2023/06/04 22:53:54 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.10.9 (main, Mar 8 2023, 10:47:38) [GCC 11.2.0] CUDA available: True numpy_random_seed: 1263609607 GPU 0,1: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/petrelfs/share/cuda-11.6 NVCC: Cuda compilation tools, release 11.6, V11.6.124 GCC: gcc (GCC) 7.5.0 PyTorch: 1.13.1 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201402 - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.6 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37 - CuDNN 8.3.2 (built against CUDA 11.5) - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.14.1 OpenCV: 4.7.0 MMEngine: 0.7.3 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None deterministic: False Distributed launcher: slurm Distributed training: True GPU number: 2 ------------------------------------------------------------ 2023/06/04 22:54:00 - mmengine - INFO - Config: optim_wrapper = dict( optimizer=dict( type='SGD', lr=0.0001, momentum=0.9, weight_decay=0.0001, _scope_='mmpretrain'), clip_grad=None) param_scheduler = [ dict(type='CosineAnnealingLR', eta_min=1e-05, by_epoch=False, begin=0) ] train_cfg = dict(by_epoch=True, max_epochs=10, val_interval=1) val_cfg = dict() test_cfg = dict() auto_scale_lr = dict(base_batch_size=512) model = dict( type='ImageClassifier', backbone=dict( frozen_stages=2, type='ResNet', depth=50, num_stages=4, out_indices=(3, ), style='pytorch', init_cfg=dict( type='Pretrained', checkpoint= 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth', prefix='backbone')), neck=dict(type='GlobalAveragePooling'), head=dict( type='LinearClsHead', num_classes=2, in_channels=2048, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=1)) dataset_type = 'CustomDataset' data_preprocessor = dict( num_classes=2, mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) bgr_mean = [103.53, 116.28, 123.675] bgr_std = [57.375, 57.12, 58.395] train_pipeline = [ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='ResizeEdge', scale=256, edge='short', backend='pillow', interpolation='bicubic'), dict(type='CenterCrop', crop_size=224), dict(type='PackInputs') ] train_dataloader = dict( pin_memory=True, persistent_workers=True, collate_fn=dict(type='default_collate'), batch_size=256, num_workers=10, dataset=dict( type='ConcatDataset', datasets=[ dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/stablediffusionV2-1-dpmsolver-25-1m.tsv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]), dict( type='CustomDataset', data_root='', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/cc1m.csv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]) ]), sampler=dict(type='DefaultSampler', shuffle=True)) val_dataloader = dict( pin_memory=True, persistent_workers=True, collate_fn=dict(type='default_collate'), batch_size=256, num_workers=10, dataset=dict( type='ConcatDataset', datasets=[ dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV2-1-dpmsolver-25-1w.tsv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]), dict( type='CustomDataset', data_root='', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/cc1w.csv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]) ]), sampler=dict(type='DefaultSampler', shuffle=False)) val_evaluator = dict(type='Accuracy', topk=1) test_dataloader = dict( pin_memory=True, persistent_workers=True, collate_fn=dict(type='default_collate'), batch_size=256, num_workers=10, dataset=dict( type='ConcatDataset', datasets=[ dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV2-1-dpmsolver-25-1w.tsv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]), dict( type='CustomDataset', data_root='', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/cc1w.csv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict(type='JPEG', compress_val=65, prob=0.5), dict(type='GaussianBlur', radius=1.5, prob=0.5), dict(type='PackInputs') ]) ]), sampler=dict(type='DefaultSampler', shuffle=False)) test_evaluator = dict(type='Accuracy', topk=1) custom_hooks = [dict(type='EMAHook', momentum=0.0001, priority='ABOVE_NORMAL')] default_scope = 'mmpretrain' 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=True)) env_cfg = dict( cudnn_benchmark=True, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='UniversalVisualizer', vis_backends=[ dict(type='LocalVisBackend'), dict(type='TensorboardVisBackend') ]) log_level = 'INFO' load_from = None resume = False randomness = dict(seed=None, deterministic=False) launcher = 'slurm' work_dir = 'workdir/resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1' 2023/06/04 22:54:13 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (ABOVE_NORMAL) EMAHook (BELOW_NORMAL) LoggerHook -------------------- after_load_checkpoint: (ABOVE_NORMAL) EMAHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (ABOVE_NORMAL) EMAHook (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 (ABOVE_NORMAL) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val_epoch: (ABOVE_NORMAL) EMAHook (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) VisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (ABOVE_NORMAL) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_save_checkpoint: (ABOVE_NORMAL) EMAHook -------------------- after_train: (VERY_LOW ) CheckpointHook -------------------- before_test_epoch: (ABOVE_NORMAL) EMAHook (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) VisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (ABOVE_NORMAL) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2023/06/04 22:54:34 - mmengine - INFO - load backbone in model from: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.1.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn1.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn1.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn2.weight - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn2.bias - torch.Size([64]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn3.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer1.2.bn3.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn1.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn2.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn2.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn3.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.bn3.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.downsample.1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.0.downsample.1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn1.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn2.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn2.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn3.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.1.bn3.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn1.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn2.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn2.bias - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn3.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.2.bn3.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.bn1.weight - torch.Size([128]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.bn1.bias - torch.Size([128]): PretrainedInit: load from 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https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer2.3.bn3.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.bn1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.bn2.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.bn2.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.downsample.1.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.0.downsample.1.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.1.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.1.bn1.bias - torch.Size([256]): PretrainedInit: 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https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.1.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.bn1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.bn2.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.bn2.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.2.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.bn1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.bn2.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.bn2.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.3.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.bn1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.bn2.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.bn2.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.4.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.bn1.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.bn1.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.bn2.weight - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.bn2.bias - torch.Size([256]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.bn3.weight - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer3.5.bn3.bias - torch.Size([1024]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn3.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.bn3.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.downsample.1.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.0.downsample.1.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn3.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.1.bn3.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn1.