2023/03/03 14:03:20 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.10.8 (main, Nov 24 2022, 14:13:03) [GCC 11.2.0] CUDA available: True numpy_random_seed: 42 GPU 0,1,2,3: NVIDIA A100-SXM4-80GB CUDA_HOME: None GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44) PyTorch: 1.12.1 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 11.3 - 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 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, 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 -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.13.1 OpenCV: 4.7.0 MMEngine: 0.6.0 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 42 Distributed launcher: pytorch Distributed training: True GPU number: 4 ------------------------------------------------------------ 2023/03/03 14:03:21 - mmengine - INFO - Config: custom_imports = dict(imports=['spts'], allow_failed_imports=False) file_client_args = dict(backend='disk') dictionary = dict( type='SPTSDictionary', dict_file= 'mmocr/projects/SPTS/config/spts/../../dicts/spts.txt', with_start=True, with_end=True, with_seq_end=True, same_start_end=False, with_padding=True, with_unknown=True, unknown_token=None) num_bins = 1000 model = dict( type='SPTS', data_preprocessor=dict( type='TextDetDataPreprocessor', mean=[0, 0, 0], std=[255, 255, 255], bgr_to_rgb=True), backbone=dict( type='mmdet.ResNet', depth=50, num_stages=4, out_indices=(3, ), frozen_stages=-1, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), style='pytorch'), encoder=dict(type='SPTSEncoder', d_backbone=2048, d_model=256), decoder=dict( type='SPTSDecoder', dictionary=dict( type='SPTSDictionary', dict_file= 'mmocr/projects/SPTS/config/spts/../../dicts/spts.txt', with_start=True, with_end=True, with_seq_end=True, same_start_end=False, with_padding=True, with_unknown=True, unknown_token=None), num_bins=1000, d_model=256, dropout=0.1, max_num_text=60, module_loss=dict( type='SPTSModuleLoss', num_bins=1000, ignore_first_char=True), postprocessor=dict(type='SPTSPostprocessor', num_bins=1000))) test_pipeline = [ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), dict( type='RescaleToShortSide', short_side_lens=[1000], long_side_bound=1824), dict( type='LoadOCRAnnotationsWithBezier', with_bbox=True, with_label=True, with_polygon=True, with_text=True), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ] train_pipeline = [ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), dict( type='LoadOCRAnnotationsWithBezier', with_bbox=True, with_label=True, with_polygon=True, with_text=True), dict(type='FixInvalidPolygon'), dict(type='RemoveIgnored'), dict(type='RandomCrop', min_side_ratio=0.5), dict( type='RandomApply', transforms=[ dict( type='RandomRotate', max_angle=30, pad_with_fixed_color=True, use_canvas=True) ], prob=0.3), dict(type='FixInvalidPolygon'), dict( type='RandomChoiceResize', scales=[(640, 1600), (672, 1600), (704, 1600), (736, 1600), (768, 1600), (800, 1600), (832, 1600), (864, 1600), (896, 1600)], keep_ratio=True), dict( type='RandomApply', transforms=[ dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5) ], prob=0.5), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ] icdar2013_textspotting_data_root = 'data/icdar2013' icdar2013_textspotting_train = dict( type='OCRDataset', data_root='data/icdar2013', ann_file='textspotting_train.json', filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=[ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), dict( type='LoadOCRAnnotationsWithBezier', with_bbox=True, with_label=True, with_polygon=True, with_text=True), dict(type='FixInvalidPolygon'), dict(type='RemoveIgnored'), dict(type='RandomCrop', min_side_ratio=0.5), dict( type='RandomApply', transforms=[ dict( type='RandomRotate', max_angle=30, pad_with_fixed_color=True, use_canvas=True) ], prob=0.3), dict(type='FixInvalidPolygon'), dict( type='RandomChoiceResize', scales=[(640, 1600), (672, 1600), (704, 1600), (736, 1600), (768, 1600), (800, 1600), (832, 1600), (864, 1600), (896, 1600)], keep_ratio=True), dict( type='RandomApply', transforms=[ dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5) ], prob=0.5), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ]) icdar2013_textspotting_test = dict( type='OCRDataset', data_root='data/icdar2013', ann_file='textspotting_test.json', test_mode=True, pipeline=[ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), dict( type='RescaleToShortSide', short_side_lens=[1000], long_side_bound=1824), dict( type='LoadOCRAnnotationsWithBezier', with_bbox=True, with_label=True, with_polygon=True, with_text=True), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ]) default_scope = 'mmocr' env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) randomness = dict(seed=42) default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=1), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', save_best='generic/hmean', rule='greater'), sampler_seed=dict(type='DistSamplerSeedHook'), sync_buffer=dict(type='SyncBuffersHook'), visualization=dict( type='VisualizationHook', interval=1, enable=False, show=False, draw_gt=False, draw_pred=False)) log_level = 'INFO' log_processor = dict(type='LogProcessor', window_size=10, by_epoch=True) load_from = 'work_dirs/spts_resnet50_150e_pretrain-spts-2/epoch_150.pth' resume = False val_evaluator = [ dict( type='E2EPointMetric', prefix='generic', lexicon_path='data/icdar2013/lexicons/GenericVocabulary_new.txt', pair_path='data/icdar2013/lexicons/GenericVocabulary_pair_list.txt', match_dist_thr=None), dict( type='E2EPointMetric', prefix='weak', lexicon_path='data/icdar2013/lexicons/ch2_test_vocabulary_new.txt', pair_path='data/icdar2013/lexicons/ch2_test_vocabulary_pair_list.txt', match_dist_thr=0.4), dict( type='E2EPointMetric', prefix='strong', lexicon_path='data/icdar2013/lexicons/lexicons/', lexicon_mapping=('(.*).jpg', 'new_voc_\\1.txt'), pair_path='data/icdar2013/lexicons/pairs/', pair_mapping=('(.*).jpg', 'pair_voc_\\1.txt'), match_dist_thr=0.4) ] test_evaluator = [ dict( type='E2EPointMetric', prefix='generic', lexicon_path='data/icdar2013/lexicons/GenericVocabulary_new.txt', pair_path='data/icdar2013/lexicons/GenericVocabulary_pair_list.txt', match_dist_thr=None), dict( type='E2EPointMetric', prefix='weak', lexicon_path='data/icdar2013/lexicons/ch2_test_vocabulary_new.txt', pair_path='data/icdar2013/lexicons/ch2_test_vocabulary_pair_list.txt', match_dist_thr=0.4), dict( type='E2EPointMetric', prefix='strong', lexicon_path='data/icdar2013/lexicons/lexicons/', lexicon_mapping=('(.*).jpg', 'new_voc_\\1.txt'), pair_path='data/icdar2013/lexicons/pairs/', pair_mapping=('(.*).jpg', 'pair_voc_\\1.txt'), match_dist_thr=0.4) ] vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='TextSpottingLocalVisualizer', name='visualizer', vis_backends=[dict(type='LocalVisBackend')]) num_epochs = 200 lr = 1e-05 optim_wrapper = dict( type='AmpOptimWrapper', accumulative_counts=2, optimizer=dict(type='AdamW', lr=1e-05, weight_decay=0.0001), paramwise_cfg=dict(custom_keys=dict(backbone=dict(lr_mult=0.1))), loss_scale='dynamic') train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=200, val_interval=10) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') train_dataloader = dict( batch_size=8, num_workers=8, pin_memory=True, persistent_workers=True, sampler=dict(type='RepeatAugSampler', shuffle=True, num_repeats=2), dataset=dict( type='OCRDataset', data_root='data/icdar2013', ann_file='textspotting_train.json', filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=[ dict( type='LoadImageFromFile', color_type='color_ignore_orientation'), dict( type='LoadOCRAnnotationsWithBezier', with_bbox=True, with_label=True, with_polygon=True, with_text=True), dict(type='FixInvalidPolygon'), dict(type='RemoveIgnored'), dict(type='RandomCrop', min_side_ratio=0.5), dict( type='RandomApply', transforms=[ dict( type='RandomRotate', max_angle=30, pad_with_fixed_color=True, use_canvas=True) ], prob=0.3), dict(type='FixInvalidPolygon'), dict( type='RandomChoiceResize', scales=[(640, 1600), (672, 1600), (704, 1600), (736, 1600), (768, 1600), (800, 1600), (832, 1600), (864, 1600), (896, 1600)], keep_ratio=True), dict( type='RandomApply', transforms=[ dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5) ], prob=0.5), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ])) val_dataloader = dict( batch_size=1, num_workers=4, pin_memory=True, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='OCRDataset', data_root='data/icdar2013', ann_file='textspotting_test.json', test_mode=True, pipeline=[ dict( type='LoadImageFromFile', color_type='color_ignore_orientation'), dict( type='RescaleToShortSide', short_side_lens=[1000], long_side_bound=1824), dict( type='LoadOCRAnnotationsWithBezier', with_bbox=True, with_label=True, with_polygon=True, with_text=True), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ])) test_dataloader = dict( batch_size=1, num_workers=4, pin_memory=True, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='OCRDataset', data_root='data/icdar2013', ann_file='textspotting_test.json', test_mode=True, pipeline=[ dict( type='LoadImageFromFile', color_type='color_ignore_orientation'), dict( type='RescaleToShortSide', short_side_lens=[1000], long_side_bound=1824), dict( type='LoadOCRAnnotationsWithBezier', with_bbox=True, with_label=True, with_polygon=True, with_text=True), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor')) ])) launcher = 'pytorch' work_dir = './work_dirs/spts_resnet50_350e_icdar2013' 2023/03/03 14:03:21 - mmengine - WARNING - The "visualizer" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:21 - mmengine - WARNING - The "vis_backend" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:23 - mmengine - WARNING - The "model" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:23 - mmengine - WARNING - The "task util" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:24 - mmengine - WARNING - The "hook" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:24 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (BELOW_NORMAL) LoggerHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (NORMAL ) SyncBuffersHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val_epoch: (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) VisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_test_epoch: (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) VisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2023/03/03 14:03:25 - mmengine - WARNING - The "loop" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:25 - mmengine - WARNING - The "dataset" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:25 - mmengine - WARNING - The "transform" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:25 - mmengine - WARNING - The "data sampler" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:25 - mmengine - WARNING - The "optimizer constructor" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.0.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.0.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.0.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.0.downsample.0.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.1.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer1.2.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.0.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.0.downsample.0.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.0.downsample.0.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.0.downsample.0.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.1.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.2.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer2.3.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.0.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.0.downsample.0.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.0.downsample.0.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.0.downsample.0.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.1.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.2.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.3.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.4.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer3.5.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.0.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.0.downsample.0.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.0.downsample.0.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.0.downsample.0.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.1.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv1.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv1.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv1.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv2.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv2.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv2.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv3.weight:lr=1.0000000000000002e-06 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv3.weight:weight_decay=0.0001 2023/03/03 14:03:25 - mmengine - INFO - paramwise_options -- backbone.layer4.2.conv3.weight:lr_mult=0.1 2023/03/03 14:03:25 - mmengine - WARNING - The "optimizer" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:25 - mmengine - WARNING - The "optim wrapper" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:25 - mmengine - WARNING - The "metric" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:27 - mmengine - WARNING - The "weight initializer" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead. 2023/03/03 14:03:27 - mmengine - INFO - load model from: torchvision://resnet50 2023/03/03 14:03:27 - mmengine - INFO - Loads checkpoint by torchvision backend from path: torchvision://resnet50 2023/03/03 14:03:27 - mmengine - WARNING - The model and loaded state dict do not match exactly unexpected key in source state_dict: fc.weight, fc.bias Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): PretrainedInit: load from torchvision://resnet50 backbone.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn3.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn3.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn3.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn3.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn3.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn3.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.downsample.1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.downsample.1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.downsample.1.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.downsample.1.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn3.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn3.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.downsample.1.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.downsample.1.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn3.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn3.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn3.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn3.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 encoder.input_proj.weight - torch.Size([256, 2048, 1, 1]): The value is the same before and after calling `init_weights` of SPTS encoder.input_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.embedding.word_embeddings.weight - torch.Size([1100, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.embedding.position_embeddings.weight - torch.Size([1621, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.embedding.LayerNorm.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.embedding.LayerNorm.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.vocab_embed.layer-0.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.vocab_embed.layer-0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.vocab_embed.layer-1.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.vocab_embed.layer-1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.vocab_embed.layer-2.weight - torch.Size([1100, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.vocab_embed.layer-2.bias - torch.Size([1100]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.0.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.1.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.2.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.3.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.4.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.layers.5.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.norm.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.encoder.norm.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.multihead_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.multihead_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.multihead_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.multihead_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.norm3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.0.norm3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.multihead_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.multihead_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.multihead_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.multihead_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.norm3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.1.norm3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.multihead_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.multihead_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.multihead_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.multihead_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.norm3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.2.norm3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.multihead_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.multihead_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.multihead_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.multihead_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.norm3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.3.norm3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.multihead_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.multihead_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.multihead_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.multihead_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.norm3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.4.norm3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.self_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.self_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.self_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.self_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.multihead_attn.in_proj_weight - torch.Size([768, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.multihead_attn.in_proj_bias - torch.Size([768]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.multihead_attn.out_proj.weight - torch.Size([256, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.multihead_attn.out_proj.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.linear1.weight - torch.Size([1024, 256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.linear1.bias - torch.Size([1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.linear2.weight - torch.Size([256, 1024]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.linear2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.norm1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.norm1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.norm2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.norm2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.norm3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.layers.5.norm3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.norm.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS decoder.decoder.norm.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of SPTS 2023/03/03 14:03:28 - mmengine - INFO - Load checkpoint from work_dirs/spts_resnet50_150e_pretrain-spts-2/epoch_150.pth 2023/03/03 14:03:28 - mmengine - WARNING - Gradient accumulative may slightly decrease performance because the model has BatchNorm layers. 2023/03/03 14:03:28 - mmengine - INFO - Checkpoints will be saved to mmocr/projects/SPTS/work_dirs/spts_resnet50_350e_icdar2013. 2023/03/03 14:03:33 - mmengine - INFO - Epoch(train) [1][ 1/15] lr: 1.0000e-06 eta: 4:40:18 time: 5.6079 data_time: 1.2967 memory: 16088 loss: 0.2231 loss_ce: 0.2231 2023/03/03 14:03:34 - mmengine - INFO - Epoch(train) [1][ 2/15] lr: 1.0000e-06 eta: 2:32:33 time: 3.0531 data_time: 0.6503 memory: 21401 loss: 0.1671 loss_ce: 0.1671 2023/03/03 14:03:35 - mmengine - INFO - Epoch(train) [1][ 3/15] lr: 1.0000e-06 eta: 2:01:34 time: 2.4340 data_time: 0.4341 memory: 15738 loss: 0.2110 loss_ce: 0.2110 2023/03/03 14:03:35 - mmengine - INFO - Epoch(train) [1][ 4/15] lr: 1.0000e-06 eta: 1:36:48 time: 1.9388 data_time: 0.3262 memory: 16976 loss: 0.2296 loss_ce: 0.2296 2023/03/03 14:03:36 - mmengine - INFO - Epoch(train) [1][ 5/15] lr: 1.0000e-06 eta: 1:19:35 time: 1.5944 data_time: 0.2614 memory: 16056 loss: 0.2077 loss_ce: 0.2077 2023/03/03 14:03:36 - mmengine - INFO - Epoch(train) [1][ 6/15] lr: 1.0000e-06 eta: 1:08:34 time: 1.3743 data_time: 0.2183 memory: 19131 loss: 0.2078 loss_ce: 0.2078 2023/03/03 14:03:36 - mmengine - INFO - Epoch(train) [1][ 7/15] lr: 1.0000e-06 eta: 1:00:17 time: 1.2087 data_time: 0.1874 memory: 16530 loss: 0.2053 loss_ce: 0.2053 2023/03/03 14:03:37 - mmengine - INFO - Epoch(train) [1][ 8/15] lr: 1.0000e-06 eta: 0:59:43 time: 1.1975 data_time: 0.1642 memory: 17421 loss: 0.2119 loss_ce: 0.2119 2023/03/03 14:03:38 - mmengine - INFO - Epoch(train) [1][ 9/15] lr: 1.0000e-06 eta: 0:55:04 time: 1.1047 data_time: 0.1462 memory: 22336 loss: 0.2048 loss_ce: 0.2048 2023/03/03 14:03:38 - mmengine - INFO - Epoch(train) [1][10/15] lr: 1.0000e-06 eta: 0:51:55 time: 1.0420 data_time: 0.1317 memory: 17572 loss: 0.2037 loss_ce: 0.2037 2023/03/03 14:03:38 - mmengine - INFO - Epoch(train) [1][11/15] lr: 1.0000e-06 eta: 0:48:10 time: 0.5030 data_time: 0.0023 memory: 16508 loss: 0.2100 loss_ce: 0.2100 2023/03/03 14:03:39 - mmengine - INFO - Epoch(train) [1][12/15] lr: 1.0000e-06 eta: 0:46:42 time: 0.5148 data_time: 0.0023 memory: 19217 loss: 0.2279 loss_ce: 0.2279 2023/03/03 14:03:39 - mmengine - INFO - Epoch(train) [1][13/15] lr: 1.0000e-06 eta: 0:43:52 time: 0.4157 data_time: 0.0024 memory: 18953 loss: 0.2090 loss_ce: 0.2090 2023/03/03 14:03:40 - mmengine - INFO - Epoch(train) [1][14/15] lr: 1.0000e-06 eta: 0:42:49 time: 0.4291 data_time: 0.0024 memory: 19680 loss: 0.2024 loss_ce: 0.2024 2023/03/03 14:03:40 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:03:40 - mmengine - INFO - Epoch(train) [1][15/15] lr: 1.0000e-06 eta: 0:40:26 time: 0.4221 data_time: 0.0027 memory: 5380 loss: 0.2215 loss_ce: 0.2215 2023/03/03 14:03:41 - mmengine - INFO - Epoch(train) [2][ 1/15] lr: 1.0000e-06 eta: 0:41:42 time: 0.5173 data_time: 0.0749 memory: 18241 loss: 0.2149 loss_ce: 0.2149 2023/03/03 14:03:41 - mmengine - INFO - Epoch(train) [2][ 2/15] lr: 1.0000e-06 eta: 0:39:52 time: 0.5176 data_time: 0.0749 memory: 16849 loss: 0.2116 loss_ce: 0.2116 2023/03/03 14:03:42 - mmengine - INFO - Epoch(train) [2][ 3/15] lr: 1.0000e-06 eta: 0:39:42 time: 0.4802 data_time: 0.0750 memory: 22208 loss: 0.2048 loss_ce: 0.2048 2023/03/03 14:03:42 - mmengine - INFO - Epoch(train) [2][ 4/15] lr: 1.0000e-06 eta: 0:38:42 time: 0.4860 data_time: 0.0750 memory: 26800 loss: 0.2108 loss_ce: 0.2108 2023/03/03 14:03:43 - mmengine - INFO - Epoch(train) [2][ 5/15] lr: 1.0000e-06 eta: 0:37:17 time: 0.4597 data_time: 0.0751 memory: 16992 loss: 0.2194 loss_ce: 0.2194 2023/03/03 14:03:43 - mmengine - INFO - Epoch(train) [2][ 6/15] lr: 1.0000e-06 eta: 0:36:00 time: 0.4592 data_time: 0.0751 memory: 15765 loss: 0.2220 loss_ce: 0.2220 2023/03/03 14:03:43 - mmengine - INFO - Epoch(train) [2][ 7/15] lr: 1.0000e-06 eta: 0:34:58 time: 0.4250 data_time: 0.0750 memory: 16976 loss: 0.2240 loss_ce: 0.2240 2023/03/03 14:03:43 - mmengine - INFO - Epoch(train) [2][ 8/15] lr: 1.0000e-06 eta: 0:33:54 time: 0.4261 data_time: 0.0753 memory: 15911 loss: 0.2318 loss_ce: 0.2318 2023/03/03 14:03:45 - mmengine - INFO - Epoch(train) [2][ 9/15] lr: 1.0000e-06 eta: 0:35:07 time: 0.4947 data_time: 0.0752 memory: 25716 loss: 0.2227 loss_ce: 0.2227 2023/03/03 14:03:45 - mmengine - INFO - Epoch(train) [2][10/15] lr: 1.0000e-06 eta: 0:34:17 time: 0.5097 data_time: 0.0750 memory: 22323 loss: 0.2097 loss_ce: 0.2097 2023/03/03 14:03:45 - mmengine - INFO - Epoch(train) [2][11/15] lr: 1.0000e-06 eta: 0:33:49 time: 0.4321 data_time: 0.0028 