2023/03/16 16:01:39 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.7.5 | packaged by conda-forge | (default, Jan 28 2021, 18:23:46) [GCC 9.3.0] CUDA available: False numpy_random_seed: 661296260 GCC: gcc (GCC) 7.3.0 PyTorch: 1.8.0a0+56b43f4 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: NO AVX - 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_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 -DMISSING_ARM_VST1 -DMISSING_ARM_VLD1 -Wno-stringop-overflow, LAPACK_INFO=generic, TORCH_VERSION=1.8.1, 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=OFF, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.14.0 OpenCV: 4.6.0 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 ------------------------------------------------------------ 2023/03/16 16:01:39 - mmengine - INFO - Config: model = dict( type='ImageClassifier', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(3, ), style='pytorch'), neck=dict(type='GlobalAveragePooling'), head=dict( type='LinearClsHead', num_classes=1000, in_channels=2048, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=(1, 5))) dataset_type = 'ImageNet' data_preprocessor = dict( num_classes=1000, mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', scale=224), 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=32, 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=32, 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=32, 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=100, val_interval=1) val_cfg = dict() test_cfg = dict() auto_scale_lr = dict(base_batch_size=256) default_scope = 'mmcls' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=100), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='VisualizationHook', enable=False)) env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='ClsVisualizer', vis_backends=[dict(type='LocalVisBackend')]) log_level = 'INFO' load_from = None resume = False randomness = dict(seed=None, deterministic=False) launcher = 'pytorch' work_dir = './work_dirs/resnet50_8xb32_in1k' 2023/03/16 16:01:39 - mmengine - WARNING - The "visualizer" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 2023/03/16 16:01:39 - mmengine - WARNING - The "vis_backend" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 2023/03/16 16:01:40 - mmengine - WARNING - The "model" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 2023/03/16 16:01:46 - mmengine - WARNING - The "hook" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 2023/03/16 16:01:46 - 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 -------------------- 2023/03/16 16:01:46 - mmengine - WARNING - The "dataset" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 2023/03/16 16:01:46 - mmengine - WARNING - The "transform" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 2023/03/16 16:02:00 - mmengine - WARNING - The "data sampler" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 2023/03/16 16:02:00 - mmengine - WARNING - The "optimizer wrapper constructor" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 2023/03/16 16:02:00 - mmengine - WARNING - The "optimizer" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 2023/03/16 16:02:00 - mmengine - WARNING - The "optimizer_wrapper" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 2023/03/16 16:02:00 - mmengine - WARNING - The "parameter scheduler" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 2023/03/16 16:02:01 - mmengine - WARNING - The "metric" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. 2023/03/16 16:02:02 - mmengine - WARNING - The "weight initializer" registry in mmcls did not set import location. Fallback to call `mmcls.utils.register_all_modules` instead. Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): Initialized by user-defined `init_weights` in ResNet backbone.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.bn2.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.bn3.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.bn3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.downsample.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.downsample.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.1.bn2.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.bn3.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.bn3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.2.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.2.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.2.bn2.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.2.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.2.bn3.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.2.bn3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.bn3.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.bn3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.downsample.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.downsample.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.bn3.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.bn3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.2.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.2.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.2.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.2.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.2.bn3.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.2.bn3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.3.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.3.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.3.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.3.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.3.bn3.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.3.bn3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.bn3.weight - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.bn3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.downsample.1.weight - torch.Size([1024]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.downsample.1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): ConstantInit: val=1, bias=0 backbone.layer3.1.bn3.weight - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.bn3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.2.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.2.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.2.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.2.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.2.bn3.weight - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.2.bn3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): ConstantInit: val=1, bias=0 backbone.layer3.3.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.3.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): ConstantInit: val=1, bias=0 backbone.layer3.3.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.3.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.3.bn3.weight - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.3.bn3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.4.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.4.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.4.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.4.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.4.bn3.weight - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.4.bn3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.5.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.5.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.5.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.5.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): ConstantInit: val=1, bias=0 backbone.layer3.5.bn3.weight - torch.Size([1024]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.5.bn3.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.bn3.weight - torch.Size([2048]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.bn3.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.downsample.1.weight - torch.Size([2048]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.downsample.1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.bn3.weight - torch.Size([2048]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.bn3.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.2.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.2.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.2.bn2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.2.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.2.bn3.weight - torch.Size([2048]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.2.bn3.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of ImageClassifier head.fc.weight - torch.Size([1000, 2048]): NormalInit: mean=0, std=0.01, bias=0 head.fc.bias - torch.Size([1000]): NormalInit: mean=0, std=0.01, bias=0 2023/03/16 16:02:09 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/03/16 16:02:09 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/03/16 16:02:09 - mmengine - INFO - Checkpoints will be saved to /home/openmmlab/mmcv_2x/mmclassification/work_dirs/resnet50_8xb32_in1k. 2023/03/16 16:03:12 - mmengine - INFO - Epoch(train) [1][ 100/5005] lr: 1.0000e-01 eta: 3 days, 14:27:00 time: 0.1845 data_time: 0.0019 loss: 6.6941 2023/03/16 16:03:30 - mmengine - INFO - Epoch(train) [1][ 200/5005] lr: 1.0000e-01 eta: 2 days, 7:55:15 time: 0.1823 data_time: 0.0020 loss: 6.5471 2023/03/16 16:03:48 - mmengine - INFO - Epoch(train) [1][ 300/5005] lr: 1.0000e-01 eta: 1 day, 21:52:12 time: 0.1925 data_time: 0.0020 loss: 6.3962 2023/03/16 16:04:07 - mmengine - INFO - Epoch(train) [1][ 400/5005] lr: 1.0000e-01 eta: 1 day, 16:52:06 time: 0.1943 data_time: 0.0019 loss: 6.2660 2023/03/16 16:04:26 - mmengine - INFO - Epoch(train) [1][ 500/5005] lr: 1.0000e-01 eta: 1 day, 14:01:55 time: 0.1961 data_time: 0.0018 loss: 6.1639 2023/03/16 16:04:47 - mmengine - INFO - Epoch(train) [1][ 600/5005] lr: 1.0000e-01 eta: 1 day, 12:27:08 time: 0.1867 data_time: 0.0018 loss: 6.1147 2023/03/16 16:05:07 - mmengine - INFO - Epoch(train) [1][ 700/5005] lr: 1.0000e-01 eta: 1 day, 11:08:34 time: 0.1878 data_time: 0.0016 loss: 5.9809 2023/03/16 16:05:25 - mmengine - INFO - Epoch(train) [1][ 800/5005] lr: 1.0000e-01 eta: 1 day, 9:57:50 time: 0.1802 data_time: 0.0020 loss: 5.9426 2023/03/16 16:05:44 - mmengine - INFO - Epoch(train) [1][ 900/5005] lr: 1.0000e-01 eta: 1 day, 9:03:30 time: 0.1893 data_time: 0.0020 loss: 5.7548 2023/03/16 16:06:03 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:06:03 - mmengine - INFO - Epoch(train) [1][1000/5005] lr: 1.0000e-01 eta: 1 day, 8:21:25 time: 0.1877 data_time: 0.0021 loss: 5.9554 2023/03/16 16:06:22 - mmengine - INFO - Epoch(train) [1][1100/5005] lr: 1.0000e-01 eta: 1 day, 7:48:55 time: 0.1968 data_time: 0.0016 loss: 5.9006 2023/03/16 16:06:41 - mmengine - INFO - Epoch(train) [1][1200/5005] lr: 1.0000e-01 eta: 1 day, 7:20:46 time: 0.1875 data_time: 0.0020 loss: 5.6663 2023/03/16 16:07:00 - mmengine - INFO - Epoch(train) [1][1300/5005] lr: 1.0000e-01 eta: 1 day, 7:01:25 time: 0.1914 data_time: 0.0022 loss: 5.6346 2023/03/16 16:07:19 - mmengine - INFO - Epoch(train) [1][1400/5005] lr: 1.0000e-01 eta: 1 day, 6:38:02 time: 0.1861 data_time: 0.0020 loss: 5.4721 2023/03/16 16:07:38 - mmengine - INFO - Epoch(train) [1][1500/5005] lr: 1.0000e-01 eta: 1 day, 6:23:38 time: 0.1949 data_time: 0.0020 loss: 5.4835 2023/03/16 16:07:59 - mmengine - INFO - Epoch(train) [1][1600/5005] lr: 1.0000e-01 eta: 1 day, 6:17:33 time: 0.2121 data_time: 0.0019 loss: 5.5242 2023/03/16 16:08:19 - mmengine - INFO - Epoch(train) [1][1700/5005] lr: 1.0000e-01 eta: 1 day, 6:09:08 time: 0.2073 data_time: 0.0020 loss: 5.4778 2023/03/16 16:08:39 - mmengine - INFO - Epoch(train) [1][1800/5005] lr: 1.0000e-01 eta: 1 day, 5:59:43 time: 0.1807 data_time: 0.0018 loss: 5.4842 2023/03/16 16:08:58 - mmengine - INFO - Epoch(train) [1][1900/5005] lr: 1.0000e-01 eta: 1 day, 5:47:12 time: 0.1874 data_time: 0.0018 loss: 5.2431 2023/03/16 16:09:18 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:09:18 - mmengine - INFO - Epoch(train) [1][2000/5005] lr: 1.0000e-01 eta: 1 day, 5:40:44 time: 0.2153 data_time: 0.0018 loss: 5.1759 2023/03/16 16:09:37 - mmengine - INFO - Epoch(train) [1][2100/5005] lr: 1.0000e-01 eta: 1 day, 5:30:28 time: 0.1944 data_time: 0.0017 loss: 5.1463 2023/03/16 16:09:56 - mmengine - INFO - Epoch(train) [1][2200/5005] lr: 1.0000e-01 eta: 1 day, 5:21:14 time: 0.1856 data_time: 0.0021 loss: 5.2168 2023/03/16 16:10:15 - mmengine - INFO - Epoch(train) [1][2300/5005] lr: 1.0000e-01 eta: 1 day, 5:13:58 time: 0.2162 data_time: 0.0022 loss: 5.0694 2023/03/16 16:10:35 - mmengine - INFO - Epoch(train) [1][2400/5005] lr: 1.0000e-01 eta: 1 day, 5:09:41 time: 0.1953 data_time: 0.0020 loss: 5.0263 2023/03/16 16:10:55 - mmengine - INFO - Epoch(train) [1][2500/5005] lr: 1.0000e-01 eta: 1 day, 5:04:04 time: 0.1944 data_time: 0.0023 loss: 4.9511 2023/03/16 16:11:14 - mmengine - INFO - Epoch(train) [1][2600/5005] lr: 1.0000e-01 eta: 1 day, 4:56:49 time: 0.1807 data_time: 0.0022 loss: 4.9117 2023/03/16 16:11:33 - mmengine - INFO - Epoch(train) [1][2700/5005] lr: 1.0000e-01 eta: 1 day, 4:51:52 time: 0.1963 data_time: 0.0021 loss: 4.8572 2023/03/16 16:11:53 - mmengine - INFO - Epoch(train) [1][2800/5005] lr: 1.0000e-01 eta: 1 day, 4:47:29 time: 0.1947 data_time: 0.0018 loss: 5.0217 2023/03/16 16:12:12 - mmengine - INFO - Epoch(train) [1][2900/5005] lr: 1.0000e-01 eta: 1 day, 4:43:15 time: 0.1920 data_time: 0.0020 loss: 4.8694 2023/03/16 16:12:32 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:12:32 - mmengine - INFO - Epoch(train) [1][3000/5005] lr: 1.0000e-01 eta: 1 day, 4:41:10 time: 0.2014 data_time: 0.0019 loss: 4.6567 2023/03/16 16:12:52 - mmengine - INFO - Epoch(train) [1][3100/5005] lr: 1.0000e-01 eta: 1 day, 4:38:38 time: 0.2024 data_time: 0.0016 loss: 4.9679 2023/03/16 16:13:11 - mmengine - INFO - Epoch(train) [1][3200/5005] lr: 1.0000e-01 eta: 1 day, 4:34:08 time: 0.1887 data_time: 0.0020 loss: 4.7730 2023/03/16 16:13:30 - mmengine - INFO - Epoch(train) [1][3300/5005] lr: 1.0000e-01 eta: 1 day, 4:29:01 time: 0.1882 data_time: 0.0021 loss: 4.7025 2023/03/16 16:13:49 - mmengine - INFO - Epoch(train) [1][3400/5005] lr: 1.0000e-01 eta: 1 day, 4:23:06 time: 0.1890 data_time: 0.0019 loss: 4.7957 2023/03/16 16:14:08 - mmengine - INFO - Epoch(train) [1][3500/5005] lr: 1.0000e-01 eta: 1 day, 4:19:32 time: 0.1793 data_time: 0.0023 loss: 4.5906 2023/03/16 16:14:27 - mmengine - INFO - Epoch(train) [1][3600/5005] lr: 1.0000e-01 eta: 1 day, 4:16:31 time: 0.2202 data_time: 0.0020 loss: 4.4831 2023/03/16 16:14:47 - mmengine - INFO - Epoch(train) [1][3700/5005] lr: 1.0000e-01 eta: 1 day, 4:14:08 time: 0.1892 data_time: 0.0022 loss: 4.2743 2023/03/16 16:15:05 - mmengine - INFO - Epoch(train) [1][3800/5005] lr: 1.0000e-01 eta: 1 day, 4:09:50 time: 0.1912 data_time: 0.0021 loss: 4.3841 2023/03/16 16:15:25 - mmengine - INFO - Epoch(train) [1][3900/5005] lr: 1.0000e-01 eta: 1 day, 4:07:47 time: 0.1886 data_time: 0.0021 loss: 4.7201 2023/03/16 16:15:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:15:45 - mmengine - INFO - Epoch(train) [1][4000/5005] lr: 1.0000e-01 eta: 1 day, 4:06:48 time: 0.1969 data_time: 0.0020 loss: 4.4908 2023/03/16 16:16:04 - mmengine - INFO - Epoch(train) [1][4100/5005] lr: 1.0000e-01 eta: 1 day, 4:03:29 time: 0.1922 data_time: 0.0021 loss: 4.2402 2023/03/16 16:16:24 - mmengine - INFO - Epoch(train) [1][4200/5005] lr: 1.0000e-01 eta: 1 day, 4:02:49 time: 0.2026 data_time: 0.0020 loss: 4.3081 2023/03/16 16:16:43 - mmengine - INFO - Epoch(train) [1][4300/5005] lr: 1.0000e-01 eta: 1 day, 4:00:03 time: 0.1939 data_time: 0.0021 loss: 4.2656 2023/03/16 16:17:02 - mmengine - INFO - Epoch(train) [1][4400/5005] lr: 1.0000e-01 eta: 1 day, 3:57:42 time: 0.1849 data_time: 0.0023 loss: 4.4683 2023/03/16 16:17:22 - mmengine - INFO - Epoch(train) [1][4500/5005] lr: 1.0000e-01 eta: 1 day, 3:55:09 time: 0.1993 data_time: 0.0022 loss: 4.2615 2023/03/16 16:17:42 - mmengine - INFO - Epoch(train) [1][4600/5005] lr: 1.0000e-01 eta: 1 day, 3:54:16 time: 0.1971 data_time: 0.0019 loss: 4.2208 2023/03/16 16:18:02 - mmengine - INFO - Epoch(train) [1][4700/5005] lr: 1.0000e-01 eta: 1 day, 3:53:35 time: 0.2017 data_time: 0.0017 loss: 4.3809 2023/03/16 16:18:21 - mmengine - INFO - Epoch(train) [1][4800/5005] lr: 1.0000e-01 eta: 1 day, 3:52:17 time: 0.2156 data_time: 0.0014 loss: 4.3678 2023/03/16 16:18:41 - mmengine - INFO - Epoch(train) [1][4900/5005] lr: 1.0000e-01 eta: 1 day, 3:50:40 time: 0.1914 data_time: 0.0022 loss: 4.1435 2023/03/16 16:19:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:19:00 - mmengine - INFO - Epoch(train) [1][5000/5005] lr: 1.0000e-01 eta: 1 day, 3:49:18 time: 0.2047 data_time: 0.0030 loss: 4.1222 2023/03/16 16:19:14 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:19:14 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/03/16 16:19:27 - mmengine - INFO - Epoch(val) [1][100/196] eta: 0:00:11 time: 0.0488 data_time: 0.0008 2023/03/16 16:19:55 - mmengine - INFO - Epoch(val) [1][196/196] accuracy/top1: 19.8820 accuracy/top5: 42.4440data_time: 0.0004 time: 0.0453 2023/03/16 16:20:18 - mmengine - INFO - Epoch(train) [2][ 100/5005] lr: 1.0000e-01 eta: 1 day, 4:14:58 time: 0.1902 data_time: 0.0020 loss: 4.1842 2023/03/16 16:20:37 - mmengine - INFO - Epoch(train) [2][ 200/5005] lr: 1.0000e-01 eta: 1 day, 4:12:17 time: 0.1932 data_time: 0.0020 loss: 4.0754 2023/03/16 16:20:57 - mmengine - INFO - Epoch(train) [2][ 300/5005] lr: 1.0000e-01 eta: 1 day, 4:10:30 time: 0.2380 data_time: 0.0019 loss: 4.2335 2023/03/16 16:21:19 - mmengine - INFO - Epoch(train) [2][ 400/5005] lr: 1.0000e-01 eta: 1 day, 4:12:36 time: 0.1840 data_time: 0.0022 loss: 4.1157 2023/03/16 16:21:39 - mmengine - INFO - Epoch(train) [2][ 500/5005] lr: 1.0000e-01 eta: 1 day, 4:10:51 time: 0.1761 data_time: 0.0021 loss: 4.1124 2023/03/16 16:21:56 - mmengine - INFO - Epoch(train) [2][ 600/5005] lr: 1.0000e-01 eta: 1 day, 4:05:59 time: 0.1746 data_time: 0.0023 loss: 3.8459 2023/03/16 16:22:14 - mmengine - INFO - Epoch(train) [2][ 700/5005] lr: 1.0000e-01 eta: 1 day, 4:01:45 time: 0.1971 data_time: 0.0019 loss: 4.2409 2023/03/16 16:22:33 - mmengine - INFO - Epoch(train) [2][ 800/5005] lr: 1.0000e-01 eta: 1 day, 4:00:19 time: 0.1871 data_time: 0.0018 loss: 3.9940 2023/03/16 16:22:55 - mmengine - INFO - Epoch(train) [2][ 900/5005] lr: 1.0000e-01 eta: 1 day, 4:01:42 time: 0.1844 data_time: 0.0019 loss: 3.8428 2023/03/16 16:23:12 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:23:14 - mmengine - INFO - Epoch(train) [2][1000/5005] lr: 1.0000e-01 eta: 1 day, 3:58:46 time: 0.2420 data_time: 0.0019 loss: 3.9675 2023/03/16 16:23:34 - mmengine - INFO - Epoch(train) [2][1100/5005] lr: 1.0000e-01 eta: 1 day, 3:58:44 time: 0.2338 data_time: 0.0018 loss: 3.9185 2023/03/16 16:23:53 - mmengine - INFO - Epoch(train) [2][1200/5005] lr: 1.0000e-01 eta: 1 day, 3:56:36 time: 0.1641 data_time: 0.0019 loss: 3.9776 2023/03/16 16:24:10 - mmengine - INFO - Epoch(train) [2][1300/5005] lr: 1.0000e-01 eta: 1 day, 3:52:06 time: 0.1735 data_time: 0.0019 loss: 3.9463 2023/03/16 16:24:28 - mmengine - INFO - Epoch(train) [2][1400/5005] lr: 1.0000e-01 eta: 1 day, 3:48:31 time: 0.1798 data_time: 0.0018 loss: 3.7394 2023/03/16 16:24:47 - mmengine - INFO - Epoch(train) [2][1500/5005] lr: 1.0000e-01 eta: 1 day, 3:46:09 time: 0.1778 data_time: 0.0020 loss: 3.9028 2023/03/16 16:25:05 - mmengine - INFO - Epoch(train) [2][1600/5005] lr: 1.0000e-01 eta: 1 day, 3:43:46 time: 0.1882 data_time: 0.0019 loss: 4.0119 2023/03/16 16:25:25 - mmengine - INFO - Epoch(train) [2][1700/5005] lr: 1.0000e-01 eta: 1 day, 3:42:47 time: 0.1945 data_time: 0.0019 loss: 3.8681 2023/03/16 16:25:46 - mmengine - INFO - Epoch(train) [2][1800/5005] lr: 1.0000e-01 eta: 1 day, 3:42:57 time: 0.1872 data_time: 0.0017 loss: 3.8029 2023/03/16 16:26:04 - mmengine - INFO - Epoch(train) [2][1900/5005] lr: 1.0000e-01 eta: 1 day, 3:40:30 time: 0.1836 data_time: 0.0019 loss: 3.5910 2023/03/16 16:26:23 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:26:24 - mmengine - INFO - Epoch(train) [2][2000/5005] lr: 1.0000e-01 eta: 1 day, 3:40:15 time: 0.1999 data_time: 0.0020 loss: 3.8112 2023/03/16 16:26:42 - mmengine - INFO - Epoch(train) [2][2100/5005] lr: 1.0000e-01 eta: 1 day, 3:36:56 time: 0.1764 data_time: 0.0018 loss: 3.6766 2023/03/16 16:27:00 - mmengine - INFO - Epoch(train) [2][2200/5005] lr: 1.0000e-01 eta: 1 day, 3:33:44 time: 0.1729 data_time: 0.0019 loss: 4.0416 2023/03/16 16:27:18 - mmengine - INFO - Epoch(train) [2][2300/5005] lr: 1.0000e-01 eta: 1 day, 3:31:06 time: 0.1792 data_time: 0.0019 loss: 3.5937 2023/03/16 16:27:37 - mmengine - INFO - Epoch(train) [2][2400/5005] lr: 1.0000e-01 eta: 1 day, 3:29:33 time: 0.1945 data_time: 0.0019 loss: 3.9306 2023/03/16 16:27:56 - mmengine - INFO - Epoch(train) [2][2500/5005] lr: 1.0000e-01 eta: 1 day, 3:28:49 time: 0.1856 data_time: 0.0019 loss: 3.6607 2023/03/16 16:28:15 - mmengine - INFO - Epoch(train) [2][2600/5005] lr: 1.0000e-01 eta: 1 day, 3:26:57 time: 0.1872 data_time: 0.0018 loss: 3.6459 2023/03/16 16:28:35 - mmengine - INFO - Epoch(train) [2][2700/5005] lr: 1.0000e-01 eta: 1 day, 3:26:22 time: 0.2402 data_time: 0.0019 loss: 3.5821 2023/03/16 16:28:53 - mmengine - INFO - Epoch(train) [2][2800/5005] lr: 1.0000e-01 eta: 1 day, 3:23:59 time: 0.1788 data_time: 0.0018 loss: 3.6790 2023/03/16 16:29:12 - mmengine - INFO - Epoch(train) [2][2900/5005] lr: 1.0000e-01 eta: 1 day, 3:22:02 time: 0.1808 data_time: 0.0019 loss: 3.8103 2023/03/16 16:29:35 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:29:36 - mmengine - INFO - Epoch(train) [2][3000/5005] lr: 1.0000e-01 eta: 1 day, 3:26:17 time: 0.2666 data_time: 0.0015 loss: 3.4722 2023/03/16 16:29:56 - mmengine - INFO - Epoch(train) [2][3100/5005] lr: 1.0000e-01 eta: 1 day, 3:25:50 time: 0.1879 data_time: 0.0019 loss: 3.6787 2023/03/16 16:30:14 - mmengine - INFO - Epoch(train) [2][3200/5005] lr: 1.0000e-01 eta: 1 day, 3:23:13 time: 0.1726 data_time: 0.0019 loss: 3.5767 2023/03/16 16:30:33 - mmengine - INFO - Epoch(train) [2][3300/5005] lr: 1.0000e-01 eta: 1 day, 3:21:54 time: 0.1766 data_time: 0.0020 loss: 3.4985 2023/03/16 16:30:52 - mmengine - INFO - Epoch(train) [2][3400/5005] lr: 1.0000e-01 eta: 1 day, 3:20:41 time: 0.1810 data_time: 0.0021 loss: 3.5823 2023/03/16 16:31:10 - mmengine - INFO - Epoch(train) [2][3500/5005] lr: 1.0000e-01 eta: 1 day, 3:18:36 time: 0.1915 data_time: 0.0018 loss: 3.5638 2023/03/16 16:31:29 - mmengine - INFO - Epoch(train) [2][3600/5005] lr: 1.0000e-01 eta: 1 day, 3:17:25 time: 0.1915 data_time: 0.0019 loss: 3.4304 2023/03/16 16:31:48 - mmengine - INFO - Epoch(train) [2][3700/5005] lr: 1.0000e-01 eta: 1 day, 3:16:03 time: 0.1878 data_time: 0.0019 loss: 3.5402 2023/03/16 16:32:07 - mmengine - INFO - Epoch(train) [2][3800/5005] lr: 1.0000e-01 eta: 1 day, 3:14:58 time: 0.2261 data_time: 0.0019 loss: 3.6032 2023/03/16 16:32:28 - mmengine - INFO - Epoch(train) [2][3900/5005] lr: 1.0000e-01 eta: 1 day, 3:15:30 time: 0.1926 data_time: 0.0019 loss: 3.5015 2023/03/16 16:32:47 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:32:48 - mmengine - INFO - Epoch(train) [2][4000/5005] lr: 1.0000e-01 eta: 1 day, 3:15:14 time: 0.2010 data_time: 0.0019 loss: 3.5052 2023/03/16 16:33:08 - mmengine - INFO - Epoch(train) [2][4100/5005] lr: 1.0000e-01 eta: 1 day, 3:14:59 time: 0.2225 data_time: 0.0018 loss: 3.3705 2023/03/16 16:33:29 - mmengine - INFO - Epoch(train) [2][4200/5005] lr: 1.0000e-01 eta: 1 day, 3:15:02 time: 0.1888 data_time: 0.0019 loss: 3.4228 2023/03/16 16:33:47 - mmengine - INFO - Epoch(train) [2][4300/5005] lr: 1.0000e-01 eta: 1 day, 3:13:03 time: 0.1785 data_time: 0.0020 loss: 3.2428 2023/03/16 16:34:05 - mmengine - INFO - Epoch(train) [2][4400/5005] lr: 1.0000e-01 eta: 1 day, 3:11:13 time: 0.1843 data_time: 0.0020 loss: 3.4410 2023/03/16 16:34:23 - mmengine - INFO - Epoch(train) [2][4500/5005] lr: 1.0000e-01 eta: 1 day, 3:09:37 time: 0.1905 data_time: 0.0019 loss: 3.3400 2023/03/16 16:34:42 - mmengine - INFO - Epoch(train) [2][4600/5005] lr: 1.0000e-01 eta: 1 day, 3:08:15 time: 0.1811 data_time: 0.0020 loss: 3.1634 2023/03/16 16:35:01 - mmengine - INFO - Epoch(train) [2][4700/5005] lr: 1.0000e-01 eta: 1 day, 3:06:59 time: 0.1841 data_time: 0.0019 loss: 3.5766 2023/03/16 16:35:19 - mmengine - INFO - Epoch(train) [2][4800/5005] lr: 1.0000e-01 eta: 1 day, 3:04:54 time: 0.1762 data_time: 0.0019 loss: 3.4271 2023/03/16 16:35:37 - mmengine - INFO - Epoch(train) [2][4900/5005] lr: 1.0000e-01 eta: 1 day, 3:03:26 time: 0.1904 data_time: 0.0019 loss: 3.4015 2023/03/16 16:35:55 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:35:56 - mmengine - INFO - Epoch(train) [2][5000/5005] lr: 1.0000e-01 eta: 1 day, 3:01:57 time: 0.1874 data_time: 0.0027 loss: 3.4495 2023/03/16 16:35:57 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:35:57 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/03/16 16:36:04 - mmengine - INFO - Epoch(val) [2][100/196] eta: 0:00:06 time: 0.0472 data_time: 0.0009 2023/03/16 16:36:29 - mmengine - INFO - Epoch(val) [2][196/196] accuracy/top1: 31.3780 accuracy/top5: 57.7860data_time: 0.0341 time: 0.0665 2023/03/16 16:36:50 - mmengine - INFO - Epoch(train) [3][ 100/5005] lr: 1.0000e-01 eta: 1 day, 3:02:38 time: 0.1762 data_time: 0.0019 loss: 3.5828 2023/03/16 16:37:07 - mmengine - INFO - Epoch(train) [3][ 200/5005] lr: 1.0000e-01 eta: 1 day, 2:59:54 time: 0.1663 data_time: 0.0017 loss: 3.2459 2023/03/16 16:37:25 - mmengine - INFO - Epoch(train) [3][ 300/5005] lr: 1.0000e-01 eta: 1 day, 2:57:55 time: 0.1802 data_time: 0.0020 loss: 3.2334 2023/03/16 16:37:43 - mmengine - INFO - Epoch(train) [3][ 400/5005] lr: 1.0000e-01 eta: 1 day, 2:56:43 time: 0.1849 data_time: 0.0019 loss: 3.3664 2023/03/16 16:38:01 - mmengine - INFO - Epoch(train) [3][ 500/5005] lr: 1.0000e-01 eta: 1 day, 2:54:55 time: 0.1761 data_time: 0.0020 loss: 3.2607 2023/03/16 16:38:19 - mmengine - INFO - Epoch(train) [3][ 600/5005] lr: 1.0000e-01 eta: 1 day, 2:53:02 time: 0.1661 data_time: 0.0019 loss: 3.0241 2023/03/16 16:38:37 - mmengine - INFO - Epoch(train) [3][ 700/5005] lr: 1.0000e-01 eta: 1 day, 2:51:17 time: 0.2067 data_time: 0.0019 loss: 3.1951 2023/03/16 16:39:00 - mmengine - INFO - Epoch(train) [3][ 800/5005] lr: 1.0000e-01 eta: 1 day, 2:53:22 time: 0.1909 data_time: 0.0020 loss: 3.1863 2023/03/16 16:39:18 - mmengine - INFO - Epoch(train) [3][ 900/5005] lr: 1.0000e-01 eta: 1 day, 2:52:00 time: 0.1803 data_time: 0.0018 loss: 3.3390 2023/03/16 16:39:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:39:36 - mmengine - INFO - Epoch(train) [3][1000/5005] lr: 1.0000e-01 eta: 1 day, 2:50:29 time: 0.1834 data_time: 0.0020 loss: 3.2262 2023/03/16 16:39:54 - mmengine - INFO - Epoch(train) [3][1100/5005] lr: 1.0000e-01 eta: 1 day, 2:48:42 time: 0.1820 data_time: 0.0020 loss: 3.1539 2023/03/16 16:40:12 - mmengine - INFO - Epoch(train) [3][1200/5005] lr: 1.0000e-01 eta: 1 day, 2:47:12 time: 0.1894 data_time: 0.0019 loss: 3.3455 2023/03/16 16:40:31 - mmengine - INFO - Epoch(train) [3][1300/5005] lr: 1.0000e-01 eta: 1 day, 2:45:53 time: 0.1850 data_time: 0.0020 loss: 3.3655 2023/03/16 16:40:50 - mmengine - INFO - Epoch(train) [3][1400/5005] lr: 1.0000e-01 eta: 1 day, 2:45:41 time: 0.2307 data_time: 0.0014 loss: 3.3243 2023/03/16 16:41:13 - mmengine - INFO - Epoch(train) [3][1500/5005] lr: 1.0000e-01 eta: 1 day, 2:47:31 time: 0.2320 data_time: 0.0019 loss: 2.9645 2023/03/16 16:41:33 - mmengine - INFO - Epoch(train) [3][1600/5005] lr: 1.0000e-01 eta: 1 day, 2:47:03 time: 0.1871 data_time: 0.0023 loss: 3.1462 2023/03/16 16:41:53 - mmengine - INFO - Epoch(train) [3][1700/5005] lr: 1.0000e-01 eta: 1 day, 2:47:13 time: 0.1849 data_time: 0.0019 loss: 3.3637 2023/03/16 16:42:12 - mmengine - INFO - Epoch(train) [3][1800/5005] lr: 1.0000e-01 eta: 1 day, 2:46:20 time: 0.1738 data_time: 0.0018 loss: 3.2115 2023/03/16 16:42:30 - mmengine - INFO - Epoch(train) [3][1900/5005] lr: 1.0000e-01 eta: 1 day, 2:44:38 time: 0.1753 data_time: 0.0019 loss: 3.2642 2023/03/16 16:42:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:42:48 - mmengine - INFO - Epoch(train) [3][2000/5005] lr: 1.0000e-01 eta: 1 day, 2:43:04 time: 0.1780 data_time: 0.0021 loss: 3.1304 2023/03/16 16:43:06 - mmengine - INFO - Epoch(train) [3][2100/5005] lr: 1.0000e-01 eta: 1 day, 2:41:46 time: 0.1862 data_time: 0.0020 loss: 2.9377 2023/03/16 16:43:24 - mmengine - INFO - Epoch(train) [3][2200/5005] lr: 1.0000e-01 eta: 1 day, 2:40:19 time: 0.1746 data_time: 0.0020 loss: 3.3038 2023/03/16 16:43:43 - mmengine - INFO - Epoch(train) [3][2300/5005] lr: 1.0000e-01 eta: 1 day, 2:39:21 time: 0.1912 data_time: 0.0018 loss: 3.2113 2023/03/16 16:44:00 - mmengine - INFO - Epoch(train) [3][2400/5005] lr: 1.0000e-01 eta: 1 day, 2:37:46 time: 0.1706 data_time: 0.0023 loss: 3.0450 2023/03/16 16:44:18 - mmengine - INFO - Epoch(train) [3][2500/5005] lr: 1.0000e-01 eta: 1 day, 2:35:55 time: 0.1801 data_time: 0.0020 loss: 3.2028 2023/03/16 16:44:35 - mmengine - INFO - Epoch(train) [3][2600/5005] lr: 1.0000e-01 eta: 1 day, 2:34:13 time: 0.1725 data_time: 0.0018 loss: 3.0938 2023/03/16 16:44:53 - mmengine - INFO - Epoch(train) [3][2700/5005] lr: 1.0000e-01 eta: 1 day, 2:32:57 time: 0.1718 data_time: 0.0018 loss: 3.1608 2023/03/16 16:45:10 - mmengine - INFO - Epoch(train) [3][2800/5005] lr: 1.0000e-01 eta: 1 day, 2:30:56 time: 0.1674 data_time: 0.0018 loss: 3.0504 2023/03/16 16:45:27 - mmengine - INFO - Epoch(train) [3][2900/5005] lr: 1.0000e-01 eta: 1 day, 2:28:59 time: 0.1814 data_time: 0.0019 loss: 2.9081 2023/03/16 16:45:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:45:45 - mmengine - INFO - Epoch(train) [3][3000/5005] lr: 1.0000e-01 eta: 1 day, 2:27:11 time: 0.1720 data_time: 0.0018 loss: 3.0660 2023/03/16 16:46:02 - mmengine - INFO - Epoch(train) [3][3100/5005] lr: 1.0000e-01 eta: 1 day, 2:25:22 time: 0.1632 data_time: 0.0020 loss: 3.1220 2023/03/16 16:46:19 - mmengine - INFO - Epoch(train) [3][3200/5005] lr: 1.0000e-01 eta: 1 day, 2:23:34 time: 0.1726 data_time: 0.0019 loss: 3.1907 2023/03/16 16:46:38 - mmengine - INFO - Epoch(train) [3][3300/5005] lr: 1.0000e-01 eta: 1 day, 2:22:49 time: 0.1866 data_time: 0.0019 loss: 3.0859 2023/03/16 16:46:57 - mmengine - INFO - Epoch(train) [3][3400/5005] lr: 1.0000e-01 eta: 1 day, 2:22:14 time: 0.1810 data_time: 0.0018 loss: 3.2266 2023/03/16 16:47:17 - mmengine - INFO - Epoch(train) [3][3500/5005] lr: 1.0000e-01 eta: 1 day, 2:22:13 time: 0.1879 data_time: 0.0019 loss: 3.0517 2023/03/16 16:47:35 - mmengine - INFO - Epoch(train) [3][3600/5005] lr: 1.0000e-01 eta: 1 day, 2:21:12 time: 0.1805 data_time: 0.0019 loss: 3.0673 2023/03/16 16:47:53 - mmengine - INFO - Epoch(train) [3][3700/5005] lr: 1.0000e-01 eta: 1 day, 2:19:55 time: 0.1763 data_time: 0.0018 loss: 2.9411 2023/03/16 16:48:12 - mmengine - INFO - Epoch(train) [3][3800/5005] lr: 1.0000e-01 eta: 1 day, 2:19:13 time: 0.1755 data_time: 0.0019 loss: 2.9418 2023/03/16 16:48:31 - mmengine - INFO - Epoch(train) [3][3900/5005] lr: 1.0000e-01 eta: 1 day, 2:18:27 time: 0.1850 data_time: 0.0018 loss: 2.9336 2023/03/16 16:48:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:48:48 - mmengine - INFO - Epoch(train) [3][4000/5005] lr: 1.0000e-01 eta: 1 day, 2:16:56 time: 0.1876 data_time: 0.0023 loss: 3.0840 2023/03/16 16:49:06 - mmengine - INFO - Epoch(train) [3][4100/5005] lr: 1.0000e-01 eta: 1 day, 2:15:38 time: 0.1733 data_time: 0.0020 loss: 3.1467 2023/03/16 16:49:24 - mmengine - INFO - Epoch(train) [3][4200/5005] lr: 1.0000e-01 eta: 1 day, 2:14:27 time: 0.1814 data_time: 0.0018 loss: 3.0014 2023/03/16 16:49:42 - mmengine - INFO - Epoch(train) [3][4300/5005] lr: 1.0000e-01 eta: 1 day, 2:13:17 time: 0.1753 data_time: 0.0018 loss: 2.8831 2023/03/16 16:49:59 - mmengine - INFO - Epoch(train) [3][4400/5005] lr: 1.0000e-01 eta: 1 day, 2:11:41 time: 0.1688 data_time: 0.0018 loss: 3.1669 2023/03/16 16:50:17 - mmengine - INFO - Epoch(train) [3][4500/5005] lr: 1.0000e-01 eta: 1 day, 2:10:36 time: 0.1745 data_time: 0.0018 loss: 2.9419 2023/03/16 16:50:34 - mmengine - INFO - Epoch(train) [3][4600/5005] lr: 1.0000e-01 eta: 1 day, 2:09:13 time: 0.1766 data_time: 0.0021 loss: 2.9957 2023/03/16 16:50:53 - mmengine - INFO - Epoch(train) [3][4700/5005] lr: 1.0000e-01 eta: 1 day, 2:08:24 time: 0.1890 data_time: 0.0017 loss: 2.9483 2023/03/16 16:51:13 - mmengine - INFO - Epoch(train) [3][4800/5005] lr: 1.0000e-01 eta: 1 day, 2:08:21 time: 0.1788 data_time: 0.0020 loss: 3.0293 2023/03/16 16:51:31 - mmengine - INFO - Epoch(train) [3][4900/5005] lr: 1.0000e-01 eta: 1 day, 2:07:15 time: 0.1744 data_time: 0.0018 loss: 3.1274 2023/03/16 16:51:47 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:51:49 - mmengine - INFO - Epoch(train) [3][5000/5005] lr: 1.0000e-01 eta: 1 day, 2:06:24 time: 0.2074 data_time: 0.0028 loss: 2.9726 2023/03/16 16:51:50 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:51:51 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/03/16 16:51:58 - mmengine - INFO - Epoch(val) [3][100/196] eta: 0:00:05 time: 0.0543 data_time: 0.0010 2023/03/16 16:52:23 - mmengine - INFO - Epoch(val) [3][196/196] accuracy/top1: 37.7000 accuracy/top5: 64.2460data_time: 0.0267 time: 0.0578 2023/03/16 16:52:42 - mmengine - INFO - Epoch(train) [4][ 100/5005] lr: 1.0000e-01 eta: 1 day, 2:06:10 time: 0.1743 data_time: 0.0019 loss: 3.1627 2023/03/16 16:53:00 - mmengine - INFO - Epoch(train) [4][ 200/5005] lr: 1.0000e-01 eta: 1 day, 2:04:56 time: 0.1654 data_time: 0.0019 loss: 2.9781 2023/03/16 16:53:17 - mmengine - INFO - Epoch(train) [4][ 300/5005] lr: 1.0000e-01 eta: 1 day, 2:03:26 time: 0.1729 data_time: 0.0020 loss: 2.6637 2023/03/16 16:53:37 - mmengine - INFO - Epoch(train) [4][ 400/5005] lr: 1.0000e-01 eta: 1 day, 2:03:21 time: 0.2374 data_time: 0.0017 loss: 2.7964 2023/03/16 16:53:56 - mmengine - INFO - Epoch(train) [4][ 500/5005] lr: 1.0000e-01 eta: 1 day, 2:03:01 time: 0.1992 data_time: 0.0020 loss: 3.0181 2023/03/16 16:54:14 - mmengine - INFO - Epoch(train) [4][ 600/5005] lr: 1.0000e-01 eta: 1 day, 2:01:51 time: 0.1692 data_time: 0.0019 loss: 2.9101 2023/03/16 16:54:30 - mmengine - INFO - Epoch(train) [4][ 700/5005] lr: 1.0000e-01 eta: 1 day, 2:00:06 time: 0.1638 data_time: 0.0018 loss: 2.8920 2023/03/16 16:54:47 - mmengine - INFO - Epoch(train) [4][ 800/5005] lr: 1.0000e-01 eta: 1 day, 1:58:36 time: 0.1980 data_time: 0.0015 loss: 2.7707 2023/03/16 16:55:07 - mmengine - INFO - Epoch(train) [4][ 900/5005] lr: 1.0000e-01 eta: 1 day, 1:58:24 time: 0.1582 data_time: 0.0018 loss: 3.2542 2023/03/16 16:55:21 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:55:24 - mmengine - INFO - Epoch(train) [4][1000/5005] lr: 1.0000e-01 eta: 1 day, 1:56:47 time: 0.1612 data_time: 0.0020 loss: 3.0316 2023/03/16 16:55:41 - mmengine - INFO - Epoch(train) [4][1100/5005] lr: 1.0000e-01 eta: 1 day, 1:55:18 time: 0.1728 data_time: 0.0019 loss: 2.7757 2023/03/16 16:55:59 - mmengine - INFO - Epoch(train) [4][1200/5005] lr: 1.0000e-01 eta: 1 day, 1:54:19 time: 0.1860 data_time: 0.0020 loss: 2.8454 2023/03/16 16:56:20 - mmengine - INFO - Epoch(train) [4][1300/5005] lr: 1.0000e-01 eta: 1 day, 1:55:00 time: 0.2236 data_time: 0.0020 loss: 2.7837 2023/03/16 16:56:41 - mmengine - INFO - Epoch(train) [4][1400/5005] lr: 1.0000e-01 eta: 1 day, 1:55:46 time: 0.1804 data_time: 0.0020 loss: 2.8514 2023/03/16 16:57:00 - mmengine - INFO - Epoch(train) [4][1500/5005] lr: 1.0000e-01 eta: 1 day, 1:55:01 time: 0.2098 data_time: 0.0020 loss: 2.9555 2023/03/16 16:57:21 - mmengine - INFO - Epoch(train) [4][1600/5005] lr: 1.0000e-01 eta: 1 day, 1:55:43 time: 0.2398 data_time: 0.0023 loss: 3.0278 2023/03/16 16:57:41 - mmengine - INFO - Epoch(train) [4][1700/5005] lr: 1.0000e-01 eta: 1 day, 1:55:31 time: 0.1865 data_time: 0.0023 loss: 2.9685 2023/03/16 16:57:59 - mmengine - INFO - Epoch(train) [4][1800/5005] lr: 1.0000e-01 eta: 1 day, 1:54:35 time: 0.1739 data_time: 0.0022 loss: 2.7644 2023/03/16 16:58:16 - mmengine - INFO - Epoch(train) [4][1900/5005] lr: 1.0000e-01 eta: 1 day, 1:53:26 time: 0.1809 data_time: 0.0023 loss: 2.8413 2023/03/16 16:58:32 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 16:58:36 - mmengine - INFO - Epoch(train) [4][2000/5005] lr: 1.0000e-01 eta: 1 day, 1:53:06 time: 0.2254 data_time: 0.0019 loss: 2.8631 2023/03/16 16:58:55 - mmengine - INFO - Epoch(train) [4][2100/5005] lr: 1.0000e-01 eta: 1 day, 1:52:41 time: 0.1888 data_time: 0.0020 loss: 2.9894 2023/03/16 16:59:14 - mmengine - INFO - Epoch(train) [4][2200/5005] lr: 1.0000e-01 eta: 1 day, 1:52:12 time: 0.2027 data_time: 0.0019 loss: 2.8358 2023/03/16 16:59:35 - mmengine - INFO - Epoch(train) [4][2300/5005] lr: 1.0000e-01 eta: 1 day, 1:52:48 time: 0.2008 data_time: 0.0018 loss: 2.9154 2023/03/16 16:59:56 - mmengine - INFO - Epoch(train) [4][2400/5005] lr: 1.0000e-01 eta: 1 day, 1:53:26 time: 0.2108 data_time: 0.0020 loss: 2.6709 2023/03/16 17:00:16 - mmengine - INFO - Epoch(train) [4][2500/5005] lr: 1.0000e-01 eta: 1 day, 1:53:28 time: 0.1874 data_time: 0.0019 loss: 3.0244 2023/03/16 17:00:36 - mmengine - INFO - Epoch(train) [4][2600/5005] lr: 1.0000e-01 eta: 1 day, 1:53:16 time: 0.2189 data_time: 0.0017 loss: 2.8368 2023/03/16 17:00:55 - mmengine - INFO - Epoch(train) [4][2700/5005] lr: 1.0000e-01 eta: 1 day, 1:52:42 time: 0.1871 data_time: 0.0020 loss: 2.7603 2023/03/16 17:01:14 - mmengine - INFO - Epoch(train) [4][2800/5005] lr: 1.0000e-01 eta: 1 day, 1:52:23 time: 0.2060 data_time: 0.0020 loss: 2.8618 2023/03/16 17:01:32 - mmengine - INFO - Epoch(train) [4][2900/5005] lr: 1.0000e-01 eta: 1 day, 1:51:40 time: 0.1810 data_time: 0.0023 loss: 2.9440 2023/03/16 17:01:48 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:01:51 - mmengine - INFO - Epoch(train) [4][3000/5005] lr: 1.0000e-01 eta: 1 day, 1:51:05 time: 0.1888 data_time: 0.0021 loss: 2.7580 2023/03/16 17:02:11 - mmengine - INFO - Epoch(train) [4][3100/5005] lr: 1.0000e-01 eta: 1 day, 1:51:06 time: 0.2039 data_time: 0.0020 loss: 2.7912 2023/03/16 17:02:32 - mmengine - INFO - Epoch(train) [4][3200/5005] lr: 1.0000e-01 eta: 1 day, 1:51:15 time: 0.1970 data_time: 0.0020 loss: 3.0050 2023/03/16 17:02:51 - mmengine - INFO - Epoch(train) [4][3300/5005] lr: 1.0000e-01 eta: 1 day, 1:51:02 time: 0.1958 data_time: 0.0020 loss: 2.7764 2023/03/16 17:03:11 - mmengine - INFO - Epoch(train) [4][3400/5005] lr: 1.0000e-01 eta: 1 day, 1:50:56 time: 0.1916 data_time: 0.0020 loss: 2.8972 2023/03/16 17:03:30 - mmengine - INFO - Epoch(train) [4][3500/5005] lr: 1.0000e-01 eta: 1 day, 1:50:39 time: 0.1973 data_time: 0.0020 loss: 2.7786 2023/03/16 17:03:50 - mmengine - INFO - Epoch(train) [4][3600/5005] lr: 1.0000e-01 eta: 1 day, 1:50:30 time: 0.1914 data_time: 0.0021 loss: 3.0168 2023/03/16 17:04:09 - mmengine - INFO - Epoch(train) [4][3700/5005] lr: 1.0000e-01 eta: 1 day, 1:50:09 time: 0.1927 data_time: 0.0021 loss: 2.8059 2023/03/16 17:04:29 - mmengine - INFO - Epoch(train) [4][3800/5005] lr: 1.0000e-01 eta: 1 day, 1:49:50 time: 0.1839 data_time: 0.0019 loss: 2.7276 2023/03/16 17:04:47 - mmengine - INFO - Epoch(train) [4][3900/5005] lr: 1.0000e-01 eta: 1 day, 1:49:14 time: 0.1828 data_time: 0.0021 loss: 3.0793 2023/03/16 17:05:03 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:05:06 - mmengine - INFO - Epoch(train) [4][4000/5005] lr: 1.0000e-01 eta: 1 day, 1:48:47 time: 0.1991 data_time: 0.0021 loss: 2.8855 2023/03/16 17:05:26 - mmengine - INFO - Epoch(train) [4][4100/5005] lr: 1.0000e-01 eta: 1 day, 1:48:28 time: 0.1889 data_time: 0.0021 loss: 2.8966 2023/03/16 17:05:45 - mmengine - INFO - Epoch(train) [4][4200/5005] lr: 1.0000e-01 eta: 1 day, 1:48:06 time: 0.1936 data_time: 0.0020 loss: 2.6220 2023/03/16 17:06:05 - mmengine - INFO - Epoch(train) [4][4300/5005] lr: 1.0000e-01 eta: 1 day, 1:48:08 time: 0.1958 data_time: 0.0021 loss: 2.9298 2023/03/16 17:06:25 - mmengine - INFO - Epoch(train) [4][4400/5005] lr: 1.0000e-01 eta: 1 day, 1:48:01 time: 0.1936 data_time: 0.0020 loss: 2.8930 2023/03/16 17:06:45 - mmengine - INFO - Epoch(train) [4][4500/5005] lr: 1.0000e-01 eta: 1 day, 1:47:53 time: 0.2151 data_time: 0.0020 loss: 2.6399 2023/03/16 17:07:03 - mmengine - INFO - Epoch(train) [4][4600/5005] lr: 1.0000e-01 eta: 1 day, 1:47:11 time: 0.1831 data_time: 0.0022 loss: 2.8885 2023/03/16 17:07:21 - mmengine - INFO - Epoch(train) [4][4700/5005] lr: 1.0000e-01 eta: 1 day, 1:46:23 time: 0.1866 data_time: 0.0021 loss: 2.4164 2023/03/16 17:07:39 - mmengine - INFO - Epoch(train) [4][4800/5005] lr: 1.0000e-01 eta: 1 day, 1:45:33 time: 0.1788 data_time: 0.0020 loss: 3.0466 2023/03/16 17:07:57 - mmengine - INFO - Epoch(train) [4][4900/5005] lr: 1.0000e-01 eta: 1 day, 1:44:42 time: 0.1817 data_time: 0.0021 loss: 2.7056 2023/03/16 17:08:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:08:16 - mmengine - INFO - Epoch(train) [4][5000/5005] lr: 1.0000e-01 eta: 1 day, 1:44:09 time: 0.1882 data_time: 0.0030 loss: 2.7643 2023/03/16 17:08:17 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:08:17 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/03/16 17:08:23 - mmengine - INFO - Epoch(val) [4][100/196] eta: 0:00:04 time: 0.0467 data_time: 0.0009 2023/03/16 17:08:48 - mmengine - INFO - Epoch(val) [4][196/196] accuracy/top1: 43.9960 accuracy/top5: 70.8420data_time: 0.0123 time: 0.0441 2023/03/16 17:09:12 - mmengine - INFO - Epoch(train) [5][ 100/5005] lr: 1.0000e-01 eta: 1 day, 1:45:33 time: 0.1790 data_time: 0.0021 loss: 2.9348 2023/03/16 17:09:32 - mmengine - INFO - Epoch(train) [5][ 200/5005] lr: 1.0000e-01 eta: 1 day, 1:45:34 time: 0.1803 data_time: 0.0023 loss: 2.8205 2023/03/16 17:09:50 - mmengine - INFO - Epoch(train) [5][ 300/5005] lr: 1.0000e-01 eta: 1 day, 1:44:40 time: 0.1777 data_time: 0.0021 loss: 2.4483 2023/03/16 17:10:09 - mmengine - INFO - Epoch(train) [5][ 400/5005] lr: 1.0000e-01 eta: 1 day, 1:44:20 time: 0.1945 data_time: 0.0022 loss: 2.7759 2023/03/16 17:10:29 - mmengine - INFO - Epoch(train) [5][ 500/5005] lr: 1.0000e-01 eta: 1 day, 1:44:18 time: 0.1898 data_time: 0.0024 loss: 2.8225 2023/03/16 17:10:50 - mmengine - INFO - Epoch(train) [5][ 600/5005] lr: 1.0000e-01 eta: 1 day, 1:44:30 time: 0.1912 data_time: 0.0019 loss: 2.4557 2023/03/16 17:11:08 - mmengine - INFO - Epoch(train) [5][ 700/5005] lr: 1.0000e-01 eta: 1 day, 1:43:45 time: 0.1773 data_time: 0.0018 loss: 2.7009 2023/03/16 17:11:26 - mmengine - INFO - Epoch(train) [5][ 800/5005] lr: 1.0000e-01 eta: 1 day, 1:42:48 time: 0.1800 data_time: 0.0022 loss: 2.8770 2023/03/16 17:11:44 - mmengine - INFO - Epoch(train) [5][ 900/5005] lr: 1.0000e-01 eta: 1 day, 1:42:02 time: 0.1832 data_time: 0.0020 loss: 2.5457 2023/03/16 17:11:59 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:12:02 - mmengine - INFO - Epoch(train) [5][1000/5005] lr: 1.0000e-01 eta: 1 day, 1:41:25 time: 0.1819 data_time: 0.0018 loss: 2.7014 2023/03/16 17:12:21 - mmengine - INFO - Epoch(train) [5][1100/5005] lr: 1.0000e-01 eta: 1 day, 1:40:41 time: 0.1794 data_time: 0.0018 loss: 2.5473 2023/03/16 17:12:40 - mmengine - INFO - Epoch(train) [5][1200/5005] lr: 1.0000e-01 eta: 1 day, 1:40:15 time: 0.1902 data_time: 0.0019 loss: 2.4819 2023/03/16 17:12:59 - mmengine - INFO - Epoch(train) [5][1300/5005] lr: 1.0000e-01 eta: 1 day, 1:39:48 time: 0.1827 data_time: 0.0022 loss: 2.6782 2023/03/16 17:13:18 - mmengine - INFO - Epoch(train) [5][1400/5005] lr: 1.0000e-01 eta: 1 day, 1:39:21 time: 0.2204 data_time: 0.0023 loss: 2.5234 2023/03/16 17:13:35 - mmengine - INFO - Epoch(train) [5][1500/5005] lr: 1.0000e-01 eta: 1 day, 1:38:26 time: 0.1767 data_time: 0.0021 loss: 2.6138 2023/03/16 17:13:52 - mmengine - INFO - Epoch(train) [5][1600/5005] lr: 1.0000e-01 eta: 1 day, 1:37:21 time: 0.1722 data_time: 0.0020 loss: 2.7004 2023/03/16 17:14:11 - mmengine - INFO - Epoch(train) [5][1700/5005] lr: 1.0000e-01 eta: 1 day, 1:36:45 time: 0.1802 data_time: 0.0020 loss: 2.6664 2023/03/16 17:14:30 - mmengine - INFO - Epoch(train) [5][1800/5005] lr: 1.0000e-01 eta: 1 day, 1:36:17 time: 0.1930 data_time: 0.0020 loss: 2.7816 2023/03/16 17:14:49 - mmengine - INFO - Epoch(train) [5][1900/5005] lr: 1.0000e-01 eta: 1 day, 1:35:51 time: 0.1824 data_time: 0.0020 loss: 2.7572 2023/03/16 17:15:03 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:15:07 - mmengine - INFO - Epoch(train) [5][2000/5005] lr: 1.0000e-01 eta: 1 day, 1:35:07 time: 0.1787 data_time: 0.0022 loss: 2.8512 2023/03/16 17:15:26 - mmengine - INFO - Epoch(train) [5][2100/5005] lr: 1.0000e-01 eta: 1 day, 1:34:45 time: 0.1816 data_time: 0.0022 loss: 2.6843 2023/03/16 17:15:44 - mmengine - INFO - Epoch(train) [5][2200/5005] lr: 1.0000e-01 eta: 1 day, 1:34:02 time: 0.1862 data_time: 0.0021 loss: 2.8170 2023/03/16 17:16:02 - mmengine - INFO - Epoch(train) [5][2300/5005] lr: 1.0000e-01 eta: 1 day, 1:33:16 time: 0.1810 data_time: 0.0022 loss: 2.6448 2023/03/16 17:16:21 - mmengine - INFO - Epoch(train) [5][2400/5005] lr: 1.0000e-01 eta: 1 day, 1:32:37 time: 0.1854 data_time: 0.0020 loss: 2.6960 2023/03/16 17:16:40 - mmengine - INFO - Epoch(train) [5][2500/5005] lr: 1.0000e-01 eta: 1 day, 1:32:16 time: 0.2204 data_time: 0.0019 loss: 2.7106 2023/03/16 17:17:01 - mmengine - INFO - Epoch(train) [5][2600/5005] lr: 1.0000e-01 eta: 1 day, 1:32:37 time: 0.1847 data_time: 0.0021 loss: 2.7682 2023/03/16 17:17:19 - mmengine - INFO - Epoch(train) [5][2700/5005] lr: 1.0000e-01 eta: 1 day, 1:31:57 time: 0.1806 data_time: 0.0021 loss: 2.7115 2023/03/16 17:17:37 - mmengine - INFO - Epoch(train) [5][2800/5005] lr: 1.0000e-01 eta: 1 day, 1:31:12 time: 0.1804 data_time: 0.0020 loss: 2.6088 2023/03/16 17:17:57 - mmengine - INFO - Epoch(train) [5][2900/5005] lr: 1.0000e-01 eta: 1 day, 1:30:56 time: 0.1923 data_time: 0.0019 loss: 2.8491 2023/03/16 17:18:12 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:18:15 - mmengine - INFO - Epoch(train) [5][3000/5005] lr: 1.0000e-01 eta: 1 day, 1:30:22 time: 0.1765 data_time: 0.0020 loss: 2.4218 2023/03/16 17:18:33 - mmengine - INFO - Epoch(train) [5][3100/5005] lr: 1.0000e-01 eta: 1 day, 1:29:38 time: 0.1766 data_time: 0.0020 loss: 2.5878 2023/03/16 17:18:51 - mmengine - INFO - Epoch(train) [5][3200/5005] lr: 1.0000e-01 eta: 1 day, 1:28:56 time: 0.1875 data_time: 0.0020 loss: 2.7110 2023/03/16 17:19:10 - mmengine - INFO - Epoch(train) [5][3300/5005] lr: 1.0000e-01 eta: 1 day, 1:28:27 time: 0.1767 data_time: 0.0021 loss: 2.6030 2023/03/16 17:19:28 - mmengine - INFO - Epoch(train) [5][3400/5005] lr: 1.0000e-01 eta: 1 day, 1:27:40 time: 0.1772 data_time: 0.0022 loss: 2.8063 2023/03/16 17:19:46 - mmengine - INFO - Epoch(train) [5][3500/5005] lr: 1.0000e-01 eta: 1 day, 1:27:00 time: 0.1880 data_time: 0.0021 loss: 2.8356 2023/03/16 17:20:05 - mmengine - INFO - Epoch(train) [5][3600/5005] lr: 1.0000e-01 eta: 1 day, 1:26:40 time: 0.2534 data_time: 0.0018 loss: 2.5356 2023/03/16 17:20:25 - mmengine - INFO - Epoch(train) [5][3700/5005] lr: 1.0000e-01 eta: 1 day, 1:26:24 time: 0.1817 data_time: 0.0021 loss: 2.7580 2023/03/16 17:20:43 - mmengine - INFO - Epoch(train) [5][3800/5005] lr: 1.0000e-01 eta: 1 day, 1:25:54 time: 0.1843 data_time: 0.0020 loss: 2.6523 2023/03/16 17:21:01 - mmengine - INFO - Epoch(train) [5][3900/5005] lr: 1.0000e-01 eta: 1 day, 1:25:11 time: 0.1798 data_time: 0.0020 loss: 2.5671 2023/03/16 17:21:16 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:21:19 - mmengine - INFO - Epoch(train) [5][4000/5005] lr: 1.0000e-01 eta: 1 day, 1:24:21 time: 0.1692 data_time: 0.0021 loss: 2.6432 2023/03/16 17:21:36 - mmengine - INFO - Epoch(train) [5][4100/5005] lr: 1.0000e-01 eta: 1 day, 1:23:23 time: 0.1783 data_time: 0.0022 loss: 2.7625 2023/03/16 17:21:58 - mmengine - INFO - Epoch(train) [5][4200/5005] lr: 1.0000e-01 eta: 1 day, 1:23:53 time: 0.2487 data_time: 0.0019 loss: 2.5724 2023/03/16 17:22:20 - mmengine - INFO - Epoch(train) [5][4300/5005] lr: 1.0000e-01 eta: 1 day, 1:24:24 time: 0.2436 data_time: 0.0017 loss: 2.5382 2023/03/16 17:22:40 - mmengine - INFO - Epoch(train) [5][4400/5005] lr: 1.0000e-01 eta: 1 day, 1:24:19 time: 0.1862 data_time: 0.0020 loss: 2.7607 2023/03/16 17:22:58 - mmengine - INFO - Epoch(train) [5][4500/5005] lr: 1.0000e-01 eta: 1 day, 1:23:47 time: 0.2107 data_time: 0.0021 loss: 2.6262 2023/03/16 17:23:17 - mmengine - INFO - Epoch(train) [5][4600/5005] lr: 1.0000e-01 eta: 1 day, 1:23:16 time: 0.1902 data_time: 0.0020 loss: 2.7946 2023/03/16 17:23:37 - mmengine - INFO - Epoch(train) [5][4700/5005] lr: 1.0000e-01 eta: 1 day, 1:23:06 time: 0.1870 data_time: 0.0022 loss: 2.6666 2023/03/16 17:23:54 - mmengine - INFO - Epoch(train) [5][4800/5005] lr: 1.0000e-01 eta: 1 day, 1:22:18 time: 0.1782 data_time: 0.0021 loss: 2.6025 2023/03/16 17:24:12 - mmengine - INFO - Epoch(train) [5][4900/5005] lr: 1.0000e-01 eta: 1 day, 1:21:31 time: 0.1767 data_time: 0.0026 loss: 2.7239 2023/03/16 17:24:27 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:24:31 - mmengine - INFO - Epoch(train) [5][5000/5005] lr: 1.0000e-01 eta: 1 day, 1:21:01 time: 0.1857 data_time: 0.0029 loss: 2.6586 2023/03/16 17:24:32 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:24:32 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/03/16 17:24:38 - mmengine - INFO - Epoch(val) [5][100/196] eta: 0:00:05 time: 0.0445 data_time: 0.0009 2023/03/16 17:25:06 - mmengine - INFO - Epoch(val) [5][196/196] accuracy/top1: 46.3520 accuracy/top5: 72.9080data_time: 0.0207 time: 0.0550 2023/03/16 17:25:31 - mmengine - INFO - Epoch(train) [6][ 100/5005] lr: 1.0000e-01 eta: 1 day, 1:22:21 time: 0.2323 data_time: 0.0020 loss: 2.6310 2023/03/16 17:25:52 - mmengine - INFO - Epoch(train) [6][ 200/5005] lr: 1.0000e-01 eta: 1 day, 1:22:43 time: 0.2339 data_time: 0.0021 loss: 2.6346 2023/03/16 17:26:12 - mmengine - INFO - Epoch(train) [6][ 300/5005] lr: 1.0000e-01 eta: 1 day, 1:22:39 time: 0.1810 data_time: 0.0023 loss: 2.7631 2023/03/16 17:26:33 - mmengine - INFO - Epoch(train) [6][ 400/5005] lr: 1.0000e-01 eta: 1 day, 1:22:40 time: 0.1895 data_time: 0.0023 loss: 2.6782 2023/03/16 17:26:52 - mmengine - INFO - Epoch(train) [6][ 500/5005] lr: 1.0000e-01 eta: 1 day, 1:22:10 time: 0.1718 data_time: 0.0022 loss: 2.7418 2023/03/16 17:27:08 - mmengine - INFO - Epoch(train) [6][ 600/5005] lr: 1.0000e-01 eta: 1 day, 1:21:08 time: 0.1687 data_time: 0.0023 loss: 2.7143 2023/03/16 17:27:25 - mmengine - INFO - Epoch(train) [6][ 700/5005] lr: 1.0000e-01 eta: 1 day, 1:20:08 time: 0.1723 data_time: 0.0021 loss: 2.8888 2023/03/16 17:27:42 - mmengine - INFO - Epoch(train) [6][ 800/5005] lr: 1.0000e-01 eta: 1 day, 1:19:07 time: 0.1643 data_time: 0.0021 loss: 2.6665 2023/03/16 17:27:59 - mmengine - INFO - Epoch(train) [6][ 900/5005] lr: 1.0000e-01 eta: 1 day, 1:18:05 time: 0.1671 data_time: 0.0020 loss: 2.6772 2023/03/16 17:28:12 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:28:17 - mmengine - INFO - Epoch(train) [6][1000/5005] lr: 1.0000e-01 eta: 1 day, 1:17:10 time: 0.1734 data_time: 0.0021 loss: 2.5642 2023/03/16 17:28:34 - mmengine - INFO - Epoch(train) [6][1100/5005] lr: 1.0000e-01 eta: 1 day, 1:16:18 time: 0.1701 data_time: 0.0021 loss: 2.8864 2023/03/16 17:28:51 - mmengine - INFO - Epoch(train) [6][1200/5005] lr: 1.0000e-01 eta: 1 day, 1:15:17 time: 0.1669 data_time: 0.0019 loss: 2.7244 2023/03/16 17:29:12 - mmengine - INFO - Epoch(train) [6][1300/5005] lr: 1.0000e-01 eta: 1 day, 1:15:32 time: 0.2618 data_time: 0.0024 loss: 2.6695 2023/03/16 17:29:35 - mmengine - INFO - Epoch(train) [6][1400/5005] lr: 1.0000e-01 eta: 1 day, 1:16:26 time: 0.1935 data_time: 0.0028 loss: 2.5840 2023/03/16 17:29:54 - mmengine - INFO - Epoch(train) [6][1500/5005] lr: 1.0000e-01 eta: 1 day, 1:16:00 time: 0.1845 data_time: 0.0023 loss: 2.7586 2023/03/16 17:30:13 - mmengine - INFO - Epoch(train) [6][1600/5005] lr: 1.0000e-01 eta: 1 day, 1:15:30 time: 0.1881 data_time: 0.0022 loss: 2.5867 2023/03/16 17:30:31 - mmengine - INFO - Epoch(train) [6][1700/5005] lr: 1.0000e-01 eta: 1 day, 1:15:01 time: 0.1899 data_time: 0.0022 loss: 2.6093 2023/03/16 17:30:55 - mmengine - INFO - Epoch(train) [6][1800/5005] lr: 1.0000e-01 eta: 1 day, 1:16:01 time: 0.2305 data_time: 0.0023 loss: 2.8573 2023/03/16 17:31:13 - mmengine - INFO - Epoch(train) [6][1900/5005] lr: 1.0000e-01 eta: 1 day, 1:15:20 time: 0.1792 data_time: 0.0023 loss: 2.4977 2023/03/16 17:31:27 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:31:32 - mmengine - INFO - Epoch(train) [6][2000/5005] lr: 1.0000e-01 eta: 1 day, 1:15:06 time: 0.2226 data_time: 0.0023 loss: 2.5734 2023/03/16 17:31:52 - mmengine - INFO - Epoch(train) [6][2100/5005] lr: 1.0000e-01 eta: 1 day, 1:14:58 time: 0.1774 data_time: 0.0022 loss: 2.4468 2023/03/16 17:32:10 - mmengine - INFO - Epoch(train) [6][2200/5005] lr: 1.0000e-01 eta: 1 day, 1:14:08 time: 0.1686 data_time: 0.0022 loss: 2.5152 2023/03/16 17:32:29 - mmengine - INFO - Epoch(train) [6][2300/5005] lr: 1.0000e-01 eta: 1 day, 1:13:51 time: 0.2091 data_time: 0.0022 loss: 2.3864 2023/03/16 17:32:47 - mmengine - INFO - Epoch(train) [6][2400/5005] lr: 1.0000e-01 eta: 1 day, 1:13:14 time: 0.1749 data_time: 0.0022 loss: 2.5513 2023/03/16 17:33:06 - mmengine - INFO - Epoch(train) [6][2500/5005] lr: 1.0000e-01 eta: 1 day, 1:12:41 time: 0.1856 data_time: 0.0024 loss: 2.7384 2023/03/16 17:33:24 - mmengine - INFO - Epoch(train) [6][2600/5005] lr: 1.0000e-01 eta: 1 day, 1:12:04 time: 0.1699 data_time: 0.0023 loss: 2.7278 2023/03/16 17:33:42 - mmengine - INFO - Epoch(train) [6][2700/5005] lr: 1.0000e-01 eta: 1 day, 1:11:21 time: 0.1841 data_time: 0.0024 loss: 2.6318 2023/03/16 17:34:01 - mmengine - INFO - Epoch(train) [6][2800/5005] lr: 1.0000e-01 eta: 1 day, 1:11:06 time: 0.1995 data_time: 0.0022 loss: 2.5611 2023/03/16 17:34:20 - mmengine - INFO - Epoch(train) [6][2900/5005] lr: 1.0000e-01 eta: 1 day, 1:10:35 time: 0.1762 data_time: 0.0023 loss: 2.6781 2023/03/16 17:34:35 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:34:40 - mmengine - INFO - Epoch(train) [6][3000/5005] lr: 1.0000e-01 eta: 1 day, 1:10:29 time: 0.1834 data_time: 0.0024 loss: 2.6170 2023/03/16 17:34:58 - mmengine - INFO - Epoch(train) [6][3100/5005] lr: 1.0000e-01 eta: 1 day, 1:09:52 time: 0.1729 data_time: 0.0024 loss: 2.6072 2023/03/16 17:35:16 - mmengine - INFO - Epoch(train) [6][3200/5005] lr: 1.0000e-01 eta: 1 day, 1:09:12 time: 0.1753 data_time: 0.0024 loss: 2.5136 2023/03/16 17:35:34 - mmengine - INFO - Epoch(train) [6][3300/5005] lr: 1.0000e-01 eta: 1 day, 1:08:34 time: 0.1827 data_time: 0.0023 loss: 2.5849 2023/03/16 17:35:52 - mmengine - INFO - Epoch(train) [6][3400/5005] lr: 1.0000e-01 eta: 1 day, 1:07:57 time: 0.1780 data_time: 0.0022 loss: 2.7349 2023/03/16 17:36:10 - mmengine - INFO - Epoch(train) [6][3500/5005] lr: 1.0000e-01 eta: 1 day, 1:07:17 time: 0.1758 data_time: 0.0022 loss: 2.4933 2023/03/16 17:36:27 - mmengine - INFO - Epoch(train) [6][3600/5005] lr: 1.0000e-01 eta: 1 day, 1:06:30 time: 0.1639 data_time: 0.0022 loss: 2.3957 2023/03/16 17:36:44 - mmengine - INFO - Epoch(train) [6][3700/5005] lr: 1.0000e-01 eta: 1 day, 1:05:34 time: 0.1742 data_time: 0.0023 loss: 2.5682 2023/03/16 17:37:02 - mmengine - INFO - Epoch(train) [6][3800/5005] lr: 1.0000e-01 eta: 1 day, 1:04:53 time: 0.1841 data_time: 0.0023 loss: 2.7332 2023/03/16 17:37:21 - mmengine - INFO - Epoch(train) [6][3900/5005] lr: 1.0000e-01 eta: 1 day, 1:04:22 time: 0.1834 data_time: 0.0024 loss: 2.5767 2023/03/16 17:37:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:37:39 - mmengine - INFO - Epoch(train) [6][4000/5005] lr: 1.0000e-01 eta: 1 day, 1:03:47 time: 0.1778 data_time: 0.0022 loss: 2.4964 2023/03/16 17:37:56 - mmengine - INFO - Epoch(train) [6][4100/5005] lr: 1.0000e-01 eta: 1 day, 1:02:57 time: 0.1692 data_time: 0.0022 loss: 2.5572 2023/03/16 17:38:13 - mmengine - INFO - Epoch(train) [6][4200/5005] lr: 1.0000e-01 eta: 1 day, 1:02:09 time: 0.1673 data_time: 0.0024 loss: 2.5535 2023/03/16 17:38:31 - mmengine - INFO - Epoch(train) [6][4300/5005] lr: 1.0000e-01 eta: 1 day, 1:01:25 time: 0.1729 data_time: 0.0023 loss: 2.5335 2023/03/16 17:38:49 - mmengine - INFO - Epoch(train) [6][4400/5005] lr: 1.0000e-01 eta: 1 day, 1:00:46 time: 0.1790 data_time: 0.0022 loss: 2.5960 2023/03/16 17:39:07 - mmengine - INFO - Epoch(train) [6][4500/5005] lr: 1.0000e-01 eta: 1 day, 1:00:14 time: 0.1821 data_time: 0.0024 loss: 2.3872 2023/03/16 17:39:26 - mmengine - INFO - Epoch(train) [6][4600/5005] lr: 1.0000e-01 eta: 1 day, 0:59:44 time: 0.1861 data_time: 0.0023 loss: 2.6697 2023/03/16 17:39:44 - mmengine - INFO - Epoch(train) [6][4700/5005] lr: 1.0000e-01 eta: 1 day, 0:59:18 time: 0.1870 data_time: 0.0023 loss: 2.3132 2023/03/16 17:40:03 - mmengine - INFO - Epoch(train) [6][4800/5005] lr: 1.0000e-01 eta: 1 day, 0:58:54 time: 0.1907 data_time: 0.0023 loss: 2.6135 2023/03/16 17:40:21 - mmengine - INFO - Epoch(train) [6][4900/5005] lr: 1.0000e-01 eta: 1 day, 0:58:16 time: 0.1737 data_time: 0.0021 loss: 2.5445 2023/03/16 17:40:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:40:39 - mmengine - INFO - Epoch(train) [6][5000/5005] lr: 1.0000e-01 eta: 1 day, 0:57:40 time: 0.1821 data_time: 0.0029 loss: 2.6282 2023/03/16 17:40:40 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:40:41 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/03/16 17:40:48 - mmengine - INFO - Epoch(val) [6][100/196] eta: 0:00:06 time: 0.0469 data_time: 0.0008 2023/03/16 17:41:12 - mmengine - INFO - Epoch(val) [6][196/196] accuracy/top1: 45.6040 accuracy/top5: 72.1060data_time: 0.0279 time: 0.0601 2023/03/16 17:41:32 - mmengine - INFO - Epoch(train) [7][ 100/5005] lr: 1.0000e-01 eta: 1 day, 0:57:29 time: 0.1852 data_time: 0.0022 loss: 2.5042 2023/03/16 17:41:51 - mmengine - INFO - Epoch(train) [7][ 200/5005] lr: 1.0000e-01 eta: 1 day, 0:57:14 time: 0.1757 data_time: 0.0022 loss: 2.6820 2023/03/16 17:42:08 - mmengine - INFO - Epoch(train) [7][ 300/5005] lr: 1.0000e-01 eta: 1 day, 0:56:20 time: 0.1708 data_time: 0.0020 loss: 2.4140 2023/03/16 17:42:31 - mmengine - INFO - Epoch(train) [7][ 400/5005] lr: 1.0000e-01 eta: 1 day, 0:56:53 time: 0.2170 data_time: 0.0019 loss: 2.4244 2023/03/16 17:42:50 - mmengine - INFO - Epoch(train) [7][ 500/5005] lr: 1.0000e-01 eta: 1 day, 0:56:37 time: 0.1770 data_time: 0.0020 loss: 2.7231 2023/03/16 17:43:07 - mmengine - INFO - Epoch(train) [7][ 600/5005] lr: 1.0000e-01 eta: 1 day, 0:55:51 time: 0.1756 data_time: 0.0021 loss: 2.3903 2023/03/16 17:43:26 - mmengine - INFO - Epoch(train) [7][ 700/5005] lr: 1.0000e-01 eta: 1 day, 0:55:17 time: 0.1802 data_time: 0.0020 loss: 2.6460 2023/03/16 17:43:44 - mmengine - INFO - Epoch(train) [7][ 800/5005] lr: 1.0000e-01 eta: 1 day, 0:54:53 time: 0.1859 data_time: 0.0019 loss: 2.7600 2023/03/16 17:44:03 - mmengine - INFO - Epoch(train) [7][ 900/5005] lr: 1.0000e-01 eta: 1 day, 0:54:28 time: 0.1864 data_time: 0.0020 loss: 2.3451 2023/03/16 17:44:17 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:44:23 - mmengine - INFO - Epoch(train) [7][1000/5005] lr: 1.0000e-01 eta: 1 day, 0:54:17 time: 0.1955 data_time: 0.0020 loss: 2.2911 2023/03/16 17:44:47 - mmengine - INFO - Epoch(train) [7][1100/5005] lr: 1.0000e-01 eta: 1 day, 0:55:17 time: 0.1911 data_time: 0.0020 loss: 2.5280 2023/03/16 17:45:05 - mmengine - INFO - Epoch(train) [7][1200/5005] lr: 1.0000e-01 eta: 1 day, 0:54:45 time: 0.1838 data_time: 0.0022 loss: 2.4193 2023/03/16 17:45:23 - mmengine - INFO - Epoch(train) [7][1300/5005] lr: 1.0000e-01 eta: 1 day, 0:54:01 time: 0.1691 data_time: 0.0024 loss: 2.4259 2023/03/16 17:45:40 - mmengine - INFO - Epoch(train) [7][1400/5005] lr: 1.0000e-01 eta: 1 day, 0:53:13 time: 0.1699 data_time: 0.0022 loss: 2.4673 2023/03/16 17:45:57 - mmengine - INFO - Epoch(train) [7][1500/5005] lr: 1.0000e-01 eta: 1 day, 0:52:27 time: 0.1716 data_time: 0.0023 loss: 2.6364 2023/03/16 17:46:15 - mmengine - INFO - Epoch(train) [7][1600/5005] lr: 1.0000e-01 eta: 1 day, 0:51:52 time: 0.1942 data_time: 0.0022 loss: 2.2132 2023/03/16 17:46:34 - mmengine - INFO - Epoch(train) [7][1700/5005] lr: 1.0000e-01 eta: 1 day, 0:51:31 time: 0.1811 data_time: 0.0020 loss: 2.3431 2023/03/16 17:46:53 - mmengine - INFO - Epoch(train) [7][1800/5005] lr: 1.0000e-01 eta: 1 day, 0:51:04 time: 0.1875 data_time: 0.0022 loss: 2.6043 2023/03/16 17:47:13 - mmengine - INFO - Epoch(train) [7][1900/5005] lr: 1.0000e-01 eta: 1 day, 0:51:02 time: 0.1885 data_time: 0.0023 loss: 2.5915 2023/03/16 17:47:27 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:47:32 - mmengine - INFO - Epoch(train) [7][2000/5005] lr: 1.0000e-01 eta: 1 day, 0:50:41 time: 0.1931 data_time: 0.0024 loss: 2.6086 2023/03/16 17:47:51 - mmengine - INFO - Epoch(train) [7][2100/5005] lr: 1.0000e-01 eta: 1 day, 0:50:14 time: 0.1814 data_time: 0.0023 loss: 2.4311 2023/03/16 17:48:09 - mmengine - INFO - Epoch(train) [7][2200/5005] lr: 1.0000e-01 eta: 1 day, 0:49:44 time: 0.1775 data_time: 0.0022 loss: 2.4344 2023/03/16 17:48:27 - mmengine - INFO - Epoch(train) [7][2300/5005] lr: 1.0000e-01 eta: 1 day, 0:49:13 time: 0.1951 data_time: 0.0020 loss: 2.2566 2023/03/16 17:48:48 - mmengine - INFO - Epoch(train) [7][2400/5005] lr: 1.0000e-01 eta: 1 day, 0:49:17 time: 0.1873 data_time: 0.0019 loss: 2.4054 2023/03/16 17:49:06 - mmengine - INFO - Epoch(train) [7][2500/5005] lr: 1.0000e-01 eta: 1 day, 0:48:41 time: 0.1811 data_time: 0.0022 loss: 2.5644 2023/03/16 17:49:24 - mmengine - INFO - Epoch(train) [7][2600/5005] lr: 1.0000e-01 eta: 1 day, 0:48:06 time: 0.1801 data_time: 0.0021 loss: 2.5271 2023/03/16 17:49:42 - mmengine - INFO - Epoch(train) [7][2700/5005] lr: 1.0000e-01 eta: 1 day, 0:47:24 time: 0.1752 data_time: 0.0021 loss: 2.4590 2023/03/16 17:49:59 - mmengine - INFO - Epoch(train) [7][2800/5005] lr: 1.0000e-01 eta: 1 day, 0:46:44 time: 0.1761 data_time: 0.0022 loss: 2.3573 2023/03/16 17:50:17 - mmengine - INFO - Epoch(train) [7][2900/5005] lr: 1.0000e-01 eta: 1 day, 0:46:13 time: 0.1779 data_time: 0.0022 loss: 2.5305 2023/03/16 17:50:33 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:50:40 - mmengine - INFO - Epoch(train) [7][3000/5005] lr: 1.0000e-01 eta: 1 day, 0:46:42 time: 0.1804 data_time: 0.0021 loss: 2.4867 2023/03/16 17:50:59 - mmengine - INFO - Epoch(train) [7][3100/5005] lr: 1.0000e-01 eta: 1 day, 0:46:24 time: 0.2480 data_time: 0.0020 loss: 2.7779 2023/03/16 17:51:19 - mmengine - INFO - Epoch(train) [7][3200/5005] lr: 1.0000e-01 eta: 1 day, 0:46:20 time: 0.1926 data_time: 0.0023 loss: 2.5751 2023/03/16 17:51:39 - mmengine - INFO - Epoch(train) [7][3300/5005] lr: 1.0000e-01 eta: 1 day, 0:46:06 time: 0.2032 data_time: 0.0021 loss: 2.5942 2023/03/16 17:51:59 - mmengine - INFO - Epoch(train) [7][3400/5005] lr: 1.0000e-01 eta: 1 day, 0:46:04 time: 0.1883 data_time: 0.0022 loss: 2.3063 2023/03/16 17:52:19 - mmengine - INFO - Epoch(train) [7][3500/5005] lr: 1.0000e-01 eta: 1 day, 0:45:54 time: 0.1846 data_time: 0.0023 loss: 2.5665 2023/03/16 17:52:37 - mmengine - INFO - Epoch(train) [7][3600/5005] lr: 1.0000e-01 eta: 1 day, 0:45:22 time: 0.1787 data_time: 0.0023 loss: 2.5269 2023/03/16 17:52:55 - mmengine - INFO - Epoch(train) [7][3700/5005] lr: 1.0000e-01 eta: 1 day, 0:44:48 time: 0.1772 data_time: 0.0022 loss: 2.5189 2023/03/16 17:53:13 - mmengine - INFO - Epoch(train) [7][3800/5005] lr: 1.0000e-01 eta: 1 day, 0:44:06 time: 0.1803 data_time: 0.0025 loss: 2.3978 2023/03/16 17:53:31 - mmengine - INFO - Epoch(train) [7][3900/5005] lr: 1.0000e-01 eta: 1 day, 0:43:42 time: 0.1894 data_time: 0.0023 loss: 2.7813 2023/03/16 17:53:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:53:51 - mmengine - INFO - Epoch(train) [7][4000/5005] lr: 1.0000e-01 eta: 1 day, 0:43:36 time: 0.1859 data_time: 0.0022 loss: 2.5775 2023/03/16 17:54:09 - mmengine - INFO - Epoch(train) [7][4100/5005] lr: 1.0000e-01 eta: 1 day, 0:43:02 time: 0.1734 data_time: 0.0021 loss: 2.6910 2023/03/16 17:54:27 - mmengine - INFO - Epoch(train) [7][4200/5005] lr: 1.0000e-01 eta: 1 day, 0:42:21 time: 0.1747 data_time: 0.0023 loss: 2.5649 2023/03/16 17:54:45 - mmengine - INFO - Epoch(train) [7][4300/5005] lr: 1.0000e-01 eta: 1 day, 0:41:46 time: 0.1773 data_time: 0.0019 loss: 2.5085 2023/03/16 17:55:04 - mmengine - INFO - Epoch(train) [7][4400/5005] lr: 1.0000e-01 eta: 1 day, 0:41:23 time: 0.2266 data_time: 0.0022 loss: 2.3880 2023/03/16 17:55:25 - mmengine - INFO - Epoch(train) [7][4500/5005] lr: 1.0000e-01 eta: 1 day, 0:41:29 time: 0.2612 data_time: 0.0022 loss: 2.4394 2023/03/16 17:55:43 - mmengine - INFO - Epoch(train) [7][4600/5005] lr: 1.0000e-01 eta: 1 day, 0:40:56 time: 0.1724 data_time: 0.0021 loss: 2.5544 2023/03/16 17:56:01 - mmengine - INFO - Epoch(train) [7][4700/5005] lr: 1.0000e-01 eta: 1 day, 0:40:28 time: 0.1811 data_time: 0.0022 loss: 2.4002 2023/03/16 17:56:19 - mmengine - INFO - Epoch(train) [7][4800/5005] lr: 1.0000e-01 eta: 1 day, 0:39:56 time: 0.1747 data_time: 0.0024 loss: 2.4483 2023/03/16 17:56:36 - mmengine - INFO - Epoch(train) [7][4900/5005] lr: 1.0000e-01 eta: 1 day, 0:39:11 time: 0.1729 data_time: 0.0023 loss: 2.5353 2023/03/16 17:56:49 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:56:54 - mmengine - INFO - Epoch(train) [7][5000/5005] lr: 1.0000e-01 eta: 1 day, 0:38:34 time: 0.1827 data_time: 0.0034 loss: 2.4323 2023/03/16 17:56:55 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 17:56:55 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/03/16 17:57:01 - mmengine - INFO - Epoch(val) [7][100/196] eta: 0:00:04 time: 0.0489 data_time: 0.0047 2023/03/16 17:57:26 - mmengine - INFO - Epoch(val) [7][196/196] accuracy/top1: 46.9780 accuracy/top5: 73.2000data_time: 0.0137 time: 0.0502 2023/03/16 17:57:53 - mmengine - INFO - Epoch(train) [8][ 100/5005] lr: 1.0000e-01 eta: 1 day, 0:40:02 time: 0.2029 data_time: 0.0023 loss: 2.5516 2023/03/16 17:58:11 - mmengine - INFO - Epoch(train) [8][ 200/5005] lr: 1.0000e-01 eta: 1 day, 0:39:33 time: 0.1943 data_time: 0.0024 loss: 2.6952 2023/03/16 17:58:30 - mmengine - INFO - Epoch(train) [8][ 300/5005] lr: 1.0000e-01 eta: 1 day, 0:39:12 time: 0.1854 data_time: 0.0024 loss: 2.5880 2023/03/16 17:58:49 - mmengine - INFO - Epoch(train) [8][ 400/5005] lr: 1.0000e-01 eta: 1 day, 0:38:49 time: 0.1907 data_time: 0.0022 loss: 2.2649 2023/03/16 17:59:08 - mmengine - INFO - Epoch(train) [8][ 500/5005] lr: 1.0000e-01 eta: 1 day, 0:38:28 time: 0.2161 data_time: 0.0022 loss: 2.5335 2023/03/16 17:59:30 - mmengine - INFO - Epoch(train) [8][ 600/5005] lr: 1.0000e-01 eta: 1 day, 0:38:45 time: 0.1921 data_time: 0.0021 loss: 2.4235 2023/03/16 17:59:48 - mmengine - INFO - Epoch(train) [8][ 700/5005] lr: 1.0000e-01 eta: 1 day, 0:38:18 time: 0.1884 data_time: 0.0019 loss: 2.7432 2023/03/16 18:00:08 - mmengine - INFO - Epoch(train) [8][ 800/5005] lr: 1.0000e-01 eta: 1 day, 0:38:03 time: 0.2024 data_time: 0.0021 loss: 2.5250 2023/03/16 18:00:29 - mmengine - INFO - Epoch(train) [8][ 900/5005] lr: 1.0000e-01 eta: 1 day, 0:38:11 time: 0.1958 data_time: 0.0022 loss: 2.5403 2023/03/16 18:00:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:00:50 - mmengine - INFO - Epoch(train) [8][1000/5005] lr: 1.0000e-01 eta: 1 day, 0:38:17 time: 0.1846 data_time: 0.0024 loss: 2.2845 2023/03/16 18:01:08 - mmengine - INFO - Epoch(train) [8][1100/5005] lr: 1.0000e-01 eta: 1 day, 0:37:49 time: 0.1762 data_time: 0.0024 loss: 2.7572 2023/03/16 18:01:27 - mmengine - INFO - Epoch(train) [8][1200/5005] lr: 1.0000e-01 eta: 1 day, 0:37:24 time: 0.1902 data_time: 0.0022 loss: 2.4313 2023/03/16 18:01:46 - mmengine - INFO - Epoch(train) [8][1300/5005] lr: 1.0000e-01 eta: 1 day, 0:37:06 time: 0.1978 data_time: 0.0023 loss: 2.4190 2023/03/16 18:02:05 - mmengine - INFO - Epoch(train) [8][1400/5005] lr: 1.0000e-01 eta: 1 day, 0:36:38 time: 0.1870 data_time: 0.0023 loss: 2.4364 2023/03/16 18:02:24 - mmengine - INFO - Epoch(train) [8][1500/5005] lr: 1.0000e-01 eta: 1 day, 0:36:17 time: 0.1831 data_time: 0.0023 loss: 2.3396 2023/03/16 18:02:41 - mmengine - INFO - Epoch(train) [8][1600/5005] lr: 1.0000e-01 eta: 1 day, 0:35:41 time: 0.1762 data_time: 0.0023 loss: 2.5910 2023/03/16 18:03:00 - mmengine - INFO - Epoch(train) [8][1700/5005] lr: 1.0000e-01 eta: 1 day, 0:35:14 time: 0.1913 data_time: 0.0024 loss: 2.4753 2023/03/16 18:03:19 - mmengine - INFO - Epoch(train) [8][1800/5005] lr: 1.0000e-01 eta: 1 day, 0:34:49 time: 0.1796 data_time: 0.0021 loss: 2.4576 2023/03/16 18:03:37 - mmengine - INFO - Epoch(train) [8][1900/5005] lr: 1.0000e-01 eta: 1 day, 0:34:24 time: 0.1937 data_time: 0.0022 loss: 2.5598 2023/03/16 18:03:51 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:04:00 - mmengine - INFO - Epoch(train) [8][2000/5005] lr: 1.0000e-01 eta: 1 day, 0:34:54 time: 0.2497 data_time: 0.0021 loss: 2.5732 2023/03/16 18:04:20 - mmengine - INFO - Epoch(train) [8][2100/5005] lr: 1.0000e-01 eta: 1 day, 0:34:45 time: 0.1771 data_time: 0.0020 loss: 2.3389 2023/03/16 18:04:38 - mmengine - INFO - Epoch(train) [8][2200/5005] lr: 1.0000e-01 eta: 1 day, 0:34:12 time: 0.1790 data_time: 0.0021 loss: 2.5552 2023/03/16 18:04:56 - mmengine - INFO - Epoch(train) [8][2300/5005] lr: 1.0000e-01 eta: 1 day, 0:33:40 time: 0.1851 data_time: 0.0019 loss: 2.5223 2023/03/16 18:05:15 - mmengine - INFO - Epoch(train) [8][2400/5005] lr: 1.0000e-01 eta: 1 day, 0:33:12 time: 0.1832 data_time: 0.0026 loss: 2.4623 2023/03/16 18:05:33 - mmengine - INFO - Epoch(train) [8][2500/5005] lr: 1.0000e-01 eta: 1 day, 0:32:48 time: 0.1873 data_time: 0.0023 loss: 2.4773 2023/03/16 18:05:52 - mmengine - INFO - Epoch(train) [8][2600/5005] lr: 1.0000e-01 eta: 1 day, 0:32:23 time: 0.1806 data_time: 0.0022 loss: 2.5079 2023/03/16 18:06:10 - mmengine - INFO - Epoch(train) [8][2700/5005] lr: 1.0000e-01 eta: 1 day, 0:31:49 time: 0.1783 data_time: 0.0021 loss: 2.3285 2023/03/16 18:06:28 - mmengine - INFO - Epoch(train) [8][2800/5005] lr: 1.0000e-01 eta: 1 day, 0:31:20 time: 0.1860 data_time: 0.0022 loss: 2.5897 2023/03/16 18:06:47 - mmengine - INFO - Epoch(train) [8][2900/5005] lr: 1.0000e-01 eta: 1 day, 0:31:02 time: 0.2571 data_time: 0.0020 loss: 2.2917 2023/03/16 18:07:02 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:07:09 - mmengine - INFO - Epoch(train) [8][3000/5005] lr: 1.0000e-01 eta: 1 day, 0:31:10 time: 0.1950 data_time: 0.0026 loss: 2.5536 2023/03/16 18:07:30 - mmengine - INFO - Epoch(train) [8][3100/5005] lr: 1.0000e-01 eta: 1 day, 0:31:18 time: 0.1755 data_time: 0.0023 loss: 2.3780 2023/03/16 18:07:48 - mmengine - INFO - Epoch(train) [8][3200/5005] lr: 1.0000e-01 eta: 1 day, 0:30:45 time: 0.1901 data_time: 0.0023 loss: 2.5722 2023/03/16 18:08:06 - mmengine - INFO - Epoch(train) [8][3300/5005] lr: 1.0000e-01 eta: 1 day, 0:30:13 time: 0.1718 data_time: 0.0022 loss: 2.4825 2023/03/16 18:08:24 - mmengine - INFO - Epoch(train) [8][3400/5005] lr: 1.0000e-01 eta: 1 day, 0:29:36 time: 0.1782 data_time: 0.0022 loss: 2.4961 2023/03/16 18:08:42 - mmengine - INFO - Epoch(train) [8][3500/5005] lr: 1.0000e-01 eta: 1 day, 0:29:05 time: 0.1861 data_time: 0.0023 loss: 2.3362 2023/03/16 18:09:00 - mmengine - INFO - Epoch(train) [8][3600/5005] lr: 1.0000e-01 eta: 1 day, 0:28:39 time: 0.1851 data_time: 0.0024 loss: 2.4212 2023/03/16 18:09:19 - mmengine - INFO - Epoch(train) [8][3700/5005] lr: 1.0000e-01 eta: 1 day, 0:28:11 time: 0.1741 data_time: 0.0024 loss: 2.2244 2023/03/16 18:09:37 - mmengine - INFO - Epoch(train) [8][3800/5005] lr: 1.0000e-01 eta: 1 day, 0:27:43 time: 0.1770 data_time: 0.0020 loss: 2.4703 2023/03/16 18:09:55 - mmengine - INFO - Epoch(train) [8][3900/5005] lr: 1.0000e-01 eta: 1 day, 0:27:13 time: 0.1729 data_time: 0.0021 loss: 2.5291 2023/03/16 18:10:06 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:10:12 - mmengine - INFO - Epoch(train) [8][4000/5005] lr: 1.0000e-01 eta: 1 day, 0:26:25 time: 0.1644 data_time: 0.0020 loss: 2.5986 2023/03/16 18:10:28 - mmengine - INFO - Epoch(train) [8][4100/5005] lr: 1.0000e-01 eta: 1 day, 0:25:29 time: 0.1630 data_time: 0.0022 loss: 2.3955 2023/03/16 18:10:45 - mmengine - INFO - Epoch(train) [8][4200/5005] lr: 1.0000e-01 eta: 1 day, 0:24:45 time: 0.1752 data_time: 0.0023 loss: 2.3701 2023/03/16 18:11:03 - mmengine - INFO - Epoch(train) [8][4300/5005] lr: 1.0000e-01 eta: 1 day, 0:24:14 time: 0.1846 data_time: 0.0025 loss: 2.5203 2023/03/16 18:11:21 - mmengine - INFO - Epoch(train) [8][4400/5005] lr: 1.0000e-01 eta: 1 day, 0:23:42 time: 0.1932 data_time: 0.0024 loss: 2.4325 2023/03/16 18:11:39 - mmengine - INFO - Epoch(train) [8][4500/5005] lr: 1.0000e-01 eta: 1 day, 0:23:10 time: 0.1737 data_time: 0.0023 loss: 2.4655 2023/03/16 18:11:56 - mmengine - INFO - Epoch(train) [8][4600/5005] lr: 1.0000e-01 eta: 1 day, 0:22:35 time: 0.1827 data_time: 0.0026 loss: 2.4171 2023/03/16 18:12:15 - mmengine - INFO - Epoch(train) [8][4700/5005] lr: 1.0000e-01 eta: 1 day, 0:22:06 time: 0.1810 data_time: 0.0024 loss: 2.4514 2023/03/16 18:12:33 - mmengine - INFO - Epoch(train) [8][4800/5005] lr: 1.0000e-01 eta: 1 day, 0:21:34 time: 0.1775 data_time: 0.0023 loss: 2.4856 2023/03/16 18:12:56 - mmengine - INFO - Epoch(train) [8][4900/5005] lr: 1.0000e-01 eta: 1 day, 0:22:05 time: 0.2358 data_time: 0.0022 loss: 2.1408 2023/03/16 18:13:14 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:13:21 - mmengine - INFO - Epoch(train) [8][5000/5005] lr: 1.0000e-01 eta: 1 day, 0:22:53 time: 0.1980 data_time: 0.0029 loss: 2.5064 2023/03/16 18:13:22 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:13:22 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/03/16 18:13:29 - mmengine - INFO - Epoch(val) [8][100/196] eta: 0:00:05 time: 0.0537 data_time: 0.0010 2023/03/16 18:13:55 - mmengine - INFO - Epoch(val) [8][196/196] accuracy/top1: 50.6700 accuracy/top5: 76.2840data_time: 0.0308 time: 0.0608 2023/03/16 18:14:14 - mmengine - INFO - Epoch(train) [9][ 100/5005] lr: 1.0000e-01 eta: 1 day, 0:22:40 time: 0.1729 data_time: 0.0021 loss: 2.5951 2023/03/16 18:14:32 - mmengine - INFO - Epoch(train) [9][ 200/5005] lr: 1.0000e-01 eta: 1 day, 0:22:05 time: 0.1796 data_time: 0.0020 loss: 2.4365 2023/03/16 18:14:50 - mmengine - INFO - Epoch(train) [9][ 300/5005] lr: 1.0000e-01 eta: 1 day, 0:21:36 time: 0.1876 data_time: 0.0021 loss: 2.3506 2023/03/16 18:15:09 - mmengine - INFO - Epoch(train) [9][ 400/5005] lr: 1.0000e-01 eta: 1 day, 0:21:16 time: 0.1879 data_time: 0.0020 loss: 2.4677 2023/03/16 18:15:29 - mmengine - INFO - Epoch(train) [9][ 500/5005] lr: 1.0000e-01 eta: 1 day, 0:21:05 time: 0.2004 data_time: 0.0021 loss: 2.2576 2023/03/16 18:15:48 - mmengine - INFO - Epoch(train) [9][ 600/5005] lr: 1.0000e-01 eta: 1 day, 0:20:50 time: 0.1881 data_time: 0.0021 loss: 2.3934 2023/03/16 18:16:07 - mmengine - INFO - Epoch(train) [9][ 700/5005] lr: 1.0000e-01 eta: 1 day, 0:20:22 time: 0.1737 data_time: 0.0020 loss: 2.4314 2023/03/16 18:16:24 - mmengine - INFO - Epoch(train) [9][ 800/5005] lr: 1.0000e-01 eta: 1 day, 0:19:46 time: 0.1745 data_time: 0.0023 loss: 2.3220 2023/03/16 18:16:43 - mmengine - INFO - Epoch(train) [9][ 900/5005] lr: 1.0000e-01 eta: 1 day, 0:19:20 time: 0.1869 data_time: 0.0022 loss: 2.6899 2023/03/16 18:16:54 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:17:02 - mmengine - INFO - Epoch(train) [9][1000/5005] lr: 1.0000e-01 eta: 1 day, 0:19:04 time: 0.1925 data_time: 0.0022 loss: 2.4260 2023/03/16 18:17:21 - mmengine - INFO - Epoch(train) [9][1100/5005] lr: 1.0000e-01 eta: 1 day, 0:18:45 time: 0.1843 data_time: 0.0022 loss: 2.7209 2023/03/16 18:17:40 - mmengine - INFO - Epoch(train) [9][1200/5005] lr: 1.0000e-01 eta: 1 day, 0:18:27 time: 0.1980 data_time: 0.0021 loss: 2.4557 2023/03/16 18:18:00 - mmengine - INFO - Epoch(train) [9][1300/5005] lr: 1.0000e-01 eta: 1 day, 0:18:11 time: 0.1929 data_time: 0.0022 loss: 2.4555 2023/03/16 18:18:19 - mmengine - INFO - Epoch(train) [9][1400/5005] lr: 1.0000e-01 eta: 1 day, 0:17:57 time: 0.1967 data_time: 0.0021 loss: 2.4635 2023/03/16 18:18:39 - mmengine - INFO - Epoch(train) [9][1500/5005] lr: 1.0000e-01 eta: 1 day, 0:17:42 time: 0.1953 data_time: 0.0021 loss: 2.3855 2023/03/16 18:18:58 - mmengine - INFO - Epoch(train) [9][1600/5005] lr: 1.0000e-01 eta: 1 day, 0:17:31 time: 0.1859 data_time: 0.0024 loss: 2.5120 2023/03/16 18:19:18 - mmengine - INFO - Epoch(train) [9][1700/5005] lr: 1.0000e-01 eta: 1 day, 0:17:19 time: 0.2007 data_time: 0.0022 loss: 2.5314 2023/03/16 18:19:38 - mmengine - INFO - Epoch(train) [9][1800/5005] lr: 1.0000e-01 eta: 1 day, 0:17:03 time: 0.1942 data_time: 0.0022 loss: 2.6014 2023/03/16 18:19:58 - mmengine - INFO - Epoch(train) [9][1900/5005] lr: 1.0000e-01 eta: 1 day, 0:16:54 time: 0.2039 data_time: 0.0021 loss: 2.3764 2023/03/16 18:20:09 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:20:18 - mmengine - INFO - Epoch(train) [9][2000/5005] lr: 1.0000e-01 eta: 1 day, 0:16:46 time: 0.2138 data_time: 0.0022 loss: 2.4888 2023/03/16 18:20:38 - mmengine - INFO - Epoch(train) [9][2100/5005] lr: 1.0000e-01 eta: 1 day, 0:16:42 time: 0.1944 data_time: 0.0021 loss: 2.3429 2023/03/16 18:20:58 - mmengine - INFO - Epoch(train) [9][2200/5005] lr: 1.0000e-01 eta: 1 day, 0:16:27 time: 0.1883 data_time: 0.0021 loss: 2.5135 2023/03/16 18:21:16 - mmengine - INFO - Epoch(train) [9][2300/5005] lr: 1.0000e-01 eta: 1 day, 0:16:04 time: 0.1820 data_time: 0.0022 loss: 2.4872 2023/03/16 18:21:35 - mmengine - INFO - Epoch(train) [9][2400/5005] lr: 1.0000e-01 eta: 1 day, 0:15:42 time: 0.1906 data_time: 0.0021 loss: 2.6248 2023/03/16 18:21:55 - mmengine - INFO - Epoch(train) [9][2500/5005] lr: 1.0000e-01 eta: 1 day, 0:15:26 time: 0.1957 data_time: 0.0022 loss: 2.5028 2023/03/16 18:22:14 - mmengine - INFO - Epoch(train) [9][2600/5005] lr: 1.0000e-01 eta: 1 day, 0:15:13 time: 0.1957 data_time: 0.0024 loss: 2.2568 2023/03/16 18:22:34 - mmengine - INFO - Epoch(train) [9][2700/5005] lr: 1.0000e-01 eta: 1 day, 0:15:04 time: 0.1928 data_time: 0.0024 loss: 2.2680 2023/03/16 18:22:54 - mmengine - INFO - Epoch(train) [9][2800/5005] lr: 1.0000e-01 eta: 1 day, 0:14:49 time: 0.1920 data_time: 0.0024 loss: 2.4570 2023/03/16 18:23:13 - mmengine - INFO - Epoch(train) [9][2900/5005] lr: 1.0000e-01 eta: 1 day, 0:14:37 time: 0.1961 data_time: 0.0027 loss: 2.5037 2023/03/16 18:23:25 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:23:33 - mmengine - INFO - Epoch(train) [9][3000/5005] lr: 1.0000e-01 eta: 1 day, 0:14:20 time: 0.1966 data_time: 0.0023 loss: 2.3190 2023/03/16 18:23:52 - mmengine - INFO - Epoch(train) [9][3100/5005] lr: 1.0000e-01 eta: 1 day, 0:14:06 time: 0.1941 data_time: 0.0023 loss: 2.7078 2023/03/16 18:24:12 - mmengine - INFO - Epoch(train) [9][3200/5005] lr: 1.0000e-01 eta: 1 day, 0:13:51 time: 0.1975 data_time: 0.0022 loss: 2.2617 2023/03/16 18:24:31 - mmengine - INFO - Epoch(train) [9][3300/5005] lr: 1.0000e-01 eta: 1 day, 0:13:29 time: 0.1806 data_time: 0.0022 loss: 2.4670 2023/03/16 18:24:50 - mmengine - INFO - Epoch(train) [9][3400/5005] lr: 1.0000e-01 eta: 1 day, 0:13:08 time: 0.2051 data_time: 0.0022 loss: 2.3253 2023/03/16 18:25:07 - mmengine - INFO - Epoch(train) [9][3500/5005] lr: 1.0000e-01 eta: 1 day, 0:12:35 time: 0.1723 data_time: 0.0022 loss: 2.4148 2023/03/16 18:25:26 - mmengine - INFO - Epoch(train) [9][3600/5005] lr: 1.0000e-01 eta: 1 day, 0:12:07 time: 0.1839 data_time: 0.0021 loss: 2.5059 2023/03/16 18:25:45 - mmengine - INFO - Epoch(train) [9][3700/5005] lr: 1.0000e-01 eta: 1 day, 0:11:48 time: 0.1885 data_time: 0.0022 loss: 2.5914 2023/03/16 18:26:04 - mmengine - INFO - Epoch(train) [9][3800/5005] lr: 1.0000e-01 eta: 1 day, 0:11:27 time: 0.1877 data_time: 0.0020 loss: 2.4956 2023/03/16 18:26:23 - mmengine - INFO - Epoch(train) [9][3900/5005] lr: 1.0000e-01 eta: 1 day, 0:11:07 time: 0.1954 data_time: 0.0021 loss: 2.4378 2023/03/16 18:26:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:26:42 - mmengine - INFO - Epoch(train) [9][4000/5005] lr: 1.0000e-01 eta: 1 day, 0:10:50 time: 0.1963 data_time: 0.0021 loss: 2.5266 2023/03/16 18:27:01 - mmengine - INFO - Epoch(train) [9][4100/5005] lr: 1.0000e-01 eta: 1 day, 0:10:35 time: 0.1937 data_time: 0.0024 loss: 2.4634 2023/03/16 18:27:21 - mmengine - INFO - Epoch(train) [9][4200/5005] lr: 1.0000e-01 eta: 1 day, 0:10:21 time: 0.1943 data_time: 0.0022 loss: 2.3033 2023/03/16 18:27:41 - mmengine - INFO - Epoch(train) [9][4300/5005] lr: 1.0000e-01 eta: 1 day, 0:10:07 time: 0.1979 data_time: 0.0022 loss: 2.4537 2023/03/16 18:28:01 - mmengine - INFO - Epoch(train) [9][4400/5005] lr: 1.0000e-01 eta: 1 day, 0:09:59 time: 0.2044 data_time: 0.0023 loss: 2.3618 2023/03/16 18:28:21 - mmengine - INFO - Epoch(train) [9][4500/5005] lr: 1.0000e-01 eta: 1 day, 0:09:52 time: 0.1914 data_time: 0.0023 loss: 2.4373 2023/03/16 18:28:40 - mmengine - INFO - Epoch(train) [9][4600/5005] lr: 1.0000e-01 eta: 1 day, 0:09:36 time: 0.2167 data_time: 0.0021 loss: 2.3544 2023/03/16 18:29:00 - mmengine - INFO - Epoch(train) [9][4700/5005] lr: 1.0000e-01 eta: 1 day, 0:09:24 time: 0.1981 data_time: 0.0023 loss: 2.2700 2023/03/16 18:29:20 - mmengine - INFO - Epoch(train) [9][4800/5005] lr: 1.0000e-01 eta: 1 day, 0:09:08 time: 0.1909 data_time: 0.0022 loss: 2.5125 2023/03/16 18:29:39 - mmengine - INFO - Epoch(train) [9][4900/5005] lr: 1.0000e-01 eta: 1 day, 0:08:52 time: 0.1910 data_time: 0.0026 loss: 2.2876 2023/03/16 18:29:51 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:29:59 - mmengine - INFO - Epoch(train) [9][5000/5005] lr: 1.0000e-01 eta: 1 day, 0:08:38 time: 0.2077 data_time: 0.0031 loss: 2.4820 2023/03/16 18:30:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:30:00 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/03/16 18:30:06 - mmengine - INFO - Epoch(val) [9][100/196] eta: 0:00:04 time: 0.0478 data_time: 0.0054 2023/03/16 18:30:34 - mmengine - INFO - Epoch(val) [9][196/196] accuracy/top1: 48.4480 accuracy/top5: 74.9080data_time: 0.0220 time: 0.0568 2023/03/16 18:30:54 - mmengine - INFO - Epoch(train) [10][ 100/5005] lr: 1.0000e-01 eta: 1 day, 0:08:30 time: 0.1852 data_time: 0.0025 loss: 2.3003 2023/03/16 18:31:13 - mmengine - INFO - Epoch(train) [10][ 200/5005] lr: 1.0000e-01 eta: 1 day, 0:08:07 time: 0.1854 data_time: 0.0025 loss: 2.3779 2023/03/16 18:31:30 - mmengine - INFO - Epoch(train) [10][ 300/5005] lr: 1.0000e-01 eta: 1 day, 0:07:34 time: 0.1754 data_time: 0.0024 loss: 2.3925 2023/03/16 18:31:49 - mmengine - INFO - Epoch(train) [10][ 400/5005] lr: 1.0000e-01 eta: 1 day, 0:07:10 time: 0.2180 data_time: 0.0023 loss: 2.4772 2023/03/16 18:32:08 - mmengine - INFO - Epoch(train) [10][ 500/5005] lr: 1.0000e-01 eta: 1 day, 0:06:48 time: 0.1783 data_time: 0.0029 loss: 2.5025 2023/03/16 18:32:26 - mmengine - INFO - Epoch(train) [10][ 600/5005] lr: 1.0000e-01 eta: 1 day, 0:06:23 time: 0.2165 data_time: 0.0024 loss: 2.5164 2023/03/16 18:32:45 - mmengine - INFO - Epoch(train) [10][ 700/5005] lr: 1.0000e-01 eta: 1 day, 0:06:04 time: 0.2244 data_time: 0.0023 loss: 2.4235 2023/03/16 18:33:03 - mmengine - INFO - Epoch(train) [10][ 800/5005] lr: 1.0000e-01 eta: 1 day, 0:05:35 time: 0.1806 data_time: 0.0022 loss: 2.3139 2023/03/16 18:33:22 - mmengine - INFO - Epoch(train) [10][ 900/5005] lr: 1.0000e-01 eta: 1 day, 0:05:06 time: 0.1812 data_time: 0.0020 loss: 2.4217 2023/03/16 18:33:31 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:33:40 - mmengine - INFO - Epoch(train) [10][1000/5005] lr: 1.0000e-01 eta: 1 day, 0:04:44 time: 0.1920 data_time: 0.0021 loss: 2.6422 2023/03/16 18:34:00 - mmengine - INFO - Epoch(train) [10][1100/5005] lr: 1.0000e-01 eta: 1 day, 0:04:30 time: 0.1926 data_time: 0.0023 loss: 2.5130 2023/03/16 18:34:20 - mmengine - INFO - Epoch(train) [10][1200/5005] lr: 1.0000e-01 eta: 1 day, 0:04:17 time: 0.2013 data_time: 0.0021 loss: 2.5866 2023/03/16 18:34:40 - mmengine - INFO - Epoch(train) [10][1300/5005] lr: 1.0000e-01 eta: 1 day, 0:04:08 time: 0.1968 data_time: 0.0022 loss: 2.3426 2023/03/16 18:34:59 - mmengine - INFO - Epoch(train) [10][1400/5005] lr: 1.0000e-01 eta: 1 day, 0:03:49 time: 0.1821 data_time: 0.0023 loss: 2.4774 2023/03/16 18:35:18 - mmengine - INFO - Epoch(train) [10][1500/5005] lr: 1.0000e-01 eta: 1 day, 0:03:32 time: 0.1925 data_time: 0.0022 loss: 2.4964 2023/03/16 18:35:37 - mmengine - INFO - Epoch(train) [10][1600/5005] lr: 1.0000e-01 eta: 1 day, 0:03:10 time: 0.1821 data_time: 0.0022 loss: 2.3340 2023/03/16 18:35:56 - mmengine - INFO - Epoch(train) [10][1700/5005] lr: 1.0000e-01 eta: 1 day, 0:02:51 time: 0.1804 data_time: 0.0024 loss: 2.3809 2023/03/16 18:36:15 - mmengine - INFO - Epoch(train) [10][1800/5005] lr: 1.0000e-01 eta: 1 day, 0:02:32 time: 0.1988 data_time: 0.0022 loss: 2.2375 2023/03/16 18:36:35 - mmengine - INFO - Epoch(train) [10][1900/5005] lr: 1.0000e-01 eta: 1 day, 0:02:15 time: 0.1922 data_time: 0.0022 loss: 2.3110 2023/03/16 18:36:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:36:53 - mmengine - INFO - Epoch(train) [10][2000/5005] lr: 1.0000e-01 eta: 1 day, 0:01:46 time: 0.1802 data_time: 0.0023 loss: 2.1173 2023/03/16 18:37:11 - mmengine - INFO - Epoch(train) [10][2100/5005] lr: 1.0000e-01 eta: 1 day, 0:01:21 time: 0.2006 data_time: 0.0022 loss: 2.3215 2023/03/16 18:37:31 - mmengine - INFO - Epoch(train) [10][2200/5005] lr: 1.0000e-01 eta: 1 day, 0:01:04 time: 0.1947 data_time: 0.0021 loss: 2.5333 2023/03/16 18:37:53 - mmengine - INFO - Epoch(train) [10][2300/5005] lr: 1.0000e-01 eta: 1 day, 0:01:12 time: 0.2445 data_time: 0.0021 loss: 2.4057 2023/03/16 18:38:15 - mmengine - INFO - Epoch(train) [10][2400/5005] lr: 1.0000e-01 eta: 1 day, 0:01:26 time: 0.2207 data_time: 0.0022 loss: 2.0437 2023/03/16 18:38:36 - mmengine - INFO - Epoch(train) [10][2500/5005] lr: 1.0000e-01 eta: 1 day, 0:01:28 time: 0.1836 data_time: 0.0024 loss: 2.3340 2023/03/16 18:38:57 - mmengine - INFO - Epoch(train) [10][2600/5005] lr: 1.0000e-01 eta: 1 day, 0:01:19 time: 0.2109 data_time: 0.0022 loss: 2.5112 2023/03/16 18:39:17 - mmengine - INFO - Epoch(train) [10][2700/5005] lr: 1.0000e-01 eta: 1 day, 0:01:09 time: 0.2078 data_time: 0.0022 loss: 2.4391 2023/03/16 18:39:38 - mmengine - INFO - Epoch(train) [10][2800/5005] lr: 1.0000e-01 eta: 1 day, 0:01:13 time: 0.1951 data_time: 0.0022 loss: 2.3712 2023/03/16 18:39:58 - mmengine - INFO - Epoch(train) [10][2900/5005] lr: 1.0000e-01 eta: 1 day, 0:00:56 time: 0.1914 data_time: 0.0021 loss: 2.3696 2023/03/16 18:40:08 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:40:17 - mmengine - INFO - Epoch(train) [10][3000/5005] lr: 1.0000e-01 eta: 1 day, 0:00:41 time: 0.1937 data_time: 0.0020 loss: 2.5263 2023/03/16 18:40:37 - mmengine - INFO - Epoch(train) [10][3100/5005] lr: 1.0000e-01 eta: 1 day, 0:00:27 time: 0.1953 data_time: 0.0023 loss: 2.1067 2023/03/16 18:40:57 - mmengine - INFO - Epoch(train) [10][3200/5005] lr: 1.0000e-01 eta: 1 day, 0:00:15 time: 0.2013 data_time: 0.0021 loss: 2.1470 2023/03/16 18:41:18 - mmengine - INFO - Epoch(train) [10][3300/5005] lr: 1.0000e-01 eta: 1 day, 0:00:13 time: 0.2142 data_time: 0.0021 loss: 2.3908 2023/03/16 18:41:39 - mmengine - INFO - Epoch(train) [10][3400/5005] lr: 1.0000e-01 eta: 1 day, 0:00:12 time: 0.2145 data_time: 0.0026 loss: 2.4592 2023/03/16 18:42:00 - mmengine - INFO - Epoch(train) [10][3500/5005] lr: 1.0000e-01 eta: 1 day, 0:00:16 time: 0.2181 data_time: 0.0022 loss: 2.2457 2023/03/16 18:42:22 - mmengine - INFO - Epoch(train) [10][3600/5005] lr: 1.0000e-01 eta: 1 day, 0:00:17 time: 0.2026 data_time: 0.0023 loss: 2.3081 2023/03/16 18:42:41 - mmengine - INFO - Epoch(train) [10][3700/5005] lr: 1.0000e-01 eta: 1 day, 0:00:03 time: 0.1895 data_time: 0.0024 loss: 2.3555 2023/03/16 18:43:00 - mmengine - INFO - Epoch(train) [10][3800/5005] lr: 1.0000e-01 eta: 23:59:42 time: 0.1859 data_time: 0.0019 loss: 2.5785 2023/03/16 18:43:20 - mmengine - INFO - Epoch(train) [10][3900/5005] lr: 1.0000e-01 eta: 23:59:24 time: 0.1917 data_time: 0.0022 loss: 2.4363 2023/03/16 18:43:30 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:43:39 - mmengine - INFO - Epoch(train) [10][4000/5005] lr: 1.0000e-01 eta: 23:59:10 time: 0.1918 data_time: 0.0023 loss: 2.2978 2023/03/16 18:43:58 - mmengine - INFO - Epoch(train) [10][4100/5005] lr: 1.0000e-01 eta: 23:58:44 time: 0.1897 data_time: 0.0021 loss: 2.3736 2023/03/16 18:44:18 - mmengine - INFO - Epoch(train) [10][4200/5005] lr: 1.0000e-01 eta: 23:58:32 time: 0.2075 data_time: 0.0021 loss: 2.3850 2023/03/16 18:44:38 - mmengine - INFO - Epoch(train) [10][4300/5005] lr: 1.0000e-01 eta: 23:58:22 time: 0.2000 data_time: 0.0021 loss: 2.2261 2023/03/16 18:44:58 - mmengine - INFO - Epoch(train) [10][4400/5005] lr: 1.0000e-01 eta: 23:58:11 time: 0.1929 data_time: 0.0023 loss: 2.4999 2023/03/16 18:45:17 - mmengine - INFO - Epoch(train) [10][4500/5005] lr: 1.0000e-01 eta: 23:57:52 time: 0.1953 data_time: 0.0024 loss: 2.1720 2023/03/16 18:45:37 - mmengine - INFO - Epoch(train) [10][4600/5005] lr: 1.0000e-01 eta: 23:57:43 time: 0.2058 data_time: 0.0022 loss: 2.3093 2023/03/16 18:45:58 - mmengine - INFO - Epoch(train) [10][4700/5005] lr: 1.0000e-01 eta: 23:57:40 time: 0.2060 data_time: 0.0021 loss: 2.4781 2023/03/16 18:46:19 - mmengine - INFO - Epoch(train) [10][4800/5005] lr: 1.0000e-01 eta: 23:57:35 time: 0.1971 data_time: 0.0022 loss: 2.6164 2023/03/16 18:46:39 - mmengine - INFO - Epoch(train) [10][4900/5005] lr: 1.0000e-01 eta: 23:57:21 time: 0.1957 data_time: 0.0023 loss: 2.4418 2023/03/16 18:46:50 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:46:59 - mmengine - INFO - Epoch(train) [10][5000/5005] lr: 1.0000e-01 eta: 23:57:10 time: 0.2076 data_time: 0.0033 loss: 2.3754 2023/03/16 18:47:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:47:00 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/03/16 18:47:06 - mmengine - INFO - Epoch(val) [10][100/196] eta: 0:00:04 time: 0.0470 data_time: 0.0114 2023/03/16 18:47:34 - mmengine - INFO - Epoch(val) [10][196/196] accuracy/top1: 51.9160 accuracy/top5: 77.4640data_time: 0.0128 time: 0.0482 2023/03/16 18:47:56 - mmengine - INFO - Epoch(train) [11][ 100/5005] lr: 1.0000e-01 eta: 23:57:19 time: 0.1801 data_time: 0.0024 loss: 2.4106 2023/03/16 18:48:14 - mmengine - INFO - Epoch(train) [11][ 200/5005] lr: 1.0000e-01 eta: 23:56:45 time: 0.1738 data_time: 0.0027 loss: 2.3167 2023/03/16 18:48:31 - mmengine - INFO - Epoch(train) [11][ 300/5005] lr: 1.0000e-01 eta: 23:56:09 time: 0.1746 data_time: 0.0024 loss: 2.2666 2023/03/16 18:48:50 - mmengine - INFO - Epoch(train) [11][ 400/5005] lr: 1.0000e-01 eta: 23:55:43 time: 0.1817 data_time: 0.0025 loss: 2.5844 2023/03/16 18:49:09 - mmengine - INFO - Epoch(train) [11][ 500/5005] lr: 1.0000e-01 eta: 23:55:27 time: 0.1910 data_time: 0.0022 loss: 2.4100 2023/03/16 18:49:28 - mmengine - INFO - Epoch(train) [11][ 600/5005] lr: 1.0000e-01 eta: 23:55:03 time: 0.1809 data_time: 0.0024 loss: 2.3565 2023/03/16 18:49:46 - mmengine - INFO - Epoch(train) [11][ 700/5005] lr: 1.0000e-01 eta: 23:54:32 time: 0.1793 data_time: 0.0024 loss: 2.2059 2023/03/16 18:50:04 - mmengine - INFO - Epoch(train) [11][ 800/5005] lr: 1.0000e-01 eta: 23:54:07 time: 0.1888 data_time: 0.0024 loss: 2.5158 2023/03/16 18:50:23 - mmengine - INFO - Epoch(train) [11][ 900/5005] lr: 1.0000e-01 eta: 23:53:46 time: 0.1854 data_time: 0.0022 loss: 2.3726 2023/03/16 18:50:32 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:50:41 - mmengine - INFO - Epoch(train) [11][1000/5005] lr: 1.0000e-01 eta: 23:53:18 time: 0.1771 data_time: 0.0023 loss: 2.3222 2023/03/16 18:50:59 - mmengine - INFO - Epoch(train) [11][1100/5005] lr: 1.0000e-01 eta: 23:52:46 time: 0.1739 data_time: 0.0023 loss: 2.4960 2023/03/16 18:51:17 - mmengine - INFO - Epoch(train) [11][1200/5005] lr: 1.0000e-01 eta: 23:52:15 time: 0.1800 data_time: 0.0022 loss: 2.3137 2023/03/16 18:51:35 - mmengine - INFO - Epoch(train) [11][1300/5005] lr: 1.0000e-01 eta: 23:51:50 time: 0.1847 data_time: 0.0024 loss: 2.3374 2023/03/16 18:51:53 - mmengine - INFO - Epoch(train) [11][1400/5005] lr: 1.0000e-01 eta: 23:51:23 time: 0.1737 data_time: 0.0023 loss: 2.3683 2023/03/16 18:52:12 - mmengine - INFO - Epoch(train) [11][1500/5005] lr: 1.0000e-01 eta: 23:51:01 time: 0.1887 data_time: 0.0026 loss: 2.3881 2023/03/16 18:52:31 - mmengine - INFO - Epoch(train) [11][1600/5005] lr: 1.0000e-01 eta: 23:50:40 time: 0.1870 data_time: 0.0025 loss: 2.4198 2023/03/16 18:52:49 - mmengine - INFO - Epoch(train) [11][1700/5005] lr: 1.0000e-01 eta: 23:50:12 time: 0.1769 data_time: 0.0024 loss: 2.3944 2023/03/16 18:53:07 - mmengine - INFO - Epoch(train) [11][1800/5005] lr: 1.0000e-01 eta: 23:49:42 time: 0.1814 data_time: 0.0024 loss: 2.4414 2023/03/16 18:53:25 - mmengine - INFO - Epoch(train) [11][1900/5005] lr: 1.0000e-01 eta: 23:49:14 time: 0.1780 data_time: 0.0022 loss: 2.3060 2023/03/16 18:53:35 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:53:44 - mmengine - INFO - Epoch(train) [11][2000/5005] lr: 1.0000e-01 eta: 23:48:54 time: 0.1860 data_time: 0.0021 loss: 2.2352 2023/03/16 18:54:03 - mmengine - INFO - Epoch(train) [11][2100/5005] lr: 1.0000e-01 eta: 23:48:31 time: 0.1967 data_time: 0.0027 loss: 2.3518 2023/03/16 18:54:22 - mmengine - INFO - Epoch(train) [11][2200/5005] lr: 1.0000e-01 eta: 23:48:09 time: 0.1773 data_time: 0.0021 loss: 2.3847 2023/03/16 18:54:40 - mmengine - INFO - Epoch(train) [11][2300/5005] lr: 1.0000e-01 eta: 23:47:41 time: 0.1826 data_time: 0.0023 loss: 2.3111 2023/03/16 18:54:59 - mmengine - INFO - Epoch(train) [11][2400/5005] lr: 1.0000e-01 eta: 23:47:24 time: 0.1945 data_time: 0.0023 loss: 2.4630 2023/03/16 18:55:19 - mmengine - INFO - Epoch(train) [11][2500/5005] lr: 1.0000e-01 eta: 23:47:06 time: 0.1885 data_time: 0.0021 loss: 2.3084 2023/03/16 18:55:38 - mmengine - INFO - Epoch(train) [11][2600/5005] lr: 1.0000e-01 eta: 23:46:51 time: 0.1976 data_time: 0.0025 loss: 2.2407 2023/03/16 18:55:58 - mmengine - INFO - Epoch(train) [11][2700/5005] lr: 1.0000e-01 eta: 23:46:38 time: 0.1952 data_time: 0.0022 loss: 2.3803 2023/03/16 18:56:18 - mmengine - INFO - Epoch(train) [11][2800/5005] lr: 1.0000e-01 eta: 23:46:25 time: 0.1923 data_time: 0.0023 loss: 2.1975 2023/03/16 18:56:38 - mmengine - INFO - Epoch(train) [11][2900/5005] lr: 1.0000e-01 eta: 23:46:12 time: 0.2006 data_time: 0.0021 loss: 2.5014 2023/03/16 18:56:48 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 18:56:58 - mmengine - INFO - Epoch(train) [11][3000/5005] lr: 1.0000e-01 eta: 23:45:58 time: 0.1853 data_time: 0.0023 loss: 2.2961 2023/03/16 18:57:16 - mmengine - INFO - Epoch(train) [11][3100/5005] lr: 1.0000e-01 eta: 23:45:31 time: 0.1797 data_time: 0.0023 loss: 2.3578 2023/03/16 18:57:34 - mmengine - INFO - Epoch(train) [11][3200/5005] lr: 1.0000e-01 eta: 23:45:00 time: 0.1719 data_time: 0.0024 loss: 2.2796 2023/03/16 18:57:52 - mmengine - INFO - Epoch(train) [11][3300/5005] lr: 1.0000e-01 eta: 23:44:32 time: 0.1868 data_time: 0.0023 loss: 2.3419 2023/03/16 18:58:11 - mmengine - INFO - Epoch(train) [11][3400/5005] lr: 1.0000e-01 eta: 23:44:13 time: 0.1884 data_time: 0.0023 loss: 2.3470 2023/03/16 18:58:30 - mmengine - INFO - Epoch(train) [11][3500/5005] lr: 1.0000e-01 eta: 23:43:56 time: 0.1934 data_time: 0.0026 loss: 2.3060 2023/03/16 18:58:50 - mmengine - INFO - Epoch(train) [11][3600/5005] lr: 1.0000e-01 eta: 23:43:42 time: 0.1908 data_time: 0.0024 loss: 2.6894 2023/03/16 18:59:09 - mmengine - INFO - Epoch(train) [11][3700/5005] lr: 1.0000e-01 eta: 23:43:22 time: 0.1934 data_time: 0.0024 loss: 2.2875 2023/03/16 18:59:29 - mmengine - INFO - Epoch(train) [11][3800/5005] lr: 1.0000e-01 eta: 23:43:07 time: 0.1810 data_time: 0.0024 loss: 2.4724 2023/03/16 18:59:47 - mmengine - INFO - Epoch(train) [11][3900/5005] lr: 1.0000e-01 eta: 23:42:42 time: 0.1823 data_time: 0.0024 loss: 2.4778 2023/03/16 18:59:57 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:00:06 - mmengine - INFO - Epoch(train) [11][4000/5005] lr: 1.0000e-01 eta: 23:42:21 time: 0.1892 data_time: 0.0024 loss: 2.3177 2023/03/16 19:00:25 - mmengine - INFO - Epoch(train) [11][4100/5005] lr: 1.0000e-01 eta: 23:41:57 time: 0.1912 data_time: 0.0023 loss: 2.2901 2023/03/16 19:00:43 - mmengine - INFO - Epoch(train) [11][4200/5005] lr: 1.0000e-01 eta: 23:41:34 time: 0.2128 data_time: 0.0021 loss: 2.5511 2023/03/16 19:01:02 - mmengine - INFO - Epoch(train) [11][4300/5005] lr: 1.0000e-01 eta: 23:41:14 time: 0.1822 data_time: 0.0020 loss: 2.2528 2023/03/16 19:01:21 - mmengine - INFO - Epoch(train) [11][4400/5005] lr: 1.0000e-01 eta: 23:40:49 time: 0.1829 data_time: 0.0023 loss: 2.5832 2023/03/16 19:01:39 - mmengine - INFO - Epoch(train) [11][4500/5005] lr: 1.0000e-01 eta: 23:40:27 time: 0.1838 data_time: 0.0023 loss: 2.1636 2023/03/16 19:01:58 - mmengine - INFO - Epoch(train) [11][4600/5005] lr: 1.0000e-01 eta: 23:40:05 time: 0.1883 data_time: 0.0022 loss: 2.6146 2023/03/16 19:02:17 - mmengine - INFO - Epoch(train) [11][4700/5005] lr: 1.0000e-01 eta: 23:39:44 time: 0.1877 data_time: 0.0023 loss: 2.2522 2023/03/16 19:02:36 - mmengine - INFO - Epoch(train) [11][4800/5005] lr: 1.0000e-01 eta: 23:39:23 time: 0.1975 data_time: 0.0021 loss: 2.2877 2023/03/16 19:02:55 - mmengine - INFO - Epoch(train) [11][4900/5005] lr: 1.0000e-01 eta: 23:39:04 time: 0.1868 data_time: 0.0027 loss: 2.2831 2023/03/16 19:03:04 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:03:14 - mmengine - INFO - Epoch(train) [11][5000/5005] lr: 1.0000e-01 eta: 23:38:38 time: 0.1848 data_time: 0.0034 loss: 2.4565 2023/03/16 19:03:15 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:03:15 - mmengine - INFO - Saving checkpoint at 11 epochs 2023/03/16 19:03:22 - mmengine - INFO - Epoch(val) [11][100/196] eta: 0:00:05 time: 0.0504 data_time: 0.0010 2023/03/16 19:03:51 - mmengine - INFO - Epoch(val) [11][196/196] accuracy/top1: 49.1160 accuracy/top5: 74.8820data_time: 0.0304 time: 0.0652 2023/03/16 19:04:10 - mmengine - INFO - Epoch(train) [12][ 100/5005] lr: 1.0000e-01 eta: 23:38:17 time: 0.1914 data_time: 0.0024 loss: 2.2712 2023/03/16 19:04:28 - mmengine - INFO - Epoch(train) [12][ 200/5005] lr: 1.0000e-01 eta: 23:37:53 time: 0.2194 data_time: 0.0026 loss: 2.4361 2023/03/16 19:04:47 - mmengine - INFO - Epoch(train) [12][ 300/5005] lr: 1.0000e-01 eta: 23:37:33 time: 0.1831 data_time: 0.0027 loss: 2.6481 2023/03/16 19:05:05 - mmengine - INFO - Epoch(train) [12][ 400/5005] lr: 1.0000e-01 eta: 23:37:04 time: 0.1774 data_time: 0.0026 loss: 2.2909 2023/03/16 19:05:28 - mmengine - INFO - Epoch(train) [12][ 500/5005] lr: 1.0000e-01 eta: 23:37:17 time: 0.2286 data_time: 0.0025 loss: 2.2803 2023/03/16 19:05:47 - mmengine - INFO - Epoch(train) [12][ 600/5005] lr: 1.0000e-01 eta: 23:36:51 time: 0.1719 data_time: 0.0022 loss: 2.1524 2023/03/16 19:06:07 - mmengine - INFO - Epoch(train) [12][ 700/5005] lr: 1.0000e-01 eta: 23:36:42 time: 0.1871 data_time: 0.0020 loss: 2.5538 2023/03/16 19:06:27 - mmengine - INFO - Epoch(train) [12][ 800/5005] lr: 1.0000e-01 eta: 23:36:25 time: 0.2343 data_time: 0.0023 loss: 2.3578 2023/03/16 19:06:47 - mmengine - INFO - Epoch(train) [12][ 900/5005] lr: 1.0000e-01 eta: 23:36:15 time: 0.1792 data_time: 0.0023 loss: 2.1514 2023/03/16 19:06:55 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:07:05 - mmengine - INFO - Epoch(train) [12][1000/5005] lr: 1.0000e-01 eta: 23:35:47 time: 0.1783 