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn1.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn2.weight - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn2.bias - torch.Size([512]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn3.weight - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth backbone.layer4.2.bn3.bias - torch.Size([2048]): PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth head.fc.weight - torch.Size([2, 2048]): NormalInit: mean=0, std=0.01, bias=0 head.fc.bias - torch.Size([2]): NormalInit: mean=0, std=0.01, bias=0 2023/06/04 22:54:34 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/06/04 22:54:34 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/06/04 22:54:34 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/luzeyu/workspace/fakebench/mmpretrain/workdir/resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1. 2023/06/04 22:55:43 - mmengine - INFO - Epoch(train) [1][ 100/3907] lr: 9.9999e-05 eta: 7:26:57 time: 0.6773 data_time: 0.2523 memory: 9436 loss: 0.6043 2023/06/04 22:56:50 - mmengine - INFO - Epoch(train) [1][ 200/3907] lr: 9.9994e-05 eta: 7:21:19 time: 0.7268 data_time: 0.0753 memory: 6319 loss: 0.5358 2023/06/04 22:57:58 - mmengine - INFO - Epoch(train) [1][ 300/3907] lr: 9.9987e-05 eta: 7:18:53 time: 0.6230 data_time: 0.0010 memory: 6319 loss: 0.4941 2023/06/04 22:59:05 - mmengine - INFO - Epoch(train) [1][ 400/3907] lr: 9.9977e-05 eta: 7:17:19 time: 0.6354 data_time: 0.0009 memory: 6319 loss: 0.4298 2023/06/04 23:00:12 - mmengine - INFO - Epoch(train) [1][ 500/3907] lr: 9.9964e-05 eta: 7:15:02 time: 0.7114 data_time: 0.0008 memory: 6319 loss: 0.4119 2023/06/04 23:01:19 - mmengine - INFO - Epoch(train) [1][ 600/3907] lr: 9.9948e-05 eta: 7:12:36 time: 0.6574 data_time: 0.0012 memory: 6319 loss: 0.3756 2023/06/04 23:02:24 - mmengine - INFO - Epoch(train) [1][ 700/3907] lr: 9.9929e-05 eta: 7:09:32 time: 0.6608 data_time: 0.0009 memory: 6319 loss: 0.3355 2023/06/04 23:03:33 - mmengine - INFO - Epoch(train) [1][ 800/3907] lr: 9.9907e-05 eta: 7:09:44 time: 0.6427 data_time: 0.0013 memory: 6319 loss: 0.3327 2023/06/04 23:04:39 - mmengine - INFO - Epoch(train) [1][ 900/3907] lr: 9.9882e-05 eta: 7:08:03 time: 0.6512 data_time: 0.0009 memory: 6319 loss: 0.3026 2023/06/04 23:05:47 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/04 23:05:47 - mmengine - INFO - Epoch(train) [1][1000/3907] lr: 9.9855e-05 eta: 7:07:24 time: 0.6902 data_time: 0.0010 memory: 6319 loss: 0.2757 2023/06/04 23:06:57 - mmengine - INFO - Epoch(train) [1][1100/3907] lr: 9.9824e-05 eta: 7:07:15 time: 0.7048 data_time: 0.0009 memory: 6319 loss: 0.2721 2023/06/04 23:08:03 - mmengine - INFO - Epoch(train) [1][1200/3907] lr: 9.9791e-05 eta: 7:05:21 time: 0.6478 data_time: 0.0010 memory: 6319 loss: 0.2623 2023/06/04 23:09:08 - mmengine - INFO - Epoch(train) [1][1300/3907] lr: 9.9755e-05 eta: 7:03:21 time: 0.6527 data_time: 0.0009 memory: 6319 loss: 0.2532 2023/06/04 23:10:15 - mmengine - INFO - Epoch(train) [1][1400/3907] lr: 9.9716e-05 eta: 7:02:12 time: 0.7147 data_time: 0.0009 memory: 6319 loss: 0.2702 2023/06/04 23:11:21 - mmengine - INFO - Epoch(train) [1][1500/3907] lr: 9.9674e-05 eta: 7:00:27 time: 0.6704 data_time: 0.0008 memory: 6319 loss: 0.2465 2023/06/04 23:12:29 - mmengine - INFO - Epoch(train) [1][1600/3907] lr: 9.9629e-05 eta: 6:59:41 time: 0.6794 data_time: 0.0011 memory: 6319 loss: 0.2376 2023/06/04 23:13:30 - mmengine - INFO - Epoch(train) [1][1700/3907] lr: 9.9581e-05 eta: 6:56:22 time: 0.6408 data_time: 0.0008 memory: 6319 loss: 0.2211 2023/06/04 23:14:35 - mmengine - INFO - Epoch(train) [1][1800/3907] lr: 9.9530e-05 eta: 6:54:22 time: 0.6415 data_time: 0.1043 memory: 6319 loss: 0.2324 2023/06/04 23:15:40 - mmengine - INFO - Epoch(train) [1][1900/3907] lr: 9.9476e-05 eta: 6:52:54 time: 0.6706 data_time: 0.5309 memory: 6319 loss: 0.2141 2023/06/04 23:16:48 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/04 23:16:48 - mmengine - INFO - Epoch(train) [1][2000/3907] lr: 9.9420e-05 eta: 6:52:10 time: 0.7201 data_time: 0.5010 memory: 6319 loss: 0.2260 2023/06/04 23:17:55 - mmengine - INFO - Epoch(train) [1][2100/3907] lr: 9.9361e-05 eta: 6:51:10 time: 0.7120 data_time: 0.5712 memory: 6319 loss: 0.2068 2023/06/04 23:19:04 - mmengine - INFO - Epoch(train) [1][2200/3907] lr: 9.9298e-05 eta: 6:50:30 time: 0.7229 data_time: 0.5833 memory: 6319 loss: 0.1956 2023/06/04 23:20:12 - mmengine - INFO - Epoch(train) [1][2300/3907] lr: 9.9233e-05 eta: 6:49:52 time: 0.6740 data_time: 0.5348 memory: 6319 loss: 0.2159 2023/06/04 23:21:21 - mmengine - INFO - Epoch(train) [1][2400/3907] lr: 9.9165e-05 eta: 6:49:13 time: 0.6373 data_time: 0.4979 memory: 6319 loss: 0.1922 2023/06/04 23:22:27 - mmengine - INFO - Epoch(train) [1][2500/3907] lr: 9.9095e-05 eta: 6:48:00 time: 0.6930 data_time: 0.5531 memory: 6319 loss: 0.2031 2023/06/04 23:23:35 - mmengine - INFO - Epoch(train) [1][2600/3907] lr: 9.9021e-05 eta: 6:47:03 time: 0.6640 data_time: 0.5237 memory: 6319 loss: 0.1767 2023/06/04 23:24:43 - mmengine - INFO - Epoch(train) [1][2700/3907] lr: 9.8944e-05 eta: 6:46:08 time: 0.6682 data_time: 0.5284 memory: 6319 loss: 0.2076 2023/06/04 23:25:51 - mmengine - INFO - Epoch(train) [1][2800/3907] lr: 9.8865e-05 eta: 6:45:11 time: 0.6659 data_time: 0.5254 memory: 6319 loss: 0.1828 2023/06/04 23:27:00 - mmengine - INFO - Epoch(train) [1][2900/3907] lr: 9.8783e-05 eta: 6:44:35 time: 0.6735 data_time: 0.5331 memory: 6319 loss: 0.1638 2023/06/04 23:28:07 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/04 23:28:07 - mmengine - INFO - Epoch(train) [1][3000/3907] lr: 9.8698e-05 eta: 6:43:29 time: 0.6812 data_time: 0.5406 memory: 6319 loss: 0.1844 2023/06/04 23:29:15 - mmengine - INFO - Epoch(train) [1][3100/3907] lr: 9.8610e-05 eta: 6:42:25 time: 0.6715 data_time: 0.5321 memory: 6319 loss: 0.1827 2023/06/04 23:30:21 - mmengine - INFO - Epoch(train) [1][3200/3907] lr: 9.8519e-05 eta: 6:41:11 time: 0.6751 data_time: 0.5345 memory: 6319 loss: 0.1714 2023/06/04 23:31:28 - mmengine - INFO - Epoch(train) [1][3300/3907] lr: 9.8426e-05 eta: 6:39:54 time: 0.6606 data_time: 0.5207 memory: 6319 loss: 0.1628 2023/06/04 23:32:35 - mmengine - INFO - Epoch(train) [1][3400/3907] lr: 9.8330e-05 eta: 6:38:50 time: 0.6689 data_time: 0.5295 memory: 6319 loss: 0.1740 2023/06/04 23:33:41 - mmengine - INFO - Epoch(train) [1][3500/3907] lr: 9.8231e-05 eta: 6:37:30 time: 0.6999 data_time: 0.5588 memory: 6319 loss: 0.1812 2023/06/04 23:34:47 - mmengine - INFO - Epoch(train) [1][3600/3907] lr: 9.8129e-05 eta: 6:36:16 time: 0.6636 data_time: 0.5235 memory: 6319 loss: 0.1638 2023/06/04 23:35:53 - mmengine - INFO - Epoch(train) [1][3700/3907] lr: 9.8024e-05 eta: 6:35:01 time: 0.6513 data_time: 0.5098 memory: 6319 loss: 0.1713 2023/06/04 23:37:00 - mmengine - INFO - Epoch(train) [1][3800/3907] lr: 9.7917e-05 eta: 6:33:52 time: 0.6676 data_time: 0.5158 memory: 6319 loss: 0.1612 2023/06/04 23:38:06 - mmengine - INFO - Epoch(train) [1][3900/3907] lr: 9.7806e-05 eta: 6:32:32 time: 0.6452 data_time: 0.5063 memory: 6319 loss: 0.1737 2023/06/04 23:38:12 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/04 23:38:12 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/06/04 23:39:03 - mmengine - INFO - Epoch(val) [1][57/57] accuracy/top1: 79.2874 data_time: 0.5959 time: 0.6884 2023/06/04 23:40:12 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/04 23:40:13 - mmengine - INFO - Epoch(train) [2][ 100/3907] lr: 9.7685e-05 eta: 6:31:58 time: 0.6768 data_time: 0.5371 memory: 6319 loss: 0.1528 2023/06/04 23:41:21 - mmengine - INFO - Epoch(train) [2][ 200/3907] lr: 9.7570e-05 eta: 6:30:56 time: 0.6940 data_time: 0.5544 memory: 6319 loss: 0.1413 2023/06/04 23:42:28 - mmengine - INFO - Epoch(train) [2][ 300/3907] lr: 9.7451e-05 eta: 6:29:47 time: 0.6467 data_time: 0.5068 memory: 6319 loss: 0.1545 2023/06/04 23:43:35 - mmengine - INFO - Epoch(train) [2][ 400/3907] lr: 9.7329e-05 eta: 6:28:42 time: 0.6443 data_time: 0.5037 memory: 6319 loss: 0.1405 2023/06/04 23:44:42 - mmengine - INFO - Epoch(train) [2][ 500/3907] lr: 9.7205e-05 eta: 6:27:34 time: 0.5851 data_time: 0.4447 memory: 6319 loss: 0.1506 2023/06/04 23:45:47 - mmengine - INFO - Epoch(train) [2][ 600/3907] lr: 9.7078e-05 eta: 6:26:12 time: 0.6615 data_time: 0.5212 memory: 6319 loss: 0.1310 2023/06/04 23:46:54 - mmengine - INFO - Epoch(train) [2][ 700/3907] lr: 9.6949e-05 eta: 6:25:03 time: 0.6660 data_time: 0.5258 memory: 6319 loss: 0.1483 2023/06/04 23:48:00 - mmengine - INFO - Epoch(train) [2][ 800/3907] lr: 9.6816e-05 eta: 6:23:50 time: 0.6336 data_time: 0.4931 memory: 6319 loss: 0.1387 2023/06/04 23:49:07 - mmengine - INFO - Epoch(train) [2][ 900/3907] lr: 9.6681e-05 eta: 6:22:38 time: 0.6974 data_time: 0.5573 memory: 6319 loss: 0.1431 2023/06/04 23:50:14 - mmengine - INFO - Epoch(train) [2][1000/3907] lr: 9.6544e-05 eta: 6:21:33 time: 0.6310 data_time: 0.4908 memory: 6319 loss: 0.1592 2023/06/04 23:51:20 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/04 23:51:21 - mmengine - INFO - Epoch(train) [2][1100/3907] lr: 9.6403e-05 eta: 6:20:29 time: 0.6652 data_time: 0.5240 memory: 6319 loss: 0.1495 2023/06/04 23:52:28 - mmengine - INFO - Epoch(train) [2][1200/3907] lr: 9.6260e-05 eta: 6:19:21 time: 0.6592 data_time: 0.5177 memory: 6319 loss: 0.1486 2023/06/04 23:53:35 - mmengine - INFO - Epoch(train) [2][1300/3907] lr: 9.6114e-05 eta: 6:18:10 