memory: 19916 loss: 0.2060 loss_ce: 0.2060 2023/03/03 14:03:46 - mmengine - INFO - Epoch(train) [2][12/15] lr: 1.0000e-06 eta: 0:32:59 time: 0.4338 data_time: 0.0029 memory: 17968 loss: 0.2030 loss_ce: 0.2030 2023/03/03 14:03:47 - mmengine - INFO - Epoch(train) [2][13/15] lr: 1.0000e-06 eta: 0:33:53 time: 0.4780 data_time: 0.0028 memory: 16654 loss: 0.2022 loss_ce: 0.2022 2023/03/03 14:03:47 - mmengine - INFO - Epoch(train) [2][14/15] lr: 1.0000e-06 eta: 0:33:03 time: 0.4556 data_time: 0.0028 memory: 17421 loss: 0.2016 loss_ce: 0.2016 2023/03/03 14:03:47 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:03:47 - mmengine - INFO - Epoch(train) [2][15/15] lr: 1.0000e-06 eta: 0:32:36 time: 0.4745 data_time: 0.0029 memory: 5279 loss: 0.2006 loss_ce: 0.2006 2023/03/03 14:03:49 - mmengine - INFO - Epoch(train) [3][ 1/15] lr: 1.0000e-06 eta: 0:33:43 time: 0.5895 data_time: 0.1078 memory: 13962 loss: 0.1903 loss_ce: 0.1903 2023/03/03 14:03:49 - mmengine - INFO - Epoch(train) [3][ 2/15] lr: 1.0000e-06 eta: 0:33:02 time: 0.5866 data_time: 0.1079 memory: 16370 loss: 0.1833 loss_ce: 0.1833 2023/03/03 14:03:49 - mmengine - INFO - Epoch(train) [3][ 3/15] lr: 1.0000e-06 eta: 0:32:17 time: 0.5832 data_time: 0.1075 memory: 16191 loss: 0.1826 loss_ce: 0.1826 2023/03/03 14:03:50 - mmengine - INFO - Epoch(train) [3][ 4/15] lr: 1.0000e-06 eta: 0:31:50 time: 0.4905 data_time: 0.1074 memory: 23502 loss: 0.2003 loss_ce: 0.2003 2023/03/03 14:03:50 - mmengine - INFO - Epoch(train) [3][ 5/15] lr: 1.0000e-06 eta: 0:31:40 time: 0.5147 data_time: 0.1074 memory: 25353 loss: 0.1995 loss_ce: 0.1995 2023/03/03 14:03:50 - mmengine - INFO - Epoch(train) [3][ 6/15] lr: 1.0000e-06 eta: 0:31:07 time: 0.4941 data_time: 0.1074 memory: 16508 loss: 0.2137 loss_ce: 0.2137 2023/03/03 14:03:51 - mmengine - INFO - Epoch(train) [3][ 7/15] lr: 1.0000e-06 eta: 0:30:31 time: 0.4898 data_time: 0.1073 memory: 18409 loss: 0.2140 loss_ce: 0.2140 2023/03/03 14:03:51 - mmengine - INFO - Epoch(train) [3][ 8/15] lr: 1.0000e-06 eta: 0:30:06 time: 0.4019 data_time: 0.1073 memory: 14093 loss: 0.2121 loss_ce: 0.2121 2023/03/03 14:03:51 - mmengine - INFO - Epoch(train) [3][ 9/15] lr: 1.0000e-06 eta: 0:29:35 time: 0.4031 data_time: 0.1072 memory: 15911 loss: 0.2111 loss_ce: 0.2111 2023/03/03 14:03:51 - mmengine - INFO - Epoch(train) [3][10/15] lr: 1.0000e-06 eta: 0:29:14 time: 0.3949 data_time: 0.1070 memory: 17848 loss: 0.2021 loss_ce: 0.2021 2023/03/03 14:03:52 - mmengine - INFO - Epoch(train) [3][11/15] lr: 1.0000e-06 eta: 0:28:44 time: 0.2776 data_time: 0.0020 memory: 17572 loss: 0.2047 loss_ce: 0.2047 2023/03/03 14:03:52 - mmengine - INFO - Epoch(train) [3][12/15] lr: 1.0000e-06 eta: 0:28:23 time: 0.2814 data_time: 0.0018 memory: 17572 loss: 0.1929 loss_ce: 0.1929 2023/03/03 14:03:52 - mmengine - INFO - Epoch(train) [3][13/15] lr: 1.0000e-06 eta: 0:28:05 time: 0.2958 data_time: 0.0018 memory: 24124 loss: 0.1882 loss_ce: 0.1882 2023/03/03 14:03:52 - mmengine - INFO - Epoch(train) [3][14/15] lr: 1.0000e-06 eta: 0:27:48 time: 0.2945 data_time: 0.0018 memory: 18766 loss: 0.1681 loss_ce: 0.1681 2023/03/03 14:03:53 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:03:53 - mmengine - INFO - Epoch(train) [3][15/15] lr: 1.0000e-06 eta: 0:27:20 time: 0.2552 data_time: 0.0019 memory: 5757 loss: 0.1743 loss_ce: 0.1743 2023/03/03 14:03:54 - mmengine - INFO - Epoch(train) [4][ 1/15] lr: 1.0000e-06 eta: 0:28:04 time: 0.3554 data_time: 0.0722 memory: 15911 loss: 0.1675 loss_ce: 0.1675 2023/03/03 14:03:54 - mmengine - INFO - Epoch(train) [4][ 2/15] lr: 1.0000e-06 eta: 0:27:42 time: 0.3581 data_time: 0.0723 memory: 16654 loss: 0.1723 loss_ce: 0.1723 2023/03/03 14:03:55 - mmengine - INFO - Epoch(train) [4][ 3/15] lr: 1.0000e-06 eta: 0:27:33 time: 0.3705 data_time: 0.0724 memory: 16212 loss: 0.1686 loss_ce: 0.1686 2023/03/03 14:03:55 - mmengine - INFO - Epoch(train) [4][ 4/15] lr: 1.0000e-06 eta: 0:27:12 time: 0.3724 data_time: 0.0724 memory: 17619 loss: 0.1622 loss_ce: 0.1622 2023/03/03 14:03:55 - mmengine - INFO - Epoch(train) [4][ 5/15] lr: 1.0000e-06 eta: 0:27:09 time: 0.3915 data_time: 0.0724 memory: 14198 loss: 0.1608 loss_ce: 0.1608 2023/03/03 14:03:56 - mmengine - INFO - Epoch(train) [4][ 6/15] lr: 1.0000e-06 eta: 0:27:25 time: 0.4561 data_time: 0.0724 memory: 33420 loss: 0.1691 loss_ce: 0.1691 2023/03/03 14:03:56 - mmengine - INFO - Epoch(train) [4][ 7/15] lr: 1.0000e-06 eta: 0:27:07 time: 0.4521 data_time: 0.0725 memory: 16223 loss: 0.1802 loss_ce: 0.1802 2023/03/03 14:03:57 - mmengine - INFO - Epoch(train) [4][ 8/15] lr: 1.0000e-06 eta: 0:26:48 time: 0.4415 data_time: 0.0726 memory: 16804 loss: 0.1824 loss_ce: 0.1824 2023/03/03 14:03:57 - mmengine - INFO - Epoch(train) [4][ 9/15] lr: 1.0000e-06 eta: 0:26:39 time: 0.4476 data_time: 0.0726 memory: 13784 loss: 0.1950 loss_ce: 0.1950 2023/03/03 14:03:57 - mmengine - INFO - Epoch(train) [4][10/15] lr: 1.0000e-06 eta: 0:26:19 time: 0.4519 data_time: 0.0725 memory: 16976 loss: 0.1976 loss_ce: 0.1976 2023/03/03 14:03:58 - mmengine - INFO - Epoch(train) [4][11/15] lr: 1.0000e-06 eta: 0:26:10 time: 0.3631 data_time: 0.0021 memory: 17572 loss: 0.1994 loss_ce: 0.1994 2023/03/03 14:03:58 - mmengine - INFO - Epoch(train) [4][12/15] lr: 1.0000e-06 eta: 0:25:52 time: 0.3621 data_time: 0.0021 memory: 15911 loss: 0.2005 loss_ce: 0.2005 2023/03/03 14:03:58 - mmengine - INFO - Epoch(train) [4][13/15] lr: 1.0000e-06 eta: 0:25:41 time: 0.3504 data_time: 0.0020 memory: 14198 loss: 0.2085 loss_ce: 0.2085 2023/03/03 14:03:58 - mmengine - INFO - Epoch(train) [4][14/15] lr: 1.0000e-06 eta: 0:25:33 time: 0.3647 data_time: 0.0019 memory: 22670 loss: 0.2227 loss_ce: 0.2227 2023/03/03 14:03:59 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:03:59 - mmengine - INFO - Epoch(train) [4][15/15] lr: 1.0000e-06 eta: 0:25:15 time: 0.3309 data_time: 0.0020 memory: 6121 loss: 0.2396 loss_ce: 0.2396 2023/03/03 14:03:59 - mmengine - INFO - Epoch(train) [5][ 1/15] lr: 1.0000e-06 eta: 0:25:32 time: 0.3344 data_time: 0.0662 memory: 16955 loss: 0.2188 loss_ce: 0.2188 2023/03/03 14:04:00 - mmengine - INFO - Epoch(train) [5][ 2/15] lr: 1.0000e-06 eta: 0:25:22 time: 0.3421 data_time: 0.0662 memory: 15202 loss: 0.2215 loss_ce: 0.2215 2023/03/03 14:04:00 - mmengine - INFO - Epoch(train) [5][ 3/15] lr: 1.0000e-06 eta: 0:25:14 time: 0.3567 data_time: 0.0662 memory: 22012 loss: 0.2146 loss_ce: 0.2146 2023/03/03 14:04:01 - mmengine - INFO - Epoch(train) [5][ 4/15] lr: 1.0000e-06 eta: 0:25:12 time: 0.3643 data_time: 0.0662 memory: 17446 loss: 0.2028 loss_ce: 0.2028 2023/03/03 14:04:01 - mmengine - INFO - Epoch(train) [5][ 5/15] lr: 1.0000e-06 eta: 0:24:58 time: 0.3671 data_time: 0.0662 memory: 16654 loss: 0.1969 loss_ce: 0.1969 2023/03/03 14:04:01 - mmengine - INFO - Epoch(train) [5][ 6/15] lr: 1.0000e-06 eta: 0:24:47 time: 0.3606 data_time: 0.0662 memory: 17120 loss: 0.1951 loss_ce: 0.1951 2023/03/03 14:04:01 - mmengine - INFO - Epoch(train) [5][ 7/15] lr: 1.0000e-06 eta: 0:24:34 time: 0.3611 data_time: 0.0661 memory: 16223 loss: 0.2016 loss_ce: 0.2016 2023/03/03 14:04:02 - mmengine - INFO - Epoch(train) [5][ 8/15] lr: 1.0000e-06 eta: 0:24:32 time: 0.3757 data_time: 0.0661 memory: 16976 loss: 0.2053 loss_ce: 0.2053 2023/03/03 14:04:02 - mmengine - INFO - Epoch(train) [5][ 9/15] lr: 1.0000e-06 eta: 0:24:19 time: 0.3604 data_time: 0.0661 memory: 16370 loss: 0.1958 loss_ce: 0.1958 2023/03/03 14:04:02 - mmengine - INFO - Epoch(train) [5][10/15] lr: 1.0000e-06 eta: 0:24:09 time: 0.3689 data_time: 0.0661 memory: 17968 loss: 0.1709 loss_ce: 0.1709 2023/03/03 14:04:02 - mmengine - INFO - Epoch(train) [5][11/15] lr: 1.0000e-06 eta: 0:23:56 time: 0.3009 data_time: 0.0019 memory: 17767 loss: 0.1731 loss_ce: 0.1731 2023/03/03 14:04:03 - mmengine - INFO - Epoch(train) [5][12/15] lr: 1.0000e-06 eta: 0:23:59 time: 0.3277 data_time: 0.0018 memory: 28423 loss: 0.1627 loss_ce: 0.1627 2023/03/03 14:04:03 - mmengine - INFO - Epoch(train) [5][13/15] lr: 1.0000e-06 eta: 0:23:51 time: 0.3215 data_time: 0.0018 memory: 22950 loss: 0.1670 loss_ce: 0.1670 2023/03/03 14:04:04 - mmengine - INFO - Epoch(train) [5][14/15] lr: 1.0000e-06 eta: 0:23:48 time: 0.3154 data_time: 0.0017 memory: 19676 loss: 0.1761 loss_ce: 0.1761 2023/03/03 14:04:04 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:04:04 - mmengine - INFO - Epoch(train) [5][15/15] lr: 1.0000e-06 eta: 0:23:32 time: 0.3041 data_time: 0.0017 memory: 6071 loss: 0.1782 loss_ce: 0.1782 2023/03/03 14:04:05 - mmengine - INFO - Epoch(train) [6][ 1/15] lr: 1.0000e-06 eta: 0:23:59 time: 0.3942 data_time: 0.0538 memory: 16958 loss: 0.1768 loss_ce: 0.1768 2023/03/03 14:04:05 - mmengine - INFO - Epoch(train) [6][ 2/15] lr: 1.0000e-06 eta: 0:23:48 time: 0.3945 data_time: 0.0538 memory: 16370 loss: 0.1722 loss_ce: 0.1722 2023/03/03 14:04:06 - mmengine - INFO - Epoch(train) [6][ 3/15] lr: 1.0000e-06 eta: 0:23:48 time: 0.3981 data_time: 0.0539 memory: 17892 loss: 0.1619 loss_ce: 0.1619 2023/03/03 14:04:06 - mmengine - INFO - Epoch(train) [6][ 4/15] lr: 1.0000e-06 eta: 0:23:40 time: 0.4051 data_time: 0.0539 memory: 20549 loss: 0.1589 loss_ce: 0.1589 2023/03/03 14:04:06 - mmengine - INFO - Epoch(train) [6][ 5/15] lr: 1.0000e-06 eta: 0:23:36 time: 0.4193 data_time: 0.0539 memory: 17638 loss: 0.1657 loss_ce: 0.1657 2023/03/03 14:04:07 - mmengine - INFO - Epoch(train) [6][ 6/15] lr: 1.0000e-06 eta: 0:23:26 time: 0.4210 data_time: 0.0539 memory: 15911 loss: 0.1689 loss_ce: 0.1689 2023/03/03 14:04:07 - mmengine - INFO - Epoch(train) [6][ 7/15] lr: 1.0000e-06 eta: 0:23:21 time: 0.3984 data_time: 0.0539 memory: 23078 loss: 0.1742 loss_ce: 0.1742 2023/03/03 14:04:07 - mmengine - INFO - Epoch(train) [6][ 8/15] lr: 1.0000e-06 eta: 0:23:10 time: 0.3869 data_time: 0.0538 memory: 17421 loss: 0.1840 loss_ce: 0.1840 2023/03/03 14:04:08 - mmengine - INFO - Epoch(train) [6][ 9/15] lr: 1.0000e-06 eta: 0:23:10 time: 0.3925 data_time: 0.0538 memory: 17421 loss: 0.1769 loss_ce: 0.1769 2023/03/03 14:04:08 - mmengine - INFO - Epoch(train) [6][10/15] lr: 1.0000e-06 eta: 0:22:58 time: 0.3989 data_time: 0.0538 memory: 14322 loss: 0.1722 loss_ce: 0.1722 2023/03/03 14:04:08 - mmengine - INFO - Epoch(train) [6][11/15] lr: 1.0000e-06 eta: 0:22:59 time: 0.3307 data_time: 0.0019 memory: 14197 loss: 0.1737 loss_ce: 0.1737 2023/03/03 14:04:09 - mmengine - INFO - Epoch(train) [6][12/15] lr: 1.0000e-06 eta: 0:22:50 time: 0.3300 data_time: 0.0019 memory: 15556 loss: 0.1658 loss_ce: 0.1658 2023/03/03 14:04:09 - mmengine - INFO - Epoch(train) [6][13/15] lr: 1.0000e-06 eta: 0:22:50 time: 0.3280 data_time: 0.0018 memory: 16976 loss: 0.1716 loss_ce: 0.1716 2023/03/03 14:04:09 - mmengine - INFO - Epoch(train) [6][14/15] lr: 1.0000e-06 eta: 0:22:41 time: 0.3200 data_time: 0.0018 memory: 15631 loss: 0.1700 loss_ce: 0.1700 2023/03/03 14:04:10 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:04:10 - mmengine - INFO - Epoch(train) [6][15/15] lr: 1.0000e-06 eta: 0:22:34 time: 0.3073 data_time: 0.0017 memory: 7262 loss: 0.1696 loss_ce: 0.1696 2023/03/03 14:04:11 - mmengine - INFO - Epoch(train) [7][ 1/15] lr: 1.0000e-06 eta: 0:22:49 time: 0.3829 data_time: 0.0685 memory: 21466 loss: 0.1627 loss_ce: 0.1627 2023/03/03 14:04:11 - mmengine - INFO - Epoch(train) [7][ 2/15] lr: 1.0000e-06 eta: 0:22:48 time: 0.3896 data_time: 0.0686 memory: 13874 loss: 0.1683 loss_ce: 0.1683 2023/03/03 14:04:11 - mmengine - INFO - Epoch(train) [7][ 3/15] lr: 1.0000e-06 eta: 0:22:38 time: 0.3879 data_time: 0.0687 memory: 12722 loss: 0.1676 loss_ce: 0.1676 2023/03/03 14:04:12 - mmengine - INFO - Epoch(train) [7][ 4/15] lr: 1.0000e-06 eta: 0:22:38 time: 0.3907 data_time: 0.0688 memory: 15315 loss: 0.1642 loss_ce: 0.1642 2023/03/03 14:04:12 - mmengine - INFO - Epoch(train) [7][ 5/15] lr: 1.0000e-06 eta: 0:22:29 time: 0.3910 data_time: 0.0688 memory: 14509 loss: 0.1672 loss_ce: 0.1672 2023/03/03 14:04:12 - mmengine - INFO - Epoch(train) [7][ 6/15] lr: 1.0000e-06 eta: 0:22:32 time: 0.3988 data_time: 0.0687 memory: 19314 loss: 0.1733 loss_ce: 0.1733 2023/03/03 14:04:13 - mmengine - INFO - Epoch(train) [7][ 7/15] lr: 1.0000e-06 eta: 0:22:24 time: 0.3988 data_time: 0.0687 memory: 16056 loss: 0.1760 loss_ce: 0.1760 2023/03/03 14:04:13 - mmengine - INFO - Epoch(train) [7][ 8/15] lr: 1.0000e-06 eta: 0:22:19 time: 0.3835 data_time: 0.0687 memory: 15767 loss: 0.1778 loss_ce: 0.1778 2023/03/03 14:04:13 - mmengine - INFO - Epoch(train) [7][ 9/15] lr: 1.0000e-06 eta: 0:22:11 time: 0.3816 data_time: 0.0687 memory: 17120 loss: 0.1851 loss_ce: 0.1851 2023/03/03 14:04:13 - mmengine - INFO - Epoch(train) [7][10/15] lr: 1.0000e-06 eta: 0:22:06 time: 0.3860 data_time: 0.0687 memory: 15494 loss: 0.1889 loss_ce: 0.1889 2023/03/03 14:04:14 - mmengine - INFO - Epoch(train) [7][11/15] lr: 1.0000e-06 eta: 0:22:00 time: 0.3143 data_time: 0.0019 memory: 19061 loss: 0.1915 loss_ce: 0.1915 2023/03/03 14:04:14 - mmengine - INFO - Epoch(train) [7][12/15] lr: 1.0000e-06 eta: 0:21:57 time: 0.3071 data_time: 0.0019 memory: 16508 loss: 0.1788 loss_ce: 0.1788 2023/03/03 14:04:14 - mmengine - INFO - Epoch(train) [7][13/15] lr: 1.0000e-06 eta: 0:21:49 time: 0.3088 data_time: 0.0018 memory: 17120 loss: 0.1760 loss_ce: 0.1760 2023/03/03 14:04:15 - mmengine - INFO - Epoch(train) [7][14/15] lr: 1.0000e-06 eta: 0:21:45 time: 0.2942 data_time: 0.0017 memory: 20919 loss: 0.1780 loss_ce: 0.1780 2023/03/03 14:04:15 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:04:15 - mmengine - INFO - Epoch(train) [7][15/15] lr: 1.0000e-06 eta: 0:21:35 time: 0.2869 data_time: 0.0017 memory: 6850 loss: 0.1890 loss_ce: 0.1890 2023/03/03 14:04:16 - mmengine - INFO - Epoch(train) [8][ 1/15] lr: 1.0000e-06 eta: 0:22:05 time: 0.3845 data_time: 0.0702 memory: 16654 loss: 0.1814 loss_ce: 0.1814 2023/03/03 14:04:16 - mmengine - INFO - Epoch(train) [8][ 2/15] lr: 1.0000e-06 eta: 0:21:57 time: 0.3803 data_time: 0.0702 memory: 14882 loss: 0.1852 loss_ce: 0.1852 2023/03/03 14:04:17 - mmengine - INFO - Epoch(train) [8][ 3/15] lr: 1.0000e-06 eta: 0:21:56 time: 0.3932 data_time: 0.0703 memory: 17421 loss: 0.1840 loss_ce: 0.1840 2023/03/03 14:04:17 - mmengine - INFO - Epoch(train) [8][ 4/15] lr: 1.0000e-06 eta: 0:21:52 time: 0.4038 data_time: 0.0703 memory: 22442 loss: 0.1761 loss_ce: 0.1761 2023/03/03 14:04:17 - mmengine - INFO - Epoch(train) [8][ 5/15] lr: 1.0000e-06 eta: 0:21:46 time: 0.3988 data_time: 0.0703 memory: 14051 loss: 0.1716 loss_ce: 0.1716 2023/03/03 14:04:18 - mmengine - INFO - Epoch(train) [8][ 6/15] lr: 1.0000e-06 eta: 0:21:39 time: 0.3949 data_time: 0.0703 memory: 15767 loss: 0.1731 loss_ce: 0.1731 2023/03/03 14:04:18 - mmengine - INFO - Epoch(train) [8][ 7/15] lr: 1.0000e-06 eta: 0:21:35 time: 0.3877 data_time: 0.0703 memory: 18070 loss: 0.1719 loss_ce: 0.1719 2023/03/03 14:04:18 - mmengine - INFO - Epoch(train) [8][ 8/15] lr: 1.0000e-06 eta: 0:21:28 time: 0.3893 data_time: 0.0703 memory: 15268 loss: 0.1677 loss_ce: 0.1677 2023/03/03 14:04:18 - mmengine - INFO - Epoch(train) [8][ 9/15] lr: 1.0000e-06 eta: 0:21:26 time: 0.3935 data_time: 0.0703 memory: 18098 loss: 0.1698 loss_ce: 0.1698 2023/03/03 14:04:19 - mmengine - INFO - Epoch(train) [8][10/15] lr: 1.0000e-06 eta: 0:21:20 time: 0.4056 data_time: 0.0703 memory: 16573 loss: 0.1559 loss_ce: 0.1559 2023/03/03 14:04:19 - mmengine - INFO - Epoch(train) [8][11/15] lr: 1.0000e-06 eta: 0:21:14 time: 0.2698 data_time: 0.0017 memory: 13941 loss: 0.1608 loss_ce: 0.1608 2023/03/03 14:04:19 - mmengine - INFO - Epoch(train) [8][12/15] lr: 1.0000e-06 eta: 0:21:08 time: 0.2752 data_time: 0.0017 memory: 17122 loss: 0.1520 loss_ce: 0.1520 2023/03/03 14:04:19 - mmengine - INFO - Epoch(train) [8][13/15] lr: 1.0000e-06 eta: 0:21:03 time: 0.2578 data_time: 0.0017 memory: 17107 loss: 0.1556 loss_ce: 0.1556 2023/03/03 14:04:20 - mmengine - INFO - Epoch(train) [8][14/15] lr: 1.0000e-06 eta: 0:20:57 time: 0.2490 data_time: 0.0016 memory: 15432 loss: 0.1640 loss_ce: 0.1640 2023/03/03 14:04:20 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:04:20 - mmengine - INFO - Epoch(train) [8][15/15] lr: 1.0000e-06 eta: 0:20:54 time: 0.2535 data_time: 0.0016 memory: 6275 loss: 0.1637 loss_ce: 0.1637 2023/03/03 14:04:21 - mmengine - INFO - Epoch(train) [9][ 1/15] lr: 1.0000e-06 eta: 0:21:05 time: 0.3222 data_time: 0.0688 memory: 17122 loss: 0.1620 loss_ce: 0.1620 2023/03/03 14:04:21 - mmengine - INFO - Epoch(train) [9][ 2/15] lr: 1.0000e-06 eta: 0:21:03 time: 0.3333 data_time: 0.0689 memory: 16223 loss: 0.1690 loss_ce: 0.1690 2023/03/03 14:04:21 - mmengine - INFO - Epoch(train) [9][ 3/15] lr: 1.0000e-06 eta: 0:20:58 time: 0.3353 data_time: 0.0690 memory: 17284 loss: 0.1646 loss_ce: 0.1646 2023/03/03 14:04:22 - mmengine - INFO - Epoch(train) [9][ 4/15] lr: 1.0000e-06 eta: 0:20:59 time: 0.3475 data_time: 0.0690 memory: 17892 loss: 0.1604 loss_ce: 0.1604 2023/03/03 14:04:22 - mmengine - INFO - Epoch(train) [9][ 5/15] lr: 1.0000e-06 eta: 0:20:53 time: 0.3473 data_time: 0.0691 memory: 16370 loss: 0.1620 loss_ce: 0.1620 2023/03/03 14:04:23 - mmengine - INFO - Epoch(train) [9][ 6/15] lr: 1.0000e-06 eta: 0:20:59 time: 0.3961 data_time: 0.0691 memory: 16742 loss: 0.1578 loss_ce: 0.1578 2023/03/03 14:04:23 - mmengine - INFO - Epoch(train) [9][ 7/15] lr: 1.0000e-06 eta: 0:20:53 time: 0.3957 data_time: 0.0692 memory: 16654 loss: 0.1637 loss_ce: 0.1637 2023/03/03 14:04:23 - mmengine - INFO - Epoch(train) [9][ 8/15] lr: 1.0000e-06 eta: 0:20:52 time: 0.4062 data_time: 0.0691 memory: 15624 loss: 0.1559 loss_ce: 0.1559 2023/03/03 14:04:24 - mmengine - INFO - Epoch(train) [9][ 9/15] lr: 1.0000e-06 eta: 0:20:46 time: 0.4033 data_time: 0.0691 memory: 15788 loss: 0.1504 loss_ce: 0.1504 2023/03/03 14:04:24 - mmengine - INFO - Epoch(train) [9][10/15] lr: 1.0000e-06 eta: 0:20:45 time: 0.4123 data_time: 0.0691 memory: 16804 loss: 0.1464 loss_ce: 0.1464 2023/03/03 14:04:24 - mmengine - INFO - Epoch(train) [9][11/15] lr: 1.0000e-06 eta: 0:20:38 time: 0.3394 data_time: 0.0019 memory: 14198 loss: 0.1514 loss_ce: 0.1514 2023/03/03 14:04:25 - mmengine - INFO - Epoch(train) [9][12/15] lr: 1.0000e-06 eta: 0:20:40 time: 0.3522 data_time: 0.0018 memory: 16804 loss: 0.1474 loss_ce: 0.1474 2023/03/03 14:04:25 - mmengine - INFO - Epoch(train) [9][13/15] lr: 1.0000e-06 eta: 0:20:34 time: 0.3491 data_time: 0.0018 memory: 15293 loss: 0.1457 loss_ce: 0.1457 2023/03/03 14:04:25 - mmengine - INFO - Epoch(train) [9][14/15] lr: 1.0000e-06 eta: 0:20:32 time: 0.3323 data_time: 0.0017 memory: 17421 loss: 0.1489 loss_ce: 0.1489 2023/03/03 14:04:25 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:04:25 - mmengine - INFO - Epoch(train) [9][15/15] lr: 1.0000e-06 eta: 0:20:25 time: 0.3220 data_time: 0.0016 memory: 6838 loss: 0.1771 loss_ce: 0.1771 2023/03/03 14:04:27 - mmengine - INFO - Epoch(train) [10][ 1/15] lr: 1.0000e-06 eta: 0:20:44 time: 0.3891 data_time: 0.0452 memory: 16976 loss: 0.1757 loss_ce: 0.1757 2023/03/03 14:04:27 - mmengine - INFO - Epoch(train) [10][ 2/15] lr: 1.0000e-06 eta: 0:20:40 time: 0.3909 data_time: 0.0451 memory: 17795 loss: 0.1743 loss_ce: 0.1743 2023/03/03 14:04:27 - mmengine - INFO - Epoch(train) [10][ 3/15] lr: 1.0000e-06 eta: 0:20:36 time: 0.3827 data_time: 0.0451 memory: 17284 loss: 0.1659 loss_ce: 0.1659 2023/03/03 14:04:28 - mmengine - INFO - Epoch(train) [10][ 4/15] lr: 1.0000e-06 eta: 0:20:32 time: 0.3871 data_time: 0.0451 memory: 16955 loss: 0.1644 loss_ce: 0.1644 2023/03/03 14:04:28 - mmengine - INFO - Epoch(train) [10][ 5/15] lr: 1.0000e-06 eta: 0:20:30 time: 0.3839 data_time: 0.0452 memory: 16310 loss: 0.1644 loss_ce: 0.1644 2023/03/03 14:04:28 - mmengine - INFO - Epoch(train) [10][ 6/15] lr: 1.0000e-06 eta: 0:20:27 time: 0.3994 data_time: 0.0452 memory: 24648 loss: 0.1636 loss_ce: 0.1636 2023/03/03 14:04:28 - mmengine - INFO - Epoch(train) [10][ 7/15] lr: 1.0000e-06 eta: 0:20:23 time: 0.3696 data_time: 0.0452 memory: 15457 loss: 0.1637 loss_ce: 0.1637 2023/03/03 14:04:29 - mmengine - INFO - Epoch(train) [10][ 8/15] lr: 1.0000e-06 eta: 0:20:18 time: 0.3716 data_time: 0.0452 memory: 16530 loss: 0.1666 loss_ce: 0.1666 2023/03/03 14:04:29 - mmengine - INFO - Epoch(train) [10][ 9/15] lr: 1.0000e-06 eta: 0:20:20 time: 0.3908 data_time: 0.0452 memory: 17876 loss: 0.1654 loss_ce: 0.1654 2023/03/03 14:04:29 - mmengine - INFO - Epoch(train) [10][10/15] lr: 1.0000e-06 eta: 0:20:14 time: 0.3962 data_time: 0.0452 memory: 14322 loss: 0.1327 loss_ce: 0.1327 2023/03/03 14:04:30 - mmengine - INFO - Epoch(train) [10][11/15] lr: 1.0000e-06 eta: 0:20:22 time: 0.3453 data_time: 0.0016 memory: 19367 loss: 0.1329 loss_ce: 0.1329 2023/03/03 14:04:30 - mmengine - INFO - Epoch(train) [10][12/15] lr: 1.0000e-06 eta: 0:20:17 time: 0.3406 data_time: 0.0016 memory: 17272 loss: 0.1343 loss_ce: 0.1343 2023/03/03 14:04:31 - mmengine - INFO - Epoch(train) [10][13/15] lr: 1.0000e-06 eta: 0:20:15 time: 0.3411 data_time: 0.0016 memory: 13611 loss: 0.1481 loss_ce: 0.1481 2023/03/03 14:04:31 - mmengine - INFO - Epoch(train) [10][14/15] lr: 1.0000e-06 eta: 0:20:10 time: 0.3413 data_time: 0.0015 memory: 17122 loss: 0.1469 loss_ce: 0.1469 2023/03/03 14:04:31 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:04:31 - mmengine - INFO - Epoch(train) [10][15/15] lr: 1.0000e-06 eta: 0:20:05 time: 0.3218 data_time: 0.0015 memory: 6940 loss: 0.1512 loss_ce: 0.1512 2023/03/03 14:04:34 - mmengine - INFO - Epoch(val) [10][ 1/59] eta: 0:02:24 time: 2.4979 data_time: 0.2138 memory: 981 2023/03/03 14:04:34 - mmengine - INFO - Epoch(val) [10][ 2/59] eta: 0:01:34 time: 1.6655 data_time: 0.1074 memory: 981 2023/03/03 14:04:36 - mmengine - INFO - Epoch(val) [10][ 3/59] eta: 0:01:27 time: 1.5581 data_time: 0.0718 memory: 1003 2023/03/03 14:04:37 - mmengine - INFO - Epoch(val) [10][ 4/59] eta: 0:01:14 time: 1.3597 data_time: 0.0541 memory: 981 2023/03/03 14:04:40 - mmengine - INFO - Epoch(val) [10][ 5/59] eta: 0:01:31 time: 1.6947 data_time: 0.0434 memory: 1016 2023/03/03 14:04:42 - mmengine - INFO - Epoch(val) [10][ 6/59] eta: 0:01:36 time: 1.8176 data_time: 0.0363 memory: 981 2023/03/03 14:04:42 - mmengine - INFO - Epoch(val) [10][ 7/59] eta: 0:01:22 time: 1.5832 data_time: 0.0313 memory: 1043 2023/03/03 14:04:43 - mmengine - INFO - Epoch(val) [10][ 8/59] eta: 0:01:17 time: 1.5105 data_time: 0.0274 memory: 1016 2023/03/03 14:04:44 - mmengine - INFO - Epoch(val) [10][ 9/59] eta: 0:01:12 time: 1.4541 data_time: 0.0245 memory: 981 2023/03/03 14:04:47 - mmengine - INFO - Epoch(val) [10][10/59] eta: 0:01:17 time: 1.5916 data_time: 0.0221 memory: 981 2023/03/03 14:04:48 - mmengine - INFO - Epoch(val) [10][11/59] eta: 0:01:11 time: 1.3920 data_time: 0.0008 memory: 981 2023/03/03 14:04:51 - mmengine - INFO - Epoch(val) [10][12/59] eta: 0:01:17 time: 1.6331 data_time: 0.0008 memory: 1016 2023/03/03 14:04:53 - mmengine - INFO - Epoch(val) [10][13/59] eta: 0:01:16 time: 1.7044 data_time: 0.0008 memory: 981 2023/03/03 14:04:54 - mmengine - INFO - Epoch(val) [10][14/59] eta: 0:01:13 time: 1.7293 data_time: 0.0008 memory: 890 2023/03/03 14:04:54 - mmengine - INFO - Epoch(val) [10][15/59] eta: 0:01:06 time: 1.4283 data_time: 0.0008 memory: 981 2023/03/03 14:04:54 - mmengine - INFO - Epoch(val) [10][16/59] eta: 0:01:02 time: 1.2352 data_time: 0.0008 memory: 981 2023/03/03 14:04:55 - mmengine - INFO - Epoch(val) [10][17/59] eta: 0:00:58 time: 1.2515 data_time: 0.0008 memory: 981 2023/03/03 14:04:55 - mmengine - INFO - Epoch(val) [10][18/59] eta: 0:00:54 time: 1.1689 data_time: 0.0008 memory: 981 2023/03/03 14:04:56 - mmengine - INFO - Epoch(val) [10][19/59] eta: 0:00:52 time: 1.1691 data_time: 0.0008 memory: 981 2023/03/03 14:04:56 - mmengine - INFO - Epoch(val) [10][20/59] eta: 0:00:48 time: 0.9199 data_time: 0.0008 memory: 981 2023/03/03 14:04:59 - mmengine - INFO - Epoch(val) [10][21/59] eta: 0:00:50 time: 1.1539 data_time: 0.0008 memory: 981 2023/03/03 14:04:59 - mmengine - INFO - Epoch(val) [10][22/59] eta: 0:00:47 time: 0.8471 data_time: 0.0008 memory: 981 2023/03/03 14:05:00 - mmengine - INFO - Epoch(val) [10][23/59] eta: 0:00:45 time: 0.7082 data_time: 0.0008 memory: 981 2023/03/03 14:05:00 - mmengine - INFO - Epoch(val) [10][24/59] eta: 0:00:42 time: 0.6410 data_time: 0.0008 memory: 962 2023/03/03 14:05:01 - mmengine - INFO - Epoch(val) [10][25/59] eta: 0:00:40 time: 0.6727 data_time: 0.0008 memory: 981 2023/03/03 14:05:01 - mmengine - INFO - Epoch(val) [10][26/59] eta: 0:00:37 time: 0.6565 data_time: 0.0008 memory: 981 2023/03/03 14:05:01 - mmengine - INFO - Epoch(val) [10][27/59] eta: 0:00:35 time: 0.6567 data_time: 0.0008 memory: 981 2023/03/03 14:05:02 - mmengine - INFO - Epoch(val) [10][28/59] eta: 0:00:33 time: 0.6731 data_time: 0.0008 memory: 981 2023/03/03 14:05:03 - mmengine - INFO - Epoch(val) [10][29/59] eta: 0:00:32 time: 0.7074 data_time: 0.0008 memory: 981 2023/03/03 14:05:04 - mmengine - INFO - Epoch(val) [10][30/59] eta: 0:00:31 