmengine - INFO - Epoch(train) [86][400/626] lr: 1.0000e-03 eta: 0:15:21 time: 0.2969 data_time: 0.0034 loss: 1.0484 03/10 11:08:58 - mmengine - INFO - Epoch(train) [86][500/626] lr: 1.0000e-03 eta: 0:14:47 time: 0.2897 data_time: 0.0031 loss: 1.0684 03/10 11:09:30 - mmengine - INFO - Epoch(train) [86][600/626] lr: 1.0000e-03 eta: 0:14:14 time: 0.3549 data_time: 0.0350 loss: 1.1003 03/10 11:09:38 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 11:09:38 - mmengine - INFO - Saving checkpoint at 86 epochs 03/10 11:09:59 - mmengine - INFO - Epoch(val) [86][98/98] accuracy/top1: 72.5200 accuracy/top5: 90.8900 03/10 11:10:32 - mmengine - INFO - Epoch(train) [87][100/626] lr: 1.0000e-03 eta: 0:13:31 time: 0.2892 data_time: 0.0035 loss: 1.0513 03/10 11:10:52 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 11:11:04 - mmengine - INFO - Epoch(train) [87][200/626] lr: 1.0000e-03 eta: 0:12:57 time: 0.3689 data_time: 0.0031 loss: 1.0050 03/10 11:11:35 - mmengine - INFO - Epoch(train) [87][300/626] lr: 1.0000e-03 eta: 0:12:23 time: 0.2703 data_time: 0.0034 loss: 1.0088 03/10 11:12:06 - mmengine - INFO - Epoch(train) [87][400/626] lr: 1.0000e-03 eta: 0:11:49 time: 0.3046 data_time: 0.0032 loss: 1.0313 03/10 11:12:37 - mmengine - INFO - Epoch(train) [87][500/626] lr: 1.0000e-03 eta: 0:11:16 time: 0.3222 data_time: 0.0034 loss: 1.0631 03/10 11:13:09 - mmengine - INFO - Epoch(train) [87][600/626] lr: 1.0000e-03 eta: 0:10:42 time: 0.3283 data_time: 0.0036 loss: 1.0216 03/10 11:13:16 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 11:13:17 - mmengine - INFO - Saving checkpoint at 87 epochs 03/10 11:13:37 - mmengine - INFO - Epoch(val) [87][98/98] accuracy/top1: 72.5340 accuracy/top5: 90.9380 03/10 11:14:10 - mmengine - INFO - Epoch(train) [88][100/626] lr: 1.0000e-03 eta: 0:09:59 time: 0.2882 data_time: 0.0077 loss: 1.0114 03/10 11:14:41 - mmengine - INFO - Epoch(train) [88][200/626] lr: 1.0000e-03 eta: 0:09:25 time: 0.2906 data_time: 0.0034 loss: 1.0295 03/10 11:15:12 - mmengine - INFO - Epoch(train) [88][300/626] lr: 1.0000e-03 eta: 0:08:52 time: 0.3600 data_time: 0.0034 loss: 1.0168 03/10 11:15:43 - mmengine - INFO - Epoch(train) [88][400/626] lr: 1.0000e-03 eta: 0:08:18 time: 0.3379 data_time: 0.0034 loss: 1.0400 03/10 11:16:14 - mmengine - INFO - Epoch(train) [88][500/626] lr: 1.0000e-03 eta: 0:07:44 time: 0.3272 data_time: 0.0364 loss: 1.0742 03/10 11:16:26 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 11:16:46 - mmengine - INFO - Epoch(train) [88][600/626] lr: 1.0000e-03 eta: 0:07:10 time: 0.3385 data_time: 0.0033 loss: 0.9902 03/10 11:16:53 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 11:16:53 - mmengine - INFO - Saving checkpoint at 88 epochs 03/10 11:17:13 - mmengine - INFO - Epoch(val) [88][98/98] accuracy/top1: 72.5720 accuracy/top5: 90.9060 03/10 11:17:48 - mmengine - INFO - Epoch(train) [89][100/626] lr: 1.0000e-03 eta: 0:06:28 time: 0.3192 data_time: 0.0032 loss: 1.0300 03/10 11:18:19 - mmengine - INFO - Epoch(train) [89][200/626] lr: 1.0000e-03 eta: 0:05:54 time: 0.3232 data_time: 0.0455 loss: 1.0248 03/10 11:18:50 - mmengine - INFO - Epoch(train) [89][300/626] lr: 1.0000e-03 eta: 0:05:20 time: 0.3225 data_time: 0.0035 loss: 1.0000 03/10 11:19:22 - mmengine - INFO - Epoch(train) [89][400/626] lr: 1.0000e-03 eta: 0:04:47 time: 0.3214 data_time: 0.0033 loss: 1.0458 03/10 11:19:53 - mmengine - INFO - Epoch(train) [89][500/626] lr: 1.0000e-03 eta: 0:04:13 time: 0.3022 data_time: 0.0032 loss: 1.0397 03/10 11:20:25 - mmengine - INFO - Epoch(train) [89][600/626] lr: 1.0000e-03 eta: 0:03:39 time: 0.3178 data_time: 0.0034 loss: 1.0073 03/10 11:20:32 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 11:20:32 - mmengine - INFO - Saving checkpoint at 89 epochs 03/10 11:20:53 - mmengine - INFO - Epoch(val) [89][98/98] accuracy/top1: 72.5900 accuracy/top5: 90.9040 03/10 11:21:27 - mmengine - INFO - Epoch(train) [90][100/626] lr: 1.0000e-03 eta: 0:02:57 time: 0.2939 data_time: 0.0033 loss: 1.0450 03/10 11:21:58 - mmengine - INFO - Epoch(train) [90][200/626] lr: 1.0000e-03 eta: 0:02:23 time: 0.2789 data_time: 0.0037 loss: 1.0501 03/10 11:22:25 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 11:22:29 - mmengine - INFO - Epoch(train) [90][300/626] lr: 1.0000e-03 eta: 0:01:49 time: 0.3097 data_time: 0.0160 loss: 1.0601 03/10 11:23:00 - mmengine - INFO - Epoch(train) [90][400/626] lr: 1.0000e-03 eta: 0:01:16 time: 0.3214 data_time: 0.0033 loss: 1.0282 03/10 11:23:32 - mmengine - INFO - Epoch(train) [90][500/626] lr: 1.0000e-03 eta: 0:00:42 time: 0.3230 data_time: 0.0031 loss: 1.0216 03/10 11:24:03 - mmengine - INFO - Epoch(train) [90][600/626] lr: 1.0000e-03 eta: 0:00:08 time: 0.3152 data_time: 0.0032 loss: 1.0606 03/10 11:24:11 - mmengine - INFO - Exp name: densenet121_4xb256_in1k_20230310_052033 03/10 11:24:11 - mmengine - INFO - Saving checkpoint at 90 epochs 03/10 11:24:32 - mmengine - INFO - Epoch(val) [90][98/98] accuracy/top1: 72.5380 accuracy/top5: 90.8560