data_time: 0.0023 loss: 2.3665 2023/03/16 19:07:24 - mmengine - INFO - Epoch(train) [12][1100/5005] lr: 1.0000e-01 eta: 23:35:24 time: 0.2007 data_time: 0.0021 loss: 2.3500 2023/03/16 19:07:43 - mmengine - INFO - Epoch(train) [12][1200/5005] lr: 1.0000e-01 eta: 23:35:10 time: 0.2006 data_time: 0.0020 loss: 2.4000 2023/03/16 19:08:02 - mmengine - INFO - Epoch(train) [12][1300/5005] lr: 1.0000e-01 eta: 23:34:49 time: 0.1787 data_time: 0.0022 loss: 2.4786 2023/03/16 19:08:21 - mmengine - INFO - Epoch(train) [12][1400/5005] lr: 1.0000e-01 eta: 23:34:24 time: 0.1768 data_time: 0.0021 loss: 2.2179 2023/03/16 19:08:39 - mmengine - INFO - Epoch(train) [12][1500/5005] lr: 1.0000e-01 eta: 23:34:00 time: 0.1831 data_time: 0.0021 loss: 2.3684 2023/03/16 19:08:59 - mmengine - INFO - Epoch(train) [12][1600/5005] lr: 1.0000e-01 eta: 23:33:45 time: 0.2006 data_time: 0.0021 loss: 2.3450 2023/03/16 19:09:19 - mmengine - INFO - Epoch(train) [12][1700/5005] lr: 1.0000e-01 eta: 23:33:34 time: 0.1954 data_time: 0.0022 loss: 2.2678 2023/03/16 19:09:38 - mmengine - INFO - Epoch(train) [12][1800/5005] lr: 1.0000e-01 eta: 23:33:16 time: 0.1929 data_time: 0.0023 loss: 2.4524 2023/03/16 19:09:57 - mmengine - INFO - Epoch(train) [12][1900/5005] lr: 1.0000e-01 eta: 23:32:57 time: 0.1860 data_time: 0.0025 loss: 2.2785 2023/03/16 19:10:06 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:10:15 - mmengine - INFO - Epoch(train) [12][2000/5005] lr: 1.0000e-01 eta: 23:32:28 time: 0.1743 data_time: 0.0022 loss: 2.4399 2023/03/16 19:10:34 - mmengine - INFO - Epoch(train) [12][2100/5005] lr: 1.0000e-01 eta: 23:32:04 time: 0.1855 data_time: 0.0027 loss: 2.4116 2023/03/16 19:10:51 - mmengine - INFO - Epoch(train) [12][2200/5005] lr: 1.0000e-01 eta: 23:31:32 time: 0.1747 data_time: 0.0024 loss: 2.3017 2023/03/16 19:11:09 - mmengine - INFO - Epoch(train) [12][2300/5005] lr: 1.0000e-01 eta: 23:31:00 time: 0.1803 data_time: 0.0025 loss: 2.5180 2023/03/16 19:11:27 - mmengine - INFO - Epoch(train) [12][2400/5005] lr: 1.0000e-01 eta: 23:30:33 time: 0.1766 data_time: 0.0022 loss: 2.3231 2023/03/16 19:11:45 - mmengine - INFO - Epoch(train) [12][2500/5005] lr: 1.0000e-01 eta: 23:30:07 time: 0.1843 data_time: 0.0019 loss: 2.2702 2023/03/16 19:12:04 - mmengine - INFO - Epoch(train) [12][2600/5005] lr: 1.0000e-01 eta: 23:29:47 time: 0.1843 data_time: 0.0020 loss: 2.4106 2023/03/16 19:12:23 - mmengine - INFO - Epoch(train) [12][2700/5005] lr: 1.0000e-01 eta: 23:29:25 time: 0.1802 data_time: 0.0027 loss: 2.4046 2023/03/16 19:12:41 - mmengine - INFO - Epoch(train) [12][2800/5005] lr: 1.0000e-01 eta: 23:29:00 time: 0.1788 data_time: 0.0024 loss: 2.4134 2023/03/16 19:13:00 - mmengine - INFO - Epoch(train) [12][2900/5005] lr: 1.0000e-01 eta: 23:28:41 time: 0.1934 data_time: 0.0024 loss: 2.3117 2023/03/16 19:13:09 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:13:20 - mmengine - INFO - Epoch(train) [12][3000/5005] lr: 1.0000e-01 eta: 23:28:24 time: 0.1919 data_time: 0.0024 loss: 2.4568 2023/03/16 19:13:39 - mmengine - INFO - Epoch(train) [12][3100/5005] lr: 1.0000e-01 eta: 23:28:02 time: 0.1881 data_time: 0.0023 loss: 2.2243 2023/03/16 19:13:57 - mmengine - INFO - Epoch(train) [12][3200/5005] lr: 1.0000e-01 eta: 23:27:36 time: 0.1775 data_time: 0.0023 loss: 2.3804 2023/03/16 19:14:15 - mmengine - INFO - Epoch(train) [12][3300/5005] lr: 1.0000e-01 eta: 23:27:10 time: 0.1823 data_time: 0.0018 loss: 2.4506 2023/03/16 19:14:33 - mmengine - INFO - Epoch(train) [12][3400/5005] lr: 1.0000e-01 eta: 23:26:42 time: 0.1821 data_time: 0.0020 loss: 2.4282 2023/03/16 19:14:52 - mmengine - INFO - Epoch(train) [12][3500/5005] lr: 1.0000e-01 eta: 23:26:22 time: 0.2043 data_time: 0.0019 loss: 2.3311 2023/03/16 19:15:11 - mmengine - INFO - Epoch(train) [12][3600/5005] lr: 1.0000e-01 eta: 23:26:01 time: 0.1872 data_time: 0.0018 loss: 2.4594 2023/03/16 19:15:29 - mmengine - INFO - Epoch(train) [12][3700/5005] lr: 1.0000e-01 eta: 23:25:37 time: 0.1854 data_time: 0.0018 loss: 2.4654 2023/03/16 19:15:48 - mmengine - INFO - Epoch(train) [12][3800/5005] lr: 1.0000e-01 eta: 23:25:14 time: 0.1767 data_time: 0.0020 loss: 2.2843 2023/03/16 19:16:06 - mmengine - INFO - Epoch(train) [12][3900/5005] lr: 1.0000e-01 eta: 23:24:48 time: 0.1857 data_time: 0.0020 loss: 2.2570 2023/03/16 19:16:14 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:16:25 - mmengine - INFO - Epoch(train) [12][4000/5005] lr: 1.0000e-01 eta: 23:24:28 time: 0.2158 data_time: 0.0022 loss: 2.3176 2023/03/16 19:16:46 - mmengine - INFO - Epoch(train) [12][4100/5005] lr: 1.0000e-01 eta: 23:24:19 time: 0.2156 data_time: 0.0020 loss: 2.5163 2023/03/16 19:17:05 - mmengine - INFO - Epoch(train) [12][4200/5005] lr: 1.0000e-01 eta: 23:23:59 time: 0.1869 data_time: 0.0021 loss: 2.3558 2023/03/16 19:17:24 - mmengine - INFO - Epoch(train) [12][4300/5005] lr: 1.0000e-01 eta: 23:23:39 time: 0.1839 data_time: 0.0023 loss: 2.2184 2023/03/16 19:17:42 - mmengine - INFO - Epoch(train) [12][4400/5005] lr: 1.0000e-01 eta: 23:23:14 time: 0.1871 data_time: 0.0022 loss: 2.4189 2023/03/16 19:18:01 - mmengine - INFO - Epoch(train) [12][4500/5005] lr: 1.0000e-01 eta: 23:22:55 time: 0.1864 data_time: 0.0021 loss: 2.2859 2023/03/16 19:18:20 - mmengine - INFO - Epoch(train) [12][4600/5005] lr: 1.0000e-01 eta: 23:22:37 time: 0.2137 data_time: 0.0021 loss: 2.3273 2023/03/16 19:18:39 - mmengine - INFO - Epoch(train) [12][4700/5005] lr: 1.0000e-01 eta: 23:22:18 time: 0.1956 data_time: 0.0021 loss: 2.5621 2023/03/16 19:19:01 - mmengine - INFO - Epoch(train) [12][4800/5005] lr: 1.0000e-01 eta: 23:22:18 time: 0.2232 data_time: 0.0026 loss: 2.3559 2023/03/16 19:19:23 - mmengine - INFO - Epoch(train) [12][4900/5005] lr: 1.0000e-01 eta: 23:22:21 time: 0.2288 data_time: 0.0020 loss: 2.3193 2023/03/16 19:19:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:19:46 - mmengine - INFO - Epoch(train) [12][5000/5005] lr: 1.0000e-01 eta: 23:22:31 time: 0.2331 data_time: 0.0029 loss: 2.3949 2023/03/16 19:19:48 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:19:48 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/03/16 19:19:55 - mmengine - INFO - Epoch(val) [12][100/196] eta: 0:00:06 time: 0.0488 data_time: 0.0010 2023/03/16 19:20:20 - mmengine - INFO - Epoch(val) [12][196/196] accuracy/top1: 48.6280 accuracy/top5: 74.5960data_time: 0.0245 time: 0.0568 2023/03/16 19:20:42 - mmengine - INFO - Epoch(train) [13][ 100/5005] lr: 1.0000e-01 eta: 23:22:38 time: 0.1886 data_time: 0.0022 loss: 2.3480 2023/03/16 19:21:01 - mmengine - INFO - Epoch(train) [13][ 200/5005] lr: 1.0000e-01 eta: 23:22:12 time: 0.1786 data_time: 0.0021 loss: 2.4301 2023/03/16 19:21:20 - mmengine - INFO - Epoch(train) [13][ 300/5005] lr: 1.0000e-01 eta: 23:21:53 time: 0.1789 data_time: 0.0021 loss: 2.1102 2023/03/16 19:21:37 - mmengine - INFO - Epoch(train) [13][ 400/5005] lr: 1.0000e-01 eta: 23:21:22 time: 0.1784 data_time: 0.0021 loss: 2.2494 2023/03/16 19:21:56 - mmengine - INFO - Epoch(train) [13][ 500/5005] lr: 1.0000e-01 eta: 23:20:58 time: 0.1828 data_time: 0.0020 loss: 2.2421 2023/03/16 19:22:14 - mmengine - INFO - Epoch(train) [13][ 600/5005] lr: 1.0000e-01 eta: 23:20:31 time: 0.1796 data_time: 0.0020 loss: 2.3189 2023/03/16 19:22:33 - mmengine - INFO - Epoch(train) [13][ 700/5005] lr: 1.0000e-01 eta: 23:20:11 time: 0.2068 data_time: 0.0023 loss: 2.3588 2023/03/16 19:22:55 - mmengine - INFO - Epoch(train) [13][ 800/5005] lr: 1.0000e-01 eta: 23:20:14 time: 0.2556 data_time: 0.0020 loss: 2.3238 2023/03/16 19:23:14 - mmengine - INFO - Epoch(train) [13][ 900/5005] lr: 1.0000e-01 eta: 23:19:55 time: 0.1697 data_time: 0.0021 loss: 2.5254 2023/03/16 19:23:21 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:23:31 - mmengine - INFO - Epoch(train) [13][1000/5005] lr: 1.0000e-01 eta: 23:19:23 time: 0.1864 data_time: 0.0020 loss: 2.3236 2023/03/16 19:23:50 - mmengine - INFO - Epoch(train) [13][1100/5005] lr: 1.0000e-01 eta: 23:18:57 time: 0.1678 data_time: 0.0021 loss: 2.4114 2023/03/16 19:24:08 - mmengine - INFO - Epoch(train) [13][1200/5005] lr: 1.0000e-01 eta: 23:18:32 time: 0.1827 data_time: 0.0022 loss: 2.0594 2023/03/16 19:24:26 - mmengine - INFO - Epoch(train) [13][1300/5005] lr: 1.0000e-01 eta: 23:18:05 time: 0.1853 data_time: 0.0024 loss: 2.3164 2023/03/16 19:24:45 - mmengine - INFO - Epoch(train) [13][1400/5005] lr: 1.0000e-01 eta: 23:17:42 time: 0.1825 data_time: 0.0021 loss: 2.3311 2023/03/16 19:25:05 - mmengine - INFO - Epoch(train) [13][1500/5005] lr: 1.0000e-01 eta: 23:17:28 time: 0.1835 data_time: 0.0023 loss: 2.3576 2023/03/16 19:25:23 - mmengine - INFO - Epoch(train) [13][1600/5005] lr: 1.0000e-01 eta: 23:17:02 time: 0.1785 data_time: 0.0024 loss: 2.2763 2023/03/16 19:25:43 - mmengine - INFO - Epoch(train) [13][1700/5005] lr: 1.0000e-01 eta: 23:16:52 time: 0.1851 data_time: 0.0021 loss: 2.5082 2023/03/16 19:26:01 - mmengine - INFO - Epoch(train) [13][1800/5005] lr: 1.0000e-01 eta: 23:16:21 time: 0.1661 data_time: 0.0021 loss: 2.2134 2023/03/16 19:26:17 - mmengine - INFO - Epoch(train) [13][1900/5005] lr: 1.0000e-01 eta: 23:15:44 time: 0.1660 data_time: 0.0022 loss: 2.3624 2023/03/16 19:26:24 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:26:34 - mmengine - INFO - Epoch(train) [13][2000/5005] lr: 1.0000e-01 eta: 23:15:11 time: 0.1684 data_time: 0.0021 loss: 2.2251 2023/03/16 19:26:52 - mmengine - INFO - Epoch(train) [13][2100/5005] lr: 1.0000e-01 eta: 23:14:43 time: 0.2000 data_time: 0.0023 loss: 2.3246 2023/03/16 19:27:11 - mmengine - INFO - Epoch(train) [13][2200/5005] lr: 1.0000e-01 eta: 23:14:25 time: 0.1804 data_time: 0.0024 loss: 2.3555 2023/03/16 19:27:29 - mmengine - INFO - Epoch(train) [13][2300/5005] lr: 1.0000e-01 eta: 23:13:53 time: 0.1767 data_time: 0.0022 loss: 2.3859 2023/03/16 19:27:47 - mmengine - INFO - Epoch(train) [13][2400/5005] lr: 1.0000e-01 eta: 23:13:28 time: 0.2057 data_time: 0.0022 loss: 2.5283 2023/03/16 19:28:08 - mmengine - INFO - Epoch(train) [13][2500/5005] lr: 1.0000e-01 eta: 23:13:21 time: 0.2601 data_time: 0.0020 loss: 2.2913 2023/03/16 19:28:29 - mmengine - INFO - Epoch(train) [13][2600/5005] lr: 1.0000e-01 eta: 23:13:13 time: 0.1800 data_time: 0.0021 loss: 2.3794 2023/03/16 19:28:47 - mmengine - INFO - Epoch(train) [13][2700/5005] lr: 1.0000e-01 eta: 23:12:50 time: 0.2156 data_time: 0.0023 loss: 1.9032 2023/03/16 19:29:05 - mmengine - INFO - Epoch(train) [13][2800/5005] lr: 1.0000e-01 eta: 23:12:19 time: 0.1749 data_time: 0.0026 loss: 2.3926 2023/03/16 19:29:22 - mmengine - INFO - Epoch(train) [13][2900/5005] lr: 1.0000e-01 eta: 23:11:48 time: 0.1719 data_time: 0.0025 loss: 2.4098 2023/03/16 19:29:29 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:29:39 - mmengine - INFO - Epoch(train) [13][3000/5005] lr: 1.0000e-01 eta: 23:11:15 time: 0.1716 data_time: 0.0023 loss: 2.3765 2023/03/16 19:29:57 - mmengine - INFO - Epoch(train) [13][3100/5005] lr: 1.0000e-01 eta: 23:10:47 time: 0.1729 data_time: 0.0024 loss: 2.3254 2023/03/16 19:30:18 - mmengine - INFO - Epoch(train) [13][3200/5005] lr: 1.0000e-01 eta: 23:10:38 time: 0.1979 data_time: 0.0022 loss: 2.4965 2023/03/16 19:30:36 - mmengine - INFO - Epoch(train) [13][3300/5005] lr: 1.0000e-01 eta: 23:10:13 time: 0.2412 data_time: 0.0024 loss: 2.2264 2023/03/16 19:30:57 - mmengine - INFO - Epoch(train) [13][3400/5005] lr: 1.0000e-01 eta: 23:10:08 time: 0.1853 data_time: 0.0023 loss: 2.2627 2023/03/16 19:31:16 - mmengine - INFO - Epoch(train) [13][3500/5005] lr: 1.0000e-01 eta: 23:09:46 time: 0.1810 data_time: 0.0023 loss: 2.4071 2023/03/16 19:31:33 - mmengine - INFO - Epoch(train) [13][3600/5005] lr: 1.0000e-01 eta: 23:09:13 time: 0.1679 data_time: 0.0024 loss: 2.4288 2023/03/16 19:31:50 - mmengine - INFO - Epoch(train) [13][3700/5005] lr: 1.0000e-01 eta: 23:08:42 time: 0.1862 data_time: 0.0025 loss: 2.5851 2023/03/16 19:32:08 - mmengine - INFO - Epoch(train) [13][3800/5005] lr: 1.0000e-01 eta: 23:08:16 time: 0.1772 data_time: 0.0024 loss: 2.1812 2023/03/16 19:32:27 - mmengine - INFO - Epoch(train) [13][3900/5005] lr: 1.0000e-01 eta: 23:07:57 time: 0.1859 data_time: 0.0026 loss: 2.4079 2023/03/16 19:32:35 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:32:46 - mmengine - INFO - Epoch(train) [13][4000/5005] lr: 1.0000e-01 eta: 23:07:36 time: 0.1885 data_time: 0.0026 loss: 2.4653 2023/03/16 19:33:05 - mmengine - INFO - Epoch(train) [13][4100/5005] lr: 1.0000e-01 eta: 23:07:13 time: 0.1820 data_time: 0.0022 loss: 2.1464 2023/03/16 19:33:23 - mmengine - INFO - Epoch(train) [13][4200/5005] lr: 1.0000e-01 eta: 23:06:47 time: 0.1770 data_time: 0.0023 loss: 2.2130 2023/03/16 19:33:41 - mmengine - INFO - Epoch(train) [13][4300/5005] lr: 1.0000e-01 eta: 23:06:21 time: 0.1866 data_time: 0.0024 loss: 2.2805 2023/03/16 19:34:02 - mmengine - INFO - Epoch(train) [13][4400/5005] lr: 1.0000e-01 eta: 23:06:16 time: 0.1900 data_time: 0.0022 loss: 2.4071 2023/03/16 19:34:20 - mmengine - INFO - Epoch(train) [13][4500/5005] lr: 1.0000e-01 eta: 23:05:50 time: 0.1793 data_time: 0.0024 loss: 2.3497 2023/03/16 19:34:38 - mmengine - INFO - Epoch(train) [13][4600/5005] lr: 1.0000e-01 eta: 23:05:24 time: 0.1775 data_time: 0.0022 loss: 2.3540 2023/03/16 19:34:57 - mmengine - INFO - Epoch(train) [13][4700/5005] lr: 1.0000e-01 eta: 23:05:00 time: 0.1824 data_time: 0.0022 loss: 2.0699 2023/03/16 19:35:14 - mmengine - INFO - Epoch(train) [13][4800/5005] lr: 1.0000e-01 eta: 23:04:30 time: 0.1736 data_time: 0.0024 loss: 2.0466 2023/03/16 19:35:32 - mmengine - INFO - Epoch(train) [13][4900/5005] lr: 1.0000e-01 eta: 23:04:03 time: 0.1765 data_time: 0.0024 loss: 2.4531 2023/03/16 19:35:40 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:35:53 - mmengine - INFO - Epoch(train) [13][5000/5005] lr: 1.0000e-01 eta: 23:03:56 time: 0.2161 data_time: 0.0031 loss: 2.3595 2023/03/16 19:35:54 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:35:54 - mmengine - INFO - Saving checkpoint at 13 epochs 2023/03/16 19:36:01 - mmengine - INFO - Epoch(val) [13][100/196] eta: 0:00:05 time: 0.0521 data_time: 0.0009 2023/03/16 19:36:30 - mmengine - INFO - Epoch(val) [13][196/196] accuracy/top1: 50.3720 accuracy/top5: 76.6880data_time: 0.0291 time: 0.0595 2023/03/16 19:36:52 - mmengine - INFO - Epoch(train) [14][ 100/5005] lr: 1.0000e-01 eta: 23:03:53 time: 0.1890 data_time: 0.0023 loss: 2.3587 2023/03/16 19:37:10 - mmengine - INFO - Epoch(train) [14][ 200/5005] lr: 1.0000e-01 eta: 23:03:28 time: 0.1730 data_time: 0.0023 loss: 2.4959 2023/03/16 19:37:30 - mmengine - INFO - Epoch(train) [14][ 300/5005] lr: 1.0000e-01 eta: 23:03:12 time: 0.1862 data_time: 0.0024 loss: 2.2753 2023/03/16 19:37:48 - mmengine - INFO - Epoch(train) [14][ 400/5005] lr: 1.0000e-01 eta: 23:02:46 time: 0.1860 data_time: 0.0022 loss: 2.4464 2023/03/16 19:38:08 - mmengine - INFO - Epoch(train) [14][ 500/5005] lr: 1.0000e-01 eta: 23:02:36 time: 0.1807 data_time: 0.0024 loss: 2.2136 2023/03/16 19:38:26 - mmengine - INFO - Epoch(train) [14][ 600/5005] lr: 1.0000e-01 eta: 23:02:12 time: 0.1801 data_time: 0.0023 loss: 2.2244 2023/03/16 19:38:45 - mmengine - INFO - Epoch(train) [14][ 700/5005] lr: 1.0000e-01 eta: 23:01:50 time: 0.1830 data_time: 0.0020 loss: 2.6032 2023/03/16 19:39:04 - mmengine - INFO - Epoch(train) [14][ 800/5005] lr: 1.0000e-01 eta: 23:01:29 time: 0.1766 data_time: 0.0028 loss: 2.3127 2023/03/16 19:39:21 - mmengine - INFO - Epoch(train) [14][ 900/5005] lr: 1.0000e-01 eta: 23:00:58 time: 0.1820 data_time: 0.0023 loss: 2.2638 2023/03/16 19:39:28 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:39:40 - mmengine - INFO - Epoch(train) [14][1000/5005] lr: 1.0000e-01 eta: 23:00:37 time: 0.2330 data_time: 0.0023 loss: 2.0814 2023/03/16 19:39:59 - mmengine - INFO - Epoch(train) [14][1100/5005] lr: 1.0000e-01 eta: 23:00:19 time: 0.1805 data_time: 0.0024 loss: 2.3301 2023/03/16 19:40:19 - mmengine - INFO - Epoch(train) [14][1200/5005] lr: 1.0000e-01 eta: 23:00:06 time: 0.2296 data_time: 0.0024 loss: 2.5105 2023/03/16 19:40:39 - mmengine - INFO - Epoch(train) [14][1300/5005] lr: 1.0000e-01 eta: 22:59:54 time: 0.2075 data_time: 0.0024 loss: 2.2964 2023/03/16 19:40:58 - mmengine - INFO - Epoch(train) [14][1400/5005] lr: 1.0000e-01 eta: 22:59:30 time: 0.1793 data_time: 0.0020 loss: 2.3764 2023/03/16 19:41:16 - mmengine - INFO - Epoch(train) [14][1500/5005] lr: 1.0000e-01 eta: 22:59:07 time: 0.1801 data_time: 0.0025 loss: 2.2054 2023/03/16 19:41:34 - mmengine - INFO - Epoch(train) [14][1600/5005] lr: 1.0000e-01 eta: 22:58:40 time: 0.1818 data_time: 0.0024 loss: 2.3751 2023/03/16 19:41:52 - mmengine - INFO - Epoch(train) [14][1700/5005] lr: 1.0000e-01 eta: 22:58:14 time: 0.1787 data_time: 0.0021 loss: 2.5631 2023/03/16 19:42:11 - mmengine - INFO - Epoch(train) [14][1800/5005] lr: 1.0000e-01 eta: 22:57:50 time: 0.1738 data_time: 0.0024 loss: 2.3117 2023/03/16 19:42:28 - mmengine - INFO - Epoch(train) [14][1900/5005] lr: 1.0000e-01 eta: 22:57:23 time: 0.1883 data_time: 0.0024 loss: 2.0927 2023/03/16 19:42:35 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:42:47 - mmengine - INFO - Epoch(train) [14][2000/5005] lr: 1.0000e-01 eta: 22:57:01 time: 0.2072 data_time: 0.0022 loss: 2.1445 2023/03/16 19:43:07 - mmengine - INFO - Epoch(train) [14][2100/5005] lr: 1.0000e-01 eta: 22:56:50 time: 0.2135 data_time: 0.0020 loss: 1.8857 2023/03/16 19:43:28 - mmengine - INFO - Epoch(train) [14][2200/5005] lr: 1.0000e-01 eta: 22:56:41 time: 0.1866 data_time: 0.0021 loss: 2.1963 2023/03/16 19:43:47 - mmengine - INFO - Epoch(train) [14][2300/5005] lr: 1.0000e-01 eta: 22:56:18 time: 0.1809 data_time: 0.0022 loss: 2.1623 2023/03/16 19:44:06 - mmengine - INFO - Epoch(train) [14][2400/5005] lr: 1.0000e-01 eta: 22:55:59 time: 0.1888 data_time: 0.0021 loss: 2.3029 2023/03/16 19:44:24 - mmengine - INFO - Epoch(train) [14][2500/5005] lr: 1.0000e-01 eta: 22:55:36 time: 0.1762 data_time: 0.0023 loss: 2.7014 2023/03/16 19:44:44 - mmengine - INFO - Epoch(train) [14][2600/5005] lr: 1.0000e-01 eta: 22:55:19 time: 0.1968 data_time: 0.0020 loss: 2.2613 2023/03/16 19:45:03 - mmengine - INFO - Epoch(train) [14][2700/5005] lr: 1.0000e-01 eta: 22:55:04 time: 0.1943 data_time: 0.0022 loss: 2.2039 2023/03/16 19:45:23 - mmengine - INFO - Epoch(train) [14][2800/5005] lr: 1.0000e-01 eta: 22:54:47 time: 0.1947 data_time: 0.0021 loss: 2.3390 2023/03/16 19:45:43 - mmengine - INFO - Epoch(train) [14][2900/5005] lr: 1.0000e-01 eta: 22:54:32 time: 0.2006 data_time: 0.0026 loss: 2.3117 2023/03/16 19:45:50 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:46:02 - mmengine - INFO - Epoch(train) [14][3000/5005] lr: 1.0000e-01 eta: 22:54:15 time: 0.1902 data_time: 0.0022 loss: 2.1752 2023/03/16 19:46:22 - mmengine - INFO - Epoch(train) [14][3100/5005] lr: 1.0000e-01 eta: 22:53:59 time: 0.1943 data_time: 0.0022 loss: 2.5099 2023/03/16 19:46:41 - mmengine - INFO - Epoch(train) [14][3200/5005] lr: 1.0000e-01 eta: 22:53:42 time: 0.1893 data_time: 0.0022 loss: 2.5018 2023/03/16 19:47:00 - mmengine - INFO - Epoch(train) [14][3300/5005] lr: 1.0000e-01 eta: 22:53:24 time: 0.1870 data_time: 0.0021 loss: 2.3294 2023/03/16 19:47:19 - mmengine - INFO - Epoch(train) [14][3400/5005] lr: 1.0000e-01 eta: 22:53:02 time: 0.1850 data_time: 0.0021 loss: 2.5808 2023/03/16 19:47:38 - mmengine - INFO - Epoch(train) [14][3500/5005] lr: 1.0000e-01 eta: 22:52:40 time: 0.1882 data_time: 0.0021 loss: 2.1403 2023/03/16 19:47:57 - mmengine - INFO - Epoch(train) [14][3600/5005] lr: 1.0000e-01 eta: 22:52:25 time: 0.2047 data_time: 0.0022 loss: 2.3583 2023/03/16 19:48:18 - mmengine - INFO - Epoch(train) [14][3700/5005] lr: 1.0000e-01 eta: 22:52:15 time: 0.2007 data_time: 0.0021 loss: 2.2802 2023/03/16 19:48:37 - mmengine - INFO - Epoch(train) [14][3800/5005] lr: 1.0000e-01 eta: 22:51:58 time: 0.1837 data_time: 0.0022 loss: 2.4088 2023/03/16 19:48:57 - mmengine - INFO - Epoch(train) [14][3900/5005] lr: 1.0000e-01 eta: 22:51:40 time: 0.1959 data_time: 0.0023 loss: 2.3118 2023/03/16 19:49:04 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:49:16 - mmengine - INFO - Epoch(train) [14][4000/5005] lr: 1.0000e-01 eta: 22:51:25 time: 0.1903 data_time: 0.0021 loss: 2.3629 2023/03/16 19:49:36 - mmengine - INFO - Epoch(train) [14][4100/5005] lr: 1.0000e-01 eta: 22:51:06 time: 0.1972 data_time: 0.0025 loss: 2.3889 2023/03/16 19:49:55 - mmengine - INFO - Epoch(train) [14][4200/5005] lr: 1.0000e-01 eta: 22:50:50 time: 0.2206 data_time: 0.0021 loss: 2.4204 2023/03/16 19:50:14 - mmengine - INFO - Epoch(train) [14][4300/5005] lr: 1.0000e-01 eta: 22:50:32 time: 0.1906 data_time: 0.0023 loss: 2.3926 2023/03/16 19:50:34 - mmengine - INFO - Epoch(train) [14][4400/5005] lr: 1.0000e-01 eta: 22:50:16 time: 0.2016 data_time: 0.0022 loss: 2.2676 2023/03/16 19:50:54 - mmengine - INFO - Epoch(train) [14][4500/5005] lr: 1.0000e-01 eta: 22:50:02 time: 0.1927 data_time: 0.0022 loss: 2.1803 2023/03/16 19:51:13 - mmengine - INFO - Epoch(train) [14][4600/5005] lr: 1.0000e-01 eta: 22:49:44 time: 0.1859 data_time: 0.0020 loss: 2.2304 2023/03/16 19:51:32 - mmengine - INFO - Epoch(train) [14][4700/5005] lr: 1.0000e-01 eta: 22:49:22 time: 0.1879 data_time: 0.0024 loss: 2.2470 2023/03/16 19:51:51 - mmengine - INFO - Epoch(train) [14][4800/5005] lr: 1.0000e-01 eta: 22:49:03 time: 0.1893 data_time: 0.0021 loss: 2.3201 2023/03/16 19:52:10 - mmengine - INFO - Epoch(train) [14][4900/5005] lr: 1.0000e-01 eta: 22:48:45 time: 0.1900 data_time: 0.0027 loss: 2.3851 2023/03/16 19:52:17 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:52:30 - mmengine - INFO - Epoch(train) [14][5000/5005] lr: 1.0000e-01 eta: 22:48:30 time: 0.2160 data_time: 0.0035 loss: 2.2539 2023/03/16 19:52:31 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:52:32 - mmengine - INFO - Saving checkpoint at 14 epochs 2023/03/16 19:52:38 - mmengine - INFO - Epoch(val) [14][100/196] eta: 0:00:05 time: 0.0475 data_time: 0.0010 2023/03/16 19:53:06 - mmengine - INFO - Epoch(val) [14][196/196] accuracy/top1: 52.5240 accuracy/top5: 78.2360data_time: 0.0262 time: 0.0580 2023/03/16 19:53:35 - mmengine - INFO - Epoch(train) [15][ 100/5005] lr: 1.0000e-01 eta: 22:49:15 time: 0.2881 data_time: 0.0023 loss: 2.2781 2023/03/16 19:53:56 - mmengine - INFO - Epoch(train) [15][ 200/5005] lr: 1.0000e-01 eta: 22:49:07 time: 0.1908 data_time: 0.0029 loss: 2.3195 2023/03/16 19:54:15 - mmengine - INFO - Epoch(train) [15][ 300/5005] lr: 1.0000e-01 eta: 22:48:47 time: 0.1989 data_time: 0.0022 loss: 2.4390 2023/03/16 19:54:33 - mmengine - INFO - Epoch(train) [15][ 400/5005] lr: 1.0000e-01 eta: 22:48:23 time: 0.1859 data_time: 0.0025 loss: 2.4237 2023/03/16 19:54:54 - mmengine - INFO - Epoch(train) [15][ 500/5005] lr: 1.0000e-01 eta: 22:48:11 time: 0.2261 data_time: 0.0022 loss: 2.3079 2023/03/16 19:55:13 - mmengine - INFO - Epoch(train) [15][ 600/5005] lr: 1.0000e-01 eta: 22:47:55 time: 0.1955 data_time: 0.0024 loss: 2.2327 2023/03/16 19:55:33 - mmengine - INFO - Epoch(train) [15][ 700/5005] lr: 1.0000e-01 eta: 22:47:39 time: 0.1823 data_time: 0.0025 loss: 2.4839 2023/03/16 19:55:51 - mmengine - INFO - Epoch(train) [15][ 800/5005] lr: 1.0000e-01 eta: 22:47:12 time: 0.1814 data_time: 0.0023 loss: 2.2770 2023/03/16 19:56:08 - mmengine - INFO - Epoch(train) [15][ 900/5005] lr: 1.0000e-01 eta: 22:46:44 time: 0.1742 data_time: 0.0019 loss: 2.3555 2023/03/16 19:56:14 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:56:26 - mmengine - INFO - Epoch(train) [15][1000/5005] lr: 1.0000e-01 eta: 22:46:18 time: 0.1864 data_time: 0.0024 loss: 2.0689 2023/03/16 19:56:45 - mmengine - INFO - Epoch(train) [15][1100/5005] lr: 1.0000e-01 eta: 22:45:54 time: 0.1836 data_time: 0.0026 loss: 2.5609 2023/03/16 19:57:05 - mmengine - INFO - Epoch(train) [15][1200/5005] lr: 1.0000e-01 eta: 22:45:44 time: 0.2405 data_time: 0.0025 loss: 2.3418 2023/03/16 19:57:27 - mmengine - INFO - Epoch(train) [15][1300/5005] lr: 1.0000e-01 eta: 22:45:39 time: 0.1839 data_time: 0.0026 loss: 2.2053 2023/03/16 19:57:46 - mmengine - INFO - Epoch(train) [15][1400/5005] lr: 1.0000e-01 eta: 22:45:21 time: 0.1773 data_time: 0.0024 loss: 2.3789 2023/03/16 19:58:07 - mmengine - INFO - Epoch(train) [15][1500/5005] lr: 1.0000e-01 eta: 22:45:10 time: 0.2381 data_time: 0.0018 loss: 2.2799 2023/03/16 19:58:31 - mmengine - INFO - Epoch(train) [15][1600/5005] lr: 1.0000e-01 eta: 22:45:24 time: 0.2455 data_time: 0.0020 loss: 2.3845 2023/03/16 19:58:52 - mmengine - INFO - Epoch(train) [15][1700/5005] lr: 1.0000e-01 eta: 22:45:11 time: 0.1868 data_time: 0.0025 loss: 2.3003 2023/03/16 19:59:10 - mmengine - INFO - Epoch(train) [15][1800/5005] lr: 1.0000e-01 eta: 22:44:47 time: 0.1842 data_time: 0.0024 loss: 2.0789 2023/03/16 19:59:28 - mmengine - INFO - Epoch(train) [15][1900/5005] lr: 1.0000e-01 eta: 22:44:21 time: 0.1821 data_time: 0.0023 loss: 2.2541 2023/03/16 19:59:33 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 19:59:46 - mmengine - INFO - Epoch(train) [15][2000/5005] lr: 1.0000e-01 eta: 22:43:57 time: 0.1773 data_time: 0.0024 loss: 2.3009 2023/03/16 20:00:04 - mmengine - INFO - Epoch(train) [15][2100/5005] lr: 1.0000e-01 eta: 22:43:30 time: 0.1730 data_time: 0.0028 loss: 2.2050 2023/03/16 20:00:21 - mmengine - INFO - Epoch(train) [15][2200/5005] lr: 1.0000e-01 eta: 22:43:01 time: 0.1847 data_time: 0.0027 loss: 2.2847 2023/03/16 20:00:39 - mmengine - INFO - Epoch(train) [15][2300/5005] lr: 1.0000e-01 eta: 22:42:35 time: 0.1738 data_time: 0.0024 loss: 2.2898 2023/03/16 20:00:57 - mmengine - INFO - Epoch(train) [15][2400/5005] lr: 1.0000e-01 eta: 22:42:10 time: 0.1783 data_time: 0.0025 loss: 2.4901 2023/03/16 20:01:16 - mmengine - INFO - Epoch(train) [15][2500/5005] lr: 1.0000e-01 eta: 22:41:48 time: 0.1870 data_time: 0.0024 loss: 2.4099 2023/03/16 20:01:36 - mmengine - INFO - Epoch(train) [15][2600/5005] lr: 1.0000e-01 eta: 22:41:32 time: 0.2145 data_time: 0.0024 loss: 2.3670 2023/03/16 20:01:56 - mmengine - INFO - Epoch(train) [15][2700/5005] lr: 1.0000e-01 eta: 22:41:21 time: 0.1896 data_time: 0.0025 loss: 2.2774 2023/03/16 20:02:16 - mmengine - INFO - Epoch(train) [15][2800/5005] lr: 1.0000e-01 eta: 22:41:07 time: 0.1898 data_time: 0.0027 loss: 2.2278 2023/03/16 20:02:36 - mmengine - INFO - Epoch(train) [15][2900/5005] lr: 1.0000e-01 eta: 22:40:51 time: 0.1943 data_time: 0.0027 loss: 2.2439 2023/03/16 20:02:42 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:02:55 - mmengine - INFO - Epoch(train) [15][3000/5005] lr: 1.0000e-01 eta: 22:40:34 time: 0.2118 data_time: 0.0025 loss: 2.4650 2023/03/16 20:03:15 - mmengine - INFO - Epoch(train) [15][3100/5005] lr: 1.0000e-01 eta: 22:40:15 time: 0.1881 data_time: 0.0027 loss: 2.2465 2023/03/16 20:03:33 - mmengine - INFO - Epoch(train) [15][3200/5005] lr: 1.0000e-01 eta: 22:39:53 time: 0.1870 data_time: 0.0026 loss: 2.3559 2023/03/16 20:03:52 - mmengine - INFO - Epoch(train) [15][3300/5005] lr: 1.0000e-01 eta: 22:39:33 time: 0.1848 data_time: 0.0025 loss: 2.2275 2023/03/16 20:04:11 - mmengine - INFO - Epoch(train) [15][3400/5005] lr: 1.0000e-01 eta: 22:39:12 time: 0.1929 data_time: 0.0027 loss: 2.5983 2023/03/16 20:04:30 - mmengine - INFO - Epoch(train) [15][3500/5005] lr: 1.0000e-01 eta: 22:38:54 time: 0.1881 data_time: 0.0027 loss: 2.1213 2023/03/16 20:04:49 - mmengine - INFO - Epoch(train) [15][3600/5005] lr: 1.0000e-01 eta: 22:38:33 time: 0.1867 data_time: 0.0026 loss: 2.1811 2023/03/16 20:05:07 - mmengine - INFO - Epoch(train) [15][3700/5005] lr: 1.0000e-01 eta: 22:38:09 time: 0.1825 data_time: 0.0026 loss: 2.3508 2023/03/16 20:05:26 - mmengine - INFO - Epoch(train) [15][3800/5005] lr: 1.0000e-01 eta: 22:37:46 time: 0.1861 data_time: 0.0023 loss: 2.2568 2023/03/16 20:05:45 - mmengine - INFO - Epoch(train) [15][3900/5005] lr: 1.0000e-01 eta: 22:37:28 time: 0.1854 data_time: 0.0022 loss: 2.4710 2023/03/16 20:05:51 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:06:05 - mmengine - INFO - Epoch(train) [15][4000/5005] lr: 1.0000e-01 eta: 22:37:10 time: 0.1874 data_time: 0.0025 loss: 2.2094 2023/03/16 20:06:24 - mmengine - INFO - Epoch(train) [15][4100/5005] lr: 1.0000e-01 eta: 22:36:53 time: 0.2013 data_time: 0.0027 loss: 2.3361 2023/03/16 20:06:44 - mmengine - INFO - Epoch(train) [15][4200/5005] lr: 1.0000e-01 eta: 22:36:36 time: 0.1846 data_time: 0.0025 loss: 2.2861 2023/03/16 20:07:03 - mmengine - INFO - Epoch(train) [15][4300/5005] lr: 1.0000e-01 eta: 22:36:18 time: 0.1840 data_time: 0.0025 loss: 2.3304 2023/03/16 20:07:22 - mmengine - INFO - Epoch(train) [15][4400/5005] lr: 1.0000e-01 eta: 22:36:00 time: 0.1912 data_time: 0.0025 loss: 2.2070 2023/03/16 20:07:42 - mmengine - INFO - Epoch(train) [15][4500/5005] lr: 1.0000e-01 eta: 22:35:43 time: 0.1952 data_time: 0.0026 loss: 2.4094 2023/03/16 20:08:01 - mmengine - INFO - Epoch(train) [15][4600/5005] lr: 1.0000e-01 eta: 22:35:26 time: 0.1931 data_time: 0.0028 loss: 2.5366 2023/03/16 20:08:21 - mmengine - INFO - Epoch(train) [15][4700/5005] lr: 1.0000e-01 eta: 22:35:09 time: 0.1959 data_time: 0.0021 loss: 2.2319 2023/03/16 20:08:39 - mmengine - INFO - Epoch(train) [15][4800/5005] lr: 1.0000e-01 eta: 22:34:48 time: 0.1889 data_time: 0.0022 loss: 2.0850 2023/03/16 20:09:00 - mmengine - INFO - Epoch(train) [15][4900/5005] lr: 1.0000e-01 eta: 22:34:36 time: 0.2035 data_time: 0.0027 loss: 2.4134 2023/03/16 20:09:06 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:09:20 - mmengine - INFO - Epoch(train) [15][5000/5005] lr: 1.0000e-01 eta: 22:34:23 time: 0.2178 data_time: 0.0031 loss: 2.2077 2023/03/16 20:09:21 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:09:22 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/03/16 20:09:28 - mmengine - INFO - Epoch(val) [15][100/196] eta: 0:00:05 time: 0.0468 data_time: 0.0009 2023/03/16 20:09:53 - mmengine - INFO - Epoch(val) [15][196/196] accuracy/top1: 51.6260 accuracy/top5: 77.4240data_time: 0.0069 time: 0.0365 2023/03/16 20:10:18 - mmengine - INFO - Epoch(train) [16][ 100/5005] lr: 1.0000e-01 eta: 22:34:35 time: 0.2023 data_time: 0.0021 loss: 2.2639 2023/03/16 20:10:37 - mmengine - INFO - Epoch(train) [16][ 200/5005] lr: 1.0000e-01 eta: 22:34:16 time: 0.1926 data_time: 0.0023 loss: 2.2370 2023/03/16 20:11:02 - mmengine - INFO - Epoch(train) [16][ 300/5005] lr: 1.0000e-01 eta: 22:34:28 time: 0.2466 data_time: 0.0018 loss: 2.3552 2023/03/16 20:11:26 - mmengine - INFO - Epoch(train) [16][ 400/5005] lr: 1.0000e-01 eta: 22:34:37 time: 0.2634 data_time: 0.0020 loss: 2.4292 2023/03/16 20:11:47 - mmengine - INFO - Epoch(train) [16][ 500/5005] lr: 1.0000e-01 eta: 22:34:31 time: 0.1953 data_time: 0.0024 loss: 2.2320 2023/03/16 20:12:11 - mmengine - INFO - Epoch(train) [16][ 600/5005] lr: 1.0000e-01 eta: 22:34:37 time: 0.2488 data_time: 0.0019 loss: 2.2450 2023/03/16 20:12:30 - mmengine - INFO - Epoch(train) [16][ 700/5005] lr: 1.0000e-01 eta: 22:34:17 time: 0.1856 data_time: 0.0022 loss: 2.3672 2023/03/16 20:12:48 - mmengine - INFO - Epoch(train) [16][ 800/5005] lr: 1.0000e-01 eta: 22:33:55 time: 0.1792 data_time: 0.0025 loss: 2.1496 2023/03/16 20:13:06 - mmengine - INFO - Epoch(train) [16][ 900/5005] lr: 1.0000e-01 eta: 22:33:25 time: 0.1715 data_time: 0.0024 loss: 2.3771 2023/03/16 20:13:10 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:13:23 - mmengine - INFO - Epoch(train) [16][1000/5005] lr: 1.0000e-01 eta: 22:32:57 time: 0.1885 data_time: 0.0024 loss: 2.2544 2023/03/16 20:13:43 - mmengine - INFO - Epoch(train) [16][1100/5005] lr: 1.0000e-01 eta: 22:32:39 time: 0.1877 data_time: 0.0025 loss: 2.3333 2023/03/16 20:14:01 - mmengine - INFO - Epoch(train) [16][1200/5005] lr: 1.0000e-01 eta: 22:32:18 time: 0.1767 data_time: 0.0024 loss: 2.2464 2023/03/16 20:14:20 - mmengine - INFO - Epoch(train) [16][1300/5005] lr: 1.0000e-01 eta: 22:31:53 time: 0.1901 data_time: 0.0025 loss: 2.3797 2023/03/16 20:14:40 - mmengine - INFO - Epoch(train) [16][1400/5005] lr: 1.0000e-01 eta: 22:31:38 time: 0.1868 data_time: 0.0024 loss: 2.3590 2023/03/16 20:15:01 - mmengine - INFO - Epoch(train) [16][1500/5005] lr: 1.0000e-01 eta: 22:31:34 time: 0.1920 data_time: 0.0025 loss: 2.0843 2023/03/16 20:15:20 - mmengine - INFO - Epoch(train) [16][1600/5005] lr: 1.0000e-01 eta: 22:31:12 time: 0.1762 data_time: 0.0023 loss: 2.4728 2023/03/16 20:15:38 - mmengine - INFO - Epoch(train) [16][1700/5005] lr: 1.0000e-01 eta: 22:30:44 time: 0.1673 data_time: 0.0026 loss: 2.2241 2023/03/16 20:15:55 - mmengine - INFO - Epoch(train) [16][1800/5005] lr: 1.0000e-01 eta: 22:30:14 time: 0.1726 data_time: 0.0024 loss: 2.1767 2023/03/16 20:16:14 - mmengine - INFO - Epoch(train) [16][1900/5005] lr: 1.0000e-01 eta: 22:29:56 time: 0.2049 data_time: 0.0024 loss: 2.2049 2023/03/16 20:16:19 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:16:32 - mmengine - INFO - Epoch(train) [16][2000/5005] lr: 1.0000e-01 eta: 22:29:31 time: 0.1773 data_time: 0.0025 loss: 2.3915 2023/03/16 20:16:51 - mmengine - INFO - Epoch(train) [16][2100/5005] lr: 1.0000e-01 eta: 22:29:10 time: 0.1946 data_time: 0.0025 loss: 2.1960 2023/03/16 20:17:10 - mmengine - INFO - Epoch(train) [16][2200/5005] lr: 1.0000e-01 eta: 22:28:48 time: 0.1798 data_time: 0.0024 loss: 2.3998 2023/03/16 20:17:28 - mmengine - INFO - Epoch(train) [16][2300/5005] lr: 1.0000e-01 eta: 22:28:25 time: 0.1772 data_time: 0.0023 loss: 2.3903 2023/03/16 20:17:46 - mmengine - INFO - Epoch(train) [16][2400/5005] lr: 1.0000e-01 eta: 22:28:00 time: 0.1769 data_time: 0.0027 loss: 2.4070 2023/03/16 20:18:05 - mmengine - INFO - Epoch(train) [16][2500/5005] lr: 1.0000e-01 eta: 22:27:40 time: 0.1901 data_time: 0.0023 loss: 2.2708 2023/03/16 20:18:24 - mmengine - INFO - Epoch(train) [16][2600/5005] lr: 1.0000e-01 eta: 22:27:18 time: 0.1771 data_time: 0.0023 loss: 2.2418 2023/03/16 20:18:43 - mmengine - INFO - Epoch(train) [16][2700/5005] lr: 1.0000e-01 eta: 22:26:59 time: 0.2407 data_time: 0.0022 loss: 2.2494 2023/03/16 20:19:05 - mmengine - INFO - Epoch(train) [16][2800/5005] lr: 1.0000e-01 eta: 22:26:56 time: 0.1752 data_time: 0.0024 loss: 2.0418 2023/03/16 20:19:24 - mmengine - INFO - Epoch(train) [16][2900/5005] lr: 1.0000e-01 eta: 22:26:34 time: 0.1929 data_time: 0.0024 loss: 2.3951 2023/03/16 20:19:29 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:19:43 - mmengine - INFO - Epoch(train) [16][3000/5005] lr: 1.0000e-01 eta: 22:26:13 time: 0.1873 data_time: 0.0023 loss: 2.3517 2023/03/16 20:20:02 - mmengine - INFO - Epoch(train) [16][3100/5005] lr: 1.0000e-01 eta: 22:25:52 time: 0.1867 data_time: 0.0025 loss: 2.1711 2023/03/16 20:20:19 - mmengine - INFO - Epoch(train) [16][3200/5005] lr: 1.0000e-01 eta: 22:25:24 time: 0.1764 data_time: 0.0025 loss: 2.3357 2023/03/16 20:20:38 - mmengine - INFO - Epoch(train) [16][3300/5005] lr: 1.0000e-01 eta: 22:25:03 time: 0.1808 data_time: 0.0024 loss: 2.4070 2023/03/16 20:20:57 - mmengine - INFO - Epoch(train) [16][3400/5005] lr: 1.0000e-01 eta: 22:24:42 time: 0.2119 data_time: 0.0018 loss: 2.2345 2023/03/16 20:21:16 - mmengine - INFO - Epoch(train) [16][3500/5005] lr: 1.0000e-01 eta: 22:24:24 time: 0.2147 data_time: 0.0022 loss: 2.3217 2023/03/16 20:21:38 - mmengine - INFO - Epoch(train) [16][3600/5005] lr: 1.0000e-01 eta: 22:24:19 time: 0.2442 data_time: 0.0021 loss: 2.2291 2023/03/16 20:21:56 - mmengine - INFO - Epoch(train) [16][3700/5005] lr: 1.0000e-01 eta: 22:23:55 time: 0.1809 data_time: 0.0023 loss: 2.3255 2023/03/16 20:22:14 - mmengine - INFO - Epoch(train) [16][3800/5005] lr: 1.0000e-01 eta: 22:23:32 time: 0.1956 data_time: 0.0024 loss: 2.1429 2023/03/16 20:22:32 - mmengine - INFO - Epoch(train) [16][3900/5005] lr: 1.0000e-01 eta: 22:23:03 time: 0.1758 data_time: 0.0025 loss: 2.2272 2023/03/16 20:22:36 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:22:50 - mmengine - INFO - Epoch(train) [16][4000/5005] lr: 1.0000e-01 eta: 22:22:39 time: 0.1829 data_time: 0.0025 loss: 2.2283 2023/03/16 20:23:09 - mmengine - INFO - Epoch(train) [16][4100/5005] lr: 1.0000e-01 eta: 22:22:17 time: 0.1818 data_time: 0.0024 loss: 2.2403 2023/03/16 20:23:27 - mmengine - INFO - Epoch(train) [16][4200/5005] lr: 1.0000e-01 eta: 22:21:55 time: 0.1848 data_time: 0.0026 loss: 2.0548 2023/03/16 20:23:46 - mmengine - INFO - Epoch(train) [16][4300/5005] lr: 1.0000e-01 eta: 22:21:35 time: 0.1859 data_time: 0.0022 loss: 2.3780 2023/03/16 20:24:05 - mmengine - INFO - Epoch(train) [16][4400/5005] lr: 1.0000e-01 eta: 22:21:13 time: 0.1754 data_time: 0.0022 loss: 2.4118 2023/03/16 20:24:23 - mmengine - INFO - Epoch(train) [16][4500/5005] lr: 1.0000e-01 eta: 22:20:49 time: 0.1833 data_time: 0.0024 loss: 2.3425 2023/03/16 20:24:42 - mmengine - INFO - Epoch(train) [16][4600/5005] lr: 1.0000e-01 eta: 22:20:27 time: 0.1833 data_time: 0.0024 loss: 2.4008 2023/03/16 20:25:01 - mmengine - INFO - Epoch(train) [16][4700/5005] lr: 1.0000e-01 eta: 22:20:09 time: 0.2471 data_time: 0.0022 loss: 2.0744 2023/03/16 20:25:26 - mmengine - INFO - Epoch(train) [16][4800/5005] lr: 1.0000e-01 eta: 22:20:18 time: 0.1931 data_time: 0.0020 loss: 2.3174 2023/03/16 20:25:44 - mmengine - INFO - Epoch(train) [16][4900/5005] lr: 1.0000e-01 eta: 22:19:56 time: 0.1849 data_time: 0.0023 loss: 2.5002 2023/03/16 20:25:49 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:26:07 - mmengine - INFO - Epoch(train) [16][5000/5005] lr: 1.0000e-01 eta: 22:19:54 time: 0.2691 data_time: 0.0030 loss: 2.2362 2023/03/16 20:26:08 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:26:09 - mmengine - INFO - Saving checkpoint at 16 epochs 2023/03/16 20:26:15 - mmengine - INFO - Epoch(val) [16][100/196] eta: 0:00:05 time: 0.0481 data_time: 0.0009 2023/03/16 20:26:39 - mmengine - INFO - Epoch(val) [16][196/196] accuracy/top1: 52.1100 accuracy/top5: 78.0320data_time: 0.0249 time: 0.0639 2023/03/16 20:27:01 - mmengine - INFO - Epoch(train) [17][ 100/5005] lr: 1.0000e-01 eta: 22:19:48 time: 0.1849 data_time: 0.0028 loss: 2.4622 2023/03/16 20:27:19 - mmengine - INFO - Epoch(train) [17][ 200/5005] lr: 1.0000e-01 eta: 22:19:26 time: 0.1876 data_time: 0.0025 loss: 2.5867 2023/03/16 20:27:38 - mmengine - INFO - Epoch(train) [17][ 300/5005] lr: 1.0000e-01 eta: 22:19:05 time: 0.1802 data_time: 0.0024 loss: 2.1620 2023/03/16 20:28:02 - mmengine - INFO - Epoch(train) [17][ 400/5005] lr: 1.0000e-01 eta: 22:19:10 time: 0.2487 data_time: 0.0017 loss: 2.2667 2023/03/16 20:28:24 - mmengine - INFO - Epoch(train) [17][ 500/5005] lr: 1.0000e-01 eta: 22:19:06 time: 0.1922 data_time: 0.0026 loss: 2.4317 2023/03/16 20:28:43 - mmengine - INFO - Epoch(train) [17][ 600/5005] lr: 1.0000e-01 eta: 22:18:45 time: 0.1931 data_time: 0.0023 loss: 2.0788 2023/03/16 20:29:00 - mmengine - INFO - Epoch(train) [17][ 700/5005] lr: 1.0000e-01 eta: 22:18:18 time: 0.1677 data_time: 0.0023 loss: 2.3365 2023/03/16 20:29:17 - mmengine - INFO - Epoch(train) [17][ 800/5005] lr: 1.0000e-01 eta: 22:17:48 time: 0.1680 data_time: 0.0024 loss: 2.2466 2023/03/16 20:29:35 - mmengine - INFO - Epoch(train) [17][ 900/5005] lr: 1.0000e-01 eta: 22:17:21 time: 0.1843 data_time: 0.0027 loss: 2.4255 2023/03/16 20:29:40 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:29:55 - mmengine - INFO - Epoch(train) [17][1000/5005] lr: 1.0000e-01 eta: 22:17:07 time: 0.1766 data_time: 0.0028 loss: 2.3420 2023/03/16 20:30:13 - mmengine - INFO - Epoch(train) [17][1100/5005] lr: 1.0000e-01 eta: 22:16:42 time: 0.1817 data_time: 0.0026 loss: 2.0818 2023/03/16 20:30:31 - mmengine - INFO - Epoch(train) [17][1200/5005] lr: 1.0000e-01 eta: 22:16:15 time: 0.1719 data_time: 0.0023 loss: 2.2257 2023/03/16 20:30:48 - mmengine - INFO - Epoch(train) [17][1300/5005] lr: 1.0000e-01 eta: 22:15:45 time: 0.1711 data_time: 0.0024 loss: 2.1791 2023/03/16 20:31:06 - mmengine - INFO - Epoch(train) [17][1400/5005] lr: 1.0000e-01 eta: 22:15:21 time: 0.1876 data_time: 0.0025 loss: 2.4325 2023/03/16 20:31:24 - mmengine - INFO - Epoch(train) [17][1500/5005] lr: 1.0000e-01 eta: 22:14:57 time: 0.2076 data_time: 0.0025 loss: 2.2741 2023/03/16 20:31:44 - mmengine - INFO - Epoch(train) [17][1600/5005] lr: 1.0000e-01 eta: 22:14:39 time: 0.1886 data_time: 0.0025 loss: 2.4863 2023/03/16 20:32:03 - mmengine - INFO - Epoch(train) [17][1700/5005] lr: 1.0000e-01 eta: 22:14:18 time: 0.1744 data_time: 0.0024 loss: 2.2227 2023/03/16 20:32:20 - mmengine - INFO - Epoch(train) [17][1800/5005] lr: 1.0000e-01 eta: 22:13:49 time: 0.1808 data_time: 0.0025 loss: 2.3928 2023/03/16 20:32:37 - mmengine - INFO - Epoch(train) [17][1900/5005] lr: 1.0000e-01 eta: 22:13:22 time: 0.1687 data_time: 0.0023 loss: 2.4528 2023/03/16 20:32:41 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:32:56 - mmengine - INFO - Epoch(train) [17][2000/5005] lr: 1.0000e-01 eta: 22:12:59 time: 0.2145 data_time: 0.0024 loss: 2.2036 2023/03/16 20:33:14 - mmengine - INFO - Epoch(train) [17][2100/5005] lr: 1.0000e-01 eta: 22:12:36 time: 0.1770 data_time: 0.0024 loss: 2.1400 2023/03/16 20:33:31 - mmengine - INFO - Epoch(train) [17][2200/5005] lr: 1.0000e-01 eta: 22:12:07 time: 0.1660 data_time: 0.0027 loss: 2.2945 2023/03/16 20:33:48 - mmengine - INFO - Epoch(train) [17][2300/5005] lr: 1.0000e-01 eta: 22:11:36 time: 0.1745 data_time: 0.0026 loss: 2.3082 2023/03/16 20:34:06 - mmengine - INFO - Epoch(train) [17][2400/5005] lr: 1.0000e-01 eta: 22:11:10 time: 0.2038 data_time: 0.0025 loss: 2.2645 2023/03/16 20:34:24 - mmengine - INFO - Epoch(train) [17][2500/5005] lr: 1.0000e-01 eta: 22:10:46 time: 0.1820 data_time: 0.0023 loss: 2.2946 2023/03/16 20:34:43 - mmengine - INFO - Epoch(train) [17][2600/5005] lr: 1.0000e-01 eta: 22:10:24 time: 0.1917 data_time: 0.0025 loss: 2.2976 2023/03/16 20:35:02 - mmengine - INFO - Epoch(train) [17][2700/5005] lr: 1.0000e-01 eta: 22:10:05 time: 0.1831 data_time: 0.0024 loss: 2.4002 2023/03/16 20:35:19 - mmengine - INFO - Epoch(train) [17][2800/5005] lr: 1.0000e-01 eta: 22:09:37 time: 0.1718 data_time: 0.0023 loss: 2.3926 2023/03/16 20:35:37 - mmengine - INFO - Epoch(train) [17][2900/5005] lr: 1.0000e-01 eta: 22:09:11 time: 0.1849 data_time: 0.0022 loss: 2.2811 2023/03/16 20:35:42 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:36:01 - mmengine - INFO - Epoch(train) [17][3000/5005] lr: 1.0000e-01 eta: 22:09:15 time: 0.1857 data_time: 0.0019 loss: 2.2503 2023/03/16 20:36:19 - mmengine - INFO - Epoch(train) [17][3100/5005] lr: 1.0000e-01 eta: 22:08:50 time: 0.1739 data_time: 0.0022 loss: 2.2234 2023/03/16 20:36:36 - mmengine - INFO - Epoch(train) [17][3200/5005] lr: 1.0000e-01 eta: 22:08:20 time: 0.1795 data_time: 0.0025 loss: 2.2980 2023/03/16 20:36:54 - mmengine - INFO - Epoch(train) [17][3300/5005] lr: 1.0000e-01 eta: 22:07:54 time: 0.1798 data_time: 0.0026 loss: 2.1211 2023/03/16 20:37:13 - mmengine - INFO - Epoch(train) [17][3400/5005] lr: 1.0000e-01 eta: 22:07:36 time: 0.1833 data_time: 0.0025 loss: 2.1729 2023/03/16 20:37:31 - mmengine - INFO - Epoch(train) [17][3500/5005] lr: 1.0000e-01 eta: 22:07:12 time: 0.1784 data_time: 0.0025 loss: 2.1184 2023/03/16 20:37:49 - mmengine - INFO - Epoch(train) [17][3600/5005] lr: 1.0000e-01 eta: 22:06:46 time: 0.1759 data_time: 0.0027 loss: 2.2607 2023/03/16 20:38:06 - mmengine - INFO - Epoch(train) [17][3700/5005] lr: 1.0000e-01 eta: 22:06:18 time: 0.1711 data_time: 0.0027 loss: 2.1554 2023/03/16 20:38:24 - mmengine - INFO - Epoch(train) [17][3800/5005] lr: 1.0000e-01 eta: 22:05:52 time: 0.1880 data_time: 0.0026 loss: 2.4831 2023/03/16 20:38:42 - mmengine - INFO - Epoch(train) [17][3900/5005] lr: 1.0000e-01 eta: 22:05:28 time: 0.1787 data_time: 0.0026 loss: 2.2427 2023/03/16 20:38:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:39:00 - mmengine - INFO - Epoch(train) [17][4000/5005] lr: 1.0000e-01 eta: 22:05:05 time: 0.1772 data_time: 0.0022 loss: 2.4979 2023/03/16 20:39:18 - mmengine - INFO - Epoch(train) [17][4100/5005] lr: 1.0000e-01 eta: 22:04:38 time: 0.1748 data_time: 0.0025 loss: 2.3331 2023/03/16 20:39:36 - mmengine - INFO - Epoch(train) [17][4200/5005] lr: 1.0000e-01 eta: 22:04:15 time: 0.1993 data_time: 0.0025 loss: 2.2042 2023/03/16 20:40:00 - mmengine - INFO - Epoch(train) [17][4300/5005] lr: 1.0000e-01 eta: 22:04:17 time: 0.1880 data_time: 0.0027 loss: 2.3025 2023/03/16 20:40:19 - mmengine - INFO - Epoch(train) [17][4400/5005] lr: 1.0000e-01 eta: 22:03:57 time: 0.1851 data_time: 0.0023 loss: 2.3957 2023/03/16 20:40:37 - mmengine - INFO - Epoch(train) [17][4500/5005] lr: 1.0000e-01 eta: 22:03:33 time: 0.1740 data_time: 0.0022 loss: 1.9743 2023/03/16 20:40:55 - mmengine - INFO - Epoch(train) [17][4600/5005] lr: 1.0000e-01 eta: 22:03:11 time: 0.1866 data_time: 0.0024 loss: 2.3303 2023/03/16 20:41:14 - mmengine - INFO - Epoch(train) [17][4700/5005] lr: 1.0000e-01 eta: 22:02:51 time: 0.1888 data_time: 0.0024 loss: 2.1567 2023/03/16 20:41:32 - mmengine - INFO - Epoch(train) [17][4800/5005] lr: 1.0000e-01 eta: 22:02:26 time: 0.1829 data_time: 0.0027 loss: 2.2200 2023/03/16 20:41:51 - mmengine - INFO - Epoch(train) [17][4900/5005] lr: 1.0000e-01 eta: 22:02:05 time: 0.1785 data_time: 0.0025 loss: 2.1946 2023/03/16 20:41:55 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:42:09 - mmengine - INFO - Epoch(train) [17][5000/5005] lr: 1.0000e-01 eta: 22:01:40 time: 0.1789 data_time: 0.0030 loss: 2.2310 2023/03/16 20:42:10 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:42:10 - mmengine - INFO - Saving checkpoint at 17 epochs 2023/03/16 20:42:17 - mmengine - INFO - Epoch(val) [17][100/196] eta: 0:00:05 time: 0.0413 data_time: 0.0053 2023/03/16 20:42:43 - mmengine - INFO - Epoch(val) [17][196/196] accuracy/top1: 51.9980 accuracy/top5: 77.5900data_time: 0.0214 time: 0.0509 2023/03/16 20:43:04 - mmengine - INFO - Epoch(train) [18][ 100/5005] lr: 1.0000e-01 eta: 22:01:26 time: 0.2089 data_time: 0.0024 loss: 2.3437 2023/03/16 20:43:22 - mmengine - INFO - Epoch(train) [18][ 200/5005] lr: 1.0000e-01 eta: 22:01:00 time: 0.1804 data_time: 0.0024 loss: 2.2661 2023/03/16 20:43:41 - mmengine - INFO - Epoch(train) [18][ 300/5005] lr: 1.0000e-01 eta: 22:00:42 time: 0.2185 data_time: 0.0022 loss: 2.0173 2023/03/16 20:43:59 - mmengine - INFO - Epoch(train) [18][ 400/5005] lr: 1.0000e-01 eta: 22:00:18 time: 0.1796 data_time: 0.0023 loss: 2.4033 2023/03/16 20:44:21 - mmengine - INFO - Epoch(train) [18][ 500/5005] lr: 1.0000e-01 eta: 22:00:10 time: 0.1854 data_time: 0.0024 loss: 2.3297 2023/03/16 20:44:39 - mmengine - INFO - Epoch(train) [18][ 600/5005] lr: 1.0000e-01 eta: 21:59:50 time: 0.1998 data_time: 0.0024 loss: 2.1319 2023/03/16 20:45:00 - mmengine - INFO - Epoch(train) [18][ 700/5005] lr: 1.0000e-01 eta: 21:59:37 time: 0.1746 data_time: 0.0024 loss: 2.4686 2023/03/16 20:45:18 - mmengine - INFO - Epoch(train) [18][ 800/5005] lr: 1.0000e-01 eta: 21:59:12 time: 0.1832 data_time: 0.0022 loss: 2.4537 2023/03/16 20:45:35 - mmengine - INFO - Epoch(train) [18][ 900/5005] lr: 1.0000e-01 eta: 21:58:45 time: 0.1746 data_time: 0.0023 loss: 2.2564 2023/03/16 20:45:38 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:45:53 - mmengine - INFO - Epoch(train) [18][1000/5005] lr: 1.0000e-01 eta: 21:58:20 time: 0.1778 data_time: 0.0026 loss: 2.2111 2023/03/16 20:46:11 - mmengine - INFO - Epoch(train) [18][1100/5005] lr: 1.0000e-01 eta: 21:57:53 time: 0.1749 data_time: 0.0025 loss: 2.4621 2023/03/16 20:46:28 - mmengine - INFO - Epoch(train) [18][1200/5005] lr: 1.0000e-01 eta: 21:57:26 time: 0.1702 data_time: 0.0026 loss: 2.3497 2023/03/16 20:46:47 - mmengine - INFO - Epoch(train) [18][1300/5005] lr: 1.0000e-01 eta: 21:57:04 time: 0.1861 data_time: 0.0027 loss: 2.4297 2023/03/16 20:47:04 - mmengine - INFO - Epoch(train) [18][1400/5005] lr: 1.0000e-01 eta: 21:56:36 time: 0.1743 data_time: 0.0023 loss: 2.3176 2023/03/16 20:47:21 - mmengine - INFO - Epoch(train) [18][1500/5005] lr: 1.0000e-01 eta: 21:56:08 time: 0.1718 data_time: 0.0022 loss: 2.4665 2023/03/16 20:47:39 - mmengine - INFO - Epoch(train) [18][1600/5005] lr: 1.0000e-01 eta: 21:55:43 time: 0.1772 data_time: 0.0025 loss: 2.4885 2023/03/16 20:47:58 - mmengine - INFO - Epoch(train) [18][1700/5005] lr: 1.0000e-01 eta: 21:55:22 time: 0.1978 data_time: 0.0022 loss: 2.3265 2023/03/16 20:48:20 - mmengine - INFO - Epoch(train) [18][1800/5005] lr: 1.0000e-01 eta: 21:55:15 time: 0.2600 data_time: 0.0025 loss: 2.2513 2023/03/16 20:48:38 - mmengine - INFO - Epoch(train) [18][1900/5005] lr: 1.0000e-01 eta: 21:54:51 time: 0.1774 data_time: 0.0024 loss: 2.3609 2023/03/16 20:48:40 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:48:56 - mmengine - INFO - Epoch(train) [18][2000/5005] lr: 1.0000e-01 eta: 21:54:27 time: 0.1791 data_time: 0.0024 loss: 2.2782 2023/03/16 20:49:14 - mmengine - INFO - Epoch(train) [18][2100/5005] lr: 1.0000e-01 eta: 21:54:06 time: 0.2087 data_time: 0.0025 loss: 2.2458 2023/03/16 20:49:33 - mmengine - INFO - Epoch(train) [18][2200/5005] lr: 1.0000e-01 eta: 21:53:46 time: 0.1855 data_time: 0.0024 loss: 2.2237 2023/03/16 20:49:52 - mmengine - INFO - Epoch(train) [18][2300/5005] lr: 1.0000e-01 eta: 21:53:24 time: 0.1879 data_time: 0.0023 loss: 2.2706 2023/03/16 20:50:10 - mmengine - INFO - Epoch(train) [18][2400/5005] lr: 1.0000e-01 eta: 21:52:59 time: 0.1747 data_time: 0.0022 loss: 2.0744 2023/03/16 20:50:27 - mmengine - INFO - Epoch(train) [18][2500/5005] lr: 1.0000e-01 eta: 21:52:32 time: 0.1768 data_time: 0.0022 loss: 2.3400 2023/03/16 20:50:45 - mmengine - INFO - Epoch(train) [18][2600/5005] lr: 1.0000e-01 eta: 21:52:08 time: 0.1789 data_time: 0.0025 loss: 2.3420 2023/03/16 20:51:03 - mmengine - INFO - Epoch(train) [18][2700/5005] lr: 1.0000e-01 eta: 21:51:45 time: 0.1749 data_time: 0.0024 loss: 2.2207 2023/03/16 20:51:22 - mmengine - INFO - Epoch(train) [18][2800/5005] lr: 1.0000e-01 eta: 21:51:21 time: 0.2056 data_time: 0.0027 loss: 2.5953 2023/03/16 20:51:40 - mmengine - INFO - Epoch(train) [18][2900/5005] lr: 1.0000e-01 eta: 21:51:00 time: 0.1724 data_time: 0.0024 loss: 2.5179 2023/03/16 20:51:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:52:03 - mmengine - INFO - Epoch(train) [18][3000/5005] lr: 1.0000e-01 eta: 21:50:57 time: 0.2059 data_time: 0.0026 loss: 2.2294 2023/03/16 20:52:21 - mmengine - INFO - Epoch(train) [18][3100/5005] lr: 1.0000e-01 eta: 21:50:35 time: 0.1813 data_time: 0.0026 loss: 2.1810 2023/03/16 20:52:40 - mmengine - INFO - Epoch(train) [18][3200/5005] lr: 1.0000e-01 eta: 21:50:12 time: 0.1843 data_time: 0.0024 loss: 2.3258 2023/03/16 20:52:58 - mmengine - INFO - Epoch(train) [18][3300/5005] lr: 1.0000e-01 eta: 21:49:48 time: 0.1774 data_time: 0.0023 loss: 2.4067 2023/03/16 20:53:16 - mmengine - INFO - Epoch(train) [18][3400/5005] lr: 1.0000e-01 eta: 21:49:25 time: 0.1829 data_time: 0.0025 loss: 2.2536 2023/03/16 20:53:34 - mmengine - INFO - Epoch(train) [18][3500/5005] lr: 1.0000e-01 eta: 21:49:02 time: 0.1805 data_time: 0.0022 loss: 2.2788 2023/03/16 20:53:52 - mmengine - INFO - Epoch(train) [18][3600/5005] lr: 1.0000e-01 eta: 21:48:38 time: 0.1772 data_time: 0.0025 loss: 2.1864 2023/03/16 20:54:10 - mmengine - INFO - Epoch(train) [18][3700/5005] lr: 1.0000e-01 eta: 21:48:13 time: 0.2042 data_time: 0.0022 loss: 2.2770 2023/03/16 20:54:29 - mmengine - INFO - Epoch(train) [18][3800/5005] lr: 1.0000e-01 eta: 21:47:51 time: 0.1853 data_time: 0.0026 loss: 2.0845 2023/03/16 20:54:47 - mmengine - INFO - Epoch(train) [18][3900/5005] lr: 1.0000e-01 eta: 21:47:29 time: 0.1829 data_time: 0.0024 loss: 2.1453 2023/03/16 20:54:50 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:55:06 - mmengine - INFO - Epoch(train) [18][4000/5005] lr: 1.0000e-01 eta: 21:47:10 time: 0.1807 data_time: 0.0022 loss: 2.4083 2023/03/16 20:55:24 - mmengine - INFO - Epoch(train) [18][4100/5005] lr: 1.0000e-01 eta: 21:46:46 time: 0.1830 data_time: 0.0024 loss: 2.0726 2023/03/16 20:55:43 - mmengine - INFO - Epoch(train) [18][4200/5005] lr: 1.0000e-01 eta: 21:46:24 time: 0.2094 data_time: 0.0022 loss: 2.5672 2023/03/16 20:56:01 - mmengine - INFO - Epoch(train) [18][4300/5005] lr: 1.0000e-01 eta: 21:46:03 time: 0.1815 data_time: 0.0021 loss: 2.4168 2023/03/16 20:56:21 - mmengine - INFO - Epoch(train) [18][4400/5005] lr: 1.0000e-01 eta: 21:45:48 time: 0.2205 data_time: 0.0025 loss: 1.8368 2023/03/16 20:56:39 - mmengine - INFO - Epoch(train) [18][4500/5005] lr: 1.0000e-01 eta: 21:45:24 time: 0.1765 data_time: 0.0023 loss: 2.3607 2023/03/16 20:56:57 - mmengine - INFO - Epoch(train) [18][4600/5005] lr: 1.0000e-01 eta: 21:45:00 time: 0.1807 data_time: 0.0024 loss: 2.2494 2023/03/16 20:57:15 - mmengine - INFO - Epoch(train) [18][4700/5005] lr: 1.0000e-01 eta: 21:44:32 time: 0.1689 data_time: 0.0024 loss: 2.6296 2023/03/16 20:57:32 - mmengine - INFO - Epoch(train) [18][4800/5005] lr: 1.0000e-01 eta: 21:44:06 time: 0.1714 data_time: 0.0022 loss: 2.0804 2023/03/16 20:57:50 - mmengine - INFO - Epoch(train) [18][4900/5005] lr: 1.0000e-01 eta: 21:43:40 time: 0.1785 data_time: 0.0023 loss: 2.5723 2023/03/16 20:57:53 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:58:09 - mmengine - INFO - Epoch(train) [18][5000/5005] lr: 1.0000e-01 eta: 21:43:20 time: 0.1857 data_time: 0.0030 loss: 2.1197 2023/03/16 20:58:10 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 20:58:10 - mmengine - INFO - Saving checkpoint at 18 epochs 2023/03/16 20:58:17 - mmengine - INFO - Epoch(val) [18][100/196] eta: 0:00:05 time: 0.0429 data_time: 0.0085 2023/03/16 20:58:44 - mmengine - INFO - Epoch(val) [18][196/196] accuracy/top1: 53.7100 accuracy/top5: 79.2920data_time: 0.0312 time: 0.0641 2023/03/16 20:59:06 - mmengine - INFO - Epoch(train) [19][ 100/5005] lr: 1.0000e-01 eta: 21:43:14 time: 0.2097 data_time: 0.0023 loss: 2.3786 2023/03/16 20:59:25 - mmengine - INFO - Epoch(train) [19][ 200/5005] lr: 1.0000e-01 eta: 21:42:54 time: 0.1829 data_time: 0.0023 loss: 2.2932 2023/03/16 20:59:44 - mmengine - INFO - Epoch(train) [19][ 300/5005] lr: 1.0000e-01 eta: 21:42:36 time: 0.1883 data_time: 0.0024 loss: 2.2160 2023/03/16 21:00:04 - mmengine - INFO - Epoch(train) [19][ 400/5005] lr: 1.0000e-01 eta: 21:42:19 time: 0.1830 data_time: 0.0023 loss: 2.2561 2023/03/16 21:00:23 - mmengine - INFO - Epoch(train) [19][ 500/5005] lr: 1.0000e-01 eta: 21:41:58 time: 0.1830 data_time: 0.0025 loss: 2.2387 2023/03/16 21:00:41 - mmengine - INFO - Epoch(train) [19][ 600/5005] lr: 1.0000e-01 eta: 21:41:35 time: 0.1820 data_time: 0.0024 loss: 2.1862 2023/03/16 21:00:59 - mmengine - INFO - Epoch(train) [19][ 700/5005] lr: 1.0000e-01 eta: 21:41:12 time: 0.1766 data_time: 0.0026 loss: 2.3537 2023/03/16 21:01:20 - mmengine - INFO - Epoch(train) [19][ 800/5005] lr: 1.0000e-01 eta: 21:41:02 time: 0.2125 data_time: 0.0023 loss: 2.2292 2023/03/16 21:01:40 - mmengine - INFO - Epoch(train) [19][ 900/5005] lr: 1.0000e-01 eta: 21:40:46 time: 0.2333 data_time: 0.0022 loss: 2.1996 2023/03/16 21:01:42 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:02:01 - mmengine - INFO - Epoch(train) [19][1000/5005] lr: 1.0000e-01 eta: 21:40:34 time: 0.1810 data_time: 0.0023 loss: 1.9934 2023/03/16 21:02:18 - mmengine - INFO - Epoch(train) [19][1100/5005] lr: 1.0000e-01 eta: 21:40:10 time: 0.1772 data_time: 0.0024 loss: 2.2622 2023/03/16 21:02:37 - mmengine - INFO - Epoch(train) [19][1200/5005] lr: 1.0000e-01 eta: 21:39:49 time: 0.2030 data_time: 0.0025 loss: 2.4725 2023/03/16 21:02:57 - mmengine - INFO - Epoch(train) [19][1300/5005] lr: 1.0000e-01 eta: 21:39:32 time: 0.1847 data_time: 0.0024 loss: 2.3639 2023/03/16 21:03:15 - mmengine - INFO - Epoch(train) [19][1400/5005] lr: 1.0000e-01 eta: 21:39:10 time: 0.1777 data_time: 0.0024 loss: 2.3669 2023/03/16 21:03:35 - mmengine - INFO - Epoch(train) [19][1500/5005] lr: 1.0000e-01 eta: 21:38:54 time: 0.2649 data_time: 0.0026 loss: 2.4831 2023/03/16 21:03:56 - mmengine - INFO - Epoch(train) [19][1600/5005] lr: 1.0000e-01 eta: 21:38:45 time: 0.1789 data_time: 0.0023 loss: 2.2998 2023/03/16 21:04:14 - mmengine - INFO - Epoch(train) [19][1700/5005] lr: 1.0000e-01 eta: 21:38:21 time: 0.1762 data_time: 0.0027 loss: 2.3486 2023/03/16 21:04:32 - mmengine - INFO - Epoch(train) [19][1800/5005] lr: 1.0000e-01 eta: 21:37:54 time: 0.1796 data_time: 0.0026 loss: 2.4305 2023/03/16 21:04:50 - mmengine - INFO - Epoch(train) [19][1900/5005] lr: 1.0000e-01 eta: 21:37:30 time: 0.1776 data_time: 0.0027 loss: 2.2876 2023/03/16 21:04:52 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:05:08 - mmengine - INFO - Epoch(train) [19][2000/5005] lr: 1.0000e-01 eta: 21:37:06 time: 0.1790 data_time: 0.0028 loss: 2.2430 2023/03/16 21:05:30 - mmengine - INFO - Epoch(train) [19][2100/5005] lr: 1.0000e-01 eta: 21:37:01 time: 0.1945 data_time: 0.0027 loss: 2.2309 2023/03/16 21:05:49 - mmengine - INFO - Epoch(train) [19][2200/5005] lr: 1.0000e-01 eta: 21:36:42 time: 0.1933 data_time: 0.0029 loss: 2.2887 2023/03/16 21:06:08 - mmengine - INFO - Epoch(train) [19][2300/5005] lr: 1.0000e-01 eta: 21:36:22 time: 0.1847 data_time: 0.0027 loss: 2.2483 2023/03/16 21:06:26 - mmengine - INFO - Epoch(train) [19][2400/5005] lr: 1.0000e-01 eta: 21:36:00 time: 0.1850 data_time: 0.0025 loss: 2.2721 2023/03/16 21:06:45 - mmengine - INFO - Epoch(train) [19][2500/5005] lr: 1.0000e-01 eta: 21:35:40 time: 0.1824 data_time: 0.0028 loss: 2.3342 2023/03/16 21:07:04 - mmengine - INFO - Epoch(train) [19][2600/5005] lr: 1.0000e-01 eta: 21:35:18 time: 0.1904 data_time: 0.0028 loss: 2.1111 2023/03/16 21:07:22 - mmengine - INFO - Epoch(train) [19][2700/5005] lr: 1.0000e-01 eta: 21:34:57 time: 0.1825 data_time: 0.0027 loss: 2.2438 2023/03/16 21:07:41 - mmengine - INFO - Epoch(train) [19][2800/5005] lr: 1.0000e-01 eta: 21:34:35 time: 0.1690 data_time: 0.0023 loss: 2.1781 2023/03/16 21:07:58 - mmengine - INFO - Epoch(train) [19][2900/5005] lr: 1.0000e-01 eta: 21:34:09 time: 0.1739 data_time: 0.0025 loss: 2.4496 2023/03/16 21:08:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:08:17 - mmengine - INFO - Epoch(train) [19][3000/5005] lr: 1.0000e-01 eta: 21:33:46 time: 0.2083 data_time: 0.0025 loss: 2.3485 2023/03/16 21:08:36 - mmengine - INFO - Epoch(train) [19][3100/5005] lr: 1.0000e-01 eta: 21:33:26 time: 0.1863 data_time: 0.0027 loss: 2.3473 2023/03/16 21:08:53 - mmengine - INFO - Epoch(train) [19][3200/5005] lr: 1.0000e-01 eta: 21:33:01 time: 0.1896 data_time: 0.0024 loss: 2.3119 2023/03/16 21:09:11 - mmengine - INFO - Epoch(train) [19][3300/5005] lr: 1.0000e-01 eta: 21:32:38 time: 0.1811 data_time: 0.0025 loss: 2.1408 2023/03/16 21:09:30 - mmengine - INFO - Epoch(train) [19][3400/5005] lr: 1.0000e-01 eta: 21:32:17 time: 0.1935 data_time: 0.0025 loss: 2.3620 2023/03/16 21:09:49 - mmengine - INFO - Epoch(train) [19][3500/5005] lr: 1.0000e-01 eta: 21:31:57 time: 0.1924 data_time: 0.0027 loss: 2.2687 2023/03/16 21:10:08 - mmengine - INFO - Epoch(train) [19][3600/5005] lr: 1.0000e-01 eta: 21:31:39 time: 0.1919 data_time: 0.0026 loss: 2.2313 2023/03/16 21:10:28 - mmengine - INFO - Epoch(train) [19][3700/5005] lr: 1.0000e-01 eta: 21:31:23 time: 0.1907 data_time: 0.0025 loss: 2.1927 2023/03/16 21:10:48 - mmengine - INFO - Epoch(train) [19][3800/5005] lr: 1.0000e-01 eta: 21:31:06 time: 0.2197 data_time: 0.0026 loss: 2.0776 2023/03/16 21:11:08 - mmengine - INFO - Epoch(train) [19][3900/5005] lr: 1.0000e-01 eta: 21:30:53 time: 0.1836 data_time: 0.0029 loss: 2.2362 2023/03/16 21:11:10 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:11:26 - mmengine - INFO - Epoch(train) [19][4000/5005] lr: 1.0000e-01 eta: 21:30:29 time: 0.1790 data_time: 0.0026 loss: 2.3224 2023/03/16 21:11:45 - mmengine - INFO - Epoch(train) [19][4100/5005] lr: 1.0000e-01 eta: 21:30:11 time: 0.1917 data_time: 0.0026 loss: 2.2369 2023/03/16 21:12:05 - mmengine - INFO - Epoch(train) [19][4200/5005] lr: 1.0000e-01 eta: 21:29:52 time: 0.1856 data_time: 0.0030 loss: 2.2093 2023/03/16 21:12:23 - mmengine - INFO - Epoch(train) [19][4300/5005] lr: 1.0000e-01 eta: 21:29:31 time: 0.1884 data_time: 0.0025 loss: 2.4066 2023/03/16 21:12:43 - mmengine - INFO - Epoch(train) [19][4400/5005] lr: 1.0000e-01 eta: 21:29:16 time: 0.2074 data_time: 0.0024 loss: 2.3963 2023/03/16 21:13:02 - mmengine - INFO - Epoch(train) [19][4500/5005] lr: 1.0000e-01 eta: 21:28:57 time: 0.1873 data_time: 0.0026 loss: 2.4095 2023/03/16 21:13:21 - mmengine - INFO - Epoch(train) [19][4600/5005] lr: 1.0000e-01 eta: 21:28:37 time: 0.1856 data_time: 0.0031 loss: 2.3301 2023/03/16 21:13:40 - mmengine - INFO - Epoch(train) [19][4700/5005] lr: 1.0000e-01 eta: 21:28:15 time: 0.1851 data_time: 0.0023 loss: 2.3188 2023/03/16 21:13:58 - mmengine - INFO - Epoch(train) [19][4800/5005] lr: 1.0000e-01 eta: 21:27:54 time: 0.1811 data_time: 0.0024 loss: 2.1981 2023/03/16 21:14:17 - mmengine - INFO - Epoch(train) [19][4900/5005] lr: 1.0000e-01 eta: 21:27:32 time: 0.1829 data_time: 0.0026 loss: 2.4630 2023/03/16 21:14:19 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:14:36 - mmengine - INFO - Epoch(train) [19][5000/5005] lr: 1.0000e-01 eta: 21:27:12 time: 0.1848 data_time: 0.0035 loss: 2.2704 2023/03/16 21:14:36 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:14:37 - mmengine - INFO - Saving checkpoint at 19 epochs 2023/03/16 21:14:44 - mmengine - INFO - Epoch(val) [19][100/196] eta: 0:00:05 time: 0.0475 data_time: 0.0009 2023/03/16 21:15:11 - mmengine - INFO - Epoch(val) [19][196/196] accuracy/top1: 51.7980 accuracy/top5: 77.3360data_time: 0.0328 time: 0.0662 2023/03/16 21:15:31 - mmengine - INFO - Epoch(train) [20][ 100/5005] lr: 1.0000e-01 eta: 21:26:55 time: 0.1776 data_time: 0.0026 loss: 2.1384 2023/03/16 21:15:49 - mmengine - INFO - Epoch(train) [20][ 200/5005] lr: 1.0000e-01 eta: 21:26:31 time: 0.1862 data_time: 0.0024 loss: 2.2773 2023/03/16 21:16:08 - mmengine - INFO - Epoch(train) [20][ 300/5005] lr: 1.0000e-01 eta: 21:26:13 time: 0.1937 data_time: 0.0026 loss: 2.0911 2023/03/16 21:16:29 - mmengine - INFO - Epoch(train) [20][ 400/5005] lr: 1.0000e-01 eta: 21:25:59 time: 0.2079 data_time: 0.0026 loss: 2.2074 2023/03/16 21:16:49 - mmengine - INFO - Epoch(train) [20][ 500/5005] lr: 1.0000e-01 eta: 21:25:46 time: 0.2013 data_time: 0.0027 loss: 2.2634 2023/03/16 21:17:09 - mmengine - INFO - Epoch(train) [20][ 600/5005] lr: 1.0000e-01 eta: 21:25:28 time: 0.1946 data_time: 0.0026 loss: 2.3573 2023/03/16 21:17:28 - mmengine - INFO - Epoch(train) [20][ 700/5005] lr: 1.0000e-01 eta: 21:25:12 time: 0.2108 data_time: 0.0026 loss: 2.2929 2023/03/16 21:17:48 - mmengine - INFO - Epoch(train) [20][ 800/5005] lr: 1.0000e-01 eta: 21:24:56 time: 0.1860 data_time: 0.0022 loss: 2.1406 2023/03/16 21:18:08 - mmengine - INFO - Epoch(train) [20][ 900/5005] lr: 1.0000e-01 eta: 21:24:41 time: 0.2073 data_time: 0.0026 loss: 2.2203 2023/03/16 21:18:09 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:18:28 - mmengine - INFO - Epoch(train) [20][1000/5005] lr: 1.0000e-01 eta: 21:24:25 time: 0.1936 data_time: 0.0029 loss: 2.4150 2023/03/16 21:18:47 - mmengine - INFO - Epoch(train) [20][1100/5005] lr: 1.0000e-01 eta: 21:24:07 time: 0.1924 data_time: 0.0030 loss: 2.2218 2023/03/16 21:19:06 - mmengine - INFO - Epoch(train) [20][1200/5005] lr: 1.0000e-01 eta: 21:23:48 time: 0.1909 data_time: 0.0027 loss: 2.3922 2023/03/16 21:19:26 - mmengine - INFO - Epoch(train) [20][1300/5005] lr: 1.0000e-01 eta: 21:23:31 time: 0.2038 data_time: 0.0026 loss: 2.2014 2023/03/16 21:19:46 - mmengine - INFO - Epoch(train) [20][1400/5005] lr: 1.0000e-01 eta: 21:23:16 time: 0.1961 data_time: 0.0023 loss: 2.0114 2023/03/16 21:20:06 - mmengine - INFO - Epoch(train) [20][1500/5005] lr: 1.0000e-01 eta: 21:22:58 time: 0.1967 data_time: 0.0022 loss: 2.4990 2023/03/16 21:20:25 - mmengine - INFO - Epoch(train) [20][1600/5005] lr: 1.0000e-01 eta: 21:22:41 time: 0.1934 data_time: 0.0025 loss: 2.0980 2023/03/16 21:20:45 - mmengine - INFO - Epoch(train) [20][1700/5005] lr: 1.0000e-01 eta: 21:22:23 time: 0.1937 data_time: 0.0028 loss: 2.0388 2023/03/16 21:21:05 - mmengine - INFO - Epoch(train) [20][1800/5005] lr: 1.0000e-01 eta: 21:22:08 time: 0.1932 data_time: 0.0024 loss: 2.2282 2023/03/16 21:21:24 - mmengine - INFO - Epoch(train) [20][1900/5005] lr: 1.0000e-01 eta: 21:21:50 time: 0.1893 data_time: 0.0026 loss: 2.2012 2023/03/16 21:21:25 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:21:43 - mmengine - INFO - Epoch(train) [20][2000/5005] lr: 1.0000e-01 eta: 21:21:30 time: 0.1886 data_time: 0.0028 loss: 2.3983 2023/03/16 21:22:02 - mmengine - INFO - Epoch(train) [20][2100/5005] lr: 1.0000e-01 eta: 21:21:10 time: 0.1891 data_time: 0.0028 loss: 2.2850 2023/03/16 21:22:21 - mmengine - INFO - Epoch(train) [20][2200/5005] lr: 1.0000e-01 eta: 21:20:52 time: 0.1865 data_time: 0.0027 loss: 2.3027 2023/03/16 21:22:40 - mmengine - INFO - Epoch(train) [20][2300/5005] lr: 1.0000e-01 eta: 21:20:32 time: 0.1845 data_time: 0.0024 loss: 2.2968 2023/03/16 21:22:59 - mmengine - INFO - Epoch(train) [20][2400/5005] lr: 1.0000e-01 eta: 21:20:12 time: 0.1859 data_time: 0.0025 loss: 2.0989 2023/03/16 21:23:17 - mmengine - INFO - Epoch(train) [20][2500/5005] lr: 1.0000e-01 eta: 21:19:51 time: 0.1856 data_time: 0.0025 loss: 2.2580 2023/03/16 21:23:36 - mmengine - INFO - Epoch(train) [20][2600/5005] lr: 1.0000e-01 eta: 21:19:29 time: 0.1854 data_time: 0.0027 loss: 2.1531 2023/03/16 21:23:54 - mmengine - INFO - Epoch(train) [20][2700/5005] lr: 1.0000e-01 eta: 21:19:06 time: 0.1777 data_time: 0.0027 loss: 2.3265 2023/03/16 21:24:12 - mmengine - INFO - Epoch(train) [20][2800/5005] lr: 1.0000e-01 eta: 21:18:43 time: 0.1811 data_time: 0.0027 loss: 2.2629 2023/03/16 21:24:31 - mmengine - INFO - Epoch(train) [20][2900/5005] lr: 1.0000e-01 eta: 21:18:21 time: 0.2086 data_time: 0.0029 loss: 2.4434 2023/03/16 21:24:32 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:24:50 - mmengine - INFO - Epoch(train) [20][3000/5005] lr: 1.0000e-01 eta: 21:18:03 time: 0.2078 data_time: 0.0025 loss: 2.2066 2023/03/16 21:25:09 - mmengine - INFO - Epoch(train) [20][3100/5005] lr: 1.0000e-01 eta: 21:17:44 time: 0.1906 data_time: 0.0029 loss: 2.4262 2023/03/16 21:25:27 - mmengine - INFO - Epoch(train) [20][3200/5005] lr: 1.0000e-01 eta: 21:17:19 time: 0.1778 data_time: 0.0026 loss: 2.3939 2023/03/16 21:25:45 - mmengine - INFO - Epoch(train) [20][3300/5005] lr: 1.0000e-01 eta: 21:16:58 time: 0.1795 data_time: 0.0025 loss: 2.2996 2023/03/16 21:26:04 - mmengine - INFO - Epoch(train) [20][3400/5005] lr: 1.0000e-01 eta: 21:16:35 time: 0.1802 data_time: 0.0024 loss: 2.0623 2023/03/16 21:26:22 - mmengine - INFO - Epoch(train) [20][3500/5005] lr: 1.0000e-01 eta: 21:16:12 time: 0.1752 data_time: 0.0025 loss: 2.2656 2023/03/16 21:26:40 - mmengine - INFO - Epoch(train) [20][3600/5005] lr: 1.0000e-01 eta: 21:15:51 time: 0.1786 data_time: 0.0028 loss: 2.2065 2023/03/16 21:26:59 - mmengine - INFO - Epoch(train) [20][3700/5005] lr: 1.0000e-01 eta: 21:15:30 time: 0.1866 data_time: 0.0025 loss: 2.1223 2023/03/16 21:27:18 - mmengine - INFO - Epoch(train) [20][3800/5005] lr: 1.0000e-01 eta: 21:15:09 time: 0.1886 data_time: 0.0025 loss: 2.3803 2023/03/16 21:27:36 - mmengine - INFO - Epoch(train) [20][3900/5005] lr: 1.0000e-01 eta: 21:14:49 time: 0.1887 data_time: 0.0028 loss: 2.2073 2023/03/16 21:27:37 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:27:56 - mmengine - INFO - Epoch(train) [20][4000/5005] lr: 1.0000e-01 eta: 21:14:31 time: 0.1926 data_time: 0.0028 loss: 2.2904 2023/03/16 21:28:14 - mmengine - INFO - Epoch(train) [20][4100/5005] lr: 1.0000e-01 eta: 21:14:11 time: 0.1998 data_time: 0.0024 loss: 2.2684 2023/03/16 21:28:34 - mmengine - INFO - Epoch(train) [20][4200/5005] lr: 1.0000e-01 eta: 21:13:52 time: 0.1775 data_time: 0.0026 loss: 2.1657 2023/03/16 21:28:52 - mmengine - INFO - Epoch(train) [20][4300/5005] lr: 1.0000e-01 eta: 21:13:30 time: 0.1856 data_time: 0.0026 loss: 2.4020 2023/03/16 21:29:11 - mmengine - INFO - Epoch(train) [20][4400/5005] lr: 1.0000e-01 eta: 21:13:10 time: 0.1870 data_time: 0.0026 loss: 2.1654 2023/03/16 21:29:30 - mmengine - INFO - Epoch(train) [20][4500/5005] lr: 1.0000e-01 eta: 21:12:50 time: 0.1895 data_time: 0.0026 loss: 2.5888 2023/03/16 21:29:49 - mmengine - INFO - Epoch(train) [20][4600/5005] lr: 1.0000e-01 eta: 21:12:31 time: 0.1906 data_time: 0.0031 loss: 2.3697 2023/03/16 21:30:09 - mmengine - INFO - Epoch(train) [20][4700/5005] lr: 1.0000e-01 eta: 21:12:14 time: 0.1915 data_time: 0.0026 loss: 2.5161 2023/03/16 21:30:27 - mmengine - INFO - Epoch(train) [20][4800/5005] lr: 1.0000e-01 eta: 21:11:53 time: 0.1872 data_time: 0.0026 loss: 2.0824 2023/03/16 21:30:45 - mmengine - INFO - Epoch(train) [20][4900/5005] lr: 1.0000e-01 eta: 21:11:30 time: 0.1780 data_time: 0.0024 loss: 2.2685 2023/03/16 21:30:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:31:04 - mmengine - INFO - Epoch(train) [20][5000/5005] lr: 1.0000e-01 eta: 21:11:08 time: 0.1851 data_time: 0.0031 loss: 2.1633 2023/03/16 21:31:05 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:31:05 - mmengine - INFO - Saving checkpoint at 20 epochs 2023/03/16 21:31:12 - mmengine - INFO - Epoch(val) [20][100/196] eta: 0:00:05 time: 0.0522 data_time: 0.0009 2023/03/16 21:31:40 - mmengine - INFO - Epoch(val) [20][196/196] accuracy/top1: 51.6960 accuracy/top5: 77.2820data_time: 0.0251 time: 0.0596 2023/03/16 21:32:00 - mmengine - INFO - Epoch(train) [21][ 100/5005] lr: 1.0000e-01 eta: 21:10:51 time: 0.1812 data_time: 0.0028 loss: 2.3621 2023/03/16 21:32:19 - mmengine - INFO - Epoch(train) [21][ 200/5005] lr: 1.0000e-01 eta: 21:10:31 time: 0.2307 data_time: 0.0026 loss: 2.1901 2023/03/16 21:32:37 - mmengine - INFO - Epoch(train) [21][ 300/5005] lr: 1.0000e-01 eta: 21:10:09 time: 0.1836 data_time: 0.0030 loss: 2.2462 2023/03/16 21:32:55 - mmengine - INFO - Epoch(train) [21][ 400/5005] lr: 1.0000e-01 eta: 21:09:47 time: 0.1875 data_time: 0.0032 loss: 2.0978 2023/03/16 21:33:14 - mmengine - INFO - Epoch(train) [21][ 500/5005] lr: 1.0000e-01 eta: 21:09:25 time: 0.1710 data_time: 0.0024 loss: 2.4010 2023/03/16 21:33:30 - mmengine - INFO - Epoch(train) [21][ 600/5005] lr: 1.0000e-01 eta: 21:08:56 time: 0.1669 data_time: 0.0022 loss: 2.1949 2023/03/16 21:33:47 - mmengine - INFO - Epoch(train) [21][ 700/5005] lr: 1.0000e-01 eta: 21:08:29 time: 0.1657 data_time: 0.0025 loss: 2.2349 2023/03/16 21:34:04 - mmengine - INFO - Epoch(train) [21][ 800/5005] lr: 1.0000e-01 eta: 21:08:00 time: 0.1591 data_time: 0.0027 loss: 2.0819 2023/03/16 21:34:21 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:34:21 - mmengine - INFO - Epoch(train) [21][ 900/5005] lr: 1.0000e-01 eta: 21:07:32 time: 0.1705 data_time: 0.0027 loss: 2.0775 2023/03/16 21:34:38 - mmengine - INFO - Epoch(train) [21][1000/5005] lr: 1.0000e-01 eta: 21:07:07 time: 0.1805 data_time: 0.0023 loss: 2.2534 2023/03/16 21:34:56 - mmengine - INFO - Epoch(train) [21][1100/5005] lr: 1.0000e-01 eta: 21:06:43 time: 0.1783 data_time: 0.0024 loss: 2.4006 2023/03/16 21:35:13 - mmengine - INFO - Epoch(train) [21][1200/5005] lr: 1.0000e-01 eta: 21:06:16 time: 0.1715 data_time: 0.0028 loss: 2.4293 2023/03/16 21:35:31 - mmengine - INFO - Epoch(train) [21][1300/5005] lr: 1.0000e-01 eta: 21:05:52 time: 0.1874 data_time: 0.0025 loss: 2.4128 2023/03/16 21:35:51 - mmengine - INFO - Epoch(train) [21][1400/5005] lr: 1.0000e-01 eta: 21:05:36 time: 0.1852 data_time: 0.0024 loss: 2.4553 2023/03/16 21:36:09 - mmengine - INFO - Epoch(train) [21][1500/5005] lr: 1.0000e-01 eta: 21:05:13 time: 0.1808 data_time: 0.0026 loss: 2.3536 2023/03/16 21:36:28 - mmengine - INFO - Epoch(train) [21][1600/5005] lr: 1.0000e-01 eta: 21:04:53 time: 0.1851 data_time: 0.0026 loss: 2.4762 2023/03/16 21:36:46 - mmengine - INFO - Epoch(train) [21][1700/5005] lr: 1.0000e-01 eta: 21:04:31 time: 0.1919 data_time: 0.0025 loss: 2.3460 2023/03/16 21:37:05 - mmengine - INFO - Epoch(train) [21][1800/5005] lr: 1.0000e-01 eta: 21:04:10 time: 0.1852 data_time: 0.0026 loss: 2.2941 2023/03/16 21:37:24 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:37:24 - mmengine - INFO - Epoch(train) [21][1900/5005] lr: 1.0000e-01 eta: 21:03:51 time: 0.2175 data_time: 0.0026 loss: 2.3201 2023/03/16 21:37:42 - mmengine - INFO - Epoch(train) [21][2000/5005] lr: 1.0000e-01 eta: 21:03:30 time: 0.1783 data_time: 0.0021 loss: 2.2844 2023/03/16 21:38:01 - mmengine - INFO - Epoch(train) [21][2100/5005] lr: 1.0000e-01 eta: 21:03:09 time: 0.1857 data_time: 0.0028 loss: 2.0858 2023/03/16 21:38:20 - mmengine - INFO - Epoch(train) [21][2200/5005] lr: 1.0000e-01 eta: 21:02:49 time: 0.1782 data_time: 0.0026 loss: 2.1963 2023/03/16 21:38:38 - mmengine - INFO - Epoch(train) [21][2300/5005] lr: 1.0000e-01 eta: 21:02:25 time: 0.1768 data_time: 0.0023 loss: 2.0806 2023/03/16 21:38:56 - mmengine - INFO - Epoch(train) [21][2400/5005] lr: 1.0000e-01 eta: 21:02:02 time: 0.1813 data_time: 0.0026 loss: 2.2673 2023/03/16 21:39:14 - mmengine - INFO - Epoch(train) [21][2500/5005] lr: 1.0000e-01 eta: 21:01:42 time: 0.1912 data_time: 0.0025 loss: 2.2740 2023/03/16 21:39:33 - mmengine - INFO - Epoch(train) [21][2600/5005] lr: 1.0000e-01 eta: 21:01:22 time: 0.1959 data_time: 0.0028 loss: 2.1501 2023/03/16 21:39:54 - mmengine - INFO - Epoch(train) [21][2700/5005] lr: 1.0000e-01 eta: 21:01:10 time: 0.1991 data_time: 0.0026 loss: 2.2022 2023/03/16 21:40:14 - mmengine - INFO - Epoch(train) [21][2800/5005] lr: 1.0000e-01 eta: 21:00:56 time: 0.2045 data_time: 0.0026 loss: 2.2352 2023/03/16 21:40:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:40:34 - mmengine - INFO - Epoch(train) [21][2900/5005] lr: 1.0000e-01 eta: 21:00:39 time: 0.2062 data_time: 0.0028 loss: 2.3861 2023/03/16 21:40:53 - mmengine - INFO - Epoch(train) [21][3000/5005] lr: 1.0000e-01 eta: 21:00:20 time: 0.1880 data_time: 0.0027 loss: 2.0686 2023/03/16 21:41:14 - mmengine - INFO - Epoch(train) [21][3100/5005] lr: 1.0000e-01 eta: 21:00:09 time: 0.2161 data_time: 0.0025 loss: 2.1376 2023/03/16 21:41:34 - mmengine - INFO - Epoch(train) [21][3200/5005] lr: 1.0000e-01 eta: 20:59:53 time: 0.1863 data_time: 0.0024 loss: 2.1413 2023/03/16 21:41:53 - mmengine - INFO - Epoch(train) [21][3300/5005] lr: 1.0000e-01 eta: 20:59:33 time: 0.1867 data_time: 0.0027 loss: 2.1778 2023/03/16 21:42:12 - mmengine - INFO - Epoch(train) [21][3400/5005] lr: 1.0000e-01 eta: 20:59:14 time: 0.1808 data_time: 0.0028 loss: 2.2726 2023/03/16 21:42:31 - mmengine - INFO - Epoch(train) [21][3500/5005] lr: 1.0000e-01 eta: 20:58:52 time: 0.1883 data_time: 0.0028 loss: 2.4209 2023/03/16 21:42:51 - mmengine - INFO - Epoch(train) [21][3600/5005] lr: 1.0000e-01 eta: 20:58:39 time: 0.2525 data_time: 0.0027 loss: 2.1078 2023/03/16 21:43:12 - mmengine - INFO - Epoch(train) [21][3700/5005] lr: 1.0000e-01 eta: 20:58:27 time: 0.2133 data_time: 0.0026 loss: 2.4277 2023/03/16 21:43:32 - mmengine - INFO - Epoch(train) [21][3800/5005] lr: 1.0000e-01 eta: 20:58:12 time: 0.2031 data_time: 0.0026 loss: 2.2472 2023/03/16 21:43:51 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:43:51 - mmengine - INFO - Epoch(train) [21][3900/5005] lr: 1.0000e-01 eta: 20:57:52 time: 0.1814 data_time: 0.0030 loss: 2.3227 2023/03/16 21:44:09 - mmengine - INFO - Epoch(train) [21][4000/5005] lr: 1.0000e-01 eta: 20:57:30 time: 0.1936 data_time: 0.0027 loss: 2.4172 2023/03/16 21:44:28 - mmengine - INFO - Epoch(train) [21][4100/5005] lr: 1.0000e-01 eta: 20:57:08 time: 0.1886 data_time: 0.0030 loss: 2.4245 2023/03/16 21:44:47 - mmengine - INFO - Epoch(train) [21][4200/5005] lr: 1.0000e-01 eta: 20:56:48 time: 0.1966 data_time: 0.0027 loss: 2.1785 2023/03/16 21:45:06 - mmengine - INFO - Epoch(train) [21][4300/5005] lr: 1.0000e-01 eta: 20:56:29 time: 0.1848 data_time: 0.0027 loss: 2.3325 2023/03/16 21:45:28 - mmengine - INFO - Epoch(train) [21][4400/5005] lr: 1.0000e-01 eta: 20:56:22 time: 0.2143 data_time: 0.0026 loss: 2.2573 2023/03/16 21:45:46 - mmengine - INFO - Epoch(train) [21][4500/5005] lr: 1.0000e-01 eta: 20:56:01 time: 0.1935 data_time: 0.0026 loss: 2.3270 2023/03/16 21:46:06 - mmengine - INFO - Epoch(train) [21][4600/5005] lr: 1.0000e-01 eta: 20:55:42 time: 0.1888 data_time: 0.0026 loss: 2.2133 2023/03/16 21:46:24 - mmengine - INFO - Epoch(train) [21][4700/5005] lr: 1.0000e-01 eta: 20:55:19 time: 0.1799 data_time: 0.0024 loss: 2.1775 2023/03/16 21:46:41 - mmengine - INFO - Epoch(train) [21][4800/5005] lr: 1.0000e-01 eta: 20:54:54 time: 0.1636 data_time: 0.0023 loss: 2.4215 2023/03/16 21:46:58 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:46:58 - mmengine - INFO - Epoch(train) [21][4900/5005] lr: 1.0000e-01 eta: 20:54:27 time: 0.1764 data_time: 0.0029 loss: 2.2587 2023/03/16 21:47:18 - mmengine - INFO - Epoch(train) [21][5000/5005] lr: 1.0000e-01 eta: 20:54:10 time: 0.1879 data_time: 0.0036 loss: 2.5238 2023/03/16 21:47:19 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:47:19 - mmengine - INFO - Saving checkpoint at 21 epochs 2023/03/16 21:47:25 - mmengine - INFO - Epoch(val) [21][100/196] eta: 0:00:04 time: 0.0422 data_time: 0.0008 2023/03/16 21:47:51 - mmengine - INFO - Epoch(val) [21][196/196] accuracy/top1: 54.1720 accuracy/top5: 79.2320data_time: 0.0270 time: 0.0612 2023/03/16 21:48:13 - mmengine - INFO - Epoch(train) [22][ 100/5005] lr: 1.0000e-01 eta: 20:54:00 time: 0.1880 data_time: 0.0027 loss: 2.2158 2023/03/16 21:48:30 - mmengine - INFO - Epoch(train) [22][ 200/5005] lr: 1.0000e-01 eta: 20:53:36 time: 0.1654 data_time: 0.0023 loss: 2.0908 2023/03/16 21:48:48 - mmengine - INFO - Epoch(train) [22][ 300/5005] lr: 1.0000e-01 eta: 20:53:13 time: 0.1915 data_time: 0.0024 loss: 2.3385 2023/03/16 21:49:07 - mmengine - INFO - Epoch(train) [22][ 400/5005] lr: 1.0000e-01 eta: 20:52:54 time: 0.1833 data_time: 0.0025 loss: 2.2854 2023/03/16 21:49:26 - mmengine - INFO - Epoch(train) [22][ 500/5005] lr: 1.0000e-01 eta: 20:52:33 time: 0.1930 data_time: 0.0025 loss: 2.2154 2023/03/16 21:49:50 - mmengine - INFO - Epoch(train) [22][ 600/5005] lr: 1.0000e-01 eta: 20:52:31 time: 0.2374 data_time: 0.0023 loss: 2.2645 2023/03/16 21:50:09 - mmengine - INFO - Epoch(train) [22][ 700/5005] lr: 1.0000e-01 eta: 20:52:13 time: 0.1886 data_time: 0.0025 loss: 2.4035 2023/03/16 21:50:28 - mmengine - INFO - Epoch(train) [22][ 800/5005] lr: 1.0000e-01 eta: 20:51:52 time: 0.1768 data_time: 0.0026 loss: 2.2828 2023/03/16 21:50:44 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:50:45 - mmengine - INFO - Epoch(train) [22][ 900/5005] lr: 1.0000e-01 eta: 20:51:27 time: 0.1763 data_time: 0.0025 loss: 2.2569 2023/03/16 21:51:05 - mmengine - INFO - Epoch(train) [22][1000/5005] lr: 1.0000e-01 eta: 20:51:11 time: 0.1888 data_time: 0.0025 loss: 2.2920 2023/03/16 21:51:23 - mmengine - INFO - Epoch(train) [22][1100/5005] lr: 1.0000e-01 eta: 20:50:48 time: 0.1721 data_time: 0.0025 loss: 2.2221 2023/03/16 21:51:41 - mmengine - INFO - Epoch(train) [22][1200/5005] lr: 1.0000e-01 eta: 20:50:24 time: 0.1863 data_time: 0.0031 loss: 2.4308 2023/03/16 21:52:00 - mmengine - INFO - Epoch(train) [22][1300/5005] lr: 1.0000e-01 eta: 20:50:07 time: 0.2004 data_time: 0.0025 loss: 2.2505 2023/03/16 21:52:20 - mmengine - INFO - Epoch(train) [22][1400/5005] lr: 1.0000e-01 eta: 20:49:49 time: 0.1891 data_time: 0.0026 loss: 2.2217 2023/03/16 21:52:39 - mmengine - INFO - Epoch(train) [22][1500/5005] lr: 1.0000e-01 eta: 20:49:29 time: 0.1845 data_time: 0.0025 loss: 2.1658 2023/03/16 21:52:57 - mmengine - INFO - Epoch(train) [22][1600/5005] lr: 1.0000e-01 eta: 20:49:08 time: 0.1845 data_time: 0.0028 loss: 2.2130 2023/03/16 21:53:15 - mmengine - INFO - Epoch(train) [22][1700/5005] lr: 1.0000e-01 eta: 20:48:46 time: 0.1809 data_time: 0.0024 loss: 2.2036 2023/03/16 21:53:33 - mmengine - INFO - Epoch(train) [22][1800/5005] lr: 1.0000e-01 eta: 20:48:22 time: 0.1696 data_time: 0.0025 loss: 2.2427 2023/03/16 21:53:49 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:53:50 - mmengine - INFO - Epoch(train) [22][1900/5005] lr: 1.0000e-01 eta: 20:47:56 time: 0.2028 data_time: 0.0024 loss: 2.1232 2023/03/16 21:54:13 - mmengine - INFO - Epoch(train) [22][2000/5005] lr: 1.0000e-01 eta: 20:47:51 time: 0.2515 data_time: 0.0024 loss: 2.2611 2023/03/16 21:54:32 - mmengine - INFO - Epoch(train) [22][2100/5005] lr: 1.0000e-01 eta: 20:47:30 time: 0.1823 data_time: 0.0026 loss: 2.3970 2023/03/16 21:54:50 - mmengine - INFO - Epoch(train) [22][2200/5005] lr: 1.0000e-01 eta: 20:47:09 time: 0.1955 data_time: 0.0026 loss: 2.1860 2023/03/16 21:55:08 - mmengine - INFO - Epoch(train) [22][2300/5005] lr: 1.0000e-01 eta: 20:46:45 time: 0.1718 data_time: 0.0024 loss: 2.2212 2023/03/16 21:55:27 - mmengine - INFO - Epoch(train) [22][2400/5005] lr: 1.0000e-01 eta: 20:46:24 time: 0.1868 data_time: 0.0025 loss: 2.2501 2023/03/16 21:55:44 - mmengine - INFO - Epoch(train) [22][2500/5005] lr: 1.0000e-01 eta: 20:46:00 time: 0.1698 data_time: 0.0025 loss: 2.3000 2023/03/16 21:56:02 - mmengine - INFO - Epoch(train) [22][2600/5005] lr: 1.0000e-01 eta: 20:45:35 time: 0.1682 data_time: 0.0025 loss: 2.3372 2023/03/16 21:56:20 - mmengine - INFO - Epoch(train) [22][2700/5005] lr: 1.0000e-01 eta: 20:45:15 time: 0.2334 data_time: 0.0025 loss: 2.3601 2023/03/16 21:56:41 - mmengine - INFO - Epoch(train) [22][2800/5005] lr: 1.0000e-01 eta: 20:45:00 time: 0.2285 data_time: 0.0024 loss: 2.2232 2023/03/16 21:57:01 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:57:02 - mmengine - INFO - Epoch(train) [22][2900/5005] lr: 1.0000e-01 eta: 20:44:48 time: 0.1702 data_time: 0.0024 loss: 2.4011 2023/03/16 21:57:19 - mmengine - INFO - Epoch(train) [22][3000/5005] lr: 1.0000e-01 eta: 20:44:21 time: 0.1701 data_time: 0.0024 loss: 2.2080 2023/03/16 21:57:36 - mmengine - INFO - Epoch(train) [22][3100/5005] lr: 1.0000e-01 eta: 20:43:55 time: 0.1685 data_time: 0.0024 loss: 2.0569 2023/03/16 21:57:53 - mmengine - INFO - Epoch(train) [22][3200/5005] lr: 1.0000e-01 eta: 20:43:30 time: 0.1791 data_time: 0.0027 loss: 2.3484 2023/03/16 21:58:11 - mmengine - INFO - Epoch(train) [22][3300/5005] lr: 1.0000e-01 eta: 20:43:05 time: 0.1767 data_time: 0.0026 loss: 2.2363 2023/03/16 21:58:29 - mmengine - INFO - Epoch(train) [22][3400/5005] lr: 1.0000e-01 eta: 20:42:44 time: 0.1865 data_time: 0.0027 loss: 1.9824 2023/03/16 21:58:47 - mmengine - INFO - Epoch(train) [22][3500/5005] lr: 1.0000e-01 eta: 20:42:20 time: 0.1789 data_time: 0.0026 loss: 2.1377 2023/03/16 21:59:05 - mmengine - INFO - Epoch(train) [22][3600/5005] lr: 1.0000e-01 eta: 20:41:59 time: 0.1902 data_time: 0.0026 loss: 2.3233 2023/03/16 21:59:23 - mmengine - INFO - Epoch(train) [22][3700/5005] lr: 1.0000e-01 eta: 20:41:33 time: 0.1667 data_time: 0.0027 loss: 2.1105 2023/03/16 21:59:40 - mmengine - INFO - Epoch(train) [22][3800/5005] lr: 1.0000e-01 eta: 20:41:07 time: 0.1731 data_time: 0.0027 loss: 2.2499 2023/03/16 21:59:57 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 21:59:57 - mmengine - INFO - Epoch(train) [22][3900/5005] lr: 1.0000e-01 eta: 20:40:44 time: 0.1767 data_time: 0.0028 loss: 2.3065 2023/03/16 22:00:16 - mmengine - INFO - Epoch(train) [22][4000/5005] lr: 1.0000e-01 eta: 20:40:22 time: 0.1753 data_time: 0.0027 loss: 2.2976 2023/03/16 22:00:35 - mmengine - INFO - Epoch(train) [22][4100/5005] lr: 1.0000e-01 eta: 20:40:04 time: 0.1820 data_time: 0.0027 loss: 2.4780 2023/03/16 22:00:53 - mmengine - INFO - Epoch(train) [22][4200/5005] lr: 1.0000e-01 eta: 20:39:39 time: 0.1768 data_time: 0.0026 loss: 2.4031 2023/03/16 22:01:11 - mmengine - INFO - Epoch(train) [22][4300/5005] lr: 1.0000e-01 eta: 20:39:17 time: 0.1931 data_time: 0.0028 loss: 2.2342 2023/03/16 22:01:30 - mmengine - INFO - Epoch(train) [22][4400/5005] lr: 1.0000e-01 eta: 20:38:58 time: 0.1857 data_time: 0.0026 loss: 2.4160 2023/03/16 22:01:50 - mmengine - INFO - Epoch(train) [22][4500/5005] lr: 1.0000e-01 eta: 20:38:40 time: 0.1873 data_time: 0.0026 loss: 2.5562 2023/03/16 22:02:08 - mmengine - INFO - Epoch(train) [22][4600/5005] lr: 1.0000e-01 eta: 20:38:18 time: 0.1873 data_time: 0.0025 loss: 2.1269 2023/03/16 22:02:27 - mmengine - INFO - Epoch(train) [22][4700/5005] lr: 1.0000e-01 eta: 20:37:59 time: 0.1962 data_time: 0.0027 loss: 2.2639 2023/03/16 22:02:46 - mmengine - INFO - Epoch(train) [22][4800/5005] lr: 1.0000e-01 eta: 20:37:41 time: 0.1956 data_time: 0.0027 loss: 2.2641 2023/03/16 22:03:04 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:03:05 - mmengine - INFO - Epoch(train) [22][4900/5005] lr: 1.0000e-01 eta: 20:37:20 time: 0.1766 data_time: 0.0027 loss: 2.4686 2023/03/16 22:03:23 - mmengine - INFO - Epoch(train) [22][5000/5005] lr: 1.0000e-01 eta: 20:36:57 time: 0.1873 data_time: 0.0036 loss: 2.2594 2023/03/16 22:03:24 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:03:24 - mmengine - INFO - Saving checkpoint at 22 epochs 2023/03/16 22:03:31 - mmengine - INFO - Epoch(val) [22][100/196] eta: 0:00:05 time: 0.0441 data_time: 0.0009 2023/03/16 22:03:59 - mmengine - INFO - Epoch(val) [22][196/196] accuracy/top1: 53.6100 accuracy/top5: 78.9280data_time: 0.0273 time: 0.0576 2023/03/16 22:04:22 - mmengine - INFO - Epoch(train) [23][ 100/5005] lr: 1.0000e-01 eta: 20:36:51 time: 0.1874 data_time: 0.0026 loss: 2.1800 2023/03/16 22:04:40 - mmengine - INFO - Epoch(train) [23][ 200/5005] lr: 1.0000e-01 eta: 20:36:28 time: 0.1733 data_time: 0.0027 loss: 2.2326 2023/03/16 22:04:58 - mmengine - INFO - Epoch(train) [23][ 300/5005] lr: 1.0000e-01 eta: 20:36:06 time: 0.1865 data_time: 0.0026 loss: 2.4134 2023/03/16 22:05:16 - mmengine - INFO - Epoch(train) [23][ 400/5005] lr: 1.0000e-01 eta: 20:35:45 time: 0.1815 data_time: 0.0029 loss: 2.1566 2023/03/16 22:05:36 - mmengine - INFO - Epoch(train) [23][ 500/5005] lr: 1.0000e-01 eta: 20:35:29 time: 0.2034 data_time: 0.0027 loss: 2.1847 2023/03/16 22:05:56 - mmengine - INFO - Epoch(train) [23][ 600/5005] lr: 1.0000e-01 eta: 20:35:12 time: 0.1954 data_time: 0.0023 loss: 2.1446 2023/03/16 22:06:15 - mmengine - INFO - Epoch(train) [23][ 700/5005] lr: 1.0000e-01 eta: 20:34:52 time: 0.1813 data_time: 0.0024 loss: 2.0630 2023/03/16 22:06:33 - mmengine - INFO - Epoch(train) [23][ 800/5005] lr: 1.0000e-01 eta: 20:34:29 time: 0.1823 data_time: 0.0024 loss: 2.2538 2023/03/16 22:06:49 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:06:51 - mmengine - INFO - Epoch(train) [23][ 900/5005] lr: 1.0000e-01 eta: 20:34:08 time: 0.1830 data_time: 0.0024 loss: 2.5072 2023/03/16 22:07:09 - mmengine - INFO - Epoch(train) [23][1000/5005] lr: 1.0000e-01 eta: 20:33:46 time: 0.1797 data_time: 0.0028 loss: 2.1341 2023/03/16 22:07:28 - mmengine - INFO - Epoch(train) [23][1100/5005] lr: 1.0000e-01 eta: 20:33:25 time: 0.1867 data_time: 0.0023 loss: 2.4078 2023/03/16 22:07:46 - mmengine - INFO - Epoch(train) [23][1200/5005] lr: 1.0000e-01 eta: 20:33:04 time: 0.1957 data_time: 0.0026 loss: 2.2190 2023/03/16 22:08:06 - mmengine - INFO - Epoch(train) [23][1300/5005] lr: 1.0000e-01 eta: 20:32:46 time: 0.1873 data_time: 0.0028 loss: 1.9060 2023/03/16 22:08:25 - mmengine - INFO - Epoch(train) [23][1400/5005] lr: 1.0000e-01 eta: 20:32:27 time: 0.1880 data_time: 0.0027 loss: 2.0816 2023/03/16 22:08:47 - mmengine - INFO - Epoch(train) [23][1500/5005] lr: 1.0000e-01 eta: 20:32:18 time: 0.2449 data_time: 0.0023 loss: 2.1791 2023/03/16 22:09:06 - mmengine - INFO - Epoch(train) [23][1600/5005] lr: 1.0000e-01 eta: 20:32:00 time: 0.1912 data_time: 0.0028 loss: 2.1656 2023/03/16 22:09:25 - mmengine - INFO - Epoch(train) [23][1700/5005] lr: 1.0000e-01 eta: 20:31:40 time: 0.1861 data_time: 0.0023 loss: 2.2269 2023/03/16 22:09:44 - mmengine - INFO - Epoch(train) [23][1800/5005] lr: 1.0000e-01 eta: 20:31:21 time: 0.1864 data_time: 0.0023 loss: 2.3187 2023/03/16 22:10:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:10:02 - mmengine - INFO - Epoch(train) [23][1900/5005] lr: 1.0000e-01 eta: 20:30:59 time: 0.1763 data_time: 0.0024 loss: 2.0941 2023/03/16 22:10:21 - mmengine - INFO - Epoch(train) [23][2000/5005] lr: 1.0000e-01 eta: 20:30:38 time: 0.1924 data_time: 0.0022 loss: 2.2916 2023/03/16 22:10:38 - mmengine - INFO - Epoch(train) [23][2100/5005] lr: 1.0000e-01 eta: 20:30:14 time: 0.1731 data_time: 0.0025 loss: 2.2394 2023/03/16 22:10:58 - mmengine - INFO - Epoch(train) [23][2200/5005] lr: 1.0000e-01 eta: 20:29:57 time: 0.1795 data_time: 0.0028 loss: 2.1262 2023/03/16 22:11:17 - mmengine - INFO - Epoch(train) [23][2300/5005] lr: 1.0000e-01 eta: 20:29:38 time: 0.1928 data_time: 0.0028 loss: 2.1629 2023/03/16 22:11:36 - mmengine - INFO - Epoch(train) [23][2400/5005] lr: 1.0000e-01 eta: 20:29:18 time: 0.1845 data_time: 0.0026 loss: 2.4639 2023/03/16 22:11:55 - mmengine - INFO - Epoch(train) [23][2500/5005] lr: 1.0000e-01 eta: 20:28:58 time: 0.1775 data_time: 0.0028 loss: 2.2851 2023/03/16 22:12:13 - mmengine - INFO - Epoch(train) [23][2600/5005] lr: 1.0000e-01 eta: 20:28:35 time: 0.1757 data_time: 0.0024 loss: 2.1966 2023/03/16 22:12:31 - mmengine - INFO - Epoch(train) [23][2700/5005] lr: 1.0000e-01 eta: 20:28:14 time: 0.1867 data_time: 0.0026 loss: 2.4905 2023/03/16 22:12:50 - mmengine - INFO - Epoch(train) [23][2800/5005] lr: 1.0000e-01 eta: 20:27:54 time: 0.1831 data_time: 0.0025 loss: 2.1888 2023/03/16 22:13:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:13:09 - mmengine - INFO - Epoch(train) [23][2900/5005] lr: 1.0000e-01 eta: 20:27:37 time: 0.1968 data_time: 0.0025 loss: 2.3683 2023/03/16 22:13:29 - mmengine - INFO - Epoch(train) [23][3000/5005] lr: 1.0000e-01 eta: 20:27:21 time: 0.1866 data_time: 0.0023 loss: 2.4171 2023/03/16 22:13:48 - mmengine - INFO - Epoch(train) [23][3100/5005] lr: 1.0000e-01 eta: 20:27:00 time: 0.1877 data_time: 0.0024 loss: 2.1909 2023/03/16 22:14:07 - mmengine - INFO - Epoch(train) [23][3200/5005] lr: 1.0000e-01 eta: 20:26:43 time: 0.1879 data_time: 0.0029 loss: 2.3768 2023/03/16 22:14:26 - mmengine - INFO - Epoch(train) [23][3300/5005] lr: 1.0000e-01 eta: 20:26:22 time: 0.1874 data_time: 0.0026 loss: 2.2256 2023/03/16 22:14:45 - mmengine - INFO - Epoch(train) [23][3400/5005] lr: 1.0000e-01 eta: 20:26:03 time: 0.1854 data_time: 0.0025 loss: 2.3344 2023/03/16 22:15:04 - mmengine - INFO - Epoch(train) [23][3500/5005] lr: 1.0000e-01 eta: 20:25:42 time: 0.1777 data_time: 0.0025 loss: 2.3096 2023/03/16 22:15:22 - mmengine - INFO - Epoch(train) [23][3600/5005] lr: 1.0000e-01 eta: 20:25:21 time: 0.1884 data_time: 0.0024 loss: 2.2785 2023/03/16 22:15:42 - mmengine - INFO - Epoch(train) [23][3700/5005] lr: 1.0000e-01 eta: 20:25:07 time: 0.1763 data_time: 0.0029 loss: 2.2611 2023/03/16 22:16:00 - mmengine - INFO - Epoch(train) [23][3800/5005] lr: 1.0000e-01 eta: 20:24:43 time: 0.1767 data_time: 0.0027 loss: 2.2652 2023/03/16 22:16:16 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:16:18 - mmengine - INFO - Epoch(train) [23][3900/5005] lr: 1.0000e-01 eta: 20:24:20 time: 0.1754 data_time: 0.0028 loss: 2.1397 2023/03/16 22:16:36 - mmengine - INFO - Epoch(train) [23][4000/5005] lr: 1.0000e-01 eta: 20:23:58 time: 0.1823 data_time: 0.0028 loss: 2.3962 2023/03/16 22:16:55 - mmengine - INFO - Epoch(train) [23][4100/5005] lr: 1.0000e-01 eta: 20:23:39 time: 0.2037 data_time: 0.0023 loss: 2.0082 2023/03/16 22:17:14 - mmengine - INFO - Epoch(train) [23][4200/5005] lr: 1.0000e-01 eta: 20:23:20 time: 0.2416 data_time: 0.0023 loss: 2.0648 2023/03/16 22:17:33 - mmengine - INFO - Epoch(train) [23][4300/5005] lr: 1.0000e-01 eta: 20:23:01 time: 0.1936 data_time: 0.0024 loss: 2.2678 2023/03/16 22:17:52 - mmengine - INFO - Epoch(train) [23][4400/5005] lr: 1.0000e-01 eta: 20:22:41 time: 0.1795 data_time: 0.0023 loss: 2.3948 2023/03/16 22:18:11 - mmengine - INFO - Epoch(train) [23][4500/5005] lr: 1.0000e-01 eta: 20:22:21 time: 0.1950 data_time: 0.0024 loss: 2.2880 2023/03/16 22:18:30 - mmengine - INFO - Epoch(train) [23][4600/5005] lr: 1.0000e-01 eta: 20:22:01 time: 0.1835 data_time: 0.0025 loss: 2.0616 2023/03/16 22:18:50 - mmengine - INFO - Epoch(train) [23][4700/5005] lr: 1.0000e-01 eta: 20:21:45 time: 0.1983 data_time: 0.0026 loss: 2.4929 2023/03/16 22:19:09 - mmengine - INFO - Epoch(train) [23][4800/5005] lr: 1.0000e-01 eta: 20:21:26 time: 0.1845 data_time: 0.0026 loss: 2.5051 2023/03/16 22:19:26 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:19:27 - mmengine - INFO - Epoch(train) [23][4900/5005] lr: 1.0000e-01 eta: 20:21:06 time: 0.1860 data_time: 0.0024 loss: 2.2673 2023/03/16 22:19:46 - mmengine - INFO - Epoch(train) [23][5000/5005] lr: 1.0000e-01 eta: 20:20:45 time: 0.1945 data_time: 0.0031 loss: 2.2656 2023/03/16 22:19:47 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:19:47 - mmengine - INFO - Saving checkpoint at 23 epochs 2023/03/16 22:19:54 - mmengine - INFO - Epoch(val) [23][100/196] eta: 0:00:05 time: 0.0469 data_time: 0.0009 2023/03/16 22:20:18 - mmengine - INFO - Epoch(val) [23][196/196] accuracy/top1: 54.2040 accuracy/top5: 79.9380data_time: 0.0234 time: 0.0549 2023/03/16 22:20:38 - mmengine - INFO - Epoch(train) [24][ 100/5005] lr: 1.0000e-01 eta: 20:20:27 time: 0.1869 data_time: 0.0025 loss: 2.1441 2023/03/16 22:20:57 - mmengine - INFO - Epoch(train) [24][ 200/5005] lr: 1.0000e-01 eta: 20:20:09 time: 0.1870 data_time: 0.0024 loss: 2.1801 2023/03/16 22:21:16 - mmengine - INFO - Epoch(train) [24][ 300/5005] lr: 1.0000e-01 eta: 20:19:48 time: 0.1974 data_time: 0.0022 loss: 2.1240 2023/03/16 22:21:34 - mmengine - INFO - Epoch(train) [24][ 400/5005] lr: 1.0000e-01 eta: 20:19:27 time: 0.1707 data_time: 0.0024 loss: 2.1471 2023/03/16 22:21:52 - mmengine - INFO - Epoch(train) [24][ 500/5005] lr: 1.0000e-01 eta: 20:19:04 time: 0.1893 data_time: 0.0024 loss: 2.0575 2023/03/16 22:22:12 - mmengine - INFO - Epoch(train) [24][ 600/5005] lr: 1.0000e-01 eta: 20:18:47 time: 0.2150 data_time: 0.0024 loss: 2.1831 2023/03/16 22:22:32 - mmengine - INFO - Epoch(train) [24][ 700/5005] lr: 1.0000e-01 eta: 20:18:32 time: 0.1990 data_time: 0.0019 loss: 2.1997 2023/03/16 22:22:52 - mmengine - INFO - Epoch(train) [24][ 800/5005] lr: 1.0000e-01 eta: 20:18:15 time: 0.2023 data_time: 0.0023 loss: 2.3509 2023/03/16 22:23:09 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:23:12 - mmengine - INFO - Epoch(train) [24][ 900/5005] lr: 1.0000e-01 eta: 20:17:59 time: 0.2024 data_time: 0.0023 loss: 2.3103 2023/03/16 22:23:32 - mmengine - INFO - Epoch(train) [24][1000/5005] lr: 1.0000e-01 eta: 20:17:43 time: 0.1975 data_time: 0.0024 loss: 1.9933 2023/03/16 22:23:51 - mmengine - INFO - Epoch(train) [24][1100/5005] lr: 1.0000e-01 eta: 20:17:23 time: 0.1853 data_time: 0.0023 loss: 2.4481 2023/03/16 22:24:10 - mmengine - INFO - Epoch(train) [24][1200/5005] lr: 1.0000e-01 eta: 20:17:05 time: 0.1942 data_time: 0.0023 loss: 2.1049 2023/03/16 22:24:29 - mmengine - INFO - Epoch(train) [24][1300/5005] lr: 1.0000e-01 eta: 20:16:47 time: 0.2064 data_time: 0.0023 loss: 2.2721 2023/03/16 22:24:48 - mmengine - INFO - Epoch(train) [24][1400/5005] lr: 1.0000e-01 eta: 20:16:27 time: 0.2017 data_time: 0.0023 loss: 2.2696 2023/03/16 22:25:07 - mmengine - INFO - Epoch(train) [24][1500/5005] lr: 1.0000e-01 eta: 20:16:08 time: 0.1853 data_time: 0.0024 loss: 2.2829 2023/03/16 22:25:26 - mmengine - INFO - Epoch(train) [24][1600/5005] lr: 1.0000e-01 eta: 20:15:48 time: 0.1887 data_time: 0.0023 loss: 2.3678 2023/03/16 22:25:44 - mmengine - INFO - Epoch(train) [24][1700/5005] lr: 1.0000e-01 eta: 20:15:27 time: 0.1831 data_time: 0.0024 loss: 2.4034 2023/03/16 22:26:03 - mmengine - INFO - Epoch(train) [24][1800/5005] lr: 1.0000e-01 eta: 20:15:06 time: 0.1788 data_time: 0.0027 loss: 2.3447 2023/03/16 22:26:18 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:26:21 - mmengine - INFO - Epoch(train) [24][1900/5005] lr: 1.0000e-01 eta: 20:14:43 time: 0.1846 data_time: 0.0023 loss: 2.3660 2023/03/16 22:26:39 - mmengine - INFO - Epoch(train) [24][2000/5005] lr: 1.0000e-01 eta: 20:14:21 time: 0.1806 data_time: 0.0023 loss: 2.2526 2023/03/16 22:26:57 - mmengine - INFO - Epoch(train) [24][2100/5005] lr: 1.0000e-01 eta: 20:14:00 time: 0.1802 data_time: 0.0023 loss: 2.2178 2023/03/16 22:27:16 - mmengine - INFO - Epoch(train) [24][2200/5005] lr: 1.0000e-01 eta: 20:13:41 time: 0.2116 data_time: 0.0023 loss: 2.1569 2023/03/16 22:27:35 - mmengine - INFO - Epoch(train) [24][2300/5005] lr: 1.0000e-01 eta: 20:13:21 time: 0.1772 data_time: 0.0037 loss: 1.9300 2023/03/16 22:27:54 - mmengine - INFO - Epoch(train) [24][2400/5005] lr: 1.0000e-01 eta: 20:13:01 time: 0.1928 data_time: 0.0023 loss: 2.1644 2023/03/16 22:28:13 - mmengine - INFO - Epoch(train) [24][2500/5005] lr: 1.0000e-01 eta: 20:12:43 time: 0.1918 data_time: 0.0023 loss: 2.2700 2023/03/16 22:28:32 - mmengine - INFO - Epoch(train) [24][2600/5005] lr: 1.0000e-01 eta: 20:12:25 time: 0.1942 data_time: 0.0026 loss: 2.4130 2023/03/16 22:28:52 - mmengine - INFO - Epoch(train) [24][2700/5005] lr: 1.0000e-01 eta: 20:12:08 time: 0.1827 data_time: 0.0026 loss: 2.1842 2023/03/16 22:29:11 - mmengine - INFO - Epoch(train) [24][2800/5005] lr: 1.0000e-01 eta: 20:11:49 time: 0.1958 data_time: 0.0023 loss: 2.2489 2023/03/16 22:29:28 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:29:31 - mmengine - INFO - Epoch(train) [24][2900/5005] lr: 1.0000e-01 eta: 20:11:31 time: 0.1838 data_time: 0.0024 loss: 2.1335 2023/03/16 22:29:50 - mmengine - INFO - Epoch(train) [24][3000/5005] lr: 1.0000e-01 eta: 20:11:11 time: 0.1873 data_time: 0.0025 loss: 2.2548 2023/03/16 22:30:09 - mmengine - INFO - Epoch(train) [24][3100/5005] lr: 1.0000e-01 eta: 20:10:52 time: 0.1872 data_time: 0.0023 loss: 2.2041 2023/03/16 22:30:28 - mmengine - INFO - Epoch(train) [24][3200/5005] lr: 1.0000e-01 eta: 20:10:33 time: 0.1884 data_time: 0.0022 loss: 2.2963 2023/03/16 22:30:48 - mmengine - INFO - Epoch(train) [24][3300/5005] lr: 1.0000e-01 eta: 20:10:17 time: 0.1999 data_time: 0.0021 loss: 2.2162 2023/03/16 22:31:07 - mmengine - INFO - Epoch(train) [24][3400/5005] lr: 1.0000e-01 eta: 20:09:59 time: 0.1881 data_time: 0.0020 loss: 2.1932 2023/03/16 22:31:26 - mmengine - INFO - Epoch(train) [24][3500/5005] lr: 1.0000e-01 eta: 20:09:41 time: 0.1961 data_time: 0.0022 loss: 2.1276 2023/03/16 22:31:45 - mmengine - INFO - Epoch(train) [24][3600/5005] lr: 1.0000e-01 eta: 20:09:22 time: 0.1852 data_time: 0.0023 loss: 2.1494 2023/03/16 22:32:04 - mmengine - INFO - Epoch(train) [24][3700/5005] lr: 1.0000e-01 eta: 20:09:03 time: 0.2125 data_time: 0.0024 loss: 2.4333 2023/03/16 22:32:24 - mmengine - INFO - Epoch(train) [24][3800/5005] lr: 1.0000e-01 eta: 20:08:44 time: 0.1941 data_time: 0.0024 loss: 2.0711 2023/03/16 22:32:39 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:32:42 - mmengine - INFO - Epoch(train) [24][3900/5005] lr: 1.0000e-01 eta: 20:08:23 time: 0.1822 data_time: 0.0023 loss: 2.3341 2023/03/16 22:33:01 - mmengine - INFO - Epoch(train) [24][4000/5005] lr: 1.0000e-01 eta: 20:08:04 time: 0.1815 data_time: 0.0024 loss: 2.1983 2023/03/16 22:33:19 - mmengine - INFO - Epoch(train) [24][4100/5005] lr: 1.0000e-01 eta: 20:07:41 time: 0.1781 data_time: 0.0027 loss: 2.1318 2023/03/16 22:33:37 - mmengine - INFO - Epoch(train) [24][4200/5005] lr: 1.0000e-01 eta: 20:07:18 time: 0.1744 data_time: 0.0025 loss: 2.0336 2023/03/16 22:33:55 - mmengine - INFO - Epoch(train) [24][4300/5005] lr: 1.0000e-01 eta: 20:06:56 time: 0.1754 data_time: 0.0029 loss: 2.1534 2023/03/16 22:34:13 - mmengine - INFO - Epoch(train) [24][4400/5005] lr: 1.0000e-01 eta: 20:06:35 time: 0.1940 data_time: 0.0023 loss: 2.2940 2023/03/16 22:34:33 - mmengine - INFO - Epoch(train) [24][4500/5005] lr: 1.0000e-01 eta: 20:06:17 time: 0.1919 data_time: 0.0023 loss: 2.2803 2023/03/16 22:34:51 - mmengine - INFO - Epoch(train) [24][4600/5005] lr: 1.0000e-01 eta: 20:05:57 time: 0.1890 data_time: 0.0024 loss: 2.3992 2023/03/16 22:35:11 - mmengine - INFO - Epoch(train) [24][4700/5005] lr: 1.0000e-01 eta: 20:05:39 time: 0.1905 data_time: 0.0023 loss: 2.6619 2023/03/16 22:35:30 - mmengine - INFO - Epoch(train) [24][4800/5005] lr: 1.0000e-01 eta: 20:05:19 time: 0.1773 data_time: 0.0022 loss: 2.1669 2023/03/16 22:35:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:35:49 - mmengine - INFO - Epoch(train) [24][4900/5005] lr: 1.0000e-01 eta: 20:05:02 time: 0.1935 data_time: 0.0024 loss: 2.0319 2023/03/16 22:36:09 - mmengine - INFO - Epoch(train) [24][5000/5005] lr: 1.0000e-01 eta: 20:04:47 time: 0.2104 data_time: 0.0032 loss: 2.2189 2023/03/16 22:36:11 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:36:11 - mmengine - INFO - Saving checkpoint at 24 epochs 2023/03/16 22:36:17 - mmengine - INFO - Epoch(val) [24][100/196] eta: 0:00:04 time: 0.0465 data_time: 0.0055 2023/03/16 22:36:41 - mmengine - INFO - Epoch(val) [24][196/196] accuracy/top1: 52.1940 accuracy/top5: 77.9220data_time: 0.0245 time: 0.0566 2023/03/16 22:37:03 - mmengine - INFO - Epoch(train) [25][ 100/5005] lr: 1.0000e-01 eta: 20:04:37 time: 0.1957 data_time: 0.0024 loss: 2.1271 2023/03/16 22:37:24 - mmengine - INFO - Epoch(train) [25][ 200/5005] lr: 1.0000e-01 eta: 20:04:23 time: 0.2152 data_time: 0.0022 loss: 2.1843 2023/03/16 22:37:44 - mmengine - INFO - Epoch(train) [25][ 300/5005] lr: 1.0000e-01 eta: 20:04:08 time: 0.2044 data_time: 0.0025 loss: 2.1660 2023/03/16 22:38:04 - mmengine - INFO - Epoch(train) [25][ 400/5005] lr: 1.0000e-01 eta: 20:03:53 time: 0.2116 data_time: 0.0027 loss: 2.3530 2023/03/16 22:38:24 - mmengine - INFO - Epoch(train) [25][ 500/5005] lr: 1.0000e-01 eta: 20:03:35 time: 0.1886 data_time: 0.0025 loss: 2.2815 2023/03/16 22:38:43 - mmengine - INFO - Epoch(train) [25][ 600/5005] lr: 1.0000e-01 eta: 20:03:17 time: 0.1953 data_time: 0.0024 loss: 2.2085 2023/03/16 22:39:03 - mmengine - INFO - Epoch(train) [25][ 700/5005] lr: 1.0000e-01 eta: 20:03:01 time: 0.1894 data_time: 0.0025 loss: 2.1709 2023/03/16 22:39:23 - mmengine - INFO - Epoch(train) [25][ 800/5005] lr: 1.0000e-01 eta: 20:02:45 time: 0.2012 data_time: 0.0025 loss: 2.1884 2023/03/16 22:39:39 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:39:43 - mmengine - INFO - Epoch(train) [25][ 900/5005] lr: 1.0000e-01 eta: 20:02:28 time: 0.1855 data_time: 0.0024 loss: 2.2870 2023/03/16 22:40:02 - mmengine - INFO - Epoch(train) [25][1000/5005] lr: 1.0000e-01 eta: 20:02:10 time: 0.1940 data_time: 0.0023 loss: 2.1038 2023/03/16 22:40:22 - mmengine - INFO - Epoch(train) [25][1100/5005] lr: 1.0000e-01 eta: 20:01:53 time: 0.1988 data_time: 0.0024 loss: 2.3050 2023/03/16 22:40:41 - mmengine - INFO - Epoch(train) [25][1200/5005] lr: 1.0000e-01 eta: 20:01:35 time: 0.1914 data_time: 0.0024 loss: 2.0578 2023/03/16 22:41:01 - mmengine - INFO - Epoch(train) [25][1300/5005] lr: 1.0000e-01 eta: 20:01:18 time: 0.1993 data_time: 0.0024 loss: 2.1705 2023/03/16 22:41:21 - mmengine - INFO - Epoch(train) [25][1400/5005] lr: 1.0000e-01 eta: 20:01:02 time: 0.2011 data_time: 0.0023 loss: 2.0785 2023/03/16 22:41:42 - mmengine - INFO - Epoch(train) [25][1500/5005] lr: 1.0000e-01 eta: 20:00:48 time: 0.2397 data_time: 0.0025 loss: 2.4961 2023/03/16 22:42:02 - mmengine - INFO - Epoch(train) [25][1600/5005] lr: 1.0000e-01 eta: 20:00:32 time: 0.1951 data_time: 0.0026 loss: 2.2783 2023/03/16 22:42:22 - mmengine - INFO - Epoch(train) [25][1700/5005] lr: 1.0000e-01 eta: 20:00:15 time: 0.1984 data_time: 0.0024 loss: 2.4957 2023/03/16 22:42:41 - mmengine - INFO - Epoch(train) [25][1800/5005] lr: 1.0000e-01 eta: 19:59:58 time: 0.1935 data_time: 0.0026 loss: 2.4822 2023/03/16 22:42:57 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:43:01 - mmengine - INFO - Epoch(train) [25][1900/5005] lr: 1.0000e-01 eta: 19:59:42 time: 0.1997 data_time: 0.0024 loss: 2.4745 2023/03/16 22:43:21 - mmengine - INFO - Epoch(train) [25][2000/5005] lr: 1.0000e-01 eta: 19:59:24 time: 0.1875 data_time: 0.0024 loss: 2.2850 2023/03/16 22:43:40 - mmengine - INFO - Epoch(train) [25][2100/5005] lr: 1.0000e-01 eta: 19:59:06 time: 0.1950 data_time: 0.0025 loss: 2.4915 2023/03/16 22:44:00 - mmengine - INFO - Epoch(train) [25][2200/5005] lr: 1.0000e-01 eta: 19:58:50 time: 0.1982 data_time: 0.0024 loss: 2.0945 2023/03/16 22:44:20 - mmengine - INFO - Epoch(train) [25][2300/5005] lr: 1.0000e-01 eta: 19:58:32 time: 0.1954 data_time: 0.0025 loss: 2.4437 2023/03/16 22:44:39 - mmengine - INFO - Epoch(train) [25][2400/5005] lr: 1.0000e-01 eta: 19:58:15 time: 0.1983 data_time: 0.0020 loss: 2.5295 2023/03/16 22:44:59 - mmengine - INFO - Epoch(train) [25][2500/5005] lr: 1.0000e-01 eta: 19:57:58 time: 0.1898 data_time: 0.0024 loss: 2.2437 2023/03/16 22:45:18 - mmengine - INFO - Epoch(train) [25][2600/5005] lr: 1.0000e-01 eta: 19:57:39 time: 0.1859 data_time: 0.0026 loss: 2.2715 2023/03/16 22:45:37 - mmengine - INFO - Epoch(train) [25][2700/5005] lr: 1.0000e-01 eta: 19:57:21 time: 0.1980 data_time: 0.0027 loss: 2.1261 2023/03/16 22:45:57 - mmengine - INFO - Epoch(train) [25][2800/5005] lr: 1.0000e-01 eta: 19:57:04 time: 0.1935 data_time: 0.0026 loss: 2.1566 2023/03/16 22:46:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:46:17 - mmengine - INFO - Epoch(train) [25][2900/5005] lr: 1.0000e-01 eta: 19:56:47 time: 0.1951 data_time: 0.0024 loss: 2.4007 2023/03/16 22:46:37 - mmengine - INFO - Epoch(train) [25][3000/5005] lr: 1.0000e-01 eta: 19:56:31 time: 0.1983 data_time: 0.0026 loss: 2.1307 2023/03/16 22:46:57 - mmengine - INFO - Epoch(train) [25][3100/5005] lr: 1.0000e-01 eta: 19:56:15 time: 0.2060 data_time: 0.0027 loss: 2.2118 2023/03/16 22:47:18 - mmengine - INFO - Epoch(train) [25][3200/5005] lr: 1.0000e-01 eta: 19:56:01 time: 0.1804 data_time: 0.0024 loss: 2.1726 2023/03/16 22:47:37 - mmengine - INFO - Epoch(train) [25][3300/5005] lr: 1.0000e-01 eta: 19:55:41 time: 0.1939 data_time: 0.0021 loss: 2.2101 2023/03/16 22:47:55 - mmengine - INFO - Epoch(train) [25][3400/5005] lr: 1.0000e-01 eta: 19:55:21 time: 0.1887 data_time: 0.0024 loss: 2.3040 2023/03/16 22:48:14 - mmengine - INFO - Epoch(train) [25][3500/5005] lr: 1.0000e-01 eta: 19:55:00 time: 0.2059 data_time: 0.0024 loss: 2.1038 2023/03/16 22:48:33 - mmengine - INFO - Epoch(train) [25][3600/5005] lr: 1.0000e-01 eta: 19:54:40 time: 0.1906 data_time: 0.0022 loss: 2.3506 2023/03/16 22:48:51 - mmengine - INFO - Epoch(train) [25][3700/5005] lr: 1.0000e-01 eta: 19:54:18 time: 0.1833 data_time: 0.0023 loss: 2.1498 2023/03/16 22:49:09 - mmengine - INFO - Epoch(train) [25][3800/5005] lr: 1.0000e-01 eta: 19:53:56 time: 0.1843 data_time: 0.0026 loss: 2.2155 2023/03/16 22:49:25 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:49:29 - mmengine - INFO - Epoch(train) [25][3900/5005] lr: 1.0000e-01 eta: 19:53:39 time: 0.1966 data_time: 0.0024 loss: 2.0371 2023/03/16 22:49:48 - mmengine - INFO - Epoch(train) [25][4000/5005] lr: 1.0000e-01 eta: 19:53:19 time: 0.1900 data_time: 0.0024 loss: 1.9089 2023/03/16 22:50:07 - mmengine - INFO - Epoch(train) [25][4100/5005] lr: 1.0000e-01 eta: 19:53:00 time: 0.1881 data_time: 0.0022 loss: 2.4869 2023/03/16 22:50:25 - mmengine - INFO - Epoch(train) [25][4200/5005] lr: 1.0000e-01 eta: 19:52:39 time: 0.1780 data_time: 0.0024 loss: 2.2212 2023/03/16 22:50:43 - mmengine - INFO - Epoch(train) [25][4300/5005] lr: 1.0000e-01 eta: 19:52:18 time: 0.1855 data_time: 0.0024 loss: 2.1871 2023/03/16 22:51:01 - mmengine - INFO - Epoch(train) [25][4400/5005] lr: 1.0000e-01 eta: 19:51:56 time: 0.1744 data_time: 0.0023 loss: 2.2442 2023/03/16 22:51:19 - mmengine - INFO - Epoch(train) [25][4500/5005] lr: 1.0000e-01 eta: 19:51:34 time: 0.1806 data_time: 0.0025 loss: 2.3097 2023/03/16 22:51:38 - mmengine - INFO - Epoch(train) [25][4600/5005] lr: 1.0000e-01 eta: 19:51:13 time: 0.1842 data_time: 0.0024 loss: 2.2901 2023/03/16 22:51:57 - mmengine - INFO - Epoch(train) [25][4700/5005] lr: 1.0000e-01 eta: 19:50:52 time: 0.1752 data_time: 0.0023 loss: 2.1815 2023/03/16 22:52:15 - mmengine - INFO - Epoch(train) [25][4800/5005] lr: 1.0000e-01 eta: 19:50:31 time: 0.1796 data_time: 0.0024 loss: 2.3418 2023/03/16 22:52:29 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:52:32 - mmengine - INFO - Epoch(train) [25][4900/5005] lr: 1.0000e-01 eta: 19:50:07 time: 0.1676 data_time: 0.0024 loss: 2.3354 2023/03/16 22:52:49 - mmengine - INFO - Epoch(train) [25][5000/5005] lr: 1.0000e-01 eta: 19:49:42 time: 0.1702 data_time: 0.0033 loss: 2.3981 2023/03/16 22:52:50 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:52:51 - mmengine - INFO - Saving checkpoint at 25 epochs 2023/03/16 22:52:57 - mmengine - INFO - Epoch(val) [25][100/196] eta: 0:00:04 time: 0.0488 data_time: 0.0090 2023/03/16 22:53:22 - mmengine - INFO - Epoch(val) [25][196/196] accuracy/top1: 52.6920 accuracy/top5: 78.5460data_time: 0.0240 time: 0.0563 2023/03/16 22:53:43 - mmengine - INFO - Epoch(train) [26][ 100/5005] lr: 1.0000e-01 eta: 19:49:28 time: 0.2025 data_time: 0.0026 loss: 2.0650 2023/03/16 22:54:03 - mmengine - INFO - Epoch(train) [26][ 200/5005] lr: 1.0000e-01 eta: 19:49:11 time: 0.2026 data_time: 0.0025 loss: 2.2055 2023/03/16 22:54:24 - mmengine - INFO - Epoch(train) [26][ 300/5005] lr: 1.0000e-01 eta: 19:48:58 time: 0.2355 data_time: 0.0025 loss: 2.0836 2023/03/16 22:54:45 - mmengine - INFO - Epoch(train) [26][ 400/5005] lr: 1.0000e-01 eta: 19:48:45 time: 0.2070 data_time: 0.0020 loss: 2.1545 2023/03/16 22:55:03 - mmengine - INFO - Epoch(train) [26][ 500/5005] lr: 1.0000e-01 eta: 19:48:25 time: 0.1808 data_time: 0.0028 loss: 2.0748 2023/03/16 22:55:21 - mmengine - INFO - Epoch(train) [26][ 600/5005] lr: 1.0000e-01 eta: 19:48:02 time: 0.1744 data_time: 0.0028 loss: 2.3549 2023/03/16 22:55:40 - mmengine - INFO - Epoch(train) [26][ 700/5005] lr: 1.0000e-01 eta: 19:47:43 time: 0.1775 data_time: 0.0023 loss: 2.1625 2023/03/16 22:55:58 - mmengine - INFO - Epoch(train) [26][ 800/5005] lr: 1.0000e-01 eta: 19:47:20 time: 0.1882 data_time: 0.0026 loss: 2.1610 2023/03/16 22:56:12 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:56:17 - mmengine - INFO - Epoch(train) [26][ 900/5005] lr: 1.0000e-01 eta: 19:47:01 time: 0.1946 data_time: 0.0027 loss: 2.3400 2023/03/16 22:56:35 - mmengine - INFO - Epoch(train) [26][1000/5005] lr: 1.0000e-01 eta: 19:46:38 time: 0.1776 data_time: 0.0024 loss: 1.9246 2023/03/16 22:56:53 - mmengine - INFO - Epoch(train) [26][1100/5005] lr: 1.0000e-01 eta: 19:46:16 time: 0.1775 data_time: 0.0025 loss: 2.5073 2023/03/16 22:57:11 - mmengine - INFO - Epoch(train) [26][1200/5005] lr: 1.0000e-01 eta: 19:45:53 time: 0.1769 data_time: 0.0026 loss: 2.0045 2023/03/16 22:57:29 - mmengine - INFO - Epoch(train) [26][1300/5005] lr: 1.0000e-01 eta: 19:45:31 time: 0.1803 data_time: 0.0024 loss: 2.1947 2023/03/16 22:57:47 - mmengine - INFO - Epoch(train) [26][1400/5005] lr: 1.0000e-01 eta: 19:45:11 time: 0.1887 data_time: 0.0023 loss: 2.2627 2023/03/16 22:58:06 - mmengine - INFO - Epoch(train) [26][1500/5005] lr: 1.0000e-01 eta: 19:44:52 time: 0.1897 data_time: 0.0027 loss: 2.0380 2023/03/16 22:58:26 - mmengine - INFO - Epoch(train) [26][1600/5005] lr: 1.0000e-01 eta: 19:44:33 time: 0.1830 data_time: 0.0028 loss: 2.4461 2023/03/16 22:58:45 - mmengine - INFO - Epoch(train) [26][1700/5005] lr: 1.0000e-01 eta: 19:44:14 time: 0.1865 data_time: 0.0030 loss: 1.9896 2023/03/16 22:59:03 - mmengine - INFO - Epoch(train) [26][1800/5005] lr: 1.0000e-01 eta: 19:43:52 time: 0.1841 data_time: 0.0029 loss: 2.3571 2023/03/16 22:59:17 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 22:59:22 - mmengine - INFO - Epoch(train) [26][1900/5005] lr: 1.0000e-01 eta: 19:43:33 time: 0.1892 data_time: 0.0027 loss: 2.0622 2023/03/16 22:59:41 - mmengine - INFO - Epoch(train) [26][2000/5005] lr: 1.0000e-01 eta: 19:43:15 time: 0.1870 data_time: 0.0033 loss: 2.1548 2023/03/16 23:00:00 - mmengine - INFO - Epoch(train) [26][2100/5005] lr: 1.0000e-01 eta: 19:42:55 time: 0.1892 data_time: 0.0028 loss: 2.0579 2023/03/16 23:00:20 - mmengine - INFO - Epoch(train) [26][2200/5005] lr: 1.0000e-01 eta: 19:42:38 time: 0.1963 data_time: 0.0026 loss: 2.1432 2023/03/16 23:00:40 - mmengine - INFO - Epoch(train) [26][2300/5005] lr: 1.0000e-01 eta: 19:42:21 time: 0.1978 data_time: 0.0026 loss: 2.3593 2023/03/16 23:01:00 - mmengine - INFO - Epoch(train) [26][2400/5005] lr: 1.0000e-01 eta: 19:42:05 time: 0.2016 data_time: 0.0027 loss: 2.1791 2023/03/16 23:01:19 - mmengine - INFO - Epoch(train) [26][2500/5005] lr: 1.0000e-01 eta: 19:41:46 time: 0.1806 data_time: 0.0028 loss: 2.3963 2023/03/16 23:01:37 - mmengine - INFO - Epoch(train) [26][2600/5005] lr: 1.0000e-01 eta: 19:41:25 time: 0.1786 data_time: 0.0027 loss: 2.1817 2023/03/16 23:01:57 - mmengine - INFO - Epoch(train) [26][2700/5005] lr: 1.0000e-01 eta: 19:41:07 time: 0.1988 data_time: 0.0026 loss: 2.0782 2023/03/16 23:02:17 - mmengine - INFO - Epoch(train) [26][2800/5005] lr: 1.0000e-01 eta: 19:40:51 time: 0.1888 data_time: 0.0026 loss: 2.0917 2023/03/16 23:02:31 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:02:36 - mmengine - INFO - Epoch(train) [26][2900/5005] lr: 1.0000e-01 eta: 19:40:31 time: 0.1884 data_time: 0.0025 loss: 2.0696 2023/03/16 23:02:55 - mmengine - INFO - Epoch(train) [26][3000/5005] lr: 1.0000e-01 eta: 19:40:13 time: 0.1956 data_time: 0.0029 loss: 2.2373 2023/03/16 23:03:14 - mmengine - INFO - Epoch(train) [26][3100/5005] lr: 1.0000e-01 eta: 19:39:54 time: 0.1956 data_time: 0.0024 loss: 2.0626 2023/03/16 23:03:34 - mmengine - INFO - Epoch(train) [26][3200/5005] lr: 1.0000e-01 eta: 19:39:39 time: 0.2048 data_time: 0.0025 loss: 2.1990 2023/03/16 23:03:54 - mmengine - INFO - Epoch(train) [26][3300/5005] lr: 1.0000e-01 eta: 19:39:20 time: 0.1903 data_time: 0.0028 loss: 2.1459 2023/03/16 23:04:13 - mmengine - INFO - Epoch(train) [26][3400/5005] lr: 1.0000e-01 eta: 19:39:01 time: 0.1846 data_time: 0.0026 loss: 2.4648 2023/03/16 23:04:31 - mmengine - INFO - Epoch(train) [26][3500/5005] lr: 1.0000e-01 eta: 19:38:40 time: 0.1813 data_time: 0.0029 loss: 2.3319 2023/03/16 23:04:50 - mmengine - INFO - Epoch(train) [26][3600/5005] lr: 1.0000e-01 eta: 19:38:21 time: 0.1860 data_time: 0.0025 loss: 2.2452 2023/03/16 23:05:09 - mmengine - INFO - Epoch(train) [26][3700/5005] lr: 1.0000e-01 eta: 19:38:02 time: 0.1849 data_time: 0.0027 loss: 2.1158 2023/03/16 23:05:28 - mmengine - INFO - Epoch(train) [26][3800/5005] lr: 1.0000e-01 eta: 19:37:42 time: 0.1836 data_time: 0.0030 loss: 2.4070 2023/03/16 23:05:42 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:05:47 - mmengine - INFO - Epoch(train) [26][3900/5005] lr: 1.0000e-01 eta: 19:37:22 time: 0.1941 data_time: 0.0027 loss: 2.2034 2023/03/16 23:06:07 - mmengine - INFO - Epoch(train) [26][4000/5005] lr: 1.0000e-01 eta: 19:37:07 time: 0.1982 data_time: 0.0027 loss: 2.0615 2023/03/16 23:06:27 - mmengine - INFO - Epoch(train) [26][4100/5005] lr: 1.0000e-01 eta: 19:36:49 time: 0.1942 data_time: 0.0027 loss: 2.3133 2023/03/16 23:06:46 - mmengine - INFO - Epoch(train) [26][4200/5005] lr: 1.0000e-01 eta: 19:36:31 time: 0.1937 data_time: 0.0028 loss: 2.1545 2023/03/16 23:07:05 - mmengine - INFO - Epoch(train) [26][4300/5005] lr: 1.0000e-01 eta: 19:36:13 time: 0.1830 data_time: 0.0026 loss: 2.0682 2023/03/16 23:07:24 - mmengine - INFO - Epoch(train) [26][4400/5005] lr: 1.0000e-01 eta: 19:35:53 time: 0.1930 data_time: 0.0026 loss: 2.4278 2023/03/16 23:07:45 - mmengine - INFO - Epoch(train) [26][4500/5005] lr: 1.0000e-01 eta: 19:35:38 time: 0.1914 data_time: 0.0026 loss: 2.1479 2023/03/16 23:08:04 - mmengine - INFO - Epoch(train) [26][4600/5005] lr: 1.0000e-01 eta: 19:35:19 time: 0.1991 data_time: 0.0027 loss: 2.3551 2023/03/16 23:08:23 - mmengine - INFO - Epoch(train) [26][4700/5005] lr: 1.0000e-01 eta: 19:35:01 time: 0.1915 data_time: 0.0027 loss: 2.2308 2023/03/16 23:08:43 - mmengine - INFO - Epoch(train) [26][4800/5005] lr: 1.0000e-01 eta: 19:34:42 time: 0.1900 data_time: 0.0028 loss: 2.2103 2023/03/16 23:08:57 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:09:02 - mmengine - INFO - Epoch(train) [26][4900/5005] lr: 1.0000e-01 eta: 19:34:25 time: 0.2138 data_time: 0.0028 loss: 2.3202 2023/03/16 23:09:22 - mmengine - INFO - Epoch(train) [26][5000/5005] lr: 1.0000e-01 eta: 19:34:07 time: 0.2036 data_time: 0.0037 loss: 2.3878 2023/03/16 23:09:23 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:09:23 - mmengine - INFO - Saving checkpoint at 26 epochs 2023/03/16 23:09:29 - mmengine - INFO - Epoch(val) [26][100/196] eta: 0:00:05 time: 0.0457 data_time: 0.0009 2023/03/16 23:09:58 - mmengine - INFO - Epoch(val) [26][196/196] accuracy/top1: 53.2580 accuracy/top5: 78.5080data_time: 0.0216 time: 0.0561 2023/03/16 23:10:18 - mmengine - INFO - Epoch(train) [27][ 100/5005] lr: 1.0000e-01 eta: 19:33:50 time: 0.2202 data_time: 0.0023 loss: 2.3169 2023/03/16 23:10:39 - mmengine - INFO - Epoch(train) [27][ 200/5005] lr: 1.0000e-01 eta: 19:33:37 time: 0.1847 data_time: 0.0028 loss: 2.2213 2023/03/16 23:10:59 - mmengine - INFO - Epoch(train) [27][ 300/5005] lr: 1.0000e-01 eta: 19:33:19 time: 0.1851 data_time: 0.0026 loss: 2.1406 2023/03/16 23:11:18 - mmengine - INFO - Epoch(train) [27][ 400/5005] lr: 1.0000e-01 eta: 19:32:59 time: 0.1937 data_time: 0.0027 loss: 2.2187 2023/03/16 23:11:36 - mmengine - INFO - Epoch(train) [27][ 500/5005] lr: 1.0000e-01 eta: 19:32:38 time: 0.1711 data_time: 0.0030 loss: 2.0718 2023/03/16 23:11:54 - mmengine - INFO - Epoch(train) [27][ 600/5005] lr: 1.0000e-01 eta: 19:32:17 time: 0.1840 data_time: 0.0026 loss: 2.0246 2023/03/16 23:12:13 - mmengine - INFO - Epoch(train) [27][ 700/5005] lr: 1.0000e-01 eta: 19:31:57 time: 0.1893 data_time: 0.0025 loss: 2.3191 2023/03/16 23:12:32 - mmengine - INFO - Epoch(train) [27][ 800/5005] lr: 1.0000e-01 eta: 19:31:37 time: 0.1880 data_time: 0.0026 loss: 2.0780 2023/03/16 23:12:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:12:51 - mmengine - INFO - Epoch(train) [27][ 900/5005] lr: 1.0000e-01 eta: 19:31:19 time: 0.1864 data_time: 0.0026 loss: 2.1758 2023/03/16 23:13:11 - mmengine - INFO - Epoch(train) [27][1000/5005] lr: 1.0000e-01 eta: 19:31:02 time: 0.1780 data_time: 0.0028 loss: 2.1394 2023/03/16 23:13:30 - mmengine - INFO - Epoch(train) [27][1100/5005] lr: 1.0000e-01 eta: 19:30:42 time: 0.1836 data_time: 0.0028 loss: 2.3286 2023/03/16 23:13:49 - mmengine - INFO - Epoch(train) [27][1200/5005] lr: 1.0000e-01 eta: 19:30:22 time: 0.1839 data_time: 0.0027 loss: 2.2342 2023/03/16 23:14:08 - mmengine - INFO - Epoch(train) [27][1300/5005] lr: 1.0000e-01 eta: 19:30:03 time: 0.1939 data_time: 0.0027 loss: 2.4101 2023/03/16 23:14:26 - mmengine - INFO - Epoch(train) [27][1400/5005] lr: 1.0000e-01 eta: 19:29:42 time: 0.1875 data_time: 0.0025 loss: 2.2467 2023/03/16 23:14:48 - mmengine - INFO - Epoch(train) [27][1500/5005] lr: 1.0000e-01 eta: 19:29:31 time: 0.2281 data_time: 0.0024 loss: 2.1371 2023/03/16 23:15:08 - mmengine - INFO - Epoch(train) [27][1600/5005] lr: 1.0000e-01 eta: 19:29:15 time: 0.1906 data_time: 0.0027 loss: 2.2877 2023/03/16 23:15:27 - mmengine - INFO - Epoch(train) [27][1700/5005] lr: 1.0000e-01 eta: 19:28:56 time: 0.1948 data_time: 0.0028 loss: 2.3043 2023/03/16 23:15:46 - mmengine - INFO - Epoch(train) [27][1800/5005] lr: 1.0000e-01 eta: 19:28:37 time: 0.2074 data_time: 0.0028 loss: 2.1242 2023/03/16 23:16:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:16:06 - mmengine - INFO - Epoch(train) [27][1900/5005] lr: 1.0000e-01 eta: 19:28:19 time: 0.1880 data_time: 0.0028 loss: 2.2792 2023/03/16 23:16:26 - mmengine - INFO - Epoch(train) [27][2000/5005] lr: 1.0000e-01 eta: 19:28:02 time: 0.2009 data_time: 0.0028 loss: 2.1806 2023/03/16 23:16:46 - mmengine - INFO - Epoch(train) [27][2100/5005] lr: 1.0000e-01 eta: 19:27:47 time: 0.2178 data_time: 0.0028 loss: 2.1554 2023/03/16 23:17:06 - mmengine - INFO - Epoch(train) [27][2200/5005] lr: 1.0000e-01 eta: 19:27:30 time: 0.1828 data_time: 0.0029 loss: 2.0795 2023/03/16 23:17:24 - mmengine - INFO - Epoch(train) [27][2300/5005] lr: 1.0000e-01 eta: 19:27:08 time: 0.1830 data_time: 0.0026 loss: 2.3938 2023/03/16 23:17:44 - mmengine - INFO - Epoch(train) [27][2400/5005] lr: 1.0000e-01 eta: 19:26:52 time: 0.1884 data_time: 0.0027 loss: 2.0890 2023/03/16 23:18:03 - mmengine - INFO - Epoch(train) [27][2500/5005] lr: 1.0000e-01 eta: 19:26:33 time: 0.1843 data_time: 0.0028 loss: 2.2923 2023/03/16 23:18:22 - mmengine - INFO - Epoch(train) [27][2600/5005] lr: 1.0000e-01 eta: 19:26:14 time: 0.1867 data_time: 0.0030 loss: 2.2815 2023/03/16 23:18:42 - mmengine - INFO - Epoch(train) [27][2700/5005] lr: 1.0000e-01 eta: 19:25:57 time: 0.1970 data_time: 0.0028 loss: 2.1950 2023/03/16 23:19:00 - mmengine - INFO - Epoch(train) [27][2800/5005] lr: 1.0000e-01 eta: 19:25:35 time: 0.1759 data_time: 0.0030 loss: 2.3496 2023/03/16 23:19:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:19:19 - mmengine - INFO - Epoch(train) [27][2900/5005] lr: 1.0000e-01 eta: 19:25:14 time: 0.2061 data_time: 0.0027 loss: 2.3334 2023/03/16 23:19:40 - mmengine - INFO - Epoch(train) [27][3000/5005] lr: 1.0000e-01 eta: 19:25:00 time: 0.2004 data_time: 0.0027 loss: 2.3933 2023/03/16 23:19:58 - mmengine - INFO - Epoch(train) [27][3100/5005] lr: 1.0000e-01 eta: 19:24:40 time: 0.1775 data_time: 0.0026 loss: 2.2094 2023/03/16 23:20:17 - mmengine - INFO - Epoch(train) [27][3200/5005] lr: 1.0000e-01 eta: 19:24:19 time: 0.1918 data_time: 0.0026 loss: 2.3259 2023/03/16 23:20:36 - mmengine - INFO - Epoch(train) [27][3300/5005] lr: 1.0000e-01 eta: 19:24:02 time: 0.1957 data_time: 0.0025 loss: 2.2941 2023/03/16 23:20:57 - mmengine - INFO - Epoch(train) [27][3400/5005] lr: 1.0000e-01 eta: 19:23:46 time: 0.2241 data_time: 0.0025 loss: 2.2852 2023/03/16 23:21:17 - mmengine - INFO - Epoch(train) [27][3500/5005] lr: 1.0000e-01 eta: 19:23:29 time: 0.2095 data_time: 0.0024 loss: 2.2880 2023/03/16 23:21:36 - mmengine - INFO - Epoch(train) [27][3600/5005] lr: 1.0000e-01 eta: 19:23:10 time: 0.1936 data_time: 0.0026 loss: 2.1570 2023/03/16 23:21:55 - mmengine - INFO - Epoch(train) [27][3700/5005] lr: 1.0000e-01 eta: 19:22:51 time: 0.1901 data_time: 0.0026 loss: 2.2629 2023/03/16 23:22:15 - mmengine - INFO - Epoch(train) [27][3800/5005] lr: 1.0000e-01 eta: 19:22:35 time: 0.2101 data_time: 0.0031 loss: 2.3064 2023/03/16 23:22:30 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:22:36 - mmengine - INFO - Epoch(train) [27][3900/5005] lr: 1.0000e-01 eta: 19:22:21 time: 0.1884 data_time: 0.0025 loss: 2.1533 2023/03/16 23:22:56 - mmengine - INFO - Epoch(train) [27][4000/5005] lr: 1.0000e-01 eta: 19:22:04 time: 0.1970 data_time: 0.0027 loss: 2.0393 2023/03/16 23:23:15 - mmengine - INFO - Epoch(train) [27][4100/5005] lr: 1.0000e-01 eta: 19:21:45 time: 0.1840 data_time: 0.0026 loss: 2.4613 2023/03/16 23:23:34 - mmengine - INFO - Epoch(train) [27][4200/5005] lr: 1.0000e-01 eta: 19:21:28 time: 0.1891 data_time: 0.0030 loss: 2.1712 2023/03/16 23:23:54 - mmengine - INFO - Epoch(train) [27][4300/5005] lr: 1.0000e-01 eta: 19:21:08 time: 0.1866 data_time: 0.0029 loss: 2.2556 2023/03/16 23:24:13 - mmengine - INFO - Epoch(train) [27][4400/5005] lr: 1.0000e-01 eta: 19:20:51 time: 0.1857 data_time: 0.0026 loss: 2.3518 2023/03/16 23:24:33 - mmengine - INFO - Epoch(train) [27][4500/5005] lr: 1.0000e-01 eta: 19:20:35 time: 0.1983 data_time: 0.0027 loss: 2.2820 2023/03/16 23:24:53 - mmengine - INFO - Epoch(train) [27][4600/5005] lr: 1.0000e-01 eta: 19:20:17 time: 0.1962 data_time: 0.0026 loss: 2.1720 2023/03/16 23:25:13 - mmengine - INFO - Epoch(train) [27][4700/5005] lr: 1.0000e-01 eta: 19:20:00 time: 0.1986 data_time: 0.0027 loss: 2.2411 2023/03/16 23:25:32 - mmengine - INFO - Epoch(train) [27][4800/5005] lr: 1.0000e-01 eta: 19:19:42 time: 0.1913 data_time: 0.0028 loss: 2.0788 2023/03/16 23:25:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:25:52 - mmengine - INFO - Epoch(train) [27][4900/5005] lr: 1.0000e-01 eta: 19:19:24 time: 0.1967 data_time: 0.0028 loss: 2.1637 2023/03/16 23:26:12 - mmengine - INFO - Epoch(train) [27][5000/5005] lr: 1.0000e-01 eta: 19:19:09 time: 0.2201 data_time: 0.0036 loss: 2.2346 2023/03/16 23:26:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:26:14 - mmengine - INFO - Saving checkpoint at 27 epochs 2023/03/16 23:26:20 - mmengine - INFO - Epoch(val) [27][100/196] eta: 0:00:05 time: 0.0478 data_time: 0.0007 2023/03/16 23:26:47 - mmengine - INFO - Epoch(val) [27][196/196] accuracy/top1: 53.5320 accuracy/top5: 79.2560data_time: 0.0023 time: 0.0350 2023/03/16 23:27:08 - mmengine - INFO - Epoch(train) [28][ 100/5005] lr: 1.0000e-01 eta: 19:18:54 time: 0.1771 data_time: 0.0026 loss: 2.1156 2023/03/16 23:27:26 - mmengine - INFO - Epoch(train) [28][ 200/5005] lr: 1.0000e-01 eta: 19:18:31 time: 0.1802 data_time: 0.0029 loss: 2.3701 2023/03/16 23:27:45 - mmengine - INFO - Epoch(train) [28][ 300/5005] lr: 1.0000e-01 eta: 19:18:11 time: 0.2123 data_time: 0.0030 loss: 2.3622 2023/03/16 23:28:02 - mmengine - INFO - Epoch(train) [28][ 400/5005] lr: 1.0000e-01 eta: 19:17:48 time: 0.1649 data_time: 0.0029 loss: 2.3480 2023/03/16 23:28:20 - mmengine - INFO - Epoch(train) [28][ 500/5005] lr: 1.0000e-01 eta: 19:17:25 time: 0.1774 data_time: 0.0029 loss: 2.0083 2023/03/16 23:28:39 - mmengine - INFO - Epoch(train) [28][ 600/5005] lr: 1.0000e-01 eta: 19:17:05 time: 0.1744 data_time: 0.0031 loss: 2.2505 2023/03/16 23:28:57 - mmengine - INFO - Epoch(train) [28][ 700/5005] lr: 1.0000e-01 eta: 19:16:44 time: 0.1793 data_time: 0.0026 loss: 2.1914 2023/03/16 23:29:15 - mmengine - INFO - Epoch(train) [28][ 800/5005] lr: 1.0000e-01 eta: 19:16:21 time: 0.1772 data_time: 0.0026 loss: 2.1942 2023/03/16 23:29:26 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:29:32 - mmengine - INFO - Epoch(train) [28][ 900/5005] lr: 1.0000e-01 eta: 19:15:58 time: 0.1704 data_time: 0.0024 loss: 2.3037 2023/03/16 23:29:51 - mmengine - INFO - Epoch(train) [28][1000/5005] lr: 1.0000e-01 eta: 19:15:37 time: 0.2145 data_time: 0.0025 loss: 2.1922 2023/03/16 23:30:10 - mmengine - INFO - Epoch(train) [28][1100/5005] lr: 1.0000e-01 eta: 19:15:18 time: 0.1786 data_time: 0.0028 loss: 2.2355 2023/03/16 23:30:28 - mmengine - INFO - Epoch(train) [28][1200/5005] lr: 1.0000e-01 eta: 19:14:56 time: 0.1815 data_time: 0.0028 loss: 2.3566 2023/03/16 23:30:47 - mmengine - INFO - Epoch(train) [28][1300/5005] lr: 1.0000e-01 eta: 19:14:38 time: 0.1946 data_time: 0.0027 loss: 2.1382 2023/03/16 23:31:07 - mmengine - INFO - Epoch(train) [28][1400/5005] lr: 1.0000e-01 eta: 19:14:20 time: 0.1932 data_time: 0.0027 loss: 1.9613 2023/03/16 23:31:26 - mmengine - INFO - Epoch(train) [28][1500/5005] lr: 1.0000e-01 eta: 19:14:02 time: 0.1918 data_time: 0.0028 loss: 2.2430 2023/03/16 23:31:46 - mmengine - INFO - Epoch(train) [28][1600/5005] lr: 1.0000e-01 eta: 19:13:44 time: 0.1884 data_time: 0.0028 loss: 2.1280 2023/03/16 23:32:05 - mmengine - INFO - Epoch(train) [28][1700/5005] lr: 1.0000e-01 eta: 19:13:26 time: 0.1894 data_time: 0.0027 loss: 2.1044 2023/03/16 23:32:25 - mmengine - INFO - Epoch(train) [28][1800/5005] lr: 1.0000e-01 eta: 19:13:09 time: 0.1956 data_time: 0.0024 loss: 2.2477 2023/03/16 23:32:38 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:32:44 - mmengine - INFO - Epoch(train) [28][1900/5005] lr: 1.0000e-01 eta: 19:12:50 time: 0.1884 data_time: 0.0023 loss: 2.2121 2023/03/16 23:33:04 - mmengine - INFO - Epoch(train) [28][2000/5005] lr: 1.0000e-01 eta: 19:12:32 time: 0.1967 data_time: 0.0024 loss: 2.2701 2023/03/16 23:33:23 - mmengine - INFO - Epoch(train) [28][2100/5005] lr: 1.0000e-01 eta: 19:12:13 time: 0.1868 data_time: 0.0025 loss: 2.1082 2023/03/16 23:33:42 - mmengine - INFO - Epoch(train) [28][2200/5005] lr: 1.0000e-01 eta: 19:11:54 time: 0.1896 data_time: 0.0023 loss: 2.1813 2023/03/16 23:34:04 - mmengine - INFO - Epoch(train) [28][2300/5005] lr: 1.0000e-01 eta: 19:11:43 time: 0.2251 data_time: 0.0026 loss: 1.9680 2023/03/16 23:34:25 - mmengine - INFO - Epoch(train) [28][2400/5005] lr: 1.0000e-01 eta: 19:11:28 time: 0.1876 data_time: 0.0027 loss: 2.3579 2023/03/16 23:34:44 - mmengine - INFO - Epoch(train) [28][2500/5005] lr: 1.0000e-01 eta: 19:11:10 time: 0.2038 data_time: 0.0029 loss: 2.2274 2023/03/16 23:35:04 - mmengine - INFO - Epoch(train) [28][2600/5005] lr: 1.0000e-01 eta: 19:10:53 time: 0.1961 data_time: 0.0023 loss: 2.2929 2023/03/16 23:35:24 - mmengine - INFO - Epoch(train) [28][2700/5005] lr: 1.0000e-01 eta: 19:10:35 time: 0.1877 data_time: 0.0026 loss: 2.1966 2023/03/16 23:35:43 - mmengine - INFO - Epoch(train) [28][2800/5005] lr: 1.0000e-01 eta: 19:10:18 time: 0.1935 data_time: 0.0024 loss: 2.0833 2023/03/16 23:35:56 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:36:03 - mmengine - INFO - Epoch(train) [28][2900/5005] lr: 1.0000e-01 eta: 19:09:59 time: 0.1859 data_time: 0.0025 loss: 2.0421 2023/03/16 23:36:23 - mmengine - INFO - Epoch(train) [28][3000/5005] lr: 1.0000e-01 eta: 19:09:43 time: 0.1895 data_time: 0.0024 loss: 2.2143 2023/03/16 23:36:42 - mmengine - INFO - Epoch(train) [28][3100/5005] lr: 1.0000e-01 eta: 19:09:23 time: 0.1887 data_time: 0.0024 loss: 2.1063 2023/03/16 23:37:01 - mmengine - INFO - Epoch(train) [28][3200/5005] lr: 1.0000e-01 eta: 19:09:06 time: 0.1982 data_time: 0.0026 loss: 2.3651 2023/03/16 23:37:21 - mmengine - INFO - Epoch(train) [28][3300/5005] lr: 1.0000e-01 eta: 19:08:48 time: 0.1896 data_time: 0.0029 loss: 2.2463 2023/03/16 23:37:40 - mmengine - INFO - Epoch(train) [28][3400/5005] lr: 1.0000e-01 eta: 19:08:29 time: 0.1905 data_time: 0.0025 loss: 2.2987 2023/03/16 23:37:59 - mmengine - INFO - Epoch(train) [28][3500/5005] lr: 1.0000e-01 eta: 19:08:11 time: 0.1984 data_time: 0.0025 loss: 1.9683 2023/03/16 23:38:20 - mmengine - INFO - Epoch(train) [28][3600/5005] lr: 1.0000e-01 eta: 19:07:55 time: 0.1733 data_time: 0.0024 loss: 2.0334 2023/03/16 23:38:37 - mmengine - INFO - Epoch(train) [28][3700/5005] lr: 1.0000e-01 eta: 19:07:31 time: 0.1713 data_time: 0.0025 loss: 2.2591 2023/03/16 23:38:54 - mmengine - INFO - Epoch(train) [28][3800/5005] lr: 1.0000e-01 eta: 19:07:08 time: 0.1765 data_time: 0.0026 loss: 2.2179 2023/03/16 23:39:06 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:39:13 - mmengine - INFO - Epoch(train) [28][3900/5005] lr: 1.0000e-01 eta: 19:06:47 time: 0.1926 data_time: 0.0027 loss: 2.0979 2023/03/16 23:39:34 - mmengine - INFO - Epoch(train) [28][4000/5005] lr: 1.0000e-01 eta: 19:06:34 time: 0.2334 data_time: 0.0025 loss: 2.1433 2023/03/16 23:39:58 - mmengine - INFO - Epoch(train) [28][4100/5005] lr: 1.0000e-01 eta: 19:06:26 time: 0.2359 data_time: 0.0026 loss: 2.3681 2023/03/16 23:40:19 - mmengine - INFO - Epoch(train) [28][4200/5005] lr: 1.0000e-01 eta: 19:06:11 time: 0.1977 data_time: 0.0022 loss: 2.2205 2023/03/16 23:40:38 - mmengine - INFO - Epoch(train) [28][4300/5005] lr: 1.0000e-01 eta: 19:05:54 time: 0.1737 data_time: 0.0021 loss: 2.3637 2023/03/16 23:40:56 - mmengine - INFO - Epoch(train) [28][4400/5005] lr: 1.0000e-01 eta: 19:05:32 time: 0.1781 data_time: 0.0025 loss: 2.4243 2023/03/16 23:41:15 - mmengine - INFO - Epoch(train) [28][4500/5005] lr: 1.0000e-01 eta: 19:05:11 time: 0.1848 data_time: 0.0027 loss: 2.2057 2023/03/16 23:41:32 - mmengine - INFO - Epoch(train) [28][4600/5005] lr: 1.0000e-01 eta: 19:04:49 time: 0.1845 data_time: 0.0026 loss: 2.4673 2023/03/16 23:41:51 - mmengine - INFO - Epoch(train) [28][4700/5005] lr: 1.0000e-01 eta: 19:04:27 time: 0.1822 data_time: 0.0026 loss: 2.1279 2023/03/16 23:42:09 - mmengine - INFO - Epoch(train) [28][4800/5005] lr: 1.0000e-01 eta: 19:04:07 time: 0.1847 data_time: 0.0024 loss: 2.2764 2023/03/16 23:42:22 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:42:29 - mmengine - INFO - Epoch(train) [28][4900/5005] lr: 1.0000e-01 eta: 19:03:50 time: 0.1942 data_time: 0.0025 loss: 2.1113 2023/03/16 23:42:50 - mmengine - INFO - Epoch(train) [28][5000/5005] lr: 1.0000e-01 eta: 19:03:34 time: 0.2220 data_time: 0.0035 loss: 2.2940 2023/03/16 23:42:51 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:42:51 - mmengine - INFO - Saving checkpoint at 28 epochs 2023/03/16 23:42:58 - mmengine - INFO - Epoch(val) [28][100/196] eta: 0:00:05 time: 0.0598 data_time: 0.0009 2023/03/16 23:43:26 - mmengine - INFO - Epoch(val) [28][196/196] accuracy/top1: 53.8380 accuracy/top5: 79.1160data_time: 0.0127 time: 0.0434 2023/03/16 23:43:49 - mmengine - INFO - Epoch(train) [29][ 100/5005] lr: 1.0000e-01 eta: 19:03:25 time: 0.2050 data_time: 0.0025 loss: 2.4073 2023/03/16 23:44:08 - mmengine - INFO - Epoch(train) [29][ 200/5005] lr: 1.0000e-01 eta: 19:03:05 time: 0.1768 data_time: 0.0025 loss: 2.1061 2023/03/16 23:44:27 - mmengine - INFO - Epoch(train) [29][ 300/5005] lr: 1.0000e-01 eta: 19:02:47 time: 0.2041 data_time: 0.0025 loss: 2.1114 2023/03/16 23:44:47 - mmengine - INFO - Epoch(train) [29][ 400/5005] lr: 1.0000e-01 eta: 19:02:28 time: 0.1848 data_time: 0.0025 loss: 2.1085 2023/03/16 23:45:05 - mmengine - INFO - Epoch(train) [29][ 500/5005] lr: 1.0000e-01 eta: 19:02:08 time: 0.1839 data_time: 0.0026 loss: 2.2518 2023/03/16 23:45:25 - mmengine - INFO - Epoch(train) [29][ 600/5005] lr: 1.0000e-01 eta: 19:01:50 time: 0.1812 data_time: 0.0023 loss: 2.0566 2023/03/16 23:45:44 - mmengine - INFO - Epoch(train) [29][ 700/5005] lr: 1.0000e-01 eta: 19:01:31 time: 0.1941 data_time: 0.0026 loss: 2.2808 2023/03/16 23:46:05 - mmengine - INFO - Epoch(train) [29][ 800/5005] lr: 1.0000e-01 eta: 19:01:17 time: 0.1913 data_time: 0.0026 loss: 2.3554 2023/03/16 23:46:17 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:46:24 - mmengine - INFO - Epoch(train) [29][ 900/5005] lr: 1.0000e-01 eta: 19:00:59 time: 0.1955 data_time: 0.0026 loss: 2.2419 2023/03/16 23:46:43 - mmengine - INFO - Epoch(train) [29][1000/5005] lr: 1.0000e-01 eta: 19:00:39 time: 0.1854 data_time: 0.0023 loss: 2.1194 2023/03/16 23:47:04 - mmengine - INFO - Epoch(train) [29][1100/5005] lr: 1.0000e-01 eta: 19:00:23 time: 0.1944 data_time: 0.0028 loss: 2.1942 2023/03/16 23:47:23 - mmengine - INFO - Epoch(train) [29][1200/5005] lr: 1.0000e-01 eta: 19:00:05 time: 0.1836 data_time: 0.0025 loss: 2.4845 2023/03/16 23:47:42 - mmengine - INFO - Epoch(train) [29][1300/5005] lr: 1.0000e-01 eta: 18:59:45 time: 0.1847 data_time: 0.0027 loss: 2.1341 2023/03/16 23:48:01 - mmengine - INFO - Epoch(train) [29][1400/5005] lr: 1.0000e-01 eta: 18:59:26 time: 0.1881 data_time: 0.0025 loss: 2.1660 2023/03/16 23:48:20 - mmengine - INFO - Epoch(train) [29][1500/5005] lr: 1.0000e-01 eta: 18:59:06 time: 0.1774 data_time: 0.0028 loss: 2.0897 2023/03/16 23:48:38 - mmengine - INFO - Epoch(train) [29][1600/5005] lr: 1.0000e-01 eta: 18:58:45 time: 0.1787 data_time: 0.0026 loss: 2.2461 2023/03/16 23:48:56 - mmengine - INFO - Epoch(train) [29][1700/5005] lr: 1.0000e-01 eta: 18:58:24 time: 0.1858 data_time: 0.0025 loss: 2.0680 2023/03/16 23:49:14 - mmengine - INFO - Epoch(train) [29][1800/5005] lr: 1.0000e-01 eta: 18:58:03 time: 0.1712 data_time: 0.0026 loss: 1.8860 2023/03/16 23:49:25 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:49:32 - mmengine - INFO - Epoch(train) [29][1900/5005] lr: 1.0000e-01 eta: 18:57:40 time: 0.1834 data_time: 0.0026 loss: 2.2122 2023/03/16 23:49:51 - mmengine - INFO - Epoch(train) [29][2000/5005] lr: 1.0000e-01 eta: 18:57:21 time: 0.1981 data_time: 0.0025 loss: 2.3326 2023/03/16 23:50:10 - mmengine - INFO - Epoch(train) [29][2100/5005] lr: 1.0000e-01 eta: 18:57:02 time: 0.1876 data_time: 0.0026 loss: 2.0351 2023/03/16 23:50:29 - mmengine - INFO - Epoch(train) [29][2200/5005] lr: 1.0000e-01 eta: 18:56:42 time: 0.1959 data_time: 0.0025 loss: 2.2599 2023/03/16 23:50:49 - mmengine - INFO - Epoch(train) [29][2300/5005] lr: 1.0000e-01 eta: 18:56:25 time: 0.1956 data_time: 0.0028 loss: 2.1385 2023/03/16 23:51:08 - mmengine - INFO - Epoch(train) [29][2400/5005] lr: 1.0000e-01 eta: 18:56:06 time: 0.1788 data_time: 0.0026 loss: 1.9879 2023/03/16 23:51:27 - mmengine - INFO - Epoch(train) [29][2500/5005] lr: 1.0000e-01 eta: 18:55:45 time: 0.1863 data_time: 0.0023 loss: 2.2981 2023/03/16 23:51:45 - mmengine - INFO - Epoch(train) [29][2600/5005] lr: 1.0000e-01 eta: 18:55:25 time: 0.1887 data_time: 0.0022 loss: 2.2432 2023/03/16 23:52:04 - mmengine - INFO - Epoch(train) [29][2700/5005] lr: 1.0000e-01 eta: 18:55:05 time: 0.1780 data_time: 0.0024 loss: 2.0656 2023/03/16 23:52:22 - mmengine - INFO - Epoch(train) [29][2800/5005] lr: 1.0000e-01 eta: 18:54:43 time: 0.1774 data_time: 0.0024 loss: 2.1952 2023/03/16 23:52:33 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:52:41 - mmengine - INFO - Epoch(train) [29][2900/5005] lr: 1.0000e-01 eta: 18:54:23 time: 0.1833 data_time: 0.0026 loss: 2.3225 2023/03/16 23:52:59 - mmengine - INFO - Epoch(train) [29][3000/5005] lr: 1.0000e-01 eta: 18:54:03 time: 0.1915 data_time: 0.0026 loss: 2.0954 2023/03/16 23:53:18 - mmengine - INFO - Epoch(train) [29][3100/5005] lr: 1.0000e-01 eta: 18:53:44 time: 0.1865 data_time: 0.0024 loss: 2.1214 2023/03/16 23:53:37 - mmengine - INFO - Epoch(train) [29][3200/5005] lr: 1.0000e-01 eta: 18:53:24 time: 0.1883 data_time: 0.0025 loss: 2.0777 2023/03/16 23:53:55 - mmengine - INFO - Epoch(train) [29][3300/5005] lr: 1.0000e-01 eta: 18:53:02 time: 0.1782 data_time: 0.0025 loss: 1.9673 2023/03/16 23:54:13 - mmengine - INFO - Epoch(train) [29][3400/5005] lr: 1.0000e-01 eta: 18:52:40 time: 0.1786 data_time: 0.0024 loss: 2.1117 2023/03/16 23:54:31 - mmengine - INFO - Epoch(train) [29][3500/5005] lr: 1.0000e-01 eta: 18:52:18 time: 0.1753 data_time: 0.0027 loss: 2.1892 2023/03/16 23:54:49 - mmengine - INFO - Epoch(train) [29][3600/5005] lr: 1.0000e-01 eta: 18:51:57 time: 0.1763 data_time: 0.0026 loss: 2.4657 2023/03/16 23:55:07 - mmengine - INFO - Epoch(train) [29][3700/5005] lr: 1.0000e-01 eta: 18:51:35 time: 0.1812 data_time: 0.0025 loss: 2.1713 2023/03/16 23:55:25 - mmengine - INFO - Epoch(train) [29][3800/5005] lr: 1.0000e-01 eta: 18:51:14 time: 0.1789 data_time: 0.0027 loss: 2.1503 2023/03/16 23:55:37 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:55:44 - mmengine - INFO - Epoch(train) [29][3900/5005] lr: 1.0000e-01 eta: 18:50:54 time: 0.1799 data_time: 0.0024 loss: 2.2533 2023/03/16 23:56:02 - mmengine - INFO - Epoch(train) [29][4000/5005] lr: 1.0000e-01 eta: 18:50:32 time: 0.1778 data_time: 0.0028 loss: 2.1171 2023/03/16 23:56:21 - mmengine - INFO - Epoch(train) [29][4100/5005] lr: 1.0000e-01 eta: 18:50:12 time: 0.1886 data_time: 0.0026 loss: 1.9606 2023/03/16 23:56:39 - mmengine - INFO - Epoch(train) [29][4200/5005] lr: 1.0000e-01 eta: 18:49:51 time: 0.1809 data_time: 0.0028 loss: 2.0365 2023/03/16 23:56:57 - mmengine - INFO - Epoch(train) [29][4300/5005] lr: 1.0000e-01 eta: 18:49:29 time: 0.1780 data_time: 0.0026 loss: 2.3649 2023/03/16 23:57:15 - mmengine - INFO - Epoch(train) [29][4400/5005] lr: 1.0000e-01 eta: 18:49:08 time: 0.1786 data_time: 0.0026 loss: 2.1889 2023/03/16 23:57:34 - mmengine - INFO - Epoch(train) [29][4500/5005] lr: 1.0000e-01 eta: 18:48:47 time: 0.1763 data_time: 0.0026 loss: 2.3991 2023/03/16 23:57:52 - mmengine - INFO - Epoch(train) [29][4600/5005] lr: 1.0000e-01 eta: 18:48:26 time: 0.1840 data_time: 0.0025 loss: 2.2292 2023/03/16 23:58:11 - mmengine - INFO - Epoch(train) [29][4700/5005] lr: 1.0000e-01 eta: 18:48:06 time: 0.1778 data_time: 0.0025 loss: 2.3653 2023/03/16 23:58:30 - mmengine - INFO - Epoch(train) [29][4800/5005] lr: 1.0000e-01 eta: 18:47:46 time: 0.1845 data_time: 0.0026 loss: 2.1192 2023/03/16 23:58:41 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:58:48 - mmengine - INFO - Epoch(train) [29][4900/5005] lr: 1.0000e-01 eta: 18:47:26 time: 0.1791 data_time: 0.0027 loss: 2.3774 2023/03/16 23:59:06 - mmengine - INFO - Epoch(train) [29][5000/5005] lr: 1.0000e-01 eta: 18:47:03 time: 0.1841 data_time: 0.0034 loss: 2.2387 2023/03/16 23:59:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/16 23:59:07 - mmengine - INFO - Saving checkpoint at 29 epochs 2023/03/16 23:59:13 - mmengine - INFO - Epoch(val) [29][100/196] eta: 0:00:04 time: 0.0506 data_time: 0.0084 2023/03/16 23:59:40 - mmengine - INFO - Epoch(val) [29][196/196] accuracy/top1: 54.7920 accuracy/top5: 79.9200data_time: 0.0200 time: 0.0503 2023/03/17 00:00:01 - mmengine - INFO - Epoch(train) [30][ 100/5005] lr: 1.0000e-01 eta: 18:46:49 time: 0.1830 data_time: 0.0025 loss: 2.2057 2023/03/17 00:00:20 - mmengine - INFO - Epoch(train) [30][ 200/5005] lr: 1.0000e-01 eta: 18:46:29 time: 0.1907 data_time: 0.0025 loss: 2.4659 2023/03/17 00:00:38 - mmengine - INFO - Epoch(train) [30][ 300/5005] lr: 1.0000e-01 eta: 18:46:08 time: 0.1609 data_time: 0.0029 loss: 2.1204 2023/03/17 00:00:55 - mmengine - INFO - Epoch(train) [30][ 400/5005] lr: 1.0000e-01 eta: 18:45:42 time: 0.1670 data_time: 0.0026 loss: 2.3335 2023/03/17 00:01:12 - mmengine - INFO - Epoch(train) [30][ 500/5005] lr: 1.0000e-01 eta: 18:45:18 time: 0.1714 data_time: 0.0024 loss: 2.1738 2023/03/17 00:01:30 - mmengine - INFO - Epoch(train) [30][ 600/5005] lr: 1.0000e-01 eta: 18:44:57 time: 0.1816 data_time: 0.0024 loss: 2.1192 2023/03/17 00:01:49 - mmengine - INFO - Epoch(train) [30][ 700/5005] lr: 1.0000e-01 eta: 18:44:38 time: 0.1978 data_time: 0.0027 loss: 2.2026 2023/03/17 00:02:09 - mmengine - INFO - Epoch(train) [30][ 800/5005] lr: 1.0000e-01 eta: 18:44:21 time: 0.2029 data_time: 0.0025 loss: 2.1104 2023/03/17 00:02:20 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:02:29 - mmengine - INFO - Epoch(train) [30][ 900/5005] lr: 1.0000e-01 eta: 18:44:05 time: 0.1994 data_time: 0.0024 loss: 2.2199 2023/03/17 00:02:49 - mmengine - INFO - Epoch(train) [30][1000/5005] lr: 1.0000e-01 eta: 18:43:48 time: 0.1964 data_time: 0.0024 loss: 2.0770 2023/03/17 00:03:09 - mmengine - INFO - Epoch(train) [30][1100/5005] lr: 1.0000e-01 eta: 18:43:31 time: 0.1803 data_time: 0.0028 loss: 2.3186 2023/03/17 00:03:29 - mmengine - INFO - Epoch(train) [30][1200/5005] lr: 1.0000e-01 eta: 18:43:12 time: 0.1804 data_time: 0.0027 loss: 2.0189 2023/03/17 00:03:47 - mmengine - INFO - Epoch(train) [30][1300/5005] lr: 1.0000e-01 eta: 18:42:51 time: 0.2017 data_time: 0.0025 loss: 2.1686 2023/03/17 00:04:06 - mmengine - INFO - Epoch(train) [30][1400/5005] lr: 1.0000e-01 eta: 18:42:33 time: 0.1932 data_time: 0.0024 loss: 2.2097 2023/03/17 00:04:29 - mmengine - INFO - Epoch(train) [30][1500/5005] lr: 1.0000e-01 eta: 18:42:23 time: 0.2263 data_time: 0.0026 loss: 2.0972 2023/03/17 00:04:51 - mmengine - INFO - Epoch(train) [30][1600/5005] lr: 1.0000e-01 eta: 18:42:12 time: 0.2179 data_time: 0.0024 loss: 2.2384 2023/03/17 00:05:14 - mmengine - INFO - Epoch(train) [30][1700/5005] lr: 1.0000e-01 eta: 18:42:01 time: 0.2149 data_time: 0.0025 loss: 2.3254 2023/03/17 00:05:36 - mmengine - INFO - Epoch(train) [30][1800/5005] lr: 1.0000e-01 eta: 18:41:50 time: 0.2311 data_time: 0.0024 loss: 2.0695 2023/03/17 00:05:47 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:05:56 - mmengine - INFO - Epoch(train) [30][1900/5005] lr: 1.0000e-01 eta: 18:41:33 time: 0.2017 data_time: 0.0024 loss: 2.2367 2023/03/17 00:06:15 - mmengine - INFO - Epoch(train) [30][2000/5005] lr: 1.0000e-01 eta: 18:41:14 time: 0.1995 data_time: 0.0024 loss: 2.0293 2023/03/17 00:06:37 - mmengine - INFO - Epoch(train) [30][2100/5005] lr: 1.0000e-01 eta: 18:41:01 time: 0.2032 data_time: 0.0026 loss: 2.3880 2023/03/17 00:06:57 - mmengine - INFO - Epoch(train) [30][2200/5005] lr: 1.0000e-01 eta: 18:40:45 time: 0.2073 data_time: 0.0027 loss: 2.2264 2023/03/17 00:07:17 - mmengine - INFO - Epoch(train) [30][2300/5005] lr: 1.0000e-01 eta: 18:40:28 time: 0.1747 data_time: 0.0025 loss: 2.2866 2023/03/17 00:07:36 - mmengine - INFO - Epoch(train) [30][2400/5005] lr: 1.0000e-01 eta: 18:40:08 time: 0.1891 data_time: 0.0028 loss: 2.3412 2023/03/17 00:07:56 - mmengine - INFO - Epoch(train) [30][2500/5005] lr: 1.0000e-01 eta: 18:39:51 time: 0.1961 data_time: 0.0029 loss: 1.9802 2023/03/17 00:08:16 - mmengine - INFO - Epoch(train) [30][2600/5005] lr: 1.0000e-01 eta: 18:39:35 time: 0.2205 data_time: 0.0025 loss: 2.3567 2023/03/17 00:08:37 - mmengine - INFO - Epoch(train) [30][2700/5005] lr: 1.0000e-01 eta: 18:39:20 time: 0.1930 data_time: 0.0025 loss: 2.2809 2023/03/17 00:08:56 - mmengine - INFO - Epoch(train) [30][2800/5005] lr: 1.0000e-01 eta: 18:39:01 time: 0.1671 data_time: 0.0025 loss: 2.2079 2023/03/17 00:09:06 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:09:14 - mmengine - INFO - Epoch(train) [30][2900/5005] lr: 1.0000e-01 eta: 18:38:38 time: 0.1755 data_time: 0.0027 loss: 2.2508 2023/03/17 00:09:32 - mmengine - INFO - Epoch(train) [30][3000/5005] lr: 1.0000e-01 eta: 18:38:16 time: 0.1725 data_time: 0.0027 loss: 2.3133 2023/03/17 00:09:50 - mmengine - INFO - Epoch(train) [30][3100/5005] lr: 1.0000e-01 eta: 18:37:54 time: 0.1795 data_time: 0.0027 loss: 2.2463 2023/03/17 00:10:09 - mmengine - INFO - Epoch(train) [30][3200/5005] lr: 1.0000e-01 eta: 18:37:35 time: 0.2116 data_time: 0.0028 loss: 2.0636 2023/03/17 00:10:30 - mmengine - INFO - Epoch(train) [30][3300/5005] lr: 1.0000e-01 eta: 18:37:21 time: 0.1967 data_time: 0.0029 loss: 2.1634 2023/03/17 00:10:49 - mmengine - INFO - Epoch(train) [30][3400/5005] lr: 1.0000e-01 eta: 18:37:01 time: 0.1845 data_time: 0.0026 loss: 2.1775 2023/03/17 00:11:08 - mmengine - INFO - Epoch(train) [30][3500/5005] lr: 1.0000e-01 eta: 18:36:44 time: 0.1979 data_time: 0.0028 loss: 2.0400 2023/03/17 00:11:28 - mmengine - INFO - Epoch(train) [30][3600/5005] lr: 1.0000e-01 eta: 18:36:25 time: 0.1927 data_time: 0.0026 loss: 2.4627 2023/03/17 00:11:48 - mmengine - INFO - Epoch(train) [30][3700/5005] lr: 1.0000e-01 eta: 18:36:08 time: 0.1968 data_time: 0.0026 loss: 2.1356 2023/03/17 00:12:08 - mmengine - INFO - Epoch(train) [30][3800/5005] lr: 1.0000e-01 eta: 18:35:51 time: 0.1955 data_time: 0.0028 loss: 2.2537 2023/03/17 00:12:18 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:12:27 - mmengine - INFO - Epoch(train) [30][3900/5005] lr: 1.0000e-01 eta: 18:35:33 time: 0.2010 data_time: 0.0027 loss: 2.3425 2023/03/17 00:12:47 - mmengine - INFO - Epoch(train) [30][4000/5005] lr: 1.0000e-01 eta: 18:35:15 time: 0.1873 data_time: 0.0026 loss: 2.4349 2023/03/17 00:13:06 - mmengine - INFO - Epoch(train) [30][4100/5005] lr: 1.0000e-01 eta: 18:34:57 time: 0.1911 data_time: 0.0026 loss: 2.2404 2023/03/17 00:13:26 - mmengine - INFO - Epoch(train) [30][4200/5005] lr: 1.0000e-01 eta: 18:34:38 time: 0.1976 data_time: 0.0027 loss: 2.2773 2023/03/17 00:13:46 - mmengine - INFO - Epoch(train) [30][4300/5005] lr: 1.0000e-01 eta: 18:34:22 time: 0.1886 data_time: 0.0027 loss: 2.3027 2023/03/17 00:14:05 - mmengine - INFO - Epoch(train) [30][4400/5005] lr: 1.0000e-01 eta: 18:34:04 time: 0.1952 data_time: 0.0030 loss: 2.0517 2023/03/17 00:14:25 - mmengine - INFO - Epoch(train) [30][4500/5005] lr: 1.0000e-01 eta: 18:33:45 time: 0.1952 data_time: 0.0029 loss: 2.1365 2023/03/17 00:14:44 - mmengine - INFO - Epoch(train) [30][4600/5005] lr: 1.0000e-01 eta: 18:33:27 time: 0.1919 data_time: 0.0028 loss: 2.1756 2023/03/17 00:15:04 - mmengine - INFO - Epoch(train) [30][4700/5005] lr: 1.0000e-01 eta: 18:33:10 time: 0.1974 data_time: 0.0029 loss: 2.2922 2023/03/17 00:15:24 - mmengine - INFO - Epoch(train) [30][4800/5005] lr: 1.0000e-01 eta: 18:32:52 time: 0.1910 data_time: 0.0030 loss: 2.1722 2023/03/17 00:15:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:15:43 - mmengine - INFO - Epoch(train) [30][4900/5005] lr: 1.0000e-01 eta: 18:32:34 time: 0.1973 data_time: 0.0030 loss: 2.1396 2023/03/17 00:16:03 - mmengine - INFO - Epoch(train) [30][5000/5005] lr: 1.0000e-01 eta: 18:32:17 time: 0.2316 data_time: 0.0041 loss: 2.3026 2023/03/17 00:16:05 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:16:05 - mmengine - INFO - Saving checkpoint at 30 epochs 2023/03/17 00:16:12 - mmengine - INFO - Epoch(val) [30][100/196] eta: 0:00:05 time: 0.0529 data_time: 0.0009 2023/03/17 00:16:38 - mmengine - INFO - Epoch(val) [30][196/196] accuracy/top1: 54.1040 accuracy/top5: 79.0960data_time: 0.0308 time: 0.0670 2023/03/17 00:16:59 - mmengine - INFO - Epoch(train) [31][ 100/5005] lr: 1.0000e-02 eta: 18:32:01 time: 0.1782 data_time: 0.0026 loss: 1.7556 2023/03/17 00:17:17 - mmengine - INFO - Epoch(train) [31][ 200/5005] lr: 1.0000e-02 eta: 18:31:39 time: 0.1736 data_time: 0.0026 loss: 1.9857 2023/03/17 00:17:36 - mmengine - INFO - Epoch(train) [31][ 300/5005] lr: 1.0000e-02 eta: 18:31:20 time: 0.1919 data_time: 0.0025 loss: 1.8654 2023/03/17 00:17:54 - mmengine - INFO - Epoch(train) [31][ 400/5005] lr: 1.0000e-02 eta: 18:30:58 time: 0.1786 data_time: 0.0023 loss: 1.7526 2023/03/17 00:18:12 - mmengine - INFO - Epoch(train) [31][ 500/5005] lr: 1.0000e-02 eta: 18:30:37 time: 0.2009 data_time: 0.0030 loss: 1.9271 2023/03/17 00:18:31 - mmengine - INFO - Epoch(train) [31][ 600/5005] lr: 1.0000e-02 eta: 18:30:17 time: 0.1888 data_time: 0.0030 loss: 1.7472 2023/03/17 00:18:49 - mmengine - INFO - Epoch(train) [31][ 700/5005] lr: 1.0000e-02 eta: 18:29:57 time: 0.1872 data_time: 0.0026 loss: 1.5084 2023/03/17 00:19:12 - mmengine - INFO - Epoch(train) [31][ 800/5005] lr: 1.0000e-02 eta: 18:29:46 time: 0.1960 data_time: 0.0027 loss: 1.7888 2023/03/17 00:19:21 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:19:30 - mmengine - INFO - Epoch(train) [31][ 900/5005] lr: 1.0000e-02 eta: 18:29:25 time: 0.1696 data_time: 0.0027 loss: 1.8936 2023/03/17 00:19:48 - mmengine - INFO - Epoch(train) [31][1000/5005] lr: 1.0000e-02 eta: 18:29:04 time: 0.1983 data_time: 0.0028 loss: 1.6910 2023/03/17 00:20:06 - mmengine - INFO - Epoch(train) [31][1100/5005] lr: 1.0000e-02 eta: 18:28:41 time: 0.1789 data_time: 0.0027 loss: 1.7028 2023/03/17 00:20:23 - mmengine - INFO - Epoch(train) [31][1200/5005] lr: 1.0000e-02 eta: 18:28:18 time: 0.1741 data_time: 0.0028 loss: 1.6062 2023/03/17 00:20:42 - mmengine - INFO - Epoch(train) [31][1300/5005] lr: 1.0000e-02 eta: 18:27:58 time: 0.1881 data_time: 0.0029 loss: 1.6402 2023/03/17 00:21:00 - mmengine - INFO - Epoch(train) [31][1400/5005] lr: 1.0000e-02 eta: 18:27:36 time: 0.1811 data_time: 0.0028 loss: 1.6801 2023/03/17 00:21:22 - mmengine - INFO - Epoch(train) [31][1500/5005] lr: 1.0000e-02 eta: 18:27:25 time: 0.1882 data_time: 0.0026 loss: 1.8651 2023/03/17 00:21:42 - mmengine - INFO - Epoch(train) [31][1600/5005] lr: 1.0000e-02 eta: 18:27:07 time: 0.2491 data_time: 0.0024 loss: 1.6143 2023/03/17 00:22:02 - mmengine - INFO - Epoch(train) [31][1700/5005] lr: 1.0000e-02 eta: 18:26:49 time: 0.1814 data_time: 0.0028 loss: 1.5685 2023/03/17 00:22:23 - mmengine - INFO - Epoch(train) [31][1800/5005] lr: 1.0000e-02 eta: 18:26:36 time: 0.2243 data_time: 0.0026 loss: 1.7170 2023/03/17 00:22:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:22:44 - mmengine - INFO - Epoch(train) [31][1900/5005] lr: 1.0000e-02 eta: 18:26:19 time: 0.1870 data_time: 0.0027 loss: 1.7126 2023/03/17 00:23:03 - mmengine - INFO - Epoch(train) [31][2000/5005] lr: 1.0000e-02 eta: 18:26:00 time: 0.1758 data_time: 0.0027 loss: 1.9181 2023/03/17 00:23:20 - mmengine - INFO - Epoch(train) [31][2100/5005] lr: 1.0000e-02 eta: 18:25:37 time: 0.1716 data_time: 0.0028 loss: 1.5803 2023/03/17 00:23:39 - mmengine - INFO - Epoch(train) [31][2200/5005] lr: 1.0000e-02 eta: 18:25:17 time: 0.1753 data_time: 0.0026 loss: 1.6154 2023/03/17 00:23:57 - mmengine - INFO - Epoch(train) [31][2300/5005] lr: 1.0000e-02 eta: 18:24:56 time: 0.1878 data_time: 0.0030 loss: 1.8641 2023/03/17 00:24:16 - mmengine - INFO - Epoch(train) [31][2400/5005] lr: 1.0000e-02 eta: 18:24:38 time: 0.1787 data_time: 0.0029 loss: 1.5951 2023/03/17 00:24:35 - mmengine - INFO - Epoch(train) [31][2500/5005] lr: 1.0000e-02 eta: 18:24:18 time: 0.1713 data_time: 0.0025 loss: 1.7385 2023/03/17 00:24:53 - mmengine - INFO - Epoch(train) [31][2600/5005] lr: 1.0000e-02 eta: 18:23:56 time: 0.1770 data_time: 0.0029 loss: 1.6018 2023/03/17 00:25:11 - mmengine - INFO - Epoch(train) [31][2700/5005] lr: 1.0000e-02 eta: 18:23:35 time: 0.1723 data_time: 0.0029 loss: 1.7805 2023/03/17 00:25:31 - mmengine - INFO - Epoch(train) [31][2800/5005] lr: 1.0000e-02 eta: 18:23:19 time: 0.2750 data_time: 0.0023 loss: 1.6192 2023/03/17 00:25:42 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:25:50 - mmengine - INFO - Epoch(train) [31][2900/5005] lr: 1.0000e-02 eta: 18:22:59 time: 0.1663 data_time: 0.0030 loss: 1.6877 2023/03/17 00:26:07 - mmengine - INFO - Epoch(train) [31][3000/5005] lr: 1.0000e-02 eta: 18:22:35 time: 0.1684 data_time: 0.0028 loss: 1.4770 2023/03/17 00:26:26 - mmengine - INFO - Epoch(train) [31][3100/5005] lr: 1.0000e-02 eta: 18:22:15 time: 0.1953 data_time: 0.0028 loss: 1.7246 2023/03/17 00:26:44 - mmengine - INFO - Epoch(train) [31][3200/5005] lr: 1.0000e-02 eta: 18:21:54 time: 0.1842 data_time: 0.0027 loss: 1.5242 2023/03/17 00:27:03 - mmengine - INFO - Epoch(train) [31][3300/5005] lr: 1.0000e-02 eta: 18:21:34 time: 0.1879 data_time: 0.0026 loss: 1.8965 2023/03/17 00:27:22 - mmengine - INFO - Epoch(train) [31][3400/5005] lr: 1.0000e-02 eta: 18:21:14 time: 0.2033 data_time: 0.0027 loss: 1.6409 2023/03/17 00:27:40 - mmengine - INFO - Epoch(train) [31][3500/5005] lr: 1.0000e-02 eta: 18:20:52 time: 0.1750 data_time: 0.0025 loss: 1.8231 2023/03/17 00:27:58 - mmengine - INFO - Epoch(train) [31][3600/5005] lr: 1.0000e-02 eta: 18:20:32 time: 0.1846 data_time: 0.0025 loss: 1.6752 2023/03/17 00:28:17 - mmengine - INFO - Epoch(train) [31][3700/5005] lr: 1.0000e-02 eta: 18:20:11 time: 0.1712 data_time: 0.0027 loss: 1.5471 2023/03/17 00:28:34 - mmengine - INFO - Epoch(train) [31][3800/5005] lr: 1.0000e-02 eta: 18:19:48 time: 0.1746 data_time: 0.0034 loss: 1.7001 2023/03/17 00:28:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:28:52 - mmengine - INFO - Epoch(train) [31][3900/5005] lr: 1.0000e-02 eta: 18:19:28 time: 0.1867 data_time: 0.0028 loss: 1.6268 2023/03/17 00:29:11 - mmengine - INFO - Epoch(train) [31][4000/5005] lr: 1.0000e-02 eta: 18:19:08 time: 0.2147 data_time: 0.0028 loss: 1.7338 2023/03/17 00:29:30 - mmengine - INFO - Epoch(train) [31][4100/5005] lr: 1.0000e-02 eta: 18:18:48 time: 0.1757 data_time: 0.0027 loss: 1.4770 2023/03/17 00:29:49 - mmengine - INFO - Epoch(train) [31][4200/5005] lr: 1.0000e-02 eta: 18:18:28 time: 0.1805 data_time: 0.0027 loss: 1.7698 2023/03/17 00:30:08 - mmengine - INFO - Epoch(train) [31][4300/5005] lr: 1.0000e-02 eta: 18:18:09 time: 0.1893 data_time: 0.0029 loss: 1.4299 2023/03/17 00:30:26 - mmengine - INFO - Epoch(train) [31][4400/5005] lr: 1.0000e-02 eta: 18:17:49 time: 0.1838 data_time: 0.0027 loss: 1.6318 2023/03/17 00:30:46 - mmengine - INFO - Epoch(train) [31][4500/5005] lr: 1.0000e-02 eta: 18:17:30 time: 0.2121 data_time: 0.0026 loss: 1.4270 2023/03/17 00:31:05 - mmengine - INFO - Epoch(train) [31][4600/5005] lr: 1.0000e-02 eta: 18:17:11 time: 0.2178 data_time: 0.0030 loss: 1.4191 2023/03/17 00:31:23 - mmengine - INFO - Epoch(train) [31][4700/5005] lr: 1.0000e-02 eta: 18:16:51 time: 0.1911 data_time: 0.0029 loss: 1.7597 2023/03/17 00:31:44 - mmengine - INFO - Epoch(train) [31][4800/5005] lr: 1.0000e-02 eta: 18:16:36 time: 0.1885 data_time: 0.0027 loss: 1.7388 2023/03/17 00:31:53 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:32:03 - mmengine - INFO - Epoch(train) [31][4900/5005] lr: 1.0000e-02 eta: 18:16:15 time: 0.1841 data_time: 0.0026 loss: 1.6667 2023/03/17 00:32:22 - mmengine - INFO - Epoch(train) [31][5000/5005] lr: 1.0000e-02 eta: 18:15:57 time: 0.1938 data_time: 0.0035 loss: 1.6638 2023/03/17 00:32:23 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:32:24 - mmengine - INFO - Saving checkpoint at 31 epochs 2023/03/17 00:32:32 - mmengine - INFO - Epoch(val) [31][100/196] eta: 0:00:06 time: 0.0591 data_time: 0.0008 2023/03/17 00:32:58 - mmengine - INFO - Epoch(val) [31][196/196] accuracy/top1: 68.3980 accuracy/top5: 88.7260data_time: 0.0119 time: 0.0444 2023/03/17 00:33:19 - mmengine - INFO - Epoch(train) [32][ 100/5005] lr: 1.0000e-02 eta: 18:15:40 time: 0.1845 data_time: 0.0028 loss: 1.5388 2023/03/17 00:33:38 - mmengine - INFO - Epoch(train) [32][ 200/5005] lr: 1.0000e-02 eta: 18:15:22 time: 0.2428 data_time: 0.0025 loss: 1.7102 2023/03/17 00:33:58 - mmengine - INFO - Epoch(train) [32][ 300/5005] lr: 1.0000e-02 eta: 18:15:05 time: 0.1898 data_time: 0.0027 loss: 1.7645 2023/03/17 00:34:18 - mmengine - INFO - Epoch(train) [32][ 400/5005] lr: 1.0000e-02 eta: 18:14:46 time: 0.1856 data_time: 0.0029 loss: 1.5893 2023/03/17 00:34:36 - mmengine - INFO - Epoch(train) [32][ 500/5005] lr: 1.0000e-02 eta: 18:14:25 time: 0.1804 data_time: 0.0027 loss: 1.4550 2023/03/17 00:34:54 - mmengine - INFO - Epoch(train) [32][ 600/5005] lr: 1.0000e-02 eta: 18:14:05 time: 0.2013 data_time: 0.0027 loss: 1.7379 2023/03/17 00:35:14 - mmengine - INFO - Epoch(train) [32][ 700/5005] lr: 1.0000e-02 eta: 18:13:47 time: 0.1884 data_time: 0.0025 loss: 1.5753 2023/03/17 00:35:34 - mmengine - INFO - Epoch(train) [32][ 800/5005] lr: 1.0000e-02 eta: 18:13:29 time: 0.1971 data_time: 0.0030 loss: 1.6263 2023/03/17 00:35:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:35:54 - mmengine - INFO - Epoch(train) [32][ 900/5005] lr: 1.0000e-02 eta: 18:13:14 time: 0.2206 data_time: 0.0025 loss: 1.6372 2023/03/17 00:36:14 - mmengine - INFO - Epoch(train) [32][1000/5005] lr: 1.0000e-02 eta: 18:12:56 time: 0.1783 data_time: 0.0026 loss: 1.7053 2023/03/17 00:36:32 - mmengine - INFO - Epoch(train) [32][1100/5005] lr: 1.0000e-02 eta: 18:12:34 time: 0.1751 data_time: 0.0025 loss: 1.7311 2023/03/17 00:36:50 - mmengine - INFO - Epoch(train) [32][1200/5005] lr: 1.0000e-02 eta: 18:12:12 time: 0.1782 data_time: 0.0027 loss: 1.5735 2023/03/17 00:37:09 - mmengine - INFO - Epoch(train) [32][1300/5005] lr: 1.0000e-02 eta: 18:11:53 time: 0.1977 data_time: 0.0027 loss: 1.5214 2023/03/17 00:37:29 - mmengine - INFO - Epoch(train) [32][1400/5005] lr: 1.0000e-02 eta: 18:11:36 time: 0.2019 data_time: 0.0027 loss: 1.8318 2023/03/17 00:37:48 - mmengine - INFO - Epoch(train) [32][1500/5005] lr: 1.0000e-02 eta: 18:11:17 time: 0.1918 data_time: 0.0031 loss: 1.5568 2023/03/17 00:38:08 - mmengine - INFO - Epoch(train) [32][1600/5005] lr: 1.0000e-02 eta: 18:10:59 time: 0.2033 data_time: 0.0027 loss: 1.5028 2023/03/17 00:38:27 - mmengine - INFO - Epoch(train) [32][1700/5005] lr: 1.0000e-02 eta: 18:10:42 time: 0.1947 data_time: 0.0030 loss: 1.5121 2023/03/17 00:38:48 - mmengine - INFO - Epoch(train) [32][1800/5005] lr: 1.0000e-02 eta: 18:10:25 time: 0.1957 data_time: 0.0023 loss: 1.3467 2023/03/17 00:38:57 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:39:07 - mmengine - INFO - Epoch(train) [32][1900/5005] lr: 1.0000e-02 eta: 18:10:07 time: 0.1897 data_time: 0.0026 loss: 1.4466 2023/03/17 00:39:27 - mmengine - INFO - Epoch(train) [32][2000/5005] lr: 1.0000e-02 eta: 18:09:50 time: 0.2091 data_time: 0.0027 loss: 1.4906 2023/03/17 00:39:46 - mmengine - INFO - Epoch(train) [32][2100/5005] lr: 1.0000e-02 eta: 18:09:30 time: 0.2141 data_time: 0.0026 loss: 1.3599 2023/03/17 00:40:06 - mmengine - INFO - Epoch(train) [32][2200/5005] lr: 1.0000e-02 eta: 18:09:13 time: 0.1973 data_time: 0.0025 loss: 1.4981 2023/03/17 00:40:26 - mmengine - INFO - Epoch(train) [32][2300/5005] lr: 1.0000e-02 eta: 18:08:57 time: 0.2115 data_time: 0.0026 loss: 1.5506 2023/03/17 00:40:47 - mmengine - INFO - Epoch(train) [32][2400/5005] lr: 1.0000e-02 eta: 18:08:41 time: 0.1857 data_time: 0.0027 loss: 1.7427 2023/03/17 00:41:06 - mmengine - INFO - Epoch(train) [32][2500/5005] lr: 1.0000e-02 eta: 18:08:22 time: 0.1944 data_time: 0.0027 loss: 1.5956 2023/03/17 00:41:26 - mmengine - INFO - Epoch(train) [32][2600/5005] lr: 1.0000e-02 eta: 18:08:04 time: 0.1958 data_time: 0.0026 loss: 1.7290 2023/03/17 00:41:46 - mmengine - INFO - Epoch(train) [32][2700/5005] lr: 1.0000e-02 eta: 18:07:47 time: 0.1961 data_time: 0.0026 loss: 1.5179 2023/03/17 00:42:06 - mmengine - INFO - Epoch(train) [32][2800/5005] lr: 1.0000e-02 eta: 18:07:30 time: 0.2201 data_time: 0.0026 loss: 1.8814 2023/03/17 00:42:15 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:42:27 - mmengine - INFO - Epoch(train) [32][2900/5005] lr: 1.0000e-02 eta: 18:07:15 time: 0.1989 data_time: 0.0027 loss: 1.5741 2023/03/17 00:42:46 - mmengine - INFO - Epoch(train) [32][3000/5005] lr: 1.0000e-02 eta: 18:06:56 time: 0.1818 data_time: 0.0032 loss: 1.4597 2023/03/17 00:43:05 - mmengine - INFO - Epoch(train) [32][3100/5005] lr: 1.0000e-02 eta: 18:06:37 time: 0.1983 data_time: 0.0025 loss: 1.6898 2023/03/17 00:43:25 - mmengine - INFO - Epoch(train) [32][3200/5005] lr: 1.0000e-02 eta: 18:06:19 time: 0.1918 data_time: 0.0028 loss: 1.7126 2023/03/17 00:43:44 - mmengine - INFO - Epoch(train) [32][3300/5005] lr: 1.0000e-02 eta: 18:06:00 time: 0.1811 data_time: 0.0027 loss: 1.6653 2023/03/17 00:44:02 - mmengine - INFO - Epoch(train) [32][3400/5005] lr: 1.0000e-02 eta: 18:05:39 time: 0.1890 data_time: 0.0029 loss: 1.7072 2023/03/17 00:44:21 - mmengine - INFO - Epoch(train) [32][3500/5005] lr: 1.0000e-02 eta: 18:05:20 time: 0.1941 data_time: 0.0028 loss: 1.5579 2023/03/17 00:44:40 - mmengine - INFO - Epoch(train) [32][3600/5005] lr: 1.0000e-02 eta: 18:05:00 time: 0.1795 data_time: 0.0026 loss: 1.5858 2023/03/17 00:44:58 - mmengine - INFO - Epoch(train) [32][3700/5005] lr: 1.0000e-02 eta: 18:04:39 time: 0.1879 data_time: 0.0029 loss: 1.4693 2023/03/17 00:45:17 - mmengine - INFO - Epoch(train) [32][3800/5005] lr: 1.0000e-02 eta: 18:04:20 time: 0.1879 data_time: 0.0027 loss: 1.5386 2023/03/17 00:45:26 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:45:36 - mmengine - INFO - Epoch(train) [32][3900/5005] lr: 1.0000e-02 eta: 18:04:00 time: 0.1859 data_time: 0.0029 loss: 1.5494 2023/03/17 00:45:56 - mmengine - INFO - Epoch(train) [32][4000/5005] lr: 1.0000e-02 eta: 18:03:42 time: 0.2013 data_time: 0.0025 loss: 1.3605 2023/03/17 00:46:16 - mmengine - INFO - Epoch(train) [32][4100/5005] lr: 1.0000e-02 eta: 18:03:26 time: 0.1932 data_time: 0.0029 loss: 1.5779 2023/03/17 00:46:35 - mmengine - INFO - Epoch(train) [32][4200/5005] lr: 1.0000e-02 eta: 18:03:07 time: 0.1839 data_time: 0.0027 loss: 1.6299 2023/03/17 00:46:55 - mmengine - INFO - Epoch(train) [32][4300/5005] lr: 1.0000e-02 eta: 18:02:49 time: 0.2327 data_time: 0.0024 loss: 1.3890 2023/03/17 00:47:15 - mmengine - INFO - Epoch(train) [32][4400/5005] lr: 1.0000e-02 eta: 18:02:31 time: 0.1960 data_time: 0.0032 loss: 1.6370 2023/03/17 00:47:33 - mmengine - INFO - Epoch(train) [32][4500/5005] lr: 1.0000e-02 eta: 18:02:11 time: 0.1876 data_time: 0.0030 loss: 1.5503 2023/03/17 00:47:53 - mmengine - INFO - Epoch(train) [32][4600/5005] lr: 1.0000e-02 eta: 18:01:53 time: 0.1984 data_time: 0.0030 loss: 1.5543 2023/03/17 00:48:12 - mmengine - INFO - Epoch(train) [32][4700/5005] lr: 1.0000e-02 eta: 18:01:34 time: 0.1816 data_time: 0.0025 loss: 1.5536 2023/03/17 00:48:30 - mmengine - INFO - Epoch(train) [32][4800/5005] lr: 1.0000e-02 eta: 18:01:11 time: 0.1751 data_time: 0.0029 loss: 1.5628 2023/03/17 00:48:37 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:48:47 - mmengine - INFO - Epoch(train) [32][4900/5005] lr: 1.0000e-02 eta: 18:00:49 time: 0.1795 data_time: 0.0030 loss: 1.5804 2023/03/17 00:49:05 - mmengine - INFO - Epoch(train) [32][5000/5005] lr: 1.0000e-02 eta: 18:00:27 time: 0.1780 data_time: 0.0037 loss: 1.7223 2023/03/17 00:49:06 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:49:06 - mmengine - INFO - Saving checkpoint at 32 epochs 2023/03/17 00:49:13 - mmengine - INFO - Epoch(val) [32][100/196] eta: 0:00:05 time: 0.0448 data_time: 0.0048 2023/03/17 00:49:37 - mmengine - INFO - Epoch(val) [32][196/196] accuracy/top1: 69.4180 accuracy/top5: 89.3460data_time: 0.0159 time: 0.0491 2023/03/17 00:49:57 - mmengine - INFO - Epoch(train) [33][ 100/5005] lr: 1.0000e-02 eta: 18:00:08 time: 0.1892 data_time: 0.0028 loss: 1.5593 2023/03/17 00:50:15 - mmengine - INFO - Epoch(train) [33][ 200/5005] lr: 1.0000e-02 eta: 17:59:48 time: 0.1975 data_time: 0.0039 loss: 1.3351 2023/03/17 00:50:36 - mmengine - INFO - Epoch(train) [33][ 300/5005] lr: 1.0000e-02 eta: 17:59:33 time: 0.2162 data_time: 0.0027 loss: 1.5332 2023/03/17 00:50:58 - mmengine - INFO - Epoch(train) [33][ 400/5005] lr: 1.0000e-02 eta: 17:59:20 time: 0.1996 data_time: 0.0028 loss: 1.4932 2023/03/17 00:51:18 - mmengine - INFO - Epoch(train) [33][ 500/5005] lr: 1.0000e-02 eta: 17:59:01 time: 0.1971 data_time: 0.0029 loss: 1.6180 2023/03/17 00:51:38 - mmengine - INFO - Epoch(train) [33][ 600/5005] lr: 1.0000e-02 eta: 17:58:44 time: 0.2130 data_time: 0.0031 loss: 1.5409 2023/03/17 00:51:58 - mmengine - INFO - Epoch(train) [33][ 700/5005] lr: 1.0000e-02 eta: 17:58:27 time: 0.1910 data_time: 0.0032 loss: 1.4482 2023/03/17 00:52:17 - mmengine - INFO - Epoch(train) [33][ 800/5005] lr: 1.0000e-02 eta: 17:58:09 time: 0.1823 data_time: 0.0029 loss: 1.5478 2023/03/17 00:52:25 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:52:37 - mmengine - INFO - Epoch(train) [33][ 900/5005] lr: 1.0000e-02 eta: 17:57:51 time: 0.1879 data_time: 0.0028 loss: 1.4731 2023/03/17 00:52:58 - mmengine - INFO - Epoch(train) [33][1000/5005] lr: 1.0000e-02 eta: 17:57:37 time: 0.1928 data_time: 0.0030 loss: 1.4067 2023/03/17 00:53:18 - mmengine - INFO - Epoch(train) [33][1100/5005] lr: 1.0000e-02 eta: 17:57:18 time: 0.1840 data_time: 0.0030 loss: 1.5148 2023/03/17 00:53:36 - mmengine - INFO - Epoch(train) [33][1200/5005] lr: 1.0000e-02 eta: 17:56:58 time: 0.1859 data_time: 0.0029 loss: 1.6677 2023/03/17 00:53:55 - mmengine - INFO - Epoch(train) [33][1300/5005] lr: 1.0000e-02 eta: 17:56:38 time: 0.1832 data_time: 0.0030 loss: 1.6345 2023/03/17 00:54:14 - mmengine - INFO - Epoch(train) [33][1400/5005] lr: 1.0000e-02 eta: 17:56:18 time: 0.1905 data_time: 0.0031 loss: 1.4161 2023/03/17 00:54:33 - mmengine - INFO - Epoch(train) [33][1500/5005] lr: 1.0000e-02 eta: 17:55:58 time: 0.1846 data_time: 0.0027 loss: 1.3795 2023/03/17 00:54:50 - mmengine - INFO - Epoch(train) [33][1600/5005] lr: 1.0000e-02 eta: 17:55:37 time: 0.1906 data_time: 0.0027 loss: 1.4551 2023/03/17 00:55:09 - mmengine - INFO - Epoch(train) [33][1700/5005] lr: 1.0000e-02 eta: 17:55:17 time: 0.1982 data_time: 0.0027 loss: 1.5120 2023/03/17 00:55:29 - mmengine - INFO - Epoch(train) [33][1800/5005] lr: 1.0000e-02 eta: 17:54:59 time: 0.1855 data_time: 0.0027 loss: 1.5240 2023/03/17 00:55:37 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:55:50 - mmengine - INFO - Epoch(train) [33][1900/5005] lr: 1.0000e-02 eta: 17:54:44 time: 0.2019 data_time: 0.0026 loss: 1.4727 2023/03/17 00:56:09 - mmengine - INFO - Epoch(train) [33][2000/5005] lr: 1.0000e-02 eta: 17:54:24 time: 0.1820 data_time: 0.0028 loss: 1.5402 2023/03/17 00:56:28 - mmengine - INFO - Epoch(train) [33][2100/5005] lr: 1.0000e-02 eta: 17:54:04 time: 0.1835 data_time: 0.0027 loss: 1.6716 2023/03/17 00:56:47 - mmengine - INFO - Epoch(train) [33][2200/5005] lr: 1.0000e-02 eta: 17:53:45 time: 0.1921 data_time: 0.0026 loss: 1.4880 2023/03/17 00:57:05 - mmengine - INFO - Epoch(train) [33][2300/5005] lr: 1.0000e-02 eta: 17:53:25 time: 0.1856 data_time: 0.0027 loss: 1.5565 2023/03/17 00:57:24 - mmengine - INFO - Epoch(train) [33][2400/5005] lr: 1.0000e-02 eta: 17:53:06 time: 0.1880 data_time: 0.0027 loss: 1.5143 2023/03/17 00:57:44 - mmengine - INFO - Epoch(train) [33][2500/5005] lr: 1.0000e-02 eta: 17:52:49 time: 0.2508 data_time: 0.0028 loss: 1.3650 2023/03/17 00:58:05 - mmengine - INFO - Epoch(train) [33][2600/5005] lr: 1.0000e-02 eta: 17:52:32 time: 0.1982 data_time: 0.0028 loss: 1.3861 2023/03/17 00:58:24 - mmengine - INFO - Epoch(train) [33][2700/5005] lr: 1.0000e-02 eta: 17:52:14 time: 0.1918 data_time: 0.0026 loss: 1.5227 2023/03/17 00:58:44 - mmengine - INFO - Epoch(train) [33][2800/5005] lr: 1.0000e-02 eta: 17:51:56 time: 0.1932 data_time: 0.0027 loss: 1.3613 2023/03/17 00:58:52 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 00:59:03 - mmengine - INFO - Epoch(train) [33][2900/5005] lr: 1.0000e-02 eta: 17:51:37 time: 0.1881 data_time: 0.0026 loss: 1.4297 2023/03/17 00:59:22 - mmengine - INFO - Epoch(train) [33][3000/5005] lr: 1.0000e-02 eta: 17:51:17 time: 0.1823 data_time: 0.0026 loss: 1.8369 2023/03/17 00:59:40 - mmengine - INFO - Epoch(train) [33][3100/5005] lr: 1.0000e-02 eta: 17:50:57 time: 0.1847 data_time: 0.0022 loss: 1.4212 2023/03/17 01:00:00 - mmengine - INFO - Epoch(train) [33][3200/5005] lr: 1.0000e-02 eta: 17:50:39 time: 0.1946 data_time: 0.0026 loss: 1.4292 2023/03/17 01:00:19 - mmengine - INFO - Epoch(train) [33][3300/5005] lr: 1.0000e-02 eta: 17:50:21 time: 0.2264 data_time: 0.0026 loss: 1.3499 2023/03/17 01:00:40 - mmengine - INFO - Epoch(train) [33][3400/5005] lr: 1.0000e-02 eta: 17:50:04 time: 0.1974 data_time: 0.0027 loss: 1.3919 2023/03/17 01:00:59 - mmengine - INFO - Epoch(train) [33][3500/5005] lr: 1.0000e-02 eta: 17:49:45 time: 0.1869 data_time: 0.0028 loss: 1.5161 2023/03/17 01:01:19 - mmengine - INFO - Epoch(train) [33][3600/5005] lr: 1.0000e-02 eta: 17:49:28 time: 0.2009 data_time: 0.0027 loss: 1.3647 2023/03/17 01:01:39 - mmengine - INFO - Epoch(train) [33][3700/5005] lr: 1.0000e-02 eta: 17:49:11 time: 0.2029 data_time: 0.0029 loss: 1.5988 2023/03/17 01:02:00 - mmengine - INFO - Epoch(train) [33][3800/5005] lr: 1.0000e-02 eta: 17:48:56 time: 0.1859 data_time: 0.0027 loss: 1.5745 2023/03/17 01:02:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:02:20 - mmengine - INFO - Epoch(train) [33][3900/5005] lr: 1.0000e-02 eta: 17:48:39 time: 0.2192 data_time: 0.0027 loss: 1.4280 2023/03/17 01:02:40 - mmengine - INFO - Epoch(train) [33][4000/5005] lr: 1.0000e-02 eta: 17:48:21 time: 0.1840 data_time: 0.0027 loss: 1.4168 2023/03/17 01:02:58 - mmengine - INFO - Epoch(train) [33][4100/5005] lr: 1.0000e-02 eta: 17:48:00 time: 0.1757 data_time: 0.0027 loss: 1.4222 2023/03/17 01:03:16 - mmengine - INFO - Epoch(train) [33][4200/5005] lr: 1.0000e-02 eta: 17:47:38 time: 0.1813 data_time: 0.0025 loss: 1.6001 2023/03/17 01:03:35 - mmengine - INFO - Epoch(train) [33][4300/5005] lr: 1.0000e-02 eta: 17:47:19 time: 0.1796 data_time: 0.0026 loss: 1.2433 2023/03/17 01:03:54 - mmengine - INFO - Epoch(train) [33][4400/5005] lr: 1.0000e-02 eta: 17:47:00 time: 0.1733 data_time: 0.0027 loss: 1.4862 2023/03/17 01:04:13 - mmengine - INFO - Epoch(train) [33][4500/5005] lr: 1.0000e-02 eta: 17:46:41 time: 0.1836 data_time: 0.0027 loss: 1.4590 2023/03/17 01:04:34 - mmengine - INFO - Epoch(train) [33][4600/5005] lr: 1.0000e-02 eta: 17:46:26 time: 0.1966 data_time: 0.0029 loss: 1.3027 2023/03/17 01:04:53 - mmengine - INFO - Epoch(train) [33][4700/5005] lr: 1.0000e-02 eta: 17:46:06 time: 0.1779 data_time: 0.0028 loss: 1.7550 2023/03/17 01:05:11 - mmengine - INFO - Epoch(train) [33][4800/5005] lr: 1.0000e-02 eta: 17:45:44 time: 0.1816 data_time: 0.0027 loss: 1.3982 2023/03/17 01:05:18 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:05:30 - mmengine - INFO - Epoch(train) [33][4900/5005] lr: 1.0000e-02 eta: 17:45:24 time: 0.1832 data_time: 0.0028 loss: 1.3806 2023/03/17 01:05:48 - mmengine - INFO - Epoch(train) [33][5000/5005] lr: 1.0000e-02 eta: 17:45:04 time: 0.1848 data_time: 0.0034 loss: 1.3909 2023/03/17 01:05:49 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:05:50 - mmengine - INFO - Saving checkpoint at 33 epochs 2023/03/17 01:05:56 - mmengine - INFO - Epoch(val) [33][100/196] eta: 0:00:04 time: 0.0449 data_time: 0.0008 2023/03/17 01:06:20 - mmengine - INFO - Epoch(val) [33][196/196] accuracy/top1: 69.7120 accuracy/top5: 89.6860data_time: 0.0229 time: 0.0531 2023/03/17 01:06:39 - mmengine - INFO - Epoch(train) [34][ 100/5005] lr: 1.0000e-02 eta: 17:44:43 time: 0.1846 data_time: 0.0026 loss: 1.5940 2023/03/17 01:06:58 - mmengine - INFO - Epoch(train) [34][ 200/5005] lr: 1.0000e-02 eta: 17:44:22 time: 0.1794 data_time: 0.0028 loss: 1.4596 2023/03/17 01:07:16 - mmengine - INFO - Epoch(train) [34][ 300/5005] lr: 1.0000e-02 eta: 17:44:02 time: 0.1848 data_time: 0.0029 loss: 1.4284 2023/03/17 01:07:34 - mmengine - INFO - Epoch(train) [34][ 400/5005] lr: 1.0000e-02 eta: 17:43:41 time: 0.1777 data_time: 0.0030 loss: 1.4950 2023/03/17 01:07:51 - mmengine - INFO - Epoch(train) [34][ 500/5005] lr: 1.0000e-02 eta: 17:43:18 time: 0.1690 data_time: 0.0027 loss: 1.3143 2023/03/17 01:08:10 - mmengine - INFO - Epoch(train) [34][ 600/5005] lr: 1.0000e-02 eta: 17:42:57 time: 0.1706 data_time: 0.0030 loss: 1.4988 2023/03/17 01:08:29 - mmengine - INFO - Epoch(train) [34][ 700/5005] lr: 1.0000e-02 eta: 17:42:38 time: 0.2000 data_time: 0.0027 loss: 1.2563 2023/03/17 01:08:48 - mmengine - INFO - Epoch(train) [34][ 800/5005] lr: 1.0000e-02 eta: 17:42:20 time: 0.1897 data_time: 0.0028 loss: 1.5942 2023/03/17 01:08:55 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:09:07 - mmengine - INFO - Epoch(train) [34][ 900/5005] lr: 1.0000e-02 eta: 17:42:00 time: 0.1845 data_time: 0.0027 loss: 1.4424 2023/03/17 01:09:26 - mmengine - INFO - Epoch(train) [34][1000/5005] lr: 1.0000e-02 eta: 17:41:40 time: 0.1879 data_time: 0.0027 loss: 1.6158 2023/03/17 01:09:45 - mmengine - INFO - Epoch(train) [34][1100/5005] lr: 1.0000e-02 eta: 17:41:21 time: 0.2134 data_time: 0.0027 loss: 1.3353 2023/03/17 01:10:04 - mmengine - INFO - Epoch(train) [34][1200/5005] lr: 1.0000e-02 eta: 17:41:03 time: 0.1669 data_time: 0.0029 loss: 1.4948 2023/03/17 01:10:22 - mmengine - INFO - Epoch(train) [34][1300/5005] lr: 1.0000e-02 eta: 17:40:41 time: 0.1791 data_time: 0.0028 loss: 1.2846 2023/03/17 01:10:42 - mmengine - INFO - Epoch(train) [34][1400/5005] lr: 1.0000e-02 eta: 17:40:23 time: 0.2199 data_time: 0.0028 loss: 1.4176 2023/03/17 01:11:01 - mmengine - INFO - Epoch(train) [34][1500/5005] lr: 1.0000e-02 eta: 17:40:05 time: 0.1900 data_time: 0.0030 loss: 1.7021 2023/03/17 01:11:22 - mmengine - INFO - Epoch(train) [34][1600/5005] lr: 1.0000e-02 eta: 17:39:48 time: 0.2030 data_time: 0.0027 loss: 1.3843 2023/03/17 01:11:42 - mmengine - INFO - Epoch(train) [34][1700/5005] lr: 1.0000e-02 eta: 17:39:31 time: 0.2146 data_time: 0.0029 loss: 1.4333 2023/03/17 01:12:01 - mmengine - INFO - Epoch(train) [34][1800/5005] lr: 1.0000e-02 eta: 17:39:13 time: 0.1838 data_time: 0.0026 loss: 1.4391 2023/03/17 01:12:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:12:19 - mmengine - INFO - Epoch(train) [34][1900/5005] lr: 1.0000e-02 eta: 17:38:52 time: 0.2065 data_time: 0.0032 loss: 1.5904 2023/03/17 01:12:37 - mmengine - INFO - Epoch(train) [34][2000/5005] lr: 1.0000e-02 eta: 17:38:30 time: 0.1869 data_time: 0.0031 loss: 1.4167 2023/03/17 01:12:56 - mmengine - INFO - Epoch(train) [34][2100/5005] lr: 1.0000e-02 eta: 17:38:11 time: 0.1780 data_time: 0.0032 loss: 1.3310 2023/03/17 01:13:14 - mmengine - INFO - Epoch(train) [34][2200/5005] lr: 1.0000e-02 eta: 17:37:49 time: 0.1856 data_time: 0.0028 loss: 1.4011 2023/03/17 01:13:33 - mmengine - INFO - Epoch(train) [34][2300/5005] lr: 1.0000e-02 eta: 17:37:29 time: 0.1883 data_time: 0.0029 loss: 1.3931 2023/03/17 01:13:52 - mmengine - INFO - Epoch(train) [34][2400/5005] lr: 1.0000e-02 eta: 17:37:10 time: 0.1820 data_time: 0.0027 loss: 1.6465 2023/03/17 01:14:11 - mmengine - INFO - Epoch(train) [34][2500/5005] lr: 1.0000e-02 eta: 17:36:50 time: 0.1877 data_time: 0.0029 loss: 1.4315 2023/03/17 01:14:31 - mmengine - INFO - Epoch(train) [34][2600/5005] lr: 1.0000e-02 eta: 17:36:33 time: 0.1808 data_time: 0.0028 loss: 1.4130 2023/03/17 01:14:50 - mmengine - INFO - Epoch(train) [34][2700/5005] lr: 1.0000e-02 eta: 17:36:14 time: 0.2150 data_time: 0.0028 loss: 1.5963 2023/03/17 01:15:10 - mmengine - INFO - Epoch(train) [34][2800/5005] lr: 1.0000e-02 eta: 17:35:56 time: 0.1877 data_time: 0.0029 loss: 1.7464 2023/03/17 01:15:16 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:15:28 - mmengine - INFO - Epoch(train) [34][2900/5005] lr: 1.0000e-02 eta: 17:35:36 time: 0.1798 data_time: 0.0027 loss: 1.5682 2023/03/17 01:15:48 - mmengine - INFO - Epoch(train) [34][3000/5005] lr: 1.0000e-02 eta: 17:35:18 time: 0.1834 data_time: 0.0024 loss: 1.3248 2023/03/17 01:16:06 - mmengine - INFO - Epoch(train) [34][3100/5005] lr: 1.0000e-02 eta: 17:34:58 time: 0.1863 data_time: 0.0031 loss: 1.2865 2023/03/17 01:16:28 - mmengine - INFO - Epoch(train) [34][3200/5005] lr: 1.0000e-02 eta: 17:34:44 time: 0.2132 data_time: 0.0028 loss: 1.4979 2023/03/17 01:16:47 - mmengine - INFO - Epoch(train) [34][3300/5005] lr: 1.0000e-02 eta: 17:34:25 time: 0.1883 data_time: 0.0032 loss: 1.5852 2023/03/17 01:17:07 - mmengine - INFO - Epoch(train) [34][3400/5005] lr: 1.0000e-02 eta: 17:34:08 time: 0.2212 data_time: 0.0027 loss: 1.4502 2023/03/17 01:17:29 - mmengine - INFO - Epoch(train) [34][3500/5005] lr: 1.0000e-02 eta: 17:33:55 time: 0.2115 data_time: 0.0028 loss: 1.5726 2023/03/17 01:17:51 - mmengine - INFO - Epoch(train) [34][3600/5005] lr: 1.0000e-02 eta: 17:33:41 time: 0.2128 data_time: 0.0024 loss: 1.4668 2023/03/17 01:18:13 - mmengine - INFO - Epoch(train) [34][3700/5005] lr: 1.0000e-02 eta: 17:33:27 time: 0.2249 data_time: 0.0027 loss: 1.4951 2023/03/17 01:18:32 - mmengine - INFO - Epoch(train) [34][3800/5005] lr: 1.0000e-02 eta: 17:33:07 time: 0.1791 data_time: 0.0032 loss: 1.7109 2023/03/17 01:18:38 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:18:50 - mmengine - INFO - Epoch(train) [34][3900/5005] lr: 1.0000e-02 eta: 17:32:46 time: 0.1872 data_time: 0.0029 loss: 1.5405 2023/03/17 01:19:09 - mmengine - INFO - Epoch(train) [34][4000/5005] lr: 1.0000e-02 eta: 17:32:28 time: 0.2160 data_time: 0.0028 loss: 1.4769 2023/03/17 01:19:31 - mmengine - INFO - Epoch(train) [34][4100/5005] lr: 1.0000e-02 eta: 17:32:13 time: 0.2023 data_time: 0.0028 loss: 1.6918 2023/03/17 01:19:54 - mmengine - INFO - Epoch(train) [34][4200/5005] lr: 1.0000e-02 eta: 17:32:01 time: 0.1775 data_time: 0.0028 loss: 1.4471 2023/03/17 01:20:12 - mmengine - INFO - Epoch(train) [34][4300/5005] lr: 1.0000e-02 eta: 17:31:40 time: 0.1795 data_time: 0.0029 loss: 1.4832 2023/03/17 01:20:30 - mmengine - INFO - Epoch(train) [34][4400/5005] lr: 1.0000e-02 eta: 17:31:20 time: 0.1812 data_time: 0.0033 loss: 1.5128 2023/03/17 01:20:48 - mmengine - INFO - Epoch(train) [34][4500/5005] lr: 1.0000e-02 eta: 17:30:59 time: 0.1763 data_time: 0.0030 loss: 1.5220 2023/03/17 01:21:08 - mmengine - INFO - Epoch(train) [34][4600/5005] lr: 1.0000e-02 eta: 17:30:41 time: 0.2118 data_time: 0.0030 loss: 1.4865 2023/03/17 01:21:28 - mmengine - INFO - Epoch(train) [34][4700/5005] lr: 1.0000e-02 eta: 17:30:23 time: 0.2249 data_time: 0.0028 loss: 1.5583 2023/03/17 01:21:46 - mmengine - INFO - Epoch(train) [34][4800/5005] lr: 1.0000e-02 eta: 17:30:02 time: 0.1706 data_time: 0.0026 loss: 1.4241 2023/03/17 01:21:52 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:22:05 - mmengine - INFO - Epoch(train) [34][4900/5005] lr: 1.0000e-02 eta: 17:29:44 time: 0.2035 data_time: 0.0029 loss: 1.3822 2023/03/17 01:22:26 - mmengine - INFO - Epoch(train) [34][5000/5005] lr: 1.0000e-02 eta: 17:29:27 time: 0.1883 data_time: 0.0039 loss: 1.5372 2023/03/17 01:22:27 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:22:27 - mmengine - INFO - Saving checkpoint at 34 epochs 2023/03/17 01:22:34 - mmengine - INFO - Epoch(val) [34][100/196] eta: 0:00:05 time: 0.0498 data_time: 0.0069 2023/03/17 01:23:02 - mmengine - INFO - Epoch(val) [34][196/196] accuracy/top1: 69.7640 accuracy/top5: 89.8580data_time: 0.0316 time: 0.0654 2023/03/17 01:23:26 - mmengine - INFO - Epoch(train) [35][ 100/5005] lr: 1.0000e-02 eta: 17:29:16 time: 0.1917 data_time: 0.0026 loss: 1.4088 2023/03/17 01:23:44 - mmengine - INFO - Epoch(train) [35][ 200/5005] lr: 1.0000e-02 eta: 17:28:55 time: 0.1870 data_time: 0.0028 loss: 1.3716 2023/03/17 01:24:02 - mmengine - INFO - Epoch(train) [35][ 300/5005] lr: 1.0000e-02 eta: 17:28:34 time: 0.1792 data_time: 0.0028 loss: 1.4543 2023/03/17 01:24:20 - mmengine - INFO - Epoch(train) [35][ 400/5005] lr: 1.0000e-02 eta: 17:28:13 time: 0.1846 data_time: 0.0029 loss: 1.4319 2023/03/17 01:24:39 - mmengine - INFO - Epoch(train) [35][ 500/5005] lr: 1.0000e-02 eta: 17:27:53 time: 0.1868 data_time: 0.0027 loss: 1.4923 2023/03/17 01:25:00 - mmengine - INFO - Epoch(train) [35][ 600/5005] lr: 1.0000e-02 eta: 17:27:37 time: 0.2082 data_time: 0.0033 loss: 1.4574 2023/03/17 01:25:20 - mmengine - INFO - Epoch(train) [35][ 700/5005] lr: 1.0000e-02 eta: 17:27:20 time: 0.1935 data_time: 0.0031 loss: 1.5215 2023/03/17 01:25:39 - mmengine - INFO - Epoch(train) [35][ 800/5005] lr: 1.0000e-02 eta: 17:27:01 time: 0.1915 data_time: 0.0026 loss: 1.5206 2023/03/17 01:25:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:25:58 - mmengine - INFO - Epoch(train) [35][ 900/5005] lr: 1.0000e-02 eta: 17:26:41 time: 0.1761 data_time: 0.0028 loss: 1.4290 2023/03/17 01:26:16 - mmengine - INFO - Epoch(train) [35][1000/5005] lr: 1.0000e-02 eta: 17:26:19 time: 0.1755 data_time: 0.0029 loss: 1.3906 2023/03/17 01:26:34 - mmengine - INFO - Epoch(train) [35][1100/5005] lr: 1.0000e-02 eta: 17:25:59 time: 0.1862 data_time: 0.0030 loss: 1.3451 2023/03/17 01:26:52 - mmengine - INFO - Epoch(train) [35][1200/5005] lr: 1.0000e-02 eta: 17:25:37 time: 0.1834 data_time: 0.0028 loss: 1.3468 2023/03/17 01:27:11 - mmengine - INFO - Epoch(train) [35][1300/5005] lr: 1.0000e-02 eta: 17:25:19 time: 0.2040 data_time: 0.0027 loss: 1.4644 2023/03/17 01:27:30 - mmengine - INFO - Epoch(train) [35][1400/5005] lr: 1.0000e-02 eta: 17:24:59 time: 0.1742 data_time: 0.0031 loss: 1.4625 2023/03/17 01:27:48 - mmengine - INFO - Epoch(train) [35][1500/5005] lr: 1.0000e-02 eta: 17:24:38 time: 0.1758 data_time: 0.0029 loss: 1.6079 2023/03/17 01:28:07 - mmengine - INFO - Epoch(train) [35][1600/5005] lr: 1.0000e-02 eta: 17:24:18 time: 0.1811 data_time: 0.0029 loss: 1.4098 2023/03/17 01:28:26 - mmengine - INFO - Epoch(train) [35][1700/5005] lr: 1.0000e-02 eta: 17:23:59 time: 0.2100 data_time: 0.0031 loss: 1.5678 2023/03/17 01:28:45 - mmengine - INFO - Epoch(train) [35][1800/5005] lr: 1.0000e-02 eta: 17:23:39 time: 0.1828 data_time: 0.0032 loss: 1.4349 2023/03/17 01:28:50 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:29:02 - mmengine - INFO - Epoch(train) [35][1900/5005] lr: 1.0000e-02 eta: 17:23:17 time: 0.1821 data_time: 0.0028 loss: 1.5798 2023/03/17 01:29:22 - mmengine - INFO - Epoch(train) [35][2000/5005] lr: 1.0000e-02 eta: 17:23:00 time: 0.1841 data_time: 0.0024 loss: 1.6063 2023/03/17 01:29:41 - mmengine - INFO - Epoch(train) [35][2100/5005] lr: 1.0000e-02 eta: 17:22:40 time: 0.1885 data_time: 0.0032 loss: 1.5069 2023/03/17 01:30:00 - mmengine - INFO - Epoch(train) [35][2200/5005] lr: 1.0000e-02 eta: 17:22:20 time: 0.1912 data_time: 0.0030 loss: 1.4636 2023/03/17 01:30:18 - mmengine - INFO - Epoch(train) [35][2300/5005] lr: 1.0000e-02 eta: 17:22:00 time: 0.1861 data_time: 0.0025 loss: 1.5015 2023/03/17 01:30:37 - mmengine - INFO - Epoch(train) [35][2400/5005] lr: 1.0000e-02 eta: 17:21:40 time: 0.1860 data_time: 0.0030 loss: 1.3343 2023/03/17 01:30:57 - mmengine - INFO - Epoch(train) [35][2500/5005] lr: 1.0000e-02 eta: 17:21:22 time: 0.1890 data_time: 0.0027 loss: 1.5378 2023/03/17 01:31:16 - mmengine - INFO - Epoch(train) [35][2600/5005] lr: 1.0000e-02 eta: 17:21:03 time: 0.2142 data_time: 0.0029 loss: 1.4006 2023/03/17 01:31:34 - mmengine - INFO - Epoch(train) [35][2700/5005] lr: 1.0000e-02 eta: 17:20:42 time: 0.1929 data_time: 0.0026 loss: 1.4606 2023/03/17 01:31:55 - mmengine - INFO - Epoch(train) [35][2800/5005] lr: 1.0000e-02 eta: 17:20:26 time: 0.2089 data_time: 0.0033 loss: 1.4301 2023/03/17 01:32:01 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:32:17 - mmengine - INFO - Epoch(train) [35][2900/5005] lr: 1.0000e-02 eta: 17:20:13 time: 0.2437 data_time: 0.0031 loss: 1.4764 2023/03/17 01:32:38 - mmengine - INFO - Epoch(train) [35][3000/5005] lr: 1.0000e-02 eta: 17:19:57 time: 0.1888 data_time: 0.0030 loss: 1.4463 2023/03/17 01:32:57 - mmengine - INFO - Epoch(train) [35][3100/5005] lr: 1.0000e-02 eta: 17:19:37 time: 0.1897 data_time: 0.0028 loss: 1.4750 2023/03/17 01:33:16 - mmengine - INFO - Epoch(train) [35][3200/5005] lr: 1.0000e-02 eta: 17:19:18 time: 0.1945 data_time: 0.0032 loss: 1.2737 2023/03/17 01:33:36 - mmengine - INFO - Epoch(train) [35][3300/5005] lr: 1.0000e-02 eta: 17:19:01 time: 0.1835 data_time: 0.0029 loss: 1.3916 2023/03/17 01:33:55 - mmengine - INFO - Epoch(train) [35][3400/5005] lr: 1.0000e-02 eta: 17:18:41 time: 0.1918 data_time: 0.0026 loss: 1.6575 2023/03/17 01:34:14 - mmengine - INFO - Epoch(train) [35][3500/5005] lr: 1.0000e-02 eta: 17:18:23 time: 0.1909 data_time: 0.0025 loss: 1.5041 2023/03/17 01:34:33 - mmengine - INFO - Epoch(train) [35][3600/5005] lr: 1.0000e-02 eta: 17:18:04 time: 0.1809 data_time: 0.0029 loss: 1.4149 2023/03/17 01:34:52 - mmengine - INFO - Epoch(train) [35][3700/5005] lr: 1.0000e-02 eta: 17:17:45 time: 0.2058 data_time: 0.0029 loss: 1.3998 2023/03/17 01:35:11 - mmengine - INFO - Epoch(train) [35][3800/5005] lr: 1.0000e-02 eta: 17:17:25 time: 0.1864 data_time: 0.0028 loss: 1.4282 2023/03/17 01:35:17 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:35:30 - mmengine - INFO - Epoch(train) [35][3900/5005] lr: 1.0000e-02 eta: 17:17:05 time: 0.1875 data_time: 0.0029 loss: 1.6209 2023/03/17 01:35:52 - mmengine - INFO - Epoch(train) [35][4000/5005] lr: 1.0000e-02 eta: 17:16:51 time: 0.2082 data_time: 0.0028 loss: 1.4113 2023/03/17 01:36:11 - mmengine - INFO - Epoch(train) [35][4100/5005] lr: 1.0000e-02 eta: 17:16:33 time: 0.1956 data_time: 0.0027 loss: 1.5772 2023/03/17 01:36:31 - mmengine - INFO - Epoch(train) [35][4200/5005] lr: 1.0000e-02 eta: 17:16:14 time: 0.1972 data_time: 0.0027 loss: 1.4159 2023/03/17 01:36:50 - mmengine - INFO - Epoch(train) [35][4300/5005] lr: 1.0000e-02 eta: 17:15:56 time: 0.1993 data_time: 0.0027 loss: 1.4322 2023/03/17 01:37:10 - mmengine - INFO - Epoch(train) [35][4400/5005] lr: 1.0000e-02 eta: 17:15:37 time: 0.1823 data_time: 0.0032 loss: 1.4516 2023/03/17 01:37:31 - mmengine - INFO - Epoch(train) [35][4500/5005] lr: 1.0000e-02 eta: 17:15:22 time: 0.2134 data_time: 0.0036 loss: 1.4633 2023/03/17 01:37:50 - mmengine - INFO - Epoch(train) [35][4600/5005] lr: 1.0000e-02 eta: 17:15:04 time: 0.1896 data_time: 0.0031 loss: 1.4893 2023/03/17 01:38:10 - mmengine - INFO - Epoch(train) [35][4700/5005] lr: 1.0000e-02 eta: 17:14:46 time: 0.2140 data_time: 0.0029 loss: 1.3166 2023/03/17 01:38:29 - mmengine - INFO - Epoch(train) [35][4800/5005] lr: 1.0000e-02 eta: 17:14:26 time: 0.1950 data_time: 0.0031 loss: 1.3293 2023/03/17 01:38:35 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:38:48 - mmengine - INFO - Epoch(train) [35][4900/5005] lr: 1.0000e-02 eta: 17:14:07 time: 0.1847 data_time: 0.0030 loss: 1.3566 2023/03/17 01:39:07 - mmengine - INFO - Epoch(train) [35][5000/5005] lr: 1.0000e-02 eta: 17:13:47 time: 0.2201 data_time: 0.0031 loss: 1.4790 2023/03/17 01:39:08 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:39:09 - mmengine - INFO - Saving checkpoint at 35 epochs 2023/03/17 01:39:15 - mmengine - INFO - Epoch(val) [35][100/196] eta: 0:00:05 time: 0.0422 data_time: 0.0009 2023/03/17 01:39:42 - mmengine - INFO - Epoch(val) [35][196/196] accuracy/top1: 70.1380 accuracy/top5: 89.8540data_time: 0.0361 time: 0.0653 2023/03/17 01:40:05 - mmengine - INFO - Epoch(train) [36][ 100/5005] lr: 1.0000e-02 eta: 17:13:34 time: 0.1873 data_time: 0.0029 loss: 1.2311 2023/03/17 01:40:23 - mmengine - INFO - Epoch(train) [36][ 200/5005] lr: 1.0000e-02 eta: 17:13:14 time: 0.1971 data_time: 0.0025 loss: 1.4601 2023/03/17 01:40:43 - mmengine - INFO - Epoch(train) [36][ 300/5005] lr: 1.0000e-02 eta: 17:12:57 time: 0.2272 data_time: 0.0027 loss: 1.6224 2023/03/17 01:41:06 - mmengine - INFO - Epoch(train) [36][ 400/5005] lr: 1.0000e-02 eta: 17:12:44 time: 0.1924 data_time: 0.0026 loss: 1.4950 2023/03/17 01:41:27 - mmengine - INFO - Epoch(train) [36][ 500/5005] lr: 1.0000e-02 eta: 17:12:29 time: 0.2207 data_time: 0.0025 loss: 1.1829 2023/03/17 01:41:47 - mmengine - INFO - Epoch(train) [36][ 600/5005] lr: 1.0000e-02 eta: 17:12:12 time: 0.1884 data_time: 0.0027 loss: 1.4505 2023/03/17 01:42:06 - mmengine - INFO - Epoch(train) [36][ 700/5005] lr: 1.0000e-02 eta: 17:11:51 time: 0.1829 data_time: 0.0028 loss: 1.5772 2023/03/17 01:42:24 - mmengine - INFO - Epoch(train) [36][ 800/5005] lr: 1.0000e-02 eta: 17:11:30 time: 0.1811 data_time: 0.0027 loss: 1.4977 2023/03/17 01:42:29 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:42:43 - mmengine - INFO - Epoch(train) [36][ 900/5005] lr: 1.0000e-02 eta: 17:11:11 time: 0.1909 data_time: 0.0025 loss: 1.3811 2023/03/17 01:43:02 - mmengine - INFO - Epoch(train) [36][1000/5005] lr: 1.0000e-02 eta: 17:10:51 time: 0.1745 data_time: 0.0028 loss: 1.3583 2023/03/17 01:43:20 - mmengine - INFO - Epoch(train) [36][1100/5005] lr: 1.0000e-02 eta: 17:10:30 time: 0.1814 data_time: 0.0031 loss: 1.3704 2023/03/17 01:43:39 - mmengine - INFO - Epoch(train) [36][1200/5005] lr: 1.0000e-02 eta: 17:10:11 time: 0.1896 data_time: 0.0029 loss: 1.4335 2023/03/17 01:43:59 - mmengine - INFO - Epoch(train) [36][1300/5005] lr: 1.0000e-02 eta: 17:09:53 time: 0.1955 data_time: 0.0029 loss: 1.4109 2023/03/17 01:44:18 - mmengine - INFO - Epoch(train) [36][1400/5005] lr: 1.0000e-02 eta: 17:09:35 time: 0.1902 data_time: 0.0029 loss: 1.4842 2023/03/17 01:44:38 - mmengine - INFO - Epoch(train) [36][1500/5005] lr: 1.0000e-02 eta: 17:09:16 time: 0.2034 data_time: 0.0024 loss: 1.5131 2023/03/17 01:44:57 - mmengine - INFO - Epoch(train) [36][1600/5005] lr: 1.0000e-02 eta: 17:08:58 time: 0.1835 data_time: 0.0029 loss: 1.6093 2023/03/17 01:45:18 - mmengine - INFO - Epoch(train) [36][1700/5005] lr: 1.0000e-02 eta: 17:08:41 time: 0.2108 data_time: 0.0031 loss: 1.5783 2023/03/17 01:45:38 - mmengine - INFO - Epoch(train) [36][1800/5005] lr: 1.0000e-02 eta: 17:08:25 time: 0.2032 data_time: 0.0030 loss: 1.4259 2023/03/17 01:45:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:45:59 - mmengine - INFO - Epoch(train) [36][1900/5005] lr: 1.0000e-02 eta: 17:08:08 time: 0.2027 data_time: 0.0029 loss: 1.5758 2023/03/17 01:46:19 - mmengine - INFO - Epoch(train) [36][2000/5005] lr: 1.0000e-02 eta: 17:07:51 time: 0.1982 data_time: 0.0025 loss: 1.3930 2023/03/17 01:46:39 - mmengine - INFO - Epoch(train) [36][2100/5005] lr: 1.0000e-02 eta: 17:07:34 time: 0.1983 data_time: 0.0024 loss: 1.6240 2023/03/17 01:47:00 - mmengine - INFO - Epoch(train) [36][2200/5005] lr: 1.0000e-02 eta: 17:07:17 time: 0.2050 data_time: 0.0028 loss: 1.3886 2023/03/17 01:47:20 - mmengine - INFO - Epoch(train) [36][2300/5005] lr: 1.0000e-02 eta: 17:07:01 time: 0.2179 data_time: 0.0027 loss: 1.4686 2023/03/17 01:47:42 - mmengine - INFO - Epoch(train) [36][2400/5005] lr: 1.0000e-02 eta: 17:06:46 time: 0.2084 data_time: 0.0029 loss: 1.3851 2023/03/17 01:48:02 - mmengine - INFO - Epoch(train) [36][2500/5005] lr: 1.0000e-02 eta: 17:06:28 time: 0.1926 data_time: 0.0027 loss: 1.4781 2023/03/17 01:48:21 - mmengine - INFO - Epoch(train) [36][2600/5005] lr: 1.0000e-02 eta: 17:06:09 time: 0.1957 data_time: 0.0029 loss: 1.4578 2023/03/17 01:48:41 - mmengine - INFO - Epoch(train) [36][2700/5005] lr: 1.0000e-02 eta: 17:05:52 time: 0.2205 data_time: 0.0030 loss: 1.5945 2023/03/17 01:49:01 - mmengine - INFO - Epoch(train) [36][2800/5005] lr: 1.0000e-02 eta: 17:05:34 time: 0.1962 data_time: 0.0032 loss: 1.3536 2023/03/17 01:49:06 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:49:21 - mmengine - INFO - Epoch(train) [36][2900/5005] lr: 1.0000e-02 eta: 17:05:17 time: 0.2050 data_time: 0.0032 loss: 1.2708 2023/03/17 01:49:41 - mmengine - INFO - Epoch(train) [36][3000/5005] lr: 1.0000e-02 eta: 17:04:59 time: 0.2205 data_time: 0.0031 loss: 1.6428 2023/03/17 01:50:00 - mmengine - INFO - Epoch(train) [36][3100/5005] lr: 1.0000e-02 eta: 17:04:41 time: 0.1949 data_time: 0.0028 loss: 1.2427 2023/03/17 01:50:20 - mmengine - INFO - Epoch(train) [36][3200/5005] lr: 1.0000e-02 eta: 17:04:22 time: 0.1908 data_time: 0.0028 loss: 1.3873 2023/03/17 01:50:39 - mmengine - INFO - Epoch(train) [36][3300/5005] lr: 1.0000e-02 eta: 17:04:03 time: 0.1964 data_time: 0.0034 loss: 1.3028 2023/03/17 01:50:59 - mmengine - INFO - Epoch(train) [36][3400/5005] lr: 1.0000e-02 eta: 17:03:45 time: 0.1927 data_time: 0.0033 loss: 1.5703 2023/03/17 01:51:19 - mmengine - INFO - Epoch(train) [36][3500/5005] lr: 1.0000e-02 eta: 17:03:27 time: 0.1928 data_time: 0.0031 loss: 1.5961 2023/03/17 01:51:38 - mmengine - INFO - Epoch(train) [36][3600/5005] lr: 1.0000e-02 eta: 17:03:08 time: 0.1935 data_time: 0.0027 loss: 1.4240 2023/03/17 01:51:57 - mmengine - INFO - Epoch(train) [36][3700/5005] lr: 1.0000e-02 eta: 17:02:49 time: 0.2037 data_time: 0.0027 loss: 1.3614 2023/03/17 01:52:17 - mmengine - INFO - Epoch(train) [36][3800/5005] lr: 1.0000e-02 eta: 17:02:31 time: 0.1974 data_time: 0.0031 loss: 1.3287 2023/03/17 01:52:22 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:52:36 - mmengine - INFO - Epoch(train) [36][3900/5005] lr: 1.0000e-02 eta: 17:02:12 time: 0.1886 data_time: 0.0028 loss: 1.3456 2023/03/17 01:52:54 - mmengine - INFO - Epoch(train) [36][4000/5005] lr: 1.0000e-02 eta: 17:01:50 time: 0.1709 data_time: 0.0031 loss: 1.5502 2023/03/17 01:53:12 - mmengine - INFO - Epoch(train) [36][4100/5005] lr: 1.0000e-02 eta: 17:01:29 time: 0.1739 data_time: 0.0027 loss: 1.3629 2023/03/17 01:53:29 - mmengine - INFO - Epoch(train) [36][4200/5005] lr: 1.0000e-02 eta: 17:01:07 time: 0.1660 data_time: 0.0028 loss: 1.5134 2023/03/17 01:53:46 - mmengine - INFO - Epoch(train) [36][4300/5005] lr: 1.0000e-02 eta: 17:00:43 time: 0.1752 data_time: 0.0031 loss: 1.5682 2023/03/17 01:54:04 - mmengine - INFO - Epoch(train) [36][4400/5005] lr: 1.0000e-02 eta: 17:00:23 time: 0.1845 data_time: 0.0029 loss: 1.4405 2023/03/17 01:54:23 - mmengine - INFO - Epoch(train) [36][4500/5005] lr: 1.0000e-02 eta: 17:00:03 time: 0.1834 data_time: 0.0028 loss: 1.5024 2023/03/17 01:54:42 - mmengine - INFO - Epoch(train) [36][4600/5005] lr: 1.0000e-02 eta: 16:59:43 time: 0.1850 data_time: 0.0028 loss: 1.5839 2023/03/17 01:55:01 - mmengine - INFO - Epoch(train) [36][4700/5005] lr: 1.0000e-02 eta: 16:59:24 time: 0.1869 data_time: 0.0029 loss: 1.4368 2023/03/17 01:55:20 - mmengine - INFO - Epoch(train) [36][4800/5005] lr: 1.0000e-02 eta: 16:59:04 time: 0.1878 data_time: 0.0030 loss: 1.6463 2023/03/17 01:55:24 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:55:39 - mmengine - INFO - Epoch(train) [36][4900/5005] lr: 1.0000e-02 eta: 16:58:46 time: 0.1902 data_time: 0.0029 loss: 1.5925 2023/03/17 01:55:58 - mmengine - INFO - Epoch(train) [36][5000/5005] lr: 1.0000e-02 eta: 16:58:27 time: 0.1982 data_time: 0.0039 loss: 1.3140 2023/03/17 01:56:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:56:00 - mmengine - INFO - Saving checkpoint at 36 epochs 2023/03/17 01:56:06 - mmengine - INFO - Epoch(val) [36][100/196] eta: 0:00:04 time: 0.0443 data_time: 0.0055 2023/03/17 01:56:34 - mmengine - INFO - Epoch(val) [36][196/196] accuracy/top1: 70.2680 accuracy/top5: 90.1700data_time: 0.0267 time: 0.0599 2023/03/17 01:56:55 - mmengine - INFO - Epoch(train) [37][ 100/5005] lr: 1.0000e-02 eta: 16:58:11 time: 0.2018 data_time: 0.0035 loss: 1.4238 2023/03/17 01:57:16 - mmengine - INFO - Epoch(train) [37][ 200/5005] lr: 1.0000e-02 eta: 16:57:55 time: 0.2049 data_time: 0.0030 loss: 1.4640 2023/03/17 01:57:36 - mmengine - INFO - Epoch(train) [37][ 300/5005] lr: 1.0000e-02 eta: 16:57:38 time: 0.2002 data_time: 0.0032 loss: 1.5496 2023/03/17 01:57:56 - mmengine - INFO - Epoch(train) [37][ 400/5005] lr: 1.0000e-02 eta: 16:57:20 time: 0.1951 data_time: 0.0032 loss: 1.5501 2023/03/17 01:58:15 - mmengine - INFO - Epoch(train) [37][ 500/5005] lr: 1.0000e-02 eta: 16:57:01 time: 0.1904 data_time: 0.0029 loss: 1.5166 2023/03/17 01:58:34 - mmengine - INFO - Epoch(train) [37][ 600/5005] lr: 1.0000e-02 eta: 16:56:42 time: 0.1873 data_time: 0.0029 loss: 1.5943 2023/03/17 01:58:54 - mmengine - INFO - Epoch(train) [37][ 700/5005] lr: 1.0000e-02 eta: 16:56:24 time: 0.2071 data_time: 0.0033 loss: 1.4017 2023/03/17 01:59:15 - mmengine - INFO - Epoch(train) [37][ 800/5005] lr: 1.0000e-02 eta: 16:56:07 time: 0.1977 data_time: 0.0033 loss: 1.4942 2023/03/17 01:59:19 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 01:59:34 - mmengine - INFO - Epoch(train) [37][ 900/5005] lr: 1.0000e-02 eta: 16:55:48 time: 0.1797 data_time: 0.0029 loss: 1.5024 2023/03/17 01:59:51 - mmengine - INFO - Epoch(train) [37][1000/5005] lr: 1.0000e-02 eta: 16:55:26 time: 0.1740 data_time: 0.0029 loss: 1.6625 2023/03/17 02:00:11 - mmengine - INFO - Epoch(train) [37][1100/5005] lr: 1.0000e-02 eta: 16:55:09 time: 0.2020 data_time: 0.0028 loss: 1.5508 2023/03/17 02:00:31 - mmengine - INFO - Epoch(train) [37][1200/5005] lr: 1.0000e-02 eta: 16:54:50 time: 0.1939 data_time: 0.0029 loss: 1.3777 2023/03/17 02:00:50 - mmengine - INFO - Epoch(train) [37][1300/5005] lr: 1.0000e-02 eta: 16:54:32 time: 0.1869 data_time: 0.0029 loss: 1.4530 2023/03/17 02:01:10 - mmengine - INFO - Epoch(train) [37][1400/5005] lr: 1.0000e-02 eta: 16:54:14 time: 0.1956 data_time: 0.0030 loss: 1.5387 2023/03/17 02:01:29 - mmengine - INFO - Epoch(train) [37][1500/5005] lr: 1.0000e-02 eta: 16:53:54 time: 0.1839 data_time: 0.0030 loss: 1.3548 2023/03/17 02:01:48 - mmengine - INFO - Epoch(train) [37][1600/5005] lr: 1.0000e-02 eta: 16:53:34 time: 0.1847 data_time: 0.0028 loss: 1.4047 2023/03/17 02:02:07 - mmengine - INFO - Epoch(train) [37][1700/5005] lr: 1.0000e-02 eta: 16:53:15 time: 0.1945 data_time: 0.0027 loss: 1.2909 2023/03/17 02:02:26 - mmengine - INFO - Epoch(train) [37][1800/5005] lr: 1.0000e-02 eta: 16:52:55 time: 0.1863 data_time: 0.0028 loss: 1.2367 2023/03/17 02:02:29 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:02:44 - mmengine - INFO - Epoch(train) [37][1900/5005] lr: 1.0000e-02 eta: 16:52:35 time: 0.1876 data_time: 0.0027 loss: 1.3405 2023/03/17 02:03:03 - mmengine - INFO - Epoch(train) [37][2000/5005] lr: 1.0000e-02 eta: 16:52:16 time: 0.1916 data_time: 0.0026 loss: 1.3776 2023/03/17 02:03:23 - mmengine - INFO - Epoch(train) [37][2100/5005] lr: 1.0000e-02 eta: 16:51:58 time: 0.2025 data_time: 0.0029 loss: 1.2865 2023/03/17 02:03:43 - mmengine - INFO - Epoch(train) [37][2200/5005] lr: 1.0000e-02 eta: 16:51:41 time: 0.1928 data_time: 0.0032 loss: 1.4710 2023/03/17 02:04:03 - mmengine - INFO - Epoch(train) [37][2300/5005] lr: 1.0000e-02 eta: 16:51:23 time: 0.1965 data_time: 0.0031 loss: 1.5456 2023/03/17 02:04:23 - mmengine - INFO - Epoch(train) [37][2400/5005] lr: 1.0000e-02 eta: 16:51:05 time: 0.1962 data_time: 0.0032 loss: 1.5519 2023/03/17 02:04:44 - mmengine - INFO - Epoch(train) [37][2500/5005] lr: 1.0000e-02 eta: 16:50:49 time: 0.1981 data_time: 0.0031 loss: 1.3684 2023/03/17 02:05:04 - mmengine - INFO - Epoch(train) [37][2600/5005] lr: 1.0000e-02 eta: 16:50:31 time: 0.1929 data_time: 0.0030 loss: 1.2738 2023/03/17 02:05:23 - mmengine - INFO - Epoch(train) [37][2700/5005] lr: 1.0000e-02 eta: 16:50:12 time: 0.1974 data_time: 0.0030 loss: 1.5450 2023/03/17 02:05:44 - mmengine - INFO - Epoch(train) [37][2800/5005] lr: 1.0000e-02 eta: 16:49:56 time: 0.2087 data_time: 0.0030 loss: 1.3998 2023/03/17 02:05:48 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:06:05 - mmengine - INFO - Epoch(train) [37][2900/5005] lr: 1.0000e-02 eta: 16:49:40 time: 0.1945 data_time: 0.0030 loss: 1.7151 2023/03/17 02:06:24 - mmengine - INFO - Epoch(train) [37][3000/5005] lr: 1.0000e-02 eta: 16:49:21 time: 0.2015 data_time: 0.0031 loss: 1.2935 2023/03/17 02:06:44 - mmengine - INFO - Epoch(train) [37][3100/5005] lr: 1.0000e-02 eta: 16:49:03 time: 0.1866 data_time: 0.0027 loss: 1.3342 2023/03/17 02:07:03 - mmengine - INFO - Epoch(train) [37][3200/5005] lr: 1.0000e-02 eta: 16:48:44 time: 0.1893 data_time: 0.0025 loss: 1.4620 2023/03/17 02:07:22 - mmengine - INFO - Epoch(train) [37][3300/5005] lr: 1.0000e-02 eta: 16:48:25 time: 0.1892 data_time: 0.0027 loss: 1.5525 2023/03/17 02:07:42 - mmengine - INFO - Epoch(train) [37][3400/5005] lr: 1.0000e-02 eta: 16:48:06 time: 0.1902 data_time: 0.0028 loss: 1.4976 2023/03/17 02:08:01 - mmengine - INFO - Epoch(train) [37][3500/5005] lr: 1.0000e-02 eta: 16:47:48 time: 0.1961 data_time: 0.0029 loss: 1.4134 2023/03/17 02:08:22 - mmengine - INFO - Epoch(train) [37][3600/5005] lr: 1.0000e-02 eta: 16:47:31 time: 0.1938 data_time: 0.0026 loss: 1.4026 2023/03/17 02:08:41 - mmengine - INFO - Epoch(train) [37][3700/5005] lr: 1.0000e-02 eta: 16:47:13 time: 0.2004 data_time: 0.0029 loss: 1.5872 2023/03/17 02:09:02 - mmengine - INFO - Epoch(train) [37][3800/5005] lr: 1.0000e-02 eta: 16:46:55 time: 0.1989 data_time: 0.0029 loss: 1.2159 2023/03/17 02:09:06 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:09:22 - mmengine - INFO - Epoch(train) [37][3900/5005] lr: 1.0000e-02 eta: 16:46:39 time: 0.1903 data_time: 0.0026 loss: 1.2429 2023/03/17 02:09:41 - mmengine - INFO - Epoch(train) [37][4000/5005] lr: 1.0000e-02 eta: 16:46:19 time: 0.1901 data_time: 0.0026 loss: 1.3326 2023/03/17 02:10:01 - mmengine - INFO - Epoch(train) [37][4100/5005] lr: 1.0000e-02 eta: 16:46:02 time: 0.1964 data_time: 0.0029 loss: 1.4239 2023/03/17 02:10:22 - mmengine - INFO - Epoch(train) [37][4200/5005] lr: 1.0000e-02 eta: 16:45:45 time: 0.2051 data_time: 0.0029 loss: 1.3093 2023/03/17 02:10:43 - mmengine - INFO - Epoch(train) [37][4300/5005] lr: 1.0000e-02 eta: 16:45:29 time: 0.2078 data_time: 0.0031 loss: 1.4305 2023/03/17 02:11:03 - mmengine - INFO - Epoch(train) [37][4400/5005] lr: 1.0000e-02 eta: 16:45:12 time: 0.2041 data_time: 0.0033 loss: 1.3744 2023/03/17 02:11:24 - mmengine - INFO - Epoch(train) [37][4500/5005] lr: 1.0000e-02 eta: 16:44:55 time: 0.1997 data_time: 0.0028 loss: 1.5159 2023/03/17 02:11:44 - mmengine - INFO - Epoch(train) [37][4600/5005] lr: 1.0000e-02 eta: 16:44:39 time: 0.2290 data_time: 0.0025 loss: 1.3987 2023/03/17 02:12:05 - mmengine - INFO - Epoch(train) [37][4700/5005] lr: 1.0000e-02 eta: 16:44:22 time: 0.2009 data_time: 0.0028 loss: 1.3883 2023/03/17 02:12:25 - mmengine - INFO - Epoch(train) [37][4800/5005] lr: 1.0000e-02 eta: 16:44:05 time: 0.2054 data_time: 0.0029 loss: 1.5633 2023/03/17 02:12:29 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:12:46 - mmengine - INFO - Epoch(train) [37][4900/5005] lr: 1.0000e-02 eta: 16:43:48 time: 0.2033 data_time: 0.0032 loss: 1.5030 2023/03/17 02:13:06 - mmengine - INFO - Epoch(train) [37][5000/5005] lr: 1.0000e-02 eta: 16:43:31 time: 0.2277 data_time: 0.0039 loss: 1.4043 2023/03/17 02:13:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:13:08 - mmengine - INFO - Saving checkpoint at 37 epochs 2023/03/17 02:13:14 - mmengine - INFO - Epoch(val) [37][100/196] eta: 0:00:05 time: 0.0483 data_time: 0.0017 2023/03/17 02:13:43 - mmengine - INFO - Epoch(val) [37][196/196] accuracy/top1: 70.2160 accuracy/top5: 89.9900data_time: 0.0301 time: 0.0599 2023/03/17 02:14:03 - mmengine - INFO - Epoch(train) [38][ 100/5005] lr: 1.0000e-02 eta: 16:43:13 time: 0.1806 data_time: 0.0029 loss: 1.6590 2023/03/17 02:14:21 - mmengine - INFO - Epoch(train) [38][ 200/5005] lr: 1.0000e-02 eta: 16:42:53 time: 0.1887 data_time: 0.0030 loss: 1.4010 2023/03/17 02:14:41 - mmengine - INFO - Epoch(train) [38][ 300/5005] lr: 1.0000e-02 eta: 16:42:34 time: 0.1924 data_time: 0.0029 loss: 1.2007 2023/03/17 02:15:00 - mmengine - INFO - Epoch(train) [38][ 400/5005] lr: 1.0000e-02 eta: 16:42:16 time: 0.1813 data_time: 0.0030 loss: 1.5603 2023/03/17 02:15:19 - mmengine - INFO - Epoch(train) [38][ 500/5005] lr: 1.0000e-02 eta: 16:41:55 time: 0.1831 data_time: 0.0030 loss: 1.4128 2023/03/17 02:15:37 - mmengine - INFO - Epoch(train) [38][ 600/5005] lr: 1.0000e-02 eta: 16:41:35 time: 0.1860 data_time: 0.0032 loss: 1.4695 2023/03/17 02:15:56 - mmengine - INFO - Epoch(train) [38][ 700/5005] lr: 1.0000e-02 eta: 16:41:15 time: 0.1861 data_time: 0.0033 loss: 1.2285 2023/03/17 02:16:15 - mmengine - INFO - Epoch(train) [38][ 800/5005] lr: 1.0000e-02 eta: 16:40:56 time: 0.2160 data_time: 0.0031 loss: 1.2664 2023/03/17 02:16:18 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:16:34 - mmengine - INFO - Epoch(train) [38][ 900/5005] lr: 1.0000e-02 eta: 16:40:37 time: 0.1821 data_time: 0.0032 loss: 1.3570 2023/03/17 02:16:52 - mmengine - INFO - Epoch(train) [38][1000/5005] lr: 1.0000e-02 eta: 16:40:16 time: 0.1845 data_time: 0.0031 loss: 1.5342 2023/03/17 02:17:10 - mmengine - INFO - Epoch(train) [38][1100/5005] lr: 1.0000e-02 eta: 16:39:54 time: 0.1735 data_time: 0.0028 loss: 1.5657 2023/03/17 02:17:27 - mmengine - INFO - Epoch(train) [38][1200/5005] lr: 1.0000e-02 eta: 16:39:32 time: 0.1691 data_time: 0.0036 loss: 1.4620 2023/03/17 02:17:45 - mmengine - INFO - Epoch(train) [38][1300/5005] lr: 1.0000e-02 eta: 16:39:10 time: 0.1807 data_time: 0.0028 loss: 1.4043 2023/03/17 02:18:05 - mmengine - INFO - Epoch(train) [38][1400/5005] lr: 1.0000e-02 eta: 16:38:52 time: 0.2066 data_time: 0.0033 loss: 1.2375 2023/03/17 02:18:25 - mmengine - INFO - Epoch(train) [38][1500/5005] lr: 1.0000e-02 eta: 16:38:34 time: 0.1876 data_time: 0.0032 loss: 1.2651 2023/03/17 02:18:44 - mmengine - INFO - Epoch(train) [38][1600/5005] lr: 1.0000e-02 eta: 16:38:15 time: 0.1787 data_time: 0.0031 loss: 1.4655 2023/03/17 02:19:02 - mmengine - INFO - Epoch(train) [38][1700/5005] lr: 1.0000e-02 eta: 16:37:54 time: 0.1761 data_time: 0.0030 loss: 1.4529 2023/03/17 02:19:20 - mmengine - INFO - Epoch(train) [38][1800/5005] lr: 1.0000e-02 eta: 16:37:33 time: 0.1814 data_time: 0.0026 loss: 1.3768 2023/03/17 02:19:23 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:19:38 - mmengine - INFO - Epoch(train) [38][1900/5005] lr: 1.0000e-02 eta: 16:37:13 time: 0.1872 data_time: 0.0032 loss: 1.5350 2023/03/17 02:19:57 - mmengine - INFO - Epoch(train) [38][2000/5005] lr: 1.0000e-02 eta: 16:36:54 time: 0.1875 data_time: 0.0031 loss: 1.5135 2023/03/17 02:20:17 - mmengine - INFO - Epoch(train) [38][2100/5005] lr: 1.0000e-02 eta: 16:36:36 time: 0.2126 data_time: 0.0029 loss: 1.3757 2023/03/17 02:20:37 - mmengine - INFO - Epoch(train) [38][2200/5005] lr: 1.0000e-02 eta: 16:36:18 time: 0.1967 data_time: 0.0029 loss: 1.3458 2023/03/17 02:20:59 - mmengine - INFO - Epoch(train) [38][2300/5005] lr: 1.0000e-02 eta: 16:36:03 time: 0.2163 data_time: 0.0031 loss: 1.5438 2023/03/17 02:21:19 - mmengine - INFO - Epoch(train) [38][2400/5005] lr: 1.0000e-02 eta: 16:35:45 time: 0.1889 data_time: 0.0029 loss: 1.4113 2023/03/17 02:21:39 - mmengine - INFO - Epoch(train) [38][2500/5005] lr: 1.0000e-02 eta: 16:35:28 time: 0.2037 data_time: 0.0033 loss: 1.4782 2023/03/17 02:21:59 - mmengine - INFO - Epoch(train) [38][2600/5005] lr: 1.0000e-02 eta: 16:35:09 time: 0.1896 data_time: 0.0033 loss: 1.3719 2023/03/17 02:22:17 - mmengine - INFO - Epoch(train) [38][2700/5005] lr: 1.0000e-02 eta: 16:34:50 time: 0.1882 data_time: 0.0029 loss: 1.5863 2023/03/17 02:22:36 - mmengine - INFO - Epoch(train) [38][2800/5005] lr: 1.0000e-02 eta: 16:34:30 time: 0.1874 data_time: 0.0031 loss: 1.3677 2023/03/17 02:22:39 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:22:55 - mmengine - INFO - Epoch(train) [38][2900/5005] lr: 1.0000e-02 eta: 16:34:11 time: 0.1837 data_time: 0.0026 loss: 1.4728 2023/03/17 02:23:15 - mmengine - INFO - Epoch(train) [38][3000/5005] lr: 1.0000e-02 eta: 16:33:52 time: 0.1944 data_time: 0.0036 loss: 1.3799 2023/03/17 02:23:34 - mmengine - INFO - Epoch(train) [38][3100/5005] lr: 1.0000e-02 eta: 16:33:34 time: 0.1920 data_time: 0.0028 loss: 1.3865 2023/03/17 02:23:54 - mmengine - INFO - Epoch(train) [38][3200/5005] lr: 1.0000e-02 eta: 16:33:15 time: 0.1865 data_time: 0.0030 loss: 1.3491 2023/03/17 02:24:13 - mmengine - INFO - Epoch(train) [38][3300/5005] lr: 1.0000e-02 eta: 16:32:56 time: 0.2103 data_time: 0.0029 loss: 1.3705 2023/03/17 02:24:34 - mmengine - INFO - Epoch(train) [38][3400/5005] lr: 1.0000e-02 eta: 16:32:39 time: 0.2119 data_time: 0.0031 loss: 1.5322 2023/03/17 02:24:54 - mmengine - INFO - Epoch(train) [38][3500/5005] lr: 1.0000e-02 eta: 16:32:21 time: 0.1947 data_time: 0.0028 loss: 1.6015 2023/03/17 02:25:13 - mmengine - INFO - Epoch(train) [38][3600/5005] lr: 1.0000e-02 eta: 16:32:03 time: 0.1939 data_time: 0.0030 loss: 1.3320 2023/03/17 02:25:33 - mmengine - INFO - Epoch(train) [38][3700/5005] lr: 1.0000e-02 eta: 16:31:45 time: 0.1962 data_time: 0.0030 loss: 1.4479 2023/03/17 02:25:52 - mmengine - INFO - Epoch(train) [38][3800/5005] lr: 1.0000e-02 eta: 16:31:26 time: 0.1850 data_time: 0.0030 loss: 1.6541 2023/03/17 02:25:55 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:26:11 - mmengine - INFO - Epoch(train) [38][3900/5005] lr: 1.0000e-02 eta: 16:31:06 time: 0.1873 data_time: 0.0027 loss: 1.4073 2023/03/17 02:26:30 - mmengine - INFO - Epoch(train) [38][4000/5005] lr: 1.0000e-02 eta: 16:30:47 time: 0.2092 data_time: 0.0029 loss: 1.2349 2023/03/17 02:26:50 - mmengine - INFO - Epoch(train) [38][4100/5005] lr: 1.0000e-02 eta: 16:30:28 time: 0.1915 data_time: 0.0029 loss: 1.4399 2023/03/17 02:27:08 - mmengine - INFO - Epoch(train) [38][4200/5005] lr: 1.0000e-02 eta: 16:30:08 time: 0.1788 data_time: 0.0028 loss: 1.4525 2023/03/17 02:27:26 - mmengine - INFO - Epoch(train) [38][4300/5005] lr: 1.0000e-02 eta: 16:29:47 time: 0.1758 data_time: 0.0028 loss: 1.2290 2023/03/17 02:27:46 - mmengine - INFO - Epoch(train) [38][4400/5005] lr: 1.0000e-02 eta: 16:29:28 time: 0.1971 data_time: 0.0028 loss: 1.3912 2023/03/17 02:28:05 - mmengine - INFO - Epoch(train) [38][4500/5005] lr: 1.0000e-02 eta: 16:29:09 time: 0.1880 data_time: 0.0028 loss: 1.4593 2023/03/17 02:28:26 - mmengine - INFO - Epoch(train) [38][4600/5005] lr: 1.0000e-02 eta: 16:28:53 time: 0.2017 data_time: 0.0030 loss: 1.4849 2023/03/17 02:28:45 - mmengine - INFO - Epoch(train) [38][4700/5005] lr: 1.0000e-02 eta: 16:28:34 time: 0.1951 data_time: 0.0028 loss: 1.3586 2023/03/17 02:29:04 - mmengine - INFO - Epoch(train) [38][4800/5005] lr: 1.0000e-02 eta: 16:28:15 time: 0.1923 data_time: 0.0032 loss: 1.2949 2023/03/17 02:29:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:29:23 - mmengine - INFO - Epoch(train) [38][4900/5005] lr: 1.0000e-02 eta: 16:27:55 time: 0.1752 data_time: 0.0027 loss: 1.3833 2023/03/17 02:29:41 - mmengine - INFO - Epoch(train) [38][5000/5005] lr: 1.0000e-02 eta: 16:27:35 time: 0.1840 data_time: 0.0041 loss: 1.5005 2023/03/17 02:29:42 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:29:43 - mmengine - INFO - Saving checkpoint at 38 epochs 2023/03/17 02:29:49 - mmengine - INFO - Epoch(val) [38][100/196] eta: 0:00:05 time: 0.0479 data_time: 0.0009 2023/03/17 02:30:17 - mmengine - INFO - Epoch(val) [38][196/196] accuracy/top1: 70.1800 accuracy/top5: 89.9920data_time: 0.0211 time: 0.0549 2023/03/17 02:30:40 - mmengine - INFO - Epoch(train) [39][ 100/5005] lr: 1.0000e-02 eta: 16:27:20 time: 0.2070 data_time: 0.0027 loss: 1.4743 2023/03/17 02:31:01 - mmengine - INFO - Epoch(train) [39][ 200/5005] lr: 1.0000e-02 eta: 16:27:04 time: 0.1905 data_time: 0.0028 loss: 1.5454 2023/03/17 02:31:20 - mmengine - INFO - Epoch(train) [39][ 300/5005] lr: 1.0000e-02 eta: 16:26:45 time: 0.1902 data_time: 0.0029 loss: 1.3878 2023/03/17 02:31:40 - mmengine - INFO - Epoch(train) [39][ 400/5005] lr: 1.0000e-02 eta: 16:26:26 time: 0.2045 data_time: 0.0029 loss: 1.2295 2023/03/17 02:32:00 - mmengine - INFO - Epoch(train) [39][ 500/5005] lr: 1.0000e-02 eta: 16:26:09 time: 0.1900 data_time: 0.0028 loss: 1.5273 2023/03/17 02:32:19 - mmengine - INFO - Epoch(train) [39][ 600/5005] lr: 1.0000e-02 eta: 16:25:50 time: 0.1998 data_time: 0.0029 loss: 1.3126 2023/03/17 02:32:38 - mmengine - INFO - Epoch(train) [39][ 700/5005] lr: 1.0000e-02 eta: 16:25:31 time: 0.1836 data_time: 0.0031 loss: 1.3992 2023/03/17 02:32:57 - mmengine - INFO - Epoch(train) [39][ 800/5005] lr: 1.0000e-02 eta: 16:25:11 time: 0.1841 data_time: 0.0028 loss: 1.4295 2023/03/17 02:32:59 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:33:17 - mmengine - INFO - Epoch(train) [39][ 900/5005] lr: 1.0000e-02 eta: 16:24:53 time: 0.2136 data_time: 0.0028 loss: 1.3701 2023/03/17 02:33:35 - mmengine - INFO - Epoch(train) [39][1000/5005] lr: 1.0000e-02 eta: 16:24:32 time: 0.1773 data_time: 0.0027 loss: 1.5174 2023/03/17 02:33:55 - mmengine - INFO - Epoch(train) [39][1100/5005] lr: 1.0000e-02 eta: 16:24:14 time: 0.2073 data_time: 0.0027 loss: 1.4816 2023/03/17 02:34:14 - mmengine - INFO - Epoch(train) [39][1200/5005] lr: 1.0000e-02 eta: 16:23:55 time: 0.1959 data_time: 0.0029 loss: 1.3616 2023/03/17 02:34:33 - mmengine - INFO - Epoch(train) [39][1300/5005] lr: 1.0000e-02 eta: 16:23:36 time: 0.1906 data_time: 0.0029 loss: 1.4325 2023/03/17 02:34:52 - mmengine - INFO - Epoch(train) [39][1400/5005] lr: 1.0000e-02 eta: 16:23:17 time: 0.1930 data_time: 0.0029 loss: 1.3380 2023/03/17 02:35:12 - mmengine - INFO - Epoch(train) [39][1500/5005] lr: 1.0000e-02 eta: 16:22:58 time: 0.1917 data_time: 0.0026 loss: 1.5344 2023/03/17 02:35:32 - mmengine - INFO - Epoch(train) [39][1600/5005] lr: 1.0000e-02 eta: 16:22:40 time: 0.1943 data_time: 0.0029 loss: 1.3214 2023/03/17 02:35:52 - mmengine - INFO - Epoch(train) [39][1700/5005] lr: 1.0000e-02 eta: 16:22:23 time: 0.1960 data_time: 0.0028 loss: 1.3717 2023/03/17 02:36:13 - mmengine - INFO - Epoch(train) [39][1800/5005] lr: 1.0000e-02 eta: 16:22:06 time: 0.1996 data_time: 0.0027 loss: 1.4771 2023/03/17 02:36:15 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:36:33 - mmengine - INFO - Epoch(train) [39][1900/5005] lr: 1.0000e-02 eta: 16:21:48 time: 0.1977 data_time: 0.0027 loss: 1.2729 2023/03/17 02:36:53 - mmengine - INFO - Epoch(train) [39][2000/5005] lr: 1.0000e-02 eta: 16:21:30 time: 0.1985 data_time: 0.0028 loss: 1.4918 2023/03/17 02:37:13 - mmengine - INFO - Epoch(train) [39][2100/5005] lr: 1.0000e-02 eta: 16:21:13 time: 0.2405 data_time: 0.0021 loss: 1.3885 2023/03/17 02:37:34 - mmengine - INFO - Epoch(train) [39][2200/5005] lr: 1.0000e-02 eta: 16:20:57 time: 0.2226 data_time: 0.0028 loss: 1.3644 2023/03/17 02:37:56 - mmengine - INFO - Epoch(train) [39][2300/5005] lr: 1.0000e-02 eta: 16:20:42 time: 0.2093 data_time: 0.0029 loss: 1.5776 2023/03/17 02:38:15 - mmengine - INFO - Epoch(train) [39][2400/5005] lr: 1.0000e-02 eta: 16:20:23 time: 0.1903 data_time: 0.0027 loss: 1.2629 2023/03/17 02:38:34 - mmengine - INFO - Epoch(train) [39][2500/5005] lr: 1.0000e-02 eta: 16:20:04 time: 0.1958 data_time: 0.0026 loss: 1.4360 2023/03/17 02:38:53 - mmengine - INFO - Epoch(train) [39][2600/5005] lr: 1.0000e-02 eta: 16:19:44 time: 0.1842 data_time: 0.0033 loss: 1.4032 2023/03/17 02:39:11 - mmengine - INFO - Epoch(train) [39][2700/5005] lr: 1.0000e-02 eta: 16:19:24 time: 0.1825 data_time: 0.0037 loss: 1.4350 2023/03/17 02:39:32 - mmengine - INFO - Epoch(train) [39][2800/5005] lr: 1.0000e-02 eta: 16:19:08 time: 0.2229 data_time: 0.0031 loss: 1.4964 2023/03/17 02:39:35 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:39:55 - mmengine - INFO - Epoch(train) [39][2900/5005] lr: 1.0000e-02 eta: 16:18:54 time: 0.2249 data_time: 0.0033 loss: 1.4276 2023/03/17 02:40:15 - mmengine - INFO - Epoch(train) [39][3000/5005] lr: 1.0000e-02 eta: 16:18:35 time: 0.1903 data_time: 0.0033 loss: 1.5110 2023/03/17 02:40:34 - mmengine - INFO - Epoch(train) [39][3100/5005] lr: 1.0000e-02 eta: 16:18:17 time: 0.2113 data_time: 0.0029 loss: 1.3474 2023/03/17 02:40:57 - mmengine - INFO - Epoch(train) [39][3200/5005] lr: 1.0000e-02 eta: 16:18:04 time: 0.2336 data_time: 0.0028 loss: 1.4778 2023/03/17 02:41:19 - mmengine - INFO - Epoch(train) [39][3300/5005] lr: 1.0000e-02 eta: 16:17:49 time: 0.1948 data_time: 0.0027 loss: 1.5285 2023/03/17 02:41:40 - mmengine - INFO - Epoch(train) [39][3400/5005] lr: 1.0000e-02 eta: 16:17:32 time: 0.2293 data_time: 0.0025 loss: 1.4106 2023/03/17 02:41:59 - mmengine - INFO - Epoch(train) [39][3500/5005] lr: 1.0000e-02 eta: 16:17:13 time: 0.1849 data_time: 0.0030 loss: 1.3419 2023/03/17 02:42:17 - mmengine - INFO - Epoch(train) [39][3600/5005] lr: 1.0000e-02 eta: 16:16:53 time: 0.1943 data_time: 0.0036 loss: 1.3001 2023/03/17 02:42:36 - mmengine - INFO - Epoch(train) [39][3700/5005] lr: 1.0000e-02 eta: 16:16:33 time: 0.1894 data_time: 0.0031 loss: 1.4192 2023/03/17 02:42:55 - mmengine - INFO - Epoch(train) [39][3800/5005] lr: 1.0000e-02 eta: 16:16:14 time: 0.1984 data_time: 0.0029 loss: 1.2661 2023/03/17 02:42:58 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:43:17 - mmengine - INFO - Epoch(train) [39][3900/5005] lr: 1.0000e-02 eta: 16:15:58 time: 0.1802 data_time: 0.0031 loss: 1.4948 2023/03/17 02:43:35 - mmengine - INFO - Epoch(train) [39][4000/5005] lr: 1.0000e-02 eta: 16:15:38 time: 0.1870 data_time: 0.0033 loss: 1.4855 2023/03/17 02:43:54 - mmengine - INFO - Epoch(train) [39][4100/5005] lr: 1.0000e-02 eta: 16:15:18 time: 0.1880 data_time: 0.0027 loss: 1.3209 2023/03/17 02:44:13 - mmengine - INFO - Epoch(train) [39][4200/5005] lr: 1.0000e-02 eta: 16:14:59 time: 0.2163 data_time: 0.0024 loss: 1.3875 2023/03/17 02:44:33 - mmengine - INFO - Epoch(train) [39][4300/5005] lr: 1.0000e-02 eta: 16:14:41 time: 0.1886 data_time: 0.0028 loss: 1.5040 2023/03/17 02:44:54 - mmengine - INFO - Epoch(train) [39][4400/5005] lr: 1.0000e-02 eta: 16:14:24 time: 0.2351 data_time: 0.0022 loss: 1.3872 2023/03/17 02:45:14 - mmengine - INFO - Epoch(train) [39][4500/5005] lr: 1.0000e-02 eta: 16:14:07 time: 0.1954 data_time: 0.0027 loss: 1.6059 2023/03/17 02:45:34 - mmengine - INFO - Epoch(train) [39][4600/5005] lr: 1.0000e-02 eta: 16:13:49 time: 0.1786 data_time: 0.0029 loss: 1.5869 2023/03/17 02:45:53 - mmengine - INFO - Epoch(train) [39][4700/5005] lr: 1.0000e-02 eta: 16:13:29 time: 0.1996 data_time: 0.0030 loss: 1.5447 2023/03/17 02:46:12 - mmengine - INFO - Epoch(train) [39][4800/5005] lr: 1.0000e-02 eta: 16:13:11 time: 0.1886 data_time: 0.0031 loss: 1.6702 2023/03/17 02:46:14 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:46:33 - mmengine - INFO - Epoch(train) [39][4900/5005] lr: 1.0000e-02 eta: 16:12:54 time: 0.1960 data_time: 0.0027 loss: 1.5728 2023/03/17 02:46:53 - mmengine - INFO - Epoch(train) [39][5000/5005] lr: 1.0000e-02 eta: 16:12:36 time: 0.2448 data_time: 0.0040 loss: 1.2991 2023/03/17 02:46:54 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:46:55 - mmengine - INFO - Saving checkpoint at 39 epochs 2023/03/17 02:47:01 - mmengine - INFO - Epoch(val) [39][100/196] eta: 0:00:05 time: 0.0494 data_time: 0.0009 2023/03/17 02:47:30 - mmengine - INFO - Epoch(val) [39][196/196] accuracy/top1: 70.0340 accuracy/top5: 89.9960data_time: 0.0254 time: 0.0551 2023/03/17 02:47:51 - mmengine - INFO - Epoch(train) [40][ 100/5005] lr: 1.0000e-02 eta: 16:12:19 time: 0.1859 data_time: 0.0033 loss: 1.4994 2023/03/17 02:48:09 - mmengine - INFO - Epoch(train) [40][ 200/5005] lr: 1.0000e-02 eta: 16:11:58 time: 0.1827 data_time: 0.0030 loss: 1.3525 2023/03/17 02:48:28 - mmengine - INFO - Epoch(train) [40][ 300/5005] lr: 1.0000e-02 eta: 16:11:38 time: 0.1842 data_time: 0.0027 loss: 1.2966 2023/03/17 02:48:46 - mmengine - INFO - Epoch(train) [40][ 400/5005] lr: 1.0000e-02 eta: 16:11:19 time: 0.1817 data_time: 0.0030 loss: 1.5230 2023/03/17 02:49:05 - mmengine - INFO - Epoch(train) [40][ 500/5005] lr: 1.0000e-02 eta: 16:10:58 time: 0.1832 data_time: 0.0032 loss: 1.3518 2023/03/17 02:49:24 - mmengine - INFO - Epoch(train) [40][ 600/5005] lr: 1.0000e-02 eta: 16:10:39 time: 0.1891 data_time: 0.0033 loss: 1.3658 2023/03/17 02:49:43 - mmengine - INFO - Epoch(train) [40][ 700/5005] lr: 1.0000e-02 eta: 16:10:19 time: 0.1820 data_time: 0.0038 loss: 1.4163 2023/03/17 02:50:02 - mmengine - INFO - Epoch(train) [40][ 800/5005] lr: 1.0000e-02 eta: 16:10:00 time: 0.1925 data_time: 0.0033 loss: 1.3332 2023/03/17 02:50:03 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:50:21 - mmengine - INFO - Epoch(train) [40][ 900/5005] lr: 1.0000e-02 eta: 16:09:41 time: 0.1942 data_time: 0.0030 loss: 1.3110 2023/03/17 02:50:40 - mmengine - INFO - Epoch(train) [40][1000/5005] lr: 1.0000e-02 eta: 16:09:21 time: 0.1821 data_time: 0.0032 loss: 1.4740 2023/03/17 02:50:59 - mmengine - INFO - Epoch(train) [40][1100/5005] lr: 1.0000e-02 eta: 16:09:02 time: 0.2079 data_time: 0.0034 loss: 1.4091 2023/03/17 02:51:20 - mmengine - INFO - Epoch(train) [40][1200/5005] lr: 1.0000e-02 eta: 16:08:45 time: 0.1983 data_time: 0.0037 loss: 1.4137 2023/03/17 02:51:40 - mmengine - INFO - Epoch(train) [40][1300/5005] lr: 1.0000e-02 eta: 16:08:27 time: 0.1883 data_time: 0.0031 loss: 1.2956 2023/03/17 02:51:58 - mmengine - INFO - Epoch(train) [40][1400/5005] lr: 1.0000e-02 eta: 16:08:07 time: 0.1801 data_time: 0.0034 loss: 1.4121 2023/03/17 02:52:17 - mmengine - INFO - Epoch(train) [40][1500/5005] lr: 1.0000e-02 eta: 16:07:48 time: 0.2050 data_time: 0.0035 loss: 1.5697 2023/03/17 02:52:37 - mmengine - INFO - Epoch(train) [40][1600/5005] lr: 1.0000e-02 eta: 16:07:30 time: 0.1939 data_time: 0.0033 loss: 1.4013 2023/03/17 02:52:57 - mmengine - INFO - Epoch(train) [40][1700/5005] lr: 1.0000e-02 eta: 16:07:11 time: 0.1943 data_time: 0.0040 loss: 1.4877 2023/03/17 02:53:16 - mmengine - INFO - Epoch(train) [40][1800/5005] lr: 1.0000e-02 eta: 16:06:52 time: 0.1911 data_time: 0.0035 loss: 1.4626 2023/03/17 02:53:17 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:53:36 - mmengine - INFO - Epoch(train) [40][1900/5005] lr: 1.0000e-02 eta: 16:06:34 time: 0.1979 data_time: 0.0035 loss: 1.4215 2023/03/17 02:53:56 - mmengine - INFO - Epoch(train) [40][2000/5005] lr: 1.0000e-02 eta: 16:06:16 time: 0.2229 data_time: 0.0033 loss: 1.4562 2023/03/17 02:54:16 - mmengine - INFO - Epoch(train) [40][2100/5005] lr: 1.0000e-02 eta: 16:05:58 time: 0.1935 data_time: 0.0035 loss: 1.2773 2023/03/17 02:54:36 - mmengine - INFO - Epoch(train) [40][2200/5005] lr: 1.0000e-02 eta: 16:05:40 time: 0.1958 data_time: 0.0035 loss: 1.5405 2023/03/17 02:54:56 - mmengine - INFO - Epoch(train) [40][2300/5005] lr: 1.0000e-02 eta: 16:05:22 time: 0.2042 data_time: 0.0039 loss: 1.3765 2023/03/17 02:55:16 - mmengine - INFO - Epoch(train) [40][2400/5005] lr: 1.0000e-02 eta: 16:05:04 time: 0.1960 data_time: 0.0032 loss: 1.4180 2023/03/17 02:55:36 - mmengine - INFO - Epoch(train) [40][2500/5005] lr: 1.0000e-02 eta: 16:04:46 time: 0.1940 data_time: 0.0034 loss: 1.4985 2023/03/17 02:55:55 - mmengine - INFO - Epoch(train) [40][2600/5005] lr: 1.0000e-02 eta: 16:04:28 time: 0.1954 data_time: 0.0034 loss: 1.4688 2023/03/17 02:56:15 - mmengine - INFO - Epoch(train) [40][2700/5005] lr: 1.0000e-02 eta: 16:04:09 time: 0.1873 data_time: 0.0030 loss: 1.6063 2023/03/17 02:56:34 - mmengine - INFO - Epoch(train) [40][2800/5005] lr: 1.0000e-02 eta: 16:03:51 time: 0.2044 data_time: 0.0028 loss: 1.2858 2023/03/17 02:56:35 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 02:56:53 - mmengine - INFO - Epoch(train) [40][2900/5005] lr: 1.0000e-02 eta: 16:03:30 time: 0.1872 data_time: 0.0038 loss: 1.2863 2023/03/17 02:57:12 - mmengine - INFO - Epoch(train) [40][3000/5005] lr: 1.0000e-02 eta: 16:03:11 time: 0.1846 data_time: 0.0033 loss: 1.4191 2023/03/17 02:57:30 - mmengine - INFO - Epoch(train) [40][3100/5005] lr: 1.0000e-02 eta: 16:02:51 time: 0.1883 data_time: 0.0034 loss: 1.1847 2023/03/17 02:57:50 - mmengine - INFO - Epoch(train) [40][3200/5005] lr: 1.0000e-02 eta: 16:02:32 time: 0.2125 data_time: 0.0026 loss: 1.2896 2023/03/17 02:58:09 - mmengine - INFO - Epoch(train) [40][3300/5005] lr: 1.0000e-02 eta: 16:02:13 time: 0.1924 data_time: 0.0030 loss: 1.7956 2023/03/17 02:58:29 - mmengine - INFO - Epoch(train) [40][3400/5005] lr: 1.0000e-02 eta: 16:01:54 time: 0.1850 data_time: 0.0030 loss: 1.4317 2023/03/17 02:58:48 - mmengine - INFO - Epoch(train) [40][3500/5005] lr: 1.0000e-02 eta: 16:01:36 time: 0.1854 data_time: 0.0033 loss: 1.1991 2023/03/17 02:59:07 - mmengine - INFO - Epoch(train) [40][3600/5005] lr: 1.0000e-02 eta: 16:01:16 time: 0.1842 data_time: 0.0031 loss: 1.4181 2023/03/17 02:59:26 - mmengine - INFO - Epoch(train) [40][3700/5005] lr: 1.0000e-02 eta: 16:00:56 time: 0.1938 data_time: 0.0033 loss: 1.3293 2023/03/17 02:59:45 - mmengine - INFO - Epoch(train) [40][3800/5005] lr: 1.0000e-02 eta: 16:00:38 time: 0.2005 data_time: 0.0031 loss: 1.4544 2023/03/17 02:59:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:00:05 - mmengine - INFO - Epoch(train) [40][3900/5005] lr: 1.0000e-02 eta: 16:00:20 time: 0.2193 data_time: 0.0029 loss: 1.4008 2023/03/17 03:00:25 - mmengine - INFO - Epoch(train) [40][4000/5005] lr: 1.0000e-02 eta: 16:00:01 time: 0.1913 data_time: 0.0032 loss: 1.5148 2023/03/17 03:00:45 - mmengine - INFO - Epoch(train) [40][4100/5005] lr: 1.0000e-02 eta: 15:59:43 time: 0.1953 data_time: 0.0030 loss: 1.4852 2023/03/17 03:01:04 - mmengine - INFO - Epoch(train) [40][4200/5005] lr: 1.0000e-02 eta: 15:59:24 time: 0.1972 data_time: 0.0037 loss: 1.3728 2023/03/17 03:01:24 - mmengine - INFO - Epoch(train) [40][4300/5005] lr: 1.0000e-02 eta: 15:59:06 time: 0.1970 data_time: 0.0034 loss: 1.5614 2023/03/17 03:01:44 - mmengine - INFO - Epoch(train) [40][4400/5005] lr: 1.0000e-02 eta: 15:58:48 time: 0.1946 data_time: 0.0030 loss: 1.6819 2023/03/17 03:02:04 - mmengine - INFO - Epoch(train) [40][4500/5005] lr: 1.0000e-02 eta: 15:58:30 time: 0.1973 data_time: 0.0034 loss: 1.4121 2023/03/17 03:02:23 - mmengine - INFO - Epoch(train) [40][4600/5005] lr: 1.0000e-02 eta: 15:58:11 time: 0.1885 data_time: 0.0034 loss: 1.5373 2023/03/17 03:02:43 - mmengine - INFO - Epoch(train) [40][4700/5005] lr: 1.0000e-02 eta: 15:57:53 time: 0.1950 data_time: 0.0029 loss: 1.2163 2023/03/17 03:03:03 - mmengine - INFO - Epoch(train) [40][4800/5005] lr: 1.0000e-02 eta: 15:57:36 time: 0.2244 data_time: 0.0034 loss: 1.5645 2023/03/17 03:03:04 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:03:24 - mmengine - INFO - Epoch(train) [40][4900/5005] lr: 1.0000e-02 eta: 15:57:19 time: 0.1978 data_time: 0.0033 loss: 1.5479 2023/03/17 03:03:44 - mmengine - INFO - Epoch(train) [40][5000/5005] lr: 1.0000e-02 eta: 15:57:01 time: 0.1960 data_time: 0.0039 loss: 1.3719 2023/03/17 03:03:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:03:45 - mmengine - INFO - Saving checkpoint at 40 epochs 2023/03/17 03:03:52 - mmengine - INFO - Epoch(val) [40][100/196] eta: 0:00:05 time: 0.0509 data_time: 0.0009 2023/03/17 03:04:17 - mmengine - INFO - Epoch(val) [40][196/196] accuracy/top1: 69.7020 accuracy/top5: 89.5860data_time: 0.0240 time: 0.0566 2023/03/17 03:04:38 - mmengine - INFO - Epoch(train) [41][ 100/5005] lr: 1.0000e-02 eta: 15:56:44 time: 0.2012 data_time: 0.0030 loss: 1.5256 2023/03/17 03:04:57 - mmengine - INFO - Epoch(train) [41][ 200/5005] lr: 1.0000e-02 eta: 15:56:24 time: 0.1825 data_time: 0.0027 loss: 1.4771 2023/03/17 03:05:15 - mmengine - INFO - Epoch(train) [41][ 300/5005] lr: 1.0000e-02 eta: 15:56:04 time: 0.1768 data_time: 0.0032 loss: 1.5271 2023/03/17 03:05:34 - mmengine - INFO - Epoch(train) [41][ 400/5005] lr: 1.0000e-02 eta: 15:55:44 time: 0.1853 data_time: 0.0027 loss: 1.5073 2023/03/17 03:05:55 - mmengine - INFO - Epoch(train) [41][ 500/5005] lr: 1.0000e-02 eta: 15:55:28 time: 0.2149 data_time: 0.0029 loss: 1.4823 2023/03/17 03:06:16 - mmengine - INFO - Epoch(train) [41][ 600/5005] lr: 1.0000e-02 eta: 15:55:11 time: 0.1976 data_time: 0.0028 loss: 1.4492 2023/03/17 03:06:34 - mmengine - INFO - Epoch(train) [41][ 700/5005] lr: 1.0000e-02 eta: 15:54:50 time: 0.1779 data_time: 0.0030 loss: 1.4737 2023/03/17 03:06:52 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:06:52 - mmengine - INFO - Epoch(train) [41][ 800/5005] lr: 1.0000e-02 eta: 15:54:30 time: 0.1859 data_time: 0.0029 loss: 1.3043 2023/03/17 03:07:14 - mmengine - INFO - Epoch(train) [41][ 900/5005] lr: 1.0000e-02 eta: 15:54:14 time: 0.1839 data_time: 0.0030 loss: 1.4144 2023/03/17 03:07:32 - mmengine - INFO - Epoch(train) [41][1000/5005] lr: 1.0000e-02 eta: 15:53:54 time: 0.1918 data_time: 0.0029 loss: 1.2470 2023/03/17 03:07:52 - mmengine - INFO - Epoch(train) [41][1100/5005] lr: 1.0000e-02 eta: 15:53:35 time: 0.1919 data_time: 0.0030 loss: 1.3230 2023/03/17 03:08:11 - mmengine - INFO - Epoch(train) [41][1200/5005] lr: 1.0000e-02 eta: 15:53:16 time: 0.1870 data_time: 0.0027 loss: 1.2488 2023/03/17 03:08:30 - mmengine - INFO - Epoch(train) [41][1300/5005] lr: 1.0000e-02 eta: 15:52:56 time: 0.1804 data_time: 0.0033 loss: 1.4360 2023/03/17 03:08:49 - mmengine - INFO - Epoch(train) [41][1400/5005] lr: 1.0000e-02 eta: 15:52:37 time: 0.1931 data_time: 0.0031 loss: 1.6848 2023/03/17 03:09:11 - mmengine - INFO - Epoch(train) [41][1500/5005] lr: 1.0000e-02 eta: 15:52:22 time: 0.1876 data_time: 0.0021 loss: 1.4623 2023/03/17 03:09:30 - mmengine - INFO - Epoch(train) [41][1600/5005] lr: 1.0000e-02 eta: 15:52:03 time: 0.2250 data_time: 0.0028 loss: 1.4392 2023/03/17 03:09:48 - mmengine - INFO - Epoch(train) [41][1700/5005] lr: 1.0000e-02 eta: 15:51:42 time: 0.1827 data_time: 0.0028 loss: 1.3746 2023/03/17 03:10:09 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:10:09 - mmengine - INFO - Epoch(train) [41][1800/5005] lr: 1.0000e-02 eta: 15:51:25 time: 0.2270 data_time: 0.0027 loss: 1.3375 2023/03/17 03:10:29 - mmengine - INFO - Epoch(train) [41][1900/5005] lr: 1.0000e-02 eta: 15:51:08 time: 0.1854 data_time: 0.0031 loss: 1.4936 2023/03/17 03:10:48 - mmengine - INFO - Epoch(train) [41][2000/5005] lr: 1.0000e-02 eta: 15:50:48 time: 0.1881 data_time: 0.0030 loss: 1.6003 2023/03/17 03:11:07 - mmengine - INFO - Epoch(train) [41][2100/5005] lr: 1.0000e-02 eta: 15:50:28 time: 0.1799 data_time: 0.0030 loss: 1.4430 2023/03/17 03:11:26 - mmengine - INFO - Epoch(train) [41][2200/5005] lr: 1.0000e-02 eta: 15:50:08 time: 0.1957 data_time: 0.0031 loss: 1.3773 2023/03/17 03:11:45 - mmengine - INFO - Epoch(train) [41][2300/5005] lr: 1.0000e-02 eta: 15:49:50 time: 0.1939 data_time: 0.0029 loss: 1.4790 2023/03/17 03:12:05 - mmengine - INFO - Epoch(train) [41][2400/5005] lr: 1.0000e-02 eta: 15:49:31 time: 0.2102 data_time: 0.0029 loss: 1.4512 2023/03/17 03:12:24 - mmengine - INFO - Epoch(train) [41][2500/5005] lr: 1.0000e-02 eta: 15:49:12 time: 0.1900 data_time: 0.0036 loss: 1.2666 2023/03/17 03:12:44 - mmengine - INFO - Epoch(train) [41][2600/5005] lr: 1.0000e-02 eta: 15:48:54 time: 0.2071 data_time: 0.0028 loss: 1.5866 2023/03/17 03:13:04 - mmengine - INFO - Epoch(train) [41][2700/5005] lr: 1.0000e-02 eta: 15:48:36 time: 0.1965 data_time: 0.0030 loss: 1.4342 2023/03/17 03:13:23 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:13:23 - mmengine - INFO - Epoch(train) [41][2800/5005] lr: 1.0000e-02 eta: 15:48:17 time: 0.1987 data_time: 0.0032 loss: 1.3341 2023/03/17 03:13:44 - mmengine - INFO - Epoch(train) [41][2900/5005] lr: 1.0000e-02 eta: 15:48:00 time: 0.1970 data_time: 0.0030 loss: 1.3557 2023/03/17 03:14:04 - mmengine - INFO - Epoch(train) [41][3000/5005] lr: 1.0000e-02 eta: 15:47:42 time: 0.2024 data_time: 0.0028 loss: 1.4938 2023/03/17 03:14:26 - mmengine - INFO - Epoch(train) [41][3100/5005] lr: 1.0000e-02 eta: 15:47:27 time: 0.1915 data_time: 0.0028 loss: 1.6424 2023/03/17 03:14:46 - mmengine - INFO - Epoch(train) [41][3200/5005] lr: 1.0000e-02 eta: 15:47:09 time: 0.1957 data_time: 0.0027 loss: 1.2937 2023/03/17 03:15:05 - mmengine - INFO - Epoch(train) [41][3300/5005] lr: 1.0000e-02 eta: 15:46:51 time: 0.1893 data_time: 0.0032 loss: 1.3417 2023/03/17 03:15:25 - mmengine - INFO - Epoch(train) [41][3400/5005] lr: 1.0000e-02 eta: 15:46:32 time: 0.2234 data_time: 0.0031 loss: 1.3565 2023/03/17 03:15:45 - mmengine - INFO - Epoch(train) [41][3500/5005] lr: 1.0000e-02 eta: 15:46:14 time: 0.1916 data_time: 0.0032 loss: 1.5474 2023/03/17 03:16:04 - mmengine - INFO - Epoch(train) [41][3600/5005] lr: 1.0000e-02 eta: 15:45:55 time: 0.1961 data_time: 0.0030 loss: 1.4829 2023/03/17 03:16:27 - mmengine - INFO - Epoch(train) [41][3700/5005] lr: 1.0000e-02 eta: 15:45:40 time: 0.2367 data_time: 0.0027 loss: 1.3393 2023/03/17 03:16:47 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:16:47 - mmengine - INFO - Epoch(train) [41][3800/5005] lr: 1.0000e-02 eta: 15:45:23 time: 0.1923 data_time: 0.0027 loss: 1.3719 2023/03/17 03:17:06 - mmengine - INFO - Epoch(train) [41][3900/5005] lr: 1.0000e-02 eta: 15:45:04 time: 0.1973 data_time: 0.0026 loss: 1.3567 2023/03/17 03:17:26 - mmengine - INFO - Epoch(train) [41][4000/5005] lr: 1.0000e-02 eta: 15:44:46 time: 0.1901 data_time: 0.0028 loss: 1.2601 2023/03/17 03:17:46 - mmengine - INFO - Epoch(train) [41][4100/5005] lr: 1.0000e-02 eta: 15:44:27 time: 0.1880 data_time: 0.0029 loss: 1.2094 2023/03/17 03:18:04 - mmengine - INFO - Epoch(train) [41][4200/5005] lr: 1.0000e-02 eta: 15:44:07 time: 0.1803 data_time: 0.0028 loss: 1.4808 2023/03/17 03:18:22 - mmengine - INFO - Epoch(train) [41][4300/5005] lr: 1.0000e-02 eta: 15:43:46 time: 0.1783 data_time: 0.0031 loss: 1.4718 2023/03/17 03:18:41 - mmengine - INFO - Epoch(train) [41][4400/5005] lr: 1.0000e-02 eta: 15:43:27 time: 0.1872 data_time: 0.0028 loss: 1.4208 2023/03/17 03:19:00 - mmengine - INFO - Epoch(train) [41][4500/5005] lr: 1.0000e-02 eta: 15:43:07 time: 0.1927 data_time: 0.0031 loss: 1.3404 2023/03/17 03:19:20 - mmengine - INFO - Epoch(train) [41][4600/5005] lr: 1.0000e-02 eta: 15:42:49 time: 0.1886 data_time: 0.0028 loss: 1.4547 2023/03/17 03:19:39 - mmengine - INFO - Epoch(train) [41][4700/5005] lr: 1.0000e-02 eta: 15:42:30 time: 0.1884 data_time: 0.0029 loss: 1.5390 2023/03/17 03:19:58 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:19:58 - mmengine - INFO - Epoch(train) [41][4800/5005] lr: 1.0000e-02 eta: 15:42:10 time: 0.1873 data_time: 0.0029 loss: 1.5865 2023/03/17 03:20:17 - mmengine - INFO - Epoch(train) [41][4900/5005] lr: 1.0000e-02 eta: 15:41:50 time: 0.1837 data_time: 0.0029 loss: 1.3895 2023/03/17 03:20:36 - mmengine - INFO - Epoch(train) [41][5000/5005] lr: 1.0000e-02 eta: 15:41:31 time: 0.1837 data_time: 0.0037 loss: 1.4430 2023/03/17 03:20:37 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:20:37 - mmengine - INFO - Saving checkpoint at 41 epochs 2023/03/17 03:20:43 - mmengine - INFO - Epoch(val) [41][100/196] eta: 0:00:04 time: 0.0460 data_time: 0.0088 2023/03/17 03:21:10 - mmengine - INFO - Epoch(val) [41][196/196] accuracy/top1: 70.3460 accuracy/top5: 90.0320data_time: 0.0088 time: 0.0708 2023/03/17 03:21:34 - mmengine - INFO - Epoch(train) [42][ 100/5005] lr: 1.0000e-02 eta: 15:41:18 time: 0.2093 data_time: 0.0027 loss: 1.5504 2023/03/17 03:21:55 - mmengine - INFO - Epoch(train) [42][ 200/5005] lr: 1.0000e-02 eta: 15:41:00 time: 0.2250 data_time: 0.0027 loss: 1.4814 2023/03/17 03:22:14 - mmengine - INFO - Epoch(train) [42][ 300/5005] lr: 1.0000e-02 eta: 15:40:41 time: 0.1844 data_time: 0.0034 loss: 1.5150 2023/03/17 03:22:33 - mmengine - INFO - Epoch(train) [42][ 400/5005] lr: 1.0000e-02 eta: 15:40:21 time: 0.1887 data_time: 0.0029 loss: 1.4035 2023/03/17 03:22:52 - mmengine - INFO - Epoch(train) [42][ 500/5005] lr: 1.0000e-02 eta: 15:40:02 time: 0.1826 data_time: 0.0029 loss: 1.6302 2023/03/17 03:23:10 - mmengine - INFO - Epoch(train) [42][ 600/5005] lr: 1.0000e-02 eta: 15:39:42 time: 0.1869 data_time: 0.0036 loss: 1.3215 2023/03/17 03:23:29 - mmengine - INFO - Epoch(train) [42][ 700/5005] lr: 1.0000e-02 eta: 15:39:21 time: 0.1870 data_time: 0.0034 loss: 1.5519 2023/03/17 03:23:47 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:23:49 - mmengine - INFO - Epoch(train) [42][ 800/5005] lr: 1.0000e-02 eta: 15:39:03 time: 0.2252 data_time: 0.0039 loss: 1.4292 2023/03/17 03:24:08 - mmengine - INFO - Epoch(train) [42][ 900/5005] lr: 1.0000e-02 eta: 15:38:44 time: 0.2055 data_time: 0.0032 loss: 1.4173 2023/03/17 03:24:28 - mmengine - INFO - Epoch(train) [42][1000/5005] lr: 1.0000e-02 eta: 15:38:26 time: 0.2000 data_time: 0.0032 loss: 1.4037 2023/03/17 03:24:48 - mmengine - INFO - Epoch(train) [42][1100/5005] lr: 1.0000e-02 eta: 15:38:08 time: 0.1822 data_time: 0.0031 loss: 1.1780 2023/03/17 03:25:06 - mmengine - INFO - Epoch(train) [42][1200/5005] lr: 1.0000e-02 eta: 15:37:47 time: 0.1879 data_time: 0.0031 loss: 1.5771 2023/03/17 03:25:26 - mmengine - INFO - Epoch(train) [42][1300/5005] lr: 1.0000e-02 eta: 15:37:29 time: 0.1951 data_time: 0.0031 loss: 1.3692 2023/03/17 03:25:46 - mmengine - INFO - Epoch(train) [42][1400/5005] lr: 1.0000e-02 eta: 15:37:11 time: 0.1975 data_time: 0.0029 loss: 1.3958 2023/03/17 03:26:06 - mmengine - INFO - Epoch(train) [42][1500/5005] lr: 1.0000e-02 eta: 15:36:54 time: 0.2101 data_time: 0.0031 loss: 1.3444 2023/03/17 03:26:26 - mmengine - INFO - Epoch(train) [42][1600/5005] lr: 1.0000e-02 eta: 15:36:35 time: 0.1937 data_time: 0.0030 loss: 1.4364 2023/03/17 03:26:45 - mmengine - INFO - Epoch(train) [42][1700/5005] lr: 1.0000e-02 eta: 15:36:16 time: 0.1990 data_time: 0.0033 loss: 1.4009 2023/03/17 03:27:04 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:27:05 - mmengine - INFO - Epoch(train) [42][1800/5005] lr: 1.0000e-02 eta: 15:35:58 time: 0.2026 data_time: 0.0028 loss: 1.4967 2023/03/17 03:27:25 - mmengine - INFO - Epoch(train) [42][1900/5005] lr: 1.0000e-02 eta: 15:35:40 time: 0.1924 data_time: 0.0031 loss: 1.6073 2023/03/17 03:27:45 - mmengine - INFO - Epoch(train) [42][2000/5005] lr: 1.0000e-02 eta: 15:35:22 time: 0.1821 data_time: 0.0041 loss: 1.3669 2023/03/17 03:28:03 - mmengine - INFO - Epoch(train) [42][2100/5005] lr: 1.0000e-02 eta: 15:35:01 time: 0.1795 data_time: 0.0028 loss: 1.4532 2023/03/17 03:28:22 - mmengine - INFO - Epoch(train) [42][2200/5005] lr: 1.0000e-02 eta: 15:34:41 time: 0.2084 data_time: 0.0031 loss: 1.3520 2023/03/17 03:28:40 - mmengine - INFO - Epoch(train) [42][2300/5005] lr: 1.0000e-02 eta: 15:34:21 time: 0.1908 data_time: 0.0031 loss: 1.5240 2023/03/17 03:28:57 - mmengine - INFO - Epoch(train) [42][2400/5005] lr: 1.0000e-02 eta: 15:33:59 time: 0.1755 data_time: 0.0035 loss: 1.5198 2023/03/17 03:29:15 - mmengine - INFO - Epoch(train) [42][2500/5005] lr: 1.0000e-02 eta: 15:33:38 time: 0.1718 data_time: 0.0032 loss: 1.4272 2023/03/17 03:29:33 - mmengine - INFO - Epoch(train) [42][2600/5005] lr: 1.0000e-02 eta: 15:33:16 time: 0.1809 data_time: 0.0032 loss: 1.5290 2023/03/17 03:29:51 - mmengine - INFO - Epoch(train) [42][2700/5005] lr: 1.0000e-02 eta: 15:32:55 time: 0.1789 data_time: 0.0032 loss: 1.4918 2023/03/17 03:30:08 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:30:09 - mmengine - INFO - Epoch(train) [42][2800/5005] lr: 1.0000e-02 eta: 15:32:34 time: 0.1807 data_time: 0.0029 loss: 1.5929 2023/03/17 03:30:27 - mmengine - INFO - Epoch(train) [42][2900/5005] lr: 1.0000e-02 eta: 15:32:14 time: 0.1954 data_time: 0.0032 loss: 1.4120 2023/03/17 03:30:46 - mmengine - INFO - Epoch(train) [42][3000/5005] lr: 1.0000e-02 eta: 15:31:55 time: 0.1878 data_time: 0.0028 loss: 1.4157 2023/03/17 03:31:06 - mmengine - INFO - Epoch(train) [42][3100/5005] lr: 1.0000e-02 eta: 15:31:36 time: 0.2087 data_time: 0.0034 loss: 1.3073 2023/03/17 03:31:25 - mmengine - INFO - Epoch(train) [42][3200/5005] lr: 1.0000e-02 eta: 15:31:17 time: 0.1806 data_time: 0.0029 loss: 1.3115 2023/03/17 03:31:44 - mmengine - INFO - Epoch(train) [42][3300/5005] lr: 1.0000e-02 eta: 15:30:57 time: 0.1902 data_time: 0.0033 loss: 1.4501 2023/03/17 03:32:03 - mmengine - INFO - Epoch(train) [42][3400/5005] lr: 1.0000e-02 eta: 15:30:38 time: 0.1980 data_time: 0.0032 loss: 1.4821 2023/03/17 03:32:23 - mmengine - INFO - Epoch(train) [42][3500/5005] lr: 1.0000e-02 eta: 15:30:20 time: 0.2069 data_time: 0.0033 loss: 1.3866 2023/03/17 03:32:43 - mmengine - INFO - Epoch(train) [42][3600/5005] lr: 1.0000e-02 eta: 15:30:02 time: 0.1825 data_time: 0.0038 loss: 1.3797 2023/03/17 03:33:02 - mmengine - INFO - Epoch(train) [42][3700/5005] lr: 1.0000e-02 eta: 15:29:42 time: 0.1869 data_time: 0.0031 loss: 1.2804 2023/03/17 03:33:20 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:33:21 - mmengine - INFO - Epoch(train) [42][3800/5005] lr: 1.0000e-02 eta: 15:29:23 time: 0.1924 data_time: 0.0032 loss: 1.3834 2023/03/17 03:33:40 - mmengine - INFO - Epoch(train) [42][3900/5005] lr: 1.0000e-02 eta: 15:29:04 time: 0.2101 data_time: 0.0031 loss: 1.3692 2023/03/17 03:33:59 - mmengine - INFO - Epoch(train) [42][4000/5005] lr: 1.0000e-02 eta: 15:28:45 time: 0.1961 data_time: 0.0035 loss: 1.3657 2023/03/17 03:34:20 - mmengine - INFO - Epoch(train) [42][4100/5005] lr: 1.0000e-02 eta: 15:28:27 time: 0.2062 data_time: 0.0030 loss: 1.3767 2023/03/17 03:34:40 - mmengine - INFO - Epoch(train) [42][4200/5005] lr: 1.0000e-02 eta: 15:28:09 time: 0.1877 data_time: 0.0033 loss: 1.4623 2023/03/17 03:34:58 - mmengine - INFO - Epoch(train) [42][4300/5005] lr: 1.0000e-02 eta: 15:27:49 time: 0.1743 data_time: 0.0031 loss: 1.2639 2023/03/17 03:35:17 - mmengine - INFO - Epoch(train) [42][4400/5005] lr: 1.0000e-02 eta: 15:27:30 time: 0.1917 data_time: 0.0033 loss: 1.5758 2023/03/17 03:35:37 - mmengine - INFO - Epoch(train) [42][4500/5005] lr: 1.0000e-02 eta: 15:27:11 time: 0.1953 data_time: 0.0031 loss: 1.3080 2023/03/17 03:35:57 - mmengine - INFO - Epoch(train) [42][4600/5005] lr: 1.0000e-02 eta: 15:26:53 time: 0.2010 data_time: 0.0030 loss: 1.4800 2023/03/17 03:36:18 - mmengine - INFO - Epoch(train) [42][4700/5005] lr: 1.0000e-02 eta: 15:26:36 time: 0.2001 data_time: 0.0034 loss: 1.5210 2023/03/17 03:36:36 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:36:37 - mmengine - INFO - Epoch(train) [42][4800/5005] lr: 1.0000e-02 eta: 15:26:17 time: 0.1898 data_time: 0.0035 loss: 1.4767 2023/03/17 03:36:57 - mmengine - INFO - Epoch(train) [42][4900/5005] lr: 1.0000e-02 eta: 15:25:59 time: 0.1961 data_time: 0.0035 loss: 1.2660 2023/03/17 03:37:17 - mmengine - INFO - Epoch(train) [42][5000/5005] lr: 1.0000e-02 eta: 15:25:40 time: 0.2078 data_time: 0.0045 loss: 1.7061 2023/03/17 03:37:18 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:37:18 - mmengine - INFO - Saving checkpoint at 42 epochs 2023/03/17 03:37:25 - mmengine - INFO - Epoch(val) [42][100/196] eta: 0:00:05 time: 0.0565 data_time: 0.0009 2023/03/17 03:37:52 - mmengine - INFO - Epoch(val) [42][196/196] accuracy/top1: 70.2380 accuracy/top5: 89.8360data_time: 0.0626 time: 0.0959 2023/03/17 03:38:13 - mmengine - INFO - Epoch(train) [43][ 100/5005] lr: 1.0000e-02 eta: 15:25:23 time: 0.1932 data_time: 0.0031 loss: 1.4961 2023/03/17 03:38:34 - mmengine - INFO - Epoch(train) [43][ 200/5005] lr: 1.0000e-02 eta: 15:25:06 time: 0.1875 data_time: 0.0033 loss: 1.3490 2023/03/17 03:38:55 - mmengine - INFO - Epoch(train) [43][ 300/5005] lr: 1.0000e-02 eta: 15:24:50 time: 0.2154 data_time: 0.0033 loss: 1.4297 2023/03/17 03:39:17 - mmengine - INFO - Epoch(train) [43][ 400/5005] lr: 1.0000e-02 eta: 15:24:34 time: 0.2395 data_time: 0.0034 loss: 1.4459 2023/03/17 03:39:36 - mmengine - INFO - Epoch(train) [43][ 500/5005] lr: 1.0000e-02 eta: 15:24:15 time: 0.1784 data_time: 0.0039 loss: 1.4143 2023/03/17 03:39:55 - mmengine - INFO - Epoch(train) [43][ 600/5005] lr: 1.0000e-02 eta: 15:23:54 time: 0.1918 data_time: 0.0033 loss: 1.4174 2023/03/17 03:40:18 - mmengine - INFO - Epoch(train) [43][ 700/5005] lr: 1.0000e-02 eta: 15:23:41 time: 0.1941 data_time: 0.0036 loss: 1.3965 2023/03/17 03:40:35 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:40:37 - mmengine - INFO - Epoch(train) [43][ 800/5005] lr: 1.0000e-02 eta: 15:23:21 time: 0.1890 data_time: 0.0032 loss: 1.5363 2023/03/17 03:40:56 - mmengine - INFO - Epoch(train) [43][ 900/5005] lr: 1.0000e-02 eta: 15:23:02 time: 0.1825 data_time: 0.0033 loss: 1.3946 2023/03/17 03:41:15 - mmengine - INFO - Epoch(train) [43][1000/5005] lr: 1.0000e-02 eta: 15:22:43 time: 0.1976 data_time: 0.0036 loss: 1.3735 2023/03/17 03:41:35 - mmengine - INFO - Epoch(train) [43][1100/5005] lr: 1.0000e-02 eta: 15:22:25 time: 0.1987 data_time: 0.0034 loss: 1.4482 2023/03/17 03:41:56 - mmengine - INFO - Epoch(train) [43][1200/5005] lr: 1.0000e-02 eta: 15:22:07 time: 0.2075 data_time: 0.0034 loss: 1.5272 2023/03/17 03:42:18 - mmengine - INFO - Epoch(train) [43][1300/5005] lr: 1.0000e-02 eta: 15:21:52 time: 0.1927 data_time: 0.0034 loss: 1.3224 2023/03/17 03:42:38 - mmengine - INFO - Epoch(train) [43][1400/5005] lr: 1.0000e-02 eta: 15:21:35 time: 0.1972 data_time: 0.0031 loss: 1.2932 2023/03/17 03:42:59 - mmengine - INFO - Epoch(train) [43][1500/5005] lr: 1.0000e-02 eta: 15:21:17 time: 0.1993 data_time: 0.0030 loss: 1.2249 2023/03/17 03:43:18 - mmengine - INFO - Epoch(train) [43][1600/5005] lr: 1.0000e-02 eta: 15:20:58 time: 0.1904 data_time: 0.0030 loss: 1.4253 2023/03/17 03:43:38 - mmengine - INFO - Epoch(train) [43][1700/5005] lr: 1.0000e-02 eta: 15:20:40 time: 0.1933 data_time: 0.0031 loss: 1.3107 2023/03/17 03:43:56 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:43:58 - mmengine - INFO - Epoch(train) [43][1800/5005] lr: 1.0000e-02 eta: 15:20:22 time: 0.1970 data_time: 0.0036 loss: 1.3058 2023/03/17 03:44:17 - mmengine - INFO - Epoch(train) [43][1900/5005] lr: 1.0000e-02 eta: 15:20:03 time: 0.1991 data_time: 0.0035 loss: 1.5281 2023/03/17 03:44:38 - mmengine - INFO - Epoch(train) [43][2000/5005] lr: 1.0000e-02 eta: 15:19:45 time: 0.2081 data_time: 0.0034 loss: 1.4415 2023/03/17 03:44:59 - mmengine - INFO - Epoch(train) [43][2100/5005] lr: 1.0000e-02 eta: 15:19:29 time: 0.1989 data_time: 0.0039 loss: 1.3086 2023/03/17 03:45:19 - mmengine - INFO - Epoch(train) [43][2200/5005] lr: 1.0000e-02 eta: 15:19:11 time: 0.2393 data_time: 0.0031 loss: 1.3776 2023/03/17 03:45:39 - mmengine - INFO - Epoch(train) [43][2300/5005] lr: 1.0000e-02 eta: 15:18:53 time: 0.2036 data_time: 0.0032 loss: 1.4672 2023/03/17 03:45:59 - mmengine - INFO - Epoch(train) [43][2400/5005] lr: 1.0000e-02 eta: 15:18:35 time: 0.1887 data_time: 0.0031 loss: 1.2438 2023/03/17 03:46:18 - mmengine - INFO - Epoch(train) [43][2500/5005] lr: 1.0000e-02 eta: 15:18:15 time: 0.1889 data_time: 0.0033 loss: 1.5996 2023/03/17 03:46:38 - mmengine - INFO - Epoch(train) [43][2600/5005] lr: 1.0000e-02 eta: 15:17:56 time: 0.1940 data_time: 0.0033 loss: 1.4097 2023/03/17 03:46:58 - mmengine - INFO - Epoch(train) [43][2700/5005] lr: 1.0000e-02 eta: 15:17:39 time: 0.2021 data_time: 0.0030 loss: 1.6843 2023/03/17 03:47:16 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:47:18 - mmengine - INFO - Epoch(train) [43][2800/5005] lr: 1.0000e-02 eta: 15:17:20 time: 0.1937 data_time: 0.0031 loss: 1.4662 2023/03/17 03:47:37 - mmengine - INFO - Epoch(train) [43][2900/5005] lr: 1.0000e-02 eta: 15:17:02 time: 0.1925 data_time: 0.0031 loss: 1.4509 2023/03/17 03:47:57 - mmengine - INFO - Epoch(train) [43][3000/5005] lr: 1.0000e-02 eta: 15:16:43 time: 0.1981 data_time: 0.0026 loss: 1.3836 2023/03/17 03:48:17 - mmengine - INFO - Epoch(train) [43][3100/5005] lr: 1.0000e-02 eta: 15:16:25 time: 0.1875 data_time: 0.0033 loss: 1.4077 2023/03/17 03:48:36 - mmengine - INFO - Epoch(train) [43][3200/5005] lr: 1.0000e-02 eta: 15:16:05 time: 0.1871 data_time: 0.0032 loss: 1.3110 2023/03/17 03:48:56 - mmengine - INFO - Epoch(train) [43][3300/5005] lr: 1.0000e-02 eta: 15:15:47 time: 0.1903 data_time: 0.0030 loss: 1.3135 2023/03/17 03:49:15 - mmengine - INFO - Epoch(train) [43][3400/5005] lr: 1.0000e-02 eta: 15:15:27 time: 0.2001 data_time: 0.0030 loss: 1.3623 2023/03/17 03:49:34 - mmengine - INFO - Epoch(train) [43][3500/5005] lr: 1.0000e-02 eta: 15:15:08 time: 0.1918 data_time: 0.0033 loss: 1.3170 2023/03/17 03:49:53 - mmengine - INFO - Epoch(train) [43][3600/5005] lr: 1.0000e-02 eta: 15:14:49 time: 0.1868 data_time: 0.0031 loss: 1.5332 2023/03/17 03:50:12 - mmengine - INFO - Epoch(train) [43][3700/5005] lr: 1.0000e-02 eta: 15:14:29 time: 0.1825 data_time: 0.0030 loss: 1.6118 2023/03/17 03:50:28 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:50:30 - mmengine - INFO - Epoch(train) [43][3800/5005] lr: 1.0000e-02 eta: 15:14:08 time: 0.1769 data_time: 0.0030 loss: 1.3553 2023/03/17 03:50:48 - mmengine - INFO - Epoch(train) [43][3900/5005] lr: 1.0000e-02 eta: 15:13:48 time: 0.1904 data_time: 0.0031 loss: 1.4715 2023/03/17 03:51:07 - mmengine - INFO - Epoch(train) [43][4000/5005] lr: 1.0000e-02 eta: 15:13:29 time: 0.1928 data_time: 0.0031 loss: 1.4201 2023/03/17 03:51:26 - mmengine - INFO - Epoch(train) [43][4100/5005] lr: 1.0000e-02 eta: 15:13:08 time: 0.1824 data_time: 0.0028 loss: 1.2613 2023/03/17 03:51:45 - mmengine - INFO - Epoch(train) [43][4200/5005] lr: 1.0000e-02 eta: 15:12:49 time: 0.1938 data_time: 0.0029 loss: 1.4281 2023/03/17 03:52:04 - mmengine - INFO - Epoch(train) [43][4300/5005] lr: 1.0000e-02 eta: 15:12:30 time: 0.1863 data_time: 0.0027 loss: 1.4581 2023/03/17 03:52:23 - mmengine - INFO - Epoch(train) [43][4400/5005] lr: 1.0000e-02 eta: 15:12:10 time: 0.1890 data_time: 0.0028 loss: 1.5816 2023/03/17 03:52:42 - mmengine - INFO - Epoch(train) [43][4500/5005] lr: 1.0000e-02 eta: 15:11:51 time: 0.1859 data_time: 0.0028 loss: 1.4410 2023/03/17 03:53:00 - mmengine - INFO - Epoch(train) [43][4600/5005] lr: 1.0000e-02 eta: 15:11:30 time: 0.1803 data_time: 0.0029 loss: 1.4113 2023/03/17 03:53:18 - mmengine - INFO - Epoch(train) [43][4700/5005] lr: 1.0000e-02 eta: 15:11:10 time: 0.1863 data_time: 0.0028 loss: 1.4132 2023/03/17 03:53:36 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:53:38 - mmengine - INFO - Epoch(train) [43][4800/5005] lr: 1.0000e-02 eta: 15:10:51 time: 0.2169 data_time: 0.0027 loss: 1.3583 2023/03/17 03:53:57 - mmengine - INFO - Epoch(train) [43][4900/5005] lr: 1.0000e-02 eta: 15:10:32 time: 0.1817 data_time: 0.0025 loss: 1.4932 2023/03/17 03:54:15 - mmengine - INFO - Epoch(train) [43][5000/5005] lr: 1.0000e-02 eta: 15:10:11 time: 0.1799 data_time: 0.0039 loss: 1.5684 2023/03/17 03:54:16 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:54:16 - mmengine - INFO - Saving checkpoint at 43 epochs 2023/03/17 03:54:23 - mmengine - INFO - Epoch(val) [43][100/196] eta: 0:00:04 time: 0.0475 data_time: 0.0132 2023/03/17 03:54:48 - mmengine - INFO - Epoch(val) [43][196/196] accuracy/top1: 69.7900 accuracy/top5: 89.7860data_time: 0.0281 time: 0.0579 2023/03/17 03:55:09 - mmengine - INFO - Epoch(train) [44][ 100/5005] lr: 1.0000e-02 eta: 15:09:54 time: 0.1759 data_time: 0.0031 loss: 1.3677 2023/03/17 03:55:27 - mmengine - INFO - Epoch(train) [44][ 200/5005] lr: 1.0000e-02 eta: 15:09:33 time: 0.1774 data_time: 0.0036 loss: 1.6455 2023/03/17 03:55:46 - mmengine - INFO - Epoch(train) [44][ 300/5005] lr: 1.0000e-02 eta: 15:09:14 time: 0.1896 data_time: 0.0036 loss: 1.3887 2023/03/17 03:56:06 - mmengine - INFO - Epoch(train) [44][ 400/5005] lr: 1.0000e-02 eta: 15:08:56 time: 0.1983 data_time: 0.0032 loss: 1.5055 2023/03/17 03:56:25 - mmengine - INFO - Epoch(train) [44][ 500/5005] lr: 1.0000e-02 eta: 15:08:36 time: 0.1832 data_time: 0.0028 loss: 1.2627 2023/03/17 03:56:44 - mmengine - INFO - Epoch(train) [44][ 600/5005] lr: 1.0000e-02 eta: 15:08:16 time: 0.1835 data_time: 0.0029 loss: 1.6607 2023/03/17 03:57:02 - mmengine - INFO - Epoch(train) [44][ 700/5005] lr: 1.0000e-02 eta: 15:07:56 time: 0.1752 data_time: 0.0029 loss: 1.3650 2023/03/17 03:57:18 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 03:57:21 - mmengine - INFO - Epoch(train) [44][ 800/5005] lr: 1.0000e-02 eta: 15:07:36 time: 0.1880 data_time: 0.0030 loss: 1.2392 2023/03/17 03:57:39 - mmengine - INFO - Epoch(train) [44][ 900/5005] lr: 1.0000e-02 eta: 15:07:16 time: 0.1873 data_time: 0.0029 loss: 1.5479 2023/03/17 03:57:59 - mmengine - INFO - Epoch(train) [44][1000/5005] lr: 1.0000e-02 eta: 15:06:57 time: 0.1972 data_time: 0.0035 loss: 1.3120 2023/03/17 03:58:20 - mmengine - INFO - Epoch(train) [44][1100/5005] lr: 1.0000e-02 eta: 15:06:41 time: 0.2397 data_time: 0.0034 loss: 1.4339 2023/03/17 03:58:40 - mmengine - INFO - Epoch(train) [44][1200/5005] lr: 1.0000e-02 eta: 15:06:22 time: 0.1957 data_time: 0.0037 loss: 1.3957 2023/03/17 03:58:59 - mmengine - INFO - Epoch(train) [44][1300/5005] lr: 1.0000e-02 eta: 15:06:03 time: 0.1944 data_time: 0.0036 loss: 1.4330 2023/03/17 03:59:18 - mmengine - INFO - Epoch(train) [44][1400/5005] lr: 1.0000e-02 eta: 15:05:44 time: 0.1976 data_time: 0.0039 loss: 1.5457 2023/03/17 03:59:38 - mmengine - INFO - Epoch(train) [44][1500/5005] lr: 1.0000e-02 eta: 15:05:25 time: 0.1881 data_time: 0.0035 loss: 1.5210 2023/03/17 03:59:57 - mmengine - INFO - Epoch(train) [44][1600/5005] lr: 1.0000e-02 eta: 15:05:06 time: 0.1986 data_time: 0.0030 loss: 1.5923 2023/03/17 04:00:16 - mmengine - INFO - Epoch(train) [44][1700/5005] lr: 1.0000e-02 eta: 15:04:47 time: 0.1898 data_time: 0.0036 loss: 1.2764 2023/03/17 04:00:33 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:00:36 - mmengine - INFO - Epoch(train) [44][1800/5005] lr: 1.0000e-02 eta: 15:04:28 time: 0.1905 data_time: 0.0029 loss: 1.4122 2023/03/17 04:00:54 - mmengine - INFO - Epoch(train) [44][1900/5005] lr: 1.0000e-02 eta: 15:04:07 time: 0.1673 data_time: 0.0030 loss: 1.3887 2023/03/17 04:01:11 - mmengine - INFO - Epoch(train) [44][2000/5005] lr: 1.0000e-02 eta: 15:03:46 time: 0.1764 data_time: 0.0031 loss: 1.3919 2023/03/17 04:01:29 - mmengine - INFO - Epoch(train) [44][2100/5005] lr: 1.0000e-02 eta: 15:03:25 time: 0.1868 data_time: 0.0035 loss: 1.4901 2023/03/17 04:01:48 - mmengine - INFO - Epoch(train) [44][2200/5005] lr: 1.0000e-02 eta: 15:03:05 time: 0.1821 data_time: 0.0032 loss: 1.4262 2023/03/17 04:02:07 - mmengine - INFO - Epoch(train) [44][2300/5005] lr: 1.0000e-02 eta: 15:02:46 time: 0.1819 data_time: 0.0028 loss: 1.5255 2023/03/17 04:02:26 - mmengine - INFO - Epoch(train) [44][2400/5005] lr: 1.0000e-02 eta: 15:02:26 time: 0.1855 data_time: 0.0030 loss: 1.3012 2023/03/17 04:02:45 - mmengine - INFO - Epoch(train) [44][2500/5005] lr: 1.0000e-02 eta: 15:02:08 time: 0.2116 data_time: 0.0031 loss: 1.4417 2023/03/17 04:03:04 - mmengine - INFO - Epoch(train) [44][2600/5005] lr: 1.0000e-02 eta: 15:01:48 time: 0.1911 data_time: 0.0030 loss: 1.7339 2023/03/17 04:03:24 - mmengine - INFO - Epoch(train) [44][2700/5005] lr: 1.0000e-02 eta: 15:01:30 time: 0.1925 data_time: 0.0034 loss: 1.2452 2023/03/17 04:03:40 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:03:43 - mmengine - INFO - Epoch(train) [44][2800/5005] lr: 1.0000e-02 eta: 15:01:10 time: 0.2017 data_time: 0.0033 loss: 1.2975 2023/03/17 04:04:03 - mmengine - INFO - Epoch(train) [44][2900/5005] lr: 1.0000e-02 eta: 15:00:52 time: 0.2288 data_time: 0.0030 loss: 1.3654 2023/03/17 04:04:22 - mmengine - INFO - Epoch(train) [44][3000/5005] lr: 1.0000e-02 eta: 15:00:33 time: 0.1957 data_time: 0.0033 loss: 1.4648 2023/03/17 04:04:42 - mmengine - INFO - Epoch(train) [44][3100/5005] lr: 1.0000e-02 eta: 15:00:14 time: 0.1919 data_time: 0.0031 loss: 1.5198 2023/03/17 04:05:05 - mmengine - INFO - Epoch(train) [44][3200/5005] lr: 1.0000e-02 eta: 15:00:00 time: 0.2380 data_time: 0.0030 loss: 1.3416 2023/03/17 04:05:27 - mmengine - INFO - Epoch(train) [44][3300/5005] lr: 1.0000e-02 eta: 14:59:44 time: 0.1993 data_time: 0.0035 loss: 1.6128 2023/03/17 04:05:47 - mmengine - INFO - Epoch(train) [44][3400/5005] lr: 1.0000e-02 eta: 14:59:26 time: 0.2236 data_time: 0.0034 loss: 1.3712 2023/03/17 04:06:06 - mmengine - INFO - Epoch(train) [44][3500/5005] lr: 1.0000e-02 eta: 14:59:07 time: 0.1863 data_time: 0.0038 loss: 1.7433 2023/03/17 04:06:26 - mmengine - INFO - Epoch(train) [44][3600/5005] lr: 1.0000e-02 eta: 14:58:49 time: 0.1967 data_time: 0.0033 loss: 1.3540 2023/03/17 04:06:46 - mmengine - INFO - Epoch(train) [44][3700/5005] lr: 1.0000e-02 eta: 14:58:30 time: 0.1920 data_time: 0.0032 loss: 1.5329 2023/03/17 04:07:02 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:07:05 - mmengine - INFO - Epoch(train) [44][3800/5005] lr: 1.0000e-02 eta: 14:58:11 time: 0.1855 data_time: 0.0032 loss: 1.6219 2023/03/17 04:07:25 - mmengine - INFO - Epoch(train) [44][3900/5005] lr: 1.0000e-02 eta: 14:57:52 time: 0.2027 data_time: 0.0038 loss: 1.5767 2023/03/17 04:07:44 - mmengine - INFO - Epoch(train) [44][4000/5005] lr: 1.0000e-02 eta: 14:57:33 time: 0.1918 data_time: 0.0037 loss: 1.3741 2023/03/17 04:08:03 - mmengine - INFO - Epoch(train) [44][4100/5005] lr: 1.0000e-02 eta: 14:57:14 time: 0.1864 data_time: 0.0035 loss: 1.3067 2023/03/17 04:08:22 - mmengine - INFO - Epoch(train) [44][4200/5005] lr: 1.0000e-02 eta: 14:56:54 time: 0.1951 data_time: 0.0039 loss: 1.3551 2023/03/17 04:08:41 - mmengine - INFO - Epoch(train) [44][4300/5005] lr: 1.0000e-02 eta: 14:56:36 time: 0.1989 data_time: 0.0034 loss: 1.3391 2023/03/17 04:09:01 - mmengine - INFO - Epoch(train) [44][4400/5005] lr: 1.0000e-02 eta: 14:56:17 time: 0.2010 data_time: 0.0034 loss: 1.2642 2023/03/17 04:09:20 - mmengine - INFO - Epoch(train) [44][4500/5005] lr: 1.0000e-02 eta: 14:55:58 time: 0.1878 data_time: 0.0032 loss: 1.5324 2023/03/17 04:09:40 - mmengine - INFO - Epoch(train) [44][4600/5005] lr: 1.0000e-02 eta: 14:55:39 time: 0.1947 data_time: 0.0033 loss: 1.4308 2023/03/17 04:10:00 - mmengine - INFO - Epoch(train) [44][4700/5005] lr: 1.0000e-02 eta: 14:55:20 time: 0.1927 data_time: 0.0033 loss: 1.5865 2023/03/17 04:10:16 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:10:19 - mmengine - INFO - Epoch(train) [44][4800/5005] lr: 1.0000e-02 eta: 14:55:01 time: 0.1810 data_time: 0.0039 loss: 1.3655 2023/03/17 04:10:38 - mmengine - INFO - Epoch(train) [44][4900/5005] lr: 1.0000e-02 eta: 14:54:42 time: 0.1940 data_time: 0.0033 loss: 1.3658 2023/03/17 04:11:02 - mmengine - INFO - Epoch(train) [44][5000/5005] lr: 1.0000e-02 eta: 14:54:29 time: 0.2567 data_time: 0.0039 loss: 1.3369 2023/03/17 04:11:03 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:11:04 - mmengine - INFO - Saving checkpoint at 44 epochs 2023/03/17 04:11:10 - mmengine - INFO - Epoch(val) [44][100/196] eta: 0:00:05 time: 0.0468 data_time: 0.0009 2023/03/17 04:11:37 - mmengine - INFO - Epoch(val) [44][196/196] accuracy/top1: 69.1580 accuracy/top5: 89.4600data_time: 0.0265 time: 0.0637 2023/03/17 04:12:00 - mmengine - INFO - Epoch(train) [45][ 100/5005] lr: 1.0000e-02 eta: 14:54:13 time: 0.2116 data_time: 0.0029 loss: 1.3979 2023/03/17 04:12:19 - mmengine - INFO - Epoch(train) [45][ 200/5005] lr: 1.0000e-02 eta: 14:53:53 time: 0.1848 data_time: 0.0029 loss: 1.3222 2023/03/17 04:12:37 - mmengine - INFO - Epoch(train) [45][ 300/5005] lr: 1.0000e-02 eta: 14:53:34 time: 0.2045 data_time: 0.0028 loss: 1.4440 2023/03/17 04:12:57 - mmengine - INFO - Epoch(train) [45][ 400/5005] lr: 1.0000e-02 eta: 14:53:15 time: 0.1926 data_time: 0.0031 loss: 1.3866 2023/03/17 04:13:19 - mmengine - INFO - Epoch(train) [45][ 500/5005] lr: 1.0000e-02 eta: 14:52:59 time: 0.2015 data_time: 0.0034 loss: 1.3667 2023/03/17 04:13:41 - mmengine - INFO - Epoch(train) [45][ 600/5005] lr: 1.0000e-02 eta: 14:52:44 time: 0.2383 data_time: 0.0032 loss: 1.4922 2023/03/17 04:14:00 - mmengine - INFO - Epoch(train) [45][ 700/5005] lr: 1.0000e-02 eta: 14:52:25 time: 0.1888 data_time: 0.0031 loss: 1.4652 2023/03/17 04:14:16 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:14:20 - mmengine - INFO - Epoch(train) [45][ 800/5005] lr: 1.0000e-02 eta: 14:52:06 time: 0.2138 data_time: 0.0029 loss: 1.2677 2023/03/17 04:14:39 - mmengine - INFO - Epoch(train) [45][ 900/5005] lr: 1.0000e-02 eta: 14:51:46 time: 0.1831 data_time: 0.0033 loss: 1.7075 2023/03/17 04:14:57 - mmengine - INFO - Epoch(train) [45][1000/5005] lr: 1.0000e-02 eta: 14:51:26 time: 0.1834 data_time: 0.0034 loss: 1.3213 2023/03/17 04:15:17 - mmengine - INFO - Epoch(train) [45][1100/5005] lr: 1.0000e-02 eta: 14:51:07 time: 0.1983 data_time: 0.0032 loss: 1.6564 2023/03/17 04:15:37 - mmengine - INFO - Epoch(train) [45][1200/5005] lr: 1.0000e-02 eta: 14:50:49 time: 0.2207 data_time: 0.0032 loss: 1.5684 2023/03/17 04:15:56 - mmengine - INFO - Epoch(train) [45][1300/5005] lr: 1.0000e-02 eta: 14:50:30 time: 0.1916 data_time: 0.0033 loss: 1.5505 2023/03/17 04:16:15 - mmengine - INFO - Epoch(train) [45][1400/5005] lr: 1.0000e-02 eta: 14:50:11 time: 0.2015 data_time: 0.0031 loss: 1.3907 2023/03/17 04:16:35 - mmengine - INFO - Epoch(train) [45][1500/5005] lr: 1.0000e-02 eta: 14:49:52 time: 0.2131 data_time: 0.0030 loss: 1.5506 2023/03/17 04:16:56 - mmengine - INFO - Epoch(train) [45][1600/5005] lr: 1.0000e-02 eta: 14:49:35 time: 0.2090 data_time: 0.0035 loss: 1.4667 2023/03/17 04:17:14 - mmengine - INFO - Epoch(train) [45][1700/5005] lr: 1.0000e-02 eta: 14:49:15 time: 0.1881 data_time: 0.0029 loss: 1.5166 2023/03/17 04:17:29 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:17:33 - mmengine - INFO - Epoch(train) [45][1800/5005] lr: 1.0000e-02 eta: 14:48:55 time: 0.1856 data_time: 0.0031 loss: 1.4089 2023/03/17 04:17:51 - mmengine - INFO - Epoch(train) [45][1900/5005] lr: 1.0000e-02 eta: 14:48:35 time: 0.1745 data_time: 0.0035 loss: 1.4599 2023/03/17 04:18:09 - mmengine - INFO - Epoch(train) [45][2000/5005] lr: 1.0000e-02 eta: 14:48:14 time: 0.1858 data_time: 0.0029 loss: 1.3777 2023/03/17 04:18:30 - mmengine - INFO - Epoch(train) [45][2100/5005] lr: 1.0000e-02 eta: 14:47:57 time: 0.1990 data_time: 0.0034 loss: 1.3947 2023/03/17 04:18:52 - mmengine - INFO - Epoch(train) [45][2200/5005] lr: 1.0000e-02 eta: 14:47:40 time: 0.2166 data_time: 0.0028 loss: 1.4835 2023/03/17 04:19:12 - mmengine - INFO - Epoch(train) [45][2300/5005] lr: 1.0000e-02 eta: 14:47:22 time: 0.1871 data_time: 0.0031 loss: 1.5208 2023/03/17 04:19:32 - mmengine - INFO - Epoch(train) [45][2400/5005] lr: 1.0000e-02 eta: 14:47:05 time: 0.2347 data_time: 0.0032 loss: 1.4654 2023/03/17 04:19:53 - mmengine - INFO - Epoch(train) [45][2500/5005] lr: 1.0000e-02 eta: 14:46:48 time: 0.2014 data_time: 0.0035 loss: 1.5008 2023/03/17 04:20:13 - mmengine - INFO - Epoch(train) [45][2600/5005] lr: 1.0000e-02 eta: 14:46:29 time: 0.1946 data_time: 0.0032 loss: 1.3935 2023/03/17 04:20:33 - mmengine - INFO - Epoch(train) [45][2700/5005] lr: 1.0000e-02 eta: 14:46:11 time: 0.1868 data_time: 0.0033 loss: 1.4791 2023/03/17 04:20:48 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:20:52 - mmengine - INFO - Epoch(train) [45][2800/5005] lr: 1.0000e-02 eta: 14:45:51 time: 0.1872 data_time: 0.0029 loss: 1.4747 2023/03/17 04:21:11 - mmengine - INFO - Epoch(train) [45][2900/5005] lr: 1.0000e-02 eta: 14:45:32 time: 0.1904 data_time: 0.0030 loss: 1.5059 2023/03/17 04:21:30 - mmengine - INFO - Epoch(train) [45][3000/5005] lr: 1.0000e-02 eta: 14:45:13 time: 0.2104 data_time: 0.0036 loss: 1.5801 2023/03/17 04:21:53 - mmengine - INFO - Epoch(train) [45][3100/5005] lr: 1.0000e-02 eta: 14:44:58 time: 0.2095 data_time: 0.0033 loss: 1.4882 2023/03/17 04:22:12 - mmengine - INFO - Epoch(train) [45][3200/5005] lr: 1.0000e-02 eta: 14:44:39 time: 0.1788 data_time: 0.0031 loss: 1.3188 2023/03/17 04:22:31 - mmengine - INFO - Epoch(train) [45][3300/5005] lr: 1.0000e-02 eta: 14:44:19 time: 0.1824 data_time: 0.0036 loss: 1.4374 2023/03/17 04:22:50 - mmengine - INFO - Epoch(train) [45][3400/5005] lr: 1.0000e-02 eta: 14:44:00 time: 0.1906 data_time: 0.0032 loss: 1.4125 2023/03/17 04:23:10 - mmengine - INFO - Epoch(train) [45][3500/5005] lr: 1.0000e-02 eta: 14:43:41 time: 0.1924 data_time: 0.0035 loss: 1.3588 2023/03/17 04:23:28 - mmengine - INFO - Epoch(train) [45][3600/5005] lr: 1.0000e-02 eta: 14:43:21 time: 0.1841 data_time: 0.0034 loss: 1.4372 2023/03/17 04:23:48 - mmengine - INFO - Epoch(train) [45][3700/5005] lr: 1.0000e-02 eta: 14:43:02 time: 0.1963 data_time: 0.0033 loss: 1.3721 2023/03/17 04:24:06 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:24:11 - mmengine - INFO - Epoch(train) [45][3800/5005] lr: 1.0000e-02 eta: 14:42:48 time: 0.2287 data_time: 0.0038 loss: 1.4167 2023/03/17 04:24:30 - mmengine - INFO - Epoch(train) [45][3900/5005] lr: 1.0000e-02 eta: 14:42:28 time: 0.1841 data_time: 0.0032 loss: 1.5570 2023/03/17 04:24:49 - mmengine - INFO - Epoch(train) [45][4000/5005] lr: 1.0000e-02 eta: 14:42:09 time: 0.2095 data_time: 0.0034 loss: 1.4749 2023/03/17 04:25:07 - mmengine - INFO - Epoch(train) [45][4100/5005] lr: 1.0000e-02 eta: 14:41:49 time: 0.1851 data_time: 0.0032 loss: 1.3208 2023/03/17 04:25:26 - mmengine - INFO - Epoch(train) [45][4200/5005] lr: 1.0000e-02 eta: 14:41:29 time: 0.2078 data_time: 0.0029 loss: 1.5027 2023/03/17 04:25:44 - mmengine - INFO - Epoch(train) [45][4300/5005] lr: 1.0000e-02 eta: 14:41:08 time: 0.1820 data_time: 0.0031 loss: 1.4073 2023/03/17 04:26:02 - mmengine - INFO - Epoch(train) [45][4400/5005] lr: 1.0000e-02 eta: 14:40:48 time: 0.1772 data_time: 0.0031 loss: 1.2622 2023/03/17 04:26:21 - mmengine - INFO - Epoch(train) [45][4500/5005] lr: 1.0000e-02 eta: 14:40:28 time: 0.1851 data_time: 0.0035 loss: 1.4489 2023/03/17 04:26:39 - mmengine - INFO - Epoch(train) [45][4600/5005] lr: 1.0000e-02 eta: 14:40:08 time: 0.1794 data_time: 0.0036 loss: 1.2749 2023/03/17 04:26:59 - mmengine - INFO - Epoch(train) [45][4700/5005] lr: 1.0000e-02 eta: 14:39:50 time: 0.2049 data_time: 0.0036 loss: 1.4594 2023/03/17 04:27:17 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:27:22 - mmengine - INFO - Epoch(train) [45][4800/5005] lr: 1.0000e-02 eta: 14:39:34 time: 0.2260 data_time: 0.0036 loss: 1.4861 2023/03/17 04:27:41 - mmengine - INFO - Epoch(train) [45][4900/5005] lr: 1.0000e-02 eta: 14:39:14 time: 0.1820 data_time: 0.0031 loss: 1.4174 2023/03/17 04:27:59 - mmengine - INFO - Epoch(train) [45][5000/5005] lr: 1.0000e-02 eta: 14:38:54 time: 0.1867 data_time: 0.0040 loss: 1.4673 2023/03/17 04:28:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:28:00 - mmengine - INFO - Saving checkpoint at 45 epochs 2023/03/17 04:28:07 - mmengine - INFO - Epoch(val) [45][100/196] eta: 0:00:05 time: 0.0453 data_time: 0.0066 2023/03/17 04:28:34 - mmengine - INFO - Epoch(val) [45][196/196] accuracy/top1: 69.3800 accuracy/top5: 89.5060data_time: 0.0153 time: 0.0455 2023/03/17 04:28:55 - mmengine - INFO - Epoch(train) [46][ 100/5005] lr: 1.0000e-02 eta: 14:38:36 time: 0.2044 data_time: 0.0030 loss: 1.5233 2023/03/17 04:29:14 - mmengine - INFO - Epoch(train) [46][ 200/5005] lr: 1.0000e-02 eta: 14:38:17 time: 0.1817 data_time: 0.0038 loss: 1.4608 2023/03/17 04:29:33 - mmengine - INFO - Epoch(train) [46][ 300/5005] lr: 1.0000e-02 eta: 14:37:57 time: 0.1874 data_time: 0.0033 loss: 1.5616 2023/03/17 04:29:53 - mmengine - INFO - Epoch(train) [46][ 400/5005] lr: 1.0000e-02 eta: 14:37:39 time: 0.1864 data_time: 0.0030 loss: 1.4543 2023/03/17 04:30:12 - mmengine - INFO - Epoch(train) [46][ 500/5005] lr: 1.0000e-02 eta: 14:37:19 time: 0.2070 data_time: 0.0030 loss: 1.5891 2023/03/17 04:30:32 - mmengine - INFO - Epoch(train) [46][ 600/5005] lr: 1.0000e-02 eta: 14:37:01 time: 0.1877 data_time: 0.0031 loss: 1.3995 2023/03/17 04:30:51 - mmengine - INFO - Epoch(train) [46][ 700/5005] lr: 1.0000e-02 eta: 14:36:41 time: 0.2174 data_time: 0.0030 loss: 1.3069 2023/03/17 04:31:06 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:31:11 - mmengine - INFO - Epoch(train) [46][ 800/5005] lr: 1.0000e-02 eta: 14:36:23 time: 0.1829 data_time: 0.0030 loss: 1.5813 2023/03/17 04:31:29 - mmengine - INFO - Epoch(train) [46][ 900/5005] lr: 1.0000e-02 eta: 14:36:03 time: 0.1897 data_time: 0.0031 loss: 1.5047 2023/03/17 04:31:48 - mmengine - INFO - Epoch(train) [46][1000/5005] lr: 1.0000e-02 eta: 14:35:44 time: 0.1742 data_time: 0.0032 loss: 1.3696 2023/03/17 04:32:06 - mmengine - INFO - Epoch(train) [46][1100/5005] lr: 1.0000e-02 eta: 14:35:22 time: 0.1783 data_time: 0.0033 loss: 1.5186 2023/03/17 04:32:25 - mmengine - INFO - Epoch(train) [46][1200/5005] lr: 1.0000e-02 eta: 14:35:03 time: 0.2066 data_time: 0.0032 loss: 1.6562 2023/03/17 04:32:43 - mmengine - INFO - Epoch(train) [46][1300/5005] lr: 1.0000e-02 eta: 14:34:43 time: 0.1783 data_time: 0.0034 loss: 1.6751 2023/03/17 04:33:01 - mmengine - INFO - Epoch(train) [46][1400/5005] lr: 1.0000e-02 eta: 14:34:22 time: 0.1943 data_time: 0.0031 loss: 1.3558 2023/03/17 04:33:20 - mmengine - INFO - Epoch(train) [46][1500/5005] lr: 1.0000e-02 eta: 14:34:02 time: 0.1898 data_time: 0.0033 loss: 1.6050 2023/03/17 04:33:41 - mmengine - INFO - Epoch(train) [46][1600/5005] lr: 1.0000e-02 eta: 14:33:45 time: 0.2416 data_time: 0.0027 loss: 1.4086 2023/03/17 04:34:03 - mmengine - INFO - Epoch(train) [46][1700/5005] lr: 1.0000e-02 eta: 14:33:30 time: 0.1977 data_time: 0.0029 loss: 1.5092 2023/03/17 04:34:19 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:34:24 - mmengine - INFO - Epoch(train) [46][1800/5005] lr: 1.0000e-02 eta: 14:33:12 time: 0.2007 data_time: 0.0031 loss: 1.4203 2023/03/17 04:34:44 - mmengine - INFO - Epoch(train) [46][1900/5005] lr: 1.0000e-02 eta: 14:32:54 time: 0.1983 data_time: 0.0032 loss: 1.2053 2023/03/17 04:35:04 - mmengine - INFO - Epoch(train) [46][2000/5005] lr: 1.0000e-02 eta: 14:32:35 time: 0.1982 data_time: 0.0032 loss: 1.2655 2023/03/17 04:35:25 - mmengine - INFO - Epoch(train) [46][2100/5005] lr: 1.0000e-02 eta: 14:32:18 time: 0.2100 data_time: 0.0028 loss: 1.4047 2023/03/17 04:35:45 - mmengine - INFO - Epoch(train) [46][2200/5005] lr: 1.0000e-02 eta: 14:32:00 time: 0.2157 data_time: 0.0035 loss: 1.2726 2023/03/17 04:36:03 - mmengine - INFO - Epoch(train) [46][2300/5005] lr: 1.0000e-02 eta: 14:31:40 time: 0.1800 data_time: 0.0034 loss: 1.5146 2023/03/17 04:36:22 - mmengine - INFO - Epoch(train) [46][2400/5005] lr: 1.0000e-02 eta: 14:31:20 time: 0.1898 data_time: 0.0030 loss: 1.4370 2023/03/17 04:36:41 - mmengine - INFO - Epoch(train) [46][2500/5005] lr: 1.0000e-02 eta: 14:31:00 time: 0.1851 data_time: 0.0033 loss: 1.5438 2023/03/17 04:36:59 - mmengine - INFO - Epoch(train) [46][2600/5005] lr: 1.0000e-02 eta: 14:30:41 time: 0.1842 data_time: 0.0032 loss: 1.4166 2023/03/17 04:37:20 - mmengine - INFO - Epoch(train) [46][2700/5005] lr: 1.0000e-02 eta: 14:30:23 time: 0.2132 data_time: 0.0028 loss: 1.4784 2023/03/17 04:37:35 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:37:40 - mmengine - INFO - Epoch(train) [46][2800/5005] lr: 1.0000e-02 eta: 14:30:04 time: 0.1871 data_time: 0.0032 loss: 1.3363 2023/03/17 04:38:00 - mmengine - INFO - Epoch(train) [46][2900/5005] lr: 1.0000e-02 eta: 14:29:47 time: 0.2021 data_time: 0.0031 loss: 1.4318 2023/03/17 04:38:21 - mmengine - INFO - Epoch(train) [46][3000/5005] lr: 1.0000e-02 eta: 14:29:29 time: 0.2145 data_time: 0.0034 loss: 1.4131 2023/03/17 04:38:43 - mmengine - INFO - Epoch(train) [46][3100/5005] lr: 1.0000e-02 eta: 14:29:14 time: 0.1967 data_time: 0.0030 loss: 1.1834 2023/03/17 04:39:05 - mmengine - INFO - Epoch(train) [46][3200/5005] lr: 1.0000e-02 eta: 14:28:58 time: 0.2007 data_time: 0.0030 loss: 1.4054 2023/03/17 04:39:26 - mmengine - INFO - Epoch(train) [46][3300/5005] lr: 1.0000e-02 eta: 14:28:40 time: 0.2041 data_time: 0.0028 loss: 1.5822 2023/03/17 04:39:46 - mmengine - INFO - Epoch(train) [46][3400/5005] lr: 1.0000e-02 eta: 14:28:22 time: 0.2039 data_time: 0.0034 loss: 1.4084 2023/03/17 04:40:05 - mmengine - INFO - Epoch(train) [46][3500/5005] lr: 1.0000e-02 eta: 14:28:03 time: 0.1829 data_time: 0.0037 loss: 1.3683 2023/03/17 04:40:25 - mmengine - INFO - Epoch(train) [46][3600/5005] lr: 1.0000e-02 eta: 14:27:45 time: 0.1998 data_time: 0.0032 loss: 1.5726 2023/03/17 04:40:44 - mmengine - INFO - Epoch(train) [46][3700/5005] lr: 1.0000e-02 eta: 14:27:25 time: 0.2041 data_time: 0.0031 loss: 1.6367 2023/03/17 04:40:59 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:41:05 - mmengine - INFO - Epoch(train) [46][3800/5005] lr: 1.0000e-02 eta: 14:27:07 time: 0.2059 data_time: 0.0033 loss: 1.4265 2023/03/17 04:41:23 - mmengine - INFO - Epoch(train) [46][3900/5005] lr: 1.0000e-02 eta: 14:26:48 time: 0.1798 data_time: 0.0030 loss: 1.3826 2023/03/17 04:41:42 - mmengine - INFO - Epoch(train) [46][4000/5005] lr: 1.0000e-02 eta: 14:26:27 time: 0.1833 data_time: 0.0037 loss: 1.3080 2023/03/17 04:42:01 - mmengine - INFO - Epoch(train) [46][4100/5005] lr: 1.0000e-02 eta: 14:26:08 time: 0.1918 data_time: 0.0030 loss: 1.2794 2023/03/17 04:42:20 - mmengine - INFO - Epoch(train) [46][4200/5005] lr: 1.0000e-02 eta: 14:25:49 time: 0.1830 data_time: 0.0030 loss: 1.3563 2023/03/17 04:42:39 - mmengine - INFO - Epoch(train) [46][4300/5005] lr: 1.0000e-02 eta: 14:25:29 time: 0.1917 data_time: 0.0033 loss: 1.5061 2023/03/17 04:43:00 - mmengine - INFO - Epoch(train) [46][4400/5005] lr: 1.0000e-02 eta: 14:25:12 time: 0.2185 data_time: 0.0027 loss: 1.4793 2023/03/17 04:43:20 - mmengine - INFO - Epoch(train) [46][4500/5005] lr: 1.0000e-02 eta: 14:24:53 time: 0.1875 data_time: 0.0031 loss: 1.4005 2023/03/17 04:43:40 - mmengine - INFO - Epoch(train) [46][4600/5005] lr: 1.0000e-02 eta: 14:24:35 time: 0.2040 data_time: 0.0025 loss: 1.3685 2023/03/17 04:43:59 - mmengine - INFO - Epoch(train) [46][4700/5005] lr: 1.0000e-02 eta: 14:24:16 time: 0.2085 data_time: 0.0030 loss: 1.4364 2023/03/17 04:44:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:44:17 - mmengine - INFO - Epoch(train) [46][4800/5005] lr: 1.0000e-02 eta: 14:23:56 time: 0.1803 data_time: 0.0029 loss: 1.4895 2023/03/17 04:44:36 - mmengine - INFO - Epoch(train) [46][4900/5005] lr: 1.0000e-02 eta: 14:23:36 time: 0.1860 data_time: 0.0031 loss: 1.6407 2023/03/17 04:44:55 - mmengine - INFO - Epoch(train) [46][5000/5005] lr: 1.0000e-02 eta: 14:23:16 time: 0.2245 data_time: 0.0044 loss: 1.4612 2023/03/17 04:44:56 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:44:56 - mmengine - INFO - Saving checkpoint at 46 epochs 2023/03/17 04:45:02 - mmengine - INFO - Epoch(val) [46][100/196] eta: 0:00:04 time: 0.0440 data_time: 0.0009 2023/03/17 04:45:30 - mmengine - INFO - Epoch(val) [46][196/196] accuracy/top1: 68.9280 accuracy/top5: 89.1260data_time: 0.0268 time: 0.0607 2023/03/17 04:45:49 - mmengine - INFO - Epoch(train) [47][ 100/5005] lr: 1.0000e-02 eta: 14:22:56 time: 0.1721 data_time: 0.0034 loss: 1.2929 2023/03/17 04:46:07 - mmengine - INFO - Epoch(train) [47][ 200/5005] lr: 1.0000e-02 eta: 14:22:35 time: 0.1797 data_time: 0.0032 loss: 1.3538 2023/03/17 04:46:25 - mmengine - INFO - Epoch(train) [47][ 300/5005] lr: 1.0000e-02 eta: 14:22:14 time: 0.2030 data_time: 0.0031 loss: 1.3898 2023/03/17 04:46:42 - mmengine - INFO - Epoch(train) [47][ 400/5005] lr: 1.0000e-02 eta: 14:21:53 time: 0.1806 data_time: 0.0032 loss: 1.4625 2023/03/17 04:47:02 - mmengine - INFO - Epoch(train) [47][ 500/5005] lr: 1.0000e-02 eta: 14:21:34 time: 0.1991 data_time: 0.0035 loss: 1.3620 2023/03/17 04:47:21 - mmengine - INFO - Epoch(train) [47][ 600/5005] lr: 1.0000e-02 eta: 14:21:15 time: 0.1861 data_time: 0.0032 loss: 1.4025 2023/03/17 04:47:40 - mmengine - INFO - Epoch(train) [47][ 700/5005] lr: 1.0000e-02 eta: 14:20:55 time: 0.1814 data_time: 0.0031 loss: 1.4742 2023/03/17 04:47:53 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:47:59 - mmengine - INFO - Epoch(train) [47][ 800/5005] lr: 1.0000e-02 eta: 14:20:36 time: 0.2011 data_time: 0.0040 loss: 1.4761 2023/03/17 04:48:20 - mmengine - INFO - Epoch(train) [47][ 900/5005] lr: 1.0000e-02 eta: 14:20:19 time: 0.2234 data_time: 0.0031 loss: 1.6262 2023/03/17 04:48:40 - mmengine - INFO - Epoch(train) [47][1000/5005] lr: 1.0000e-02 eta: 14:20:00 time: 0.1990 data_time: 0.0031 loss: 1.5235 2023/03/17 04:49:00 - mmengine - INFO - Epoch(train) [47][1100/5005] lr: 1.0000e-02 eta: 14:19:42 time: 0.2280 data_time: 0.0031 loss: 1.5574 2023/03/17 04:49:20 - mmengine - INFO - Epoch(train) [47][1200/5005] lr: 1.0000e-02 eta: 14:19:24 time: 0.2050 data_time: 0.0036 loss: 1.3684 2023/03/17 04:49:41 - mmengine - INFO - Epoch(train) [47][1300/5005] lr: 1.0000e-02 eta: 14:19:07 time: 0.2048 data_time: 0.0036 loss: 1.5515 2023/03/17 04:50:01 - mmengine - INFO - Epoch(train) [47][1400/5005] lr: 1.0000e-02 eta: 14:18:48 time: 0.1917 data_time: 0.0033 loss: 1.3944 2023/03/17 04:50:21 - mmengine - INFO - Epoch(train) [47][1500/5005] lr: 1.0000e-02 eta: 14:18:30 time: 0.1959 data_time: 0.0034 loss: 1.5372 2023/03/17 04:50:41 - mmengine - INFO - Epoch(train) [47][1600/5005] lr: 1.0000e-02 eta: 14:18:11 time: 0.1978 data_time: 0.0033 loss: 1.4948 2023/03/17 04:51:01 - mmengine - INFO - Epoch(train) [47][1700/5005] lr: 1.0000e-02 eta: 14:17:53 time: 0.1887 data_time: 0.0029 loss: 1.5451 2023/03/17 04:51:14 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:51:20 - mmengine - INFO - Epoch(train) [47][1800/5005] lr: 1.0000e-02 eta: 14:17:34 time: 0.1961 data_time: 0.0031 loss: 1.5970 2023/03/17 04:51:40 - mmengine - INFO - Epoch(train) [47][1900/5005] lr: 1.0000e-02 eta: 14:17:16 time: 0.2006 data_time: 0.0027 loss: 1.2878 2023/03/17 04:52:01 - mmengine - INFO - Epoch(train) [47][2000/5005] lr: 1.0000e-02 eta: 14:16:58 time: 0.2005 data_time: 0.0037 loss: 1.4523 2023/03/17 04:52:21 - mmengine - INFO - Epoch(train) [47][2100/5005] lr: 1.0000e-02 eta: 14:16:40 time: 0.1844 data_time: 0.0033 loss: 1.3058 2023/03/17 04:52:40 - mmengine - INFO - Epoch(train) [47][2200/5005] lr: 1.0000e-02 eta: 14:16:20 time: 0.1762 data_time: 0.0034 loss: 1.4443 2023/03/17 04:52:59 - mmengine - INFO - Epoch(train) [47][2300/5005] lr: 1.0000e-02 eta: 14:16:01 time: 0.2163 data_time: 0.0034 loss: 1.3951 2023/03/17 04:53:19 - mmengine - INFO - Epoch(train) [47][2400/5005] lr: 1.0000e-02 eta: 14:15:43 time: 0.1955 data_time: 0.0035 loss: 1.5720 2023/03/17 04:53:38 - mmengine - INFO - Epoch(train) [47][2500/5005] lr: 1.0000e-02 eta: 14:15:23 time: 0.1866 data_time: 0.0033 loss: 1.4508 2023/03/17 04:53:57 - mmengine - INFO - Epoch(train) [47][2600/5005] lr: 1.0000e-02 eta: 14:15:04 time: 0.1834 data_time: 0.0030 loss: 1.4842 2023/03/17 04:54:16 - mmengine - INFO - Epoch(train) [47][2700/5005] lr: 1.0000e-02 eta: 14:14:44 time: 0.1867 data_time: 0.0031 loss: 1.4465 2023/03/17 04:54:29 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:54:35 - mmengine - INFO - Epoch(train) [47][2800/5005] lr: 1.0000e-02 eta: 14:14:25 time: 0.1944 data_time: 0.0034 loss: 1.4552 2023/03/17 04:54:56 - mmengine - INFO - Epoch(train) [47][2900/5005] lr: 1.0000e-02 eta: 14:14:07 time: 0.2229 data_time: 0.0032 loss: 1.3835 2023/03/17 04:55:15 - mmengine - INFO - Epoch(train) [47][3000/5005] lr: 1.0000e-02 eta: 14:13:48 time: 0.1947 data_time: 0.0030 loss: 1.4384 2023/03/17 04:55:34 - mmengine - INFO - Epoch(train) [47][3100/5005] lr: 1.0000e-02 eta: 14:13:29 time: 0.1840 data_time: 0.0029 loss: 1.4921 2023/03/17 04:55:53 - mmengine - INFO - Epoch(train) [47][3200/5005] lr: 1.0000e-02 eta: 14:13:10 time: 0.1857 data_time: 0.0030 loss: 1.3500 2023/03/17 04:56:13 - mmengine - INFO - Epoch(train) [47][3300/5005] lr: 1.0000e-02 eta: 14:12:51 time: 0.2096 data_time: 0.0033 loss: 1.6354 2023/03/17 04:56:33 - mmengine - INFO - Epoch(train) [47][3400/5005] lr: 1.0000e-02 eta: 14:12:32 time: 0.1975 data_time: 0.0030 loss: 1.3069 2023/03/17 04:56:53 - mmengine - INFO - Epoch(train) [47][3500/5005] lr: 1.0000e-02 eta: 14:12:14 time: 0.1958 data_time: 0.0027 loss: 1.4152 2023/03/17 04:57:13 - mmengine - INFO - Epoch(train) [47][3600/5005] lr: 1.0000e-02 eta: 14:11:56 time: 0.2076 data_time: 0.0032 loss: 1.3036 2023/03/17 04:57:33 - mmengine - INFO - Epoch(train) [47][3700/5005] lr: 1.0000e-02 eta: 14:11:37 time: 0.1990 data_time: 0.0027 loss: 1.3369 2023/03/17 04:57:47 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 04:57:53 - mmengine - INFO - Epoch(train) [47][3800/5005] lr: 1.0000e-02 eta: 14:11:19 time: 0.2005 data_time: 0.0028 loss: 1.5090 2023/03/17 04:58:13 - mmengine - INFO - Epoch(train) [47][3900/5005] lr: 1.0000e-02 eta: 14:11:01 time: 0.2247 data_time: 0.0027 loss: 1.4740 2023/03/17 04:58:33 - mmengine - INFO - Epoch(train) [47][4000/5005] lr: 1.0000e-02 eta: 14:10:43 time: 0.1953 data_time: 0.0028 loss: 1.4017 2023/03/17 04:58:53 - mmengine - INFO - Epoch(train) [47][4100/5005] lr: 1.0000e-02 eta: 14:10:24 time: 0.2038 data_time: 0.0032 loss: 1.3497 2023/03/17 04:59:13 - mmengine - INFO - Epoch(train) [47][4200/5005] lr: 1.0000e-02 eta: 14:10:06 time: 0.1914 data_time: 0.0030 loss: 1.4346 2023/03/17 04:59:33 - mmengine - INFO - Epoch(train) [47][4300/5005] lr: 1.0000e-02 eta: 14:09:48 time: 0.2065 data_time: 0.0029 loss: 1.3080 2023/03/17 04:59:53 - mmengine - INFO - Epoch(train) [47][4400/5005] lr: 1.0000e-02 eta: 14:09:29 time: 0.1887 data_time: 0.0036 loss: 1.4463 2023/03/17 05:00:12 - mmengine - INFO - Epoch(train) [47][4500/5005] lr: 1.0000e-02 eta: 14:09:10 time: 0.1928 data_time: 0.0037 loss: 1.6410 2023/03/17 05:00:31 - mmengine - INFO - Epoch(train) [47][4600/5005] lr: 1.0000e-02 eta: 14:08:51 time: 0.1930 data_time: 0.0033 loss: 1.5767 2023/03/17 05:00:51 - mmengine - INFO - Epoch(train) [47][4700/5005] lr: 1.0000e-02 eta: 14:08:32 time: 0.1920 data_time: 0.0030 loss: 1.5552 2023/03/17 05:01:04 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:01:10 - mmengine - INFO - Epoch(train) [47][4800/5005] lr: 1.0000e-02 eta: 14:08:12 time: 0.1866 data_time: 0.0029 loss: 1.3894 2023/03/17 05:01:29 - mmengine - INFO - Epoch(train) [47][4900/5005] lr: 1.0000e-02 eta: 14:07:53 time: 0.1861 data_time: 0.0030 loss: 1.4606 2023/03/17 05:01:49 - mmengine - INFO - Epoch(train) [47][5000/5005] lr: 1.0000e-02 eta: 14:07:34 time: 0.2166 data_time: 0.0043 loss: 1.4228 2023/03/17 05:01:50 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:01:50 - mmengine - INFO - Saving checkpoint at 47 epochs 2023/03/17 05:01:56 - mmengine - INFO - Epoch(val) [47][100/196] eta: 0:00:05 time: 0.0441 data_time: 0.0009 2023/03/17 05:02:24 - mmengine - INFO - Epoch(val) [47][196/196] accuracy/top1: 69.9240 accuracy/top5: 89.9280data_time: 0.0358 time: 0.0655 2023/03/17 05:02:44 - mmengine - INFO - Epoch(train) [48][ 100/5005] lr: 1.0000e-02 eta: 14:07:15 time: 0.1852 data_time: 0.0029 loss: 1.4792 2023/03/17 05:03:03 - mmengine - INFO - Epoch(train) [48][ 200/5005] lr: 1.0000e-02 eta: 14:06:55 time: 0.1977 data_time: 0.0030 loss: 1.4166 2023/03/17 05:03:22 - mmengine - INFO - Epoch(train) [48][ 300/5005] lr: 1.0000e-02 eta: 14:06:36 time: 0.1865 data_time: 0.0031 loss: 1.4696 2023/03/17 05:03:41 - mmengine - INFO - Epoch(train) [48][ 400/5005] lr: 1.0000e-02 eta: 14:06:16 time: 0.2196 data_time: 0.0032 loss: 1.1895 2023/03/17 05:04:00 - mmengine - INFO - Epoch(train) [48][ 500/5005] lr: 1.0000e-02 eta: 14:05:57 time: 0.1835 data_time: 0.0032 loss: 1.3675 2023/03/17 05:04:19 - mmengine - INFO - Epoch(train) [48][ 600/5005] lr: 1.0000e-02 eta: 14:05:38 time: 0.1834 data_time: 0.0033 loss: 1.1635 2023/03/17 05:04:38 - mmengine - INFO - Epoch(train) [48][ 700/5005] lr: 1.0000e-02 eta: 14:05:18 time: 0.1780 data_time: 0.0034 loss: 1.4518 2023/03/17 05:04:49 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:04:56 - mmengine - INFO - Epoch(train) [48][ 800/5005] lr: 1.0000e-02 eta: 14:04:57 time: 0.1845 data_time: 0.0041 loss: 1.5164 2023/03/17 05:05:15 - mmengine - INFO - Epoch(train) [48][ 900/5005] lr: 1.0000e-02 eta: 14:04:39 time: 0.2275 data_time: 0.0033 loss: 1.4170 2023/03/17 05:05:36 - mmengine - INFO - Epoch(train) [48][1000/5005] lr: 1.0000e-02 eta: 14:04:21 time: 0.2015 data_time: 0.0033 loss: 1.3965 2023/03/17 05:05:57 - mmengine - INFO - Epoch(train) [48][1100/5005] lr: 1.0000e-02 eta: 14:04:04 time: 0.2096 data_time: 0.0032 loss: 1.4643 2023/03/17 05:06:18 - mmengine - INFO - Epoch(train) [48][1200/5005] lr: 1.0000e-02 eta: 14:03:46 time: 0.2296 data_time: 0.0029 loss: 1.4238 2023/03/17 05:06:38 - mmengine - INFO - Epoch(train) [48][1300/5005] lr: 1.0000e-02 eta: 14:03:28 time: 0.1965 data_time: 0.0031 loss: 1.4631 2023/03/17 05:06:57 - mmengine - INFO - Epoch(train) [48][1400/5005] lr: 1.0000e-02 eta: 14:03:08 time: 0.1838 data_time: 0.0034 loss: 1.5331 2023/03/17 05:07:17 - mmengine - INFO - Epoch(train) [48][1500/5005] lr: 1.0000e-02 eta: 14:02:50 time: 0.2091 data_time: 0.0035 loss: 1.6077 2023/03/17 05:07:37 - mmengine - INFO - Epoch(train) [48][1600/5005] lr: 1.0000e-02 eta: 14:02:31 time: 0.1878 data_time: 0.0034 loss: 1.4322 2023/03/17 05:07:57 - mmengine - INFO - Epoch(train) [48][1700/5005] lr: 1.0000e-02 eta: 14:02:13 time: 0.2151 data_time: 0.0037 loss: 1.4356 2023/03/17 05:08:09 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:08:15 - mmengine - INFO - Epoch(train) [48][1800/5005] lr: 1.0000e-02 eta: 14:01:53 time: 0.1900 data_time: 0.0032 loss: 1.3374 2023/03/17 05:08:35 - mmengine - INFO - Epoch(train) [48][1900/5005] lr: 1.0000e-02 eta: 14:01:35 time: 0.1951 data_time: 0.0033 loss: 1.4382 2023/03/17 05:08:56 - mmengine - INFO - Epoch(train) [48][2000/5005] lr: 1.0000e-02 eta: 14:01:17 time: 0.2130 data_time: 0.0031 loss: 1.3839 2023/03/17 05:09:15 - mmengine - INFO - Epoch(train) [48][2100/5005] lr: 1.0000e-02 eta: 14:00:58 time: 0.1921 data_time: 0.0035 loss: 1.4664 2023/03/17 05:09:35 - mmengine - INFO - Epoch(train) [48][2200/5005] lr: 1.0000e-02 eta: 14:00:40 time: 0.1915 data_time: 0.0037 loss: 1.4907 2023/03/17 05:09:56 - mmengine - INFO - Epoch(train) [48][2300/5005] lr: 1.0000e-02 eta: 14:00:22 time: 0.2432 data_time: 0.0033 loss: 1.4454 2023/03/17 05:10:16 - mmengine - INFO - Epoch(train) [48][2400/5005] lr: 1.0000e-02 eta: 14:00:04 time: 0.1813 data_time: 0.0032 loss: 1.4071 2023/03/17 05:10:34 - mmengine - INFO - Epoch(train) [48][2500/5005] lr: 1.0000e-02 eta: 13:59:43 time: 0.1768 data_time: 0.0031 loss: 1.3156 2023/03/17 05:10:53 - mmengine - INFO - Epoch(train) [48][2600/5005] lr: 1.0000e-02 eta: 13:59:23 time: 0.1889 data_time: 0.0036 loss: 1.4640 2023/03/17 05:11:13 - mmengine - INFO - Epoch(train) [48][2700/5005] lr: 1.0000e-02 eta: 13:59:06 time: 0.1950 data_time: 0.0033 loss: 1.1724 2023/03/17 05:11:26 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:11:33 - mmengine - INFO - Epoch(train) [48][2800/5005] lr: 1.0000e-02 eta: 13:58:47 time: 0.1889 data_time: 0.0032 loss: 1.3376 2023/03/17 05:11:51 - mmengine - INFO - Epoch(train) [48][2900/5005] lr: 1.0000e-02 eta: 13:58:27 time: 0.1678 data_time: 0.0032 loss: 1.4226 2023/03/17 05:12:09 - mmengine - INFO - Epoch(train) [48][3000/5005] lr: 1.0000e-02 eta: 13:58:06 time: 0.1790 data_time: 0.0029 loss: 1.3716 2023/03/17 05:12:29 - mmengine - INFO - Epoch(train) [48][3100/5005] lr: 1.0000e-02 eta: 13:57:47 time: 0.1896 data_time: 0.0029 loss: 1.5246 2023/03/17 05:12:49 - mmengine - INFO - Epoch(train) [48][3200/5005] lr: 1.0000e-02 eta: 13:57:29 time: 0.2214 data_time: 0.0029 loss: 1.5175 2023/03/17 05:13:10 - mmengine - INFO - Epoch(train) [48][3300/5005] lr: 1.0000e-02 eta: 13:57:12 time: 0.2024 data_time: 0.0030 loss: 1.3617 2023/03/17 05:13:30 - mmengine - INFO - Epoch(train) [48][3400/5005] lr: 1.0000e-02 eta: 13:56:54 time: 0.1946 data_time: 0.0030 loss: 1.4921 2023/03/17 05:13:50 - mmengine - INFO - Epoch(train) [48][3500/5005] lr: 1.0000e-02 eta: 13:56:35 time: 0.1919 data_time: 0.0032 loss: 1.4912 2023/03/17 05:14:10 - mmengine - INFO - Epoch(train) [48][3600/5005] lr: 1.0000e-02 eta: 13:56:16 time: 0.2082 data_time: 0.0028 loss: 1.1408 2023/03/17 05:14:29 - mmengine - INFO - Epoch(train) [48][3700/5005] lr: 1.0000e-02 eta: 13:55:57 time: 0.1757 data_time: 0.0036 loss: 1.5960 2023/03/17 05:14:40 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:14:47 - mmengine - INFO - Epoch(train) [48][3800/5005] lr: 1.0000e-02 eta: 13:55:36 time: 0.1834 data_time: 0.0031 loss: 1.3956 2023/03/17 05:15:08 - mmengine - INFO - Epoch(train) [48][3900/5005] lr: 1.0000e-02 eta: 13:55:19 time: 0.1739 data_time: 0.0031 loss: 1.3673 2023/03/17 05:15:26 - mmengine - INFO - Epoch(train) [48][4000/5005] lr: 1.0000e-02 eta: 13:54:58 time: 0.1706 data_time: 0.0035 loss: 1.5380 2023/03/17 05:15:44 - mmengine - INFO - Epoch(train) [48][4100/5005] lr: 1.0000e-02 eta: 13:54:38 time: 0.1828 data_time: 0.0032 loss: 1.4654 2023/03/17 05:16:03 - mmengine - INFO - Epoch(train) [48][4200/5005] lr: 1.0000e-02 eta: 13:54:18 time: 0.1935 data_time: 0.0031 loss: 1.5307 2023/03/17 05:16:24 - mmengine - INFO - Epoch(train) [48][4300/5005] lr: 1.0000e-02 eta: 13:54:01 time: 0.2580 data_time: 0.0029 loss: 1.5013 2023/03/17 05:16:45 - mmengine - INFO - Epoch(train) [48][4400/5005] lr: 1.0000e-02 eta: 13:53:44 time: 0.2271 data_time: 0.0029 loss: 1.2708 2023/03/17 05:17:05 - mmengine - INFO - Epoch(train) [48][4500/5005] lr: 1.0000e-02 eta: 13:53:26 time: 0.1846 data_time: 0.0033 loss: 1.3200 2023/03/17 05:17:24 - mmengine - INFO - Epoch(train) [48][4600/5005] lr: 1.0000e-02 eta: 13:53:06 time: 0.1769 data_time: 0.0037 loss: 1.3125 2023/03/17 05:17:41 - mmengine - INFO - Epoch(train) [48][4700/5005] lr: 1.0000e-02 eta: 13:52:45 time: 0.1809 data_time: 0.0032 loss: 1.3044 2023/03/17 05:17:53 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:17:59 - mmengine - INFO - Epoch(train) [48][4800/5005] lr: 1.0000e-02 eta: 13:52:24 time: 0.1916 data_time: 0.0035 loss: 1.3120 2023/03/17 05:18:19 - mmengine - INFO - Epoch(train) [48][4900/5005] lr: 1.0000e-02 eta: 13:52:05 time: 0.1838 data_time: 0.0032 loss: 1.3367 2023/03/17 05:18:37 - mmengine - INFO - Epoch(train) [48][5000/5005] lr: 1.0000e-02 eta: 13:51:45 time: 0.1871 data_time: 0.0045 loss: 1.3214 2023/03/17 05:18:38 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:18:39 - mmengine - INFO - Saving checkpoint at 48 epochs 2023/03/17 05:18:46 - mmengine - INFO - Epoch(val) [48][100/196] eta: 0:00:05 time: 0.0581 data_time: 0.0008 2023/03/17 05:19:14 - mmengine - INFO - Epoch(val) [48][196/196] accuracy/top1: 70.0200 accuracy/top5: 89.9280data_time: 0.0120 time: 0.0426 2023/03/17 05:19:40 - mmengine - INFO - Epoch(train) [49][ 100/5005] lr: 1.0000e-02 eta: 13:51:33 time: 0.2579 data_time: 0.0032 loss: 1.3982 2023/03/17 05:20:00 - mmengine - INFO - Epoch(train) [49][ 200/5005] lr: 1.0000e-02 eta: 13:51:14 time: 0.1985 data_time: 0.0036 loss: 1.5035 2023/03/17 05:20:21 - mmengine - INFO - Epoch(train) [49][ 300/5005] lr: 1.0000e-02 eta: 13:50:56 time: 0.1855 data_time: 0.0038 loss: 1.4412 2023/03/17 05:20:39 - mmengine - INFO - Epoch(train) [49][ 400/5005] lr: 1.0000e-02 eta: 13:50:36 time: 0.1802 data_time: 0.0033 loss: 1.4065 2023/03/17 05:20:58 - mmengine - INFO - Epoch(train) [49][ 500/5005] lr: 1.0000e-02 eta: 13:50:17 time: 0.1860 data_time: 0.0030 loss: 1.2191 2023/03/17 05:21:16 - mmengine - INFO - Epoch(train) [49][ 600/5005] lr: 1.0000e-02 eta: 13:49:57 time: 0.1827 data_time: 0.0033 loss: 1.4378 2023/03/17 05:21:36 - mmengine - INFO - Epoch(train) [49][ 700/5005] lr: 1.0000e-02 eta: 13:49:38 time: 0.1869 data_time: 0.0031 loss: 1.3422 2023/03/17 05:21:48 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:21:55 - mmengine - INFO - Epoch(train) [49][ 800/5005] lr: 1.0000e-02 eta: 13:49:18 time: 0.1811 data_time: 0.0031 loss: 1.4912 2023/03/17 05:22:12 - mmengine - INFO - Epoch(train) [49][ 900/5005] lr: 1.0000e-02 eta: 13:48:57 time: 0.1749 data_time: 0.0029 loss: 1.3868 2023/03/17 05:22:31 - mmengine - INFO - Epoch(train) [49][1000/5005] lr: 1.0000e-02 eta: 13:48:37 time: 0.1847 data_time: 0.0033 loss: 1.3730 2023/03/17 05:22:50 - mmengine - INFO - Epoch(train) [49][1100/5005] lr: 1.0000e-02 eta: 13:48:18 time: 0.1838 data_time: 0.0033 loss: 1.4167 2023/03/17 05:23:08 - mmengine - INFO - Epoch(train) [49][1200/5005] lr: 1.0000e-02 eta: 13:47:58 time: 0.1772 data_time: 0.0032 loss: 1.3729 2023/03/17 05:23:26 - mmengine - INFO - Epoch(train) [49][1300/5005] lr: 1.0000e-02 eta: 13:47:37 time: 0.2130 data_time: 0.0034 loss: 1.3204 2023/03/17 05:23:45 - mmengine - INFO - Epoch(train) [49][1400/5005] lr: 1.0000e-02 eta: 13:47:17 time: 0.1748 data_time: 0.0034 loss: 1.4294 2023/03/17 05:24:04 - mmengine - INFO - Epoch(train) [49][1500/5005] lr: 1.0000e-02 eta: 13:46:58 time: 0.1836 data_time: 0.0036 loss: 1.4897 2023/03/17 05:24:23 - mmengine - INFO - Epoch(train) [49][1600/5005] lr: 1.0000e-02 eta: 13:46:38 time: 0.1817 data_time: 0.0038 loss: 1.4705 2023/03/17 05:24:42 - mmengine - INFO - Epoch(train) [49][1700/5005] lr: 1.0000e-02 eta: 13:46:19 time: 0.1988 data_time: 0.0038 loss: 1.4146 2023/03/17 05:24:54 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:25:01 - mmengine - INFO - Epoch(train) [49][1800/5005] lr: 1.0000e-02 eta: 13:45:59 time: 0.1800 data_time: 0.0034 loss: 1.5254 2023/03/17 05:25:20 - mmengine - INFO - Epoch(train) [49][1900/5005] lr: 1.0000e-02 eta: 13:45:39 time: 0.1782 data_time: 0.0030 loss: 1.4383 2023/03/17 05:25:39 - mmengine - INFO - Epoch(train) [49][2000/5005] lr: 1.0000e-02 eta: 13:45:20 time: 0.1848 data_time: 0.0033 loss: 1.4790 2023/03/17 05:25:57 - mmengine - INFO - Epoch(train) [49][2100/5005] lr: 1.0000e-02 eta: 13:45:00 time: 0.1812 data_time: 0.0031 loss: 1.2262 2023/03/17 05:26:16 - mmengine - INFO - Epoch(train) [49][2200/5005] lr: 1.0000e-02 eta: 13:44:41 time: 0.1917 data_time: 0.0034 loss: 1.3611 2023/03/17 05:26:36 - mmengine - INFO - Epoch(train) [49][2300/5005] lr: 1.0000e-02 eta: 13:44:22 time: 0.1863 data_time: 0.0031 loss: 1.4282 2023/03/17 05:26:55 - mmengine - INFO - Epoch(train) [49][2400/5005] lr: 1.0000e-02 eta: 13:44:03 time: 0.1930 data_time: 0.0036 loss: 1.4959 2023/03/17 05:27:14 - mmengine - INFO - Epoch(train) [49][2500/5005] lr: 1.0000e-02 eta: 13:43:43 time: 0.1866 data_time: 0.0044 loss: 1.5029 2023/03/17 05:27:34 - mmengine - INFO - Epoch(train) [49][2600/5005] lr: 1.0000e-02 eta: 13:43:25 time: 0.1972 data_time: 0.0036 loss: 1.2463 2023/03/17 05:27:54 - mmengine - INFO - Epoch(train) [49][2700/5005] lr: 1.0000e-02 eta: 13:43:07 time: 0.1953 data_time: 0.0034 loss: 1.3850 2023/03/17 05:28:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:28:14 - mmengine - INFO - Epoch(train) [49][2800/5005] lr: 1.0000e-02 eta: 13:42:48 time: 0.2024 data_time: 0.0032 loss: 1.4698 2023/03/17 05:28:35 - mmengine - INFO - Epoch(train) [49][2900/5005] lr: 1.0000e-02 eta: 13:42:30 time: 0.2051 data_time: 0.0037 loss: 1.2916 2023/03/17 05:28:56 - mmengine - INFO - Epoch(train) [49][3000/5005] lr: 1.0000e-02 eta: 13:42:13 time: 0.1974 data_time: 0.0035 loss: 1.3424 2023/03/17 05:29:17 - mmengine - INFO - Epoch(train) [49][3100/5005] lr: 1.0000e-02 eta: 13:41:56 time: 0.2034 data_time: 0.0034 loss: 1.4388 2023/03/17 05:29:38 - mmengine - INFO - Epoch(train) [49][3200/5005] lr: 1.0000e-02 eta: 13:41:38 time: 0.2048 data_time: 0.0037 loss: 1.5073 2023/03/17 05:29:58 - mmengine - INFO - Epoch(train) [49][3300/5005] lr: 1.0000e-02 eta: 13:41:20 time: 0.2181 data_time: 0.0038 loss: 1.3196 2023/03/17 05:30:17 - mmengine - INFO - Epoch(train) [49][3400/5005] lr: 1.0000e-02 eta: 13:41:01 time: 0.1898 data_time: 0.0034 loss: 1.6920 2023/03/17 05:30:38 - mmengine - INFO - Epoch(train) [49][3500/5005] lr: 1.0000e-02 eta: 13:40:43 time: 0.2091 data_time: 0.0036 loss: 1.4028 2023/03/17 05:30:59 - mmengine - INFO - Epoch(train) [49][3600/5005] lr: 1.0000e-02 eta: 13:40:26 time: 0.1957 data_time: 0.0034 loss: 1.4727 2023/03/17 05:31:19 - mmengine - INFO - Epoch(train) [49][3700/5005] lr: 1.0000e-02 eta: 13:40:07 time: 0.2083 data_time: 0.0030 loss: 1.3883 2023/03/17 05:31:32 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:31:39 - mmengine - INFO - Epoch(train) [49][3800/5005] lr: 1.0000e-02 eta: 13:39:49 time: 0.1920 data_time: 0.0030 loss: 1.4567 2023/03/17 05:31:59 - mmengine - INFO - Epoch(train) [49][3900/5005] lr: 1.0000e-02 eta: 13:39:30 time: 0.1931 data_time: 0.0030 loss: 1.5291 2023/03/17 05:32:18 - mmengine - INFO - Epoch(train) [49][4000/5005] lr: 1.0000e-02 eta: 13:39:11 time: 0.1928 data_time: 0.0030 loss: 1.3786 2023/03/17 05:32:38 - mmengine - INFO - Epoch(train) [49][4100/5005] lr: 1.0000e-02 eta: 13:38:53 time: 0.1960 data_time: 0.0030 loss: 1.3574 2023/03/17 05:32:58 - mmengine - INFO - Epoch(train) [49][4200/5005] lr: 1.0000e-02 eta: 13:38:34 time: 0.1889 data_time: 0.0033 loss: 1.3220 2023/03/17 05:33:17 - mmengine - INFO - Epoch(train) [49][4300/5005] lr: 1.0000e-02 eta: 13:38:15 time: 0.1985 data_time: 0.0031 loss: 1.5111 2023/03/17 05:33:37 - mmengine - INFO - Epoch(train) [49][4400/5005] lr: 1.0000e-02 eta: 13:37:57 time: 0.1974 data_time: 0.0034 loss: 1.2097 2023/03/17 05:33:58 - mmengine - INFO - Epoch(train) [49][4500/5005] lr: 1.0000e-02 eta: 13:37:39 time: 0.2057 data_time: 0.0036 loss: 1.5411 2023/03/17 05:34:18 - mmengine - INFO - Epoch(train) [49][4600/5005] lr: 1.0000e-02 eta: 13:37:20 time: 0.1981 data_time: 0.0036 loss: 1.3130 2023/03/17 05:34:38 - mmengine - INFO - Epoch(train) [49][4700/5005] lr: 1.0000e-02 eta: 13:37:02 time: 0.1974 data_time: 0.0035 loss: 1.5237 2023/03/17 05:34:50 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:34:59 - mmengine - INFO - Epoch(train) [49][4800/5005] lr: 1.0000e-02 eta: 13:36:44 time: 0.2114 data_time: 0.0031 loss: 1.4593 2023/03/17 05:35:19 - mmengine - INFO - Epoch(train) [49][4900/5005] lr: 1.0000e-02 eta: 13:36:26 time: 0.1956 data_time: 0.0034 loss: 1.4805 2023/03/17 05:35:39 - mmengine - INFO - Epoch(train) [49][5000/5005] lr: 1.0000e-02 eta: 13:36:08 time: 0.2206 data_time: 0.0042 loss: 1.2747 2023/03/17 05:35:40 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:35:41 - mmengine - INFO - Saving checkpoint at 49 epochs 2023/03/17 05:35:47 - mmengine - INFO - Epoch(val) [49][100/196] eta: 0:00:05 time: 0.0483 data_time: 0.0009 2023/03/17 05:36:14 - mmengine - INFO - Epoch(val) [49][196/196] accuracy/top1: 69.7080 accuracy/top5: 89.8800data_time: 0.0104 time: 0.0411 2023/03/17 05:36:35 - mmengine - INFO - Epoch(train) [50][ 100/5005] lr: 1.0000e-02 eta: 13:35:50 time: 0.2017 data_time: 0.0031 loss: 1.4849 2023/03/17 05:36:55 - mmengine - INFO - Epoch(train) [50][ 200/5005] lr: 1.0000e-02 eta: 13:35:31 time: 0.1828 data_time: 0.0031 loss: 1.3494 2023/03/17 05:37:13 - mmengine - INFO - Epoch(train) [50][ 300/5005] lr: 1.0000e-02 eta: 13:35:10 time: 0.1811 data_time: 0.0033 loss: 1.4092 2023/03/17 05:37:31 - mmengine - INFO - Epoch(train) [50][ 400/5005] lr: 1.0000e-02 eta: 13:34:50 time: 0.1808 data_time: 0.0037 loss: 1.4638 2023/03/17 05:37:51 - mmengine - INFO - Epoch(train) [50][ 500/5005] lr: 1.0000e-02 eta: 13:34:31 time: 0.1828 data_time: 0.0033 loss: 1.4407 2023/03/17 05:38:10 - mmengine - INFO - Epoch(train) [50][ 600/5005] lr: 1.0000e-02 eta: 13:34:12 time: 0.2262 data_time: 0.0037 loss: 1.3533 2023/03/17 05:38:29 - mmengine - INFO - Epoch(train) [50][ 700/5005] lr: 1.0000e-02 eta: 13:33:53 time: 0.1842 data_time: 0.0043 loss: 1.4893 2023/03/17 05:38:39 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:38:48 - mmengine - INFO - Epoch(train) [50][ 800/5005] lr: 1.0000e-02 eta: 13:33:33 time: 0.1979 