time: 0.7569 data_time: 0.0008 memory: 999 2023/03/03 14:05:04 - mmengine - INFO - Epoch(val) [10][31/59] eta: 0:00:30 time: 0.5397 data_time: 0.0008 memory: 981 2023/03/03 14:05:06 - mmengine - INFO - Epoch(val) [10][32/59] eta: 0:00:29 time: 0.6394 data_time: 0.0008 memory: 981 2023/03/03 14:05:06 - mmengine - INFO - Epoch(val) [10][33/59] eta: 0:00:27 time: 0.5752 data_time: 0.0009 memory: 981 2023/03/03 14:05:06 - mmengine - INFO - Epoch(val) [10][34/59] eta: 0:00:25 time: 0.5586 data_time: 0.0009 memory: 981 2023/03/03 14:05:07 - mmengine - INFO - Epoch(val) [10][35/59] eta: 0:00:24 time: 0.5910 data_time: 0.0008 memory: 981 2023/03/03 14:05:07 - mmengine - INFO - Epoch(val) [10][36/59] eta: 0:00:22 time: 0.6107 data_time: 0.0008 memory: 981 2023/03/03 14:05:07 - mmengine - INFO - Epoch(val) [10][37/59] eta: 0:00:21 time: 0.5943 data_time: 0.0009 memory: 981 2023/03/03 14:05:08 - mmengine - INFO - Epoch(val) [10][38/59] eta: 0:00:20 time: 0.6267 data_time: 0.0009 memory: 981 2023/03/03 14:05:08 - mmengine - INFO - Epoch(val) [10][39/59] eta: 0:00:19 time: 0.5424 data_time: 0.0009 memory: 987 2023/03/03 14:05:09 - mmengine - INFO - Epoch(val) [10][40/59] eta: 0:00:18 time: 0.5258 data_time: 0.0008 memory: 981 2023/03/03 14:05:10 - mmengine - INFO - Epoch(val) [10][41/59] eta: 0:00:17 time: 0.5768 data_time: 0.0008 memory: 986 2023/03/03 14:05:11 - mmengine - INFO - Epoch(val) [10][42/59] eta: 0:00:16 time: 0.5264 data_time: 0.0009 memory: 981 2023/03/03 14:05:12 - mmengine - INFO - Epoch(val) [10][43/59] eta: 0:00:15 time: 0.6071 data_time: 0.0008 memory: 976 2023/03/03 14:05:12 - mmengine - INFO - Epoch(val) [10][44/59] eta: 0:00:14 time: 0.6396 data_time: 0.0008 memory: 1003 2023/03/03 14:05:14 - mmengine - INFO - Epoch(val) [10][45/59] eta: 0:00:13 time: 0.7776 data_time: 0.0008 memory: 981 2023/03/03 14:05:15 - mmengine - INFO - Epoch(val) [10][46/59] eta: 0:00:12 time: 0.8068 data_time: 0.0008 memory: 981 2023/03/03 14:05:16 - mmengine - INFO - Epoch(val) [10][47/59] eta: 0:00:11 time: 0.8390 data_time: 0.0008 memory: 936 2023/03/03 14:05:16 - mmengine - INFO - Epoch(val) [10][48/59] eta: 0:00:10 time: 0.8227 data_time: 0.0008 memory: 1000 2023/03/03 14:05:17 - mmengine - INFO - Epoch(val) [10][49/59] eta: 0:00:09 time: 0.8720 data_time: 0.0008 memory: 981 2023/03/03 14:05:18 - mmengine - INFO - Epoch(val) [10][50/59] eta: 0:00:08 time: 0.8882 data_time: 0.0009 memory: 987 2023/03/03 14:05:20 - mmengine - INFO - Epoch(val) [10][51/59] eta: 0:00:07 time: 0.9387 data_time: 0.0009 memory: 981 2023/03/03 14:05:21 - mmengine - INFO - Epoch(val) [10][52/59] eta: 0:00:06 time: 0.9877 data_time: 0.0008 memory: 981 2023/03/03 14:05:22 - mmengine - INFO - Epoch(val) [10][53/59] eta: 0:00:05 time: 0.9871 data_time: 0.0008 memory: 962 2023/03/03 14:05:22 - mmengine - INFO - Epoch(val) [10][54/59] eta: 0:00:04 time: 1.0031 data_time: 0.0008 memory: 981 2023/03/03 14:05:23 - mmengine - INFO - Epoch(val) [10][55/59] eta: 0:00:03 time: 0.8647 data_time: 0.0008 memory: 981 2023/03/03 14:05:24 - mmengine - INFO - Epoch(val) [10][56/59] eta: 0:00:02 time: 0.8480 data_time: 0.0008 memory: 981 2023/03/03 14:05:26 - mmengine - INFO - Epoch(val) [10][57/59] eta: 0:00:01 time: 1.0216 data_time: 0.0008 memory: 981 2023/03/03 14:05:27 - mmengine - INFO - Epoch(val) [10][58/59] eta: 0:00:00 time: 1.1118 data_time: 0.0008 memory: 1016 2023/03/03 14:05:28 - mmengine - INFO - Epoch(val) [10][59/59] eta: 0:00:00 time: 1.0622 data_time: 0.0008 memory: 981 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.80, recall: 0.7963, precision: 0.8305, hmean: 0.8131 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.81, recall: 0.7963, precision: 0.8344, hmean: 0.8150 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.82, recall: 0.7963, precision: 0.8385, hmean: 0.8169 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.83, recall: 0.7945, precision: 0.8406, hmean: 0.8169 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.84, recall: 0.7936, precision: 0.8421, hmean: 0.8171 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.85, recall: 0.7918, precision: 0.8459, hmean: 0.8179 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.86, recall: 0.7900, precision: 0.8497, hmean: 0.8187 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.87, recall: 0.7890, precision: 0.8563, hmean: 0.8213 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.88, recall: 0.7854, precision: 0.8617, hmean: 0.8218 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.89, recall: 0.7836, precision: 0.8649, hmean: 0.8222 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.90, recall: 0.7790, precision: 0.8660, hmean: 0.8202 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.91, recall: 0.7753, precision: 0.8726, hmean: 0.8211 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.92, recall: 0.7689, precision: 0.8725, hmean: 0.8175 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.93, recall: 0.7580, precision: 0.8746, hmean: 0.8121 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.94, recall: 0.7470, precision: 0.8767, hmean: 0.8067 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.95, recall: 0.7342, precision: 0.8806, hmean: 0.8008 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.96, recall: 0.7215, precision: 0.8827, hmean: 0.7940 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.97, recall: 0.7032, precision: 0.8871, hmean: 0.7845 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.98, recall: 0.6822, precision: 0.8946, hmean: 0.7741 2023/03/03 14:06:17 - mmengine - INFO - text score threshold: 0.99, recall: 0.6493, precision: 0.9011, hmean: 0.7548 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.80, recall: 0.8100, precision: 0.9033, hmean: 0.8541 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.81, recall: 0.8100, precision: 0.9070, hmean: 0.8558 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.82, recall: 0.8091, precision: 0.9097, hmean: 0.8565 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.83, recall: 0.8064, precision: 0.9112, hmean: 0.8556 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.84, recall: 0.8055, precision: 0.9121, hmean: 0.8555 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.85, recall: 0.8037, precision: 0.9138, hmean: 0.8552 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.86, recall: 0.8018, precision: 0.9165, hmean: 0.8553 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.87, recall: 0.8009, precision: 0.9203, hmean: 0.8564 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.88, recall: 0.7963, precision: 0.9208, hmean: 0.8541 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.89, recall: 0.7936, precision: 0.9225, hmean: 0.8532 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.90, recall: 0.7890, precision: 0.9221, hmean: 0.8504 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.91, recall: 0.7826, precision: 0.9235, hmean: 0.8473 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.92, recall: 0.7763, precision: 0.9229, hmean: 0.8433 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.93, recall: 0.7662, precision: 0.9240, hmean: 0.8377 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.94, recall: 0.7562, precision: 0.9241, hmean: 0.8317 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.95, recall: 0.7416, precision: 0.9259, hmean: 0.8235 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.96, recall: 0.7269, precision: 0.9267, hmean: 0.8147 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.97, recall: 0.7087, precision: 0.9282, hmean: 0.8037 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.98, recall: 0.6858, precision: 0.9341, hmean: 0.7909 2023/03/03 14:06:20 - mmengine - INFO - text score threshold: 0.99, recall: 0.6502, precision: 0.9381, hmean: 0.7681 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.80, recall: 0.7315, precision: 0.9547, hmean: 0.8283 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.81, recall: 0.7306, precision: 0.9547, hmean: 0.8277 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.82, recall: 0.7288, precision: 0.9545, hmean: 0.8265 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.83, recall: 0.7269, precision: 0.9556, hmean: 0.8257 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.84, recall: 0.7260, precision: 0.9555, hmean: 0.8251 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.85, recall: 0.7233, precision: 0.9554, hmean: 0.8233 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.86, recall: 0.7215, precision: 0.9553, hmean: 0.8221 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.87, recall: 0.7205, precision: 0.9552, hmean: 0.8214 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.88, recall: 0.7160, precision: 0.9549, hmean: 0.8184 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.89, recall: 0.7132, precision: 0.9571, hmean: 0.8174 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.90, recall: 0.7096, precision: 0.9569, hmean: 0.8149 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.91, recall: 0.7023, precision: 0.9565, hmean: 0.8099 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.92, recall: 0.6968, precision: 0.9561, hmean: 0.8061 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.93, recall: 0.6886, precision: 0.9581, hmean: 0.8013 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.94, recall: 0.6804, precision: 0.9601, hmean: 0.7964 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.95, recall: 0.6667, precision: 0.9593, hmean: 0.7866 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.96, recall: 0.6539, precision: 0.9624, hmean: 0.7787 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.97, recall: 0.6393, precision: 0.9655, hmean: 0.7692 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.98, recall: 0.6183, precision: 0.9658, hmean: 0.7539 2023/03/03 14:06:22 - mmengine - INFO - text score threshold: 0.99, recall: 0.5836, precision: 0.9667, hmean: 0.7278 2023/03/03 14:06:22 - mmengine - INFO - Epoch(val) [10][59/59] generic/precision: 0.8649 generic/recall: 0.7836 generic/hmean: 0.8222 weak/precision: 0.9097 weak/recall: 0.8091 weak/hmean: 0.8565 strong/precision: 0.9547 strong/recall: 0.7315 strong/hmean: 0.8283 2023/03/03 14:06:25 - mmengine - INFO - The best checkpoint with 0.8222 generic/hmean at 10 epoch is saved to best_generic/hmean_epoch_10.pth. 2023/03/03 14:06:25 - mmengine - INFO - Epoch(train) [11][ 1/15] lr: 1.0000e-06 eta: 0:20:10 time: 0.3618 data_time: 0.0439 memory: 21186 loss: 0.1449 loss_ce: 0.1449 2023/03/03 14:06:26 - mmengine - INFO - Epoch(train) [11][ 2/15] lr: 1.0000e-06 eta: 0:20:07 time: 0.3642 data_time: 0.0440 memory: 17284 loss: 0.1372 loss_ce: 0.1372 2023/03/03 14:06:26 - mmengine - INFO - Epoch(train) [11][ 3/15] lr: 1.0000e-06 eta: 0:20:02 time: 0.3628 data_time: 0.0440 memory: 17711 loss: 0.1441 loss_ce: 0.1441 2023/03/03 14:06:26 - mmengine - INFO - Epoch(train) [11][ 4/15] lr: 1.0000e-06 eta: 0:19:59 time: 0.3398 data_time: 0.0441 memory: 17272 loss: 0.1511 loss_ce: 0.1511 2023/03/03 14:06:26 - mmengine - INFO - Epoch(train) [11][ 5/15] lr: 1.0000e-06 eta: 0:19:56 time: 0.3508 data_time: 0.0441 memory: 16930 loss: 0.1487 loss_ce: 0.1487 2023/03/03 14:06:27 - mmengine - INFO - Epoch(train) [11][ 6/15] lr: 1.0000e-06 eta: 0:19:54 time: 0.2962 data_time: 0.0442 memory: 19131 loss: 0.1452 loss_ce: 0.1452 2023/03/03 14:06:27 - mmengine - INFO - Epoch(train) [11][ 7/15] lr: 1.0000e-06 eta: 0:19:50 time: 0.2980 data_time: 0.0442 memory: 15767 loss: 0.1431 loss_ce: 0.1431 2023/03/03 14:06:27 - mmengine - INFO - Epoch(train) [11][ 8/15] lr: 1.0000e-06 eta: 0:19:50 time: 0.3114 data_time: 0.0442 memory: 16654 loss: 0.1324 loss_ce: 0.1324 2023/03/03 14:06:27 - mmengine - INFO - Epoch(train) [11][ 9/15] lr: 1.0000e-06 eta: 0:19:45 time: 0.3081 data_time: 0.0442 memory: 17272 loss: 0.1313 loss_ce: 0.1313 2023/03/03 14:06:28 - mmengine - INFO - Epoch(train) [11][10/15] lr: 1.0000e-06 eta: 0:19:42 time: 0.3166 data_time: 0.0442 memory: 17572 loss: 0.1280 loss_ce: 0.1280 2023/03/03 14:06:28 - mmengine - INFO - Epoch(train) [11][11/15] lr: 1.0000e-06 eta: 0:19:40 time: 0.2769 data_time: 0.0018 memory: 24491 loss: 0.1272 loss_ce: 0.1272 2023/03/03 14:06:28 - mmengine - INFO - Epoch(train) [11][12/15] lr: 1.0000e-06 eta: 0:19:40 time: 0.2970 data_time: 0.0018 memory: 19593 loss: 0.1324 loss_ce: 0.1324 2023/03/03 14:06:29 - mmengine - INFO - Epoch(train) [11][13/15] lr: 1.0000e-06 eta: 0:19:36 time: 0.2969 data_time: 0.0018 memory: 14821 loss: 0.1259 loss_ce: 0.1259 2023/03/03 14:06:29 - mmengine - INFO - Epoch(train) [11][14/15] lr: 1.0000e-06 eta: 0:19:33 time: 0.2921 data_time: 0.0016 memory: 17788 loss: 0.1156 loss_ce: 0.1156 2023/03/03 14:06:29 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:06:29 - mmengine - INFO - Epoch(train) [11][15/15] lr: 1.0000e-06 eta: 0:19:28 time: 0.2797 data_time: 0.0016 memory: 6134 loss: 0.1197 loss_ce: 0.1197 2023/03/03 14:06:30 - mmengine - INFO - Epoch(train) [12][ 1/15] lr: 1.0000e-06 eta: 0:19:39 time: 0.3582 data_time: 0.0640 memory: 19282 loss: 0.1205 loss_ce: 0.1205 2023/03/03 14:06:30 - mmengine - INFO - Epoch(train) [12][ 2/15] lr: 1.0000e-06 eta: 0:19:36 time: 0.3602 data_time: 0.0640 memory: 16477 loss: 0.1179 loss_ce: 0.1179 2023/03/03 14:06:31 - mmengine - INFO - Epoch(train) [12][ 3/15] lr: 1.0000e-06 eta: 0:19:33 time: 0.3418 data_time: 0.0641 memory: 14717 loss: 0.1243 loss_ce: 0.1243 2023/03/03 14:06:31 - mmengine - INFO - Epoch(train) [12][ 4/15] lr: 1.0000e-06 eta: 0:19:30 time: 0.3546 data_time: 0.0641 memory: 16020 loss: 0.1248 loss_ce: 0.1248 2023/03/03 14:06:31 - mmengine - INFO - Epoch(train) [12][ 5/15] lr: 1.0000e-06 eta: 0:19:27 time: 0.3528 data_time: 0.0641 memory: 16804 loss: 0.1245 loss_ce: 0.1245 2023/03/03 14:06:31 - mmengine - INFO - Epoch(train) [12][ 6/15] lr: 1.0000e-06 eta: 0:19:23 time: 0.3370 data_time: 0.0642 memory: 15389 loss: 0.1249 loss_ce: 0.1249 2023/03/03 14:06:32 - mmengine - INFO - Epoch(train) [12][ 7/15] lr: 1.0000e-06 eta: 0:19:22 time: 0.3311 data_time: 0.0642 memory: 17948 loss: 0.1281 loss_ce: 0.1281 2023/03/03 14:06:32 - mmengine - INFO - Epoch(train) [12][ 8/15] lr: 1.0000e-06 eta: 0:19:18 time: 0.3300 data_time: 0.0641 memory: 17120 loss: 0.1290 loss_ce: 0.1290 2023/03/03 14:06:32 - mmengine - INFO - Epoch(train) [12][ 9/15] lr: 1.0000e-06 eta: 0:19:18 time: 0.3497 data_time: 0.0641 memory: 20915 loss: 0.1361 loss_ce: 0.1361 2023/03/03 14:06:33 - mmengine - INFO - Epoch(train) [12][10/15] lr: 1.0000e-06 eta: 0:19:15 time: 0.3593 data_time: 0.0641 memory: 17533 loss: 0.1369 loss_ce: 0.1369 2023/03/03 14:06:33 - mmengine - INFO - Epoch(train) [12][11/15] lr: 1.0000e-06 eta: 0:19:13 time: 0.2790 data_time: 0.0017 memory: 19383 loss: 0.1403 loss_ce: 0.1403 2023/03/03 14:06:33 - mmengine - INFO - Epoch(train) [12][12/15] lr: 1.0000e-06 eta: 0:19:09 time: 0.2731 data_time: 0.0017 memory: 14322 loss: 0.1433 loss_ce: 0.1433 2023/03/03 14:06:33 - mmengine - INFO - Epoch(train) [12][13/15] lr: 1.0000e-06 eta: 0:19:07 time: 0.2809 data_time: 0.0016 memory: 17024 loss: 0.1370 loss_ce: 0.1370 2023/03/03 14:06:34 - mmengine - INFO - Epoch(train) [12][14/15] lr: 1.0000e-06 eta: 0:19:05 time: 0.2815 data_time: 0.0015 memory: 18486 loss: 0.1333 loss_ce: 0.1333 2023/03/03 14:06:34 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:06:34 - mmengine - INFO - Epoch(train) [12][15/15] lr: 1.0000e-06 eta: 0:19:01 time: 0.2742 data_time: 0.0015 memory: 5124 loss: 0.1373 loss_ce: 0.1373 2023/03/03 14:06:35 - mmengine - INFO - Epoch(train) [13][ 1/15] lr: 1.0000e-06 eta: 0:19:08 time: 0.3431 data_time: 0.0564 memory: 18662 loss: 0.1374 loss_ce: 0.1374 2023/03/03 14:06:35 - mmengine - INFO - Epoch(train) [13][ 2/15] lr: 1.0000e-06 eta: 0:19:09 time: 0.3504 data_time: 0.0564 memory: 18070 loss: 0.1293 loss_ce: 0.1293 2023/03/03 14:06:36 - mmengine - INFO - Epoch(train) [13][ 3/15] lr: 1.0000e-06 eta: 0:19:05 time: 0.3515 data_time: 0.0564 memory: 18766 loss: 0.1229 loss_ce: 0.1229 2023/03/03 14:06:36 - mmengine - INFO - Epoch(train) [13][ 4/15] lr: 1.0000e-06 eta: 0:19:06 time: 0.3546 data_time: 0.0565 memory: 16508 loss: 0.1198 loss_ce: 0.1198 2023/03/03 14:06:36 - mmengine - INFO - Epoch(train) [13][ 5/15] lr: 1.0000e-06 eta: 0:19:02 time: 0.3473 data_time: 0.0565 memory: 15849 loss: 0.1135 loss_ce: 0.1135 2023/03/03 14:06:37 - mmengine - INFO - Epoch(train) [13][ 6/15] lr: 1.0000e-06 eta: 0:19:02 time: 0.3653 data_time: 0.0565 memory: 16804 loss: 0.1092 loss_ce: 0.1092 2023/03/03 14:06:37 - mmengine - INFO - Epoch(train) [13][ 7/15] lr: 1.0000e-06 eta: 0:19:02 time: 0.3917 data_time: 0.0565 memory: 19958 loss: 0.1097 loss_ce: 0.1097 2023/03/03 14:06:37 - mmengine - INFO - Epoch(train) [13][ 8/15] lr: 1.0000e-06 eta: 0:19:00 time: 0.3854 data_time: 0.0565 memory: 17619 loss: 0.1083 loss_ce: 0.1083 2023/03/03 14:06:38 - mmengine - INFO - Epoch(train) [13][ 9/15] lr: 1.0000e-06 eta: 0:18:59 time: 0.3868 data_time: 0.0566 memory: 19270 loss: 0.1161 loss_ce: 0.1161 2023/03/03 14:06:38 - mmengine - INFO - Epoch(train) [13][10/15] lr: 1.0000e-06 eta: 0:18:56 time: 0.3963 data_time: 0.0566 memory: 15631 loss: 0.1134 loss_ce: 0.1134 2023/03/03 14:06:38 - mmengine - INFO - Epoch(train) [13][11/15] lr: 1.0000e-06 eta: 0:18:52 time: 0.3288 data_time: 0.0018 memory: 16509 loss: 0.1187 loss_ce: 0.1187 2023/03/03 14:06:38 - mmengine - INFO - Epoch(train) [13][12/15] lr: 1.0000e-06 eta: 0:18:50 time: 0.3072 data_time: 0.0017 memory: 17730 loss: 0.1272 loss_ce: 0.1272 2023/03/03 14:06:39 - mmengine - INFO - Epoch(train) [13][13/15] lr: 1.0000e-06 eta: 0:18:46 time: 0.3062 data_time: 0.0017 memory: 17120 loss: 0.1347 loss_ce: 0.1347 2023/03/03 14:06:39 - mmengine - INFO - Epoch(train) [13][14/15] lr: 1.0000e-06 eta: 0:18:47 time: 0.3068 data_time: 0.0016 memory: 17406 loss: 0.1317 loss_ce: 0.1317 2023/03/03 14:06:39 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:06:39 - mmengine - INFO - Epoch(train) [13][15/15] lr: 1.0000e-06 eta: 0:18:43 time: 0.3035 data_time: 0.0016 memory: 4114 loss: 0.1408 loss_ce: 0.1408 2023/03/03 14:06:40 - mmengine - INFO - Epoch(train) [14][ 1/15] lr: 1.0000e-06 eta: 0:18:51 time: 0.3518 data_time: 0.0746 memory: 17572 loss: 0.1390 loss_ce: 0.1390 2023/03/03 14:06:40 - mmengine - INFO - Epoch(train) [14][ 2/15] lr: 1.0000e-06 eta: 0:18:48 time: 0.3314 data_time: 0.0746 memory: 17122 loss: 0.1347 loss_ce: 0.1347 2023/03/03 14:06:41 - mmengine - INFO - Epoch(train) [14][ 3/15] lr: 1.0000e-06 eta: 0:18:50 time: 0.3625 data_time: 0.0746 memory: 18409 loss: 0.1355 loss_ce: 0.1355 2023/03/03 14:06:41 - mmengine - INFO - Epoch(train) [14][ 4/15] lr: 1.0000e-06 eta: 0:18:47 time: 0.3504 data_time: 0.0747 memory: 16370 loss: 0.1370 loss_ce: 0.1370 2023/03/03 14:06:42 - mmengine - INFO - Epoch(train) [14][ 5/15] lr: 1.0000e-06 eta: 0:18:45 time: 0.3563 data_time: 0.0746 memory: 16849 loss: 0.1336 loss_ce: 0.1336 2023/03/03 14:06:42 - mmengine - INFO - Epoch(train) [14][ 6/15] lr: 1.0000e-06 eta: 0:18:42 time: 0.3549 data_time: 0.0746 memory: 14461 loss: 0.1319 loss_ce: 0.1319 2023/03/03 14:06:42 - mmengine - INFO - Epoch(train) [14][ 7/15] lr: 1.0000e-06 eta: 0:18:42 time: 0.3740 data_time: 0.0746 memory: 17572 loss: 0.1266 loss_ce: 0.1266 2023/03/03 14:06:42 - mmengine - INFO - Epoch(train) [14][ 8/15] lr: 1.0000e-06 eta: 0:18:39 time: 0.3760 data_time: 0.0746 memory: 19340 loss: 0.1194 loss_ce: 0.1194 2023/03/03 14:06:43 - mmengine - INFO - Epoch(train) [14][ 9/15] lr: 1.0000e-06 eta: 0:18:38 time: 0.3634 data_time: 0.0746 memory: 14906 loss: 0.1280 loss_ce: 0.1280 2023/03/03 14:06:43 - mmengine - INFO - Epoch(train) [14][10/15] lr: 1.0000e-06 eta: 0:18:35 time: 0.3724 data_time: 0.0746 memory: 17788 loss: 0.1172 loss_ce: 0.1172 2023/03/03 14:06:44 - mmengine - INFO - Epoch(train) [14][11/15] lr: 1.0000e-06 eta: 0:18:37 time: 0.3353 data_time: 0.0016 memory: 17572 loss: 0.1208 loss_ce: 0.1208 2023/03/03 14:06:44 - mmengine - INFO - Epoch(train) [14][12/15] lr: 1.0000e-06 eta: 0:18:34 time: 0.3315 data_time: 0.0016 memory: 17572 loss: 0.1198 loss_ce: 0.1198 2023/03/03 14:06:44 - mmengine - INFO - Epoch(train) [14][13/15] lr: 1.0000e-06 eta: 0:18:32 time: 0.2978 data_time: 0.0015 memory: 16976 loss: 0.1233 loss_ce: 0.1233 2023/03/03 14:06:44 - mmengine - INFO - Epoch(train) [14][14/15] lr: 1.0000e-06 eta: 0:18:28 time: 0.2952 data_time: 0.0015 memory: 17421 loss: 0.1182 loss_ce: 0.1182 2023/03/03 14:06:44 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:06:44 - mmengine - INFO - Epoch(train) [14][15/15] lr: 1.0000e-06 eta: 0:18:25 time: 0.2819 data_time: 0.0014 memory: 5456 loss: 0.1409 loss_ce: 0.1409 2023/03/03 14:06:45 - mmengine - INFO - Epoch(train) [15][ 1/15] lr: 1.0000e-06 eta: 0:18:27 time: 0.3195 data_time: 0.0331 memory: 17333 loss: 0.1391 loss_ce: 0.1391 2023/03/03 14:06:45 - mmengine - INFO - Epoch(train) [15][ 2/15] lr: 1.0000e-06 eta: 0:18:28 time: 0.3243 data_time: 0.0333 memory: 16530 loss: 0.1370 loss_ce: 0.1370 2023/03/03 14:06:46 - mmengine - INFO - Epoch(train) [15][ 3/15] lr: 1.0000e-06 eta: 0:18:25 time: 0.3229 data_time: 0.0333 memory: 17424 loss: 0.1413 loss_ce: 0.1413 2023/03/03 14:06:46 - mmengine - INFO - Epoch(train) [15][ 4/15] lr: 1.0000e-06 eta: 0:18:25 time: 0.3284 data_time: 0.0334 memory: 20226 loss: 0.1395 loss_ce: 0.1395 2023/03/03 14:06:46 - mmengine - INFO - Epoch(train) [15][ 5/15] lr: 1.0000e-06 eta: 0:18:21 time: 0.3244 data_time: 0.0334 memory: 17475 loss: 0.1419 loss_ce: 0.1419 2023/03/03 14:06:47 - mmengine - INFO - Epoch(train) [15][ 6/15] lr: 1.0000e-06 eta: 0:18:22 time: 0.3149 data_time: 0.0334 memory: 16508 loss: 0.1425 loss_ce: 0.1425 2023/03/03 14:06:47 - mmengine - INFO - Epoch(train) [15][ 7/15] lr: 1.0000e-06 eta: 0:18:20 time: 0.3175 data_time: 0.0334 memory: 16370 loss: 0.1466 loss_ce: 0.1466 2023/03/03 14:06:47 - mmengine - INFO - Epoch(train) [15][ 8/15] lr: 1.0000e-06 eta: 0:18:19 time: 0.3300 data_time: 0.0334 memory: 16212 loss: 0.1418 loss_ce: 0.1418 2023/03/03 14:06:47 - mmengine - INFO - Epoch(train) [15][ 9/15] lr: 1.0000e-06 eta: 0:18:16 time: 0.3303 data_time: 0.0334 memory: 18409 loss: 0.1378 loss_ce: 0.1378 2023/03/03 14:06:48 - mmengine - INFO - Epoch(train) [15][10/15] lr: 1.0000e-06 eta: 0:18:15 time: 0.3469 data_time: 0.0334 memory: 16598 loss: 0.1122 loss_ce: 0.1122 2023/03/03 14:06:48 - mmengine - INFO - Epoch(train) [15][11/15] lr: 1.0000e-06 eta: 0:18:12 time: 0.3140 data_time: 0.0018 memory: 16223 loss: 0.1109 loss_ce: 0.1109 2023/03/03 14:06:48 - mmengine - INFO - Epoch(train) [15][12/15] lr: 1.0000e-06 eta: 0:18:12 time: 0.3064 data_time: 0.0016 memory: 15575 loss: 0.1104 loss_ce: 0.1104 2023/03/03 14:06:49 - mmengine - INFO - Epoch(train) [15][13/15] lr: 1.0000e-06 eta: 0:18:11 time: 0.3155 data_time: 0.0016 memory: 20088 loss: 0.1137 loss_ce: 0.1137 2023/03/03 14:06:49 - mmengine - INFO - Epoch(train) [15][14/15] lr: 1.0000e-06 eta: 0:18:08 time: 0.2978 data_time: 0.0016 memory: 16685 loss: 0.1049 loss_ce: 0.1049 2023/03/03 14:06:49 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:06:49 - mmengine - INFO - Epoch(train) [15][15/15] lr: 1.0000e-06 eta: 0:18:05 time: 0.2929 data_time: 0.0015 memory: 4819 loss: 0.1084 loss_ce: 0.1084 2023/03/03 14:06:50 - mmengine - INFO - Epoch(train) [16][ 1/15] lr: 1.0000e-06 eta: 0:18:14 time: 0.3583 data_time: 0.0690 memory: 17272 loss: 0.1128 loss_ce: 0.1128 2023/03/03 14:06:51 - mmengine - INFO - Epoch(train) [16][ 2/15] lr: 1.0000e-06 eta: 0:18:13 time: 0.3710 data_time: 0.0690 memory: 18778 loss: 0.1114 loss_ce: 0.1114 2023/03/03 14:06:51 - mmengine - INFO - Epoch(train) [16][ 3/15] lr: 1.0000e-06 eta: 0:18:12 time: 0.3685 data_time: 0.0691 memory: 18070 loss: 0.1132 loss_ce: 0.1132 2023/03/03 14:06:51 - mmengine - INFO - Epoch(train) [16][ 4/15] lr: 1.0000e-06 eta: 0:18:09 time: 0.3704 data_time: 0.0691 memory: 15865 loss: 0.1176 loss_ce: 0.1176 2023/03/03 14:06:51 - mmengine - INFO - Epoch(train) [16][ 5/15] lr: 1.0000e-06 eta: 0:18:08 time: 0.3652 data_time: 0.0692 memory: 17837 loss: 0.1207 loss_ce: 0.1207 2023/03/03 14:06:52 - mmengine - INFO - Epoch(train) [16][ 6/15] lr: 1.0000e-06 eta: 0:18:05 time: 0.3626 data_time: 0.0692 memory: 17175 loss: 0.1253 loss_ce: 0.1253 2023/03/03 14:06:52 - mmengine - INFO - Epoch(train) [16][ 7/15] lr: 1.0000e-06 eta: 0:18:04 time: 0.3534 data_time: 0.0692 memory: 17120 loss: 0.1292 loss_ce: 0.1292 2023/03/03 14:06:52 - mmengine - INFO - Epoch(train) [16][ 8/15] lr: 1.0000e-06 eta: 0:18:01 time: 0.3448 data_time: 0.0691 memory: 15286 loss: 0.1255 loss_ce: 0.1255 2023/03/03 14:06:52 - mmengine - INFO - Epoch(train) [16][ 9/15] lr: 1.0000e-06 eta: 0:17:59 time: 0.3452 data_time: 0.0691 memory: 15903 loss: 0.1269 loss_ce: 0.1269 2023/03/03 14:06:53 - mmengine - INFO - Epoch(train) [16][10/15] lr: 1.0000e-06 eta: 0:17:56 time: 0.3498 data_time: 0.0691 memory: 16976 loss: 0.1326 loss_ce: 0.1326 2023/03/03 14:06:53 - mmengine - INFO - Epoch(train) [16][11/15] lr: 1.0000e-06 eta: 0:17:56 time: 0.2774 data_time: 0.0017 memory: 16654 loss: 0.1273 loss_ce: 0.1273 2023/03/03 14:06:53 - mmengine - INFO - Epoch(train) [16][12/15] lr: 1.0000e-06 eta: 0:17:54 time: 0.2647 data_time: 0.0016 memory: 16370 loss: 0.1294 loss_ce: 0.1294 2023/03/03 14:06:54 - mmengine - INFO - Epoch(train) [16][13/15] lr: 1.0000e-06 eta: 0:17:54 time: 0.2729 data_time: 0.0015 memory: 24942 loss: 0.1310 loss_ce: 0.1310 2023/03/03 14:06:54 - mmengine - INFO - Epoch(train) [16][14/15] lr: 1.0000e-06 eta: 0:17:51 time: 0.2726 data_time: 0.0015 memory: 19324 loss: 0.1267 loss_ce: 0.1267 2023/03/03 14:06:54 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:06:54 - mmengine - INFO - Epoch(train) [16][15/15] lr: 1.0000e-06 eta: 0:17:49 time: 0.2609 data_time: 0.0015 memory: 6850 loss: 0.1422 loss_ce: 0.1422 2023/03/03 14:06:55 - mmengine - INFO - Epoch(train) [17][ 1/15] lr: 1.0000e-06 eta: 0:17:55 time: 0.3382 data_time: 0.0559 memory: 18782 loss: 0.1389 loss_ce: 0.1389 2023/03/03 14:06:55 - mmengine - INFO - Epoch(train) [17][ 2/15] lr: 1.0000e-06 eta: 0:17:53 time: 0.3340 data_time: 0.0560 memory: 17256 loss: 0.1384 loss_ce: 0.1384 2023/03/03 14:06:56 - mmengine - INFO - Epoch(train) [17][ 3/15] lr: 1.0000e-06 eta: 0:17:51 time: 0.3349 data_time: 0.0560 memory: 15686 loss: 0.1407 loss_ce: 0.1407 2023/03/03 14:06:56 - mmengine - INFO - Epoch(train) [17][ 4/15] lr: 1.0000e-06 eta: 0:17:50 time: 0.3501 data_time: 0.0561 memory: 17120 loss: 0.1502 loss_ce: 0.1502 2023/03/03 14:06:56 - mmengine - INFO - Epoch(train) [17][ 5/15] lr: 1.0000e-06 eta: 0:17:49 time: 0.3606 data_time: 0.0561 memory: 17609 loss: 0.1423 loss_ce: 0.1423 2023/03/03 14:06:57 - mmengine - INFO - Epoch(train) [17][ 6/15] lr: 1.0000e-06 eta: 0:17:48 time: 0.3495 data_time: 0.0561 memory: 20576 loss: 0.1372 loss_ce: 0.1372 2023/03/03 14:06:57 - mmengine - INFO - Epoch(train) [17][ 7/15] lr: 1.0000e-06 eta: 0:17:45 time: 0.3486 data_time: 0.0562 memory: 13971 loss: 0.1451 loss_ce: 0.1451 2023/03/03 14:06:57 - mmengine - INFO - Epoch(train) [17][ 8/15] lr: 1.0000e-06 eta: 0:17:46 time: 0.3533 data_time: 0.0562 memory: 13388 loss: 0.1494 loss_ce: 0.1494 2023/03/03 14:06:57 - mmengine - INFO - Epoch(train) [17][ 9/15] lr: 1.0000e-06 eta: 0:17:44 time: 0.3574 data_time: 0.0562 memory: 17377 loss: 0.1528 loss_ce: 0.1528 2023/03/03 14:06:58 - mmengine - INFO - Epoch(train) [17][10/15] lr: 1.0000e-06 eta: 0:17:42 time: 0.3618 data_time: 0.0562 memory: 15432 loss: 0.1417 loss_ce: 0.1417 2023/03/03 14:06:58 - mmengine - INFO - Epoch(train) [17][11/15] lr: 1.0000e-06 eta: 0:17:40 time: 0.2929 data_time: 0.0017 memory: 16635 loss: 0.1400 loss_ce: 0.1400 2023/03/03 14:06:58 - mmengine - INFO - Epoch(train) [17][12/15] lr: 1.0000e-06 eta: 0:17:39 time: 0.2930 data_time: 0.0017 memory: 15432 loss: 0.1433 loss_ce: 0.1433 2023/03/03 14:06:59 - mmengine - INFO - Epoch(train) [17][13/15] lr: 1.0000e-06 eta: 0:17:38 time: 0.3105 data_time: 0.0017 memory: 23607 loss: 0.1355 loss_ce: 0.1355 2023/03/03 14:06:59 - mmengine - INFO - Epoch(train) [17][14/15] lr: 1.0000e-06 eta: 0:17:38 time: 0.3142 data_time: 0.0016 memory: 17421 loss: 0.1286 loss_ce: 0.1286 2023/03/03 14:06:59 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:06:59 - mmengine - INFO - Epoch(train) [17][15/15] lr: 1.0000e-06 eta: 0:17:36 time: 0.2999 data_time: 0.0016 memory: 5249 loss: 0.1364 loss_ce: 0.1364 2023/03/03 14:07:00 - mmengine - INFO - Epoch(train) [18][ 1/15] lr: 1.0000e-06 eta: 0:17:40 time: 0.3550 data_time: 0.0466 memory: 17878 loss: 0.1403 loss_ce: 0.1403 2023/03/03 14:07:00 - mmengine - INFO - Epoch(train) [18][ 2/15] lr: 1.0000e-06 eta: 0:17:38 time: 0.3546 data_time: 0.0467 memory: 16976 loss: 0.1353 loss_ce: 0.1353 2023/03/03 14:07:01 - mmengine - INFO - Epoch(train) [18][ 3/15] lr: 1.0000e-06 eta: 0:17:36 time: 0.3341 data_time: 0.0468 memory: 17198 loss: 0.1252 loss_ce: 0.1252 2023/03/03 14:07:01 - mmengine - INFO - Epoch(train) [18][ 4/15] lr: 1.0000e-06 eta: 0:17:35 time: 0.3375 data_time: 0.0469 memory: 14532 loss: 0.1257 loss_ce: 0.1257 2023/03/03 14:07:01 - mmengine - INFO - Epoch(train) [18][ 5/15] lr: 1.0000e-06 eta: 0:17:34 time: 0.3517 data_time: 0.0469 memory: 16508 loss: 0.1281 loss_ce: 0.1281 2023/03/03 14:07:02 - mmengine - INFO - Epoch(train) [18][ 6/15] lr: 1.0000e-06 eta: 0:17:34 time: 0.3633 data_time: 0.0469 memory: 21793 loss: 0.1352 loss_ce: 0.1352 2023/03/03 14:07:02 - mmengine - INFO - Epoch(train) [18][ 7/15] lr: 1.0000e-06 eta: 0:17:32 time: 0.3566 data_time: 0.0469 memory: 17421 loss: 0.1297 loss_ce: 0.1297 2023/03/03 14:07:02 - mmengine - INFO - Epoch(train) [18][ 8/15] lr: 1.0000e-06 eta: 0:17:30 time: 0.3404 data_time: 0.0469 memory: 17122 loss: 0.1323 loss_ce: 0.1323 2023/03/03 14:07:02 - mmengine - INFO - Epoch(train) [18][ 9/15] lr: 1.0000e-06 eta: 0:17:30 time: 0.3396 data_time: 0.0469 memory: 16955 loss: 0.1345 loss_ce: 0.1345 2023/03/03 14:07:03 - mmengine - INFO - Epoch(train) [18][10/15] lr: 1.0000e-06 eta: 0:17:28 time: 0.3467 data_time: 0.0469 memory: 16897 loss: 0.1245 loss_ce: 0.1245 2023/03/03 14:07:03 - mmengine - INFO - Epoch(train) [18][11/15] lr: 1.0000e-06 eta: 0:17:27 time: 0.2962 data_time: 0.0019 memory: 17572 loss: 0.1249 loss_ce: 0.1249 2023/03/03 14:07:03 - mmengine - INFO - Epoch(train) [18][12/15] lr: 1.0000e-06 eta: 0:17:25 time: 0.2983 data_time: 0.0018 memory: 17325 loss: 0.1204 loss_ce: 0.1204 2023/03/03 14:07:04 - mmengine - INFO - Epoch(train) [18][13/15] lr: 1.0000e-06 eta: 0:17:24 time: 0.3054 data_time: 0.0016 memory: 22834 loss: 0.1230 loss_ce: 0.1230 2023/03/03 14:07:04 - mmengine - INFO - Epoch(train) [18][14/15] lr: 1.0000e-06 eta: 0:17:22 time: 0.3003 data_time: 0.0015 memory: 17788 loss: 0.1225 loss_ce: 0.1225 2023/03/03 14:07:04 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:07:04 - mmengine - INFO - Epoch(train) [18][15/15] lr: 1.0000e-06 eta: 0:17:20 time: 0.2816 data_time: 0.0015 memory: 4694 loss: 0.1239 loss_ce: 0.1239 2023/03/03 14:07:05 - mmengine - INFO - Epoch(train) [19][ 1/15] lr: 1.0000e-06 eta: 0:17:24 time: 0.3217 data_time: 0.0600 memory: 17892 loss: 0.1182 loss_ce: 0.1182 2023/03/03 14:07:05 - mmengine - INFO - Epoch(train) [19][ 2/15] lr: 1.0000e-06 eta: 0:17:23 time: 0.3369 data_time: 0.0601 memory: 13916 loss: 0.1233 loss_ce: 0.1233 2023/03/03 14:07:05 - mmengine - INFO - Epoch(train) [19][ 3/15] lr: 1.0000e-06 eta: 0:17:21 time: 0.3375 data_time: 0.0601 memory: 16199 loss: 0.1342 loss_ce: 0.1342 2023/03/03 14:07:06 - mmengine - INFO - Epoch(train) [19][ 4/15] lr: 1.0000e-06 eta: 0:17:21 time: 0.3289 data_time: 0.0602 memory: 16976 loss: 0.1397 loss_ce: 0.1397 2023/03/03 14:07:06 - mmengine - INFO - Epoch(train) [19][ 5/15] lr: 1.0000e-06 eta: 0:17:18 time: 0.3276 data_time: 0.0601 memory: 15767 loss: 0.1403 loss_ce: 0.1403 2023/03/03 14:07:07 - mmengine - INFO - Epoch(train) [19][ 6/15] lr: 1.0000e-06 eta: 0:17:20 time: 0.3457 data_time: 0.0601 memory: 17004 loss: 0.1376 loss_ce: 0.1376 2023/03/03 14:07:07 - mmengine - INFO - Epoch(train) [19][ 7/15] lr: 1.0000e-06 eta: 0:17:20 time: 0.3686 data_time: 0.0602 memory: 20225 loss: 0.1421 loss_ce: 0.1421 2023/03/03 14:07:07 - mmengine - INFO - Epoch(train) [19][ 8/15] lr: 1.0000e-06 eta: 0:17:19 time: 0.3628 data_time: 0.0603 memory: 18307 loss: 0.1426 loss_ce: 0.1426 2023/03/03 14:07:07 - mmengine - INFO - Epoch(train) [19][ 9/15] lr: 1.0000e-06 eta: 0:17:16 time: 0.3588 data_time: 0.0603 memory: 18409 loss: 0.1369 loss_ce: 0.1369 2023/03/03 14:07:08 - mmengine - INFO - Epoch(train) [19][10/15] lr: 1.0000e-06 eta: 0:17:15 time: 0.3719 data_time: 0.0603 memory: 23760 loss: 0.1324 loss_ce: 0.1324 2023/03/03 14:07:08 - mmengine - INFO - Epoch(train) [19][11/15] lr: 1.0000e-06 eta: 0:17:13 time: 0.3115 data_time: 0.0018 memory: 13674 loss: 0.1303 loss_ce: 0.1303 2023/03/03 14:07:08 - mmengine - INFO - Epoch(train) [19][12/15] lr: 1.0000e-06 eta: 0:17:13 time: 0.3155 data_time: 0.0017 memory: 16212 loss: 0.1347 loss_ce: 0.1347 2023/03/03 14:07:09 - mmengine - INFO - Epoch(train) [19][13/15] lr: 1.0000e-06 eta: 0:17:11 time: 0.3093 data_time: 0.0017 memory: 14781 loss: 0.1266 loss_ce: 0.1266 2023/03/03 14:07:09 - mmengine - INFO - Epoch(train) [19][14/15] lr: 1.0000e-06 eta: 0:17:10 time: 0.3123 data_time: 0.0016 memory: 17272 loss: 0.1194 loss_ce: 0.1194 2023/03/03 14:07:09 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:07:09 - mmengine - INFO - Epoch(train) [19][15/15] lr: 1.0000e-06 eta: 0:17:08 time: 0.3113 data_time: 0.0016 memory: 8773 loss: 0.1243 loss_ce: 0.1243 2023/03/03 14:07:10 - mmengine - INFO - Epoch(train) [20][ 1/15] lr: 1.0000e-06 eta: 0:17:11 time: 0.3362 data_time: 0.0573 memory: 17272 loss: 0.1292 loss_ce: 0.1292 2023/03/03 14:07:10 - mmengine - INFO - Epoch(train) [20][ 2/15] lr: 1.0000e-06 eta: 0:17:09 time: 0.3107 data_time: 0.0573 memory: 13782 loss: 0.1248 loss_ce: 0.1248 2023/03/03 14:07:11 - mmengine - INFO - Epoch(train) [20][ 3/15] lr: 1.0000e-06 eta: 0:17:10 time: 0.3276 data_time: 0.0573 memory: 14549 loss: 0.1259 loss_ce: 0.1259 2023/03/03 14:07:11 - mmengine - INFO - Epoch(train) [20][ 4/15] lr: 1.0000e-06 eta: 0:17:09 time: 0.3453 data_time: 0.0573 memory: 19795 loss: 0.1288 loss_ce: 0.1288 2023/03/03 14:07:11 - mmengine - INFO - Epoch(train) [20][ 5/15] lr: 1.0000e-06 eta: 0:17:09 time: 0.3572 data_time: 0.0574 memory: 15767 loss: 0.1262 loss_ce: 0.1262 2023/03/03 14:07:12 - mmengine - INFO - Epoch(train) [20][ 6/15] lr: 1.0000e-06 eta: 0:17:07 time: 0.3594 data_time: 0.0575 memory: 15432 loss: 0.1299 loss_ce: 0.1299 2023/03/03 14:07:12 - mmengine - INFO - Epoch(train) [20][ 7/15] lr: 1.0000e-06 eta: 0:17:06 time: 0.3497 data_time: 0.0575 memory: 17572 loss: 0.1195 loss_ce: 0.1195 2023/03/03 14:07:12 - mmengine - INFO - Epoch(train) [20][ 8/15] lr: 1.0000e-06 eta: 0:17:06 time: 0.3703 data_time: 0.0575 memory: 28391 loss: 0.1200 loss_ce: 0.1200 2023/03/03 14:07:12 - mmengine - INFO - Epoch(train) [20][ 9/15] lr: 1.0000e-06 eta: 0:17:04 time: 0.3580 data_time: 0.0575 memory: 17215 loss: 0.1172 loss_ce: 0.1172 2023/03/03 14:07:13 - mmengine - INFO - Epoch(train) [20][10/15] lr: 1.0000e-06 eta: 0:17:03 time: 0.3627 data_time: 0.0575 memory: 13512 loss: 0.1163 loss_ce: 0.1163 2023/03/03 14:07:13 - mmengine - INFO - Epoch(train) [20][11/15] lr: 1.0000e-06 eta: 0:17:02 time: 0.3225 data_time: 0.0018 memory: 21895 loss: 0.1126 loss_ce: 0.1126 2023/03/03 14:07:13 - mmengine - INFO - Epoch(train) [20][12/15] lr: 1.0000e-06 eta: 0:17:00 time: 0.3213 data_time: 0.0018 memory: 17120 loss: 0.1142 loss_ce: 0.1142 2023/03/03 14:07:14 - mmengine - INFO - Epoch(train) [20][13/15] lr: 1.0000e-06 eta: 0:17:01 time: 0.3280 data_time: 0.0017 memory: 17540 loss: 0.1168 loss_ce: 0.1168 2023/03/03 14:07:14 - mmengine - INFO - Epoch(train) [20][14/15] lr: 1.0000e-06 eta: 0:16:59 time: 0.3109 data_time: 0.0017 memory: 18586 loss: 0.1164 loss_ce: 0.1164 2023/03/03 14:07:14 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:07:14 - mmengine - INFO - Epoch(train) [20][15/15] lr: 1.0000e-06 eta: 0:16:57 time: 0.2866 data_time: 0.0016 memory: 6122 loss: 0.1163 loss_ce: 0.1163 2023/03/03 14:07:16 - mmengine - INFO - Epoch(val) [20][ 1/59] eta: 0:01:29 time: 1.1343 data_time: 0.0047 memory: 981 2023/03/03 14:07:17 - mmengine - INFO - Epoch(val) [20][ 2/59] eta: 0:01:07 time: 1.0488 data_time: 0.0047 memory: 981 2023/03/03 14:07:18 - mmengine - INFO - Epoch(val) [20][ 3/59] eta: 0:01:09 time: 1.0663 data_time: 0.0047 memory: 1003 2023/03/03 14:07:18 - mmengine - INFO - Epoch(val) [20][ 4/59] eta: 0:00:53 time: 1.0013 data_time: 0.0048 memory: 981 2023/03/03 14:07:21 - mmengine - INFO - Epoch(val) [20][ 5/59] eta: 0:01:16 time: 1.2557 data_time: 0.0048 memory: 1016 2023/03/03 14:07:24 - mmengine - INFO - Epoch(val) [20][ 6/59] eta: 0:01:25 time: 1.4534 data_time: 0.0048 memory: 981 2023/03/03 14:07:24 - mmengine - INFO - Epoch(val) [20][ 7/59] eta: 0:01:13 time: 1.4049 data_time: 0.0048 memory: 1043 2023/03/03 14:07:25 - mmengine - INFO - Epoch(val) [20][ 8/59] eta: 0:01:09 time: 1.2817 data_time: 0.0049 memory: 1016 2023/03/03 14:07:26 - mmengine - INFO - Epoch(val) [20][ 9/59] eta: 0:01:06 time: 1.2420 data_time: 0.0048 memory: 981 2023/03/03 14:07:27 - mmengine - INFO - Epoch(val) [20][10/59] eta: 0:01:03 time: 1.2887 data_time: 0.0049 memory: 981 2023/03/03 14:07:28 - mmengine - INFO - Epoch(val) [20][11/59] eta: 0:00:58 time: 1.1822 data_time: 0.0009 memory: 981 2023/03/03 14:07:31 - mmengine - INFO - Epoch(val) [20][12/59] eta: 0:01:05 time: 1.4246 data_time: 0.0010 memory: 1016 2023/03/03 14:07:33 - mmengine - INFO - Epoch(val) [20][13/59] eta: 0:01:06 time: 1.4976 data_time: 0.0010 memory: 981 2023/03/03 14:07:34 - mmengine - INFO - Epoch(val) [20][14/59] eta: 0:01:03 time: 1.5809 data_time: 0.0010 memory: 890 2023/03/03 14:07:34 - mmengine - INFO - Epoch(val) [20][15/59] eta: 0:00:57 time: 1.2631 data_time: 0.0010 memory: 981 2023/03/03 14:07:34 - mmengine - INFO - Epoch(val) [20][16/59] eta: 0:00:54 time: 1.0493 data_time: 0.0010 memory: 981 2023/03/03 14:07:35 - mmengine - INFO - Epoch(val) [20][17/59] eta: 0:00:50 time: 1.0655 data_time: 0.0010 memory: 981 2023/03/03 14:07:35 - mmengine - INFO - Epoch(val) [20][18/59] eta: 0:00:47 time: 0.9992 data_time: 0.0010 memory: 981 2023/03/03 14:07:36 - mmengine - INFO - Epoch(val) [20][19/59] eta: 0:00:46 time: 0.9984 data_time: 0.0011 memory: 981 2023/03/03 14:07:36 - mmengine - INFO - Epoch(val) [20][20/59] eta: 0:00:43 time: 0.9355 data_time: 0.0010 memory: 981 2023/03/03 14:07:38 - mmengine - INFO - Epoch(val) [20][21/59] eta: 0:00:42 time: 1.0379 data_time: 0.0010 memory: 981 2023/03/03 14:07:38 - mmengine - INFO - Epoch(val) [20][22/59] eta: 0:00:40 time: 0.7300 data_time: 0.0010 memory: 981 2023/03/03 14:07:39 - mmengine - INFO - Epoch(val) [20][23/59] eta: 0:00:38 time: 0.5895 data_time: 0.0009 memory: 981 2023/03/03 14:07:39 - mmengine - INFO - Epoch(val) [20][24/59] eta: 0:00:36 time: 0.5211 data_time: 0.0009 memory: 962 2023/03/03 14:07:39 - mmengine - INFO - Epoch(val) [20][25/59] eta: 0:00:34 time: 0.5526 data_time: 0.0009 memory: 981 2023/03/03 14:07:40 - mmengine - INFO - Epoch(val) [20][26/59] eta: 0:00:32 time: 0.5365 data_time: 0.0009 memory: 981 2023/03/03 14:07:40 - mmengine - INFO - Epoch(val) [20][27/59] eta: 0:00:30 time: 0.5367 data_time: 0.0010 memory: 981 2023/03/03 14:07:40 - mmengine - INFO - Epoch(val) [20][28/59] eta: 0:00:29 time: 0.5366 data_time: 0.0010 memory: 981 2023/03/03 14:07:42 - mmengine - INFO - Epoch(val) [20][29/59] eta: 0:00:28 time: 0.5706 data_time: 0.0009 memory: 981 2023/03/03 14:07:43 - mmengine - INFO - Epoch(val) [20][30/59] eta: 0:00:27 time: 0.6191 data_time: 0.0009 memory: 999 2023/03/03 14:07:43 - mmengine - INFO - Epoch(val) [20][31/59] eta: 0:00:26 time: 0.5335 data_time: 0.0010 memory: 981 2023/03/03 14:07:45 - mmengine - INFO - Epoch(val) [20][32/59] eta: 0:00:25 time: 0.6360 data_time: 0.0010 memory: 981 2023/03/03 14:07:45 - mmengine - INFO - Epoch(val) [20][33/59] eta: 0:00:23 time: 0.5725 data_time: 0.0010 memory: 981 2023/03/03 14:07:45 - mmengine - INFO - Epoch(val) [20][34/59] eta: 0:00:22 time: 0.5579 data_time: 0.0010 memory: 981 2023/03/03 14:07:45 - mmengine - INFO - Epoch(val) [20][35/59] eta: 0:00:21 time: 0.5924 data_time: 0.0010 memory: 981 2023/03/03 14:07:46 - mmengine - INFO - Epoch(val) [20][36/59] eta: 0:00:20 time: 0.6094 data_time: 0.0010 memory: 981 2023/03/03 14:07:46 - mmengine - INFO - Epoch(val) [20][37/59] eta: 0:00:18 time: 0.5932 data_time: 0.0009 memory: 981 2023/03/03 14:07:47 - mmengine - INFO - Epoch(val) [20][38/59] eta: 0:00:17 time: 0.6280 data_time: 0.0009 memory: 981 2023/03/03 14:07:47 - mmengine - INFO - Epoch(val) [20][39/59] eta: 0:00:16 time: 0.5452 data_time: 0.0010 memory: 987 2023/03/03 14:07:48 - mmengine - INFO - Epoch(val) [20][40/59] eta: 0:00:16 time: 0.5309 data_time: 0.0010 memory: 981 2023/03/03 14:07:49 - mmengine - INFO - Epoch(val) [20][41/59] eta: 0:00:15 time: 0.5855 data_time: 0.0009 memory: 986 2023/03/03 14:07:50 - mmengine - INFO - Epoch(val) [20][42/59] eta: 0:00:14 time: 0.5299 data_time: 0.0010 memory: 981 2023/03/03 14:07:51 - mmengine - INFO - Epoch(val) [20][43/59] eta: 0:00:13 time: 0.6096 data_time: 0.0010 memory: 976 2023/03/03 14:07:51 - mmengine - INFO - Epoch(val) [20][44/59] eta: 0:00:12 time: 0.6421 data_time: 0.0010 memory: 1003 2023/03/03 14:07:53 - mmengine - INFO - Epoch(val) [20][45/59] eta: 0:00:12 time: 0.7606 data_time: 0.0010 memory: 981 2023/03/03 14:07:54 - mmengine - INFO - Epoch(val) [20][46/59] eta: 0:00:11 time: 0.7929 data_time: 0.0014 memory: 981 2023/03/03 14:07:54 - mmengine - INFO - Epoch(val) [20][47/59] eta: 0:00:10 time: 0.8243 data_time: 0.0014 memory: 936 2023/03/03 14:07:55 - mmengine - INFO - Epoch(val) [20][48/59] eta: 0:00:09 time: 0.8069 data_time: 0.0014 memory: 1000 2023/03/03 14:07:56 - mmengine - INFO - Epoch(val) [20][49/59] eta: 0:00:08 time: 0.8586 data_time: 0.0014 memory: 981 2023/03/03 14:07:57 - mmengine - INFO - Epoch(val) [20][50/59] eta: 0:00:07 time: 0.8754 data_time: 0.0015 memory: 987 2023/03/03 14:07:58 - mmengine - INFO - Epoch(val) [20][51/59] eta: 0:00:06 time: 0.9299 data_time: 0.0015 memory: 981 2023/03/03 14:08:00 - mmengine - INFO - Epoch(val) [20][52/59] eta: 0:00:06 time: 0.9853 data_time: 0.0017 memory: 981 2023/03/03 14:08:00 - mmengine - INFO - Epoch(val) [20][53/59] eta: 0:00:05 time: 0.9693 data_time: 0.0017 memory: 962 2023/03/03 14:08:01 - mmengine - INFO - Epoch(val) [20][54/59] eta: 0:00:04 time: 0.9832 data_time: 0.0018 memory: 981 2023/03/03 14:08:02 - mmengine - INFO - Epoch(val) [20][55/59] eta: 0:00:03 time: 0.8622 data_time: 0.0018 memory: 981 2023/03/03 14:08:02 - mmengine - INFO - Epoch(val) [20][56/59] eta: 0:00:02 time: 0.8467 data_time: 0.0014 memory: 981 2023/03/03 14:08:05 - mmengine - INFO - Epoch(val) [20][57/59] eta: 0:00:01 time: 1.0253 data_time: 0.0013 memory: 981 2023/03/03 14:08:06 - mmengine - INFO - Epoch(val) [20][58/59] eta: 0:00:00 time: 1.1092 data_time: 0.0014 memory: 1016 2023/03/03 14:08:06 - mmengine - INFO - Epoch(val) [20][59/59] eta: 0:00:00 time: 1.0576 data_time: 0.0014 memory: 981 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.80, recall: 0.8055, precision: 0.8368, hmean: 0.8208 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.81, recall: 0.8055, precision: 0.8392, hmean: 0.8220 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.82, recall: 0.8046, precision: 0.8439, hmean: 0.8237 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.83, recall: 0.7991, precision: 0.8446, hmean: 0.8212 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.84, recall: 0.7982, precision: 0.8477, hmean: 0.8222 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.85, recall: 0.7982, precision: 0.8535, hmean: 0.8249 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.86, recall: 0.7963, precision: 0.8557, hmean: 0.8250 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.87, recall: 0.7954, precision: 0.8598, hmean: 0.8264 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.88, recall: 0.7936, precision: 0.8621, hmean: 0.8264 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.89, recall: 0.7909, precision: 0.8643, hmean: 0.8259 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.90, recall: 0.7881, precision: 0.8717, hmean: 0.8278 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.91, recall: 0.7826, precision: 0.8727, hmean: 0.8252 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.92, recall: 0.7708, precision: 0.8755, hmean: 0.8198 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.93, recall: 0.7616, precision: 0.8770, hmean: 0.8152 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.94, recall: 0.7516, precision: 0.8802, hmean: 0.8108 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.95, recall: 0.7434, precision: 0.8810, hmean: 0.8063 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.96, recall: 0.7333, precision: 0.8853, hmean: 0.8022 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.97, recall: 0.7205, precision: 0.8885, hmean: 0.7958 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.98, recall: 0.6959, precision: 0.8954, hmean: 0.7831 2023/03/03 14:08:35 - mmengine - INFO - text score threshold: 0.99, recall: 0.6630, precision: 0.9030, hmean: 0.7646 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.80, recall: 0.8192, precision: 0.9033, hmean: 0.8592 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.81, recall: 0.8192, precision: 0.9042, hmean: 0.8596 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.82, recall: 0.8174, precision: 0.9077, hmean: 0.8602 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.83, recall: 0.8119, precision: 0.9071, hmean: 0.8569 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.84, recall: 0.8100, precision: 0.9079, hmean: 0.8562 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.85, recall: 0.8100, precision: 0.9126, hmean: 0.8582 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.86, recall: 0.8082, precision: 0.9133, hmean: 0.8576 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.87, recall: 0.8064, precision: 0.9160, hmean: 0.8577 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.88, recall: 0.8046, precision: 0.9187, hmean: 0.8578 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.89, recall: 0.8000, precision: 0.9192, hmean: 0.8555 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.90, recall: 0.7973, precision: 0.9228, hmean: 0.8555 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.91, recall: 0.7918, precision: 0.9223, hmean: 0.8521 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.92, recall: 0.7790, precision: 0.9222, hmean: 0.8446 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.93, recall: 0.7699, precision: 0.9233, hmean: 0.8396 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.94, recall: 0.7598, precision: 0.9244, hmean: 0.8341 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.95, recall: 0.7516, precision: 0.9258, hmean: 0.8296 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.96, recall: 0.7397, precision: 0.9278, hmean: 0.8232 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.97, recall: 0.7269, precision: 0.9321, hmean: 0.8168 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.98, recall: 0.6995, precision: 0.9341, hmean: 0.8000 2023/03/03 14:08:37 - mmengine - INFO - text score threshold: 0.99, recall: 0.6648, precision: 0.9394, hmean: 0.7786 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.80, recall: 0.7416, precision: 0.9575, hmean: 0.8358 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.81, recall: 0.7406, precision: 0.9575, hmean: 0.8352 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.82, recall: 0.7379, precision: 0.9573, hmean: 0.8334 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.83, recall: 0.7342, precision: 0.9571, hmean: 0.8310 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.84, recall: 0.7333, precision: 0.9594, hmean: 0.8313 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.85, recall: 0.7306, precision: 0.9592, hmean: 0.8294 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.86, recall: 0.7297, precision: 0.9592, hmean: 0.8288 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.87, recall: 0.7269, precision: 0.9590, hmean: 0.8270 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.88, recall: 0.7251, precision: 0.9601, hmean: 0.8262 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.89, recall: 0.7205, precision: 0.9599, hmean: 0.8232 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.90, recall: 0.7187, precision: 0.9598, hmean: 0.8219 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.91, recall: 0.7132, precision: 0.9595, hmean: 0.8182 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.92, recall: 0.7014, precision: 0.9588, hmean: 0.8101 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.93, recall: 0.6922, precision: 0.9607, hmean: 0.8047 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.94, recall: 0.6840, precision: 0.9627, hmean: 0.7998 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.95, recall: 0.6749, precision: 0.9622, hmean: 0.7933 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.96, recall: 0.6621, precision: 0.9615, hmean: 0.7842 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.97, recall: 0.6511, precision: 0.9648, hmean: 0.7775 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.98, recall: 0.6292, precision: 0.9691, hmean: 0.7630 2023/03/03 14:08:40 - mmengine - INFO - text score threshold: 