2023/05/31 17:32:56 - 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: 151848001 GPU 0,1,2,3: 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) 4.8.5 20150623 (Red Hat 4.8.5-44) 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: 4 ------------------------------------------------------------ 2023/05/31 17:32:57 - mmengine - INFO - Config: optim_wrapper = dict( optimizer=dict( type='AdamW', lr=0.004, weight_decay=0.05, eps=1e-08, betas=(0.9, 0.999), _scope_='mmpretrain'), paramwise_cfg=dict( norm_decay_mult=0.0, bias_decay_mult=0.0, flat_decay_mult=0.0, custom_keys=dict({ '.absolute_pos_embed': dict(decay_mult=0.0), '.relative_position_bias_table': dict(decay_mult=0.0) })), type='AmpOptimWrapper', dtype='bfloat16', clip_grad=None) param_scheduler = [ dict(type='CosineAnnealingLR', eta_min=1e-05, by_epoch=True, begin=0) ] train_cfg = dict(by_epoch=True, max_epochs=20, val_interval=1) val_cfg = dict() test_cfg = dict() auto_scale_lr = dict(base_batch_size=4096) model = dict( type='ImageClassifier', backbone=dict(type='ConvNeXt', arch='small', drop_path_rate=0.4), head=dict( type='LinearClsHead', num_classes=2, in_channels=768, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), init_cfg=None), init_cfg=dict( type='TruncNormal', layer=['Conv2d', 'Linear'], std=0.02, bias=0.0), train_cfg=None) 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='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='CustomDataset', data_root='', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/stablediffusionV1-5R2-dpmsolver-25-5m.csv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), 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='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV1-5R2-dpmsolver-25-1w.tsv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='ResizeEdge', scale=256, edge='short', backend='pillow', interpolation='bicubic'), dict(type='CenterCrop', crop_size=224), dict(type='PackInputs') ]), sampler=dict(type='DefaultSampler', shuffle=False)) val_evaluator = [ dict(type='Accuracy', topk=1), dict(type='SingleLabelMetric', average=None) ] test_dataloader = dict( pin_memory=True, persistent_workers=True, collate_fn=dict(type='default_collate'), batch_size=256, num_workers=10, dataset=dict( type='CustomDataset', data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', ann_file= '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV1-5R2-dpmsolver-25-1w.tsv', pipeline=[ dict(type='LoadImageFromFile'), dict( type='ResizeEdge', scale=256, edge='short', backend='pillow', interpolation='bicubic'), dict(type='CenterCrop', crop_size=224), dict(type='PackInputs') ]), sampler=dict(type='DefaultSampler', shuffle=False)) test_evaluator = [ dict(type='Accuracy', topk=1), dict(type='SingleLabelMetric', average=None) ] 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/convnext_small_4xb256_4e-3lr_5m' 2023/05/31 17:33:00 - 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 -------------------- 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.0.0.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.0.1.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.0.1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.1.0.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.1.0.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.1.1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.2.0.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.2.0.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.2.1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.3.0.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.3.0.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.downsample_layers.3.1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.0.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.1.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.0.2.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - 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mmengine - INFO - paramwise_options -- backbone.stages.2.1.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.1.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.2.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.2.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.2.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - 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mmengine - INFO - paramwise_options -- backbone.stages.2.4.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.4.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.4.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.4.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.4.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.4.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.5.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.5.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.5.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.5.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.5.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.5.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.6.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.6.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.6.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.6.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.6.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.6.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.7.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.7.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.7.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.7.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.7.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.7.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.8.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.8.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.8.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.8.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.8.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.8.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.9.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.9.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.9.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.9.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.9.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.9.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.10.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.10.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.10.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.10.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.10.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.10.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.11.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.11.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.11.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.11.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.11.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.11.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.12.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.12.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.12.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.12.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.12.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.12.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.13.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.13.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.13.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.13.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.13.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.13.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.14.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.14.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.14.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.14.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.14.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.14.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.15.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.15.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.15.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.15.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.15.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.15.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.16.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.16.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.16.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.16.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.16.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.16.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.17.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.17.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.17.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.17.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.17.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.17.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.18.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.18.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.18.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.18.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.18.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.18.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.19.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.19.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.19.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.19.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.19.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.19.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.20.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.20.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.20.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.20.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.20.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.20.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.21.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.21.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.21.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.21.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.21.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.21.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.22.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.22.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.22.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.22.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.22.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.22.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.23.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.23.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.23.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.23.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.23.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.23.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.24.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.24.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.24.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.24.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.24.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.24.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.25.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.25.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.25.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.25.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.25.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.25.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.26.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.26.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.26.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.26.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.26.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.2.26.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.0.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.0.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.0.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.0.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.0.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.0.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.1.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.gamma:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.depthwise_conv.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.norm.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.norm.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.pointwise_conv1.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.stages.3.2.pointwise_conv2.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.norm3.weight:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- backbone.norm3.bias:weight_decay=0.0 2023/05/31 17:33:27 - mmengine - INFO - paramwise_options -- head.fc.bias:weight_decay=0.0 Name of parameter - Initialization information backbone.downsample_layers.0.0.weight - torch.Size([96, 3, 4, 4]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.downsample_layers.0.0.bias - torch.Size([96]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.downsample_layers.0.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.downsample_layers.0.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.downsample_layers.1.0.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.downsample_layers.1.0.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.downsample_layers.1.1.weight - torch.Size([192, 96, 2, 2]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.downsample_layers.1.1.bias - torch.Size([192]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.downsample_layers.2.0.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.downsample_layers.2.0.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.downsample_layers.2.1.weight - torch.Size([384, 192, 2, 2]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.downsample_layers.2.1.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.downsample_layers.3.0.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.downsample_layers.3.0.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.downsample_layers.3.1.weight - torch.Size([768, 384, 2, 2]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.downsample_layers.3.1.bias - torch.Size([768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.0.gamma - torch.Size([96]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.0.0.depthwise_conv.weight - torch.Size([96, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.0.depthwise_conv.bias - torch.Size([96]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.0.norm.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.0.0.norm.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.0.0.pointwise_conv1.weight - torch.Size([384, 96]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.0.pointwise_conv1.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.0.pointwise_conv2.weight - torch.Size([96, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.0.pointwise_conv2.bias - torch.Size([96]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.1.gamma - torch.Size([96]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.0.1.depthwise_conv.weight - torch.Size([96, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.1.depthwise_conv.bias - torch.Size([96]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.1.norm.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.0.1.norm.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.0.1.pointwise_conv1.weight - torch.Size([384, 96]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.1.pointwise_conv1.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.1.pointwise_conv2.weight - torch.Size([96, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.1.pointwise_conv2.bias - torch.Size([96]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.2.gamma - torch.Size([96]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.0.2.depthwise_conv.weight - torch.Size([96, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.2.depthwise_conv.bias - torch.Size([96]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.2.norm.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.0.2.norm.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.0.2.pointwise_conv1.weight - torch.Size([384, 96]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.2.pointwise_conv1.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.2.pointwise_conv2.weight - torch.Size([96, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.0.2.pointwise_conv2.bias - torch.Size([96]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.0.gamma - torch.Size([192]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.1.0.depthwise_conv.weight - torch.Size([192, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.0.depthwise_conv.bias - torch.Size([192]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.0.norm.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.1.0.norm.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.1.0.pointwise_conv1.weight - torch.Size([768, 192]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.0.pointwise_conv1.bias - torch.Size([768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.0.pointwise_conv2.weight - torch.Size([192, 768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.0.pointwise_conv2.bias - torch.Size([192]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.1.gamma - torch.Size([192]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.1.1.depthwise_conv.weight - torch.Size([192, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.1.depthwise_conv.bias - torch.Size([192]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.1.norm.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.1.1.norm.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.1.1.pointwise_conv1.weight - torch.Size([768, 192]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.1.pointwise_conv1.bias - torch.Size([768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.1.pointwise_conv2.weight - torch.Size([192, 768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.1.pointwise_conv2.bias - torch.Size([192]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.2.gamma - torch.Size([192]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.1.2.depthwise_conv.weight - torch.Size([192, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.2.depthwise_conv.bias - torch.Size([192]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.2.norm.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.1.2.norm.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.1.2.pointwise_conv1.weight - torch.Size([768, 192]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.2.pointwise_conv1.bias - torch.Size([768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.2.pointwise_conv2.weight - torch.Size([192, 768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.1.2.pointwise_conv2.bias - torch.Size([192]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.0.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.0.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.0.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.0.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.0.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.0.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.0.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.0.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.0.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.1.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.1.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.1.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.1.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.1.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.1.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.1.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.1.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.1.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.2.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.2.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.2.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.2.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.2.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.2.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.2.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.2.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.2.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.3.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.3.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.3.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.3.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.3.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.3.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.3.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.3.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.3.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.4.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.4.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.4.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.4.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.4.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.4.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.4.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.4.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.4.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.5.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.5.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.5.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.5.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.5.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.5.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.5.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.5.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.5.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.6.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.6.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.6.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.6.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.6.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.6.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.6.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.6.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.6.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.7.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.7.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.7.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.7.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.7.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.7.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.7.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.7.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.7.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.8.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.8.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.8.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.8.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.8.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.8.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.8.