time: 0.7580 data_time: 0.6181 memory: 6319 loss: 0.1456 2023/06/04 23:54:41 - mmengine - INFO - Epoch(train) [2][1400/3907] lr: 9.5966e-05 eta: 6:16:57 time: 0.6575 data_time: 0.5168 memory: 6319 loss: 0.1564 2023/06/04 23:55:48 - mmengine - INFO - Epoch(train) [2][1500/3907] lr: 9.5815e-05 eta: 6:15:52 time: 0.6685 data_time: 0.5288 memory: 6319 loss: 0.1406 2023/06/04 23:56:54 - mmengine - INFO - Epoch(train) [2][1600/3907] lr: 9.5661e-05 eta: 6:14:36 time: 0.7404 data_time: 0.6000 memory: 6319 loss: 0.1410 2023/06/04 23:57:58 - mmengine - INFO - Epoch(train) [2][1700/3907] lr: 9.5505e-05 eta: 6:13:15 time: 0.6693 data_time: 0.5292 memory: 6319 loss: 0.1464 2023/06/04 23:59:04 - mmengine - INFO - Epoch(train) [2][1800/3907] lr: 9.5346e-05 eta: 6:12:01 time: 0.6595 data_time: 0.5193 memory: 6319 loss: 0.1251 2023/06/05 00:00:10 - mmengine - INFO - Epoch(train) [2][1900/3907] lr: 9.5184e-05 eta: 6:10:51 time: 0.6812 data_time: 0.5405 memory: 6319 loss: 0.1406 2023/06/05 00:01:17 - mmengine - INFO - Epoch(train) [2][2000/3907] lr: 9.5020e-05 eta: 6:09:43 time: 0.6625 data_time: 0.5032 memory: 6319 loss: 0.1317 2023/06/05 00:02:22 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 00:02:23 - mmengine - INFO - Epoch(train) [2][2100/3907] lr: 9.4854e-05 eta: 6:08:34 time: 0.6838 data_time: 0.5442 memory: 6319 loss: 0.1416 2023/06/05 00:03:30 - mmengine - INFO - Epoch(train) [2][2200/3907] lr: 9.4684e-05 eta: 6:07:27 time: 0.6704 data_time: 0.5202 memory: 6319 loss: 0.1402 2023/06/05 00:04:36 - mmengine - INFO - Epoch(train) [2][2300/3907] lr: 9.4512e-05 eta: 6:06:12 time: 0.6754 data_time: 0.5359 memory: 6319 loss: 0.1390 2023/06/05 00:05:42 - mmengine - INFO - Epoch(train) [2][2400/3907] lr: 9.4338e-05 eta: 6:05:01 time: 0.6629 data_time: 0.4885 memory: 6319 loss: 0.1325 2023/06/05 00:06:49 - mmengine - INFO - Epoch(train) [2][2500/3907] lr: 9.4161e-05 eta: 6:03:57 time: 0.6119 data_time: 0.4711 memory: 6319 loss: 0.1328 2023/06/05 00:07:55 - mmengine - INFO - Epoch(train) [2][2600/3907] lr: 9.3981e-05 eta: 6:02:43 time: 0.6861 data_time: 0.5423 memory: 6319 loss: 0.1291 2023/06/05 00:09:00 - mmengine - INFO - Epoch(train) [2][2700/3907] lr: 9.3799e-05 eta: 6:01:30 time: 0.6600 data_time: 0.5194 memory: 6319 loss: 0.1384 2023/06/05 00:10:06 - mmengine - INFO - Epoch(train) [2][2800/3907] lr: 9.3615e-05 eta: 6:00:19 time: 0.6556 data_time: 0.5155 memory: 6319 loss: 0.1473 2023/06/05 00:11:11 - mmengine - INFO - Epoch(train) [2][2900/3907] lr: 9.3428e-05 eta: 5:59:04 time: 0.6514 data_time: 0.5120 memory: 6319 loss: 0.1209 2023/06/05 00:12:19 - mmengine - INFO - Epoch(train) [2][3000/3907] lr: 9.3238e-05 eta: 5:58:00 time: 0.7055 data_time: 0.5649 memory: 6319 loss: 0.1435 2023/06/05 00:13:24 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 00:13:25 - mmengine - INFO - Epoch(train) [2][3100/3907] lr: 9.3046e-05 eta: 5:56:49 time: 0.6247 data_time: 0.4848 memory: 6319 loss: 0.1390 2023/06/05 00:14:31 - mmengine - INFO - Epoch(train) [2][3200/3907] lr: 9.2852e-05 eta: 5:55:41 time: 0.6478 data_time: 0.5071 memory: 6319 loss: 0.1205 2023/06/05 00:15:39 - mmengine - INFO - Epoch(train) [2][3300/3907] lr: 9.2655e-05 eta: 5:54:39 time: 0.6395 data_time: 0.4996 memory: 6319 loss: 0.1324 2023/06/05 00:16:44 - mmengine - INFO - Epoch(train) [2][3400/3907] lr: 9.2456e-05 eta: 5:53:25 time: 0.6907 data_time: 0.5493 memory: 6319 loss: 0.1269 2023/06/05 00:17:52 - mmengine - INFO - Epoch(train) [2][3500/3907] lr: 9.2254e-05 eta: 5:52:22 time: 0.7101 data_time: 0.5684 memory: 6319 loss: 0.1229 2023/06/05 00:19:00 - mmengine - INFO - Epoch(train) [2][3600/3907] lr: 9.2050e-05 eta: 5:51:23 time: 0.6303 data_time: 0.4899 memory: 6319 loss: 0.1143 2023/06/05 00:20:08 - mmengine - INFO - Epoch(train) [2][3700/3907] lr: 9.1843e-05 eta: 5:50:20 time: 0.6717 data_time: 0.5326 memory: 6319 loss: 0.1283 2023/06/05 00:21:13 - mmengine - INFO - Epoch(train) [2][3800/3907] lr: 9.1634e-05 eta: 5:49:07 time: 0.6564 data_time: 0.5166 memory: 6319 loss: 0.1219 2023/06/05 00:22:19 - mmengine - INFO - Epoch(train) [2][3900/3907] lr: 9.1423e-05 eta: 5:47:55 time: 0.6266 data_time: 0.4867 memory: 6319 loss: 0.1200 2023/06/05 00:22:24 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 00:22:24 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/06/05 00:23:09 - mmengine - INFO - Epoch(val) [2][57/57] accuracy/top1: 84.4160 data_time: 0.4754 time: 0.5640 2023/06/05 00:24:18 - mmengine - INFO - Epoch(train) [3][ 100/3907] lr: 9.1194e-05 eta: 5:46:55 time: 0.6764 data_time: 0.5369 memory: 6319 loss: 0.1321 2023/06/05 00:25:16 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 00:25:23 - mmengine - INFO - Epoch(train) [3][ 200/3907] lr: 9.0978e-05 eta: 5:45:43 time: 0.6464 data_time: 0.4976 memory: 6319 loss: 0.1009 2023/06/05 00:26:30 - mmengine - INFO - Epoch(train) [3][ 300/3907] lr: 9.0759e-05 eta: 5:44:35 time: 0.6661 data_time: 0.5266 memory: 6319 loss: 0.1469 2023/06/05 00:27:36 - mmengine - INFO - Epoch(train) [3][ 400/3907] lr: 9.0539e-05 eta: 5:43:27 time: 0.6567 data_time: 0.5115 memory: 6319 loss: 0.1193 2023/06/05 00:28:42 - mmengine - INFO - Epoch(train) [3][ 500/3907] lr: 9.0315e-05 eta: 5:42:17 time: 0.6791 data_time: 0.5386 memory: 6319 loss: 0.1172 2023/06/05 00:29:49 - mmengine - INFO - Epoch(train) [3][ 600/3907] lr: 9.0090e-05 eta: 5:41:11 time: 0.6794 data_time: 0.5395 memory: 6319 loss: 0.1280 2023/06/05 00:30:55 - mmengine - INFO - Epoch(train) [3][ 700/3907] lr: 8.9862e-05 eta: 5:40:01 time: 0.6367 data_time: 0.4958 memory: 6319 loss: 0.1217 2023/06/05 00:32:11 - mmengine - INFO - Epoch(train) [3][ 800/3907] lr: 8.9632e-05 eta: 5:39:25 time: 0.7412 data_time: 0.6008 memory: 6319 loss: 0.1163 2023/06/05 00:33:15 - mmengine - INFO - Epoch(train) [3][ 900/3907] lr: 8.9400e-05 eta: 5:38:08 time: 0.6671 data_time: 0.5272 memory: 6319 loss: 0.1225 2023/06/05 00:34:19 - mmengine - INFO - Epoch(train) [3][1000/3907] lr: 8.9166e-05 eta: 5:36:52 time: 0.6386 data_time: 0.4976 memory: 6319 loss: 0.1387 2023/06/05 00:35:24 - mmengine - INFO - Epoch(train) [3][1100/3907] lr: 8.8929e-05 eta: 5:35:41 time: 0.6426 data_time: 0.5019 memory: 6319 loss: 0.1089 2023/06/05 00:36:23 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 00:36:30 - mmengine - INFO - Epoch(train) [3][1200/3907] lr: 8.8691e-05 eta: 5:34:30 time: 0.6732 data_time: 0.5331 memory: 6319 loss: 0.1227 2023/06/05 00:37:36 - mmengine - INFO - Epoch(train) [3][1300/3907] lr: 8.8450e-05 eta: 5:33:22 time: 0.6398 data_time: 0.4999 memory: 6319 loss: 0.1164 2023/06/05 00:38:42 - mmengine - INFO - Epoch(train) [3][1400/3907] lr: 8.8206e-05 eta: 5:32:12 time: 0.6419 data_time: 0.5013 memory: 6319 loss: 0.1125 2023/06/05 00:39:49 - mmengine - INFO - Epoch(train) [3][1500/3907] lr: 8.7961e-05 eta: 5:31:06 time: 0.6775 data_time: 0.5387 memory: 6319 loss: 0.1149 2023/06/05 00:40:56 - mmengine - INFO - Epoch(train) [3][1600/3907] lr: 8.7714e-05 eta: 5:29:59 time: 0.6409 data_time: 0.5014 memory: 6319 loss: 0.1108 2023/06/05 00:42:12 - mmengine - INFO - Epoch(train) [3][1700/3907] lr: 8.7464e-05 eta: 5:29:20 time: 0.8585 data_time: 0.7175 memory: 6319 loss: 0.1080 2023/06/05 00:43:18 - mmengine - INFO - Epoch(train) [3][1800/3907] lr: 8.7213e-05 eta: 5:28:11 time: 0.6469 data_time: 0.5071 memory: 6319 loss: 0.1144 2023/06/05 00:44:22 - mmengine - INFO - Epoch(train) [3][1900/3907] lr: 8.6959e-05 eta: 5:26:56 time: 0.6303 data_time: 0.4912 memory: 6319 loss: 0.1140 2023/06/05 00:45:28 - mmengine - INFO - Epoch(train) [3][2000/3907] lr: 8.6703e-05 eta: 5:25:48 time: 0.6445 data_time: 0.5045 memory: 6319 loss: 0.1196 2023/06/05 00:46:35 - mmengine - INFO - Epoch(train) [3][2100/3907] lr: 8.6445e-05 eta: 5:24:41 time: 0.6851 data_time: 0.5450 memory: 6319 loss: 0.1173 2023/06/05 00:47:35 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 00:47:43 - mmengine - INFO - Epoch(train) [3][2200/3907] lr: 8.6186e-05 eta: 5:23:36 time: 0.6610 data_time: 0.5212 memory: 6319 loss: 0.1034 2023/06/05 00:48:48 - mmengine - INFO - Epoch(train) [3][2300/3907] lr: 8.5924e-05 eta: 5:22:27 time: 0.6864 data_time: 0.5474 memory: 6319 loss: 0.1198 2023/06/05 00:49:55 - mmengine - INFO - Epoch(train) [3][2400/3907] lr: 8.5660e-05 eta: 5:21:20 time: 0.7293 data_time: 0.5904 memory: 6319 loss: 0.1248 2023/06/05 00:51:02 - mmengine - INFO - Epoch(train) [3][2500/3907] lr: 8.5394e-05 eta: 5:20:14 time: 0.6492 data_time: 0.5097 memory: 6319 loss: 0.1360 2023/06/05 00:52:12 - mmengine - INFO - Epoch(train) [3][2600/3907] lr: 8.5126e-05 eta: 5:19:14 time: 0.8287 data_time: 0.6894 memory: 6319 loss: 0.1009 2023/06/05 00:53:28 - mmengine - INFO - Epoch(train) [3][2700/3907] lr: 8.4856e-05 eta: 5:18:33 time: 0.7133 data_time: 0.5736 memory: 6319 loss: 0.1160 2023/06/05 00:54:39 - mmengine - INFO - Epoch(train) [3][2800/3907] lr: 8.4585e-05 eta: 5:17:37 time: 0.6500 data_time: 0.5090 memory: 6319 loss: 0.0941 2023/06/05 00:55:45 - mmengine - INFO - Epoch(train) [3][2900/3907] lr: 8.4311e-05 eta: 5:16:26 time: 0.6542 data_time: 0.5090 memory: 6319 loss: 0.1214 2023/06/05 00:56:51 - mmengine - INFO - Epoch(train) [3][3000/3907] lr: 8.4036e-05 eta: 5:15:17 time: 0.6606 data_time: 0.5095 memory: 6319 loss: 0.1028 2023/06/05 00:57:57 - mmengine - INFO - Epoch(train) [3][3100/3907] lr: 8.3758e-05 eta: 5:14:09 time: 0.6595 data_time: 0.5198 memory: 6319 loss: 0.1071 2023/06/05 00:58:57 