0.99, recall: 0.5973, precision: 0.9703, hmean: 0.7394 2023/03/03 14:08:40 - mmengine - INFO - Epoch(val) [20][59/59] generic/precision: 0.8717 generic/recall: 0.7881 generic/hmean: 0.8278 weak/precision: 0.9077 weak/recall: 0.8174 weak/hmean: 0.8602 strong/precision: 0.9575 strong/recall: 0.7416 strong/hmean: 0.8358 2023/03/03 14:08:40 - mmengine - INFO - The previous best checkpoint mmocr/projects/SPTS/work_dirs/spts_resnet50_350e_icdar2013/best_generic/hmean_epoch_10.pth is removed 2023/03/03 14:08:42 - mmengine - INFO - The best checkpoint with 0.8278 generic/hmean at 20 epoch is saved to best_generic/hmean_epoch_20.pth. 2023/03/03 14:08:43 - mmengine - INFO - Epoch(train) [21][ 1/15] lr: 1.0000e-06 eta: 0:17:01 time: 0.3493 data_time: 0.0638 memory: 18070 loss: 0.1154 loss_ce: 0.1154 2023/03/03 14:08:43 - mmengine - INFO - Epoch(train) [21][ 2/15] lr: 1.0000e-06 eta: 0:16:59 time: 0.3409 data_time: 0.0638 memory: 17572 loss: 0.1162 loss_ce: 0.1162 2023/03/03 14:08:43 - mmengine - INFO - Epoch(train) [21][ 3/15] lr: 1.0000e-06 eta: 0:16:57 time: 0.3228 data_time: 0.0638 memory: 18407 loss: 0.1127 loss_ce: 0.1127 2023/03/03 14:08:44 - mmengine - INFO - Epoch(train) [21][ 4/15] lr: 1.0000e-06 eta: 0:16:58 time: 0.3560 data_time: 0.0638 memory: 33371 loss: 0.1121 loss_ce: 0.1121 2023/03/03 14:08:44 - mmengine - INFO - Epoch(train) [21][ 5/15] lr: 1.0000e-06 eta: 0:16:56 time: 0.3504 data_time: 0.0639 memory: 16976 loss: 0.1090 loss_ce: 0.1090 2023/03/03 14:08:44 - mmengine - INFO - Epoch(train) [21][ 6/15] lr: 1.0000e-06 eta: 0:16:56 time: 0.3543 data_time: 0.0639 memory: 15494 loss: 0.1118 loss_ce: 0.1118 2023/03/03 14:08:45 - mmengine - INFO - Epoch(train) [21][ 7/15] lr: 1.0000e-06 eta: 0:16:55 time: 0.3571 data_time: 0.0639 memory: 16508 loss: 0.1118 loss_ce: 0.1118 2023/03/03 14:08:45 - mmengine - INFO - Epoch(train) [21][ 8/15] lr: 1.0000e-06 eta: 0:16:53 time: 0.3331 data_time: 0.0639 memory: 16199 loss: 0.1125 loss_ce: 0.1125 2023/03/03 14:08:45 - mmengine - INFO - Epoch(train) [21][ 9/15] lr: 1.0000e-06 eta: 0:16:51 time: 0.3324 data_time: 0.0639 memory: 17730 loss: 0.1177 loss_ce: 0.1177 2023/03/03 14:08:45 - mmengine - INFO - Epoch(train) [21][10/15] lr: 1.0000e-06 eta: 0:16:51 time: 0.3448 data_time: 0.0639 memory: 13444 loss: 0.1181 loss_ce: 0.1181 2023/03/03 14:08:46 - mmengine - INFO - Epoch(train) [21][11/15] lr: 1.0000e-06 eta: 0:16:50 time: 0.2967 data_time: 0.0016 memory: 21516 loss: 0.1173 loss_ce: 0.1173 2023/03/03 14:08:46 - mmengine - INFO - Epoch(train) [21][12/15] lr: 1.0000e-06 eta: 0:16:49 time: 0.3015 data_time: 0.0015 memory: 17122 loss: 0.1174 loss_ce: 0.1174 2023/03/03 14:08:46 - mmengine - INFO - Epoch(train) [21][13/15] lr: 1.0000e-06 eta: 0:16:48 time: 0.3125 data_time: 0.0015 memory: 18348 loss: 0.1242 loss_ce: 0.1242 2023/03/03 14:08:47 - mmengine - INFO - Epoch(train) [21][14/15] lr: 1.0000e-06 eta: 0:16:47 time: 0.2845 data_time: 0.0015 memory: 17120 loss: 0.1258 loss_ce: 0.1258 2023/03/03 14:08:47 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:08:47 - mmengine - INFO - Epoch(train) [21][15/15] lr: 1.0000e-06 eta: 0:16:44 time: 0.2809 data_time: 0.0015 memory: 6347 loss: 0.1316 loss_ce: 0.1316 2023/03/03 14:08:47 - mmengine - INFO - Epoch(train) [22][ 1/15] lr: 1.0000e-06 eta: 0:16:47 time: 0.3162 data_time: 0.0509 memory: 18070 loss: 0.1311 loss_ce: 0.1311 2023/03/03 14:08:48 - mmengine - INFO - Epoch(train) [22][ 2/15] lr: 1.0000e-06 eta: 0:16:46 time: 0.3170 data_time: 0.0509 memory: 17284 loss: 0.1227 loss_ce: 0.1227 2023/03/03 14:08:48 - mmengine - INFO - Epoch(train) [22][ 3/15] lr: 1.0000e-06 eta: 0:16:45 time: 0.3181 data_time: 0.0510 memory: 15457 loss: 0.1184 loss_ce: 0.1184 2023/03/03 14:08:48 - mmengine - INFO - Epoch(train) [22][ 4/15] lr: 1.0000e-06 eta: 0:16:43 time: 0.3210 data_time: 0.0510 memory: 16804 loss: 0.1145 loss_ce: 0.1145 2023/03/03 14:08:49 - mmengine - INFO - Epoch(train) [22][ 5/15] lr: 1.0000e-06 eta: 0:16:43 time: 0.3265 data_time: 0.0511 memory: 15262 loss: 0.1135 loss_ce: 0.1135 2023/03/03 14:08:49 - mmengine - INFO - Epoch(train) [22][ 6/15] lr: 1.0000e-06 eta: 0:16:41 time: 0.3106 data_time: 0.0511 memory: 18241 loss: 0.1113 loss_ce: 0.1113 2023/03/03 14:08:49 - mmengine - INFO - Epoch(train) [22][ 7/15] lr: 1.0000e-06 eta: 0:16:41 time: 0.3328 data_time: 0.0511 memory: 16370 loss: 0.1153 loss_ce: 0.1153 2023/03/03 14:08:49 - mmengine - INFO - Epoch(train) [22][ 8/15] lr: 1.0000e-06 eta: 0:16:40 time: 0.3242 data_time: 0.0512 memory: 16508 loss: 0.1118 loss_ce: 0.1118 2023/03/03 14:08:50 - mmengine - INFO - Epoch(train) [22][ 9/15] lr: 1.0000e-06 eta: 0:16:40 time: 0.3440 data_time: 0.0511 memory: 15494 loss: 0.1203 loss_ce: 0.1203 2023/03/03 14:08:50 - mmengine - INFO - Epoch(train) [22][10/15] lr: 1.0000e-06 eta: 0:16:39 time: 0.3586 data_time: 0.0511 memory: 17143 loss: 0.1126 loss_ce: 0.1126 2023/03/03 14:08:51 - mmengine - INFO - Epoch(train) [22][11/15] lr: 1.0000e-06 eta: 0:16:39 time: 0.3120 data_time: 0.0018 memory: 16370 loss: 0.1105 loss_ce: 0.1105 2023/03/03 14:08:51 - mmengine - INFO - Epoch(train) [22][12/15] lr: 1.0000e-06 eta: 0:16:37 time: 0.3083 data_time: 0.0017 memory: 17272 loss: 0.1131 loss_ce: 0.1131 2023/03/03 14:08:51 - mmengine - INFO - Epoch(train) [22][13/15] lr: 1.0000e-06 eta: 0:16:36 time: 0.3124 data_time: 0.0016 memory: 22766 loss: 0.1194 loss_ce: 0.1194 2023/03/03 14:08:51 - mmengine - INFO - Epoch(train) [22][14/15] lr: 1.0000e-06 eta: 0:16:35 time: 0.3196 data_time: 0.0016 memory: 17540 loss: 0.1229 loss_ce: 0.1229 2023/03/03 14:08:52 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:08:52 - mmengine - INFO - Epoch(train) [22][15/15] lr: 1.0000e-06 eta: 0:16:33 time: 0.3042 data_time: 0.0015 memory: 10348 loss: 0.1269 loss_ce: 0.1269 2023/03/03 14:08:53 - mmengine - INFO - Epoch(train) [23][ 1/15] lr: 1.0000e-06 eta: 0:16:37 time: 0.3766 data_time: 0.0722 memory: 15631 loss: 0.1320 loss_ce: 0.1320 2023/03/03 14:08:53 - mmengine - INFO - Epoch(train) [23][ 2/15] lr: 1.0000e-06 eta: 0:16:37 time: 0.3713 data_time: 0.0764 memory: 17788 loss: 0.1264 loss_ce: 0.1264 2023/03/03 14:08:53 - mmengine - INFO - Epoch(train) [23][ 3/15] lr: 1.0000e-06 eta: 0:16:36 time: 0.3678 data_time: 0.0764 memory: 13028 loss: 0.1268 loss_ce: 0.1268 2023/03/03 14:08:54 - mmengine - INFO - Epoch(train) [23][ 4/15] lr: 1.0000e-06 eta: 0:16:35 time: 0.3546 data_time: 0.0764 memory: 17272 loss: 0.1172 loss_ce: 0.1172 2023/03/03 14:08:54 - mmengine - INFO - Epoch(train) [23][ 5/15] lr: 1.0000e-06 eta: 0:16:35 time: 0.3683 data_time: 0.0764 memory: 32467 loss: 0.1182 loss_ce: 0.1182 2023/03/03 14:08:54 - mmengine - INFO - Epoch(train) [23][ 6/15] lr: 1.0000e-06 eta: 0:16:35 time: 0.3794 data_time: 0.0765 memory: 21487 loss: 0.1133 loss_ce: 0.1133 2023/03/03 14:08:55 - mmengine - INFO - Epoch(train) [23][ 7/15] lr: 1.0000e-06 eta: 0:16:34 time: 0.3824 data_time: 0.0765 memory: 16804 loss: 0.1127 loss_ce: 0.1127 2023/03/03 14:08:55 - mmengine - INFO - Epoch(train) [23][ 8/15] lr: 1.0000e-06 eta: 0:16:34 time: 0.3912 data_time: 0.0765 memory: 11234 loss: 0.1088 loss_ce: 0.1088 2023/03/03 14:08:55 - mmengine - INFO - Epoch(train) [23][ 9/15] lr: 1.0000e-06 eta: 0:16:33 time: 0.3910 data_time: 0.0765 memory: 21971 loss: 0.1095 loss_ce: 0.1095 2023/03/03 14:08:56 - mmengine - INFO - Epoch(train) [23][10/15] lr: 1.0000e-06 eta: 0:16:32 time: 0.4003 data_time: 0.0765 memory: 17572 loss: 0.1073 loss_ce: 0.1073 2023/03/03 14:08:56 - mmengine - INFO - Epoch(train) [23][11/15] lr: 1.0000e-06 eta: 0:16:30 time: 0.3312 data_time: 0.0059 memory: 17619 loss: 0.1020 loss_ce: 0.1020 2023/03/03 14:08:56 - mmengine - INFO - Epoch(train) [23][12/15] lr: 1.0000e-06 eta: 0:16:29 time: 0.3100 data_time: 0.0017 memory: 17120 loss: 0.1073 loss_ce: 0.1073 2023/03/03 14:08:56 - mmengine - INFO - Epoch(train) [23][13/15] lr: 1.0000e-06 eta: 0:16:27 time: 0.3106 data_time: 0.0016 memory: 16976 loss: 0.1152 loss_ce: 0.1152 2023/03/03 14:08:57 - mmengine - INFO - Epoch(train) [23][14/15] lr: 1.0000e-06 eta: 0:16:26 time: 0.3007 data_time: 0.0016 memory: 18241 loss: 0.1165 loss_ce: 0.1165 2023/03/03 14:08:57 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:08:57 - mmengine - INFO - Epoch(train) [23][15/15] lr: 1.0000e-06 eta: 0:16:23 time: 0.2659 data_time: 0.0015 memory: 5705 loss: 0.1256 loss_ce: 0.1256 2023/03/03 14:08:58 - mmengine - INFO - Epoch(train) [24][ 1/15] lr: 1.0000e-06 eta: 0:16:27 time: 0.3176 data_time: 0.0680 memory: 17488 loss: 0.1298 loss_ce: 0.1298 2023/03/03 14:08:58 - mmengine - INFO - Epoch(train) [24][ 2/15] lr: 1.0000e-06 eta: 0:16:25 time: 0.3153 data_time: 0.0681 memory: 17421 loss: 0.1327 loss_ce: 0.1327 2023/03/03 14:08:58 - mmengine - INFO - Epoch(train) [24][ 3/15] lr: 1.0000e-06 eta: 0:16:26 time: 0.3166 data_time: 0.0681 memory: 32021 loss: 0.1259 loss_ce: 0.1259 2023/03/03 14:08:58 - mmengine - INFO - Epoch(train) [24][ 4/15] lr: 1.0000e-06 eta: 0:16:24 time: 0.3067 data_time: 0.0681 memory: 17572 loss: 0.1213 loss_ce: 0.1213 2023/03/03 14:08:59 - mmengine - INFO - Epoch(train) [24][ 5/15] lr: 1.0000e-06 eta: 0:16:25 time: 0.3354 data_time: 0.0682 memory: 13722 loss: 0.1208 loss_ce: 0.1208 2023/03/03 14:08:59 - mmengine - INFO - Epoch(train) [24][ 6/15] lr: 1.0000e-06 eta: 0:16:25 time: 0.3532 data_time: 0.0682 memory: 23395 loss: 0.1248 loss_ce: 0.1248 2023/03/03 14:09:00 - mmengine - INFO - Epoch(train) [24][ 7/15] lr: 1.0000e-06 eta: 0:16:24 time: 0.3632 data_time: 0.0682 memory: 15911 loss: 0.1225 loss_ce: 0.1225 2023/03/03 14:09:00 - mmengine - INFO - Epoch(train) [24][ 8/15] lr: 1.0000e-06 eta: 0:16:23 time: 0.3659 data_time: 0.0682 memory: 16223 loss: 0.1178 loss_ce: 0.1178 2023/03/03 14:09:00 - mmengine - INFO - Epoch(train) [24][ 9/15] lr: 1.0000e-06 eta: 0:16:22 time: 0.3678 data_time: 0.0682 memory: 15631 loss: 0.1185 loss_ce: 0.1185 2023/03/03 14:09:00 - mmengine - INFO - Epoch(train) [24][10/15] lr: 1.0000e-06 eta: 0:16:20 time: 0.3807 data_time: 0.0683 memory: 16508 loss: 0.1068 loss_ce: 0.1068 2023/03/03 14:09:01 - mmengine - INFO - Epoch(train) [24][11/15] lr: 1.0000e-06 eta: 0:16:20 time: 0.3218 data_time: 0.0017 memory: 15631 loss: 0.1074 loss_ce: 0.1074 2023/03/03 14:09:01 - mmengine - INFO - Epoch(train) [24][12/15] lr: 1.0000e-06 eta: 0:16:18 time: 0.3212 data_time: 0.0017 memory: 17421 loss: 0.1047 loss_ce: 0.1047 2023/03/03 14:09:01 - mmengine - INFO - Epoch(train) [24][13/15] lr: 1.0000e-06 eta: 0:16:17 time: 0.3067 data_time: 0.0016 memory: 15037 loss: 0.1127 loss_ce: 0.1127 2023/03/03 14:09:01 - mmengine - INFO - Epoch(train) [24][14/15] lr: 1.0000e-06 eta: 0:16:15 time: 0.3055 data_time: 0.0016 memory: 14436 loss: 0.1103 loss_ce: 0.1103 2023/03/03 14:09:02 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:09:02 - mmengine - INFO - Epoch(train) [24][15/15] lr: 1.0000e-06 eta: 0:16:15 time: 0.2809 data_time: 0.0016 memory: 5528 loss: 0.1127 loss_ce: 0.1127 2023/03/03 14:09:03 - mmengine - INFO - Epoch(train) [25][ 1/15] lr: 1.0000e-06 eta: 0:16:20 time: 0.3511 data_time: 0.0769 memory: 18651 loss: 0.1145 loss_ce: 0.1145 2023/03/03 14:09:03 - mmengine - INFO - Epoch(train) [25][ 2/15] lr: 1.0000e-06 eta: 0:16:19 time: 0.3461 data_time: 0.0769 memory: 18239 loss: 0.1095 loss_ce: 0.1095 2023/03/03 14:09:03 - mmengine - INFO - Epoch(train) [25][ 3/15] lr: 1.0000e-06 eta: 0:16:18 time: 0.3527 data_time: 0.0770 memory: 14413 loss: 0.1087 loss_ce: 0.1087 2023/03/03 14:09:04 - mmengine - INFO - Epoch(train) [25][ 4/15] lr: 1.0000e-06 eta: 0:16:18 time: 0.3639 data_time: 0.0770 memory: 17622 loss: 0.1107 loss_ce: 0.1107 2023/03/03 14:09:04 - mmengine - INFO - Epoch(train) [25][ 5/15] lr: 1.0000e-06 eta: 0:16:17 time: 0.3736 data_time: 0.0770 memory: 20005 loss: 0.1135 loss_ce: 0.1135 2023/03/03 14:09:04 - mmengine - INFO - Epoch(train) [25][ 6/15] lr: 1.0000e-06 eta: 0:16:16 time: 0.3645 data_time: 0.0770 memory: 15532 loss: 0.1113 loss_ce: 0.1113 2023/03/03 14:09:05 - mmengine - INFO - Epoch(train) [25][ 7/15] lr: 1.0000e-06 eta: 0:16:14 time: 0.3669 data_time: 0.0771 memory: 16369 loss: 0.1120 loss_ce: 0.1120 2023/03/03 14:09:05 - mmengine - INFO - Epoch(train) [25][ 8/15] lr: 1.0000e-06 eta: 0:16:13 time: 0.3622 data_time: 0.0772 memory: 15297 loss: 0.1063 loss_ce: 0.1063 2023/03/03 14:09:05 - mmengine - INFO - Epoch(train) [25][ 9/15] lr: 1.0000e-06 eta: 0:16:11 time: 0.3634 data_time: 0.0772 memory: 17421 loss: 0.1088 loss_ce: 0.1088 2023/03/03 14:09:05 - mmengine - INFO - Epoch(train) [25][10/15] lr: 1.0000e-06 eta: 0:16:11 time: 0.3644 data_time: 0.0772 memory: 13613 loss: 0.1067 loss_ce: 0.1067 2023/03/03 14:09:06 - mmengine - INFO - Epoch(train) [25][11/15] lr: 1.0000e-06 eta: 0:16:10 time: 0.2849 data_time: 0.0021 memory: 16112 loss: 0.1027 loss_ce: 0.1027 2023/03/03 14:09:06 - mmengine - INFO - Epoch(train) [25][12/15] lr: 1.0000e-06 eta: 0:16:09 time: 0.2812 data_time: 0.0020 memory: 19226 loss: 0.1028 loss_ce: 0.1028 2023/03/03 14:09:06 - mmengine - INFO - Epoch(train) [25][13/15] lr: 1.0000e-06 eta: 0:16:07 time: 0.2738 data_time: 0.0020 memory: 15631 loss: 0.1014 loss_ce: 0.1014 2023/03/03 14:09:07 - mmengine - INFO - Epoch(train) [25][14/15] lr: 1.0000e-06 eta: 0:16:07 time: 0.2698 data_time: 0.0020 memory: 16536 loss: 0.0960 loss_ce: 0.0960 2023/03/03 14:09:07 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:09:07 - mmengine - INFO - Epoch(train) [25][15/15] lr: 1.0000e-06 eta: 0:16:05 time: 0.2492 data_time: 0.0020 memory: 6398 loss: 0.1007 loss_ce: 0.1007 2023/03/03 14:09:08 - mmengine - INFO - Epoch(train) [26][ 1/15] lr: 1.0000e-06 eta: 0:16:08 time: 0.3189 data_time: 0.0574 memory: 21723 loss: 0.1050 loss_ce: 0.1050 2023/03/03 14:09:08 - mmengine - INFO - Epoch(train) [26][ 2/15] lr: 1.0000e-06 eta: 0:16:07 time: 0.3224 data_time: 0.0575 memory: 18652 loss: 0.1034 loss_ce: 0.1034 2023/03/03 14:09:08 - mmengine - INFO - Epoch(train) [26][ 3/15] lr: 1.0000e-06 eta: 0:16:07 time: 0.3397 data_time: 0.0575 memory: 17572 loss: 0.1042 loss_ce: 0.1042 2023/03/03 14:09:09 - mmengine - INFO - Epoch(train) [26][ 4/15] lr: 1.0000e-06 eta: 0:16:07 time: 0.3627 data_time: 0.0575 memory: 26390 loss: 0.1041 loss_ce: 0.1041 2023/03/03 14:09:09 - mmengine - INFO - Epoch(train) [26][ 5/15] lr: 1.0000e-06 eta: 0:16:06 time: 0.3534 data_time: 0.0575 memory: 18528 loss: 0.1000 loss_ce: 0.1000 2023/03/03 14:09:09 - mmengine - INFO - Epoch(train) [26][ 6/15] lr: 1.0000e-06 eta: 0:16:04 time: 0.3426 data_time: 0.0573 memory: 18953 loss: 0.0973 loss_ce: 0.0973 2023/03/03 14:09:10 - mmengine - INFO - Epoch(train) [26][ 7/15] lr: 1.0000e-06 eta: 0:16:07 time: 0.3929 data_time: 0.0573 memory: 18070 loss: 0.0994 loss_ce: 0.0994 2023/03/03 14:09:10 - mmengine - INFO - Epoch(train) [26][ 8/15] lr: 1.0000e-06 eta: 0:16:06 time: 0.4042 data_time: 0.0574 memory: 19171 loss: 0.1023 loss_ce: 0.1023 2023/03/03 14:09:10 - mmengine - INFO - Epoch(train) [26][ 9/15] lr: 1.0000e-06 eta: 0:16:05 time: 0.3905 data_time: 0.0574 memory: 15449 loss: 0.1033 loss_ce: 0.1033 2023/03/03 14:09:11 - mmengine - INFO - Epoch(train) [26][10/15] lr: 1.0000e-06 eta: 0:16:04 time: 0.4136 data_time: 0.0574 memory: 25536 loss: 0.0993 loss_ce: 0.0993 2023/03/03 14:09:11 - mmengine - INFO - Epoch(train) [26][11/15] lr: 1.0000e-06 eta: 0:16:02 time: 0.3383 data_time: 0.0020 memory: 14827 loss: 0.0994 loss_ce: 0.0994 2023/03/03 14:09:11 - mmengine - INFO - Epoch(train) [26][12/15] lr: 1.0000e-06 eta: 0:16:01 time: 0.3325 data_time: 0.0018 memory: 17572 loss: 0.0984 loss_ce: 0.0984 2023/03/03 14:09:12 - mmengine - INFO - Epoch(train) [26][13/15] lr: 1.0000e-06 eta: 0:16:00 time: 0.3257 data_time: 0.0017 memory: 17284 loss: 0.0963 loss_ce: 0.0963 2023/03/03 14:09:12 - mmengine - INFO - Epoch(train) [26][14/15] lr: 1.0000e-06 eta: 0:15:59 time: 0.3026 data_time: 0.0016 memory: 17272 loss: 0.0988 loss_ce: 0.0988 2023/03/03 14:09:12 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:09:12 - mmengine - INFO - Epoch(train) [26][15/15] lr: 1.0000e-06 eta: 0:15:58 time: 0.3043 data_time: 0.0016 memory: 6850 loss: 0.1112 loss_ce: 0.1112 2023/03/03 14:09:13 - mmengine - INFO - Epoch(train) [27][ 1/15] lr: 1.0000e-06 eta: 0:16:00 time: 0.3567 data_time: 0.0506 memory: 17791 loss: 0.1153 loss_ce: 0.1153 2023/03/03 14:09:13 - mmengine - INFO - Epoch(train) [27][ 2/15] lr: 1.0000e-06 eta: 0:15:59 time: 0.3153 data_time: 0.0507 memory: 17272 loss: 0.1177 loss_ce: 0.1177 2023/03/03 14:09:13 - mmengine - INFO - Epoch(train) [27][ 3/15] lr: 1.0000e-06 eta: 0:15:58 time: 0.3036 data_time: 0.0507 memory: 15315 loss: 0.1104 loss_ce: 0.1104 2023/03/03 14:09:14 - mmengine - INFO - Epoch(train) [27][ 4/15] lr: 1.0000e-06 eta: 0:15:57 time: 0.3053 data_time: 0.0507 memory: 13842 loss: 0.1079 loss_ce: 0.1079 2023/03/03 14:09:14 - mmengine - INFO - Epoch(train) [27][ 5/15] lr: 1.0000e-06 eta: 0:15:56 time: 0.3005 data_time: 0.0507 memory: 16076 loss: 0.1116 loss_ce: 0.1116 2023/03/03 14:09:14 - mmengine - INFO - Epoch(train) [27][ 6/15] lr: 1.0000e-06 eta: 0:15:56 time: 0.3197 data_time: 0.0508 memory: 20120 loss: 0.1051 loss_ce: 0.1051 2023/03/03 14:09:14 - mmengine - INFO - Epoch(train) [27][ 7/15] lr: 1.0000e-06 eta: 0:15:54 time: 0.3223 data_time: 0.0508 memory: 16370 loss: 0.1098 loss_ce: 0.1098 2023/03/03 14:09:15 - mmengine - INFO - Epoch(train) [27][ 8/15] lr: 1.0000e-06 eta: 0:15:54 time: 0.3288 data_time: 0.0511 memory: 16976 loss: 0.1246 loss_ce: 0.1246 2023/03/03 14:09:15 - mmengine - INFO - Epoch(train) [27][ 9/15] lr: 1.0000e-06 eta: 0:15:53 time: 0.3287 data_time: 0.0511 memory: 16976 loss: 0.1230 loss_ce: 0.1230 2023/03/03 14:09:15 - mmengine - INFO - Epoch(train) [27][10/15] lr: 1.0000e-06 eta: 0:15:52 time: 0.3361 data_time: 0.0511 memory: 18159 loss: 0.1146 loss_ce: 0.1146 2023/03/03 14:09:16 - mmengine - INFO - Epoch(train) [27][11/15] lr: 1.0000e-06 eta: 0:15:51 time: 0.2805 data_time: 0.0021 memory: 15190 loss: 0.1114 loss_ce: 0.1114 2023/03/03 14:09:16 - mmengine - INFO - Epoch(train) [27][12/15] lr: 1.0000e-06 eta: 0:15:51 time: 0.2969 data_time: 0.0020 memory: 29468 loss: 0.1125 loss_ce: 0.1125 2023/03/03 14:09:16 - mmengine - INFO - Epoch(train) [27][13/15] lr: 1.0000e-06 eta: 0:15:49 time: 0.2935 data_time: 0.0019 memory: 14461 loss: 0.1153 loss_ce: 0.1153 2023/03/03 14:09:17 - mmengine - INFO - Epoch(train) [27][14/15] lr: 1.0000e-06 eta: 0:15:50 time: 0.3148 data_time: 0.0019 memory: 17121 loss: 0.1144 loss_ce: 0.1144 2023/03/03 14:09:17 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:09:17 - mmengine - INFO - Epoch(train) [27][15/15] lr: 1.0000e-06 eta: 0:15:48 time: 0.2999 data_time: 0.0018 memory: 6570 loss: 0.1156 loss_ce: 0.1156 2023/03/03 14:09:18 - mmengine - INFO - Epoch(train) [28][ 1/15] lr: 1.0000e-06 eta: 0:15:51 time: 0.3494 data_time: 0.0677 memory: 19144 loss: 0.1131 loss_ce: 0.1131 2023/03/03 14:09:18 - mmengine - INFO - Epoch(train) [28][ 2/15] lr: 1.0000e-06 eta: 0:15:49 time: 0.3476 data_time: 0.0677 memory: 17272 loss: 0.1122 loss_ce: 0.1122 2023/03/03 14:09:18 - mmengine - INFO - Epoch(train) [28][ 3/15] lr: 1.0000e-06 eta: 0:15:49 time: 0.3462 data_time: 0.0675 memory: 17272 loss: 0.1011 loss_ce: 0.1011 2023/03/03 14:09:19 - mmengine - INFO - Epoch(train) [28][ 4/15] lr: 1.0000e-06 eta: 0:15:48 time: 0.3489 data_time: 0.0675 memory: 16223 loss: 0.1029 loss_ce: 0.1029 2023/03/03 14:09:19 - mmengine - INFO - Epoch(train) [28][ 5/15] lr: 1.0000e-06 eta: 0:15:48 time: 0.3568 data_time: 0.0675 memory: 30476 loss: 0.1006 loss_ce: 0.1006 2023/03/03 14:09:19 - mmengine - INFO - Epoch(train) [28][ 6/15] lr: 1.0000e-06 eta: 0:15:46 time: 0.3586 data_time: 0.0675 memory: 16976 loss: 0.1060 loss_ce: 0.1060 2023/03/03 14:09:19 - mmengine - INFO - Epoch(train) [28][ 7/15] lr: 1.0000e-06 eta: 0:15:46 time: 0.3444 data_time: 0.0675 memory: 16432 loss: 0.1051 loss_ce: 0.1051 2023/03/03 14:09:20 - mmengine - INFO - Epoch(train) [28][ 8/15] lr: 1.0000e-06 eta: 0:15:46 time: 0.3628 data_time: 0.0675 memory: 17528 loss: 0.1009 loss_ce: 0.1009 2023/03/03 14:09:20 - mmengine - INFO - Epoch(train) [28][ 9/15] lr: 1.0000e-06 eta: 0:15:44 time: 0.3409 data_time: 0.0675 memory: 15631 loss: 0.1048 loss_ce: 0.1048 2023/03/03 14:09:21 - mmengine - INFO - Epoch(train) [28][10/15] lr: 1.0000e-06 eta: 0:15:45 time: 0.3756 data_time: 0.0675 memory: 30972 loss: 0.0974 loss_ce: 0.0974 2023/03/03 14:09:21 - mmengine - INFO - Epoch(train) [28][11/15] lr: 1.0000e-06 eta: 0:15:45 time: 0.3286 data_time: 0.0017 memory: 24233 loss: 0.0967 loss_ce: 0.0967 2023/03/03 14:09:21 - mmengine - INFO - Epoch(train) [28][12/15] lr: 1.0000e-06 eta: 0:15:43 time: 0.3298 data_time: 0.0017 memory: 15824 loss: 0.0950 loss_ce: 0.0950 2023/03/03 14:09:21 - mmengine - INFO - Epoch(train) [28][13/15] lr: 1.0000e-06 eta: 0:15:42 time: 0.3175 data_time: 0.0017 memory: 15767 loss: 0.0973 loss_ce: 0.0973 2023/03/03 14:09:22 - mmengine - INFO - Epoch(train) [28][14/15] lr: 1.0000e-06 eta: 0:15:41 time: 0.3178 data_time: 0.0016 memory: 16301 loss: 0.0913 loss_ce: 0.0913 2023/03/03 14:09:22 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:09:22 - mmengine - INFO - Epoch(train) [28][15/15] lr: 1.0000e-06 eta: 0:15:41 time: 0.3050 data_time: 0.0016 memory: 6850 loss: 0.0999 loss_ce: 0.0999 2023/03/03 14:09:23 - mmengine - INFO - Epoch(train) [29][ 1/15] lr: 1.0000e-06 eta: 0:15:41 time: 0.3444 data_time: 0.0399 memory: 16976 loss: 0.1046 loss_ce: 0.1046 2023/03/03 14:09:23 - mmengine - INFO - Epoch(train) [29][ 2/15] lr: 1.0000e-06 eta: 0:15:41 time: 0.3449 data_time: 0.0400 memory: 16976 loss: 0.1112 loss_ce: 0.1112 2023/03/03 14:09:23 - mmengine - INFO - Epoch(train) [29][ 3/15] lr: 1.0000e-06 eta: 0:15:40 time: 0.3290 data_time: 0.0401 memory: 18070 loss: 0.1099 loss_ce: 0.1099 2023/03/03 14:09:23 - mmengine - INFO - Epoch(train) [29][ 4/15] lr: 1.0000e-06 eta: 0:15:39 time: 0.3348 data_time: 0.0401 memory: 16056 loss: 0.1067 loss_ce: 0.1067 2023/03/03 14:09:24 - mmengine - INFO - Epoch(train) [29][ 5/15] lr: 1.0000e-06 eta: 0:15:37 time: 0.3053 data_time: 0.0402 memory: 18070 loss: 0.1051 loss_ce: 0.1051 2023/03/03 14:09:24 - mmengine - INFO - Epoch(train) [29][ 6/15] lr: 1.0000e-06 eta: 0:15:38 time: 0.3075 data_time: 0.0401 memory: 18154 loss: 0.1106 loss_ce: 0.1106 2023/03/03 14:09:24 - mmengine - INFO - Epoch(train) [29][ 7/15] lr: 1.0000e-06 eta: 0:15:36 time: 0.3058 data_time: 0.0401 memory: 17892 loss: 0.1112 loss_ce: 0.1112 2023/03/03 14:09:25 - mmengine - INFO - Epoch(train) [29][ 8/15] lr: 1.0000e-06 eta: 0:15:37 time: 0.3338 data_time: 0.0401 memory: 19747 loss: 0.1041 loss_ce: 0.1041 2023/03/03 14:09:25 - mmengine - INFO - Epoch(train) [29][ 9/15] lr: 1.0000e-06 eta: 0:15:36 time: 0.3333 data_time: 0.0402 memory: 16014 loss: 0.1043 loss_ce: 0.1043 2023/03/03 14:09:25 - mmengine - INFO - Epoch(train) [29][10/15] lr: 1.0000e-06 eta: 0:15:35 time: 0.3320 data_time: 0.0402 memory: 16370 loss: 0.0992 loss_ce: 0.0992 2023/03/03 14:09:26 - mmengine - INFO - Epoch(train) [29][11/15] lr: 1.0000e-06 eta: 0:15:35 time: 0.3108 data_time: 0.0018 memory: 20625 loss: 0.0927 loss_ce: 0.0927 2023/03/03 14:09:26 - mmengine - INFO - Epoch(train) [29][12/15] lr: 1.0000e-06 eta: 0:15:33 time: 0.2965 data_time: 0.0018 memory: 17421 loss: 0.0817 loss_ce: 0.0817 2023/03/03 14:09:26 - mmengine - INFO - Epoch(train) [29][13/15] lr: 1.0000e-06 eta: 0:15:32 time: 0.2988 data_time: 0.0018 memory: 16804 loss: 0.0855 loss_ce: 0.0855 2023/03/03 14:09:27 - mmengine - INFO - Epoch(train) [29][14/15] lr: 1.0000e-06 eta: 0:15:32 time: 0.3143 data_time: 0.0017 memory: 18586 loss: 0.0863 loss_ce: 0.0863 2023/03/03 14:09:27 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:09:27 - mmengine - INFO - Epoch(train) [29][15/15] lr: 1.0000e-06 eta: 0:15:31 time: 0.3092 data_time: 0.0017 memory: 4261 loss: 0.0895 loss_ce: 0.0895 2023/03/03 14:09:28 - mmengine - INFO - Epoch(train) [30][ 1/15] lr: 1.0000e-06 eta: 0:15:33 time: 0.3475 data_time: 0.0410 memory: 15911 loss: 0.0904 loss_ce: 0.0904 2023/03/03 14:09:28 - mmengine - INFO - Epoch(train) [30][ 2/15] lr: 1.0000e-06 eta: 0:15:32 time: 0.3607 data_time: 0.0514 memory: 16508 loss: 0.0924 loss_ce: 0.0924 2023/03/03 14:09:28 - mmengine - INFO - Epoch(train) [30][ 3/15] lr: 1.0000e-06 eta: 0:15:32 time: 0.3416 data_time: 0.0515 memory: 16804 loss: 0.0957 loss_ce: 0.0957 2023/03/03 14:09:28 - mmengine - INFO - Epoch(train) [30][ 4/15] lr: 1.0000e-06 eta: 0:15:31 time: 0.3393 data_time: 0.0515 memory: 17272 loss: 0.0981 loss_ce: 0.0981 2023/03/03 14:09:29 - mmengine - INFO - Epoch(train) [30][ 5/15] lr: 1.0000e-06 eta: 0:15:31 time: 0.3625 data_time: 0.0515 memory: 20056 loss: 0.0967 loss_ce: 0.0967 2023/03/03 14:09:29 - mmengine - INFO - Epoch(train) [30][ 6/15] lr: 1.0000e-06 eta: 0:15:30 time: 0.3446 data_time: 0.0515 memory: 18070 loss: 0.0929 loss_ce: 