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.8.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.8.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.9.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.9.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.9.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.9.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.9.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.9.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.9.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.9.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.9.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.10.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.10.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.10.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.10.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.10.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.10.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.10.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.10.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.10.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.11.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.11.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.11.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.11.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.11.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.11.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.11.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.11.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.11.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.12.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.12.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.12.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.12.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.12.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.12.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.12.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.12.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.12.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.13.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.13.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.13.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.13.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.13.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.13.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.13.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.13.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.13.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.14.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.14.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.14.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.14.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.14.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.14.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.14.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.14.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.14.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.15.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.15.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.15.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.15.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.15.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.15.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.15.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.15.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.15.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.16.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.16.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.16.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.16.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.16.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.16.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.16.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.16.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.16.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.17.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.17.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.17.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.17.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.17.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.17.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.17.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.17.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.17.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.18.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.18.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.18.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.18.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.18.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.18.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.18.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.18.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.18.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.19.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.19.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.19.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.19.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.19.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.19.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.19.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.19.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.19.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.20.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.20.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.20.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.20.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.20.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.20.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.20.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.20.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.20.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.21.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.21.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.21.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.21.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.21.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.21.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.21.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.21.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.21.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.22.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.22.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.22.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.22.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.22.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.22.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.22.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.22.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.22.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.23.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.23.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.23.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.23.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.23.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.23.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.23.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.23.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.23.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.24.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.24.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.24.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.24.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.24.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.24.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.24.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.24.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.24.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.25.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.25.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.25.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.25.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.25.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.25.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.25.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.25.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.25.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.26.gamma - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.26.depthwise_conv.weight - torch.Size([384, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.26.depthwise_conv.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.26.norm.weight - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.26.norm.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.2.26.pointwise_conv1.weight - torch.Size([1536, 384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.26.pointwise_conv1.bias - torch.Size([1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.26.pointwise_conv2.weight - torch.Size([384, 1536]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.2.26.pointwise_conv2.bias - torch.Size([384]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.0.gamma - torch.Size([768]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.3.0.depthwise_conv.weight - torch.Size([768, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.0.depthwise_conv.bias - torch.Size([768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.0.norm.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.3.0.norm.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.3.0.pointwise_conv1.weight - torch.Size([3072, 768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.0.pointwise_conv1.bias - torch.Size([3072]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.0.pointwise_conv2.weight - torch.Size([768, 3072]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.0.pointwise_conv2.bias - torch.Size([768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.1.gamma - torch.Size([768]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.3.1.depthwise_conv.weight - torch.Size([768, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.1.depthwise_conv.bias - torch.Size([768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.1.norm.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.3.1.norm.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.3.1.pointwise_conv1.weight - torch.Size([3072, 768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.1.pointwise_conv1.bias - torch.Size([3072]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.1.pointwise_conv2.weight - torch.Size([768, 3072]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.1.pointwise_conv2.bias - torch.Size([768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.2.gamma - torch.Size([768]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.3.2.depthwise_conv.weight - torch.Size([768, 1, 7, 7]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.2.depthwise_conv.bias - torch.Size([768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.2.norm.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.3.2.norm.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.stages.3.2.pointwise_conv1.weight - torch.Size([3072, 768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.2.pointwise_conv1.bias - torch.Size([3072]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.2.pointwise_conv2.weight - torch.Size([768, 3072]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.stages.3.2.pointwise_conv2.bias - torch.Size([768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 backbone.norm3.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.norm3.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of ImageClassifier head.fc.weight - torch.Size([2, 768]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 head.fc.bias - torch.Size([2]): TruncNormalInit: a=-2, b=2, mean=0, std=0.02, bias=0.0 2023/05/31 17:33:28 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/05/31 17:33:28 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/05/31 17:33:28 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/luzeyu/workspace/fakebench/mmpretrain/workdir/convnext_small_4xb256_4e-3lr_5m. 2023/05/31 17:35:30 - mmengine - INFO - Epoch(train) [1][ 100/5758] lr: 4.0000e-03 eta: 1 day, 15:02:45 time: 1.1037 data_time: 0.2584 memory: 25071 loss: 0.6135 2023/05/31 17:37:22 - mmengine - INFO - Epoch(train) [1][ 200/5758] lr: 4.0000e-03 eta: 1 day, 13:21:05 time: 1.0568 data_time: 0.0020 memory: 25071 loss: 0.5921 2023/05/31 17:39:12 - mmengine - INFO - Epoch(train) [1][ 300/5758] lr: 4.0000e-03 eta: 1 day, 12:35:36 time: 1.0625 data_time: 0.0015 memory: 25071 loss: 0.5693 2023/05/31 17:41:03 - mmengine - INFO - Epoch(train) [1][ 400/5758] lr: 4.0000e-03 eta: 1 day, 12:17:30 time: 1.0132 data_time: 0.0018 memory: 25071 loss: 0.5560 2023/05/31 17:42:49 - mmengine - INFO - Epoch(train) [1][ 500/5758] lr: 4.0000e-03 eta: 1 day, 11:45:23 time: 1.0576 data_time: 0.0018 memory: 25071 loss: 0.5387 2023/05/31 17:44:33 - mmengine - INFO - Epoch(train) [1][ 600/5758] lr: 4.0000e-03 eta: 1 day, 11:17:54 time: 1.0222 data_time: 0.0015 memory: 25071 loss: 0.5206 2023/05/31 17:46:16 - mmengine - INFO - Epoch(train) [1][ 700/5758] lr: 4.0000e-03 eta: 1 day, 10:54:07 time: 1.0578 data_time: 0.0011 memory: 25071 loss: 0.5122 2023/05/31 17:47:55 - mmengine - INFO - Epoch(train) [1][ 800/5758] lr: 4.0000e-03 eta: 1 day, 10:24:46 time: 0.9828 data_time: 0.0019 memory: 25071 loss: 0.5337 2023/05/31 17:49:35 - mmengine - INFO - Epoch(train) [1][ 900/5758] lr: 4.0000e-03 eta: 1 day, 10:05:36 time: 1.0143 data_time: 0.0015 memory: 25071 loss: 0.4814 2023/05/31 17:51:11 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 17:51:11 - mmengine - INFO - Epoch(train) [1][1000/5758] lr: 4.0000e-03 eta: 1 day, 9:43:16 time: 1.0005 data_time: 0.0016 memory: 25071 loss: 0.4887 2023/05/31 17:52:51 - mmengine - INFO - Epoch(train) [1][1100/5758] lr: 4.0000e-03 eta: 1 day, 9:29:42 time: 0.9755 data_time: 0.0021 memory: 25071 loss: 0.4961 2023/05/31 17:54:30 - mmengine - INFO - Epoch(train) [1][1200/5758] lr: 4.0000e-03 eta: 1 day, 9:17:48 time: 0.9678 data_time: 0.0019 memory: 25071 loss: 0.4578 2023/05/31 17:56:11 - mmengine - INFO - Epoch(train) [1][1300/5758] lr: 4.0000e-03 eta: 1 day, 9:09:25 time: 1.0431 data_time: 0.0017 memory: 25071 loss: 0.4498 2023/05/31 17:57:50 - mmengine - INFO - Epoch(train) [1][1400/5758] lr: 4.0000e-03 eta: 1 day, 9:00:00 time: 0.9596 data_time: 0.0015 memory: 25071 loss: 0.4368 2023/05/31 17:59:31 - mmengine - INFO - Epoch(train) [1][1500/5758] lr: 4.0000e-03 eta: 1 day, 8:53:57 time: 0.9089 data_time: 0.0018 memory: 25071 loss: 0.5320 2023/05/31 18:01:11 - mmengine - INFO - Epoch(train) [1][1600/5758] lr: 4.0000e-03 eta: 1 day, 8:47:28 time: 0.9892 data_time: 0.0015 memory: 25071 loss: 0.4219 2023/05/31 18:02:49 - mmengine - INFO - Epoch(train) [1][1700/5758] lr: 4.0000e-03 eta: 1 day, 8:38:27 time: 0.9990 data_time: 0.0017 memory: 25071 loss: 0.4142 2023/05/31 18:04:24 - mmengine - INFO - Epoch(train) [1][1800/5758] lr: 4.0000e-03 eta: 1 day, 8:27:46 time: 1.1330 data_time: 0.0014 memory: 25071 loss: 0.3887 2023/05/31 18:06:00 - mmengine - INFO - Epoch(train) [1][1900/5758] lr: 4.0000e-03 eta: 1 day, 8:19:25 time: 0.9008 data_time: 0.0014 memory: 25071 loss: 0.3969 2023/05/31 18:07:34 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 18:07:34 - mmengine - INFO - Epoch(train) [1][2000/5758] lr: 4.0000e-03 eta: 1 day, 8:09:46 time: 1.0660 data_time: 0.0014 memory: 25071 loss: 0.3817 2023/05/31 18:09:11 - mmengine - INFO - Epoch(train) [1][2100/5758] lr: 4.0000e-03 eta: 1 day, 8:02:39 time: 1.0678 data_time: 0.0016 memory: 25071 loss: 0.4004 2023/05/31 18:10:42 - mmengine - INFO - Epoch(train) [1][2200/5758] lr: 4.0000e-03 eta: 1 day, 7:52:14 time: 0.8623 data_time: 0.0015 memory: 25071 loss: 0.3590 2023/05/31 18:12:12 - mmengine - INFO - Epoch(train) [1][2300/5758] lr: 4.0000e-03 eta: 1 day, 7:40:20 time: 0.8956 data_time: 0.0015 memory: 25071 loss: 0.2965 2023/05/31 18:13:41 - mmengine - INFO - Epoch(train) [1][2400/5758] lr: 4.0000e-03 eta: 1 day, 7:29:22 time: 0.9355 data_time: 0.0015 memory: 25071 loss: 0.3110 2023/05/31 18:15:08 - mmengine - INFO - Epoch(train) [1][2500/5758] lr: 4.0000e-03 eta: 1 day, 7:17:56 time: 0.8769 data_time: 0.0016 memory: 25071 loss: 0.2893 2023/05/31 18:16:33 - mmengine - INFO - Epoch(train) [1][2600/5758] lr: 4.0000e-03 eta: 1 day, 7:05:08 time: 0.8800 data_time: 0.0020 memory: 25071 loss: 0.2850 2023/05/31 18:18:04 - mmengine - INFO - Epoch(train) [1][2700/5758] lr: 4.0000e-03 eta: 1 day, 6:57:39 time: 0.9100 data_time: 0.0014 memory: 25071 loss: 0.2896 2023/05/31 18:19:42 - mmengine - INFO - Epoch(train) [1][2800/5758] lr: 4.0000e-03 eta: 1 day, 6:55:00 time: 0.9779 data_time: 0.0015 memory: 25071 loss: 0.3225 2023/05/31 18:21:16 - mmengine - INFO - Epoch(train) [1][2900/5758] lr: 4.0000e-03 eta: 1 day, 6:50:30 time: 0.9152 data_time: 0.0013 memory: 25071 loss: 0.2772 2023/05/31 18:22:52 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 18:22:52 - mmengine - INFO - Epoch(train) [1][3000/5758] lr: 4.0000e-03 eta: 1 day, 6:47:02 time: 0.9313 data_time: 0.0020 memory: 25071 loss: 0.2216 2023/05/31 18:24:27 - mmengine - INFO - Epoch(train) [1][3100/5758] lr: 4.0000e-03 eta: 1 day, 6:42:54 time: 0.9810 data_time: 0.0017 memory: 25071 loss: 0.2503 2023/05/31 18:26:03 - mmengine - INFO - Epoch(train) [1][3200/5758] lr: 4.0000e-03 eta: 1 day, 6:39:59 time: 0.9946 data_time: 0.0014 memory: 25071 loss: 0.2145 2023/05/31 18:27:36 - mmengine - INFO - Epoch(train) [1][3300/5758] lr: 4.0000e-03 eta: 1 day, 6:35:12 time: 0.9269 data_time: 0.0025 memory: 25071 loss: 0.2393 2023/05/31 18:29:08 - mmengine - INFO - Epoch(train) [1][3400/5758] lr: 4.0000e-03 eta: 1 day, 6:30:04 time: 0.9523 data_time: 0.0017 memory: 25071 loss: 0.2123 2023/05/31 18:30:38 - mmengine - INFO - Epoch(train) [1][3500/5758] lr: 4.0000e-03 eta: 1 day, 6:23:36 time: 0.9016 data_time: 0.0015 memory: 25071 loss: 0.2155 2023/05/31 18:32:07 - mmengine - INFO - Epoch(train) [1][3600/5758] lr: 4.0000e-03 eta: 1 day, 6:17:30 time: 1.0201 data_time: 0.0224 memory: 25071 loss: 0.2121 2023/05/31 18:33:37 - mmengine - INFO - Epoch(train) [1][3700/5758] lr: 4.0000e-03 eta: 1 day, 6:12:02 time: 0.8195 data_time: 0.0015 memory: 25071 loss: 0.1702 2023/05/31 18:35:09 - mmengine - INFO - Epoch(train) [1][3800/5758] lr: 4.0000e-03 eta: 1 day, 6:07:50 time: 1.0012 data_time: 0.0014 memory: 25071 loss: 0.1883 2023/05/31 18:36:37 - mmengine - INFO - Epoch(train) [1][3900/5758] lr: 4.0000e-03 eta: 1 day, 6:01:21 time: 0.8362 data_time: 0.0015 memory: 25071 loss: 0.1630 2023/05/31 18:38:07 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 18:38:07 - mmengine - INFO - Epoch(train) [1][4000/5758] lr: 4.0000e-03 eta: 1 day, 5:56:27 time: 0.8800 data_time: 0.0015 memory: 25071 loss: 0.1774 2023/05/31 18:39:37 - mmengine - INFO - Epoch(train) [1][4100/5758] lr: 4.0000e-03 eta: 1 day, 5:51:57 time: 0.9191 data_time: 0.0016 memory: 25071 loss: 0.1531 2023/05/31 18:41:08 - mmengine - INFO - Epoch(train) [1][4200/5758] lr: 4.0000e-03 eta: 1 day, 5:47:53 time: 0.9319 data_time: 0.0019 memory: 25071 loss: 0.1537 2023/05/31 18:42:39 - mmengine - INFO - Epoch(train) [1][4300/5758] lr: 4.0000e-03 eta: 1 day, 5:43:35 time: 0.8985 data_time: 0.0016 memory: 25071 loss: 0.1297 2023/05/31 18:44:09 - mmengine - INFO - Epoch(train) [1][4400/5758] lr: 4.0000e-03 eta: 1 day, 5:39:24 time: 0.8515 data_time: 0.0014 memory: 25071 loss: 0.1656 2023/05/31 18:45:39 - mmengine - INFO - Epoch(train) [1][4500/5758] lr: 4.0000e-03 eta: 1 day, 5:35:04 time: 0.9596 data_time: 0.0013 memory: 25071 loss: 0.1451 2023/05/31 18:47:10 - mmengine - INFO - Epoch(train) [1][4600/5758] lr: 4.0000e-03 eta: 1 day, 5:31:21 time: 0.9197 data_time: 0.0014 memory: 25071 loss: 0.1515 2023/05/31 18:48:51 - mmengine - INFO - Epoch(train) [1][4700/5758] lr: 4.0000e-03 eta: 1 day, 5:31:47 time: 0.9126 data_time: 0.0022 memory: 25071 loss: 0.1102 2023/05/31 18:50:38 - mmengine - INFO - Epoch(train) [1][4800/5758] lr: 4.0000e-03 eta: 1 day, 5:34:02 time: 0.9485 data_time: 0.0018 memory: 25071 loss: 0.1259 2023/05/31 18:52:23 - mmengine - INFO - Epoch(train) [1][4900/5758] lr: 4.0000e-03 eta: 1 day, 5:35:44 time: 1.0158 data_time: 0.0012 memory: 25071 loss: 0.0999 2023/05/31 18:54:05 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 18:54:05 - mmengine - INFO - Epoch(train) [1][5000/5758] lr: 4.0000e-03 eta: 1 day, 5:36:10 time: 0.9244 data_time: 0.0014 memory: 25071 loss: 0.1139 2023/05/31 18:55:47 - mmengine - INFO - Epoch(train) [1][5100/5758] lr: 4.0000e-03 eta: 1 day, 5:36:21 time: 0.9541 data_time: 0.0016 memory: 25071 loss: 0.1296 2023/05/31 18:57:23 - mmengine - INFO - Epoch(train) [1][5200/5758] lr: 4.0000e-03 eta: 1 day, 5:34:25 time: 0.8874 data_time: 0.0022 memory: 25071 loss: 0.0850 2023/05/31 18:59:01 - mmengine - INFO - Epoch(train) [1][5300/5758] lr: 4.0000e-03 eta: 1 day, 5:33:19 time: 0.9663 data_time: 0.0015 memory: 25071 loss: 0.0990 2023/05/31 19:00:38 - mmengine - INFO - Epoch(train) [1][5400/5758] lr: 4.0000e-03 eta: 1 day, 5:31:44 time: 0.9788 data_time: 0.0020 memory: 25071 loss: 0.0649 2023/05/31 19:02:07 - mmengine - INFO - Epoch(train) [1][5500/5758] lr: 4.0000e-03 eta: 1 day, 5:27:34 time: 0.8545 data_time: 0.0015 memory: 25071 loss: 0.0742 2023/05/31 19:03:37 - mmengine - INFO - Epoch(train) [1][5600/5758] lr: 4.0000e-03 eta: 1 day, 5:23:51 time: 0.9005 data_time: 0.0014 memory: 25071 loss: 0.0684 2023/05/31 19:05:11 - mmengine - INFO - Epoch(train) [1][5700/5758] lr: 4.0000e-03 eta: 1 day, 5:21:18 time: 1.0014 data_time: 0.0017 memory: 25071 loss: 0.0647 2023/05/31 19:06:07 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 19:06:07 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/05/31 19:06:27 - mmengine - INFO - Epoch(val) [1][16/16] accuracy/top1: 71.3339 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [71.33387756347656, 0.0] single-label/f1-score_classwise: [83.26885223388672, 0.0] data_time: 0.2987 time: 0.8163 2023/05/31 19:08:22 - mmengine - INFO - Epoch(train) [2][ 100/5758] lr: 3.9754e-03 eta: 1 day, 5:24:16 time: 0.9840 data_time: 0.0016 memory: 25074 loss: 0.4454 2023/05/31 19:10:12 - mmengine - INFO - Epoch(train) [2][ 200/5758] lr: 3.9754e-03 eta: 1 day, 5:26:30 time: 0.9971 data_time: 0.0021 memory: 25074 loss: 0.4071 2023/05/31 19:11:01 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 19:11:59 - mmengine - INFO - Epoch(train) [2][ 300/5758] lr: 3.9754e-03 eta: 1 day, 5:27:59 time: 1.0498 data_time: 0.0013 memory: 25074 loss: 0.2401 2023/05/31 19:13:32 - mmengine - INFO - Epoch(train) [2][ 400/5758] lr: 3.9754e-03 eta: 1 day, 5:25:06 time: 0.9421 data_time: 0.0016 memory: 25074 loss: 0.2418 2023/05/31 19:15:07 - mmengine - INFO - Epoch(train) [2][ 500/5758] lr: 3.9754e-03 eta: 1 day, 5:22:56 time: 0.8745 data_time: 0.0018 memory: 25074 loss: 0.0975 2023/05/31 19:16:41 - mmengine - INFO - Epoch(train) [2][ 600/5758] lr: 3.9754e-03 eta: 1 day, 5:20:23 time: 0.9066 data_time: 0.0022 memory: 25074 loss: 0.0629 2023/05/31 19:18:13 - mmengine - INFO - Epoch(train) [2][ 700/5758] lr: 3.9754e-03 eta: 1 day, 5:17:22 time: 0.9185 data_time: 0.0019 memory: 25074 loss: 0.0615 2023/05/31 19:19:46 - mmengine - INFO - Epoch(train) [2][ 800/5758] lr: 3.9754e-03 eta: 1 day, 5:14:27 time: 0.8759 data_time: 0.0015 memory: 25074 loss: 0.0705 2023/05/31 19:21:18 - mmengine - INFO - Epoch(train) [2][ 900/5758] lr: 3.9754e-03 eta: 1 day, 5:11:31 time: 0.9211 data_time: 0.0016 memory: 25074 loss: 0.0725 2023/05/31 19:22:48 - mmengine - INFO - Epoch(train) [2][1000/5758] lr: 3.9754e-03 eta: 1 day, 5:08:08 time: 1.0462 data_time: 0.0015 memory: 25074 loss: 0.0804 2023/05/31 19:24:19 - mmengine - INFO - Epoch(train) [2][1100/5758] lr: 3.9754e-03 eta: 1 day, 5:05:02 time: 0.8867 data_time: 0.0019 memory: 25074 loss: 0.0675 2023/05/31 19:25:52 - mmengine - INFO - Epoch(train) [2][1200/5758] lr: 3.9754e-03 eta: 1 day, 5:02:22 time: 0.8682 data_time: 0.0016 memory: 25074 loss: 0.0524 2023/05/31 19:26:33 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 19:27:26 - mmengine - INFO - Epoch(train) [2][1300/5758] lr: 3.9754e-03 eta: 1 day, 5:00:14 time: 0.9431 data_time: 0.0019 memory: 25074 loss: 0.0816 2023/05/31 19:29:01 - mmengine - INFO - Epoch(train) [2][1400/5758] lr: 3.9754e-03 eta: 1 day, 4:58:11 time: 0.9383 data_time: 0.0021 memory: 25074 loss: 0.0454 2023/05/31 19:30:34 - mmengine - INFO - Epoch(train) [2][1500/5758] lr: 3.9754e-03 eta: 1 day, 4:55:48 time: 0.8265 data_time: 0.0018 memory: 25074 loss: 0.0543 2023/05/31 19:32:06 - mmengine - INFO - Epoch(train) [2][1600/5758] lr: 3.9754e-03 eta: 1 day, 4:52:52 time: 0.9104 data_time: 0.0023 memory: 25074 loss: 0.0614 2023/05/31 19:33:35 - mmengine - INFO - Epoch(train) [2][1700/5758] lr: 3.9754e-03 eta: 1 day, 4:49:38 time: 0.8602 data_time: 0.0013 memory: 25074 loss: 0.0497 2023/05/31 19:35:06 - mmengine - INFO - Epoch(train) [2][1800/5758] lr: 3.9754e-03 eta: 1 day, 4:46:38 time: 0.9751 data_time: 0.0024 memory: 25074 loss: 0.0506 2023/05/31 19:36:32 - mmengine - INFO - Epoch(train) [2][1900/5758] lr: 3.9754e-03 eta: 1 day, 4:42:36 time: 0.7512 data_time: 0.0018 memory: 25074 loss: 0.5002 2023/05/31 19:37:56 - mmengine - INFO - Epoch(train) [2][2000/5758] lr: 3.9754e-03 eta: 1 day, 4:38:23 time: 0.8483 data_time: 0.0014 memory: 25074 loss: 0.4878 2023/05/31 19:39:22 - mmengine - INFO - Epoch(train) [2][2100/5758] lr: 3.9754e-03 eta: 1 day, 4:34:25 time: 0.8354 data_time: 0.0019 memory: 25074 loss: 0.4480 2023/05/31 19:40:50 - mmengine - INFO - Epoch(train) [2][2200/5758] lr: 3.9754e-03 eta: 1 day, 4:30:57 time: 0.8249 data_time: 0.0019 memory: 25074 loss: 0.2145 2023/05/31 19:41:26 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 19:42:15 - mmengine - INFO - Epoch(train) [2][2300/5758] lr: 3.9754e-03 eta: 1 day, 4:27:07 time: 0.8128 data_time: 0.0020 memory: 25074 loss: 0.0894 2023/05/31 19:43:41 - mmengine - INFO - Epoch(train) [2][2400/5758] lr: 3.9754e-03 eta: 1 day, 4:23:20 time: 0.8492 data_time: 0.0019 memory: 25074 loss: 0.0481 2023/05/31 19:45:05 - mmengine - INFO - Epoch(train) [2][2500/5758] lr: 3.9754e-03 eta: 1 day, 4:19:11 time: 0.8987 data_time: 0.0022 memory: 25074 loss: 0.0582 2023/05/31 19:46:24 - mmengine - INFO - Epoch(train) [2][2600/5758] lr: 3.9754e-03 eta: 1 day, 4:14:15 time: 0.7931 data_time: 0.0016 memory: 25074 loss: 0.0499 2023/05/31 19:47:46 - mmengine - INFO - Epoch(train) [2][2700/5758] lr: 3.9754e-03 eta: 1 day, 4:09:45 time: 0.8968 data_time: 0.0023 memory: 25074 loss: 0.0692 2023/05/31 19:49:07 - mmengine - INFO - Epoch(train) [2][2800/5758] lr: 3.9754e-03 eta: 1 day, 4:05:23 time: 0.8449 data_time: 0.0022 memory: 25074 loss: 0.0493 2023/05/31 19:50:26 - mmengine - INFO - Epoch(train) [2][2900/5758] lr: 3.9754e-03 eta: 1 day, 4:00:33 time: 0.7910 data_time: 0.0027 memory: 25074 loss: 0.0368 2023/05/31 19:51:46 - mmengine - INFO - Epoch(train) [2][3000/5758] lr: 3.9754e-03 eta: 1 day, 3:55:57 time: 0.7935 data_time: 0.0017 memory: 25074 loss: 0.0463 2023/05/31 19:53:07 - mmengine - INFO - Epoch(train) [2][3100/5758] lr: 3.9754e-03 eta: 1 day, 3:51:37 time: 0.8450 data_time: 0.0017 memory: 25074 loss: 0.0452 2023/05/31 19:54:28 - mmengine - INFO - Epoch(train) [2][3200/5758] lr: 3.9754e-03 eta: 1 day, 3:47:31 time: 0.8160 data_time: 0.0022 memory: 25074 loss: 0.0492 2023/05/31 19:55:02 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 19:55:51 - mmengine - INFO - Epoch(train) [2][3300/5758] lr: 3.9754e-03 eta: 1 day, 3:43:39 time: 0.8127 data_time: 0.0021 memory: 25074 loss: 0.0413 2023/05/31 19:57:13 - mmengine - INFO - Epoch(train) [2][3400/5758] lr: 3.9754e-03 eta: 1 day, 3:39:43 time: 0.8368 data_time: 0.0014 memory: 25074 loss: 0.0402 2023/05/31 19:58:36 - mmengine - INFO - Epoch(train) [2][3500/5758] lr: 3.9754e-03 eta: 1 day, 3:36:04 time: 0.8621 data_time: 0.0015 memory: 25074 loss: 0.0767 2023/05/31 20:00:00 - mmengine - INFO - Epoch(train) [2][3600/5758] lr: 3.9754e-03 eta: 1 day, 3:32:46 time: 0.7853 data_time: 0.0017 memory: 25074 loss: 0.0275 2023/05/31 20:01:23 - mmengine - INFO - Epoch(train) [2][3700/5758] lr: 3.9754e-03 eta: 1 day, 3:29:15 time: 0.8450 data_time: 0.0018 memory: 25074 loss: 0.0290 2023/05/31 20:02:48 - mmengine - INFO - Epoch(train) [2][3800/5758] lr: 3.9754e-03 eta: 1 day, 3:25:58 time: 0.8254 data_time: 0.0016 memory: 25074 loss: 0.0305 2023/05/31 20:04:11 - mmengine - INFO - Epoch(train) [2][3900/5758] lr: 3.9754e-03 eta: 1 day, 3:22:38 time: 0.9337 data_time: 0.0017 memory: 25074 loss: 0.0382 2023/05/31 20:05:35 - mmengine - INFO - Epoch(train) [2][4000/5758] lr: 3.9754e-03 eta: 1 day, 3:19:16 time: 0.8491 data_time: 0.0016 memory: 25074 loss: 0.0606 2023/05/31 20:07:02 - mmengine - INFO - Epoch(train) [2][4100/5758] lr: 3.9754e-03 eta: 1 day, 3:16:39 time: 0.9263 data_time: 0.0018 memory: 25074 loss: 0.0396 2023/05/31 20:08:32 - mmengine - INFO - Epoch(train) [2][4200/5758] lr: 3.9754e-03 eta: 1 day, 3:14:34 time: 0.9409 data_time: 0.0016 memory: 25074 loss: 0.0302 2023/05/31 20:09:10 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 20:10:02 - mmengine - INFO - Epoch(train) [2][4300/5758] lr: 3.9754e-03 eta: 1 day, 3:12:21 time: 0.8561 data_time: 0.0016 memory: 25074 loss: 0.0355 2023/05/31 20:11:30 - mmengine - INFO - Epoch(train) [2][4400/5758] lr: 3.9754e-03 eta: 1 day, 3:09:51 time: 0.8507 data_time: 0.0020 memory: 25074 loss: 0.0249 2023/05/31 20:12:59 - mmengine - INFO - Epoch(train) [2][4500/5758] lr: 3.9754e-03 eta: 1 day, 3:07:44 time: 0.9130 data_time: 0.0017 memory: 25074 loss: 0.0247 2023/05/31 20:14:28 - mmengine - INFO - Epoch(train) [2][4600/5758] lr: 3.9754e-03 eta: 1 day, 3:05:28 time: 0.8855 data_time: 0.0013 memory: 25074 loss: 0.4744 2023/05/31 20:15:55 - mmengine - INFO - Epoch(train) [2][4700/5758] lr: 3.9754e-03 eta: 1 day, 3:02:55 time: 0.8346 data_time: 0.0012 memory: 25074 loss: 0.2807 2023/05/31 20:17:22 - mmengine - INFO - Epoch(train) [2][4800/5758] lr: 3.9754e-03 eta: 1 day, 3:00:21 time: 0.9134 data_time: 0.0013 memory: 25074 loss: 0.5717 2023/05/31 20:18:53 - mmengine - INFO - Epoch(train) [2][4900/5758] lr: 3.9754e-03 eta: 1 day, 2:58:26 time: 0.8857 data_time: 0.0015 memory: 25074 loss: 0.5091 2023/05/31 20:20:24 - mmengine - INFO - Epoch(train) [2][5000/5758] lr: 3.9754e-03 eta: 1 day, 2:56:31 time: 0.9126 data_time: 0.0020 memory: 25074 loss: 0.3839 2023/05/31 20:21:53 - mmengine - INFO - Epoch(train) [2][5100/5758] lr: 3.9754e-03 eta: 1 day, 2:54:27 time: 0.9338 data_time: 0.0021 memory: 25074 loss: 0.4082 2023/05/31 20:23:19 - mmengine - INFO - Epoch(train) [2][5200/5758] lr: 3.9754e-03 eta: 1 day, 2:51:43 time: 0.7848 data_time: 0.0013 memory: 25074 loss: 0.3371 2023/05/31 20:23:56 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 20:24:47 - mmengine - INFO - Epoch(train) [2][5300/5758] lr: 3.9754e-03 eta: 1 day, 2:49:29 time: 0.8530 data_time: 0.0013 memory: 25074 loss: 0.3264 2023/05/31 20:26:18 - mmengine - INFO - Epoch(train) [2][5400/5758] lr: 3.9754e-03 eta: 1 day, 2:47:37 time: 0.9370 data_time: 0.0013 memory: 25074 loss: 0.1496 2023/05/31 20:27:48 - mmengine - INFO - Epoch(train) [2][5500/5758] lr: 3.9754e-03 eta: 1 day, 2:45:44 time: 0.8929 data_time: 0.0013 memory: 25074 loss: 0.0755 2023/05/31 20:29:16 - mmengine - INFO - Epoch(train) [2][5600/5758] lr: 3.9754e-03 eta: 1 day, 2:43:21 time: 0.8584 data_time: 0.0022 memory: 25074 loss: 0.7301 2023/05/31 20:30:45 - mmengine - INFO - Epoch(train) [2][5700/5758] lr: 3.9754e-03 eta: 1 day, 2:41:22 time: 0.9257 data_time: 0.0014 memory: 25074 loss: 0.6506 2023/05/31 20:31:36 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 20:31:36 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/05/31 20:31:55 - mmengine - INFO - Epoch(val) [2][16/16] accuracy/top1: 100.0000 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [100.0, 0.0] single-label/f1-score_classwise: [100.0, 0.0] data_time: 0.2579 time: 0.6472 2023/05/31 20:33:33 - mmengine - INFO - Epoch(train) [3][ 100/5758] lr: 3.9024e-03 eta: 1 day, 2:39:10 time: 0.8862 data_time: 0.0020 memory: 25074 loss: 0.5309 2023/05/31 20:35:06 - mmengine - INFO - Epoch(train) [3][ 200/5758] lr: 3.9024e-03 eta: 1 day, 2:37:42 time: 0.9476 data_time: 0.0017 memory: 25074 loss: 0.4704 2023/05/31 20:36:34 - mmengine - INFO - Epoch(train) [3][ 300/5758] lr: 3.9024e-03 eta: 1 day, 2:35:32 time: 0.8306 data_time: 0.0023 memory: 25074 loss: 0.4299 2023/05/31 20:38:02 - mmengine - INFO - Epoch(train) [3][ 400/5758] lr: 3.9024e-03 eta: 1 day, 2:33:13 time: 0.9029 data_time: 0.0019 memory: 25074 loss: 0.3691 2023/05/31 20:39:17 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 20:39:30 - mmengine - INFO - Epoch(train) [3][ 500/5758] lr: 3.9024e-03 eta: 1 day, 2:31:03 time: 0.8394 data_time: 0.0020 memory: 25074 loss: 0.3560 2023/05/31 20:40:59 - mmengine - INFO - Epoch(train) [3][ 600/5758] lr: 3.9024e-03 eta: 1 day, 2:29:01 time: 0.9999 data_time: 0.0019 memory: 25074 loss: 0.3092 2023/05/31 20:42:27 - mmengine - INFO - Epoch(train) [3][ 700/5758] lr: 3.9024e-03 eta: 1 day, 2:26:52 time: 0.8323 data_time: 0.0020 memory: 25074 loss: 0.2898 2023/05/31 20:43:55 - mmengine - INFO - Epoch(train) [3][ 800/5758] lr: 3.9024e-03 eta: 1 day, 2:24:42 time: 0.8981 data_time: 0.0020 memory: 25074 loss: 0.2832 2023/05/31 20:45:25 - mmengine - INFO - Epoch(train) [3][ 900/5758] lr: 3.9024e-03 eta: 1 day, 2:22:44 time: 0.8585 data_time: 0.0022 memory: 25074 loss: 0.2297 2023/05/31 20:46:56 - mmengine - INFO - Epoch(train) [3][1000/5758] lr: 3.9024e-03 eta: 1 day, 2:21:01 time: 0.9057 data_time: 0.0031 memory: 25074 loss: 0.2236 2023/05/31 20:48:24 - mmengine - INFO - Epoch(train) [3][1100/5758] lr: 3.9024e-03 eta: 1 day, 2:18:56 time: 0.9525 data_time: 0.0022 memory: 25074 loss: 0.2103 2023/05/31 20:49:54 - mmengine - INFO - Epoch(train) [3][1200/5758] lr: 3.9024e-03 eta: 1 day, 2:17:00 time: 0.8641 data_time: 0.0022 memory: 25074 loss: 0.1976 2023/05/31 20:51:20 - mmengine - INFO - Epoch(train) [3][1300/5758] lr: 3.9024e-03 eta: 1 day, 2:14:39 time: 0.8039 data_time: 0.0020 memory: 25074 loss: 0.2243 2023/05/31 20:52:47 - mmengine - INFO - Epoch(train) [3][1400/5758] lr: 3.9024e-03 eta: 1 day, 2:12:22 time: 0.8570 data_time: 0.0018 memory: 25074 loss: 0.1269 2023/05/31 20:54:01 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 20:54:14 - mmengine - INFO - Epoch(train) [3][1500/5758] lr: 3.9024e-03 eta: 1 day, 2:10:14 time: 0.9195 data_time: 0.0023 memory: 25074 loss: 0.1365 2023/05/31 20:55:44 - mmengine - INFO - Epoch(train) [3][1600/5758] lr: 3.9024e-03 eta: 1 day, 2:08:25 time: 0.9113 data_time: 0.0026 memory: 25074 loss: 0.1514 2023/05/31 20:57:12 - mmengine - INFO - Epoch(train) [3][1700/5758] lr: 3.9024e-03 eta: 1 day, 2:06:16 time: 0.8294 data_time: 0.0020 memory: 25074 loss: 0.1175 2023/05/31 20:58:40 - mmengine - INFO - Epoch(train) [3][1800/5758] lr: 3.9024e-03 eta: 1 day, 2:04:11 time: 0.9456 data_time: 0.0019 memory: 25074 loss: 0.0811 2023/05/31 21:00:05 - mmengine - INFO - Epoch(train) [3][1900/5758] lr: 3.9024e-03 eta: 1 day, 2:01:50 time: 0.8765 data_time: 0.0015 memory: 25074 loss: 0.0680 2023/05/31 21:01:32 - mmengine - INFO - Epoch(train) [3][2000/5758] lr: 3.9024e-03 eta: 1 day, 1:59:34 time: 0.8908 data_time: 0.0019 memory: 25074 loss: 0.0674 2023/05/31 21:02:57 - mmengine - INFO - Epoch(train) [3][2100/5758] lr: 3.9024e-03 eta: 1 day, 1:57:08 time: 0.8105 data_time: 0.0014 memory: 25074 loss: 0.0715 2023/05/31 21:04:23 - mmengine - INFO - Epoch(train) [3][2200/5758] lr: 3.9024e-03 eta: 1 day, 1:54:54 time: 0.8420 data_time: 0.0013 memory: 25074 loss: 0.0617 2023/05/31 21:05:48 - mmengine - INFO - Epoch(train) [3][2300/5758] lr: 3.9024e-03 eta: 1 day, 1:52:34 time: 0.7945 data_time: 0.0021 memory: 25074 loss: 0.0454 2023/05/31 21:07:16 - mmengine - INFO - Epoch(train) [3][2400/5758] lr: 3.9024e-03 eta: 1 day, 1:50:33 time: 0.8628 data_time: 0.0018 memory: 25074 loss: 0.0485 2023/05/31 21:08:31 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 21:08:44 - mmengine - INFO - Epoch(train) [3][2500/5758] lr: 3.9024e-03 eta: 1 day, 1:48:31 time: 0.8765 data_time: 0.0022 memory: 25074 loss: 0.0497 2023/05/31 21:10:10 - mmengine - INFO - Epoch(train) [3][2600/5758] lr: 3.9024e-03 eta: 1 day, 1:46:18 time: 0.7989 data_time: 0.0016 memory: 25074 loss: 0.0700 2023/05/31 21:11:35 - mmengine - INFO - Epoch(train) [3][2700/5758] lr: 3.9024e-03 eta: 1 day, 1:43:55 time: 0.8280 data_time: 0.0014 memory: 25074 loss: 0.0505 2023/05/31 21:13:01 - mmengine - INFO - Epoch(train) [3][2800/5758] lr: 3.9024e-03 eta: 1 day, 1:41:43 time: 0.8680 data_time: 0.0016 memory: 25074 loss: 0.0507 2023/05/31 21:14:26 - mmengine - INFO - Epoch(train) [3][2900/5758] lr: 3.9024e-03 eta: 1 day, 1:39:27 time: 0.8109 data_time: 0.0024 memory: 25074 loss: 0.0536 2023/05/31 21:15:51 - mmengine - INFO - Epoch(train) [3][3000/5758] lr: 3.9024e-03 eta: 1 day, 1:37:09 time: 0.8751 data_time: 0.0022 memory: 25074 loss: 0.0404 2023/05/31 21:17:15 - mmengine - INFO - Epoch(train) [3][3100/5758] lr: 3.9024e-03 eta: 1 day, 1:34:46 time: 0.8424 data_time: 0.0021 memory: 25074 loss: 0.0417 2023/05/31 21:18:40 - mmengine - INFO - Epoch(train) [3][3200/5758] lr: 3.9024e-03 eta: 1 day, 1:32:27 time: 0.8206 data_time: 0.0014 memory: 25074 loss: 0.1048 2023/05/31 21:20:05 - mmengine - INFO - Epoch(train) [3][3300/5758] lr: 3.9024e-03 eta: 1 day, 1:30:08 time: 0.8065 data_time: 0.0017 memory: 25074 loss: 0.5778 2023/05/31 21:21:30 - mmengine - INFO - Epoch(train) [3][3400/5758] lr: 3.9024e-03 eta: 1 day, 1:27:57 time: 0.8386 data_time: 0.0020 memory: 25074 loss: 0.4217 2023/05/31 21:22:43 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 21:22:56 - mmengine - INFO - Epoch(train) [3][3500/5758] lr: 3.9024e-03 eta: 1 day, 1:25:51 time: 0.8775 data_time: 0.0014 memory: 25074 loss: 0.3421 2023/05/31 21:24:26 - mmengine - INFO - Epoch(train) [3][3600/5758] lr: 3.9024e-03 eta: 1 day, 1:24:05 time: 0.8238 data_time: 0.0021 memory: 25074 loss: 0.2083 2023/05/31 21:25:55 - mmengine - INFO - Epoch(train) [3][3700/5758] lr: 3.9024e-03 eta: 1 day, 1:22:23 time: 0.8463 data_time: 0.0019 memory: 25074 loss: 0.0917 2023/05/31 21:27:25 - mmengine - INFO - Epoch(train) [3][3800/5758] lr: 3.9024e-03 eta: 1 day, 1:20:41 time: 0.8383 data_time: 0.0014 memory: 25074 loss: 0.0916 2023/05/31 21:28:54 - mmengine - INFO - Epoch(train) [3][3900/5758] lr: 3.9024e-03 eta: 1 day, 1:18:51 time: 0.8325 data_time: 0.0015 memory: 25074 loss: 0.0407 2023/05/31 21:30:20 - mmengine - INFO - Epoch(train) [3][4000/5758] lr: 3.9024e-03 eta: 1 day, 1:16:45 time: 0.8978 data_time: 0.0016 memory: 25074 loss: 0.0456 2023/05/31 21:31:46 - mmengine - INFO - Epoch(train) [3][4100/5758] lr: 3.9024e-03 eta: 1 day, 1:14:41 time: 0.9011 data_time: 0.0015 memory: 25074 loss: 0.0519 2023/05/31 21:33:15 - mmengine - INFO - Epoch(train) [3][4200/5758] lr: 3.9024e-03 eta: 1 day, 1:12:55 time: 0.9069 data_time: 0.0016 memory: 25074 loss: 0.0254 2023/05/31 21:34:42 - mmengine - INFO - Epoch(train) [3][4300/5758] lr: 3.9024e-03 eta: 1 day, 1:10:58 time: 0.8678 data_time: 0.0015 memory: 25074 loss: 0.0338 2023/05/31 21:36:10 - mmengine - INFO - Epoch(train) [3][4400/5758] lr: 3.9024e-03 eta: 1 day, 1:09:04 time: 0.8704 data_time: 0.0015 memory: 25074 loss: 0.0263 2023/05/31 21:37:26 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 21:37:39 - mmengine - INFO - Epoch(train) [3][4500/5758] lr: 3.9024e-03 eta: 1 day, 1:07:17 time: 0.8488 data_time: 0.0015 memory: 25074 loss: 0.0240 2023/05/31 21:39:05 - mmengine - INFO - Epoch(train) [3][4600/5758] lr: 3.9024e-03 eta: 1 day, 1:05:18 time: 0.8706 data_time: 0.0015 memory: 25074 loss: 0.0922 2023/05/31 21:40:34 - mmengine - INFO - Epoch(train) [3][4700/5758] lr: 3.9024e-03 eta: 1 day, 1:03:30 time: 0.9396 data_time: 0.0015 memory: 25074 loss: 0.0390 2023/05/31 21:42:02 - mmengine - INFO - Epoch(train) [3][4800/5758] lr: 3.9024e-03 eta: 1 day, 1:01:44 time: 0.9234 data_time: 0.0013 memory: 25074 loss: 0.0330 2023/05/31 21:43:33 - mmengine - INFO - Epoch(train) [3][4900/5758] lr: 3.9024e-03 eta: 1 day, 1:00:09 time: 0.9162 data_time: 0.0013 memory: 25074 loss: 0.0281 2023/05/31 21:44:56 - mmengine - INFO - Epoch(train) [3][5000/5758] lr: 3.9024e-03 eta: 1 day, 0:57:52 time: 0.8040 data_time: 0.0017 memory: 25074 loss: 0.0249 2023/05/31 21:46:20 - mmengine - INFO - Epoch(train) [3][5100/5758] lr: 3.9024e-03 eta: 1 day, 0:55:34 time: 0.8070 data_time: 0.0013 memory: 25074 loss: 0.0303 2023/05/31 21:47:47 - mmengine - INFO - Epoch(train) [3][5200/5758] lr: 3.9024e-03 eta: 1 day, 0:53:41 time: 0.9348 data_time: 0.0015 memory: 25074 loss: 0.3830 2023/05/31 21:49:14 - mmengine - INFO - Epoch(train) [3][5300/5758] lr: 3.9024e-03 eta: 1 day, 0:51:46 time: 0.8739 data_time: 0.0014 memory: 25074 loss: 0.1110 2023/05/31 21:50:39 - mmengine - INFO - Epoch(train) [3][5400/5758] lr: 3.9024e-03 eta: 1 day, 0:49:42 time: 0.8635 data_time: 0.0015 memory: 25074 loss: 0.1760 2023/05/31 21:51:55 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 21:52:08 - mmengine - INFO - Epoch(train) [3][5500/5758] lr: 3.9024e-03 eta: 1 day, 0:47:58 time: 0.8102 data_time: 0.0013 memory: 25074 loss: 0.0392 2023/05/31 21:53:38 - mmengine - INFO - Epoch(train) [3][5600/5758] lr: 3.9024e-03 eta: 1 day, 0:46:23 time: 0.9931 data_time: 0.0015 memory: 25074 loss: 0.0389 2023/05/31 21:55:06 - mmengine - INFO - Epoch(train) [3][5700/5758] lr: 3.9024e-03 eta: 1 day, 0:44:33 time: 0.9239 data_time: 0.0020 memory: 25074 loss: 0.0255 2023/05/31 21:55:54 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 21:55:54 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/05/31 21:56:14 - mmengine - INFO - Epoch(val) [3][16/16] accuracy/top1: 100.0000 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [100.0, 0.0] single-label/f1-score_classwise: [100.0, 0.0] data_time: 0.2431 time: 0.6312 2023/05/31 21:57:48 - mmengine - INFO - Epoch(train) [4][ 100/5758] lr: 3.7826e-03 eta: 1 day, 0:42:06 time: 0.8111 data_time: 0.0020 memory: 25074 loss: 0.0245 2023/05/31 21:59:16 - mmengine - INFO - Epoch(train) [4][ 200/5758] lr: 3.7826e-03 eta: 1 day, 0:40:17 time: 0.8649 data_time: 0.0026 memory: 25074 loss: 0.0179 2023/05/31 22:00:43 - mmengine - INFO - Epoch(train) [4][ 300/5758] lr: 3.7826e-03 eta: 1 day, 0:38:23 time: 0.8393 data_time: 0.0021 memory: 25074 loss: 0.0241 2023/05/31 22:02:13 - mmengine - INFO - Epoch(train) [4][ 400/5758] lr: 3.7826e-03 eta: 1 day, 0:36:46 time: 0.8186 data_time: 0.0023 memory: 25074 loss: 0.0163 2023/05/31 22:03:43 - mmengine - INFO - Epoch(train) [4][ 500/5758] lr: 3.7826e-03 eta: 1 day, 0:35:13 time: 0.8512 data_time: 0.0025 memory: 25074 loss: 0.0226 2023/05/31 22:05:11 - mmengine - INFO - Epoch(train) [4][ 600/5758] lr: 3.7826e-03 eta: 1 day, 0:33:26 time: 0.8193 data_time: 0.0021 memory: 25074 loss: 0.0241 2023/05/31 22:06:44 - mmengine - INFO - Epoch(train) [4][ 700/5758] lr: 3.7826e-03 eta: 1 day, 0:32:04 time: 0.8932 data_time: 0.0024 memory: 25074 loss: 0.0193 2023/05/31 22:07:05 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 22:08:08 - mmengine - INFO - Epoch(train) [4][ 800/5758] lr: 3.7826e-03 eta: 1 day, 0:29:56 time: 0.8333 data_time: 0.0021 memory: 25074 loss: 0.0156 2023/05/31 22:09:29 - mmengine - INFO - Epoch(train) [4][ 900/5758] lr: 3.7826e-03 eta: 1 day, 0:27:36 time: 0.8374 data_time: 0.0022 memory: 25074 loss: 0.3952 2023/05/31 22:10:53 - mmengine - INFO - Epoch(train) [4][1000/5758] lr: 3.7826e-03 eta: 1 day, 0:25:26 time: 0.8616 data_time: 0.0027 memory: 25074 loss: 0.3264 2023/05/31 22:12:16 - mmengine - INFO - Epoch(train) [4][1100/5758] lr: 3.7826e-03 eta: 1 day, 0:23:16 time: 0.8392 data_time: 0.0016 memory: 25074 loss: 0.2365 2023/05/31 22:13:38 - mmengine - INFO - Epoch(train) [4][1200/5758] lr: 3.7826e-03 eta: 1 day, 0:21:00 time: 0.8178 data_time: 0.0021 memory: 25074 loss: 0.3408 2023/05/31 22:15:05 - mmengine - INFO - Epoch(train) [4][1300/5758] lr: 3.7826e-03 eta: 1 day, 0:19:10 time: 0.8166 data_time: 0.0025 memory: 25074 loss: 0.1928 2023/05/31 22:16:29 - mmengine - INFO - Epoch(train) [4][1400/5758] lr: 3.7826e-03 eta: 1 day, 0:17:05 time: 0.8161 data_time: 0.0014 memory: 25074 loss: 0.0683 2023/05/31 22:17:53 - mmengine - INFO - Epoch(train) [4][1500/5758] lr: 3.7826e-03 eta: 1 day, 0:15:02 time: 0.8285 data_time: 0.0015 memory: 25074 loss: 0.0416 2023/05/31 22:19:14 - mmengine - INFO - Epoch(train) [4][1600/5758] lr: 3.7826e-03 eta: 1 day, 0:12:43 time: 0.8103 data_time: 0.0014 memory: 25074 loss: 0.0294 2023/05/31 22:20:38 - mmengine - INFO - Epoch(train) [4][1700/5758] lr: 3.7826e-03 eta: 1 day, 0:10:37 time: 0.8247 data_time: 0.0016 memory: 25074 loss: 0.0448 2023/05/31 22:20:59 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 22:22:00 - mmengine - INFO - Epoch(train) [4][1800/5758] lr: 3.7826e-03 eta: 1 day, 0:08:22 time: 0.8886 data_time: 0.0017 memory: 25074 loss: 0.0440 2023/05/31 22:23:22 - mmengine - INFO - Epoch(train) [4][1900/5758] lr: 3.7826e-03 eta: 1 day, 0:06:12 time: 0.8481 data_time: 0.0019 memory: 25074 loss: 0.0296 2023/05/31 22:24:46 - mmengine - INFO - Epoch(train) [4][2000/5758] lr: 3.7826e-03 eta: 1 day, 0:04:06 time: 0.9165 data_time: 0.0022 memory: 25074 loss: 0.0245 2023/05/31 22:26:06 - mmengine - INFO - Epoch(train) [4][2100/5758] lr: 3.7826e-03 eta: 1 day, 0:01:46 time: 0.7630 data_time: 0.0016 memory: 25074 loss: 0.0210 2023/05/31 22:27:28 - mmengine - INFO - Epoch(train) [4][2200/5758] lr: 3.7826e-03 eta: 23:59:38 time: 0.8513 data_time: 0.0017 memory: 25074 loss: 0.9873 2023/05/31 22:28:53 - mmengine - INFO - Epoch(train) [4][2300/5758] lr: 3.7826e-03 eta: 23:57:39 time: 0.8507 data_time: 0.0017 memory: 25074 loss: 0.6863 2023/05/31 22:30:15 - mmengine - INFO - Epoch(train) [4][2400/5758] lr: 3.7826e-03 eta: 23:55:32 time: 0.8141 data_time: 0.0020 memory: 25074 loss: 0.6228 2023/05/31 22:31:39 - mmengine - INFO - Epoch(train) [4][2500/5758] lr: 3.7826e-03 eta: 23:53:29 time: 0.8711 data_time: 0.0022 memory: 25074 loss: 0.6230 2023/05/31 22:32:59 - mmengine - INFO - Epoch(train) [4][2600/5758] lr: 3.7826e-03 eta: 23:51:10 time: 0.7790 data_time: 0.0016 memory: 25074 loss: 0.5823 2023/05/31 22:34:20 - mmengine - INFO - Epoch(train) [4][2700/5758] lr: 3.7826e-03 eta: 23:48:57 time: 0.8447 data_time: 0.0021 memory: 25074 loss: 0.5363 2023/05/31 22:34:41 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 22:35:41 - mmengine - INFO - Epoch(train) [4][2800/5758] lr: 3.7826e-03 eta: 23:46:45 time: 0.8090 data_time: 0.0014 memory: 25074 loss: 0.5363 2023/05/31 22:37:03 - mmengine - INFO - Epoch(train) [4][2900/5758] lr: 3.7826e-03 eta: 23:44:36 time: 0.7996 data_time: 0.0017 memory: 25074 loss: 0.5080 2023/05/31 22:38:27 - mmengine - INFO - Epoch(train) [4][3000/5758] lr: 3.7826e-03 eta: 23:42:37 time: 0.8065 data_time: 0.0025 memory: 25074 loss: 0.4858 2023/05/31 22:39:50 - mmengine - INFO - Epoch(train) [4][3100/5758] lr: 3.7826e-03 eta: 23:40:35 time: 0.8865 data_time: 0.0018 memory: 25074 loss: 0.4641 2023/05/31 22:41:09 - mmengine - INFO - Epoch(train) [4][3200/5758] lr: 3.7826e-03 eta: 23:38:15 time: 0.8055 data_time: 0.0020 memory: 25074 loss: 0.4372 2023/05/31 22:42:31 - mmengine - INFO - Epoch(train) [4][3300/5758] lr: 3.7826e-03 eta: 23:36:07 time: 0.8218 data_time: 0.0017 memory: 25074 loss: 0.4226 2023/05/31 22:43:52 - mmengine - INFO - Epoch(train) [4][3400/5758] lr: 3.7826e-03 eta: 23:33:57 time: 0.7935 data_time: 0.0022 memory: 25074 loss: 0.4066 2023/05/31 22:45:15 - mmengine - INFO - Epoch(train) [4][3500/5758] lr: 3.7826e-03 eta: 23:31:59 time: 0.8749 data_time: 0.0027 memory: 25074 loss: 0.4147 2023/05/31 22:46:37 - mmengine - INFO - Epoch(train) [4][3600/5758] lr: 3.7826e-03 eta: 23:29:53 time: 0.8124 data_time: 0.0032 memory: 25074 loss: 0.3632 2023/05/31 22:47:59 - mmengine - INFO - Epoch(train) [4][3700/5758] lr: 3.7826e-03 eta: 23:27:50 time: 0.8755 data_time: 0.0019 memory: 25074 loss: 0.3610 2023/05/31 22:48:22 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 22:49:26 - mmengine - INFO - Epoch(train) [4][3800/5758] lr: 3.7826e-03 eta: 23:26:06 time: 0.8620 data_time: 0.0017 memory: 25074 loss: 0.3225 2023/05/31 22:50:51 - mmengine - INFO - Epoch(train) [4][3900/5758] lr: 3.7826e-03 eta: 23:24:15 time: 0.9055 data_time: 0.0018 memory: 25074 loss: 0.3151 2023/05/31 22:52:14 - mmengine - INFO - Epoch(train) [4][4000/5758] lr: 3.7826e-03 eta: 23:22:17 time: 0.7909 data_time: 0.0019 memory: 25074 loss: 0.3306 2023/05/31 22:53:35 - mmengine - INFO - Epoch(train) [4][4100/5758] lr: 3.7826e-03 eta: 23:20:08 time: 0.7740 data_time: 0.0014 memory: 25074 loss: 0.2978 2023/05/31 22:54:54 - mmengine - INFO - Epoch(train) [4][4200/5758] lr: 3.7826e-03 eta: 23:17:54 time: 0.8174 data_time: 0.0018 memory: 25074 loss: 0.2693 2023/05/31 22:56:14 - mmengine - INFO - Epoch(train) [4][4300/5758] lr: 3.7826e-03 eta: 23:15:42 time: 0.7717 data_time: 0.0016 memory: 25074 loss: 0.2740 2023/05/31 22:57:34 - mmengine - INFO - Epoch(train) [4][4400/5758] lr: 3.7826e-03 eta: 23:13:34 time: 0.8106 data_time: 0.0017 memory: 25074 loss: 0.2659 2023/05/31 22:58:55 - mmengine - INFO - Epoch(train) [4][4500/5758] lr: 3.7826e-03 eta: 23:11:26 time: 0.8459 data_time: 0.0019 memory: 25074 loss: 0.2477 2023/05/31 23:00:17 - mmengine - INFO - Epoch(train) [4][4600/5758] lr: 3.7826e-03 eta: 23:09:28 time: 0.8613 data_time: 0.0021 memory: 25074 loss: 0.2430 2023/05/31 23:01:38 - mmengine - INFO - Epoch(train) [4][4700/5758] lr: 3.7826e-03 eta: 23:07:21 time: 0.7738 data_time: 0.0015 memory: 25074 loss: 0.2166 2023/05/31 23:02:00 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 23:02:59 - mmengine - INFO - Epoch(train) [4][4800/5758] lr: 3.7826e-03 eta: 23:05:17 time: 0.8245 data_time: 0.0018 memory: 25074 loss: 0.2208 2023/05/31 23:04:22 - mmengine - INFO - Epoch(train) [4][4900/5758] lr: 3.7826e-03 eta: 23:03:20 time: 0.8350 data_time: 0.0020 memory: 25074 loss: 0.2657 2023/05/31 23:05:45 - mmengine - INFO - Epoch(train) [4][5000/5758] lr: 3.7826e-03 eta: 23:01:27 time: 0.9054 data_time: 0.0013 memory: 25074 loss: 0.1990 2023/05/31 23:07:08 - mmengine - INFO - Epoch(train) [4][5100/5758] lr: 3.7826e-03 eta: 22:59:30 time: 0.8268 data_time: 0.0015 memory: 25074 loss: 0.1768 2023/05/31 23:08:29 - mmengine - INFO - Epoch(train) [4][5200/5758] lr: 3.7826e-03 eta: 22:57:27 time: 0.8090 data_time: 0.0028 memory: 25074 loss: 0.1815 2023/05/31 23:09:50 - mmengine - INFO - Epoch(train) [4][5300/5758] lr: 3.7826e-03 eta: 22:55:25 time: 0.7551 data_time: 0.0025 memory: 25074 loss: 0.1644 2023/05/31 23:11:12 - mmengine - INFO - Epoch(train) [4][5400/5758] lr: 3.7826e-03 eta: 22:53:25 time: 0.9045 data_time: 0.0014 memory: 25074 loss: 0.2193 2023/05/31 23:12:32 - mmengine - INFO - Epoch(train) [4][5500/5758] lr: 3.7826e-03 eta: 22:51:21 time: 0.8169 data_time: 0.0019 memory: 25074 loss: 0.2136 2023/05/31 23:13:53 - mmengine - INFO - Epoch(train) [4][5600/5758] lr: 3.7826e-03 eta: 22:49:19 time: 0.9057 data_time: 0.0017 memory: 25074 loss: 0.1699 2023/05/31 23:15:16 - mmengine - INFO - Epoch(train) [4][5700/5758] lr: 3.7826e-03 eta: 22:47:27 time: 0.8601 data_time: 0.0020 memory: 25074 loss: 0.1526 2023/05/31 23:15:38 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 23:16:03 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 23:16:03 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/05/31 23:16:22 - mmengine - INFO - Epoch(val) [4][16/16] accuracy/top1: 100.0000 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [100.0, 0.0] single-label/f1-score_classwise: [100.0, 0.0] data_time: 0.2402 time: 0.6277 2023/05/31 23:17:51 - mmengine - INFO - Epoch(train) [5][ 100/5758] lr: 3.6190e-03 eta: 22:44:49 time: 0.7362 data_time: 0.0028 memory: 25074 loss: 0.1410 2023/05/31 23:19:13 - mmengine - INFO - Epoch(train) [5][ 200/5758] lr: 3.6190e-03 eta: 22:42:52 time: 0.7838 data_time: 0.0024 memory: 25074 loss: 0.1311 2023/05/31 23:20:38 - mmengine - INFO - Epoch(train) [5][ 300/5758] lr: 3.6190e-03 eta: 22:41:08 time: 0.7731 data_time: 0.0022 memory: 25074 loss: 0.1291 2023/05/31 23:22:02 - mmengine - INFO - Epoch(train) [5][ 400/5758] lr: 3.6190e-03 eta: 22:39:17 time: 0.8351 data_time: 0.0021 memory: 25074 loss: 0.1430 2023/05/31 23:23:24 - mmengine - INFO - Epoch(train) [5][ 500/5758] lr: 3.6190e-03 eta: 22:37:21 time: 0.8327 data_time: 0.0020 memory: 25074 loss: 0.1098 2023/05/31 23:24:43 - mmengine - INFO - Epoch(train) [5][ 600/5758] lr: 3.6190e-03 eta: 22:35:15 time: 0.8027 data_time: 0.0021 memory: 25074 loss: 0.1494 2023/05/31 23:26:05 - mmengine - INFO - Epoch(train) [5][ 700/5758] lr: 3.6190e-03 eta: 22:33:18 time: 0.8583 data_time: 0.0016 memory: 25074 loss: 0.1001 2023/05/31 23:27:27 - mmengine - INFO - Epoch(train) [5][ 800/5758] lr: 3.6190e-03 eta: 22:31:24 time: 0.8757 data_time: 0.0023 memory: 25074 loss: 0.1192 2023/05/31 23:28:48 - mmengine - INFO - Epoch(train) [5][ 900/5758] lr: 3.6190e-03 eta: 22:29:27 time: 0.7414 data_time: 0.0020 memory: 25074 loss: 0.0992 2023/05/31 23:29:43 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 23:30:10 - mmengine - INFO - Epoch(train) [5][1000/5758] lr: 3.6190e-03 eta: 22:27:31 time: 0.8163 data_time: 0.0018 memory: 25074 loss: 0.5075 2023/05/31 23:31:32 - mmengine - INFO - Epoch(train) [5][1100/5758] lr: 3.6190e-03 eta: 22:25:38 time: 0.7812 data_time: 0.0021 memory: 25074 loss: 0.2001 2023/05/31 23:32:53 - mmengine - INFO - Epoch(train) [5][1200/5758] lr: 3.6190e-03 eta: 22:23:41 time: 0.9015 data_time: 0.0019 memory: 25074 loss: 0.1282 2023/05/31 23:34:13 - mmengine - INFO - Epoch(train) [5][1300/5758] lr: 3.6190e-03 eta: 22:21:39 time: 0.7681 data_time: 0.0019 memory: 25074 loss: 0.0742 2023/05/31 23:35:35 - mmengine - INFO - Epoch(train) [5][1400/5758] lr: 3.6190e-03 eta: 22:19:45 time: 0.8026 data_time: 0.0017 memory: 25074 loss: 0.1416 2023/05/31 23:36:58 - mmengine - INFO - Epoch(train) [5][1500/5758] lr: 3.6190e-03 eta: 22:17:55 time: 1.0372 data_time: 0.0021 memory: 25074 loss: 0.0798 2023/05/31 23:38:20 - mmengine - INFO - Epoch(train) [5][1600/5758] lr: 3.6190e-03 eta: 22:16:03 time: 0.8295 data_time: 0.0020 memory: 25074 loss: 0.0977 2023/05/31 23:39:42 - mmengine - INFO - Epoch(train) [5][1700/5758] lr: 3.6190e-03 eta: 22:14:11 time: 0.8011 data_time: 0.0015 memory: 25074 loss: 0.0776 2023/05/31 23:41:03 - mmengine - INFO - Epoch(train) [5][1800/5758] lr: 3.6190e-03 eta: 22:12:15 time: 0.7675 data_time: 0.0018 memory: 25074 loss: 0.0825 2023/05/31 23:42:23 - mmengine - INFO - Epoch(train) [5][1900/5758] lr: 3.6190e-03 eta: 22:10:15 time: 0.8050 data_time: 0.0023 memory: 25074 loss: 0.1585 2023/05/31 23:43:18 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 23:43:44 - mmengine - INFO - Epoch(train) [5][2000/5758] lr: 3.6190e-03 eta: 22:08:19 time: 0.8166 data_time: 0.0022 memory: 25074 loss: 0.0716 2023/05/31 23:45:07 - mmengine - INFO - Epoch(train) [5][2100/5758] lr: 3.6190e-03 eta: 22:06:33 time: 0.9307 data_time: 0.0021 memory: 25074 loss: 0.0528 2023/05/31 23:46:27 - mmengine - INFO - Epoch(train) [5][2200/5758] lr: 3.6190e-03 eta: 22:04:33 time: 0.7789 data_time: 0.0016 memory: 25074 loss: 0.0555 2023/05/31 23:47:48 - mmengine - INFO - Epoch(train) [5][2300/5758] lr: 3.6190e-03 eta: 22:02:40 time: 0.7973 data_time: 0.0028 memory: 25074 loss: 0.0683 2023/05/31 23:49:10 - mmengine - INFO - Epoch(train) [5][2400/5758] lr: 3.6190e-03 eta: 22:00:48 time: 0.8169 data_time: 0.0019 memory: 25074 loss: 0.1626 2023/05/31 23:50:32 - mmengine - INFO - Epoch(train) [5][2500/5758] lr: 3.6190e-03 eta: 21:58:58 time: 0.8373 data_time: 0.0020 memory: 25074 loss: 0.0514 2023/05/31 23:51:52 - mmengine - INFO - Epoch(train) [5][2600/5758] lr: 3.6190e-03 eta: 21:57:00 time: 0.8783 data_time: 0.0022 memory: 25074 loss: 0.0432 2023/05/31 23:53:10 - mmengine - INFO - Epoch(train) [5][2700/5758] lr: 3.6190e-03 eta: 21:54:58 time: 0.7888 data_time: 0.0023 memory: 25074 loss: 0.0879 2023/05/31 23:54:31 - mmengine - INFO - Epoch(train) [5][2800/5758] lr: 3.6190e-03 eta: 21:53:05 time: 0.8403 data_time: 0.0021 memory: 25074 loss: 0.0391 2023/05/31 23:55:54 - mmengine - INFO - Epoch(train) [5][2900/5758] lr: 3.6190e-03 eta: 21:51:17 time: 0.8360 data_time: 0.0019 memory: 25074 loss: 0.3418 2023/05/31 23:56:49 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/05/31 23:57:14 - mmengine - INFO - Epoch(train) [5][3000/5758] lr: 3.6190e-03 eta: 21:49:22 time: 0.7920 data_time: 0.0015 memory: 25074 loss: 0.0532 2023/05/31 23:58:36 - mmengine - INFO - Epoch(train) [5][3100/5758] lr: 3.6190e-03 eta: 21:47:33 time: 0.8395 data_time: 0.0018 memory: 25074 loss: 0.0482 2023/05/31 23:59:58 - mmengine - INFO - Epoch(train) [5][3200/5758] lr: 3.6190e-03 eta: 21:45:43 time: 0.8029 data_time: 0.0022 memory: 25074 loss: 0.5153 2023/06/01 00:01:18 - mmengine - INFO - Epoch(train) [5][3300/5758] lr: 3.6190e-03 eta: 21:43:49 time: 0.8329 data_time: 0.0022 memory: 25074 loss: 0.2922 2023/06/01 00:02:39 - mmengine - INFO - Epoch(train) [5][3400/5758] lr: 3.6190e-03 eta: 21:41:55 time: 0.8191 data_time: 0.0021 memory: 25074 loss: 0.1692 2023/06/01 00:03:59 - mmengine - INFO - Epoch(train) [5][3500/5758] lr: 3.6190e-03 eta: 21:40:02 time: 0.8360 data_time: 0.0020 memory: 25074 loss: 0.0718 2023/06/01 00:05:20 - mmengine - INFO - Epoch(train) [5][3600/5758] lr: 3.6190e-03 eta: 21:38:09 time: 0.8147 data_time: 0.0019 memory: 25074 loss: 0.0571 2023/06/01 00:06:40 - mmengine - INFO - Epoch(train) [5][3700/5758] lr: 3.6190e-03 eta: 21:36:16 time: 0.7919 data_time: 0.0022 memory: 25074 loss: 0.0399 2023/06/01 00:08:01 - mmengine - INFO - Epoch(train) [5][3800/5758] lr: 3.6190e-03 eta: 21:34:26 time: 0.7573 data_time: 0.0017 memory: 25074 loss: 0.0239 2023/06/01 00:09:22 - mmengine - INFO - Epoch(train) [5][3900/5758] lr: 3.6190e-03 eta: 21:32:33 time: 0.8103 data_time: 0.0018 memory: 25074 loss: 0.0489 2023/06/01 00:10:16 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 00:10:42 - mmengine - INFO - Epoch(train) [5][4000/5758] lr: 3.6190e-03 eta: 21:30:40 time: 0.8083 data_time: 0.0020 memory: 25074 loss: 0.0225 2023/06/01 00:12:03 - mmengine - INFO - Epoch(train) [5][4100/5758] lr: 3.6190e-03 eta: 21:28:49 time: 0.8008 data_time: 0.0024 memory: 25074 loss: 0.0307 2023/06/01 00:13:23 - mmengine - INFO - Epoch(train) [5][4200/5758] lr: 3.6190e-03 eta: 21:26:57 time: 0.7805 data_time: 0.0019 memory: 25074 loss: 0.0404 2023/06/01 00:14:43 - mmengine - INFO - Epoch(train) [5][4300/5758] lr: 3.6190e-03 eta: 21:25:05 time: 0.8094 data_time: 0.0015 memory: 25074 loss: 0.0219 2023/06/01 00:16:04 - mmengine - INFO - Epoch(train) [5][4400/5758] lr: 3.6190e-03 eta: 21:23:15 time: 0.8124 data_time: 0.0025 memory: 25074 loss: 0.0218 2023/06/01 00:17:20 - mmengine - INFO - Epoch(train) [5][4500/5758] lr: 3.6190e-03 eta: 21:21:07 time: 0.7535 data_time: 0.0016 memory: 25074 loss: 0.0240 2023/06/01 00:18:39 - mmengine - INFO - Epoch(train) [5][4600/5758] lr: 3.6190e-03 eta: 21:19:14 time: 0.7911 data_time: 0.0022 memory: 25074 loss: 0.2776 2023/06/01 00:20:00 - mmengine - INFO - Epoch(train) [5][4700/5758] lr: 3.6190e-03 eta: 21:17:24 time: 0.7885 data_time: 0.0015 memory: 25074 loss: 0.0282 2023/06/01 00:21:20 - mmengine - INFO - Epoch(train) [5][4800/5758] lr: 3.6190e-03 eta: 21:15:33 time: 0.7951 data_time: 0.0021 memory: 25074 loss: 0.0301 2023/06/01 00:22:40 - mmengine - INFO - Epoch(train) [5][4900/5758] lr: 3.6190e-03 eta: 21:13:41 time: 0.7606 data_time: 0.0017 memory: 25074 loss: 0.0214 2023/06/01 00:23:37 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 00:24:02 - mmengine - INFO - Epoch(train) [5][5000/5758] lr: 3.6190e-03 eta: 21:11:57 time: 0.8411 data_time: 0.0020 memory: 25074 loss: 0.0209 2023/06/01 00:25:22 - mmengine - INFO - Epoch(train) [5][5100/5758] lr: 3.6190e-03 eta: 21:10:06 time: 0.7920 data_time: 0.0023 memory: 25074 loss: 0.0308 2023/06/01 00:26:44 - mmengine - INFO - Epoch(train) [5][5200/5758] lr: 3.6190e-03 eta: 21:08:21 time: 0.8144 data_time: 0.0018 memory: 25074 loss: 0.7453 2023/06/01 00:28:04 - mmengine - INFO - Epoch(train) [5][5300/5758] lr: 3.6190e-03 eta: 21:06:28 time: 0.7838 data_time: 0.0017 memory: 25074 loss: 0.5257 2023/06/01 00:29:24 - mmengine - INFO - Epoch(train) [5][5400/5758] lr: 3.6190e-03 eta: 21:04:40 time: 0.7523 data_time: 0.0016 memory: 25074 loss: 0.3817 2023/06/01 00:30:44 - mmengine - INFO - Epoch(train) [5][5500/5758] lr: 3.6190e-03 eta: 21:02:48 time: 0.7719 data_time: 0.0016 memory: 25074 loss: 0.2653 2023/06/01 00:32:03 - mmengine - INFO - Epoch(train) [5][5600/5758] lr: 3.6190e-03 eta: 21:00:55 time: 0.8409 data_time: 0.0018 memory: 25074 loss: 0.5123 2023/06/01 00:33:23 - mmengine - INFO - Epoch(train) [5][5700/5758] lr: 3.6190e-03 eta: 20:59:06 time: 0.7827 data_time: 0.0016 memory: 25074 loss: 0.2876 2023/06/01 00:34:09 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 00:34:09 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/06/01 00:34:28 - mmengine - INFO - Epoch(val) [5][16/16] accuracy/top1: 100.0000 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [100.0, 0.0] single-label/f1-score_classwise: [100.0, 0.0] data_time: 0.2348 time: 0.6202 2023/06/01 00:35:55 - mmengine - INFO - Epoch(train) [6][ 100/5758] lr: 3.4157e-03 eta: 20:56:33 time: 0.7910 data_time: 0.0018 memory: 25074 loss: 0.1717 2023/06/01 00:37:15 - mmengine - INFO - Epoch(train) [6][ 200/5758] lr: 3.4157e-03 eta: 20:54:42 time: 0.8032 data_time: 0.0018 memory: 25074 loss: 0.0633 2023/06/01 00:37:23 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 00:38:34 - mmengine - INFO - Epoch(train) [6][ 300/5758] lr: 3.4157e-03 eta: 20:52:53 time: 0.7925 data_time: 0.0019 memory: 25074 loss: 0.0350 2023/06/01 00:39:55 - mmengine - INFO - Epoch(train) [6][ 400/5758] lr: 3.4157e-03 eta: 20:51:06 time: 0.8220 data_time: 0.0024 memory: 25074 loss: 0.0298 2023/06/01 00:41:16 - mmengine - INFO - Epoch(train) [6][ 500/5758] lr: 3.4157e-03 eta: 20:49:19 time: 0.7986 data_time: 0.0015 memory: 25074 loss: 0.0448 2023/06/01 00:42:36 - mmengine - INFO - Epoch(train) [6][ 600/5758] lr: 3.4157e-03 eta: 20:47:31 time: 0.7858 data_time: 0.0024 memory: 25074 loss: 0.0284 2023/06/01 00:43:56 - mmengine - INFO - Epoch(train) [6][ 700/5758] lr: 3.4157e-03 eta: 20:45:44 time: 0.8088 data_time: 0.0019 memory: 25074 loss: 0.0374 2023/06/01 00:45:17 - mmengine - INFO - Epoch(train) [6][ 800/5758] lr: 3.4157e-03 eta: 20:43:58 time: 0.7831 data_time: 0.0023 memory: 25074 loss: 0.0244 2023/06/01 00:46:38 - mmengine - INFO - Epoch(train) [6][ 900/5758] lr: 3.4157e-03 eta: 20:42:12 time: 0.8390 data_time: 0.0027 memory: 25074 loss: 0.0272 2023/06/01 00:47:58 - mmengine - INFO - Epoch(train) [6][1000/5758] lr: 3.4157e-03 eta: 20:40:24 time: 0.7135 data_time: 0.0023 memory: 25074 loss: 0.0431 2023/06/01 00:49:20 - mmengine - INFO - Epoch(train) [6][1100/5758] lr: 3.4157e-03 eta: 20:38:41 time: 0.8424 data_time: 0.0020 memory: 25074 loss: 0.0196 2023/06/01 00:50:41 - mmengine - INFO - Epoch(train) [6][1200/5758] lr: 3.4157e-03 eta: 20:36:58 time: 0.7580 data_time: 0.0021 memory: 25074 loss: 0.0258 2023/06/01 00:50:49 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 00:52:02 - mmengine - INFO - Epoch(train) [6][1300/5758] lr: 3.4157e-03 eta: 20:35:12 time: 0.8078 data_time: 0.0020 memory: 25074 loss: 0.0160 2023/06/01 00:53:22 - mmengine - INFO - Epoch(train) [6][1400/5758] lr: 3.4157e-03 eta: 20:33:26 time: 0.8338 data_time: 0.0022 memory: 25074 loss: 0.0233 2023/06/01 00:54:41 - mmengine - INFO - Epoch(train) [6][1500/5758] lr: 3.4157e-03 eta: 20:31:36 time: 0.7886 data_time: 0.0017 memory: 25074 loss: 0.0263 2023/06/01 00:56:00 - mmengine - INFO - Epoch(train) [6][1600/5758] lr: 3.4157e-03 eta: 20:29:47 time: 0.7699 data_time: 0.0017 memory: 25074 loss: 0.0194 2023/06/01 00:57:21 - mmengine - INFO - Epoch(train) [6][1700/5758] lr: 3.4157e-03 eta: 20:28:02 time: 0.7650 data_time: 0.0019 memory: 25074 loss: 0.0245 2023/06/01 00:58:41 - mmengine - INFO - Epoch(train) [6][1800/5758] lr: 3.4157e-03 eta: 20:26:16 time: 0.7781 data_time: 0.0014 memory: 25074 loss: 0.9105 2023/06/01 01:00:00 - mmengine - INFO - Epoch(train) [6][1900/5758] lr: 3.4157e-03 eta: 20:24:27 time: 0.7518 data_time: 0.0016 memory: 25074 loss: 0.6870 2023/06/01 01:01:19 - mmengine - INFO - Epoch(train) [6][2000/5758] lr: 3.4157e-03 eta: 20:22:38 time: 0.8090 data_time: 0.0018 memory: 25074 loss: 0.6859 2023/06/01 01:02:38 - mmengine - INFO - Epoch(train) [6][2100/5758] lr: 3.4157e-03 eta: 20:20:50 time: 0.8212 data_time: 0.0016 memory: 25074 loss: 0.6863 2023/06/01 01:03:57 - mmengine - INFO - Epoch(train) [6][2200/5758] lr: 3.4157e-03 eta: 20:19:01 time: 0.7603 data_time: 0.0015 memory: 25074 loss: 0.6893 2023/06/01 01:04:05 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 01:05:18 - mmengine - INFO - Epoch(train) [6][2300/5758] lr: 3.4157e-03 eta: 20:17:19 time: 0.7956 data_time: 0.0017 memory: 25074 loss: 0.6890 2023/06/01 01:06:38 - mmengine - INFO - Epoch(train) [6][2400/5758] lr: 3.4157e-03 eta: 20:15:33 time: 0.8149 data_time: 0.0014 memory: 25074 loss: 0.6837 2023/06/01 01:09:00 - mmengine - INFO - Epoch(train) [6][2500/5758] lr: 3.4157e-03 eta: 20:16:33 time: 0.7687 data_time: 0.0615 memory: 25074 loss: 0.6795 2023/06/01 01:10:12 - mmengine - INFO - Epoch(train) [6][2600/5758] lr: 3.4157e-03 eta: 20:14:27 time: 0.7203 data_time: 0.0013 memory: 25074 loss: 0.6829 2023/06/01 01:11:30 - mmengine - INFO - Epoch(train) [6][2700/5758] lr: 3.4157e-03 eta: 20:12:36 time: 0.8305 data_time: 0.0014 memory: 25074 loss: 0.6826 2023/06/01 01:12:46 - mmengine - INFO - Epoch(train) [6][2800/5758] lr: 3.4157e-03 eta: 20:10:39 time: 0.8051 data_time: 0.0018 memory: 25074 loss: 0.6878 2023/06/01 01:14:01 - mmengine - INFO - Epoch(train) [6][2900/5758] lr: 3.4157e-03 eta: 20:08:40 time: 0.7747 data_time: 0.0014 memory: 25074 loss: 0.6877 2023/06/01 01:15:17 - mmengine - INFO - Epoch(train) [6][3000/5758] lr: 3.4157e-03 eta: 20:06:45 time: 0.7355 data_time: 0.0015 memory: 25074 loss: 0.6831 2023/06/01 01:16:33 - mmengine - INFO - Epoch(train) [6][3100/5758] lr: 3.4157e-03 eta: 20:04:50 time: 0.7252 data_time: 0.0018 memory: 25074 loss: 0.6801 2023/06/01 01:17:50 - mmengine - INFO - Epoch(train) [6][3200/5758] lr: 3.4157e-03 eta: 20:02:58 time: 0.7361 data_time: 0.0016 memory: 25074 loss: 0.6558 2023/06/01 01:17:57 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 01:19:04 - mmengine - INFO - Epoch(train) [6][3300/5758] lr: 3.4157e-03 eta: 20:00:59 time: 0.7466 data_time: 0.0020 memory: 25074 loss: 0.6396 2023/06/01 01:20:20 - mmengine - INFO - Epoch(train) [6][3400/5758] lr: 3.4157e-03 eta: 19:59:04 time: 0.7108 data_time: 0.0015 memory: 25074 loss: 0.5973 2023/06/01 01:21:36 - mmengine - INFO - Epoch(train) [6][3500/5758] lr: 3.4157e-03 eta: 19:57:11 time: 0.7146 data_time: 0.0015 memory: 25074 loss: 0.5695 2023/06/01 01:22:56 - mmengine - INFO - Epoch(train) [6][3600/5758] lr: 3.4157e-03 eta: 19:55:27 time: 0.7867 data_time: 0.0019 memory: 25074 loss: 0.5324 2023/06/01 01:24:13 - mmengine - INFO - Epoch(train) [6][3700/5758] lr: 3.4157e-03 eta: 19:53:34 time: 0.7765 data_time: 0.0017 memory: 25074 loss: 0.5393 2023/06/01 01:25:30 - mmengine - INFO - Epoch(train) [6][3800/5758] lr: 3.4157e-03 eta: 19:51:42 time: 0.8012 data_time: 0.0020 memory: 25074 loss: 0.4775 2023/06/01 01:26:51 - mmengine - INFO - Epoch(train) [6][3900/5758] lr: 3.4157e-03 eta: 19:50:03 time: 0.7955 data_time: 0.0019 memory: 25074 loss: 0.4625 2023/06/01 01:28:12 - mmengine - INFO - Epoch(train) [6][4000/5758] lr: 3.4157e-03 eta: 19:48:23 time: 0.8331 data_time: 0.0021 memory: 25074 loss: 0.4338 2023/06/01 01:29:34 - mmengine - INFO - Epoch(train) [6][4100/5758] lr: 3.4157e-03 eta: 19:46:43 time: 0.8732 data_time: 0.0017 memory: 25074 loss: 0.3927 2023/06/01 01:30:53 - mmengine - INFO - Epoch(train) [6][4200/5758] lr: 3.4157e-03 eta: 19:44:58 time: 0.8073 data_time: 0.0019 memory: 25074 loss: 0.3973 2023/06/01 01:31:00 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 01:32:11 - mmengine - INFO - Epoch(train) [6][4300/5758] lr: 3.4157e-03 eta: 19:43:11 time: 0.8198 data_time: 0.0019 memory: 25074 loss: 0.3450 2023/06/01 01:33:30 - mmengine - INFO - Epoch(train) [6][4400/5758] lr: 3.4157e-03 eta: 19:41:27 time: 0.8256 data_time: 0.0017 memory: 25074 loss: 0.3450 2023/06/01 01:34:50 - mmengine - INFO - Epoch(train) [6][4500/5758] lr: 3.4157e-03 eta: 19:39:44 time: 0.8408 data_time: 0.0016 memory: 25074 loss: 0.3008 2023/06/01 01:36:11 - mmengine - INFO - Epoch(train) [6][4600/5758] lr: 3.4157e-03 eta: 19:38:03 time: 0.8218 data_time: 0.0018 memory: 25074 loss: 0.2861 2023/06/01 01:37:34 - mmengine - INFO - Epoch(train) [6][4700/5758] lr: 3.4157e-03 eta: 19:36:29 time: 0.8694 data_time: 0.0021 memory: 25074 loss: 0.2748 2023/06/01 01:38:55 - mmengine - INFO - Epoch(train) [6][4800/5758] lr: 3.4157e-03 eta: 19:34:49 time: 0.8727 data_time: 0.0017 memory: 25074 loss: 0.2403 2023/06/01 01:40:16 - mmengine - INFO - Epoch(train) [6][4900/5758] lr: 3.4157e-03 eta: 19:33:11 time: 0.8610 data_time: 0.0024 memory: 25074 loss: 0.1954 2023/06/01 01:41:36 - mmengine - INFO - Epoch(train) [6][5000/5758] lr: 3.4157e-03 eta: 19:31:27 time: 0.7790 data_time: 0.0020 memory: 25074 loss: 0.2252 2023/06/01 01:42:57 - mmengine - INFO - Epoch(train) [6][5100/5758] lr: 3.4157e-03 eta: 19:29:49 time: 0.8055 data_time: 0.0020 memory: 25074 loss: 0.2168 2023/06/01 01:44:17 - mmengine - INFO - Epoch(train) [6][5200/5758] lr: 3.4157e-03 eta: 19:28:08 time: 0.7842 data_time: 0.0019 memory: 25074 loss: 0.1787 2023/06/01 01:44:26 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 01:45:39 - mmengine - INFO - Epoch(train) [6][5300/5758] lr: 3.4157e-03 eta: 19:26:31 time: 0.8257 data_time: 0.0021 memory: 25074 loss: 0.3113 2023/06/01 01:47:01 - mmengine - INFO - Epoch(train) [6][5400/5758] lr: 3.4157e-03 eta: 19:24:53 time: 0.7858 data_time: 0.0019 memory: 25074 loss: 0.1942 2023/06/01 01:48:23 - mmengine - INFO - Epoch(train) [6][5500/5758] lr: 3.4157e-03 eta: 19:23:17 time: 0.8522 data_time: 0.0022 memory: 25074 loss: 0.1610 2023/06/01 01:49:44 - mmengine - INFO - Epoch(train) [6][5600/5758] lr: 3.4157e-03 eta: 19:21:39 time: 0.8028 data_time: 0.0022 memory: 25074 loss: 0.1281 2023/06/01 01:51:07 - mmengine - INFO - Epoch(train) [6][5700/5758] lr: 3.4157e-03 eta: 19:20:03 time: 0.8249 data_time: 0.0020 memory: 25074 loss: 0.0844 2023/06/01 01:51:53 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 01:51:53 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/06/01 01:52:12 - mmengine - INFO - Epoch(val) [6][16/16] accuracy/top1: 100.0000 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [100.0, 0.0] single-label/f1-score_classwise: [100.0, 0.0] data_time: 0.2262 time: 0.6205 2023/06/01 01:53:41 - mmengine - INFO - Epoch(train) [7][ 100/5758] lr: 3.1776e-03 eta: 