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 00:59:04 - mmengine - INFO - Epoch(train) [3][3200/3907] lr: 8.3479e-05 eta: 5:13:02 time: 0.6474 data_time: 0.4932 memory: 6319 loss: 0.1060 2023/06/05 01:00:12 - mmengine - INFO - Epoch(train) [3][3300/3907] lr: 8.3198e-05 eta: 5:11:56 time: 0.6125 data_time: 0.4724 memory: 6319 loss: 0.1152 2023/06/05 01:01:17 - mmengine - INFO - Epoch(train) [3][3400/3907] lr: 8.2915e-05 eta: 5:10:45 time: 0.6834 data_time: 0.5436 memory: 6319 loss: 0.0990 2023/06/05 01:02:25 - mmengine - INFO - Epoch(train) [3][3500/3907] lr: 8.2630e-05 eta: 5:09:42 time: 0.6846 data_time: 0.5444 memory: 6319 loss: 0.1035 2023/06/05 01:03:33 - mmengine - INFO - Epoch(train) [3][3600/3907] lr: 8.2344e-05 eta: 5:08:37 time: 0.6770 data_time: 0.5355 memory: 6319 loss: 0.1068 2023/06/05 01:04:42 - mmengine - INFO - Epoch(train) [3][3700/3907] lr: 8.2056e-05 eta: 5:07:34 time: 0.6740 data_time: 0.5349 memory: 6319 loss: 0.1099 2023/06/05 01:05:50 - mmengine - INFO - Epoch(train) [3][3800/3907] lr: 8.1765e-05 eta: 5:06:29 time: 0.6752 data_time: 0.5361 memory: 6319 loss: 0.1061 2023/06/05 01:06:57 - mmengine - INFO - Epoch(train) [3][3900/3907] lr: 8.1474e-05 eta: 5:05:22 time: 0.6158 data_time: 0.4763 memory: 6319 loss: 0.1085 2023/06/05 01:07:02 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 01:07:02 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/06/05 01:07:51 - mmengine - INFO - Epoch(val) [3][57/57] accuracy/top1: 87.3421 data_time: 0.5443 time: 0.6337 2023/06/05 01:09:01 - mmengine - INFO - Epoch(train) [4][ 100/3907] lr: 8.1160e-05 eta: 5:04:19 time: 0.7443 data_time: 0.5888 memory: 6319 loss: 0.1141 2023/06/05 01:10:08 - mmengine - INFO - Epoch(train) [4][ 200/3907] lr: 8.0864e-05 eta: 5:03:12 time: 0.6592 data_time: 0.5061 memory: 6319 loss: 0.1029 2023/06/05 01:11:02 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 01:11:17 - mmengine - INFO - Epoch(train) [4][ 300/3907] lr: 8.0567e-05 eta: 5:02:09 time: 0.6956 data_time: 0.4117 memory: 6319 loss: 0.1024 2023/06/05 01:12:23 - mmengine - INFO - Epoch(train) [4][ 400/3907] lr: 8.0269e-05 eta: 5:01:00 time: 0.6918 data_time: 0.3840 memory: 6319 loss: 0.1088 2023/06/05 01:13:31 - mmengine - INFO - Epoch(train) [4][ 500/3907] lr: 7.9969e-05 eta: 4:59:55 time: 0.6833 data_time: 0.3068 memory: 6319 loss: 0.1142 2023/06/05 01:14:39 - mmengine - INFO - Epoch(train) [4][ 600/3907] lr: 7.9667e-05 eta: 4:58:51 time: 0.6850 data_time: 0.3399 memory: 6319 loss: 0.1026 2023/06/05 01:15:47 - mmengine - INFO - Epoch(train) [4][ 700/3907] lr: 7.9363e-05 eta: 4:57:46 time: 0.6532 data_time: 0.3745 memory: 6319 loss: 0.1052 2023/06/05 01:16:55 - mmengine - INFO - Epoch(train) [4][ 800/3907] lr: 7.9058e-05 eta: 4:56:40 time: 0.6609 data_time: 0.4166 memory: 6319 loss: 0.1049 2023/06/05 01:18:03 - mmengine - INFO - Epoch(train) [4][ 900/3907] lr: 7.8752e-05 eta: 4:55:35 time: 0.6547 data_time: 0.3144 memory: 6319 loss: 0.0911 2023/06/05 01:19:10 - mmengine - INFO - Epoch(train) [4][1000/3907] lr: 7.8444e-05 eta: 4:54:29 time: 0.7190 data_time: 0.2799 memory: 6319 loss: 0.1041 2023/06/05 01:20:17 - mmengine - INFO - Epoch(train) [4][1100/3907] lr: 7.8134e-05 eta: 4:53:21 time: 0.6120 data_time: 0.3072 memory: 6319 loss: 0.0889 2023/06/05 01:21:22 - mmengine - INFO - Epoch(train) [4][1200/3907] lr: 7.7823e-05 eta: 4:52:10 time: 0.6498 data_time: 0.5094 memory: 6319 loss: 0.1102 2023/06/05 01:22:15 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 01:22:29 - mmengine - INFO - Epoch(train) [4][1300/3907] lr: 7.7510e-05 eta: 4:51:02 time: 0.6814 data_time: 0.5414 memory: 6319 loss: 0.1199 2023/06/05 01:23:37 - mmengine - INFO - Epoch(train) [4][1400/3907] lr: 7.7196e-05 eta: 4:49:58 time: 0.7173 data_time: 0.5784 memory: 6319 loss: 0.1049 2023/06/05 01:24:45 - mmengine - INFO - Epoch(train) [4][1500/3907] lr: 7.6881e-05 eta: 4:48:52 time: 0.6452 data_time: 0.5041 memory: 6319 loss: 0.1167 2023/06/05 01:25:52 - mmengine - INFO - Epoch(train) [4][1600/3907] lr: 7.6564e-05 eta: 4:47:46 time: 0.6744 data_time: 0.5350 memory: 6319 loss: 0.1014 2023/06/05 01:27:00 - mmengine - INFO - Epoch(train) [4][1700/3907] lr: 7.6246e-05 eta: 4:46:40 time: 0.7068 data_time: 0.5671 memory: 6319 loss: 0.1028 2023/06/05 01:28:07 - mmengine - INFO - Epoch(train) [4][1800/3907] lr: 7.5926e-05 eta: 4:45:34 time: 0.6507 data_time: 0.5110 memory: 6319 loss: 0.1068 2023/06/05 01:29:17 - mmengine - INFO - Epoch(train) [4][1900/3907] lr: 7.5605e-05 eta: 4:44:31 time: 0.7270 data_time: 0.5814 memory: 6319 loss: 0.1104 2023/06/05 01:30:25 - mmengine - INFO - Epoch(train) [4][2000/3907] lr: 7.5283e-05 eta: 4:43:26 time: 0.7088 data_time: 0.5673 memory: 6319 loss: 0.1058 2023/06/05 01:31:34 - mmengine - INFO - Epoch(train) [4][2100/3907] lr: 7.4959e-05 eta: 4:42:22 time: 0.7210 data_time: 0.5114 memory: 6319 loss: 0.0918 2023/06/05 01:32:41 - mmengine - INFO - Epoch(train) [4][2200/3907] lr: 7.4634e-05 eta: 4:41:16 time: 0.6922 data_time: 0.5522 memory: 6319 loss: 0.1060 2023/06/05 01:33:37 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 01:33:51 - mmengine - INFO - Epoch(train) [4][2300/3907] lr: 7.4308e-05 eta: 4:40:13 time: 0.6872 data_time: 0.5371 memory: 6319 loss: 0.1012 2023/06/05 01:34:57 - mmengine - INFO - Epoch(train) [4][2400/3907] lr: 7.3980e-05 eta: 4:39:04 time: 0.6490 data_time: 0.5098 memory: 6319 loss: 0.1126 2023/06/05 01:36:13 - mmengine - INFO - Epoch(train) [4][2500/3907] lr: 7.3652e-05 eta: 4:38:13 time: 0.7904 data_time: 0.6447 memory: 6319 loss: 0.0988 2023/06/05 01:37:20 - mmengine - INFO - Epoch(train) [4][2600/3907] lr: 7.3322e-05 eta: 4:37:06 time: 0.6249 data_time: 0.4853 memory: 6319 loss: 0.1030 2023/06/05 01:38:24 - mmengine - INFO - Epoch(train) [4][2700/3907] lr: 7.2991e-05 eta: 4:35:53 time: 0.6624 data_time: 0.5232 memory: 6319 loss: 0.0952 2023/06/05 01:39:32 - mmengine - INFO - Epoch(train) [4][2800/3907] lr: 7.2659e-05 eta: 4:34:47 time: 0.6820 data_time: 0.5393 memory: 6319 loss: 0.0991 2023/06/05 01:40:41 - mmengine - INFO - Epoch(train) [4][2900/3907] lr: 7.2325e-05 eta: 4:33:43 time: 0.6450 data_time: 0.5042 memory: 6319 loss: 0.0953 2023/06/05 01:41:49 - mmengine - INFO - Epoch(train) [4][3000/3907] lr: 7.1991e-05 eta: 4:32:37 time: 0.6776 data_time: 0.5369 memory: 6319 loss: 0.0873 2023/06/05 01:42:56 - mmengine - INFO - Epoch(train) [4][3100/3907] lr: 7.1655e-05 eta: 4:31:30 time: 0.7061 data_time: 0.5643 memory: 6319 loss: 0.1050 2023/06/05 01:44:04 - mmengine - INFO - Epoch(train) [4][3200/3907] lr: 7.1318e-05 eta: 4:30:23 time: 0.6815 data_time: 0.5413 memory: 6319 loss: 0.0882 2023/06/05 01:44:58 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 01:45:11 - mmengine - INFO - Epoch(train) [4][3300/3907] lr: 7.0981e-05 eta: 4:29:17 time: 0.6491 data_time: 0.5089 memory: 6319 loss: 0.0940 2023/06/05 01:46:19 - mmengine - INFO - Epoch(train) [4][3400/3907] lr: 7.0642e-05 eta: 4:28:10 time: 0.6593 data_time: 0.5177 memory: 6319 loss: 0.0992 2023/06/05 01:47:26 - mmengine - INFO - Epoch(train) [4][3500/3907] lr: 7.0302e-05 eta: 4:27:03 time: 0.6801 data_time: 0.5397 memory: 6319 loss: 0.0944 2023/06/05 01:48:37 - mmengine - INFO - Epoch(train) [4][3600/3907] lr: 6.9961e-05 eta: 4:26:02 time: 0.7637 data_time: 0.6162 memory: 6319 loss: 0.0895 2023/06/05 01:49:50 - mmengine - INFO - Epoch(train) [4][3700/3907] lr: 6.9620e-05 eta: 4:25:03 time: 0.6629 data_time: 0.5222 memory: 6319 loss: 0.0996 2023/06/05 01:50:56 - mmengine - INFO - Epoch(train) [4][3800/3907] lr: 6.9277e-05 eta: 4:23:54 time: 0.6424 data_time: 0.4960 memory: 6319 loss: 0.0930 2023/06/05 01:52:02 - mmengine - INFO - Epoch(train) [4][3900/3907] lr: 6.8933e-05 eta: 4:22:45 time: 0.6563 data_time: 0.5162 memory: 6319 loss: 0.0981 2023/06/05 01:52:08 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 01:52:08 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/06/05 01:52:58 - mmengine - INFO - Epoch(val) [4][57/57] accuracy/top1: 90.2509 data_time: 0.5514 time: 0.6400 2023/06/05 01:54:07 - mmengine - INFO - Epoch(train) [5][ 100/3907] lr: 6.8565e-05 eta: 4:21:37 time: 0.7089 data_time: 0.5262 memory: 6319 loss: 0.0847 2023/06/05 01:55:15 - mmengine - INFO - Epoch(train) [5][ 200/3907] lr: 6.8219e-05 eta: 4:20:31 time: 0.6575 data_time: 0.5166 memory: 6319 loss: 0.0910 2023/06/05 01:56:22 - mmengine - INFO - Epoch(train) [5][ 300/3907] lr: 6.7873e-05 eta: 4:19:24 time: 0.6636 data_time: 0.5228 memory: 6319 loss: 0.1058 2023/06/05 01:57:16 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 01:57:31 - mmengine - INFO - Epoch(train) [5][ 400/3907] lr: 6.7526e-05 eta: 4:18:19 time: 0.7193 data_time: 0.5789 memory: 6319 loss: 0.0884 2023/06/05 01:58:37 - mmengine - INFO - Epoch(train) [5][ 500/3907] lr: 6.7178e-05 eta: 4:17:10 time: 0.6873 data_time: 0.5475 memory: 6319 loss: 0.0965 2023/06/05 01:59:45 - mmengine - INFO - Epoch(train) [5][ 600/3907] lr: 6.6829e-05 eta: 4:16:03 time: 0.6608 data_time: 0.5068 memory: 6319 loss: 0.1029 2023/06/05 02:00:53 - mmengine - INFO - Epoch(train) [5][ 700/3907] lr: 6.6480e-05 eta: 4:14:57 time: 0.6656 data_time: 0.5259 memory: 6319 loss: 0.0936 2023/06/05 02:02:04 - mmengine - INFO - Epoch(train) [5][ 800/3907] lr: 6.6129e-05 eta: 4:13:55 time: 0.8690 data_time: 0.7285 memory: 6319 loss: 0.1000 2023/06/05 02:03:15 - mmengine - INFO - Epoch(train) [5][ 900/3907] lr: 6.5778e-05 eta: 4:12:53 time: 0.6115 