0.0929 2023/03/03 14:09:29 - mmengine - INFO - Epoch(train) [30][ 7/15] lr: 1.0000e-06 eta: 0:15:29 time: 0.3479 data_time: 0.0514 memory: 16370 loss: 0.1005 loss_ce: 0.1005 2023/03/03 14:09:30 - mmengine - INFO - Epoch(train) [30][ 8/15] lr: 1.0000e-06 eta: 0:15:27 time: 0.3452 data_time: 0.0514 memory: 17572 loss: 0.0965 loss_ce: 0.0965 2023/03/03 14:09:30 - mmengine - INFO - Epoch(train) [30][ 9/15] lr: 1.0000e-06 eta: 0:15:28 time: 0.3441 data_time: 0.0515 memory: 18241 loss: 0.0955 loss_ce: 0.0955 2023/03/03 14:09:31 - mmengine - INFO - Epoch(train) [30][10/15] lr: 1.0000e-06 eta: 0:15:29 time: 0.3997 data_time: 0.0515 memory: 31213 loss: 0.0941 loss_ce: 0.0941 2023/03/03 14:09:31 - mmengine - INFO - Epoch(train) [30][11/15] lr: 1.0000e-06 eta: 0:15:28 time: 0.3384 data_time: 0.0121 memory: 17272 loss: 0.0906 loss_ce: 0.0906 2023/03/03 14:09:31 - mmengine - INFO - Epoch(train) [30][12/15] lr: 1.0000e-06 eta: 0:15:26 time: 0.3248 data_time: 0.0017 memory: 16976 loss: 0.0956 loss_ce: 0.0956 2023/03/03 14:09:32 - mmengine - INFO - Epoch(train) [30][13/15] lr: 1.0000e-06 eta: 0:15:26 time: 0.3282 data_time: 0.0016 memory: 17421 loss: 0.0976 loss_ce: 0.0976 2023/03/03 14:09:32 - mmengine - INFO - Epoch(train) [30][14/15] lr: 1.0000e-06 eta: 0:15:25 time: 0.3311 data_time: 0.0016 memory: 16804 loss: 0.0956 loss_ce: 0.0956 2023/03/03 14:09:32 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:09:32 - mmengine - INFO - Epoch(train) [30][15/15] lr: 1.0000e-06 eta: 0:15:24 time: 0.3024 data_time: 0.0015 memory: 4593 loss: 0.0963 loss_ce: 0.0963 2023/03/03 14:09:34 - mmengine - INFO - Epoch(val) [30][ 1/59] eta: 0:01:29 time: 1.1272 data_time: 0.0042 memory: 981 2023/03/03 14:09:34 - mmengine - INFO - Epoch(val) [30][ 2/59] eta: 0:01:07 time: 1.0358 data_time: 0.0042 memory: 981 2023/03/03 14:09:36 - mmengine - INFO - Epoch(val) [30][ 3/59] eta: 0:01:09 time: 1.0499 data_time: 0.0039 memory: 1003 2023/03/03 14:09:36 - mmengine - INFO - Epoch(val) [30][ 4/59] eta: 0:00:55 time: 1.0174 data_time: 0.0038 memory: 981 2023/03/03 14:09:39 - mmengine - INFO - Epoch(val) [30][ 5/59] eta: 0:01:18 time: 1.2750 data_time: 0.0038 memory: 1016 2023/03/03 14:09:42 - mmengine - INFO - Epoch(val) [30][ 6/59] eta: 0:01:27 time: 1.4694 data_time: 0.0038 memory: 981 2023/03/03 14:09:42 - mmengine - INFO - Epoch(val) [30][ 7/59] eta: 0:01:14 time: 1.4191 data_time: 0.0038 memory: 1043 2023/03/03 14:09:43 - mmengine - INFO - Epoch(val) [30][ 8/59] eta: 0:01:10 time: 1.2901 data_time: 0.0038 memory: 1016 2023/03/03 14:09:44 - mmengine - INFO - Epoch(val) [30][ 9/59] eta: 0:01:06 time: 1.2539 data_time: 0.0037 memory: 981 2023/03/03 14:09:45 - mmengine - INFO - Epoch(val) [30][10/59] eta: 0:01:04 time: 1.3183 data_time: 0.0037 memory: 981 2023/03/03 14:09:46 - mmengine - INFO - Epoch(val) [30][11/59] eta: 0:00:59 time: 1.2138 data_time: 0.0008 memory: 981 2023/03/03 14:09:49 - mmengine - INFO - Epoch(val) [30][12/59] eta: 0:01:06 time: 1.4523 data_time: 0.0008 memory: 1016 2023/03/03 14:09:51 - mmengine - INFO - Epoch(val) [30][13/59] eta: 0:01:07 time: 1.5247 data_time: 0.0008 memory: 981 2023/03/03 14:09:52 - mmengine - INFO - Epoch(val) [30][14/59] eta: 0:01:04 time: 1.5919 data_time: 0.0008 memory: 890 2023/03/03 14:09:52 - mmengine - INFO - Epoch(val) [30][15/59] eta: 0:00:58 time: 1.2725 data_time: 0.0008 memory: 981 2023/03/03 14:09:52 - mmengine - INFO - Epoch(val) [30][16/59] eta: 0:00:55 time: 1.0622 data_time: 0.0008 memory: 981 2023/03/03 14:09:53 - mmengine - INFO - Epoch(val) [30][17/59] eta: 0:00:51 time: 1.0784 data_time: 0.0008 memory: 981 2023/03/03 14:09:53 - mmengine - INFO - Epoch(val) [30][18/59] eta: 0:00:48 time: 1.0132 data_time: 0.0008 memory: 981 2023/03/03 14:09:54 - mmengine - INFO - Epoch(val) [30][19/59] eta: 0:00:46 time: 1.0141 data_time: 0.0008 memory: 981 2023/03/03 14:09:54 - mmengine - INFO - Epoch(val) [30][20/59] eta: 0:00:43 time: 0.9320 data_time: 0.0008 memory: 981 2023/03/03 14:09:56 - mmengine - INFO - Epoch(val) [30][21/59] eta: 0:00:43 time: 1.0335 data_time: 0.0008 memory: 981 2023/03/03 14:09:56 - mmengine - INFO - Epoch(val) [30][22/59] eta: 0:00:40 time: 0.7294 data_time: 0.0008 memory: 981 2023/03/03 14:09:57 - mmengine - INFO - Epoch(val) [30][23/59] eta: 0:00:38 time: 0.5890 data_time: 0.0007 memory: 981 2023/03/03 14:09:57 - mmengine - INFO - Epoch(val) [30][24/59] eta: 0:00:36 time: 0.5215 data_time: 0.0007 memory: 962 2023/03/03 14:09:58 - mmengine - INFO - Epoch(val) [30][25/59] eta: 0:00:34 time: 0.5525 data_time: 0.0007 memory: 981 2023/03/03 14:09:58 - mmengine - INFO - Epoch(val) [30][26/59] eta: 0:00:32 time: 0.5360 data_time: 0.0007 memory: 981 2023/03/03 14:09:58 - mmengine - INFO - Epoch(val) [30][27/59] eta: 0:00:31 time: 0.5358 data_time: 0.0007 memory: 981 2023/03/03 14:09:59 - mmengine - INFO - Epoch(val) [30][28/59] eta: 0:00:29 time: 0.5348 data_time: 0.0007 memory: 981 2023/03/03 14:10:00 - mmengine - INFO - Epoch(val) [30][29/59] eta: 0:00:28 time: 0.5667 data_time: 0.0007 memory: 981 2023/03/03 14:10:01 - mmengine - INFO - Epoch(val) [30][30/59] eta: 0:00:27 time: 0.6157 data_time: 0.0007 memory: 999 2023/03/03 14:10:01 - mmengine - INFO - Epoch(val) [30][31/59] eta: 0:00:26 time: 0.5306 data_time: 0.0007 memory: 981 2023/03/03 14:10:02 - mmengine - INFO - Epoch(val) [30][32/59] eta: 0:00:25 time: 0.6289 data_time: 0.0007 memory: 981 2023/03/03 14:10:02 - mmengine - INFO - Epoch(val) [30][33/59] eta: 0:00:24 time: 0.5655 data_time: 0.0007 memory: 981 2023/03/03 14:10:03 - mmengine - INFO - Epoch(val) [30][34/59] eta: 0:00:22 time: 0.5495 data_time: 0.0008 memory: 981 2023/03/03 14:10:03 - mmengine - INFO - Epoch(val) [30][35/59] eta: 0:00:21 time: 0.5333 data_time: 0.0008 memory: 981 2023/03/03 14:10:03 - mmengine - INFO - Epoch(val) [30][36/59] eta: 0:00:20 time: 0.5494 data_time: 0.0008 memory: 981 2023/03/03 14:10:04 - mmengine - INFO - Epoch(val) [30][37/59] eta: 0:00:18 time: 0.5332 data_time: 0.0008 memory: 981 2023/03/03 14:10:04 - mmengine - INFO - Epoch(val) [30][38/59] eta: 0:00:17 time: 0.5666 data_time: 0.0008 memory: 981 2023/03/03 14:10:05 - mmengine - INFO - Epoch(val) [30][39/59] eta: 0:00:16 time: 0.4845 data_time: 0.0008 memory: 987 2023/03/03 14:10:05 - mmengine - INFO - Epoch(val) [30][40/59] eta: 0:00:15 time: 0.4846 data_time: 0.0007 memory: 981 2023/03/03 14:10:07 - mmengine - INFO - Epoch(val) [30][41/59] eta: 0:00:15 time: 0.5349 data_time: 0.0008 memory: 986 2023/03/03 14:10:07 - mmengine - INFO - Epoch(val) [30][42/59] eta: 0:00:14 time: 0.4852 data_time: 0.0008 memory: 981 2023/03/03 14:10:08 - mmengine - INFO - Epoch(val) [30][43/59] eta: 0:00:13 time: 0.5655 data_time: 0.0007 memory: 976 2023/03/03 14:10:09 - mmengine - INFO - Epoch(val) [30][44/59] eta: 0:00:12 time: 0.5977 data_time: 0.0007 memory: 1003 2023/03/03 14:10:11 - mmengine - INFO - Epoch(val) [30][45/59] eta: 0:00:11 time: 0.7669 data_time: 0.0007 memory: 981 2023/03/03 14:10:11 - mmengine - INFO - Epoch(val) [30][46/59] eta: 0:00:11 time: 0.8001 data_time: 0.0008 memory: 981 2023/03/03 14:10:12 - mmengine - INFO - Epoch(val) [30][47/59] eta: 0:00:10 time: 0.8323 data_time: 0.0008 memory: 936 2023/03/03 14:10:12 - mmengine - INFO - Epoch(val) [30][48/59] eta: 0:00:09 time: 0.8163 data_time: 0.0008 memory: 1000 2023/03/03 14:10:13 - mmengine - INFO - Epoch(val) [30][49/59] eta: 0:00:08 time: 0.8663 data_time: 0.0008 memory: 981 2023/03/03 14:10:14 - mmengine - INFO - Epoch(val) [30][50/59] eta: 0:00:07 time: 0.8662 data_time: 0.0008 memory: 987 2023/03/03 14:10:16 - mmengine - INFO - Epoch(val) [30][51/59] eta: 0:00:06 time: 0.9177 data_time: 0.0008 memory: 981 2023/03/03 14:10:17 - mmengine - INFO - Epoch(val) [30][52/59] eta: 0:00:06 time: 0.9679 data_time: 0.0008 memory: 981 2023/03/03 14:10:18 - mmengine - INFO - Epoch(val) [30][53/59] eta: 0:00:05 time: 0.9679 data_time: 0.0008 memory: 962 2023/03/03 14:10:18 - mmengine - INFO - Epoch(val) [30][54/59] eta: 0:00:04 time: 0.9846 data_time: 0.0008 memory: 981 2023/03/03 14:10:19 - mmengine - INFO - Epoch(val) [30][55/59] eta: 0:00:03 time: 0.8645 data_time: 0.0008 memory: 981 2023/03/03 14:10:20 - mmengine - INFO - Epoch(val) [30][56/59] eta: 0:00:02 time: 0.8643 data_time: 0.0007 memory: 981 2023/03/03 14:10:22 - mmengine - INFO - Epoch(val) [30][57/59] eta: 0:00:01 time: 1.0375 data_time: 0.0007 memory: 981 2023/03/03 14:10:24 - mmengine - INFO - Epoch(val) [30][58/59] eta: 0:00:00 time: 1.1203 data_time: 0.0007 memory: 1016 2023/03/03 14:10:24 - mmengine - INFO - Epoch(val) [30][59/59] eta: 0:00:00 time: 1.0707 data_time: 0.0007 memory: 981 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.80, recall: 0.8128, precision: 0.8326, hmean: 0.8226 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.81, recall: 0.8119, precision: 0.8355, hmean: 0.8235 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.82, recall: 0.8119, precision: 0.8371, hmean: 0.8243 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.83, recall: 0.8119, precision: 0.8387, hmean: 0.8251 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.84, recall: 0.8091, precision: 0.8414, hmean: 0.8250 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.85, recall: 0.8073, precision: 0.8443, hmean: 0.8254 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.86, recall: 0.8055, precision: 0.8481, hmean: 0.8262 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.87, recall: 0.8009, precision: 0.8515, hmean: 0.8254 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.88, recall: 0.7982, precision: 0.8543, hmean: 0.8253 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.89, recall: 0.7945, precision: 0.8588, hmean: 0.8254 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.90, recall: 0.7881, precision: 0.8596, hmean: 0.8223 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.91, recall: 0.7854, precision: 0.8600, hmean: 0.8210 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.92, recall: 0.7799, precision: 0.8626, hmean: 0.8192 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.93, recall: 0.7717, precision: 0.8640, hmean: 0.8152 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.94, recall: 0.7616, precision: 0.8669, hmean: 0.8109 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.95, recall: 0.7516, precision: 0.8672, hmean: 0.8053 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.96, recall: 0.7434, precision: 0.8734, hmean: 0.8032 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.97, recall: 0.7315, precision: 0.8783, hmean: 0.7982 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.98, recall: 0.7142, precision: 0.8836, hmean: 0.7899 2023/03/03 14:10:53 - mmengine - INFO - text score threshold: 0.99, recall: 0.6785, precision: 0.8941, hmean: 0.7715 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.80, recall: 0.8256, precision: 0.9067, hmean: 0.8642 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.81, recall: 0.8237, precision: 0.9074, hmean: 0.8636 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.82, recall: 0.8237, precision: 0.9084, hmean: 0.8640 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.83, recall: 0.8237, precision: 0.9093, hmean: 0.8644 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.84, recall: 0.8210, precision: 0.9099, hmean: 0.8632 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.85, recall: 0.8174, precision: 0.9114, hmean: 0.8618 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.86, recall: 0.8146, precision: 0.9139, hmean: 0.8614 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.87, recall: 0.8100, precision: 0.9154, hmean: 0.8595 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.88, recall: 0.8073, precision: 0.9161, hmean: 0.8583 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.89, recall: 0.8027, precision: 0.9175, hmean: 0.8563 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.90, recall: 0.7954, precision: 0.9168, hmean: 0.8518 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.91, recall: 0.7927, precision: 0.9175, hmean: 0.8506 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.92, recall: 0.7872, precision: 0.9180, hmean: 0.8476 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.93, recall: 0.7781, precision: 0.9191, hmean: 0.8427 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.94, recall: 0.7662, precision: 0.9189, hmean: 0.8357 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.95, recall: 0.7562, precision: 0.9190, hmean: 0.8297 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.96, recall: 0.7479, precision: 0.9254, hmean: 0.8273 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.97, recall: 0.7352, precision: 0.9285, hmean: 0.8206 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.98, recall: 0.7169, precision: 0.9301, hmean: 0.8097 2023/03/03 14:10:56 - mmengine - INFO - text score threshold: 0.99, recall: 0.6804, precision: 0.9348, hmean: 0.7875 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.80, recall: 0.7461, precision: 0.9578, hmean: 0.8388 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.81, recall: 0.7443, precision: 0.9577, hmean: 0.8376 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.82, recall: 0.7443, precision: 0.9577, hmean: 0.8376 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.83, recall: 0.7434, precision: 0.9576, hmean: 0.8370 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.84, recall: 0.7406, precision: 0.9575, hmean: 0.8352 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.85, recall: 0.7379, precision: 0.9585, hmean: 0.8338 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.86, recall: 0.7361, precision: 0.9607, hmean: 0.8335 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.87, recall: 0.7306, precision: 0.9604, hmean: 0.8299 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.88, recall: 0.7297, precision: 0.9603, hmean: 0.8293 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.89, recall: 0.7251, precision: 0.9601, hmean: 0.8262 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.90, recall: 0.7178, precision: 0.9597, hmean: 0.8213 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.91, recall: 0.7142, precision: 0.9595, hmean: 0.8188 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.92, recall: 0.7096, precision: 0.9593, hmean: 0.8157 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.93, recall: 0.7023, precision: 0.9589, hmean: 0.8108 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.94, recall: 0.6913, precision: 0.9594, hmean: 0.8036 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.95, recall: 0.6813, precision: 0.9601, hmean: 0.7970 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.96, recall: 0.6721, precision: 0.9621, hmean: 0.7914 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.97, recall: 0.6612, precision: 0.9640, hmean: 0.7844 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.98, recall: 0.6438, precision: 0.9644, hmean: 0.7722 2023/03/03 14:10:59 - mmengine - INFO - text score threshold: 0.99, recall: 0.6091, precision: 0.9639, hmean: 0.7465 2023/03/03 14:10:59 - mmengine - INFO - Epoch(val) [30][59/59] generic/precision: 0.8481 generic/recall: 0.8055 generic/hmean: 0.8262 weak/precision: 0.9093 weak/recall: 0.8237 weak/hmean: 0.8644 strong/precision: 0.9578 strong/recall: 0.7461 strong/hmean: 0.8388 2023/03/03 14:11:00 - mmengine - INFO - Epoch(train) [31][ 1/15] lr: 1.0000e-06 eta: 0:15:26 time: 0.3687 data_time: 0.0558 memory: 21663 loss: 0.1026 loss_ce: 0.1026 2023/03/03 14:11:00 - mmengine - INFO - Epoch(train) [31][ 2/15] lr: 1.0000e-06 eta: 0:15:26 time: 0.3702 data_time: 0.0559 memory: 16955 loss: 0.0978 loss_ce: 0.0978 2023/03/03 14:11:00 - mmengine - INFO - Epoch(train) [31][ 3/15] lr: 1.0000e-06 eta: 0:15:24 time: 0.3705 data_time: 0.0559 memory: 17272 loss: 0.1001 loss_ce: 0.1001 2023/03/03 14:11:00 - mmengine - INFO - Epoch(train) [31][ 4/15] lr: 1.0000e-06 eta: 0:15:24 time: 0.3658 data_time: 0.0560 memory: 18902 loss: 0.1040 loss_ce: 0.1040 2023/03/03 14:11:01 - mmengine - INFO - Epoch(train) [31][ 5/15] lr: 1.0000e-06 eta: 0:15:23 time: 0.3237 data_time: 0.0561 memory: 16549 loss: 0.1034 loss_ce: 0.1034 2023/03/03 14:11:01 - mmengine - INFO - Epoch(train) [31][ 6/15] lr: 1.0000e-06 eta: 0:15:22 time: 0.3307 data_time: 0.0561 memory: 16976 loss: 0.1112 loss_ce: 0.1112 2023/03/03 14:11:01 - mmengine - INFO - Epoch(train) [31][ 7/15] lr: 1.0000e-06 eta: 0:15:22 time: 0.3412 data_time: 0.0561 memory: 17513 loss: 0.1060 loss_ce: 0.1060 2023/03/03 14:11:02 - mmengine - INFO - Epoch(train) [31][ 8/15] lr: 1.0000e-06 eta: 0:15:22 time: 0.3437 data_time: 0.0561 memory: 17795 loss: 0.1044 loss_ce: 0.1044 2023/03/03 14:11:02 - mmengine - INFO - Epoch(train) [31][ 9/15] lr: 1.0000e-06 eta: 0:15:20 time: 0.3407 data_time: 0.0562 memory: 17120 loss: 0.1075 loss_ce: 0.1075 2023/03/03 14:11:02 - mmengine - INFO - Epoch(train) [31][10/15] lr: 1.0000e-06 eta: 0:15:19 time: 0.3432 data_time: 0.0562 memory: 17120 loss: 0.1058 loss_ce: 0.1058 2023/03/03 14:11:02 - mmengine - INFO - Epoch(train) [31][11/15] lr: 1.0000e-06 eta: 0:15:18 time: 0.2797 data_time: 0.0019 memory: 12337 loss: 0.1040 loss_ce: 0.1040 2023/03/03 14:11:03 - mmengine - INFO - Epoch(train) [31][12/15] lr: 1.0000e-06 eta: 0:15:18 time: 0.2897 data_time: 0.0018 memory: 17681 loss: 0.0988 loss_ce: 0.0988 2023/03/03 14:11:03 - mmengine - INFO - Epoch(train) [31][13/15] lr: 1.0000e-06 eta: 0:15:17 time: 0.2898 data_time: 0.0018 memory: 18140 loss: 0.0972 loss_ce: 0.0972 2023/03/03 14:11:03 - mmengine - INFO - Epoch(train) [31][14/15] lr: 1.0000e-06 eta: 0:15:16 time: 0.2827 data_time: 0.0018 memory: 16781 loss: 0.0992 loss_ce: 0.0992 2023/03/03 14:11:03 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:11:03 - mmengine - INFO - Epoch(train) [31][15/15] lr: 1.0000e-06 eta: 0:15:15 time: 0.2716 data_time: 0.0017 memory: 5019 loss: 0.1046 loss_ce: 0.1046 2023/03/03 14:11:05 - mmengine - INFO - Epoch(train) [32][ 1/15] lr: 1.0000e-06 eta: 0:15:19 time: 0.3728 data_time: 0.0673 memory: 18392 loss: 0.1036 loss_ce: 0.1036 2023/03/03 14:11:05 - mmengine - INFO - Epoch(train) [32][ 2/15] lr: 1.0000e-06 eta: 0:15:18 time: 0.3655 data_time: 0.0673 memory: 16370 loss: 0.1038 loss_ce: 0.1038 2023/03/03 14:11:05 - mmengine - INFO - Epoch(train) [32][ 3/15] lr: 1.0000e-06 eta: 0:15:17 time: 0.3470 data_time: 0.0673 memory: 16976 loss: 0.1085 loss_ce: 0.1085 2023/03/03 14:11:05 - mmengine - INFO - Epoch(train) [32][ 4/15] lr: 1.0000e-06 eta: 0:15:16 time: 0.3527 data_time: 0.0673 memory: 18797 loss: 0.1133 loss_ce: 0.1133 2023/03/03 14:11:06 - mmengine - INFO - Epoch(train) [32][ 5/15] lr: 1.0000e-06 eta: 0:15:16 time: 0.3699 data_time: 0.0674 memory: 17215 loss: 0.1103 loss_ce: 0.1103 2023/03/03 14:11:06 - mmengine - INFO - Epoch(train) [32][ 6/15] lr: 1.0000e-06 eta: 0:15:15 time: 0.3664 data_time: 0.0674 memory: 13462 loss: 0.1076 loss_ce: 0.1076 2023/03/03 14:11:07 - mmengine - INFO - Epoch(train) [32][ 7/15] lr: 1.0000e-06 eta: 0:15:17 time: 0.4137 data_time: 0.0674 memory: 18766 loss: 0.1085 loss_ce: 0.1085 2023/03/03 14:11:07 - mmengine - INFO - Epoch(train) [32][ 8/15] lr: 1.0000e-06 eta: 0:15:16 time: 0.4229 data_time: 0.0674 memory: 20517 loss: 0.1097 loss_ce: 0.1097 2023/03/03 14:11:07 - mmengine - INFO - Epoch(train) [32][ 9/15] lr: 1.0000e-06 eta: 0:15:15 time: 0.4126 data_time: 0.0674 memory: 17272 loss: 0.1050 loss_ce: 0.1050 2023/03/03 14:11:08 - mmengine - INFO - Epoch(train) [32][10/15] lr: 1.0000e-06 eta: 0:15:15 time: 0.4345 data_time: 0.0674 memory: 23607 loss: 0.0987 loss_ce: 0.0987 2023/03/03 14:11:08 - mmengine - INFO - Epoch(train) [32][11/15] lr: 1.0000e-06 eta: 0:15:14 time: 0.3264 data_time: 0.0018 memory: 17421 loss: 0.0943 loss_ce: 0.0943 2023/03/03 14:11:08 - mmengine - INFO - Epoch(train) [32][12/15] lr: 1.0000e-06 eta: 0:15:12 time: 0.3234 data_time: 0.0017 memory: 17272 loss: 0.0937 loss_ce: 0.0937 2023/03/03 14:11:08 - mmengine - INFO - Epoch(train) [32][13/15] lr: 1.0000e-06 eta: 0:15:12 time: 0.3264 data_time: 0.0017 memory: 17572 loss: 0.0873 loss_ce: 0.0873 2023/03/03 14:11:09 - mmengine - INFO - Epoch(train) [32][14/15] lr: 1.0000e-06 eta: 0:15:10 time: 0.3207 data_time: 0.0017 memory: 17272 loss: 0.0806 loss_ce: 0.0806 2023/03/03 14:11:09 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:11:09 - mmengine - INFO - Epoch(train) [32][15/15] lr: 1.0000e-06 eta: 0:15:09 time: 0.2972 data_time: 0.0016 memory: 5696 loss: 0.0870 loss_ce: 0.0870 2023/03/03 14:11:09 - mmengine - INFO - Epoch(train) [33][ 1/15] lr: 1.0000e-06 eta: 0:15:10 time: 0.3358 data_time: 0.0363 memory: 16522 loss: 0.0887 loss_ce: 0.0887 2023/03/03 14:11:10 - mmengine - INFO - Epoch(train) [33][ 2/15] lr: 1.0000e-06 eta: 0:15:11 time: 0.3221 data_time: 0.0364 memory: 12907 loss: 0.0902 loss_ce: 0.0902 2023/03/03 14:11:10 - mmengine - INFO - Epoch(train) [33][ 3/15] lr: 1.0000e-06 eta: 0:15:10 time: 0.3160 data_time: 0.0365 memory: 16804 loss: 0.0918 loss_ce: 0.0918 2023/03/03 14:11:11 - mmengine - INFO - Epoch(train) [33][ 4/15] lr: 1.0000e-06 eta: 0:15:10 time: 0.3341 data_time: 0.0365 memory: 18070 loss: 0.0919 loss_ce: 0.0919 2023/03/03 14:11:11 - mmengine - INFO - Epoch(train) [33][ 5/15] lr: 1.0000e-06 eta: 0:15:09 time: 0.3128 data_time: 0.0365 memory: 14461 loss: 0.0980 loss_ce: 0.0980 2023/03/03 14:11:11 - mmengine - INFO - Epoch(train) [33][ 6/15] lr: 1.0000e-06 eta: 0:15:09 time: 0.3392 data_time: 0.0365 memory: 17572 loss: 0.1016 loss_ce: 0.1016 2023/03/03 14:11:12 - mmengine - INFO - Epoch(train) [33][ 7/15] lr: 1.0000e-06 eta: 0:15:08 time: 0.3411 data_time: 0.0365 memory: 15911 loss: 0.1032 loss_ce: 0.1032 2023/03/03 14:11:12 - mmengine - INFO - Epoch(train) [33][ 8/15] lr: 1.0000e-06 eta: 0:15:07 time: 0.3483 data_time: 0.0365 memory: 21889 loss: 0.1098 loss_ce: 0.1098 2023/03/03 14:11:12 - mmengine - INFO - Epoch(train) [33][ 9/15] lr: 1.0000e-06 eta: 0:15:06 time: 0.3510 data_time: 0.0365 memory: 16370 loss: 0.1106 loss_ce: 0.1106 2023/03/03 14:11:12 - mmengine - INFO - Epoch(train) [33][10/15] lr: 1.0000e-06 eta: 0:15:06 time: 0.3661 data_time: 0.0365 memory: 15826 loss: 0.1079 loss_ce: 0.1079 2023/03/03 14:11:13 - mmengine - INFO - Epoch(train) [33][11/15] lr: 1.0000e-06 eta: 0:15:05 time: 0.3284 data_time: 0.0018 memory: 17572 loss: 0.1042 loss_ce: 0.1042 2023/03/03 14:11:13 - mmengine - INFO - Epoch(train) [33][12/15] lr: 1.0000e-06 eta: 0:15:04 time: 0.2851 data_time: 0.0017 memory: 16986 loss: 0.1055 loss_ce: 0.1055 2023/03/03 14:11:13 - mmengine - INFO - Epoch(train) [33][13/15] lr: 1.0000e-06 eta: 0:15:03 time: 0.2859 data_time: 0.0016 memory: 18182 loss: 0.1033 loss_ce: 0.1033 2023/03/03 14:11:13 - mmengine - INFO - Epoch(train) [33][14/15] lr: 1.0000e-06 eta: 0:15:02 time: 0.2756 data_time: 0.0016 memory: 15767 loss: 0.1027 loss_ce: 0.1027 2023/03/03 14:11:14 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:11:14 - mmengine - INFO - Epoch(train) [33][15/15] lr: 1.0000e-06 eta: 0:15:00 time: 0.2695 data_time: 0.0016 memory: 6570 loss: 0.1004 loss_ce: 0.1004 2023/03/03 14:11:15 - mmengine - INFO - Epoch(train) [34][ 1/15] lr: 1.0000e-06 eta: 0:15:03 time: 0.3251 data_time: 0.0441 memory: 24437 loss: 0.0970 loss_ce: 0.0970 2023/03/03 14:11:15 - mmengine - INFO - Epoch(train) [34][ 2/15] lr: 1.0000e-06 eta: 0:15:02 time: 0.3255 data_time: 0.0442 memory: 15631 loss: 0.0931 loss_ce: 0.0931 2023/03/03 14:11:15 - mmengine - INFO - Epoch(train) [34][ 3/15] lr: 1.0000e-06 eta: 0:15:01 time: 0.3160 data_time: 0.0443 memory: 18070 loss: 0.0859 loss_ce: 0.0859 2023/03/03 14:11:15 - mmengine - INFO - Epoch(train) [34][ 4/15] lr: 1.0000e-06 eta: 0:15:00 time: 0.3137 data_time: 0.0443 memory: 18409 loss: 0.0834 loss_ce: 0.0834 2023/03/03 14:11:16 - mmengine - INFO - Epoch(train) [34][ 5/15] lr: 1.0000e-06 eta: 0:14:59 time: 0.3063 data_time: 0.0443 memory: 16508 loss: 0.0826 loss_ce: 0.0826 2023/03/03 14:11:16 - mmengine - INFO - Epoch(train) [34][ 6/15] lr: 1.0000e-06 eta: 0:14:58 time: 0.3080 data_time: 0.0443 memory: 15545 loss: 0.0858 loss_ce: 0.0858 2023/03/03 14:11:16 - mmengine - INFO - Epoch(train) [34][ 7/15] lr: 1.0000e-06 eta: 0:14:58 time: 0.3134 data_time: 0.0443 memory: 16396 loss: 0.0867 loss_ce: 0.0867 2023/03/03 14:11:16 - mmengine - INFO - Epoch(train) [34][ 8/15] lr: 1.0000e-06 eta: 0:14:57 time: 0.3099 data_time: 0.0443 memory: 18766 loss: 0.0863 loss_ce: 0.0863 2023/03/03 14:11:17 - mmengine - INFO - Epoch(train) [34][ 9/15] lr: 1.0000e-06 eta: 0:14:57 time: 0.3299 data_time: 0.0443 memory: 13114 loss: 0.0851 loss_ce: 0.0851 2023/03/03 14:11:17 - mmengine - INFO - Epoch(train) [34][10/15] lr: 1.0000e-06 eta: 0:14:56 time: 0.3380 data_time: 0.0443 memory: 17272 loss: 0.0819 loss_ce: 0.0819 2023/03/03 14:11:17 - mmengine - INFO - Epoch(train) [34][11/15] lr: 1.0000e-06 eta: 0:14:56 time: 0.2755 data_time: 0.0018 memory: 20985 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:11:18 - mmengine - INFO - Epoch(train) [34][12/15] lr: 1.0000e-06 eta: 0:14:54 time: 0.2737 data_time: 0.0016 memory: 18409 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:11:18 - mmengine - INFO - Epoch(train) [34][13/15] lr: 1.0000e-06 eta: 0:14:54 time: 0.2889 data_time: 0.0016 memory: 16976 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:11:18 - mmengine - INFO - Epoch(train) [34][14/15] lr: 1.0000e-06 eta: 0:14:54 time: 0.3029 data_time: 0.0016 memory: 13019 loss: 0.0843 loss_ce: 0.0843 2023/03/03 14:11:18 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:11:18 - mmengine - INFO - Epoch(train) [34][15/15] lr: 1.0000e-06 eta: 0:14:52 time: 0.2879 data_time: 0.0016 memory: 6570 loss: 0.0839 loss_ce: 0.0839 2023/03/03 14:11:19 - mmengine - INFO - Epoch(train) [35][ 1/15] lr: 1.0000e-06 eta: 0:14:54 time: 0.3472 data_time: 0.0592 memory: 16199 loss: 0.0881 loss_ce: 0.0881 2023/03/03 14:11:20 - mmengine - INFO - Epoch(train) [35][ 2/15] lr: 1.0000e-06 eta: 0:14:54 time: 0.3554 data_time: 0.0593 memory: 16056 loss: 0.0896 loss_ce: 0.0896 2023/03/03 14:11:20 - mmengine - INFO - Epoch(train) [35][ 3/15] lr: 1.0000e-06 eta: 0:14:53 time: 0.3643 data_time: 0.0594 memory: 16767 loss: 0.0895 loss_ce: 0.0895 2023/03/03 14:11:20 - mmengine - INFO - Epoch(train) [35][ 4/15] lr: 1.0000e-06 eta: 0:14:52 time: 0.3415 data_time: 0.0594 memory: 16804 loss: 0.0940 loss_ce: 0.0940 2023/03/03 14:11:20 - mmengine - INFO - Epoch(train) [35][ 5/15] lr: 1.0000e-06 eta: 0:14:51 time: 0.3421 data_time: 0.0594 memory: 16976 loss: 0.0998 loss_ce: 0.0998 2023/03/03 14:11:21 - mmengine - INFO - Epoch(train) [35][ 6/15] lr: 1.0000e-06 eta: 0:14:51 time: 0.3404 data_time: 0.0594 memory: 15561 loss: 0.1025 loss_ce: 0.1025 2023/03/03 14:11:21 - mmengine - INFO - Epoch(train) [35][ 7/15] lr: 1.0000e-06 eta: 0:14:50 time: 0.3401 data_time: 0.0594 memory: 17572 loss: 0.1078 loss_ce: 0.1078 2023/03/03 14:11:21 - mmengine - INFO - Epoch(train) [35][ 8/15] lr: 1.0000e-06 eta: 0:14:50 time: 0.3524 data_time: 0.0594 memory: 16199 loss: 0.1082 loss_ce: 0.1082 2023/03/03 14:11:22 - mmengine - INFO - Epoch(train) [35][ 9/15] lr: 1.0000e-06 eta: 0:14:49 time: 0.3437 data_time: 0.0594 memory: 19129 loss: 0.1024 loss_ce: 0.1024 2023/03/03 14:11:22 - mmengine - INFO - Epoch(train) [35][10/15] lr: 1.0000e-06 eta: 0:14:50 time: 0.3746 data_time: 0.0594 memory: 15631 loss: 0.1033 loss_ce: 0.1033 2023/03/03 14:11:22 - mmengine - INFO - Epoch(train) [35][11/15] lr: 1.0000e-06 eta: 0:14:48 time: 0.3133 data_time: 0.0017 memory: 16976 loss: 0.1025 loss_ce: 0.1025 2023/03/03 14:11:23 - mmengine - INFO - Epoch(train) [35][12/15] lr: 1.0000e-06 eta: 0:14:48 time: 0.3140 data_time: 0.0016 memory: 15228 loss: 0.1055 loss_ce: 0.1055 2023/03/03 14:11:23 - mmengine - INFO - Epoch(train) [35][13/15] lr: 1.0000e-06 eta: 0:14:47 time: 0.3041 data_time: 0.0016 memory: 17223 loss: 0.1100 loss_ce: 0.1100 2023/03/03 14:11:23 - mmengine - INFO - Epoch(train) [35][14/15] lr: 1.0000e-06 eta: 0:14:47 time: 0.3299 data_time: 0.0016 memory: 17421 loss: 0.1095 loss_ce: 0.1095 2023/03/03 14:11:24 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:11:24 - mmengine - INFO - Epoch(train) [35][15/15] lr: 1.0000e-06 eta: 0:14:46 time: 0.3235 data_time: 0.0016 memory: 5798 loss: 0.1083 loss_ce: 0.1083 2023/03/03 14:11:25 - mmengine - INFO - Epoch(train) [36][ 1/15] lr: 1.0000e-06 eta: 0:14:49 time: 0.3983 data_time: 0.0397 memory: 17272 loss: 0.1073 loss_ce: 0.1073 2023/03/03 14:11:25 - mmengine - INFO - Epoch(train) [36][ 2/15] lr: 1.0000e-06 eta: 0:14:48 time: 0.3972 data_time: 0.0397 memory: 12471 loss: 0.1058 loss_ce: 0.1058 2023/03/03 14:11:25 - mmengine - INFO - Epoch(train) [36][ 3/15] lr: 1.0000e-06 eta: 0:14:48 time: 0.3925 data_time: 0.0397 memory: 18953 loss: 0.1017 loss_ce: 0.1017 2023/03/03 14:11:26 - mmengine - INFO - Epoch(train) [36][ 4/15] lr: 1.0000e-06 eta: 0:14:47 time: 0.3870 data_time: 0.0397 memory: 17572 loss: 0.1066 loss_ce: 0.1066 2023/03/03 14:11:26 - mmengine - INFO - Epoch(train) [36][ 5/15] lr: 1.0000e-06 eta: 0:14:47 time: 0.3783 data_time: 0.0397 memory: 16681 loss: 0.1078 loss_ce: 0.1078 2023/03/03 14:11:26 - mmengine - INFO - Epoch(train) [36][ 6/15] lr: 1.0000e-06 eta: 0:14:46 time: 0.3784 data_time: 0.0397 memory: 16976 loss: 0.1162 loss_ce: 0.1162 2023/03/03 14:11:27 - mmengine - INFO - Epoch(train) [36][ 7/15] lr: 1.0000e-06 eta: 0:14:46 time: 0.3958 data_time: 0.0398 memory: 15085 loss: 0.1142 loss_ce: 0.1142 2023/03/03 14:11:27 - mmengine - INFO - Epoch(train) [36][ 8/15] lr: 1.0000e-06 eta: 0:14:45 time: 0.3961 data_time: 0.0398 memory: 14474 loss: 0.1168 loss_ce: 0.1168 2023/03/03 14:11:27 - mmengine - INFO - Epoch(train) [36][ 9/15] lr: 1.0000e-06 eta: 0:14:45 time: 0.3843 data_time: 0.0398 memory: 16542 loss: 0.1168 loss_ce: 0.1168 2023/03/03 14:11:27 - mmengine - INFO - Epoch(train) [36][10/15] lr: 1.0000e-06 eta: 0:14:44 time: 0.3902 data_time: 0.0398 memory: 18070 loss: 0.1101 loss_ce: 0.1101 2023/03/03 14:11:28 - mmengine - INFO - Epoch(train) [36][11/15] lr: 1.0000e-06 eta: 0:14:44 time: 0.3149 data_time: 0.0017 memory: 16849 loss: 0.1081 loss_ce: 0.1081 2023/03/03 14:11:28 - mmengine - INFO - Epoch(train) [36][12/15] lr: 1.0000e-06 eta: 0:14:43 time: 0.3319 data_time: 0.0017 memory: 26520 loss: 0.1042 loss_ce: 0.1042 2023/03/03 14:11:28 - mmengine - INFO - Epoch(train) [36][13/15] lr: 1.0000e-06 eta: 0:14:42 time: 0.3085 data_time: 0.0016 memory: 17651 loss: 0.1040 loss_ce: 0.1040 2023/03/03 14:11:29 - mmengine - INFO - Epoch(train) [36][14/15] lr: 1.0000e-06 eta: 0:14:41 time: 0.3112 data_time: 0.0016 memory: 16199 loss: 0.1097 loss_ce: 0.1097 2023/03/03 14:11:29 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:11:29 - mmengine - INFO - Epoch(train) [36][15/15] lr: 1.0000e-06 eta: 0:14:40 time: 0.2913 data_time: 0.0016 memory: 6071 loss: 0.1160 loss_ce: 0.1160 2023/03/03 14:11:30 - mmengine - INFO - Epoch(train) [37][ 1/15] lr: 1.0000e-06 eta: 0:14:42 time: 0.3669 data_time: 0.0741 memory: 19747 loss: 0.0975 loss_ce: 0.0975 2023/03/03 14:11:30 - mmengine - INFO - Epoch(train) [37][ 2/15] lr: 1.0000e-06 eta: 0:14:42 time: 0.3350 data_time: 0.0741 memory: 17198 loss: 0.0984 loss_ce: 0.0984 2023/03/03 14:11:30 - mmengine - INFO - Epoch(train) [37][ 3/15] lr: 1.0000e-06 eta: 0:14:41 time: 0.3378 data_time: 0.0741 memory: 20557 loss: 0.0954 loss_ce: 0.0954 2023/03/03 14:11:31 - mmengine - INFO - Epoch(train) [37][ 4/15] lr: 1.0000e-06 eta: 0:14:40 time: 0.3302 data_time: 0.0742 memory: 16223 loss: 0.0967 loss_ce: 0.0967 2023/03/03 14:11:31 - mmengine - INFO - Epoch(train) [37][ 5/15] lr: 1.0000e-06 eta: 0:14:39 time: 0.3329 data_time: 0.0742 memory: 16573 loss: 0.0990 loss_ce: 0.0990 2023/03/03 14:11:31 - mmengine - INFO - Epoch(train) [37][ 6/15] lr: 1.0000e-06 eta: 0:14:39 time: 0.3299 data_time: 0.0742 memory: 17312 loss: 0.1009 loss_ce: 0.1009 2023/03/03 14:11:31 - mmengine - INFO - Epoch(train) [37][ 7/15] lr: 1.0000e-06 eta: 0:14:38 time: 0.3139 data_time: 0.0742 memory: 17421 loss: 0.1031 loss_ce: 0.1031 2023/03/03 14:11:32 - mmengine - INFO - Epoch(train) [37][ 8/15] lr: 1.0000e-06 eta: 0:14:38 time: 0.3318 data_time: 0.0742 memory: 22668 loss: 0.1040 loss_ce: 0.1040 2023/03/03 14:11:32 - mmengine - INFO - Epoch(train) [37][ 9/15] lr: 1.0000e-06 eta: 0:14:37 time: 0.3290 data_time: 0.0742 memory: 17572 loss: 0.0960 loss_ce: 0.0960 2023/03/03 14:11:32 - mmengine - INFO - Epoch(train) [37][10/15] lr: 1.0000e-06 eta: 0:14:36 time: 0.3414 data_time: 0.0742 memory: 15621 loss: 0.0860 loss_ce: 0.0860 2023/03/03 14:11:33 - mmengine - INFO - Epoch(train) [37][11/15] lr: 1.0000e-06 eta: 0:14:36 time: 0.2853 data_time: 0.0017 memory: 25888 loss: 0.0855 loss_ce: 0.0855 2023/03/03 14:11:33 - mmengine - INFO - Epoch(train) [37][12/15] lr: 1.0000e-06 eta: 0:14:35 time: 0.2906 data_time: 0.0016 memory: 15175 loss: 0.0839 loss_ce: 0.0839 2023/03/03 14:11:33 - mmengine - INFO - Epoch(train) [37][13/15] lr: 1.0000e-06 eta: 0:14:34 time: 0.2874 data_time: 0.0016 memory: 17421 loss: 0.0811 loss_ce: 0.0811 2023/03/03 14:11:34 - mmengine - INFO - Epoch(train) [37][14/15] lr: 1.0000e-06 eta: 0:14:34 time: 0.2966 data_time: 0.0015 memory: 31311 loss: 0.0794 loss_ce: 0.0794 2023/03/03 14:11:34 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:11:34 - mmengine - INFO - Epoch(train) [37][15/15] lr: 1.0000e-06 eta: 0:14:33 time: 0.2897 data_time: 0.0015 memory: 5338 loss: 0.0836 loss_ce: 0.0836 2023/03/03 14:11:35 - mmengine - INFO - Epoch(train) [38][ 1/15] lr: 1.0000e-06 eta: 0:14:35 time: 0.3553 data_time: 0.0669 memory: 15767 loss: 0.0833 loss_ce: 0.0833 2023/03/03 14:11:35 - mmengine - INFO - Epoch(train) [38][ 2/15] lr: 1.0000e-06 eta: 0:14:35 time: 0.3689 data_time: 0.0670 memory: 21845 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:11:35 - mmengine - INFO - Epoch(train) [38][ 3/15] lr: 1.0000e-06 eta: 0:14:34 time: 0.3558 data_time: 0.0670 memory: 19739 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:11:36 - mmengine - INFO - Epoch(train) [38][ 4/15] lr: 1.0000e-06 eta: 0:14:33 time: 0.3629 data_time: 0.0671 memory: 11846 loss: 0.0899 loss_ce: 0.0899 2023/03/03 14:11:36 - mmengine - INFO - Epoch(train) [38][ 5/15] lr: 1.0000e-06 eta: 0:14:32 time: 0.3588 data_time: 0.0671 memory: 16508 loss: 0.0929 loss_ce: 0.0929 2023/03/03 14:11:36 - mmengine - INFO - Epoch(train) [38][ 6/15] lr: 1.0000e-06 eta: 0:14:31 time: 0.3419 data_time: 0.0671 memory: 16199 loss: 0.1021 loss_ce: 0.1021 2023/03/03 14:11:36 - mmengine - INFO - Epoch(train) [38][ 7/15] lr: 1.0000e-06 eta: 0:14:31 time: 0.3379 data_time: 0.0671 memory: 17619 loss: 0.0993 loss_ce: 0.0993 2023/03/03 14:11:37 - mmengine - INFO - Epoch(train) [38][ 8/15] lr: 1.0000e-06 eta: 0:14:31 time: 0.3758 data_time: 0.0671 memory: 36593 loss: 0.1047 loss_ce: 0.1047 2023/03/03 14:11:37 - mmengine - INFO - Epoch(train) [38][ 9/15] lr: 1.0000e-06 eta: 0:14:30 time: 0.3552 data_time: 0.0671 memory: 17572 loss: 0.1037 loss_ce: 0.1037 2023/03/03 14:11:37 - mmengine - INFO - Epoch(train) [38][10/15] lr: 1.0000e-06 eta: 0:14:29 time: 0.3619 data_time: 0.0671 memory: 16654 loss: 0.0983 loss_ce: 0.0983 2023/03/03 14:11:38 - mmengine - INFO - Epoch(train) [38][11/15] lr: 1.0000e-06 eta: 0:14:29 time: 0.3007 data_time: 0.0017 memory: 17572 loss: 0.0972 loss_ce: 0.0972 2023/03/03 14:11:38 - mmengine - INFO - Epoch(train) [38][12/15] lr: 1.0000e-06 eta: 0:14:28 time: 0.2871 data_time: 0.0016 memory: 17120 loss: 0.1026 loss_ce: 0.1026 2023/03/03 14:11:39 - mmengine - INFO - Epoch(train) [38][13/15] lr: 1.0000e-06 eta: 0:14:29 time: 0.3250 data_time: 0.0016 memory: 17272 loss: 0.1024 loss_ce: 0.1024 2023/03/03 14:11:39 - mmengine - INFO - Epoch(train) [38][14/15] lr: 1.0000e-06 eta: 0:14:29 time: 0.3337 data_time: 0.0016 memory: 19263 loss: 0.0929 loss_ce: 0.0929 2023/03/03 14:11:39 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:11:39 - mmengine - INFO - Epoch(train) [38][15/15] lr: 1.0000e-06 eta: 0:14:27 time: 0.3264 data_time: 0.0015 memory: 4941 loss: 0.0957 loss_ce: 0.0957 2023/03/03 14:11:40 - mmengine - INFO - Epoch(train) [39][ 1/15] lr: 1.0000e-06 eta: 0:14:28 time: 0.3640 data_time: 0.0408 memory: 17572 loss: 0.0901 loss_ce: 0.0901 2023/03/03 14:11:40 - mmengine - INFO - Epoch(train) [39][ 2/15] lr: 1.0000e-06 eta: 0:14:28 time: 0.3892 data_time: 0.0540 memory: 18070 loss: 0.0892 loss_ce: 0.0892 2023/03/03 14:11:40 - mmengine - INFO - Epoch(train) [39][ 3/15] lr: 1.0000e-06 eta: 0:14:27 time: 0.3515 data_time: 0.0541 memory: 17272 loss: 0.0872 loss_ce: 0.0872 2023/03/03 14:11:41 - mmengine - INFO - Epoch(train) [39][ 4/15] lr: 1.0000e-06 eta: 0:14:27 time: 0.3611 data_time: 0.0541 memory: 11561 loss: 0.0908 loss_ce: 0.0908 2023/03/03 14:11:41 - mmengine - INFO - Epoch(train) [39][ 5/15] lr: 1.0000e-06 eta: 0:14:26 time: 0.3584 data_time: 0.0542 memory: 17120 loss: 0.0936 loss_ce: 0.0936 2023/03/03 14:11:41 - mmengine - INFO - Epoch(train) [39][ 6/15] lr: 1.0000e-06 eta: 0:14:26 time: 0.3592 data_time: 0.0542 memory: 17978 loss: 0.0991 loss_ce: 0.0991 2023/03/03 14:11:42 - mmengine - INFO - Epoch(train) [39][ 7/15] lr: 1.0000e-06 eta: 0:14:25 time: 0.3629 data_time: 0.0542 memory: 17619 loss: 0.0950 loss_ce: 0.0950 2023/03/03 14:11:42 - mmengine - INFO - Epoch(train) [39][ 8/15] lr: 1.0000e-06 eta: 0:14:25 time: 0.3472 data_time: 0.0543 memory: 16976 loss: 0.0954 loss_ce: 0.0954 2023/03/03 14:11:42 - mmengine - INFO - Epoch(train) [39][ 9/15] lr: 1.0000e-06 eta: 0:14:24 time: 0.3330 data_time: 0.0543 memory: 15494 loss: 0.1022 loss_ce: 0.1022 2023/03/03 14:11:43 - mmengine - INFO - Epoch(train) [39][10/15] lr: 1.0000e-06 eta: 0:14:24 time: 0.3524 data_time: 0.0543 memory: 16976 loss: 0.0997 loss_ce: 0.0997 2023/03/03 14:11:43 - mmengine - INFO - Epoch(train) [39][11/15] lr: 1.0000e-06 eta: 0:14:23 time: 0.3147 data_time: 0.0150 memory: 16223 loss: 0.1022 loss_ce: 0.1022 2023/03/03 14:11:43 - mmengine - INFO - Epoch(train) [39][12/15] lr: 1.0000e-06 eta: 0:14:22 time: 0.2891 data_time: 0.0018 memory: 17572 loss: 0.1052 loss_ce: 0.1052 2023/03/03 14:11:43 - mmengine - INFO - Epoch(train) [39][13/15] lr: 1.0000e-06 eta: 0:14:21 time: 0.3007 data_time: 0.0017 memory: 15845 loss: 0.1021 loss_ce: 0.1021 2023/03/03 14:11:44 - mmengine - INFO - Epoch(train) [39][14/15] lr: 1.0000e-06 eta: 0:14:20 time: 0.2913 data_time: 0.0017 memory: 17272 loss: 0.0977 loss_ce: 0.0977 2023/03/03 14:11:44 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:11:44 - mmengine - INFO - Epoch(train) [39][15/15] lr: 1.0000e-06 eta: 0:14:19 time: 0.2861 data_time: 0.0017 memory: 3982 loss: 0.1083 loss_ce: 0.1083 2023/03/03 14:11:45 - mmengine - INFO - Epoch(train) [40][ 1/15] lr: 1.0000e-06 eta: 0:14:21 time: 0.3384 data_time: 0.0564 memory: 17131 loss: 0.1035 loss_ce: 0.1035 2023/03/03 14:11:45 - mmengine - INFO - Epoch(train) [40][ 2/15] lr: 1.0000e-06 eta: 0:14:20 time: 0.3379 data_time: 0.0565 memory: 16654 loss: 0.1087 loss_ce: 0.1087 2023/03/03 14:11:45 - mmengine - INFO - Epoch(train) [40][ 3/15] lr: 1.0000e-06 eta: 0:14:20 time: 0.3154 data_time: 0.0564 memory: 14622 loss: 0.1036 loss_ce: 0.1036 2023/03/03 14:11:45 - mmengine - INFO - Epoch(train) [40][ 4/15] lr: 1.0000e-06 eta: 0:14:18 time: 0.3107 data_time: 0.0565 memory: 13722 loss: 0.0994 loss_ce: 0.0994 2023/03/03 14:11:46 - mmengine - INFO - Epoch(train) [40][ 5/15] lr: 1.0000e-06 eta: 0:14:19 time: 0.3364 data_time: 0.0564 memory: 20327 loss: 0.0986 loss_ce: 0.0986 2023/03/03 14:11:46 - mmengine - INFO - Epoch(train) [40][ 6/15] lr: 1.0000e-06 eta: 0:14:18 time: 0.3341 data_time: 0.0565 memory: 17572 loss: 0.0960 loss_ce: 0.0960 2023/03/03 14:11:47 - mmengine - INFO - Epoch(train) [40][ 7/15] lr: 1.0000e-06 eta: 0:14:18 time: 0.3489 data_time: 0.0565 memory: 16654 loss: 0.0939 loss_ce: 0.0939 2023/03/03 14:11:47 - mmengine - INFO - Epoch(train) [40][ 8/15] lr: 1.0000e-06 eta: 0:14:17 time: 0.3371 data_time: 0.0565 memory: 17209 loss: 0.0926 loss_ce: 0.0926 2023/03/03 14:11:47 - mmengine - INFO - Epoch(train) [40][ 9/15] lr: 1.0000e-06 eta: 0:14:17 time: 0.3592 data_time: 0.0565 memory: 23941 loss: 0.0933 loss_ce: 0.0933 2023/03/03 14:11:48 - mmengine - INFO - Epoch(train) [40][10/15] lr: 1.0000e-06 eta: 0:14:17 time: 0.3826 data_time: 0.0565 memory: 22721 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:11:48 - mmengine - INFO - Epoch(train) [40][11/15] lr: 1.0000e-06 eta: 0:14:17 time: 0.3312 data_time: 0.0017 memory: 20744 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:11:48 - mmengine - INFO - Epoch(train) [40][12/15] lr: 1.0000e-06 eta: 0:14:16 time: 0.3318 data_time: 0.0017 memory: 17446 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:11:49 - mmengine - INFO - Epoch(train) [40][13/15] lr: 1.0000e-06 eta: 0:14:16 time: 0.3572 data_time: 0.0016 memory: 16654 loss: 0.0802 loss_ce: 0.0802 2023/03/03 14:11:49 - mmengine - INFO - Epoch(train) [40][14/15] lr: 1.0000e-06 eta: 0:14:15 time: 0.3629 data_time: 0.0016 memory: 16508 loss: 0.0816 loss_ce: 0.0816 2023/03/03 14:11:49 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:11:49 - mmengine - INFO - Epoch(train) [40][15/15] lr: 1.0000e-06 eta: 0:14:14 time: 0.3276 data_time: 0.0016 memory: 5937 loss: 0.0861 loss_ce: 0.0861 2023/03/03 14:11:51 - mmengine - INFO - Epoch(val) [40][ 1/59] eta: 0:01:30 time: 1.1440 data_time: 0.0033 memory: 981 2023/03/03 14:11:52 - mmengine - INFO - Epoch(val) [40][ 2/59] eta: 0:01:08 time: 1.0598 data_time: 0.0033 memory: 981 2023/03/03 14:11:53 - mmengine - INFO - Epoch(val) [40][ 3/59] eta: 0:01:13 time: 1.0955 data_time: 0.0033 memory: 1003 2023/03/03 14:11:54 - mmengine - INFO - Epoch(val) [40][ 4/59] eta: 0:00:58 time: 1.0468 data_time: 0.0033 memory: 981 2023/03/03 14:11:57 - mmengine - INFO - Epoch(val) [40][ 5/59] eta: 0:01:20 time: 1.3046 data_time: 0.0033 memory: 1016 2023/03/03 14:11:59 - mmengine - INFO - Epoch(val) [40][ 6/59] eta: 0:01:29 time: 1.5031 data_time: 0.0033 memory: 981 2023/03/03 14:12:00 - mmengine - INFO - Epoch(val) [40][ 7/59] eta: 0:01:16 time: 1.4384 data_time: 0.0034 memory: 1043 2023/03/03 14:12:01 - mmengine - INFO - Epoch(val) [40][ 8/59] eta: 0:01:12 time: 1.3162 data_time: 0.0034 memory: 1016 2023/03/03 14:12:02 - mmengine - INFO - Epoch(val) [40][ 9/59] eta: 0:01:08 time: 1.2834 data_time: 0.0034 memory: 981 2023/03/03 14:12:03 - mmengine - INFO - Epoch(val) [40][10/59] eta: 0:01:07 time: 1.3853 data_time: 0.0034 memory: 981 2023/03/03 14:12:04 - mmengine - INFO - Epoch(val) [40][11/59] eta: 0:01:02 time: 1.2801 data_time: 0.0009 memory: 981 2023/03/03 14:12:07 - mmengine - INFO - Epoch(val) [40][12/59] eta: 0:01:09 time: 1.5447 data_time: 0.0009 memory: 1016 2023/03/03 14:12:09 - mmengine - INFO - Epoch(val) [40][13/59] eta: 0:01:10 time: 1.6016 data_time: 0.0010 memory: 981 2023/03/03 14:12:10 - mmengine - INFO - Epoch(val) [40][14/59] eta: 0:01:07 time: 1.6855 data_time: 0.0010 memory: 890 2023/03/03 14:12:10 - mmengine - INFO - Epoch(val) [40][15/59] eta: 0:01:02 time: 1.3639 data_time: 0.0010 memory: 981 2023/03/03 14:12:11 - mmengine - INFO - Epoch(val) [40][16/59] eta: 0:00:58 time: 1.1491 data_time: 0.0010 memory: 981 2023/03/03 14:12:11 - mmengine - INFO - Epoch(val) [40][17/59] eta: 0:00:54 time: 1.1646 data_time: 0.0010 memory: 981 2023/03/03 14:12:12 - mmengine - INFO - Epoch(val) [40][18/59] eta: 0:00:50 time: 1.0975 data_time: 0.0010 memory: 981 2023/03/03 14:12:13 - mmengine - INFO - Epoch(val) [40][19/59] eta: 0:00:49 time: 1.0999 data_time: 0.0009 memory: 981 2023/03/03 14:12:13 - mmengine - INFO - Epoch(val) [40][20/59] eta: 0:00:46 time: 0.9821 data_time: 0.0009 memory: 981 2023/03/03 14:12:15 - mmengine - INFO - Epoch(val) [40][21/59] eta: 0:00:46 time: 1.1209 data_time: 0.0009 memory: 981 2023/03/03 14:12:15 - mmengine - INFO - Epoch(val) [40][22/59] eta: 0:00:43 time: 0.7898 data_time: 0.0009 memory: 981 2023/03/03 14:12:16 - mmengine - INFO - Epoch(val) [40][23/59] eta: 0:00:41 time: 0.6470 data_time: 0.0008 memory: 981 2023/03/03 14:12:16 - mmengine - INFO - Epoch(val) [40][24/59] eta: 0:00:39 time: 0.5631 data_time: 0.0008 memory: 962 2023/03/03 14:12:16 - mmengine - INFO - Epoch(val) [40][25/59] eta: 0:00:36 time: 0.5942 data_time: 0.0008 memory: 981 2023/03/03 14:12:17 - mmengine - INFO - Epoch(val) [40][26/59] eta: 0:00:34 time: 0.5777 data_time: 0.0008 memory: 981 2023/03/03 14:12:17 - mmengine - INFO - Epoch(val) [40][27/59] eta: 0:00:32 time: 0.5777 data_time: 0.0008 memory: 981 2023/03/03 14:12:17 - mmengine - INFO - Epoch(val) [40][28/59] eta: 0:00:31 time: 0.5830 data_time: 0.0008 memory: 981 2023/03/03 14:12:19 - mmengine - INFO - Epoch(val) [40][29/59] eta: 0:00:30 time: 0.6161 data_time: 0.0008 memory: 981 2023/03/03 14:12:20 - mmengine - INFO - Epoch(val) [40][30/59] eta: 0:00:29 time: 0.6654 data_time: 0.0008 memory: 999 2023/03/03 14:12:20 - mmengine - INFO - Epoch(val) [40][31/59] eta: 0:00:27 time: 0.5427 data_time: 0.0008 memory: 981 2023/03/03 14:12:21 - mmengine - INFO - Epoch(val) [40][32/59] eta: 0:00:27 time: 0.6414 data_time: 0.0008 memory: 981 2023/03/03 14:12:21 - mmengine - INFO - Epoch(val) [40][33/59] eta: 0:00:25 time: 0.5776 data_time: 0.0008 memory: 981 2023/03/03 14:12:22 - mmengine - INFO - Epoch(val) [40][34/59] eta: 0:00:23 time: 0.5611 data_time: 0.0008 memory: 981 2023/03/03 14:12:22 - mmengine - INFO - Epoch(val) [40][35/59] eta: 0:00:22 time: 0.5448 data_time: 0.0008 memory: 981 2023/03/03 14:12:22 - mmengine - INFO - Epoch(val) [40][36/59] eta: 0:00:21 time: 0.5612 data_time: 0.0008 memory: 981 2023/03/03 14:12:22 - mmengine - INFO - Epoch(val) [40][37/59] eta: 0:00:19 time: 0.5450 data_time: 0.0008 memory: 981 2023/03/03 14:12:23 - mmengine - INFO - Epoch(val) [40][38/59] eta: 0:00:18 time: 0.5725 data_time: 0.0008 memory: 981 2023/03/03 14:12:24 - mmengine - INFO - Epoch(val) [40][39/59] eta: 0:00:17 time: 0.4861 data_time: 0.0008 memory: 987 2023/03/03 14:12:24 - mmengine - INFO - Epoch(val) [40][40/59] eta: 0:00:16 time: 0.4856 data_time: 0.0007 memory: 981 2023/03/03 14:12:26 - mmengine - INFO - Epoch(val) [40][41/59] eta: 0:00:15 time: 0.5352 data_time: 0.0007 memory: 986 2023/03/03 14:12:26 - mmengine - INFO - Epoch(val) [40][42/59] eta: 0:00:14 time: 0.4854 data_time: 0.0007 memory: 981 2023/03/03 14:12:27 - mmengine - INFO - Epoch(val) [40][43/59] eta: 0:00:14 time: 0.5654 data_time: 0.0007 memory: 976 2023/03/03 14:12:28 - mmengine - INFO - Epoch(val) [40][44/59] eta: 0:00:13 time: 0.5977 data_time: 0.0007 memory: 1003 2023/03/03 14:12:29 - mmengine - INFO - Epoch(val) [40][45/59] eta: 0:00:12 time: 0.7668 data_time: 0.0007 memory: 981 2023/03/03 14:12:30 - mmengine - INFO - Epoch(val) [40][46/59] eta: 0:00:11 time: 0.8001 data_time: 0.0008 memory: 981 2023/03/03 14:12:31 - mmengine - INFO - Epoch(val) [40][47/59] eta: 0:00:10 time: 0.8322 data_time: 0.0008 memory: 936 2023/03/03 14:12:31 - mmengine - INFO - Epoch(val) [40][48/59] eta: 0:00:09 time: 0.8163 data_time: 0.0008 memory: 1000 2023/03/03 14:12:32 - mmengine - INFO - Epoch(val) [40][49/59] eta: 0:00:08 time: 0.8662 data_time: 0.0008 memory: 981 2023/03/03 14:12:33 - mmengine - INFO - Epoch(val) [40][50/59] eta: 0:00:07 time: 0.8664 data_time: 0.0008 memory: 987 2023/03/03 14:12:35 - mmengine - INFO - Epoch(val) [40][51/59] eta: 0:00:07 time: 0.9177 data_time: 0.0008 memory: 981 2023/03/03 14:12:36 - mmengine - INFO - Epoch(val) [40][52/59] eta: 0:00:06 time: 0.9663 data_time: 0.0008 memory: 981 2023/03/03 14:12:37 - mmengine - INFO - Epoch(val) [40][53/59] eta: 0:00:05 time: 0.9674 data_time: 0.0008 memory: 962 2023/03/03 14:12:37 - mmengine - INFO - Epoch(val) [40][54/59] eta: 0:00:04 time: 0.9854 data_time: 0.0008 memory: 981 2023/03/03 14:12:38 - mmengine - INFO - Epoch(val) [40][55/59] eta: 0:00:03 time: 0.8661 data_time: 0.0008 memory: 981 2023/03/03 14:12:39 - mmengine - INFO - Epoch(val) [40][56/59] eta: 0:00:02 time: 0.8672 data_time: 0.0008 memory: 981 2023/03/03 14:12:41 - mmengine - INFO - Epoch(val) [40][57/59] eta: 0:00:01 time: 1.0424 data_time: 0.0008 memory: 981 2023/03/03 14:12:43 - mmengine - INFO - Epoch(val) [40][58/59] eta: 0:00:00 time: 1.1260 data_time: 0.0008 memory: 1016 2023/03/03 14:12:43 - mmengine - INFO - Epoch(val) [40][59/59] eta: 0:00:00 time: 1.0772 data_time: 0.0009 memory: 981 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.80, recall: 0.8183, precision: 0.8296, hmean: 0.8239 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.81, recall: 0.8183, precision: 0.8343, hmean: 0.8262 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.82, recall: 0.8174, precision: 0.8341, hmean: 0.8256 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.83, recall: 0.8174, precision: 0.8364, hmean: 0.8268 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.84, recall: 0.8164, precision: 0.8379, hmean: 0.8270 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.85, recall: 0.8146, precision: 0.8447, hmean: 0.8294 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.86, recall: 0.8128, precision: 0.8476, hmean: 0.8298 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.87, recall: 0.8110, precision: 0.8481, hmean: 0.8291 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.88, recall: 0.8073, precision: 0.8516, hmean: 0.8289 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.89, recall: 0.8055, precision: 0.8547, hmean: 0.8293 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.90, recall: 0.8018, precision: 0.8583, hmean: 0.8291 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.91, recall: 0.7963, precision: 0.8600, hmean: 0.8269 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.92, recall: 