19:17:43 time: 0.7843 data_time: 0.0017 memory: 25074 loss: 0.0823 2023/06/01 01:55:02 - mmengine - INFO - Epoch(train) [7][ 200/5758] lr: 3.1776e-03 eta: 19:16:07 time: 0.7684 data_time: 0.0023 memory: 25074 loss: 0.0858 2023/06/01 01:56:25 - mmengine - INFO - Epoch(train) [7][ 300/5758] lr: 3.1776e-03 eta: 19:14:31 time: 0.8051 data_time: 0.0017 memory: 25074 loss: 0.0816 2023/06/01 01:57:48 - mmengine - INFO - Epoch(train) [7][ 400/5758] lr: 3.1776e-03 eta: 19:12:58 time: 0.8809 data_time: 0.0019 memory: 25074 loss: 0.1065 2023/06/01 01:58:30 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 01:59:11 - mmengine - INFO - Epoch(train) [7][ 500/5758] lr: 3.1776e-03 eta: 19:11:24 time: 0.7991 data_time: 0.0022 memory: 25074 loss: 0.0550 2023/06/01 02:00:32 - mmengine - INFO - Epoch(train) [7][ 600/5758] lr: 3.1776e-03 eta: 19:09:47 time: 0.8405 data_time: 0.0021 memory: 25074 loss: 0.0631 2023/06/01 02:01:54 - mmengine - INFO - Epoch(train) [7][ 700/5758] lr: 3.1776e-03 eta: 19:08:11 time: 0.8097 data_time: 0.0020 memory: 25074 loss: 0.0530 2023/06/01 02:03:17 - mmengine - INFO - Epoch(train) [7][ 800/5758] lr: 3.1776e-03 eta: 19:06:37 time: 0.8274 data_time: 0.0023 memory: 25074 loss: 0.0548 2023/06/01 02:04:40 - mmengine - INFO - Epoch(train) [7][ 900/5758] lr: 3.1776e-03 eta: 19:05:04 time: 0.8286 data_time: 0.0019 memory: 25074 loss: 0.0599 2023/06/01 02:06:02 - mmengine - INFO - Epoch(train) [7][1000/5758] lr: 3.1776e-03 eta: 19:03:27 time: 0.8614 data_time: 0.0021 memory: 25074 loss: 0.0369 2023/06/01 02:07:24 - mmengine - INFO - Epoch(train) [7][1100/5758] lr: 3.1776e-03 eta: 19:01:52 time: 0.8106 data_time: 0.0020 memory: 25074 loss: 0.0259 2023/06/01 02:08:47 - mmengine - INFO - Epoch(train) [7][1200/5758] lr: 3.1776e-03 eta: 19:00:19 time: 0.7781 data_time: 0.0016 memory: 25074 loss: 0.0549 2023/06/01 02:10:07 - mmengine - INFO - Epoch(train) [7][1300/5758] lr: 3.1776e-03 eta: 18:58:39 time: 0.8170 data_time: 0.0018 memory: 25074 loss: 0.0450 2023/06/01 02:11:30 - mmengine - INFO - Epoch(train) [7][1400/5758] lr: 3.1776e-03 eta: 18:57:07 time: 0.9130 data_time: 0.0028 memory: 25074 loss: 0.0345 2023/06/01 02:12:12 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 02:12:52 - mmengine - INFO - Epoch(train) [7][1500/5758] lr: 3.1776e-03 eta: 18:55:31 time: 0.8565 data_time: 0.0017 memory: 25074 loss: 0.5377 2023/06/01 02:14:13 - mmengine - INFO - Epoch(train) [7][1600/5758] lr: 3.1776e-03 eta: 18:53:54 time: 0.8048 data_time: 0.0023 memory: 25074 loss: 0.3871 2023/06/01 02:15:34 - mmengine - INFO - Epoch(train) [7][1700/5758] lr: 3.1776e-03 eta: 18:52:17 time: 0.8087 data_time: 0.0019 memory: 25074 loss: 0.2551 2023/06/01 02:16:55 - mmengine - INFO - Epoch(train) [7][1800/5758] lr: 3.1776e-03 eta: 18:50:40 time: 0.7995 data_time: 0.0016 memory: 25074 loss: 0.1017 2023/06/01 02:18:16 - mmengine - INFO - Epoch(train) [7][1900/5758] lr: 3.1776e-03 eta: 18:49:03 time: 0.7761 data_time: 0.0018 memory: 25074 loss: 0.0550 2023/06/01 02:19:38 - mmengine - INFO - Epoch(train) [7][2000/5758] lr: 3.1776e-03 eta: 18:47:28 time: 0.8210 data_time: 0.0016 memory: 25074 loss: 0.0397 2023/06/01 02:21:01 - mmengine - INFO - Epoch(train) [7][2100/5758] lr: 3.1776e-03 eta: 18:45:54 time: 0.8073 data_time: 0.0020 memory: 25074 loss: 0.0337 2023/06/01 02:22:22 - mmengine - INFO - Epoch(train) [7][2200/5758] lr: 3.1776e-03 eta: 18:44:17 time: 0.8329 data_time: 0.0015 memory: 25074 loss: 0.0510 2023/06/01 02:23:42 - mmengine - INFO - Epoch(train) [7][2300/5758] lr: 3.1776e-03 eta: 18:42:40 time: 0.8236 data_time: 0.0018 memory: 25074 loss: 0.0374 2023/06/01 02:25:03 - mmengine - INFO - Epoch(train) [7][2400/5758] lr: 3.1776e-03 eta: 18:41:03 time: 0.7547 data_time: 0.0018 memory: 25074 loss: 0.0335 2023/06/01 02:25:45 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 02:26:25 - mmengine - INFO - Epoch(train) [7][2500/5758] lr: 3.1776e-03 eta: 18:39:27 time: 0.8111 data_time: 0.0015 memory: 25074 loss: 0.0330 2023/06/01 02:27:48 - mmengine - INFO - Epoch(train) [7][2600/5758] lr: 3.1776e-03 eta: 18:37:55 time: 0.7962 data_time: 0.0020 memory: 25074 loss: 0.0280 2023/06/01 02:29:09 - mmengine - INFO - Epoch(train) [7][2700/5758] lr: 3.1776e-03 eta: 18:36:19 time: 0.8027 data_time: 0.0015 memory: 25074 loss: 0.0194 2023/06/01 02:30:30 - mmengine - INFO - Epoch(train) [7][2800/5758] lr: 3.1776e-03 eta: 18:34:43 time: 0.7862 data_time: 0.0013 memory: 25074 loss: 0.4901 2023/06/01 02:31:51 - mmengine - INFO - Epoch(train) [7][2900/5758] lr: 3.1776e-03 eta: 18:33:07 time: 0.8663 data_time: 0.0016 memory: 25074 loss: 0.3818 2023/06/01 02:33:12 - mmengine - INFO - Epoch(train) [7][3000/5758] lr: 3.1776e-03 eta: 18:31:31 time: 0.8492 data_time: 0.0015 memory: 25074 loss: 0.3002 2023/06/01 02:34:33 - mmengine - INFO - Epoch(train) [7][3100/5758] lr: 3.1776e-03 eta: 18:29:54 time: 0.7984 data_time: 0.0015 memory: 25074 loss: 0.2209 2023/06/01 02:35:55 - mmengine - INFO - Epoch(train) [7][3200/5758] lr: 3.1776e-03 eta: 18:28:20 time: 0.8250 data_time: 0.0019 memory: 25074 loss: 0.1824 2023/06/01 02:37:16 - mmengine - INFO - Epoch(train) [7][3300/5758] lr: 3.1776e-03 eta: 18:26:45 time: 0.7949 data_time: 0.0029 memory: 25074 loss: 0.0875 2023/06/01 02:38:39 - mmengine - INFO - Epoch(train) [7][3400/5758] lr: 3.1776e-03 eta: 18:25:13 time: 0.8398 data_time: 0.0020 memory: 25074 loss: 0.0338 2023/06/01 02:39:21 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 02:40:00 - mmengine - INFO - Epoch(train) [7][3500/5758] lr: 3.1776e-03 eta: 18:23:38 time: 0.7995 data_time: 0.0018 memory: 25074 loss: 0.0624 2023/06/01 02:41:22 - mmengine - INFO - Epoch(train) [7][3600/5758] lr: 3.1776e-03 eta: 18:22:03 time: 0.7869 data_time: 0.0017 memory: 25074 loss: 0.0265 2023/06/01 02:42:43 - mmengine - INFO - Epoch(train) [7][3700/5758] lr: 3.1776e-03 eta: 18:20:27 time: 0.8373 data_time: 0.0021 memory: 25074 loss: 0.0237 2023/06/01 02:44:06 - mmengine - INFO - Epoch(train) [7][3800/5758] lr: 3.1776e-03 eta: 18:18:56 time: 0.9143 data_time: 0.0021 memory: 25074 loss: 0.0428 2023/06/01 02:45:27 - mmengine - INFO - Epoch(train) [7][3900/5758] lr: 3.1776e-03 eta: 18:17:21 time: 0.7826 data_time: 0.0024 memory: 25074 loss: 0.0203 2023/06/01 02:46:48 - mmengine - INFO - Epoch(train) [7][4000/5758] lr: 3.1776e-03 eta: 18:15:46 time: 0.8423 data_time: 0.0016 memory: 25074 loss: 0.0184 2023/06/01 02:48:10 - mmengine - INFO - Epoch(train) [7][4100/5758] lr: 3.1776e-03 eta: 18:14:11 time: 0.8426 data_time: 0.0017 memory: 25074 loss: 0.0317 2023/06/01 02:49:31 - mmengine - INFO - Epoch(train) [7][4200/5758] lr: 3.1776e-03 eta: 18:12:37 time: 0.7893 data_time: 0.0019 memory: 25074 loss: 0.0680 2023/06/01 02:50:54 - mmengine - INFO - Epoch(train) [7][4300/5758] lr: 3.1776e-03 eta: 18:11:06 time: 0.8710 data_time: 0.0022 memory: 25074 loss: 0.0158 2023/06/01 02:52:17 - mmengine - INFO - Epoch(train) [7][4400/5758] lr: 3.1776e-03 eta: 18:09:33 time: 0.8603 data_time: 0.0018 memory: 25074 loss: 0.0189 2023/06/01 02:52:58 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 02:53:39 - mmengine - INFO - Epoch(train) [7][4500/5758] lr: 3.1776e-03 eta: 18:08:01 time: 0.8458 data_time: 0.0016 memory: 25074 loss: 0.0184 2023/06/01 02:55:02 - mmengine - INFO - Epoch(train) [7][4600/5758] lr: 3.1776e-03 eta: 18:06:30 time: 0.8067 data_time: 0.0017 memory: 25074 loss: 0.0191 2023/06/01 02:56:24 - mmengine - INFO - Epoch(train) [7][4700/5758] lr: 3.1776e-03 eta: 18:04:57 time: 0.8204 data_time: 0.0021 memory: 25074 loss: 0.0132 2023/06/01 02:57:46 - mmengine - INFO - Epoch(train) [7][4800/5758] lr: 3.1776e-03 eta: 18:03:24 time: 0.7940 data_time: 0.0016 memory: 25074 loss: 0.0251 2023/06/01 02:59:09 - mmengine - INFO - Epoch(train) [7][4900/5758] lr: 3.1776e-03 eta: 18:01:53 time: 0.9075 data_time: 0.0016 memory: 25074 loss: 0.0212 2023/06/01 03:00:32 - mmengine - INFO - Epoch(train) [7][5000/5758] lr: 3.1776e-03 eta: 18:00:22 time: 0.7914 data_time: 0.0014 memory: 25074 loss: 0.6647 2023/06/01 03:01:54 - mmengine - INFO - Epoch(train) [7][5100/5758] lr: 3.1776e-03 eta: 17:58:48 time: 0.8294 data_time: 0.0015 memory: 25074 loss: 0.6365 2023/06/01 03:03:15 - mmengine - INFO - Epoch(train) [7][5200/5758] lr: 3.1776e-03 eta: 17:57:13 time: 0.7879 data_time: 0.0016 memory: 25074 loss: 0.6290 2023/06/01 03:04:37 - mmengine - INFO - Epoch(train) [7][5300/5758] lr: 3.1776e-03 eta: 17:55:41 time: 0.8508 data_time: 0.0018 memory: 25074 loss: 0.6199 2023/06/01 03:05:58 - mmengine - INFO - Epoch(train) [7][5400/5758] lr: 3.1776e-03 eta: 17:54:07 time: 0.8093 data_time: 0.0017 memory: 25074 loss: 0.6043 2023/06/01 03:06:40 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 03:07:19 - mmengine - INFO - Epoch(train) [7][5500/5758] lr: 3.1776e-03 eta: 17:52:32 time: 0.7967 data_time: 0.0021 memory: 25074 loss: 0.5974 2023/06/01 03:08:42 - mmengine - INFO - Epoch(train) [7][5600/5758] lr: 3.1776e-03 eta: 17:51:02 time: 0.7883 data_time: 0.0016 memory: 25074 loss: 0.5756 2023/06/01 03:10:05 - mmengine - INFO - Epoch(train) [7][5700/5758] lr: 3.1776e-03 eta: 17:49:30 time: 0.8141 data_time: 0.0013 memory: 25074 loss: 0.5639 2023/06/01 03:10:52 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 03:10:52 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/06/01 03:11:10 - mmengine - INFO - Epoch(val) [7][16/16] accuracy/top1: 100.0000 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [100.0, 0.0] single-label/f1-score_classwise: [100.0, 0.0] data_time: 0.2255 time: 0.6097 2023/06/01 03:12:39 - mmengine - INFO - Epoch(train) [8][ 100/5758] lr: 2.9107e-03 eta: 17:47:17 time: 0.7893 data_time: 0.0018 memory: 25074 loss: 0.5362 2023/06/01 03:14:00 - mmengine - INFO - Epoch(train) [8][ 200/5758] lr: 2.9107e-03 eta: 17:45:43 time: 0.8145 data_time: 0.0021 memory: 25074 loss: 0.5108 2023/06/01 03:15:22 - mmengine - INFO - Epoch(train) [8][ 300/5758] lr: 2.9107e-03 eta: 17:44:10 time: 0.8064 data_time: 0.0027 memory: 25074 loss: 0.4770 2023/06/01 03:16:44 - mmengine - INFO - Epoch(train) [8][ 400/5758] lr: 2.9107e-03 eta: 17:42:37 time: 0.7941 data_time: 0.0021 memory: 25074 loss: 0.4469 2023/06/01 03:18:02 - mmengine - INFO - Epoch(train) [8][ 500/5758] lr: 2.9107e-03 eta: 17:40:58 time: 0.7754 data_time: 0.0014 memory: 25074 loss: 0.4381 2023/06/01 03:19:22 - mmengine - INFO - Epoch(train) [8][ 600/5758] lr: 2.9107e-03 eta: 17:39:23 time: 0.8069 data_time: 0.0020 memory: 25074 loss: 0.4025 2023/06/01 03:20:40 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 03:20:44 - mmengine - INFO - Epoch(train) [8][ 700/5758] lr: 2.9107e-03 eta: 17:37:51 time: 0.8387 data_time: 0.0016 memory: 25074 loss: 0.3908 2023/06/01 03:22:07 - mmengine - INFO - Epoch(train) [8][ 800/5758] lr: 2.9107e-03 eta: 17:36:21 time: 0.7854 data_time: 0.0015 memory: 25074 loss: 0.3861 2023/06/01 03:23:30 - mmengine - INFO - Epoch(train) [8][ 900/5758] lr: 2.9107e-03 eta: 17:34:50 time: 0.8259 data_time: 0.0020 memory: 25074 loss: 0.3816 2023/06/01 03:24:50 - mmengine - INFO - Epoch(train) [8][1000/5758] lr: 2.9107e-03 eta: 17:33:15 time: 0.8218 data_time: 0.0014 memory: 25074 loss: 0.3461 2023/06/01 03:26:11 - mmengine - INFO - Epoch(train) [8][1100/5758] lr: 2.9107e-03 eta: 17:31:41 time: 0.8307 data_time: 0.0017 memory: 25074 loss: 0.3338 2023/06/01 03:27:34 - mmengine - INFO - Epoch(train) [8][1200/5758] lr: 2.9107e-03 eta: 17:30:11 time: 0.8040 data_time: 0.0013 memory: 25074 loss: 0.3185 2023/06/01 03:28:57 - mmengine - INFO - Epoch(train) [8][1300/5758] lr: 2.9107e-03 eta: 17:28:41 time: 0.8786 data_time: 0.0014 memory: 25074 loss: 0.3485 2023/06/01 03:30:20 - mmengine - INFO - Epoch(train) [8][1400/5758] lr: 2.9107e-03 eta: 17:27:10 time: 0.8875 data_time: 0.0014 memory: 25074 loss: 0.2783 2023/06/01 03:31:42 - mmengine - INFO - Epoch(train) [8][1500/5758] lr: 2.9107e-03 eta: 17:25:39 time: 0.8517 data_time: 0.0013 memory: 25074 loss: 0.3125 2023/06/01 03:33:04 - mmengine - INFO - Epoch(train) [8][1600/5758] lr: 2.9107e-03 eta: 17:24:06 time: 0.8422 data_time: 0.0014 memory: 25074 loss: 0.2974 2023/06/01 03:34:21 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 03:34:25 - mmengine - INFO - Epoch(train) [8][1700/5758] lr: 2.9107e-03 eta: 17:22:34 time: 0.7978 data_time: 0.0016 memory: 25074 loss: 0.2832 2023/06/01 03:35:48 - mmengine - INFO - Epoch(train) [8][1800/5758] lr: 2.9107e-03 eta: 17:21:04 time: 0.8021 data_time: 0.0013 memory: 25074 loss: 0.2620 2023/06/01 03:37:09 - mmengine - INFO - Epoch(train) [8][1900/5758] lr: 2.9107e-03 eta: 17:19:30 time: 0.7949 data_time: 0.0015 memory: 25074 loss: 0.2613 2023/06/01 03:38:32 - mmengine - INFO - Epoch(train) [8][2000/5758] lr: 2.9107e-03 eta: 17:18:00 time: 0.8208 data_time: 0.0014 memory: 25074 loss: 0.2649 2023/06/01 03:39:55 - mmengine - INFO - Epoch(train) [8][2100/5758] lr: 2.9107e-03 eta: 17:16:30 time: 0.8449 data_time: 0.0014 memory: 25074 loss: 0.2695 2023/06/01 03:41:15 - mmengine - INFO - Epoch(train) [8][2200/5758] lr: 2.9107e-03 eta: 17:14:56 time: 0.8236 data_time: 0.0023 memory: 25074 loss: 0.2874 2023/06/01 03:42:36 - mmengine - INFO - Epoch(train) [8][2300/5758] lr: 2.9107e-03 eta: 17:13:23 time: 0.8314 data_time: 0.0021 memory: 25074 loss: 0.2447 2023/06/01 03:43:58 - mmengine - INFO - Epoch(train) [8][2400/5758] lr: 2.9107e-03 eta: 17:11:51 time: 0.8474 data_time: 0.0021 memory: 25074 loss: 0.2398 2023/06/01 03:45:19 - mmengine - INFO - Epoch(train) [8][2500/5758] lr: 2.9107e-03 eta: 17:10:18 time: 0.7882 data_time: 0.0022 memory: 25074 loss: 0.2349 2023/06/01 03:46:42 - mmengine - INFO - Epoch(train) [8][2600/5758] lr: 2.9107e-03 eta: 17:08:49 time: 0.8113 data_time: 0.0023 memory: 25074 loss: 0.2343 2023/06/01 03:47:57 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 03:48:02 - mmengine - INFO - Epoch(train) [8][2700/5758] lr: 2.9107e-03 eta: 17:07:14 time: 0.8289 data_time: 0.0024 memory: 25074 loss: 0.2269 2023/06/01 03:49:24 - mmengine - INFO - Epoch(train) [8][2800/5758] lr: 2.9107e-03 eta: 17:05:43 time: 0.7623 data_time: 0.0019 memory: 25074 loss: 0.2178 2023/06/01 03:50:44 - mmengine - INFO - Epoch(train) [8][2900/5758] lr: 2.9107e-03 eta: 17:04:09 time: 0.7880 data_time: 0.0022 memory: 25074 loss: 0.2173 2023/06/01 03:52:05 - mmengine - INFO - Epoch(train) [8][3000/5758] lr: 2.9107e-03 eta: 17:02:35 time: 0.8095 data_time: 0.0021 memory: 25074 loss: 0.2091 2023/06/01 03:53:26 - mmengine - INFO - Epoch(train) [8][3100/5758] lr: 2.9107e-03 eta: 17:01:03 time: 0.8101 data_time: 0.0019 memory: 25074 loss: 0.1714 2023/06/01 03:54:49 - mmengine - INFO - Epoch(train) [8][3200/5758] lr: 2.9107e-03 eta: 16:59:34 time: 0.8593 data_time: 0.0020 memory: 25074 loss: 0.2080 2023/06/01 03:56:11 - mmengine - INFO - Epoch(train) [8][3300/5758] lr: 2.9107e-03 eta: 16:58:03 time: 0.8132 data_time: 0.0016 memory: 25074 loss: 0.1868 2023/06/01 03:57:33 - mmengine - INFO - Epoch(train) [8][3400/5758] lr: 2.9107e-03 eta: 16:56:32 time: 0.7774 data_time: 0.0022 memory: 25074 loss: 0.1665 2023/06/01 03:58:54 - mmengine - INFO - Epoch(train) [8][3500/5758] lr: 2.9107e-03 eta: 16:55:00 time: 0.8707 data_time: 0.0021 memory: 25074 loss: 0.1845 2023/06/01 04:00:17 - mmengine - INFO - Epoch(train) [8][3600/5758] lr: 2.9107e-03 eta: 16:53:30 time: 0.7724 data_time: 0.0024 memory: 25074 loss: 0.1662 2023/06/01 04:01:29 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 04:01:34 - mmengine - INFO - Epoch(train) [8][3700/5758] lr: 2.9107e-03 eta: 16:51:51 time: 0.7987 data_time: 0.0017 memory: 25074 loss: 0.1619 2023/06/01 04:02:56 - mmengine - INFO - Epoch(train) [8][3800/5758] lr: 2.9107e-03 eta: 16:50:21 time: 0.8377 data_time: 0.0014 memory: 25074 loss: 0.1478 2023/06/01 04:04:19 - mmengine - INFO - Epoch(train) [8][3900/5758] lr: 2.9107e-03 eta: 16:48:52 time: 0.8408 data_time: 0.0015 memory: 25074 loss: 0.1582 2023/06/01 04:05:40 - mmengine - INFO - Epoch(train) [8][4000/5758] lr: 2.9107e-03 eta: 16:47:20 time: 0.8367 data_time: 0.0016 memory: 25074 loss: 0.1327 2023/06/01 04:07:04 - mmengine - INFO - Epoch(train) [8][4100/5758] lr: 2.9107e-03 eta: 16:45:52 time: 0.8209 data_time: 0.0017 memory: 25074 loss: 0.1277 2023/06/01 04:08:25 - mmengine - INFO - Epoch(train) [8][4200/5758] lr: 2.9107e-03 eta: 16:44:20 time: 0.8249 data_time: 0.0017 memory: 25074 loss: 0.1300 2023/06/01 04:09:48 - mmengine - INFO - Epoch(train) [8][4300/5758] lr: 2.9107e-03 eta: 16:42:51 time: 0.7803 data_time: 0.0018 memory: 25074 loss: 0.1400 2023/06/01 04:11:10 - mmengine - INFO - Epoch(train) [8][4400/5758] lr: 2.9107e-03 eta: 16:41:21 time: 0.8321 data_time: 0.0019 memory: 25074 loss: 0.1150 2023/06/01 04:12:31 - mmengine - INFO - Epoch(train) [8][4500/5758] lr: 2.9107e-03 eta: 16:39:48 time: 0.8187 data_time: 0.0019 memory: 25074 loss: 0.1016 2023/06/01 04:13:53 - mmengine - INFO - Epoch(train) [8][4600/5758] lr: 2.9107e-03 eta: 16:38:18 time: 0.7879 data_time: 0.0021 memory: 25074 loss: 0.0986 2023/06/01 04:15:11 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 04:15:15 - mmengine - INFO - Epoch(train) [8][4700/5758] lr: 2.9107e-03 eta: 16:36:49 time: 0.7465 data_time: 0.0029 memory: 25074 loss: 0.0792 2023/06/01 04:16:37 - mmengine - INFO - Epoch(train) [8][4800/5758] lr: 2.9107e-03 eta: 16:35:17 time: 0.7901 data_time: 0.0026 memory: 25074 loss: 0.0931 2023/06/01 04:17:59 - mmengine - INFO - Epoch(train) [8][4900/5758] lr: 2.9107e-03 eta: 16:33:47 time: 0.8152 data_time: 0.0021 memory: 25074 loss: 0.0984 2023/06/01 04:19:22 - mmengine - INFO - Epoch(train) [8][5000/5758] lr: 2.9107e-03 eta: 16:32:18 time: 0.8074 data_time: 0.0022 memory: 25074 loss: 0.0778 2023/06/01 04:20:44 - mmengine - INFO - Epoch(train) [8][5100/5758] lr: 2.9107e-03 eta: 16:30:48 time: 0.8148 data_time: 0.0018 memory: 25074 loss: 0.0694 2023/06/01 04:22:05 - mmengine - INFO - Epoch(train) [8][5200/5758] lr: 2.9107e-03 eta: 16:29:16 time: 0.8064 data_time: 0.0014 memory: 25074 loss: 0.0627 2023/06/01 04:23:27 - mmengine - INFO - Epoch(train) [8][5300/5758] lr: 2.9107e-03 eta: 16:27:46 time: 0.7676 data_time: 0.0017 memory: 25074 loss: 0.0830 2023/06/01 04:24:49 - mmengine - INFO - Epoch(train) [8][5400/5758] lr: 2.9107e-03 eta: 16:26:16 time: 0.8051 data_time: 0.0019 memory: 25074 loss: 0.0650 2023/06/01 04:26:10 - mmengine - INFO - Epoch(train) [8][5500/5758] lr: 2.9107e-03 eta: 16:24:45 time: 0.8106 data_time: 0.0023 memory: 25074 loss: 0.0770 2023/06/01 04:27:31 - mmengine - INFO - Epoch(train) [8][5600/5758] lr: 2.9107e-03 eta: 16:23:13 time: 0.8026 data_time: 0.0015 memory: 25074 loss: 0.0426 2023/06/01 04:28:48 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 04:28:52 - mmengine - INFO - Epoch(train) [8][5700/5758] lr: 2.9107e-03 eta: 16:21:42 time: 0.8076 data_time: 0.0020 memory: 25074 loss: 0.0647 2023/06/01 04:29:43 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 04:29:43 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/06/01 04:30:03 - mmengine - INFO - Epoch(val) [8][16/16] accuracy/top1: 100.0000 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [100.0, 0.0] single-label/f1-score_classwise: [100.0, 0.0] data_time: 0.2316 time: 0.6260 2023/06/01 04:31:32 - mmengine - INFO - Epoch(train) [9][ 100/5758] lr: 2.6215e-03 eta: 16:19:36 time: 0.7938 data_time: 0.0020 memory: 25074 loss: 0.0473 2023/06/01 04:32:54 - mmengine - INFO - Epoch(train) [9][ 200/5758] lr: 2.6215e-03 eta: 16:18:06 time: 0.8486 data_time: 0.0014 memory: 25074 loss: 0.0379 2023/06/01 04:34:17 - mmengine - INFO - Epoch(train) [9][ 300/5758] lr: 2.6215e-03 eta: 16:16:37 time: 0.8137 data_time: 0.0022 memory: 25074 loss: 0.0486 2023/06/01 04:35:38 - mmengine - INFO - Epoch(train) [9][ 400/5758] lr: 2.6215e-03 eta: 16:15:05 time: 0.8413 data_time: 0.0022 memory: 25074 loss: 0.0392 2023/06/01 04:36:58 - mmengine - INFO - Epoch(train) [9][ 500/5758] lr: 2.6215e-03 eta: 16:13:34 time: 0.8025 data_time: 0.0021 memory: 25074 loss: 0.0501 2023/06/01 04:38:21 - mmengine - INFO - Epoch(train) [9][ 600/5758] lr: 2.6215e-03 eta: 16:12:04 time: 0.8108 data_time: 0.0019 memory: 25074 loss: 0.0653 2023/06/01 04:39:42 - mmengine - INFO - Epoch(train) [9][ 700/5758] lr: 2.6215e-03 eta: 16:10:33 time: 0.7812 data_time: 0.0016 memory: 25074 loss: 0.0389 2023/06/01 04:41:01 - mmengine - INFO - Epoch(train) [9][ 800/5758] lr: 2.6215e-03 eta: 16:08:59 time: 0.8008 data_time: 0.0023 memory: 25074 loss: 0.0421 2023/06/01 04:42:24 - mmengine - INFO - Epoch(train) [9][ 900/5758] lr: 2.6215e-03 eta: 16:07:31 time: 0.7815 data_time: 0.0017 memory: 25074 loss: 0.0425 2023/06/01 04:42:54 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 04:43:45 - mmengine - INFO - Epoch(train) [9][1000/5758] lr: 2.6215e-03 eta: 16:06:01 time: 0.8031 data_time: 0.0023 memory: 25074 loss: 0.0496 2023/06/01 04:45:08 - mmengine - INFO - Epoch(train) [9][1100/5758] lr: 2.6215e-03 eta: 16:04:32 time: 0.8107 data_time: 0.0018 memory: 25074 loss: 0.0354 2023/06/01 04:46:31 - mmengine - INFO - Epoch(train) [9][1200/5758] lr: 2.6215e-03 eta: 16:03:04 time: 0.8082 data_time: 0.0021 memory: 25074 loss: 0.0424 2023/06/01 04:47:54 - mmengine - INFO - Epoch(train) [9][1300/5758] lr: 2.6215e-03 eta: 16:01:36 time: 0.8648 data_time: 0.0016 memory: 25074 loss: 0.0406 2023/06/01 04:49:16 - mmengine - INFO - Epoch(train) [9][1400/5758] lr: 2.6215e-03 eta: 16:00:07 time: 0.8223 data_time: 0.0023 memory: 25074 loss: 0.0584 2023/06/01 04:50:38 - mmengine - INFO - Epoch(train) [9][1500/5758] lr: 2.6215e-03 eta: 15:58:36 time: 0.7938 data_time: 0.0019 memory: 25074 loss: 0.0369 2023/06/01 04:51:59 - mmengine - INFO - Epoch(train) [9][1600/5758] lr: 2.6215e-03 eta: 15:57:06 time: 0.7814 data_time: 0.0021 memory: 25074 loss: 0.0269 2023/06/01 04:53:20 - mmengine - INFO - Epoch(train) [9][1700/5758] lr: 2.6215e-03 eta: 15:55:34 time: 0.7761 data_time: 0.0018 memory: 25074 loss: 0.0410 2023/06/01 04:54:42 - mmengine - INFO - Epoch(train) [9][1800/5758] lr: 2.6215e-03 eta: 15:54:05 time: 0.8536 data_time: 0.0019 memory: 25074 loss: 0.0302 2023/06/01 04:56:04 - mmengine - INFO - Epoch(train) [9][1900/5758] lr: 2.6215e-03 eta: 15:52:36 time: 0.8638 data_time: 0.0019 memory: 25074 loss: 0.1361 2023/06/01 04:56:34 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 04:57:26 - mmengine - INFO - Epoch(train) [9][2000/5758] lr: 2.6215e-03 eta: 15:51:07 time: 0.7930 data_time: 0.0021 memory: 25074 loss: 0.0336 2023/06/01 04:58:47 - mmengine - INFO - Epoch(train) [9][2100/5758] lr: 2.6215e-03 eta: 15:49:36 time: 0.7864 data_time: 0.0017 memory: 25074 loss: 0.0320 2023/06/01 05:00:08 - mmengine - INFO - Epoch(train) [9][2200/5758] lr: 2.6215e-03 eta: 15:48:05 time: 0.8411 data_time: 0.0023 memory: 25074 loss: 0.0256 2023/06/01 05:01:31 - mmengine - INFO - Epoch(train) [9][2300/5758] lr: 2.6215e-03 eta: 15:46:37 time: 0.7890 data_time: 0.0015 memory: 25074 loss: 0.0304 2023/06/01 05:02:52 - mmengine - INFO - Epoch(train) [9][2400/5758] lr: 2.6215e-03 eta: 15:45:07 time: 0.8084 data_time: 0.0020 memory: 25074 loss: 0.0360 2023/06/01 05:04:14 - mmengine - INFO - Epoch(train) [9][2500/5758] lr: 2.6215e-03 eta: 15:43:37 time: 0.7925 data_time: 0.0023 memory: 25074 loss: 0.0213 2023/06/01 05:05:37 - mmengine - INFO - Epoch(train) [9][2600/5758] lr: 2.6215e-03 eta: 15:42:10 time: 0.8228 data_time: 0.0025 memory: 25074 loss: 0.0444 2023/06/01 05:06:58 - mmengine - INFO - Epoch(train) [9][2700/5758] lr: 2.6215e-03 eta: 15:40:39 time: 0.7708 data_time: 0.0022 memory: 25074 loss: 0.0278 2023/06/01 05:08:20 - mmengine - INFO - Epoch(train) [9][2800/5758] lr: 2.6215e-03 eta: 15:39:10 time: 0.8667 data_time: 0.0017 memory: 25074 loss: 0.0206 2023/06/01 05:09:42 - mmengine - INFO - Epoch(train) [9][2900/5758] lr: 2.6215e-03 eta: 15:37:41 time: 0.8383 data_time: 0.0017 memory: 25074 loss: 0.0244 2023/06/01 05:10:11 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 