data_time: 0.4692 memory: 6319 loss: 0.0940 2023/06/05 02:04:19 - mmengine - INFO - Epoch(train) [5][1000/3907] lr: 6.5427e-05 eta: 4:11:42 time: 0.6595 data_time: 0.5046 memory: 6319 loss: 0.0937 2023/06/05 02:05:25 - mmengine - INFO - Epoch(train) [5][1100/3907] lr: 6.5074e-05 eta: 4:10:32 time: 0.6369 data_time: 0.4959 memory: 6319 loss: 0.0941 2023/06/05 02:06:33 - mmengine - INFO - Epoch(train) [5][1200/3907] lr: 6.4721e-05 eta: 4:09:26 time: 0.6507 data_time: 0.4961 memory: 6319 loss: 0.1007 2023/06/05 02:07:40 - mmengine - INFO - Epoch(train) [5][1300/3907] lr: 6.4368e-05 eta: 4:08:17 time: 0.6899 data_time: 0.5493 memory: 6319 loss: 0.0979 2023/06/05 02:08:33 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 02:08:48 - mmengine - INFO - Epoch(train) [5][1400/3907] lr: 6.4014e-05 eta: 4:07:11 time: 0.6991 data_time: 0.5583 memory: 6319 loss: 0.0972 2023/06/05 02:09:55 - mmengine - INFO - Epoch(train) [5][1500/3907] lr: 6.3659e-05 eta: 4:06:04 time: 0.7026 data_time: 0.5615 memory: 6319 loss: 0.0773 2023/06/05 02:11:04 - mmengine - INFO - Epoch(train) [5][1600/3907] lr: 6.3303e-05 eta: 4:04:59 time: 0.6768 data_time: 0.5357 memory: 6319 loss: 0.0957 2023/06/05 02:12:13 - mmengine - INFO - Epoch(train) [5][1700/3907] lr: 6.2948e-05 eta: 4:03:54 time: 0.6474 data_time: 0.5066 memory: 6319 loss: 0.0933 2023/06/05 02:13:21 - mmengine - INFO - Epoch(train) [5][1800/3907] lr: 6.2591e-05 eta: 4:02:47 time: 0.6274 data_time: 0.4877 memory: 6319 loss: 0.1000 2023/06/05 02:14:27 - mmengine - INFO - Epoch(train) [5][1900/3907] lr: 6.2234e-05 eta: 4:01:39 time: 0.6816 data_time: 0.5397 memory: 6319 loss: 0.0879 2023/06/05 02:15:35 - mmengine - INFO - Epoch(train) [5][2000/3907] lr: 6.1877e-05 eta: 4:00:32 time: 0.6615 data_time: 0.5211 memory: 6319 loss: 0.0972 2023/06/05 02:16:42 - mmengine - INFO - Epoch(train) [5][2100/3907] lr: 6.1519e-05 eta: 3:59:24 time: 0.6226 data_time: 0.4726 memory: 6319 loss: 0.0888 2023/06/05 02:17:49 - mmengine - INFO - Epoch(train) [5][2200/3907] lr: 6.1161e-05 eta: 3:58:17 time: 0.6426 data_time: 0.5019 memory: 6319 loss: 0.0908 2023/06/05 02:19:01 - mmengine - INFO - Epoch(train) [5][2300/3907] lr: 6.0802e-05 eta: 3:57:15 time: 0.7770 data_time: 0.6354 memory: 6319 loss: 0.1021 2023/06/05 02:20:04 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 02:20:20 - mmengine - INFO - Epoch(train) [5][2400/3907] lr: 6.0443e-05 eta: 3:56:21 time: 0.7664 data_time: 0.6275 memory: 6319 loss: 0.0956 2023/06/05 02:21:24 - mmengine - INFO - Epoch(train) [5][2500/3907] lr: 6.0084e-05 eta: 3:55:10 time: 0.6135 data_time: 0.4720 memory: 6319 loss: 0.0871 2023/06/05 02:22:27 - mmengine - INFO - Epoch(train) [5][2600/3907] lr: 5.9724e-05 eta: 3:53:58 time: 0.6515 data_time: 0.5098 memory: 6319 loss: 0.0901 2023/06/05 02:23:34 - mmengine - INFO - Epoch(train) [5][2700/3907] lr: 5.9364e-05 eta: 3:52:49 time: 0.6207 data_time: 0.4803 memory: 6319 loss: 0.0891 2023/06/05 02:24:39 - mmengine - INFO - Epoch(train) [5][2800/3907] lr: 5.9004e-05 eta: 3:51:40 time: 0.6623 data_time: 0.5215 memory: 6319 loss: 0.1049 2023/06/05 02:25:46 - mmengine - INFO - Epoch(train) [5][2900/3907] lr: 5.8643e-05 eta: 3:50:32 time: 0.6878 data_time: 0.5449 memory: 6319 loss: 0.0943 2023/06/05 02:26:53 - mmengine - INFO - Epoch(train) [5][3000/3907] lr: 5.8283e-05 eta: 3:49:25 time: 0.7121 data_time: 0.5710 memory: 6319 loss: 0.0935 2023/06/05 02:28:01 - mmengine - INFO - Epoch(train) [5][3100/3907] lr: 5.7922e-05 eta: 3:48:18 time: 0.6972 data_time: 0.5558 memory: 6319 loss: 0.0996 2023/06/05 02:29:09 - mmengine - INFO - Epoch(train) [5][3200/3907] lr: 5.7560e-05 eta: 3:47:10 time: 0.6685 data_time: 0.5280 memory: 6319 loss: 0.1042 2023/06/05 02:30:16 - mmengine - INFO - Epoch(train) [5][3300/3907] lr: 5.7199e-05 eta: 3:46:03 time: 0.6613 data_time: 0.5208 memory: 6319 loss: 0.1008 2023/06/05 02:31:09 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 02:31:24 - mmengine - INFO - Epoch(train) [5][3400/3907] lr: 5.6838e-05 eta: 3:44:56 time: 0.6708 data_time: 0.5302 memory: 6319 loss: 0.0858 2023/06/05 02:32:31 - mmengine - INFO - Epoch(train) [5][3500/3907] lr: 5.6476e-05 eta: 3:43:48 time: 0.6429 data_time: 0.5027 memory: 6319 loss: 0.1171 2023/06/05 02:33:37 - mmengine - INFO - Epoch(train) [5][3600/3907] lr: 5.6114e-05 eta: 3:42:40 time: 0.6569 data_time: 0.5160 memory: 6319 loss: 0.0920 2023/06/05 02:34:45 - mmengine - INFO - Epoch(train) [5][3700/3907] lr: 5.5753e-05 eta: 3:41:33 time: 0.6830 data_time: 0.5427 memory: 6319 loss: 0.1029 2023/06/05 02:35:52 - mmengine - INFO - Epoch(train) [5][3800/3907] lr: 5.5391e-05 eta: 3:40:25 time: 0.6381 data_time: 0.4972 memory: 6319 loss: 0.0870 2023/06/05 02:36:59 - mmengine - INFO - Epoch(train) [5][3900/3907] lr: 5.5029e-05 eta: 3:39:18 time: 0.6577 data_time: 0.5175 memory: 6319 loss: 0.1026 2023/06/05 02:37:04 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 02:37:04 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/06/05 02:37:59 - mmengine - INFO - Epoch(val) [5][57/57] accuracy/top1: 93.7302 data_time: 0.6467 time: 0.7327 2023/06/05 02:39:32 - mmengine - INFO - Epoch(train) [6][ 100/3907] lr: 5.4642e-05 eta: 3:38:32 time: 1.0088 data_time: 0.0313 memory: 6319 loss: 0.1072 2023/06/05 02:40:54 - mmengine - INFO - Epoch(train) [6][ 200/3907] lr: 5.4280e-05 eta: 3:37:39 time: 0.5342 data_time: 0.1992 memory: 6319 loss: 0.1047 2023/06/05 02:41:53 - mmengine - INFO - Epoch(train) [6][ 300/3907] lr: 5.3918e-05 eta: 3:36:23 time: 0.6258 data_time: 0.4796 memory: 6319 loss: 0.0882 2023/06/05 02:42:59 - mmengine - INFO - Epoch(train) [6][ 400/3907] lr: 5.3556e-05 eta: 3:35:14 time: 0.6749 data_time: 0.4100 memory: 6319 loss: 0.0793 2023/06/05 02:43:42 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 02:44:04 - mmengine - INFO - Epoch(train) [6][ 500/3907] lr: 5.3195e-05 eta: 3:34:04 time: 0.6482 data_time: 0.3807 memory: 6319 loss: 0.0931 2023/06/05 02:45:10 - mmengine - INFO - Epoch(train) [6][ 600/3907] lr: 5.2833e-05 eta: 3:32:56 time: 0.7213 data_time: 0.5792 memory: 6319 loss: 0.0903 2023/06/05 02:46:19 - mmengine - INFO - Epoch(train) [6][ 700/3907] lr: 5.2472e-05 eta: 3:31:50 time: 0.8090 data_time: 0.6684 memory: 6319 loss: 0.0896 2023/06/05 02:47:26 - mmengine - INFO - Epoch(train) [6][ 800/3907] lr: 5.2111e-05 eta: 3:30:42 time: 0.6541 data_time: 0.5131 memory: 6319 loss: 0.0865 2023/06/05 02:48:33 - mmengine - INFO - Epoch(train) [6][ 900/3907] lr: 5.1750e-05 eta: 3:29:34 time: 0.6661 data_time: 0.5252 memory: 6319 loss: 0.0908 2023/06/05 02:49:40 - mmengine - INFO - Epoch(train) [6][1000/3907] lr: 5.1389e-05 eta: 3:28:26 time: 0.6543 data_time: 0.5123 memory: 6319 loss: 0.0951 2023/06/05 02:50:47 - mmengine - INFO - Epoch(train) [6][1100/3907] lr: 5.1029e-05 eta: 3:27:18 time: 0.6567 data_time: 0.5156 memory: 6319 loss: 0.1042 2023/06/05 02:51:54 - mmengine - INFO - Epoch(train) [6][1200/3907] lr: 5.0668e-05 eta: 3:26:10 time: 0.6560 data_time: 0.5148 memory: 6319 loss: 0.0892 2023/06/05 02:53:01 - mmengine - INFO - Epoch(train) [6][1300/3907] lr: 5.0308e-05 eta: 3:25:02 time: 0.6526 data_time: 0.5123 memory: 6319 loss: 0.0874 2023/06/05 02:54:07 - mmengine - INFO - Epoch(train) [6][1400/3907] lr: 4.9949e-05 eta: 3:23:53 time: 0.6498 data_time: 0.5093 memory: 6319 loss: 0.0833 2023/06/05 02:54:53 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 02:55:13 - mmengine - INFO - Epoch(train) [6][1500/3907] lr: 4.9589e-05 eta: 3:22:45 time: 0.6572 data_time: 0.5166 memory: 6319 loss: 0.0813 2023/06/05 02:56:19 - mmengine - INFO - Epoch(train) [6][1600/3907] lr: 4.9230e-05 eta: 3:21:36 time: 0.6470 data_time: 0.5068 memory: 6319 loss: 0.1099 2023/06/05 02:57:26 - mmengine - INFO - Epoch(train) [6][1700/3907] lr: 4.8871e-05 eta: 3:20:28 time: 0.7002 data_time: 0.5566 memory: 6319 loss: 0.0949 2023/06/05 02:58:33 - mmengine - INFO - Epoch(train) [6][1800/3907] lr: 4.8513e-05 eta: 3:19:20 time: 0.6985 data_time: 0.5592 memory: 6319 loss: 0.0785 2023/06/05 02:59:39 - mmengine - INFO - Epoch(train) [6][1900/3907] lr: 4.8155e-05 eta: 3:18:11 time: 0.6720 data_time: 0.5205 memory: 6319 loss: 0.0885 2023/06/05 03:00:44 - mmengine - INFO - Epoch(train) [6][2000/3907] lr: 4.7798e-05 eta: 3:17:02 time: 0.6435 data_time: 0.5039 memory: 6319 loss: 0.0847 2023/06/05 03:01:51 - mmengine - INFO - Epoch(train) [6][2100/3907] lr: 4.7441e-05 eta: 3:15:55 time: 0.6574 data_time: 0.5169 memory: 6319 loss: 0.0869 2023/06/05 03:02:57 - mmengine - INFO - Epoch(train) [6][2200/3907] lr: 4.7084e-05 eta: 3:14:46 time: 0.6593 data_time: 0.5160 memory: 6319 loss: 0.0849 2023/06/05 03:04:05 - mmengine - INFO - Epoch(train) [6][2300/3907] lr: 4.6729e-05 eta: 3:13:39 time: 0.6877 data_time: 0.5460 memory: 6319 loss: 0.0828 2023/06/05 03:05:11 - mmengine - INFO - Epoch(train) [6][2400/3907] lr: 4.6373e-05 eta: 3:12:30 time: 0.6736 data_time: 0.5129 memory: 6319 loss: 0.0953 2023/06/05 03:05:57 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 03:06:17 - mmengine - INFO - Epoch(train) [6][2500/3907] lr: 4.6018e-05 eta: 3:11:22 time: 0.6585 data_time: 0.5153 memory: 6319 loss: 0.0898 2023/06/05 03:07:22 - mmengine - INFO - Epoch(train) [6][2600/3907] lr: 4.5664e-05 eta: 3:10:13 time: 0.6338 data_time: 0.4810 memory: 6319 loss: 0.1028 2023/06/05 03:08:29 - mmengine - INFO - Epoch(train) [6][2700/3907] lr: 4.5310e-05 eta: 3:09:05 time: 0.6423 data_time: 0.5007 memory: 