data_time: 0.0032 loss: 1.3524 2023/03/17 05:39:09 - mmengine - INFO - Epoch(train) [50][ 900/5005] lr: 1.0000e-02 eta: 13:33:16 time: 0.2280 data_time: 0.0033 loss: 1.5284 2023/03/17 05:39:29 - mmengine - INFO - Epoch(train) [50][1000/5005] lr: 1.0000e-02 eta: 13:32:57 time: 0.1870 data_time: 0.0038 loss: 1.3770 2023/03/17 05:39:48 - mmengine - INFO - Epoch(train) [50][1100/5005] lr: 1.0000e-02 eta: 13:32:38 time: 0.1924 data_time: 0.0030 loss: 1.3201 2023/03/17 05:40:07 - mmengine - INFO - Epoch(train) [50][1200/5005] lr: 1.0000e-02 eta: 13:32:19 time: 0.1880 data_time: 0.0030 loss: 1.5155 2023/03/17 05:40:26 - mmengine - INFO - Epoch(train) [50][1300/5005] lr: 1.0000e-02 eta: 13:31:59 time: 0.1832 data_time: 0.0032 loss: 1.3693 2023/03/17 05:40:45 - mmengine - INFO - Epoch(train) [50][1400/5005] lr: 1.0000e-02 eta: 13:31:39 time: 0.1922 data_time: 0.0035 loss: 1.6701 2023/03/17 05:41:05 - mmengine - INFO - Epoch(train) [50][1500/5005] lr: 1.0000e-02 eta: 13:31:20 time: 0.1920 data_time: 0.0034 loss: 1.5995 2023/03/17 05:41:24 - mmengine - INFO - Epoch(train) [50][1600/5005] lr: 1.0000e-02 eta: 13:31:02 time: 0.1971 data_time: 0.0035 loss: 1.4751 2023/03/17 05:41:44 - mmengine - INFO - Epoch(train) [50][1700/5005] lr: 1.0000e-02 eta: 13:30:43 time: 0.1917 data_time: 0.0033 loss: 1.3175 2023/03/17 05:41:55 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:42:03 - mmengine - INFO - Epoch(train) [50][1800/5005] lr: 1.0000e-02 eta: 13:30:23 time: 0.1910 data_time: 0.0030 loss: 1.4460 2023/03/17 05:42:23 - mmengine - INFO - Epoch(train) [50][1900/5005] lr: 1.0000e-02 eta: 13:30:05 time: 0.2269 data_time: 0.0033 loss: 1.3225 2023/03/17 05:42:43 - mmengine - INFO - Epoch(train) [50][2000/5005] lr: 1.0000e-02 eta: 13:29:47 time: 0.1954 data_time: 0.0034 loss: 1.3222 2023/03/17 05:43:02 - mmengine - INFO - Epoch(train) [50][2100/5005] lr: 1.0000e-02 eta: 13:29:27 time: 0.1720 data_time: 0.0033 loss: 1.2306 2023/03/17 05:43:20 - mmengine - INFO - Epoch(train) [50][2200/5005] lr: 1.0000e-02 eta: 13:29:06 time: 0.1804 data_time: 0.0038 loss: 1.5392 2023/03/17 05:43:37 - mmengine - INFO - Epoch(train) [50][2300/5005] lr: 1.0000e-02 eta: 13:28:45 time: 0.1760 data_time: 0.0035 loss: 1.3741 2023/03/17 05:43:56 - mmengine - INFO - Epoch(train) [50][2400/5005] lr: 1.0000e-02 eta: 13:28:25 time: 0.1799 data_time: 0.0032 loss: 1.7199 2023/03/17 05:44:15 - mmengine - INFO - Epoch(train) [50][2500/5005] lr: 1.0000e-02 eta: 13:28:06 time: 0.1887 data_time: 0.0029 loss: 1.3744 2023/03/17 05:44:34 - mmengine - INFO - Epoch(train) [50][2600/5005] lr: 1.0000e-02 eta: 13:27:46 time: 0.1949 data_time: 0.0030 loss: 1.4313 2023/03/17 05:44:53 - mmengine - INFO - Epoch(train) [50][2700/5005] lr: 1.0000e-02 eta: 13:27:27 time: 0.1788 data_time: 0.0033 loss: 1.3538 2023/03/17 05:45:04 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:45:13 - mmengine - INFO - Epoch(train) [50][2800/5005] lr: 1.0000e-02 eta: 13:27:08 time: 0.1936 data_time: 0.0029 loss: 1.4116 2023/03/17 05:45:31 - mmengine - INFO - Epoch(train) [50][2900/5005] lr: 1.0000e-02 eta: 13:26:48 time: 0.1845 data_time: 0.0030 loss: 1.5340 2023/03/17 05:45:49 - mmengine - INFO - Epoch(train) [50][3000/5005] lr: 1.0000e-02 eta: 13:26:28 time: 0.1861 data_time: 0.0031 loss: 1.6456 2023/03/17 05:46:08 - mmengine - INFO - Epoch(train) [50][3100/5005] lr: 1.0000e-02 eta: 13:26:08 time: 0.1778 data_time: 0.0031 loss: 1.2843 2023/03/17 05:46:27 - mmengine - INFO - Epoch(train) [50][3200/5005] lr: 1.0000e-02 eta: 13:25:48 time: 0.2092 data_time: 0.0030 loss: 1.3506 2023/03/17 05:46:46 - mmengine - INFO - Epoch(train) [50][3300/5005] lr: 1.0000e-02 eta: 13:25:29 time: 0.2019 data_time: 0.0034 loss: 1.8102 2023/03/17 05:47:06 - mmengine - INFO - Epoch(train) [50][3400/5005] lr: 1.0000e-02 eta: 13:25:11 time: 0.2010 data_time: 0.0033 loss: 1.4965 2023/03/17 05:47:26 - mmengine - INFO - Epoch(train) [50][3500/5005] lr: 1.0000e-02 eta: 13:24:52 time: 0.1867 data_time: 0.0029 loss: 1.1580 2023/03/17 05:47:46 - mmengine - INFO - Epoch(train) [50][3600/5005] lr: 1.0000e-02 eta: 13:24:33 time: 0.2242 data_time: 0.0029 loss: 1.4509 2023/03/17 05:48:05 - mmengine - INFO - Epoch(train) [50][3700/5005] lr: 1.0000e-02 eta: 13:24:14 time: 0.1950 data_time: 0.0031 loss: 1.4748 2023/03/17 05:48:16 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:48:25 - mmengine - INFO - Epoch(train) [50][3800/5005] lr: 1.0000e-02 eta: 13:23:55 time: 0.1980 data_time: 0.0036 loss: 1.3109 2023/03/17 05:48:45 - mmengine - INFO - Epoch(train) [50][3900/5005] lr: 1.0000e-02 eta: 13:23:38 time: 0.2017 data_time: 0.0027 loss: 1.4909 2023/03/17 05:49:06 - mmengine - INFO - Epoch(train) [50][4000/5005] lr: 1.0000e-02 eta: 13:23:20 time: 0.2172 data_time: 0.0037 loss: 1.1815 2023/03/17 05:49:27 - mmengine - INFO - Epoch(train) [50][4100/5005] lr: 1.0000e-02 eta: 13:23:02 time: 0.2031 data_time: 0.0032 loss: 1.5388 2023/03/17 05:49:47 - mmengine - INFO - Epoch(train) [50][4200/5005] lr: 1.0000e-02 eta: 13:22:44 time: 0.1985 data_time: 0.0035 loss: 1.2884 2023/03/17 05:50:07 - mmengine - INFO - Epoch(train) [50][4300/5005] lr: 1.0000e-02 eta: 13:22:25 time: 0.1898 data_time: 0.0034 loss: 1.4042 2023/03/17 05:50:26 - mmengine - INFO - Epoch(train) [50][4400/5005] lr: 1.0000e-02 eta: 13:22:06 time: 0.1962 data_time: 0.0036 loss: 1.4447 2023/03/17 05:50:46 - mmengine - INFO - Epoch(train) [50][4500/5005] lr: 1.0000e-02 eta: 13:21:47 time: 0.2011 data_time: 0.0037 loss: 1.2883 2023/03/17 05:51:06 - mmengine - INFO - Epoch(train) [50][4600/5005] lr: 1.0000e-02 eta: 13:21:29 time: 0.1949 data_time: 0.0032 loss: 1.3625 2023/03/17 05:51:25 - mmengine - INFO - Epoch(train) [50][4700/5005] lr: 1.0000e-02 eta: 13:21:10 time: 0.1889 data_time: 0.0031 loss: 1.2830 2023/03/17 05:51:36 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:51:45 - mmengine - INFO - Epoch(train) [50][4800/5005] lr: 1.0000e-02 eta: 13:20:51 time: 0.2025 data_time: 0.0036 loss: 1.3117 2023/03/17 05:52:04 - mmengine - INFO - Epoch(train) [50][4900/5005] lr: 1.0000e-02 eta: 13:20:32 time: 0.1973 data_time: 0.0034 loss: 1.3788 2023/03/17 05:52:25 - mmengine - INFO - Epoch(train) [50][5000/5005] lr: 1.0000e-02 eta: 13:20:14 time: 0.2284 data_time: 0.0041 loss: 1.4685 2023/03/17 05:52:26 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:52:27 - mmengine - INFO - Saving checkpoint at 50 epochs 2023/03/17 05:52:33 - mmengine - INFO - Epoch(val) [50][100/196] eta: 0:00:05 time: 0.0502 data_time: 0.0009 2023/03/17 05:53:00 - mmengine - INFO - Epoch(val) [50][196/196] accuracy/top1: 69.0440 accuracy/top5: 89.3720data_time: 0.0294 time: 0.0618 2023/03/17 05:53:23 - mmengine - INFO - Epoch(train) [51][ 100/5005] lr: 1.0000e-02 eta: 13:19:58 time: 0.2029 data_time: 0.0034 loss: 1.3475 2023/03/17 05:53:42 - mmengine - INFO - Epoch(train) [51][ 200/5005] lr: 1.0000e-02 eta: 13:19:38 time: 0.1972 data_time: 0.0032 loss: 1.3280 2023/03/17 05:54:04 - mmengine - INFO - Epoch(train) [51][ 300/5005] lr: 1.0000e-02 eta: 13:19:21 time: 0.2074 data_time: 0.0038 loss: 1.3177 2023/03/17 05:54:24 - mmengine - INFO - Epoch(train) [51][ 400/5005] lr: 1.0000e-02 eta: 13:19:03 time: 0.2050 data_time: 0.0034 loss: 1.3541 2023/03/17 05:54:45 - mmengine - INFO - Epoch(train) [51][ 500/5005] lr: 1.0000e-02 eta: 13:18:46 time: 0.2146 data_time: 0.0036 loss: 1.2452 2023/03/17 05:55:06 - mmengine - INFO - Epoch(train) [51][ 600/5005] lr: 1.0000e-02 eta: 13:18:28 time: 0.1938 data_time: 0.0037 loss: 1.4740 2023/03/17 05:55:25 - mmengine - INFO - Epoch(train) [51][ 700/5005] lr: 1.0000e-02 eta: 13:18:08 time: 0.1766 data_time: 0.0036 loss: 1.3882 2023/03/17 05:55:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:55:43 - mmengine - INFO - Epoch(train) [51][ 800/5005] lr: 1.0000e-02 eta: 13:17:48 time: 0.1833 data_time: 0.0039 loss: 1.3201 2023/03/17 05:56:02 - mmengine - INFO - Epoch(train) [51][ 900/5005] lr: 1.0000e-02 eta: 13:17:29 time: 0.2225 data_time: 0.0037 loss: 1.4608 2023/03/17 05:56:22 - mmengine - INFO - Epoch(train) [51][1000/5005] lr: 1.0000e-02 eta: 13:17:10 time: 0.1866 data_time: 0.0038 loss: 1.4216 2023/03/17 05:56:41 - mmengine - INFO - Epoch(train) [51][1100/5005] lr: 1.0000e-02 eta: 13:16:50 time: 0.1872 data_time: 0.0036 loss: 1.3970 2023/03/17 05:57:02 - mmengine - INFO - Epoch(train) [51][1200/5005] lr: 1.0000e-02 eta: 13:16:33 time: 0.2168 data_time: 0.0035 loss: 1.5019 2023/03/17 05:57:23 - mmengine - INFO - Epoch(train) [51][1300/5005] lr: 1.0000e-02 eta: 13:16:16 time: 0.2103 data_time: 0.0038 loss: 1.3781 2023/03/17 05:57:43 - mmengine - INFO - Epoch(train) [51][1400/5005] lr: 1.0000e-02 eta: 13:15:57 time: 0.1990 data_time: 0.0037 loss: 1.4378 2023/03/17 05:58:02 - mmengine - INFO - Epoch(train) [51][1500/5005] lr: 1.0000e-02 eta: 13:15:37 time: 0.1852 data_time: 0.0032 loss: 1.3685 2023/03/17 05:58:19 - mmengine - INFO - Epoch(train) [51][1600/5005] lr: 1.0000e-02 eta: 13:15:17 time: 0.1693 data_time: 0.0033 loss: 1.4909 2023/03/17 05:58:37 - mmengine - INFO - Epoch(train) [51][1700/5005] lr: 1.0000e-02 eta: 13:14:56 time: 0.1829 data_time: 0.0038 loss: 1.2446 2023/03/17 05:58:47 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 05:58:56 - mmengine - INFO - Epoch(train) [51][1800/5005] lr: 1.0000e-02 eta: 13:14:36 time: 0.1921 data_time: 0.0036 loss: 1.4998 2023/03/17 05:59:16 - mmengine - INFO - Epoch(train) [51][1900/5005] lr: 1.0000e-02 eta: 13:14:18 time: 0.2006 data_time: 0.0038 loss: 1.3414 2023/03/17 05:59:35 - mmengine - INFO - Epoch(train) [51][2000/5005] lr: 1.0000e-02 eta: 13:13:58 time: 0.1805 data_time: 0.0037 loss: 1.4583 2023/03/17 05:59:56 - mmengine - INFO - Epoch(train) [51][2100/5005] lr: 1.0000e-02 eta: 13:13:40 time: 0.1889 data_time: 0.0036 loss: 1.4773 2023/03/17 06:00:15 - mmengine - INFO - Epoch(train) [51][2200/5005] lr: 1.0000e-02 eta: 13:13:21 time: 0.1901 data_time: 0.0040 loss: 1.5505 2023/03/17 06:00:33 - mmengine - INFO - Epoch(train) [51][2300/5005] lr: 1.0000e-02 eta: 13:13:01 time: 0.1802 data_time: 0.0034 loss: 1.5458 2023/03/17 06:00:52 - mmengine - INFO - Epoch(train) [51][2400/5005] lr: 1.0000e-02 eta: 13:12:41 time: 0.1858 data_time: 0.0039 loss: 1.5475 2023/03/17 06:01:11 - mmengine - INFO - Epoch(train) [51][2500/5005] lr: 1.0000e-02 eta: 13:12:22 time: 0.1825 data_time: 0.0032 loss: 1.3466 2023/03/17 06:01:31 - mmengine - INFO - Epoch(train) [51][2600/5005] lr: 1.0000e-02 eta: 13:12:03 time: 0.1789 data_time: 0.0032 loss: 1.4324 2023/03/17 06:01:49 - mmengine - INFO - Epoch(train) [51][2700/5005] lr: 1.0000e-02 eta: 13:11:42 time: 0.1817 data_time: 0.0031 loss: 1.2980 2023/03/17 06:01:58 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:02:08 - mmengine - INFO - Epoch(train) [51][2800/5005] lr: 1.0000e-02 eta: 13:11:23 time: 0.2093 data_time: 0.0031 loss: 1.4725 2023/03/17 06:02:28 - mmengine - INFO - Epoch(train) [51][2900/5005] lr: 1.0000e-02 eta: 13:11:05 time: 0.1824 data_time: 0.0036 loss: 1.4865 2023/03/17 06:02:47 - mmengine - INFO - Epoch(train) [51][3000/5005] lr: 1.0000e-02 eta: 13:10:45 time: 0.1802 data_time: 0.0041 loss: 1.4735 2023/03/17 06:03:05 - mmengine - INFO - Epoch(train) [51][3100/5005] lr: 1.0000e-02 eta: 13:10:25 time: 0.1871 data_time: 0.0038 loss: 1.4890 2023/03/17 06:03:24 - mmengine - INFO - Epoch(train) [51][3200/5005] lr: 1.0000e-02 eta: 13:10:06 time: 0.1838 data_time: 0.0035 loss: 1.4771 2023/03/17 06:03:44 - mmengine - INFO - Epoch(train) [51][3300/5005] lr: 1.0000e-02 eta: 13:09:47 time: 0.1877 data_time: 0.0032 loss: 1.6453 2023/03/17 06:04:03 - mmengine - INFO - Epoch(train) [51][3400/5005] lr: 1.0000e-02 eta: 13:09:27 time: 0.1868 data_time: 0.0033 loss: 1.5674 2023/03/17 06:04:23 - mmengine - INFO - Epoch(train) [51][3500/5005] lr: 1.0000e-02 eta: 13:09:09 time: 0.1908 data_time: 0.0030 loss: 1.4211 2023/03/17 06:04:42 - mmengine - INFO - Epoch(train) [51][3600/5005] lr: 1.0000e-02 eta: 13:08:49 time: 0.1964 data_time: 0.0035 loss: 1.4846 2023/03/17 06:05:02 - mmengine - INFO - Epoch(train) [51][3700/5005] lr: 1.0000e-02 eta: 13:08:31 time: 0.2010 data_time: 0.0032 loss: 1.2531 2023/03/17 06:05:12 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:05:22 - mmengine - INFO - Epoch(train) [51][3800/5005] lr: 1.0000e-02 eta: 13:08:13 time: 0.2022 data_time: 0.0034 loss: 1.3222 2023/03/17 06:05:42 - mmengine - INFO - Epoch(train) [51][3900/5005] lr: 1.0000e-02 eta: 13:07:54 time: 0.1853 data_time: 0.0035 loss: 1.3363 2023/03/17 06:06:01 - mmengine - INFO - Epoch(train) [51][4000/5005] lr: 1.0000e-02 eta: 13:07:35 time: 0.1956 data_time: 0.0035 loss: 1.1081 2023/03/17 06:06:22 - mmengine - INFO - Epoch(train) [51][4100/5005] lr: 1.0000e-02 eta: 13:07:17 time: 0.2191 data_time: 0.0032 loss: 1.3729 2023/03/17 06:06:43 - mmengine - INFO - Epoch(train) [51][4200/5005] lr: 1.0000e-02 eta: 13:06:59 time: 0.2153 data_time: 0.0034 loss: 1.3516 2023/03/17 06:07:02 - mmengine - INFO - Epoch(train) [51][4300/5005] lr: 1.0000e-02 eta: 13:06:40 time: 0.1950 data_time: 0.0035 loss: 1.3965 2023/03/17 06:07:22 - mmengine - INFO - Epoch(train) [51][4400/5005] lr: 1.0000e-02 eta: 13:06:21 time: 0.1902 data_time: 0.0034 loss: 1.3859 2023/03/17 06:07:41 - mmengine - INFO - Epoch(train) [51][4500/5005] lr: 1.0000e-02 eta: 13:06:02 time: 0.1908 data_time: 0.0034 loss: 1.6367 2023/03/17 06:08:01 - mmengine - INFO - Epoch(train) [51][4600/5005] lr: 1.0000e-02 eta: 13:05:43 time: 0.2017 data_time: 0.0038 loss: 1.4271 2023/03/17 06:08:22 - mmengine - INFO - Epoch(train) [51][4700/5005] lr: 1.0000e-02 eta: 13:05:25 time: 0.2198 data_time: 0.0034 loss: 1.2421 2023/03/17 06:08:32 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:08:41 - mmengine - INFO - Epoch(train) [51][4800/5005] lr: 1.0000e-02 eta: 13:05:06 time: 0.1827 data_time: 0.0034 loss: 1.4172 2023/03/17 06:08:59 - mmengine - INFO - Epoch(train) [51][4900/5005] lr: 1.0000e-02 eta: 13:04:46 time: 0.1827 data_time: 0.0034 loss: 1.3381 2023/03/17 06:09:18 - mmengine - INFO - Epoch(train) [51][5000/5005] lr: 1.0000e-02 eta: 13:04:26 time: 0.1893 data_time: 0.0040 loss: 1.4143 2023/03/17 06:09:19 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:09:20 - mmengine - INFO - Saving checkpoint at 51 epochs 2023/03/17 06:09:26 - mmengine - INFO - Epoch(val) [51][100/196] eta: 0:00:04 time: 0.0453 data_time: 0.0008 2023/03/17 06:09:52 - mmengine - INFO - Epoch(val) [51][196/196] accuracy/top1: 69.8560 accuracy/top5: 89.6660data_time: 0.0171 time: 0.0546 2023/03/17 06:10:14 - mmengine - INFO - Epoch(train) [52][ 100/5005] lr: 1.0000e-02 eta: 13:04:09 time: 0.1768 data_time: 0.0032 loss: 1.4313 2023/03/17 06:10:33 - mmengine - INFO - Epoch(train) [52][ 200/5005] lr: 1.0000e-02 eta: 13:03:49 time: 0.1914 data_time: 0.0035 loss: 1.5219 2023/03/17 06:10:51 - mmengine - INFO - Epoch(train) [52][ 300/5005] lr: 1.0000e-02 eta: 13:03:29 time: 0.1791 data_time: 0.0032 loss: 1.3624 2023/03/17 06:11:09 - mmengine - INFO - Epoch(train) [52][ 400/5005] lr: 1.0000e-02 eta: 13:03:08 time: 0.2007 data_time: 0.0036 loss: 1.3460 2023/03/17 06:11:30 - mmengine - INFO - Epoch(train) [52][ 500/5005] lr: 1.0000e-02 eta: 13:02:51 time: 0.1935 data_time: 0.0036 loss: 1.3196 2023/03/17 06:11:48 - mmengine - INFO - Epoch(train) [52][ 600/5005] lr: 1.0000e-02 eta: 13:02:31 time: 0.1770 data_time: 0.0038 loss: 1.3962 2023/03/17 06:12:07 - mmengine - INFO - Epoch(train) [52][ 700/5005] lr: 1.0000e-02 eta: 13:02:11 time: 0.1799 data_time: 0.0034 loss: 1.2767 2023/03/17 06:12:15 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:12:26 - mmengine - INFO - Epoch(train) [52][ 800/5005] lr: 1.0000e-02 eta: 13:01:51 time: 0.1839 data_time: 0.0031 loss: 1.4305 2023/03/17 06:12:44 - mmengine - INFO - Epoch(train) [52][ 900/5005] lr: 1.0000e-02 eta: 13:01:31 time: 0.1832 data_time: 0.0037 loss: 1.5988 2023/03/17 06:13:02 - mmengine - INFO - Epoch(train) [52][1000/5005] lr: 1.0000e-02 eta: 13:01:11 time: 0.1803 data_time: 0.0029 loss: 1.2734 2023/03/17 06:13:21 - mmengine - INFO - Epoch(train) [52][1100/5005] lr: 1.0000e-02 eta: 13:00:51 time: 0.1885 data_time: 0.0035 loss: 1.4157 2023/03/17 06:13:41 - mmengine - INFO - Epoch(train) [52][1200/5005] lr: 1.0000e-02 eta: 13:00:32 time: 0.1976 data_time: 0.0031 loss: 1.4182 2023/03/17 06:14:01 - mmengine - INFO - Epoch(train) [52][1300/5005] lr: 1.0000e-02 eta: 13:00:14 time: 0.1916 data_time: 0.0039 loss: 1.3100 2023/03/17 06:14:20 - mmengine - INFO - Epoch(train) [52][1400/5005] lr: 1.0000e-02 eta: 12:59:54 time: 0.1857 data_time: 0.0037 loss: 1.2272 2023/03/17 06:14:44 - mmengine - INFO - Epoch(train) [52][1500/5005] lr: 1.0000e-02 eta: 12:59:40 time: 0.1958 data_time: 0.0035 loss: 1.3609 2023/03/17 06:15:03 - mmengine - INFO - Epoch(train) [52][1600/5005] lr: 1.0000e-02 eta: 12:59:20 time: 0.1792 data_time: 0.0034 loss: 1.5057 2023/03/17 06:15:22 - mmengine - INFO - Epoch(train) [52][1700/5005] lr: 1.0000e-02 eta: 12:59:01 time: 0.1761 data_time: 0.0036 loss: 1.4255 2023/03/17 06:15:30 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:15:40 - mmengine - INFO - Epoch(train) [52][1800/5005] lr: 1.0000e-02 eta: 12:58:41 time: 0.1838 data_time: 0.0036 loss: 1.3802 2023/03/17 06:15:58 - mmengine - INFO - Epoch(train) [52][1900/5005] lr: 1.0000e-02 eta: 12:58:20 time: 0.1799 data_time: 0.0036 loss: 1.3738 2023/03/17 06:16:17 - mmengine - INFO - Epoch(train) [52][2000/5005] lr: 1.0000e-02 eta: 12:58:00 time: 0.1914 data_time: 0.0035 loss: 1.3942 2023/03/17 06:16:36 - mmengine - INFO - Epoch(train) [52][2100/5005] lr: 1.0000e-02 eta: 12:57:41 time: 0.1881 data_time: 0.0039 loss: 1.4264 2023/03/17 06:16:55 - mmengine - INFO - Epoch(train) [52][2200/5005] lr: 1.0000e-02 eta: 12:57:21 time: 0.1803 data_time: 0.0033 loss: 1.4032 2023/03/17 06:17:14 - mmengine - INFO - Epoch(train) [52][2300/5005] lr: 1.0000e-02 eta: 12:57:02 time: 0.1851 data_time: 0.0040 loss: 1.2991 2023/03/17 06:17:35 - mmengine - INFO - Epoch(train) [52][2400/5005] lr: 1.0000e-02 eta: 12:56:44 time: 0.2615 data_time: 0.0031 loss: 1.2433 2023/03/17 06:17:59 - mmengine - INFO - Epoch(train) [52][2500/5005] lr: 1.0000e-02 eta: 12:56:30 time: 0.1886 data_time: 0.0036 loss: 1.3293 2023/03/17 06:18:19 - mmengine - INFO - Epoch(train) [52][2600/5005] lr: 1.0000e-02 eta: 12:56:11 time: 0.1877 data_time: 0.0035 loss: 1.4461 2023/03/17 06:18:38 - mmengine - INFO - Epoch(train) [52][2700/5005] lr: 1.0000e-02 eta: 12:55:52 time: 0.1919 data_time: 0.0038 loss: 1.2060 2023/03/17 06:18:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:18:57 - mmengine - INFO - Epoch(train) [52][2800/5005] lr: 1.0000e-02 eta: 12:55:32 time: 0.1904 data_time: 0.0038 loss: 1.3809 2023/03/17 06:19:16 - mmengine - INFO - Epoch(train) [52][2900/5005] lr: 1.0000e-02 eta: 12:55:13 time: 0.1883 data_time: 0.0034 loss: 1.3625 2023/03/17 06:19:35 - mmengine - INFO - Epoch(train) [52][3000/5005] lr: 1.0000e-02 eta: 12:54:53 time: 0.1942 data_time: 0.0030 loss: 1.3656 2023/03/17 06:19:54 - mmengine - INFO - Epoch(train) [52][3100/5005] lr: 1.0000e-02 eta: 12:54:34 time: 0.1959 data_time: 0.0035 loss: 1.4647 2023/03/17 06:20:13 - mmengine - INFO - Epoch(train) [52][3200/5005] lr: 1.0000e-02 eta: 12:54:15 time: 0.1847 data_time: 0.0039 loss: 1.2405 2023/03/17 06:20:32 - mmengine - INFO - Epoch(train) [52][3300/5005] lr: 1.0000e-02 eta: 12:53:55 time: 0.1922 data_time: 0.0034 loss: 1.3494 2023/03/17 06:20:52 - mmengine - INFO - Epoch(train) [52][3400/5005] lr: 1.0000e-02 eta: 12:53:36 time: 0.2324 data_time: 0.0032 loss: 1.5202 2023/03/17 06:21:11 - mmengine - INFO - Epoch(train) [52][3500/5005] lr: 1.0000e-02 eta: 12:53:17 time: 0.1871 data_time: 0.0033 loss: 1.3082 2023/03/17 06:21:30 - mmengine - INFO - Epoch(train) [52][3600/5005] lr: 1.0000e-02 eta: 12:52:58 time: 0.1993 data_time: 0.0034 loss: 1.2620 2023/03/17 06:21:51 - mmengine - INFO - Epoch(train) [52][3700/5005] lr: 1.0000e-02 eta: 12:52:39 time: 0.2515 data_time: 0.0031 loss: 1.2417 2023/03/17 06:22:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:22:12 - mmengine - INFO - Epoch(train) [52][3800/5005] lr: 1.0000e-02 eta: 12:52:22 time: 0.2246 data_time: 0.0033 loss: 1.4998 2023/03/17 06:22:32 - mmengine - INFO - Epoch(train) [52][3900/5005] lr: 1.0000e-02 eta: 12:52:03 time: 0.1855 data_time: 0.0037 loss: 1.4294 2023/03/17 06:22:53 - mmengine - INFO - Epoch(train) [52][4000/5005] lr: 1.0000e-02 eta: 12:51:45 time: 0.2546 data_time: 0.0033 loss: 1.5950 2023/03/17 06:23:15 - mmengine - INFO - Epoch(train) [52][4100/5005] lr: 1.0000e-02 eta: 12:51:29 time: 0.1944 data_time: 0.0037 loss: 1.2263 2023/03/17 06:23:36 - mmengine - INFO - Epoch(train) [52][4200/5005] lr: 1.0000e-02 eta: 12:51:11 time: 0.1916 data_time: 0.0037 loss: 1.3856 2023/03/17 06:23:56 - mmengine - INFO - Epoch(train) [52][4300/5005] lr: 1.0000e-02 eta: 12:50:53 time: 0.1982 data_time: 0.0033 loss: 1.3312 2023/03/17 06:24:15 - mmengine - INFO - Epoch(train) [52][4400/5005] lr: 1.0000e-02 eta: 12:50:33 time: 0.1801 data_time: 0.0036 loss: 1.3273 2023/03/17 06:24:32 - mmengine - INFO - Epoch(train) [52][4500/5005] lr: 1.0000e-02 eta: 12:50:13 time: 0.1777 data_time: 0.0037 loss: 1.4253 2023/03/17 06:24:51 - mmengine - INFO - Epoch(train) [52][4600/5005] lr: 1.0000e-02 eta: 12:49:53 time: 0.1826 data_time: 0.0034 loss: 1.4470 2023/03/17 06:25:11 - mmengine - INFO - Epoch(train) [52][4700/5005] lr: 1.0000e-02 eta: 12:49:34 time: 0.1853 data_time: 0.0035 loss: 1.3857 2023/03/17 06:25:19 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:25:29 - mmengine - INFO - Epoch(train) [52][4800/5005] lr: 1.0000e-02 eta: 12:49:14 time: 0.2144 data_time: 0.0034 loss: 1.4202 2023/03/17 06:25:49 - mmengine - INFO - Epoch(train) [52][4900/5005] lr: 1.0000e-02 eta: 12:48:55 time: 0.1858 data_time: 0.0042 loss: 1.3097 2023/03/17 06:26:08 - mmengine - INFO - Epoch(train) [52][5000/5005] lr: 1.0000e-02 eta: 12:48:35 time: 0.1979 data_time: 0.0043 loss: 1.5262 2023/03/17 06:26:09 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:26:10 - mmengine - INFO - Saving checkpoint at 52 epochs 2023/03/17 06:26:16 - mmengine - INFO - Epoch(val) [52][100/196] eta: 0:00:04 time: 0.0438 data_time: 0.0024 2023/03/17 06:26:45 - mmengine - INFO - Epoch(val) [52][196/196] accuracy/top1: 69.6700 accuracy/top5: 89.6360data_time: 0.0113 time: 0.0450 2023/03/17 06:27:05 - mmengine - INFO - Epoch(train) [53][ 100/5005] lr: 1.0000e-02 eta: 12:48:16 time: 0.1804 data_time: 0.0031 loss: 1.4471 2023/03/17 06:27:24 - mmengine - INFO - Epoch(train) [53][ 200/5005] lr: 1.0000e-02 eta: 12:47:57 time: 0.1938 data_time: 0.0035 loss: 1.4622 2023/03/17 06:27:45 - mmengine - INFO - Epoch(train) [53][ 300/5005] lr: 1.0000e-02 eta: 12:47:40 time: 0.1858 data_time: 0.0033 loss: 1.3741 2023/03/17 06:28:04 - mmengine - INFO - Epoch(train) [53][ 400/5005] lr: 1.0000e-02 eta: 12:47:20 time: 0.1833 data_time: 0.0035 loss: 1.2059 2023/03/17 06:28:28 - mmengine - INFO - Epoch(train) [53][ 500/5005] lr: 1.0000e-02 eta: 12:47:05 time: 0.2502 data_time: 0.0033 loss: 1.4508 2023/03/17 06:28:48 - mmengine - INFO - Epoch(train) [53][ 600/5005] lr: 1.0000e-02 eta: 12:46:46 time: 0.1850 data_time: 0.0036 loss: 1.5309 2023/03/17 06:29:08 - mmengine - INFO - Epoch(train) [53][ 700/5005] lr: 1.0000e-02 eta: 12:46:28 time: 0.2166 data_time: 0.0035 loss: 1.2790 2023/03/17 06:29:17 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:29:29 - mmengine - INFO - Epoch(train) [53][ 800/5005] lr: 1.0000e-02 eta: 12:46:11 time: 0.2113 data_time: 0.0028 loss: 1.2751 2023/03/17 06:29:51 - mmengine - INFO - Epoch(train) [53][ 900/5005] lr: 1.0000e-02 eta: 12:45:54 time: 0.2237 data_time: 0.0032 loss: 1.5733 2023/03/17 06:30:12 - mmengine - INFO - Epoch(train) [53][1000/5005] lr: 1.0000e-02 eta: 12:45:36 time: 0.2269 data_time: 0.0030 loss: 1.3193 2023/03/17 06:30:34 - mmengine - INFO - Epoch(train) [53][1100/5005] lr: 1.0000e-02 eta: 12:45:19 time: 0.2394 data_time: 0.0034 loss: 1.4368 2023/03/17 06:30:56 - mmengine - INFO - Epoch(train) [53][1200/5005] lr: 1.0000e-02 eta: 12:45:02 time: 0.1986 data_time: 0.0033 loss: 1.2708 2023/03/17 06:31:16 - mmengine - INFO - Epoch(train) [53][1300/5005] lr: 1.0000e-02 eta: 12:44:44 time: 0.2115 data_time: 0.0037 loss: 1.3375 2023/03/17 06:31:38 - mmengine - INFO - Epoch(train) [53][1400/5005] lr: 1.0000e-02 eta: 12:44:27 time: 0.2455 data_time: 0.0029 loss: 1.3511 2023/03/17 06:32:00 - mmengine - INFO - Epoch(train) [53][1500/5005] lr: 1.0000e-02 eta: 12:44:10 time: 0.1868 data_time: 0.0034 loss: 1.4119 2023/03/17 06:32:21 - mmengine - INFO - Epoch(train) [53][1600/5005] lr: 1.0000e-02 eta: 12:43:52 time: 0.1883 data_time: 0.0030 loss: 1.6754 2023/03/17 06:32:46 - mmengine - INFO - Epoch(train) [53][1700/5005] lr: 1.0000e-02 eta: 12:43:38 time: 0.2208 data_time: 0.0033 loss: 1.4001 2023/03/17 06:32:56 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:33:07 - mmengine - INFO - Epoch(train) [53][1800/5005] lr: 1.0000e-02 eta: 12:43:21 time: 0.1801 data_time: 0.0036 loss: 1.3686 2023/03/17 06:33:24 - mmengine - INFO - Epoch(train) [53][1900/5005] lr: 1.0000e-02 eta: 12:43:00 time: 0.1672 data_time: 0.0037 loss: 1.4285 2023/03/17 06:33:42 - mmengine - INFO - Epoch(train) [53][2000/5005] lr: 1.0000e-02 eta: 12:42:39 time: 0.1720 data_time: 0.0035 loss: 1.4481 2023/03/17 06:33:59 - mmengine - INFO - Epoch(train) [53][2100/5005] lr: 1.0000e-02 eta: 12:42:18 time: 0.1689 data_time: 0.0038 loss: 1.2521 2023/03/17 06:34:17 - mmengine - INFO - Epoch(train) [53][2200/5005] lr: 1.0000e-02 eta: 12:41:57 time: 0.1733 data_time: 0.0038 loss: 1.3125 2023/03/17 06:34:35 - mmengine - INFO - Epoch(train) [53][2300/5005] lr: 1.0000e-02 eta: 12:41:37 time: 0.1793 data_time: 0.0035 loss: 1.3513 2023/03/17 06:34:53 - mmengine - INFO - Epoch(train) [53][2400/5005] lr: 1.0000e-02 eta: 12:41:16 time: 0.1734 data_time: 0.0037 loss: 1.4105 2023/03/17 06:35:15 - mmengine - INFO - Epoch(train) [53][2500/5005] lr: 1.0000e-02 eta: 12:41:00 time: 0.2351 data_time: 0.0034 loss: 1.6208 2023/03/17 06:35:35 - mmengine - INFO - Epoch(train) [53][2600/5005] lr: 1.0000e-02 eta: 12:40:41 time: 0.1827 data_time: 0.0036 loss: 1.5587 2023/03/17 06:35:53 - mmengine - INFO - Epoch(train) [53][2700/5005] lr: 1.0000e-02 eta: 12:40:21 time: 0.1763 data_time: 0.0039 loss: 1.2561 2023/03/17 06:36:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:36:11 - mmengine - INFO - Epoch(train) [53][2800/5005] lr: 1.0000e-02 eta: 12:40:00 time: 0.1797 data_time: 0.0040 loss: 1.3749 2023/03/17 06:36:30 - mmengine - INFO - Epoch(train) [53][2900/5005] lr: 1.0000e-02 eta: 12:39:41 time: 0.1836 data_time: 0.0042 loss: 1.4140 2023/03/17 06:36:49 - mmengine - INFO - Epoch(train) [53][3000/5005] lr: 1.0000e-02 eta: 12:39:21 time: 0.1797 data_time: 0.0038 loss: 1.4484 2023/03/17 06:37:07 - mmengine - INFO - Epoch(train) [53][3100/5005] lr: 1.0000e-02 eta: 12:39:01 time: 0.1784 data_time: 0.0040 loss: 1.3704 2023/03/17 06:37:24 - mmengine - INFO - Epoch(train) [53][3200/5005] lr: 1.0000e-02 eta: 12:38:40 time: 0.1769 data_time: 0.0032 loss: 1.4172 2023/03/17 06:37:43 - mmengine - INFO - Epoch(train) [53][3300/5005] lr: 1.0000e-02 eta: 12:38:20 time: 0.2003 data_time: 0.0032 loss: 1.3495 2023/03/17 06:38:02 - mmengine - INFO - Epoch(train) [53][3400/5005] lr: 1.0000e-02 eta: 12:38:01 time: 0.1745 data_time: 0.0032 loss: 1.4588 2023/03/17 06:38:20 - mmengine - INFO - Epoch(train) [53][3500/5005] lr: 1.0000e-02 eta: 12:37:40 time: 0.1806 data_time: 0.0034 loss: 1.4973 2023/03/17 06:38:38 - mmengine - INFO - Epoch(train) [53][3600/5005] lr: 1.0000e-02 eta: 12:37:20 time: 0.1897 data_time: 0.0031 loss: 1.4766 2023/03/17 06:38:58 - mmengine - INFO - Epoch(train) [53][3700/5005] lr: 1.0000e-02 eta: 12:37:01 time: 0.1992 data_time: 0.0036 loss: 1.3655 2023/03/17 06:39:06 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:39:19 - mmengine - INFO - Epoch(train) [53][3800/5005] lr: 1.0000e-02 eta: 12:36:43 time: 0.2232 data_time: 0.0040 loss: 1.6312 2023/03/17 06:39:40 - mmengine - INFO - Epoch(train) [53][3900/5005] lr: 1.0000e-02 eta: 12:36:26 time: 0.1900 data_time: 0.0032 loss: 1.3782 2023/03/17 06:39:59 - mmengine - INFO - Epoch(train) [53][4000/5005] lr: 1.0000e-02 eta: 12:36:07 time: 0.2213 data_time: 0.0032 loss: 1.5680 2023/03/17 06:40:20 - mmengine - INFO - Epoch(train) [53][4100/5005] lr: 1.0000e-02 eta: 12:35:49 time: 0.2070 data_time: 0.0034 loss: 1.3100 2023/03/17 06:40:40 - mmengine - INFO - Epoch(train) [53][4200/5005] lr: 1.0000e-02 eta: 12:35:30 time: 0.1793 data_time: 0.0033 loss: 1.4840 2023/03/17 06:41:00 - mmengine - INFO - Epoch(train) [53][4300/5005] lr: 1.0000e-02 eta: 12:35:11 time: 0.1871 data_time: 0.0035 loss: 1.3508 2023/03/17 06:41:19 - mmengine - INFO - Epoch(train) [53][4400/5005] lr: 1.0000e-02 eta: 12:34:52 time: 0.2222 data_time: 0.0027 loss: 1.3598 2023/03/17 06:41:39 - mmengine - INFO - Epoch(train) [53][4500/5005] lr: 1.0000e-02 eta: 12:34:34 time: 0.1983 data_time: 0.0035 loss: 1.2384 2023/03/17 06:42:00 - mmengine - INFO - Epoch(train) [53][4600/5005] lr: 1.0000e-02 eta: 12:34:16 time: 0.1824 data_time: 0.0032 loss: 1.3715 2023/03/17 06:42:18 - mmengine - INFO - Epoch(train) [53][4700/5005] lr: 1.0000e-02 eta: 12:33:56 time: 0.1882 data_time: 0.0033 loss: 1.3601 2023/03/17 06:42:26 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:42:39 - mmengine - INFO - Epoch(train) [53][4800/5005] lr: 1.0000e-02 eta: 12:33:38 time: 0.1848 data_time: 0.0033 loss: 1.3497 2023/03/17 06:42:57 - mmengine - INFO - Epoch(train) [53][4900/5005] lr: 1.0000e-02 eta: 12:33:17 time: 0.1793 data_time: 0.0035 loss: 1.1990 2023/03/17 06:43:15 - mmengine - INFO - Epoch(train) [53][5000/5005] lr: 1.0000e-02 eta: 12:32:57 time: 0.1821 data_time: 0.0045 loss: 1.2302 2023/03/17 06:43:16 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:43:16 - mmengine - INFO - Saving checkpoint at 53 epochs 2023/03/17 06:43:22 - mmengine - INFO - Epoch(val) [53][100/196] eta: 0:00:04 time: 0.0474 data_time: 0.0074 2023/03/17 06:43:49 - mmengine - INFO - Epoch(val) [53][196/196] accuracy/top1: 69.3740 accuracy/top5: 89.4360data_time: 0.0153 time: 0.0464 2023/03/17 06:44:09 - mmengine - INFO - Epoch(train) [54][ 100/5005] lr: 1.0000e-02 eta: 12:32:37 time: 0.2236 data_time: 0.0032 loss: 1.3879 2023/03/17 06:44:28 - mmengine - INFO - Epoch(train) [54][ 200/5005] lr: 1.0000e-02 eta: 12:32:18 time: 0.2002 data_time: 0.0032 loss: 1.4989 2023/03/17 06:44:47 - mmengine - INFO - Epoch(train) [54][ 300/5005] lr: 1.0000e-02 eta: 12:31:58 time: 0.1827 data_time: 0.0030 loss: 1.3255 2023/03/17 06:45:05 - mmengine - INFO - Epoch(train) [54][ 400/5005] lr: 1.0000e-02 eta: 12:31:38 time: 0.1839 data_time: 0.0032 loss: 1.3613 2023/03/17 06:45:23 - mmengine - INFO - Epoch(train) [54][ 500/5005] lr: 1.0000e-02 eta: 12:31:17 time: 0.1813 data_time: 0.0034 loss: 1.2771 2023/03/17 06:45:42 - mmengine - INFO - Epoch(train) [54][ 600/5005] lr: 1.0000e-02 eta: 12:30:58 time: 0.2098 data_time: 0.0036 loss: 1.2506 2023/03/17 06:46:04 - mmengine - INFO - Epoch(train) [54][ 700/5005] lr: 1.0000e-02 eta: 12:30:41 time: 0.1909 data_time: 0.0029 loss: 1.5426 2023/03/17 06:46:11 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:46:24 - mmengine - INFO - Epoch(train) [54][ 800/5005] lr: 1.0000e-02 eta: 12:30:22 time: 0.2012 data_time: 0.0041 loss: 1.3863 2023/03/17 06:46:44 - mmengine - INFO - Epoch(train) [54][ 900/5005] lr: 1.0000e-02 eta: 12:30:04 time: 0.1995 data_time: 0.0033 loss: 1.3243 2023/03/17 06:47:05 - mmengine - INFO - Epoch(train) [54][1000/5005] lr: 1.0000e-02 eta: 12:29:46 time: 0.2282 data_time: 0.0035 loss: 1.5682 2023/03/17 06:47:26 - mmengine - INFO - Epoch(train) [54][1100/5005] lr: 1.0000e-02 eta: 12:29:29 time: 0.2055 data_time: 0.0034 loss: 1.5289 2023/03/17 06:47:46 - mmengine - INFO - Epoch(train) [54][1200/5005] lr: 1.0000e-02 eta: 12:29:10 time: 0.1951 data_time: 0.0033 loss: 1.5074 2023/03/17 06:48:07 - mmengine - INFO - Epoch(train) [54][1300/5005] lr: 1.0000e-02 eta: 12:28:52 time: 0.2363 data_time: 0.0035 loss: 1.3923 2023/03/17 06:48:28 - mmengine - INFO - Epoch(train) [54][1400/5005] lr: 1.0000e-02 eta: 12:28:34 time: 0.1997 data_time: 0.0032 loss: 1.3393 2023/03/17 06:48:49 - mmengine - INFO - Epoch(train) [54][1500/5005] lr: 1.0000e-02 eta: 12:28:16 time: 0.2109 data_time: 0.0027 loss: 1.4632 2023/03/17 06:49:11 - mmengine - INFO - Epoch(train) [54][1600/5005] lr: 1.0000e-02 eta: 12:27:59 time: 0.2376 data_time: 0.0032 loss: 1.6913 2023/03/17 06:49:31 - mmengine - INFO - Epoch(train) [54][1700/5005] lr: 1.0000e-02 eta: 12:27:41 time: 0.1930 data_time: 0.0033 loss: 1.2813 2023/03/17 06:49:38 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:49:51 - mmengine - INFO - Epoch(train) [54][1800/5005] lr: 1.0000e-02 eta: 12:27:22 time: 0.1899 data_time: 0.0037 loss: 1.4634 2023/03/17 06:50:10 - mmengine - INFO - Epoch(train) [54][1900/5005] lr: 1.0000e-02 eta: 12:27:03 time: 0.1961 data_time: 0.0032 loss: 1.5405 2023/03/17 06:50:30 - mmengine - INFO - Epoch(train) [54][2000/5005] lr: 1.0000e-02 eta: 12:26:45 time: 0.1967 data_time: 0.0037 loss: 1.5621 2023/03/17 06:50:50 - mmengine - INFO - Epoch(train) [54][2100/5005] lr: 1.0000e-02 eta: 12:26:26 time: 0.1911 data_time: 0.0035 loss: 1.5026 2023/03/17 06:51:10 - mmengine - INFO - Epoch(train) [54][2200/5005] lr: 1.0000e-02 eta: 12:26:07 time: 0.1962 data_time: 0.0034 loss: 1.4741 2023/03/17 06:51:31 - mmengine - INFO - Epoch(train) [54][2300/5005] lr: 1.0000e-02 eta: 12:25:50 time: 0.2096 data_time: 0.0031 loss: 1.2302 2023/03/17 06:51:51 - mmengine - INFO - Epoch(train) [54][2400/5005] lr: 1.0000e-02 eta: 12:25:31 time: 0.1901 data_time: 0.0035 loss: 1.4240 2023/03/17 06:52:09 - mmengine - INFO - Epoch(train) [54][2500/5005] lr: 1.0000e-02 eta: 12:25:11 time: 0.1768 data_time: 0.0030 loss: 1.6653 2023/03/17 06:52:28 - mmengine - INFO - Epoch(train) [54][2600/5005] lr: 1.0000e-02 eta: 12:24:51 time: 0.2273 data_time: 0.0032 loss: 1.3114 2023/03/17 06:52:47 - mmengine - INFO - Epoch(train) [54][2700/5005] lr: 1.0000e-02 eta: 12:24:32 time: 0.1698 data_time: 0.0030 loss: 1.4583 2023/03/17 06:52:54 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:53:06 - mmengine - INFO - Epoch(train) [54][2800/5005] lr: 1.0000e-02 eta: 12:24:12 time: 0.1928 data_time: 0.0034 loss: 1.2684 2023/03/17 06:53:24 - mmengine - INFO - Epoch(train) [54][2900/5005] lr: 1.0000e-02 eta: 12:23:51 time: 0.1833 data_time: 0.0032 loss: 1.4671 2023/03/17 06:53:42 - mmengine - INFO - Epoch(train) [54][3000/5005] lr: 1.0000e-02 eta: 12:23:31 time: 0.1915 data_time: 0.0034 loss: 1.4663 2023/03/17 06:54:02 - mmengine - INFO - Epoch(train) [54][3100/5005] lr: 1.0000e-02 eta: 12:23:13 time: 0.1870 data_time: 0.0033 loss: 1.5186 2023/03/17 06:54:21 - mmengine - INFO - Epoch(train) [54][3200/5005] lr: 1.0000e-02 eta: 12:22:53 time: 0.1882 data_time: 0.0032 loss: 1.6374 2023/03/17 06:54:40 - mmengine - INFO - Epoch(train) [54][3300/5005] lr: 1.0000e-02 eta: 12:22:34 time: 0.1937 data_time: 0.0038 loss: 1.4285 2023/03/17 06:54:59 - mmengine - INFO - Epoch(train) [54][3400/5005] lr: 1.0000e-02 eta: 12:22:14 time: 0.1889 data_time: 0.0034 loss: 1.6338 2023/03/17 06:55:19 - mmengine - INFO - Epoch(train) [54][3500/5005] lr: 1.0000e-02 eta: 12:21:55 time: 0.1818 data_time: 0.0033 loss: 1.2841 2023/03/17 06:55:37 - mmengine - INFO - Epoch(train) [54][3600/5005] lr: 1.0000e-02 eta: 12:21:35 time: 0.1766 data_time: 0.0034 loss: 1.4917 2023/03/17 06:55:57 - mmengine - INFO - Epoch(train) [54][3700/5005] lr: 1.0000e-02 eta: 12:21:16 time: 0.2177 data_time: 0.0034 loss: 1.3850 2023/03/17 06:56:04 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:56:17 - mmengine - INFO - Epoch(train) [54][3800/5005] lr: 1.0000e-02 eta: 12:20:57 time: 0.1968 data_time: 0.0039 loss: 1.4453 2023/03/17 06:56:38 - mmengine - INFO - Epoch(train) [54][3900/5005] lr: 1.0000e-02 eta: 12:20:40 time: 0.2018 data_time: 0.0033 loss: 1.3538 2023/03/17 06:56:58 - mmengine - INFO - Epoch(train) [54][4000/5005] lr: 1.0000e-02 eta: 12:20:21 time: 0.1978 data_time: 0.0034 loss: 1.5261 2023/03/17 06:57:18 - mmengine - INFO - Epoch(train) [54][4100/5005] lr: 1.0000e-02 eta: 12:20:03 time: 0.1974 data_time: 0.0028 loss: 1.3595 2023/03/17 06:57:38 - mmengine - INFO - Epoch(train) [54][4200/5005] lr: 1.0000e-02 eta: 12:19:44 time: 0.1915 data_time: 0.0031 loss: 1.4099 2023/03/17 06:57:57 - mmengine - INFO - Epoch(train) [54][4300/5005] lr: 1.0000e-02 eta: 12:19:25 time: 0.1955 data_time: 0.0032 loss: 1.4010 2023/03/17 06:58:17 - mmengine - INFO - Epoch(train) [54][4400/5005] lr: 1.0000e-02 eta: 12:19:06 time: 0.2058 data_time: 0.0034 loss: 1.5711 2023/03/17 06:58:38 - mmengine - INFO - Epoch(train) [54][4500/5005] lr: 1.0000e-02 eta: 12:18:48 time: 0.2011 data_time: 0.0035 loss: 1.3717 2023/03/17 06:58:58 - mmengine - INFO - Epoch(train) [54][4600/5005] lr: 1.0000e-02 eta: 12:18:30 time: 0.2003 data_time: 0.0034 loss: 1.2983 2023/03/17 06:59:18 - mmengine - INFO - Epoch(train) [54][4700/5005] lr: 1.0000e-02 eta: 12:18:11 time: 0.1973 data_time: 0.0033 loss: 1.6270 2023/03/17 06:59:26 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 06:59:39 - mmengine - INFO - Epoch(train) [54][4800/5005] lr: 1.0000e-02 eta: 12:17:53 time: 0.2033 data_time: 0.0037 loss: 1.3406 2023/03/17 06:59:59 - mmengine - INFO - Epoch(train) [54][4900/5005] lr: 1.0000e-02 eta: 12:17:34 time: 0.1874 data_time: 0.0036 loss: 1.2275 2023/03/17 07:00:17 - mmengine - INFO - Epoch(train) [54][5000/5005] lr: 1.0000e-02 eta: 12:17:15 time: 0.1869 data_time: 0.0042 loss: 1.4342 2023/03/17 07:00:18 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:00:19 - mmengine - INFO - Saving checkpoint at 54 epochs 2023/03/17 07:00:25 - mmengine - INFO - Epoch(val) [54][100/196] eta: 0:00:05 time: 0.0447 data_time: 0.0010 2023/03/17 07:00:52 - mmengine - INFO - Epoch(val) [54][196/196] accuracy/top1: 69.2900 accuracy/top5: 89.5360data_time: 0.0217 time: 0.0516 2023/03/17 07:01:14 - mmengine - INFO - Epoch(train) [55][ 100/5005] lr: 1.0000e-02 eta: 12:16:56 time: 0.2015 data_time: 0.0036 loss: 1.3718 2023/03/17 07:01:34 - mmengine - INFO - Epoch(train) [55][ 200/5005] lr: 1.0000e-02 eta: 12:16:38 time: 0.1951 data_time: 0.0034 loss: 1.4624 2023/03/17 07:01:54 - mmengine - INFO - Epoch(train) [55][ 300/5005] lr: 1.0000e-02 eta: 12:16:19 time: 0.1990 data_time: 0.0035 loss: 1.4289 2023/03/17 07:02:15 - mmengine - INFO - Epoch(train) [55][ 400/5005] lr: 1.0000e-02 eta: 12:16:02 time: 0.1964 data_time: 0.0037 loss: 1.3040 2023/03/17 07:02:34 - mmengine - INFO - Epoch(train) [55][ 500/5005] lr: 1.0000e-02 eta: 12:15:42 time: 0.1858 data_time: 0.0035 loss: 1.3322 2023/03/17 07:02:54 - mmengine - INFO - Epoch(train) [55][ 600/5005] lr: 1.0000e-02 eta: 12:15:23 time: 0.2012 data_time: 0.0035 loss: 1.5078 2023/03/17 07:03:14 - mmengine - INFO - Epoch(train) [55][ 700/5005] lr: 1.0000e-02 eta: 12:15:05 time: 0.1955 data_time: 0.0041 loss: 1.4140 2023/03/17 07:03:21 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:03:36 - mmengine - INFO - Epoch(train) [55][ 800/5005] lr: 1.0000e-02 eta: 12:14:48 time: 0.1869 data_time: 0.0034 loss: 1.3941 2023/03/17 07:03:56 - mmengine - INFO - Epoch(train) [55][ 900/5005] lr: 1.0000e-02 eta: 12:14:29 time: 0.1930 data_time: 0.0034 loss: 1.5137 2023/03/17 07:04:16 - mmengine - INFO - Epoch(train) [55][1000/5005] lr: 1.0000e-02 eta: 12:14:10 time: 0.1894 data_time: 0.0037 loss: 1.1768 2023/03/17 07:04:35 - mmengine - INFO - Epoch(train) [55][1100/5005] lr: 1.0000e-02 eta: 12:13:51 time: 0.1953 data_time: 0.0036 loss: 1.3092 2023/03/17 07:04:56 - mmengine - INFO - Epoch(train) [55][1200/5005] lr: 1.0000e-02 eta: 12:13:32 time: 0.1854 data_time: 0.0038 loss: 1.4518 2023/03/17 07:05:14 - mmengine - INFO - Epoch(train) [55][1300/5005] lr: 1.0000e-02 eta: 12:13:12 time: 0.1856 data_time: 0.0033 loss: 1.4210 2023/03/17 07:05:32 - mmengine - INFO - Epoch(train) [55][1400/5005] lr: 1.0000e-02 eta: 12:12:52 time: 0.1779 data_time: 0.0037 loss: 1.4016 2023/03/17 07:05:50 - mmengine - INFO - Epoch(train) [55][1500/5005] lr: 1.0000e-02 eta: 12:12:32 time: 0.1914 data_time: 0.0034 loss: 1.4261 2023/03/17 07:06:09 - mmengine - INFO - Epoch(train) [55][1600/5005] lr: 1.0000e-02 eta: 12:12:13 time: 0.1993 data_time: 0.0037 loss: 1.4822 2023/03/17 07:06:29 - mmengine - INFO - Epoch(train) [55][1700/5005] lr: 1.0000e-02 eta: 12:11:54 time: 0.1929 data_time: 0.0038 loss: 1.2363 2023/03/17 07:06:35 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:06:49 - mmengine - INFO - Epoch(train) [55][1800/5005] lr: 1.0000e-02 eta: 12:11:35 time: 0.1942 data_time: 0.0034 loss: 1.4998 2023/03/17 07:07:12 - mmengine - INFO - Epoch(train) [55][1900/5005] lr: 1.0000e-02 eta: 12:11:19 time: 0.2560 data_time: 0.0031 loss: 1.4455 2023/03/17 07:07:35 - mmengine - INFO - Epoch(train) [55][2000/5005] lr: 1.0000e-02 eta: 12:11:03 time: 0.2097 data_time: 0.0034 loss: 1.4834 2023/03/17 07:07:55 - mmengine - INFO - Epoch(train) [55][2100/5005] lr: 1.0000e-02 eta: 12:10:44 time: 0.1891 data_time: 0.0036 loss: 1.4000 2023/03/17 07:08:15 - mmengine - INFO - Epoch(train) [55][2200/5005] lr: 1.0000e-02 eta: 12:10:25 time: 0.1855 data_time: 0.0034 loss: 1.4676 2023/03/17 07:08:34 - mmengine - INFO - Epoch(train) [55][2300/5005] lr: 1.0000e-02 eta: 12:10:06 time: 0.1958 data_time: 0.0035 loss: 1.2614 2023/03/17 07:08:55 - mmengine - INFO - Epoch(train) [55][2400/5005] lr: 1.0000e-02 eta: 12:09:48 time: 0.2018 data_time: 0.0034 loss: 1.3133 2023/03/17 07:09:14 - mmengine - INFO - Epoch(train) [55][2500/5005] lr: 1.0000e-02 eta: 12:09:29 time: 0.1952 data_time: 0.0035 loss: 1.3718 2023/03/17 07:09:34 - mmengine - INFO - Epoch(train) [55][2600/5005] lr: 1.0000e-02 eta: 12:09:10 time: 0.1940 data_time: 0.0034 loss: 1.3155 2023/03/17 07:09:55 - mmengine - INFO - Epoch(train) [55][2700/5005] lr: 1.0000e-02 eta: 12:08:52 time: 0.1850 data_time: 0.0039 loss: 1.4005 2023/03/17 07:10:01 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:10:16 - mmengine - INFO - Epoch(train) [55][2800/5005] lr: 1.0000e-02 eta: 12:08:34 time: 0.2174 data_time: 0.0030 loss: 1.4777 2023/03/17 07:10:38 - mmengine - INFO - Epoch(train) [55][2900/5005] lr: 1.0000e-02 eta: 12:08:17 time: 0.2303 data_time: 0.0029 loss: 1.4281 2023/03/17 07:10:58 - mmengine - INFO - Epoch(train) [55][3000/5005] lr: 1.0000e-02 eta: 12:07:59 time: 0.1841 data_time: 0.0033 loss: 1.5966 2023/03/17 07:11:17 - mmengine - INFO - Epoch(train) [55][3100/5005] lr: 1.0000e-02 eta: 12:07:39 time: 0.1849 data_time: 0.0041 loss: 1.4707 2023/03/17 07:11:37 - mmengine - INFO - Epoch(train) [55][3200/5005] lr: 1.0000e-02 eta: 12:07:21 time: 0.1939 data_time: 0.0033 loss: 1.5942 2023/03/17 07:11:57 - mmengine - INFO - Epoch(train) [55][3300/5005] lr: 1.0000e-02 eta: 12:07:02 time: 0.1949 data_time: 0.0035 loss: 1.2944 2023/03/17 07:12:17 - mmengine - INFO - Epoch(train) [55][3400/5005] lr: 1.0000e-02 eta: 12:06:43 time: 0.1928 data_time: 0.0032 loss: 1.5405 2023/03/17 07:12:36 - mmengine - INFO - Epoch(train) [55][3500/5005] lr: 1.0000e-02 eta: 12:06:24 time: 0.1905 data_time: 0.0034 loss: 1.2894 2023/03/17 07:12:56 - mmengine - INFO - Epoch(train) [55][3600/5005] lr: 1.0000e-02 eta: 12:06:05 time: 0.1911 data_time: 0.0035 loss: 1.3293 2023/03/17 07:13:16 - mmengine - INFO - Epoch(train) [55][3700/5005] lr: 1.0000e-02 eta: 12:05:46 time: 0.1910 data_time: 0.0035 loss: 1.4215 2023/03/17 07:13:22 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:13:36 - mmengine - INFO - Epoch(train) [55][3800/5005] lr: 1.0000e-02 eta: 12:05:27 time: 0.2205 data_time: 0.0032 loss: 1.4164 2023/03/17 07:13:55 - mmengine - INFO - Epoch(train) [55][3900/5005] lr: 1.0000e-02 eta: 12:05:08 time: 0.2002 data_time: 0.0035 loss: 1.7566 2023/03/17 07:14:15 - mmengine - INFO - Epoch(train) [55][4000/5005] lr: 1.0000e-02 eta: 12:04:49 time: 0.1998 data_time: 0.0034 loss: 1.4711 2023/03/17 07:14:35 - mmengine - INFO - Epoch(train) [55][4100/5005] lr: 1.0000e-02 eta: 12:04:30 time: 0.1843 data_time: 0.0034 loss: 1.4223 2023/03/17 07:14:55 - mmengine - INFO - Epoch(train) [55][4200/5005] lr: 1.0000e-02 eta: 12:04:12 time: 0.1951 data_time: 0.0035 loss: 1.1778 2023/03/17 07:15:14 - mmengine - INFO - Epoch(train) [55][4300/5005] lr: 1.0000e-02 eta: 12:03:53 time: 0.1817 data_time: 0.0039 loss: 1.3054 2023/03/17 07:15:35 - mmengine - INFO - Epoch(train) [55][4400/5005] lr: 1.0000e-02 eta: 12:03:34 time: 0.1970 data_time: 0.0036 loss: 1.4555 2023/03/17 07:15:55 - mmengine - INFO - Epoch(train) [55][4500/5005] lr: 1.0000e-02 eta: 12:03:16 time: 0.2378 data_time: 0.0033 loss: 1.4509 2023/03/17 07:16:16 - mmengine - INFO - Epoch(train) [55][4600/5005] lr: 1.0000e-02 eta: 12:02:58 time: 0.1925 data_time: 0.0032 loss: 1.1238 2023/03/17 07:16:36 - mmengine - INFO - Epoch(train) [55][4700/5005] lr: 1.0000e-02 eta: 12:02:39 time: 0.1893 data_time: 0.0038 loss: 1.4787 2023/03/17 07:16:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:16:58 - mmengine - INFO - Epoch(train) [55][4800/5005] lr: 1.0000e-02 eta: 12:02:22 time: 0.2323 data_time: 0.0034 loss: 1.2555 2023/03/17 07:17:19 - mmengine - INFO - Epoch(train) [55][4900/5005] lr: 1.0000e-02 eta: 12:02:04 time: 0.1990 data_time: 0.0029 loss: 1.5407 2023/03/17 07:17:38 - mmengine - INFO - Epoch(train) [55][5000/5005] lr: 1.0000e-02 eta: 12:01:45 time: 0.2015 data_time: 0.0046 loss: 1.3820 2023/03/17 07:17:40 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:17:40 - mmengine - INFO - Saving checkpoint at 55 epochs 2023/03/17 07:17:47 - mmengine - INFO - Epoch(val) [55][100/196] eta: 0:00:05 time: 0.0569 data_time: 0.0009 2023/03/17 07:18:15 - mmengine - INFO - Epoch(val) [55][196/196] accuracy/top1: 69.6120 accuracy/top5: 89.4020data_time: 0.0301 time: 0.0629 2023/03/17 07:18:41 - mmengine - INFO - Epoch(train) [56][ 100/5005] lr: 1.0000e-02 eta: 12:01:30 time: 0.1991 data_time: 0.0034 loss: 1.2860 2023/03/17 07:19:01 - mmengine - INFO - Epoch(train) [56][ 200/5005] lr: 1.0000e-02 eta: 12:01:11 time: 0.2139 data_time: 0.0037 loss: 1.5861 2023/03/17 07:19:23 - mmengine - INFO - Epoch(train) [56][ 300/5005] lr: 1.0000e-02 eta: 12:00:54 time: 0.2221 data_time: 0.0037 loss: 1.2725 2023/03/17 07:19:44 - mmengine - INFO - Epoch(train) [56][ 400/5005] lr: 1.0000e-02 eta: 12:00:37 time: 0.2043 data_time: 0.0034 loss: 1.4629 2023/03/17 07:20:04 - mmengine - INFO - Epoch(train) [56][ 500/5005] lr: 1.0000e-02 eta: 12:00:18 time: 0.2087 data_time: 0.0035 loss: 1.3568 2023/03/17 07:20:23 - mmengine - INFO - Epoch(train) [56][ 600/5005] lr: 1.0000e-02 eta: 11:59:59 time: 0.1666 data_time: 0.0030 loss: 1.3977 2023/03/17 07:20:41 - mmengine - INFO - Epoch(train) [56][ 700/5005] lr: 1.0000e-02 eta: 11:59:38 time: 0.1775 data_time: 0.0036 loss: 1.6787 2023/03/17 07:20:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:21:00 - mmengine - INFO - Epoch(train) [56][ 800/5005] lr: 1.0000e-02 eta: 11:59:18 time: 0.2022 data_time: 0.0035 loss: 1.5086 2023/03/17 07:21:19 - mmengine - INFO - Epoch(train) [56][ 900/5005] lr: 1.0000e-02 eta: 11:58:59 time: 0.1869 data_time: 0.0038 loss: 1.2968 2023/03/17 07:21:37 - mmengine - INFO - Epoch(train) [56][1000/5005] lr: 1.0000e-02 eta: 11:58:39 time: 0.1774 data_time: 0.0035 loss: 1.5252 2023/03/17 07:21:58 - mmengine - INFO - Epoch(train) [56][1100/5005] lr: 1.0000e-02 eta: 11:58:21 time: 0.1883 data_time: 0.0033 loss: 1.4387 2023/03/17 07:22:16 - mmengine - INFO - Epoch(train) [56][1200/5005] lr: 1.0000e-02 eta: 11:58:01 time: 0.1791 data_time: 0.0032 loss: 1.3730 2023/03/17 07:22:36 - mmengine - INFO - Epoch(train) [56][1300/5005] lr: 1.0000e-02 eta: 11:57:42 time: 0.2355 data_time: 0.0034 loss: 1.5611 2023/03/17 07:23:00 - mmengine - INFO - Epoch(train) [56][1400/5005] lr: 1.0000e-02 eta: 11:57:26 time: 0.2167 data_time: 0.0034 loss: 1.5033 2023/03/17 07:23:23 - mmengine - INFO - Epoch(train) [56][1500/5005] lr: 1.0000e-02 eta: 11:57:10 time: 0.2536 data_time: 0.0036 loss: 1.3312 2023/03/17 07:23:47 - mmengine - INFO - Epoch(train) [56][1600/5005] lr: 1.0000e-02 eta: 11:56:54 time: 0.2479 data_time: 0.0032 loss: 1.3531 2023/03/17 07:24:06 - mmengine - INFO - Epoch(train) [56][1700/5005] lr: 1.0000e-02 eta: 11:56:35 time: 0.1788 data_time: 0.0035 loss: 1.5635 2023/03/17 07:24:10 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:24:24 - mmengine - INFO - Epoch(train) [56][1800/5005] lr: 1.0000e-02 eta: 11:56:15 time: 0.1821 data_time: 0.0036 loss: 1.1980 2023/03/17 07:24:43 - mmengine - INFO - Epoch(train) [56][1900/5005] lr: 1.0000e-02 eta: 11:55:55 time: 0.2052 data_time: 0.0032 loss: 1.3241 2023/03/17 07:25:00 - mmengine - INFO - Epoch(train) [56][2000/5005] lr: 1.0000e-02 eta: 11:55:35 time: 0.1751 data_time: 0.0038 loss: 1.3586 2023/03/17 07:25:19 - mmengine - INFO - Epoch(train) [56][2100/5005] lr: 1.0000e-02 eta: 11:55:15 time: 0.1755 data_time: 0.0040 loss: 1.4365 2023/03/17 07:25:38 - mmengine - INFO - Epoch(train) [56][2200/5005] lr: 1.0000e-02 eta: 11:54:55 time: 0.2318 data_time: 0.0038 loss: 1.4723 2023/03/17 07:26:00 - mmengine - INFO - Epoch(train) [56][2300/5005] lr: 1.0000e-02 eta: 11:54:38 time: 0.1856 data_time: 0.0034 loss: 1.5667 2023/03/17 07:26:18 - mmengine - INFO - Epoch(train) [56][2400/5005] lr: 1.0000e-02 eta: 11:54:18 time: 0.1747 data_time: 0.0039 loss: 1.4562 2023/03/17 07:26:39 - mmengine - INFO - Epoch(train) [56][2500/5005] lr: 1.0000e-02 eta: 11:54:00 time: 0.2056 data_time: 0.0037 loss: 1.6410 2023/03/17 07:27:01 - mmengine - INFO - Epoch(train) [56][2600/5005] lr: 1.0000e-02 eta: 11:53:42 time: 0.1826 data_time: 0.0039 loss: 1.5039 2023/03/17 07:27:19 - mmengine - INFO - Epoch(train) [56][2700/5005] lr: 1.0000e-02 eta: 11:53:22 time: 0.1815 data_time: 0.0035 loss: 1.4634 2023/03/17 07:27:24 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:27:39 - mmengine - INFO - Epoch(train) [56][2800/5005] lr: 1.0000e-02 eta: 11:53:03 time: 0.2237 data_time: 0.0034 loss: 1.1757 2023/03/17 07:28:01 - mmengine - INFO - Epoch(train) [56][2900/5005] lr: 1.0000e-02 eta: 11:52:47 time: 0.1897 data_time: 0.0037 loss: 1.3617 2023/03/17 07:28:19 - mmengine - INFO - Epoch(train) [56][3000/5005] lr: 1.0000e-02 eta: 11:52:27 time: 0.1768 data_time: 0.0032 loss: 1.3719 2023/03/17 07:28:37 - mmengine - INFO - Epoch(train) [56][3100/5005] lr: 1.0000e-02 eta: 11:52:06 time: 0.1776 data_time: 0.0041 loss: 1.4593 2023/03/17 07:28:56 - mmengine - INFO - Epoch(train) [56][3200/5005] lr: 1.0000e-02 eta: 11:51:46 time: 0.1772 data_time: 0.0040 loss: 1.5610 2023/03/17 07:29:15 - mmengine - INFO - Epoch(train) [56][3300/5005] lr: 1.0000e-02 eta: 11:51:27 time: 0.1816 data_time: 0.0036 loss: 1.3086 2023/03/17 07:29:33 - mmengine - INFO - Epoch(train) [56][3400/5005] lr: 1.0000e-02 eta: 11:51:07 time: 0.1837 data_time: 0.0038 loss: 1.3524 2023/03/17 07:29:52 - mmengine - INFO - Epoch(train) [56][3500/5005] lr: 1.0000e-02 eta: 11:50:47 time: 0.1828 data_time: 0.0034 loss: 1.4529 2023/03/17 07:30:10 - mmengine - INFO - Epoch(train) [56][3600/5005] lr: 1.0000e-02 eta: 11:50:27 time: 0.2081 data_time: 0.0034 loss: 1.4135 2023/03/17 07:30:29 - mmengine - INFO - Epoch(train) [56][3700/5005] lr: 1.0000e-02 eta: 11:50:07 time: 0.1931 data_time: 0.0035 loss: 1.5483 2023/03/17 07:30:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:30:48 - mmengine - INFO - Epoch(train) [56][3800/5005] lr: 1.0000e-02 eta: 11:49:48 time: 0.1752 data_time: 0.0037 loss: 1.3004 2023/03/17 07:31:06 - mmengine - INFO - Epoch(train) [56][3900/5005] lr: 1.0000e-02 eta: 11:49:27 time: 0.1766 data_time: 0.0034 loss: 1.4461 2023/03/17 07:31:27 - mmengine - INFO - Epoch(train) [56][4000/5005] lr: 1.0000e-02 eta: 11:49:09 time: 0.2001 data_time: 0.0030 loss: 1.3453 2023/03/17 07:31:45 - mmengine - INFO - Epoch(train) [56][4100/5005] lr: 1.0000e-02 eta: 11:48:49 time: 0.1777 data_time: 0.0037 loss: 1.4027 2023/03/17 07:32:03 - mmengine - INFO - Epoch(train) [56][4200/5005] lr: 1.0000e-02 eta: 11:48:29 time: 0.1970 data_time: 0.0037 loss: 1.1892 2023/03/17 07:32:21 - mmengine - INFO - Epoch(train) [56][4300/5005] lr: 1.0000e-02 eta: 11:48:09 time: 0.1744 data_time: 0.0039 loss: 1.5203 2023/03/17 07:32:43 - mmengine - INFO - Epoch(train) [56][4400/5005] lr: 1.0000e-02 eta: 11:47:51 time: 0.2486 data_time: 0.0033 loss: 1.4666 2023/03/17 07:33:08 - mmengine - INFO - Epoch(train) [56][4500/5005] lr: 1.0000e-02 eta: 11:47:37 time: 0.2477 data_time: 0.0035 loss: 1.3240 2023/03/17 07:33:29 - mmengine - INFO - Epoch(train) [56][4600/5005] lr: 1.0000e-02 eta: 11:47:19 time: 0.1918 data_time: 0.0033 loss: 1.4453 2023/03/17 07:33:48 - mmengine - INFO - Epoch(train) [56][4700/5005] lr: 1.0000e-02 eta: 11:46:59 time: 0.1866 data_time: 0.0036 loss: 1.2620 2023/03/17 07:33:53 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:34:08 - mmengine - INFO - Epoch(train) [56][4800/5005] lr: 1.0000e-02 eta: 11:46:41 time: 0.2211 data_time: 0.0034 loss: 1.4012 2023/03/17 07:34:28 - mmengine - INFO - Epoch(train) [56][4900/5005] lr: 1.0000e-02 eta: 11:46:21 time: 0.1798 data_time: 0.0040 loss: 1.2837 2023/03/17 07:34:47 - mmengine - INFO - Epoch(train) [56][5000/5005] lr: 1.0000e-02 eta: 11:46:02 time: 0.1895 data_time: 0.0044 loss: 1.2990 2023/03/17 07:34:48 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:34:48 - mmengine - INFO - Saving checkpoint at 56 epochs 2023/03/17 07:34:54 - mmengine - INFO - Epoch(val) [56][100/196] eta: 0:00:04 time: 0.0461 data_time: 0.0016 2023/03/17 07:35:21 - mmengine - INFO - Epoch(val) [56][196/196] accuracy/top1: 69.9980 accuracy/top5: 89.8960data_time: 0.0189 time: 0.0550 2023/03/17 07:35:42 - mmengine - INFO - Epoch(train) [57][ 100/5005] lr: 1.0000e-02 eta: 11:45:43 time: 0.1877 data_time: 0.0032 loss: 1.7655 2023/03/17 07:36:00 - mmengine - INFO - Epoch(train) [57][ 200/5005] lr: 1.0000e-02 eta: 11:45:23 time: 0.1789 data_time: 0.0029 loss: 1.5683 2023/03/17 07:36:18 - mmengine - INFO - Epoch(train) [57][ 300/5005] lr: 1.0000e-02 eta: 11:45:02 time: 0.1889 data_time: 0.0039 loss: 1.3247 2023/03/17 07:36:37 - mmengine - INFO - Epoch(train) [57][ 400/5005] lr: 1.0000e-02 eta: 11:44:43 time: 0.1913 data_time: 0.0034 loss: 1.3376 2023/03/17 07:36:55 - mmengine - INFO - Epoch(train) [57][ 500/5005] lr: 1.0000e-02 eta: 11:44:22 time: 0.1829 data_time: 0.0032 loss: 1.5315 2023/03/17 07:37:16 - mmengine - INFO - Epoch(train) [57][ 600/5005] lr: 1.0000e-02 eta: 11:44:04 time: 0.2180 data_time: 0.0037 loss: 1.4358 2023/03/17 07:37:34 - mmengine - INFO - Epoch(train) [57][ 700/5005] lr: 1.0000e-02 eta: 11:43:44 time: 0.1818 data_time: 0.0033 loss: 1.3344 2023/03/17 07:37:38 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:37:54 - mmengine - INFO - Epoch(train) [57][ 800/5005] lr: 1.0000e-02 eta: 11:43:25 time: 0.2208 data_time: 0.0031 loss: 1.5159 2023/03/17 07:38:12 - mmengine - INFO - Epoch(train) [57][ 900/5005] lr: 1.0000e-02 eta: 11:43:05 time: 0.1864 data_time: 0.0032 loss: 1.4127 2023/03/17 07:38:31 - mmengine - INFO - Epoch(train) [57][1000/5005] lr: 1.0000e-02 eta: 11:42:45 time: 0.1835 data_time: 0.0036 loss: 1.3627 2023/03/17 07:38:49 - mmengine - INFO - Epoch(train) [57][1100/5005] lr: 1.0000e-02 eta: 11:42:26 time: 0.1795 data_time: 0.0031 loss: 1.4345 2023/03/17 07:39:09 - mmengine - INFO - Epoch(train) [57][1200/5005] lr: 1.0000e-02 eta: 11:42:06 time: 0.1908 data_time: 0.0036 loss: 1.4380 2023/03/17 07:39:29 - mmengine - INFO - Epoch(train) [57][1300/5005] lr: 1.0000e-02 eta: 11:41:47 time: 0.2078 data_time: 0.0044 loss: 1.5558 2023/03/17 07:39:46 - mmengine - INFO - Epoch(train) [57][1400/5005] lr: 1.0000e-02 eta: 11:41:27 time: 0.1743 data_time: 0.0041 loss: 1.4260 2023/03/17 07:40:04 - mmengine - INFO - Epoch(train) [57][1500/5005] lr: 1.0000e-02 eta: 11:41:06 time: 0.1755 data_time: 0.0037 loss: 1.3915 2023/03/17 07:40:22 - mmengine - INFO - Epoch(train) [57][1600/5005] lr: 1.0000e-02 eta: 11:40:46 time: 0.1814 data_time: 0.0038 loss: 1.2231 2023/03/17 07:40:40 - mmengine - INFO - Epoch(train) [57][1700/5005] lr: 1.0000e-02 eta: 11:40:26 time: 0.1786 data_time: 0.0037 loss: 1.3395 2023/03/17 07:40:44 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:40:59 - mmengine - INFO - Epoch(train) [57][1800/5005] lr: 1.0000e-02 eta: 11:40:06 time: 0.1908 data_time: 0.0034 loss: 1.4002 2023/03/17 07:41:17 - mmengine - INFO - Epoch(train) [57][1900/5005] lr: 1.0000e-02 eta: 11:39:46 time: 0.1882 data_time: 0.0041 loss: 1.4459 2023/03/17 07:41:36 - mmengine - INFO - Epoch(train) [57][2000/5005] lr: 1.0000e-02 eta: 11:39:27 time: 0.1856 data_time: 0.0035 loss: 1.3607 2023/03/17 07:41:57 - mmengine - INFO - Epoch(train) [57][2100/5005] lr: 1.0000e-02 eta: 11:39:08 time: 0.1966 data_time: 0.0035 loss: 1.3156 2023/03/17 07:42:16 - mmengine - INFO - Epoch(train) [57][2200/5005] lr: 1.0000e-02 eta: 11:38:49 time: 0.1744 data_time: 0.0035 loss: 1.3209 2023/03/17 07:42:34 - mmengine - INFO - Epoch(train) [57][2300/5005] lr: 1.0000e-02 eta: 11:38:29 time: 0.1785 data_time: 0.0032 loss: 1.2631 2023/03/17 07:42:53 - mmengine - INFO - Epoch(train) [57][2400/5005] lr: 1.0000e-02 eta: 11:38:10 time: 0.1777 data_time: 0.0033 loss: 1.2984 2023/03/17 07:43:12 - mmengine - INFO - Epoch(train) [57][2500/5005] lr: 1.0000e-02 eta: 11:37:50 time: 0.1746 data_time: 0.0034 loss: 1.6243 2023/03/17 07:43:32 - mmengine - INFO - Epoch(train) [57][2600/5005] lr: 1.0000e-02 eta: 11:37:31 time: 0.2040 data_time: 0.0029 loss: 1.4783 2023/03/17 07:43:53 - mmengine - INFO - Epoch(train) [57][2700/5005] lr: 1.0000e-02 eta: 11:37:13 time: 0.2397 data_time: 0.0035 loss: 1.5145 2023/03/17 07:43:58 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:44:14 - mmengine - INFO - Epoch(train) [57][2800/5005] lr: 1.0000e-02 eta: 11:36:55 time: 0.1816 data_time: 0.0032 loss: 1.1939 2023/03/17 07:44:34 - mmengine - INFO - Epoch(train) [57][2900/5005] lr: 1.0000e-02 eta: 11:36:37 time: 0.2093 data_time: 0.0032 loss: 1.2773 2023/03/17 07:44:56 - mmengine - INFO - Epoch(train) [57][3000/5005] lr: 1.0000e-02 eta: 11:36:19 time: 0.2144 data_time: 0.0031 loss: 1.4390 2023/03/17 07:45:15 - mmengine - INFO - Epoch(train) [57][3100/5005] lr: 1.0000e-02 eta: 11:36:00 time: 0.1910 data_time: 0.0037 loss: 1.4361 2023/03/17 07:45:34 - mmengine - INFO - Epoch(train) [57][3200/5005] lr: 1.0000e-02 eta: 11:35:40 time: 0.1838 data_time: 0.0032 loss: 1.3853 2023/03/17 07:45:51 - mmengine - INFO - Epoch(train) [57][3300/5005] lr: 1.0000e-02 eta: 11:35:20 time: 0.1738 data_time: 0.0034 loss: 1.4575 2023/03/17 07:46:09 - mmengine - INFO - Epoch(train) [57][3400/5005] lr: 1.0000e-02 eta: 11:34:59 time: 0.1835 data_time: 0.0035 loss: 1.4635 2023/03/17 07:46:28 - mmengine - INFO - Epoch(train) [57][3500/5005] lr: 1.0000e-02 eta: 11:34:39 time: 0.1903 data_time: 0.0040 loss: 1.2459 2023/03/17 07:46:46 - mmengine - INFO - Epoch(train) [57][3600/5005] lr: 1.0000e-02 eta: 11:34:19 time: 0.1757 data_time: 0.0037 loss: 1.5691 2023/03/17 07:47:05 - mmengine - INFO - Epoch(train) [57][3700/5005] lr: 1.0000e-02 eta: 11:34:00 time: 0.1882 data_time: 0.0039 loss: 1.5114 2023/03/17 07:47:09 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:47:24 - mmengine - INFO - Epoch(train) [57][3800/5005] lr: 1.0000e-02 eta: 11:33:40 time: 0.1917 data_time: 0.0035 loss: 1.2428 2023/03/17 07:47:48 - mmengine - INFO - Epoch(train) [57][3900/5005] lr: 1.0000e-02 eta: 11:33:25 time: 0.1906 data_time: 0.0043 loss: 1.4944 2023/03/17 07:48:07 - mmengine - INFO - Epoch(train) [57][4000/5005] lr: 1.0000e-02 eta: 11:33:05 time: 0.1860 data_time: 0.0034 loss: 1.4457 2023/03/17 07:48:25 - mmengine - INFO - Epoch(train) [57][4100/5005] lr: 1.0000e-02 eta: 11:32:45 time: 0.1761 data_time: 0.0033 loss: 1.3577 2023/03/17 07:48:44 - mmengine - INFO - Epoch(train) [57][4200/5005] lr: 1.0000e-02 eta: 11:32:25 time: 0.2215 data_time: 0.0035 loss: 1.3137 2023/03/17 07:49:03 - mmengine - INFO - Epoch(train) [57][4300/5005] lr: 1.0000e-02 eta: 11:32:06 time: 0.1857 data_time: 0.0036 loss: 1.5335 2023/03/17 07:49:25 - mmengine - INFO - Epoch(train) [57][4400/5005] lr: 1.0000e-02 eta: 11:31:48 time: 0.2523 data_time: 0.0033 loss: 1.4503 2023/03/17 07:49:46 - mmengine - INFO - Epoch(train) [57][4500/5005] lr: 1.0000e-02 eta: 11:31:30 time: 0.1837 data_time: 0.0034 loss: 1.4415 2023/03/17 07:50:05 - mmengine - INFO - Epoch(train) [57][4600/5005] lr: 1.0000e-02 eta: 11:31:11 time: 0.1894 data_time: 0.0036 loss: 1.4708 2023/03/17 07:50:23 - mmengine - INFO - Epoch(train) [57][4700/5005] lr: 1.0000e-02 eta: 11:30:51 time: 0.1814 data_time: 0.0037 loss: 1.3683 2023/03/17 07:50:27 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:50:42 - mmengine - INFO - Epoch(train) [57][4800/5005] lr: 1.0000e-02 eta: 11:30:31 time: 0.1778 data_time: 0.0035 loss: 1.4357 2023/03/17 07:51:05 - mmengine - INFO - Epoch(train) [57][4900/5005] lr: 1.0000e-02 eta: 11:30:14 time: 0.2335 data_time: 0.0036 loss: 1.5338 2023/03/17 07:51:29 - mmengine - INFO - Epoch(train) [57][5000/5005] lr: 1.0000e-02 eta: 11:29:59 time: 0.2496 data_time: 0.0046 loss: 1.4380 2023/03/17 07:51:30 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:51:31 - mmengine - INFO - Saving checkpoint at 57 epochs 2023/03/17 07:51:37 - mmengine - INFO - Epoch(val) [57][100/196] eta: 0:00:05 time: 0.0462 data_time: 0.0020 2023/03/17 07:52:02 - mmengine - INFO - Epoch(val) [57][196/196] accuracy/top1: 69.9680 accuracy/top5: 90.0000data_time: 0.0360 time: 0.0656 2023/03/17 07:52:24 - mmengine - INFO - Epoch(train) [58][ 100/5005] lr: 1.0000e-02 eta: 11:29:41 time: 0.1885 data_time: 0.0035 loss: 1.3359 2023/03/17 07:52:46 - mmengine - INFO - Epoch(train) [58][ 200/5005] lr: 1.0000e-02 eta: 11:29:24 time: 0.1796 data_time: 0.0033 loss: 1.4155 2023/03/17 07:53:05 - mmengine - INFO - Epoch(train) [58][ 300/5005] lr: 1.0000e-02 eta: 11:29:04 time: 0.2337 data_time: 0.0035 loss: 1.5027 2023/03/17 07:53:26 - mmengine - INFO - Epoch(train) [58][ 400/5005] lr: 1.0000e-02 eta: 11:28:46 time: 0.2331 data_time: 0.0042 loss: 1.4282 2023/03/17 07:53:45 - mmengine - INFO - Epoch(train) [58][ 500/5005] lr: 1.0000e-02 eta: 11:28:27 time: 0.1931 data_time: 0.0037 loss: 1.2813 2023/03/17 07:54:04 - mmengine - INFO - Epoch(train) [58][ 600/5005] lr: 1.0000e-02 eta: 11:28:07 time: 0.1824 data_time: 0.0037 loss: 1.3603 2023/03/17 07:54:23 - mmengine - INFO - Epoch(train) [58][ 700/5005] lr: 1.0000e-02 eta: 11:27:47 time: 0.1879 data_time: 0.0036 loss: 1.4234 2023/03/17 07:54:25 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:54:42 - mmengine - INFO - Epoch(train) [58][ 800/5005] lr: 1.0000e-02 eta: 11:27:28 time: 0.1944 data_time: 0.0034 loss: 1.1798 2023/03/17 07:55:01 - mmengine - INFO - Epoch(train) [58][ 900/5005] lr: 1.0000e-02 eta: 11:27:09 time: 0.2103 data_time: 0.0033 loss: 1.2697 2023/03/17 07:55:20 - mmengine - INFO - Epoch(train) [58][1000/5005] lr: 1.0000e-02 eta: 11:26:49 time: 0.1790 data_time: 0.0038 loss: 1.4409 2023/03/17 07:55:39 - mmengine - INFO - Epoch(train) [58][1100/5005] lr: 1.0000e-02 eta: 11:26:29 time: 0.1914 data_time: 0.0037 loss: 1.4117 2023/03/17 07:55:57 - mmengine - INFO - Epoch(train) [58][1200/5005] lr: 1.0000e-02 eta: 11:26:09 time: 0.1726 data_time: 0.0037 loss: 1.3387 2023/03/17 07:56:14 - mmengine - INFO - Epoch(train) [58][1300/5005] lr: 1.0000e-02 eta: 11:25:49 time: 0.1718 data_time: 0.0037 loss: 1.2894 2023/03/17 07:56:32 - mmengine - INFO - Epoch(train) [58][1400/5005] lr: 1.0000e-02 eta: 11:25:28 time: 0.1732 data_time: 0.0038 loss: 1.5259 2023/03/17 07:56:51 - mmengine - INFO - Epoch(train) [58][1500/5005] lr: 1.0000e-02 eta: 11:25:09 time: 0.1896 data_time: 0.0039 loss: 1.4735 2023/03/17 07:57:10 - mmengine - INFO - Epoch(train) [58][1600/5005] lr: 1.0000e-02 eta: 11:24:49 time: 0.1867 data_time: 0.0037 loss: 1.2722 2023/03/17 07:57:29 - mmengine - INFO - Epoch(train) [58][1700/5005] lr: 1.0000e-02 eta: 11:24:30 time: 0.2338 data_time: 0.0035 loss: 1.4731 2023/03/17 07:57:32 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 07:57:49 - mmengine - INFO - Epoch(train) [58][1800/5005] lr: 1.0000e-02 eta: 11:24:11 time: 0.1933 data_time: 0.0030 loss: 1.5052 2023/03/17 07:58:09 - mmengine - INFO - Epoch(train) [58][1900/5005] lr: 1.0000e-02 eta: 11:23:52 time: 0.1864 data_time: 0.0035 loss: 1.5379 2023/03/17 07:58:31 - mmengine - INFO - Epoch(train) [58][2000/5005] lr: 1.0000e-02 eta: 11:23:35 time: 0.2491 data_time: 0.0033 loss: 1.4638 2023/03/17 07:58:53 - mmengine - INFO - Epoch(train) [58][2100/5005] lr: 1.0000e-02 eta: 11:23:18 time: 0.2332 data_time: 0.0032 loss: 1.4945 2023/03/17 07:59:11 - mmengine - INFO - Epoch(train) [58][2200/5005] lr: 1.0000e-02 eta: 11:22:58 time: 0.1799 data_time: 0.0034 loss: 1.5299 2023/03/17 07:59:29 - mmengine - INFO - Epoch(train) [58][2300/5005] lr: 1.0000e-02 eta: 11:22:38 time: 0.1804 data_time: 0.0038 loss: 1.3957 2023/03/17 07:59:48 - mmengine - INFO - Epoch(train) [58][2400/5005] lr: 1.0000e-02 eta: 11:22:18 time: 0.1815 data_time: 0.0034 loss: 1.5777 2023/03/17 08:00:06 - mmengine - INFO - Epoch(train) [58][2500/5005] lr: 1.0000e-02 eta: 11:21:58 time: 0.1792 data_time: 0.0032 loss: 1.2905 2023/03/17 08:00:24 - mmengine - INFO - Epoch(train) [58][2600/5005] lr: 1.0000e-02 eta: 11:21:38 time: 0.1866 data_time: 0.0037 loss: 1.3753 2023/03/17 08:00:43 - mmengine - INFO - Epoch(train) [58][2700/5005] lr: 1.0000e-02 eta: 11:21:18 time: 0.1812 data_time: 0.0037 loss: 1.3657 2023/03/17 08:00:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:01:01 - mmengine - INFO - Epoch(train) [58][2800/5005] lr: 1.0000e-02 eta: 11:20:58 time: 0.1818 data_time: 0.0040 loss: 1.4821 2023/03/17 08:01:20 - mmengine - INFO - Epoch(train) [58][2900/5005] lr: 1.0000e-02 eta: 11:20:38 time: 0.1790 data_time: 0.0043 loss: 1.3336 2023/03/17 08:01:38 - mmengine - INFO - Epoch(train) [58][3000/5005] lr: 1.0000e-02 eta: 11:20:18 time: 0.1799 data_time: 0.0033 loss: 1.2773 2023/03/17 08:01:58 - mmengine - INFO - Epoch(train) [58][3100/5005] lr: 1.0000e-02 eta: 11:19:59 time: 0.1949 data_time: 0.0034 loss: 1.4272 2023/03/17 08:02:20 - mmengine - INFO - Epoch(train) [58][3200/5005] lr: 1.0000e-02 eta: 11:19:41 time: 0.1897 data_time: 0.0036 loss: 1.4876 2023/03/17 08:02:39 - mmengine - INFO - Epoch(train) [58][3300/5005] lr: 1.0000e-02 eta: 11:19:22 time: 0.1834 data_time: 0.0039 loss: 1.3400 2023/03/17 08:02:57 - mmengine - INFO - Epoch(train) [58][3400/5005] lr: 1.0000e-02 eta: 11:19:02 time: 0.1787 data_time: 0.0034 loss: 1.5552 2023/03/17 08:03:15 - mmengine - INFO - Epoch(train) [58][3500/5005] lr: 1.0000e-02 eta: 11:18:42 time: 0.1806 data_time: 0.0034 loss: 1.3871 2023/03/17 08:03:35 - mmengine - INFO - Epoch(train) [58][3600/5005] lr: 1.0000e-02 eta: 11:18:23 time: 0.2005 data_time: 0.0030 loss: 1.4523 2023/03/17 08:03:57 - mmengine - INFO - Epoch(train) [58][3700/5005] lr: 1.0000e-02 eta: 11:18:05 time: 0.2086 data_time: 0.0032 loss: 1.5162 2023/03/17 08:04:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:04:16 - mmengine - INFO - Epoch(train) [58][3800/5005] lr: 1.0000e-02 eta: 11:17:46 time: 0.1814 data_time: 0.0037 loss: 1.6025 2023/03/17 08:04:35 - mmengine - INFO - Epoch(train) [58][3900/5005] lr: 1.0000e-02 eta: 11:17:27 time: 0.1824 data_time: 0.0039 loss: 1.4634 2023/03/17 08:04:55 - mmengine - INFO - Epoch(train) [58][4000/5005] lr: 1.0000e-02 eta: 11:17:08 time: 0.1953 data_time: 0.0032 loss: 1.2317 2023/03/17 08:05:15 - mmengine - INFO - Epoch(train) [58][4100/5005] lr: 1.0000e-02 eta: 11:16:49 time: 0.1875 data_time: 0.0038 loss: 1.6461 2023/03/17 08:05:36 - mmengine - INFO - Epoch(train) [58][4200/5005] lr: 1.0000e-02 eta: 11:16:31 time: 0.2097 data_time: 0.0036 loss: 1.4819 2023/03/17 08:05:55 - mmengine - INFO - Epoch(train) [58][4300/5005] lr: 1.0000e-02 eta: 11:16:12 time: 0.1883 data_time: 0.0035 loss: 1.2558 2023/03/17 08:06:14 - mmengine - INFO - Epoch(train) [58][4400/5005] lr: 1.0000e-02 eta: 11:15:52 time: 0.1794 data_time: 0.0033 loss: 1.3828 2023/03/17 08:06:32 - mmengine - INFO - Epoch(train) [58][4500/5005] lr: 1.0000e-02 eta: 11:15:32 time: 0.1814 data_time: 0.0032 loss: 1.3486 2023/03/17 08:06:51 - mmengine - INFO - Epoch(train) [58][4600/5005] lr: 1.0000e-02 eta: 11:15:13 time: 0.1906 data_time: 0.0031 loss: 1.5135 2023/03/17 08:07:11 - mmengine - INFO - Epoch(train) [58][4700/5005] lr: 1.0000e-02 eta: 11:14:54 time: 0.2006 data_time: 0.0029 loss: 1.3731 2023/03/17 08:07:14 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:07:32 - mmengine - INFO - Epoch(train) [58][4800/5005] lr: 1.0000e-02 eta: 11:14:35 time: 0.2034 data_time: 0.0033 loss: 1.3571 2023/03/17 08:07:52 - mmengine - INFO - Epoch(train) [58][4900/5005] lr: 1.0000e-02 eta: 11:14:17 time: 0.2131 data_time: 0.0038 loss: 1.4925 2023/03/17 08:08:11 - mmengine - INFO - Epoch(train) [58][5000/5005] lr: 1.0000e-02 eta: 11:13:57 time: 0.1902 data_time: 0.0045 loss: 1.4600 2023/03/17 08:08:12 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:08:13 - mmengine - INFO - Saving checkpoint at 58 epochs 2023/03/17 08:08:19 - mmengine - INFO - Epoch(val) [58][100/196] eta: 0:00:05 time: 0.0660 data_time: 0.0009 2023/03/17 08:08:46 - mmengine - INFO - Epoch(val) [58][196/196] accuracy/top1: 70.1880 accuracy/top5: 89.9580data_time: 0.0044 time: 0.0388 2023/03/17 08:09:09 - mmengine - INFO - Epoch(train) [59][ 100/5005] lr: 1.0000e-02 eta: 11:13:40 time: 0.2300 data_time: 0.0031 loss: 1.2999 2023/03/17 08:09:27 - mmengine - INFO - Epoch(train) [59][ 200/5005] lr: 1.0000e-02 eta: 11:13:20 time: 0.1855 data_time: 0.0035 loss: 1.5241 2023/03/17 08:09:46 - mmengine - INFO - Epoch(train) [59][ 300/5005] lr: 1.0000e-02 eta: 11:13:00 time: 0.1886 data_time: 0.0033 loss: 1.3501 2023/03/17 08:10:05 - mmengine - INFO - Epoch(train) [59][ 400/5005] lr: 1.0000e-02 eta: 11:12:41 time: 0.1879 data_time: 0.0034 loss: 1.4542 2023/03/17 08:10:25 - mmengine - INFO - Epoch(train) [59][ 500/5005] lr: 1.0000e-02 eta: 11:12:22 time: 0.1865 data_time: 0.0035 loss: 1.5190 2023/03/17 08:10:47 - mmengine - INFO - Epoch(train) [59][ 600/5005] lr: 1.0000e-02 eta: 11:12:05 time: 0.2493 data_time: 0.0036 loss: 1.3454 2023/03/17 08:11:09 - mmengine - INFO - Epoch(train) [59][ 700/5005] lr: 1.0000e-02 eta: 11:11:47 time: 0.1911 data_time: 0.0032 loss: 1.3010 2023/03/17 08:11:11 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:11:28 - mmengine - INFO - Epoch(train) [59][ 800/5005] lr: 1.0000e-02 eta: 11:11:28 time: 0.1892 data_time: 0.0033 loss: 1.3622 2023/03/17 08:11:47 - mmengine - INFO - Epoch(train) [59][ 900/5005] lr: 1.0000e-02 eta: 11:11:08 time: 0.1911 data_time: 0.0032 loss: 1.4036 2023/03/17 08:12:07 - mmengine - INFO - Epoch(train) [59][1000/5005] lr: 1.0000e-02 eta: 11:10:49 time: 0.1829 data_time: 0.0034 loss: 1.3769 2023/03/17 08:12:26 - mmengine - INFO - Epoch(train) [59][1100/5005] lr: 1.0000e-02 eta: 11:10:30 time: 0.1908 data_time: 0.0034 loss: 1.4210 2023/03/17 08:12:45 - mmengine - INFO - Epoch(train) [59][1200/5005] lr: 1.0000e-02 eta: 11:10:10 time: 0.1803 data_time: 0.0032 loss: 1.4083 2023/03/17 08:13:03 - mmengine - INFO - Epoch(train) [59][1300/5005] lr: 1.0000e-02 eta: 11:09:50 time: 0.1837 data_time: 0.0036 loss: 1.3832 2023/03/17 08:13:21 - mmengine - INFO - Epoch(train) [59][1400/5005] lr: 1.0000e-02 eta: 11:09:30 time: 0.1897 data_time: 0.0035 loss: 1.5567 2023/03/17 08:13:40 - mmengine - INFO - Epoch(train) [59][1500/5005] lr: 1.0000e-02 eta: 11:09:10 time: 0.1813 data_time: 0.0037 loss: 1.4192 2023/03/17 08:13:58 - mmengine - INFO - Epoch(train) [59][1600/5005] lr: 1.0000e-02 eta: 11:08:50 time: 0.1937 data_time: 0.0033 loss: 1.5535 2023/03/17 08:14:18 - mmengine - INFO - Epoch(train) [59][1700/5005] lr: 1.0000e-02 eta: 11:08:31 time: 0.1898 data_time: 0.0030 loss: 1.3833 2023/03/17 08:14:20 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:14:37 - mmengine - INFO - Epoch(train) [59][1800/5005] lr: 1.0000e-02 eta: 11:08:12 time: 0.1846 data_time: 0.0037 loss: 1.3534 2023/03/17 08:14:56 - mmengine - INFO - Epoch(train) [59][1900/5005] lr: 1.0000e-02 eta: 11:07:52 time: 0.1969 data_time: 0.0029 loss: 1.3507 2023/03/17 08:15:20 - mmengine - INFO - Epoch(train) [59][2000/5005] lr: 1.0000e-02 eta: 11:07:37 time: 0.2615 data_time: 0.0031 loss: 1.2540 2023/03/17 08:15:41 - mmengine - INFO - Epoch(train) [59][2100/5005] lr: 1.0000e-02 eta: 11:07:19 time: 0.2537 data_time: 0.0023 loss: 1.3048 2023/03/17 08:16:07 - mmengine - INFO - Epoch(train) [59][2200/5005] lr: 1.0000e-02 eta: 11:07:04 time: 0.2524 data_time: 0.0031 loss: 1.3254 2023/03/17 08:16:27 - mmengine - INFO - Epoch(train) [59][2300/5005] lr: 1.0000e-02 eta: 11:06:45 time: 0.1934 data_time: 0.0034 loss: 1.3420 2023/03/17 08:16:46 - mmengine - INFO - Epoch(train) [59][2400/5005] lr: 1.0000e-02 eta: 11:06:26 time: 0.2051 data_time: 0.0034 loss: 1.2607 2023/03/17 08:17:09 - mmengine - INFO - Epoch(train) [59][2500/5005] lr: 1.0000e-02 eta: 11:06:09 time: 0.2426 data_time: 0.0034 loss: 1.6848 2023/03/17 08:17:29 - mmengine - INFO - Epoch(train) [59][2600/5005] lr: 1.0000e-02 eta: 11:05:50 time: 0.1824 data_time: 0.0038 loss: 1.3605 2023/03/17 08:17:48 - mmengine - INFO - Epoch(train) [59][2700/5005] lr: 1.0000e-02 eta: 11:05:30 time: 0.1790 data_time: 0.0034 loss: 1.5080 2023/03/17 08:17:49 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:18:06 - mmengine - INFO - Epoch(train) [59][2800/5005] lr: 1.0000e-02 eta: 11:05:10 time: 0.1876 data_time: 0.0036 loss: 1.5273 2023/03/17 08:18:26 - mmengine - INFO - Epoch(train) [59][2900/5005] lr: 1.0000e-02 eta: 11:04:52 time: 0.2581 data_time: 0.0033 loss: 1.5441 2023/03/17 08:18:48 - mmengine - INFO - Epoch(train) [59][3000/5005] lr: 1.0000e-02 eta: 11:04:35 time: 0.1887 data_time: 0.0032 loss: 1.3016 2023/03/17 08:19:08 - mmengine - INFO - Epoch(train) [59][3100/5005] lr: 1.0000e-02 eta: 11:04:16 time: 0.2124 data_time: 0.0032 loss: 1.3440 2023/03/17 08:19:31 - mmengine - INFO - Epoch(train) [59][3200/5005] lr: 1.0000e-02 eta: 11:03:59 time: 0.1981 data_time: 0.0037 loss: 1.3544 2023/03/17 08:19:52 - mmengine - INFO - Epoch(train) [59][3300/5005] lr: 1.0000e-02 eta: 11:03:41 time: 0.1813 data_time: 0.0032 loss: 1.5165 2023/03/17 08:20:11 - mmengine - INFO - Epoch(train) [59][3400/5005] lr: 1.0000e-02 eta: 11:03:22 time: 0.1847 data_time: 0.0036 loss: 1.4998 2023/03/17 08:20:31 - mmengine - INFO - Epoch(train) [59][3500/5005] lr: 1.0000e-02 eta: 11:03:03 time: 0.1809 data_time: 0.0032 loss: 1.3149 2023/03/17 08:20:50 - mmengine - INFO - Epoch(train) [59][3600/5005] lr: 1.0000e-02 eta: 11:02:43 time: 0.1979 data_time: 0.0029 loss: 1.3546 2023/03/17 08:21:10 - mmengine - INFO - Epoch(train) [59][3700/5005] lr: 1.0000e-02 eta: 11:02:24 time: 0.1850 data_time: 0.0035 loss: 1.4227 2023/03/17 08:21:12 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:21:29 - mmengine - INFO - Epoch(train) [59][3800/5005] lr: 1.0000e-02 eta: 11:02:05 time: 0.1964 data_time: 0.0033 loss: 1.3863 2023/03/17 08:21:48 - mmengine - INFO - Epoch(train) [59][3900/5005] lr: 1.0000e-02 eta: 11:01:45 time: 0.1839 data_time: 0.0035 loss: 1.4774 2023/03/17 08:22:07 - mmengine - INFO - Epoch(train) [59][4000/5005] lr: 1.0000e-02 eta: 11:01:26 time: 0.1907 data_time: 0.0035 loss: 1.3368 2023/03/17 08:22:25 - mmengine - INFO - Epoch(train) [59][4100/5005] lr: 1.0000e-02 eta: 11:01:05 time: 0.1785 data_time: 0.0038 loss: 1.3201 2023/03/17 08:22:43 - mmengine - INFO - Epoch(train) [59][4200/5005] lr: 1.0000e-02 eta: 11:00:45 time: 0.1798 data_time: 0.0039 loss: 1.2747 2023/03/17 08:23:01 - mmengine - INFO - Epoch(train) [59][4300/5005] lr: 1.0000e-02 eta: 11:00:25 time: 0.1768 data_time: 0.0037 loss: 1.1658 2023/03/17 08:23:20 - mmengine - INFO - Epoch(train) [59][4400/5005] lr: 1.0000e-02 eta: 11:00:05 time: 0.2030 data_time: 0.0036 loss: 1.2833 2023/03/17 08:23:38 - mmengine - INFO - Epoch(train) [59][4500/5005] lr: 1.0000e-02 eta: 10:59:46 time: 0.1834 data_time: 0.0032 loss: 1.3037 2023/03/17 08:23:57 - mmengine - INFO - Epoch(train) [59][4600/5005] lr: 1.0000e-02 eta: 10:59:26 time: 0.1810 data_time: 0.0034 loss: 1.3293 2023/03/17 08:24:15 - mmengine - INFO - Epoch(train) [59][4700/5005] lr: 1.0000e-02 eta: 10:59:06 time: 0.1779 data_time: 0.0041 loss: 1.4634 2023/03/17 08:24:17 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:24:37 - mmengine - INFO - Epoch(train) [59][4800/5005] lr: 1.0000e-02 eta: 10:58:48 time: 0.2821 data_time: 0.0034 loss: 1.3387 2023/03/17 08:24:59 - mmengine - INFO - Epoch(train) [59][4900/5005] lr: 1.0000e-02 eta: 10:58:31 time: 0.1852 data_time: 0.0037 loss: 1.4998 2023/03/17 08:25:18 - mmengine - INFO - Epoch(train) [59][5000/5005] lr: 1.0000e-02 eta: 10:58:11 time: 0.1837 data_time: 0.0047 loss: 1.3317 2023/03/17 08:25:19 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:25:20 - mmengine - INFO - Saving checkpoint at 59 epochs 2023/03/17 08:25:27 - mmengine - INFO - Epoch(val) [59][100/196] eta: 0:00:06 time: 0.0775 data_time: 0.0008 2023/03/17 08:25:53 - mmengine - INFO - Epoch(val) [59][196/196] accuracy/top1: 69.4240 accuracy/top5: 89.5620data_time: 0.0006 time: 0.0495 2023/03/17 08:26:18 - mmengine - INFO - Epoch(train) [60][ 100/5005] lr: 1.0000e-02 eta: 10:57:55 time: 0.2070 data_time: 0.0036 loss: 1.5098 2023/03/17 08:26:39 - mmengine - INFO - Epoch(train) [60][ 200/5005] lr: 1.0000e-02 eta: 10:57:37 time: 0.2076 data_time: 0.0035 loss: 1.3333 2023/03/17 08:27:04 - mmengine - INFO - Epoch(train) [60][ 300/5005] lr: 1.0000e-02 eta: 10:57:22 time: 0.2090 data_time: 0.0034 loss: 1.4456 2023/03/17 08:27:27 - mmengine - INFO - Epoch(train) [60][ 400/5005] lr: 1.0000e-02 eta: 10:57:05 time: 0.1883 data_time: 0.0033 loss: 1.2653 2023/03/17 08:27:45 - mmengine - INFO - Epoch(train) [60][ 500/5005] lr: 1.0000e-02 eta: 10:56:45 time: 0.1850 data_time: 0.0039 loss: 1.4941 2023/03/17 08:28:04 - mmengine - INFO - Epoch(train) [60][ 600/5005] lr: 1.0000e-02 eta: 10:56:26 time: 0.1893 data_time: 0.0037 loss: 1.2830 2023/03/17 08:28:23 - mmengine - INFO - Epoch(train) [60][ 700/5005] lr: 1.0000e-02 eta: 10:56:06 time: 0.2179 data_time: 0.0040 loss: 1.6423 2023/03/17 08:28:24 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:28:42 - mmengine - INFO - Epoch(train) [60][ 800/5005] lr: 1.0000e-02 eta: 10:55:46 time: 0.1838 data_time: 0.0035 loss: 1.3239 2023/03/17 08:29:01 - mmengine - INFO - Epoch(train) [60][ 900/5005] lr: 1.0000e-02 eta: 10:55:27 time: 0.1958 data_time: 0.0037 loss: 1.5340 2023/03/17 08:29:20 - mmengine - INFO - Epoch(train) [60][1000/5005] lr: 1.0000e-02 eta: 10:55:07 time: 0.1839 data_time: 0.0038 loss: 1.2946 2023/03/17 08:29:38 - mmengine - INFO - Epoch(train) [60][1100/5005] lr: 1.0000e-02 eta: 10:54:47 time: 0.1665 data_time: 0.0032 loss: 1.3999 2023/03/17 08:29:55 - mmengine - INFO - Epoch(train) [60][1200/5005] lr: 1.0000e-02 eta: 10:54:26 time: 0.1785 data_time: 0.0035 loss: 1.4522 2023/03/17 08:30:14 - mmengine - INFO - Epoch(train) [60][1300/5005] lr: 1.0000e-02 eta: 10:54:07 time: 0.1812 data_time: 0.0034 loss: 1.5107 2023/03/17 08:30:33 - mmengine - INFO - Epoch(train) [60][1400/5005] lr: 1.0000e-02 eta: 10:53:47 time: 0.1898 data_time: 0.0038 loss: 1.2993 2023/03/17 08:30:54 - mmengine - INFO - Epoch(train) [60][1500/5005] lr: 1.0000e-02 eta: 10:53:29 time: 0.2128 data_time: 0.0035 loss: 1.4555 2023/03/17 08:31:15 - mmengine - INFO - Epoch(train) [60][1600/5005] lr: 1.0000e-02 eta: 10:53:11 time: 0.1827 data_time: 0.0036 loss: 1.5277 2023/03/17 08:31:34 - mmengine - INFO - Epoch(train) [60][1700/5005] lr: 1.0000e-02 eta: 10:52:51 time: 0.1948 data_time: 0.0036 loss: 1.5077 2023/03/17 08:31:35 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:31:52 - mmengine - INFO - Epoch(train) [60][1800/5005] lr: 1.0000e-02 eta: 10:52:31 time: 0.1744 data_time: 0.0037 loss: 1.3241 2023/03/17 08:32:10 - mmengine - INFO - Epoch(train) [60][1900/5005] lr: 1.0000e-02 eta: 10:52:11 time: 0.1667 data_time: 0.0042 loss: 1.3334 2023/03/17 08:32:29 - mmengine - INFO - Epoch(train) [60][2000/5005] lr: 1.0000e-02 eta: 10:51:52 time: 0.1931 data_time: 0.0039 loss: 1.3850 2023/03/17 08:32:50 - mmengine - INFO - Epoch(train) [60][2100/5005] lr: 1.0000e-02 eta: 10:51:33 time: 0.2393 data_time: 0.0030 loss: 1.3752 2023/03/17 08:33:09 - mmengine - INFO - Epoch(train) [60][2200/5005] lr: 1.0000e-02 eta: 10:51:14 time: 0.1756 data_time: 0.0031 loss: 1.2345 2023/03/17 08:33:29 - mmengine - INFO - Epoch(train) [60][2300/5005] lr: 1.0000e-02 eta: 10:50:55 time: 0.1923 data_time: 0.0043 loss: 1.1269 2023/03/17 08:33:47 - mmengine - INFO - Epoch(train) [60][2400/5005] lr: 1.0000e-02 eta: 10:50:35 time: 0.1795 data_time: 0.0035 loss: 1.2972 2023/03/17 08:34:05 - mmengine - INFO - Epoch(train) [60][2500/5005] lr: 1.0000e-02 eta: 10:50:15 time: 0.1757 data_time: 0.0037 loss: 1.4170 2023/03/17 08:34:23 - mmengine - INFO - Epoch(train) [60][2600/5005] lr: 1.0000e-02 eta: 10:49:54 time: 0.1719 data_time: 0.0043 loss: 1.6398 2023/03/17 08:34:40 - mmengine - INFO - Epoch(train) [60][2700/5005] lr: 1.0000e-02 eta: 10:49:34 time: 0.1740 data_time: 0.0036 loss: 1.3756 2023/03/17 08:34:41 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:34:58 - mmengine - INFO - Epoch(train) [60][2800/5005] lr: 1.0000e-02 eta: 10:49:14 time: 0.1761 data_time: 0.0035 loss: 1.2699 2023/03/17 08:35:17 - mmengine - INFO - Epoch(train) [60][2900/5005] lr: 1.0000e-02 eta: 10:48:54 time: 0.1860 data_time: 0.0040 loss: 1.4603 2023/03/17 08:35:37 - mmengine - INFO - Epoch(train) [60][3000/5005] lr: 1.0000e-02 eta: 10:48:35 time: 0.2112 data_time: 0.0033 loss: 1.3934 2023/03/17 08:35:57 - mmengine - INFO - Epoch(train) [60][3100/5005] lr: 1.0000e-02 eta: 10:48:16 time: 0.1894 data_time: 0.0037 loss: 1.4876 2023/03/17 08:36:15 - mmengine - INFO - Epoch(train) [60][3200/5005] lr: 1.0000e-02 eta: 10:47:56 time: 0.1774 data_time: 0.0037 loss: 1.4179 2023/03/17 08:36:34 - mmengine - INFO - Epoch(train) [60][3300/5005] lr: 1.0000e-02 eta: 10:47:36 time: 0.1800 data_time: 0.0038 loss: 1.3428 2023/03/17 08:36:52 - mmengine - INFO - Epoch(train) [60][3400/5005] lr: 1.0000e-02 eta: 10:47:16 time: 0.1767 data_time: 0.0037 loss: 1.4042 2023/03/17 08:37:10 - mmengine - INFO - Epoch(train) [60][3500/5005] lr: 1.0000e-02 eta: 10:46:56 time: 0.1779 data_time: 0.0035 loss: 1.4038 2023/03/17 08:37:29 - mmengine - INFO - Epoch(train) [60][3600/5005] lr: 1.0000e-02 eta: 10:46:37 time: 0.1896 data_time: 0.0039 loss: 1.4197 2023/03/17 08:37:48 - mmengine - INFO - Epoch(train) [60][3700/5005] lr: 1.0000e-02 eta: 10:46:17 time: 0.1838 data_time: 0.0038 loss: 1.2439 2023/03/17 08:37:49 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:38:07 - mmengine - INFO - Epoch(train) [60][3800/5005] lr: 1.0000e-02 eta: 10:45:58 time: 0.1824 data_time: 0.0037 loss: 1.3096 2023/03/17 08:38:28 - mmengine - INFO - Epoch(train) [60][3900/5005] lr: 1.0000e-02 eta: 10:45:40 time: 0.1794 data_time: 0.0035 loss: 1.4662 2023/03/17 08:38:46 - mmengine - INFO - Epoch(train) [60][4000/5005] lr: 1.0000e-02 eta: 10:45:19 time: 0.1796 data_time: 0.0033 loss: 1.3813 2023/03/17 08:39:04 - mmengine - INFO - Epoch(train) [60][4100/5005] lr: 1.0000e-02 eta: 10:44:59 time: 0.1751 data_time: 0.0030 loss: 1.5987 2023/03/17 08:39:23 - mmengine - INFO - Epoch(train) [60][4200/5005] lr: 1.0000e-02 eta: 10:44:40 time: 0.1813 data_time: 0.0038 loss: 1.4419 2023/03/17 08:39:45 - mmengine - INFO - Epoch(train) [60][4300/5005] lr: 1.0000e-02 eta: 10:44:22 time: 0.2507 data_time: 0.0035 loss: 1.4756 2023/03/17 08:40:04 - mmengine - INFO - Epoch(train) [60][4400/5005] lr: 1.0000e-02 eta: 10:44:03 time: 0.1870 data_time: 0.0037 loss: 1.3781 2023/03/17 08:40:22 - mmengine - INFO - Epoch(train) [60][4500/5005] lr: 1.0000e-02 eta: 10:43:43 time: 0.1746 data_time: 0.0036 loss: 1.4103 2023/03/17 08:40:40 - mmengine - INFO - Epoch(train) [60][4600/5005] lr: 1.0000e-02 eta: 10:43:22 time: 0.1740 data_time: 0.0035 loss: 1.3611 2023/03/17 08:40:58 - mmengine - INFO - Epoch(train) [60][4700/5005] lr: 1.0000e-02 eta: 10:43:02 time: 0.1749 data_time: 0.0039 loss: 1.6073 2023/03/17 08:40:59 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:41:17 - mmengine - INFO - Epoch(train) [60][4800/5005] lr: 1.0000e-02 eta: 10:42:43 time: 0.2036 data_time: 0.0036 loss: 1.3920 2023/03/17 08:41:35 - mmengine - INFO - Epoch(train) [60][4900/5005] lr: 1.0000e-02 eta: 10:42:23 time: 0.1769 data_time: 0.0035 loss: 1.6395 2023/03/17 08:41:54 - mmengine - INFO - Epoch(train) [60][5000/5005] lr: 1.0000e-02 eta: 10:42:03 time: 0.1889 data_time: 0.0043 loss: 1.3608 2023/03/17 08:41:55 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:41:56 - mmengine - INFO - Saving checkpoint at 60 epochs 2023/03/17 08:42:02 - mmengine - INFO - Epoch(val) [60][100/196] eta: 0:00:05 time: 0.0458 data_time: 0.0009 2023/03/17 08:42:30 - mmengine - INFO - Epoch(val) [60][196/196] accuracy/top1: 69.6180 accuracy/top5: 89.4760data_time: 0.0208 time: 0.0551 2023/03/17 08:42:54 - mmengine - INFO - Epoch(train) [61][ 100/5005] lr: 1.0000e-03 eta: 10:41:46 time: 0.1836 data_time: 0.0034 loss: 1.3580 2023/03/17 08:43:13 - mmengine - INFO - Epoch(train) [61][ 200/5005] lr: 1.0000e-03 eta: 10:41:27 time: 0.1955 data_time: 0.0032 loss: 1.3346 2023/03/17 08:43:32 - mmengine - INFO - Epoch(train) [61][ 300/5005] lr: 1.0000e-03 eta: 10:41:07 time: 0.1788 data_time: 0.0034 loss: 1.2098 2023/03/17 08:43:51 - mmengine - INFO - Epoch(train) [61][ 400/5005] lr: 1.0000e-03 eta: 10:40:48 time: 0.2124 data_time: 0.0040 loss: 1.1177 2023/03/17 08:44:10 - mmengine - INFO - Epoch(train) [61][ 500/5005] lr: 1.0000e-03 eta: 10:40:29 time: 0.1860 data_time: 0.0035 loss: 1.2845 2023/03/17 08:44:29 - mmengine - INFO - Epoch(train) [61][ 600/5005] lr: 1.0000e-03 eta: 10:40:09 time: 0.1866 data_time: 0.0033 loss: 1.1108 2023/03/17 08:44:48 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:44:48 - mmengine - INFO - Epoch(train) [61][ 700/5005] lr: 1.0000e-03 eta: 10:39:49 time: 0.1860 data_time: 0.0035 loss: 1.1052 2023/03/17 08:45:07 - mmengine - INFO - Epoch(train) [61][ 800/5005] lr: 1.0000e-03 eta: 10:39:30 time: 0.1928 data_time: 0.0037 loss: 1.2184 2023/03/17 08:45:27 - mmengine - INFO - Epoch(train) [61][ 900/5005] lr: 1.0000e-03 eta: 10:39:11 time: 0.1739 data_time: 0.0035 loss: 1.3318 2023/03/17 08:45:46 - mmengine - INFO - Epoch(train) [61][1000/5005] lr: 1.0000e-03 eta: 10:38:52 time: 0.1901 data_time: 0.0037 loss: 1.3111 2023/03/17 08:46:05 - mmengine - INFO - Epoch(train) [61][1100/5005] lr: 1.0000e-03 eta: 10:38:32 time: 0.1991 data_time: 0.0040 loss: 1.0900 2023/03/17 08:46:27 - mmengine - INFO - Epoch(train) [61][1200/5005] lr: 1.0000e-03 eta: 10:38:15 time: 0.1889 data_time: 0.0038 loss: 1.3892 2023/03/17 08:46:47 - mmengine - INFO - Epoch(train) [61][1300/5005] lr: 1.0000e-03 eta: 10:37:56 time: 0.1870 data_time: 0.0037 loss: 1.1001 2023/03/17 08:47:06 - mmengine - INFO - Epoch(train) [61][1400/5005] lr: 1.0000e-03 eta: 10:37:36 time: 0.1888 data_time: 0.0033 loss: 1.4846 2023/03/17 08:47:25 - mmengine - INFO - Epoch(train) [61][1500/5005] lr: 1.0000e-03 eta: 10:37:17 time: 0.1861 data_time: 0.0036 loss: 1.1048 2023/03/17 08:47:44 - mmengine - INFO - Epoch(train) [61][1600/5005] lr: 1.0000e-03 eta: 10:36:57 time: 0.1891 data_time: 0.0031 loss: 1.0949 2023/03/17 08:48:03 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:48:03 - mmengine - INFO - Epoch(train) [61][1700/5005] lr: 1.0000e-03 eta: 10:36:38 time: 0.1943 data_time: 0.0034 loss: 1.1379 2023/03/17 08:48:23 - mmengine - INFO - Epoch(train) [61][1800/5005] lr: 1.0000e-03 eta: 10:36:19 time: 0.1870 data_time: 0.0035 loss: 1.3103 2023/03/17 08:48:42 - mmengine - INFO - Epoch(train) [61][1900/5005] lr: 1.0000e-03 eta: 10:36:00 time: 0.1980 data_time: 0.0033 loss: 1.2517 2023/03/17 08:49:00 - mmengine - INFO - Epoch(train) [61][2000/5005] lr: 1.0000e-03 eta: 10:35:40 time: 0.1832 data_time: 0.0035 loss: 1.2460 2023/03/17 08:49:20 - mmengine - INFO - Epoch(train) [61][2100/5005] lr: 1.0000e-03 eta: 10:35:21 time: 0.1894 data_time: 0.0038 loss: 1.1211 2023/03/17 08:49:38 - mmengine - INFO - Epoch(train) [61][2200/5005] lr: 1.0000e-03 eta: 10:35:01 time: 0.1735 data_time: 0.0042 loss: 1.3364 2023/03/17 08:49:57 - mmengine - INFO - Epoch(train) [61][2300/5005] lr: 1.0000e-03 eta: 10:34:41 time: 0.2146 data_time: 0.0036 loss: 1.1487 2023/03/17 08:50:16 - mmengine - INFO - Epoch(train) [61][2400/5005] lr: 1.0000e-03 eta: 10:34:22 time: 0.1902 data_time: 0.0039 loss: 1.1015 2023/03/17 08:50:35 - mmengine - INFO - Epoch(train) [61][2500/5005] lr: 1.0000e-03 eta: 10:34:02 time: 0.2016 data_time: 0.0035 loss: 1.3958 2023/03/17 08:50:56 - mmengine - INFO - Epoch(train) [61][2600/5005] lr: 1.0000e-03 eta: 10:33:44 time: 0.2028 data_time: 0.0035 loss: 1.2367 2023/03/17 08:51:15 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:51:15 - mmengine - INFO - Epoch(train) [61][2700/5005] lr: 1.0000e-03 eta: 10:33:24 time: 0.1848 data_time: 0.0036 loss: 1.3567 2023/03/17 08:51:34 - mmengine - INFO - Epoch(train) [61][2800/5005] lr: 1.0000e-03 eta: 10:33:05 time: 0.1957 data_time: 0.0036 loss: 1.1713 2023/03/17 08:51:53 - mmengine - INFO - Epoch(train) [61][2900/5005] lr: 1.0000e-03 eta: 10:32:45 time: 0.1899 data_time: 0.0038 loss: 1.0772 2023/03/17 08:52:11 - mmengine - INFO - Epoch(train) [61][3000/5005] lr: 1.0000e-03 eta: 10:32:26 time: 0.1880 data_time: 0.0033 loss: 1.1563 2023/03/17 08:52:30 - mmengine - INFO - Epoch(train) [61][3100/5005] lr: 1.0000e-03 eta: 10:32:06 time: 0.1934 data_time: 0.0039 loss: 1.1412 2023/03/17 08:52:50 - mmengine - INFO - Epoch(train) [61][3200/5005] lr: 1.0000e-03 eta: 10:31:47 time: 0.1982 data_time: 0.0038 loss: 1.2795 2023/03/17 08:53:11 - mmengine - INFO - Epoch(train) [61][3300/5005] lr: 1.0000e-03 eta: 10:31:29 time: 0.1911 data_time: 0.0035 loss: 1.2214 2023/03/17 08:53:31 - mmengine - INFO - Epoch(train) [61][3400/5005] lr: 1.0000e-03 eta: 10:31:10 time: 0.1865 data_time: 0.0036 loss: 1.3262 2023/03/17 08:53:50 - mmengine - INFO - Epoch(train) [61][3500/5005] lr: 1.0000e-03 eta: 10:30:51 time: 0.2186 data_time: 0.0035 loss: 1.2236 2023/03/17 08:54:10 - mmengine - INFO - Epoch(train) [61][3600/5005] lr: 1.0000e-03 eta: 10:30:32 time: 0.1990 data_time: 0.0031 loss: 1.1610 2023/03/17 08:54:31 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:54:31 - mmengine - INFO - Epoch(train) [61][3700/5005] lr: 1.0000e-03 eta: 10:30:13 time: 0.2030 data_time: 0.0041 loss: 1.1746 2023/03/17 08:54:51 - mmengine - INFO - Epoch(train) [61][3800/5005] lr: 1.0000e-03 eta: 10:29:55 time: 0.2082 data_time: 0.0027 loss: 1.0771 2023/03/17 08:55:11 - mmengine - INFO - Epoch(train) [61][3900/5005] lr: 1.0000e-03 eta: 10:29:36 time: 0.1902 data_time: 0.0040 loss: 1.1164 2023/03/17 08:55:30 - mmengine - INFO - Epoch(train) [61][4000/5005] lr: 1.0000e-03 eta: 10:29:16 time: 0.1919 data_time: 0.0041 loss: 1.1346 2023/03/17 08:55:50 - mmengine - INFO - Epoch(train) [61][4100/5005] lr: 1.0000e-03 eta: 10:28:58 time: 0.2074 data_time: 0.0038 loss: 1.1029 2023/03/17 08:56:10 - mmengine - INFO - Epoch(train) [61][4200/5005] lr: 1.0000e-03 eta: 10:28:39 time: 0.1867 data_time: 0.0038 loss: 1.2010 2023/03/17 08:56:30 - mmengine - INFO - Epoch(train) [61][4300/5005] lr: 1.0000e-03 eta: 10:28:20 time: 0.2135 data_time: 0.0033 loss: 1.1175 2023/03/17 08:56:53 - mmengine - INFO - Epoch(train) [61][4400/5005] lr: 1.0000e-03 eta: 10:28:03 time: 0.2160 data_time: 0.0037 loss: 1.2539 2023/03/17 08:57:12 - mmengine - INFO - Epoch(train) [61][4500/5005] lr: 1.0000e-03 eta: 10:27:44 time: 0.1826 data_time: 0.0039 loss: 1.1001 2023/03/17 08:57:31 - mmengine - INFO - Epoch(train) [61][4600/5005] lr: 1.0000e-03 eta: 10:27:24 time: 0.1868 data_time: 0.0035 loss: 1.1811 2023/03/17 08:57:51 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:57:51 - mmengine - INFO - Epoch(train) [61][4700/5005] lr: 1.0000e-03 eta: 10:27:05 time: 0.2280 data_time: 0.0035 loss: 1.1699 2023/03/17 08:58:10 - mmengine - INFO - Epoch(train) [61][4800/5005] lr: 1.0000e-03 eta: 10:26:45 time: 0.1846 data_time: 0.0034 loss: 1.0723 2023/03/17 08:58:29 - mmengine - INFO - Epoch(train) [61][4900/5005] lr: 1.0000e-03 eta: 10:26:26 time: 0.1841 data_time: 0.0040 loss: 1.2221 2023/03/17 08:58:49 - mmengine - INFO - Epoch(train) [61][5000/5005] lr: 1.0000e-03 eta: 10:26:07 time: 0.2375 data_time: 0.0043 loss: 1.2761 2023/03/17 08:58:50 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 08:58:51 - mmengine - INFO - Saving checkpoint at 61 epochs 2023/03/17 08:58:57 - mmengine - INFO - Epoch(val) [61][100/196] eta: 0:00:05 time: 0.0451 data_time: 0.0009 2023/03/17 08:59:25 - mmengine - INFO - Epoch(val) [61][196/196] accuracy/top1: 74.3860 accuracy/top5: 92.1340data_time: 0.0381 time: 0.0684 2023/03/17 08:59:46 - mmengine - INFO - Epoch(train) [62][ 100/5005] lr: 1.0000e-03 eta: 10:25:48 time: 0.2000 data_time: 0.0037 loss: 1.0137 2023/03/17 09:00:06 - mmengine - INFO - Epoch(train) [62][ 200/5005] lr: 1.0000e-03 eta: 10:25:30 time: 0.2152 data_time: 0.0034 loss: 1.1834 2023/03/17 09:00:28 - mmengine - INFO - Epoch(train) [62][ 300/5005] lr: 1.0000e-03 eta: 10:25:12 time: 0.2201 data_time: 0.0036 loss: 1.0713 2023/03/17 09:00:47 - mmengine - INFO - Epoch(train) [62][ 400/5005] lr: 1.0000e-03 eta: 10:24:53 time: 0.1919 data_time: 0.0033 loss: 1.2326 2023/03/17 09:01:07 - mmengine - INFO - Epoch(train) [62][ 500/5005] lr: 1.0000e-03 eta: 10:24:34 time: 0.1967 data_time: 0.0035 loss: 1.1692 2023/03/17 09:01:27 - mmengine - INFO - Epoch(train) [62][ 600/5005] lr: 1.0000e-03 eta: 10:24:15 time: 0.1889 data_time: 0.0035 loss: 1.1052 2023/03/17 09:01:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:01:46 - mmengine - INFO - Epoch(train) [62][ 700/5005] lr: 1.0000e-03 eta: 10:23:55 time: 0.1982 data_time: 0.0037 loss: 1.0961 2023/03/17 09:02:07 - mmengine - INFO - Epoch(train) [62][ 800/5005] lr: 1.0000e-03 eta: 10:23:37 time: 0.2004 data_time: 0.0041 loss: 1.1406 2023/03/17 09:02:27 - mmengine - INFO - Epoch(train) [62][ 900/5005] lr: 1.0000e-03 eta: 10:23:18 time: 0.1963 data_time: 0.0041 loss: 1.0711 2023/03/17 09:02:47 - mmengine - INFO - Epoch(train) [62][1000/5005] lr: 1.0000e-03 eta: 10:22:59 time: 0.1808 data_time: 0.0039 loss: 0.9921 2023/03/17 09:03:06 - mmengine - INFO - Epoch(train) [62][1100/5005] lr: 1.0000e-03 eta: 10:22:40 time: 0.1853 data_time: 0.0044 loss: 0.8816 2023/03/17 09:03:24 - mmengine - INFO - Epoch(train) [62][1200/5005] lr: 1.0000e-03 eta: 10:22:20 time: 0.1799 data_time: 0.0035 loss: 1.0471 2023/03/17 09:03:44 - mmengine - INFO - Epoch(train) [62][1300/5005] lr: 1.0000e-03 eta: 10:22:01 time: 0.1902 data_time: 0.0038 loss: 1.2089 2023/03/17 09:04:04 - mmengine - INFO - Epoch(train) [62][1400/5005] lr: 1.0000e-03 eta: 10:21:42 time: 0.2005 data_time: 0.0035 loss: 1.1425 2023/03/17 09:04:23 - mmengine - INFO - Epoch(train) [62][1500/5005] lr: 1.0000e-03 eta: 10:21:22 time: 0.1881 data_time: 0.0037 loss: 1.1884 2023/03/17 09:04:42 - mmengine - INFO - Epoch(train) [62][1600/5005] lr: 1.0000e-03 eta: 10:21:03 time: 0.1891 data_time: 0.0038 loss: 1.0849 2023/03/17 09:05:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:05:01 - mmengine - INFO - Epoch(train) [62][1700/5005] lr: 1.0000e-03 eta: 10:20:44 time: 0.1993 data_time: 0.0033 loss: 1.0487 2023/03/17 09:05:22 - mmengine - INFO - Epoch(train) [62][1800/5005] lr: 1.0000e-03 eta: 10:20:25 time: 0.1882 data_time: 0.0034 loss: 1.0579 2023/03/17 09:05:42 - mmengine - INFO - Epoch(train) [62][1900/5005] lr: 1.0000e-03 eta: 10:20:06 time: 0.2085 data_time: 0.0037 loss: 1.2029 2023/03/17 09:06:02 - mmengine - INFO - Epoch(train) [62][2000/5005] lr: 1.0000e-03 eta: 10:19:48 time: 0.2001 data_time: 0.0036 loss: 1.0194 2023/03/17 09:06:23 - mmengine - INFO - Epoch(train) [62][2100/5005] lr: 1.0000e-03 eta: 10:19:29 time: 0.2206 data_time: 0.0037 loss: 1.1183 2023/03/17 09:06:43 - mmengine - INFO - Epoch(train) [62][2200/5005] lr: 1.0000e-03 eta: 10:19:11 time: 0.1996 data_time: 0.0038 loss: 1.2269 2023/03/17 09:07:04 - mmengine - INFO - Epoch(train) [62][2300/5005] lr: 1.0000e-03 eta: 10:18:52 time: 0.2026 data_time: 0.0041 loss: 1.0610 2023/03/17 09:07:24 - mmengine - INFO - Epoch(train) [62][2400/5005] lr: 1.0000e-03 eta: 10:18:33 time: 0.2016 data_time: 0.0031 loss: 1.2845 2023/03/17 09:07:45 - mmengine - INFO - Epoch(train) [62][2500/5005] lr: 1.0000e-03 eta: 10:18:15 time: 0.2116 data_time: 0.0034 loss: 1.2343 2023/03/17 09:08:02 - mmengine - INFO - Epoch(train) [62][2600/5005] lr: 1.0000e-03 eta: 10:17:55 time: 0.1760 data_time: 0.0036 loss: 1.0935 2023/03/17 09:08:19 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:08:20 - mmengine - INFO - Epoch(train) [62][2700/5005] lr: 1.0000e-03 eta: 10:17:34 time: 0.1835 data_time: 0.0039 loss: 1.2132 2023/03/17 09:08:39 - mmengine - INFO - Epoch(train) [62][2800/5005] lr: 1.0000e-03 eta: 10:17:15 time: 0.1913 data_time: 0.0034 loss: 1.1915 2023/03/17 09:08:58 - mmengine - INFO - Epoch(train) [62][2900/5005] lr: 1.0000e-03 eta: 10:16:55 time: 0.1879 data_time: 0.0032 loss: 1.0185 2023/03/17 09:09:18 - mmengine - INFO - Epoch(train) [62][3000/5005] lr: 1.0000e-03 eta: 10:16:36 time: 0.1921 data_time: 0.0032 loss: 1.1259 2023/03/17 09:09:38 - mmengine - INFO - Epoch(train) [62][3100/5005] lr: 1.0000e-03 eta: 10:16:18 time: 0.1995 data_time: 0.0035 loss: 1.3724 2023/03/17 09:09:58 - mmengine - INFO - Epoch(train) [62][3200/5005] lr: 1.0000e-03 eta: 10:15:58 time: 0.2100 data_time: 0.0035 loss: 1.1944 2023/03/17 09:10:18 - mmengine - INFO - Epoch(train) [62][3300/5005] lr: 1.0000e-03 eta: 10:15:40 time: 0.2124 data_time: 0.0033 loss: 1.1762 2023/03/17 09:10:39 - mmengine - INFO - Epoch(train) [62][3400/5005] lr: 1.0000e-03 eta: 10:15:22 time: 0.2286 data_time: 0.0037 loss: 1.2981 2023/03/17 09:10:59 - mmengine - INFO - Epoch(train) [62][3500/5005] lr: 1.0000e-03 eta: 10:15:03 time: 0.2015 data_time: 0.0033 loss: 1.1237 2023/03/17 09:11:20 - mmengine - INFO - Epoch(train) [62][3600/5005] lr: 1.0000e-03 eta: 10:14:44 time: 0.2054 data_time: 0.0041 loss: 1.4277 2023/03/17 09:11:39 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:11:40 - mmengine - INFO - Epoch(train) [62][3700/5005] lr: 1.0000e-03 eta: 10:14:26 time: 0.2059 data_time: 0.0033 loss: 1.0725 2023/03/17 09:12:00 - mmengine - INFO - Epoch(train) [62][3800/5005] lr: 1.0000e-03 eta: 10:14:07 time: 0.1980 data_time: 0.0041 loss: 1.0634 2023/03/17 09:12:21 - mmengine - INFO - Epoch(train) [62][3900/5005] lr: 1.0000e-03 eta: 10:13:49 time: 0.2043 data_time: 0.0039 loss: 1.1560 2023/03/17 09:12:42 - mmengine - INFO - Epoch(train) [62][4000/5005] lr: 1.0000e-03 eta: 10:13:30 time: 0.2013 data_time: 0.0041 loss: 1.0185 2023/03/17 09:13:02 - mmengine - INFO - Epoch(train) [62][4100/5005] lr: 1.0000e-03 eta: 10:13:11 time: 0.1947 data_time: 0.0043 loss: 1.1745 2023/03/17 09:13:22 - mmengine - INFO - Epoch(train) [62][4200/5005] lr: 1.0000e-03 eta: 10:12:52 time: 0.2075 data_time: 0.0044 loss: 1.0645 2023/03/17 09:13:43 - mmengine - INFO - Epoch(train) [62][4300/5005] lr: 1.0000e-03 eta: 10:12:34 time: 0.2035 data_time: 0.0039 loss: 1.2343 2023/03/17 09:14:03 - mmengine - INFO - Epoch(train) [62][4400/5005] lr: 1.0000e-03 eta: 10:12:15 time: 0.1857 data_time: 0.0041 loss: 1.1191 2023/03/17 09:14:23 - mmengine - INFO - Epoch(train) [62][4500/5005] lr: 1.0000e-03 eta: 10:11:56 time: 0.1963 data_time: 0.0040 loss: 1.1099 2023/03/17 09:14:43 - mmengine - INFO - Epoch(train) [62][4600/5005] lr: 1.0000e-03 eta: 10:11:37 time: 0.1993 data_time: 0.0038 loss: 1.2514 2023/03/17 09:15:02 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:15:03 - mmengine - INFO - Epoch(train) [62][4700/5005] lr: 1.0000e-03 eta: 10:11:19 time: 0.2047 data_time: 0.0038 loss: 1.2769 2023/03/17 09:15:23 - mmengine - INFO - Epoch(train) [62][4800/5005] lr: 1.0000e-03 eta: 10:10:59 time: 0.1872 data_time: 0.0035 loss: 1.0618 2023/03/17 09:15:42 - mmengine - INFO - Epoch(train) [62][4900/5005] lr: 1.0000e-03 eta: 10:10:40 time: 0.1976 data_time: 0.0036 loss: 1.0433 2023/03/17 09:16:02 - mmengine - INFO - Epoch(train) [62][5000/5005] lr: 1.0000e-03 eta: 10:10:21 time: 0.2106 data_time: 0.0047 loss: 1.0505 2023/03/17 09:16:03 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:16:04 - mmengine - INFO - Saving checkpoint at 62 epochs 2023/03/17 09:16:11 - mmengine - INFO - Epoch(val) [62][100/196] eta: 0:00:05 time: 0.0513 data_time: 0.0009 2023/03/17 09:16:39 - mmengine - INFO - Epoch(val) [62][196/196] accuracy/top1: 74.6560 accuracy/top5: 92.3100data_time: 0.0262 time: 0.0604 2023/03/17 09:17:00 - mmengine - INFO - Epoch(train) [63][ 100/5005] lr: 1.0000e-03 eta: 10:10:02 time: 0.1849 data_time: 0.0046 loss: 1.0575 2023/03/17 09:17:19 - mmengine - INFO - Epoch(train) [63][ 200/5005] lr: 1.0000e-03 eta: 10:09:43 time: 0.1893 data_time: 0.0037 loss: 1.3023 2023/03/17 09:17:38 - mmengine - INFO - Epoch(train) [63][ 300/5005] lr: 1.0000e-03 eta: 10:09:23 time: 0.1862 data_time: 0.0034 loss: 1.1488 2023/03/17 09:17:58 - mmengine - INFO - Epoch(train) [63][ 400/5005] lr: 1.0000e-03 eta: 10:09:04 time: 0.1995 data_time: 0.0037 loss: 1.0869 2023/03/17 09:18:18 - mmengine - INFO - Epoch(train) [63][ 500/5005] lr: 1.0000e-03 eta: 10:08:45 time: 0.1978 data_time: 0.0034 loss: 0.9029 2023/03/17 09:18:38 - mmengine - INFO - Epoch(train) [63][ 600/5005] lr: 1.0000e-03 eta: 10:08:27 time: 0.2284 data_time: 0.0034 loss: 0.9624 2023/03/17 09:18:56 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:18:58 - mmengine - INFO - Epoch(train) [63][ 700/5005] lr: 1.0000e-03 eta: 10:08:07 time: 0.1942 data_time: 0.0035 loss: 1.1226 2023/03/17 09:19:17 - mmengine - INFO - Epoch(train) [63][ 800/5005] lr: 1.0000e-03 eta: 10:07:48 time: 0.1887 data_time: 0.0039 loss: 1.1804 2023/03/17 09:19:36 - mmengine - INFO - Epoch(train) [63][ 900/5005] lr: 1.0000e-03 eta: 10:07:29 time: 0.1875 data_time: 0.0041 loss: 1.0892 2023/03/17 09:19:56 - mmengine - INFO - Epoch(train) [63][1000/5005] lr: 1.0000e-03 eta: 10:07:10 time: 0.2258 data_time: 0.0038 loss: 0.9490 2023/03/17 09:20:16 - mmengine - INFO - Epoch(train) [63][1100/5005] lr: 1.0000e-03 eta: 10:06:50 time: 0.1827 data_time: 0.0036 loss: 1.0724 2023/03/17 09:20:34 - mmengine - INFO - Epoch(train) [63][1200/5005] lr: 1.0000e-03 eta: 10:06:31 time: 0.1858 data_time: 0.0039 loss: 1.0982 2023/03/17 09:20:53 - mmengine - INFO - Epoch(train) [63][1300/5005] lr: 1.0000e-03 eta: 10:06:11 time: 0.1836 data_time: 0.0036 loss: 1.0637 2023/03/17 09:21:12 - mmengine - INFO - Epoch(train) [63][1400/5005] lr: 1.0000e-03 eta: 10:05:52 time: 0.1973 data_time: 0.0043 loss: 1.2198 2023/03/17 09:21:33 - mmengine - INFO - Epoch(train) [63][1500/5005] lr: 1.0000e-03 eta: 10:05:34 time: 0.2056 data_time: 0.0037 loss: 1.0472 2023/03/17 09:21:54 - mmengine - INFO - Epoch(train) [63][1600/5005] lr: 1.0000e-03 eta: 10:05:15 time: 0.1950 data_time: 0.0035 loss: 1.0831 2023/03/17 09:22:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:22:15 - mmengine - INFO - Epoch(train) [63][1700/5005] lr: 1.0000e-03 eta: 10:04:57 time: 0.2112 data_time: 0.0036 loss: 1.0090 2023/03/17 09:22:36 - mmengine - INFO - Epoch(train) [63][1800/5005] lr: 1.0000e-03 eta: 10:04:39 time: 0.1844 data_time: 0.0038 loss: 1.0821 2023/03/17 09:22:55 - mmengine - INFO - Epoch(train) [63][1900/5005] lr: 1.0000e-03 eta: 10:04:19 time: 0.1809 data_time: 0.0043 loss: 1.3014 2023/03/17 09:23:13 - mmengine - INFO - Epoch(train) [63][2000/5005] lr: 1.0000e-03 eta: 10:03:59 time: 0.1783 data_time: 0.0040 loss: 0.9541 2023/03/17 09:23:32 - mmengine - INFO - Epoch(train) [63][2100/5005] lr: 1.0000e-03 eta: 10:03:40 time: 0.1946 data_time: 0.0046 loss: 1.2306 2023/03/17 09:23:52 - mmengine - INFO - Epoch(train) [63][2200/5005] lr: 1.0000e-03 eta: 10:03:21 time: 0.1896 data_time: 0.0039 loss: 1.1037 2023/03/17 09:24:11 - mmengine - INFO - Epoch(train) [63][2300/5005] lr: 1.0000e-03 eta: 10:03:01 time: 0.1879 data_time: 0.0043 loss: 1.1564 2023/03/17 09:24:30 - mmengine - INFO - Epoch(train) [63][2400/5005] lr: 1.0000e-03 eta: 10:02:42 time: 0.1972 data_time: 0.0032 loss: 1.1600 2023/03/17 09:24:50 - mmengine - INFO - Epoch(train) [63][2500/5005] lr: 1.0000e-03 eta: 10:02:23 time: 0.2025 data_time: 0.0040 loss: 1.1108 2023/03/17 09:25:10 - mmengine - INFO - Epoch(train) [63][2600/5005] lr: 1.0000e-03 eta: 10:02:04 time: 0.1780 data_time: 0.0038 loss: 1.1505 2023/03/17 09:25:26 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:25:28 - mmengine - INFO - Epoch(train) [63][2700/5005] lr: 1.0000e-03 eta: 10:01:44 time: 0.1983 data_time: 0.0043 loss: 1.1501 2023/03/17 09:25:47 - mmengine - INFO - Epoch(train) [63][2800/5005] lr: 1.0000e-03 eta: 10:01:24 time: 0.1855 data_time: 0.0039 loss: 1.1266 2023/03/17 09:26:06 - mmengine - INFO - Epoch(train) [63][2900/5005] lr: 1.0000e-03 eta: 10:01:05 time: 0.1866 data_time: 0.0041 loss: 1.3114 2023/03/17 09:26:26 - mmengine - INFO - Epoch(train) [63][3000/5005] lr: 1.0000e-03 eta: 10:00:46 time: 0.1911 data_time: 0.0041 loss: 1.0949 2023/03/17 09:26:46 - mmengine - INFO - Epoch(train) [63][3100/5005] lr: 1.0000e-03 eta: 10:00:27 time: 0.1961 data_time: 0.0035 loss: 1.1829 2023/03/17 09:27:06 - mmengine - INFO - Epoch(train) [63][3200/5005] lr: 1.0000e-03 eta: 10:00:08 time: 0.2049 data_time: 0.0042 loss: 1.1112 2023/03/17 09:27:27 - mmengine - INFO - Epoch(train) [63][3300/5005] lr: 1.0000e-03 eta: 9:59:50 time: 0.2011 data_time: 0.0038 loss: 1.0158 2023/03/17 09:27:47 - mmengine - INFO - Epoch(train) [63][3400/5005] lr: 1.0000e-03 eta: 9:59:31 time: 0.2055 data_time: 0.0038 loss: 1.1060 2023/03/17 09:28:09 - mmengine - INFO - Epoch(train) [63][3500/5005] lr: 1.0000e-03 eta: 9:59:13 time: 0.2103 data_time: 0.0042 loss: 1.2684 2023/03/17 09:28:30 - mmengine - INFO - Epoch(train) [63][3600/5005] lr: 1.0000e-03 eta: 9:58:55 time: 0.2035 data_time: 0.0045 loss: 1.2216 2023/03/17 09:28:49 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:28:51 - mmengine - INFO - Epoch(train) [63][3700/5005] lr: 1.0000e-03 eta: 9:58:36 time: 0.2117 data_time: 0.0042 loss: 1.2473 2023/03/17 09:29:12 - mmengine - INFO - Epoch(train) [63][3800/5005] lr: 1.0000e-03 eta: 9:58:18 time: 0.2139 data_time: 0.0042 loss: 1.2339 2023/03/17 09:29:32 - mmengine - INFO - Epoch(train) [63][3900/5005] lr: 1.0000e-03 eta: 9:57:59 time: 0.1982 data_time: 0.0031 loss: 1.1115 2023/03/17 09:29:52 - mmengine - INFO - Epoch(train) [63][4000/5005] lr: 1.0000e-03 eta: 9:57:40 time: 0.1949 data_time: 0.0033 loss: 1.0075 2023/03/17 09:30:12 - mmengine - INFO - Epoch(train) [63][4100/5005] lr: 1.0000e-03 eta: 9:57:21 time: 0.1949 data_time: 0.0034 loss: 1.0543 2023/03/17 09:30:32 - mmengine - INFO - Epoch(train) [63][4200/5005] lr: 1.0000e-03 eta: 9:57:03 time: 0.1948 data_time: 0.0033 loss: 1.2660 2023/03/17 09:30:51 - mmengine - INFO - Epoch(train) [63][4300/5005] lr: 1.0000e-03 eta: 9:56:43 time: 0.1970 data_time: 0.0031 loss: 1.2384 2023/03/17 09:31:11 - mmengine - INFO - Epoch(train) [63][4400/5005] lr: 1.0000e-03 eta: 9:56:24 time: 0.1938 data_time: 0.0032 loss: 1.0328 2023/03/17 09:31:30 - mmengine - INFO - Epoch(train) [63][4500/5005] lr: 1.0000e-03 eta: 9:56:05 time: 0.1954 data_time: 0.0040 loss: 1.0439 2023/03/17 09:31:50 - mmengine - INFO - Epoch(train) [63][4600/5005] lr: 1.0000e-03 eta: 9:55:46 time: 0.2034 data_time: 0.0041 loss: 1.1230 2023/03/17 09:32:09 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:32:11 - mmengine - INFO - Epoch(train) [63][4700/5005] lr: 1.0000e-03 eta: 9:55:27 time: 0.2019 data_time: 0.0043 loss: 1.2709 2023/03/17 09:32:31 - mmengine - INFO - Epoch(train) [63][4800/5005] lr: 1.0000e-03 eta: 9:55:09 time: 0.1972 data_time: 0.0038 loss: 1.2376 2023/03/17 09:32:51 - mmengine - INFO - Epoch(train) [63][4900/5005] lr: 1.0000e-03 eta: 9:54:50 time: 0.2011 data_time: 0.0040 loss: 1.0991 2023/03/17 09:33:12 - mmengine - INFO - Epoch(train) [63][5000/5005] lr: 1.0000e-03 eta: 9:54:31 time: 0.2358 data_time: 0.0048 loss: 1.1410 2023/03/17 09:33:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:33:13 - mmengine - INFO - Saving checkpoint at 63 epochs 2023/03/17 09:33:20 - mmengine - INFO - Epoch(val) [63][100/196] eta: 0:00:05 time: 0.0572 data_time: 0.0009 2023/03/17 09:33:48 - mmengine - INFO - Epoch(val) [63][196/196] accuracy/top1: 74.8040 accuracy/top5: 92.4420data_time: 0.0147 time: 0.0480 2023/03/17 09:34:11 - mmengine - INFO - Epoch(train) [64][ 100/5005] lr: 1.0000e-03 eta: 9:54:13 time: 0.2579 data_time: 0.0036 loss: 1.1012 2023/03/17 09:34:35 - mmengine - INFO - Epoch(train) [64][ 200/5005] lr: 1.0000e-03 eta: 9:53:56 time: 0.2008 data_time: 0.0042 loss: 1.1734 2023/03/17 09:34:54 - mmengine - INFO - Epoch(train) [64][ 300/5005] lr: 1.0000e-03 eta: 9:53:37 time: 0.2073 data_time: 0.0035 loss: 1.1536 2023/03/17 09:35:13 - mmengine - INFO - Epoch(train) [64][ 400/5005] lr: 1.0000e-03 eta: 9:53:17 time: 0.1831 data_time: 0.0040 loss: 1.1442 2023/03/17 09:35:32 - mmengine - INFO - Epoch(train) [64][ 500/5005] lr: 1.0000e-03 eta: 9:52:58 time: 0.1920 data_time: 0.0036 loss: 1.1363 2023/03/17 09:35:52 - mmengine - INFO - Epoch(train) [64][ 600/5005] lr: 1.0000e-03 eta: 9:52:39 time: 0.1987 data_time: 0.0039 loss: 1.2498 2023/03/17 09:36:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:36:17 - mmengine - INFO - Epoch(train) [64][ 700/5005] lr: 1.0000e-03 eta: 9:52:23 time: 0.2534 data_time: 0.0039 loss: 1.0723 2023/03/17 09:36:41 - mmengine - INFO - Epoch(train) [64][ 800/5005] lr: 1.0000e-03 eta: 9:52:06 time: 0.2079 data_time: 0.0038 loss: 1.1361 2023/03/17 09:37:01 - mmengine - INFO - Epoch(train) [64][ 900/5005] lr: 1.0000e-03 eta: 9:51:47 time: 0.1802 data_time: 0.0037 loss: 1.2322 2023/03/17 09:37:21 - mmengine - INFO - Epoch(train) [64][1000/5005] lr: 1.0000e-03 eta: 9:51:28 time: 0.1969 data_time: 0.0043 loss: 1.1953 2023/03/17 09:37:41 - mmengine - INFO - Epoch(train) [64][1100/5005] lr: 1.0000e-03 eta: 9:51:09 time: 0.2180 data_time: 0.0041 loss: 1.1066 2023/03/17 09:38:01 - mmengine - INFO - Epoch(train) [64][1200/5005] lr: 1.0000e-03 eta: 9:50:51 time: 0.1975 data_time: 0.0040 loss: 1.1182 2023/03/17 09:38:21 - mmengine - INFO - Epoch(train) [64][1300/5005] lr: 1.0000e-03 eta: 9:50:32 time: 0.1972 data_time: 0.0042 loss: 1.0140 2023/03/17 09:38:41 - mmengine - INFO - Epoch(train) [64][1400/5005] lr: 1.0000e-03 eta: 9:50:13 time: 0.1865 data_time: 0.0041 loss: 1.1936 2023/03/17 09:38:59 - mmengine - INFO - Epoch(train) [64][1500/5005] lr: 1.0000e-03 eta: 9:49:53 time: 0.1747 data_time: 0.0037 loss: 1.0651 2023/03/17 09:39:17 - mmengine - INFO - Epoch(train) [64][1600/5005] lr: 1.0000e-03 eta: 9:49:33 time: 0.1925 data_time: 0.0043 loss: 1.0285 2023/03/17 09:39:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:39:37 - mmengine - INFO - Epoch(train) [64][1700/5005] lr: 1.0000e-03 eta: 9:49:14 time: 0.1909 data_time: 0.0045 loss: 1.0500 2023/03/17 09:39:57 - mmengine - INFO - Epoch(train) [64][1800/5005] lr: 1.0000e-03 eta: 9:48:55 time: 0.2233 data_time: 0.0039 loss: 1.1249 2023/03/17 09:40:20 - mmengine - INFO - Epoch(train) [64][1900/5005] lr: 1.0000e-03 eta: 9:48:38 time: 0.2038 data_time: 0.0040 loss: 1.1987 2023/03/17 09:40:38 - mmengine - INFO - Epoch(train) [64][2000/5005] lr: 1.0000e-03 eta: 9:48:18 time: 0.1810 data_time: 0.0037 loss: 1.0235 2023/03/17 09:40:57 - mmengine - INFO - Epoch(train) [64][2100/5005] lr: 1.0000e-03 eta: 9:47:58 time: 0.1723 data_time: 0.0039 loss: 1.1340 2023/03/17 09:41:14 - mmengine - INFO - Epoch(train) [64][2200/5005] lr: 1.0000e-03 eta: 9:47:38 time: 0.1777 data_time: 0.0042 loss: 0.9763 2023/03/17 09:41:34 - mmengine - INFO - Epoch(train) [64][2300/5005] lr: 1.0000e-03 eta: 9:47:18 time: 0.1890 data_time: 0.0042 loss: 1.1656 2023/03/17 09:41:54 - mmengine - INFO - Epoch(train) [64][2400/5005] lr: 1.0000e-03 eta: 9:46:59 time: 0.1877 data_time: 0.0039 loss: 0.9904 2023/03/17 09:42:12 - mmengine - INFO - Epoch(train) [64][2500/5005] lr: 1.0000e-03 eta: 9:46:40 time: 0.1804 data_time: 0.0036 loss: 1.1651 2023/03/17 09:42:30 - mmengine - INFO - Epoch(train) [64][2600/5005] lr: 1.0000e-03 eta: 9:46:20 time: 0.1824 data_time: 0.0039 loss: 1.3147 2023/03/17 09:42:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:42:49 - mmengine - INFO - Epoch(train) [64][2700/5005] lr: 1.0000e-03 eta: 9:46:00 time: 0.1815 data_time: 0.0041 loss: 1.1964 2023/03/17 09:43:09 - mmengine - INFO - Epoch(train) [64][2800/5005] lr: 1.0000e-03 eta: 9:45:41 time: 0.2039 data_time: 0.0043 loss: 1.0473 2023/03/17 09:43:29 - mmengine - INFO - Epoch(train) [64][2900/5005] lr: 1.0000e-03 eta: 9:45:22 time: 0.2021 data_time: 0.0040 loss: 1.0212 2023/03/17 09:43:48 - mmengine - INFO - Epoch(train) [64][3000/5005] lr: 1.0000e-03 eta: 9:45:03 time: 0.1926 data_time: 0.0039 loss: 1.1093 2023/03/17 09:44:07 - mmengine - INFO - Epoch(train) [64][3100/5005] lr: 1.0000e-03 eta: 9:44:43 time: 0.1912 data_time: 0.0034 loss: 0.8903 2023/03/17 09:44:28 - mmengine - INFO - Epoch(train) [64][3200/5005] lr: 1.0000e-03 eta: 9:44:24 time: 0.1983 data_time: 0.0035 loss: 1.0302 2023/03/17 09:44:47 - mmengine - INFO - Epoch(train) [64][3300/5005] lr: 1.0000e-03 eta: 9:44:05 time: 0.1792 data_time: 0.0034 loss: 1.1208 2023/03/17 09:45:05 - mmengine - INFO - Epoch(train) [64][3400/5005] lr: 1.0000e-03 eta: 9:43:45 time: 0.1867 data_time: 0.0037 loss: 1.2152 2023/03/17 09:45:23 - mmengine - INFO - Epoch(train) [64][3500/5005] lr: 1.0000e-03 eta: 9:43:25 time: 0.1693 data_time: 0.0041 loss: 1.1871 2023/03/17 09:45:42 - mmengine - INFO - Epoch(train) [64][3600/5005] lr: 1.0000e-03 eta: 9:43:06 time: 0.1838 data_time: 0.0040 loss: 1.1703 2023/03/17 09:45:58 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:46:01 - mmengine - INFO - Epoch(train) [64][3700/5005] lr: 1.0000e-03 eta: 9:42:46 time: 0.1827 data_time: 0.0036 loss: 1.2186 2023/03/17 09:46:19 - mmengine - INFO - Epoch(train) [64][3800/5005] lr: 1.0000e-03 eta: 9:42:26 time: 0.1923 data_time: 0.0036 loss: 1.1252 2023/03/17 09:46:38 - mmengine - INFO - Epoch(train) [64][3900/5005] lr: 1.0000e-03 eta: 9:42:06 time: 0.1881 data_time: 0.0039 loss: 1.1631 2023/03/17 09:46:56 - mmengine - INFO - Epoch(train) [64][4000/5005] lr: 1.0000e-03 eta: 9:41:47 time: 0.1801 data_time: 0.0037 loss: 1.3935 2023/03/17 09:47:14 - mmengine - INFO - Epoch(train) [64][4100/5005] lr: 1.0000e-03 eta: 9:41:27 time: 0.1797 data_time: 0.0040 loss: 1.2515 2023/03/17 09:47:34 - mmengine - INFO - Epoch(train) [64][4200/5005] lr: 1.0000e-03 eta: 9:41:07 time: 0.1995 data_time: 0.0038 loss: 1.1527 2023/03/17 09:47:53 - mmengine - INFO - Epoch(train) [64][4300/5005] lr: 1.0000e-03 eta: 9:40:48 time: 0.1819 data_time: 0.0036 loss: 1.2620 2023/03/17 09:48:12 - mmengine - INFO - Epoch(train) [64][4400/5005] lr: 1.0000e-03 eta: 9:40:29 time: 0.1981 data_time: 0.0039 loss: 1.1714 2023/03/17 09:48:32 - mmengine - INFO - Epoch(train) [64][4500/5005] lr: 1.0000e-03 eta: 9:40:10 time: 0.2249 data_time: 0.0040 loss: 1.0807 2023/03/17 09:48:52 - mmengine - INFO - Epoch(train) [64][4600/5005] lr: 1.0000e-03 eta: 9:39:50 time: 0.2049 data_time: 0.0039 loss: 1.0030 2023/03/17 09:49:08 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:49:10 - mmengine - INFO - Epoch(train) [64][4700/5005] lr: 1.0000e-03 eta: 9:39:31 time: 0.1902 data_time: 0.0046 loss: 1.0640 2023/03/17 09:49:30 - mmengine - INFO - Epoch(train) [64][4800/5005] lr: 1.0000e-03 eta: 9:39:11 time: 0.1898 data_time: 0.0039 loss: 1.0848 2023/03/17 09:49:50 - mmengine - INFO - Epoch(train) [64][4900/5005] lr: 1.0000e-03 eta: 9:38:52 time: 0.2082 data_time: 0.0042 loss: 1.1190 2023/03/17 09:50:10 - mmengine - INFO - Epoch(train) [64][5000/5005] lr: 1.0000e-03 eta: 9:38:34 time: 0.2113 data_time: 0.0050 loss: 0.8814 2023/03/17 09:50:11 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:50:12 - mmengine - INFO - Saving checkpoint at 64 epochs 2023/03/17 09:50:18 - mmengine - INFO - Epoch(val) [64][100/196] eta: 0:00:05 time: 0.0442 data_time: 0.0010 2023/03/17 09:50:43 - mmengine - INFO - Epoch(val) [64][196/196] accuracy/top1: 74.9380 accuracy/top5: 92.4360data_time: 0.0314 time: 0.0613 2023/03/17 09:51:03 - mmengine - INFO - Epoch(train) [65][ 100/5005] lr: 1.0000e-03 eta: 9:38:14 time: 0.1880 data_time: 0.0040 loss: 0.9532 2023/03/17 09:51:21 - mmengine - INFO - Epoch(train) [65][ 200/5005] lr: 1.0000e-03 eta: 9:37:54 time: 0.1770 data_time: 0.0037 loss: 1.1722 2023/03/17 09:51:39 - mmengine - INFO - Epoch(train) [65][ 300/5005] lr: 1.0000e-03 eta: 9:37:34 time: 0.1796 data_time: 0.0038 loss: 1.1407 2023/03/17 09:51:57 - mmengine - INFO - Epoch(train) [65][ 400/5005] lr: 1.0000e-03 eta: 9:37:14 time: 0.1841 data_time: 0.0035 loss: 1.1418 2023/03/17 09:52:16 - mmengine - INFO - Epoch(train) [65][ 500/5005] lr: 1.0000e-03 eta: 9:36:55 time: 0.1819 data_time: 0.0040 loss: 1.0197 2023/03/17 09:52:35 - mmengine - INFO - Epoch(train) [65][ 600/5005] lr: 1.0000e-03 eta: 9:36:35 time: 0.1905 data_time: 0.0037 loss: 0.9328 2023/03/17 09:52:50 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:52:54 - mmengine - INFO - Epoch(train) [65][ 700/5005] lr: 1.0000e-03 eta: 9:36:16 time: 0.1896 data_time: 0.0038 loss: 0.9893 2023/03/17 09:53:14 - mmengine - INFO - Epoch(train) [65][ 800/5005] lr: 1.0000e-03 eta: 9:35:56 time: 0.2194 data_time: 0.0041 loss: 1.0450 2023/03/17 09:53:34 - mmengine - INFO - Epoch(train) [65][ 900/5005] lr: 1.0000e-03 eta: 9:35:37 time: 0.2028 data_time: 0.0040 loss: 1.2464 2023/03/17 09:53:54 - mmengine - INFO - Epoch(train) [65][1000/5005] lr: 1.0000e-03 eta: 9:35:19 time: 0.2043 data_time: 0.0040 loss: 1.1146 2023/03/17 09:54:15 - mmengine - INFO - Epoch(train) [65][1100/5005] lr: 1.0000e-03 eta: 9:35:00 time: 0.2116 data_time: 0.0044 loss: 0.9760 2023/03/17 09:54:36 - mmengine - INFO - Epoch(train) [65][1200/5005] lr: 1.0000e-03 eta: 9:34:42 time: 0.2128 data_time: 0.0043 loss: 1.0804 2023/03/17 09:54:57 - mmengine - INFO - Epoch(train) [65][1300/5005] lr: 1.0000e-03 eta: 9:34:24 time: 0.2023 data_time: 0.0043 loss: 1.2146 2023/03/17 09:55:18 - mmengine - INFO - Epoch(train) [65][1400/5005] lr: 1.0000e-03 eta: 9:34:05 time: 0.2009 data_time: 0.0041 loss: 1.1158 2023/03/17 09:55:37 - mmengine - INFO - Epoch(train) [65][1500/5005] lr: 1.0000e-03 eta: 9:33:46 time: 0.1930 data_time: 0.0039 loss: 0.9956 2023/03/17 09:55:57 - mmengine - INFO - Epoch(train) [65][1600/5005] lr: 1.0000e-03 eta: 9:33:27 time: 0.1983 data_time: 0.0041 loss: 1.1107 2023/03/17 09:56:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:56:17 - mmengine - INFO - Epoch(train) [65][1700/5005] lr: 1.0000e-03 eta: 9:33:08 time: 0.2030 data_time: 0.0037 loss: 1.0512 2023/03/17 09:56:37 - mmengine - INFO - Epoch(train) [65][1800/5005] lr: 1.0000e-03 eta: 9:32:49 time: 0.1954 data_time: 0.0038 loss: 0.8863 2023/03/17 09:56:58 - mmengine - INFO - Epoch(train) [65][1900/5005] lr: 1.0000e-03 eta: 9:32:31 time: 0.1962 data_time: 0.0043 loss: 1.0801 2023/03/17 09:57:19 - mmengine - INFO - Epoch(train) [65][2000/5005] lr: 1.0000e-03 eta: 9:32:12 time: 0.2084 data_time: 0.0036 loss: 1.0583 2023/03/17 09:57:39 - mmengine - INFO - Epoch(train) [65][2100/5005] lr: 1.0000e-03 eta: 9:31:53 time: 0.1935 data_time: 0.0037 loss: 0.9965 2023/03/17 09:58:00 - mmengine - INFO - Epoch(train) [65][2200/5005] lr: 1.0000e-03 eta: 9:31:35 time: 0.2016 data_time: 0.0041 loss: 1.0788 2023/03/17 09:58:20 - mmengine - INFO - Epoch(train) [65][2300/5005] lr: 1.0000e-03 eta: 9:31:16 time: 0.2091 data_time: 0.0042 loss: 1.0289 2023/03/17 09:58:40 - mmengine - INFO - Epoch(train) [65][2400/5005] lr: 1.0000e-03 eta: 9:30:57 time: 0.1898 data_time: 0.0042 loss: 1.0688 2023/03/17 09:58:59 - mmengine - INFO - Epoch(train) [65][2500/5005] lr: 1.0000e-03 eta: 9:30:38 time: 0.1981 data_time: 0.0042 loss: 1.1253 2023/03/17 09:59:20 - mmengine - INFO - Epoch(train) [65][2600/5005] lr: 1.0000e-03 eta: 9:30:19 time: 0.2059 data_time: 0.0050 loss: 1.1337 2023/03/17 09:59:37 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 09:59:40 - mmengine - INFO - Epoch(train) [65][2700/5005] lr: 1.0000e-03 eta: 9:30:00 time: 0.1882 data_time: 0.0038 loss: 1.1071 2023/03/17 10:00:00 - mmengine - INFO - Epoch(train) [65][2800/5005] lr: 1.0000e-03 eta: 9:29:41 time: 0.1927 data_time: 0.0040 loss: 1.0895 2023/03/17 10:00:20 - mmengine - INFO - Epoch(train) [65][2900/5005] lr: 1.0000e-03 eta: 9:29:22 time: 0.2044 data_time: 0.0046 loss: 0.9245 2023/03/17 10:00:40 - mmengine - INFO - Epoch(train) [65][3000/5005] lr: 1.0000e-03 eta: 9:29:04 time: 0.1969 data_time: 0.0040 loss: 1.0125 2023/03/17 10:01:00 - mmengine - INFO - Epoch(train) [65][3100/5005] lr: 1.0000e-03 eta: 9:28:45 time: 0.1920 data_time: 0.0037 loss: 1.1798 2023/03/17 10:01:20 - mmengine - INFO - Epoch(train) [65][3200/5005] lr: 1.0000e-03 eta: 9:28:26 time: 0.2009 data_time: 0.0039 loss: 1.1044 2023/03/17 10:01:41 - mmengine - INFO - Epoch(train) [65][3300/5005] lr: 1.0000e-03 eta: 9:28:07 time: 0.1996 data_time: 0.0037 loss: 1.0939 2023/03/17 10:02:00 - mmengine - INFO - Epoch(train) [65][3400/5005] lr: 1.0000e-03 eta: 9:27:48 time: 0.1863 data_time: 0.0038 loss: 1.3357 2023/03/17 10:02:20 - mmengine - INFO - Epoch(train) [65][3500/5005] lr: 1.0000e-03 eta: 9:27:29 time: 0.1993 data_time: 0.0039 loss: 1.0904 2023/03/17 10:02:40 - mmengine - INFO - Epoch(train) [65][3600/5005] lr: 1.0000e-03 eta: 9:27:10 time: 0.2013 data_time: 0.0041 loss: 1.1348 2023/03/17 10:02:57 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:03:01 - mmengine - INFO - Epoch(train) [65][3700/5005] lr: 1.0000e-03 eta: 9:26:51 time: 0.2042 data_time: 0.0040 loss: 1.0010 2023/03/17 10:03:22 - mmengine - INFO - Epoch(train) [65][3800/5005] lr: 1.0000e-03 eta: 9:26:33 time: 0.2103 data_time: 0.0042 loss: 1.3240 2023/03/17 10:03:44 - mmengine - INFO - Epoch(train) [65][3900/5005] lr: 1.0000e-03 eta: 9:26:15 time: 0.2040 data_time: 0.0039 loss: 0.8832 2023/03/17 10:04:04 - mmengine - INFO - Epoch(train) [65][4000/5005] lr: 1.0000e-03 eta: 9:25:56 time: 0.2086 data_time: 0.0045 loss: 1.0435 2023/03/17 10:04:25 - mmengine - INFO - Epoch(train) [65][4100/5005] lr: 1.0000e-03 eta: 9:25:38 time: 0.1967 data_time: 0.0039 loss: 0.9374 2023/03/17 10:04:45 - mmengine - INFO - Epoch(train) [65][4200/5005] lr: 1.0000e-03 eta: 9:25:19 time: 0.2109 data_time: 0.0043 loss: 1.2898 2023/03/17 10:05:06 - mmengine - INFO - Epoch(train) [65][4300/5005] lr: 1.0000e-03 eta: 9:25:00 time: 0.2065 data_time: 0.0040 loss: 1.0960 2023/03/17 10:05:27 - mmengine - INFO - Epoch(train) [65][4400/5005] lr: 1.0000e-03 eta: 9:24:42 time: 0.2045 data_time: 0.0040 loss: 1.2361 2023/03/17 10:05:48 - mmengine - INFO - Epoch(train) [65][4500/5005] lr: 1.0000e-03 eta: 9:24:24 time: 0.2098 data_time: 0.0037 loss: 1.1780 2023/03/17 10:06:09 - mmengine - INFO - Epoch(train) [65][4600/5005] lr: 1.0000e-03 eta: 9:24:05 time: 0.2062 data_time: 0.0042 loss: 1.1032 2023/03/17 10:06:26 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:06:30 - mmengine - INFO - Epoch(train) [65][4700/5005] lr: 1.0000e-03 eta: 9:23:47 time: 0.2051 data_time: 0.0042 loss: 1.1464 2023/03/17 10:06:51 - mmengine - INFO - Epoch(train) [65][4800/5005] lr: 1.0000e-03 eta: 9:23:28 time: 0.2067 data_time: 0.0040 loss: 1.1476 2023/03/17 10:07:11 - mmengine - INFO - Epoch(train) [65][4900/5005] lr: 1.0000e-03 eta: 9:23:09 time: 0.2014 data_time: 0.0043 loss: 1.0918 2023/03/17 10:07:32 - mmengine - INFO - Epoch(train) [65][5000/5005] lr: 1.0000e-03 eta: 9:22:50 time: 0.1938 data_time: 0.0049 loss: 1.0943 2023/03/17 10:07:33 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:07:33 - mmengine - INFO - Saving checkpoint at 65 epochs 2023/03/17 10:07:40 - mmengine - INFO - Epoch(val) [65][100/196] eta: 0:00:06 time: 0.0628 data_time: 0.0009 2023/03/17 10:08:09 - mmengine - INFO - Epoch(val) [65][196/196] accuracy/top1: 75.2520 accuracy/top5: 92.5560data_time: 0.0306 time: 0.0686 2023/03/17 10:08:29 - mmengine - INFO - Epoch(train) [66][ 100/5005] lr: 1.0000e-03 eta: 9:22:31 time: 0.1942 data_time: 0.0038 loss: 1.0618 2023/03/17 10:08:48 - mmengine - INFO - Epoch(train) [66][ 200/5005] lr: 1.0000e-03 eta: 9:22:11 time: 0.1831 data_time: 0.0049 loss: 0.9995 2023/03/17 10:09:06 - mmengine - INFO - Epoch(train) [66][ 300/5005] lr: 1.0000e-03 eta: 9:21:51 time: 0.1827 data_time: 0.0041 loss: 1.0599 2023/03/17 10:09:25 - mmengine - INFO - Epoch(train) [66][ 400/5005] lr: 1.0000e-03 eta: 9:21:31 time: 0.1759 data_time: 0.0044 loss: 1.2557 2023/03/17 10:09:42 - mmengine - INFO - Epoch(train) [66][ 500/5005] lr: 1.0000e-03 eta: 9:21:11 time: 0.1678 data_time: 0.0035 loss: 1.1695 2023/03/17 10:10:00 - mmengine - INFO - Epoch(train) [66][ 600/5005] lr: 1.0000e-03 eta: 9:20:51 time: 0.1850 data_time: 0.0039 loss: 1.0138 2023/03/17 10:10:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:10:18 - mmengine - INFO - Epoch(train) [66][ 700/5005] lr: 1.0000e-03 eta: 9:20:31 time: 0.1813 data_time: 0.0040 loss: 1.0985 2023/03/17 10:10:36 - mmengine - INFO - Epoch(train) [66][ 800/5005] lr: 1.0000e-03 eta: 9:20:11 time: 0.1832 data_time: 0.0039 loss: 1.1056 2023/03/17 10:10:54 - mmengine - INFO - Epoch(train) [66][ 900/5005] lr: 1.0000e-03 eta: 9:19:51 time: 0.1761 data_time: 0.0042 loss: 1.0316 2023/03/17 10:11:13 - mmengine - INFO - Epoch(train) [66][1000/5005] lr: 1.0000e-03 eta: 9:19:31 time: 0.1921 data_time: 0.0043 loss: 0.9673 2023/03/17 10:11:33 - mmengine - INFO - Epoch(train) [66][1100/5005] lr: 1.0000e-03 eta: 9:19:12 time: 0.2263 data_time: 0.0032 loss: 1.0902 2023/03/17 10:11:53 - mmengine - INFO - Epoch(train) [66][1200/5005] lr: 1.0000e-03 eta: 9:18:53 time: 0.1827 data_time: 0.0042 loss: 1.1522 2023/03/17 10:12:11 - mmengine - INFO - Epoch(train) [66][1300/5005] lr: 1.0000e-03 eta: 9:18:34 time: 0.1766 data_time: 0.0040 loss: 0.9683 2023/03/17 10:12:29 - mmengine - INFO - Epoch(train) [66][1400/5005] lr: 1.0000e-03 eta: 9:18:13 time: 0.1699 data_time: 0.0039 loss: 1.1572 2023/03/17 10:12:47 - mmengine - INFO - Epoch(train) [66][1500/5005] lr: 1.0000e-03 eta: 9:17:53 time: 0.1970 data_time: 0.0043 loss: 1.2370 2023/03/17 10:13:05 - mmengine - INFO - Epoch(train) [66][1600/5005] lr: 1.0000e-03 eta: 9:17:33 time: 0.1835 data_time: 0.0038 loss: 1.3006 2023/03/17 10:13:20 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:13:24 - mmengine - INFO - Epoch(train) [66][1700/5005] lr: 1.0000e-03 eta: 9:17:14 time: 0.1923 data_time: 0.0042 loss: 1.0481 2023/03/17 10:13:44 - mmengine - INFO - Epoch(train) [66][1800/5005] lr: 1.0000e-03 eta: 9:16:55 time: 0.2006 data_time: 0.0040 loss: 1.0493 2023/03/17 10:14:04 - mmengine - INFO - Epoch(train) [66][1900/5005] lr: 1.0000e-03 eta: 9:16:36 time: 0.1964 data_time: 0.0038 loss: 1.0859 2023/03/17 10:14:25 - mmengine - INFO - Epoch(train) [66][2000/5005] lr: 1.0000e-03 eta: 9:16:17 time: 0.2130 data_time: 0.0040 loss: 1.2418 2023/03/17 10:14:46 - mmengine - INFO - Epoch(train) [66][2100/5005] lr: 1.0000e-03 eta: 9:15:59 time: 0.2126 data_time: 0.0038 loss: 0.9357 2023/03/17 10:15:06 - mmengine - INFO - Epoch(train) [66][2200/5005] lr: 1.0000e-03 eta: 9:15:40 time: 0.1919 data_time: 0.0042 loss: 0.9843 2023/03/17 10:15:25 - mmengine - INFO - Epoch(train) [66][2300/5005] lr: 1.0000e-03 eta: 9:15:21 time: 0.2048 data_time: 0.0036 loss: 1.1580 2023/03/17 10:15:46 - mmengine - INFO - Epoch(train) [66][2400/5005] lr: 1.0000e-03 eta: 9:15:02 time: 0.1977 data_time: 0.0039 loss: 1.2270 2023/03/17 10:16:06 - mmengine - INFO - Epoch(train) [66][2500/5005] lr: 1.0000e-03 eta: 9:14:43 time: 0.1982 data_time: 0.0036 loss: 1.1647 2023/03/17 10:16:26 - mmengine - INFO - Epoch(train) [66][2600/5005] lr: 1.0000e-03 eta: 9:14:24 time: 0.2037 data_time: 0.0039 loss: 1.1610 2023/03/17 10:16:41 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:16:46 - mmengine - INFO - Epoch(train) [66][2700/5005] lr: 1.0000e-03 eta: 9:14:05 time: 0.2191 data_time: 0.0045 loss: 0.9289 2023/03/17 10:17:07 - mmengine - INFO - Epoch(train) [66][2800/5005] lr: 1.0000e-03 eta: 9:13:47 time: 0.1750 data_time: 0.0036 loss: 1.0041 2023/03/17 10:17:25 - mmengine - INFO - Epoch(train) [66][2900/5005] lr: 1.0000e-03 eta: 9:13:27 time: 0.1745 data_time: 0.0039 loss: 1.0723 2023/03/17 10:17:43 - mmengine - INFO - Epoch(train) [66][3000/5005] lr: 1.0000e-03 eta: 9:13:07 time: 0.1827 data_time: 0.0041 loss: 1.1989 2023/03/17 10:18:01 - mmengine - INFO - Epoch(train) [66][3100/5005] lr: 1.0000e-03 eta: 9:12:46 time: 0.1635 data_time: 0.0045 loss: 1.1674 2023/03/17 10:18:20 - mmengine - INFO - Epoch(train) [66][3200/5005] lr: 1.0000e-03 eta: 9:12:27 time: 0.2069 data_time: 0.0040 loss: 1.1663 2023/03/17 10:18:40 - mmengine - INFO - Epoch(train) [66][3300/5005] lr: 1.0000e-03 eta: 9:12:08 time: 0.2208 data_time: 0.0037 loss: 1.0329 2023/03/17 10:18:58 - mmengine - INFO - Epoch(train) [66][3400/5005] lr: 1.0000e-03 eta: 9:11:48 time: 0.1822 data_time: 0.0036 loss: 0.9875 2023/03/17 10:19:18 - mmengine - INFO - Epoch(train) [66][3500/5005] lr: 1.0000e-03 eta: 9:11:29 time: 0.2223 data_time: 0.0043 loss: 1.1122 2023/03/17 10:19:36 - mmengine - INFO - Epoch(train) [66][3600/5005] lr: 1.0000e-03 eta: 9:11:09 time: 0.1872 data_time: 0.0034 loss: 1.0886 2023/03/17 10:19:50 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:19:54 - mmengine - INFO - Epoch(train) [66][3700/5005] lr: 1.0000e-03 eta: 9:10:49 time: 0.1745 data_time: 0.0046 loss: 1.0576 2023/03/17 10:20:13 - mmengine - INFO - Epoch(train) [66][3800/5005] lr: 1.0000e-03 eta: 9:10:29 time: 0.1823 data_time: 0.0038 loss: 1.0331 2023/03/17 10:20:32 - mmengine - INFO - Epoch(train) [66][3900/5005] lr: 1.0000e-03 eta: 9:10:10 time: 0.1946 data_time: 0.0039 loss: 1.0961 2023/03/17 10:20:51 - mmengine - INFO - Epoch(train) [66][4000/5005] lr: 1.0000e-03 eta: 9:09:51 time: 0.1908 data_time: 0.0039 loss: 1.0274 2023/03/17 10:21:10 - mmengine - INFO - Epoch(train) [66][4100/5005] lr: 1.0000e-03 eta: 9:09:31 time: 0.1898 data_time: 0.0039 loss: 1.2663 2023/03/17 10:21:30 - mmengine - INFO - Epoch(train) [66][4200/5005] lr: 1.0000e-03 eta: 9:09:12 time: 0.1819 data_time: 0.0036 loss: 1.2213 2023/03/17 10:21:51 - mmengine - INFO - Epoch(train) [66][4300/5005] lr: 1.0000e-03 eta: 9:08:54 time: 0.2065 data_time: 0.0040 loss: 1.1017 2023/03/17 10:22:10 - mmengine - INFO - Epoch(train) [66][4400/5005] lr: 1.0000e-03 eta: 9:08:34 time: 0.1985 data_time: 0.0033 loss: 1.1896 2023/03/17 10:22:31 - mmengine - INFO - Epoch(train) [66][4500/5005] lr: 1.0000e-03 eta: 9:08:16 time: 0.2026 data_time: 0.0034 loss: 0.9499 2023/03/17 10:22:51 - mmengine - INFO - Epoch(train) [66][4600/5005] lr: 1.0000e-03 eta: 9:07:57 time: 0.1765 data_time: 0.0040 loss: 1.1605 2023/03/17 10:23:05 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:23:09 - mmengine - INFO - Epoch(train) [66][4700/5005] lr: 1.0000e-03 eta: 9:07:37 time: 0.1750 data_time: 0.0042 loss: 0.9966 2023/03/17 10:23:29 - mmengine - INFO - Epoch(train) [66][4800/5005] lr: 1.0000e-03 eta: 9:07:18 time: 0.2075 data_time: 0.0034 loss: 1.0047 2023/03/17 10:23:48 - mmengine - INFO - Epoch(train) [66][4900/5005] lr: 1.0000e-03 eta: 9:06:58 time: 0.1901 data_time: 0.0042 loss: 1.1688 2023/03/17 10:24:07 - mmengine - INFO - Epoch(train) [66][5000/5005] lr: 1.0000e-03 eta: 9:06:39 time: 0.1856 data_time: 0.0047 loss: 1.3738 2023/03/17 10:24:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:24:08 - mmengine - INFO - Saving checkpoint at 66 epochs 2023/03/17 10:24:14 - mmengine - INFO - Epoch(val) [66][100/196] eta: 0:00:05 time: 0.0449 data_time: 0.0021 2023/03/17 10:24:39 - mmengine - INFO - Epoch(val) [66][196/196] accuracy/top1: 75.1700 accuracy/top5: 92.5660data_time: 0.0192 time: 0.0486 2023/03/17 10:25:02 - mmengine - INFO - Epoch(train) [67][ 100/5005] lr: 1.0000e-03 eta: 9:06:20 time: 0.2238 data_time: 0.0045 loss: 1.1701 2023/03/17 10:25:22 - mmengine - INFO - Epoch(train) [67][ 200/5005] lr: 1.0000e-03 eta: 9:06:01 time: 0.1937 data_time: 0.0040 loss: 1.0762 2023/03/17 10:25:42 - mmengine - INFO - Epoch(train) [67][ 300/5005] lr: 1.0000e-03 eta: 9:05:42 time: 0.2089 data_time: 0.0040 loss: 1.0370 2023/03/17 10:26:01 - mmengine - INFO - Epoch(train) [67][ 400/5005] lr: 1.0000e-03 eta: 9:05:23 time: 0.1780 data_time: 0.0037 loss: 1.2630 2023/03/17 10:26:20 - mmengine - INFO - Epoch(train) [67][ 500/5005] lr: 1.0000e-03 eta: 9:05:03 time: 0.1832 data_time: 0.0040 loss: 1.3162 2023/03/17 10:26:38 - mmengine - INFO - Epoch(train) [67][ 600/5005] lr: 1.0000e-03 eta: 9:04:43 time: 0.1818 data_time: 0.0042 loss: 0.9986 2023/03/17 10:26:51 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:26:57 - mmengine - INFO - Epoch(train) [67][ 700/5005] lr: 1.0000e-03 eta: 9:04:24 time: 0.1886 data_time: 0.0037 loss: 1.2414 2023/03/17 10:27:16 - mmengine - INFO - Epoch(train) [67][ 800/5005] lr: 1.0000e-03 eta: 9:04:04 time: 0.1882 data_time: 0.0038 loss: 1.0103 2023/03/17 10:27:34 - mmengine - INFO - Epoch(train) [67][ 900/5005] lr: 1.0000e-03 eta: 9:03:45 time: 0.1848 data_time: 0.0036 loss: 1.1049 2023/03/17 10:27:55 - mmengine - INFO - Epoch(train) [67][1000/5005] lr: 1.0000e-03 eta: 9:03:26 time: 0.1904 data_time: 0.0038 loss: 1.2450 2023/03/17 10:28:16 - mmengine - INFO - Epoch(train) [67][1100/5005] lr: 1.0000e-03 eta: 9:03:08 time: 0.2329 data_time: 0.0040 loss: 1.0420 2023/03/17 10:28:37 - mmengine - INFO - Epoch(train) [67][1200/5005] lr: 1.0000e-03 eta: 9:02:49 time: 0.1929 data_time: 0.0034 loss: 1.1567 2023/03/17 10:28:57 - mmengine - INFO - Epoch(train) [67][1300/5005] lr: 1.0000e-03 eta: 9:02:30 time: 0.1932 data_time: 0.0040 loss: 1.0376 2023/03/17 10:29:18 - mmengine - INFO - Epoch(train) [67][1400/5005] lr: 1.0000e-03 eta: 9:02:11 time: 0.2169 data_time: 0.0042 loss: 1.1853 2023/03/17 10:29:38 - mmengine - INFO - Epoch(train) [67][1500/5005] lr: 1.0000e-03 eta: 9:01:53 time: 0.2219 data_time: 0.0038 loss: 1.1796 2023/03/17 10:30:00 - mmengine - INFO - Epoch(train) [67][1600/5005] lr: 1.0000e-03 eta: 9:01:34 time: 0.2018 data_time: 0.0043 loss: 1.0423 2023/03/17 10:30:14 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:30:20 - mmengine - INFO - Epoch(train) [67][1700/5005] lr: 1.0000e-03 eta: 9:01:16 time: 0.2001 data_time: 0.0041 loss: 0.9901 2023/03/17 10:30:40 - mmengine - INFO - Epoch(train) [67][1800/5005] lr: 1.0000e-03 eta: 9:00:56 time: 0.1991 data_time: 0.0043 loss: 1.0651 2023/03/17 10:31:00 - mmengine - INFO - Epoch(train) [67][1900/5005] lr: 1.0000e-03 eta: 9:00:38 time: 0.2152 data_time: 0.0041 loss: 1.0195 2023/03/17 10:31:19 - mmengine - INFO - Epoch(train) [67][2000/5005] lr: 1.0000e-03 eta: 9:00:18 time: 0.1874 data_time: 0.0036 loss: 1.1932 2023/03/17 10:31:38 - mmengine - INFO - Epoch(train) [67][2100/5005] lr: 1.0000e-03 eta: 8:59:59 time: 0.1963 data_time: 0.0036 loss: 1.0908 2023/03/17 10:31:57 - mmengine - INFO - Epoch(train) [67][2200/5005] lr: 1.0000e-03 eta: 8:59:39 time: 0.1904 data_time: 0.0038 loss: 1.0640 2023/03/17 10:32:17 - mmengine - INFO - Epoch(train) [67][2300/5005] lr: 1.0000e-03 eta: 8:59:20 time: 0.1920 data_time: 0.0045 loss: 0.9528 2023/03/17 10:32:36 - mmengine - INFO - Epoch(train) [67][2400/5005] lr: 1.0000e-03 eta: 8:59:01 time: 0.1907 data_time: 0.0041 loss: 1.1817 2023/03/17 10:32:55 - mmengine - INFO - Epoch(train) [67][2500/5005] lr: 1.0000e-03 eta: 8:58:41 time: 0.2020 data_time: 0.0041 loss: 1.0987 2023/03/17 10:33:15 - mmengine - INFO - Epoch(train) [67][2600/5005] lr: 1.0000e-03 eta: 8:58:22 time: 0.2121 data_time: 0.0042 loss: 1.0331 2023/03/17 10:33:29 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:33:34 - mmengine - INFO - Epoch(train) [67][2700/5005] lr: 1.0000e-03 eta: 8:58:03 time: 0.1855 data_time: 0.0039 loss: 0.9422 2023/03/17 10:33:54 - mmengine - INFO - Epoch(train) [67][2800/5005] lr: 1.0000e-03 eta: 8:57:44 time: 0.1967 data_time: 0.0037 loss: 1.1020 2023/03/17 10:34:14 - mmengine - INFO - Epoch(train) [67][2900/5005] lr: 1.0000e-03 eta: 8:57:25 time: 0.2388 data_time: 0.0038 loss: 1.1898 2023/03/17 10:34:35 - mmengine - INFO - Epoch(train) [67][3000/5005] lr: 1.0000e-03 eta: 8:57:06 time: 0.1903 data_time: 0.0035 loss: 1.2802 2023/03/17 10:34:55 - mmengine - INFO - Epoch(train) [67][3100/5005] lr: 1.0000e-03 eta: 8:56:47 time: 0.1936 data_time: 0.0037 loss: 0.9698 2023/03/17 10:35:14 - mmengine - INFO - Epoch(train) [67][3200/5005] lr: 1.0000e-03 eta: 8:56:28 time: 0.1947 data_time: 0.0040 loss: 0.9815 2023/03/17 10:35:33 - mmengine - INFO - Epoch(train) [67][3300/5005] lr: 1.0000e-03 eta: 8:56:08 time: 0.1850 data_time: 0.0037 loss: 0.8896 2023/03/17 10:35:52 - mmengine - INFO - Epoch(train) [67][3400/5005] lr: 1.0000e-03 eta: 8:55:49 time: 0.1926 data_time: 0.0036 loss: 1.1095 2023/03/17 10:36:12 - mmengine - INFO - Epoch(train) [67][3500/5005] lr: 1.0000e-03 eta: 8:55:30 time: 0.1858 data_time: 0.0035 loss: 1.0119 2023/03/17 10:36:31 - mmengine - INFO - Epoch(train) [67][3600/5005] lr: 1.0000e-03 eta: 8:55:10 time: 0.1862 data_time: 0.0038 loss: 1.2562 2023/03/17 10:36:44 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:36:49 - mmengine - INFO - Epoch(train) [67][3700/5005] lr: 1.0000e-03 eta: 8:54:50 time: 0.1788 data_time: 0.0039 loss: 1.0337 2023/03/17 10:37:07 - mmengine - INFO - Epoch(train) [67][3800/5005] lr: 1.0000e-03 eta: 8:54:31 time: 0.1760 data_time: 0.0045 loss: 0.9715 2023/03/17 10:37:25 - mmengine - INFO - Epoch(train) [67][3900/5005] lr: 1.0000e-03 eta: 8:54:10 time: 0.1771 data_time: 0.0038 loss: 1.0618 2023/03/17 10:37:44 - mmengine - INFO - Epoch(train) [67][4000/5005] lr: 1.0000e-03 eta: 8:53:51 time: 0.1910 data_time: 0.0037 loss: 1.0708 2023/03/17 10:38:03 - mmengine - INFO - Epoch(train) [67][4100/5005] lr: 1.0000e-03 eta: 8:53:31 time: 0.2013 data_time: 0.0034 loss: 1.0388 2023/03/17 10:38:22 - mmengine - INFO - Epoch(train) [67][4200/5005] lr: 1.0000e-03 eta: 8:53:12 time: 0.1829 data_time: 0.0039 loss: 0.9597 2023/03/17 10:38:41 - mmengine - INFO - Epoch(train) [67][4300/5005] lr: 1.0000e-03 eta: 8:52:52 time: 0.1881 data_time: 0.0037 loss: 1.0387 2023/03/17 10:39:00 - mmengine - INFO - Epoch(train) [67][4400/5005] lr: 1.0000e-03 eta: 8:52:33 time: 0.1966 data_time: 0.0044 loss: 1.0821 2023/03/17 10:39:20 - mmengine - INFO - Epoch(train) [67][4500/5005] lr: 1.0000e-03 eta: 8:52:14 time: 0.1984 data_time: 0.0043 loss: 1.1460 2023/03/17 10:39:41 - mmengine - INFO - Epoch(train) [67][4600/5005] lr: 1.0000e-03 eta: 8:51:55 time: 0.2030 data_time: 0.0044 loss: 0.9822 2023/03/17 10:39:55 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:40:00 - mmengine - INFO - Epoch(train) [67][4700/5005] lr: 1.0000e-03 eta: 8:51:36 time: 0.1969 data_time: 0.0044 loss: 0.9510 2023/03/17 10:40:21 - mmengine - INFO - Epoch(train) [67][4800/5005] lr: 1.0000e-03 eta: 8:51:17 time: 0.1997 data_time: 0.0041 loss: 1.1812 2023/03/17 10:40:41 - mmengine - INFO - Epoch(train) [67][4900/5005] lr: 1.0000e-03 eta: 8:50:58 time: 0.1971 data_time: 0.0044 loss: 1.0556 2023/03/17 10:41:02 - mmengine - INFO - Epoch(train) [67][5000/5005] lr: 1.0000e-03 eta: 8:50:40 time: 0.2254 data_time: 0.0050 loss: 1.2533 2023/03/17 10:41:03 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:41:03 - mmengine - INFO - Saving checkpoint at 67 epochs 2023/03/17 10:41:10 - mmengine - INFO - Epoch(val) [67][100/196] eta: 0:00:05 time: 0.0522 data_time: 0.0008 2023/03/17 10:41:35 - mmengine - INFO - Epoch(val) [67][196/196] accuracy/top1: 75.2420 accuracy/top5: 92.5740data_time: 0.0216 time: 0.0533 2023/03/17 10:42:02 - mmengine - INFO - Epoch(train) [68][ 100/5005] lr: 1.0000e-03 eta: 8:50:24 time: 0.2670 data_time: 0.0037 loss: 1.0648 2023/03/17 10:42:26 - mmengine - INFO - Epoch(train) [68][ 200/5005] lr: 1.0000e-03 eta: 8:50:06 time: 0.2048 data_time: 0.0040 loss: 1.2086 2023/03/17 10:42:45 - mmengine - INFO - Epoch(train) [68][ 300/5005] lr: 1.0000e-03 eta: 8:49:47 time: 0.1869 data_time: 0.0039 loss: 1.0979 2023/03/17 10:43:05 - mmengine - INFO - Epoch(train) [68][ 400/5005] lr: 1.0000e-03 eta: 8:49:28 time: 0.1870 data_time: 0.0039 loss: 1.0106 2023/03/17 10:43:24 - mmengine - INFO - Epoch(train) [68][ 500/5005] lr: 1.0000e-03 eta: 8:49:09 time: 0.1827 data_time: 0.0034 loss: 1.1419 2023/03/17 10:43:44 - mmengine - INFO - Epoch(train) [68][ 600/5005] lr: 1.0000e-03 eta: 8:48:50 time: 0.2166 data_time: 0.0041 loss: 1.0612 2023/03/17 10:43:58 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:44:04 - mmengine - INFO - Epoch(train) [68][ 700/5005] lr: 1.0000e-03 eta: 8:48:31 time: 0.1904 data_time: 0.0035 loss: 0.7960 2023/03/17 10:44:24 - mmengine - INFO - Epoch(train) [68][ 800/5005] lr: 1.0000e-03 eta: 8:48:12 time: 0.1922 data_time: 0.0040 loss: 1.0120 2023/03/17 10:44:43 - mmengine - INFO - Epoch(train) [68][ 900/5005] lr: 1.0000e-03 eta: 8:47:52 time: 0.1780 data_time: 0.0039 loss: 1.1420 2023/03/17 10:45:01 - mmengine - INFO - Epoch(train) [68][1000/5005] lr: 1.0000e-03 eta: 8:47:32 time: 0.1854 data_time: 0.0045 loss: 1.1077 2023/03/17 10:45:21 - mmengine - INFO - Epoch(train) [68][1100/5005] lr: 1.0000e-03 eta: 8:47:13 time: 0.2158 data_time: 0.0039 loss: 1.1434 2023/03/17 10:45:39 - mmengine - INFO - Epoch(train) [68][1200/5005] lr: 1.0000e-03 eta: 8:46:53 time: 0.1757 data_time: 0.0042 loss: 1.3660 2023/03/17 10:45:56 - mmengine - INFO - Epoch(train) [68][1300/5005] lr: 1.0000e-03 eta: 8:46:33 time: 0.1739 data_time: 0.0036 loss: 1.2899 2023/03/17 10:46:14 - mmengine - INFO - Epoch(train) [68][1400/5005] lr: 1.0000e-03 eta: 8:46:13 time: 0.1956 data_time: 0.0039 loss: 1.0874 2023/03/17 10:46:34 - mmengine - INFO - Epoch(train) [68][1500/5005] lr: 1.0000e-03 eta: 8:45:54 time: 0.1849 data_time: 0.0040 loss: 1.0037 2023/03/17 10:46:53 - mmengine - INFO - Epoch(train) [68][1600/5005] lr: 1.0000e-03 eta: 8:45:34 time: 0.1879 data_time: 0.0039 loss: 1.0285 2023/03/17 10:47:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:47:14 - mmengine - INFO - Epoch(train) [68][1700/5005] lr: 1.0000e-03 eta: 8:45:16 time: 0.1936 data_time: 0.0034 loss: 1.0048 2023/03/17 10:47:33 - mmengine - INFO - Epoch(train) [68][1800/5005] lr: 1.0000e-03 eta: 8:44:56 time: 0.1821 data_time: 0.0041 loss: 1.0008 2023/03/17 10:47:51 - mmengine - INFO - Epoch(train) [68][1900/5005] lr: 1.0000e-03 eta: 8:44:36 time: 0.1766 data_time: 0.0038 loss: 1.0492 2023/03/17 10:48:09 - mmengine - INFO - Epoch(train) [68][2000/5005] lr: 1.0000e-03 eta: 8:44:16 time: 0.1796 data_time: 0.0037 loss: 1.0592 2023/03/17 10:48:27 - mmengine - INFO - Epoch(train) [68][2100/5005] lr: 1.0000e-03 eta: 8:43:56 time: 0.2007 data_time: 0.0043 loss: 1.0084 2023/03/17 10:48:46 - mmengine - INFO - Epoch(train) [68][2200/5005] lr: 1.0000e-03 eta: 8:43:37 time: 0.2407 data_time: 0.0040 loss: 1.2118 2023/03/17 10:49:07 - mmengine - INFO - Epoch(train) [68][2300/5005] lr: 1.0000e-03 eta: 8:43:19 time: 0.1871 data_time: 0.0039 loss: 1.1359 2023/03/17 10:49:27 - mmengine - INFO - Epoch(train) [68][2400/5005] lr: 1.0000e-03 eta: 8:42:59 time: 0.1918 data_time: 0.0043 loss: 1.1734 2023/03/17 10:49:46 - mmengine - INFO - Epoch(train) [68][2500/5005] lr: 1.0000e-03 eta: 8:42:40 time: 0.2010 data_time: 0.0045 loss: 1.0807 2023/03/17 10:50:04 - mmengine - INFO - Epoch(train) [68][2600/5005] lr: 1.0000e-03 eta: 8:42:20 time: 0.1907 data_time: 0.0037 loss: 1.2067 2023/03/17 10:50:16 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:50:23 - mmengine - INFO - Epoch(train) [68][2700/5005] lr: 1.0000e-03 eta: 8:42:00 time: 0.1893 data_time: 0.0045 loss: 1.1295 2023/03/17 10:50:42 - mmengine - INFO - Epoch(train) [68][2800/5005] lr: 1.0000e-03 eta: 8:41:41 time: 0.1937 data_time: 0.0047 loss: 0.8701 2023/03/17 10:51:04 - mmengine - INFO - Epoch(train) [68][2900/5005] lr: 1.0000e-03 eta: 8:41:23 time: 0.2310 data_time: 0.0040 loss: 1.0801 2023/03/17 10:51:23 - mmengine - INFO - Epoch(train) [68][3000/5005] lr: 1.0000e-03 eta: 8:41:03 time: 0.1900 data_time: 0.0033 loss: 0.8599 2023/03/17 10:51:41 - mmengine - INFO - Epoch(train) [68][3100/5005] lr: 1.0000e-03 eta: 8:40:44 time: 0.1807 data_time: 0.0050 loss: 0.9790 2023/03/17 10:52:00 - mmengine - INFO - Epoch(train) [68][3200/5005] lr: 1.0000e-03 eta: 8:40:24 time: 0.1762 data_time: 0.0045 loss: 0.9445 2023/03/17 10:52:18 - mmengine - INFO - Epoch(train) [68][3300/5005] lr: 1.0000e-03 eta: 8:40:04 time: 0.1819 data_time: 0.0037 loss: 0.9949 2023/03/17 10:52:39 - mmengine - INFO - Epoch(train) [68][3400/5005] lr: 1.0000e-03 eta: 8:39:45 time: 0.2004 data_time: 0.0043 loss: 1.1757 2023/03/17 10:52:59 - mmengine - INFO - Epoch(train) [68][3500/5005] lr: 1.0000e-03 eta: 8:39:26 time: 0.2252 data_time: 0.0040 loss: 1.1043 2023/03/17 10:53:21 - mmengine - INFO - Epoch(train) [68][3600/5005] lr: 1.0000e-03 eta: 8:39:08 time: 0.1986 data_time: 0.0040 loss: 0.9029 2023/03/17 10:53:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:53:41 - mmengine - INFO - Epoch(train) [68][3700/5005] lr: 1.0000e-03 eta: 8:38:49 time: 0.2041 data_time: 0.0038 loss: 1.0549 2023/03/17 10:54:05 - mmengine - INFO - Epoch(train) [68][3800/5005] lr: 1.0000e-03 eta: 8:38:32 time: 0.2640 data_time: 0.0039 loss: 1.0109 2023/03/17 10:54:32 - mmengine - INFO - Epoch(train) [68][3900/5005] lr: 1.0000e-03 eta: 8:38:17 time: 0.2144 data_time: 0.0036 loss: 1.0325 2023/03/17 10:54:51 - mmengine - INFO - Epoch(train) [68][4000/5005] lr: 1.0000e-03 eta: 8:37:57 time: 0.1920 data_time: 0.0039 loss: 1.2292 2023/03/17 10:55:10 - mmengine - INFO - Epoch(train) [68][4100/5005] lr: 1.0000e-03 eta: 8:37:38 time: 0.1920 data_time: 0.0038 loss: 1.0577 2023/03/17 10:55:29 - mmengine - INFO - Epoch(train) [68][4200/5005] lr: 1.0000e-03 eta: 8:37:18 time: 0.1894 data_time: 0.0046 loss: 1.2155 2023/03/17 10:55:48 - mmengine - INFO - Epoch(train) [68][4300/5005] lr: 1.0000e-03 eta: 8:36:59 time: 0.1864 data_time: 0.0035 loss: 1.1121 2023/03/17 10:56:07 - mmengine - INFO - Epoch(train) [68][4400/5005] lr: 1.0000e-03 eta: 8:36:39 time: 0.1883 data_time: 0.0040 loss: 1.0429 2023/03/17 10:56:27 - mmengine - INFO - Epoch(train) [68][4500/5005] lr: 1.0000e-03 eta: 8:36:20 time: 0.1898 data_time: 0.0041 loss: 0.9921 2023/03/17 10:56:46 - mmengine - INFO - Epoch(train) [68][4600/5005] lr: 1.0000e-03 eta: 8:36:01 time: 0.1813 data_time: 0.0040 loss: 1.2496 2023/03/17 10:56:58 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:57:05 - mmengine - INFO - Epoch(train) [68][4700/5005] lr: 1.0000e-03 eta: 8:35:41 time: 0.2183 data_time: 0.0037 loss: 1.0490 2023/03/17 10:57:24 - mmengine - INFO - Epoch(train) [68][4800/5005] lr: 1.0000e-03 eta: 8:35:22 time: 0.1899 data_time: 0.0035 loss: 1.2641 2023/03/17 10:57:43 - mmengine - INFO - Epoch(train) [68][4900/5005] lr: 1.0000e-03 eta: 8:35:02 time: 0.1886 data_time: 0.0038 loss: 1.2294 2023/03/17 10:58:03 - mmengine - INFO - Epoch(train) [68][5000/5005] lr: 1.0000e-03 eta: 8:34:43 time: 0.2160 data_time: 0.0047 loss: 1.1438 2023/03/17 10:58:04 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 10:58:05 - mmengine - INFO - Saving checkpoint at 68 epochs 2023/03/17 10:58:12 - mmengine - INFO - Epoch(val) [68][100/196] eta: 0:00:05 time: 0.0448 data_time: 0.0009 2023/03/17 10:58:40 - mmengine - INFO - Epoch(val) [68][196/196] accuracy/top1: 75.3340 accuracy/top5: 92.6580data_time: 0.0363 time: 0.0660 2023/03/17 10:59:01 - mmengine - INFO - Epoch(train) [69][ 100/5005] lr: 1.0000e-03 eta: 8:34:24 time: 0.1863 data_time: 0.0045 loss: 1.1971 2023/03/17 10:59:19 - mmengine - INFO - Epoch(train) [69][ 200/5005] lr: 1.0000e-03 eta: 8:34:04 time: 0.1891 data_time: 0.0042 loss: 1.1219 2023/03/17 10:59:39 - mmengine - INFO - Epoch(train) [69][ 300/5005] lr: 1.0000e-03 eta: 8:33:45 time: 0.1948 data_time: 0.0039 loss: 0.8454 2023/03/17 10:59:58 - mmengine - INFO - Epoch(train) [69][ 400/5005] lr: 1.0000e-03 eta: 8:33:26 time: 0.1854 data_time: 0.0041 loss: 1.0178 2023/03/17 11:00:18 - mmengine - INFO - Epoch(train) [69][ 500/5005] lr: 1.0000e-03 eta: 8:33:06 time: 0.1931 data_time: 0.0036 loss: 0.9900 2023/03/17 11:00:37 - mmengine - INFO - Epoch(train) [69][ 600/5005] lr: 1.0000e-03 eta: 8:32:47 time: 0.1897 data_time: 0.0040 loss: 0.9935 2023/03/17 11:00:49 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:00:57 - mmengine - INFO - Epoch(train) [69][ 700/5005] lr: 1.0000e-03 eta: 8:32:28 time: 0.2264 data_time: 0.0042 loss: 1.2013 2023/03/17 11:01:18 - mmengine - INFO - Epoch(train) [69][ 800/5005] lr: 1.0000e-03 eta: 8:32:10 time: 0.2014 data_time: 0.0043 loss: 1.0072 2023/03/17 11:01:39 - mmengine - INFO - Epoch(train) [69][ 900/5005] lr: 1.0000e-03 eta: 8:31:51 time: 0.2008 data_time: 0.0044 loss: 1.1373 2023/03/17 11:01:59 - mmengine - INFO - Epoch(train) [69][1000/5005] lr: 1.0000e-03 eta: 8:31:32 time: 0.2146 data_time: 0.0033 loss: 0.8504 2023/03/17 11:02:18 - mmengine - INFO - Epoch(train) [69][1100/5005] lr: 1.0000e-03 eta: 8:31:12 time: 0.2005 data_time: 0.0035 loss: 1.1342 2023/03/17 11:02:42 - mmengine - INFO - Epoch(train) [69][1200/5005] lr: 1.0000e-03 eta: 8:30:55 time: 0.2402 data_time: 0.0042 loss: 1.1856 2023/03/17 11:03:01 - mmengine - INFO - Epoch(train) [69][1300/5005] lr: 1.0000e-03 eta: 8:30:36 time: 0.1783 data_time: 0.0039 loss: 1.1250 2023/03/17 11:03:24 - mmengine - INFO - Epoch(train) [69][1400/5005] lr: 1.0000e-03 eta: 8:30:18 time: 0.2398 data_time: 0.0038 loss: 1.1256 2023/03/17 11:03:43 - mmengine - INFO - Epoch(train) [69][1500/5005] lr: 1.0000e-03 eta: 8:29:59 time: 0.1929 data_time: 0.0038 loss: 1.1021 2023/03/17 11:04:02 - mmengine - INFO - Epoch(train) [69][1600/5005] lr: 1.0000e-03 eta: 8:29:39 time: 0.1864 data_time: 0.0039 loss: 0.9994 2023/03/17 11:04:14 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:04:22 - mmengine - INFO - Epoch(train) [69][1700/5005] lr: 1.0000e-03 eta: 8:29:20 time: 0.1852 data_time: 0.0048 loss: 1.0589 2023/03/17 11:04:41 - mmengine - INFO - Epoch(train) [69][1800/5005] lr: 1.0000e-03 eta: 8:29:01 time: 0.1800 data_time: 0.0045 loss: 1.1086 2023/03/17 11:05:00 - mmengine - INFO - Epoch(train) [69][1900/5005] lr: 1.0000e-03 eta: 8:28:41 time: 0.1883 data_time: 0.0038 loss: 0.9973 2023/03/17 11:05:19 - mmengine - INFO - Epoch(train) [69][2000/5005] lr: 1.0000e-03 eta: 8:28:22 time: 0.1813 data_time: 0.0041 loss: 0.9253 2023/03/17 11:05:37 - mmengine - INFO - Epoch(train) [69][2100/5005] lr: 1.0000e-03 eta: 8:28:02 time: 0.1880 data_time: 0.0044 loss: 1.2228 2023/03/17 11:05:56 - mmengine - INFO - Epoch(train) [69][2200/5005] lr: 1.0000e-03 eta: 8:27:42 time: 0.1897 data_time: 0.0043 loss: 0.8859 2023/03/17 11:06:16 - mmengine - INFO - Epoch(train) [69][2300/5005] lr: 1.0000e-03 eta: 8:27:23 time: 0.1947 data_time: 0.0037 loss: 0.9653 2023/03/17 11:06:35 - mmengine - INFO - Epoch(train) [69][2400/5005] lr: 1.0000e-03 eta: 8:27:04 time: 0.1896 data_time: 0.0042 loss: 1.1628 2023/03/17 11:06:56 - mmengine - INFO - Epoch(train) [69][2500/5005] lr: 1.0000e-03 eta: 8:26:45 time: 0.1981 data_time: 0.0037 loss: 0.9298 2023/03/17 11:07:15 - mmengine - INFO - Epoch(train) [69][2600/5005] lr: 1.0000e-03 eta: 8:26:26 time: 0.1901 data_time: 0.0039 loss: 0.9336 2023/03/17 11:07:26 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:07:34 - mmengine - INFO - Epoch(train) [69][2700/5005] lr: 1.0000e-03 eta: 8:26:06 time: 0.1971 data_time: 0.0044 loss: 1.1564 2023/03/17 11:07:54 - mmengine - INFO - Epoch(train) [69][2800/5005] lr: 1.0000e-03 eta: 8:25:47 time: 0.2213 data_time: 0.0040 loss: 0.9877 2023/03/17 11:08:13 - mmengine - INFO - Epoch(train) [69][2900/5005] lr: 1.0000e-03 eta: 8:25:28 time: 0.1865 data_time: 0.0038 loss: 1.1839 2023/03/17 11:08:31 - mmengine - INFO - Epoch(train) [69][3000/5005] lr: 1.0000e-03 eta: 8:25:08 time: 0.1793 data_time: 0.0040 loss: 0.9374 2023/03/17 11:08:50 - mmengine - INFO - Epoch(train) [69][3100/5005] lr: 1.0000e-03 eta: 8:24:48 time: 0.1761 data_time: 0.0041 loss: 1.0219 2023/03/17 11:09:07 - mmengine - INFO - Epoch(train) [69][3200/5005] lr: 1.0000e-03 eta: 8:24:28 time: 0.1799 data_time: 0.0039 loss: 1.1593 2023/03/17 11:09:26 - mmengine - INFO - Epoch(train) [69][3300/5005] lr: 1.0000e-03 eta: 8:24:09 time: 0.1903 data_time: 0.0036 loss: 1.1597 2023/03/17 11:09:45 - mmengine - INFO - Epoch(train) [69][3400/5005] lr: 1.0000e-03 eta: 8:23:49 time: 0.1820 data_time: 0.0041 loss: 1.0483 2023/03/17 11:10:04 - mmengine - INFO - Epoch(train) [69][3500/5005] lr: 1.0000e-03 eta: 8:23:30 time: 0.1905 data_time: 0.0040 loss: 0.9654 2023/03/17 11:10:25 - mmengine - INFO - Epoch(train) [69][3600/5005] lr: 1.0000e-03 eta: 8:23:11 time: 0.2356 data_time: 0.0037 loss: 1.1876 2023/03/17 11:10:37 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:10:46 - mmengine - INFO - Epoch(train) [69][3700/5005] lr: 1.0000e-03 eta: 8:22:52 time: 0.2371 data_time: 0.0038 loss: 1.1693 2023/03/17 11:11:05 - mmengine - INFO - Epoch(train) [69][3800/5005] lr: 1.0000e-03 eta: 8:22:33 time: 0.1870 data_time: 0.0035 loss: 0.9079 2023/03/17 11:11:24 - mmengine - INFO - Epoch(train) [69][3900/5005] lr: 1.0000e-03 eta: 8:22:14 time: 0.2591 data_time: 0.0038 loss: 1.0316 2023/03/17 11:11:49 - mmengine - INFO - Epoch(train) [69][4000/5005] lr: 1.0000e-03 eta: 8:21:56 time: 0.2432 data_time: 0.0034 loss: 1.2680 2023/03/17 11:12:08 - mmengine - INFO - Epoch(train) [69][4100/5005] lr: 1.0000e-03 eta: 8:21:37 time: 0.2186 data_time: 0.0036 loss: 1.0369 2023/03/17 11:12:28 - mmengine - INFO - Epoch(train) [69][4200/5005] lr: 1.0000e-03 eta: 8:21:18 time: 0.1974 data_time: 0.0035 loss: 1.0183 2023/03/17 11:12:49 - mmengine - INFO - Epoch(train) [69][4300/5005] lr: 1.0000e-03 eta: 8:20:59 time: 0.2145 data_time: 0.0039 loss: 1.1152 2023/03/17 11:13:10 - mmengine - INFO - Epoch(train) [69][4400/5005] lr: 1.0000e-03 eta: 8:20:41 time: 0.1904 data_time: 0.0034 loss: 1.1487 2023/03/17 11:13:30 - mmengine - INFO - Epoch(train) [69][4500/5005] lr: 1.0000e-03 eta: 8:20:22 time: 0.1962 data_time: 0.0040 loss: 1.1267 2023/03/17 11:13:50 - mmengine - INFO - Epoch(train) [69][4600/5005] lr: 1.0000e-03 eta: 8:20:03 time: 0.1879 data_time: 0.0041 loss: 0.9921 2023/03/17 11:14:01 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:14:08 - mmengine - INFO - Epoch(train) [69][4700/5005] lr: 1.0000e-03 eta: 8:19:43 time: 0.1823 data_time: 0.0039 loss: 1.1681 2023/03/17 11:14:26 - mmengine - INFO - Epoch(train) [69][4800/5005] lr: 1.0000e-03 eta: 8:19:23 time: 0.1883 data_time: 0.0038 loss: 1.0430 2023/03/17 11:14:45 - mmengine - INFO - Epoch(train) [69][4900/5005] lr: 1.0000e-03 eta: 8:19:04 time: 0.1917 data_time: 0.0037 loss: 1.1624 2023/03/17 11:15:07 - mmengine - INFO - Epoch(train) [69][5000/5005] lr: 1.0000e-03 eta: 8:18:45 time: 0.1936 data_time: 0.0046 loss: 0.9709 2023/03/17 11:15:08 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:15:08 - mmengine - INFO - Saving checkpoint at 69 epochs 2023/03/17 11:15:14 - mmengine - INFO - Epoch(val) [69][100/196] eta: 0:00:05 time: 0.0464 data_time: 0.0026 2023/03/17 11:15:42 - mmengine - INFO - Epoch(val) [69][196/196] accuracy/top1: 75.3820 accuracy/top5: 92.6840data_time: 0.0344 time: 0.0642 2023/03/17 11:16:02 - mmengine - INFO - Epoch(train) [70][ 100/5005] lr: 1.0000e-03 eta: 8:18:25 time: 0.1931 data_time: 0.0038 loss: 1.0174 2023/03/17 11:16:21 - mmengine - INFO - Epoch(train) [70][ 200/5005] lr: 1.0000e-03 eta: 8:18:06 time: 0.1825 data_time: 0.0040 loss: 1.0619 2023/03/17 11:16:42 - mmengine - INFO - Epoch(train) [70][ 300/5005] lr: 1.0000e-03 eta: 8:17:47 time: 0.2055 data_time: 0.0045 loss: 1.0459 2023/03/17 11:17:02 - mmengine - INFO - Epoch(train) [70][ 400/5005] lr: 1.0000e-03 eta: 8:17:28 time: 0.1863 data_time: 0.0045 loss: 1.0073 2023/03/17 11:17:22 - mmengine - INFO - Epoch(train) [70][ 500/5005] lr: 1.0000e-03 eta: 8:17:09 time: 0.1847 data_time: 0.0042 loss: 1.1193 2023/03/17 11:17:41 - mmengine - INFO - Epoch(train) [70][ 600/5005] lr: 1.0000e-03 eta: 8:16:50 time: 0.1975 data_time: 0.0044 loss: 1.0093 2023/03/17 11:17:52 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:18:01 - mmengine - INFO - Epoch(train) [70][ 700/5005] lr: 1.0000e-03 eta: 8:16:31 time: 0.2337 data_time: 0.0044 loss: 0.9568 2023/03/17 11:18:22 - mmengine - INFO - Epoch(train) [70][ 800/5005] lr: 1.0000e-03 eta: 8:16:12 time: 0.1838 data_time: 0.0047 loss: 1.2809 2023/03/17 11:18:41 - mmengine - INFO - Epoch(train) [70][ 900/5005] lr: 1.0000e-03 eta: 8:15:52 time: 0.1923 data_time: 0.0049 loss: 1.0031 2023/03/17 11:19:00 - mmengine - INFO - Epoch(train) [70][1000/5005] lr: 1.0000e-03 eta: 8:15:33 time: 0.1903 data_time: 0.0042 loss: 1.0668 2023/03/17 11:19:20 - mmengine - INFO - Epoch(train) [70][1100/5005] lr: 1.0000e-03 eta: 8:15:14 time: 0.1915 data_time: 0.0044 loss: 1.0626 2023/03/17 11:19:40 - mmengine - INFO - Epoch(train) [70][1200/5005] lr: 1.0000e-03 eta: 8:14:55 time: 0.1977 data_time: 0.0043 loss: 1.0470 2023/03/17 11:20:01 - mmengine - INFO - Epoch(train) [70][1300/5005] lr: 1.0000e-03 eta: 8:14:36 time: 0.1980 data_time: 0.0048 loss: 1.2059 2023/03/17 11:20:22 - mmengine - INFO - Epoch(train) [70][1400/5005] lr: 1.0000e-03 eta: 8:14:18 time: 0.2028 data_time: 0.0048 loss: 1.0467 2023/03/17 11:20:41 - mmengine - INFO - Epoch(train) [70][1500/5005] lr: 1.0000e-03 eta: 8:13:58 time: 0.1844 data_time: 0.0042 loss: 0.9348 2023/03/17 11:21:01 - mmengine - INFO - Epoch(train) [70][1600/5005] lr: 1.0000e-03 eta: 8:13:39 time: 0.1979 data_time: 0.0037 loss: 0.8889 2023/03/17 11:21:12 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:21:22 - mmengine - INFO - Epoch(train) [70][1700/5005] lr: 1.0000e-03 eta: 8:13:21 time: 0.2078 data_time: 0.0043 loss: 1.2295 2023/03/17 11:21:42 - mmengine - INFO - Epoch(train) [70][1800/5005] lr: 1.0000e-03 eta: 8:13:02 time: 0.2009 data_time: 0.0045 loss: 1.0315 2023/03/17 11:22:02 - mmengine - INFO - Epoch(train) [70][1900/5005] lr: 1.0000e-03 eta: 8:12:42 time: 0.1910 data_time: 0.0041 loss: 1.0992 2023/03/17 11:22:20 - mmengine - INFO - Epoch(train) [70][2000/5005] lr: 1.0000e-03 eta: 8:12:23 time: 0.1832 data_time: 0.0039 loss: 1.0561 2023/03/17 11:22:39 - mmengine - INFO - Epoch(train) [70][2100/5005] lr: 1.0000e-03 eta: 8:12:03 time: 0.1793 data_time: 0.0044 loss: 0.9336 2023/03/17 11:22:57 - mmengine - INFO - Epoch(train) [70][2200/5005] lr: 1.0000e-03 eta: 8:11:43 time: 0.1881 data_time: 0.0040 loss: 0.9875 2023/03/17 11:23:17 - mmengine - INFO - Epoch(train) [70][2300/5005] lr: 1.0000e-03 eta: 8:11:24 time: 0.2163 data_time: 0.0035 loss: 1.0095 2023/03/17 11:23:36 - mmengine - INFO - Epoch(train) [70][2400/5005] lr: 1.0000e-03 eta: 8:11:05 time: 0.1903 data_time: 0.0037 loss: 0.9274 2023/03/17 11:23:56 - mmengine - INFO - Epoch(train) [70][2500/5005] lr: 1.0000e-03 eta: 8:10:45 time: 0.1911 data_time: 0.0040 loss: 1.1184 2023/03/17 11:24:15 - mmengine - INFO - Epoch(train) [70][2600/5005] lr: 1.0000e-03 eta: 8:10:26 time: 0.1978 data_time: 0.0046 loss: 1.0529 2023/03/17 11:24:26 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:24:35 - mmengine - INFO - Epoch(train) [70][2700/5005] lr: 1.0000e-03 eta: 8:10:07 time: 0.2028 data_time: 0.0042 loss: 1.1506 2023/03/17 11:24:54 - mmengine - INFO - Epoch(train) [70][2800/5005] lr: 1.0000e-03 eta: 8:09:48 time: 0.1878 data_time: 0.0039 loss: 1.2007 2023/03/17 11:25:14 - mmengine - INFO - Epoch(train) [70][2900/5005] lr: 1.0000e-03 eta: 8:09:29 time: 0.1941 data_time: 0.0043 loss: 0.9973 2023/03/17 11:25:34 - mmengine - INFO - Epoch(train) [70][3000/5005] lr: 1.0000e-03 eta: 8:09:09 time: 0.1921 data_time: 0.0040 loss: 1.0531 2023/03/17 11:25:54 - mmengine - INFO - Epoch(train) [70][3100/5005] lr: 1.0000e-03 eta: 8:08:50 time: 0.1975 data_time: 0.0046 loss: 1.1309 2023/03/17 11:26:14 - mmengine - INFO - Epoch(train) [70][3200/5005] lr: 1.0000e-03 eta: 8:08:31 time: 0.1948 data_time: 0.0045 loss: 1.0602 2023/03/17 11:26:34 - mmengine - INFO - Epoch(train) [70][3300/5005] lr: 1.0000e-03 eta: 8:08:12 time: 0.1985 data_time: 0.0045 loss: 0.9921 2023/03/17 11:26:53 - mmengine - INFO - Epoch(train) [70][3400/5005] lr: 1.0000e-03 eta: 8:07:53 time: 0.1857 data_time: 0.0046 loss: 1.1385 2023/03/17 11:27:13 - mmengine - INFO - Epoch(train) [70][3500/5005] lr: 1.0000e-03 eta: 8:07:34 time: 0.1942 data_time: 0.0042 loss: 1.1459 2023/03/17 11:27:33 - mmengine - INFO - Epoch(train) [70][3600/5005] lr: 1.0000e-03 eta: 8:07:15 time: 0.2030 data_time: 0.0046 loss: 1.1075 2023/03/17 11:27:44 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:27:53 - mmengine - INFO - Epoch(train) [70][3700/5005] lr: 1.0000e-03 eta: 8:06:56 time: 0.1901 data_time: 0.0045 loss: 0.9820 2023/03/17 11:28:12 - mmengine - INFO - Epoch(train) [70][3800/5005] lr: 1.0000e-03 eta: 8:06:36 time: 0.1910 data_time: 0.0035 loss: 1.0447 2023/03/17 11:28:32 - mmengine - INFO - Epoch(train) [70][3900/5005] lr: 1.0000e-03 eta: 8:06:17 time: 0.2014 data_time: 0.0046 loss: 1.2012 2023/03/17 11:28:52 - mmengine - INFO - Epoch(train) [70][4000/5005] lr: 1.0000e-03 eta: 8:05:58 time: 0.1922 data_time: 0.0048 loss: 1.2468 2023/03/17 11:29:12 - mmengine - INFO - Epoch(train) [70][4100/5005] lr: 1.0000e-03 eta: 8:05:39 time: 0.1922 data_time: 0.0038 loss: 1.0363 2023/03/17 11:29:31 - mmengine - INFO - Epoch(train) [70][4200/5005] lr: 1.0000e-03 eta: 8:05:20 time: 0.1868 data_time: 0.0046 loss: 1.1554 2023/03/17 11:29:50 - mmengine - INFO - Epoch(train) [70][4300/5005] lr: 1.0000e-03 eta: 8:05:00 time: 0.1895 data_time: 0.0040 loss: 1.1660 2023/03/17 11:30:09 - mmengine - INFO - Epoch(train) [70][4400/5005] lr: 1.0000e-03 eta: 8:04:41 time: 0.1949 data_time: 0.0041 loss: 0.9288 2023/03/17 11:30:29 - mmengine - INFO - Epoch(train) [70][4500/5005] lr: 1.0000e-03 eta: 8:04:22 time: 0.1924 data_time: 0.0046 loss: 1.3239 2023/03/17 11:30:49 - mmengine - INFO - Epoch(train) [70][4600/5005] lr: 1.0000e-03 eta: 8:04:03 time: 0.2125 data_time: 0.0041 loss: 1.1291 2023/03/17 11:31:02 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:31:12 - mmengine - INFO - Epoch(train) [70][4700/5005] lr: 1.0000e-03 eta: 8:03:45 time: 0.2227 data_time: 0.0041 loss: 0.9848 2023/03/17 11:31:32 - mmengine - INFO - Epoch(train) [70][4800/5005] lr: 1.0000e-03 eta: 8:03:26 time: 0.1968 data_time: 0.0042 loss: 1.3521 2023/03/17 11:31:52 - mmengine - INFO - Epoch(train) [70][4900/5005] lr: 1.0000e-03 eta: 8:03:07 time: 0.1937 data_time: 0.0048 loss: 1.0180 2023/03/17 11:32:11 - mmengine - INFO - Epoch(train) [70][5000/5005] lr: 1.0000e-03 eta: 8:02:47 time: 0.1986 data_time: 0.0045 loss: 0.9753 2023/03/17 11:32:12 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:32:13 - mmengine - INFO - Saving checkpoint at 70 epochs 2023/03/17 11:32:19 - mmengine - INFO - Epoch(val) [70][100/196] eta: 0:00:05 time: 0.0471 data_time: 0.0041 2023/03/17 11:32:47 - mmengine - INFO - Epoch(val) [70][196/196] accuracy/top1: 75.5180 accuracy/top5: 92.7360data_time: 0.0311 time: 0.0673 2023/03/17 11:33:08 - mmengine - INFO - Epoch(train) [71][ 100/5005] lr: 1.0000e-03 eta: 8:02:28 time: 0.1921 data_time: 0.0042 loss: 1.0565 2023/03/17 11:33:27 - mmengine - INFO - Epoch(train) [71][ 200/5005] lr: 1.0000e-03 eta: 8:02:08 time: 0.1984 data_time: 0.0036 loss: 1.1431 2023/03/17 11:33:46 - mmengine - INFO - Epoch(train) [71][ 300/5005] lr: 1.0000e-03 eta: 8:01:49 time: 0.1850 data_time: 0.0039 loss: 1.0569 2023/03/17 11:34:05 - mmengine - INFO - Epoch(train) [71][ 400/5005] lr: 1.0000e-03 eta: 8:01:29 time: 0.2133 data_time: 0.0035 loss: 1.1441 2023/03/17 11:34:26 - mmengine - INFO - Epoch(train) [71][ 500/5005] lr: 1.0000e-03 eta: 8:01:11 time: 0.2133 data_time: 0.0044 loss: 1.0902 2023/03/17 11:34:47 - mmengine - INFO - Epoch(train) [71][ 600/5005] lr: 1.0000e-03 eta: 8:00:52 time: 0.1912 data_time: 0.0040 loss: 1.0683 2023/03/17 11:34:57 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:35:07 - mmengine - INFO - Epoch(train) [71][ 700/5005] lr: 1.0000e-03 eta: 8:00:33 time: 0.1971 data_time: 0.0040 loss: 1.1429 2023/03/17 11:35:26 - mmengine - INFO - Epoch(train) [71][ 800/5005] lr: 1.0000e-03 eta: 8:00:13 time: 0.1736 data_time: 0.0047 loss: 1.0001 2023/03/17 11:35:44 - mmengine - INFO - Epoch(train) [71][ 900/5005] lr: 1.0000e-03 eta: 7:59:54 time: 0.1890 data_time: 0.0034 loss: 1.0154 2023/03/17 11:36:04 - mmengine - INFO - Epoch(train) [71][1000/5005] lr: 1.0000e-03 eta: 7:59:35 time: 0.1920 data_time: 0.0035 loss: 1.1297 2023/03/17 11:36:25 - mmengine - INFO - Epoch(train) [71][1100/5005] lr: 1.0000e-03 eta: 7:59:16 time: 0.2251 data_time: 0.0033 loss: 1.0523 2023/03/17 11:36:45 - mmengine - INFO - Epoch(train) [71][1200/5005] lr: 1.0000e-03 eta: 7:58:57 time: 0.1822 data_time: 0.0032 loss: 1.1208 2023/03/17 11:37:04 - mmengine - INFO - Epoch(train) [71][1300/5005] lr: 1.0000e-03 eta: 7:58:37 time: 0.1804 data_time: 0.0047 loss: 1.0549 2023/03/17 11:37:23 - mmengine - INFO - Epoch(train) [71][1400/5005] lr: 1.0000e-03 eta: 7:58:18 time: 0.1878 data_time: 0.0036 loss: 1.2754 2023/03/17 11:37:43 - mmengine - INFO - Epoch(train) [71][1500/5005] lr: 1.0000e-03 eta: 7:57:59 time: 0.1849 data_time: 0.0036 loss: 1.0048 2023/03/17 11:38:02 - mmengine - INFO - Epoch(train) [71][1600/5005] lr: 1.0000e-03 eta: 7:57:39 time: 0.2400 data_time: 0.0031 loss: 0.9488 2023/03/17 11:38:14 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:38:24 - mmengine - INFO - Epoch(train) [71][1700/5005] lr: 1.0000e-03 eta: 7:57:21 time: 0.1894 data_time: 0.0033 loss: 1.0693 2023/03/17 11:38:43 - mmengine - INFO - Epoch(train) [71][1800/5005] lr: 1.0000e-03 eta: 7:57:02 time: 0.1946 data_time: 0.0040 loss: 1.1729 2023/03/17 11:39:02 - mmengine - INFO - Epoch(train) [71][1900/5005] lr: 1.0000e-03 eta: 7:56:42 time: 0.1919 data_time: 0.0035 loss: 1.0143 2023/03/17 11:39:21 - mmengine - INFO - Epoch(train) [71][2000/5005] lr: 1.0000e-03 eta: 7:56:23 time: 0.1913 data_time: 0.0034 loss: 0.9686 2023/03/17 11:39:41 - mmengine - INFO - Epoch(train) [71][2100/5005] lr: 1.0000e-03 eta: 7:56:04 time: 0.1922 data_time: 0.0041 loss: 1.1402 2023/03/17 11:40:03 - mmengine - INFO - Epoch(train) [71][2200/5005] lr: 1.0000e-03 eta: 7:55:46 time: 0.2272 data_time: 0.0042 loss: 0.9653 2023/03/17 11:40:22 - mmengine - INFO - Epoch(train) [71][2300/5005] lr: 1.0000e-03 eta: 7:55:26 time: 0.1813 data_time: 0.0042 loss: 0.9593 2023/03/17 11:40:41 - mmengine - INFO - Epoch(train) [71][2400/5005] lr: 1.0000e-03 eta: 7:55:06 time: 0.1846 data_time: 0.0044 loss: 1.1808 2023/03/17 11:40:59 - mmengine - INFO - Epoch(train) [71][2500/5005] lr: 1.0000e-03 eta: 7:54:47 time: 0.1821 data_time: 0.0042 loss: 0.9914 2023/03/17 11:41:18 - mmengine - INFO - Epoch(train) [71][2600/5005] lr: 1.0000e-03 eta: 7:54:27 time: 0.1830 data_time: 0.0041 loss: 1.0667 2023/03/17 11:41:27 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:41:37 - mmengine - INFO - Epoch(train) [71][2700/5005] lr: 1.0000e-03 eta: 7:54:08 time: 0.1945 data_time: 0.0041 loss: 1.0683 2023/03/17 11:41:57 - mmengine - INFO - Epoch(train) [71][2800/5005] lr: 1.0000e-03 eta: 7:53:48 time: 0.2051 data_time: 0.0041 loss: 1.0781 2023/03/17 11:42:16 - mmengine - INFO - Epoch(train) [71][2900/5005] lr: 1.0000e-03 eta: 7:53:29 time: 0.1799 data_time: 0.0038 loss: 1.0397 2023/03/17 11:42:35 - mmengine - INFO - Epoch(train) [71][3000/5005] lr: 1.0000e-03 eta: 7:53:09 time: 0.2082 data_time: 0.0039 loss: 1.1060 2023/03/17 11:42:53 - mmengine - INFO - Epoch(train) [71][3100/5005] lr: 1.0000e-03 eta: 7:52:50 time: 0.1910 data_time: 0.0041 loss: 1.0998 2023/03/17 11:43:13 - mmengine - INFO - Epoch(train) [71][3200/5005] lr: 1.0000e-03 eta: 7:52:31 time: 0.2373 data_time: 0.0037 loss: 1.0410 2023/03/17 11:43:34 - mmengine - INFO - Epoch(train) [71][3300/5005] lr: 1.0000e-03 eta: 7:52:12 time: 0.2238 data_time: 0.0041 loss: 1.2183 2023/03/17 11:43:56 - mmengine - INFO - Epoch(train) [71][3400/5005] lr: 1.0000e-03 eta: 7:51:54 time: 0.2066 data_time: 0.0044 loss: 1.0144 2023/03/17 11:44:16 - mmengine - INFO - Epoch(train) [71][3500/5005] lr: 1.0000e-03 eta: 7:51:34 time: 0.1947 data_time: 0.0040 loss: 1.2076 2023/03/17 11:44:34 - mmengine - INFO - Epoch(train) [71][3600/5005] lr: 1.0000e-03 eta: 7:51:15 time: 0.1801 data_time: 0.0045 loss: 1.1387 2023/03/17 11:44:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:44:56 - mmengine - INFO - Epoch(train) [71][3700/5005] lr: 1.0000e-03 eta: 7:50:57 time: 0.2345 data_time: 0.0040 loss: 0.8659 2023/03/17 11:45:18 - mmengine - INFO - Epoch(train) [71][3800/5005] lr: 1.0000e-03 eta: 7:50:38 time: 0.1921 data_time: 0.0038 loss: 1.0434 2023/03/17 11:45:37 - mmengine - INFO - Epoch(train) [71][3900/5005] lr: 1.0000e-03 eta: 7:50:19 time: 0.1833 data_time: 0.0037 loss: 1.0927 2023/03/17 11:45:56 - mmengine - INFO - Epoch(train) [71][4000/5005] lr: 1.0000e-03 eta: 7:49:59 time: 0.1916 data_time: 0.0040 loss: 1.1474 2023/03/17 11:46:15 - mmengine - INFO - Epoch(train) [71][4100/5005] lr: 1.0000e-03 eta: 7:49:40 time: 0.1830 data_time: 0.0040 loss: 1.0697 2023/03/17 11:46:35 - mmengine - INFO - Epoch(train) [71][4200/5005] lr: 1.0000e-03 eta: 7:49:21 time: 0.1802 data_time: 0.0040 loss: 1.1591 2023/03/17 11:46:54 - mmengine - INFO - Epoch(train) [71][4300/5005] lr: 1.0000e-03 eta: 7:49:01 time: 0.1916 data_time: 0.0040 loss: 1.0456 2023/03/17 11:47:14 - mmengine - INFO - Epoch(train) [71][4400/5005] lr: 1.0000e-03 eta: 7:48:42 time: 0.1776 data_time: 0.0044 loss: 1.1681 2023/03/17 11:47:32 - mmengine - INFO - Epoch(train) [71][4500/5005] lr: 1.0000e-03 eta: 7:48:22 time: 0.1836 data_time: 0.0040 loss: 1.2298 2023/03/17 11:47:51 - mmengine - INFO - Epoch(train) [71][4600/5005] lr: 1.0000e-03 eta: 7:48:03 time: 0.1869 data_time: 0.0043 loss: 1.0720 2023/03/17 11:48:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:48:10 - mmengine - INFO - Epoch(train) [71][4700/5005] lr: 1.0000e-03 eta: 7:47:43 time: 0.1853 data_time: 0.0037 loss: 0.9596 2023/03/17 11:48:33 - mmengine - INFO - Epoch(train) [71][4800/5005] lr: 1.0000e-03 eta: 7:47:26 time: 0.2483 data_time: 0.0041 loss: 1.1600 2023/03/17 11:48:53 - mmengine - INFO - Epoch(train) [71][4900/5005] lr: 1.0000e-03 eta: 7:47:06 time: 0.2004 data_time: 0.0037 loss: 1.0913 2023/03/17 11:49:12 - mmengine - INFO - Epoch(train) [71][5000/5005] lr: 1.0000e-03 eta: 7:46:47 time: 0.1891 data_time: 0.0049 loss: 1.0475 2023/03/17 11:49:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:49:14 - mmengine - INFO - Saving checkpoint at 71 epochs 2023/03/17 11:49:20 - mmengine - INFO - Epoch(val) [71][100/196] eta: 0:00:05 time: 0.0411 data_time: 0.0008 2023/03/17 11:49:45 - mmengine - INFO - Epoch(val) [71][196/196] accuracy/top1: 75.5400 accuracy/top5: 92.6800data_time: 0.0204 time: 0.0520 2023/03/17 11:50:04 - mmengine - INFO - Epoch(train) [72][ 100/5005] lr: 1.0000e-03 eta: 7:46:26 time: 0.1730 data_time: 0.0042 loss: 1.1499 2023/03/17 11:50:22 - mmengine - INFO - Epoch(train) [72][ 200/5005] lr: 1.0000e-03 eta: 7:46:07 time: 0.1744 data_time: 0.0040 loss: 0.9365 2023/03/17 11:50:41 - mmengine - INFO - Epoch(train) [72][ 300/5005] lr: 1.0000e-03 eta: 7:45:47 time: 0.1893 data_time: 0.0039 loss: 0.9888 2023/03/17 11:51:03 - mmengine - INFO - Epoch(train) [72][ 400/5005] lr: 1.0000e-03 eta: 7:45:29 time: 0.1980 data_time: 0.0042 loss: 0.9907 2023/03/17 11:51:26 - mmengine - INFO - Epoch(train) [72][ 500/5005] lr: 1.0000e-03 eta: 7:45:11 time: 0.2569 data_time: 0.0040 loss: 0.9539 2023/03/17 11:51:51 - mmengine - INFO - Epoch(train) [72][ 600/5005] lr: 1.0000e-03 eta: 7:44:54 time: 0.2847 data_time: 0.0043 loss: 1.0780 2023/03/17 11:52:03 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:52:14 - mmengine - INFO - Epoch(train) [72][ 700/5005] lr: 1.0000e-03 eta: 7:44:36 time: 0.1891 data_time: 0.0044 loss: 1.1203 2023/03/17 11:52:37 - mmengine - INFO - Epoch(train) [72][ 800/5005] lr: 1.0000e-03 eta: 7:44:18 time: 0.2160 data_time: 0.0042 loss: 0.8869 2023/03/17 11:52:58 - mmengine - INFO - Epoch(train) [72][ 900/5005] lr: 1.0000e-03 eta: 7:44:00 time: 0.2318 data_time: 0.0044 loss: 1.0180 2023/03/17 11:53:19 - mmengine - INFO - Epoch(train) [72][1000/5005] lr: 1.0000e-03 eta: 7:43:41 time: 0.2113 data_time: 0.0042 loss: 1.0814 2023/03/17 11:53:39 - mmengine - INFO - Epoch(train) [72][1100/5005] lr: 1.0000e-03 eta: 7:43:22 time: 0.1991 data_time: 0.0045 loss: 1.0569 2023/03/17 11:54:00 - mmengine - INFO - Epoch(train) [72][1200/5005] lr: 1.0000e-03 eta: 7:43:03 time: 0.2133 data_time: 0.0046 loss: 1.0460 2023/03/17 11:54:19 - mmengine - INFO - Epoch(train) [72][1300/5005] lr: 1.0000e-03 eta: 7:42:44 time: 0.1900 data_time: 0.0043 loss: 1.0653 2023/03/17 11:54:39 - mmengine - INFO - Epoch(train) [72][1400/5005] lr: 1.0000e-03 eta: 7:42:25 time: 0.1901 data_time: 0.0044 loss: 1.0428 2023/03/17 11:54:58 - mmengine - INFO - Epoch(train) [72][1500/5005] lr: 1.0000e-03 eta: 7:42:05 time: 0.1887 data_time: 0.0048 loss: 0.9885 2023/03/17 11:55:19 - mmengine - INFO - Epoch(train) [72][1600/5005] lr: 1.0000e-03 eta: 7:41:46 time: 0.1974 data_time: 0.0046 loss: 1.0206 2023/03/17 11:55:28 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:55:39 - mmengine - INFO - Epoch(train) [72][1700/5005] lr: 1.0000e-03 eta: 7:41:27 time: 0.1903 data_time: 0.0041 loss: 1.1660 2023/03/17 11:55:59 - mmengine - INFO - Epoch(train) [72][1800/5005] lr: 1.0000e-03 eta: 7:41:08 time: 0.1821 data_time: 0.0044 loss: 1.0009 2023/03/17 11:56:18 - mmengine - INFO - Epoch(train) [72][1900/5005] lr: 1.0000e-03 eta: 7:40:49 time: 0.1816 data_time: 0.0043 loss: 0.8557 2023/03/17 11:56:37 - mmengine - INFO - Epoch(train) [72][2000/5005] lr: 1.0000e-03 eta: 7:40:29 time: 0.1924 data_time: 0.0044 loss: 1.1506 2023/03/17 11:56:57 - mmengine - INFO - Epoch(train) [72][2100/5005] lr: 1.0000e-03 eta: 7:40:10 time: 0.1994 data_time: 0.0046 loss: 1.1477 2023/03/17 11:57:17 - mmengine - INFO - Epoch(train) [72][2200/5005] lr: 1.0000e-03 eta: 7:39:51 time: 0.1928 data_time: 0.0041 loss: 1.0635 2023/03/17 11:57:36 - mmengine - INFO - Epoch(train) [72][2300/5005] lr: 1.0000e-03 eta: 7:39:32 time: 0.1796 data_time: 0.0039 loss: 0.9945 2023/03/17 11:57:55 - mmengine - INFO - Epoch(train) [72][2400/5005] lr: 1.0000e-03 eta: 7:39:12 time: 0.2351 data_time: 0.0037 loss: 0.9240 2023/03/17 11:58:16 - mmengine - INFO - Epoch(train) [72][2500/5005] lr: 1.0000e-03 eta: 7:38:53 time: 0.1909 data_time: 0.0044 loss: 0.9916 2023/03/17 11:58:35 - mmengine - INFO - Epoch(train) [72][2600/5005] lr: 1.0000e-03 eta: 7:38:34 time: 0.1819 data_time: 0.0040 loss: 1.1197 2023/03/17 11:58:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 11:58:56 - mmengine - INFO - Epoch(train) [72][2700/5005] lr: 1.0000e-03 eta: 7:38:15 time: 0.2208 data_time: 0.0032 loss: 0.9126 2023/03/17 11:59:16 - mmengine - INFO - Epoch(train) [72][2800/5005] lr: 1.0000e-03 eta: 7:37:56 time: 0.2051 data_time: 0.0042 loss: 1.0506 2023/03/17 11:59:38 - mmengine - INFO - Epoch(train) [72][2900/5005] lr: 1.0000e-03 eta: 7:37:38 time: 0.2243 data_time: 0.0034 loss: 1.0727 2023/03/17 12:00:01 - mmengine - INFO - Epoch(train) [72][3000/5005] lr: 1.0000e-03 eta: 7:37:20 time: 0.1928 data_time: 0.0038 loss: 1.1637 2023/03/17 12:00:24 - mmengine - INFO - Epoch(train) [72][3100/5005] lr: 1.0000e-03 eta: 7:37:02 time: 0.2576 data_time: 0.0027 loss: 0.9880 2023/03/17 12:00:49 - mmengine - INFO - Epoch(train) [72][3200/5005] lr: 1.0000e-03 eta: 7:36:45 time: 0.2021 data_time: 0.0042 loss: 1.0308 2023/03/17 12:01:09 - mmengine - INFO - Epoch(train) [72][3300/5005] lr: 1.0000e-03 eta: 7:36:26 time: 0.1836 data_time: 0.0037 loss: 0.9958 2023/03/17 12:01:31 - mmengine - INFO - Epoch(train) [72][3400/5005] lr: 1.0000e-03 eta: 7:36:07 time: 0.1949 data_time: 0.0043 loss: 1.1051 2023/03/17 12:01:51 - mmengine - INFO - Epoch(train) [72][3500/5005] lr: 1.0000e-03 eta: 7:35:48 time: 0.1980 data_time: 0.0044 loss: 1.1136 2023/03/17 12:02:13 - mmengine - INFO - Epoch(train) [72][3600/5005] lr: 1.0000e-03 eta: 7:35:30 time: 0.2483 data_time: 0.0037 loss: 1.1027 2023/03/17 12:02:23 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:02:33 - mmengine - INFO - Epoch(train) [72][3700/5005] lr: 1.0000e-03 eta: 7:35:11 time: 0.1945 data_time: 0.0043 loss: 0.9856 2023/03/17 12:02:51 - mmengine - INFO - Epoch(train) [72][3800/5005] lr: 1.0000e-03 eta: 7:34:51 time: 0.1885 data_time: 0.0041 loss: 1.0465 2023/03/17 12:03:10 - mmengine - INFO - Epoch(train) [72][3900/5005] lr: 1.0000e-03 eta: 7:34:32 time: 0.1835 data_time: 0.0043 loss: 1.0120 2023/03/17 12:03:29 - mmengine - INFO - Epoch(train) [72][4000/5005] lr: 1.0000e-03 eta: 7:34:12 time: 0.1840 data_time: 0.0045 loss: 0.9420 2023/03/17 12:03:48 - mmengine - INFO - Epoch(train) [72][4100/5005] lr: 1.0000e-03 eta: 7:33:53 time: 0.1840 data_time: 0.0038 loss: 1.0234 2023/03/17 12:04:06 - mmengine - INFO - Epoch(train) [72][4200/5005] lr: 1.0000e-03 eta: 7:33:33 time: 0.1819 data_time: 0.0037 loss: 1.1800 2023/03/17 12:04:25 - mmengine - INFO - Epoch(train) [72][4300/5005] lr: 1.0000e-03 eta: 7:33:13 time: 0.1848 data_time: 0.0042 loss: 1.0890 2023/03/17 12:04:44 - mmengine - INFO - Epoch(train) [72][4400/5005] lr: 1.0000e-03 eta: 7:32:54 time: 0.1811 data_time: 0.0040 loss: 1.1888 2023/03/17 12:05:02 - mmengine - INFO - Epoch(train) [72][4500/5005] lr: 1.0000e-03 eta: 7:32:34 time: 0.1835 data_time: 0.0045 loss: 1.0057 2023/03/17 12:05:21 - mmengine - INFO - Epoch(train) [72][4600/5005] lr: 1.0000e-03 eta: 7:32:14 time: 0.1795 data_time: 0.0039 loss: 1.1324 2023/03/17 12:05:30 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:05:40 - mmengine - INFO - Epoch(train) [72][4700/5005] lr: 1.0000e-03 eta: 7:31:55 time: 0.1956 data_time: 0.0042 loss: 0.9558 2023/03/17 12:05:59 - mmengine - INFO - Epoch(train) [72][4800/5005] lr: 1.0000e-03 eta: 7:31:35 time: 0.1899 data_time: 0.0042 loss: 1.1088 2023/03/17 12:06:18 - mmengine - INFO - Epoch(train) [72][4900/5005] lr: 1.0000e-03 eta: 7:31:16 time: 0.1830 data_time: 0.0037 loss: 0.9871 2023/03/17 12:06:37 - mmengine - INFO - Epoch(train) [72][5000/5005] lr: 1.0000e-03 eta: 7:30:57 time: 0.2036 data_time: 0.0048 loss: 0.9856 2023/03/17 12:06:39 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:06:39 - mmengine - INFO - Saving checkpoint at 72 epochs 2023/03/17 12:06:46 - mmengine - INFO - Epoch(val) [72][100/196] eta: 0:00:05 time: 0.0470 data_time: 0.0010 2023/03/17 12:07:12 - mmengine - INFO - Epoch(val) [72][196/196] accuracy/top1: 75.6340 accuracy/top5: 92.6360data_time: 0.0269 time: 0.0607 2023/03/17 12:07:32 - mmengine - INFO - Epoch(train) [73][ 100/5005] lr: 1.0000e-03 eta: 7:30:37 time: 0.1870 data_time: 0.0038 loss: 1.0490 2023/03/17 12:07:50 - mmengine - INFO - Epoch(train) [73][ 200/5005] lr: 1.0000e-03 eta: 7:30:17 time: 0.1727 data_time: 0.0049 loss: 1.0220 2023/03/17 12:08:09 - mmengine - INFO - Epoch(train) [73][ 300/5005] lr: 1.0000e-03 eta: 7:29:57 time: 0.1878 data_time: 0.0044 loss: 1.0669 2023/03/17 12:08:29 - mmengine - INFO - Epoch(train) [73][ 400/5005] lr: 1.0000e-03 eta: 7:29:38 time: 0.1938 data_time: 0.0047 loss: 0.8595 2023/03/17 12:08:48 - mmengine - INFO - Epoch(train) [73][ 500/5005] lr: 1.0000e-03 eta: 7:29:19 time: 0.1778 data_time: 0.0040 loss: 1.0441 2023/03/17 12:09:06 - mmengine - INFO - Epoch(train) [73][ 600/5005] lr: 1.0000e-03 eta: 7:28:59 time: 0.1823 data_time: 0.0041 loss: 0.9260 2023/03/17 12:09:14 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:09:25 - mmengine - INFO - Epoch(train) [73][ 700/5005] lr: 1.0000e-03 eta: 7:28:40 time: 0.1897 data_time: 0.0048 loss: 1.0459 2023/03/17 12:09:44 - mmengine - INFO - Epoch(train) [73][ 800/5005] lr: 1.0000e-03 eta: 7:28:20 time: 0.2043 data_time: 0.0047 loss: 1.0024 2023/03/17 12:10:02 - mmengine - INFO - Epoch(train) [73][ 900/5005] lr: 1.0000e-03 eta: 7:28:00 time: 0.1765 data_time: 0.0056 loss: 1.1213 2023/03/17 12:10:20 - mmengine - INFO - Epoch(train) [73][1000/5005] lr: 1.0000e-03 eta: 7:27:40 time: 0.1750 data_time: 0.0050 loss: 1.0280 2023/03/17 12:10:39 - mmengine - INFO - Epoch(train) [73][1100/5005] lr: 1.0000e-03 eta: 7:27:21 time: 0.2160 data_time: 0.0041 loss: 1.1012 2023/03/17 12:11:00 - mmengine - INFO - Epoch(train) [73][1200/5005] lr: 1.0000e-03 eta: 7:27:02 time: 0.2181 data_time: 0.0044 loss: 1.1997 2023/03/17 12:11:23 - mmengine - INFO - Epoch(train) [73][1300/5005] lr: 1.0000e-03 eta: 7:26:44 time: 0.2228 data_time: 0.0039 loss: 0.9638 2023/03/17 12:11:42 - mmengine - INFO - Epoch(train) [73][1400/5005] lr: 1.0000e-03 eta: 7:26:25 time: 0.1891 data_time: 0.0041 loss: 0.8353 2023/03/17 12:12:02 - mmengine - INFO - Epoch(train) [73][1500/5005] lr: 1.0000e-03 eta: 7:26:06 time: 0.1864 data_time: 0.0047 loss: 1.0255 2023/03/17 12:12:23 - mmengine - INFO - Epoch(train) [73][1600/5005] lr: 1.0000e-03 eta: 7:25:47 time: 0.2107 data_time: 0.0044 loss: 1.1960 2023/03/17 12:12:31 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:12:42 - mmengine - INFO - Epoch(train) [73][1700/5005] lr: 1.0000e-03 eta: 7:25:27 time: 0.1851 data_time: 0.0045 loss: 1.0401 2023/03/17 12:13:01 - mmengine - INFO - Epoch(train) [73][1800/5005] lr: 1.0000e-03 eta: 7:25:08 time: 0.2183 data_time: 0.0035 loss: 0.9420 2023/03/17 12:13:23 - mmengine - INFO - Epoch(train) [73][1900/5005] lr: 1.0000e-03 eta: 7:24:50 time: 0.2244 data_time: 0.0042 loss: 0.8993 2023/03/17 12:13:42 - mmengine - INFO - Epoch(train) [73][2000/5005] lr: 1.0000e-03 eta: 7:24:30 time: 0.1787 data_time: 0.0048 loss: 1.0184 2023/03/17 12:14:00 - mmengine - INFO - Epoch(train) [73][2100/5005] lr: 1.0000e-03 eta: 7:24:10 time: 0.1765 data_time: 0.0050 loss: 1.0861 2023/03/17 12:14:20 - mmengine - INFO - Epoch(train) [73][2200/5005] lr: 1.0000e-03 eta: 7:23:51 time: 0.2011 data_time: 0.0048 loss: 1.2271 2023/03/17 12:14:40 - mmengine - INFO - Epoch(train) [73][2300/5005] lr: 1.0000e-03 eta: 7:23:32 time: 0.2127 data_time: 0.0045 loss: 1.1604 2023/03/17 12:15:01 - mmengine - INFO - Epoch(train) [73][2400/5005] lr: 1.0000e-03 eta: 7:23:13 time: 0.2248 data_time: 0.0035 loss: 1.1127 2023/03/17 12:15:20 - mmengine - INFO - Epoch(train) [73][2500/5005] lr: 1.0000e-03 eta: 7:22:54 time: 0.1828 data_time: 0.0043 loss: 1.1606 2023/03/17 12:15:40 - mmengine - INFO - Epoch(train) [73][2600/5005] lr: 1.0000e-03 eta: 7:22:35 time: 0.1938 data_time: 0.0042 loss: 0.9505 2023/03/17 12:15:48 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:16:00 - mmengine - INFO - Epoch(train) [73][2700/5005] lr: 1.0000e-03 eta: 7:22:16 time: 0.1984 data_time: 0.0039 loss: 1.0801 2023/03/17 12:16:20 - mmengine - INFO - Epoch(train) [73][2800/5005] lr: 1.0000e-03 eta: 7:21:56 time: 0.1946 data_time: 0.0039 loss: 1.0293 2023/03/17 12:16:39 - mmengine - INFO - Epoch(train) [73][2900/5005] lr: 1.0000e-03 eta: 7:21:37 time: 0.1935 data_time: 0.0035 loss: 1.0251 2023/03/17 12:16:58 - mmengine - INFO - Epoch(train) [73][3000/5005] lr: 1.0000e-03 eta: 7:21:17 time: 0.1854 data_time: 0.0047 loss: 1.0494 2023/03/17 12:17:18 - mmengine - INFO - Epoch(train) [73][3100/5005] lr: 1.0000e-03 eta: 7:20:58 time: 0.2060 data_time: 0.0046 loss: 0.9551 2023/03/17 12:17:38 - mmengine - INFO - Epoch(train) [73][3200/5005] lr: 1.0000e-03 eta: 7:20:39 time: 0.1916 data_time: 0.0043 loss: 1.0215 2023/03/17 12:17:58 - mmengine - INFO - Epoch(train) [73][3300/5005] lr: 1.0000e-03 eta: 7:20:20 time: 0.1999 data_time: 0.0035 loss: 1.0181 2023/03/17 12:18:19 - mmengine - INFO - Epoch(train) [73][3400/5005] lr: 1.0000e-03 eta: 7:20:02 time: 0.1895 data_time: 0.0045 loss: 1.0476 2023/03/17 12:18:39 - mmengine - INFO - Epoch(train) [73][3500/5005] lr: 1.0000e-03 eta: 7:19:42 time: 0.1869 data_time: 0.0039 loss: 1.0487 2023/03/17 12:18:58 - mmengine - INFO - Epoch(train) [73][3600/5005] lr: 1.0000e-03 eta: 7:19:23 time: 0.1874 data_time: 0.0046 loss: 1.0279 2023/03/17 12:19:05 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:19:17 - mmengine - INFO - Epoch(train) [73][3700/5005] lr: 1.0000e-03 eta: 7:19:03 time: 0.1803 data_time: 0.0042 loss: 1.1076 2023/03/17 12:19:36 - mmengine - INFO - Epoch(train) [73][3800/5005] lr: 1.0000e-03 eta: 7:18:44 time: 0.1858 data_time: 0.0044 loss: 0.9712 2023/03/17 12:19:54 - mmengine - INFO - Epoch(train) [73][3900/5005] lr: 1.0000e-03 eta: 7:18:24 time: 0.1853 data_time: 0.0041 loss: 1.0495 2023/03/17 12:20:13 - mmengine - INFO - Epoch(train) [73][4000/5005] lr: 1.0000e-03 eta: 7:18:05 time: 0.1829 data_time: 0.0041 loss: 0.8558 2023/03/17 12:20:32 - mmengine - INFO - Epoch(train) [73][4100/5005] lr: 1.0000e-03 eta: 7:17:45 time: 0.1825 data_time: 0.0041 loss: 1.0237 2023/03/17 12:20:51 - mmengine - INFO - Epoch(train) [73][4200/5005] lr: 1.0000e-03 eta: 7:17:26 time: 0.1928 data_time: 0.0046 loss: 1.0504 2023/03/17 12:21:10 - mmengine - INFO - Epoch(train) [73][4300/5005] lr: 1.0000e-03 eta: 7:17:06 time: 0.1798 data_time: 0.0039 loss: 1.1613 2023/03/17 12:21:29 - mmengine - INFO - Epoch(train) [73][4400/5005] lr: 1.0000e-03 eta: 7:16:47 time: 0.2049 data_time: 0.0037 loss: 1.0333 2023/03/17 12:21:50 - mmengine - INFO - Epoch(train) [73][4500/5005] lr: 1.0000e-03 eta: 7:16:28 time: 0.1980 data_time: 0.0045 loss: 1.0368 2023/03/17 12:22:09 - mmengine - INFO - Epoch(train) [73][4600/5005] lr: 1.0000e-03 eta: 7:16:09 time: 0.2025 data_time: 0.0044 loss: 0.9841 2023/03/17 12:22:17 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:22:29 - mmengine - INFO - Epoch(train) [73][4700/5005] lr: 1.0000e-03 eta: 7:15:49 time: 0.1903 data_time: 0.0041 loss: 1.0007 2023/03/17 12:22:49 - mmengine - INFO - Epoch(train) [73][4800/5005] lr: 1.0000e-03 eta: 7:15:30 time: 0.2296 data_time: 0.0038 loss: 0.9554 2023/03/17 12:23:10 - mmengine - INFO - Epoch(train) [73][4900/5005] lr: 1.0000e-03 eta: 7:15:11 time: 0.1909 data_time: 0.0048 loss: 1.1403 2023/03/17 12:23:29 - mmengine - INFO - Epoch(train) [73][5000/5005] lr: 1.0000e-03 eta: 7:14:52 time: 0.1930 data_time: 0.0050 loss: 1.0393 2023/03/17 12:23:30 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:23:30 - mmengine - INFO - Saving checkpoint at 73 epochs 2023/03/17 12:23:36 - mmengine - INFO - Epoch(val) [73][100/196] eta: 0:00:05 time: 0.0422 data_time: 0.0010 2023/03/17 12:24:01 - mmengine - INFO - Epoch(val) [73][196/196] accuracy/top1: 75.5200 accuracy/top5: 92.7760data_time: 0.0299 time: 0.0601 2023/03/17 12:24:31 - mmengine - INFO - Epoch(train) [74][ 100/5005] lr: 1.0000e-03 eta: 7:14:35 time: 0.2695 data_time: 0.0024 loss: 1.1313 2023/03/17 12:24:51 - mmengine - INFO - Epoch(train) [74][ 200/5005] lr: 1.0000e-03 eta: 7:14:16 time: 0.1805 data_time: 0.0047 loss: 1.0911 2023/03/17 12:25:10 - mmengine - INFO - Epoch(train) [74][ 300/5005] lr: 1.0000e-03 eta: 7:13:57 time: 0.1773 data_time: 0.0043 loss: 1.0336 2023/03/17 12:25:28 - mmengine - INFO - Epoch(train) [74][ 400/5005] lr: 1.0000e-03 eta: 7:13:37 time: 0.1923 data_time: 0.0043 loss: 1.0474 2023/03/17 12:25:47 - mmengine - INFO - Epoch(train) [74][ 500/5005] lr: 1.0000e-03 eta: 7:13:17 time: 0.1759 data_time: 0.0042 loss: 0.9220 2023/03/17 12:26:05 - mmengine - INFO - Epoch(train) [74][ 600/5005] lr: 1.0000e-03 eta: 7:12:58 time: 0.1806 data_time: 0.0045 loss: 1.0075 2023/03/17 12:26:12 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:26:24 - mmengine - INFO - Epoch(train) [74][ 700/5005] lr: 1.0000e-03 eta: 7:12:38 time: 0.1816 data_time: 0.0051 loss: 1.0954 2023/03/17 12:26:44 - mmengine - INFO - Epoch(train) [74][ 800/5005] lr: 1.0000e-03 eta: 7:12:19 time: 0.1843 data_time: 0.0041 loss: 0.8633 2023/03/17 12:27:04 - mmengine - INFO - Epoch(train) [74][ 900/5005] lr: 1.0000e-03 eta: 7:12:00 time: 0.2073 data_time: 0.0043 loss: 1.0567 2023/03/17 12:27:25 - mmengine - INFO - Epoch(train) [74][1000/5005] lr: 1.0000e-03 eta: 7:11:41 time: 0.2055 data_time: 0.0033 loss: 0.9680 2023/03/17 12:27:46 - mmengine - INFO - Epoch(train) [74][1100/5005] lr: 1.0000e-03 eta: 7:11:22 time: 0.2007 data_time: 0.0043 loss: 1.0753 2023/03/17 12:28:06 - mmengine - INFO - Epoch(train) [74][1200/5005] lr: 1.0000e-03 eta: 7:11:03 time: 0.1923 data_time: 0.0041 loss: 1.1571 2023/03/17 12:28:26 - mmengine - INFO - Epoch(train) [74][1300/5005] lr: 1.0000e-03 eta: 7:10:44 time: 0.1989 data_time: 0.0042 loss: 1.0704 2023/03/17 12:28:45 - mmengine - INFO - Epoch(train) [74][1400/5005] lr: 1.0000e-03 eta: 7:10:25 time: 0.1788 data_time: 0.0040 loss: 0.9005 2023/03/17 12:29:04 - mmengine - INFO - Epoch(train) [74][1500/5005] lr: 1.0000e-03 eta: 7:10:05 time: 0.1855 data_time: 0.0036 loss: 0.9997 2023/03/17 12:29:23 - mmengine - INFO - Epoch(train) [74][1600/5005] lr: 1.0000e-03 eta: 7:09:46 time: 0.1915 data_time: 0.0045 loss: 1.0173 2023/03/17 12:29:30 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:29:43 - mmengine - INFO - Epoch(train) [74][1700/5005] lr: 1.0000e-03 eta: 7:09:27 time: 0.1977 data_time: 0.0038 loss: 0.9715 2023/03/17 12:30:03 - mmengine - INFO - Epoch(train) [74][1800/5005] lr: 1.0000e-03 eta: 7:09:07 time: 0.1915 data_time: 0.0042 loss: 1.0237 2023/03/17 12:30:22 - mmengine - INFO - Epoch(train) [74][1900/5005] lr: 1.0000e-03 eta: 7:08:48 time: 0.1911 data_time: 0.0044 loss: 1.0532 2023/03/17 12:30:43 - mmengine - INFO - Epoch(train) [74][2000/5005] lr: 1.0000e-03 eta: 7:08:29 time: 0.2251 data_time: 0.0049 loss: 0.9018 2023/03/17 12:31:05 - mmengine - INFO - Epoch(train) [74][2100/5005] lr: 1.0000e-03 eta: 7:08:11 time: 0.1936 data_time: 0.0040 loss: 1.1392 2023/03/17 12:31:24 - mmengine - INFO - Epoch(train) [74][2200/5005] lr: 1.0000e-03 eta: 7:07:51 time: 0.1867 data_time: 0.0040 loss: 0.9809 2023/03/17 12:31:42 - mmengine - INFO - Epoch(train) [74][2300/5005] lr: 1.0000e-03 eta: 7:07:32 time: 0.1771 data_time: 0.0049 loss: 0.9924 2023/03/17 12:32:01 - mmengine - INFO - Epoch(train) [74][2400/5005] lr: 1.0000e-03 eta: 7:07:12 time: 0.1914 data_time: 0.0043 loss: 0.9940 2023/03/17 12:32:20 - mmengine - INFO - Epoch(train) [74][2500/5005] lr: 1.0000e-03 eta: 7:06:53 time: 0.1907 data_time: 0.0036 loss: 1.2349 2023/03/17 12:32:39 - mmengine - INFO - Epoch(train) [74][2600/5005] lr: 1.0000e-03 eta: 7:06:33 time: 0.1908 data_time: 0.0036 loss: 1.2256 2023/03/17 12:32:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:32:58 - mmengine - INFO - Epoch(train) [74][2700/5005] lr: 1.0000e-03 eta: 7:06:14 time: 0.1855 data_time: 0.0044 loss: 1.0840 2023/03/17 12:33:18 - mmengine - INFO - Epoch(train) [74][2800/5005] lr: 1.0000e-03 eta: 7:05:55 time: 0.2006 data_time: 0.0046 loss: 1.1594 2023/03/17 12:33:38 - mmengine - INFO - Epoch(train) [74][2900/5005] lr: 1.0000e-03 eta: 7:05:35 time: 0.1981 data_time: 0.0045 loss: 1.0377 2023/03/17 12:33:59 - mmengine - INFO - Epoch(train) [74][3000/5005] lr: 1.0000e-03 eta: 7:05:17 time: 0.2278 data_time: 0.0045 loss: 1.0207 2023/03/17 12:34:19 - mmengine - INFO - Epoch(train) [74][3100/5005] lr: 1.0000e-03 eta: 7:04:57 time: 0.1929 data_time: 0.0046 loss: 0.9510 2023/03/17 12:34:39 - mmengine - INFO - Epoch(train) [74][3200/5005] lr: 1.0000e-03 eta: 7:04:38 time: 0.1983 data_time: 0.0039 loss: 1.0693 2023/03/17 12:34:59 - mmengine - INFO - Epoch(train) [74][3300/5005] lr: 1.0000e-03 eta: 7:04:19 time: 0.2058 data_time: 0.0046 loss: 1.1300 2023/03/17 12:35:19 - mmengine - INFO - Epoch(train) [74][3400/5005] lr: 1.0000e-03 eta: 7:04:00 time: 0.2018 data_time: 0.0049 loss: 0.9998 2023/03/17 12:35:39 - mmengine - INFO - Epoch(train) [74][3500/5005] lr: 1.0000e-03 eta: 7:03:41 time: 0.1986 data_time: 0.0047 loss: 0.9639 2023/03/17 12:35:59 - mmengine - INFO - Epoch(train) [74][3600/5005] lr: 1.0000e-03 eta: 7:03:22 time: 0.1886 data_time: 0.0046 loss: 1.0503 2023/03/17 12:36:06 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:36:18 - mmengine - INFO - Epoch(train) [74][3700/5005] lr: 1.0000e-03 eta: 7:03:02 time: 0.1824 data_time: 0.0050 loss: 0.9975 2023/03/17 12:36:37 - mmengine - INFO - Epoch(train) [74][3800/5005] lr: 1.0000e-03 eta: 7:02:43 time: 0.1822 data_time: 0.0044 loss: 1.0365 2023/03/17 12:36:56 - mmengine - INFO - Epoch(train) [74][3900/5005] lr: 1.0000e-03 eta: 7:02:23 time: 0.1983 data_time: 0.0049 loss: 1.1462 2023/03/17 12:37:16 - mmengine - INFO - Epoch(train) [74][4000/5005] lr: 1.0000e-03 eta: 7:02:04 time: 0.1963 data_time: 0.0048 loss: 0.9366 2023/03/17 12:37:36 - mmengine - INFO - Epoch(train) [74][4100/5005] lr: 1.0000e-03 eta: 7:01:45 time: 0.1925 data_time: 0.0044 loss: 1.1708 2023/03/17 12:37:57 - mmengine - INFO - Epoch(train) [74][4200/5005] lr: 1.0000e-03 eta: 7:01:26 time: 0.2036 data_time: 0.0049 loss: 1.0230 2023/03/17 12:38:17 - mmengine - INFO - Epoch(train) [74][4300/5005] lr: 1.0000e-03 eta: 7:01:07 time: 0.1924 data_time: 0.0046 loss: 1.2862 2023/03/17 12:38:36 - mmengine - INFO - Epoch(train) [74][4400/5005] lr: 1.0000e-03 eta: 7:00:48 time: 0.1891 data_time: 0.0045 loss: 0.9474 2023/03/17 12:38:56 - mmengine - INFO - Epoch(train) [74][4500/5005] lr: 1.0000e-03 eta: 7:00:29 time: 0.1930 data_time: 0.0042 loss: 1.1138 2023/03/17 12:39:16 - mmengine - INFO - Epoch(train) [74][4600/5005] lr: 1.0000e-03 eta: 7:00:09 time: 0.1921 data_time: 0.0042 loss: 1.0026 2023/03/17 12:39:23 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:39:35 - mmengine - INFO - Epoch(train) [74][4700/5005] lr: 1.0000e-03 eta: 6:59:50 time: 0.1894 data_time: 0.0044 loss: 0.9603 2023/03/17 12:39:55 - mmengine - INFO - Epoch(train) [74][4800/5005] lr: 1.0000e-03 eta: 6:59:31 time: 0.1959 data_time: 0.0044 loss: 0.9306 2023/03/17 12:40:18 - mmengine - INFO - Epoch(train) [74][4900/5005] lr: 1.0000e-03 eta: 6:59:13 time: 0.2064 data_time: 0.0046 loss: 0.9498 2023/03/17 12:40:38 - mmengine - INFO - Epoch(train) [74][5000/5005] lr: 1.0000e-03 eta: 6:58:54 time: 0.2028 data_time: 0.0052 loss: 1.0114 2023/03/17 12:40:39 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:40:39 - mmengine - INFO - Saving checkpoint at 74 epochs 2023/03/17 12:40:46 - mmengine - INFO - Epoch(val) [74][100/196] eta: 0:00:05 time: 0.0501 data_time: 0.0009 2023/03/17 12:41:14 - mmengine - INFO - Epoch(val) [74][196/196] accuracy/top1: 75.6260 accuracy/top5: 92.7720data_time: 0.0259 time: 0.0623 2023/03/17 12:41:37 - mmengine - INFO - Epoch(train) [75][ 100/5005] lr: 1.0000e-03 eta: 6:58:35 time: 0.2126 data_time: 0.0046 loss: 0.9384 2023/03/17 12:41:57 - mmengine - INFO - Epoch(train) [75][ 200/5005] lr: 1.0000e-03 eta: 6:58:15 time: 0.1825 data_time: 0.0042 loss: 1.2056 2023/03/17 12:42:17 - mmengine - INFO - Epoch(train) [75][ 300/5005] lr: 1.0000e-03 eta: 6:57:56 time: 0.2003 data_time: 0.0040 loss: 1.0553 2023/03/17 12:42:40 - mmengine - INFO - Epoch(train) [75][ 400/5005] lr: 1.0000e-03 eta: 6:57:38 time: 0.2337 data_time: 0.0039 loss: 1.2758 2023/03/17 12:43:02 - mmengine - INFO - Epoch(train) [75][ 500/5005] lr: 1.0000e-03 eta: 6:57:20 time: 0.1988 data_time: 0.0044 loss: 0.9323 2023/03/17 12:43:22 - mmengine - INFO - Epoch(train) [75][ 600/5005] lr: 1.0000e-03 eta: 6:57:00 time: 0.1923 data_time: 0.0036 loss: 0.8748 2023/03/17 12:43:27 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:43:41 - mmengine - INFO - Epoch(train) [75][ 700/5005] lr: 1.0000e-03 eta: 6:56:41 time: 0.1925 data_time: 0.0042 loss: 1.0472 2023/03/17 12:44:00 - mmengine - INFO - Epoch(train) [75][ 800/5005] lr: 1.0000e-03 eta: 6:56:22 time: 0.1918 data_time: 0.0047 loss: 0.8409 2023/03/17 12:44:20 - mmengine - INFO - Epoch(train) [75][ 900/5005] lr: 1.0000e-03 eta: 6:56:02 time: 0.1976 data_time: 0.0045 loss: 0.8169 2023/03/17 12:44:39 - mmengine - INFO - Epoch(train) [75][1000/5005] lr: 1.0000e-03 eta: 6:55:43 time: 0.2006 data_time: 0.0044 loss: 0.9981 2023/03/17 12:44:59 - mmengine - INFO - Epoch(train) [75][1100/5005] lr: 1.0000e-03 eta: 6:55:24 time: 0.1981 data_time: 0.0042 loss: 1.1687 2023/03/17 12:45:18 - mmengine - INFO - Epoch(train) [75][1200/5005] lr: 1.0000e-03 eta: 6:55:05 time: 0.1816 data_time: 0.0043 loss: 0.9538 2023/03/17 12:45:38 - mmengine - INFO - Epoch(train) [75][1300/5005] lr: 1.0000e-03 eta: 6:54:45 time: 0.1891 data_time: 0.0044 loss: 1.2078 2023/03/17 12:45:58 - mmengine - INFO - Epoch(train) [75][1400/5005] lr: 1.0000e-03 eta: 6:54:26 time: 0.2014 data_time: 0.0034 loss: 0.7992 2023/03/17 12:46:17 - mmengine - INFO - Epoch(train) [75][1500/5005] lr: 1.0000e-03 eta: 6:54:07 time: 0.1844 data_time: 0.0047 loss: 1.0573 2023/03/17 12:46:36 - mmengine - INFO - Epoch(train) [75][1600/5005] lr: 1.0000e-03 eta: 6:53:47 time: 0.2020 data_time: 0.0039 loss: 0.9326 2023/03/17 12:46:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:46:57 - mmengine - INFO - Epoch(train) [75][1700/5005] lr: 1.0000e-03 eta: 6:53:28 time: 0.1976 data_time: 0.0038 loss: 1.0192 2023/03/17 12:47:17 - mmengine - INFO - Epoch(train) [75][1800/5005] lr: 1.0000e-03 eta: 6:53:09 time: 0.1852 data_time: 0.0043 loss: 1.1598 2023/03/17 12:47:36 - mmengine - INFO - Epoch(train) [75][1900/5005] lr: 1.0000e-03 eta: 6:52:50 time: 0.1970 data_time: 0.0048 loss: 1.0330 2023/03/17 12:47:57 - mmengine - INFO - Epoch(train) [75][2000/5005] lr: 1.0000e-03 eta: 6:52:31 time: 0.2580 data_time: 0.0052 loss: 1.1203 2023/03/17 12:48:19 - mmengine - INFO - Epoch(train) [75][2100/5005] lr: 1.0000e-03 eta: 6:52:12 time: 0.1964 data_time: 0.0053 loss: 1.1319 2023/03/17 12:48:39 - mmengine - INFO - Epoch(train) [75][2200/5005] lr: 1.0000e-03 eta: 6:51:53 time: 0.2026 data_time: 0.0043 loss: 1.0687 2023/03/17 12:48:58 - mmengine - INFO - Epoch(train) [75][2300/5005] lr: 1.0000e-03 eta: 6:51:34 time: 0.1837 data_time: 0.0042 loss: 1.1386 2023/03/17 12:49:17 - mmengine - INFO - Epoch(train) [75][2400/5005] lr: 1.0000e-03 eta: 6:51:14 time: 0.1814 data_time: 0.0041 loss: 1.1191 2023/03/17 12:49:37 - mmengine - INFO - Epoch(train) [75][2500/5005] lr: 1.0000e-03 eta: 6:50:55 time: 0.1988 data_time: 0.0040 loss: 1.0584 2023/03/17 12:49:57 - mmengine - INFO - Epoch(train) [75][2600/5005] lr: 1.0000e-03 eta: 6:50:36 time: 0.1990 data_time: 0.0039 loss: 0.9499 2023/03/17 12:50:03 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:50:17 - mmengine - INFO - Epoch(train) [75][2700/5005] lr: 1.0000e-03 eta: 6:50:17 time: 0.1967 data_time: 0.0042 loss: 1.0853 2023/03/17 12:50:37 - mmengine - INFO - Epoch(train) [75][2800/5005] lr: 1.0000e-03 eta: 6:49:58 time: 0.2047 data_time: 0.0042 loss: 1.2591 2023/03/17 12:50:58 - mmengine - INFO - Epoch(train) [75][2900/5005] lr: 1.0000e-03 eta: 6:49:39 time: 0.2005 data_time: 0.0038 loss: 0.9148 2023/03/17 12:51:18 - mmengine - INFO - Epoch(train) [75][3000/5005] lr: 1.0000e-03 eta: 6:49:20 time: 0.2070 data_time: 0.0044 loss: 1.0853 2023/03/17 12:51:40 - mmengine - INFO - Epoch(train) [75][3100/5005] lr: 1.0000e-03 eta: 6:49:01 time: 0.2363 data_time: 0.0047 loss: 1.1631 2023/03/17 12:51:59 - mmengine - INFO - Epoch(train) [75][3200/5005] lr: 1.0000e-03 eta: 6:48:42 time: 0.1923 data_time: 0.0049 loss: 1.1484 2023/03/17 12:52:18 - mmengine - INFO - Epoch(train) [75][3300/5005] lr: 1.0000e-03 eta: 6:48:22 time: 0.1901 data_time: 0.0044 loss: 0.9149 2023/03/17 12:52:36 - mmengine - INFO - Epoch(train) [75][3400/5005] lr: 1.0000e-03 eta: 6:48:03 time: 0.1789 data_time: 0.0045 loss: 0.8628 2023/03/17 12:52:55 - mmengine - INFO - Epoch(train) [75][3500/5005] lr: 1.0000e-03 eta: 6:47:43 time: 0.1857 data_time: 0.0050 loss: 0.9997 2023/03/17 12:53:13 - mmengine - INFO - Epoch(train) [75][3600/5005] lr: 1.0000e-03 eta: 6:47:23 time: 0.1824 data_time: 0.0047 loss: 1.1959 2023/03/17 12:53:19 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:53:34 - mmengine - INFO - Epoch(train) [75][3700/5005] lr: 1.0000e-03 eta: 6:47:05 time: 0.2355 data_time: 0.0040 loss: 1.3347 2023/03/17 12:53:56 - mmengine - INFO - Epoch(train) [75][3800/5005] lr: 1.0000e-03 eta: 6:46:46 time: 0.1854 data_time: 0.0055 loss: 1.0292 2023/03/17 12:54:17 - mmengine - INFO - Epoch(train) [75][3900/5005] lr: 1.0000e-03 eta: 6:46:27 time: 0.2234 data_time: 0.0041 loss: 0.8711 2023/03/17 12:54:38 - mmengine - INFO - Epoch(train) [75][4000/5005] lr: 1.0000e-03 eta: 6:46:09 time: 0.2292 data_time: 0.0043 loss: 0.9669 2023/03/17 12:55:02 - mmengine - INFO - Epoch(train) [75][4100/5005] lr: 1.0000e-03 eta: 6:45:51 time: 0.2245 data_time: 0.0038 loss: 1.1759 2023/03/17 12:55:21 - mmengine - INFO - Epoch(train) [75][4200/5005] lr: 1.0000e-03 eta: 6:45:31 time: 0.1870 data_time: 0.0041 loss: 1.1113 2023/03/17 12:55:41 - mmengine - INFO - Epoch(train) [75][4300/5005] lr: 1.0000e-03 eta: 6:45:12 time: 0.1948 data_time: 0.0043 loss: 1.2167 2023/03/17 12:56:01 - mmengine - INFO - Epoch(train) [75][4400/5005] lr: 1.0000e-03 eta: 6:44:53 time: 0.1947 data_time: 0.0047 loss: 0.9707 2023/03/17 12:56:20 - mmengine - INFO - Epoch(train) [75][4500/5005] lr: 1.0000e-03 eta: 6:44:33 time: 0.1834 data_time: 0.0038 loss: 1.0640 2023/03/17 12:56:39 - mmengine - INFO - Epoch(train) [75][4600/5005] lr: 1.0000e-03 eta: 6:44:14 time: 0.1927 data_time: 0.0037 loss: 0.8779 2023/03/17 12:56:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:56:58 - mmengine - INFO - Epoch(train) [75][4700/5005] lr: 1.0000e-03 eta: 6:43:54 time: 0.1793 data_time: 0.0041 loss: 1.1637 2023/03/17 12:57:17 - mmengine - INFO - Epoch(train) [75][4800/5005] lr: 1.0000e-03 eta: 6:43:35 time: 0.1942 data_time: 0.0049 loss: 1.0455 2023/03/17 12:57:37 - mmengine - INFO - Epoch(train) [75][4900/5005] lr: 1.0000e-03 eta: 6:43:16 time: 0.2008 data_time: 0.0041 loss: 1.0075 2023/03/17 12:57:58 - mmengine - INFO - Epoch(train) [75][5000/5005] lr: 1.0000e-03 eta: 6:42:57 time: 0.2183 data_time: 0.0047 loss: 1.0134 2023/03/17 12:57:59 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 12:58:00 - mmengine - INFO - Saving checkpoint at 75 epochs 2023/03/17 12:58:06 - mmengine - INFO - Epoch(val) [75][100/196] eta: 0:00:04 time: 0.0408 data_time: 0.0067 2023/03/17 12:58:31 - mmengine - INFO - Epoch(val) [75][196/196] accuracy/top1: 75.6840 accuracy/top5: 92.8320data_time: 0.0137 time: 0.0451 2023/03/17 12:58:53 - mmengine - INFO - Epoch(train) [76][ 100/5005] lr: 1.0000e-03 eta: 6:42:37 time: 0.1967 data_time: 0.0044 loss: 1.0128 2023/03/17 12:59:15 - mmengine - INFO - Epoch(train) [76][ 200/5005] lr: 1.0000e-03 eta: 6:42:19 time: 0.2463 data_time: 0.0040 loss: 1.0159 2023/03/17 12:59:39 - mmengine - INFO - Epoch(train) [76][ 300/5005] lr: 1.0000e-03 eta: 6:42:01 time: 0.2023 data_time: 0.0049 loss: 1.0626 2023/03/17 12:59:58 - mmengine - INFO - Epoch(train) [76][ 400/5005] lr: 1.0000e-03 eta: 6:41:42 time: 0.1955 data_time: 0.0043 loss: 1.0951 2023/03/17 13:00:20 - mmengine - INFO - Epoch(train) [76][ 500/5005] lr: 1.0000e-03 eta: 6:41:23 time: 0.1874 data_time: 0.0049 loss: 0.9668 2023/03/17 13:00:39 - mmengine - INFO - Epoch(train) [76][ 600/5005] lr: 1.0000e-03 eta: 6:41:04 time: 0.2048 data_time: 0.0042 loss: 0.9431 2023/03/17 13:00:44 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:00:59 - mmengine - INFO - Epoch(train) [76][ 700/5005] lr: 1.0000e-03 eta: 6:40:45 time: 0.1976 data_time: 0.0036 loss: 1.0382 2023/03/17 13:01:18 - mmengine - INFO - Epoch(train) [76][ 800/5005] lr: 1.0000e-03 eta: 6:40:25 time: 0.1897 data_time: 0.0044 loss: 0.8701 2023/03/17 13:01:37 - mmengine - INFO - Epoch(train) [76][ 900/5005] lr: 1.0000e-03 eta: 6:40:06 time: 0.1958 data_time: 0.0048 loss: 0.8968 2023/03/17 13:01:57 - mmengine - INFO - Epoch(train) [76][1000/5005] lr: 1.0000e-03 eta: 6:39:46 time: 0.1913 data_time: 0.0039 loss: 1.1502 2023/03/17 13:02:16 - mmengine - INFO - Epoch(train) [76][1100/5005] lr: 1.0000e-03 eta: 6:39:27 time: 0.1833 data_time: 0.0043 loss: 1.0030 2023/03/17 13:02:35 - mmengine - INFO - Epoch(train) [76][1200/5005] lr: 1.0000e-03 eta: 6:39:08 time: 0.1868 data_time: 0.0040 loss: 1.1680 2023/03/17 13:02:55 - mmengine - INFO - Epoch(train) [76][1300/5005] lr: 1.0000e-03 eta: 6:38:48 time: 0.1890 data_time: 0.0041 loss: 0.9922 2023/03/17 13:03:14 - mmengine - INFO - Epoch(train) [76][1400/5005] lr: 1.0000e-03 eta: 6:38:29 time: 0.1974 data_time: 0.0040 loss: 1.0627 2023/03/17 13:03:33 - mmengine - INFO - Epoch(train) [76][1500/5005] lr: 1.0000e-03 eta: 6:38:09 time: 0.1803 data_time: 0.0042 loss: 0.9009 2023/03/17 13:03:52 - mmengine - INFO - Epoch(train) [76][1600/5005] lr: 1.0000e-03 eta: 6:37:50 time: 0.1797 data_time: 0.0043 loss: 1.0829 2023/03/17 13:03:56 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:04:10 - mmengine - INFO - Epoch(train) [76][1700/5005] lr: 1.0000e-03 eta: 6:37:30 time: 0.1921 data_time: 0.0043 loss: 0.9903 2023/03/17 13:04:29 - mmengine - INFO - Epoch(train) [76][1800/5005] lr: 1.0000e-03 eta: 6:37:11 time: 0.1797 data_time: 0.0047 loss: 0.9199 2023/03/17 13:04:47 - mmengine - INFO - Epoch(train) [76][1900/5005] lr: 1.0000e-03 eta: 6:36:51 time: 0.1793 data_time: 0.0048 loss: 0.9595 2023/03/17 13:05:06 - mmengine - INFO - Epoch(train) [76][2000/5005] lr: 1.0000e-03 eta: 6:36:31 time: 0.2050 data_time: 0.0045 loss: 1.0250 2023/03/17 13:05:28 - mmengine - INFO - Epoch(train) [76][2100/5005] lr: 1.0000e-03 eta: 6:36:13 time: 0.2418 data_time: 0.0040 loss: 0.9907 2023/03/17 13:05:47 - mmengine - INFO - Epoch(train) [76][2200/5005] lr: 1.0000e-03 eta: 6:35:53 time: 0.2002 data_time: 0.0043 loss: 1.0656 2023/03/17 13:06:06 - mmengine - INFO - Epoch(train) [76][2300/5005] lr: 1.0000e-03 eta: 6:35:34 time: 0.1906 data_time: 0.0047 loss: 0.9583 2023/03/17 13:06:25 - mmengine - INFO - Epoch(train) [76][2400/5005] lr: 1.0000e-03 eta: 6:35:14 time: 0.1815 data_time: 0.0040 loss: 0.9512 2023/03/17 13:06:44 - mmengine - INFO - Epoch(train) [76][2500/5005] lr: 1.0000e-03 eta: 6:34:55 time: 0.1976 data_time: 0.0041 loss: 0.8671 2023/03/17 13:07:03 - mmengine - INFO - Epoch(train) [76][2600/5005] lr: 1.0000e-03 eta: 6:34:35 time: 0.1766 data_time: 0.0038 loss: 0.8753 2023/03/17 13:07:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:07:21 - mmengine - INFO - Epoch(train) [76][2700/5005] lr: 1.0000e-03 eta: 6:34:16 time: 0.1814 data_time: 0.0048 loss: 1.0158 2023/03/17 13:07:40 - mmengine - INFO - Epoch(train) [76][2800/5005] lr: 1.0000e-03 eta: 6:33:56 time: 0.1882 data_time: 0.0046 loss: 0.9831 2023/03/17 13:08:00 - mmengine - INFO - Epoch(train) [76][2900/5005] lr: 1.0000e-03 eta: 6:33:37 time: 0.1903 data_time: 0.0048 loss: 0.9634 2023/03/17 13:08:18 - mmengine - INFO - Epoch(train) [76][3000/5005] lr: 1.0000e-03 eta: 6:33:17 time: 0.1860 data_time: 0.0041 loss: 0.8978 2023/03/17 13:08:37 - mmengine - INFO - Epoch(train) [76][3100/5005] lr: 1.0000e-03 eta: 6:32:58 time: 0.1827 data_time: 0.0048 loss: 1.0356 2023/03/17 13:08:55 - mmengine - INFO - Epoch(train) [76][3200/5005] lr: 1.0000e-03 eta: 6:32:38 time: 0.1824 data_time: 0.0042 loss: 1.0837 2023/03/17 13:09:14 - mmengine - INFO - Epoch(train) [76][3300/5005] lr: 1.0000e-03 eta: 6:32:18 time: 0.1971 data_time: 0.0046 loss: 1.0546 2023/03/17 13:09:33 - mmengine - INFO - Epoch(train) [76][3400/5005] lr: 1.0000e-03 eta: 6:31:59 time: 0.1924 data_time: 0.0042 loss: 1.1900 2023/03/17 13:09:56 - mmengine - INFO - Epoch(train) [76][3500/5005] lr: 1.0000e-03 eta: 6:31:41 time: 0.2060 data_time: 0.0047 loss: 1.1697 2023/03/17 13:10:16 - mmengine - INFO - Epoch(train) [76][3600/5005] lr: 1.0000e-03 eta: 6:31:22 time: 0.2372 data_time: 0.0047 loss: 0.9690 2023/03/17 13:10:22 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:10:37 - mmengine - INFO - Epoch(train) [76][3700/5005] lr: 1.0000e-03 eta: 6:31:03 time: 0.1910 data_time: 0.0037 loss: 1.1044 2023/03/17 13:10:57 - mmengine - INFO - Epoch(train) [76][3800/5005] lr: 1.0000e-03 eta: 6:30:43 time: 0.2000 data_time: 0.0038 loss: 1.1548 2023/03/17 13:11:15 - mmengine - INFO - Epoch(train) [76][3900/5005] lr: 1.0000e-03 eta: 6:30:24 time: 0.1844 data_time: 0.0041 loss: 0.9404 2023/03/17 13:11:34 - mmengine - INFO - Epoch(train) [76][4000/5005] lr: 1.0000e-03 eta: 6:30:04 time: 0.1779 data_time: 0.0042 loss: 1.0655 2023/03/17 13:11:54 - mmengine - INFO - Epoch(train) [76][4100/5005] lr: 1.0000e-03 eta: 6:29:45 time: 0.2234 data_time: 0.0042 loss: 1.2551 2023/03/17 13:12:15 - mmengine - INFO - Epoch(train) [76][4200/5005] lr: 1.0000e-03 eta: 6:29:26 time: 0.2242 data_time: 0.0044 loss: 1.0464 2023/03/17 13:12:38 - mmengine - INFO - Epoch(train) [76][4300/5005] lr: 1.0000e-03 eta: 6:29:08 time: 0.1949 data_time: 0.0049 loss: 1.0898 2023/03/17 13:12:57 - mmengine - INFO - Epoch(train) [76][4400/5005] lr: 1.0000e-03 eta: 6:28:49 time: 0.1953 data_time: 0.0047 loss: 1.0765 2023/03/17 13:13:22 - mmengine - INFO - Epoch(train) [76][4500/5005] lr: 1.0000e-03 eta: 6:28:31 time: 0.2576 data_time: 0.0037 loss: 1.0668 2023/03/17 13:13:44 - mmengine - INFO - Epoch(train) [76][4600/5005] lr: 1.0000e-03 eta: 6:28:12 time: 0.1834 data_time: 0.0038 loss: 1.0862 2023/03/17 13:13:48 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:14:03 - mmengine - INFO - Epoch(train) [76][4700/5005] lr: 1.0000e-03 eta: 6:27:53 time: 0.1892 data_time: 0.0041 loss: 0.9503 2023/03/17 13:14:22 - mmengine - INFO - Epoch(train) [76][4800/5005] lr: 1.0000e-03 eta: 6:27:34 time: 0.2043 data_time: 0.0043 loss: 1.1085 2023/03/17 13:14:42 - mmengine - INFO - Epoch(train) [76][4900/5005] lr: 1.0000e-03 eta: 6:27:14 time: 0.1866 data_time: 0.0036 loss: 0.9145 2023/03/17 13:15:00 - mmengine - INFO - Epoch(train) [76][5000/5005] lr: 1.0000e-03 eta: 6:26:55 time: 0.1795 data_time: 0.0055 loss: 1.1351 2023/03/17 13:15:01 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:15:02 - mmengine - INFO - Saving checkpoint at 76 epochs 2023/03/17 13:15:08 - mmengine - INFO - Epoch(val) [76][100/196] eta: 0:00:04 time: 0.0439 data_time: 0.0044 2023/03/17 13:15:36 - mmengine - INFO - Epoch(val) [76][196/196] accuracy/top1: 75.6780 accuracy/top5: 92.7760data_time: 0.0334 time: 0.0643 2023/03/17 13:15:57 - mmengine - INFO - Epoch(train) [77][ 100/5005] lr: 1.0000e-03 eta: 6:26:35 time: 0.1955 data_time: 0.0043 loss: 1.0368 2023/03/17 13:16:17 - mmengine - INFO - Epoch(train) [77][ 200/5005] lr: 1.0000e-03 eta: 6:26:16 time: 0.2220 data_time: 0.0048 loss: 0.8521 2023/03/17 13:16:38 - mmengine - INFO - Epoch(train) [77][ 300/5005] lr: 1.0000e-03 eta: 6:25:57 time: 0.2092 data_time: 0.0046 loss: 0.8772 2023/03/17 13:16:59 - mmengine - INFO - Epoch(train) [77][ 400/5005] lr: 1.0000e-03 eta: 6:25:38 time: 0.2111 data_time: 0.0040 loss: 1.0811 2023/03/17 13:17:20 - mmengine - INFO - Epoch(train) [77][ 500/5005] lr: 1.0000e-03 eta: 6:25:19 time: 0.2065 data_time: 0.0043 loss: 0.9252 2023/03/17 13:17:40 - mmengine - INFO - Epoch(train) [77][ 600/5005] lr: 1.0000e-03 eta: 6:25:00 time: 0.1897 data_time: 0.0042 loss: 1.1007 2023/03/17 13:17:44 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:18:00 - mmengine - INFO - Epoch(train) [77][ 700/5005] lr: 1.0000e-03 eta: 6:24:41 time: 0.1963 data_time: 0.0040 loss: 1.0876 2023/03/17 13:18:20 - mmengine - INFO - Epoch(train) [77][ 800/5005] lr: 1.0000e-03 eta: 6:24:22 time: 0.2077 data_time: 0.0042 loss: 1.0229 2023/03/17 13:18:41 - mmengine - INFO - Epoch(train) [77][ 900/5005] lr: 1.0000e-03 eta: 6:24:03 time: 0.1935 data_time: 0.0038 loss: 1.0638 2023/03/17 13:19:01 - mmengine - INFO - Epoch(train) [77][1000/5005] lr: 1.0000e-03 eta: 6:23:44 time: 0.2043 data_time: 0.0044 loss: 1.0410 2023/03/17 13:19:21 - mmengine - INFO - Epoch(train) [77][1100/5005] lr: 1.0000e-03 eta: 6:23:24 time: 0.1915 data_time: 0.0043 loss: 0.9835 2023/03/17 13:19:41 - mmengine - INFO - Epoch(train) [77][1200/5005] lr: 1.0000e-03 eta: 6:23:05 time: 0.2007 data_time: 0.0054 loss: 1.1081 2023/03/17 13:20:01 - mmengine - INFO - Epoch(train) [77][1300/5005] lr: 1.0000e-03 eta: 6:22:46 time: 0.1858 data_time: 0.0037 loss: 0.9232 2023/03/17 13:20:20 - mmengine - INFO - Epoch(train) [77][1400/5005] lr: 1.0000e-03 eta: 6:22:26 time: 0.1868 data_time: 0.0041 loss: 0.8825 2023/03/17 13:20:39 - mmengine - INFO - Epoch(train) [77][1500/5005] lr: 1.0000e-03 eta: 6:22:07 time: 0.2133 data_time: 0.0039 loss: 1.0029 2023/03/17 13:21:03 - mmengine - INFO - Epoch(train) [77][1600/5005] lr: 1.0000e-03 eta: 6:21:49 time: 0.2316 data_time: 0.0038 loss: 1.0821 2023/03/17 13:21:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:21:23 - mmengine - INFO - Epoch(train) [77][1700/5005] lr: 1.0000e-03 eta: 6:21:30 time: 0.2023 data_time: 0.0042 loss: 0.8800 2023/03/17 13:21:43 - mmengine - INFO - Epoch(train) [77][1800/5005] lr: 1.0000e-03 eta: 6:21:11 time: 0.1977 data_time: 0.0040 loss: 0.9460 2023/03/17 13:22:03 - mmengine - INFO - Epoch(train) [77][1900/5005] lr: 1.0000e-03 eta: 6:20:52 time: 0.1888 data_time: 0.0039 loss: 0.8956 2023/03/17 13:22:23 - mmengine - INFO - Epoch(train) [77][2000/5005] lr: 1.0000e-03 eta: 6:20:33 time: 0.2012 data_time: 0.0042 loss: 1.0555 2023/03/17 13:22:44 - mmengine - INFO - Epoch(train) [77][2100/5005] lr: 1.0000e-03 eta: 6:20:14 time: 0.2020 data_time: 0.0039 loss: 0.9960 2023/03/17 13:23:06 - mmengine - INFO - Epoch(train) [77][2200/5005] lr: 1.0000e-03 eta: 6:19:55 time: 0.2416 data_time: 0.0041 loss: 0.8928 2023/03/17 13:23:26 - mmengine - INFO - Epoch(train) [77][2300/5005] lr: 1.0000e-03 eta: 6:19:36 time: 0.2014 data_time: 0.0038 loss: 1.0013 2023/03/17 13:23:47 - mmengine - INFO - Epoch(train) [77][2400/5005] lr: 1.0000e-03 eta: 6:19:17 time: 0.2113 data_time: 0.0040 loss: 0.9349 2023/03/17 13:24:08 - mmengine - INFO - Epoch(train) [77][2500/5005] lr: 1.0000e-03 eta: 6:18:58 time: 0.2079 data_time: 0.0046 loss: 0.9706 2023/03/17 13:24:30 - mmengine - INFO - Epoch(train) [77][2600/5005] lr: 1.0000e-03 eta: 6:18:39 time: 0.2157 data_time: 0.0051 loss: 1.1976 2023/03/17 13:24:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:24:50 - mmengine - INFO - Epoch(train) [77][2700/5005] lr: 1.0000e-03 eta: 6:18:20 time: 0.1993 data_time: 0.0041 loss: 0.8849 2023/03/17 13:25:10 - mmengine - INFO - Epoch(train) [77][2800/5005] lr: 1.0000e-03 eta: 6:18:01 time: 0.1951 data_time: 0.0045 loss: 0.9327 2023/03/17 13:25:30 - mmengine - INFO - Epoch(train) [77][2900/5005] lr: 1.0000e-03 eta: 6:17:42 time: 0.1894 data_time: 0.0045 loss: 0.9662 2023/03/17 13:25:50 - mmengine - INFO - Epoch(train) [77][3000/5005] lr: 1.0000e-03 eta: 6:17:23 time: 0.2076 data_time: 0.0047 loss: 1.1317 2023/03/17 13:26:10 - mmengine - INFO - Epoch(train) [77][3100/5005] lr: 1.0000e-03 eta: 6:17:04 time: 0.2080 data_time: 0.0047 loss: 1.0881 2023/03/17 13:26:30 - mmengine - INFO - Epoch(train) [77][3200/5005] lr: 1.0000e-03 eta: 6:16:45 time: 0.2079 data_time: 0.0048 loss: 1.0083 2023/03/17 13:26:50 - mmengine - INFO - Epoch(train) [77][3300/5005] lr: 1.0000e-03 eta: 6:16:25 time: 0.1876 data_time: 0.0035 loss: 1.0394 2023/03/17 13:27:10 - mmengine - INFO - Epoch(train) [77][3400/5005] lr: 1.0000e-03 eta: 6:16:06 time: 0.2042 data_time: 0.0040 loss: 0.9429 2023/03/17 13:27:29 - mmengine - INFO - Epoch(train) [77][3500/5005] lr: 1.0000e-03 eta: 6:15:47 time: 0.1941 data_time: 0.0042 loss: 0.9542 2023/03/17 13:27:50 - mmengine - INFO - Epoch(train) [77][3600/5005] lr: 1.0000e-03 eta: 6:15:28 time: 0.1886 data_time: 0.0042 loss: 0.9894 2023/03/17 13:27:54 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:28:10 - mmengine - INFO - Epoch(train) [77][3700/5005] lr: 1.0000e-03 eta: 6:15:09 time: 0.1897 data_time: 0.0044 loss: 0.9010 2023/03/17 13:28:30 - mmengine - INFO - Epoch(train) [77][3800/5005] lr: 1.0000e-03 eta: 6:14:49 time: 0.1985 data_time: 0.0042 loss: 1.0313 2023/03/17 13:28:50 - mmengine - INFO - Epoch(train) [77][3900/5005] lr: 1.0000e-03 eta: 6:14:30 time: 0.2155 data_time: 0.0049 loss: 0.9126 2023/03/17 13:29:11 - mmengine - INFO - Epoch(train) [77][4000/5005] lr: 1.0000e-03 eta: 6:14:11 time: 0.2467 data_time: 0.0040 loss: 0.9938 2023/03/17 13:29:32 - mmengine - INFO - Epoch(train) [77][4100/5005] lr: 1.0000e-03 eta: 6:13:52 time: 0.1903 data_time: 0.0039 loss: 1.0425 2023/03/17 13:29:53 - mmengine - INFO - Epoch(train) [77][4200/5005] lr: 1.0000e-03 eta: 6:13:34 time: 0.2057 data_time: 0.0042 loss: 1.0261 2023/03/17 13:30:14 - mmengine - INFO - Epoch(train) [77][4300/5005] lr: 1.0000e-03 eta: 6:13:15 time: 0.1830 data_time: 0.0040 loss: 0.9848 2023/03/17 13:30:33 - mmengine - INFO - Epoch(train) [77][4400/5005] lr: 1.0000e-03 eta: 6:12:55 time: 0.2145 data_time: 0.0042 loss: 0.9191 2023/03/17 13:30:57 - mmengine - INFO - Epoch(train) [77][4500/5005] lr: 1.0000e-03 eta: 6:12:37 time: 0.2430 data_time: 0.0026 loss: 1.0706 2023/03/17 13:31:18 - mmengine - INFO - Epoch(train) [77][4600/5005] lr: 1.0000e-03 eta: 6:12:18 time: 0.2061 data_time: 0.0045 loss: 1.1365 2023/03/17 13:31:22 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:31:39 - mmengine - INFO - Epoch(train) [77][4700/5005] lr: 1.0000e-03 eta: 6:12:00 time: 0.2096 data_time: 0.0040 loss: 0.9043 2023/03/17 13:32:01 - mmengine - INFO - Epoch(train) [77][4800/5005] lr: 1.0000e-03 eta: 6:11:41 time: 0.2286 data_time: 0.0047 loss: 1.0146 2023/03/17 13:32:21 - mmengine - INFO - Epoch(train) [77][4900/5005] lr: 1.0000e-03 eta: 6:11:22 time: 0.2077 data_time: 0.0042 loss: 0.9337 2023/03/17 13:32:42 - mmengine - INFO - Epoch(train) [77][5000/5005] lr: 1.0000e-03 eta: 6:11:03 time: 0.2071 data_time: 0.0060 loss: 1.0355 2023/03/17 13:32:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:32:43 - mmengine - INFO - Saving checkpoint at 77 epochs 2023/03/17 13:32:50 - mmengine - INFO - Epoch(val) [77][100/196] eta: 0:00:05 time: 0.0454 data_time: 0.0009 2023/03/17 13:33:18 - mmengine - INFO - Epoch(val) [77][196/196] accuracy/top1: 75.5720 accuracy/top5: 92.9140data_time: 0.0289 time: 0.0617 2023/03/17 13:33:43 - mmengine - INFO - Epoch(train) [78][ 100/5005] lr: 1.0000e-03 eta: 6:10:44 time: 0.1921 data_time: 0.0044 loss: 0.9173 2023/03/17 13:34:06 - mmengine - INFO - Epoch(train) [78][ 200/5005] lr: 1.0000e-03 eta: 6:10:26 time: 0.2058 data_time: 0.0040 loss: 1.0219 2023/03/17 13:34:26 - mmengine - INFO - Epoch(train) [78][ 300/5005] lr: 1.0000e-03 eta: 6:10:06 time: 0.1903 data_time: 0.0041 loss: 0.9582 2023/03/17 13:34:46 - mmengine - INFO - Epoch(train) [78][ 400/5005] lr: 1.0000e-03 eta: 6:09:47 time: 0.1902 data_time: 0.0042 loss: 0.9784 2023/03/17 13:35:06 - mmengine - INFO - Epoch(train) [78][ 500/5005] lr: 1.0000e-03 eta: 6:09:28 time: 0.2328 data_time: 0.0042 loss: 1.0756 2023/03/17 13:35:29 - mmengine - INFO - Epoch(train) [78][ 600/5005] lr: 1.0000e-03 eta: 6:09:10 time: 0.2311 data_time: 0.0038 loss: 1.0431 2023/03/17 13:35:32 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:35:49 - mmengine - INFO - Epoch(train) [78][ 700/5005] lr: 1.0000e-03 eta: 6:08:51 time: 0.2189 data_time: 0.0040 loss: 1.0279 2023/03/17 13:36:10 - mmengine - INFO - Epoch(train) [78][ 800/5005] lr: 1.0000e-03 eta: 6:08:32 time: 0.2376 data_time: 0.0036 loss: 1.0072 2023/03/17 13:36:31 - mmengine - INFO - Epoch(train) [78][ 900/5005] lr: 1.0000e-03 eta: 6:08:13 time: 0.1943 data_time: 0.0042 loss: 1.0614 2023/03/17 13:36:50 - mmengine - INFO - Epoch(train) [78][1000/5005] lr: 1.0000e-03 eta: 6:07:53 time: 0.2082 data_time: 0.0038 loss: 1.0396 2023/03/17 13:37:10 - mmengine - INFO - Epoch(train) [78][1100/5005] lr: 1.0000e-03 eta: 6:07:34 time: 0.1892 data_time: 0.0040 loss: 1.0894 2023/03/17 13:37:30 - mmengine - INFO - Epoch(train) [78][1200/5005] lr: 1.0000e-03 eta: 6:07:15 time: 0.1980 data_time: 0.0043 loss: 1.1418 2023/03/17 13:37:50 - mmengine - INFO - Epoch(train) [78][1300/5005] lr: 1.0000e-03 eta: 6:06:56 time: 0.2019 data_time: 0.0042 loss: 1.2089 2023/03/17 13:38:11 - mmengine - INFO - Epoch(train) [78][1400/5005] lr: 1.0000e-03 eta: 6:06:37 time: 0.1987 data_time: 0.0049 loss: 1.0633 2023/03/17 13:38:31 - mmengine - INFO - Epoch(train) [78][1500/5005] lr: 1.0000e-03 eta: 6:06:18 time: 0.1883 data_time: 0.0036 loss: 1.0280 2023/03/17 13:38:50 - mmengine - INFO - Epoch(train) [78][1600/5005] lr: 1.0000e-03 eta: 6:05:58 time: 0.1975 data_time: 0.0036 loss: 1.1038 2023/03/17 13:38:53 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:39:10 - mmengine - INFO - Epoch(train) [78][1700/5005] lr: 1.0000e-03 eta: 6:05:39 time: 0.1995 data_time: 0.0034 loss: 0.9848 2023/03/17 13:39:30 - mmengine - INFO - Epoch(train) [78][1800/5005] lr: 1.0000e-03 eta: 6:05:20 time: 0.1872 data_time: 0.0048 loss: 1.0706 2023/03/17 13:39:50 - mmengine - INFO - Epoch(train) [78][1900/5005] lr: 1.0000e-03 eta: 6:05:00 time: 0.1958 data_time: 0.0041 loss: 0.8805 2023/03/17 13:40:09 - mmengine - INFO - Epoch(train) [78][2000/5005] lr: 1.0000e-03 eta: 6:04:41 time: 0.1881 data_time: 0.0044 loss: 1.3371 2023/03/17 13:40:30 - mmengine - INFO - Epoch(train) [78][2100/5005] lr: 1.0000e-03 eta: 6:04:22 time: 0.2248 data_time: 0.0036 loss: 0.9916 2023/03/17 13:40:50 - mmengine - INFO - Epoch(train) [78][2200/5005] lr: 1.0000e-03 eta: 6:04:03 time: 0.2199 data_time: 0.0041 loss: 0.8847 2023/03/17 13:41:10 - mmengine - INFO - Epoch(train) [78][2300/5005] lr: 1.0000e-03 eta: 6:03:44 time: 0.2015 data_time: 0.0044 loss: 1.1533 2023/03/17 13:41:29 - mmengine - INFO - Epoch(train) [78][2400/5005] lr: 1.0000e-03 eta: 6:03:24 time: 0.1897 data_time: 0.0041 loss: 0.8632 2023/03/17 13:41:49 - mmengine - INFO - Epoch(train) [78][2500/5005] lr: 1.0000e-03 eta: 6:03:05 time: 0.2259 data_time: 0.0043 loss: 0.9962 2023/03/17 13:42:12 - mmengine - INFO - Epoch(train) [78][2600/5005] lr: 1.0000e-03 eta: 6:02:47 time: 0.2428 data_time: 0.0045 loss: 1.0160 2023/03/17 13:42:15 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:42:31 - mmengine - INFO - Epoch(train) [78][2700/5005] lr: 1.0000e-03 eta: 6:02:27 time: 0.1863 data_time: 0.0039 loss: 1.0868 2023/03/17 13:42:52 - mmengine - INFO - Epoch(train) [78][2800/5005] lr: 1.0000e-03 eta: 6:02:08 time: 0.1905 data_time: 0.0046 loss: 0.9358 2023/03/17 13:43:12 - mmengine - INFO - Epoch(train) [78][2900/5005] lr: 1.0000e-03 eta: 6:01:49 time: 0.2017 data_time: 0.0049 loss: 1.0424 2023/03/17 13:43:32 - mmengine - INFO - Epoch(train) [78][3000/5005] lr: 1.0000e-03 eta: 6:01:30 time: 0.1821 data_time: 0.0039 loss: 1.0600 2023/03/17 13:43:51 - mmengine - INFO - Epoch(train) [78][3100/5005] lr: 1.0000e-03 eta: 6:01:10 time: 0.1876 data_time: 0.0039 loss: 1.0158 2023/03/17 13:44:12 - mmengine - INFO - Epoch(train) [78][3200/5005] lr: 1.0000e-03 eta: 6:00:51 time: 0.2345 data_time: 0.0034 loss: 0.9485 2023/03/17 13:44:33 - mmengine - INFO - Epoch(train) [78][3300/5005] lr: 1.0000e-03 eta: 6:00:33 time: 0.1940 data_time: 0.0042 loss: 1.0513 2023/03/17 13:44:53 - mmengine - INFO - Epoch(train) [78][3400/5005] lr: 1.0000e-03 eta: 6:00:13 time: 0.2029 data_time: 0.0037 loss: 1.1832 2023/03/17 13:45:13 - mmengine - INFO - Epoch(train) [78][3500/5005] lr: 1.0000e-03 eta: 5:59:54 time: 0.1892 data_time: 0.0038 loss: 1.1219 2023/03/17 13:45:31 - mmengine - INFO - Epoch(train) [78][3600/5005] lr: 1.0000e-03 eta: 5:59:34 time: 0.1765 data_time: 0.0049 loss: 1.0540 2023/03/17 13:45:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:45:49 - mmengine - INFO - Epoch(train) [78][3700/5005] lr: 1.0000e-03 eta: 5:59:15 time: 0.1844 data_time: 0.0046 loss: 1.0791 2023/03/17 13:46:08 - mmengine - INFO - Epoch(train) [78][3800/5005] lr: 1.0000e-03 eta: 5:58:55 time: 0.1854 data_time: 0.0036 loss: 0.9738 2023/03/17 13:46:27 - mmengine - INFO - Epoch(train) [78][3900/5005] lr: 1.0000e-03 eta: 5:58:36 time: 0.1793 data_time: 0.0043 loss: 1.0824 2023/03/17 13:46:45 - mmengine - INFO - Epoch(train) [78][4000/5005] lr: 1.0000e-03 eta: 5:58:16 time: 0.1813 data_time: 0.0045 loss: 0.8998 2023/03/17 13:47:04 - mmengine - INFO - Epoch(train) [78][4100/5005] lr: 1.0000e-03 eta: 5:57:56 time: 0.1878 data_time: 0.0040 loss: 1.0300 2023/03/17 13:47:24 - mmengine - INFO - Epoch(train) [78][4200/5005] lr: 1.0000e-03 eta: 5:57:37 time: 0.1895 data_time: 0.0043 loss: 0.9160 2023/03/17 13:47:42 - mmengine - INFO - Epoch(train) [78][4300/5005] lr: 1.0000e-03 eta: 5:57:17 time: 0.1760 data_time: 0.0046 loss: 0.8788 2023/03/17 13:48:00 - mmengine - INFO - Epoch(train) [78][4400/5005] lr: 1.0000e-03 eta: 5:56:58 time: 0.1811 data_time: 0.0041 loss: 0.9439 2023/03/17 13:48:19 - mmengine - INFO - Epoch(train) [78][4500/5005] lr: 1.0000e-03 eta: 5:56:38 time: 0.1835 data_time: 0.0047 loss: 0.9824 2023/03/17 13:48:37 - mmengine - INFO - Epoch(train) [78][4600/5005] lr: 1.0000e-03 eta: 5:56:18 time: 0.1782 data_time: 0.0049 loss: 0.8338 2023/03/17 13:48:40 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:48:55 - mmengine - INFO - Epoch(train) [78][4700/5005] lr: 1.0000e-03 eta: 5:55:59 time: 0.1857 data_time: 0.0048 loss: 0.9921 2023/03/17 13:49:14 - mmengine - INFO - Epoch(train) [78][4800/5005] lr: 1.0000e-03 eta: 5:55:39 time: 0.2081 data_time: 0.0049 loss: 1.1415 2023/03/17 13:49:32 - mmengine - INFO - Epoch(train) [78][4900/5005] lr: 1.0000e-03 eta: 5:55:19 time: 0.1904 data_time: 0.0039 loss: 0.9911 2023/03/17 13:49:51 - mmengine - INFO - Epoch(train) [78][5000/5005] lr: 1.0000e-03 eta: 5:55:00 time: 0.1884 data_time: 0.0057 loss: 1.1309 2023/03/17 13:49:52 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:49:53 - mmengine - INFO - Saving checkpoint at 78 epochs 2023/03/17 13:50:00 - mmengine - INFO - Epoch(val) [78][100/196] eta: 0:00:05 time: 0.0474 data_time: 0.0009 2023/03/17 13:50:24 - mmengine - INFO - Epoch(val) [78][196/196] accuracy/top1: 75.6140 accuracy/top5: 92.7840data_time: 0.0289 time: 0.0659 2023/03/17 13:50:44 - mmengine - INFO - Epoch(train) [79][ 100/5005] lr: 1.0000e-03 eta: 5:54:40 time: 0.1869 data_time: 0.0039 loss: 0.9327 2023/03/17 13:51:04 - mmengine - INFO - Epoch(train) [79][ 200/5005] lr: 1.0000e-03 eta: 5:54:20 time: 0.2090 data_time: 0.0047 loss: 0.9993 2023/03/17 13:51:24 - mmengine - INFO - Epoch(train) [79][ 300/5005] lr: 1.0000e-03 eta: 5:54:01 time: 0.1842 data_time: 0.0046 loss: 0.9986 2023/03/17 13:51:43 - mmengine - INFO - Epoch(train) [79][ 400/5005] lr: 1.0000e-03 eta: 5:53:42 time: 0.2028 data_time: 0.0042 loss: 1.0003 2023/03/17 13:52:03 - mmengine - INFO - Epoch(train) [79][ 500/5005] lr: 1.0000e-03 eta: 5:53:22 time: 0.1936 data_time: 0.0045 loss: 1.0428 2023/03/17 13:52:23 - mmengine - INFO - Epoch(train) [79][ 600/5005] lr: 1.0000e-03 eta: 5:53:03 time: 0.1956 data_time: 0.0047 loss: 1.1144 2023/03/17 13:52:25 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:52:42 - mmengine - INFO - Epoch(train) [79][ 700/5005] lr: 1.0000e-03 eta: 5:52:44 time: 0.1914 data_time: 0.0047 loss: 0.9356 2023/03/17 13:53:01 - mmengine - INFO - Epoch(train) [79][ 800/5005] lr: 1.0000e-03 eta: 5:52:24 time: 0.1890 data_time: 0.0050 loss: 1.0028 2023/03/17 13:53:22 - mmengine - INFO - Epoch(train) [79][ 900/5005] lr: 1.0000e-03 eta: 5:52:05 time: 0.2020 data_time: 0.0048 loss: 1.0314 2023/03/17 13:53:42 - mmengine - INFO - Epoch(train) [79][1000/5005] lr: 1.0000e-03 eta: 5:51:46 time: 0.1976 data_time: 0.0041 loss: 0.8314 2023/03/17 13:54:02 - mmengine - INFO - Epoch(train) [79][1100/5005] lr: 1.0000e-03 eta: 5:51:27 time: 0.1991 data_time: 0.0042 loss: 1.0040 2023/03/17 13:54:23 - mmengine - INFO - Epoch(train) [79][1200/5005] lr: 1.0000e-03 eta: 5:51:08 time: 0.1919 data_time: 0.0045 loss: 1.0791 2023/03/17 13:54:42 - mmengine - INFO - Epoch(train) [79][1300/5005] lr: 1.0000e-03 eta: 5:50:49 time: 0.1911 data_time: 0.0046 loss: 1.0937 2023/03/17 13:55:02 - mmengine - INFO - Epoch(train) [79][1400/5005] lr: 1.0000e-03 eta: 5:50:29 time: 0.1942 data_time: 0.0052 loss: 1.1459 2023/03/17 13:55:22 - mmengine - INFO - Epoch(train) [79][1500/5005] lr: 1.0000e-03 eta: 5:50:10 time: 0.1904 data_time: 0.0044 loss: 1.1484 2023/03/17 13:55:43 - mmengine - INFO - Epoch(train) [79][1600/5005] lr: 1.0000e-03 eta: 5:49:51 time: 0.1950 data_time: 0.0050 loss: 0.8356 2023/03/17 13:55:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:56:03 - mmengine - INFO - Epoch(train) [79][1700/5005] lr: 1.0000e-03 eta: 5:49:32 time: 0.2116 data_time: 0.0039 loss: 1.0063 2023/03/17 13:56:24 - mmengine - INFO - Epoch(train) [79][1800/5005] lr: 1.0000e-03 eta: 5:49:13 time: 0.2008 data_time: 0.0046 loss: 1.0302 2023/03/17 13:56:44 - mmengine - INFO - Epoch(train) [79][1900/5005] lr: 1.0000e-03 eta: 5:48:54 time: 0.1864 data_time: 0.0045 loss: 1.0722 2023/03/17 13:57:03 - mmengine - INFO - Epoch(train) [79][2000/5005] lr: 1.0000e-03 eta: 5:48:34 time: 0.1927 data_time: 0.0044 loss: 1.0670 2023/03/17 13:57:23 - mmengine - INFO - Epoch(train) [79][2100/5005] lr: 1.0000e-03 eta: 5:48:15 time: 0.2063 data_time: 0.0044 loss: 0.8759 2023/03/17 13:57:42 - mmengine - INFO - Epoch(train) [79][2200/5005] lr: 1.0000e-03 eta: 5:47:56 time: 0.1887 data_time: 0.0041 loss: 0.9900 2023/03/17 13:58:02 - mmengine - INFO - Epoch(train) [79][2300/5005] lr: 1.0000e-03 eta: 5:47:36 time: 0.2224 data_time: 0.0053 loss: 1.0139 2023/03/17 13:58:23 - mmengine - INFO - Epoch(train) [79][2400/5005] lr: 1.0000e-03 eta: 5:47:17 time: 0.1932 data_time: 0.0040 loss: 0.9030 2023/03/17 13:58:44 - mmengine - INFO - Epoch(train) [79][2500/5005] lr: 1.0000e-03 eta: 5:46:58 time: 0.2020 data_time: 0.0043 loss: 1.0434 2023/03/17 13:59:05 - mmengine - INFO - Epoch(train) [79][2600/5005] lr: 1.0000e-03 eta: 5:46:39 time: 0.2287 data_time: 0.0047 loss: 0.8409 2023/03/17 13:59:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 13:59:27 - mmengine - INFO - Epoch(train) [79][2700/5005] lr: 1.0000e-03 eta: 5:46:21 time: 0.2148 data_time: 0.0054 loss: 1.1200 2023/03/17 13:59:47 - mmengine - INFO - Epoch(train) [79][2800/5005] lr: 1.0000e-03 eta: 5:46:02 time: 0.1935 data_time: 0.0048 loss: 1.0571 2023/03/17 14:00:07 - mmengine - INFO - Epoch(train) [79][2900/5005] lr: 1.0000e-03 eta: 5:45:43 time: 0.2236 data_time: 0.0042 loss: 1.1100 2023/03/17 14:00:28 - mmengine - INFO - Epoch(train) [79][3000/5005] lr: 1.0000e-03 eta: 5:45:23 time: 0.1912 data_time: 0.0047 loss: 0.9551 2023/03/17 14:00:46 - mmengine - INFO - Epoch(train) [79][3100/5005] lr: 1.0000e-03 eta: 5:45:04 time: 0.1902 data_time: 0.0042 loss: 0.9872 2023/03/17 14:01:07 - mmengine - INFO - Epoch(train) [79][3200/5005] lr: 1.0000e-03 eta: 5:44:45 time: 0.1989 data_time: 0.0045 loss: 1.0488 2023/03/17 14:01:27 - mmengine - INFO - Epoch(train) [79][3300/5005] lr: 1.0000e-03 eta: 5:44:25 time: 0.1898 data_time: 0.0044 loss: 1.2349 2023/03/17 14:01:46 - mmengine - INFO - Epoch(train) [79][3400/5005] lr: 1.0000e-03 eta: 5:44:06 time: 0.1775 data_time: 0.0052 loss: 1.0759 2023/03/17 14:02:04 - mmengine - INFO - Epoch(train) [79][3500/5005] lr: 1.0000e-03 eta: 5:43:46 time: 0.1844 data_time: 0.0050 loss: 0.9408 2023/03/17 14:02:23 - mmengine - INFO - Epoch(train) [79][3600/5005] lr: 1.0000e-03 eta: 5:43:27 time: 0.1849 data_time: 0.0044 loss: 1.0340 2023/03/17 14:02:25 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:02:43 - mmengine - INFO - Epoch(train) [79][3700/5005] lr: 1.0000e-03 eta: 5:43:08 time: 0.1915 data_time: 0.0049 loss: 1.1505 2023/03/17 14:03:02 - mmengine - INFO - Epoch(train) [79][3800/5005] lr: 1.0000e-03 eta: 5:42:48 time: 0.1886 data_time: 0.0045 loss: 0.9129 2023/03/17 14:03:21 - mmengine - INFO - Epoch(train) [79][3900/5005] lr: 1.0000e-03 eta: 5:42:29 time: 0.1945 data_time: 0.0048 loss: 1.0260 2023/03/17 14:03:41 - mmengine - INFO - Epoch(train) [79][4000/5005] lr: 1.0000e-03 eta: 5:42:09 time: 0.1970 data_time: 0.0040 loss: 1.0212 2023/03/17 14:04:02 - mmengine - INFO - Epoch(train) [79][4100/5005] lr: 1.0000e-03 eta: 5:41:50 time: 0.2032 data_time: 0.0043 loss: 1.0246 2023/03/17 14:04:21 - mmengine - INFO - Epoch(train) [79][4200/5005] lr: 1.0000e-03 eta: 5:41:31 time: 0.1950 data_time: 0.0042 loss: 1.0112 2023/03/17 14:04:41 - mmengine - INFO - Epoch(train) [79][4300/5005] lr: 1.0000e-03 eta: 5:41:12 time: 0.2180 data_time: 0.0044 loss: 1.1309 2023/03/17 14:05:03 - mmengine - INFO - Epoch(train) [79][4400/5005] lr: 1.0000e-03 eta: 5:40:53 time: 0.2534 data_time: 0.0041 loss: 1.0401 2023/03/17 14:05:24 - mmengine - INFO - Epoch(train) [79][4500/5005] lr: 1.0000e-03 eta: 5:40:34 time: 0.2009 data_time: 0.0041 loss: 1.0463 2023/03/17 14:05:45 - mmengine - INFO - Epoch(train) [79][4600/5005] lr: 1.0000e-03 eta: 5:40:15 time: 0.2193 data_time: 0.0048 loss: 1.2549 2023/03/17 14:05:47 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:06:07 - mmengine - INFO - Epoch(train) [79][4700/5005] lr: 1.0000e-03 eta: 5:39:56 time: 0.2110 data_time: 0.0043 loss: 1.0814 2023/03/17 14:06:28 - mmengine - INFO - Epoch(train) [79][4800/5005] lr: 1.0000e-03 eta: 5:39:37 time: 0.2068 data_time: 0.0044 loss: 1.1135 2023/03/17 14:06:48 - mmengine - INFO - Epoch(train) [79][4900/5005] lr: 1.0000e-03 eta: 5:39:18 time: 0.1948 data_time: 0.0048 loss: 1.0241 2023/03/17 14:07:08 - mmengine - INFO - Epoch(train) [79][5000/5005] lr: 1.0000e-03 eta: 5:38:59 time: 0.2253 data_time: 0.0056 loss: 1.1322 2023/03/17 14:07:10 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:07:10 - mmengine - INFO - Saving checkpoint at 79 epochs 2023/03/17 14:07:17 - mmengine - INFO - Epoch(val) [79][100/196] eta: 0:00:05 time: 0.0414 data_time: 0.0009 2023/03/17 14:07:41 - mmengine - INFO - Epoch(val) [79][196/196] accuracy/top1: 75.6420 accuracy/top5: 92.8360data_time: 0.0361 time: 0.0654 2023/03/17 14:08:02 - mmengine - INFO - Epoch(train) [80][ 100/5005] lr: 1.0000e-03 eta: 5:38:40 time: 0.2056 data_time: 0.0041 loss: 1.0797 2023/03/17 14:08:26 - mmengine - INFO - Epoch(train) [80][ 200/5005] lr: 1.0000e-03 eta: 5:38:21 time: 0.2186 data_time: 0.0042 loss: 0.9649 2023/03/17 14:08:48 - mmengine - INFO - Epoch(train) [80][ 300/5005] lr: 1.0000e-03 eta: 5:38:03 time: 0.1946 data_time: 0.0048 loss: 1.0258 2023/03/17 14:09:08 - mmengine - INFO - Epoch(train) [80][ 400/5005] lr: 1.0000e-03 eta: 5:37:43 time: 0.1897 data_time: 0.0042 loss: 1.2048 2023/03/17 14:09:26 - mmengine - INFO - Epoch(train) [80][ 500/5005] lr: 1.0000e-03 eta: 5:37:24 time: 0.1785 data_time: 0.0043 loss: 1.1346 2023/03/17 14:09:46 - mmengine - INFO - Epoch(train) [80][ 600/5005] lr: 1.0000e-03 eta: 5:37:04 time: 0.1792 data_time: 0.0043 loss: 1.0352 2023/03/17 14:09:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:10:03 - mmengine - INFO - Epoch(train) [80][ 700/5005] lr: 1.0000e-03 eta: 5:36:44 time: 0.1810 data_time: 0.0047 loss: 0.9194 2023/03/17 14:10:22 - mmengine - INFO - Epoch(train) [80][ 800/5005] lr: 1.0000e-03 eta: 5:36:25 time: 0.1853 data_time: 0.0044 loss: 0.9644 2023/03/17 14:10:42 - mmengine - INFO - Epoch(train) [80][ 900/5005] lr: 1.0000e-03 eta: 5:36:06 time: 0.2111 data_time: 0.0048 loss: 0.9914 2023/03/17 14:11:01 - mmengine - INFO - Epoch(train) [80][1000/5005] lr: 1.0000e-03 eta: 5:35:46 time: 0.1924 data_time: 0.0038 loss: 0.9167 2023/03/17 14:11:20 - mmengine - INFO - Epoch(train) [80][1100/5005] lr: 1.0000e-03 eta: 5:35:27 time: 0.1851 data_time: 0.0049 loss: 0.9412 2023/03/17 14:11:39 - mmengine - INFO - Epoch(train) [80][1200/5005] lr: 1.0000e-03 eta: 5:35:07 time: 0.1862 data_time: 0.0041 loss: 1.0597 2023/03/17 14:11:59 - mmengine - INFO - Epoch(train) [80][1300/5005] lr: 1.0000e-03 eta: 5:34:48 time: 0.2164 data_time: 0.0046 loss: 1.0571 2023/03/17 14:12:21 - mmengine - INFO - Epoch(train) [80][1400/5005] lr: 1.0000e-03 eta: 5:34:29 time: 0.2009 data_time: 0.0041 loss: 0.9423 2023/03/17 14:12:42 - mmengine - INFO - Epoch(train) [80][1500/5005] lr: 1.0000e-03 eta: 5:34:10 time: 0.2153 data_time: 0.0036 loss: 1.0823 2023/03/17 14:13:03 - mmengine - INFO - Epoch(train) [80][1600/5005] lr: 1.0000e-03 eta: 5:33:51 time: 0.2241 data_time: 0.0038 loss: 0.9691 2023/03/17 14:13:04 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:13:24 - mmengine - INFO - Epoch(train) [80][1700/5005] lr: 1.0000e-03 eta: 5:33:32 time: 0.2081 data_time: 0.0035 loss: 1.1057 2023/03/17 14:13:44 - mmengine - INFO - Epoch(train) [80][1800/5005] lr: 1.0000e-03 eta: 5:33:13 time: 0.1865 data_time: 0.0043 loss: 0.8575 2023/03/17 14:14:05 - mmengine - INFO - Epoch(train) [80][1900/5005] lr: 1.0000e-03 eta: 5:32:54 time: 0.2025 data_time: 0.0036 loss: 1.0716 2023/03/17 14:14:24 - mmengine - INFO - Epoch(train) [80][2000/5005] lr: 1.0000e-03 eta: 5:32:35 time: 0.1934 data_time: 0.0037 loss: 1.0205 2023/03/17 14:14:43 - mmengine - INFO - Epoch(train) [80][2100/5005] lr: 1.0000e-03 eta: 5:32:15 time: 0.1867 data_time: 0.0044 loss: 0.8216 2023/03/17 14:15:03 - mmengine - INFO - Epoch(train) [80][2200/5005] lr: 1.0000e-03 eta: 5:31:56 time: 0.1922 data_time: 0.0044 loss: 0.9633 2023/03/17 14:15:23 - mmengine - INFO - Epoch(train) [80][2300/5005] lr: 1.0000e-03 eta: 5:31:37 time: 0.1901 data_time: 0.0045 loss: 1.0513 2023/03/17 14:15:42 - mmengine - INFO - Epoch(train) [80][2400/5005] lr: 1.0000e-03 eta: 5:31:17 time: 0.1982 data_time: 0.0044 loss: 0.8445 2023/03/17 14:16:00 - mmengine - INFO - Epoch(train) [80][2500/5005] lr: 1.0000e-03 eta: 5:30:58 time: 0.1904 data_time: 0.0044 loss: 1.0506 2023/03/17 14:16:20 - mmengine - INFO - Epoch(train) [80][2600/5005] lr: 1.0000e-03 eta: 5:30:38 time: 0.2073 data_time: 0.0035 loss: 0.9100 2023/03/17 14:16:21 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:16:40 - mmengine - INFO - Epoch(train) [80][2700/5005] lr: 1.0000e-03 eta: 5:30:19 time: 0.2112 data_time: 0.0048 loss: 0.9390 2023/03/17 14:17:02 - mmengine - INFO - Epoch(train) [80][2800/5005] lr: 1.0000e-03 eta: 5:30:00 time: 0.1930 data_time: 0.0045 loss: 1.0815 2023/03/17 14:17:21 - mmengine - INFO - Epoch(train) [80][2900/5005] lr: 1.0000e-03 eta: 5:29:41 time: 0.1927 data_time: 0.0052 loss: 1.0491 2023/03/17 14:17:40 - mmengine - INFO - Epoch(train) [80][3000/5005] lr: 1.0000e-03 eta: 5:29:21 time: 0.1856 data_time: 0.0047 loss: 0.9346 2023/03/17 14:18:01 - mmengine - INFO - Epoch(train) [80][3100/5005] lr: 1.0000e-03 eta: 5:29:02 time: 0.1986 data_time: 0.0045 loss: 1.0153 2023/03/17 14:18:21 - mmengine - INFO - Epoch(train) [80][3200/5005] lr: 1.0000e-03 eta: 5:28:43 time: 0.1966 data_time: 0.0041 loss: 1.0383 2023/03/17 14:18:42 - mmengine - INFO - Epoch(train) [80][3300/5005] lr: 1.0000e-03 eta: 5:28:24 time: 0.2245 data_time: 0.0058 loss: 0.9000 2023/03/17 14:19:04 - mmengine - INFO - Epoch(train) [80][3400/5005] lr: 1.0000e-03 eta: 5:28:05 time: 0.2372 data_time: 0.0052 loss: 1.0844 2023/03/17 14:19:24 - mmengine - INFO - Epoch(train) [80][3500/5005] lr: 1.0000e-03 eta: 5:27:46 time: 0.1868 data_time: 0.0050 loss: 0.8221 2023/03/17 14:19:44 - mmengine - INFO - Epoch(train) [80][3600/5005] lr: 1.0000e-03 eta: 5:27:27 time: 0.1973 data_time: 0.0045 loss: 0.9000 2023/03/17 14:19:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:20:04 - mmengine - INFO - Epoch(train) [80][3700/5005] lr: 1.0000e-03 eta: 5:27:08 time: 0.2021 data_time: 0.0049 loss: 1.1305 2023/03/17 14:20:24 - mmengine - INFO - Epoch(train) [80][3800/5005] lr: 1.0000e-03 eta: 5:26:49 time: 0.2022 data_time: 0.0051 loss: 1.0465 2023/03/17 14:20:45 - mmengine - INFO - Epoch(train) [80][3900/5005] lr: 1.0000e-03 eta: 5:26:30 time: 0.1976 data_time: 0.0041 loss: 1.1361 2023/03/17 14:21:04 - mmengine - INFO - Epoch(train) [80][4000/5005] lr: 1.0000e-03 eta: 5:26:10 time: 0.1899 data_time: 0.0038 loss: 1.0691 2023/03/17 14:21:24 - mmengine - INFO - Epoch(train) [80][4100/5005] lr: 1.0000e-03 eta: 5:25:51 time: 0.2019 data_time: 0.0049 loss: 1.0503 2023/03/17 14:21:45 - mmengine - INFO - Epoch(train) [80][4200/5005] lr: 1.0000e-03 eta: 5:25:32 time: 0.2038 data_time: 0.0040 loss: 0.9392 2023/03/17 14:22:07 - mmengine - INFO - Epoch(train) [80][4300/5005] lr: 1.0000e-03 eta: 5:25:13 time: 0.2061 data_time: 0.0034 loss: 1.0115 2023/03/17 14:22:27 - mmengine - INFO - Epoch(train) [80][4400/5005] lr: 1.0000e-03 eta: 5:24:54 time: 0.1946 data_time: 0.0038 loss: 1.1257 2023/03/17 14:22:47 - mmengine - INFO - Epoch(train) [80][4500/5005] lr: 1.0000e-03 eta: 5:24:35 time: 0.1983 data_time: 0.0041 loss: 1.0638 2023/03/17 14:23:08 - mmengine - INFO - Epoch(train) [80][4600/5005] lr: 1.0000e-03 eta: 5:24:16 time: 0.2334 data_time: 0.0049 loss: 0.9443 2023/03/17 14:23:09 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:23:30 - mmengine - INFO - Epoch(train) [80][4700/5005] lr: 1.0000e-03 eta: 5:23:57 time: 0.2050 data_time: 0.0039 loss: 1.0380 2023/03/17 14:23:51 - mmengine - INFO - Epoch(train) [80][4800/5005] lr: 1.0000e-03 eta: 5:23:38 time: 0.1905 data_time: 0.0040 loss: 0.9481 2023/03/17 14:24:10 - mmengine - INFO - Epoch(train) [80][4900/5005] lr: 1.0000e-03 eta: 5:23:18 time: 0.1832 data_time: 0.0039 loss: 1.1836 2023/03/17 14:24:31 - mmengine - INFO - Epoch(train) [80][5000/5005] lr: 1.0000e-03 eta: 5:22:59 time: 0.2179 data_time: 0.0052 loss: 1.1515 2023/03/17 14:24:32 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:24:33 - mmengine - INFO - Saving checkpoint at 80 epochs 2023/03/17 14:24:39 - mmengine - INFO - Epoch(val) [80][100/196] eta: 0:00:05 time: 0.0406 data_time: 0.0041 2023/03/17 14:25:03 - mmengine - INFO - Epoch(val) [80][196/196] accuracy/top1: 75.7500 accuracy/top5: 92.7860data_time: 0.0176 time: 0.0516 2023/03/17 14:25:22 - mmengine - INFO - Epoch(train) [81][ 100/5005] lr: 1.0000e-03 eta: 5:22:39 time: 0.1786 data_time: 0.0043 loss: 0.7771 2023/03/17 14:25:43 - mmengine - INFO - Epoch(train) [81][ 200/5005] lr: 1.0000e-03 eta: 5:22:20 time: 0.2042 data_time: 0.0049 loss: 0.9818 2023/03/17 14:26:04 - mmengine - INFO - Epoch(train) [81][ 300/5005] lr: 1.0000e-03 eta: 5:22:01 time: 0.2035 data_time: 0.0045 loss: 1.0904 2023/03/17 14:26:25 - mmengine - INFO - Epoch(train) [81][ 400/5005] lr: 1.0000e-03 eta: 5:21:42 time: 0.1987 data_time: 0.0050 loss: 1.0335 2023/03/17 14:26:44 - mmengine - INFO - Epoch(train) [81][ 500/5005] lr: 1.0000e-03 eta: 5:21:22 time: 0.1836 data_time: 0.0047 loss: 0.9262 2023/03/17 14:27:03 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:27:03 - mmengine - INFO - Epoch(train) [81][ 600/5005] lr: 1.0000e-03 eta: 5:21:03 time: 0.1985 data_time: 0.0044 loss: 0.9332 2023/03/17 14:27:27 - mmengine - INFO - Epoch(train) [81][ 700/5005] lr: 1.0000e-03 eta: 5:20:45 time: 0.2659 data_time: 0.0041 loss: 1.0347 2023/03/17 14:27:49 - mmengine - INFO - Epoch(train) [81][ 800/5005] lr: 1.0000e-03 eta: 5:20:26 time: 0.1963 data_time: 0.0046 loss: 0.9425 2023/03/17 14:28:09 - mmengine - INFO - Epoch(train) [81][ 900/5005] lr: 1.0000e-03 eta: 5:20:07 time: 0.2377 data_time: 0.0043 loss: 0.8856 2023/03/17 14:28:32 - mmengine - INFO - Epoch(train) [81][1000/5005] lr: 1.0000e-03 eta: 5:19:48 time: 0.1777 data_time: 0.0039 loss: 0.9738 2023/03/17 14:28:51 - mmengine - INFO - Epoch(train) [81][1100/5005] lr: 1.0000e-03 eta: 5:19:29 time: 0.1897 data_time: 0.0044 loss: 1.1359 2023/03/17 14:29:12 - mmengine - INFO - Epoch(train) [81][1200/5005] lr: 1.0000e-03 eta: 5:19:10 time: 0.1986 data_time: 0.0048 loss: 1.1281 2023/03/17 14:29:31 - mmengine - INFO - Epoch(train) [81][1300/5005] lr: 1.0000e-03 eta: 5:18:50 time: 0.1890 data_time: 0.0046 loss: 0.9818 2023/03/17 14:29:50 - mmengine - INFO - Epoch(train) [81][1400/5005] lr: 1.0000e-03 eta: 5:18:31 time: 0.2039 data_time: 0.0044 loss: 0.9737 2023/03/17 14:30:09 - mmengine - INFO - Epoch(train) [81][1500/5005] lr: 1.0000e-03 eta: 5:18:11 time: 0.1868 data_time: 0.0044 loss: 1.0389 2023/03/17 14:30:29 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:30:29 - mmengine - INFO - Epoch(train) [81][1600/5005] lr: 1.0000e-03 eta: 5:17:52 time: 0.1934 data_time: 0.0052 loss: 1.1241 2023/03/17 14:30:49 - mmengine - INFO - Epoch(train) [81][1700/5005] lr: 1.0000e-03 eta: 5:17:33 time: 0.1846 data_time: 0.0046 loss: 0.8940 2023/03/17 14:31:07 - mmengine - INFO - Epoch(train) [81][1800/5005] lr: 1.0000e-03 eta: 5:17:13 time: 0.1695 data_time: 0.0057 loss: 0.9009 2023/03/17 14:31:25 - mmengine - INFO - Epoch(train) [81][1900/5005] lr: 1.0000e-03 eta: 5:16:53 time: 0.1917 data_time: 0.0039 loss: 1.1260 2023/03/17 14:31:45 - mmengine - INFO - Epoch(train) [81][2000/5005] lr: 1.0000e-03 eta: 5:16:34 time: 0.1874 data_time: 0.0041 loss: 1.0806 2023/03/17 14:32:03 - mmengine - INFO - Epoch(train) [81][2100/5005] lr: 1.0000e-03 eta: 5:16:14 time: 0.1791 data_time: 0.0043 loss: 0.9546 2023/03/17 14:32:22 - mmengine - INFO - Epoch(train) [81][2200/5005] lr: 1.0000e-03 eta: 5:15:55 time: 0.1855 data_time: 0.0041 loss: 1.1294 2023/03/17 14:32:41 - mmengine - INFO - Epoch(train) [81][2300/5005] lr: 1.0000e-03 eta: 5:15:35 time: 0.1949 data_time: 0.0042 loss: 0.8401 2023/03/17 14:33:01 - mmengine - INFO - Epoch(train) [81][2400/5005] lr: 1.0000e-03 eta: 5:15:16 time: 0.1806 data_time: 0.0044 loss: 0.8546 2023/03/17 14:33:20 - mmengine - INFO - Epoch(train) [81][2500/5005] lr: 1.0000e-03 eta: 5:14:56 time: 0.2153 data_time: 0.0048 loss: 1.0218 2023/03/17 14:33:40 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:33:40 - mmengine - INFO - Epoch(train) [81][2600/5005] lr: 1.0000e-03 eta: 5:14:37 time: 0.2054 data_time: 0.0049 loss: 0.9454 2023/03/17 14:34:01 - mmengine - INFO - Epoch(train) [81][2700/5005] lr: 1.0000e-03 eta: 5:14:18 time: 0.2451 data_time: 0.0048 loss: 1.0397 2023/03/17 14:34:22 - mmengine - INFO - Epoch(train) [81][2800/5005] lr: 1.0000e-03 eta: 5:13:59 time: 0.1871 data_time: 0.0044 loss: 0.8975 2023/03/17 14:34:42 - mmengine - INFO - Epoch(train) [81][2900/5005] lr: 1.0000e-03 eta: 5:13:40 time: 0.2013 data_time: 0.0047 loss: 0.7809 2023/03/17 14:35:02 - mmengine - INFO - Epoch(train) [81][3000/5005] lr: 1.0000e-03 eta: 5:13:21 time: 0.1936 data_time: 0.0046 loss: 0.9498 2023/03/17 14:35:22 - mmengine - INFO - Epoch(train) [81][3100/5005] lr: 1.0000e-03 eta: 5:13:01 time: 0.1875 data_time: 0.0045 loss: 1.0036 2023/03/17 14:35:42 - mmengine - INFO - Epoch(train) [81][3200/5005] lr: 1.0000e-03 eta: 5:12:42 time: 0.1923 data_time: 0.0039 loss: 0.9717 2023/03/17 14:36:01 - mmengine - INFO - Epoch(train) [81][3300/5005] lr: 1.0000e-03 eta: 5:12:23 time: 0.2564 data_time: 0.0044 loss: 0.9903 2023/03/17 14:36:24 - mmengine - INFO - Epoch(train) [81][3400/5005] lr: 1.0000e-03 eta: 5:12:04 time: 0.1961 data_time: 0.0042 loss: 0.8522 2023/03/17 14:36:45 - mmengine - INFO - Epoch(train) [81][3500/5005] lr: 1.0000e-03 eta: 5:11:45 time: 0.2264 data_time: 0.0054 loss: 0.8109 2023/03/17 14:37:06 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:37:06 - mmengine - INFO - Epoch(train) [81][3600/5005] lr: 1.0000e-03 eta: 5:11:26 time: 0.1943 data_time: 0.0048 loss: 0.9921 2023/03/17 14:37:26 - mmengine - INFO - Epoch(train) [81][3700/5005] lr: 1.0000e-03 eta: 5:11:07 time: 0.1974 data_time: 0.0039 loss: 1.0715 2023/03/17 14:37:45 - mmengine - INFO - Epoch(train) [81][3800/5005] lr: 1.0000e-03 eta: 5:10:48 time: 0.1949 data_time: 0.0048 loss: 1.0351 2023/03/17 14:38:06 - mmengine - INFO - Epoch(train) [81][3900/5005] lr: 1.0000e-03 eta: 5:10:28 time: 0.1884 data_time: 0.0042 loss: 1.0497 2023/03/17 14:38:25 - mmengine - INFO - Epoch(train) [81][4000/5005] lr: 1.0000e-03 eta: 5:10:09 time: 0.1898 data_time: 0.0041 loss: 1.0288 2023/03/17 14:38:43 - mmengine - INFO - Epoch(train) [81][4100/5005] lr: 1.0000e-03 eta: 5:09:49 time: 0.1844 data_time: 0.0050 loss: 0.9611 2023/03/17 14:39:03 - mmengine - INFO - Epoch(train) [81][4200/5005] lr: 1.0000e-03 eta: 5:09:30 time: 0.1842 data_time: 0.0046 loss: 1.0786 2023/03/17 14:39:22 - mmengine - INFO - Epoch(train) [81][4300/5005] lr: 1.0000e-03 eta: 5:09:10 time: 0.1880 data_time: 0.0042 loss: 1.0871 2023/03/17 14:39:41 - mmengine - INFO - Epoch(train) [81][4400/5005] lr: 1.0000e-03 eta: 5:08:51 time: 0.2126 data_time: 0.0045 loss: 0.9903 2023/03/17 14:40:01 - mmengine - INFO - Epoch(train) [81][4500/5005] lr: 1.0000e-03 eta: 5:08:32 time: 0.1924 data_time: 0.0044 loss: 1.0918 2023/03/17 14:40:21 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:40:21 - mmengine - INFO - Epoch(train) [81][4600/5005] lr: 1.0000e-03 eta: 5:08:12 time: 0.1967 data_time: 0.0047 loss: 1.0154 2023/03/17 14:40:40 - mmengine - INFO - Epoch(train) [81][4700/5005] lr: 1.0000e-03 eta: 5:07:53 time: 0.1922 data_time: 0.0045 loss: 1.1022 2023/03/17 14:40:58 - mmengine - INFO - Epoch(train) [81][4800/5005] lr: 1.0000e-03 eta: 5:07:33 time: 0.1782 data_time: 0.0050 loss: 1.0437 2023/03/17 14:41:17 - mmengine - INFO - Epoch(train) [81][4900/5005] lr: 1.0000e-03 eta: 5:07:14 time: 0.1887 data_time: 0.0049 loss: 0.9834 2023/03/17 14:41:36 - mmengine - INFO - Epoch(train) [81][5000/5005] lr: 1.0000e-03 eta: 5:06:54 time: 0.1755 data_time: 0.0054 loss: 1.0300 2023/03/17 14:41:37 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:41:37 - mmengine - INFO - Saving checkpoint at 81 epochs 2023/03/17 14:41:44 - mmengine - INFO - Epoch(val) [81][100/196] eta: 0:00:05 time: 0.0446 data_time: 0.0009 2023/03/17 14:42:08 - mmengine - INFO - Epoch(val) [81][196/196] accuracy/top1: 75.6300 accuracy/top5: 92.8700data_time: 0.0173 time: 0.0498 2023/03/17 14:42:28 - mmengine - INFO - Epoch(train) [82][ 100/5005] lr: 1.0000e-03 eta: 5:06:34 time: 0.1777 data_time: 0.0048 loss: 1.0834 2023/03/17 14:42:47 - mmengine - INFO - Epoch(train) [82][ 200/5005] lr: 1.0000e-03 eta: 5:06:15 time: 0.1887 data_time: 0.0046 loss: 1.0152 2023/03/17 14:43:06 - mmengine - INFO - Epoch(train) [82][ 300/5005] lr: 1.0000e-03 eta: 5:05:55 time: 0.2364 data_time: 0.0048 loss: 1.0209 2023/03/17 14:43:29 - mmengine - INFO - Epoch(train) [82][ 400/5005] lr: 1.0000e-03 eta: 5:05:36 time: 0.2021 data_time: 0.0043 loss: 0.9769 2023/03/17 14:43:48 - mmengine - INFO - Epoch(train) [82][ 500/5005] lr: 1.0000e-03 eta: 5:05:17 time: 0.2016 data_time: 0.0051 loss: 0.9882 2023/03/17 14:44:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:44:08 - mmengine - INFO - Epoch(train) [82][ 600/5005] lr: 1.0000e-03 eta: 5:04:58 time: 0.2329 data_time: 0.0046 loss: 0.9989 2023/03/17 14:44:29 - mmengine - INFO - Epoch(train) [82][ 700/5005] lr: 1.0000e-03 eta: 5:04:39 time: 0.1952 data_time: 0.0048 loss: 0.7700 2023/03/17 14:44:49 - mmengine - INFO - Epoch(train) [82][ 800/5005] lr: 1.0000e-03 eta: 5:04:19 time: 0.1899 data_time: 0.0049 loss: 1.0716 2023/03/17 14:45:07 - mmengine - INFO - Epoch(train) [82][ 900/5005] lr: 1.0000e-03 eta: 5:04:00 time: 0.1865 data_time: 0.0048 loss: 1.1320 2023/03/17 14:45:27 - mmengine - INFO - Epoch(train) [82][1000/5005] lr: 1.0000e-03 eta: 5:03:40 time: 0.2161 data_time: 0.0043 loss: 0.9037 2023/03/17 14:45:49 - mmengine - INFO - Epoch(train) [82][1100/5005] lr: 1.0000e-03 eta: 5:03:22 time: 0.1921 data_time: 0.0040 loss: 1.0053 2023/03/17 14:46:11 - mmengine - INFO - Epoch(train) [82][1200/5005] lr: 1.0000e-03 eta: 5:03:03 time: 0.2410 data_time: 0.0047 loss: 0.9681 2023/03/17 14:46:33 - mmengine - INFO - Epoch(train) [82][1300/5005] lr: 1.0000e-03 eta: 5:02:44 time: 0.2136 data_time: 0.0041 loss: 1.0012 2023/03/17 14:46:51 - mmengine - INFO - Epoch(train) [82][1400/5005] lr: 1.0000e-03 eta: 5:02:24 time: 0.1896 data_time: 0.0043 loss: 0.9600 2023/03/17 14:47:11 - mmengine - INFO - Epoch(train) [82][1500/5005] lr: 1.0000e-03 eta: 5:02:05 time: 0.1969 data_time: 0.0041 loss: 1.2417 2023/03/17 14:47:31 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:47:33 - mmengine - INFO - Epoch(train) [82][1600/5005] lr: 1.0000e-03 eta: 5:01:46 time: 0.2195 data_time: 0.0043 loss: 0.9566 2023/03/17 14:47:53 - mmengine - INFO - Epoch(train) [82][1700/5005] lr: 1.0000e-03 eta: 5:01:27 time: 0.1888 data_time: 0.0040 loss: 1.0819 2023/03/17 14:48:12 - mmengine - INFO - Epoch(train) [82][1800/5005] lr: 1.0000e-03 eta: 5:01:08 time: 0.1803 data_time: 0.0051 loss: 1.0167 2023/03/17 14:48:30 - mmengine - INFO - Epoch(train) [82][1900/5005] lr: 1.0000e-03 eta: 5:00:48 time: 0.1848 data_time: 0.0052 loss: 1.2107 2023/03/17 14:48:50 - mmengine - INFO - Epoch(train) [82][2000/5005] lr: 1.0000e-03 eta: 5:00:28 time: 0.1895 data_time: 0.0037 loss: 0.9309 2023/03/17 14:49:09 - mmengine - INFO - Epoch(train) [82][2100/5005] lr: 1.0000e-03 eta: 5:00:09 time: 0.1924 data_time: 0.0047 loss: 0.9589 2023/03/17 14:49:30 - mmengine - INFO - Epoch(train) [82][2200/5005] lr: 1.0000e-03 eta: 4:59:50 time: 0.2005 data_time: 0.0048 loss: 1.0391 2023/03/17 14:49:52 - mmengine - INFO - Epoch(train) [82][2300/5005] lr: 1.0000e-03 eta: 4:59:31 time: 0.2065 data_time: 0.0049 loss: 1.1713 2023/03/17 14:50:12 - mmengine - INFO - Epoch(train) [82][2400/5005] lr: 1.0000e-03 eta: 4:59:12 time: 0.2328 data_time: 0.0042 loss: 1.0528 2023/03/17 14:50:33 - mmengine - INFO - Epoch(train) [82][2500/5005] lr: 1.0000e-03 eta: 4:58:53 time: 0.2057 data_time: 0.0052 loss: 1.0852 2023/03/17 14:50:52 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:50:53 - mmengine - INFO - Epoch(train) [82][2600/5005] lr: 1.0000e-03 eta: 4:58:34 time: 0.1989 data_time: 0.0042 loss: 0.9407 2023/03/17 14:51:14 - mmengine - INFO - Epoch(train) [82][2700/5005] lr: 1.0000e-03 eta: 4:58:15 time: 0.1982 data_time: 0.0045 loss: 1.0840 2023/03/17 14:51:34 - mmengine - INFO - Epoch(train) [82][2800/5005] lr: 1.0000e-03 eta: 4:57:55 time: 0.2014 data_time: 0.0050 loss: 1.0185 2023/03/17 14:51:54 - mmengine - INFO - Epoch(train) [82][2900/5005] lr: 1.0000e-03 eta: 4:57:36 time: 0.2050 data_time: 0.0046 loss: 0.9909 2023/03/17 14:52:14 - mmengine - INFO - Epoch(train) [82][3000/5005] lr: 1.0000e-03 eta: 4:57:17 time: 0.1957 data_time: 0.0044 loss: 1.0718 2023/03/17 14:52:34 - mmengine - INFO - Epoch(train) [82][3100/5005] lr: 1.0000e-03 eta: 4:56:58 time: 0.2013 data_time: 0.0044 loss: 1.0903 2023/03/17 14:52:54 - mmengine - INFO - Epoch(train) [82][3200/5005] lr: 1.0000e-03 eta: 4:56:38 time: 0.1922 data_time: 0.0045 loss: 0.9141 2023/03/17 14:53:14 - mmengine - INFO - Epoch(train) [82][3300/5005] lr: 1.0000e-03 eta: 4:56:19 time: 0.1876 data_time: 0.0049 loss: 0.8526 2023/03/17 14:53:33 - mmengine - INFO - Epoch(train) [82][3400/5005] lr: 1.0000e-03 eta: 4:56:00 time: 0.1908 data_time: 0.0045 loss: 0.9886 2023/03/17 14:53:52 - mmengine - INFO - Epoch(train) [82][3500/5005] lr: 1.0000e-03 eta: 4:55:40 time: 0.1877 data_time: 0.0045 loss: 0.7875 2023/03/17 14:54:10 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:54:11 - mmengine - INFO - Epoch(train) [82][3600/5005] lr: 1.0000e-03 eta: 4:55:21 time: 0.1897 data_time: 0.0045 loss: 0.9839 2023/03/17 14:54:32 - mmengine - INFO - Epoch(train) [82][3700/5005] lr: 1.0000e-03 eta: 4:55:01 time: 0.1916 data_time: 0.0041 loss: 0.8477 2023/03/17 14:54:51 - mmengine - INFO - Epoch(train) [82][3800/5005] lr: 1.0000e-03 eta: 4:54:42 time: 0.1925 data_time: 0.0041 loss: 1.2122 2023/03/17 14:55:11 - mmengine - INFO - Epoch(train) [82][3900/5005] lr: 1.0000e-03 eta: 4:54:23 time: 0.1974 data_time: 0.0046 loss: 1.0493 2023/03/17 14:55:31 - mmengine - INFO - Epoch(train) [82][4000/5005] lr: 1.0000e-03 eta: 4:54:03 time: 0.2169 data_time: 0.0045 loss: 0.9242 2023/03/17 14:55:52 - mmengine - INFO - Epoch(train) [82][4100/5005] lr: 1.0000e-03 eta: 4:53:44 time: 0.2009 data_time: 0.0050 loss: 0.9405 2023/03/17 14:56:12 - mmengine - INFO - Epoch(train) [82][4200/5005] lr: 1.0000e-03 eta: 4:53:25 time: 0.1899 data_time: 0.0051 loss: 1.0367 2023/03/17 14:56:32 - mmengine - INFO - Epoch(train) [82][4300/5005] lr: 1.0000e-03 eta: 4:53:06 time: 0.2144 data_time: 0.0047 loss: 0.9153 2023/03/17 14:56:52 - mmengine - INFO - Epoch(train) [82][4400/5005] lr: 1.0000e-03 eta: 4:52:47 time: 0.1907 data_time: 0.0047 loss: 1.0391 2023/03/17 14:57:13 - mmengine - INFO - Epoch(train) [82][4500/5005] lr: 1.0000e-03 eta: 4:52:27 time: 0.2004 data_time: 0.0047 loss: 0.9873 2023/03/17 14:57:31 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:57:32 - mmengine - INFO - Epoch(train) [82][4600/5005] lr: 1.0000e-03 eta: 4:52:08 time: 0.1956 data_time: 0.0043 loss: 0.9541 2023/03/17 14:57:52 - mmengine - INFO - Epoch(train) [82][4700/5005] lr: 1.0000e-03 eta: 4:51:49 time: 0.2005 data_time: 0.0050 loss: 0.8576 2023/03/17 14:58:14 - mmengine - INFO - Epoch(train) [82][4800/5005] lr: 1.0000e-03 eta: 4:51:30 time: 0.2236 data_time: 0.0044 loss: 1.0458 2023/03/17 14:58:35 - mmengine - INFO - Epoch(train) [82][4900/5005] lr: 1.0000e-03 eta: 4:51:11 time: 0.2003 data_time: 0.0051 loss: 1.0306 2023/03/17 14:58:55 - mmengine - INFO - Epoch(train) [82][5000/5005] lr: 1.0000e-03 eta: 4:50:52 time: 0.2142 data_time: 0.0057 loss: 1.0257 2023/03/17 14:58:56 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 14:58:57 - mmengine - INFO - Saving checkpoint at 82 epochs 2023/03/17 14:59:03 - mmengine - INFO - Epoch(val) [82][100/196] eta: 0:00:05 time: 0.0619 data_time: 0.0009 2023/03/17 14:59:32 - mmengine - INFO - Epoch(val) [82][196/196] accuracy/top1: 75.6780 accuracy/top5: 92.7780data_time: 0.0322 time: 0.0702 2023/03/17 14:59:55 - mmengine - INFO - Epoch(train) [83][ 100/5005] lr: 1.0000e-03 eta: 4:50:32 time: 0.2781 data_time: 0.0041 loss: 1.0475 2023/03/17 15:00:16 - mmengine - INFO - Epoch(train) [83][ 200/5005] lr: 1.0000e-03 eta: 4:50:13 time: 0.1817 data_time: 0.0045 loss: 1.0754 2023/03/17 15:00:36 - mmengine - INFO - Epoch(train) [83][ 300/5005] lr: 1.0000e-03 eta: 4:49:54 time: 0.1865 data_time: 0.0044 loss: 0.9990 2023/03/17 15:00:53 - mmengine - INFO - Epoch(train) [83][ 400/5005] lr: 1.0000e-03 eta: 4:49:34 time: 0.1798 data_time: 0.0053 loss: 1.1870 2023/03/17 15:01:12 - mmengine - INFO - Epoch(train) [83][ 500/5005] lr: 1.0000e-03 eta: 4:49:14 time: 0.1823 data_time: 0.0052 loss: 1.0009 2023/03/17 15:01:29 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:01:31 - mmengine - INFO - Epoch(train) [83][ 600/5005] lr: 1.0000e-03 eta: 4:48:55 time: 0.2185 data_time: 0.0043 loss: 0.9523 2023/03/17 15:01:53 - mmengine - INFO - Epoch(train) [83][ 700/5005] lr: 1.0000e-03 eta: 4:48:36 time: 0.2257 data_time: 0.0046 loss: 0.9425 2023/03/17 15:02:13 - mmengine - INFO - Epoch(train) [83][ 800/5005] lr: 1.0000e-03 eta: 4:48:17 time: 0.1755 data_time: 0.0056 loss: 0.9103 2023/03/17 15:02:31 - mmengine - INFO - Epoch(train) [83][ 900/5005] lr: 1.0000e-03 eta: 4:47:57 time: 0.1866 data_time: 0.0045 loss: 0.9614 2023/03/17 15:02:50 - mmengine - INFO - Epoch(train) [83][1000/5005] lr: 1.0000e-03 eta: 4:47:37 time: 0.1789 data_time: 0.0052 loss: 1.1737 2023/03/17 15:03:09 - mmengine - INFO - Epoch(train) [83][1100/5005] lr: 1.0000e-03 eta: 4:47:18 time: 0.1811 data_time: 0.0052 loss: 0.9490 2023/03/17 15:03:27 - mmengine - INFO - Epoch(train) [83][1200/5005] lr: 1.0000e-03 eta: 4:46:58 time: 0.1710 data_time: 0.0053 loss: 0.9818 2023/03/17 15:03:45 - mmengine - INFO - Epoch(train) [83][1300/5005] lr: 1.0000e-03 eta: 4:46:38 time: 0.1888 data_time: 0.0044 loss: 0.9480 2023/03/17 15:04:04 - mmengine - INFO - Epoch(train) [83][1400/5005] lr: 1.0000e-03 eta: 4:46:19 time: 0.1928 data_time: 0.0043 loss: 0.8688 2023/03/17 15:04:23 - mmengine - INFO - Epoch(train) [83][1500/5005] lr: 1.0000e-03 eta: 4:45:59 time: 0.1864 data_time: 0.0041 loss: 1.0629 2023/03/17 15:04:40 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:04:42 - mmengine - INFO - Epoch(train) [83][1600/5005] lr: 1.0000e-03 eta: 4:45:40 time: 0.1865 data_time: 0.0044 loss: 1.0566 2023/03/17 15:05:01 - mmengine - INFO - Epoch(train) [83][1700/5005] lr: 1.0000e-03 eta: 4:45:21 time: 0.1979 data_time: 0.0042 loss: 1.0315 2023/03/17 15:05:22 - mmengine - INFO - Epoch(train) [83][1800/5005] lr: 1.0000e-03 eta: 4:45:01 time: 0.2032 data_time: 0.0039 loss: 0.9285 2023/03/17 15:05:44 - mmengine - INFO - Epoch(train) [83][1900/5005] lr: 1.0000e-03 eta: 4:44:43 time: 0.2373 data_time: 0.0042 loss: 1.1094 2023/03/17 15:06:08 - mmengine - INFO - Epoch(train) [83][2000/5005] lr: 1.0000e-03 eta: 4:44:24 time: 0.2339 data_time: 0.0043 loss: 1.0335 2023/03/17 15:06:29 - mmengine - INFO - Epoch(train) [83][2100/5005] lr: 1.0000e-03 eta: 4:44:05 time: 0.1821 data_time: 0.0047 loss: 0.8654 2023/03/17 15:06:47 - mmengine - INFO - Epoch(train) [83][2200/5005] lr: 1.0000e-03 eta: 4:43:45 time: 0.1780 data_time: 0.0044 loss: 0.8916 2023/03/17 15:07:08 - mmengine - INFO - Epoch(train) [83][2300/5005] lr: 1.0000e-03 eta: 4:43:26 time: 0.1846 data_time: 0.0044 loss: 0.9661 2023/03/17 15:07:26 - mmengine - INFO - Epoch(train) [83][2400/5005] lr: 1.0000e-03 eta: 4:43:07 time: 0.2122 data_time: 0.0045 loss: 0.9703 2023/03/17 15:07:44 - mmengine - INFO - Epoch(train) [83][2500/5005] lr: 1.0000e-03 eta: 4:42:47 time: 0.1708 data_time: 0.0043 loss: 1.0108 2023/03/17 15:08:01 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:08:03 - mmengine - INFO - Epoch(train) [83][2600/5005] lr: 1.0000e-03 eta: 4:42:27 time: 0.1714 data_time: 0.0054 loss: 0.9234 2023/03/17 15:08:22 - mmengine - INFO - Epoch(train) [83][2700/5005] lr: 1.0000e-03 eta: 4:42:08 time: 0.2324 data_time: 0.0041 loss: 1.0898 2023/03/17 15:08:45 - mmengine - INFO - Epoch(train) [83][2800/5005] lr: 1.0000e-03 eta: 4:41:49 time: 0.2099 data_time: 0.0045 loss: 0.9784 2023/03/17 15:09:05 - mmengine - INFO - Epoch(train) [83][2900/5005] lr: 1.0000e-03 eta: 4:41:30 time: 0.1733 data_time: 0.0041 loss: 1.0253 2023/03/17 15:09:23 - mmengine - INFO - Epoch(train) [83][3000/5005] lr: 1.0000e-03 eta: 4:41:10 time: 0.1794 data_time: 0.0042 loss: 1.1163 2023/03/17 15:09:43 - mmengine - INFO - Epoch(train) [83][3100/5005] lr: 1.0000e-03 eta: 4:40:51 time: 0.2278 data_time: 0.0043 loss: 1.0920 2023/03/17 15:10:04 - mmengine - INFO - Epoch(train) [83][3200/5005] lr: 1.0000e-03 eta: 4:40:32 time: 0.2155 data_time: 0.0043 loss: 1.0273 2023/03/17 15:10:23 - mmengine - INFO - Epoch(train) [83][3300/5005] lr: 1.0000e-03 eta: 4:40:13 time: 0.1852 data_time: 0.0040 loss: 1.0456 2023/03/17 15:10:42 - mmengine - INFO - Epoch(train) [83][3400/5005] lr: 1.0000e-03 eta: 4:39:53 time: 0.1816 data_time: 0.0047 loss: 0.9061 2023/03/17 15:11:01 - mmengine - INFO - Epoch(train) [83][3500/5005] lr: 1.0000e-03 eta: 4:39:34 time: 0.1846 data_time: 0.0051 loss: 0.8932 2023/03/17 15:11:20 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:11:22 - mmengine - INFO - Epoch(train) [83][3600/5005] lr: 1.0000e-03 eta: 4:39:14 time: 0.2146 data_time: 0.0044 loss: 0.9698 2023/03/17 15:11:43 - mmengine - INFO - Epoch(train) [83][3700/5005] lr: 1.0000e-03 eta: 4:38:55 time: 0.2172 data_time: 0.0047 loss: 0.9547 2023/03/17 15:12:04 - mmengine - INFO - Epoch(train) [83][3800/5005] lr: 1.0000e-03 eta: 4:38:36 time: 0.2232 data_time: 0.0042 loss: 0.9304 2023/03/17 15:12:23 - mmengine - INFO - Epoch(train) [83][3900/5005] lr: 1.0000e-03 eta: 4:38:17 time: 0.1851 data_time: 0.0039 loss: 1.1522 2023/03/17 15:12:45 - mmengine - INFO - Epoch(train) [83][4000/5005] lr: 1.0000e-03 eta: 4:37:58 time: 0.2231 data_time: 0.0039 loss: 0.8504 2023/03/17 15:13:09 - mmengine - INFO - Epoch(train) [83][4100/5005] lr: 1.0000e-03 eta: 4:37:39 time: 0.2528 data_time: 0.0045 loss: 0.9578 2023/03/17 15:13:33 - mmengine - INFO - Epoch(train) [83][4200/5005] lr: 1.0000e-03 eta: 4:37:21 time: 0.1925 data_time: 0.0046 loss: 1.0074 2023/03/17 15:13:54 - mmengine - INFO - Epoch(train) [83][4300/5005] lr: 1.0000e-03 eta: 4:37:02 time: 0.2111 data_time: 0.0043 loss: 1.0970 2023/03/17 15:14:16 - mmengine - INFO - Epoch(train) [83][4400/5005] lr: 1.0000e-03 eta: 4:36:43 time: 0.1889 data_time: 0.0050 loss: 1.0253 2023/03/17 15:14:35 - mmengine - INFO - Epoch(train) [83][4500/5005] lr: 1.0000e-03 eta: 4:36:24 time: 0.1766 data_time: 0.0054 loss: 0.8406 2023/03/17 15:14:52 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:14:54 - mmengine - INFO - Epoch(train) [83][4600/5005] lr: 1.0000e-03 eta: 4:36:04 time: 0.2437 data_time: 0.0046 loss: 1.1092 2023/03/17 15:15:14 - mmengine - INFO - Epoch(train) [83][4700/5005] lr: 1.0000e-03 eta: 4:35:45 time: 0.1725 data_time: 0.0057 loss: 1.1522 2023/03/17 15:15:32 - mmengine - INFO - Epoch(train) [83][4800/5005] lr: 1.0000e-03 eta: 4:35:25 time: 0.1769 data_time: 0.0055 loss: 1.0691 2023/03/17 15:15:54 - mmengine - INFO - Epoch(train) [83][4900/5005] lr: 1.0000e-03 eta: 4:35:06 time: 0.2340 data_time: 0.0042 loss: 1.0091 2023/03/17 15:16:15 - mmengine - INFO - Epoch(train) [83][5000/5005] lr: 1.0000e-03 eta: 4:34:47 time: 0.1957 data_time: 0.0056 loss: 0.9033 2023/03/17 15:16:16 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:16:16 - mmengine - INFO - Saving checkpoint at 83 epochs 2023/03/17 15:16:22 - mmengine - INFO - Epoch(val) [83][100/196] eta: 0:00:04 time: 0.0413 data_time: 0.0072 2023/03/17 15:16:47 - mmengine - INFO - Epoch(val) [83][196/196] accuracy/top1: 75.7000 accuracy/top5: 92.8500data_time: 0.0279 time: 0.0679 2023/03/17 15:17:10 - mmengine - INFO - Epoch(train) [84][ 100/5005] lr: 1.0000e-03 eta: 4:34:28 time: 0.2241 data_time: 0.0044 loss: 1.0549 2023/03/17 15:17:34 - mmengine - INFO - Epoch(train) [84][ 200/5005] lr: 1.0000e-03 eta: 4:34:09 time: 0.2394 data_time: 0.0032 loss: 1.0770 2023/03/17 15:17:55 - mmengine - INFO - Epoch(train) [84][ 300/5005] lr: 1.0000e-03 eta: 4:33:50 time: 0.1869 data_time: 0.0052 loss: 0.9033 2023/03/17 15:18:15 - mmengine - INFO - Epoch(train) [84][ 400/5005] lr: 1.0000e-03 eta: 4:33:31 time: 0.2120 data_time: 0.0048 loss: 0.9399 2023/03/17 15:18:35 - mmengine - INFO - Epoch(train) [84][ 500/5005] lr: 1.0000e-03 eta: 4:33:11 time: 0.1933 data_time: 0.0046 loss: 0.9643 2023/03/17 15:18:56 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:19:00 - mmengine - INFO - Epoch(train) [84][ 600/5005] lr: 1.0000e-03 eta: 4:32:53 time: 0.2605 data_time: 0.0034 loss: 0.8564 2023/03/17 15:19:20 - mmengine - INFO - Epoch(train) [84][ 700/5005] lr: 1.0000e-03 eta: 4:32:34 time: 0.1980 data_time: 0.0041 loss: 0.9850 2023/03/17 15:19:39 - mmengine - INFO - Epoch(train) [84][ 800/5005] lr: 1.0000e-03 eta: 4:32:14 time: 0.1880 data_time: 0.0052 loss: 1.0065 2023/03/17 15:19:58 - mmengine - INFO - Epoch(train) [84][ 900/5005] lr: 1.0000e-03 eta: 4:31:55 time: 0.1825 data_time: 0.0050 loss: 0.9021 2023/03/17 15:20:16 - mmengine - INFO - Epoch(train) [84][1000/5005] lr: 1.0000e-03 eta: 4:31:35 time: 0.1814 data_time: 0.0046 loss: 0.9886 2023/03/17 15:20:35 - mmengine - INFO - Epoch(train) [84][1100/5005] lr: 1.0000e-03 eta: 4:31:16 time: 0.1796 data_time: 0.0055 loss: 1.0008 2023/03/17 15:20:53 - mmengine - INFO - Epoch(train) [84][1200/5005] lr: 1.0000e-03 eta: 4:30:56 time: 0.1790 data_time: 0.0051 loss: 0.9277 2023/03/17 15:21:12 - mmengine - INFO - Epoch(train) [84][1300/5005] lr: 1.0000e-03 eta: 4:30:36 time: 0.1851 data_time: 0.0055 loss: 0.9964 2023/03/17 15:21:30 - mmengine - INFO - Epoch(train) [84][1400/5005] lr: 1.0000e-03 eta: 4:30:17 time: 0.1757 data_time: 0.0056 loss: 1.0110 2023/03/17 15:21:48 - mmengine - INFO - Epoch(train) [84][1500/5005] lr: 1.0000e-03 eta: 4:29:57 time: 0.1772 data_time: 0.0065 loss: 1.0307 2023/03/17 15:22:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:22:10 - mmengine - INFO - Epoch(train) [84][1600/5005] lr: 1.0000e-03 eta: 4:29:38 time: 0.2226 data_time: 0.0039 loss: 1.1759 2023/03/17 15:22:31 - mmengine - INFO - Epoch(train) [84][1700/5005] lr: 1.0000e-03 eta: 4:29:19 time: 0.2325 data_time: 0.0038 loss: 0.9686 2023/03/17 15:22:55 - mmengine - INFO - Epoch(train) [84][1800/5005] lr: 1.0000e-03 eta: 4:29:01 time: 0.1919 data_time: 0.0047 loss: 1.0107 2023/03/17 15:23:14 - mmengine - INFO - Epoch(train) [84][1900/5005] lr: 1.0000e-03 eta: 4:28:41 time: 0.1835 data_time: 0.0057 loss: 1.0079 2023/03/17 15:23:33 - mmengine - INFO - Epoch(train) [84][2000/5005] lr: 1.0000e-03 eta: 4:28:22 time: 0.1824 data_time: 0.0047 loss: 1.0769 2023/03/17 15:23:52 - mmengine - INFO - Epoch(train) [84][2100/5005] lr: 1.0000e-03 eta: 4:28:02 time: 0.1810 data_time: 0.0048 loss: 0.9405 2023/03/17 15:24:11 - mmengine - INFO - Epoch(train) [84][2200/5005] lr: 1.0000e-03 eta: 4:27:43 time: 0.1913 data_time: 0.0052 loss: 1.0034 2023/03/17 15:24:30 - mmengine - INFO - Epoch(train) [84][2300/5005] lr: 1.0000e-03 eta: 4:27:23 time: 0.2192 data_time: 0.0050 loss: 0.9812 2023/03/17 15:24:49 - mmengine - INFO - Epoch(train) [84][2400/5005] lr: 1.0000e-03 eta: 4:27:04 time: 0.1841 data_time: 0.0050 loss: 1.0833 2023/03/17 15:25:08 - mmengine - INFO - Epoch(train) [84][2500/5005] lr: 1.0000e-03 eta: 4:26:44 time: 0.1889 data_time: 0.0046 loss: 0.9278 2023/03/17 15:25:24 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:25:27 - mmengine - INFO - Epoch(train) [84][2600/5005] lr: 1.0000e-03 eta: 4:26:25 time: 0.1930 data_time: 0.0045 loss: 1.1410 2023/03/17 15:25:47 - mmengine - INFO - Epoch(train) [84][2700/5005] lr: 1.0000e-03 eta: 4:26:05 time: 0.1929 data_time: 0.0043 loss: 1.1118 2023/03/17 15:26:06 - mmengine - INFO - Epoch(train) [84][2800/5005] lr: 1.0000e-03 eta: 4:25:46 time: 0.1817 data_time: 0.0057 loss: 0.8619 2023/03/17 15:26:24 - mmengine - INFO - Epoch(train) [84][2900/5005] lr: 1.0000e-03 eta: 4:25:26 time: 0.1749 data_time: 0.0047 loss: 1.1631 2023/03/17 15:26:42 - mmengine - INFO - Epoch(train) [84][3000/5005] lr: 1.0000e-03 eta: 4:25:07 time: 0.1812 data_time: 0.0056 loss: 1.0428 2023/03/17 15:27:01 - mmengine - INFO - Epoch(train) [84][3100/5005] lr: 1.0000e-03 eta: 4:24:47 time: 0.1991 data_time: 0.0051 loss: 1.0009 2023/03/17 15:27:21 - mmengine - INFO - Epoch(train) [84][3200/5005] lr: 1.0000e-03 eta: 4:24:28 time: 0.1911 data_time: 0.0047 loss: 1.0455 2023/03/17 15:27:40 - mmengine - INFO - Epoch(train) [84][3300/5005] lr: 1.0000e-03 eta: 4:24:08 time: 0.1832 data_time: 0.0042 loss: 1.0954 2023/03/17 15:27:59 - mmengine - INFO - Epoch(train) [84][3400/5005] lr: 1.0000e-03 eta: 4:23:49 time: 0.2021 data_time: 0.0048 loss: 0.9617 2023/03/17 15:28:19 - mmengine - INFO - Epoch(train) [84][3500/5005] lr: 1.0000e-03 eta: 4:23:29 time: 0.1944 data_time: 0.0052 loss: 0.8910 2023/03/17 15:28:35 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:28:38 - mmengine - INFO - Epoch(train) [84][3600/5005] lr: 1.0000e-03 eta: 4:23:10 time: 0.1872 data_time: 0.0057 loss: 0.9738 2023/03/17 15:28:57 - mmengine - INFO - Epoch(train) [84][3700/5005] lr: 1.0000e-03 eta: 4:22:50 time: 0.1844 data_time: 0.0049 loss: 0.9888 2023/03/17 15:29:16 - mmengine - INFO - Epoch(train) [84][3800/5005] lr: 1.0000e-03 eta: 4:22:31 time: 0.2155 data_time: 0.0055 loss: 1.1601 2023/03/17 15:29:38 - mmengine - INFO - Epoch(train) [84][3900/5005] lr: 1.0000e-03 eta: 4:22:12 time: 0.2059 data_time: 0.0048 loss: 1.1495 2023/03/17 15:30:00 - mmengine - INFO - Epoch(train) [84][4000/5005] lr: 1.0000e-03 eta: 4:21:53 time: 0.2203 data_time: 0.0045 loss: 1.0282 2023/03/17 15:30:21 - mmengine - INFO - Epoch(train) [84][4100/5005] lr: 1.0000e-03 eta: 4:21:34 time: 0.2266 data_time: 0.0049 loss: 0.9980 2023/03/17 15:30:43 - mmengine - INFO - Epoch(train) [84][4200/5005] lr: 1.0000e-03 eta: 4:21:15 time: 0.1949 data_time: 0.0044 loss: 0.8814 2023/03/17 15:31:02 - mmengine - INFO - Epoch(train) [84][4300/5005] lr: 1.0000e-03 eta: 4:20:56 time: 0.1927 data_time: 0.0054 loss: 1.0695 2023/03/17 15:31:26 - mmengine - INFO - Epoch(train) [84][4400/5005] lr: 1.0000e-03 eta: 4:20:37 time: 0.2490 data_time: 0.0036 loss: 0.8480 2023/03/17 15:31:44 - mmengine - INFO - Epoch(train) [84][4500/5005] lr: 1.0000e-03 eta: 4:20:17 time: 0.1769 data_time: 0.0051 loss: 0.9594 2023/03/17 15:32:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:32:03 - mmengine - INFO - Epoch(train) [84][4600/5005] lr: 1.0000e-03 eta: 4:19:58 time: 0.1818 data_time: 0.0056 loss: 1.1814 2023/03/17 15:32:22 - mmengine - INFO - Epoch(train) [84][4700/5005] lr: 1.0000e-03 eta: 4:19:38 time: 0.1902 data_time: 0.0056 loss: 1.1265 2023/03/17 15:32:41 - mmengine - INFO - Epoch(train) [84][4800/5005] lr: 1.0000e-03 eta: 4:19:19 time: 0.1869 data_time: 0.0043 loss: 1.1531 2023/03/17 15:33:03 - mmengine - INFO - Epoch(train) [84][4900/5005] lr: 1.0000e-03 eta: 4:19:00 time: 0.2188 data_time: 0.0044 loss: 0.8496 2023/03/17 15:33:25 - mmengine - INFO - Epoch(train) [84][5000/5005] lr: 1.0000e-03 eta: 4:18:41 time: 0.2309 data_time: 0.0058 loss: 1.0723 2023/03/17 15:33:27 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:33:27 - mmengine - INFO - Saving checkpoint at 84 epochs 2023/03/17 15:33:34 - mmengine - INFO - Epoch(val) [84][100/196] eta: 0:00:05 time: 0.0446 data_time: 0.0037 2023/03/17 15:34:01 - mmengine - INFO - Epoch(val) [84][196/196] accuracy/top1: 75.7320 accuracy/top5: 92.7700data_time: 0.0268 time: 0.0587 2023/03/17 15:34:29 - mmengine - INFO - Epoch(train) [85][ 100/5005] lr: 1.0000e-03 eta: 4:18:23 time: 0.2484 data_time: 0.0040 loss: 0.9819 2023/03/17 15:34:50 - mmengine - INFO - Epoch(train) [85][ 200/5005] lr: 1.0000e-03 eta: 4:18:03 time: 0.2214 data_time: 0.0038 loss: 1.0716 2023/03/17 15:35:09 - mmengine - INFO - Epoch(train) [85][ 300/5005] lr: 1.0000e-03 eta: 4:17:44 time: 0.1867 data_time: 0.0040 loss: 1.0066 2023/03/17 15:35:28 - mmengine - INFO - Epoch(train) [85][ 400/5005] lr: 1.0000e-03 eta: 4:17:24 time: 0.1922 data_time: 0.0045 loss: 0.9899 2023/03/17 15:35:47 - mmengine - INFO - Epoch(train) [85][ 500/5005] lr: 1.0000e-03 eta: 4:17:05 time: 0.1797 data_time: 0.0047 loss: 1.1357 2023/03/17 15:36:01 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:36:05 - mmengine - INFO - Epoch(train) [85][ 600/5005] lr: 1.0000e-03 eta: 4:16:45 time: 0.1880 data_time: 0.0049 loss: 0.9857 2023/03/17 15:36:25 - mmengine - INFO - Epoch(train) [85][ 700/5005] lr: 1.0000e-03 eta: 4:16:26 time: 0.1939 data_time: 0.0060 loss: 1.0314 2023/03/17 15:36:44 - mmengine - INFO - Epoch(train) [85][ 800/5005] lr: 1.0000e-03 eta: 4:16:06 time: 0.1875 data_time: 0.0055 loss: 1.0901 2023/03/17 15:37:03 - mmengine - INFO - Epoch(train) [85][ 900/5005] lr: 1.0000e-03 eta: 4:15:47 time: 0.2053 data_time: 0.0054 loss: 1.0645 2023/03/17 15:37:23 - mmengine - INFO - Epoch(train) [85][1000/5005] lr: 1.0000e-03 eta: 4:15:28 time: 0.1931 data_time: 0.0037 loss: 1.0574 2023/03/17 15:37:42 - mmengine - INFO - Epoch(train) [85][1100/5005] lr: 1.0000e-03 eta: 4:15:08 time: 0.1896 data_time: 0.0044 loss: 1.0247 2023/03/17 15:38:01 - mmengine - INFO - Epoch(train) [85][1200/5005] lr: 1.0000e-03 eta: 4:14:49 time: 0.1897 data_time: 0.0047 loss: 0.9374 2023/03/17 15:38:21 - mmengine - INFO - Epoch(train) [85][1300/5005] lr: 1.0000e-03 eta: 4:14:29 time: 0.1834 data_time: 0.0051 loss: 0.9519 2023/03/17 15:38:41 - mmengine - INFO - Epoch(train) [85][1400/5005] lr: 1.0000e-03 eta: 4:14:10 time: 0.1840 data_time: 0.0050 loss: 0.9894 2023/03/17 15:38:59 - mmengine - INFO - Epoch(train) [85][1500/5005] lr: 1.0000e-03 eta: 4:13:51 time: 0.1876 data_time: 0.0045 loss: 1.0414 2023/03/17 15:39:16 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:39:20 - mmengine - INFO - Epoch(train) [85][1600/5005] lr: 1.0000e-03 eta: 4:13:31 time: 0.2085 data_time: 0.0040 loss: 1.0493 2023/03/17 15:39:41 - mmengine - INFO - Epoch(train) [85][1700/5005] lr: 1.0000e-03 eta: 4:13:12 time: 0.2002 data_time: 0.0042 loss: 0.7880 2023/03/17 15:40:03 - mmengine - INFO - Epoch(train) [85][1800/5005] lr: 1.0000e-03 eta: 4:12:53 time: 0.2170 data_time: 0.0045 loss: 0.9045 2023/03/17 15:40:26 - mmengine - INFO - Epoch(train) [85][1900/5005] lr: 1.0000e-03 eta: 4:12:34 time: 0.2297 data_time: 0.0051 loss: 1.0290 2023/03/17 15:40:48 - mmengine - INFO - Epoch(train) [85][2000/5005] lr: 1.0000e-03 eta: 4:12:16 time: 0.2050 data_time: 0.0040 loss: 0.8853 2023/03/17 15:41:08 - mmengine - INFO - Epoch(train) [85][2100/5005] lr: 1.0000e-03 eta: 4:11:56 time: 0.2169 data_time: 0.0051 loss: 1.0278 2023/03/17 15:41:29 - mmengine - INFO - Epoch(train) [85][2200/5005] lr: 1.0000e-03 eta: 4:11:37 time: 0.1845 data_time: 0.0045 loss: 1.0849 2023/03/17 15:41:49 - mmengine - INFO - Epoch(train) [85][2300/5005] lr: 1.0000e-03 eta: 4:11:18 time: 0.2176 data_time: 0.0045 loss: 0.9809 2023/03/17 15:42:09 - mmengine - INFO - Epoch(train) [85][2400/5005] lr: 1.0000e-03 eta: 4:10:59 time: 0.1924 data_time: 0.0050 loss: 1.0531 2023/03/17 15:42:29 - mmengine - INFO - Epoch(train) [85][2500/5005] lr: 1.0000e-03 eta: 4:10:39 time: 0.2020 data_time: 0.0064 loss: 0.9533 2023/03/17 15:42:46 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:42:50 - mmengine - INFO - Epoch(train) [85][2600/5005] lr: 1.0000e-03 eta: 4:10:20 time: 0.1915 data_time: 0.0043 loss: 0.9781 2023/03/17 15:43:10 - mmengine - INFO - Epoch(train) [85][2700/5005] lr: 1.0000e-03 eta: 4:10:01 time: 0.1967 data_time: 0.0042 loss: 0.9854 2023/03/17 15:43:30 - mmengine - INFO - Epoch(train) [85][2800/5005] lr: 1.0000e-03 eta: 4:09:42 time: 0.2047 data_time: 0.0044 loss: 0.9511 2023/03/17 15:43:50 - mmengine - INFO - Epoch(train) [85][2900/5005] lr: 1.0000e-03 eta: 4:09:22 time: 0.1978 data_time: 0.0050 loss: 0.9897 2023/03/17 15:44:11 - mmengine - INFO - Epoch(train) [85][3000/5005] lr: 1.0000e-03 eta: 4:09:03 time: 0.2009 data_time: 0.0048 loss: 1.0593 2023/03/17 15:44:30 - mmengine - INFO - Epoch(train) [85][3100/5005] lr: 1.0000e-03 eta: 4:08:44 time: 0.1881 data_time: 0.0050 loss: 0.9619 2023/03/17 15:44:50 - mmengine - INFO - Epoch(train) [85][3200/5005] lr: 1.0000e-03 eta: 4:08:24 time: 0.2314 data_time: 0.0052 loss: 0.9947 2023/03/17 15:45:11 - mmengine - INFO - Epoch(train) [85][3300/5005] lr: 1.0000e-03 eta: 4:08:05 time: 0.2294 data_time: 0.0057 loss: 0.9162 2023/03/17 15:45:32 - mmengine - INFO - Epoch(train) [85][3400/5005] lr: 1.0000e-03 eta: 4:07:46 time: 0.2076 data_time: 0.0051 loss: 0.9099 2023/03/17 15:45:53 - mmengine - INFO - Epoch(train) [85][3500/5005] lr: 1.0000e-03 eta: 4:07:27 time: 0.2091 data_time: 0.0052 loss: 0.9686 2023/03/17 15:46:10 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:46:14 - mmengine - INFO - Epoch(train) [85][3600/5005] lr: 1.0000e-03 eta: 4:07:08 time: 0.2143 data_time: 0.0054 loss: 0.9785 2023/03/17 15:46:36 - mmengine - INFO - Epoch(train) [85][3700/5005] lr: 1.0000e-03 eta: 4:06:49 time: 0.2040 data_time: 0.0036 loss: 0.9614 2023/03/17 15:46:57 - mmengine - INFO - Epoch(train) [85][3800/5005] lr: 1.0000e-03 eta: 4:06:30 time: 0.2108 data_time: 0.0048 loss: 0.9950 2023/03/17 15:47:17 - mmengine - INFO - Epoch(train) [85][3900/5005] lr: 1.0000e-03 eta: 4:06:10 time: 0.1955 data_time: 0.0049 loss: 1.2009 2023/03/17 15:47:38 - mmengine - INFO - Epoch(train) [85][4000/5005] lr: 1.0000e-03 eta: 4:05:51 time: 0.2077 data_time: 0.0054 loss: 0.9657 2023/03/17 15:48:00 - mmengine - INFO - Epoch(train) [85][4100/5005] lr: 1.0000e-03 eta: 4:05:32 time: 0.2400 data_time: 0.0041 loss: 0.8845 2023/03/17 15:48:20 - mmengine - INFO - Epoch(train) [85][4200/5005] lr: 1.0000e-03 eta: 4:05:13 time: 0.2056 data_time: 0.0052 loss: 0.8063 2023/03/17 15:48:40 - mmengine - INFO - Epoch(train) [85][4300/5005] lr: 1.0000e-03 eta: 4:04:54 time: 0.1887 data_time: 0.0041 loss: 0.9346 2023/03/17 15:49:01 - mmengine - INFO - Epoch(train) [85][4400/5005] lr: 1.0000e-03 eta: 4:04:34 time: 0.1995 data_time: 0.0039 loss: 1.0373 2023/03/17 15:49:21 - mmengine - INFO - Epoch(train) [85][4500/5005] lr: 1.0000e-03 eta: 4:04:15 time: 0.2008 data_time: 0.0041 loss: 0.9091 2023/03/17 15:49:37 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:49:41 - mmengine - INFO - Epoch(train) [85][4600/5005] lr: 1.0000e-03 eta: 4:03:56 time: 0.1930 data_time: 0.0042 loss: 0.9116 2023/03/17 15:50:02 - mmengine - INFO - Epoch(train) [85][4700/5005] lr: 1.0000e-03 eta: 4:03:37 time: 0.2191 data_time: 0.0041 loss: 1.0698 2023/03/17 15:50:22 - mmengine - INFO - Epoch(train) [85][4800/5005] lr: 1.0000e-03 eta: 4:03:17 time: 0.2017 data_time: 0.0047 loss: 1.1096 2023/03/17 15:50:42 - mmengine - INFO - Epoch(train) [85][4900/5005] lr: 1.0000e-03 eta: 4:02:58 time: 0.2045 data_time: 0.0048 loss: 1.0546 2023/03/17 15:51:04 - mmengine - INFO - Epoch(train) [85][5000/5005] lr: 1.0000e-03 eta: 4:02:39 time: 0.2243 data_time: 0.0055 loss: 1.0505 2023/03/17 15:51:05 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:51:05 - mmengine - INFO - Saving checkpoint at 85 epochs 2023/03/17 15:51:12 - mmengine - INFO - Epoch(val) [85][100/196] eta: 0:00:05 time: 0.0457 data_time: 0.0009 2023/03/17 15:51:39 - mmengine - INFO - Epoch(val) [85][196/196] accuracy/top1: 75.6940 accuracy/top5: 92.8260data_time: 0.0418 time: 0.0721 2023/03/17 15:51:58 - mmengine - INFO - Epoch(train) [86][ 100/5005] lr: 1.0000e-03 eta: 4:02:19 time: 0.1881 data_time: 0.0050 loss: 1.0639 2023/03/17 15:52:21 - mmengine - INFO - Epoch(train) [86][ 200/5005] lr: 1.0000e-03 eta: 4:02:00 time: 0.2237 data_time: 0.0046 loss: 1.2314 2023/03/17 15:52:40 - mmengine - INFO - Epoch(train) [86][ 300/5005] lr: 1.0000e-03 eta: 4:01:40 time: 0.1879 data_time: 0.0043 loss: 1.0123 2023/03/17 15:53:00 - mmengine - INFO - Epoch(train) [86][ 400/5005] lr: 1.0000e-03 eta: 4:01:21 time: 0.1927 data_time: 0.0048 loss: 0.9836 2023/03/17 15:53:20 - mmengine - INFO - Epoch(train) [86][ 500/5005] lr: 1.0000e-03 eta: 4:01:02 time: 0.1903 data_time: 0.0044 loss: 0.7380 2023/03/17 15:53:37 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:53:44 - mmengine - INFO - Epoch(train) [86][ 600/5005] lr: 1.0000e-03 eta: 4:00:43 time: 0.2606 data_time: 0.0043 loss: 1.0284 2023/03/17 15:54:03 - mmengine - INFO - Epoch(train) [86][ 700/5005] lr: 1.0000e-03 eta: 4:00:24 time: 0.2175 data_time: 0.0044 loss: 0.8056 2023/03/17 15:54:22 - mmengine - INFO - Epoch(train) [86][ 800/5005] lr: 1.0000e-03 eta: 4:00:04 time: 0.1784 data_time: 0.0047 loss: 1.0222 2023/03/17 15:54:43 - mmengine - INFO - Epoch(train) [86][ 900/5005] lr: 1.0000e-03 eta: 3:59:45 time: 0.2260 data_time: 0.0050 loss: 0.8535 2023/03/17 15:55:03 - mmengine - INFO - Epoch(train) [86][1000/5005] lr: 1.0000e-03 eta: 3:59:26 time: 0.2020 data_time: 0.0044 loss: 0.9634 2023/03/17 15:55:23 - mmengine - INFO - Epoch(train) [86][1100/5005] lr: 1.0000e-03 eta: 3:59:06 time: 0.1856 data_time: 0.0044 loss: 0.8001 2023/03/17 15:55:43 - mmengine - INFO - Epoch(train) [86][1200/5005] lr: 1.0000e-03 eta: 3:58:47 time: 0.1961 data_time: 0.0045 loss: 1.0161 2023/03/17 15:56:02 - mmengine - INFO - Epoch(train) [86][1300/5005] lr: 1.0000e-03 eta: 3:58:28 time: 0.1999 data_time: 0.0049 loss: 0.9718 2023/03/17 15:56:22 - mmengine - INFO - Epoch(train) [86][1400/5005] lr: 1.0000e-03 eta: 3:58:08 time: 0.1947 data_time: 0.0049 loss: 0.9258 2023/03/17 15:56:42 - mmengine - INFO - Epoch(train) [86][1500/5005] lr: 1.0000e-03 eta: 3:57:49 time: 0.1927 data_time: 0.0045 loss: 1.1556 2023/03/17 15:56:57 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 15:57:01 - mmengine - INFO - Epoch(train) [86][1600/5005] lr: 1.0000e-03 eta: 3:57:29 time: 0.1904 data_time: 0.0047 loss: 0.8374 2023/03/17 15:57:21 - mmengine - INFO - Epoch(train) [86][1700/5005] lr: 1.0000e-03 eta: 3:57:10 time: 0.2102 data_time: 0.0045 loss: 0.9160 2023/03/17 15:57:40 - mmengine - INFO - Epoch(train) [86][1800/5005] lr: 1.0000e-03 eta: 3:56:51 time: 0.1876 data_time: 0.0049 loss: 1.1671 2023/03/17 15:58:00 - mmengine - INFO - Epoch(train) [86][1900/5005] lr: 1.0000e-03 eta: 3:56:31 time: 0.1815 data_time: 0.0045 loss: 0.9771 2023/03/17 15:58:19 - mmengine - INFO - Epoch(train) [86][2000/5005] lr: 1.0000e-03 eta: 3:56:12 time: 0.2262 data_time: 0.0039 loss: 0.8886 2023/03/17 15:58:42 - mmengine - INFO - Epoch(train) [86][2100/5005] lr: 1.0000e-03 eta: 3:55:53 time: 0.2452 data_time: 0.0036 loss: 0.9573 2023/03/17 15:59:01 - mmengine - INFO - Epoch(train) [86][2200/5005] lr: 1.0000e-03 eta: 3:55:34 time: 0.1842 data_time: 0.0040 loss: 0.9272 2023/03/17 15:59:20 - mmengine - INFO - Epoch(train) [86][2300/5005] lr: 1.0000e-03 eta: 3:55:14 time: 0.1766 data_time: 0.0045 loss: 0.9050 2023/03/17 15:59:38 - mmengine - INFO - Epoch(train) [86][2400/5005] lr: 1.0000e-03 eta: 3:54:54 time: 0.1948 data_time: 0.0045 loss: 1.0521 2023/03/17 15:59:57 - mmengine - INFO - Epoch(train) [86][2500/5005] lr: 1.0000e-03 eta: 3:54:35 time: 0.1920 data_time: 0.0045 loss: 0.9589 2023/03/17 16:00:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:00:18 - mmengine - INFO - Epoch(train) [86][2600/5005] lr: 1.0000e-03 eta: 3:54:16 time: 0.1884 data_time: 0.0045 loss: 0.9663 2023/03/17 16:00:38 - mmengine - INFO - Epoch(train) [86][2700/5005] lr: 1.0000e-03 eta: 3:53:56 time: 0.1950 data_time: 0.0048 loss: 0.9325 2023/03/17 16:00:57 - mmengine - INFO - Epoch(train) [86][2800/5005] lr: 1.0000e-03 eta: 3:53:37 time: 0.1867 data_time: 0.0049 loss: 1.1367 2023/03/17 16:01:18 - mmengine - INFO - Epoch(train) [86][2900/5005] lr: 1.0000e-03 eta: 3:53:18 time: 0.2154 data_time: 0.0043 loss: 1.0155 2023/03/17 16:01:38 - mmengine - INFO - Epoch(train) [86][3000/5005] lr: 1.0000e-03 eta: 3:52:58 time: 0.1814 data_time: 0.0043 loss: 0.8382 2023/03/17 16:01:57 - mmengine - INFO - Epoch(train) [86][3100/5005] lr: 1.0000e-03 eta: 3:52:39 time: 0.1932 data_time: 0.0046 loss: 0.9604 2023/03/17 16:02:16 - mmengine - INFO - Epoch(train) [86][3200/5005] lr: 1.0000e-03 eta: 3:52:19 time: 0.1944 data_time: 0.0048 loss: 0.9706 2023/03/17 16:02:38 - mmengine - INFO - Epoch(train) [86][3300/5005] lr: 1.0000e-03 eta: 3:52:00 time: 0.2176 data_time: 0.0047 loss: 1.0633 2023/03/17 16:02:59 - mmengine - INFO - Epoch(train) [86][3400/5005] lr: 1.0000e-03 eta: 3:51:41 time: 0.2007 data_time: 0.0042 loss: 1.1443 2023/03/17 16:03:19 - mmengine - INFO - Epoch(train) [86][3500/5005] lr: 1.0000e-03 eta: 3:51:22 time: 0.2493 data_time: 0.0048 loss: 1.0340 2023/03/17 16:03:36 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:03:40 - mmengine - INFO - Epoch(train) [86][3600/5005] lr: 1.0000e-03 eta: 3:51:03 time: 0.1819 data_time: 0.0046 loss: 1.0637 2023/03/17 16:03:59 - mmengine - INFO - Epoch(train) [86][3700/5005] lr: 1.0000e-03 eta: 3:50:43 time: 0.1993 data_time: 0.0046 loss: 0.8840 2023/03/17 16:04:20 - mmengine - INFO - Epoch(train) [86][3800/5005] lr: 1.0000e-03 eta: 3:50:24 time: 0.1920 data_time: 0.0045 loss: 1.0154 2023/03/17 16:04:38 - mmengine - INFO - Epoch(train) [86][3900/5005] lr: 1.0000e-03 eta: 3:50:04 time: 0.1995 data_time: 0.0041 loss: 0.9709 2023/03/17 16:04:57 - mmengine - INFO - Epoch(train) [86][4000/5005] lr: 1.0000e-03 eta: 3:49:45 time: 0.1822 data_time: 0.0053 loss: 0.9103 2023/03/17 16:05:16 - mmengine - INFO - Epoch(train) [86][4100/5005] lr: 1.0000e-03 eta: 3:49:25 time: 0.1898 data_time: 0.0049 loss: 0.9527 2023/03/17 16:05:35 - mmengine - INFO - Epoch(train) [86][4200/5005] lr: 1.0000e-03 eta: 3:49:06 time: 0.1987 data_time: 0.0050 loss: 1.0846 2023/03/17 16:05:54 - mmengine - INFO - Epoch(train) [86][4300/5005] lr: 1.0000e-03 eta: 3:48:47 time: 0.1769 data_time: 0.0054 loss: 0.9918 2023/03/17 16:06:14 - mmengine - INFO - Epoch(train) [86][4400/5005] lr: 1.0000e-03 eta: 3:48:27 time: 0.1929 data_time: 0.0051 loss: 0.9650 2023/03/17 16:06:32 - mmengine - INFO - Epoch(train) [86][4500/5005] lr: 1.0000e-03 eta: 3:48:07 time: 0.1699 data_time: 0.0042 loss: 1.1024 2023/03/17 16:06:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:06:50 - mmengine - INFO - Epoch(train) [86][4600/5005] lr: 1.0000e-03 eta: 3:47:48 time: 0.1899 data_time: 0.0050 loss: 1.0488 2023/03/17 16:07:09 - mmengine - INFO - Epoch(train) [86][4700/5005] lr: 1.0000e-03 eta: 3:47:28 time: 0.1912 data_time: 0.0048 loss: 0.9531 2023/03/17 16:07:31 - mmengine - INFO - Epoch(train) [86][4800/5005] lr: 1.0000e-03 eta: 3:47:09 time: 0.2021 data_time: 0.0048 loss: 1.1461 2023/03/17 16:07:52 - mmengine - INFO - Epoch(train) [86][4900/5005] lr: 1.0000e-03 eta: 3:46:50 time: 0.2503 data_time: 0.0050 loss: 1.1526 2023/03/17 16:08:16 - mmengine - INFO - Epoch(train) [86][5000/5005] lr: 1.0000e-03 eta: 3:46:32 time: 0.2030 data_time: 0.0061 loss: 1.0182 2023/03/17 16:08:17 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:08:18 - mmengine - INFO - Saving checkpoint at 86 epochs 2023/03/17 16:08:24 - mmengine - INFO - Epoch(val) [86][100/196] eta: 0:00:05 time: 0.0422 data_time: 0.0009 2023/03/17 16:08:52 - mmengine - INFO - Epoch(val) [86][196/196] accuracy/top1: 75.7640 accuracy/top5: 92.8300data_time: 0.0315 time: 0.0624 2023/03/17 16:09:13 - mmengine - INFO - Epoch(train) [87][ 100/5005] lr: 1.0000e-03 eta: 3:46:11 time: 0.1874 data_time: 0.0052 loss: 0.8678 2023/03/17 16:09:32 - mmengine - INFO - Epoch(train) [87][ 200/5005] lr: 1.0000e-03 eta: 3:45:52 time: 0.1914 data_time: 0.0051 loss: 0.8652 2023/03/17 16:09:51 - mmengine - INFO - Epoch(train) [87][ 300/5005] lr: 1.0000e-03 eta: 3:45:32 time: 0.1884 data_time: 0.0052 loss: 0.9858 2023/03/17 16:10:11 - mmengine - INFO - Epoch(train) [87][ 400/5005] lr: 1.0000e-03 eta: 3:45:13 time: 0.1904 data_time: 0.0044 loss: 1.0249 2023/03/17 16:10:31 - mmengine - INFO - Epoch(train) [87][ 500/5005] lr: 1.0000e-03 eta: 3:44:54 time: 0.1899 data_time: 0.0051 loss: 1.0950 2023/03/17 16:10:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:10:50 - mmengine - INFO - Epoch(train) [87][ 600/5005] lr: 1.0000e-03 eta: 3:44:34 time: 0.1858 data_time: 0.0052 loss: 0.8380 2023/03/17 16:11:11 - mmengine - INFO - Epoch(train) [87][ 700/5005] lr: 1.0000e-03 eta: 3:44:15 time: 0.2057 data_time: 0.0047 loss: 1.0306 2023/03/17 16:11:33 - mmengine - INFO - Epoch(train) [87][ 800/5005] lr: 1.0000e-03 eta: 3:43:56 time: 0.2283 data_time: 0.0049 loss: 1.0640 2023/03/17 16:11:54 - mmengine - INFO - Epoch(train) [87][ 900/5005] lr: 1.0000e-03 eta: 3:43:37 time: 0.1858 data_time: 0.0043 loss: 1.0046 2023/03/17 16:12:14 - mmengine - INFO - Epoch(train) [87][1000/5005] lr: 1.0000e-03 eta: 3:43:18 time: 0.1735 data_time: 0.0042 loss: 0.9600 2023/03/17 16:12:32 - mmengine - INFO - Epoch(train) [87][1100/5005] lr: 1.0000e-03 eta: 3:42:58 time: 0.1808 data_time: 0.0043 loss: 1.0761 2023/03/17 16:12:51 - mmengine - INFO - Epoch(train) [87][1200/5005] lr: 1.0000e-03 eta: 3:42:38 time: 0.1872 data_time: 0.0044 loss: 0.8560 2023/03/17 16:13:10 - mmengine - INFO - Epoch(train) [87][1300/5005] lr: 1.0000e-03 eta: 3:42:19 time: 0.2080 data_time: 0.0039 loss: 0.9230 2023/03/17 16:13:29 - mmengine - INFO - Epoch(train) [87][1400/5005] lr: 1.0000e-03 eta: 3:41:59 time: 0.1777 data_time: 0.0054 loss: 0.9430 2023/03/17 16:13:48 - mmengine - INFO - Epoch(train) [87][1500/5005] lr: 1.0000e-03 eta: 3:41:40 time: 0.1865 data_time: 0.0052 loss: 0.9256 2023/03/17 16:14:01 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:14:07 - mmengine - INFO - Epoch(train) [87][1600/5005] lr: 1.0000e-03 eta: 3:41:20 time: 0.2007 data_time: 0.0042 loss: 1.1541 2023/03/17 16:14:30 - mmengine - INFO - Epoch(train) [87][1700/5005] lr: 1.0000e-03 eta: 3:41:01 time: 0.2414 data_time: 0.0056 loss: 1.2032 2023/03/17 16:14:54 - mmengine - INFO - Epoch(train) [87][1800/5005] lr: 1.0000e-03 eta: 3:40:43 time: 0.2435 data_time: 0.0051 loss: 1.1401 2023/03/17 16:15:17 - mmengine - INFO - Epoch(train) [87][1900/5005] lr: 1.0000e-03 eta: 3:40:24 time: 0.2086 data_time: 0.0045 loss: 1.1138 2023/03/17 16:15:37 - mmengine - INFO - Epoch(train) [87][2000/5005] lr: 1.0000e-03 eta: 3:40:05 time: 0.1863 data_time: 0.0047 loss: 1.0125 2023/03/17 16:15:59 - mmengine - INFO - Epoch(train) [87][2100/5005] lr: 1.0000e-03 eta: 3:39:46 time: 0.2491 data_time: 0.0052 loss: 0.9417 2023/03/17 16:16:21 - mmengine - INFO - Epoch(train) [87][2200/5005] lr: 1.0000e-03 eta: 3:39:27 time: 0.1820 data_time: 0.0051 loss: 1.1165 2023/03/17 16:16:40 - mmengine - INFO - Epoch(train) [87][2300/5005] lr: 1.0000e-03 eta: 3:39:07 time: 0.1918 data_time: 0.0050 loss: 1.0365 2023/03/17 16:16:59 - mmengine - INFO - Epoch(train) [87][2400/5005] lr: 1.0000e-03 eta: 3:38:48 time: 0.1984 data_time: 0.0045 loss: 1.1649 2023/03/17 16:17:18 - mmengine - INFO - Epoch(train) [87][2500/5005] lr: 1.0000e-03 eta: 3:38:28 time: 0.1892 data_time: 0.0047 loss: 1.0576 2023/03/17 16:17:32 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:17:37 - mmengine - INFO - Epoch(train) [87][2600/5005] lr: 1.0000e-03 eta: 3:38:09 time: 0.1830 data_time: 0.0051 loss: 0.8761 2023/03/17 16:17:56 - mmengine - INFO - Epoch(train) [87][2700/5005] lr: 1.0000e-03 eta: 3:37:49 time: 0.1847 data_time: 0.0048 loss: 0.8383 2023/03/17 16:18:15 - mmengine - INFO - Epoch(train) [87][2800/5005] lr: 1.0000e-03 eta: 3:37:30 time: 0.1898 data_time: 0.0052 loss: 1.1349 2023/03/17 16:18:34 - mmengine - INFO - Epoch(train) [87][2900/5005] lr: 1.0000e-03 eta: 3:37:10 time: 0.1904 data_time: 0.0049 loss: 1.1334 2023/03/17 16:18:53 - mmengine - INFO - Epoch(train) [87][3000/5005] lr: 1.0000e-03 eta: 3:36:51 time: 0.1883 data_time: 0.0053 loss: 1.0134 2023/03/17 16:19:13 - mmengine - INFO - Epoch(train) [87][3100/5005] lr: 1.0000e-03 eta: 3:36:31 time: 0.1869 data_time: 0.0048 loss: 0.8262 2023/03/17 16:19:31 - mmengine - INFO - Epoch(train) [87][3200/5005] lr: 1.0000e-03 eta: 3:36:12 time: 0.1841 data_time: 0.0049 loss: 1.0020 2023/03/17 16:19:51 - mmengine - INFO - Epoch(train) [87][3300/5005] lr: 1.0000e-03 eta: 3:35:52 time: 0.1982 data_time: 0.0050 loss: 0.9939 2023/03/17 16:20:10 - mmengine - INFO - Epoch(train) [87][3400/5005] lr: 1.0000e-03 eta: 3:35:33 time: 0.2086 data_time: 0.0050 loss: 1.0784 2023/03/17 16:20:30 - mmengine - INFO - Epoch(train) [87][3500/5005] lr: 1.0000e-03 eta: 3:35:13 time: 0.1844 data_time: 0.0043 loss: 0.9184 2023/03/17 16:20:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:20:49 - mmengine - INFO - Epoch(train) [87][3600/5005] lr: 1.0000e-03 eta: 3:34:54 time: 0.1812 data_time: 0.0048 loss: 0.9052 2023/03/17 16:21:07 - mmengine - INFO - Epoch(train) [87][3700/5005] lr: 1.0000e-03 eta: 3:34:34 time: 0.1915 data_time: 0.0049 loss: 1.1526 2023/03/17 16:21:27 - mmengine - INFO - Epoch(train) [87][3800/5005] lr: 1.0000e-03 eta: 3:34:15 time: 0.1936 data_time: 0.0045 loss: 1.0113 2023/03/17 16:21:46 - mmengine - INFO - Epoch(train) [87][3900/5005] lr: 1.0000e-03 eta: 3:33:56 time: 0.1802 data_time: 0.0046 loss: 0.9893 2023/03/17 16:22:04 - mmengine - INFO - Epoch(train) [87][4000/5005] lr: 1.0000e-03 eta: 3:33:36 time: 0.1862 data_time: 0.0046 loss: 0.9692 2023/03/17 16:22:23 - mmengine - INFO - Epoch(train) [87][4100/5005] lr: 1.0000e-03 eta: 3:33:16 time: 0.1845 data_time: 0.0050 loss: 1.0150 2023/03/17 16:22:44 - mmengine - INFO - Epoch(train) [87][4200/5005] lr: 1.0000e-03 eta: 3:32:57 time: 0.2414 data_time: 0.0056 loss: 0.9892 2023/03/17 16:23:04 - mmengine - INFO - Epoch(train) [87][4300/5005] lr: 1.0000e-03 eta: 3:32:38 time: 0.1880 data_time: 0.0044 loss: 1.1612 2023/03/17 16:23:25 - mmengine - INFO - Epoch(train) [87][4400/5005] lr: 1.0000e-03 eta: 3:32:19 time: 0.2558 data_time: 0.0050 loss: 1.1127 2023/03/17 16:23:45 - mmengine - INFO - Epoch(train) [87][4500/5005] lr: 1.0000e-03 eta: 3:31:59 time: 0.1934 data_time: 0.0039 loss: 0.9837 2023/03/17 16:23:59 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:24:05 - mmengine - INFO - Epoch(train) [87][4600/5005] lr: 1.0000e-03 eta: 3:31:40 time: 0.1928 data_time: 0.0043 loss: 1.0157 2023/03/17 16:24:25 - mmengine - INFO - Epoch(train) [87][4700/5005] lr: 1.0000e-03 eta: 3:31:21 time: 0.2029 data_time: 0.0041 loss: 1.0443 2023/03/17 16:24:45 - mmengine - INFO - Epoch(train) [87][4800/5005] lr: 1.0000e-03 eta: 3:31:01 time: 0.1959 data_time: 0.0041 loss: 0.9732 2023/03/17 16:25:05 - mmengine - INFO - Epoch(train) [87][4900/5005] lr: 1.0000e-03 eta: 3:30:42 time: 0.1909 data_time: 0.0045 loss: 1.0005 2023/03/17 16:25:26 - mmengine - INFO - Epoch(train) [87][5000/5005] lr: 1.0000e-03 eta: 3:30:23 time: 0.1945 data_time: 0.0056 loss: 0.9889 2023/03/17 16:25:27 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:25:28 - mmengine - INFO - Saving checkpoint at 87 epochs 2023/03/17 16:25:35 - mmengine - INFO - Epoch(val) [87][100/196] eta: 0:00:05 time: 0.0590 data_time: 0.0009 2023/03/17 16:26:02 - mmengine - INFO - Epoch(val) [87][196/196] accuracy/top1: 75.6780 accuracy/top5: 92.7480data_time: 0.0294 time: 0.0592 2023/03/17 16:26:25 - mmengine - INFO - Epoch(train) [88][ 100/5005] lr: 1.0000e-03 eta: 3:30:03 time: 0.2090 data_time: 0.0054 loss: 0.9792 2023/03/17 16:26:46 - mmengine - INFO - Epoch(train) [88][ 200/5005] lr: 1.0000e-03 eta: 3:29:44 time: 0.2070 data_time: 0.0047 loss: 1.0945 2023/03/17 16:27:07 - mmengine - INFO - Epoch(train) [88][ 300/5005] lr: 1.0000e-03 eta: 3:29:25 time: 0.2012 data_time: 0.0041 loss: 1.0944 2023/03/17 16:27:27 - mmengine - INFO - Epoch(train) [88][ 400/5005] lr: 1.0000e-03 eta: 3:29:05 time: 0.1959 data_time: 0.0043 loss: 1.0090 2023/03/17 16:27:47 - mmengine - INFO - Epoch(train) [88][ 500/5005] lr: 1.0000e-03 eta: 3:28:46 time: 0.2165 data_time: 0.0047 loss: 0.9122 2023/03/17 16:28:02 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:28:09 - mmengine - INFO - Epoch(train) [88][ 600/5005] lr: 1.0000e-03 eta: 3:28:27 time: 0.2035 data_time: 0.0051 loss: 0.7215 2023/03/17 16:28:30 - mmengine - INFO - Epoch(train) [88][ 700/5005] lr: 1.0000e-03 eta: 3:28:08 time: 0.2128 data_time: 0.0059 loss: 1.0342 2023/03/17 16:28:51 - mmengine - INFO - Epoch(train) [88][ 800/5005] lr: 1.0000e-03 eta: 3:27:49 time: 0.2248 data_time: 0.0054 loss: 1.0486 2023/03/17 16:29:12 - mmengine - INFO - Epoch(train) [88][ 900/5005] lr: 1.0000e-03 eta: 3:27:29 time: 0.2164 data_time: 0.0049 loss: 1.1414 2023/03/17 16:29:33 - mmengine - INFO - Epoch(train) [88][1000/5005] lr: 1.0000e-03 eta: 3:27:10 time: 0.1952 data_time: 0.0051 loss: 0.9065 2023/03/17 16:29:53 - mmengine - INFO - Epoch(train) [88][1100/5005] lr: 1.0000e-03 eta: 3:26:51 time: 0.2206 data_time: 0.0039 loss: 0.9112 2023/03/17 16:30:12 - mmengine - INFO - Epoch(train) [88][1200/5005] lr: 1.0000e-03 eta: 3:26:31 time: 0.1898 data_time: 0.0051 loss: 0.9774 2023/03/17 16:30:32 - mmengine - INFO - Epoch(train) [88][1300/5005] lr: 1.0000e-03 eta: 3:26:12 time: 0.1942 data_time: 0.0045 loss: 1.0527 2023/03/17 16:30:51 - mmengine - INFO - Epoch(train) [88][1400/5005] lr: 1.0000e-03 eta: 3:25:53 time: 0.1878 data_time: 0.0049 loss: 1.0150 2023/03/17 16:31:11 - mmengine - INFO - Epoch(train) [88][1500/5005] lr: 1.0000e-03 eta: 3:25:33 