05:11:03 - mmengine - INFO - Epoch(train) [9][3000/5758] lr: 2.6215e-03 eta: 15:36:10 time: 0.8165 data_time: 0.0015 memory: 25074 loss: 0.0269 2023/06/01 05:12:26 - mmengine - INFO - Epoch(train) [9][3100/5758] lr: 2.6215e-03 eta: 15:34:44 time: 0.8457 data_time: 0.0017 memory: 25074 loss: 0.0242 2023/06/01 05:13:49 - mmengine - INFO - Epoch(train) [9][3200/5758] lr: 2.6215e-03 eta: 15:33:15 time: 0.8112 data_time: 0.0014 memory: 25074 loss: 0.0352 2023/06/01 05:15:10 - mmengine - INFO - Epoch(train) [9][3300/5758] lr: 2.6215e-03 eta: 15:31:46 time: 0.8152 data_time: 0.0019 memory: 25074 loss: 0.2452 2023/06/01 05:16:31 - mmengine - INFO - Epoch(train) [9][3400/5758] lr: 2.6215e-03 eta: 15:30:16 time: 0.8276 data_time: 0.0014 memory: 25074 loss: 0.0282 2023/06/01 05:17:54 - mmengine - INFO - Epoch(train) [9][3500/5758] lr: 2.6215e-03 eta: 15:28:47 time: 0.7728 data_time: 0.0015 memory: 25074 loss: 0.0234 2023/06/01 05:19:16 - mmengine - INFO - Epoch(train) [9][3600/5758] lr: 2.6215e-03 eta: 15:27:19 time: 0.8695 data_time: 0.0017 memory: 25074 loss: 0.0234 2023/06/01 05:20:39 - mmengine - INFO - Epoch(train) [9][3700/5758] lr: 2.6215e-03 eta: 15:25:51 time: 0.8025 data_time: 0.0014 memory: 25074 loss: 0.0151 2023/06/01 05:21:57 - mmengine - INFO - Epoch(train) [9][3800/5758] lr: 2.6215e-03 eta: 15:24:17 time: 0.8283 data_time: 0.0017 memory: 25074 loss: 0.0320 2023/06/01 05:23:17 - mmengine - INFO - Epoch(train) [9][3900/5758] lr: 2.6215e-03 eta: 15:22:46 time: 0.7985 data_time: 0.0023 memory: 25074 loss: 0.0139 2023/06/01 05:23:47 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 05:24:39 - mmengine - INFO - Epoch(train) [9][4000/5758] lr: 2.6215e-03 eta: 15:21:16 time: 0.7803 data_time: 0.0020 memory: 25074 loss: 0.0388 2023/06/01 05:25:58 - mmengine - INFO - Epoch(train) [9][4100/5758] lr: 2.6215e-03 eta: 15:19:44 time: 0.7932 data_time: 0.0015 memory: 25074 loss: 0.0264 2023/06/01 05:27:20 - mmengine - INFO - Epoch(train) [9][4200/5758] lr: 2.6215e-03 eta: 15:18:15 time: 0.8094 data_time: 0.0020 memory: 25074 loss: 0.0261 2023/06/01 05:28:41 - mmengine - INFO - Epoch(train) [9][4300/5758] lr: 2.6215e-03 eta: 15:16:46 time: 0.8204 data_time: 0.0025 memory: 25074 loss: 0.0220 2023/06/01 05:30:02 - mmengine - INFO - Epoch(train) [9][4400/5758] lr: 2.6215e-03 eta: 15:15:16 time: 0.7742 data_time: 0.0019 memory: 25074 loss: 0.0302 2023/06/01 05:31:24 - mmengine - INFO - Epoch(train) [9][4500/5758] lr: 2.6215e-03 eta: 15:13:47 time: 0.8320 data_time: 0.0026 memory: 25074 loss: 0.0197 2023/06/01 05:32:44 - mmengine - INFO - Epoch(train) [9][4600/5758] lr: 2.6215e-03 eta: 15:12:16 time: 0.8003 data_time: 0.0019 memory: 25074 loss: 0.0249 2023/06/01 05:34:06 - mmengine - INFO - Epoch(train) [9][4700/5758] lr: 2.6215e-03 eta: 15:10:48 time: 0.8219 data_time: 0.0015 memory: 25074 loss: 0.0292 2023/06/01 05:35:28 - mmengine - INFO - Epoch(train) [9][4800/5758] lr: 2.6215e-03 eta: 15:09:19 time: 0.7957 data_time: 0.0016 memory: 25074 loss: 0.0230 2023/06/01 05:36:48 - mmengine - INFO - Epoch(train) [9][4900/5758] lr: 2.6215e-03 eta: 15:07:48 time: 0.8086 data_time: 0.0019 memory: 25074 loss: 0.0224 2023/06/01 05:37:18 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 05:38:10 - mmengine - INFO - Epoch(train) [9][5000/5758] lr: 2.6215e-03 eta: 15:06:19 time: 0.7607 data_time: 0.0018 memory: 25074 loss: 0.0186 2023/06/01 05:39:32 - mmengine - INFO - Epoch(train) [9][5100/5758] lr: 2.6215e-03 eta: 15:04:51 time: 0.8408 data_time: 0.0020 memory: 25074 loss: 0.0331 2023/06/01 05:40:53 - mmengine - INFO - Epoch(train) [9][5200/5758] lr: 2.6215e-03 eta: 15:03:21 time: 0.8428 data_time: 0.0016 memory: 25074 loss: 0.0149 2023/06/01 05:42:13 - mmengine - INFO - Epoch(train) [9][5300/5758] lr: 2.6215e-03 eta: 15:01:51 time: 0.8012 data_time: 0.0027 memory: 25074 loss: 0.0286 2023/06/01 05:43:35 - mmengine - INFO - Epoch(train) [9][5400/5758] lr: 2.6215e-03 eta: 15:00:22 time: 0.8069 data_time: 0.0024 memory: 25074 loss: 0.0266 2023/06/01 05:44:55 - mmengine - INFO - Epoch(train) [9][5500/5758] lr: 2.6215e-03 eta: 14:58:52 time: 0.8241 data_time: 0.0019 memory: 25074 loss: 0.0203 2023/06/01 05:46:17 - mmengine - INFO - Epoch(train) [9][5600/5758] lr: 2.6215e-03 eta: 14:57:23 time: 0.8194 data_time: 0.0019 memory: 25074 loss: 0.0232 2023/06/01 05:47:39 - mmengine - INFO - Epoch(train) [9][5700/5758] lr: 2.6215e-03 eta: 14:55:56 time: 0.8480 data_time: 0.0022 memory: 25074 loss: 0.0144 2023/06/01 05:48:26 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 05:48:26 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/06/01 05:48:45 - mmengine - INFO - Epoch(val) [9][16/16] accuracy/top1: 99.3308 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [99.33084869384766, 0.0] single-label/f1-score_classwise: [99.664306640625, 0.0] data_time: 0.2375 time: 0.6248 2023/06/01 05:50:14 - mmengine - INFO - Epoch(train) [10][ 100/5758] lr: 2.3171e-03 eta: 14:53:44 time: 0.8207 data_time: 0.0020 memory: 25074 loss: 0.0175 2023/06/01 05:51:18 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 05:51:36 - mmengine - INFO - Epoch(train) [10][ 200/5758] lr: 2.3171e-03 eta: 14:52:16 time: 0.7813 data_time: 0.0016 memory: 25074 loss: 0.0177 2023/06/01 05:52:57 - mmengine - INFO - Epoch(train) [10][ 300/5758] lr: 2.3171e-03 eta: 14:50:46 time: 0.8036 data_time: 0.0025 memory: 25074 loss: 0.0118 2023/06/01 05:54:20 - mmengine - INFO - Epoch(train) [10][ 400/5758] lr: 2.3171e-03 eta: 14:49:19 time: 0.7797 data_time: 0.0017 memory: 25074 loss: 0.0121 2023/06/01 05:55:40 - mmengine - INFO - Epoch(train) [10][ 500/5758] lr: 2.3171e-03 eta: 14:47:49 time: 0.8295 data_time: 0.0017 memory: 25074 loss: 0.0131 2023/06/01 05:57:03 - mmengine - INFO - Epoch(train) [10][ 600/5758] lr: 2.3171e-03 eta: 14:46:22 time: 0.8365 data_time: 0.0016 memory: 25074 loss: 0.0196 2023/06/01 05:58:25 - mmengine - INFO - Epoch(train) [10][ 700/5758] lr: 2.3171e-03 eta: 14:44:54 time: 0.8344 data_time: 0.0026 memory: 25074 loss: 0.0111 2023/06/01 05:59:45 - mmengine - INFO - Epoch(train) [10][ 800/5758] lr: 2.3171e-03 eta: 14:43:24 time: 0.7452 data_time: 0.0020 memory: 25074 loss: 0.0180 2023/06/01 06:01:09 - mmengine - INFO - Epoch(train) [10][ 900/5758] lr: 2.3171e-03 eta: 14:41:58 time: 0.7868 data_time: 0.0018 memory: 25074 loss: 0.0188 2023/06/01 06:02:29 - mmengine - INFO - Epoch(train) [10][1000/5758] lr: 2.3171e-03 eta: 14:40:28 time: 0.7551 data_time: 0.0026 memory: 25074 loss: 0.0201 2023/06/01 06:03:50 - mmengine - INFO - Epoch(train) [10][1100/5758] lr: 2.3171e-03 eta: 14:38:58 time: 0.7882 data_time: 0.0021 memory: 25074 loss: 0.0149 2023/06/01 06:04:54 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 06:05:12 - mmengine - INFO - Epoch(train) [10][1200/5758] lr: 2.3171e-03 eta: 14:37:30 time: 0.8209 data_time: 0.0020 memory: 25074 loss: 0.0153 2023/06/01 06:06:33 - mmengine - INFO - Epoch(train) [10][1300/5758] lr: 2.3171e-03 eta: 14:36:02 time: 0.7906 data_time: 0.0017 memory: 25074 loss: 0.0131 2023/06/01 06:07:56 - mmengine - INFO - Epoch(train) [10][1400/5758] lr: 2.3171e-03 eta: 14:34:34 time: 0.7908 data_time: 0.0018 memory: 25074 loss: 0.0138 2023/06/01 06:09:16 - mmengine - INFO - Epoch(train) [10][1500/5758] lr: 2.3171e-03 eta: 14:33:04 time: 0.7822 data_time: 0.0022 memory: 25074 loss: 0.0182 2023/06/01 06:10:39 - mmengine - INFO - Epoch(train) [10][1600/5758] lr: 2.3171e-03 eta: 14:31:37 time: 0.8082 data_time: 0.0027 memory: 25074 loss: 0.0141 2023/06/01 06:12:00 - mmengine - INFO - Epoch(train) [10][1700/5758] lr: 2.3171e-03 eta: 14:30:08 time: 0.8055 data_time: 0.0020 memory: 25074 loss: 0.0160 2023/06/01 06:13:23 - mmengine - INFO - Epoch(train) [10][1800/5758] lr: 2.3171e-03 eta: 14:28:42 time: 0.8374 data_time: 0.0020 memory: 25074 loss: 0.0092 2023/06/01 06:14:47 - mmengine - INFO - Epoch(train) [10][1900/5758] lr: 2.3171e-03 eta: 14:27:16 time: 0.8446 data_time: 0.0017 memory: 25074 loss: 0.0152 2023/06/01 06:16:09 - mmengine - INFO - Epoch(train) [10][2000/5758] lr: 2.3171e-03 eta: 14:25:48 time: 0.8119 data_time: 0.0019 memory: 25074 loss: 0.0116 2023/06/01 06:17:32 - mmengine - INFO - Epoch(train) [10][2100/5758] lr: 2.3171e-03 eta: 14:24:22 time: 0.8511 data_time: 0.0022 memory: 25074 loss: 0.0124 2023/06/01 06:18:36 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 06:18:54 - mmengine - INFO - Epoch(train) [10][2200/5758] lr: 2.3171e-03 eta: 14:22:54 time: 0.8317 data_time: 0.0018 memory: 25074 loss: 0.0117 2023/06/01 06:20:17 - mmengine - INFO - Epoch(train) [10][2300/5758] lr: 2.3171e-03 eta: 14:21:27 time: 0.8639 data_time: 0.0026 memory: 25074 loss: 0.0246 2023/06/01 06:21:39 - mmengine - INFO - Epoch(train) [10][2400/5758] lr: 2.3171e-03 eta: 14:20:00 time: 0.7997 data_time: 0.0022 memory: 25074 loss: 0.0215 2023/06/01 06:23:00 - mmengine - INFO - Epoch(train) [10][2500/5758] lr: 2.3171e-03 eta: 14:18:31 time: 0.8154 data_time: 0.0026 memory: 25074 loss: 0.0185 2023/06/01 06:24:21 - mmengine - INFO - Epoch(train) [10][2600/5758] lr: 2.3171e-03 eta: 14:17:02 time: 0.8356 data_time: 0.0022 memory: 25074 loss: 0.6881 2023/06/01 06:25:43 - mmengine - INFO - Epoch(train) [10][2700/5758] lr: 2.3171e-03 eta: 14:15:34 time: 0.8412 data_time: 0.0021 memory: 25074 loss: 0.6841 2023/06/01 06:27:05 - mmengine - INFO - Epoch(train) [10][2800/5758] lr: 2.3171e-03 eta: 14:14:07 time: 0.8091 data_time: 0.0022 memory: 25074 loss: 0.6862 2023/06/01 06:28:28 - mmengine - INFO - Epoch(train) [10][2900/5758] lr: 2.3171e-03 eta: 14:12:40 time: 0.8718 data_time: 0.0019 memory: 25074 loss: 0.6753 2023/06/01 06:29:50 - mmengine - INFO - Epoch(train) [10][3000/5758] lr: 2.3171e-03 eta: 14:11:13 time: 0.8286 data_time: 0.0019 memory: 25074 loss: 0.5613 2023/06/01 06:31:12 - mmengine - INFO - Epoch(train) [10][3100/5758] lr: 2.3171e-03 eta: 14:09:45 time: 0.8195 data_time: 0.0024 memory: 25074 loss: 0.4973 2023/06/01 06:32:16 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 06:32:33 - mmengine - INFO - Epoch(train) [10][3200/5758] lr: 2.3171e-03 eta: 14:08:17 time: 0.8235 data_time: 0.0015 memory: 25074 loss: 0.4726 2023/06/01 06:33:55 - mmengine - INFO - Epoch(train) [10][3300/5758] lr: 2.3171e-03 eta: 14:06:49 time: 0.7902 data_time: 0.0016 memory: 25074 loss: 0.4224 2023/06/01 06:35:18 - mmengine - INFO - Epoch(train) [10][3400/5758] lr: 2.3171e-03 eta: 14:05:22 time: 0.7914 data_time: 0.0023 memory: 25074 loss: 0.4256 2023/06/01 06:36:41 - mmengine - INFO - Epoch(train) [10][3500/5758] lr: 2.3171e-03 eta: 14:03:56 time: 0.8414 data_time: 0.0018 memory: 25074 loss: 0.3666 2023/06/01 06:38:03 - mmengine - INFO - Epoch(train) [10][3600/5758] lr: 2.3171e-03 eta: 14:02:29 time: 0.7918 data_time: 0.0016 memory: 25074 loss: 0.3317 2023/06/01 06:39:27 - mmengine - INFO - Epoch(train) [10][3700/5758] lr: 2.3171e-03 eta: 14:01:03 time: 0.8647 data_time: 0.0022 memory: 25074 loss: 0.2783 2023/06/01 06:40:49 - mmengine - INFO - Epoch(train) [10][3800/5758] lr: 2.3171e-03 eta: 13:59:36 time: 0.7787 data_time: 0.0023 memory: 25074 loss: 0.2840 2023/06/01 06:42:12 - mmengine - INFO - Epoch(train) [10][3900/5758] lr: 2.3171e-03 eta: 13:58:09 time: 0.8275 data_time: 0.0023 memory: 25074 loss: 0.2700 2023/06/01 06:43:34 - mmengine - INFO - Epoch(train) [10][4000/5758] lr: 2.3171e-03 eta: 13:56:42 time: 0.8666 data_time: 0.0025 memory: 25074 loss: 0.2077 2023/06/01 06:44:56 - mmengine - INFO - Epoch(train) [10][4100/5758] lr: 2.3171e-03 eta: 13:55:15 time: 0.8322 data_time: 0.0022 memory: 25074 loss: 0.1953 2023/06/01 06:46:00 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 06:46:18 - mmengine - INFO - Epoch(train) [10][4200/5758] lr: 2.3171e-03 eta: 13:53:48 time: 0.8302 data_time: 0.0020 memory: 25074 loss: 0.1650 2023/06/01 06:47:39 - mmengine - INFO - Epoch(train) [10][4300/5758] lr: 2.3171e-03 eta: 13:52:19 time: 0.7660 data_time: 0.0022 memory: 25074 loss: 0.1104 2023/06/01 06:49:02 - mmengine - INFO - Epoch(train) [10][4400/5758] lr: 2.3171e-03 eta: 13:50:53 time: 0.9145 data_time: 0.0023 memory: 25074 loss: 0.0837 2023/06/01 06:50:25 - mmengine - INFO - Epoch(train) [10][4500/5758] lr: 2.3171e-03 eta: 13:49:27 time: 0.8665 data_time: 0.0025 memory: 25074 loss: 0.0865 2023/06/01 06:51:48 - mmengine - INFO - Epoch(train) [10][4600/5758] lr: 2.3171e-03 eta: 13:48:01 time: 0.9035 data_time: 0.0022 memory: 25074 loss: 0.0692 2023/06/01 06:53:09 - mmengine - INFO - Epoch(train) [10][4700/5758] lr: 2.3171e-03 eta: 13:46:33 time: 0.8001 data_time: 0.0015 memory: 25074 loss: 0.0423 2023/06/01 06:54:32 - mmengine - INFO - Epoch(train) [10][4800/5758] lr: 2.3171e-03 eta: 13:45:06 time: 0.8566 data_time: 0.0020 memory: 25074 loss: 0.0463 2023/06/01 06:55:53 - mmengine - INFO - Epoch(train) [10][4900/5758] lr: 2.3171e-03 eta: 13:43:38 time: 0.8226 data_time: 0.0017 memory: 25074 loss: 0.0312 2023/06/01 06:57:14 - mmengine - INFO - Epoch(train) [10][5000/5758] lr: 2.3171e-03 eta: 13:42:10 time: 0.8224 data_time: 0.0026 memory: 25074 loss: 0.0410 2023/06/01 06:58:35 - mmengine - INFO - Epoch(train) [10][5100/5758] lr: 2.3171e-03 eta: 13:40:41 time: 0.7936 data_time: 0.0020 memory: 25074 loss: 0.0424 2023/06/01 06:59:39 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 06:59:57 - mmengine - INFO - Epoch(train) [10][5200/5758] lr: 2.3171e-03 eta: 13:39:14 time: 0.7964 data_time: 0.0025 memory: 25074 loss: 0.0250 2023/06/01 07:01:19 - mmengine - INFO - Epoch(train) [10][5300/5758] lr: 2.3171e-03 eta: 13:37:47 time: 0.8108 data_time: 0.0023 memory: 25074 loss: 0.0514 2023/06/01 07:02:42 - mmengine - INFO - Epoch(train) [10][5400/5758] lr: 2.3171e-03 eta: 13:36:21 time: 0.8978 data_time: 0.0021 memory: 25074 loss: 0.0274 2023/06/01 07:04:04 - mmengine - INFO - Epoch(train) [10][5500/5758] lr: 2.3171e-03 eta: 13:34:54 time: 0.8460 data_time: 0.0024 memory: 25074 loss: 0.0313 2023/06/01 07:05:27 - mmengine - INFO - Epoch(train) [10][5600/5758] lr: 2.3171e-03 eta: 13:33:28 time: 0.8046 data_time: 0.0026 memory: 25074 loss: 0.0202 2023/06/01 07:06:49 - mmengine - INFO - Epoch(train) [10][5700/5758] lr: 2.3171e-03 eta: 13:32:00 time: 0.8253 data_time: 0.0024 memory: 25074 loss: 0.0205 2023/06/01 07:07:36 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 07:07:36 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/06/01 07:07:54 - mmengine - INFO - Epoch(val) [10][16/16] accuracy/top1: 100.0000 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [100.0, 0.0] single-label/f1-score_classwise: [100.0, 0.0] data_time: 0.2228 time: 0.6108 2023/06/01 07:09:25 - mmengine - INFO - Epoch(train) [11][ 100/5758] lr: 2.0050e-03 eta: 13:29:51 time: 0.7896 data_time: 0.0026 memory: 25074 loss: 0.0207 2023/06/01 07:10:48 - mmengine - INFO - Epoch(train) [11][ 200/5758] lr: 2.0050e-03 eta: 13:28:24 time: 0.8085 data_time: 0.0021 memory: 25074 loss: 0.0184 2023/06/01 07:12:07 - mmengine - INFO - Epoch(train) [11][ 300/5758] lr: 2.0050e-03 eta: 13:26:55 time: 0.7892 data_time: 0.0019 memory: 25074 loss: 0.0260 2023/06/01 07:13:29 - mmengine - INFO - Epoch(train) [11][ 400/5758] lr: 2.0050e-03 eta: 13:25:28 time: 0.8114 data_time: 0.0018 memory: 25074 loss: 0.0138 2023/06/01 07:13:46 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 07:14:51 - mmengine - INFO - Epoch(train) [11][ 500/5758] lr: 2.0050e-03 eta: 13:24:01 time: 0.8287 data_time: 0.0017 memory: 25074 loss: 0.0174 2023/06/01 07:16:13 - mmengine - INFO - Epoch(train) [11][ 600/5758] lr: 2.0050e-03 eta: 13:22:34 time: 0.7944 data_time: 0.0019 memory: 25074 loss: 0.0243 2023/06/01 07:17:35 - mmengine - INFO - Epoch(train) [11][ 700/5758] lr: 2.0050e-03 eta: 13:21:07 time: 0.7873 data_time: 0.0029 memory: 25074 loss: 0.0201 2023/06/01 07:18:57 - mmengine - INFO - Epoch(train) [11][ 800/5758] lr: 2.0050e-03 eta: 13:19:40 time: 0.8720 data_time: 0.0029 memory: 25074 loss: 0.0144 2023/06/01 07:20:21 - mmengine - INFO - Epoch(train) [11][ 900/5758] lr: 2.0050e-03 eta: 13:18:15 time: 0.8665 data_time: 0.0025 memory: 25074 loss: 0.0190 2023/06/01 07:21:43 - mmengine - INFO - Epoch(train) [11][1000/5758] lr: 2.0050e-03 eta: 13:16:48 time: 0.7974 data_time: 0.0020 memory: 25074 loss: 0.0185 2023/06/01 07:23:05 - mmengine - INFO - Epoch(train) [11][1100/5758] lr: 2.0050e-03 eta: 13:15:21 time: 0.7817 data_time: 0.0018 memory: 25074 loss: 0.0580 2023/06/01 07:24:27 - mmengine - INFO - Epoch(train) [11][1200/5758] lr: 2.0050e-03 eta: 13:13:54 time: 0.8420 data_time: 0.0017 memory: 25074 loss: 0.0130 2023/06/01 07:25:49 - mmengine - INFO - Epoch(train) [11][1300/5758] lr: 2.0050e-03 eta: 13:12:28 time: 0.7899 data_time: 0.0022 memory: 25074 loss: 0.0250 2023/06/01 07:27:12 - mmengine - INFO - Epoch(train) [11][1400/5758] lr: 2.0050e-03 eta: 13:11:01 time: 0.8453 data_time: 0.0021 memory: 25074 loss: 0.0197 2023/06/01 07:27:28 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 07:28:33 - mmengine - INFO - Epoch(train) [11][1500/5758] lr: 2.0050e-03 eta: 13:09:34 time: 0.7987 data_time: 0.0023 memory: 25074 loss: 0.0098 2023/06/01 07:29:56 - mmengine - INFO - Epoch(train) [11][1600/5758] lr: 2.0050e-03 eta: 13:08:07 time: 0.8288 data_time: 0.0019 memory: 25074 loss: 0.0116 2023/06/01 07:31:19 - mmengine - INFO - Epoch(train) [11][1700/5758] lr: 2.0050e-03 eta: 13:06:42 time: 0.8637 data_time: 0.0020 memory: 25074 loss: 0.0153 2023/06/01 07:32:41 - mmengine - INFO - Epoch(train) [11][1800/5758] lr: 2.0050e-03 eta: 13:05:15 time: 0.8098 data_time: 0.0022 memory: 25074 loss: 0.0148 2023/06/01 07:34:02 - mmengine - INFO - Epoch(train) [11][1900/5758] lr: 2.0050e-03 eta: 13:03:47 time: 0.8413 data_time: 0.0025 memory: 25074 loss: 0.0159 2023/06/01 07:35:25 - mmengine - INFO - Epoch(train) [11][2000/5758] lr: 2.0050e-03 eta: 13:02:21 time: 0.7999 data_time: 0.0022 memory: 25074 loss: 0.5658 2023/06/01 07:36:47 - mmengine - INFO - Epoch(train) [11][2100/5758] lr: 2.0050e-03 eta: 13:00:55 time: 0.8692 data_time: 0.0019 memory: 25074 loss: 0.4891 2023/06/01 07:38:09 - mmengine - INFO - Epoch(train) [11][2200/5758] lr: 2.0050e-03 eta: 12:59:28 time: 0.8257 data_time: 0.0017 memory: 25074 loss: 0.4329 2023/06/01 07:39:32 - mmengine - INFO - Epoch(train) [11][2300/5758] lr: 2.0050e-03 eta: 12:58:02 time: 0.8492 data_time: 0.0016 memory: 25074 loss: 0.3923 2023/06/01 07:40:53 - mmengine - INFO - Epoch(train) [11][2400/5758] lr: 2.0050e-03 eta: 12:56:34 time: 0.8267 data_time: 0.0022 memory: 25074 loss: 0.3799 2023/06/01 07:41:09 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 07:42:14 - mmengine - INFO - Epoch(train) [11][2500/5758] lr: 2.0050e-03 eta: 12:55:07 time: 0.7978 data_time: 0.0022 memory: 25074 loss: 0.3877 2023/06/01 07:43:37 - mmengine - INFO - Epoch(train) [11][2600/5758] lr: 2.0050e-03 eta: 12:53:41 time: 0.8705 data_time: 0.0024 memory: 25074 loss: 0.3053 2023/06/01 07:44:59 - mmengine - INFO - Epoch(train) [11][2700/5758] lr: 2.0050e-03 eta: 12:52:15 time: 0.7852 data_time: 0.0021 memory: 25074 loss: 0.2704 2023/06/01 07:46:22 - mmengine - INFO - Epoch(train) [11][2800/5758] lr: 2.0050e-03 eta: 12:50:49 time: 0.8448 data_time: 0.0024 memory: 25074 loss: 0.2515 2023/06/01 07:47:42 - mmengine - INFO - Epoch(train) [11][2900/5758] lr: 2.0050e-03 eta: 12:49:20 time: 0.8257 data_time: 0.0015 memory: 25074 loss: 0.1319 2023/06/01 07:48:59 - mmengine - INFO - Epoch(train) [11][3000/5758] lr: 2.0050e-03 eta: 12:47:49 time: 0.7994 data_time: 0.0015 memory: 25074 loss: 0.0617 2023/06/01 07:50:18 - mmengine - INFO - Epoch(train) [11][3100/5758] lr: 2.0050e-03 eta: 12:46:20 time: 0.8148 data_time: 0.0015 memory: 25074 loss: 0.0287 2023/06/01 07:51:40 - mmengine - INFO - Epoch(train) [11][3200/5758] lr: 2.0050e-03 eta: 12:44:53 time: 0.7376 data_time: 0.0014 memory: 25074 loss: 0.0306 2023/06/01 07:53:02 - mmengine - INFO - Epoch(train) [11][3300/5758] lr: 2.0050e-03 eta: 12:43:26 time: 0.8499 data_time: 0.0015 memory: 25074 loss: 0.0208 2023/06/01 07:54:23 - mmengine - INFO - Epoch(train) [11][3400/5758] lr: 2.0050e-03 eta: 12:42:00 time: 0.8704 data_time: 0.0022 memory: 25074 loss: 0.0297 2023/06/01 07:54:39 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 07:55:44 - mmengine - INFO - Epoch(train) [11][3500/5758] lr: 2.0050e-03 eta: 12:40:32 time: 0.8545 data_time: 0.0023 memory: 25074 loss: 0.0147 2023/06/01 07:57:07 - mmengine - INFO - Epoch(train) [11][3600/5758] lr: 2.0050e-03 eta: 12:39:07 time: 0.8331 data_time: 0.0019 memory: 25074 loss: 0.0234 2023/06/01 07:58:31 - mmengine - INFO - Epoch(train) [11][3700/5758] lr: 2.0050e-03 eta: 12:37:41 time: 0.8547 data_time: 0.0018 memory: 25074 loss: 0.0217 2023/06/01 07:59:54 - mmengine - INFO - Epoch(train) [11][3800/5758] lr: 2.0050e-03 eta: 12:36:16 time: 0.7948 data_time: 0.0015 memory: 25074 loss: 0.0193 2023/06/01 08:01:18 - mmengine - INFO - Epoch(train) [11][3900/5758] lr: 2.0050e-03 eta: 12:34:51 time: 0.8642 data_time: 0.0017 memory: 25074 loss: 0.0179 2023/06/01 08:02:42 - mmengine - INFO - Epoch(train) [11][4000/5758] lr: 2.0050e-03 eta: 12:33:27 time: 0.8782 data_time: 0.0024 memory: 25074 loss: 0.0182 2023/06/01 08:04:05 - mmengine - INFO - Epoch(train) [11][4100/5758] lr: 2.0050e-03 eta: 12:32:01 time: 0.8442 data_time: 0.0015 memory: 25074 loss: 0.0158 2023/06/01 08:05:29 - mmengine - INFO - Epoch(train) [11][4200/5758] lr: 2.0050e-03 eta: 12:30:37 time: 0.8643 data_time: 0.0021 memory: 25074 loss: 0.0352 2023/06/01 08:06:55 - mmengine - INFO - Epoch(train) [11][4300/5758] lr: 2.0050e-03 eta: 12:29:13 time: 0.9276 data_time: 0.0023 memory: 25074 loss: 0.0172 2023/06/01 08:08:18 - mmengine - INFO - Epoch(train) [11][4400/5758] lr: 2.0050e-03 eta: 12:27:47 time: 0.8090 data_time: 0.0021 memory: 25074 loss: 0.0138 2023/06/01 08:08:35 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 08:09:43 - mmengine - INFO - Epoch(train) [11][4500/5758] lr: 2.0050e-03 eta: 12:26:24 time: 0.8844 data_time: 0.0017 memory: 25074 loss: 0.0128 2023/06/01 08:11:07 - mmengine - INFO - Epoch(train) [11][4600/5758] lr: 2.0050e-03 eta: 12:24:59 time: 0.8389 data_time: 0.0018 memory: 25074 loss: 0.0173 2023/06/01 08:12:32 - mmengine - INFO - Epoch(train) [11][4700/5758] lr: 2.0050e-03 eta: 12:23:36 time: 0.8496 data_time: 0.0015 memory: 25074 loss: 0.0152 2023/06/01 08:13:59 - mmengine - INFO - Epoch(train) [11][4800/5758] lr: 2.0050e-03 eta: 12:22:13 time: 0.9049 data_time: 0.0027 memory: 25074 loss: 0.0134 2023/06/01 08:15:23 - mmengine - INFO - Epoch(train) [11][4900/5758] lr: 2.0050e-03 eta: 12:20:48 time: 0.8099 data_time: 0.0019 memory: 25074 loss: 0.0127 2023/06/01 08:16:49 - mmengine - INFO - Epoch(train) [11][5000/5758] lr: 2.0050e-03 eta: 12:19:25 time: 0.8768 data_time: 0.0014 memory: 25074 loss: 0.0189 2023/06/01 08:18:13 - mmengine - INFO - Epoch(train) [11][5100/5758] lr: 2.0050e-03 eta: 12:18:01 time: 0.8187 data_time: 0.0022 memory: 25074 loss: 0.0098 2023/06/01 08:19:37 - mmengine - INFO - Epoch(train) [11][5200/5758] lr: 2.0050e-03 eta: 12:16:36 time: 0.8418 data_time: 0.0021 memory: 25074 loss: 0.0167 2023/06/01 08:21:01 - mmengine - INFO - Epoch(train) [11][5300/5758] lr: 2.0050e-03 eta: 12:15:12 time: 0.8278 data_time: 0.0017 memory: 25074 loss: 0.0103 2023/06/01 08:22:25 - mmengine - INFO - Epoch(train) [11][5400/5758] lr: 2.0050e-03 eta: 12:13:47 time: 0.8843 data_time: 0.0020 memory: 25074 loss: 0.0097 2023/06/01 08:22:42 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 08:23:49 - mmengine - INFO - Epoch(train) [11][5500/5758] lr: 2.0050e-03 eta: 12:12:22 time: 0.7989 data_time: 0.0020 memory: 25074 loss: 0.0108 2023/06/01 08:25:13 - mmengine - INFO - Epoch(train) [11][5600/5758] lr: 2.0050e-03 eta: 12:10:57 time: 0.8645 data_time: 0.0017 memory: 25074 loss: 0.0130 2023/06/01 08:26:37 - mmengine - INFO - Epoch(train) [11][5700/5758] lr: 2.0050e-03 eta: 12:09:32 time: 0.7989 data_time: 0.0015 memory: 25074 loss: 