6319 loss: 0.0992 2023/06/05 03:09:35 - mmengine - INFO - Epoch(train) [6][2800/3907] lr: 4.4957e-05 eta: 3:07:57 time: 0.6834 data_time: 0.5392 memory: 6319 loss: 0.0924 2023/06/05 03:10:42 - mmengine - INFO - Epoch(train) [6][2900/3907] lr: 4.4605e-05 eta: 3:06:49 time: 0.6495 data_time: 0.5094 memory: 6319 loss: 0.0798 2023/06/05 03:11:50 - mmengine - INFO - Epoch(train) [6][3000/3907] lr: 4.4253e-05 eta: 3:05:42 time: 0.6808 data_time: 0.5398 memory: 6319 loss: 0.0837 2023/06/05 03:12:56 - mmengine - INFO - Epoch(train) [6][3100/3907] lr: 4.3902e-05 eta: 3:04:33 time: 0.6207 data_time: 0.4800 memory: 6319 loss: 0.0891 2023/06/05 03:14:03 - mmengine - INFO - Epoch(train) [6][3200/3907] lr: 4.3552e-05 eta: 3:03:26 time: 0.6707 data_time: 0.5293 memory: 6319 loss: 0.0951 2023/06/05 03:15:11 - mmengine - INFO - Epoch(train) [6][3300/3907] lr: 4.3202e-05 eta: 3:02:19 time: 0.6986 data_time: 0.5575 memory: 6319 loss: 0.0866 2023/06/05 03:16:16 - mmengine - INFO - Epoch(train) [6][3400/3907] lr: 4.2854e-05 eta: 3:01:10 time: 0.6644 data_time: 0.5238 memory: 6319 loss: 0.0825 2023/06/05 03:17:02 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 03:17:23 - mmengine - INFO - Epoch(train) [6][3500/3907] lr: 4.2506e-05 eta: 3:00:02 time: 0.6671 data_time: 0.5263 memory: 6319 loss: 0.0943 2023/06/05 03:18:31 - mmengine - INFO - Epoch(train) [6][3600/3907] lr: 4.2158e-05 eta: 2:58:55 time: 0.6680 data_time: 0.5269 memory: 6319 loss: 0.1002 2023/06/05 03:19:38 - mmengine - INFO - Epoch(train) [6][3700/3907] lr: 4.1812e-05 eta: 2:57:47 time: 0.6261 data_time: 0.4859 memory: 6319 loss: 0.0942 2023/06/05 03:20:45 - mmengine - INFO - Epoch(train) [6][3800/3907] lr: 4.1466e-05 eta: 2:56:40 time: 0.6174 data_time: 0.4777 memory: 6319 loss: 0.0874 2023/06/05 03:21:51 - mmengine - INFO - Epoch(train) [6][3900/3907] lr: 4.1122e-05 eta: 2:55:32 time: 0.6479 data_time: 0.5053 memory: 6319 loss: 0.0733 2023/06/05 03:21:56 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 03:21:56 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/06/05 03:22:45 - mmengine - INFO - Epoch(val) [6][57/57] accuracy/top1: 95.1428 data_time: 0.5447 time: 0.6323 2023/06/05 03:23:54 - mmengine - INFO - Epoch(train) [7][ 100/3907] lr: 4.0754e-05 eta: 2:54:21 time: 0.6833 data_time: 0.4709 memory: 6319 loss: 0.0830 2023/06/05 03:25:01 - mmengine - INFO - Epoch(train) [7][ 200/3907] lr: 4.0411e-05 eta: 2:53:13 time: 0.6511 data_time: 0.4820 memory: 6319 loss: 0.0863 2023/06/05 03:26:06 - mmengine - INFO - Epoch(train) [7][ 300/3907] lr: 4.0069e-05 eta: 2:52:05 time: 0.6624 data_time: 0.5034 memory: 6319 loss: 0.0899 2023/06/05 03:27:13 - mmengine - INFO - Epoch(train) [7][ 400/3907] lr: 3.9729e-05 eta: 2:50:57 time: 0.6492 data_time: 0.3304 memory: 6319 loss: 0.0917 2023/06/05 03:28:20 - mmengine - INFO - Epoch(train) [7][ 500/3907] lr: 3.9389e-05 eta: 2:49:50 time: 0.6958 data_time: 0.2444 memory: 6319 loss: 0.0931 2023/06/05 03:29:00 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 03:29:27 - mmengine - INFO - Epoch(train) [7][ 600/3907] lr: 3.9050e-05 eta: 2:48:42 time: 0.6798 data_time: 0.3008 memory: 6319 loss: 0.0904 2023/06/05 03:30:35 - mmengine - INFO - Epoch(train) [7][ 700/3907] lr: 3.8712e-05 eta: 2:47:35 time: 0.6649 data_time: 0.1281 memory: 6319 loss: 0.0933 2023/06/05 03:31:41 - mmengine - INFO - Epoch(train) [7][ 800/3907] lr: 3.8375e-05 eta: 2:46:27 time: 0.6324 data_time: 0.2489 memory: 6319 loss: 0.0722 2023/06/05 03:32:48 - mmengine - INFO - Epoch(train) [7][ 900/3907] lr: 3.8039e-05 eta: 2:45:19 time: 0.6473 data_time: 0.3247 memory: 6319 loss: 0.0875 2023/06/05 03:33:53 - mmengine - INFO - Epoch(train) [7][1000/3907] lr: 3.7705e-05 eta: 2:44:11 time: 0.6407 data_time: 0.2841 memory: 6319 loss: 0.1027 2023/06/05 03:35:00 - mmengine - INFO - Epoch(train) [7][1100/3907] lr: 3.7371e-05 eta: 2:43:03 time: 0.7046 data_time: 0.3224 memory: 6319 loss: 0.0939 2023/06/05 03:36:05 - mmengine - INFO - Epoch(train) [7][1200/3907] lr: 3.7039e-05 eta: 2:41:54 time: 0.6629 data_time: 0.3817 memory: 6319 loss: 0.0884 2023/06/05 03:37:11 - mmengine - INFO - Epoch(train) [7][1300/3907] lr: 3.6708e-05 eta: 2:40:46 time: 0.6343 data_time: 0.3548 memory: 6319 loss: 0.0907 2023/06/05 03:38:17 - mmengine - INFO - Epoch(train) [7][1400/3907] lr: 3.6378e-05 eta: 2:39:38 time: 0.6865 data_time: 0.5104 memory: 6319 loss: 0.0993 2023/06/05 03:39:24 - mmengine - INFO - Epoch(train) [7][1500/3907] lr: 3.6049e-05 eta: 2:38:31 time: 0.6687 data_time: 0.5267 memory: 6319 loss: 0.0951 2023/06/05 03:40:04 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 03:40:31 - mmengine - INFO - Epoch(train) [7][1600/3907] lr: 3.5721e-05 eta: 2:37:23 time: 0.6518 data_time: 0.5122 memory: 6319 loss: 0.0814 2023/06/05 03:41:38 - mmengine - INFO - Epoch(train) [7][1700/3907] lr: 3.5395e-05 eta: 2:36:15 time: 0.6637 data_time: 0.5005 memory: 6319 loss: 0.0833 2023/06/05 03:42:45 - mmengine - INFO - Epoch(train) [7][1800/3907] lr: 3.5070e-05 eta: 2:35:08 time: 0.6965 data_time: 0.4331 memory: 6319 loss: 0.0814 2023/06/05 03:43:53 - mmengine - INFO - Epoch(train) [7][1900/3907] lr: 3.4746e-05 eta: 2:34:01 time: 0.6273 data_time: 0.2228 memory: 6319 loss: 0.0851 2023/06/05 03:44:59 - mmengine - INFO - Epoch(train) [7][2000/3907] lr: 3.4424e-05 eta: 2:32:53 time: 0.6682 data_time: 0.3571 memory: 6319 loss: 0.0795 2023/06/05 03:46:05 - mmengine - INFO - Epoch(train) [7][2100/3907] lr: 3.4103e-05 eta: 2:31:45 time: 0.6555 data_time: 0.2025 memory: 6319 loss: 0.0941 2023/06/05 03:47:10 - mmengine - INFO - Epoch(train) [7][2200/3907] lr: 3.3783e-05 eta: 2:30:36 time: 0.6488 data_time: 0.1938 memory: 6319 loss: 0.0736 2023/06/05 03:48:17 - mmengine - INFO - Epoch(train) [7][2300/3907] lr: 3.3465e-05 eta: 2:29:29 time: 0.6764 data_time: 0.0210 memory: 6319 loss: 0.0829 2023/06/05 03:49:23 - mmengine - INFO - Epoch(train) [7][2400/3907] lr: 3.3148e-05 eta: 2:28:21 time: 0.6985 data_time: 0.0012 memory: 6319 loss: 0.0913 2023/06/05 03:50:30 - mmengine - INFO - Epoch(train) [7][2500/3907] lr: 3.2832e-05 eta: 2:27:13 time: 0.6535 data_time: 0.0012 memory: 6319 loss: 0.0767 2023/06/05 03:51:10 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 03:51:37 - mmengine - INFO - Epoch(train) [7][2600/3907] lr: 3.2518e-05 eta: 2:26:06 time: 0.6932 data_time: 0.0014 memory: 6319 loss: 0.0775 2023/06/05 03:52:42 - mmengine - INFO - Epoch(train) [7][2700/3907] lr: 3.2205e-05 eta: 2:24:57 time: 0.6336 data_time: 0.0013 memory: 6319 loss: 0.0764 2023/06/05 03:53:46 - mmengine - INFO - Epoch(train) [7][2800/3907] lr: 3.1894e-05 eta: 2:23:49 time: 0.6317 data_time: 0.1095 memory: 6319 loss: 0.0750 2023/06/05 03:54:52 - mmengine - INFO - Epoch(train) [7][2900/3907] lr: 3.1584e-05 eta: 2:22:41 time: 0.6450 data_time: 0.2127 memory: 6319 loss: 0.0994 2023/06/05 03:55:58 - mmengine - INFO - Epoch(train) [7][3000/3907] lr: 3.1276e-05 eta: 2:21:33 time: 0.6719 data_time: 0.2100 memory: 6319 loss: 0.0889 2023/06/05 03:57:03 - mmengine - INFO - Epoch(train) [7][3100/3907] lr: 3.0969e-05 eta: 2:20:25 time: 0.6451 data_time: 0.2017 memory: 6319 loss: 0.0774 2023/06/05 03:58:10 - mmengine - INFO - Epoch(train) [7][3200/3907] lr: 3.0664e-05 eta: 2:19:17 time: 0.6313 data_time: 0.0578 memory: 6319 loss: 0.0933 2023/06/05 03:59:16 - mmengine - INFO - Epoch(train) [7][3300/3907] lr: 3.0360e-05 eta: 2:18:09 time: 0.6147 data_time: 0.0367 memory: 6319 loss: 0.0919 2023/06/05 04:00:23 - mmengine - INFO - Epoch(train) [7][3400/3907] lr: 3.0058e-05 eta: 2:17:02 time: 0.6930 data_time: 0.0251 memory: 6319 loss: 0.0838 2023/06/05 04:01:29 - mmengine - INFO - Epoch(train) [7][3500/3907] lr: 2.9758e-05 eta: 2:15:54 time: 0.6763 data_time: 0.0023 memory: 6319 loss: 0.0886 2023/06/05 04:02:08 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 04:02:35 - mmengine - INFO - Epoch(train) [7][3600/3907] lr: 2.9459e-05 eta: 2:14:46 time: 0.6263 data_time: 0.0017 memory: 6319 loss: 0.0850 2023/06/05 04:03:43 - mmengine - INFO - Epoch(train) [7][3700/3907] lr: 2.9162e-05 eta: 2:13:40 time: 0.6992 data_time: 0.0011 memory: 6319 loss: 0.0823 2023/06/05 04:04:49 - mmengine - INFO - Epoch(train) [7][3800/3907] lr: 2.8867e-05 eta: 2:12:32 time: 0.6734 data_time: 0.0016 memory: 6319 loss: 0.0807 2023/06/05 04:05:53 - mmengine - INFO - Epoch(train) [7][3900/3907] lr: 2.8573e-05 eta: 2:11:23 time: 0.6396 data_time: 0.0008 memory: 6319 loss: 0.0829 2023/06/05 04:05:58 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 04:05:58 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/06/05 04:06:47 - mmengine - INFO - Epoch(val) [7][57/57] accuracy/top1: 95.8770 data_time: 0.5131 time: 0.6030 2023/06/05 04:07:54 - mmengine - INFO - Epoch(train) [8][ 100/3907] lr: 2.8261e-05 eta: 2:10:11 time: 0.6539 data_time: 0.5128 memory: 6319 loss: 0.0746 2023/06/05 04:09:00 - mmengine - INFO - Epoch(train) [8][ 200/3907] lr: 2.7971e-05 eta: 2:09:04 time: 0.6469 data_time: 0.4983 memory: 6319 loss: 0.0821 2023/06/05 04:10:07 - mmengine - INFO - Epoch(train) [8][ 300/3907] lr: 2.7682e-05 eta: 2:07:57 time: 0.6515 data_time: 0.5047 memory: 6319 loss: 0.0866 2023/06/05 04:11:13 - mmengine - INFO - Epoch(train) [8][ 400/3907] lr: 2.7395e-05 eta: 2:06:49 time: 0.6198 data_time: 0.4775 memory: 6319 loss: 0.1056 2023/06/05 04:12:19 - mmengine - INFO - Epoch(train) [8][ 500/3907] lr: 2.7111e-05 eta: 2:05:41 time: 0.6706 data_time: 0.5294 