time: 0.1900 data_time: 0.0045 loss: 1.1362 2023/03/17 16:31:24 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:31:31 - mmengine - INFO - Epoch(train) [88][1600/5005] lr: 1.0000e-03 eta: 3:25:14 time: 0.2147 data_time: 0.0051 loss: 1.0802 2023/03/17 16:31:54 - mmengine - INFO - Epoch(train) [88][1700/5005] lr: 1.0000e-03 eta: 3:24:55 time: 0.2173 data_time: 0.0053 loss: 0.7672 2023/03/17 16:32:16 - mmengine - INFO - Epoch(train) [88][1800/5005] lr: 1.0000e-03 eta: 3:24:36 time: 0.2201 data_time: 0.0054 loss: 0.9569 2023/03/17 16:32:36 - mmengine - INFO - Epoch(train) [88][1900/5005] lr: 1.0000e-03 eta: 3:24:16 time: 0.1989 data_time: 0.0051 loss: 0.8716 2023/03/17 16:32:56 - mmengine - INFO - Epoch(train) [88][2000/5005] lr: 1.0000e-03 eta: 3:23:57 time: 0.2004 data_time: 0.0041 loss: 0.8887 2023/03/17 16:33:17 - mmengine - INFO - Epoch(train) [88][2100/5005] lr: 1.0000e-03 eta: 3:23:38 time: 0.1905 data_time: 0.0048 loss: 1.0471 2023/03/17 16:33:38 - mmengine - INFO - Epoch(train) [88][2200/5005] lr: 1.0000e-03 eta: 3:23:19 time: 0.2018 data_time: 0.0051 loss: 0.9724 2023/03/17 16:33:58 - mmengine - INFO - Epoch(train) [88][2300/5005] lr: 1.0000e-03 eta: 3:22:59 time: 0.2027 data_time: 0.0048 loss: 0.8044 2023/03/17 16:34:18 - mmengine - INFO - Epoch(train) [88][2400/5005] lr: 1.0000e-03 eta: 3:22:40 time: 0.2018 data_time: 0.0056 loss: 0.9665 2023/03/17 16:34:38 - mmengine - INFO - Epoch(train) [88][2500/5005] lr: 1.0000e-03 eta: 3:22:21 time: 0.1816 data_time: 0.0053 loss: 0.9967 2023/03/17 16:34:52 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:34:59 - mmengine - INFO - Epoch(train) [88][2600/5005] lr: 1.0000e-03 eta: 3:22:01 time: 0.2009 data_time: 0.0049 loss: 0.9441 2023/03/17 16:35:20 - mmengine - INFO - Epoch(train) [88][2700/5005] lr: 1.0000e-03 eta: 3:21:42 time: 0.2084 data_time: 0.0054 loss: 0.9083 2023/03/17 16:35:40 - mmengine - INFO - Epoch(train) [88][2800/5005] lr: 1.0000e-03 eta: 3:21:23 time: 0.1968 data_time: 0.0054 loss: 1.0955 2023/03/17 16:36:00 - mmengine - INFO - Epoch(train) [88][2900/5005] lr: 1.0000e-03 eta: 3:21:04 time: 0.1907 data_time: 0.0061 loss: 1.0809 2023/03/17 16:36:20 - mmengine - INFO - Epoch(train) [88][3000/5005] lr: 1.0000e-03 eta: 3:20:44 time: 0.1958 data_time: 0.0052 loss: 0.9687 2023/03/17 16:36:40 - mmengine - INFO - Epoch(train) [88][3100/5005] lr: 1.0000e-03 eta: 3:20:25 time: 0.1901 data_time: 0.0053 loss: 0.9313 2023/03/17 16:37:00 - mmengine - INFO - Epoch(train) [88][3200/5005] lr: 1.0000e-03 eta: 3:20:06 time: 0.2007 data_time: 0.0053 loss: 1.0734 2023/03/17 16:37:19 - mmengine - INFO - Epoch(train) [88][3300/5005] lr: 1.0000e-03 eta: 3:19:46 time: 0.1838 data_time: 0.0050 loss: 1.0035 2023/03/17 16:37:38 - mmengine - INFO - Epoch(train) [88][3400/5005] lr: 1.0000e-03 eta: 3:19:26 time: 0.1895 data_time: 0.0048 loss: 0.9721 2023/03/17 16:37:57 - mmengine - INFO - Epoch(train) [88][3500/5005] lr: 1.0000e-03 eta: 3:19:07 time: 0.1901 data_time: 0.0052 loss: 1.0641 2023/03/17 16:38:09 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:38:16 - mmengine - INFO - Epoch(train) [88][3600/5005] lr: 1.0000e-03 eta: 3:18:47 time: 0.1897 data_time: 0.0052 loss: 1.1124 2023/03/17 16:38:35 - mmengine - INFO - Epoch(train) [88][3700/5005] lr: 1.0000e-03 eta: 3:18:28 time: 0.2089 data_time: 0.0046 loss: 1.1088 2023/03/17 16:38:56 - mmengine - INFO - Epoch(train) [88][3800/5005] lr: 1.0000e-03 eta: 3:18:09 time: 0.1945 data_time: 0.0047 loss: 0.8691 2023/03/17 16:39:16 - mmengine - INFO - Epoch(train) [88][3900/5005] lr: 1.0000e-03 eta: 3:17:49 time: 0.1994 data_time: 0.0047 loss: 0.8715 2023/03/17 16:39:35 - mmengine - INFO - Epoch(train) [88][4000/5005] lr: 1.0000e-03 eta: 3:17:30 time: 0.1915 data_time: 0.0046 loss: 1.1223 2023/03/17 16:39:55 - mmengine - INFO - Epoch(train) [88][4100/5005] lr: 1.0000e-03 eta: 3:17:11 time: 0.1941 data_time: 0.0045 loss: 0.9921 2023/03/17 16:40:15 - mmengine - INFO - Epoch(train) [88][4200/5005] lr: 1.0000e-03 eta: 3:16:51 time: 0.1934 data_time: 0.0057 loss: 1.0496 2023/03/17 16:40:34 - mmengine - INFO - Epoch(train) [88][4300/5005] lr: 1.0000e-03 eta: 3:16:32 time: 0.2015 data_time: 0.0053 loss: 0.9762 2023/03/17 16:40:55 - mmengine - INFO - Epoch(train) [88][4400/5005] lr: 1.0000e-03 eta: 3:16:13 time: 0.1991 data_time: 0.0047 loss: 0.8753 2023/03/17 16:41:16 - mmengine - INFO - Epoch(train) [88][4500/5005] lr: 1.0000e-03 eta: 3:15:53 time: 0.2086 data_time: 0.0053 loss: 0.8831 2023/03/17 16:41:30 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:41:37 - mmengine - INFO - Epoch(train) [88][4600/5005] lr: 1.0000e-03 eta: 3:15:34 time: 0.2035 data_time: 0.0040 loss: 0.9167 2023/03/17 16:41:57 - mmengine - INFO - Epoch(train) [88][4700/5005] lr: 1.0000e-03 eta: 3:15:15 time: 0.1984 data_time: 0.0041 loss: 0.9014 2023/03/17 16:42:17 - mmengine - INFO - Epoch(train) [88][4800/5005] lr: 1.0000e-03 eta: 3:14:55 time: 0.2172 data_time: 0.0045 loss: 0.9560 2023/03/17 16:42:39 - mmengine - INFO - Epoch(train) [88][4900/5005] lr: 1.0000e-03 eta: 3:14:36 time: 0.2009 data_time: 0.0050 loss: 1.0099 2023/03/17 16:42:59 - mmengine - INFO - Epoch(train) [88][5000/5005] lr: 1.0000e-03 eta: 3:14:17 time: 0.2172 data_time: 0.0059 loss: 1.1001 2023/03/17 16:43:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:43:01 - mmengine - INFO - Saving checkpoint at 88 epochs 2023/03/17 16:43:09 - mmengine - INFO - Epoch(val) [88][100/196] eta: 0:00:07 time: 0.0956 data_time: 0.0009 2023/03/17 16:43:35 - mmengine - INFO - Epoch(val) [88][196/196] accuracy/top1: 75.6260 accuracy/top5: 92.7660data_time: 0.0286 time: 0.0592 2023/03/17 16:43:57 - mmengine - INFO - Epoch(train) [89][ 100/5005] lr: 1.0000e-03 eta: 3:13:57 time: 0.2425 data_time: 0.0048 loss: 0.7526 2023/03/17 16:44:17 - mmengine - INFO - Epoch(train) [89][ 200/5005] lr: 1.0000e-03 eta: 3:13:38 time: 0.1930 data_time: 0.0043 loss: 1.0459 2023/03/17 16:44:41 - mmengine - INFO - Epoch(train) [89][ 300/5005] lr: 1.0000e-03 eta: 3:13:19 time: 0.2305 data_time: 0.0041 loss: 1.0188 2023/03/17 16:45:01 - mmengine - INFO - Epoch(train) [89][ 400/5005] lr: 1.0000e-03 eta: 3:12:59 time: 0.1795 data_time: 0.0043 loss: 0.8952 2023/03/17 16:45:19 - mmengine - INFO - Epoch(train) [89][ 500/5005] lr: 1.0000e-03 eta: 3:12:40 time: 0.1752 data_time: 0.0052 loss: 0.9551 2023/03/17 16:45:30 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:45:37 - mmengine - INFO - Epoch(train) [89][ 600/5005] lr: 1.0000e-03 eta: 3:12:20 time: 0.1729 data_time: 0.0049 loss: 0.9381 2023/03/17 16:45:55 - mmengine - INFO - Epoch(train) [89][ 700/5005] lr: 1.0000e-03 eta: 3:12:01 time: 0.1795 data_time: 0.0047 loss: 0.9903 2023/03/17 16:46:14 - mmengine - INFO - Epoch(train) [89][ 800/5005] lr: 1.0000e-03 eta: 3:11:41 time: 0.1793 data_time: 0.0048 loss: 0.9377 2023/03/17 16:46:33 - mmengine - INFO - Epoch(train) [89][ 900/5005] lr: 1.0000e-03 eta: 3:11:22 time: 0.1997 data_time: 0.0039 loss: 1.0115 2023/03/17 16:46:52 - mmengine - INFO - Epoch(train) [89][1000/5005] lr: 1.0000e-03 eta: 3:11:02 time: 0.1793 data_time: 0.0046 loss: 1.0661 2023/03/17 16:47:10 - mmengine - INFO - Epoch(train) [89][1100/5005] lr: 1.0000e-03 eta: 3:10:43 time: 0.1776 data_time: 0.0045 loss: 0.9648 2023/03/17 16:47:29 - mmengine - INFO - Epoch(train) [89][1200/5005] lr: 1.0000e-03 eta: 3:10:23 time: 0.1884 data_time: 0.0051 loss: 0.9835 2023/03/17 16:47:48 - mmengine - INFO - Epoch(train) [89][1300/5005] lr: 1.0000e-03 eta: 3:10:04 time: 0.1867 data_time: 0.0050 loss: 0.8969 2023/03/17 16:48:07 - mmengine - INFO - Epoch(train) [89][1400/5005] lr: 1.0000e-03 eta: 3:09:44 time: 0.1854 data_time: 0.0042 loss: 0.7409 2023/03/17 16:48:27 - mmengine - INFO - Epoch(train) [89][1500/5005] lr: 1.0000e-03 eta: 3:09:25 time: 0.2046 data_time: 0.0050 loss: 0.9949 2023/03/17 16:48:39 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:48:47 - mmengine - INFO - Epoch(train) [89][1600/5005] lr: 1.0000e-03 eta: 3:09:05 time: 0.2218 data_time: 0.0047 loss: 1.0662 2023/03/17 16:49:08 - mmengine - INFO - Epoch(train) [89][1700/5005] lr: 1.0000e-03 eta: 3:08:46 time: 0.1982 data_time: 0.0046 loss: 1.0818 2023/03/17 16:49:28 - mmengine - INFO - Epoch(train) [89][1800/5005] lr: 1.0000e-03 eta: 3:08:27 time: 0.1938 data_time: 0.0041 loss: 1.0402 2023/03/17 16:49:47 - mmengine - INFO - Epoch(train) [89][1900/5005] lr: 1.0000e-03 eta: 3:08:07 time: 0.1812 data_time: 0.0049 loss: 0.9839 2023/03/17 16:50:06 - mmengine - INFO - Epoch(train) [89][2000/5005] lr: 1.0000e-03 eta: 3:07:48 time: 0.1862 data_time: 0.0046 loss: 1.0168 2023/03/17 16:50:27 - mmengine - INFO - Epoch(train) [89][2100/5005] lr: 1.0000e-03 eta: 3:07:29 time: 0.2221 data_time: 0.0042 loss: 1.0383 2023/03/17 16:50:49 - mmengine - INFO - Epoch(train) [89][2200/5005] lr: 1.0000e-03 eta: 3:07:09 time: 0.1783 data_time: 0.0045 loss: 0.9051 2023/03/17 16:51:08 - mmengine - INFO - Epoch(train) [89][2300/5005] lr: 1.0000e-03 eta: 3:06:50 time: 0.2081 data_time: 0.0046 loss: 1.0174 2023/03/17 16:51:31 - mmengine - INFO - Epoch(train) [89][2400/5005] lr: 1.0000e-03 eta: 3:06:31 time: 0.2067 data_time: 0.0034 loss: 0.9254 2023/03/17 16:51:55 - mmengine - INFO - Epoch(train) [89][2500/5005] lr: 1.0000e-03 eta: 3:06:12 time: 0.2492 data_time: 0.0028 loss: 1.0707 2023/03/17 16:52:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:52:15 - mmengine - INFO - Epoch(train) [89][2600/5005] lr: 1.0000e-03 eta: 3:05:53 time: 0.1807 data_time: 0.0047 loss: 0.8566 2023/03/17 16:52:38 - mmengine - INFO - Epoch(train) [89][2700/5005] lr: 1.0000e-03 eta: 3:05:34 time: 0.2259 data_time: 0.0050 loss: 0.9122 2023/03/17 16:53:01 - mmengine - INFO - Epoch(train) [89][2800/5005] lr: 1.0000e-03 eta: 3:05:15 time: 0.2428 data_time: 0.0046 loss: 0.8696 2023/03/17 16:53:24 - mmengine - INFO - Epoch(train) [89][2900/5005] lr: 1.0000e-03 eta: 3:04:56 time: 0.1899 data_time: 0.0050 loss: 0.9518 2023/03/17 16:53:42 - mmengine - INFO - Epoch(train) [89][3000/5005] lr: 1.0000e-03 eta: 3:04:36 time: 0.1723 data_time: 0.0041 loss: 1.0290 2023/03/17 16:54:02 - mmengine - INFO - Epoch(train) [89][3100/5005] lr: 1.0000e-03 eta: 3:04:17 time: 0.1941 data_time: 0.0045 loss: 1.1333 2023/03/17 16:54:21 - mmengine - INFO - Epoch(train) [89][3200/5005] lr: 1.0000e-03 eta: 3:03:57 time: 0.1931 data_time: 0.0048 loss: 1.1240 2023/03/17 16:54:41 - mmengine - INFO - Epoch(train) [89][3300/5005] lr: 1.0000e-03 eta: 3:03:38 time: 0.1939 data_time: 0.0047 loss: 0.9527 2023/03/17 16:55:00 - mmengine - INFO - Epoch(train) [89][3400/5005] lr: 1.0000e-03 eta: 3:03:19 time: 0.1861 data_time: 0.0047 loss: 0.9326 2023/03/17 16:55:19 - mmengine - INFO - Epoch(train) [89][3500/5005] lr: 1.0000e-03 eta: 3:02:59 time: 0.1830 data_time: 0.0046 loss: 0.9497 2023/03/17 16:55:30 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:55:38 - mmengine - INFO - Epoch(train) [89][3600/5005] lr: 1.0000e-03 eta: 3:02:39 time: 0.1829 data_time: 0.0048 loss: 0.8455 2023/03/17 16:55:57 - mmengine - INFO - Epoch(train) [89][3700/5005] lr: 1.0000e-03 eta: 3:02:20 time: 0.1887 data_time: 0.0048 loss: 1.0430 2023/03/17 16:56:16 - mmengine - INFO - Epoch(train) [89][3800/5005] lr: 1.0000e-03 eta: 3:02:01 time: 0.1829 data_time: 0.0048 loss: 1.0014 2023/03/17 16:56:36 - mmengine - INFO - Epoch(train) [89][3900/5005] lr: 1.0000e-03 eta: 3:01:41 time: 0.1915 data_time: 0.0048 loss: 1.0523 2023/03/17 16:56:54 - mmengine - INFO - Epoch(train) [89][4000/5005] lr: 1.0000e-03 eta: 3:01:22 time: 0.1769 data_time: 0.0049 loss: 0.9330 2023/03/17 16:57:13 - mmengine - INFO - Epoch(train) [89][4100/5005] lr: 1.0000e-03 eta: 3:01:02 time: 0.1762 data_time: 0.0060 loss: 0.8502 2023/03/17 16:57:31 - mmengine - INFO - Epoch(train) [89][4200/5005] lr: 1.0000e-03 eta: 3:00:42 time: 0.1754 data_time: 0.0041 loss: 1.0007 2023/03/17 16:57:49 - mmengine - INFO - Epoch(train) [89][4300/5005] lr: 1.0000e-03 eta: 3:00:23 time: 0.1863 data_time: 0.0050 loss: 0.8423 2023/03/17 16:58:09 - mmengine - INFO - Epoch(train) [89][4400/5005] lr: 1.0000e-03 eta: 3:00:03 time: 0.1931 data_time: 0.0045 loss: 0.8301 2023/03/17 16:58:27 - mmengine - INFO - Epoch(train) [89][4500/5005] lr: 1.0000e-03 eta: 2:59:44 time: 0.1750 data_time: 0.0047 loss: 0.8903 2023/03/17 16:58:38 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 16:58:46 - mmengine - INFO - Epoch(train) [89][4600/5005] lr: 1.0000e-03 eta: 2:59:24 time: 0.1830 data_time: 0.0051 loss: 0.8589 2023/03/17 16:59:05 - mmengine - INFO - Epoch(train) [89][4700/5005] lr: 1.0000e-03 eta: 2:59:05 time: 0.1870 data_time: 0.0050 loss: 1.1860 2023/03/17 16:59:25 - mmengine - INFO - Epoch(train) [89][4800/5005] lr: 1.0000e-03 eta: 2:58:45 time: 0.2068 data_time: 0.0049 loss: 1.0574 2023/03/17 16:59:47 - mmengine - INFO - Epoch(train) [89][4900/5005] lr: 1.0000e-03 eta: 2:58:26 time: 0.2187 data_time: 0.0054 loss: 1.0152 2023/03/17 17:00:08 - mmengine - INFO - Epoch(train) [89][5000/5005] lr: 1.0000e-03 eta: 2:58:07 time: 0.1931 data_time: 0.0053 loss: 0.8302 2023/03/17 17:00:09 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:00:09 - mmengine - INFO - Saving checkpoint at 89 epochs 2023/03/17 17:00:16 - mmengine - INFO - Epoch(val) [89][100/196] eta: 0:00:04 time: 0.0453 data_time: 0.0063 2023/03/17 17:00:41 - mmengine - INFO - Epoch(val) [89][196/196] accuracy/top1: 75.7820 accuracy/top5: 92.8520data_time: 0.0248 time: 0.0581 2023/03/17 17:01:07 - mmengine - INFO - Epoch(train) [90][ 100/5005] lr: 1.0000e-03 eta: 2:57:48 time: 0.2332 data_time: 0.0035 loss: 1.0068 2023/03/17 17:01:29 - mmengine - INFO - Epoch(train) [90][ 200/5005] lr: 1.0000e-03 eta: 2:57:28 time: 0.1729 data_time: 0.0053 loss: 0.8525 2023/03/17 17:01:47 - mmengine - INFO - Epoch(train) [90][ 300/5005] lr: 1.0000e-03 eta: 2:57:09 time: 0.1789 data_time: 0.0052 loss: 0.9175 2023/03/17 17:02:06 - mmengine - INFO - Epoch(train) [90][ 400/5005] lr: 1.0000e-03 eta: 2:56:49 time: 0.1927 data_time: 0.0051 loss: 1.0505 2023/03/17 17:02:25 - mmengine - INFO - Epoch(train) [90][ 500/5005] lr: 1.0000e-03 eta: 2:56:30 time: 0.1801 data_time: 0.0051 loss: 0.9355 2023/03/17 17:02:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:02:43 - mmengine - INFO - Epoch(train) [90][ 600/5005] lr: 1.0000e-03 eta: 2:56:10 time: 0.1797 data_time: 0.0052 loss: 0.8161 2023/03/17 17:03:02 - mmengine - INFO - Epoch(train) [90][ 700/5005] lr: 1.0000e-03 eta: 2:55:51 time: 0.1875 data_time: 0.0047 loss: 1.0166 2023/03/17 17:03:21 - mmengine - INFO - Epoch(train) [90][ 800/5005] lr: 1.0000e-03 eta: 2:55:31 time: 0.1914 data_time: 0.0040 loss: 1.1098 2023/03/17 17:03:41 - mmengine - INFO - Epoch(train) [90][ 900/5005] lr: 1.0000e-03 eta: 2:55:12 time: 0.1919 data_time: 0.0042 loss: 0.9304 2023/03/17 17:04:00 - mmengine - INFO - Epoch(train) [90][1000/5005] lr: 1.0000e-03 eta: 2:54:52 time: 0.1835 data_time: 0.0058 loss: 1.0500 2023/03/17 17:04:20 - mmengine - INFO - Epoch(train) [90][1100/5005] lr: 1.0000e-03 eta: 2:54:33 time: 0.1929 data_time: 0.0054 loss: 0.9493 2023/03/17 17:04:39 - mmengine - INFO - Epoch(train) [90][1200/5005] lr: 1.0000e-03 eta: 2:54:14 time: 0.1868 data_time: 0.0053 loss: 0.9253 2023/03/17 17:04:58 - mmengine - INFO - Epoch(train) [90][1300/5005] lr: 1.0000e-03 eta: 2:53:54 time: 0.1906 data_time: 0.0052 loss: 0.9036 2023/03/17 17:05:19 - mmengine - INFO - Epoch(train) [90][1400/5005] lr: 1.0000e-03 eta: 2:53:35 time: 0.2176 data_time: 0.0048 loss: 0.8820 2023/03/17 17:05:44 - mmengine - INFO - Epoch(train) [90][1500/5005] lr: 1.0000e-03 eta: 2:53:16 time: 0.2490 data_time: 0.0044 loss: 1.0775 2023/03/17 17:05:56 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:06:05 - mmengine - INFO - Epoch(train) [90][1600/5005] lr: 1.0000e-03 eta: 2:52:57 time: 0.2017 data_time: 0.0046 loss: 1.0705 2023/03/17 17:06:25 - mmengine - INFO - Epoch(train) [90][1700/5005] lr: 1.0000e-03 eta: 2:52:37 time: 0.1970 data_time: 0.0051 loss: 0.9382 2023/03/17 17:06:45 - mmengine - INFO - Epoch(train) [90][1800/5005] lr: 1.0000e-03 eta: 2:52:18 time: 0.1888 data_time: 0.0050 loss: 0.9497 2023/03/17 17:07:06 - mmengine - INFO - Epoch(train) [90][1900/5005] lr: 1.0000e-03 eta: 2:51:59 time: 0.1767 data_time: 0.0044 loss: 0.7223 2023/03/17 17:07:24 - mmengine - INFO - Epoch(train) [90][2000/5005] lr: 1.0000e-03 eta: 2:51:39 time: 0.1776 data_time: 0.0046 loss: 0.9273 2023/03/17 17:07:43 - mmengine - INFO - Epoch(train) [90][2100/5005] lr: 1.0000e-03 eta: 2:51:20 time: 0.2409 data_time: 0.0048 loss: 1.0306 2023/03/17 17:08:09 - mmengine - INFO - Epoch(train) [90][2200/5005] lr: 1.0000e-03 eta: 2:51:01 time: 0.2572 data_time: 0.0048 loss: 0.9278 2023/03/17 17:08:29 - mmengine - INFO - Epoch(train) [90][2300/5005] lr: 1.0000e-03 eta: 2:50:42 time: 0.2071 data_time: 0.0045 loss: 1.0457 2023/03/17 17:08:52 - mmengine - INFO - Epoch(train) [90][2400/5005] lr: 1.0000e-03 eta: 2:50:23 time: 0.2481 data_time: 0.0030 loss: 1.0167 2023/03/17 17:09:14 - mmengine - INFO - Epoch(train) [90][2500/5005] lr: 1.0000e-03 eta: 2:50:04 time: 0.1914 data_time: 0.0048 loss: 1.0712 2023/03/17 17:09:25 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:09:34 - mmengine - INFO - Epoch(train) [90][2600/5005] lr: 1.0000e-03 eta: 2:49:44 time: 0.1910 data_time: 0.0042 loss: 0.9001 2023/03/17 17:09:53 - mmengine - INFO - Epoch(train) [90][2700/5005] lr: 1.0000e-03 eta: 2:49:25 time: 0.1848 data_time: 0.0049 loss: 1.0298 2023/03/17 17:10:17 - mmengine - INFO - Epoch(train) [90][2800/5005] lr: 1.0000e-03 eta: 2:49:06 time: 0.2024 data_time: 0.0049 loss: 0.9574 2023/03/17 17:10:37 - mmengine - INFO - Epoch(train) [90][2900/5005] lr: 1.0000e-03 eta: 2:48:46 time: 0.1955 data_time: 0.0050 loss: 1.0573 2023/03/17 17:10:59 - mmengine - INFO - Epoch(train) [90][3000/5005] lr: 1.0000e-03 eta: 2:48:27 time: 0.1976 data_time: 0.0046 loss: 0.8996 2023/03/17 17:11:20 - mmengine - INFO - Epoch(train) [90][3100/5005] lr: 1.0000e-03 eta: 2:48:08 time: 0.2204 data_time: 0.0037 loss: 0.8283 2023/03/17 17:11:41 - mmengine - INFO - Epoch(train) [90][3200/5005] lr: 1.0000e-03 eta: 2:47:49 time: 0.2176 data_time: 0.0037 loss: 0.8355 2023/03/17 17:12:02 - mmengine - INFO - Epoch(train) [90][3300/5005] lr: 1.0000e-03 eta: 2:47:30 time: 0.2279 data_time: 0.0049 loss: 0.9499 2023/03/17 17:12:25 - mmengine - INFO - Epoch(train) [90][3400/5005] lr: 1.0000e-03 eta: 2:47:10 time: 0.1917 data_time: 0.0045 loss: 1.0145 2023/03/17 17:12:43 - mmengine - INFO - Epoch(train) [90][3500/5005] lr: 1.0000e-03 eta: 2:46:51 time: 0.1760 data_time: 0.0052 loss: 1.0488 2023/03/17 17:12:53 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:13:02 - mmengine - INFO - Epoch(train) [90][3600/5005] lr: 1.0000e-03 eta: 2:46:31 time: 0.1919 data_time: 0.0040 loss: 0.9048 2023/03/17 17:13:24 - mmengine - INFO - Epoch(train) [90][3700/5005] lr: 1.0000e-03 eta: 2:46:12 time: 0.2477 data_time: 0.0052 loss: 0.9677 2023/03/17 17:13:44 - mmengine - INFO - Epoch(train) [90][3800/5005] lr: 1.0000e-03 eta: 2:45:53 time: 0.1937 data_time: 0.0045 loss: 1.0587 2023/03/17 17:14:04 - mmengine - INFO - Epoch(train) [90][3900/5005] lr: 1.0000e-03 eta: 2:45:33 time: 0.1905 data_time: 0.0048 loss: 1.0611 2023/03/17 17:14:23 - mmengine - INFO - Epoch(train) [90][4000/5005] lr: 1.0000e-03 eta: 2:45:14 time: 0.1934 data_time: 0.0054 loss: 0.9437 2023/03/17 17:14:43 - mmengine - INFO - Epoch(train) [90][4100/5005] lr: 1.0000e-03 eta: 2:44:55 time: 0.2011 data_time: 0.0051 loss: 1.0206 2023/03/17 17:15:03 - mmengine - INFO - Epoch(train) [90][4200/5005] lr: 1.0000e-03 eta: 2:44:35 time: 0.1978 data_time: 0.0054 loss: 0.9454 2023/03/17 17:15:25 - mmengine - INFO - Epoch(train) [90][4300/5005] lr: 1.0000e-03 eta: 2:44:16 time: 0.2123 data_time: 0.0052 loss: 0.9628 2023/03/17 17:15:47 - mmengine - INFO - Epoch(train) [90][4400/5005] lr: 1.0000e-03 eta: 2:43:57 time: 0.2260 data_time: 0.0051 loss: 1.0754 2023/03/17 17:16:10 - mmengine - INFO - Epoch(train) [90][4500/5005] lr: 1.0000e-03 eta: 2:43:38 time: 0.2411 data_time: 0.0051 loss: 0.9795 2023/03/17 17:16:23 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:16:32 - mmengine - INFO - Epoch(train) [90][4600/5005] lr: 1.0000e-03 eta: 2:43:19 time: 0.1927 data_time: 0.0050 loss: 0.9643 2023/03/17 17:16:51 - mmengine - INFO - Epoch(train) [90][4700/5005] lr: 1.0000e-03 eta: 2:42:59 time: 0.1875 data_time: 0.0046 loss: 0.8679 2023/03/17 17:17:13 - mmengine - INFO - Epoch(train) [90][4800/5005] lr: 1.0000e-03 eta: 2:42:40 time: 0.1912 data_time: 0.0049 loss: 0.9093 2023/03/17 17:17:32 - mmengine - INFO - Epoch(train) [90][4900/5005] lr: 1.0000e-03 eta: 2:42:21 time: 0.1927 data_time: 0.0049 loss: 1.0090 2023/03/17 17:17:52 - mmengine - INFO - Epoch(train) [90][5000/5005] lr: 1.0000e-03 eta: 2:42:01 time: 0.1994 data_time: 0.0052 loss: 0.9649 2023/03/17 17:17:53 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:17:53 - mmengine - INFO - Saving checkpoint at 90 epochs 2023/03/17 17:18:00 - mmengine - INFO - Epoch(val) [90][100/196] eta: 0:00:05 time: 0.0630 data_time: 0.0008 2023/03/17 17:18:27 - mmengine - INFO - Epoch(val) [90][196/196] accuracy/top1: 75.7380 accuracy/top5: 92.8700data_time: 0.0306 time: 0.0677 2023/03/17 17:18:50 - mmengine - INFO - Epoch(train) [91][ 100/5005] lr: 1.0000e-04 eta: 2:41:41 time: 0.2210 data_time: 0.0044 loss: 0.9110 2023/03/17 17:19:08 - mmengine - INFO - Epoch(train) [91][ 200/5005] lr: 1.0000e-04 eta: 2:41:22 time: 0.1966 data_time: 0.0047 loss: 1.1516 2023/03/17 17:19:30 - mmengine - INFO - Epoch(train) [91][ 300/5005] lr: 1.0000e-04 eta: 2:41:02 time: 0.2028 data_time: 0.0047 loss: 0.8728 2023/03/17 17:19:51 - mmengine - INFO - Epoch(train) [91][ 400/5005] lr: 1.0000e-04 eta: 2:40:43 time: 0.2069 data_time: 0.0048 loss: 1.0734 2023/03/17 17:20:12 - mmengine - INFO - Epoch(train) [91][ 500/5005] lr: 1.0000e-04 eta: 2:40:24 time: 0.2447 data_time: 0.0042 loss: 0.8495 2023/03/17 17:20:22 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:20:31 - mmengine - INFO - Epoch(train) [91][ 600/5005] lr: 1.0000e-04 eta: 2:40:04 time: 0.1812 data_time: 0.0059 loss: 0.9088 2023/03/17 17:20:49 - mmengine - INFO - Epoch(train) [91][ 700/5005] lr: 1.0000e-04 eta: 2:39:45 time: 0.1771 data_time: 0.0055 loss: 0.8397 2023/03/17 17:21:08 - mmengine - INFO - Epoch(train) [91][ 800/5005] lr: 1.0000e-04 eta: 2:39:25 time: 0.1858 data_time: 0.0050 loss: 0.8747 2023/03/17 17:21:27 - mmengine - INFO - Epoch(train) [91][ 900/5005] lr: 1.0000e-04 eta: 2:39:06 time: 0.1808 data_time: 0.0055 loss: 0.9933 2023/03/17 17:21:46 - mmengine - INFO - Epoch(train) [91][1000/5005] lr: 1.0000e-04 eta: 2:38:46 time: 0.1921 data_time: 0.0050 loss: 1.1678 2023/03/17 17:22:04 - mmengine - INFO - Epoch(train) [91][1100/5005] lr: 1.0000e-04 eta: 2:38:27 time: 0.1750 data_time: 0.0051 loss: 0.9087 2023/03/17 17:22:22 - mmengine - INFO - Epoch(train) [91][1200/5005] lr: 1.0000e-04 eta: 2:38:07 time: 0.1785 data_time: 0.0060 loss: 1.0634 2023/03/17 17:22:42 - mmengine - INFO - Epoch(train) [91][1300/5005] lr: 1.0000e-04 eta: 2:37:48 time: 0.1787 data_time: 0.0045 loss: 0.9540 2023/03/17 17:23:00 - mmengine - INFO - Epoch(train) [91][1400/5005] lr: 1.0000e-04 eta: 2:37:28 time: 0.1763 data_time: 0.0040 loss: 1.0680 2023/03/17 17:23:19 - mmengine - INFO - Epoch(train) [91][1500/5005] lr: 1.0000e-04 eta: 2:37:09 time: 0.1820 data_time: 0.0046 loss: 0.8897 2023/03/17 17:23:28 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:23:37 - mmengine - INFO - Epoch(train) [91][1600/5005] lr: 1.0000e-04 eta: 2:36:49 time: 0.1868 data_time: 0.0054 loss: 0.7954 2023/03/17 17:23:57 - mmengine - INFO - Epoch(train) [91][1700/5005] lr: 1.0000e-04 eta: 2:36:30 time: 0.1869 data_time: 0.0060 loss: 0.8541 2023/03/17 17:24:17 - mmengine - INFO - Epoch(train) [91][1800/5005] lr: 1.0000e-04 eta: 2:36:10 time: 0.1839 data_time: 0.0052 loss: 0.9091 2023/03/17 17:24:36 - mmengine - INFO - Epoch(train) [91][1900/5005] lr: 1.0000e-04 eta: 2:35:51 time: 0.1908 data_time: 0.0052 loss: 0.9075 2023/03/17 17:24:56 - mmengine - INFO - Epoch(train) [91][2000/5005] lr: 1.0000e-04 eta: 2:35:31 time: 0.2018 data_time: 0.0047 loss: 1.0236 2023/03/17 17:25:16 - mmengine - INFO - Epoch(train) [91][2100/5005] lr: 1.0000e-04 eta: 2:35:12 time: 0.1838 data_time: 0.0057 loss: 1.0770 2023/03/17 17:25:35 - mmengine - INFO - Epoch(train) [91][2200/5005] lr: 1.0000e-04 eta: 2:34:52 time: 0.2091 data_time: 0.0040 loss: 0.8842 2023/03/17 17:25:54 - mmengine - INFO - Epoch(train) [91][2300/5005] lr: 1.0000e-04 eta: 2:34:33 time: 0.1867 data_time: 0.0050 loss: 0.9569 2023/03/17 17:26:13 - mmengine - INFO - Epoch(train) [91][2400/5005] lr: 1.0000e-04 eta: 2:34:13 time: 0.1858 data_time: 0.0043 loss: 0.9134 2023/03/17 17:26:31 - mmengine - INFO - Epoch(train) [91][2500/5005] lr: 1.0000e-04 eta: 2:33:54 time: 0.1789 data_time: 0.0053 loss: 1.0544 2023/03/17 17:26:40 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:26:50 - mmengine - INFO - Epoch(train) [91][2600/5005] lr: 1.0000e-04 eta: 2:33:34 time: 0.1898 data_time: 0.0046 loss: 1.0280 2023/03/17 17:27:08 - mmengine - INFO - Epoch(train) [91][2700/5005] lr: 1.0000e-04 eta: 2:33:15 time: 0.1778 data_time: 0.0061 loss: 0.8572 2023/03/17 17:27:27 - mmengine - INFO - Epoch(train) [91][2800/5005] lr: 1.0000e-04 eta: 2:32:55 time: 0.1801 data_time: 0.0058 loss: 0.9451 2023/03/17 17:27:45 - mmengine - INFO - Epoch(train) [91][2900/5005] lr: 1.0000e-04 eta: 2:32:36 time: 0.2070 data_time: 0.0041 loss: 0.9800 2023/03/17 17:28:06 - mmengine - INFO - Epoch(train) [91][3000/5005] lr: 1.0000e-04 eta: 2:32:16 time: 0.1931 data_time: 0.0049 loss: 0.8485 2023/03/17 17:28:26 - mmengine - INFO - Epoch(train) [91][3100/5005] lr: 1.0000e-04 eta: 2:31:57 time: 0.1877 data_time: 0.0049 loss: 0.9477 2023/03/17 17:28:46 - mmengine - INFO - Epoch(train) [91][3200/5005] lr: 1.0000e-04 eta: 2:31:38 time: 0.1985 data_time: 0.0050 loss: 1.0447 2023/03/17 17:29:06 - mmengine - INFO - Epoch(train) [91][3300/5005] lr: 1.0000e-04 eta: 2:31:18 time: 0.1887 data_time: 0.0048 loss: 0.9053 2023/03/17 17:29:25 - mmengine - INFO - Epoch(train) [91][3400/5005] lr: 1.0000e-04 eta: 2:30:59 time: 0.1836 data_time: 0.0049 loss: 0.8711 2023/03/17 17:29:45 - mmengine - INFO - Epoch(train) [91][3500/5005] lr: 1.0000e-04 eta: 2:30:39 time: 0.1857 data_time: 0.0048 loss: 0.7747 2023/03/17 17:29:54 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:30:04 - mmengine - INFO - Epoch(train) [91][3600/5005] lr: 1.0000e-04 eta: 2:30:20 time: 0.1867 data_time: 0.0044 loss: 1.0566 2023/03/17 17:30:23 - mmengine - INFO - Epoch(train) [91][3700/5005] lr: 1.0000e-04 eta: 2:30:01 time: 0.1941 data_time: 0.0050 loss: 0.9162 2023/03/17 17:30:43 - mmengine - INFO - Epoch(train) [91][3800/5005] lr: 1.0000e-04 eta: 2:29:41 time: 0.2098 data_time: 0.0052 loss: 0.9687 2023/03/17 17:31:04 - mmengine - INFO - Epoch(train) [91][3900/5005] lr: 1.0000e-04 eta: 2:29:22 time: 0.1991 data_time: 0.0050 loss: 0.9772 2023/03/17 17:31:23 - mmengine - INFO - Epoch(train) [91][4000/5005] lr: 1.0000e-04 eta: 2:29:02 time: 0.1875 data_time: 0.0049 loss: 0.8759 2023/03/17 17:31:43 - mmengine - INFO - Epoch(train) [91][4100/5005] lr: 1.0000e-04 eta: 2:28:43 time: 0.1939 data_time: 0.0047 loss: 0.9699 2023/03/17 17:32:02 - mmengine - INFO - Epoch(train) [91][4200/5005] lr: 1.0000e-04 eta: 2:28:24 time: 0.1849 data_time: 0.0046 loss: 0.9748 2023/03/17 17:32:23 - mmengine - INFO - Epoch(train) [91][4300/5005] lr: 1.0000e-04 eta: 2:28:04 time: 0.1999 data_time: 0.0056 loss: 0.8818 2023/03/17 17:32:44 - mmengine - INFO - Epoch(train) [91][4400/5005] lr: 1.0000e-04 eta: 2:27:45 time: 0.2134 data_time: 0.0051 loss: 0.9370 2023/03/17 17:33:04 - mmengine - INFO - Epoch(train) [91][4500/5005] lr: 1.0000e-04 eta: 2:27:26 time: 0.2187 data_time: 0.0054 loss: 1.0295 2023/03/17 17:33:15 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:33:25 - mmengine - INFO - Epoch(train) [91][4600/5005] lr: 1.0000e-04 eta: 2:27:06 time: 0.1907 data_time: 0.0048 loss: 0.8533 2023/03/17 17:33:44 - mmengine - INFO - Epoch(train) [91][4700/5005] lr: 1.0000e-04 eta: 2:26:47 time: 0.1904 data_time: 0.0049 loss: 0.8963 2023/03/17 17:34:03 - mmengine - INFO - Epoch(train) [91][4800/5005] lr: 1.0000e-04 eta: 2:26:27 time: 0.1759 data_time: 0.0046 loss: 1.1239 2023/03/17 17:34:25 - mmengine - INFO - Epoch(train) [91][4900/5005] lr: 1.0000e-04 eta: 2:26:08 time: 0.2287 data_time: 0.0053 loss: 0.7976 2023/03/17 17:34:46 - mmengine - INFO - Epoch(train) [91][5000/5005] lr: 1.0000e-04 eta: 2:25:49 time: 0.1969 data_time: 0.0054 loss: 0.9861 2023/03/17 17:34:47 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:34:48 - mmengine - INFO - Saving checkpoint at 91 epochs 2023/03/17 17:34:54 - mmengine - INFO - Epoch(val) [91][100/196] eta: 0:00:04 time: 0.0437 data_time: 0.0008 2023/03/17 17:35:23 - mmengine - INFO - Epoch(val) [91][196/196] accuracy/top1: 76.2200 accuracy/top5: 93.0580data_time: 0.0333 time: 0.0690 2023/03/17 17:35:45 - mmengine - INFO - Epoch(train) [92][ 100/5005] lr: 1.0000e-04 eta: 2:25:29 time: 0.2435 data_time: 0.0052 loss: 0.9401 2023/03/17 17:36:06 - mmengine - INFO - Epoch(train) [92][ 200/5005] lr: 1.0000e-04 eta: 2:25:09 time: 0.1972 data_time: 0.0049 loss: 1.0143 2023/03/17 17:36:26 - mmengine - INFO - Epoch(train) [92][ 300/5005] lr: 1.0000e-04 eta: 2:24:50 time: 0.1947 data_time: 0.0046 loss: 0.9190 2023/03/17 17:36:47 - mmengine - INFO - Epoch(train) [92][ 400/5005] lr: 1.0000e-04 eta: 2:24:31 time: 0.2125 data_time: 0.0050 loss: 0.9430 2023/03/17 17:37:10 - mmengine - INFO - Epoch(train) [92][ 500/5005] lr: 1.0000e-04 eta: 2:24:12 time: 0.2299 data_time: 0.0044 loss: 1.0589 2023/03/17 17:37:20 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:37:31 - mmengine - INFO - Epoch(train) [92][ 600/5005] lr: 1.0000e-04 eta: 2:23:52 time: 0.2274 data_time: 0.0049 loss: 0.9882 2023/03/17 17:37:56 - mmengine - INFO - Epoch(train) [92][ 700/5005] lr: 1.0000e-04 eta: 2:23:33 time: 0.2600 data_time: 0.0050 loss: 0.9945 2023/03/17 17:38:20 - mmengine - INFO - Epoch(train) [92][ 800/5005] lr: 1.0000e-04 eta: 2:23:14 time: 0.2352 data_time: 0.0057 loss: 1.0943 2023/03/17 17:38:42 - mmengine - INFO - Epoch(train) [92][ 900/5005] lr: 1.0000e-04 eta: 2:22:55 time: 0.1958 data_time: 0.0057 loss: 0.8162 2023/03/17 17:39:03 - mmengine - INFO - Epoch(train) [92][1000/5005] lr: 1.0000e-04 eta: 2:22:36 time: 0.2327 data_time: 0.0049 loss: 0.9530 2023/03/17 17:39:27 - mmengine - INFO - Epoch(train) [92][1100/5005] lr: 1.0000e-04 eta: 2:22:17 time: 0.1886 data_time: 0.0049 loss: 0.9105 2023/03/17 17:39:47 - mmengine - INFO - Epoch(train) [92][1200/5005] lr: 1.0000e-04 eta: 2:21:58 time: 0.2345 data_time: 0.0052 loss: 0.8380 2023/03/17 17:40:07 - mmengine - INFO - Epoch(train) [92][1300/5005] lr: 1.0000e-04 eta: 2:21:38 time: 0.1968 data_time: 0.0056 loss: 0.9464 2023/03/17 17:40:27 - mmengine - INFO - Epoch(train) [92][1400/5005] lr: 1.0000e-04 eta: 2:21:19 time: 0.1840 data_time: 0.0048 loss: 0.7940 2023/03/17 17:40:46 - mmengine - INFO - Epoch(train) [92][1500/5005] lr: 1.0000e-04 eta: 2:20:59 time: 0.1915 data_time: 0.0057 loss: 1.2547 2023/03/17 17:40:54 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:41:06 - mmengine - INFO - Epoch(train) [92][1600/5005] lr: 1.0000e-04 eta: 2:20:40 time: 0.2344 data_time: 0.0050 loss: 1.0232 2023/03/17 17:41:26 - mmengine - INFO - Epoch(train) [92][1700/5005] lr: 1.0000e-04 eta: 2:20:20 time: 0.1979 data_time: 0.0052 loss: 1.0618 2023/03/17 17:41:46 - mmengine - INFO - Epoch(train) [92][1800/5005] lr: 1.0000e-04 eta: 2:20:01 time: 0.2034 data_time: 0.0055 loss: 0.9496 2023/03/17 17:42:06 - mmengine - INFO - Epoch(train) [92][1900/5005] lr: 1.0000e-04 eta: 2:19:42 time: 0.1937 data_time: 0.0049 loss: 0.8158 2023/03/17 17:42:27 - mmengine - INFO - Epoch(train) [92][2000/5005] lr: 1.0000e-04 eta: 2:19:22 time: 0.2059 data_time: 0.0049 loss: 1.0232 2023/03/17 17:42:46 - mmengine - INFO - Epoch(train) [92][2100/5005] lr: 1.0000e-04 eta: 2:19:03 time: 0.2014 data_time: 0.0062 loss: 1.0283 2023/03/17 17:43:05 - mmengine - INFO - Epoch(train) [92][2200/5005] lr: 1.0000e-04 eta: 2:18:43 time: 0.2163 data_time: 0.0046 loss: 0.9556 2023/03/17 17:43:24 - mmengine - INFO - Epoch(train) [92][2300/5005] lr: 1.0000e-04 eta: 2:18:24 time: 0.1969 data_time: 0.0055 loss: 0.9276 2023/03/17 17:43:44 - mmengine - INFO - Epoch(train) [92][2400/5005] lr: 1.0000e-04 eta: 2:18:04 time: 0.1966 data_time: 0.0047 loss: 0.8161 2023/03/17 17:44:05 - mmengine - INFO - Epoch(train) [92][2500/5005] lr: 1.0000e-04 eta: 2:17:45 time: 0.1981 data_time: 0.0051 loss: 0.8707 2023/03/17 17:44:14 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:44:25 - mmengine - INFO - Epoch(train) [92][2600/5005] lr: 1.0000e-04 eta: 2:17:26 time: 0.1869 data_time: 0.0050 loss: 0.9675 2023/03/17 17:44:44 - mmengine - INFO - Epoch(train) [92][2700/5005] lr: 1.0000e-04 eta: 2:17:06 time: 0.1907 data_time: 0.0053 loss: 1.0259 2023/03/17 17:45:04 - mmengine - INFO - Epoch(train) [92][2800/5005] lr: 1.0000e-04 eta: 2:16:47 time: 0.2025 data_time: 0.0050 loss: 1.0199 2023/03/17 17:45:25 - mmengine - INFO - Epoch(train) [92][2900/5005] lr: 1.0000e-04 eta: 2:16:28 time: 0.2058 data_time: 0.0055 loss: 0.8357 2023/03/17 17:45:45 - mmengine - INFO - Epoch(train) [92][3000/5005] lr: 1.0000e-04 eta: 2:16:08 time: 0.2078 data_time: 0.0058 loss: 0.9848 2023/03/17 17:46:07 - mmengine - INFO - Epoch(train) [92][3100/5005] lr: 1.0000e-04 eta: 2:15:49 time: 0.2027 data_time: 0.0050 loss: 0.9183 2023/03/17 17:46:27 - mmengine - INFO - Epoch(train) [92][3200/5005] lr: 1.0000e-04 eta: 2:15:30 time: 0.2003 data_time: 0.0046 loss: 0.7630 2023/03/17 17:46:48 - mmengine - INFO - Epoch(train) [92][3300/5005] lr: 1.0000e-04 eta: 2:15:10 time: 0.2125 data_time: 0.0050 loss: 0.9615 2023/03/17 17:47:08 - mmengine - INFO - Epoch(train) [92][3400/5005] lr: 1.0000e-04 eta: 2:14:51 time: 0.1927 data_time: 0.0054 loss: 1.1343 2023/03/17 17:47:28 - mmengine - INFO - Epoch(train) [92][3500/5005] lr: 1.0000e-04 eta: 2:14:31 time: 0.1921 data_time: 0.0053 loss: 0.9214 2023/03/17 17:47:37 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:47:47 - mmengine - INFO - Epoch(train) [92][3600/5005] lr: 1.0000e-04 eta: 2:14:12 time: 0.1917 data_time: 0.0043 loss: 1.1025 2023/03/17 17:48:07 - mmengine - INFO - Epoch(train) [92][3700/5005] lr: 1.0000e-04 eta: 2:13:53 time: 0.2253 data_time: 0.0066 loss: 0.9113 2023/03/17 17:48:28 - mmengine - INFO - Epoch(train) [92][3800/5005] lr: 1.0000e-04 eta: 2:13:33 time: 0.1908 data_time: 0.0057 loss: 1.0773 2023/03/17 17:48:48 - mmengine - INFO - Epoch(train) [92][3900/5005] lr: 1.0000e-04 eta: 2:13:14 time: 0.1902 data_time: 0.0052 loss: 0.7995 2023/03/17 17:49:08 - mmengine - INFO - Epoch(train) [92][4000/5005] lr: 1.0000e-04 eta: 2:12:54 time: 0.1971 data_time: 0.0051 loss: 1.0164 2023/03/17 17:49:28 - mmengine - INFO - Epoch(train) [92][4100/5005] lr: 1.0000e-04 eta: 2:12:35 time: 0.2215 data_time: 0.0051 loss: 0.8939 2023/03/17 17:49:51 - mmengine - INFO - Epoch(train) [92][4200/5005] lr: 1.0000e-04 eta: 2:12:16 time: 0.2175 data_time: 0.0051 loss: 0.9909 2023/03/17 17:50:11 - mmengine - INFO - Epoch(train) [92][4300/5005] lr: 1.0000e-04 eta: 2:11:57 time: 0.1907 data_time: 0.0045 loss: 1.0478 2023/03/17 17:50:31 - mmengine - INFO - Epoch(train) [92][4400/5005] lr: 1.0000e-04 eta: 2:11:37 time: 0.1986 data_time: 0.0051 loss: 0.9741 2023/03/17 17:50:51 - mmengine - INFO - Epoch(train) [92][4500/5005] lr: 1.0000e-04 eta: 2:11:18 time: 0.1912 data_time: 0.0047 loss: 1.0214 2023/03/17 17:51:00 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:51:11 - mmengine - INFO - Epoch(train) [92][4600/5005] lr: 1.0000e-04 eta: 2:10:58 time: 0.1997 data_time: 0.0056 loss: 0.9606 2023/03/17 17:51:31 - mmengine - INFO - Epoch(train) [92][4700/5005] lr: 1.0000e-04 eta: 2:10:39 time: 0.1929 data_time: 0.0058 loss: 0.7906 2023/03/17 17:51:51 - mmengine - INFO - Epoch(train) [92][4800/5005] lr: 1.0000e-04 eta: 2:10:20 time: 0.2117 data_time: 0.0056 loss: 0.9926 2023/03/17 17:52:11 - mmengine - INFO - Epoch(train) [92][4900/5005] lr: 1.0000e-04 eta: 2:10:00 time: 0.2052 data_time: 0.0054 loss: 0.7185 2023/03/17 17:52:33 - mmengine - INFO - Epoch(train) [92][5000/5005] lr: 1.0000e-04 eta: 2:09:41 time: 0.2333 data_time: 0.0060 loss: 0.9076 2023/03/17 17:52:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:52:35 - mmengine - INFO - Saving checkpoint at 92 epochs 2023/03/17 17:52:41 - mmengine - INFO - Epoch(val) [92][100/196] eta: 0:00:04 time: 0.0386 data_time: 0.0009 2023/03/17 17:53:06 - mmengine - INFO - Epoch(val) [92][196/196] accuracy/top1: 76.3200 accuracy/top5: 93.0940data_time: 0.0169 time: 0.0590 2023/03/17 17:53:29 - mmengine - INFO - Epoch(train) [93][ 100/5005] lr: 1.0000e-04 eta: 2:09:21 time: 0.2225 data_time: 0.0044 loss: 0.9520 2023/03/17 17:53:49 - mmengine - INFO - Epoch(train) [93][ 200/5005] lr: 1.0000e-04 eta: 2:09:01 time: 0.1957 data_time: 0.0051 loss: 1.1354 2023/03/17 17:54:13 - mmengine - INFO - Epoch(train) [93][ 300/5005] lr: 1.0000e-04 eta: 2:08:42 time: 0.2482 data_time: 0.0054 loss: 0.8127 2023/03/17 17:54:33 - mmengine - INFO - Epoch(train) [93][ 400/5005] lr: 1.0000e-04 eta: 2:08:23 time: 0.1813 data_time: 0.0061 loss: 1.0966 2023/03/17 17:54:54 - mmengine - INFO - Epoch(train) [93][ 500/5005] lr: 1.0000e-04 eta: 2:08:04 time: 0.2019 data_time: 0.0053 loss: 0.8818 2023/03/17 17:55:01 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:55:13 - mmengine - INFO - Epoch(train) [93][ 600/5005] lr: 1.0000e-04 eta: 2:07:44 time: 0.1862 data_time: 0.0061 loss: 0.7722 2023/03/17 17:55:34 - mmengine - INFO - Epoch(train) [93][ 700/5005] lr: 1.0000e-04 eta: 2:07:25 time: 0.2456 data_time: 0.0052 loss: 0.8889 2023/03/17 17:55:54 - mmengine - INFO - Epoch(train) [93][ 800/5005] lr: 1.0000e-04 eta: 2:07:05 time: 0.1895 data_time: 0.0057 loss: 0.9208 2023/03/17 17:56:13 - mmengine - INFO - Epoch(train) [93][ 900/5005] lr: 1.0000e-04 eta: 2:06:46 time: 0.1899 data_time: 0.0054 loss: 1.0823 2023/03/17 17:56:33 - mmengine - INFO - Epoch(train) [93][1000/5005] lr: 1.0000e-04 eta: 2:06:27 time: 0.2197 data_time: 0.0054 loss: 0.9909 2023/03/17 17:56:56 - mmengine - INFO - Epoch(train) [93][1100/5005] lr: 1.0000e-04 eta: 2:06:08 time: 0.2130 data_time: 0.0047 loss: 0.8313 2023/03/17 17:57:16 - mmengine - INFO - Epoch(train) [93][1200/5005] lr: 1.0000e-04 eta: 2:05:48 time: 0.1901 data_time: 0.0047 loss: 0.9993 2023/03/17 17:57:38 - mmengine - INFO - Epoch(train) [93][1300/5005] lr: 1.0000e-04 eta: 2:05:29 time: 0.2431 data_time: 0.0057 loss: 1.0380 2023/03/17 17:58:01 - mmengine - INFO - Epoch(train) [93][1400/5005] lr: 1.0000e-04 eta: 2:05:10 time: 0.1982 data_time: 0.0055 loss: 0.9384 2023/03/17 17:58:24 - mmengine - INFO - Epoch(train) [93][1500/5005] lr: 1.0000e-04 eta: 2:04:51 time: 0.2466 data_time: 0.0048 loss: 1.0873 2023/03/17 17:58:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 17:58:47 - mmengine - INFO - Epoch(train) [93][1600/5005] lr: 1.0000e-04 eta: 2:04:31 time: 0.1977 data_time: 0.0058 loss: 0.8808 2023/03/17 17:59:07 - mmengine - INFO - Epoch(train) [93][1700/5005] lr: 1.0000e-04 eta: 2:04:12 time: 0.1980 data_time: 0.0054 loss: 0.8747 2023/03/17 17:59:29 - mmengine - INFO - Epoch(train) [93][1800/5005] lr: 1.0000e-04 eta: 2:03:53 time: 0.1985 data_time: 0.0052 loss: 0.8824 2023/03/17 17:59:48 - mmengine - INFO - Epoch(train) [93][1900/5005] lr: 1.0000e-04 eta: 2:03:33 time: 0.1914 data_time: 0.0043 loss: 1.0574 2023/03/17 18:00:08 - mmengine - INFO - Epoch(train) [93][2000/5005] lr: 1.0000e-04 eta: 2:03:14 time: 0.1984 data_time: 0.0047 loss: 0.9442 2023/03/17 18:00:29 - mmengine - INFO - Epoch(train) [93][2100/5005] lr: 1.0000e-04 eta: 2:02:55 time: 0.2286 data_time: 0.0047 loss: 0.7725 2023/03/17 18:00:51 - mmengine - INFO - Epoch(train) [93][2200/5005] lr: 1.0000e-04 eta: 2:02:35 time: 0.2398 data_time: 0.0047 loss: 0.7756 2023/03/17 18:01:12 - mmengine - INFO - Epoch(train) [93][2300/5005] lr: 1.0000e-04 eta: 2:02:16 time: 0.1917 data_time: 0.0046 loss: 0.9044 2023/03/17 18:01:31 - mmengine - INFO - Epoch(train) [93][2400/5005] lr: 1.0000e-04 eta: 2:01:56 time: 0.1993 data_time: 0.0046 loss: 0.9239 2023/03/17 18:01:51 - mmengine - INFO - Epoch(train) [93][2500/5005] lr: 1.0000e-04 eta: 2:01:37 time: 0.1929 data_time: 0.0053 loss: 0.9566 2023/03/17 18:01:58 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:02:11 - mmengine - INFO - Epoch(train) [93][2600/5005] lr: 1.0000e-04 eta: 2:01:18 time: 0.2075 data_time: 0.0046 loss: 0.8895 2023/03/17 18:02:32 - mmengine - INFO - Epoch(train) [93][2700/5005] lr: 1.0000e-04 eta: 2:00:58 time: 0.2164 data_time: 0.0043 loss: 0.9723 2023/03/17 18:02:52 - mmengine - INFO - Epoch(train) [93][2800/5005] lr: 1.0000e-04 eta: 2:00:39 time: 0.1923 data_time: 0.0039 loss: 0.9329 2023/03/17 18:03:12 - mmengine - INFO - Epoch(train) [93][2900/5005] lr: 1.0000e-04 eta: 2:00:19 time: 0.1879 data_time: 0.0039 loss: 0.9692 2023/03/17 18:03:31 - mmengine - INFO - Epoch(train) [93][3000/5005] lr: 1.0000e-04 eta: 2:00:00 time: 0.1861 data_time: 0.0051 loss: 0.8827 2023/03/17 18:03:51 - mmengine - INFO - Epoch(train) [93][3100/5005] lr: 1.0000e-04 eta: 1:59:41 time: 0.1930 data_time: 0.0044 loss: 0.8321 2023/03/17 18:04:11 - mmengine - INFO - Epoch(train) [93][3200/5005] lr: 1.0000e-04 eta: 1:59:21 time: 0.2423 data_time: 0.0049 loss: 0.8942 2023/03/17 18:04:33 - mmengine - INFO - Epoch(train) [93][3300/5005] lr: 1.0000e-04 eta: 1:59:02 time: 0.2208 data_time: 0.0052 loss: 1.0889 2023/03/17 18:04:53 - mmengine - INFO - Epoch(train) [93][3400/5005] lr: 1.0000e-04 eta: 1:58:42 time: 0.1944 data_time: 0.0042 loss: 0.9230 2023/03/17 18:05:12 - mmengine - INFO - Epoch(train) [93][3500/5005] lr: 1.0000e-04 eta: 1:58:23 time: 0.1922 data_time: 0.0042 loss: 0.9404 2023/03/17 18:05:20 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:05:32 - mmengine - INFO - Epoch(train) [93][3600/5005] lr: 1.0000e-04 eta: 1:58:04 time: 0.1896 data_time: 0.0045 loss: 0.8989 2023/03/17 18:05:51 - mmengine - INFO - Epoch(train) [93][3700/5005] lr: 1.0000e-04 eta: 1:57:44 time: 0.2101 data_time: 0.0059 loss: 0.8062 2023/03/17 18:06:12 - mmengine - INFO - Epoch(train) [93][3800/5005] lr: 1.0000e-04 eta: 1:57:25 time: 0.2026 data_time: 0.0040 loss: 0.9571 2023/03/17 18:06:33 - mmengine - INFO - Epoch(train) [93][3900/5005] lr: 1.0000e-04 eta: 1:57:05 time: 0.2165 data_time: 0.0061 loss: 0.8111 2023/03/17 18:06:54 - mmengine - INFO - Epoch(train) [93][4000/5005] lr: 1.0000e-04 eta: 1:56:46 time: 0.1909 data_time: 0.0041 loss: 0.9764 2023/03/17 18:07:14 - mmengine - INFO - Epoch(train) [93][4100/5005] lr: 1.0000e-04 eta: 1:56:27 time: 0.1993 data_time: 0.0045 loss: 0.9564 2023/03/17 18:07:35 - mmengine - INFO - Epoch(train) [93][4200/5005] lr: 1.0000e-04 eta: 1:56:07 time: 0.2067 data_time: 0.0052 loss: 0.9718 2023/03/17 18:07:55 - mmengine - INFO - Epoch(train) [93][4300/5005] lr: 1.0000e-04 eta: 1:55:48 time: 0.1977 data_time: 0.0042 loss: 0.8733 2023/03/17 18:08:15 - mmengine - INFO - Epoch(train) [93][4400/5005] lr: 1.0000e-04 eta: 1:55:28 time: 0.2014 data_time: 0.0038 loss: 0.9567 2023/03/17 18:08:35 - mmengine - INFO - Epoch(train) [93][4500/5005] lr: 1.0000e-04 eta: 1:55:09 time: 0.1978 data_time: 0.0040 loss: 1.0174 2023/03/17 18:08:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:08:55 - mmengine - INFO - Epoch(train) [93][4600/5005] lr: 1.0000e-04 eta: 1:54:50 time: 0.2024 data_time: 0.0036 loss: 0.8841 2023/03/17 18:09:15 - mmengine - INFO - Epoch(train) [93][4700/5005] lr: 1.0000e-04 eta: 1:54:30 time: 0.1913 data_time: 0.0044 loss: 1.0473 2023/03/17 18:09:35 - mmengine - INFO - Epoch(train) [93][4800/5005] lr: 1.0000e-04 eta: 1:54:11 time: 0.2045 data_time: 0.0053 loss: 0.9638 2023/03/17 18:09:55 - mmengine - INFO - Epoch(train) [93][4900/5005] lr: 1.0000e-04 eta: 1:53:51 time: 0.1959 data_time: 0.0044 loss: 0.9048 2023/03/17 18:10:16 - mmengine - INFO - Epoch(train) [93][5000/5005] lr: 1.0000e-04 eta: 1:53:32 time: 0.2181 data_time: 0.0057 loss: 1.0704 2023/03/17 18:10:17 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:10:18 - mmengine - INFO - Saving checkpoint at 93 epochs 2023/03/17 18:10:24 - mmengine - INFO - Epoch(val) [93][100/196] eta: 0:00:05 time: 0.0480 data_time: 0.0009 2023/03/17 18:10:52 - mmengine - INFO - Epoch(val) [93][196/196] accuracy/top1: 76.3680 accuracy/top5: 93.0940data_time: 0.0258 time: 0.0590 2023/03/17 18:11:18 - mmengine - INFO - Epoch(train) [94][ 100/5005] lr: 1.0000e-04 eta: 1:53:12 time: 0.2346 data_time: 0.0048 loss: 1.1678 2023/03/17 18:11:40 - mmengine - INFO - Epoch(train) [94][ 200/5005] lr: 1.0000e-04 eta: 1:52:53 time: 0.1959 data_time: 0.0054 loss: 1.0254 2023/03/17 18:11:59 - mmengine - INFO - Epoch(train) [94][ 300/5005] lr: 1.0000e-04 eta: 1:52:33 time: 0.1855 data_time: 0.0058 loss: 0.9202 2023/03/17 18:12:17 - mmengine - INFO - Epoch(train) [94][ 400/5005] lr: 1.0000e-04 eta: 1:52:14 time: 0.1890 data_time: 0.0051 loss: 0.8850 2023/03/17 18:12:37 - mmengine - INFO - Epoch(train) [94][ 500/5005] lr: 1.0000e-04 eta: 1:51:54 time: 0.2014 data_time: 0.0051 loss: 1.0745 2023/03/17 18:12:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:12:59 - mmengine - INFO - Epoch(train) [94][ 600/5005] lr: 1.0000e-04 eta: 1:51:35 time: 0.1786 data_time: 0.0053 loss: 0.8330 2023/03/17 18:13:18 - mmengine - INFO - Epoch(train) [94][ 700/5005] lr: 1.0000e-04 eta: 1:51:16 time: 0.1905 data_time: 0.0040 loss: 0.9036 2023/03/17 18:13:36 - mmengine - INFO - Epoch(train) [94][ 800/5005] lr: 1.0000e-04 eta: 1:50:56 time: 0.1832 data_time: 0.0062 loss: 1.0322 2023/03/17 18:13:55 - mmengine - INFO - Epoch(train) [94][ 900/5005] lr: 1.0000e-04 eta: 1:50:37 time: 0.1812 data_time: 0.0050 loss: 1.0253 2023/03/17 18:14:14 - mmengine - INFO - Epoch(train) [94][1000/5005] lr: 1.0000e-04 eta: 1:50:17 time: 0.1909 data_time: 0.0039 loss: 0.8665 2023/03/17 18:14:36 - mmengine - INFO - Epoch(train) [94][1100/5005] lr: 1.0000e-04 eta: 1:49:58 time: 0.2022 data_time: 0.0044 loss: 1.0609 2023/03/17 18:14:56 - mmengine - INFO - Epoch(train) [94][1200/5005] lr: 1.0000e-04 eta: 1:49:38 time: 0.2041 data_time: 0.0042 loss: 0.9155 2023/03/17 18:15:19 - mmengine - INFO - Epoch(train) [94][1300/5005] lr: 1.0000e-04 eta: 1:49:19 time: 0.2506 data_time: 0.0047 loss: 1.0106 2023/03/17 18:15:41 - mmengine - INFO - Epoch(train) [94][1400/5005] lr: 1.0000e-04 eta: 1:49:00 time: 0.1872 data_time: 0.0054 loss: 0.7741 2023/03/17 18:16:00 - mmengine - INFO - Epoch(train) [94][1500/5005] lr: 1.0000e-04 eta: 1:48:41 time: 0.1784 data_time: 0.0057 loss: 0.7826 2023/03/17 18:16:07 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:16:20 - mmengine - INFO - Epoch(train) [94][1600/5005] lr: 1.0000e-04 eta: 1:48:21 time: 0.1962 data_time: 0.0045 loss: 0.9063 2023/03/17 18:16:39 - mmengine - INFO - Epoch(train) [94][1700/5005] lr: 1.0000e-04 eta: 1:48:02 time: 0.1949 data_time: 0.0050 loss: 0.9287 2023/03/17 18:16:58 - mmengine - INFO - Epoch(train) [94][1800/5005] lr: 1.0000e-04 eta: 1:47:42 time: 0.1993 data_time: 0.0051 loss: 0.7612 2023/03/17 18:17:18 - mmengine - INFO - Epoch(train) [94][1900/5005] lr: 1.0000e-04 eta: 1:47:23 time: 0.1939 data_time: 0.0058 loss: 0.9373 2023/03/17 18:17:39 - mmengine - INFO - Epoch(train) [94][2000/5005] lr: 1.0000e-04 eta: 1:47:03 time: 0.2700 data_time: 0.0041 loss: 0.9045 2023/03/17 18:18:01 - mmengine - INFO - Epoch(train) [94][2100/5005] lr: 1.0000e-04 eta: 1:46:44 time: 0.2289 data_time: 0.0051 loss: 1.0132 2023/03/17 18:18:22 - mmengine - INFO - Epoch(train) [94][2200/5005] lr: 1.0000e-04 eta: 1:46:25 time: 0.2123 data_time: 0.0048 loss: 0.9286 2023/03/17 18:18:46 - mmengine - INFO - Epoch(train) [94][2300/5005] lr: 1.0000e-04 eta: 1:46:06 time: 0.2536 data_time: 0.0049 loss: 0.7493 2023/03/17 18:19:08 - mmengine - INFO - Epoch(train) [94][2400/5005] lr: 1.0000e-04 eta: 1:45:46 time: 0.1868 data_time: 0.0058 loss: 0.9139 2023/03/17 18:19:27 - mmengine - INFO - Epoch(train) [94][2500/5005] lr: 1.0000e-04 eta: 1:45:27 time: 0.1898 data_time: 0.0050 loss: 1.1316 2023/03/17 18:19:33 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:19:46 - mmengine - INFO - Epoch(train) [94][2600/5005] lr: 1.0000e-04 eta: 1:45:07 time: 0.1917 data_time: 0.0052 loss: 1.0669 2023/03/17 18:20:05 - mmengine - INFO - Epoch(train) [94][2700/5005] lr: 1.0000e-04 eta: 1:44:48 time: 0.1778 data_time: 0.0062 loss: 0.9167 2023/03/17 18:20:23 - mmengine - INFO - Epoch(train) [94][2800/5005] lr: 1.0000e-04 eta: 1:44:28 time: 0.1833 data_time: 0.0056 loss: 0.7776 2023/03/17 18:20:44 - mmengine - INFO - Epoch(train) [94][2900/5005] lr: 1.0000e-04 eta: 1:44:09 time: 0.2301 data_time: 0.0048 loss: 0.9898 2023/03/17 18:21:07 - mmengine - INFO - Epoch(train) [94][3000/5005] lr: 1.0000e-04 eta: 1:43:50 time: 0.2018 data_time: 0.0036 loss: 0.8678 2023/03/17 18:21:27 - mmengine - INFO - Epoch(train) [94][3100/5005] lr: 1.0000e-04 eta: 1:43:30 time: 0.1956 data_time: 0.0044 loss: 1.0515 2023/03/17 18:21:46 - mmengine - INFO - Epoch(train) [94][3200/5005] lr: 1.0000e-04 eta: 1:43:11 time: 0.1820 data_time: 0.0052 loss: 0.9648 2023/03/17 18:22:04 - mmengine - INFO - Epoch(train) [94][3300/5005] lr: 1.0000e-04 eta: 1:42:51 time: 0.1852 data_time: 0.0053 loss: 0.9227 2023/03/17 18:22:23 - mmengine - INFO - Epoch(train) [94][3400/5005] lr: 1.0000e-04 eta: 1:42:32 time: 0.1791 data_time: 0.0050 loss: 0.8554 2023/03/17 18:22:43 - mmengine - INFO - Epoch(train) [94][3500/5005] lr: 1.0000e-04 eta: 1:42:12 time: 0.2472 data_time: 0.0047 loss: 1.0904 2023/03/17 18:22:52 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:23:09 - mmengine - INFO - Epoch(train) [94][3600/5005] lr: 1.0000e-04 eta: 1:41:53 time: 0.2579 data_time: 0.0046 loss: 0.9446 2023/03/17 18:23:29 - mmengine - INFO - Epoch(train) [94][3700/5005] lr: 1.0000e-04 eta: 1:41:34 time: 0.1943 data_time: 0.0057 loss: 0.8186 2023/03/17 18:23:49 - mmengine - INFO - Epoch(train) [94][3800/5005] lr: 1.0000e-04 eta: 1:41:14 time: 0.1871 data_time: 0.0052 loss: 0.9196 2023/03/17 18:24:07 - mmengine - INFO - Epoch(train) [94][3900/5005] lr: 1.0000e-04 eta: 1:40:55 time: 0.1783 data_time: 0.0052 loss: 0.7941 2023/03/17 18:24:27 - mmengine - INFO - Epoch(train) [94][4000/5005] lr: 1.0000e-04 eta: 1:40:35 time: 0.1885 data_time: 0.0055 loss: 0.9218 2023/03/17 18:24:45 - mmengine - INFO - Epoch(train) [94][4100/5005] lr: 1.0000e-04 eta: 1:40:16 time: 0.1783 data_time: 0.0053 loss: 1.0339 2023/03/17 18:25:04 - mmengine - INFO - Epoch(train) [94][4200/5005] lr: 1.0000e-04 eta: 1:39:56 time: 0.1862 data_time: 0.0050 loss: 0.8951 2023/03/17 18:25:24 - mmengine - INFO - Epoch(train) [94][4300/5005] lr: 1.0000e-04 eta: 1:39:37 time: 0.2006 data_time: 0.0051 loss: 1.1527 2023/03/17 18:25:44 - mmengine - INFO - Epoch(train) [94][4400/5005] lr: 1.0000e-04 eta: 1:39:17 time: 0.1893 data_time: 0.0055 loss: 0.8989 2023/03/17 18:26:05 - mmengine - INFO - Epoch(train) [94][4500/5005] lr: 1.0000e-04 eta: 1:38:58 time: 0.1977 data_time: 0.0049 loss: 1.0093 2023/03/17 18:26:11 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:26:24 - mmengine - INFO - Epoch(train) [94][4600/5005] lr: 1.0000e-04 eta: 1:38:39 time: 0.1929 data_time: 0.0045 loss: 1.0029 2023/03/17 18:26:48 - mmengine - INFO - Epoch(train) [94][4700/5005] lr: 1.0000e-04 eta: 1:38:19 time: 0.1922 data_time: 0.0046 loss: 0.8942 2023/03/17 18:27:09 - mmengine - INFO - Epoch(train) [94][4800/5005] lr: 1.0000e-04 eta: 1:38:00 time: 0.2124 data_time: 0.0046 loss: 0.7951 2023/03/17 18:27:32 - mmengine - INFO - Epoch(train) [94][4900/5005] lr: 1.0000e-04 eta: 1:37:41 time: 0.2104 data_time: 0.0051 loss: 0.7437 2023/03/17 18:27:53 - mmengine - INFO - Epoch(train) [94][5000/5005] lr: 1.0000e-04 eta: 1:37:21 time: 0.2484 data_time: 0.0054 loss: 0.9585 2023/03/17 18:27:54 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:27:55 - mmengine - INFO - Saving checkpoint at 94 epochs 2023/03/17 18:28:02 - mmengine - INFO - Epoch(val) [94][100/196] eta: 0:00:06 time: 0.0584 data_time: 0.0009 2023/03/17 18:28:31 - mmengine - INFO - Epoch(val) [94][196/196] accuracy/top1: 76.2960 accuracy/top5: 93.0980data_time: 0.0258 time: 0.0607 2023/03/17 18:28:57 - mmengine - INFO - Epoch(train) [95][ 100/5005] lr: 1.0000e-04 eta: 1:37:01 time: 0.2304 data_time: 0.0048 loss: 0.7552 2023/03/17 18:29:20 - mmengine - INFO - Epoch(train) [95][ 200/5005] lr: 1.0000e-04 eta: 1:36:42 time: 0.2240 data_time: 0.0048 loss: 1.1371 2023/03/17 18:29:43 - mmengine - INFO - Epoch(train) [95][ 300/5005] lr: 1.0000e-04 eta: 1:36:23 time: 0.2407 data_time: 0.0043 loss: 0.9422 2023/03/17 18:30:05 - mmengine - INFO - Epoch(train) [95][ 400/5005] lr: 1.0000e-04 eta: 1:36:04 time: 0.1884 data_time: 0.0050 loss: 1.0983 2023/03/17 18:30:25 - mmengine - INFO - Epoch(train) [95][ 500/5005] lr: 1.0000e-04 eta: 1:35:44 time: 0.1860 data_time: 0.0052 loss: 0.9246 2023/03/17 18:30:30 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:30:45 - mmengine - INFO - Epoch(train) [95][ 600/5005] lr: 1.0000e-04 eta: 1:35:25 time: 0.1910 data_time: 0.0050 loss: 0.8990 2023/03/17 18:31:05 - mmengine - INFO - Epoch(train) [95][ 700/5005] lr: 1.0000e-04 eta: 1:35:05 time: 0.2064 data_time: 0.0055 loss: 0.9433 2023/03/17 18:31:27 - mmengine - INFO - Epoch(train) [95][ 800/5005] lr: 1.0000e-04 eta: 1:34:46 time: 0.2070 data_time: 0.0055 loss: 0.8166 2023/03/17 18:31:49 - mmengine - INFO - Epoch(train) [95][ 900/5005] lr: 1.0000e-04 eta: 1:34:27 time: 0.1982 data_time: 0.0051 loss: 0.9649 2023/03/17 18:32:10 - mmengine - INFO - Epoch(train) [95][1000/5005] lr: 1.0000e-04 eta: 1:34:07 time: 0.2333 data_time: 0.0045 loss: 0.9803 2023/03/17 18:32:32 - mmengine - INFO - Epoch(train) [95][1100/5005] lr: 1.0000e-04 eta: 1:33:48 time: 0.2018 data_time: 0.0049 loss: 1.1024 2023/03/17 18:32:52 - mmengine - INFO - Epoch(train) [95][1200/5005] lr: 1.0000e-04 eta: 1:33:29 time: 0.2029 data_time: 0.0055 loss: 1.0258 2023/03/17 18:33:11 - mmengine - INFO - Epoch(train) [95][1300/5005] lr: 1.0000e-04 eta: 1:33:09 time: 0.1873 data_time: 0.0054 loss: 1.0578 2023/03/17 18:33:30 - mmengine - INFO - Epoch(train) [95][1400/5005] lr: 1.0000e-04 eta: 1:32:50 time: 0.1906 data_time: 0.0048 loss: 0.7969 2023/03/17 18:33:49 - mmengine - INFO - Epoch(train) [95][1500/5005] lr: 1.0000e-04 eta: 1:32:30 time: 0.1817 data_time: 0.0050 loss: 0.9718 2023/03/17 18:33:55 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:34:08 - mmengine - INFO - Epoch(train) [95][1600/5005] lr: 1.0000e-04 eta: 1:32:11 time: 0.1827 data_time: 0.0057 loss: 0.8267 2023/03/17 18:34:28 - mmengine - INFO - Epoch(train) [95][1700/5005] lr: 1.0000e-04 eta: 1:31:51 time: 0.1907 data_time: 0.0051 loss: 0.9076 2023/03/17 18:34:47 - mmengine - INFO - Epoch(train) [95][1800/5005] lr: 1.0000e-04 eta: 1:31:32 time: 0.1876 data_time: 0.0046 loss: 0.8674 2023/03/17 18:35:06 - mmengine - INFO - Epoch(train) [95][1900/5005] lr: 1.0000e-04 eta: 1:31:12 time: 0.1788 data_time: 0.0048 loss: 1.0631 2023/03/17 18:35:24 - mmengine - INFO - Epoch(train) [95][2000/5005] lr: 1.0000e-04 eta: 1:30:53 time: 0.1743 data_time: 0.0040 loss: 0.9808 2023/03/17 18:35:43 - mmengine - INFO - Epoch(train) [95][2100/5005] lr: 1.0000e-04 eta: 1:30:33 time: 0.1886 data_time: 0.0050 loss: 0.9400 2023/03/17 18:36:01 - mmengine - INFO - Epoch(train) [95][2200/5005] lr: 1.0000e-04 eta: 1:30:14 time: 0.1810 data_time: 0.0049 loss: 0.8645 2023/03/17 18:36:20 - mmengine - INFO - Epoch(train) [95][2300/5005] lr: 1.0000e-04 eta: 1:29:54 time: 0.1899 data_time: 0.0052 loss: 0.7891 2023/03/17 18:36:38 - mmengine - INFO - Epoch(train) [95][2400/5005] lr: 1.0000e-04 eta: 1:29:35 time: 0.1859 data_time: 0.0051 loss: 0.8605 2023/03/17 18:36:58 - mmengine - INFO - Epoch(train) [95][2500/5005] lr: 1.0000e-04 eta: 1:29:15 time: 0.1941 data_time: 0.0053 loss: 0.9811 2023/03/17 18:37:04 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:37:17 - mmengine - INFO - Epoch(train) [95][2600/5005] lr: 1.0000e-04 eta: 1:28:56 time: 0.1869 data_time: 0.0051 loss: 0.8939 2023/03/17 18:37:37 - mmengine - INFO - Epoch(train) [95][2700/5005] lr: 1.0000e-04 eta: 1:28:36 time: 0.2198 data_time: 0.0050 loss: 1.2432 2023/03/17 18:38:01 - mmengine - INFO - Epoch(train) [95][2800/5005] lr: 1.0000e-04 eta: 1:28:17 time: 0.1958 data_time: 0.0050 loss: 0.8539 2023/03/17 18:38:20 - mmengine - INFO - Epoch(train) [95][2900/5005] lr: 1.0000e-04 eta: 1:27:58 time: 0.2116 data_time: 0.0049 loss: 0.8416 2023/03/17 18:38:40 - mmengine - INFO - Epoch(train) [95][3000/5005] lr: 1.0000e-04 eta: 1:27:38 time: 0.2060 data_time: 0.0045 loss: 0.9321 2023/03/17 18:38:59 - mmengine - INFO - Epoch(train) [95][3100/5005] lr: 1.0000e-04 eta: 1:27:19 time: 0.2585 data_time: 0.0052 loss: 0.8260 2023/03/17 18:39:20 - mmengine - INFO - Epoch(train) [95][3200/5005] lr: 1.0000e-04 eta: 1:26:59 time: 0.1827 data_time: 0.0048 loss: 0.9926 2023/03/17 18:39:39 - mmengine - INFO - Epoch(train) [95][3300/5005] lr: 1.0000e-04 eta: 1:26:40 time: 0.1901 data_time: 0.0051 loss: 0.8805 2023/03/17 18:39:59 - mmengine - INFO - Epoch(train) [95][3400/5005] lr: 1.0000e-04 eta: 1:26:20 time: 0.1926 data_time: 0.0049 loss: 0.9205 2023/03/17 18:40:20 - mmengine - INFO - Epoch(train) [95][3500/5005] lr: 1.0000e-04 eta: 1:26:01 time: 0.1926 data_time: 0.0053 loss: 1.0113 2023/03/17 18:40:26 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:40:43 - mmengine - INFO - Epoch(train) [95][3600/5005] lr: 1.0000e-04 eta: 1:25:42 time: 0.2510 data_time: 0.0049 loss: 0.9108 2023/03/17 18:41:08 - mmengine - INFO - Epoch(train) [95][3700/5005] lr: 1.0000e-04 eta: 1:25:22 time: 0.2364 data_time: 0.0053 loss: 0.8415 2023/03/17 18:41:28 - mmengine - INFO - Epoch(train) [95][3800/5005] lr: 1.0000e-04 eta: 1:25:03 time: 0.1935 data_time: 0.0054 loss: 0.9695 2023/03/17 18:41:48 - mmengine - INFO - Epoch(train) [95][3900/5005] lr: 1.0000e-04 eta: 1:24:44 time: 0.1866 data_time: 0.0051 loss: 0.8640 2023/03/17 18:42:07 - mmengine - INFO - Epoch(train) [95][4000/5005] lr: 1.0000e-04 eta: 1:24:24 time: 0.1899 data_time: 0.0048 loss: 0.8254 2023/03/17 18:42:29 - mmengine - INFO - Epoch(train) [95][4100/5005] lr: 1.0000e-04 eta: 1:24:05 time: 0.2171 data_time: 0.0050 loss: 0.9462 2023/03/17 18:42:50 - mmengine - INFO - Epoch(train) [95][4200/5005] lr: 1.0000e-04 eta: 1:23:45 time: 0.1935 data_time: 0.0061 loss: 0.9403 2023/03/17 18:43:09 - mmengine - INFO - Epoch(train) [95][4300/5005] lr: 1.0000e-04 eta: 1:23:26 time: 0.1971 data_time: 0.0053 loss: 0.9779 2023/03/17 18:43:29 - mmengine - INFO - Epoch(train) [95][4400/5005] lr: 1.0000e-04 eta: 1:23:06 time: 0.1978 data_time: 0.0051 loss: 0.9859 2023/03/17 18:43:48 - mmengine - INFO - Epoch(train) [95][4500/5005] lr: 1.0000e-04 eta: 1:22:47 time: 0.1900 data_time: 0.0049 loss: 0.9728 2023/03/17 18:43:54 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:44:08 - mmengine - INFO - Epoch(train) [95][4600/5005] lr: 1.0000e-04 eta: 1:22:28 time: 0.2001 data_time: 0.0058 loss: 0.9083 2023/03/17 18:44:29 - mmengine - INFO - Epoch(train) [95][4700/5005] lr: 1.0000e-04 eta: 1:22:08 time: 0.2166 data_time: 0.0054 loss: 0.7769 2023/03/17 18:44:52 - mmengine - INFO - Epoch(train) [95][4800/5005] lr: 1.0000e-04 eta: 1:21:49 time: 0.1989 data_time: 0.0052 loss: 0.7875 2023/03/17 18:45:13 - mmengine - INFO - Epoch(train) [95][4900/5005] lr: 1.0000e-04 eta: 1:21:29 time: 0.2075 data_time: 0.0057 loss: 0.8224 2023/03/17 18:45:35 - mmengine - INFO - Epoch(train) [95][5000/5005] lr: 1.0000e-04 eta: 1:21:10 time: 0.2087 data_time: 0.0061 loss: 0.9329 2023/03/17 18:45:36 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:45:37 - mmengine - INFO - Saving checkpoint at 95 epochs 2023/03/17 18:45:43 - mmengine - INFO - Epoch(val) [95][100/196] eta: 0:00:05 time: 0.0508 data_time: 0.0009 2023/03/17 18:46:09 - mmengine - INFO - Epoch(val) [95][196/196] accuracy/top1: 76.3540 accuracy/top5: 93.1260data_time: 0.0096 time: 0.0402 2023/03/17 18:46:31 - mmengine - INFO - Epoch(train) [96][ 100/5005] lr: 1.0000e-04 eta: 1:20:50 time: 0.2017 data_time: 0.0053 loss: 1.0046 2023/03/17 18:46:50 - mmengine - INFO - Epoch(train) [96][ 200/5005] lr: 1.0000e-04 eta: 1:20:30 time: 0.2016 data_time: 0.0056 loss: 1.0576 2023/03/17 18:47:09 - mmengine - INFO - Epoch(train) [96][ 300/5005] lr: 1.0000e-04 eta: 1:20:11 time: 0.1903 data_time: 0.0041 loss: 0.9874 2023/03/17 18:47:29 - mmengine - INFO - Epoch(train) [96][ 400/5005] lr: 1.0000e-04 eta: 1:19:51 time: 0.2022 data_time: 0.0050 loss: 1.0703 2023/03/17 18:47:49 - mmengine - INFO - Epoch(train) [96][ 500/5005] lr: 1.0000e-04 eta: 1:19:32 time: 0.2023 data_time: 0.0038 loss: 0.9280 2023/03/17 18:47:54 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:48:09 - mmengine - INFO - Epoch(train) [96][ 600/5005] lr: 1.0000e-04 eta: 1:19:12 time: 0.1916 data_time: 0.0042 loss: 0.9100 2023/03/17 18:48:28 - mmengine - INFO - Epoch(train) [96][ 700/5005] lr: 1.0000e-04 eta: 1:18:53 time: 0.1997 data_time: 0.0041 loss: 0.9555 2023/03/17 18:48:48 - mmengine - INFO - Epoch(train) [96][ 800/5005] lr: 1.0000e-04 eta: 1:18:34 time: 0.1940 data_time: 0.0052 loss: 0.8325 2023/03/17 18:49:10 - mmengine - INFO - Epoch(train) [96][ 900/5005] lr: 1.0000e-04 eta: 1:18:14 time: 0.2531 data_time: 0.0039 loss: 1.0128 2023/03/17 18:49:34 - mmengine - INFO - Epoch(train) [96][1000/5005] lr: 1.0000e-04 eta: 1:17:55 time: 0.1917 data_time: 0.0058 loss: 0.9869 2023/03/17 18:49:53 - mmengine - INFO - Epoch(train) [96][1100/5005] lr: 1.0000e-04 eta: 1:17:36 time: 0.1860 data_time: 0.0053 loss: 0.9429 2023/03/17 18:50:13 - mmengine - INFO - Epoch(train) [96][1200/5005] lr: 1.0000e-04 eta: 1:17:16 time: 0.1931 data_time: 0.0044 loss: 1.0497 2023/03/17 18:50:33 - mmengine - INFO - Epoch(train) [96][1300/5005] lr: 1.0000e-04 eta: 1:16:57 time: 0.1928 data_time: 0.0041 loss: 0.7549 2023/03/17 18:50:51 - mmengine - INFO - Epoch(train) [96][1400/5005] lr: 1.0000e-04 eta: 1:16:37 time: 0.1927 data_time: 0.0048 loss: 0.9173 2023/03/17 18:51:11 - mmengine - INFO - Epoch(train) [96][1500/5005] lr: 1.0000e-04 eta: 1:16:18 time: 0.1904 data_time: 0.0048 loss: 0.9319 2023/03/17 18:51:16 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:51:32 - mmengine - INFO - Epoch(train) [96][1600/5005] lr: 1.0000e-04 eta: 1:15:58 time: 0.2055 data_time: 0.0058 loss: 0.8554 2023/03/17 18:51:52 - mmengine - INFO - Epoch(train) [96][1700/5005] lr: 1.0000e-04 eta: 1:15:39 time: 0.2022 data_time: 0.0059 loss: 0.9493 2023/03/17 18:52:13 - mmengine - INFO - Epoch(train) [96][1800/5005] lr: 1.0000e-04 eta: 1:15:19 time: 0.2626 data_time: 0.0041 loss: 1.0319 2023/03/17 18:52:33 - mmengine - INFO - Epoch(train) [96][1900/5005] lr: 1.0000e-04 eta: 1:15:00 time: 0.1983 data_time: 0.0040 loss: 0.8816 2023/03/17 18:52:53 - mmengine - INFO - Epoch(train) [96][2000/5005] lr: 1.0000e-04 eta: 1:14:41 time: 0.1884 data_time: 0.0050 loss: 0.8913 2023/03/17 18:53:13 - mmengine - INFO - Epoch(train) [96][2100/5005] lr: 1.0000e-04 eta: 1:14:21 time: 0.1985 data_time: 0.0041 loss: 0.7946 2023/03/17 18:53:33 - mmengine - INFO - Epoch(train) [96][2200/5005] lr: 1.0000e-04 eta: 1:14:02 time: 0.2193 data_time: 0.0042 loss: 0.8285 2023/03/17 18:53:55 - mmengine - INFO - Epoch(train) [96][2300/5005] lr: 1.0000e-04 eta: 1:13:42 time: 0.2000 data_time: 0.0054 loss: 0.9139 2023/03/17 18:54:17 - mmengine - INFO - Epoch(train) [96][2400/5005] lr: 1.0000e-04 eta: 1:13:23 time: 0.2221 data_time: 0.0052 loss: 1.0925 2023/03/17 18:54:39 - mmengine - INFO - Epoch(train) [96][2500/5005] lr: 1.0000e-04 eta: 1:13:04 time: 0.2106 data_time: 0.0052 loss: 1.0213 2023/03/17 18:54:45 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:55:01 - mmengine - INFO - Epoch(train) [96][2600/5005] lr: 1.0000e-04 eta: 1:12:44 time: 0.2139 data_time: 0.0054 loss: 0.7671 2023/03/17 18:55:23 - mmengine - INFO - Epoch(train) [96][2700/5005] lr: 1.0000e-04 eta: 1:12:25 time: 0.2282 data_time: 0.0055 loss: 0.9183 2023/03/17 18:55:45 - mmengine - INFO - Epoch(train) [96][2800/5005] lr: 1.0000e-04 eta: 1:12:06 time: 0.1958 data_time: 0.0049 loss: 0.8875 2023/03/17 18:56:08 - mmengine - INFO - Epoch(train) [96][2900/5005] lr: 1.0000e-04 eta: 1:11:46 time: 0.2179 data_time: 0.0057 loss: 0.8584 2023/03/17 18:56:28 - mmengine - INFO - Epoch(train) [96][3000/5005] lr: 1.0000e-04 eta: 1:11:27 time: 0.1957 data_time: 0.0050 loss: 0.8828 2023/03/17 18:56:48 - mmengine - INFO - Epoch(train) [96][3100/5005] lr: 1.0000e-04 eta: 1:11:07 time: 0.1990 data_time: 0.0053 loss: 0.8250 2023/03/17 18:57:09 - mmengine - INFO - Epoch(train) [96][3200/5005] lr: 1.0000e-04 eta: 1:10:48 time: 0.1950 data_time: 0.0054 loss: 0.9145 2023/03/17 18:57:29 - mmengine - INFO - Epoch(train) [96][3300/5005] lr: 1.0000e-04 eta: 1:10:28 time: 0.1933 data_time: 0.0044 loss: 1.0492 2023/03/17 18:57:48 - mmengine - INFO - Epoch(train) [96][3400/5005] lr: 1.0000e-04 eta: 1:10:09 time: 0.1961 data_time: 0.0052 loss: 0.9012 2023/03/17 18:58:08 - mmengine - INFO - Epoch(train) [96][3500/5005] lr: 1.0000e-04 eta: 1:09:49 time: 0.1947 data_time: 0.0050 loss: 0.8338 2023/03/17 18:58:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 18:58:30 - mmengine - INFO - Epoch(train) [96][3600/5005] lr: 1.0000e-04 eta: 1:09:30 time: 0.2421 data_time: 0.0051 loss: 0.9768 2023/03/17 18:58:52 - mmengine - INFO - Epoch(train) [96][3700/5005] lr: 1.0000e-04 eta: 1:09:11 time: 0.2091 data_time: 0.0051 loss: 0.8151 2023/03/17 18:59:13 - mmengine - INFO - Epoch(train) [96][3800/5005] lr: 1.0000e-04 eta: 1:08:51 time: 0.2054 data_time: 0.0048 loss: 0.8194 2023/03/17 18:59:34 - mmengine - INFO - Epoch(train) [96][3900/5005] lr: 1.0000e-04 eta: 1:08:32 time: 0.2636 data_time: 0.0048 loss: 1.1824 2023/03/17 18:59:54 - mmengine - INFO - Epoch(train) [96][4000/5005] lr: 1.0000e-04 eta: 1:08:12 time: 0.1844 data_time: 0.0048 loss: 1.0524 2023/03/17 19:00:13 - mmengine - INFO - Epoch(train) [96][4100/5005] lr: 1.0000e-04 eta: 1:07:53 time: 0.1873 data_time: 0.0045 loss: 0.8981 2023/03/17 19:00:36 - mmengine - INFO - Epoch(train) [96][4200/5005] lr: 1.0000e-04 eta: 1:07:34 time: 0.2338 data_time: 0.0057 loss: 0.9514 2023/03/17 19:01:00 - mmengine - INFO - Epoch(train) [96][4300/5005] lr: 1.0000e-04 eta: 1:07:14 time: 0.2495 data_time: 0.0052 loss: 0.8819 2023/03/17 19:01:20 - mmengine - INFO - Epoch(train) [96][4400/5005] lr: 1.0000e-04 eta: 1:06:55 time: 0.1834 data_time: 0.0053 loss: 0.8236 2023/03/17 19:01:41 - mmengine - INFO - Epoch(train) [96][4500/5005] lr: 1.0000e-04 eta: 1:06:35 time: 0.2542 data_time: 0.0039 loss: 0.9158 2023/03/17 19:01:47 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:02:05 - mmengine - INFO - Epoch(train) [96][4600/5005] lr: 1.0000e-04 eta: 1:06:16 time: 0.2071 data_time: 0.0057 loss: 0.9864 2023/03/17 19:02:26 - mmengine - INFO - Epoch(train) [96][4700/5005] lr: 1.0000e-04 eta: 1:05:57 time: 0.2100 data_time: 0.0053 loss: 0.8901 2023/03/17 19:02:46 - mmengine - INFO - Epoch(train) [96][4800/5005] lr: 1.0000e-04 eta: 1:05:37 time: 0.2089 data_time: 0.0052 loss: 0.9643 2023/03/17 19:03:07 - mmengine - INFO - Epoch(train) [96][4900/5005] lr: 1.0000e-04 eta: 1:05:18 time: 0.1917 data_time: 0.0045 loss: 1.1557 2023/03/17 19:03:27 - mmengine - INFO - Epoch(train) [96][5000/5005] lr: 1.0000e-04 eta: 1:04:58 time: 0.2119 data_time: 0.0061 loss: 0.9850 2023/03/17 19:03:28 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:03:28 - mmengine - INFO - Saving checkpoint at 96 epochs 2023/03/17 19:03:36 - mmengine - INFO - Epoch(val) [96][100/196] eta: 0:00:06 time: 0.0487 data_time: 0.0011 2023/03/17 19:04:02 - mmengine - INFO - Epoch(val) [96][196/196] accuracy/top1: 76.3840 accuracy/top5: 93.1280data_time: 0.0352 time: 0.0727 2023/03/17 19:04:22 - mmengine - INFO - Epoch(train) [97][ 100/5005] lr: 1.0000e-04 eta: 1:04:38 time: 0.1923 data_time: 0.0047 loss: 0.8706 2023/03/17 19:04:41 - mmengine - INFO - Epoch(train) [97][ 200/5005] lr: 1.0000e-04 eta: 1:04:19 time: 0.1930 data_time: 0.0052 loss: 1.1627 2023/03/17 19:05:01 - mmengine - INFO - Epoch(train) [97][ 300/5005] lr: 1.0000e-04 eta: 1:03:59 time: 0.1896 data_time: 0.0055 loss: 1.0106 2023/03/17 19:05:21 - mmengine - INFO - Epoch(train) [97][ 400/5005] lr: 1.0000e-04 eta: 1:03:40 time: 0.2109 data_time: 0.0047 loss: 0.9258 2023/03/17 19:05:40 - mmengine - INFO - Epoch(train) [97][ 500/5005] lr: 1.0000e-04 eta: 1:03:20 time: 0.1797 data_time: 0.0057 loss: 0.8128 2023/03/17 19:05:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:06:00 - mmengine - INFO - Epoch(train) [97][ 600/5005] lr: 1.0000e-04 eta: 1:03:01 time: 0.2447 data_time: 0.0058 loss: 0.8589 2023/03/17 19:06:24 - mmengine - INFO - Epoch(train) [97][ 700/5005] lr: 1.0000e-04 eta: 1:02:41 time: 0.1858 data_time: 0.0053 loss: 0.8703 2023/03/17 19:06:43 - mmengine - INFO - Epoch(train) [97][ 800/5005] lr: 1.0000e-04 eta: 1:02:22 time: 0.2173 data_time: 0.0045 loss: 0.9431 2023/03/17 19:07:07 - mmengine - INFO - Epoch(train) [97][ 900/5005] lr: 1.0000e-04 eta: 1:02:03 time: 0.2132 data_time: 0.0053 loss: 0.9346 2023/03/17 19:07:27 - mmengine - INFO - Epoch(train) [97][1000/5005] lr: 1.0000e-04 eta: 1:01:43 time: 0.1907 data_time: 0.0047 loss: 0.8092 2023/03/17 19:07:46 - mmengine - INFO - Epoch(train) [97][1100/5005] lr: 1.0000e-04 eta: 1:01:24 time: 0.1956 data_time: 0.0058 loss: 0.9340 2023/03/17 19:08:09 - mmengine - INFO - Epoch(train) [97][1200/5005] lr: 1.0000e-04 eta: 1:01:04 time: 0.2692 data_time: 0.0043 loss: 0.9697 2023/03/17 19:08:34 - mmengine - INFO - Epoch(train) [97][1300/5005] lr: 1.0000e-04 eta: 1:00:45 time: 0.2023 data_time: 0.0038 loss: 1.1121 2023/03/17 19:08:55 - mmengine - INFO - Epoch(train) [97][1400/5005] lr: 1.0000e-04 eta: 1:00:26 time: 0.2431 data_time: 0.0037 loss: 1.0642 2023/03/17 19:09:17 - mmengine - INFO - Epoch(train) [97][1500/5005] lr: 1.0000e-04 eta: 1:00:06 time: 0.1948 data_time: 0.0046 loss: 1.0998 2023/03/17 19:09:21 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:09:37 - mmengine - INFO - Epoch(train) [97][1600/5005] lr: 1.0000e-04 eta: 0:59:47 time: 0.1956 data_time: 0.0052 loss: 0.9393 2023/03/17 19:09:57 - mmengine - INFO - Epoch(train) [97][1700/5005] lr: 1.0000e-04 eta: 0:59:27 time: 0.1936 data_time: 0.0056 loss: 0.8180 2023/03/17 19:10:16 - mmengine - INFO - Epoch(train) [97][1800/5005] lr: 1.0000e-04 eta: 0:59:08 time: 0.1892 data_time: 0.0045 loss: 0.9600 2023/03/17 19:10:37 - mmengine - INFO - Epoch(train) [97][1900/5005] lr: 1.0000e-04 eta: 0:58:48 time: 0.2354 data_time: 0.0045 loss: 0.9217 2023/03/17 19:11:01 - mmengine - INFO - Epoch(train) [97][2000/5005] lr: 1.0000e-04 eta: 0:58:29 time: 0.2408 data_time: 0.0039 loss: 0.9732 2023/03/17 19:11:23 - mmengine - INFO - Epoch(train) [97][2100/5005] lr: 1.0000e-04 eta: 0:58:10 time: 0.1939 data_time: 0.0046 loss: 0.9568 2023/03/17 19:11:42 - mmengine - INFO - Epoch(train) [97][2200/5005] lr: 1.0000e-04 eta: 0:57:50 time: 0.1823 data_time: 0.0058 loss: 0.8994 2023/03/17 19:12:00 - mmengine - INFO - Epoch(train) [97][2300/5005] lr: 1.0000e-04 eta: 0:57:31 time: 0.1800 data_time: 0.0062 loss: 0.9905 2023/03/17 19:12:18 - mmengine - INFO - Epoch(train) [97][2400/5005] lr: 1.0000e-04 eta: 0:57:11 time: 0.1844 data_time: 0.0049 loss: 0.9762 2023/03/17 19:12:38 - mmengine - INFO - Epoch(train) [97][2500/5005] lr: 1.0000e-04 eta: 0:56:52 time: 0.1839 data_time: 0.0054 loss: 1.0867 2023/03/17 19:12:42 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:12:57 - mmengine - INFO - Epoch(train) [97][2600/5005] lr: 1.0000e-04 eta: 0:56:32 time: 0.1762 data_time: 0.0055 loss: 0.8932 2023/03/17 19:13:16 - mmengine - INFO - Epoch(train) [97][2700/5005] lr: 1.0000e-04 eta: 0:56:13 time: 0.1926 data_time: 0.0052 loss: 0.9577 2023/03/17 19:13:35 - mmengine - INFO - Epoch(train) [97][2800/5005] lr: 1.0000e-04 eta: 0:55:53 time: 0.1950 data_time: 0.0051 loss: 0.8479 2023/03/17 19:13:54 - mmengine - INFO - Epoch(train) [97][2900/5005] lr: 1.0000e-04 eta: 0:55:34 time: 0.1914 data_time: 0.0048 loss: 0.8254 2023/03/17 19:14:13 - mmengine - INFO - Epoch(train) [97][3000/5005] lr: 1.0000e-04 eta: 0:55:14 time: 0.1825 data_time: 0.0049 loss: 0.8781 2023/03/17 19:14:33 - mmengine - INFO - Epoch(train) [97][3100/5005] lr: 1.0000e-04 eta: 0:54:55 time: 0.2271 data_time: 0.0057 loss: 0.8909 2023/03/17 19:14:53 - mmengine - INFO - Epoch(train) [97][3200/5005] lr: 1.0000e-04 eta: 0:54:35 time: 0.2090 data_time: 0.0059 loss: 0.7638 2023/03/17 19:15:16 - mmengine - INFO - Epoch(train) [97][3300/5005] lr: 1.0000e-04 eta: 0:54:16 time: 0.2271 data_time: 0.0040 loss: 0.9813 2023/03/17 19:15:38 - mmengine - INFO - Epoch(train) [97][3400/5005] lr: 1.0000e-04 eta: 0:53:56 time: 0.2348 data_time: 0.0049 loss: 1.1042 2023/03/17 19:15:59 - mmengine - INFO - Epoch(train) [97][3500/5005] lr: 1.0000e-04 eta: 0:53:37 time: 0.2465 data_time: 0.0047 loss: 0.8394 2023/03/17 19:16:04 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:16:23 - mmengine - INFO - Epoch(train) [97][3600/5005] lr: 1.0000e-04 eta: 0:53:18 time: 0.2526 data_time: 0.0044 loss: 0.8457 2023/03/17 19:16:46 - mmengine - INFO - Epoch(train) [97][3700/5005] lr: 1.0000e-04 eta: 0:52:58 time: 0.2187 data_time: 0.0042 loss: 0.8295 2023/03/17 19:17:09 - mmengine - INFO - Epoch(train) [97][3800/5005] lr: 1.0000e-04 eta: 0:52:39 time: 0.2243 data_time: 0.0050 loss: 0.8220 2023/03/17 19:17:33 - mmengine - INFO - Epoch(train) [97][3900/5005] lr: 1.0000e-04 eta: 0:52:20 time: 0.2400 data_time: 0.0048 loss: 0.9951 2023/03/17 19:17:57 - mmengine - INFO - Epoch(train) [97][4000/5005] lr: 1.0000e-04 eta: 0:52:00 time: 0.2410 data_time: 0.0041 loss: 0.9624 2023/03/17 19:18:16 - mmengine - INFO - Epoch(train) [97][4100/5005] lr: 1.0000e-04 eta: 0:51:41 time: 0.1815 data_time: 0.0050 loss: 0.9003 2023/03/17 19:18:37 - mmengine - INFO - Epoch(train) [97][4200/5005] lr: 1.0000e-04 eta: 0:51:21 time: 0.2010 data_time: 0.0055 loss: 0.8478 2023/03/17 19:18:56 - mmengine - INFO - Epoch(train) [97][4300/5005] lr: 1.0000e-04 eta: 0:51:02 time: 0.1822 data_time: 0.0056 loss: 0.8642 2023/03/17 19:19:16 - mmengine - INFO - Epoch(train) [97][4400/5005] lr: 1.0000e-04 eta: 0:50:42 time: 0.1871 data_time: 0.0053 loss: 0.9750 2023/03/17 19:19:35 - mmengine - INFO - Epoch(train) [97][4500/5005] lr: 1.0000e-04 eta: 0:50:23 time: 0.1838 data_time: 0.0052 loss: 1.0033 2023/03/17 19:19:39 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:19:54 - mmengine - INFO - Epoch(train) [97][4600/5005] lr: 1.0000e-04 eta: 0:50:03 time: 0.1942 data_time: 0.0047 loss: 0.8750 2023/03/17 19:20:14 - mmengine - INFO - Epoch(train) [97][4700/5005] lr: 1.0000e-04 eta: 0:49:44 time: 0.1946 data_time: 0.0042 loss: 0.9410 2023/03/17 19:20:33 - mmengine - INFO - Epoch(train) [97][4800/5005] lr: 1.0000e-04 eta: 0:49:24 time: 0.1867 data_time: 0.0049 loss: 0.8311 2023/03/17 19:20:52 - mmengine - INFO - Epoch(train) [97][4900/5005] lr: 1.0000e-04 eta: 0:49:05 time: 0.1928 data_time: 0.0048 loss: 0.7828 2023/03/17 19:21:12 - mmengine - INFO - Epoch(train) [97][5000/5005] lr: 1.0000e-04 eta: 0:48:45 time: 0.1877 data_time: 0.0051 loss: 0.7592 2023/03/17 19:21:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:21:13 - mmengine - INFO - Saving checkpoint at 97 epochs 2023/03/17 19:21:20 - mmengine - INFO - Epoch(val) [97][100/196] eta: 0:00:04 time: 0.0454 data_time: 0.0008 2023/03/17 19:21:44 - mmengine - INFO - Epoch(val) [97][196/196] accuracy/top1: 76.3100 accuracy/top5: 93.1460data_time: 0.0190 time: 0.0507 2023/03/17 19:22:08 - mmengine - INFO - Epoch(train) [98][ 100/5005] lr: 1.0000e-04 eta: 0:48:25 time: 0.1993 data_time: 0.0048 loss: 0.9987 2023/03/17 19:22:28 - mmengine - INFO - Epoch(train) [98][ 200/5005] lr: 1.0000e-04 eta: 0:48:06 time: 0.1986 data_time: 0.0055 loss: 0.8742 2023/03/17 19:22:47 - mmengine - INFO - Epoch(train) [98][ 300/5005] lr: 1.0000e-04 eta: 0:47:46 time: 0.2068 data_time: 0.0049 loss: 0.9198 2023/03/17 19:23:08 - mmengine - INFO - Epoch(train) [98][ 400/5005] lr: 1.0000e-04 eta: 0:47:27 time: 0.1910 data_time: 0.0063 loss: 0.9537 2023/03/17 19:23:28 - mmengine - INFO - Epoch(train) [98][ 500/5005] lr: 1.0000e-04 eta: 0:47:07 time: 0.1807 data_time: 0.0059 loss: 0.7775 2023/03/17 19:23:31 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:23:48 - mmengine - INFO - Epoch(train) [98][ 600/5005] lr: 1.0000e-04 eta: 0:46:48 time: 0.2321 data_time: 0.0050 loss: 0.8677 2023/03/17 19:24:10 - mmengine - INFO - Epoch(train) [98][ 700/5005] lr: 1.0000e-04 eta: 0:46:28 time: 0.2061 data_time: 0.0052 loss: 0.9121 2023/03/17 19:24:32 - mmengine - INFO - Epoch(train) [98][ 800/5005] lr: 1.0000e-04 eta: 0:46:09 time: 0.2249 data_time: 0.0048 loss: 0.9996 2023/03/17 19:24:55 - mmengine - INFO - Epoch(train) [98][ 900/5005] lr: 1.0000e-04 eta: 0:45:49 time: 0.2315 data_time: 0.0047 loss: 0.9452 2023/03/17 19:25:17 - mmengine - INFO - Epoch(train) [98][1000/5005] lr: 1.0000e-04 eta: 0:45:30 time: 0.2188 data_time: 0.0047 loss: 0.8459 2023/03/17 19:25:38 - mmengine - INFO - Epoch(train) [98][1100/5005] lr: 1.0000e-04 eta: 0:45:11 time: 0.1844 data_time: 0.0048 loss: 0.9333 2023/03/17 19:25:56 - mmengine - INFO - Epoch(train) [98][1200/5005] lr: 1.0000e-04 eta: 0:44:51 time: 0.1757 data_time: 0.0049 loss: 0.9997 2023/03/17 19:26:14 - mmengine - INFO - Epoch(train) [98][1300/5005] lr: 1.0000e-04 eta: 0:44:32 time: 0.1934 data_time: 0.0056 loss: 0.8757 2023/03/17 19:26:35 - mmengine - INFO - Epoch(train) [98][1400/5005] lr: 1.0000e-04 eta: 0:44:12 time: 0.2009 data_time: 0.0052 loss: 0.7744 2023/03/17 19:26:55 - mmengine - INFO - Epoch(train) [98][1500/5005] lr: 1.0000e-04 eta: 0:43:53 time: 0.1823 data_time: 0.0054 loss: 0.9894 2023/03/17 19:26:58 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:27:16 - mmengine - INFO - Epoch(train) [98][1600/5005] lr: 1.0000e-04 eta: 0:43:33 time: 0.2361 data_time: 0.0045 loss: 1.0527 2023/03/17 19:27:40 - mmengine - INFO - Epoch(train) [98][1700/5005] lr: 1.0000e-04 eta: 0:43:14 time: 0.2559 data_time: 0.0045 loss: 1.1629 2023/03/17 19:28:03 - mmengine - INFO - Epoch(train) [98][1800/5005] lr: 1.0000e-04 eta: 0:42:54 time: 0.2128 data_time: 0.0044 loss: 0.9431 2023/03/17 19:28:22 - mmengine - INFO - Epoch(train) [98][1900/5005] lr: 1.0000e-04 eta: 0:42:35 time: 0.1856 data_time: 0.0047 loss: 0.9654 2023/03/17 19:28:41 - mmengine - INFO - Epoch(train) [98][2000/5005] lr: 1.0000e-04 eta: 0:42:15 time: 0.1704 data_time: 0.0055 loss: 0.8948 2023/03/17 19:29:00 - mmengine - INFO - Epoch(train) [98][2100/5005] lr: 1.0000e-04 eta: 0:41:56 time: 0.2331 data_time: 0.0048 loss: 0.9540 2023/03/17 19:29:21 - mmengine - INFO - Epoch(train) [98][2200/5005] lr: 1.0000e-04 eta: 0:41:36 time: 0.1755 data_time: 0.0066 loss: 0.8516 2023/03/17 19:29:44 - mmengine - INFO - Epoch(train) [98][2300/5005] lr: 1.0000e-04 eta: 0:41:17 time: 0.1924 data_time: 0.0057 loss: 0.9151 2023/03/17 19:30:04 - mmengine - INFO - Epoch(train) [98][2400/5005] lr: 1.0000e-04 eta: 0:40:58 time: 0.1884 data_time: 0.0045 loss: 0.9976 2023/03/17 19:30:23 - mmengine - INFO - Epoch(train) [98][2500/5005] lr: 1.0000e-04 eta: 0:40:38 time: 0.2022 data_time: 0.0047 loss: 0.9812 2023/03/17 19:30:26 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:30:47 - mmengine - INFO - Epoch(train) [98][2600/5005] lr: 1.0000e-04 eta: 0:40:19 time: 0.2340 data_time: 0.0045 loss: 0.8549 2023/03/17 19:31:13 - mmengine - INFO - Epoch(train) [98][2700/5005] lr: 1.0000e-04 eta: 0:39:59 time: 0.2480 data_time: 0.0038 loss: 0.9265 2023/03/17 19:31:36 - mmengine - INFO - Epoch(train) [98][2800/5005] lr: 1.0000e-04 eta: 0:39:40 time: 0.1988 data_time: 0.0054 loss: 0.8523 2023/03/17 19:31:55 - mmengine - INFO - Epoch(train) [98][2900/5005] lr: 1.0000e-04 eta: 0:39:20 time: 0.2016 data_time: 0.0040 loss: 0.8841 2023/03/17 19:32:17 - mmengine - INFO - Epoch(train) [98][3000/5005] lr: 1.0000e-04 eta: 0:39:01 time: 0.2619 data_time: 0.0049 loss: 0.8608 2023/03/17 19:32:39 - mmengine - INFO - Epoch(train) [98][3100/5005] lr: 1.0000e-04 eta: 0:38:42 time: 0.2280 data_time: 0.0054 loss: 1.0377 2023/03/17 19:33:04 - mmengine - INFO - Epoch(train) [98][3200/5005] lr: 1.0000e-04 eta: 0:38:22 time: 0.1963 data_time: 0.0057 loss: 0.8841 2023/03/17 19:33:23 - mmengine - INFO - Epoch(train) [98][3300/5005] lr: 1.0000e-04 eta: 0:38:03 time: 0.1826 data_time: 0.0048 loss: 0.7319 2023/03/17 19:33:42 - mmengine - INFO - Epoch(train) [98][3400/5005] lr: 1.0000e-04 eta: 0:37:43 time: 0.2026 data_time: 0.0041 loss: 1.0488 2023/03/17 19:34:00 - mmengine - INFO - Epoch(train) [98][3500/5005] lr: 1.0000e-04 eta: 0:37:24 time: 0.1870 data_time: 0.0053 loss: 0.8989 2023/03/17 19:34:03 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:34:19 - mmengine - INFO - Epoch(train) [98][3600/5005] lr: 1.0000e-04 eta: 0:37:04 time: 0.1858 data_time: 0.0051 loss: 1.1110 2023/03/17 19:34:38 - mmengine - INFO - Epoch(train) [98][3700/5005] lr: 1.0000e-04 eta: 0:36:45 time: 0.1760 data_time: 0.0056 loss: 0.8243 2023/03/17 19:34:56 - mmengine - INFO - Epoch(train) [98][3800/5005] lr: 1.0000e-04 eta: 0:36:25 time: 0.1794 data_time: 0.0053 loss: 0.7651 2023/03/17 19:35:14 - mmengine - INFO - Epoch(train) [98][3900/5005] lr: 1.0000e-04 eta: 0:36:06 time: 0.1728 data_time: 0.0063 loss: 1.0553 2023/03/17 19:35:34 - mmengine - INFO - Epoch(train) [98][4000/5005] lr: 1.0000e-04 eta: 0:35:46 time: 0.2240 data_time: 0.0047 loss: 0.9655 2023/03/17 19:35:55 - mmengine - INFO - Epoch(train) [98][4100/5005] lr: 1.0000e-04 eta: 0:35:27 time: 0.1942 data_time: 0.0056 loss: 0.7947 2023/03/17 19:36:15 - mmengine - INFO - Epoch(train) [98][4200/5005] lr: 1.0000e-04 eta: 0:35:07 time: 0.1885 data_time: 0.0052 loss: 0.7972 2023/03/17 19:36:34 - mmengine - INFO - Epoch(train) [98][4300/5005] lr: 1.0000e-04 eta: 0:34:48 time: 0.1859 data_time: 0.0046 loss: 0.9830 2023/03/17 19:36:53 - mmengine - INFO - Epoch(train) [98][4400/5005] lr: 1.0000e-04 eta: 0:34:28 time: 0.1971 data_time: 0.0055 loss: 0.9677 2023/03/17 19:37:15 - mmengine - INFO - Epoch(train) [98][4500/5005] lr: 1.0000e-04 eta: 0:34:09 time: 0.1985 data_time: 0.0056 loss: 1.0761 2023/03/17 19:37:18 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:37:34 - mmengine - INFO - Epoch(train) [98][4600/5005] lr: 1.0000e-04 eta: 0:33:49 time: 0.1862 data_time: 0.0053 loss: 0.9799 2023/03/17 19:37:53 - mmengine - INFO - Epoch(train) [98][4700/5005] lr: 1.0000e-04 eta: 0:33:30 time: 0.1775 data_time: 0.0058 loss: 0.8982 2023/03/17 19:38:11 - mmengine - INFO - Epoch(train) [98][4800/5005] lr: 1.0000e-04 eta: 0:33:10 time: 0.1788 data_time: 0.0040 loss: 0.9612 2023/03/17 19:38:31 - mmengine - INFO - Epoch(train) [98][4900/5005] lr: 1.0000e-04 eta: 0:32:51 time: 0.1901 data_time: 0.0052 loss: 0.8651 2023/03/17 19:38:50 - mmengine - INFO - Epoch(train) [98][5000/5005] lr: 1.0000e-04 eta: 0:32:31 time: 0.1899 data_time: 0.0056 loss: 0.9418 2023/03/17 19:38:51 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:38:52 - mmengine - INFO - Saving checkpoint at 98 epochs 2023/03/17 19:38:59 - mmengine - INFO - Epoch(val) [98][100/196] eta: 0:00:05 time: 0.0631 data_time: 0.0008 2023/03/17 19:39:27 - mmengine - INFO - Epoch(val) [98][196/196] accuracy/top1: 76.4380 accuracy/top5: 93.1260data_time: 0.0272 time: 0.0586 2023/03/17 19:39:52 - mmengine - INFO - Epoch(train) [99][ 100/5005] lr: 1.0000e-04 eta: 0:32:11 time: 0.2017 data_time: 0.0047 loss: 1.0011 2023/03/17 19:40:12 - mmengine - INFO - Epoch(train) [99][ 200/5005] lr: 1.0000e-04 eta: 0:31:51 time: 0.1863 data_time: 0.0052 loss: 0.8465 2023/03/17 19:40:32 - mmengine - INFO - Epoch(train) [99][ 300/5005] lr: 1.0000e-04 eta: 0:31:32 time: 0.1904 data_time: 0.0050 loss: 0.8274 2023/03/17 19:40:51 - mmengine - INFO - Epoch(train) [99][ 400/5005] lr: 1.0000e-04 eta: 0:31:12 time: 0.1810 data_time: 0.0045 loss: 0.8845 2023/03/17 19:41:09 - mmengine - INFO - Epoch(train) [99][ 500/5005] lr: 1.0000e-04 eta: 0:30:53 time: 0.1850 data_time: 0.0051 loss: 0.8853 2023/03/17 19:41:10 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:41:28 - mmengine - INFO - Epoch(train) [99][ 600/5005] lr: 1.0000e-04 eta: 0:30:33 time: 0.1866 data_time: 0.0044 loss: 0.9342 2023/03/17 19:41:47 - mmengine - INFO - Epoch(train) [99][ 700/5005] lr: 1.0000e-04 eta: 0:30:14 time: 0.1934 data_time: 0.0052 loss: 0.8540 2023/03/17 19:42:06 - mmengine - INFO - Epoch(train) [99][ 800/5005] lr: 1.0000e-04 eta: 0:29:54 time: 0.1956 data_time: 0.0046 loss: 0.8588 2023/03/17 19:42:26 - mmengine - INFO - Epoch(train) [99][ 900/5005] lr: 1.0000e-04 eta: 0:29:35 time: 0.2141 data_time: 0.0045 loss: 0.9327 2023/03/17 19:42:46 - mmengine - INFO - Epoch(train) [99][1000/5005] lr: 1.0000e-04 eta: 0:29:15 time: 0.2003 data_time: 0.0051 loss: 0.8569 2023/03/17 19:43:05 - mmengine - INFO - Epoch(train) [99][1100/5005] lr: 1.0000e-04 eta: 0:28:56 time: 0.1842 data_time: 0.0044 loss: 1.0584 2023/03/17 19:43:24 - mmengine - INFO - Epoch(train) [99][1200/5005] lr: 1.0000e-04 eta: 0:28:36 time: 0.1658 data_time: 0.0058 loss: 1.0707 2023/03/17 19:43:42 - mmengine - INFO - Epoch(train) [99][1300/5005] lr: 1.0000e-04 eta: 0:28:17 time: 0.1933 data_time: 0.0046 loss: 0.9317 2023/03/17 19:44:01 - mmengine - INFO - Epoch(train) [99][1400/5005] lr: 1.0000e-04 eta: 0:27:57 time: 0.2082 data_time: 0.0055 loss: 0.7350 2023/03/17 19:44:22 - mmengine - INFO - Epoch(train) [99][1500/5005] lr: 1.0000e-04 eta: 0:27:38 time: 0.1963 data_time: 0.0055 loss: 1.0240 2023/03/17 19:44:24 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:44:41 - mmengine - INFO - Epoch(train) [99][1600/5005] lr: 1.0000e-04 eta: 0:27:18 time: 0.1944 data_time: 0.0046 loss: 0.8738 2023/03/17 19:45:02 - mmengine - INFO - Epoch(train) [99][1700/5005] lr: 1.0000e-04 eta: 0:26:59 time: 0.2396 data_time: 0.0046 loss: 0.9567 2023/03/17 19:45:26 - mmengine - INFO - Epoch(train) [99][1800/5005] lr: 1.0000e-04 eta: 0:26:40 time: 0.2107 data_time: 0.0048 loss: 0.9247 2023/03/17 19:45:46 - mmengine - INFO - Epoch(train) [99][1900/5005] lr: 1.0000e-04 eta: 0:26:20 time: 0.2001 data_time: 0.0053 loss: 0.9231 2023/03/17 19:46:06 - mmengine - INFO - Epoch(train) [99][2000/5005] lr: 1.0000e-04 eta: 0:26:01 time: 0.2138 data_time: 0.0051 loss: 0.8903 2023/03/17 19:46:27 - mmengine - INFO - Epoch(train) [99][2100/5005] lr: 1.0000e-04 eta: 0:25:41 time: 0.2203 data_time: 0.0054 loss: 0.8788 2023/03/17 19:46:46 - mmengine - INFO - Epoch(train) [99][2200/5005] lr: 1.0000e-04 eta: 0:25:22 time: 0.1790 data_time: 0.0059 loss: 0.8308 2023/03/17 19:47:05 - mmengine - INFO - Epoch(train) [99][2300/5005] lr: 1.0000e-04 eta: 0:25:02 time: 0.1846 data_time: 0.0059 loss: 1.0372 2023/03/17 19:47:25 - mmengine - INFO - Epoch(train) [99][2400/5005] lr: 1.0000e-04 eta: 0:24:43 time: 0.2409 data_time: 0.0054 loss: 0.8663 2023/03/17 19:47:48 - mmengine - INFO - Epoch(train) [99][2500/5005] lr: 1.0000e-04 eta: 0:24:23 time: 0.2572 data_time: 0.0051 loss: 0.9478 2023/03/17 19:47:51 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:48:09 - mmengine - INFO - Epoch(train) [99][2600/5005] lr: 1.0000e-04 eta: 0:24:04 time: 0.1958 data_time: 0.0052 loss: 0.9830 2023/03/17 19:48:29 - mmengine - INFO - Epoch(train) [99][2700/5005] lr: 1.0000e-04 eta: 0:23:44 time: 0.2226 data_time: 0.0055 loss: 0.9521 2023/03/17 19:48:48 - mmengine - INFO - Epoch(train) [99][2800/5005] lr: 1.0000e-04 eta: 0:23:25 time: 0.1871 data_time: 0.0050 loss: 0.9043 2023/03/17 19:49:08 - mmengine - INFO - Epoch(train) [99][2900/5005] lr: 1.0000e-04 eta: 0:23:05 time: 0.1921 data_time: 0.0048 loss: 0.9087 2023/03/17 19:49:27 - mmengine - INFO - Epoch(train) [99][3000/5005] lr: 1.0000e-04 eta: 0:22:46 time: 0.1937 data_time: 0.0057 loss: 1.0795 2023/03/17 19:49:47 - mmengine - INFO - Epoch(train) [99][3100/5005] lr: 1.0000e-04 eta: 0:22:26 time: 0.2174 data_time: 0.0053 loss: 0.9816 2023/03/17 19:50:08 - mmengine - INFO - Epoch(train) [99][3200/5005] lr: 1.0000e-04 eta: 0:22:07 time: 0.2042 data_time: 0.0051 loss: 0.8989 2023/03/17 19:50:28 - mmengine - INFO - Epoch(train) [99][3300/5005] lr: 1.0000e-04 eta: 0:21:47 time: 0.1902 data_time: 0.0051 loss: 0.8596 2023/03/17 19:50:47 - mmengine - INFO - Epoch(train) [99][3400/5005] lr: 1.0000e-04 eta: 0:21:28 time: 0.2258 data_time: 0.0051 loss: 0.9351 2023/03/17 19:51:11 - mmengine - INFO - Epoch(train) [99][3500/5005] lr: 1.0000e-04 eta: 0:21:08 time: 0.2008 data_time: 0.0049 loss: 1.0159 2023/03/17 19:51:13 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:51:32 - mmengine - INFO - Epoch(train) [99][3600/5005] lr: 1.0000e-04 eta: 0:20:49 time: 0.2490 data_time: 0.0052 loss: 0.8037 2023/03/17 19:51:55 - mmengine - INFO - Epoch(train) [99][3700/5005] lr: 1.0000e-04 eta: 0:20:29 time: 0.1962 data_time: 0.0054 loss: 0.9127 2023/03/17 19:52:15 - mmengine - INFO - Epoch(train) [99][3800/5005] lr: 1.0000e-04 eta: 0:20:10 time: 0.1957 data_time: 0.0051 loss: 0.9317 2023/03/17 19:52:35 - mmengine - INFO - Epoch(train) [99][3900/5005] lr: 1.0000e-04 eta: 0:19:50 time: 0.1894 data_time: 0.0050 loss: 0.9483 2023/03/17 19:52:54 - mmengine - INFO - Epoch(train) [99][4000/5005] lr: 1.0000e-04 eta: 0:19:31 time: 0.2040 data_time: 0.0048 loss: 0.9786 2023/03/17 19:53:14 - mmengine - INFO - Epoch(train) [99][4100/5005] lr: 1.0000e-04 eta: 0:19:11 time: 0.1825 data_time: 0.0046 loss: 0.8296 2023/03/17 19:53:32 - mmengine - INFO - Epoch(train) [99][4200/5005] lr: 1.0000e-04 eta: 0:18:52 time: 0.1827 data_time: 0.0044 loss: 0.9695 2023/03/17 19:53:51 - mmengine - INFO - Epoch(train) [99][4300/5005] lr: 1.0000e-04 eta: 0:18:32 time: 0.1876 data_time: 0.0049 loss: 0.9721 2023/03/17 19:54:10 - mmengine - INFO - Epoch(train) [99][4400/5005] lr: 1.0000e-04 eta: 0:18:13 time: 0.1815 data_time: 0.0045 loss: 0.8114 2023/03/17 19:54:32 - mmengine - INFO - Epoch(train) [99][4500/5005] lr: 1.0000e-04 eta: 0:17:54 time: 0.1942 data_time: 0.0051 loss: 0.9221 2023/03/17 19:54:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:54:51 - mmengine - INFO - Epoch(train) [99][4600/5005] lr: 1.0000e-04 eta: 0:17:34 time: 0.1852 data_time: 0.0044 loss: 0.8884 2023/03/17 19:55:11 - mmengine - INFO - Epoch(train) [99][4700/5005] lr: 1.0000e-04 eta: 0:17:15 time: 0.1956 data_time: 0.0047 loss: 0.9031 2023/03/17 19:55:30 - mmengine - INFO - Epoch(train) [99][4800/5005] lr: 1.0000e-04 eta: 0:16:55 time: 0.1921 data_time: 0.0045 loss: 0.8632 2023/03/17 19:55:50 - mmengine - INFO - Epoch(train) [99][4900/5005] lr: 1.0000e-04 eta: 0:16:36 time: 0.2036 data_time: 0.0053 loss: 1.0390 2023/03/17 19:56:10 - mmengine - INFO - Epoch(train) [99][5000/5005] lr: 1.0000e-04 eta: 0:16:16 time: 0.1919 data_time: 0.0058 loss: 0.9483 2023/03/17 19:56:11 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:56:11 - mmengine - INFO - Saving checkpoint at 99 epochs 2023/03/17 19:56:18 - mmengine - INFO - Epoch(val) [99][100/196] eta: 0:00:05 time: 0.0446 data_time: 0.0078 2023/03/17 19:56:42 - mmengine - INFO - Epoch(val) [99][196/196] accuracy/top1: 76.3960 accuracy/top5: 93.1200data_time: 0.0330 time: 0.0643 2023/03/17 19:57:05 - mmengine - INFO - Epoch(train) [100][ 100/5005] lr: 1.0000e-04 eta: 0:15:56 time: 0.2208 data_time: 0.0061 loss: 1.0514 2023/03/17 19:57:24 - mmengine - INFO - Epoch(train) [100][ 200/5005] lr: 1.0000e-04 eta: 0:15:36 time: 0.1871 data_time: 0.0043 loss: 0.9127 2023/03/17 19:57:43 - mmengine - INFO - Epoch(train) [100][ 300/5005] lr: 1.0000e-04 eta: 0:15:17 time: 0.1828 data_time: 0.0063 loss: 0.8910 2023/03/17 19:58:06 - mmengine - INFO - Epoch(train) [100][ 400/5005] lr: 1.0000e-04 eta: 0:14:57 time: 0.2314 data_time: 0.0044 loss: 0.8339 2023/03/17 19:58:26 - mmengine - INFO - Epoch(train) [100][ 500/5005] lr: 1.0000e-04 eta: 0:14:38 time: 0.1922 data_time: 0.0054 loss: 0.8848 2023/03/17 19:58:27 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 19:58:45 - mmengine - INFO - Epoch(train) [100][ 600/5005] lr: 1.0000e-04 eta: 0:14:18 time: 0.1914 data_time: 0.0048 loss: 0.9474 2023/03/17 19:59:05 - mmengine - INFO - Epoch(train) [100][ 700/5005] lr: 1.0000e-04 eta: 0:13:59 time: 0.2156 data_time: 0.0058 loss: 0.8219 2023/03/17 19:59:26 - mmengine - INFO - Epoch(train) [100][ 800/5005] lr: 1.0000e-04 eta: 0:13:39 time: 0.2034 data_time: 0.0048 loss: 1.0045 2023/03/17 19:59:47 - mmengine - INFO - Epoch(train) [100][ 900/5005] lr: 1.0000e-04 eta: 0:13:20 time: 0.1936 data_time: 0.0055 loss: 0.8647 2023/03/17 20:00:09 - mmengine - INFO - Epoch(train) [100][1000/5005] lr: 1.0000e-04 eta: 0:13:00 time: 0.2304 data_time: 0.0062 loss: 0.8616 2023/03/17 20:00:32 - mmengine - INFO - Epoch(train) [100][1100/5005] lr: 1.0000e-04 eta: 0:12:41 time: 0.1919 data_time: 0.0047 loss: 0.8833 2023/03/17 20:00:56 - mmengine - INFO - Epoch(train) [100][1200/5005] lr: 1.0000e-04 eta: 0:12:21 time: 0.2404 data_time: 0.0041 loss: 1.0561 2023/03/17 20:01:15 - mmengine - INFO - Epoch(train) [100][1300/5005] lr: 1.0000e-04 eta: 0:12:02 time: 0.1862 data_time: 0.0046 loss: 0.8900 2023/03/17 20:01:34 - mmengine - INFO - Epoch(train) [100][1400/5005] lr: 1.0000e-04 eta: 0:11:42 time: 0.1861 data_time: 0.0051 loss: 0.9075 2023/03/17 20:01:55 - mmengine - INFO - Epoch(train) [100][1500/5005] lr: 1.0000e-04 eta: 0:11:23 time: 0.2040 data_time: 0.0047 loss: 0.8935 2023/03/17 20:01:56 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 20:02:15 - mmengine - INFO - Epoch(train) [100][1600/5005] lr: 1.0000e-04 eta: 0:11:03 time: 0.2049 data_time: 0.0054 loss: 0.8779 2023/03/17 20:02:35 - mmengine - INFO - Epoch(train) [100][1700/5005] lr: 1.0000e-04 eta: 0:10:44 time: 0.1953 data_time: 0.0051 loss: 0.8357 2023/03/17 20:02:55 - mmengine - INFO - Epoch(train) [100][1800/5005] lr: 1.0000e-04 eta: 0:10:24 time: 0.1931 data_time: 0.0052 loss: 1.0157 2023/03/17 20:03:14 - mmengine - INFO - Epoch(train) [100][1900/5005] lr: 1.0000e-04 eta: 0:10:05 time: 0.1764 data_time: 0.0059 loss: 1.0138 2023/03/17 20:03:33 - mmengine - INFO - Epoch(train) [100][2000/5005] lr: 1.0000e-04 eta: 0:09:45 time: 0.1975 data_time: 0.0043 loss: 0.9448 2023/03/17 20:03:54 - mmengine - INFO - Epoch(train) [100][2100/5005] lr: 1.0000e-04 eta: 0:09:26 time: 0.1965 data_time: 0.0051 loss: 0.8771 2023/03/17 20:04:12 - mmengine - INFO - Epoch(train) [100][2200/5005] lr: 1.0000e-04 eta: 0:09:06 time: 0.1857 data_time: 0.0044 loss: 1.0232 2023/03/17 20:04:32 - mmengine - INFO - Epoch(train) [100][2300/5005] lr: 1.0000e-04 eta: 0:08:47 time: 0.2027 data_time: 0.0044 loss: 0.9588 2023/03/17 20:04:50 - mmengine - INFO - Epoch(train) [100][2400/5005] lr: 1.0000e-04 eta: 0:08:27 time: 0.1816 data_time: 0.0044 loss: 0.8942 2023/03/17 20:05:09 - mmengine - INFO - Epoch(train) [100][2500/5005] lr: 1.0000e-04 eta: 0:08:08 time: 0.1807 data_time: 0.0043 loss: 0.9282 2023/03/17 20:05:09 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 20:05:27 - mmengine - INFO - Epoch(train) [100][2600/5005] lr: 1.0000e-04 eta: 0:07:48 time: 0.1797 data_time: 0.0047 loss: 0.9085 2023/03/17 20:05:46 - mmengine - INFO - Epoch(train) [100][2700/5005] lr: 1.0000e-04 eta: 0:07:29 time: 0.1830 data_time: 0.0048 loss: 0.9886 2023/03/17 20:06:05 - mmengine - INFO - Epoch(train) [100][2800/5005] lr: 1.0000e-04 eta: 0:07:09 time: 0.1929 data_time: 0.0052 loss: 1.0659 2023/03/17 20:06:25 - mmengine - INFO - Epoch(train) [100][2900/5005] lr: 1.0000e-04 eta: 0:06:50 time: 0.1894 data_time: 0.0048 loss: 0.8969 2023/03/17 20:06:45 - mmengine - INFO - Epoch(train) [100][3000/5005] lr: 1.0000e-04 eta: 0:06:30 time: 0.1823 data_time: 0.0048 loss: 0.7712 2023/03/17 20:07:03 - mmengine - INFO - Epoch(train) [100][3100/5005] lr: 1.0000e-04 eta: 0:06:11 time: 0.1843 data_time: 0.0044 loss: 1.0580 2023/03/17 20:07:24 - mmengine - INFO - Epoch(train) [100][3200/5005] lr: 1.0000e-04 eta: 0:05:51 time: 0.2133 data_time: 0.0059 loss: 0.9490 2023/03/17 20:07:48 - mmengine - INFO - Epoch(train) [100][3300/5005] lr: 1.0000e-04 eta: 0:05:32 time: 0.2069 data_time: 0.0042 loss: 1.0048 2023/03/17 20:08:11 - mmengine - INFO - Epoch(train) [100][3400/5005] lr: 1.0000e-04 eta: 0:05:12 time: 0.2327 data_time: 0.0046 loss: 0.7963 2023/03/17 20:08:33 - mmengine - INFO - Epoch(train) [100][3500/5005] lr: 1.0000e-04 eta: 0:04:53 time: 0.1935 data_time: 0.0054 loss: 0.9770 2023/03/17 20:08:34 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 20:08:53 - mmengine - INFO - Epoch(train) [100][3600/5005] lr: 1.0000e-04 eta: 0:04:33 time: 0.1870 data_time: 0.0061 loss: 0.8486 2023/03/17 20:09:13 - mmengine - INFO - Epoch(train) [100][3700/5005] lr: 1.0000e-04 eta: 0:04:14 time: 0.2529 data_time: 0.0038 loss: 0.9874 2023/03/17 20:09:32 - mmengine - INFO - Epoch(train) [100][3800/5005] lr: 1.0000e-04 eta: 0:03:54 time: 0.1841 data_time: 0.0053 loss: 0.9270 2023/03/17 20:09:51 - mmengine - INFO - Epoch(train) [100][3900/5005] lr: 1.0000e-04 eta: 0:03:35 time: 0.1819 data_time: 0.0061 loss: 0.8218 2023/03/17 20:10:09 - mmengine - INFO - Epoch(train) [100][4000/5005] lr: 1.0000e-04 eta: 0:03:15 time: 0.1749 data_time: 0.0058 loss: 0.8615 2023/03/17 20:10:27 - mmengine - INFO - Epoch(train) [100][4100/5005] lr: 1.0000e-04 eta: 0:02:56 time: 0.1830 data_time: 0.0059 loss: 0.9020 2023/03/17 20:10:46 - mmengine - INFO - Epoch(train) [100][4200/5005] lr: 1.0000e-04 eta: 0:02:36 time: 0.1760 data_time: 0.0062 loss: 0.9506 2023/03/17 20:11:03 - mmengine - INFO - Epoch(train) [100][4300/5005] lr: 1.0000e-04 eta: 0:02:17 time: 0.1812 data_time: 0.0063 loss: 0.9039 2023/03/17 20:11:23 - mmengine - INFO - Epoch(train) [100][4400/5005] lr: 1.0000e-04 eta: 0:01:57 time: 0.1874 data_time: 0.0049 loss: 1.0091 2023/03/17 20:11:42 - mmengine - INFO - Epoch(train) [100][4500/5005] lr: 1.0000e-04 eta: 0:01:38 time: 0.1776 data_time: 0.0062 loss: 1.1075 2023/03/17 20:11:43 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 20:12:01 - mmengine - INFO - Epoch(train) [100][4600/5005] lr: 1.0000e-04 eta: 0:01:18 time: 0.1878 data_time: 0.0053 loss: 0.8834 2023/03/17 20:12:19 - mmengine - INFO - Epoch(train) [100][4700/5005] lr: 1.0000e-04 eta: 0:00:59 time: 0.1836 data_time: 0.0054 loss: 1.0201 2023/03/17 20:12:38 - mmengine - INFO - Epoch(train) [100][4800/5005] lr: 1.0000e-04 eta: 0:00:39 time: 0.1897 data_time: 0.0056 loss: 0.9271 2023/03/17 20:12:58 - mmengine - INFO - Epoch(train) [100][4900/5005] lr: 1.0000e-04 eta: 0:00:20 time: 0.1912 data_time: 0.0052 loss: 1.1212 2023/03/17 20:13:17 - mmengine - INFO - Epoch(train) [100][5000/5005] lr: 1.0000e-04 eta: 0:00:00 time: 0.1792 data_time: 0.0068 loss: 0.9042 2023/03/17 20:13:18 - mmengine - INFO - Exp name: resnet50_8xb32_in1k_20230316_160134 2023/03/17 20:13:18 - mmengine - INFO - Saving checkpoint at 100 epochs 2023/03/17 20:13:24 - mmengine - INFO - Epoch(val) [100][100/196] eta: 0:00:04 time: 0.0446 data_time: 0.0117 2023/03/17 20:13:53 - mmengine - INFO - Epoch(val) [100][196/196] accuracy/top1: 76.4040 accuracy/top5: 93.1040data_time: 0.0111 time: 0.0445