0.0108 2023/06/01 08:27:24 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 08:27:24 - mmengine - INFO - Saving checkpoint at 11 epochs 2023/06/01 08:27:44 - mmengine - INFO - Epoch(val) [11][16/16] accuracy/top1: 100.0000 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [100.0, 0.0] single-label/f1-score_classwise: [100.0, 0.0] data_time: 0.2367 time: 0.6233 2023/06/01 08:29:15 - mmengine - INFO - Epoch(train) [12][ 100/5758] lr: 1.6929e-03 eta: 12:07:24 time: 0.8847 data_time: 0.0017 memory: 25074 loss: 0.0126 2023/06/01 08:30:41 - mmengine - INFO - Epoch(train) [12][ 200/5758] lr: 1.6929e-03 eta: 12:06:01 time: 0.8630 data_time: 0.0022 memory: 25074 loss: 0.0124 2023/06/01 08:32:05 - mmengine - INFO - Epoch(train) [12][ 300/5758] lr: 1.6929e-03 eta: 12:04:36 time: 0.8486 data_time: 0.0022 memory: 25074 loss: 0.0069 2023/06/01 08:33:28 - mmengine - INFO - Epoch(train) [12][ 400/5758] lr: 1.6929e-03 eta: 12:03:11 time: 0.8659 data_time: 0.0014 memory: 25074 loss: 0.0096 2023/06/01 08:34:52 - mmengine - INFO - Epoch(train) [12][ 500/5758] lr: 1.6929e-03 eta: 12:01:46 time: 0.8526 data_time: 0.0019 memory: 25074 loss: 0.0052 2023/06/01 08:36:17 - mmengine - INFO - Epoch(train) [12][ 600/5758] lr: 1.6929e-03 eta: 12:00:22 time: 0.8559 data_time: 0.0015 memory: 25074 loss: 0.5257 2023/06/01 08:37:09 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 08:37:41 - mmengine - INFO - Epoch(train) [12][ 700/5758] lr: 1.6929e-03 eta: 11:58:57 time: 0.8218 data_time: 0.0024 memory: 25074 loss: 0.0174 2023/06/01 08:39:06 - mmengine - INFO - Epoch(train) [12][ 800/5758] lr: 1.6929e-03 eta: 11:57:34 time: 0.8379 data_time: 0.0019 memory: 25074 loss: 0.0184 2023/06/01 08:40:31 - mmengine - INFO - Epoch(train) [12][ 900/5758] lr: 1.6929e-03 eta: 11:56:09 time: 0.8501 data_time: 0.0016 memory: 25074 loss: 0.0157 2023/06/01 08:41:56 - mmengine - INFO - Epoch(train) [12][1000/5758] lr: 1.6929e-03 eta: 11:54:45 time: 0.8576 data_time: 0.0016 memory: 25074 loss: 0.0174 2023/06/01 08:43:19 - mmengine - INFO - Epoch(train) [12][1100/5758] lr: 1.6929e-03 eta: 11:53:20 time: 0.8440 data_time: 0.0018 memory: 25074 loss: 0.0087 2023/06/01 08:44:45 - mmengine - INFO - Epoch(train) [12][1200/5758] lr: 1.6929e-03 eta: 11:51:57 time: 0.8774 data_time: 0.0017 memory: 25074 loss: 0.0048 2023/06/01 08:46:11 - mmengine - INFO - Epoch(train) [12][1300/5758] lr: 1.6929e-03 eta: 11:50:33 time: 0.8194 data_time: 0.0016 memory: 25074 loss: 0.0122 2023/06/01 08:47:36 - mmengine - INFO - Epoch(train) [12][1400/5758] lr: 1.6929e-03 eta: 11:49:10 time: 0.8386 data_time: 0.0014 memory: 25074 loss: 0.0067 2023/06/01 08:49:00 - mmengine - INFO - Epoch(train) [12][1500/5758] lr: 1.6929e-03 eta: 11:47:45 time: 0.8878 data_time: 0.0017 memory: 25074 loss: 0.0128 2023/06/01 08:50:21 - mmengine - INFO - Epoch(train) [12][1600/5758] lr: 1.6929e-03 eta: 11:46:18 time: 0.7722 data_time: 0.0016 memory: 25074 loss: 0.0047 2023/06/01 08:51:10 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 08:51:42 - mmengine - INFO - Epoch(train) [12][1700/5758] lr: 1.6929e-03 eta: 11:44:51 time: 0.8705 data_time: 0.0017 memory: 25074 loss: 0.0095 2023/06/01 08:53:08 - mmengine - INFO - Epoch(train) [12][1800/5758] lr: 1.6929e-03 eta: 11:43:28 time: 0.8121 data_time: 0.0017 memory: 25074 loss: 0.0117 2023/06/01 08:54:34 - mmengine - INFO - Epoch(train) [12][1900/5758] lr: 1.6929e-03 eta: 11:42:04 time: 0.8418 data_time: 0.0023 memory: 25074 loss: 0.0113 2023/06/01 08:55:59 - mmengine - INFO - Epoch(train) [12][2000/5758] lr: 1.6929e-03 eta: 11:40:41 time: 0.8386 data_time: 0.0016 memory: 25074 loss: 0.0080 2023/06/01 08:57:24 - mmengine - INFO - Epoch(train) [12][2100/5758] lr: 1.6929e-03 eta: 11:39:17 time: 0.9014 data_time: 0.0015 memory: 25074 loss: 0.0139 2023/06/01 08:58:48 - mmengine - INFO - Epoch(train) [12][2200/5758] lr: 1.6929e-03 eta: 11:37:52 time: 0.8340 data_time: 0.0015 memory: 25074 loss: 0.0080 2023/06/01 09:00:13 - mmengine - INFO - Epoch(train) [12][2300/5758] lr: 1.6929e-03 eta: 11:36:29 time: 0.8477 data_time: 0.0015 memory: 25074 loss: 0.0104 2023/06/01 09:01:37 - mmengine - INFO - Epoch(train) [12][2400/5758] lr: 1.6929e-03 eta: 11:35:04 time: 0.8631 data_time: 0.0014 memory: 25074 loss: 0.0102 2023/06/01 09:03:02 - mmengine - INFO - Epoch(train) [12][2500/5758] lr: 1.6929e-03 eta: 11:33:40 time: 0.8444 data_time: 0.0019 memory: 25074 loss: 0.0047 2023/06/01 09:04:26 - mmengine - INFO - Epoch(train) [12][2600/5758] lr: 1.6929e-03 eta: 11:32:15 time: 0.8416 data_time: 0.0015 memory: 25074 loss: 0.0079 2023/06/01 09:05:19 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 09:05:52 - mmengine - INFO - Epoch(train) [12][2700/5758] lr: 1.6929e-03 eta: 11:30:52 time: 0.8767 data_time: 0.0015 memory: 25074 loss: 0.0047 2023/06/01 09:07:17 - mmengine - INFO - Epoch(train) [12][2800/5758] lr: 1.6929e-03 eta: 11:29:28 time: 0.7927 data_time: 0.0017 memory: 25074 loss: 0.6126 2023/06/01 09:08:41 - mmengine - INFO - Epoch(train) [12][2900/5758] lr: 1.6929e-03 eta: 11:28:03 time: 0.7909 data_time: 0.0015 memory: 25074 loss: 0.6206 2023/06/01 09:10:06 - mmengine - INFO - Epoch(train) [12][3000/5758] lr: 1.6929e-03 eta: 11:26:39 time: 0.8380 data_time: 0.0015 memory: 25074 loss: 0.5530 2023/06/01 09:11:30 - mmengine - INFO - Epoch(train) [12][3100/5758] lr: 1.6929e-03 eta: 11:25:15 time: 0.8861 data_time: 0.0016 memory: 25074 loss: 0.4458 2023/06/01 09:13:20 - mmengine - INFO - Epoch(train) [12][3200/5758] lr: 1.6929e-03 eta: 11:24:09 time: 2.0128 data_time: 0.0019 memory: 25074 loss: 0.3750 2023/06/01 09:14:57 - mmengine - INFO - Epoch(train) [12][3300/5758] lr: 1.6929e-03 eta: 11:22:53 time: 0.8876 data_time: 0.0028 memory: 25074 loss: 0.3053 2023/06/01 09:16:22 - mmengine - INFO - Epoch(train) [12][3400/5758] lr: 1.6929e-03 eta: 11:21:29 time: 0.8399 data_time: 0.0019 memory: 25074 loss: 0.2901 2023/06/01 09:17:45 - mmengine - INFO - Epoch(train) [12][3500/5758] lr: 1.6929e-03 eta: 11:20:04 time: 0.8448 data_time: 0.0019 memory: 25074 loss: 0.3042 2023/06/01 09:19:10 - mmengine - INFO - Epoch(train) [12][3600/5758] lr: 1.6929e-03 eta: 11:18:40 time: 0.8764 data_time: 0.0027 memory: 25074 loss: 0.2410 2023/06/01 09:20:02 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 09:20:35 - mmengine - INFO - Epoch(train) [12][3700/5758] lr: 1.6929e-03 eta: 11:17:16 time: 0.8524 data_time: 0.0022 memory: 25074 loss: 0.2603 2023/06/01 09:22:00 - mmengine - INFO - Epoch(train) [12][3800/5758] lr: 1.6929e-03 eta: 11:15:52 time: 0.8215 data_time: 0.0017 memory: 25074 loss: 0.2199 2023/06/01 09:23:24 - mmengine - INFO - Epoch(train) [12][3900/5758] lr: 1.6929e-03 eta: 11:14:27 time: 0.8601 data_time: 0.0017 memory: 25074 loss: 0.1977 2023/06/01 09:24:50 - mmengine - INFO - Epoch(train) [12][4000/5758] lr: 1.6929e-03 eta: 11:13:03 time: 0.8335 data_time: 0.0017 memory: 25074 loss: 0.2058 2023/06/01 09:26:14 - mmengine - INFO - Epoch(train) [12][4100/5758] lr: 1.6929e-03 eta: 11:11:39 time: 0.8400 data_time: 0.0015 memory: 25074 loss: 0.1739 2023/06/01 09:27:40 - mmengine - INFO - Epoch(train) [12][4200/5758] lr: 1.6929e-03 eta: 11:10:15 time: 0.8414 data_time: 0.0017 memory: 25074 loss: 0.1664 2023/06/01 09:29:05 - mmengine - INFO - Epoch(train) [12][4300/5758] lr: 1.6929e-03 eta: 11:08:51 time: 0.8928 data_time: 0.0016 memory: 25074 loss: 0.1331 2023/06/01 09:30:29 - mmengine - INFO - Epoch(train) [12][4400/5758] lr: 1.6929e-03 eta: 11:07:27 time: 0.8252 data_time: 0.0023 memory: 25074 loss: 0.0725 2023/06/01 09:31:54 - mmengine - INFO - Epoch(train) [12][4500/5758] lr: 1.6929e-03 eta: 11:06:03 time: 0.8944 data_time: 0.0017 memory: 25074 loss: 0.0562 2023/06/01 09:33:32 - mmengine - INFO - Epoch(train) [12][4600/5758] lr: 1.6929e-03 eta: 11:04:48 time: 0.9269 data_time: 0.0022 memory: 25074 loss: 0.0484 2023/06/01 09:34:23 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 09:34:56 - mmengine - INFO - Epoch(train) [12][4700/5758] lr: 1.6929e-03 eta: 11:03:23 time: 0.8407 data_time: 0.0018 memory: 25074 loss: 0.0265 2023/06/01 09:36:21 - mmengine - INFO - Epoch(train) [12][4800/5758] lr: 1.6929e-03 eta: 11:01:59 time: 0.8709 data_time: 0.0020 memory: 25074 loss: 0.0359 2023/06/01 09:37:47 - mmengine - INFO - Epoch(train) [12][4900/5758] lr: 1.6929e-03 eta: 11:00:35 time: 0.8374 data_time: 0.0022 memory: 25074 loss: 0.0321 2023/06/01 09:39:10 - mmengine - INFO - Epoch(train) [12][5000/5758] lr: 1.6929e-03 eta: 10:59:10 time: 0.8044 data_time: 0.0027 memory: 25074 loss: 0.0322 2023/06/01 09:40:36 - mmengine - INFO - Epoch(train) [12][5100/5758] lr: 1.6929e-03 eta: 10:57:46 time: 0.8549 data_time: 0.0020 memory: 25074 loss: 0.0260 2023/06/01 09:42:00 - mmengine - INFO - Epoch(train) [12][5200/5758] lr: 1.6929e-03 eta: 10:56:21 time: 0.8272 data_time: 0.0019 memory: 25074 loss: 0.0193 2023/06/01 09:43:24 - mmengine - INFO - Epoch(train) [12][5300/5758] lr: 1.6929e-03 eta: 10:54:57 time: 0.9095 data_time: 0.0021 memory: 25074 loss: 0.0188 2023/06/01 09:44:49 - mmengine - INFO - Epoch(train) [12][5400/5758] lr: 1.6929e-03 eta: 10:53:33 time: 0.7878 data_time: 0.0022 memory: 25074 loss: 0.0276 2023/06/01 09:46:15 - mmengine - INFO - Epoch(train) [12][5500/5758] lr: 1.6929e-03 eta: 10:52:09 time: 0.8668 data_time: 0.0023 memory: 25074 loss: 0.0198 2023/06/01 09:47:39 - mmengine - INFO - Epoch(train) [12][5600/5758] lr: 1.6929e-03 eta: 10:50:45 time: 0.8528 data_time: 0.0029 memory: 25074 loss: 0.0221 2023/06/01 09:48:30 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 09:49:02 - mmengine - INFO - Epoch(train) [12][5700/5758] lr: 1.6929e-03 eta: 10:49:19 time: 0.8659 data_time: 0.0022 memory: 25074 loss: 0.0213 2023/06/01 09:49:51 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 09:49:51 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/06/01 09:50:10 - mmengine - INFO - Epoch(val) [12][16/16] accuracy/top1: 100.0000 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [100.0, 0.0] single-label/f1-score_classwise: [100.0, 0.0] data_time: 0.2396 time: 0.6476 2023/06/01 09:51:41 - mmengine - INFO - Epoch(train) [13][ 100/5758] lr: 1.3885e-03 eta: 10:47:10 time: 0.8154 data_time: 0.0022 memory: 25074 loss: 0.0151 2023/06/01 09:53:04 - mmengine - INFO - Epoch(train) [13][ 200/5758] lr: 1.3885e-03 eta: 10:45:44 time: 0.8094 data_time: 0.0023 memory: 25074 loss: 0.0128 2023/06/01 09:54:28 - mmengine - INFO - Epoch(train) [13][ 300/5758] lr: 1.3885e-03 eta: 10:44:20 time: 0.8432 data_time: 0.0028 memory: 25074 loss: 0.0146 2023/06/01 09:55:54 - mmengine - INFO - Epoch(train) [13][ 400/5758] lr: 1.3885e-03 eta: 10:42:56 time: 0.8767 data_time: 0.0020 memory: 25074 loss: 0.0190 2023/06/01 09:57:20 - mmengine - INFO - Epoch(train) [13][ 500/5758] lr: 1.3885e-03 eta: 10:41:32 time: 0.8396 data_time: 0.0025 memory: 25074 loss: 0.0278 2023/06/01 09:58:45 - mmengine - INFO - Epoch(train) [13][ 600/5758] lr: 1.3885e-03 eta: 10:40:08 time: 0.8231 data_time: 0.0019 memory: 25074 loss: 0.0105 2023/06/01 10:00:11 - mmengine - INFO - Epoch(train) [13][ 700/5758] lr: 1.3885e-03 eta: 10:38:45 time: 0.8371 data_time: 0.0017 memory: 25074 loss: 0.0100 2023/06/01 10:01:38 - mmengine - INFO - Epoch(train) [13][ 800/5758] lr: 1.3885e-03 eta: 10:37:22 time: 0.8564 data_time: 0.0022 memory: 25074 loss: 0.0084 2023/06/01 10:03:03 - mmengine - INFO - Epoch(train) [13][ 900/5758] lr: 1.3885e-03 eta: 10:35:58 time: 0.9230 data_time: 0.0022 memory: 25074 loss: 0.0126 2023/06/01 10:03:08 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 10:04:28 - mmengine - INFO - Epoch(train) [13][1000/5758] lr: 1.3885e-03 eta: 10:34:34 time: 0.8306 data_time: 0.0021 memory: 25074 loss: 0.0064 2023/06/01 10:05:52 - mmengine - INFO - Epoch(train) [13][1100/5758] lr: 1.3885e-03 eta: 10:33:09 time: 0.8508 data_time: 0.0021 memory: 25074 loss: 0.0105 2023/06/01 10:07:18 - mmengine - INFO - Epoch(train) [13][1200/5758] lr: 1.3885e-03 eta: 10:31:45 time: 0.8331 data_time: 0.0020 memory: 25074 loss: 0.0174 2023/06/01 10:08:45 - mmengine - INFO - Epoch(train) [13][1300/5758] lr: 1.3885e-03 eta: 10:30:22 time: 0.8436 data_time: 0.0019 memory: 25074 loss: 0.0175 2023/06/01 10:10:10 - mmengine - INFO - Epoch(train) [13][1400/5758] lr: 1.3885e-03 eta: 10:28:58 time: 0.8395 data_time: 0.0019 memory: 25074 loss: 0.0113 2023/06/01 10:11:35 - mmengine - INFO - Epoch(train) [13][1500/5758] lr: 1.3885e-03 eta: 10:27:34 time: 0.8369 data_time: 0.0022 memory: 25074 loss: 0.0235 2023/06/01 10:13:01 - mmengine - INFO - Epoch(train) [13][1600/5758] lr: 1.3885e-03 eta: 10:26:10 time: 0.8792 data_time: 0.0026 memory: 25074 loss: 0.0064 2023/06/01 10:14:26 - mmengine - INFO - Epoch(train) [13][1700/5758] lr: 1.3885e-03 eta: 10:24:46 time: 0.8195 data_time: 0.0022 memory: 25074 loss: 0.0120 2023/06/01 10:15:53 - mmengine - INFO - Epoch(train) [13][1800/5758] lr: 1.3885e-03 eta: 10:23:23 time: 0.8364 data_time: 0.0020 memory: 25074 loss: 0.0083 2023/06/01 10:17:18 - mmengine - INFO - Epoch(train) [13][1900/5758] lr: 1.3885e-03 eta: 10:21:59 time: 0.8775 data_time: 0.0026 memory: 25074 loss: 0.0128 2023/06/01 10:17:22 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 10:18:43 - mmengine - INFO - Epoch(train) [13][2000/5758] lr: 1.3885e-03 eta: 10:20:35 time: 0.8430 data_time: 0.0021 memory: 25074 loss: 0.0170 2023/06/01 10:20:09 - mmengine - INFO - Epoch(train) [13][2100/5758] lr: 1.3885e-03 eta: 10:19:11 time: 0.8354 data_time: 0.0020 memory: 25074 loss: 0.0083 2023/06/01 10:21:34 - mmengine - INFO - Epoch(train) [13][2200/5758] lr: 1.3885e-03 eta: 10:17:47 time: 0.8647 data_time: 0.0025 memory: 25074 loss: 0.0133 2023/06/01 10:23:00 - mmengine - INFO - Epoch(train) [13][2300/5758] lr: 1.3885e-03 eta: 10:16:23 time: 0.8228 data_time: 0.0021 memory: 25074 loss: 0.0046 2023/06/01 10:24:24 - mmengine - INFO - Epoch(train) [13][2400/5758] lr: 1.3885e-03 eta: 10:14:59 time: 0.8153 data_time: 0.0015 memory: 25074 loss: 0.0054 2023/06/01 10:25:44 - mmengine - INFO - Epoch(train) [13][2500/5758] lr: 1.3885e-03 eta: 10:13:31 time: 0.8246 data_time: 0.0019 memory: 25074 loss: 0.0088 2023/06/01 10:27:05 - mmengine - INFO - Epoch(train) [13][2600/5758] lr: 1.3885e-03 eta: 10:12:05 time: 0.7858 data_time: 0.0024 memory: 25074 loss: 0.6324 2023/06/01 10:28:27 - mmengine - INFO - Epoch(train) [13][2700/5758] lr: 1.3885e-03 eta: 10:10:39 time: 0.8469 data_time: 0.0021 memory: 25074 loss: 0.4092 2023/06/01 10:29:52 - mmengine - INFO - Epoch(train) [13][2800/5758] lr: 1.3885e-03 eta: 10:09:14 time: 0.8175 data_time: 0.0019 memory: 25074 loss: 0.3070 2023/06/01 10:31:18 - mmengine - INFO - Epoch(train) [13][2900/5758] lr: 1.3885e-03 eta: 10:07:51 time: 0.8586 data_time: 0.0018 memory: 25074 loss: 0.2704 2023/06/01 10:31:22 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 10:32:42 - mmengine - INFO - Epoch(train) [13][3000/5758] lr: 1.3885e-03 eta: 10:06:26 time: 0.8356 data_time: 0.0023 memory: 25074 loss: 0.2208 2023/06/01 10:34:06 - mmengine - INFO - Epoch(train) [13][3100/5758] lr: 1.3885e-03 eta: 10:05:02 time: 0.8541 data_time: 0.0024 memory: 25074 loss: 0.1664 2023/06/01 10:35:33 - mmengine - INFO - Epoch(train) [13][3200/5758] lr: 1.3885e-03 eta: 10:03:39 time: 0.8588 data_time: 0.0017 memory: 25074 loss: 0.0688 2023/06/01 10:37:00 - mmengine - INFO - Epoch(train) [13][3300/5758] lr: 1.3885e-03 eta: 10:02:15 time: 0.8716 data_time: 0.0018 memory: 25074 loss: 0.0412 2023/06/01 10:38:26 - mmengine - INFO - Epoch(train) [13][3400/5758] lr: 1.3885e-03 eta: 10:00:52 time: 0.8019 data_time: 0.0023 memory: 25074 loss: 0.0353 2023/06/01 10:39:52 - mmengine - INFO - Epoch(train) [13][3500/5758] lr: 1.3885e-03 eta: 9:59:28 time: 0.8152 data_time: 0.0021 memory: 25074 loss: 0.0256 2023/06/01 10:41:18 - mmengine - INFO - Epoch(train) [13][3600/5758] lr: 1.3885e-03 eta: 9:58:05 time: 0.8970 data_time: 0.0026 memory: 25074 loss: 0.0196 2023/06/01 10:42:43 - mmengine - INFO - Epoch(train) [13][3700/5758] lr: 1.3885e-03 eta: 9:56:40 time: 0.8235 data_time: 0.0025 memory: 25074 loss: 0.0193 2023/06/01 10:44:08 - mmengine - INFO - Epoch(train) [13][3800/5758] lr: 1.3885e-03 eta: 9:55:16 time: 0.8693 data_time: 0.0029 memory: 25074 loss: 0.0148 2023/06/01 10:45:33 - mmengine - INFO - Epoch(train) [13][3900/5758] lr: 1.3885e-03 eta: 9:53:51 time: 0.9129 data_time: 0.0024 memory: 25074 loss: 0.0473 2023/06/01 10:45:37 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 10:46:59 - mmengine - INFO - Epoch(train) [13][4000/5758] lr: 1.3885e-03 eta: 9:52:28 time: 0.9482 data_time: 0.0023 memory: 25074 loss: 0.2544 2023/06/01 10:48:26 - mmengine - INFO - Epoch(train) [13][4100/5758] lr: 1.3885e-03 eta: 9:51:05 time: 0.8873 data_time: 0.0025 memory: 25074 loss: 0.0586 2023/06/01 10:49:53 - mmengine - INFO - Epoch(train) [13][4200/5758] lr: 1.3885e-03 eta: 9:49:42 time: 0.8858 data_time: 0.0029 memory: 25074 loss: 0.0363 2023/06/01 10:51:18 - mmengine - INFO - Epoch(train) [13][4300/5758] lr: 1.3885e-03 eta: 9:48:18 time: 0.8361 data_time: 0.0020 memory: 25074 loss: 0.0183 2023/06/01 10:52:44 - mmengine - INFO - Epoch(train) [13][4400/5758] lr: 1.3885e-03 eta: 9:46:54 time: 0.8416 data_time: 0.0018 memory: 25074 loss: 0.0225 2023/06/01 10:54:12 - mmengine - INFO - Epoch(train) [13][4500/5758] lr: 1.3885e-03 eta: 9:45:31 time: 0.9435 data_time: 0.0022 memory: 25074 loss: 0.0200 2023/06/01 10:55:39 - mmengine - INFO - Epoch(train) [13][4600/5758] lr: 1.3885e-03 eta: 9:44:08 time: 0.8584 data_time: 0.0022 memory: 25074 loss: 0.0063 2023/06/01 10:57:06 - mmengine - INFO - Epoch(train) [13][4700/5758] lr: 1.3885e-03 eta: 9:42:45 time: 0.8500 data_time: 0.0020 memory: 25074 loss: 0.0158 2023/06/01 10:58:32 - mmengine - INFO - Epoch(train) [13][4800/5758] lr: 1.3885e-03 eta: 9:41:21 time: 0.8819 data_time: 0.0025 memory: 25074 loss: 0.0109 2023/06/01 10:59:57 - mmengine - INFO - Epoch(train) [13][4900/5758] lr: 1.3885e-03 eta: 9:39:57 time: 0.8574 data_time: 0.0019 memory: 25074 loss: 0.0097 2023/06/01 11:00:02 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 11:01:23 - mmengine - INFO - Epoch(train) [13][5000/5758] lr: 1.3885e-03 eta: 9:38:33 time: 0.8520 data_time: 0.0023 memory: 25074 loss: 0.0216 2023/06/01 11:02:47 - mmengine - INFO - Epoch(train) [13][5100/5758] lr: 1.3885e-03 eta: 9:37:09 time: 0.9194 data_time: 0.0026 memory: 25074 loss: 0.0122 2023/06/01 11:04:11 - mmengine - INFO - Epoch(train) [13][5200/5758] lr: 1.3885e-03 eta: 9:35:44 time: 0.8667 data_time: 0.0017 memory: 25074 loss: 0.0090 2023/06/01 11:05:37 - mmengine - INFO - Epoch(train) [13][5300/5758] lr: 1.3885e-03 eta: 9:34:20 time: 0.8207 data_time: 0.0024 memory: 25074 loss: 0.0094 2023/06/01 11:07:00 - mmengine - INFO - Epoch(train) [13][5400/5758] lr: 1.3885e-03 eta: 9:32:54 time: 0.8605 data_time: 0.0021 memory: 25074 loss: 0.0091 2023/06/01 11:08:23 - mmengine - INFO - Epoch(train) [13][5500/5758] lr: 1.3885e-03 eta: 9:31:29 time: 0.8658 data_time: 0.0019 memory: 25074 loss: 0.0064 2023/06/01 11:09:46 - mmengine - INFO - Epoch(train) [13][5600/5758] lr: 1.3885e-03 eta: 9:30:04 time: 0.8472 data_time: 0.0021 memory: 25074 loss: 0.0172 2023/06/01 11:11:10 - mmengine - INFO - Epoch(train) [13][5700/5758] lr: 1.3885e-03 eta: 9:28:39 time: 0.8367 data_time: 0.0024 memory: 25074 loss: 0.0111 2023/06/01 11:11:59 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 11:11:59 - mmengine - INFO - Saving checkpoint at 13 epochs 2023/06/01 11:12:18 - mmengine - INFO - Epoch(val) [13][16/16] accuracy/top1: 99.9369 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [99.93687438964844, 0.0] single-label/f1-score_classwise: [99.96842956542969, 0.0] data_time: 0.2643 time: 0.6520 2023/06/01 11:13:50 - mmengine - INFO - Epoch(train) [14][ 100/5758] lr: 1.0993e-03 eta: 9:26:29 time: 0.8829 data_time: 0.0022 memory: 25074 loss: 0.0060 2023/06/01 11:14:31 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 11:15:17 - mmengine - INFO - Epoch(train) [14][ 200/5758] lr: 1.0993e-03 eta: 9:25:06 time: 0.9074 data_time: 0.0019 memory: 25074 loss: 0.0025 2023/06/01 11:16:43 - mmengine - INFO - Epoch(train) [14][ 300/5758] lr: 1.0993e-03 eta: 9:23:42 time: 0.8835 data_time: 0.0026 memory: 25074 loss: 0.0047 2023/06/01 11:18:06 - mmengine - INFO - Epoch(train) [14][ 400/5758] lr: 1.0993e-03 eta: 9:22:17 time: 0.8699 data_time: 0.0019 memory: 25074 loss: 0.0124 2023/06/01 11:19:30 - mmengine - INFO - Epoch(train) [14][ 500/5758] lr: 1.0993e-03 eta: 9:20:52 time: 0.8357 data_time: 0.0020 memory: 25074 loss: 0.0090 2023/06/01 11:20:54 - mmengine - INFO - Epoch(train) [14][ 600/5758] lr: 1.0993e-03 eta: 9:19:27 time: 0.8575 data_time: 0.0021 memory: 25074 loss: 0.0077 2023/06/01 11:22:22 - mmengine - INFO - Epoch(train) [14][ 700/5758] lr: 1.0993e-03 eta: 9:18:04 time: 0.8786 data_time: 0.0017 memory: 25074 loss: 0.0072 2023/06/01 11:23:49 - mmengine - INFO - Epoch(train) [14][ 800/5758] lr: 1.0993e-03 eta: 9:16:41 time: 0.9102 data_time: 0.0023 memory: 25074 loss: 0.0094 2023/06/01 11:25:16 - mmengine - INFO - Epoch(train) [14][ 900/5758] lr: 1.0993e-03 eta: 9:15:18 time: 0.9484 data_time: 0.0021 memory: 25074 loss: 0.0063 2023/06/01 11:26:44 - mmengine - INFO - Epoch(train) [14][1000/5758] lr: 1.0993e-03 eta: 9:13:55 time: 0.8318 data_time: 0.0021 memory: 25074 loss: 0.0025 2023/06/01 11:28:10 - mmengine - INFO - Epoch(train) [14][1100/5758] lr: 1.0993e-03 eta: 9:12:31 time: 0.8653 data_time: 0.0024 memory: 25074 loss: 0.0106 2023/06/01 11:28:52 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 11:29:36 - mmengine - INFO - Epoch(train) [14][1200/5758] lr: 1.0993e-03 eta: 9:11:08 time: 0.8250 data_time: 0.0016 memory: 25074 loss: 0.0046 2023/06/01 11:31:02 - mmengine - INFO - Epoch(train) [14][1300/5758] lr: 1.0993e-03 eta: 9:09:44 time: 0.8512 data_time: 0.0027 memory: 25074 loss: 0.0058 2023/06/01 11:32:30 - mmengine - INFO - Epoch(train) [14][1400/5758] lr: 1.0993e-03 eta: 9:08:21 time: 0.9002 data_time: 0.0023 memory: 25074 loss: 0.0057 2023/06/01 11:33:59 - mmengine - INFO - Epoch(train) [14][1500/5758] lr: 1.0993e-03 eta: 9:06:59 time: 0.8559 data_time: 0.0020 memory: 25074 loss: 0.0079 2023/06/01 11:35:28 - mmengine - INFO - Epoch(train) [14][1600/5758] lr: 1.0993e-03 eta: 9:05:36 time: 0.8499 data_time: 0.0021 memory: 25074 loss: 0.0034 2023/06/01 11:36:57 - mmengine - INFO - Epoch(train) [14][1700/5758] lr: 1.0993e-03 eta: 9:04:14 time: 0.8800 data_time: 0.0027 memory: 25074 loss: 0.0107 2023/06/01 11:38:26 - mmengine - INFO - Epoch(train) [14][1800/5758] lr: 1.0993e-03 eta: 9:02:51 time: 0.8587 data_time: 0.0024 memory: 25074 loss: 0.0053 2023/06/01 11:39:54 - mmengine - INFO - Epoch(train) [14][1900/5758] lr: 1.0993e-03 eta: 9:01:28 time: 0.9227 data_time: 0.0025 memory: 25074 loss: 0.0077 2023/06/01 11:41:20 - mmengine - INFO - Epoch(train) [14][2000/5758] lr: 1.0993e-03 eta: 9:00:04 time: 0.8529 data_time: 0.0022 memory: 25074 loss: 0.0114 2023/06/01 11:42:46 - mmengine - INFO - Epoch(train) [14][2100/5758] lr: 1.0993e-03 eta: 8:58:41 time: 0.8198 data_time: 0.0023 memory: 25074 loss: 0.0048 2023/06/01 11:43:24 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 11:44:08 - mmengine - INFO - Epoch(train) [14][2200/5758] lr: 1.0993e-03 eta: 8:57:15 time: 0.7742 data_time: 0.0015 memory: 25074 loss: 0.0091 2023/06/01 11:45:26 - mmengine - INFO - Epoch(train) [14][2300/5758] lr: 1.0993e-03 eta: 8:55:47 time: 0.8021 data_time: 0.0015 memory: 25074 loss: 0.0048 2023/06/01 11:46:49 - mmengine - INFO - Epoch(train) [14][2400/5758] lr: 1.0993e-03 eta: 8:54:22 time: 0.7940 data_time: 0.0020 memory: 25074 loss: 0.0043 2023/06/01 11:48:12 - mmengine - INFO - Epoch(train) [14][2500/5758] lr: 1.0993e-03 eta: 8:52:56 time: 0.8532 data_time: 0.0019 memory: 25074 loss: 0.0065 2023/06/01 11:49:36 - mmengine - INFO - Epoch(train) [14][2600/5758] lr: 1.0993e-03 eta: 8:51:31 time: 0.7793 