memory: 6319 loss: 0.0904 2023/06/05 04:13:25 - mmengine - INFO - Epoch(train) [8][ 600/3907] lr: 2.6828e-05 eta: 2:04:33 time: 0.6807 data_time: 0.5398 memory: 6319 loss: 0.0981 2023/06/05 04:13:59 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 04:14:33 - mmengine - INFO - Epoch(train) [8][ 700/3907] lr: 2.6546e-05 eta: 2:03:26 time: 0.6687 data_time: 0.4495 memory: 6319 loss: 0.0908 2023/06/05 04:15:39 - mmengine - INFO - Epoch(train) [8][ 800/3907] lr: 2.6267e-05 eta: 2:02:19 time: 0.6814 data_time: 0.5386 memory: 6319 loss: 0.0912 2023/06/05 04:16:45 - mmengine - INFO - Epoch(train) [8][ 900/3907] lr: 2.5989e-05 eta: 2:01:11 time: 0.6589 data_time: 0.5184 memory: 6319 loss: 0.1057 2023/06/05 04:17:51 - mmengine - INFO - Epoch(train) [8][1000/3907] lr: 2.5714e-05 eta: 2:00:04 time: 0.6964 data_time: 0.5535 memory: 6319 loss: 0.1015 2023/06/05 04:18:59 - mmengine - INFO - Epoch(train) [8][1100/3907] lr: 2.5440e-05 eta: 1:58:56 time: 0.6736 data_time: 0.5336 memory: 6319 loss: 0.0967 2023/06/05 04:20:05 - mmengine - INFO - Epoch(train) [8][1200/3907] lr: 2.5168e-05 eta: 1:57:49 time: 0.6763 data_time: 0.5342 memory: 6319 loss: 0.0763 2023/06/05 04:21:12 - mmengine - INFO - Epoch(train) [8][1300/3907] lr: 2.4898e-05 eta: 1:56:42 time: 0.6497 data_time: 0.5070 memory: 6319 loss: 0.0863 2023/06/05 04:22:18 - mmengine - INFO - Epoch(train) [8][1400/3907] lr: 2.4630e-05 eta: 1:55:34 time: 0.6296 data_time: 0.4846 memory: 6319 loss: 0.0780 2023/06/05 04:23:24 - mmengine - INFO - Epoch(train) [8][1500/3907] lr: 2.4364e-05 eta: 1:54:26 time: 0.6721 data_time: 0.5297 memory: 6319 loss: 0.0711 2023/06/05 04:24:31 - mmengine - INFO - Epoch(train) [8][1600/3907] lr: 2.4100e-05 eta: 1:53:19 time: 0.6428 data_time: 0.5019 memory: 6319 loss: 0.0866 2023/06/05 04:25:04 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 04:25:36 - mmengine - INFO - Epoch(train) [8][1700/3907] lr: 2.3838e-05 eta: 1:52:11 time: 0.6370 data_time: 0.4974 memory: 6319 loss: 0.0868 2023/06/05 04:26:42 - mmengine - INFO - Epoch(train) [8][1800/3907] lr: 2.3578e-05 eta: 1:51:04 time: 0.6740 data_time: 0.5338 memory: 6319 loss: 0.0866 2023/06/05 04:27:49 - mmengine - INFO - Epoch(train) [8][1900/3907] lr: 2.3320e-05 eta: 1:49:56 time: 0.6760 data_time: 0.5322 memory: 6319 loss: 0.0691 2023/06/05 04:28:55 - mmengine - INFO - Epoch(train) [8][2000/3907] lr: 2.3064e-05 eta: 1:48:49 time: 0.6792 data_time: 0.5385 memory: 6319 loss: 0.0872 2023/06/05 04:30:00 - mmengine - INFO - Epoch(train) [8][2100/3907] lr: 2.2810e-05 eta: 1:47:41 time: 0.6652 data_time: 0.5235 memory: 6319 loss: 0.0788 2023/06/05 04:31:08 - mmengine - INFO - Epoch(train) [8][2200/3907] lr: 2.2558e-05 eta: 1:46:34 time: 0.6289 data_time: 0.4888 memory: 6319 loss: 0.0879 2023/06/05 04:32:13 - mmengine - INFO - Epoch(train) [8][2300/3907] lr: 2.2309e-05 eta: 1:45:26 time: 0.6296 data_time: 0.4862 memory: 6319 loss: 0.0855 2023/06/05 04:33:19 - mmengine - INFO - Epoch(train) [8][2400/3907] lr: 2.2061e-05 eta: 1:44:19 time: 0.6510 data_time: 0.5056 memory: 6319 loss: 0.0799 2023/06/05 04:34:24 - mmengine - INFO - Epoch(train) [8][2500/3907] lr: 2.1816e-05 eta: 1:43:11 time: 0.6673 data_time: 0.5047 memory: 6319 loss: 0.0879 2023/06/05 04:35:30 - mmengine - INFO - Epoch(train) [8][2600/3907] lr: 2.1572e-05 eta: 1:42:03 time: 0.6555 data_time: 0.5138 memory: 6319 loss: 0.0794 2023/06/05 04:36:03 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 04:36:35 - mmengine - INFO - Epoch(train) [8][2700/3907] lr: 2.1331e-05 eta: 1:40:56 time: 0.6874 data_time: 0.5457 memory: 6319 loss: 0.0835 2023/06/05 04:37:41 - mmengine - INFO - Epoch(train) [8][2800/3907] lr: 2.1092e-05 eta: 1:39:48 time: 0.6198 data_time: 0.4772 memory: 6319 loss: 0.0770 2023/06/05 04:38:46 - mmengine - INFO - Epoch(train) [8][2900/3907] lr: 2.0855e-05 eta: 1:38:40 time: 0.6416 data_time: 0.4771 memory: 6319 loss: 0.0769 2023/06/05 04:39:50 - mmengine - INFO - Epoch(train) [8][3000/3907] lr: 2.0621e-05 eta: 1:37:32 time: 0.5951 data_time: 0.4539 memory: 6319 loss: 0.0880 2023/06/05 04:40:56 - mmengine - INFO - Epoch(train) [8][3100/3907] lr: 2.0388e-05 eta: 1:36:25 time: 0.6554 data_time: 0.5135 memory: 6319 loss: 0.0979 2023/06/05 04:42:01 - mmengine - INFO - Epoch(train) [8][3200/3907] lr: 2.0158e-05 eta: 1:35:17 time: 0.6210 data_time: 0.4787 memory: 6319 loss: 0.0819 2023/06/05 04:43:07 - mmengine - INFO - Epoch(train) [8][3300/3907] lr: 1.9930e-05 eta: 1:34:10 time: 0.6211 data_time: 0.4796 memory: 6319 loss: 0.0671 2023/06/05 04:44:13 - mmengine - INFO - Epoch(train) [8][3400/3907] lr: 1.9705e-05 eta: 1:33:02 time: 0.7155 data_time: 0.5479 memory: 6319 loss: 0.0779 2023/06/05 04:45:18 - mmengine - INFO - Epoch(train) [8][3500/3907] lr: 1.9481e-05 eta: 1:31:55 time: 0.6287 data_time: 0.4846 memory: 6319 loss: 0.0861 2023/06/05 04:46:23 - mmengine - INFO - Epoch(train) [8][3600/3907] lr: 1.9260e-05 eta: 1:30:47 time: 0.6590 data_time: 0.5063 memory: 6319 loss: 0.0887 2023/06/05 04:46:56 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 04:47:28 - mmengine - INFO - Epoch(train) [8][3700/3907] lr: 1.9042e-05 eta: 1:29:40 time: 0.6220 data_time: 0.4814 memory: 6319 loss: 0.0826 2023/06/05 04:48:35 - mmengine - INFO - Epoch(train) [8][3800/3907] lr: 1.8825e-05 eta: 1:28:32 time: 0.6637 data_time: 0.5231 memory: 6319 loss: 0.0693 2023/06/05 04:49:40 - mmengine - INFO - Epoch(train) [8][3900/3907] lr: 1.8611e-05 eta: 1:27:25 time: 0.6353 data_time: 0.4940 memory: 6319 loss: 0.0868 2023/06/05 04:49:46 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 04:49:46 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/06/05 04:50:35 - mmengine - INFO - Epoch(val) [8][57/57] accuracy/top1: 96.5067 data_time: 0.5388 time: 0.6272 2023/06/05 04:51:42 - mmengine - INFO - Epoch(train) [9][ 100/3907] lr: 1.8385e-05 eta: 1:26:13 time: 0.5908 data_time: 0.4504 memory: 6319 loss: 0.0774 2023/06/05 04:52:48 - mmengine - INFO - Epoch(train) [9][ 200/3907] lr: 1.8176e-05 eta: 1:25:06 time: 0.6101 data_time: 0.4683 memory: 6319 loss: 0.0846 2023/06/05 04:53:53 - mmengine - INFO - Epoch(train) [9][ 300/3907] lr: 1.7969e-05 eta: 1:23:58 time: 0.6208 data_time: 0.4809 memory: 6319 loss: 0.0809 2023/06/05 04:54:58 - mmengine - INFO - Epoch(train) [9][ 400/3907] lr: 1.7765e-05 eta: 1:22:51 time: 0.6318 data_time: 0.4907 memory: 6319 loss: 0.0857 2023/06/05 04:56:03 - mmengine - INFO - Epoch(train) [9][ 500/3907] lr: 1.7563e-05 eta: 1:21:43 time: 0.6543 data_time: 0.5134 memory: 6319 loss: 0.0786 2023/06/05 04:57:09 - mmengine - INFO - Epoch(train) [9][ 600/3907] lr: 1.7363e-05 eta: 1:20:36 time: 0.6742 data_time: 0.4887 memory: 6319 loss: 0.0888 2023/06/05 04:58:14 - mmengine - INFO - Epoch(train) [9][ 700/3907] lr: 1.7166e-05 eta: 1:19:29 time: 0.6330 data_time: 0.4915 memory: 6319 loss: 0.0832 2023/06/05 04:58:46 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 04:59:20 - mmengine - INFO - Epoch(train) [9][ 800/3907] lr: 1.6971e-05 eta: 1:18:21 time: 0.6416 data_time: 0.5001 memory: 6319 loss: 0.0735 2023/06/05 05:00:25 - mmengine - INFO - Epoch(train) [9][ 900/3907] lr: 1.6779e-05 eta: 1:17:14 time: 0.6127 data_time: 0.4580 memory: 6319 loss: 0.0807 2023/06/05 05:01:30 - mmengine - INFO - Epoch(train) [9][1000/3907] lr: 1.6589e-05 eta: 1:16:06 time: 0.6638 data_time: 0.5103 memory: 6319 loss: 0.0787 2023/06/05 05:02:36 - mmengine - INFO - Epoch(train) [9][1100/3907] lr: 1.6402e-05 eta: 1:14:59 time: 0.7057 data_time: 0.4995 memory: 6319 loss: 0.0781 2023/06/05 05:03:42 - mmengine - INFO - Epoch(train) [9][1200/3907] lr: 1.6217e-05 eta: 1:13:52 time: 0.7084 data_time: 0.4486 memory: 6319 loss: 0.0836 2023/06/05 05:04:47 - mmengine - INFO - Epoch(train) [9][1300/3907] lr: 1.6035e-05 eta: 1:12:44 time: 0.7299 data_time: 0.5895 memory: 6319 loss: 0.0897 2023/06/05 05:05:51 - mmengine - INFO - Epoch(train) [9][1400/3907] lr: 1.5855e-05 eta: 1:11:37 time: 0.6415 data_time: 0.5001 memory: 6319 loss: 0.0734 2023/06/05 05:06:59 - mmengine - INFO - Epoch(train) [9][1500/3907] lr: 1.5678e-05 eta: 1:10:30 time: 0.7241 data_time: 0.5831 memory: 6319 loss: 0.0838 2023/06/05 05:08:06 - mmengine - INFO - Epoch(train) [9][1600/3907] lr: 1.5503e-05 eta: 1:09:23 time: 0.6703 data_time: 0.5291 memory: 6319 loss: 0.0905 2023/06/05 05:09:13 - mmengine - INFO - Epoch(train) [9][1700/3907] lr: 1.5331e-05 eta: 1:08:16 time: 0.6597 data_time: 0.5190 memory: 6319 loss: 0.0885 2023/06/05 05:09:45 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 05:10:20 - mmengine - INFO - Epoch(train) [9][1800/3907] lr: 1.5162e-05 eta: 1:07:09 time: 0.6580 data_time: 0.5171 memory: 6319 loss: 0.0948 2023/06/05 05:11:25 - mmengine - INFO - Epoch(train) [9][1900/3907] lr: 1.4995e-05 eta: 1:06:02 time: 0.6293 data_time: 0.4896 memory: 6319 loss: 0.0805 2023/06/05 05:12:30 - mmengine - INFO - Epoch(train) [9][2000/3907] lr: 1.4830e-05 eta: 1:04:54 time: 0.6350 data_time: 0.4890 memory: 6319 loss: 0.0831 2023/06/05 05:13:34 - mmengine - INFO - Epoch(train) [9][2100/3907] lr: 1.4668e-05 eta: 1:03:47 time: 0.6452 data_time: 0.5043 memory: 6319 loss: 0.1006 2023/06/05 05:14:40 - mmengine - INFO - Epoch(train) [9][2200/3907] lr: 1.4509e-05 eta: 1:02:40 time: 0.6242 data_time: 0.4838 memory: 6319 loss: 0.0748 2023/06/05 05:15:45 - mmengine - INFO - Epoch(train) [9][2300/3907] lr: 1.4353e-05 eta: 1:01:32 time: 0.6371 data_time: 0.4971 memory: 6319 loss: 0.0890 2023/06/05 05:16:51 - mmengine - INFO - Epoch(train) [9][2400/3907] lr: 1.4199e-05 eta: 1:00:25 time: 0.6479 data_time: 0.5083 memory: 6319 loss: 0.0932 2023/06/05 05:17:56 - mmengine - INFO - Epoch(train) [9][2500/3907] lr: 1.4047e-05 eta: 0:59:18 time: 0.6291 data_time: 0.4894 memory: 6319 loss: 0.0795 2023/06/05 05:19:00 - mmengine - INFO - Epoch(train) [9][2600/3907] lr: 1.3899e-05 eta: 0:58:10 time: 0.6274 data_time: 0.4867 memory: 6319 loss: 0.0787 2023/06/05 05:20:05 - mmengine - INFO - Epoch(train) [9][2700/3907] lr: 1.3753e-05 eta: 0:57:03 time: 0.6523 data_time: 0.5119 memory: 6319 loss: 0.0862 2023/06/05 05:20:37 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 05:21:09 - mmengine - INFO - Epoch(train) [9][2800/3907] lr: 1.3609e-05 eta: 0:55:56 time: 0.6593 data_time: 0.5193 memory: 6319 loss: 0.0787 2023/06/05 05:22:13 - mmengine - INFO - Epoch(train) [9][2900/3907] lr: 1.3469e-05 eta: 0:54:49 time: 0.6560 data_time: 0.5041 memory: 6319 loss: 0.0808 2023/06/05 05:23:16 - mmengine - INFO - Epoch(train) [9][3000/3907] lr: 1.3331e-05 eta: 0:53:41 time: 0.5918 data_time: 0.4517 memory: 6319 loss: 0.0821 2023/06/05 05:24:23 - mmengine - INFO - Epoch(train) [9][3100/3907] lr: 1.3196e-05 eta: 0:52:34 time: 0.6678 data_time: 0.5188 memory: 6319 loss: 0.0774 2023/06/05 05:25:28 - mmengine - INFO - Epoch(train) [9][3200/3907] lr: 1.3063e-05 eta: 0:51:27 time: 0.6356 data_time: 0.4950 memory: 6319 loss: 0.0837 2023/06/05 05:26:33 - mmengine - INFO - Epoch(train) [9][3300/3907] lr: 1.2933e-05 eta: 0:50:20 time: 0.6414 data_time: 0.4888 memory: 6319 loss: 0.0862 2023/06/05 05:27:39 - mmengine - INFO - Epoch(train) [9][3400/3907] lr: 1.2806e-05 eta: 0:49:13 time: 0.7346 data_time: 0.5944 memory: 6319 loss: 0.0854 2023/06/05 05:28:46 - mmengine - INFO - Epoch(train) [9][3500/3907] lr: 1.2682e-05 eta: 0:48:06 time: 0.6734 data_time: 0.5329 memory: 6319 loss: 0.0963 2023/06/05 05:29:52 - mmengine - INFO - Epoch(train) [9][3600/3907] lr: 1.2560e-05 eta: 0:46:59 time: 0.6625 data_time: 0.5211 memory: 6319 loss: 0.0763 2023/06/05 05:30:58 - mmengine - INFO - Epoch(train) [9][3700/3907] lr: 1.2441e-05 eta: 0:45:52 time: 0.6801 data_time: 0.5393 memory: 6319 loss: 0.0787 2023/06/05 05:31:30 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 05:32:05 - mmengine - INFO - Epoch(train) [9][3800/3907] lr: 1.2325e-05 eta: 0:44:45 time: 0.6657 data_time: 0.5266 memory: 6319 loss: 0.0835 2023/06/05 05:33:10 - mmengine - INFO - Epoch(train) [9][3900/3907] lr: 1.2211e-05 eta: 0:43:38 time: 0.6446 data_time: 0.5052 memory: 6319 loss: 0.0826 2023/06/05 05:33:16 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 05:33:16 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/06/05 05:34:04 - mmengine - INFO - Epoch(val) [9][57/57] accuracy/top1: 96.4754 data_time: 0.5188 time: 0.6089 2023/06/05 05:35:11 - mmengine - INFO - Epoch(train) [10][ 100/3907] lr: 1.2093e-05 eta: 0:42:26 time: 0.6700 data_time: 0.5301 memory: 6319 loss: 0.0877 2023/06/05 05:36:18 - mmengine - INFO - Epoch(train) [10][ 200/3907] lr: 1.1985e-05 eta: 0:41:20 time: 0.6757 data_time: 0.5218 memory: 6319 loss: 0.0822 2023/06/05 05:37:25 - mmengine - INFO - Epoch(train) [10][ 300/3907] lr: 1.1881e-05 eta: 0:40:13 time: 0.6407 data_time: 0.5014 memory: 6319 loss: 0.0712 2023/06/05 05:38:31 - mmengine - INFO - Epoch(train) [10][ 400/3907] lr: 1.1778e-05 eta: 0:39:06 time: 0.6248 data_time: 0.4735 memory: 6319 loss: 0.0819 2023/06/05 05:39:36 - mmengine - INFO - Epoch(train) [10][ 500/3907] lr: 1.1679e-05 eta: 0:37:59 time: 0.6288 data_time: 0.4895 memory: 6319 loss: 0.0824 2023/06/05 05:40:42 - mmengine - INFO - Epoch(train) [10][ 600/3907] lr: 1.1583e-05 eta: 0:36:52 time: 0.6897 data_time: 0.5363 memory: 6319 loss: 0.0834 2023/06/05 05:41:49 - mmengine - INFO - Epoch(train) [10][ 700/3907] lr: 1.1489e-05 eta: 0:35:45 time: 0.6838 data_time: 0.5400 memory: 6319 loss: 0.0853 2023/06/05 05:42:54 - mmengine - INFO - Epoch(train) [10][ 800/3907] lr: 1.1398e-05 eta: 0:34:38 time: 0.7402 data_time: 0.5999 memory: 6319 loss: 0.0971 2023/06/05 05:43:20 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 05:43:59 - mmengine - INFO - Epoch(train) [10][ 900/3907] lr: 1.1310e-05 eta: 0:33:31 time: 0.6521 data_time: 0.5114 memory: 6319 loss: 0.0844 2023/06/05 05:45:04 - mmengine - INFO - Epoch(train) [10][1000/3907] lr: 1.1225e-05 eta: 0:32:24 time: 0.7041 data_time: 0.5624 memory: 6319 loss: 0.0777 2023/06/05 05:46:11 - mmengine - INFO - Epoch(train) [10][1100/3907] lr: 1.1142e-05 eta: 0:31:17 time: 0.6838 data_time: 0.5429 memory: 6319 loss: 0.0802 2023/06/05 05:47:16 - mmengine - INFO - Epoch(train) [10][1200/3907] lr: 1.1063e-05 eta: 0:30:10 time: 0.6186 data_time: 0.4776 memory: 6319 loss: 0.0894 2023/06/05 05:48:22 - mmengine - INFO - Epoch(train) [10][1300/3907] lr: 1.0986e-05 eta: 0:29:03 time: 0.6485 data_time: 0.5084 memory: 6319 loss: 0.0824 2023/06/05 05:49:27 - mmengine - INFO - Epoch(train) [10][1400/3907] lr: 1.0912e-05 eta: 0:27:56 time: 0.6370 data_time: 0.4960 memory: 6319 loss: 0.0967 2023/06/05 05:50:33 - mmengine - INFO - Epoch(train) [10][1500/3907] lr: 1.0841e-05 eta: 0:26:49 time: 0.6530 data_time: 0.5139 memory: 6319 loss: 0.0790 2023/06/05 05:51:38 - mmengine - INFO - Epoch(train) [10][1600/3907] lr: 1.0773e-05 eta: 0:25:42 time: 0.6111 data_time: 0.4700 memory: 6319 loss: 0.0812 2023/06/05 05:52:44 - mmengine - INFO - Epoch(train) [10][1700/3907] lr: 1.0707e-05 eta: 0:24:35 time: 0.6769 data_time: 0.5374 memory: 6319 loss: 0.0772 2023/06/05 05:53:50 - mmengine - INFO - Epoch(train) [10][1800/3907] lr: 1.0645e-05 eta: 0:23:28 time: 0.6857 data_time: 0.5459 memory: 6319 loss: 0.0683 2023/06/05 05:54:15 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 05:54:55 - mmengine - INFO - Epoch(train) [10][1900/3907] lr: 1.0585e-05 eta: 0:22:21 time: 0.6731 data_time: 0.5325 memory: 6319 loss: 0.0693 2023/06/05 05:56:01 - mmengine - INFO - Epoch(train) [10][2000/3907] lr: 1.0529e-05 eta: 0:21:14 time: 0.6679 data_time: 0.5285 memory: 6319 loss: 0.0746 2023/06/05 05:57:08 - mmengine - INFO - Epoch(train) [10][2100/3907] lr: 1.0475e-05 eta: 0:20:07 time: 0.6382 data_time: 0.4974 memory: 6319 loss: 0.0836 2023/06/05 05:58:13 - mmengine - INFO - Epoch(train) [10][2200/3907] lr: 1.0424e-05 eta: 0:19:00 time: 0.6249 data_time: 0.4841 memory: 6319 loss: 0.0870 2023/06/05 05:59:21 - mmengine - INFO - Epoch(train) [10][2300/3907] lr: 1.0376e-05 eta: 0:17:54 time: 0.7029 data_time: 0.5610 memory: 6319 loss: 0.0942 2023/06/05 06:00:28 - mmengine - INFO - Epoch(train) [10][2400/3907] lr: 1.0330e-05 eta: 0:16:47 time: 0.6636 data_time: 0.5243 memory: 6319 loss: 0.0782 2023/06/05 06:01:34 - mmengine - INFO - Epoch(train) [10][2500/3907] lr: 1.0288e-05 eta: 0:15:40 time: 0.6514 data_time: 0.5105 memory: 6319 loss: 0.0888 2023/06/05 06:02:40 - mmengine - INFO - Epoch(train) [10][2600/3907] lr: 1.0249e-05 eta: 0:14:33 time: 0.6535 data_time: 0.5124 memory: 6319 loss: 0.0817 2023/06/05 06:03:46 - mmengine - INFO - Epoch(train) [10][2700/3907] lr: 1.0212e-05 eta: 0:13:26 time: 0.6815 data_time: 0.5410 memory: 6319 loss: 0.0876 2023/06/05 06:04:52 - mmengine - INFO - Epoch(train) [10][2800/3907] lr: 1.0178e-05 eta: 0:12:19 time: 0.6742 data_time: 0.5327 memory: 6319 loss: 0.0873 2023/06/05 06:05:18 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 06:05:57 - mmengine - INFO - Epoch(train) [10][2900/3907] lr: 1.0148e-05 eta: 0:11:12 time: 0.6848 data_time: 0.5445 memory: 6319 loss: 0.0851 2023/06/05 06:07:05 - mmengine - INFO - Epoch(train) [10][3000/3907] lr: 1.0120e-05 eta: 0:10:06 time: 0.7787 data_time: 0.6379 memory: 6319 loss: 0.0841 2023/06/05 06:08:11 - mmengine - INFO - Epoch(train) [10][3100/3907] lr: 1.0095e-05 eta: 0:08:59 time: 0.6587 data_time: 0.5178 memory: 6319 loss: 0.0948 2023/06/05 06:09:17 - mmengine - INFO - Epoch(train) [10][3200/3907] lr: 1.0073e-05 eta: 0:07:52 time: 0.6501 data_time: 0.5099 memory: 6319 loss: 0.0908 2023/06/05 06:10:24 - mmengine - INFO - Epoch(train) [10][3300/3907] lr: 1.0054e-05 eta: 0:06:45 time: 0.6800 data_time: 0.5393 memory: 6319 loss: 0.0735 2023/06/05 06:11:29 - mmengine - INFO - Epoch(train) [10][3400/3907] lr: 1.0038e-05 eta: 0:05:38 time: 0.6487 data_time: 0.5076 memory: 6319 loss: 0.0899 2023/06/05 06:12:35 - mmengine - INFO - Epoch(train) [10][3500/3907] lr: 1.0024e-05 eta: 0:04:31 time: 0.6477 data_time: 0.5074 memory: 6319 loss: 0.0803 2023/06/05 06:13:41 - mmengine - INFO - Epoch(train) [10][3600/3907] lr: 1.0014e-05 eta: 0:03:25 time: 0.6441 data_time: 0.5041 memory: 6319 loss: 0.0884 2023/06/05 06:14:47 - mmengine - INFO - Epoch(train) [10][3700/3907] lr: 1.0006e-05 eta: 0:02:18 time: 0.6623 data_time: 0.5217 memory: 6319 loss: 0.0832 2023/06/05 06:15:53 - mmengine - INFO - Epoch(train) [10][3800/3907] lr: 1.0002e-05 eta: 0:01:11 time: 0.6792 data_time: 0.5377 memory: 6319 loss: 0.0842 2023/06/05 06:16:20 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 06:17:00 - mmengine - INFO - Epoch(train) [10][3900/3907] lr: 1.0000e-05 eta: 0:00:04 time: 0.6661 data_time: 0.5254 memory: 6319 loss: 0.0833 2023/06/05 06:17:05 - mmengine - INFO - Exp name: resnet50_2xb256_sdv2_1m_lr1e-4_aug_5e-1_20230604_225351 2023/06/05 06:17:05 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/06/05 06:17:54 - mmengine - INFO - Epoch(val) [10][57/57] accuracy/top1: 96.8686 data_time: 0.5283 time: 0.6142