data_time: 0.0014 memory: 25074 loss: 0.0092 2023/06/01 11:50:58 - mmengine - INFO - Epoch(train) [14][2700/5758] lr: 1.0993e-03 eta: 8:50:06 time: 0.8605 data_time: 0.0014 memory: 25074 loss: 0.0042 2023/06/01 11:52:23 - mmengine - INFO - Epoch(train) [14][2800/5758] lr: 1.0993e-03 eta: 8:48:41 time: 0.8461 data_time: 0.0014 memory: 25074 loss: 0.0034 2023/06/01 11:53:48 - mmengine - INFO - Epoch(train) [14][2900/5758] lr: 1.0993e-03 eta: 8:47:17 time: 0.8399 data_time: 0.0024 memory: 25074 loss: 0.0122 2023/06/01 11:55:10 - mmengine - INFO - Epoch(train) [14][3000/5758] lr: 1.0993e-03 eta: 8:45:51 time: 0.8112 data_time: 0.0017 memory: 25074 loss: 0.0035 2023/06/01 11:56:33 - mmengine - INFO - Epoch(train) [14][3100/5758] lr: 1.0993e-03 eta: 8:44:26 time: 0.8249 data_time: 0.0018 memory: 25074 loss: 0.0011 2023/06/01 11:57:12 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 11:57:56 - mmengine - INFO - Epoch(train) [14][3200/5758] lr: 1.0993e-03 eta: 8:43:00 time: 0.8364 data_time: 0.0025 memory: 25074 loss: 0.0101 2023/06/01 11:59:17 - mmengine - INFO - Epoch(train) [14][3300/5758] lr: 1.0993e-03 eta: 8:41:34 time: 0.8085 data_time: 0.0026 memory: 25074 loss: 0.0073 2023/06/01 12:00:38 - mmengine - INFO - Epoch(train) [14][3400/5758] lr: 1.0993e-03 eta: 8:40:08 time: 0.8355 data_time: 0.0023 memory: 25074 loss: 0.0097 2023/06/01 12:01:58 - mmengine - INFO - Epoch(train) [14][3500/5758] lr: 1.0993e-03 eta: 8:38:41 time: 0.8418 data_time: 0.0023 memory: 25074 loss: 0.0069 2023/06/01 12:03:21 - mmengine - INFO - Epoch(train) [14][3600/5758] lr: 1.0993e-03 eta: 8:37:16 time: 0.7828 data_time: 0.0019 memory: 25074 loss: 0.0057 2023/06/01 12:04:42 - mmengine - INFO - Epoch(train) [14][3700/5758] lr: 1.0993e-03 eta: 8:35:50 time: 0.8424 data_time: 0.0024 memory: 25074 loss: 0.0085 2023/06/01 12:06:04 - mmengine - INFO - Epoch(train) [14][3800/5758] lr: 1.0993e-03 eta: 8:34:24 time: 0.8067 data_time: 0.0020 memory: 25074 loss: 0.0078 2023/06/01 12:07:26 - mmengine - INFO - Epoch(train) [14][3900/5758] lr: 1.0993e-03 eta: 8:32:58 time: 0.8274 data_time: 0.0022 memory: 25074 loss: 0.0046 2023/06/01 12:08:46 - mmengine - INFO - Epoch(train) [14][4000/5758] lr: 1.0993e-03 eta: 8:31:31 time: 0.7971 data_time: 0.0017 memory: 25074 loss: 0.0110 2023/06/01 12:10:09 - mmengine - INFO - Epoch(train) [14][4100/5758] lr: 1.0993e-03 eta: 8:30:06 time: 0.8197 data_time: 0.0022 memory: 25074 loss: 0.0107 2023/06/01 12:10:46 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 12:11:31 - mmengine - INFO - Epoch(train) [14][4200/5758] lr: 1.0993e-03 eta: 8:28:40 time: 0.8274 data_time: 0.0026 memory: 25074 loss: 0.0026 2023/06/01 12:12:54 - mmengine - INFO - Epoch(train) [14][4300/5758] lr: 1.0993e-03 eta: 8:27:15 time: 0.8829 data_time: 0.0019 memory: 25074 loss: 0.0069 2023/06/01 12:14:16 - mmengine - INFO - Epoch(train) [14][4400/5758] lr: 1.0993e-03 eta: 8:25:49 time: 0.8273 data_time: 0.0023 memory: 25074 loss: 0.0029 2023/06/01 12:15:38 - mmengine - INFO - Epoch(train) [14][4500/5758] lr: 1.0993e-03 eta: 8:24:24 time: 0.8197 data_time: 0.0020 memory: 25074 loss: 0.0032 2023/06/01 12:17:00 - mmengine - INFO - Epoch(train) [14][4600/5758] lr: 1.0993e-03 eta: 8:22:58 time: 0.8215 data_time: 0.0019 memory: 25074 loss: 0.0017 2023/06/01 12:18:24 - mmengine - INFO - Epoch(train) [14][4700/5758] lr: 1.0993e-03 eta: 8:21:33 time: 0.8593 data_time: 0.0021 memory: 25074 loss: 0.0084 2023/06/01 12:19:48 - mmengine - INFO - Epoch(train) [14][4800/5758] lr: 1.0993e-03 eta: 8:20:08 time: 0.8848 data_time: 0.0021 memory: 25074 loss: 0.0056 2023/06/01 12:21:12 - mmengine - INFO - Epoch(train) [14][4900/5758] lr: 1.0993e-03 eta: 8:18:44 time: 0.8677 data_time: 0.0019 memory: 25074 loss: 0.0101 2023/06/01 12:22:35 - mmengine - INFO - Epoch(train) [14][5000/5758] lr: 1.0993e-03 eta: 8:17:19 time: 0.8044 data_time: 0.0019 memory: 25074 loss: 0.0105 2023/06/01 12:24:01 - mmengine - INFO - Epoch(train) [14][5100/5758] lr: 1.0993e-03 eta: 8:15:55 time: 0.8106 data_time: 0.0017 memory: 25074 loss: 0.0049 2023/06/01 12:24:39 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 12:25:22 - mmengine - INFO - Epoch(train) [14][5200/5758] lr: 1.0993e-03 eta: 8:14:29 time: 0.7801 data_time: 0.0020 memory: 25074 loss: 0.0027 2023/06/01 12:26:45 - mmengine - INFO - Epoch(train) [14][5300/5758] lr: 1.0993e-03 eta: 8:13:03 time: 0.8744 data_time: 0.0016 memory: 25074 loss: 0.0039 2023/06/01 12:28:06 - mmengine - INFO - Epoch(train) [14][5400/5758] lr: 1.0993e-03 eta: 8:11:38 time: 0.8251 data_time: 0.0015 memory: 25074 loss: 0.0070 2023/06/01 12:29:28 - mmengine - INFO - Epoch(train) [14][5500/5758] lr: 1.0993e-03 eta: 8:10:12 time: 0.8248 data_time: 0.0020 memory: 25074 loss: 0.0071 2023/06/01 12:30:50 - mmengine - INFO - Epoch(train) [14][5600/5758] lr: 1.0993e-03 eta: 8:08:46 time: 0.8233 data_time: 0.0022 memory: 25074 loss: 0.0075 2023/06/01 12:32:11 - mmengine - INFO - Epoch(train) [14][5700/5758] lr: 1.0993e-03 eta: 8:07:20 time: 0.7905 data_time: 0.0017 memory: 25074 loss: 0.0051 2023/06/01 12:32:59 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 12:32:59 - mmengine - INFO - Saving checkpoint at 14 epochs 2023/06/01 12:33:17 - mmengine - INFO - Epoch(val) [14][16/16] accuracy/top1: 99.9874 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [99.98737335205078, 0.0] single-label/f1-score_classwise: [99.99369049072266, 0.0] data_time: 0.2449 time: 0.6382 2023/06/01 12:34:47 - mmengine - INFO - Epoch(train) [15][ 100/5758] lr: 8.3237e-04 eta: 8:05:08 time: 0.8145 data_time: 0.0021 memory: 25074 loss: 0.0082 2023/06/01 12:36:08 - mmengine - INFO - Epoch(train) [15][ 200/5758] lr: 8.3237e-04 eta: 8:03:42 time: 0.7930 data_time: 0.0017 memory: 25074 loss: 0.0009 2023/06/01 12:37:30 - mmengine - INFO - Epoch(train) [15][ 300/5758] lr: 8.3237e-04 eta: 8:02:17 time: 0.8182 data_time: 0.0017 memory: 25074 loss: 0.0074 2023/06/01 12:38:41 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 12:38:50 - mmengine - INFO - Epoch(train) [15][ 400/5758] lr: 8.3237e-04 eta: 8:00:50 time: 0.7837 data_time: 0.0019 memory: 25074 loss: 0.0030 2023/06/01 12:40:13 - mmengine - INFO - Epoch(train) [15][ 500/5758] lr: 8.3237e-04 eta: 7:59:25 time: 0.8062 data_time: 0.0017 memory: 25074 loss: 0.0063 2023/06/01 12:41:33 - mmengine - INFO - Epoch(train) [15][ 600/5758] lr: 8.3237e-04 eta: 7:57:59 time: 0.7703 data_time: 0.0016 memory: 25074 loss: 0.0045 2023/06/01 12:42:54 - mmengine - INFO - Epoch(train) [15][ 700/5758] lr: 8.3237e-04 eta: 7:56:33 time: 0.8155 data_time: 0.0020 memory: 25074 loss: 0.0062 2023/06/01 12:44:16 - mmengine - INFO - Epoch(train) [15][ 800/5758] lr: 8.3237e-04 eta: 7:55:08 time: 0.8437 data_time: 0.0020 memory: 25074 loss: 0.0076 2023/06/01 12:45:39 - mmengine - INFO - Epoch(train) [15][ 900/5758] lr: 8.3237e-04 eta: 7:53:42 time: 0.7929 data_time: 0.0017 memory: 25074 loss: 0.0222 2023/06/01 12:47:00 - mmengine - INFO - Epoch(train) [15][1000/5758] lr: 8.3237e-04 eta: 7:52:16 time: 0.7770 data_time: 0.0016 memory: 25074 loss: 0.0040 2023/06/01 12:48:22 - mmengine - INFO - Epoch(train) [15][1100/5758] lr: 8.3237e-04 eta: 7:50:51 time: 0.8139 data_time: 0.0025 memory: 25074 loss: 0.0044 2023/06/01 12:49:45 - mmengine - INFO - Epoch(train) [15][1200/5758] lr: 8.3237e-04 eta: 7:49:26 time: 0.8529 data_time: 0.0018 memory: 25074 loss: 0.0032 2023/06/01 12:51:06 - mmengine - INFO - Epoch(train) [15][1300/5758] lr: 8.3237e-04 eta: 7:48:00 time: 0.8502 data_time: 0.0014 memory: 25074 loss: 0.0038 2023/06/01 12:52:19 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 12:52:29 - mmengine - INFO - Epoch(train) [15][1400/5758] lr: 8.3237e-04 eta: 7:46:35 time: 0.7973 data_time: 0.0014 memory: 25074 loss: 0.0046 2023/06/01 12:53:48 - mmengine - INFO - Epoch(train) [15][1500/5758] lr: 8.3237e-04 eta: 7:45:08 time: 0.7630 data_time: 0.0021 memory: 25074 loss: 0.0030 2023/06/01 12:55:10 - mmengine - INFO - Epoch(train) [15][1600/5758] lr: 8.3237e-04 eta: 7:43:43 time: 0.8240 data_time: 0.0017 memory: 25074 loss: 0.0034 2023/06/01 12:56:31 - mmengine - INFO - Epoch(train) [15][1700/5758] lr: 8.3237e-04 eta: 7:42:17 time: 0.8517 data_time: 0.0015 memory: 25074 loss: 0.0033 2023/06/01 12:57:52 - mmengine - INFO - Epoch(train) [15][1800/5758] lr: 8.3237e-04 eta: 7:40:51 time: 0.8344 data_time: 0.0016 memory: 25074 loss: 0.0069 2023/06/01 12:59:15 - mmengine - INFO - Epoch(train) [15][1900/5758] lr: 8.3237e-04 eta: 7:39:26 time: 0.8258 data_time: 0.0026 memory: 25074 loss: 0.0046 2023/06/01 13:00:35 - mmengine - INFO - Epoch(train) [15][2000/5758] lr: 8.3237e-04 eta: 7:38:00 time: 0.7366 data_time: 0.0016 memory: 25074 loss: 0.0028 2023/06/01 13:01:58 - mmengine - INFO - Epoch(train) [15][2100/5758] lr: 8.3237e-04 eta: 7:36:35 time: 0.9010 data_time: 0.0016 memory: 25074 loss: 0.0041 2023/06/01 13:03:22 - mmengine - INFO - Epoch(train) [15][2200/5758] lr: 8.3237e-04 eta: 7:35:11 time: 0.8672 data_time: 0.0022 memory: 25074 loss: 0.0032 2023/06/01 13:04:46 - mmengine - INFO - Epoch(train) [15][2300/5758] lr: 8.3237e-04 eta: 7:33:46 time: 0.8019 data_time: 0.0015 memory: 25074 loss: 0.0036 2023/06/01 13:05:58 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 13:06:08 - mmengine - INFO - Epoch(train) [15][2400/5758] lr: 8.3237e-04 eta: 7:32:20 time: 0.8077 data_time: 0.0016 memory: 25074 loss: 0.0034 2023/06/01 13:07:31 - mmengine - INFO - Epoch(train) [15][2500/5758] lr: 8.3237e-04 eta: 7:30:55 time: 0.7925 data_time: 0.0022 memory: 25074 loss: 0.0026 2023/06/01 13:08:54 - mmengine - INFO - Epoch(train) [15][2600/5758] lr: 8.3237e-04 eta: 7:29:30 time: 0.8363 data_time: 0.0016 memory: 25074 loss: 0.0026 2023/06/01 13:10:17 - mmengine - INFO - Epoch(train) [15][2700/5758] lr: 8.3237e-04 eta: 7:28:05 time: 0.8434 data_time: 0.0026 memory: 25074 loss: 0.0013 2023/06/01 13:11:41 - mmengine - INFO - Epoch(train) [15][2800/5758] lr: 8.3237e-04 eta: 7:26:41 time: 0.8503 data_time: 0.0015 memory: 25074 loss: 0.0040 2023/06/01 13:13:04 - mmengine - INFO - Epoch(train) [15][2900/5758] lr: 8.3237e-04 eta: 7:25:16 time: 0.8352 data_time: 0.0020 memory: 25074 loss: 0.0017 2023/06/01 13:14:28 - mmengine - INFO - Epoch(train) [15][3000/5758] lr: 8.3237e-04 eta: 7:23:51 time: 0.8708 data_time: 0.0016 memory: 25074 loss: 0.0037 2023/06/01 13:15:53 - mmengine - INFO - Epoch(train) [15][3100/5758] lr: 8.3237e-04 eta: 7:22:27 time: 0.8685 data_time: 0.0019 memory: 25074 loss: 0.0023 2023/06/01 13:17:27 - mmengine - INFO - Epoch(train) [15][3200/5758] lr: 8.3237e-04 eta: 7:21:06 time: 0.9061 data_time: 0.0015 memory: 25074 loss: 0.0080 2023/06/01 13:18:49 - mmengine - INFO - Epoch(train) [15][3300/5758] lr: 8.3237e-04 eta: 7:19:41 time: 0.7958 data_time: 0.0020 memory: 25074 loss: 0.0023 2023/06/01 13:20:05 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 13:20:15 - mmengine - INFO - Epoch(train) [15][3400/5758] lr: 8.3237e-04 eta: 7:18:17 time: 0.8611 data_time: 0.0022 memory: 25074 loss: 0.0024 2023/06/01 13:21:39 - mmengine - INFO - Epoch(train) [15][3500/5758] lr: 8.3237e-04 eta: 7:16:52 time: 0.8284 data_time: 0.0022 memory: 25074 loss: 0.0028 2023/06/01 13:23:02 - mmengine - INFO - Epoch(train) [15][3600/5758] lr: 8.3237e-04 eta: 7:15:28 time: 0.8111 data_time: 0.0021 memory: 25074 loss: 0.0021 2023/06/01 13:24:27 - mmengine - INFO - Epoch(train) [15][3700/5758] lr: 8.3237e-04 eta: 7:14:03 time: 0.8174 data_time: 0.0022 memory: 25074 loss: 0.0019 2023/06/01 13:25:50 - mmengine - INFO - Epoch(train) [15][3800/5758] lr: 8.3237e-04 eta: 7:12:39 time: 0.8291 data_time: 0.0021 memory: 25074 loss: 0.0028 2023/06/01 13:27:17 - mmengine - INFO - Epoch(train) [15][3900/5758] lr: 8.3237e-04 eta: 7:11:15 time: 0.8151 data_time: 0.0024 memory: 25074 loss: 0.0019 2023/06/01 13:28:44 - mmengine - INFO - Epoch(train) [15][4000/5758] lr: 8.3237e-04 eta: 7:09:51 time: 0.8477 data_time: 0.0020 memory: 25074 loss: 0.0044 2023/06/01 13:30:11 - mmengine - INFO - Epoch(train) [15][4100/5758] lr: 8.3237e-04 eta: 7:08:28 time: 0.8792 data_time: 0.0021 memory: 25074 loss: 0.0070 2023/06/01 13:31:33 - mmengine - INFO - Epoch(train) [15][4200/5758] lr: 8.3237e-04 eta: 7:07:03 time: 0.8185 data_time: 0.0025 memory: 25074 loss: 0.0017 2023/06/01 13:32:57 - mmengine - INFO - Epoch(train) [15][4300/5758] lr: 8.3237e-04 eta: 7:05:38 time: 0.8046 data_time: 0.0025 memory: 25074 loss: 0.0031 2023/06/01 13:34:11 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 13:34:21 - mmengine - INFO - Epoch(train) [15][4400/5758] lr: 8.3237e-04 eta: 7:04:13 time: 0.8695 data_time: 0.0018 memory: 25074 loss: 0.0020 2023/06/01 13:35:45 - mmengine - INFO - Epoch(train) [15][4500/5758] lr: 8.3237e-04 eta: 7:02:49 time: 0.7930 data_time: 0.0016 memory: 25074 loss: 0.0016 2023/06/01 13:37:10 - mmengine - INFO - Epoch(train) [15][4600/5758] lr: 8.3237e-04 eta: 7:01:25 time: 0.9227 data_time: 0.0021 memory: 25074 loss: 0.0031 2023/06/01 13:38:32 - mmengine - INFO - Epoch(train) [15][4700/5758] lr: 8.3237e-04 eta: 6:59:59 time: 0.8556 data_time: 0.0016 memory: 25074 loss: 0.0043 2023/06/01 13:39:57 - mmengine - INFO - Epoch(train) [15][4800/5758] lr: 8.3237e-04 eta: 6:58:35 time: 0.8675 data_time: 0.0017 memory: 25074 loss: 0.0056 2023/06/01 13:41:21 - mmengine - INFO - Epoch(train) [15][4900/5758] lr: 8.3237e-04 eta: 6:57:10 time: 0.8150 data_time: 0.0017 memory: 25074 loss: 0.0049 2023/06/01 13:42:45 - mmengine - INFO - Epoch(train) [15][5000/5758] lr: 8.3237e-04 eta: 6:55:46 time: 0.9148 data_time: 0.0023 memory: 25074 loss: 0.0025 2023/06/01 13:44:08 - mmengine - INFO - Epoch(train) [15][5100/5758] lr: 8.3237e-04 eta: 6:54:21 time: 0.8296 data_time: 0.0019 memory: 25074 loss: 0.0025 2023/06/01 13:45:33 - mmengine - INFO - Epoch(train) [15][5200/5758] lr: 8.3237e-04 eta: 6:52:57 time: 0.8046 data_time: 0.0030 memory: 25074 loss: 0.0059 2023/06/01 13:46:56 - mmengine - INFO - Epoch(train) [15][5300/5758] lr: 8.3237e-04 eta: 6:51:32 time: 0.8163 data_time: 0.0021 memory: 25074 loss: 0.0048 2023/06/01 13:48:10 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 13:48:20 - mmengine - INFO - Epoch(train) [15][5400/5758] lr: 8.3237e-04 eta: 6:50:07 time: 0.8333 data_time: 0.0026 memory: 25074 loss: 0.0042 2023/06/01 13:49:42 - mmengine - INFO - Epoch(train) [15][5500/5758] lr: 8.3237e-04 eta: 6:48:42 time: 0.8160 data_time: 0.0020 memory: 25074 loss: 0.0024 2023/06/01 13:51:05 - mmengine - INFO - Epoch(train) [15][5600/5758] lr: 8.3237e-04 eta: 6:47:17 time: 0.8412 data_time: 0.0019 memory: 25074 loss: 0.0033 2023/06/01 13:52:27 - mmengine - INFO - Epoch(train) [15][5700/5758] lr: 8.3237e-04 eta: 6:45:52 time: 0.8080 data_time: 0.0018 memory: 25074 loss: 0.0039 2023/06/01 13:53:15 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 13:53:15 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/06/01 13:53:34 - mmengine - INFO - Epoch(val) [15][16/16] accuracy/top1: 100.0000 single-label/precision_classwise: [100.0, 0.0] single-label/recall_classwise: [100.0, 0.0] single-label/f1-score_classwise: [100.0, 0.0] data_time: 0.2341 time: 0.6188 2023/06/01 13:55:04 - mmengine - INFO - Epoch(train) [16][ 100/5758] lr: 5.9432e-04 eta: 6:43:40 time: 0.8635 data_time: 0.0019 memory: 25074 loss: 0.0025 2023/06/01 13:56:28 - mmengine - INFO - Epoch(train) [16][ 200/5758] lr: 5.9432e-04 eta: 6:42:15 time: 0.8420 data_time: 0.0024 memory: 25074 loss: 0.0034 2023/06/01 13:57:51 - mmengine - INFO - Epoch(train) [16][ 300/5758] lr: 5.9432e-04 eta: 6:40:51 time: 0.7625 data_time: 0.0021 memory: 25074 loss: 0.0030 2023/06/01 13:59:15 - mmengine - INFO - Epoch(train) [16][ 400/5758] lr: 5.9432e-04 eta: 6:39:26 time: 0.8603 data_time: 0.0021 memory: 25074 loss: 0.0035 2023/06/01 14:00:38 - mmengine - INFO - Epoch(train) [16][ 500/5758] lr: 5.9432e-04 eta: 6:38:01 time: 0.7584 data_time: 0.0020 memory: 25074 loss: 0.0015 2023/06/01 14:02:01 - mmengine - INFO - Epoch(train) [16][ 600/5758] lr: 5.9432e-04 eta: 6:36:36 time: 0.8660 data_time: 0.0018 memory: 25074 loss: 0.0032 2023/06/01 14:02:26 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 14:03:24 - mmengine - INFO - Epoch(train) [16][ 700/5758] lr: 5.9432e-04 eta: 6:35:11 time: 0.7694 data_time: 0.0018 memory: 25074 loss: 0.0005 2023/06/01 14:04:47 - mmengine - INFO - Epoch(train) [16][ 800/5758] lr: 5.9432e-04 eta: 6:33:47 time: 0.8643 data_time: 0.0020 memory: 25074 loss: 0.0007 2023/06/01 14:06:10 - mmengine - INFO - Epoch(train) [16][ 900/5758] lr: 5.9432e-04 eta: 6:32:22 time: 0.8344 data_time: 0.0027 memory: 25074 loss: 0.0025 2023/06/01 14:07:33 - mmengine - INFO - Epoch(train) [16][1000/5758] lr: 5.9432e-04 eta: 6:30:57 time: 0.8328 data_time: 0.0017 memory: 25074 loss: 0.0021 2023/06/01 14:08:56 - mmengine - INFO - Epoch(train) [16][1100/5758] lr: 5.9432e-04 eta: 6:29:32 time: 0.8298 data_time: 0.0018 memory: 25074 loss: 0.0033 2023/06/01 14:10:20 - mmengine - INFO - Epoch(train) [16][1200/5758] lr: 5.9432e-04 eta: 6:28:07 time: 0.8674 data_time: 0.0015 memory: 25074 loss: 0.0030 2023/06/01 14:11:43 - mmengine - INFO - Epoch(train) [16][1300/5758] lr: 5.9432e-04 eta: 6:26:43 time: 0.8187 data_time: 0.0027 memory: 25074 loss: 0.0004 2023/06/01 14:13:08 - mmengine - INFO - Epoch(train) [16][1400/5758] lr: 5.9432e-04 eta: 6:25:18 time: 0.9097 data_time: 0.0023 memory: 25074 loss: 0.0027 2023/06/01 14:14:32 - mmengine - INFO - Epoch(train) [16][1500/5758] lr: 5.9432e-04 eta: 6:23:54 time: 0.8454 data_time: 0.0019 memory: 25074 loss: 0.0023 2023/06/01 14:15:56 - mmengine - INFO - Epoch(train) [16][1600/5758] lr: 5.9432e-04 eta: 6:22:29 time: 0.8380 data_time: 0.0019 memory: 25074 loss: 0.0039 2023/06/01 14:16:21 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 14:17:21 - mmengine - INFO - Epoch(train) [16][1700/5758] lr: 5.9432e-04 eta: 6:21:05 time: 0.8268 data_time: 0.0026 memory: 25074 loss: 0.0022 2023/06/01 14:18:46 - mmengine - INFO - Epoch(train) [16][1800/5758] lr: 5.9432e-04 eta: 6:19:41 time: 0.8713 data_time: 0.0025 memory: 25074 loss: 0.0016 2023/06/01 14:20:09 - mmengine - INFO - Epoch(train) [16][1900/5758] lr: 5.9432e-04 eta: 6:18:16 time: 0.8646 data_time: 0.0015 memory: 25074 loss: 0.0030 2023/06/01 14:21:34 - mmengine - INFO - Epoch(train) [16][2000/5758] lr: 5.9432e-04 eta: 6:16:52 time: 0.8700 data_time: 0.0016 memory: 25074 loss: 0.0009 2023/06/01 14:23:02 - mmengine - INFO - Epoch(train) [16][2100/5758] lr: 5.9432e-04 eta: 6:15:28 time: 0.8489 data_time: 0.0020 memory: 25074 loss: 0.0027 2023/06/01 14:24:26 - mmengine - INFO - Epoch(train) [16][2200/5758] lr: 5.9432e-04 eta: 6:14:04 time: 0.8313 data_time: 0.0025 memory: 25074 loss: 0.0015 2023/06/01 14:25:51 - mmengine - INFO - Epoch(train) [16][2300/5758] lr: 5.9432e-04 eta: 6:12:40 time: 0.8532 data_time: 0.0019 memory: 25074 loss: 0.0009 2023/06/01 14:27:16 - mmengine - INFO - Epoch(train) [16][2400/5758] lr: 5.9432e-04 eta: 6:11:15 time: 0.8587 data_time: 0.0022 memory: 25074 loss: 0.0008 2023/06/01 14:28:42 - mmengine - INFO - Epoch(train) [16][2500/5758] lr: 5.9432e-04 eta: 6:09:52 time: 0.8913 data_time: 0.0016 memory: 25074 loss: 0.0037 2023/06/01 14:30:13 - mmengine - INFO - Epoch(train) [16][2600/5758] lr: 5.9432e-04 eta: 6:08:29 time: 0.9534 data_time: 0.0038 memory: 25074 loss: 0.0010 2023/06/01 14:30:39 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 14:31:41 - mmengine - INFO - Epoch(train) [16][2700/5758] lr: 5.9432e-04 eta: 6:07:06 time: 0.9603 data_time: 0.0021 memory: 25074 loss: 0.0016 2023/06/01 14:33:10 - mmengine - INFO - Epoch(train) [16][2800/5758] lr: 5.9432e-04 eta: 6:05:42 time: 0.9243 data_time: 0.0022 memory: 25074 loss: 0.0005 2023/06/01 14:34:41 - mmengine - INFO - Epoch(train) [16][2900/5758] lr: 5.9432e-04 eta: 6:04:20 time: 0.9090 data_time: 0.0023 memory: 25074 loss: 0.0108 2023/06/01 14:36:10 - mmengine - INFO - Epoch(train) [16][3000/5758] lr: 5.9432e-04 eta: 6:02:57 time: 0.8929 data_time: 0.0015 memory: 25074 loss: 0.0019 2023/06/01 14:37:36 - mmengine - INFO - Epoch(train) [16][3100/5758] lr: 5.9432e-04 eta: 6:01:33 time: 0.9010 data_time: 0.0016 memory: 25074 loss: 0.0008 2023/06/01 14:39:01 - mmengine - INFO - Epoch(train) [16][3200/5758] lr: 5.9432e-04 eta: 6:00:08 time: 0.8695 data_time: 0.0015 memory: 25074 loss: 0.0018 2023/06/01 14:40:26 - mmengine - INFO - Epoch(train) [16][3300/5758] lr: 5.9432e-04 eta: 5:58:44 time: 0.8774 data_time: 0.0016 memory: 25074 loss: 0.0006 2023/06/01 14:41:50 - mmengine - INFO - Epoch(train) [16][3400/5758] lr: 5.9432e-04 eta: 5:57:20 time: 0.8348 data_time: 0.0016 memory: 25074 loss: 0.0029 2023/06/01 14:43:15 - mmengine - INFO - Epoch(train) [16][3500/5758] lr: 5.9432e-04 eta: 5:55:55 time: 0.8082 data_time: 0.0023 memory: 25074 loss: 0.0031 2023/06/01 14:44:38 - mmengine - INFO - Epoch(train) [16][3600/5758] lr: 5.9432e-04 eta: 5:54:31 time: 0.7833 data_time: 0.0016 memory: 25074 loss: 0.0014 2023/06/01 14:45:02 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 14:46:00 - mmengine - INFO - Epoch(train) [16][3700/5758] lr: 5.9432e-04 eta: 5:53:05 time: 0.8314 data_time: 0.0016 memory: 25074 loss: 0.0019 2023/06/01 14:47:25 - mmengine - INFO - Epoch(train) [16][3800/5758] lr: 5.9432e-04 eta: 5:51:41 time: 0.8320 data_time: 0.0019 memory: 25074 loss: 0.0008 2023/06/01 14:48:48 - mmengine - INFO - Epoch(train) [16][3900/5758] lr: 5.9432e-04 eta: 5:50:16 time: 0.8242 data_time: 0.0014 memory: 25074 loss: 0.0035 2023/06/01 14:50:12 - mmengine - INFO - Epoch(train) [16][4000/5758] lr: 5.9432e-04 eta: 5:48:52 time: 0.8209 data_time: 0.0024 memory: 25074 loss: 0.0022 2023/06/01 14:51:34 - mmengine - INFO - Epoch(train) [16][4100/5758] lr: 5.9432e-04 eta: 5:47:27 time: 0.7744 data_time: 0.0027 memory: 25074 loss: 0.0025 2023/06/01 14:52:56 - mmengine - INFO - Epoch(train) [16][4200/5758] lr: 5.9432e-04 eta: 5:46:01 time: 0.7740 data_time: 0.0016 memory: 25074 loss: 0.0040 2023/06/01 14:54:18 - mmengine - INFO - Epoch(train) [16][4300/5758] lr: 5.9432e-04 eta: 5:44:36 time: 0.8169 data_time: 0.0025 memory: 25074 loss: 0.0027 2023/06/01 14:55:38 - mmengine - INFO - Epoch(train) [16][4400/5758] lr: 5.9432e-04 eta: 5:43:11 time: 0.8274 data_time: 0.0024 memory: 25074 loss: 0.0066 2023/06/01 14:56:57 - mmengine - INFO - Epoch(train) [16][4500/5758] lr: 5.9432e-04 eta: 5:41:45 time: 0.8252 data_time: 0.0016 memory: 25074 loss: 0.0027 2023/06/01 14:58:17 - mmengine - INFO - Epoch(train) [16][4600/5758] lr: 5.9432e-04 eta: 5:40:19 time: 0.7876 data_time: 0.0017 memory: 25074 loss: 0.0054 2023/06/01 14:58:42 - mmengine - INFO - Exp name: convnext_small_4xb256_fake5m_20230531_173252 2023/06/01 14:59:38 - mmengine - INFO - Epoch(train) [16][4700/5758] lr: 5.9432e-04 eta: 5:38:54 time: 0.8089 data_time: 0.0026 memory: 25074 loss: 0.0014 2023/06/01 15:00:58 - mmengine - INFO - Epoch(train) [16][4800/5758] lr: 5.9432e-04 eta: 5:37:28 time: 0.7445 data_time: 0.0015 memory: 25074 loss: 0.0009 2023/06/01 15:02:16 - mmengine - INFO - Epoch(train) [16][4900/5758] lr: 5.9432e-04 eta: 5:36:02 time: 0.7698 data_time: 0.0021 memory: 25074 loss: 0.0019 2023/06/01 15:03:36 - mmengine - INFO - Epoch(train) [16][5000/5758] lr: 5.9432e-04 eta: 5:34:37 time: 0.7804 data_time: 0.0017 memory: 25074 loss: 0.0008 2023/06/01 15:04:57 - mmengine - INFO - Epoch(train) [16][5100/5758] lr: 5.9432e-04 eta: 5:33:12 time: 0.7461 data_time: 0.0020 memory: 25074 loss: 0.0056 2023/06/01 15:06:17 - mmengine - INFO - Epoch(train) [16][5200/5758] lr: 5.9432e-04 eta: 5:31:46 time: 0.8317 data_time: 0.0025 memory: 25074 loss: 0.0006 2023/06/01 15:07:37 - mmengine - INFO - Epoch(train) [16][5300/5758] lr: 5.9432e-04 eta: 5:30:20 time: 0.7502 data_time: 0.0016 memory: 25074 loss: 0.0013 2023/06/01 15:08:56 - mmengine - INFO - Epoch(train) [16][5400/5758] lr: 5.9432e-04 eta: 5:28:55 time: 0.7772 data_time: 0.0020 memory: 25074 loss: 0.0012