0.7900, precision: 0.8624, hmean: 0.8246 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.93, recall: 0.7808, precision: 0.8619, hmean: 0.8194 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.94, recall: 0.7726, precision: 0.8641, hmean: 0.8158 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.95, recall: 0.7626, precision: 0.8680, hmean: 0.8119 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.96, recall: 0.7534, precision: 0.8749, hmean: 0.8096 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.97, recall: 0.7406, precision: 0.8787, hmean: 0.8038 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.98, recall: 0.7251, precision: 0.8812, hmean: 0.7956 2023/03/03 14:13:12 - mmengine - INFO - text score threshold: 0.99, recall: 0.7014, precision: 0.8879, hmean: 0.7837 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.80, recall: 0.8274, precision: 0.9015, hmean: 0.8629 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.81, recall: 0.8274, precision: 0.9042, hmean: 0.8641 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.82, recall: 0.8274, precision: 0.9042, hmean: 0.8641 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.83, recall: 0.8274, precision: 0.9051, hmean: 0.8645 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.84, recall: 0.8274, precision: 0.9078, hmean: 0.8657 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.85, recall: 0.8247, precision: 0.9121, hmean: 0.8662 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.86, recall: 0.8228, precision: 0.9138, hmean: 0.8659 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.87, recall: 0.8210, precision: 0.9145, hmean: 0.8653 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.88, recall: 0.8174, precision: 0.9170, hmean: 0.8643 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.89, recall: 0.8146, precision: 0.9177, hmean: 0.8631 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.90, recall: 0.8100, precision: 0.9192, hmean: 0.8612 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.91, recall: 0.8027, precision: 0.9185, hmean: 0.8567 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.92, recall: 0.7963, precision: 0.9198, hmean: 0.8536 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.93, recall: 0.7872, precision: 0.9190, hmean: 0.8480 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.94, recall: 0.7781, precision: 0.9181, hmean: 0.8423 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.95, recall: 0.7671, precision: 0.9211, hmean: 0.8371 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.96, recall: 0.7571, precision: 0.9232, hmean: 0.8319 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.97, recall: 0.7434, precision: 0.9250, hmean: 0.8243 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.98, recall: 0.7260, precision: 0.9266, hmean: 0.8141 2023/03/03 14:13:15 - mmengine - INFO - text score threshold: 0.99, recall: 0.7032, precision: 0.9277, hmean: 0.8000 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.80, recall: 0.7489, precision: 0.9557, hmean: 0.8397 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.81, recall: 0.7489, precision: 0.9568, hmean: 0.8402 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.82, recall: 0.7489, precision: 0.9568, hmean: 0.8402 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.83, recall: 0.7479, precision: 0.9568, hmean: 0.8396 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.84, recall: 0.7461, precision: 0.9578, hmean: 0.8388 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.85, recall: 0.7434, precision: 0.9610, hmean: 0.8383 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.86, recall: 0.7416, precision: 0.9609, hmean: 0.8371 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.87, recall: 0.7406, precision: 0.9609, hmean: 0.8365 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.88, recall: 0.7361, precision: 0.9607, hmean: 0.8335 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.89, recall: 0.7342, precision: 0.9617, hmean: 0.8327 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.90, recall: 0.7297, precision: 0.9627, hmean: 0.8301 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.91, recall: 0.7224, precision: 0.9623, hmean: 0.8252 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.92, recall: 0.7178, precision: 0.9621, hmean: 0.8222 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.93, recall: 0.7105, precision: 0.9617, hmean: 0.8172 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.94, recall: 0.7014, precision: 0.9612, hmean: 0.8110 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.95, recall: 0.6895, precision: 0.9618, hmean: 0.8032 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.96, recall: 0.6822, precision: 0.9639, hmean: 0.7989 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.97, recall: 0.6694, precision: 0.9645, hmean: 0.7903 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.98, recall: 0.6539, precision: 0.9650, hmean: 0.7795 2023/03/03 14:13:17 - mmengine - INFO - text score threshold: 0.99, recall: 0.6311, precision: 0.9651, hmean: 0.7631 2023/03/03 14:13:17 - mmengine - INFO - Epoch(val) [40][59/59] generic/precision: 0.8476 generic/recall: 0.8128 generic/hmean: 0.8298 weak/precision: 0.9121 weak/recall: 0.8247 weak/hmean: 0.8662 strong/precision: 0.9568 strong/recall: 0.7489 strong/hmean: 0.8402 2023/03/03 14:13:17 - mmengine - INFO - The previous best checkpoint mmocr/projects/SPTS/work_dirs/spts_resnet50_350e_icdar2013/best_generic/hmean_epoch_20.pth is removed 2023/03/03 14:13:19 - mmengine - INFO - The best checkpoint with 0.8298 generic/hmean at 40 epoch is saved to best_generic/hmean_epoch_40.pth. 2023/03/03 14:13:20 - mmengine - INFO - Epoch(train) [41][ 1/15] lr: 1.0000e-06 eta: 0:14:17 time: 0.4047 data_time: 0.0704 memory: 14504 loss: 0.0878 loss_ce: 0.0878 2023/03/03 14:13:20 - mmengine - INFO - Epoch(train) [41][ 2/15] lr: 1.0000e-06 eta: 0:14:16 time: 0.3869 data_time: 0.0704 memory: 15631 loss: 0.0931 loss_ce: 0.0931 2023/03/03 14:13:21 - mmengine - INFO - Epoch(train) [41][ 3/15] lr: 1.0000e-06 eta: 0:14:15 time: 0.3894 data_time: 0.0704 memory: 19747 loss: 0.0929 loss_ce: 0.0929 2023/03/03 14:13:21 - mmengine - INFO - Epoch(train) [41][ 4/15] lr: 1.0000e-06 eta: 0:14:15 time: 0.3838 data_time: 0.0704 memory: 16369 loss: 0.0940 loss_ce: 0.0940 2023/03/03 14:13:21 - mmengine - INFO - Epoch(train) [41][ 5/15] lr: 1.0000e-06 eta: 0:14:14 time: 0.3683 data_time: 0.0704 memory: 16654 loss: 0.0971 loss_ce: 0.0971 2023/03/03 14:13:21 - mmengine - INFO - Epoch(train) [41][ 6/15] lr: 1.0000e-06 eta: 0:14:13 time: 0.3551 data_time: 0.0704 memory: 16456 loss: 0.1041 loss_ce: 0.1041 2023/03/03 14:13:22 - mmengine - INFO - Epoch(train) [41][ 7/15] lr: 1.0000e-06 eta: 0:14:12 time: 0.3557 data_time: 0.0704 memory: 15241 loss: 0.1106 loss_ce: 0.1106 2023/03/03 14:13:22 - mmengine - INFO - Epoch(train) [41][ 8/15] lr: 1.0000e-06 eta: 0:14:12 time: 0.3357 data_time: 0.0704 memory: 16223 loss: 0.1138 loss_ce: 0.1138 2023/03/03 14:13:22 - mmengine - INFO - Epoch(train) [41][ 9/15] lr: 1.0000e-06 eta: 0:14:11 time: 0.3348 data_time: 0.0705 memory: 15494 loss: 0.1161 loss_ce: 0.1161 2023/03/03 14:13:23 - mmengine - INFO - Epoch(train) [41][10/15] lr: 1.0000e-06 eta: 0:14:11 time: 0.3551 data_time: 0.0704 memory: 36301 loss: 0.1074 loss_ce: 0.1074 2023/03/03 14:13:23 - mmengine - INFO - Epoch(train) [41][11/15] lr: 1.0000e-06 eta: 0:14:10 time: 0.2934 data_time: 0.0016 memory: 19683 loss: 0.1060 loss_ce: 0.1060 2023/03/03 14:13:23 - mmengine - INFO - Epoch(train) [41][12/15] lr: 1.0000e-06 eta: 0:14:10 time: 0.2965 data_time: 0.0016 memory: 17311 loss: 0.1031 loss_ce: 0.1031 2023/03/03 14:13:24 - mmengine - INFO - Epoch(train) [41][13/15] lr: 1.0000e-06 eta: 0:14:09 time: 0.2965 data_time: 0.0016 memory: 16530 loss: 0.1054 loss_ce: 0.1054 2023/03/03 14:13:24 - mmengine - INFO - Epoch(train) [41][14/15] lr: 1.0000e-06 eta: 0:14:09 time: 0.3074 data_time: 0.0016 memory: 17190 loss: 0.1047 loss_ce: 0.1047 2023/03/03 14:13:24 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:13:24 - mmengine - INFO - Epoch(train) [41][15/15] lr: 1.0000e-06 eta: 0:14:08 time: 0.2994 data_time: 0.0016 memory: 5930 loss: 0.1082 loss_ce: 0.1082 2023/03/03 14:13:25 - mmengine - INFO - Epoch(train) [42][ 1/15] lr: 1.0000e-06 eta: 0:14:10 time: 0.3679 data_time: 0.0643 memory: 18241 loss: 0.1011 loss_ce: 0.1011 2023/03/03 14:13:25 - mmengine - INFO - Epoch(train) [42][ 2/15] lr: 1.0000e-06 eta: 0:14:09 time: 0.3641 data_time: 0.0643 memory: 17730 loss: 0.0949 loss_ce: 0.0949 2023/03/03 14:13:26 - mmengine - INFO - Epoch(train) [42][ 3/15] lr: 1.0000e-06 eta: 0:14:08 time: 0.3628 data_time: 0.0644 memory: 22406 loss: 0.0889 loss_ce: 0.0889 2023/03/03 14:13:26 - mmengine - INFO - Epoch(train) [42][ 4/15] lr: 1.0000e-06 eta: 0:14:08 time: 0.3871 data_time: 0.0644 memory: 23930 loss: 0.0897 loss_ce: 0.0897 2023/03/03 14:13:26 - mmengine - INFO - Epoch(train) [42][ 5/15] lr: 1.0000e-06 eta: 0:14:08 time: 0.3705 data_time: 0.0644 memory: 16223 loss: 0.0986 loss_ce: 0.0986 2023/03/03 14:13:27 - mmengine - INFO - Epoch(train) [42][ 6/15] lr: 1.0000e-06 eta: 0:14:08 time: 0.3722 data_time: 0.0645 memory: 17792 loss: 0.0987 loss_ce: 0.0987 2023/03/03 14:13:27 - mmengine - INFO - Epoch(train) [42][ 7/15] lr: 1.0000e-06 eta: 0:14:07 time: 0.3764 data_time: 0.0645 memory: 17607 loss: 0.1033 loss_ce: 0.1033 2023/03/03 14:13:27 - mmengine - INFO - Epoch(train) [42][ 8/15] lr: 1.0000e-06 eta: 0:14:06 time: 0.3766 data_time: 0.0644 memory: 16508 loss: 0.1005 loss_ce: 0.1005 2023/03/03 14:13:28 - mmengine - INFO - Epoch(train) [42][ 9/15] lr: 1.0000e-06 eta: 0:14:05 time: 0.3545 data_time: 0.0644 memory: 18876 loss: 0.0980 loss_ce: 0.0980 2023/03/03 14:13:28 - mmengine - INFO - Epoch(train) [42][10/15] lr: 1.0000e-06 eta: 0:14:06 time: 0.3904 data_time: 0.0644 memory: 36810 loss: 0.0904 loss_ce: 0.0904 2023/03/03 14:13:28 - mmengine - INFO - Epoch(train) [42][11/15] lr: 1.0000e-06 eta: 0:14:05 time: 0.3258 data_time: 0.0017 memory: 16122 loss: 0.0943 loss_ce: 0.0943 2023/03/03 14:13:29 - mmengine - INFO - Epoch(train) [42][12/15] lr: 1.0000e-06 eta: 0:14:04 time: 0.3342 data_time: 0.0017 memory: 17050 loss: 0.0928 loss_ce: 0.0928 2023/03/03 14:13:29 - mmengine - INFO - Epoch(train) [42][13/15] lr: 1.0000e-06 eta: 0:14:04 time: 0.3330 data_time: 0.0016 memory: 17596 loss: 0.0918 loss_ce: 0.0918 2023/03/03 14:13:29 - mmengine - INFO - Epoch(train) [42][14/15] lr: 1.0000e-06 eta: 0:14:03 time: 0.3176 data_time: 0.0016 memory: 18182 loss: 0.0859 loss_ce: 0.0859 2023/03/03 14:13:29 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:13:29 - mmengine - INFO - Epoch(train) [42][15/15] lr: 1.0000e-06 eta: 0:14:02 time: 0.3040 data_time: 0.0016 memory: 7262 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:13:30 - mmengine - INFO - Epoch(train) [43][ 1/15] lr: 1.0000e-06 eta: 0:14:03 time: 0.3439 data_time: 0.0447 memory: 19908 loss: 0.0852 loss_ce: 0.0852 2023/03/03 14:13:30 - mmengine - INFO - Epoch(train) [43][ 2/15] lr: 1.0000e-06 eta: 0:14:03 time: 0.3372 data_time: 0.0447 memory: 16530 loss: 0.0766 loss_ce: 0.0766 2023/03/03 14:13:31 - mmengine - INFO - Epoch(train) [43][ 3/15] lr: 1.0000e-06 eta: 0:14:02 time: 0.3384 data_time: 0.0448 memory: 16508 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:13:31 - mmengine - INFO - Epoch(train) [43][ 4/15] lr: 1.0000e-06 eta: 0:14:02 time: 0.3495 data_time: 0.0449 memory: 17198 loss: 0.0780 loss_ce: 0.0780 2023/03/03 14:13:32 - mmengine - INFO - Epoch(train) [43][ 5/15] lr: 1.0000e-06 eta: 0:14:02 time: 0.3505 data_time: 0.0449 memory: 37909 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:13:32 - mmengine - INFO - Epoch(train) [43][ 6/15] lr: 1.0000e-06 eta: 0:14:01 time: 0.3401 data_time: 0.0449 memory: 14761 loss: 0.0829 loss_ce: 0.0829 2023/03/03 14:13:32 - mmengine - INFO - Epoch(train) [43][ 7/15] lr: 1.0000e-06 eta: 0:14:00 time: 0.3333 data_time: 0.0449 memory: 15336 loss: 0.0869 loss_ce: 0.0869 2023/03/03 14:13:32 - mmengine - INFO - Epoch(train) [43][ 8/15] lr: 1.0000e-06 eta: 0:14:00 time: 0.3434 data_time: 0.0449 memory: 17751 loss: 0.0890 loss_ce: 0.0890 2023/03/03 14:13:33 - mmengine - INFO - Epoch(train) [43][ 9/15] lr: 1.0000e-06 eta: 0:13:59 time: 0.3347 data_time: 0.0449 memory: 16056 loss: 0.0898 loss_ce: 0.0898 2023/03/03 14:13:33 - mmengine - INFO - Epoch(train) [43][10/15] lr: 1.0000e-06 eta: 0:13:59 time: 0.3620 data_time: 0.0449 memory: 13405 loss: 0.0890 loss_ce: 0.0890 2023/03/03 14:13:33 - mmengine - INFO - Epoch(train) [43][11/15] lr: 1.0000e-06 eta: 0:13:58 time: 0.3078 data_time: 0.0017 memory: 16537 loss: 0.0886 loss_ce: 0.0886 2023/03/03 14:13:34 - mmengine - INFO - Epoch(train) [43][12/15] lr: 1.0000e-06 eta: 0:13:58 time: 0.3222 data_time: 0.0017 memory: 12427 loss: 0.0884 loss_ce: 0.0884 2023/03/03 14:13:34 - mmengine - INFO - Epoch(train) [43][13/15] lr: 1.0000e-06 eta: 0:13:57 time: 0.3285 data_time: 0.0016 memory: 16232 loss: 0.0904 loss_ce: 0.0904 2023/03/03 14:13:34 - mmengine - INFO - Epoch(train) [43][14/15] lr: 1.0000e-06 eta: 0:13:57 time: 0.3193 data_time: 0.0016 memory: 23915 loss: 0.0976 loss_ce: 0.0976 2023/03/03 14:13:34 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:13:34 - mmengine - INFO - Epoch(train) [43][15/15] lr: 1.0000e-06 eta: 0:13:55 time: 0.2794 data_time: 0.0016 memory: 6267 loss: 0.1029 loss_ce: 0.1029 2023/03/03 14:13:35 - mmengine - INFO - Epoch(train) [44][ 1/15] lr: 1.0000e-06 eta: 0:13:57 time: 0.3469 data_time: 0.0356 memory: 16508 loss: 0.0995 loss_ce: 0.0995 2023/03/03 14:13:36 - mmengine - INFO - Epoch(train) [44][ 2/15] lr: 1.0000e-06 eta: 0:13:56 time: 0.3486 data_time: 0.0357 memory: 16804 loss: 0.0965 loss_ce: 0.0965 2023/03/03 14:13:36 - mmengine - INFO - Epoch(train) [44][ 3/15] lr: 1.0000e-06 eta: 0:13:56 time: 0.3387 data_time: 0.0357 memory: 14525 loss: 0.0990 loss_ce: 0.0990 2023/03/03 14:13:36 - mmengine - INFO - Epoch(train) [44][ 4/15] lr: 1.0000e-06 eta: 0:13:55 time: 0.3392 data_time: 0.0358 memory: 16369 loss: 0.1018 loss_ce: 0.1018 2023/03/03 14:13:37 - mmengine - INFO - Epoch(train) [44][ 5/15] lr: 1.0000e-06 eta: 0:13:55 time: 0.3512 data_time: 0.0359 memory: 17122 loss: 0.0975 loss_ce: 0.0975 2023/03/03 14:13:37 - mmengine - INFO - Epoch(train) [44][ 6/15] lr: 1.0000e-06 eta: 0:13:54 time: 0.3504 data_time: 0.0358 memory: 16212 loss: 0.0943 loss_ce: 0.0943 2023/03/03 14:13:37 - mmengine - INFO - Epoch(train) [44][ 7/15] lr: 1.0000e-06 eta: 0:13:54 time: 0.3412 data_time: 0.0358 memory: 17439 loss: 0.0960 loss_ce: 0.0960 2023/03/03 14:13:37 - mmengine - INFO - Epoch(train) [44][ 8/15] lr: 1.0000e-06 eta: 0:13:53 time: 0.3304 data_time: 0.0358 memory: 13019 loss: 0.0905 loss_ce: 0.0905 2023/03/03 14:13:38 - mmengine - INFO - Epoch(train) [44][ 9/15] lr: 1.0000e-06 eta: 0:13:53 time: 0.3416 data_time: 0.0358 memory: 13162 loss: 0.0850 loss_ce: 0.0850 2023/03/03 14:13:38 - mmengine - INFO - Epoch(train) [44][10/15] lr: 1.0000e-06 eta: 0:13:52 time: 0.3525 data_time: 0.0358 memory: 16199 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:13:38 - mmengine - INFO - Epoch(train) [44][11/15] lr: 1.0000e-06 eta: 0:13:52 time: 0.3000 data_time: 0.0018 memory: 15911 loss: 0.0811 loss_ce: 0.0811 2023/03/03 14:13:38 - mmengine - INFO - Epoch(train) [44][12/15] lr: 1.0000e-06 eta: 0:13:50 time: 0.2922 data_time: 0.0017 memory: 13236 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:13:39 - mmengine - INFO - Epoch(train) [44][13/15] lr: 1.0000e-06 eta: 0:13:51 time: 0.3151 data_time: 0.0016 memory: 37937 loss: 0.0857 loss_ce: 0.0857 2023/03/03 14:13:39 - mmengine - INFO - Epoch(train) [44][14/15] lr: 1.0000e-06 eta: 0:13:50 time: 0.3127 data_time: 0.0016 memory: 17572 loss: 0.0834 loss_ce: 0.0834 2023/03/03 14:13:40 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:13:40 - mmengine - INFO - Epoch(train) [44][15/15] lr: 1.0000e-06 eta: 0:13:49 time: 0.2921 data_time: 0.0015 memory: 4071 loss: 0.0949 loss_ce: 0.0949 2023/03/03 14:13:40 - mmengine - INFO - Epoch(train) [45][ 1/15] lr: 1.0000e-06 eta: 0:13:51 time: 0.3615 data_time: 0.0698 memory: 16654 loss: 0.0992 loss_ce: 0.0992 2023/03/03 14:13:41 - mmengine - INFO - Epoch(train) [45][ 2/15] lr: 1.0000e-06 eta: 0:13:51 time: 0.3717 data_time: 0.0699 memory: 18812 loss: 0.1012 loss_ce: 0.1012 2023/03/03 14:13:41 - mmengine - INFO - Epoch(train) [45][ 3/15] lr: 1.0000e-06 eta: 0:13:50 time: 0.3814 data_time: 0.0699 memory: 21101 loss: 0.1066 loss_ce: 0.1066 2023/03/03 14:13:42 - mmengine - INFO - Epoch(train) [45][ 4/15] lr: 1.0000e-06 eta: 0:13:51 time: 0.3992 data_time: 0.0700 memory: 34413 loss: 0.1111 loss_ce: 0.1111 2023/03/03 14:13:42 - mmengine - INFO - Epoch(train) [45][ 5/15] lr: 1.0000e-06 eta: 0:13:50 time: 0.3997 data_time: 0.0700 memory: 16804 loss: 0.1101 loss_ce: 0.1101 2023/03/03 14:13:42 - mmengine - INFO - Epoch(train) [45][ 6/15] lr: 1.0000e-06 eta: 0:13:49 time: 0.3956 data_time: 0.0700 memory: 16370 loss: 0.1118 loss_ce: 0.1118 2023/03/03 14:13:42 - mmengine - INFO - Epoch(train) [45][ 7/15] lr: 1.0000e-06 eta: 0:13:48 time: 0.3996 data_time: 0.0700 memory: 17572 loss: 0.1100 loss_ce: 0.1100 2023/03/03 14:13:43 - mmengine - INFO - Epoch(train) [45][ 8/15] lr: 1.0000e-06 eta: 0:13:48 time: 0.3854 data_time: 0.0701 memory: 23091 loss: 0.1027 loss_ce: 0.1027 2023/03/03 14:13:43 - mmengine - INFO - Epoch(train) [45][ 9/15] lr: 1.0000e-06 eta: 0:13:47 time: 0.3880 data_time: 0.0701 memory: 16508 loss: 0.1034 loss_ce: 0.1034 2023/03/03 14:13:43 - mmengine - INFO - Epoch(train) [45][10/15] lr: 1.0000e-06 eta: 0:13:47 time: 0.3828 data_time: 0.0701 memory: 19384 loss: 0.0955 loss_ce: 0.0955 2023/03/03 14:13:44 - mmengine - INFO - Epoch(train) [45][11/15] lr: 1.0000e-06 eta: 0:13:46 time: 0.3133 data_time: 0.0018 memory: 16056 loss: 0.0906 loss_ce: 0.0906 2023/03/03 14:13:44 - mmengine - INFO - Epoch(train) [45][12/15] lr: 1.0000e-06 eta: 0:13:45 time: 0.3030 data_time: 0.0017 memory: 16370 loss: 0.0894 loss_ce: 0.0894 2023/03/03 14:13:44 - mmengine - INFO - Epoch(train) [45][13/15] lr: 1.0000e-06 eta: 0:13:45 time: 0.2987 data_time: 0.0016 memory: 18271 loss: 0.0854 loss_ce: 0.0854 2023/03/03 14:13:44 - mmengine - INFO - Epoch(train) [45][14/15] lr: 1.0000e-06 eta: 0:13:44 time: 0.2651 data_time: 0.0016 memory: 16530 loss: 0.0841 loss_ce: 0.0841 2023/03/03 14:13:45 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:13:45 - mmengine - INFO - Epoch(train) [45][15/15] lr: 1.0000e-06 eta: 0:13:43 time: 0.2585 data_time: 0.0015 memory: 6289 loss: 0.0831 loss_ce: 0.0831 2023/03/03 14:13:45 - mmengine - INFO - Epoch(train) [46][ 1/15] lr: 1.0000e-06 eta: 0:13:45 time: 0.3231 data_time: 0.0771 memory: 17272 loss: 0.0809 loss_ce: 0.0809 2023/03/03 14:13:46 - mmengine - INFO - Epoch(train) [46][ 2/15] lr: 1.0000e-06 eta: 0:13:44 time: 0.3276 data_time: 0.0772 memory: 17788 loss: 0.0792 loss_ce: 0.0792 2023/03/03 14:13:46 - mmengine - INFO - Epoch(train) [46][ 3/15] lr: 1.0000e-06 eta: 0:13:44 time: 0.3350 data_time: 0.0772 memory: 23477 loss: 0.0825 loss_ce: 0.0825 2023/03/03 14:13:46 - mmengine - INFO - Epoch(train) [46][ 4/15] lr: 1.0000e-06 eta: 0:13:43 time: 0.3396 data_time: 0.0773 memory: 17183 loss: 0.0816 loss_ce: 0.0816 2023/03/03 14:13:47 - mmengine - INFO - Epoch(train) [46][ 5/15] lr: 1.0000e-06 eta: 0:13:43 time: 0.3378 data_time: 0.0773 memory: 16508 loss: 0.0829 loss_ce: 0.0829 2023/03/03 14:13:47 - mmengine - INFO - Epoch(train) [46][ 6/15] lr: 1.0000e-06 eta: 0:13:42 time: 0.3385 data_time: 0.0773 memory: 16654 loss: 0.0835 loss_ce: 0.0835 2023/03/03 14:13:47 - mmengine - INFO - Epoch(train) [46][ 7/15] lr: 1.0000e-06 eta: 0:13:42 time: 0.3462 data_time: 0.0772 memory: 16849 loss: 0.0843 loss_ce: 0.0843 2023/03/03 14:13:48 - mmengine - INFO - Epoch(train) [46][ 8/15] lr: 1.0000e-06 eta: 0:13:41 time: 0.3412 data_time: 0.0773 memory: 16976 loss: 0.0898 loss_ce: 0.0898 2023/03/03 14:13:48 - mmengine - INFO - Epoch(train) [46][ 9/15] lr: 1.0000e-06 eta: 0:13:40 time: 0.3534 data_time: 0.0773 memory: 16370 loss: 0.0912 loss_ce: 0.0912 2023/03/03 14:13:48 - mmengine - INFO - Epoch(train) [46][10/15] lr: 1.0000e-06 eta: 0:13:39 time: 0.3571 data_time: 0.0772 memory: 17572 loss: 0.0909 loss_ce: 0.0909 2023/03/03 14:13:48 - mmengine - INFO - Epoch(train) [46][11/15] lr: 1.0000e-06 eta: 0:13:39 time: 0.3005 data_time: 0.0016 memory: 16976 loss: 0.0903 loss_ce: 0.0903 2023/03/03 14:13:49 - mmengine - INFO - Epoch(train) [46][12/15] lr: 1.0000e-06 eta: 0:13:39 time: 0.3049 data_time: 0.0016 memory: 21100 loss: 0.0956 loss_ce: 0.0956 2023/03/03 14:13:49 - mmengine - INFO - Epoch(train) [46][13/15] lr: 1.0000e-06 eta: 0:13:39 time: 0.3135 data_time: 0.0015 memory: 16089 loss: 0.0914 loss_ce: 0.0914 2023/03/03 14:13:50 - mmengine - INFO - Epoch(train) [46][14/15] lr: 1.0000e-06 eta: 0:13:38 time: 0.3062 data_time: 0.0015 memory: 17421 loss: 0.0926 loss_ce: 0.0926 2023/03/03 14:13:50 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:13:50 - mmengine - INFO - Epoch(train) [46][15/15] lr: 1.0000e-06 eta: 0:13:37 time: 0.2963 data_time: 0.0015 memory: 6850 loss: 0.0989 loss_ce: 0.0989 2023/03/03 14:13:51 - mmengine - INFO - Epoch(train) [47][ 1/15] lr: 1.0000e-06 eta: 0:13:39 time: 0.3681 data_time: 0.0731 memory: 16370 loss: 0.1037 loss_ce: 0.1037 2023/03/03 14:13:51 - mmengine - INFO - Epoch(train) [47][ 2/15] lr: 1.0000e-06 eta: 0:13:38 time: 0.3608 data_time: 0.0818 memory: 17272 loss: 0.1071 loss_ce: 0.1071 2023/03/03 14:13:51 - mmengine - INFO - Epoch(train) [47][ 3/15] lr: 1.0000e-06 eta: 0:13:37 time: 0.3611 data_time: 0.0818 memory: 16976 loss: 0.1064 loss_ce: 0.1064 2023/03/03 14:13:52 - mmengine - INFO - Epoch(train) [47][ 4/15] lr: 1.0000e-06 eta: 0:13:37 time: 0.3618 data_time: 0.0818 memory: 18586 loss: 0.1017 loss_ce: 0.1017 2023/03/03 14:13:52 - mmengine - INFO - Epoch(train) [47][ 5/15] lr: 1.0000e-06 eta: 0:13:36 time: 0.3616 data_time: 0.0819 memory: 16976 loss: 0.1044 loss_ce: 0.1044 2023/03/03 14:13:52 - mmengine - INFO - Epoch(train) [47][ 6/15] lr: 1.0000e-06 eta: 0:13:36 time: 0.3593 data_time: 0.0819 memory: 11205 loss: 0.1028 loss_ce: 0.1028 2023/03/03 14:13:52 - mmengine - INFO - Epoch(train) [47][ 7/15] lr: 1.0000e-06 eta: 0:13:35 time: 0.3523 data_time: 0.0819 memory: 15767 loss: 0.1034 loss_ce: 0.1034 2023/03/03 14:13:53 - mmengine - INFO - Epoch(train) [47][ 8/15] lr: 1.0000e-06 eta: 0:13:35 time: 0.3451 data_time: 0.0819 memory: 17284 loss: 0.1065 loss_ce: 0.1065 2023/03/03 14:13:53 - mmengine - INFO - Epoch(train) [47][ 9/15] lr: 1.0000e-06 eta: 0:13:34 time: 0.3454 data_time: 0.0819 memory: 17730 loss: 0.1051 loss_ce: 0.1051 2023/03/03 14:13:53 - mmengine - INFO - Epoch(train) [47][10/15] lr: 1.0000e-06 eta: 0:13:34 time: 0.3582 data_time: 0.0819 memory: 17400 loss: 0.0950 loss_ce: 0.0950 2023/03/03 14:13:53 - mmengine - INFO - Epoch(train) [47][11/15] lr: 1.0000e-06 eta: 0:13:33 time: 0.2841 data_time: 0.0104 memory: 18241 loss: 0.0883 loss_ce: 0.0883 2023/03/03 14:13:54 - mmengine - INFO - Epoch(train) [47][12/15] lr: 1.0000e-06 eta: 0:13:33 time: 0.3136 data_time: 0.0017 memory: 37744 loss: 0.0895 loss_ce: 0.0895 2023/03/03 14:13:55 - mmengine - INFO - Epoch(train) [47][13/15] lr: 1.0000e-06 eta: 0:13:33 time: 0.3369 data_time: 0.0017 memory: 19207 loss: 0.0840 loss_ce: 0.0840 2023/03/03 14:13:55 - mmengine - INFO - Epoch(train) [47][14/15] lr: 1.0000e-06 eta: 0:13:32 time: 0.3193 data_time: 0.0016 memory: 14875 loss: 0.0868 loss_ce: 0.0868 2023/03/03 14:13:55 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:13:55 - mmengine - INFO - Epoch(train) [47][15/15] lr: 1.0000e-06 eta: 0:13:31 time: 0.3139 data_time: 0.0016 memory: 6814 loss: 0.0942 loss_ce: 0.0942 2023/03/03 14:13:56 - mmengine - INFO - Epoch(train) [48][ 1/15] lr: 1.0000e-06 eta: 0:13:33 time: 0.3850 data_time: 0.0886 memory: 18586 loss: 0.0931 loss_ce: 0.0931 2023/03/03 14:13:57 - mmengine - INFO - Epoch(train) [48][ 2/15] lr: 1.0000e-06 eta: 0:13:34 time: 0.4287 data_time: 0.0886 memory: 31107 loss: 0.0880 loss_ce: 0.0880 2023/03/03 14:13:57 - mmengine - INFO - Epoch(train) [48][ 3/15] lr: 1.0000e-06 eta: 0:13:34 time: 0.4115 data_time: 0.0887 memory: 21816 loss: 0.0841 loss_ce: 0.0841 2023/03/03 14:13:57 - mmengine - INFO - Epoch(train) [48][ 4/15] lr: 1.0000e-06 eta: 0:13:33 time: 0.4114 data_time: 0.0887 memory: 18070 loss: 0.0849 loss_ce: 0.0849 2023/03/03 14:13:57 - mmengine - INFO - Epoch(train) [48][ 5/15] lr: 1.0000e-06 eta: 0:13:32 time: 0.4183 data_time: 0.0887 memory: 16654 loss: 0.0845 loss_ce: 0.0845 2023/03/03 14:13:58 - mmengine - INFO - Epoch(train) [48][ 6/15] lr: 1.0000e-06 eta: 0:13:31 time: 0.4136 data_time: 0.0886 memory: 12269 loss: 0.0902 loss_ce: 0.0902 2023/03/03 14:13:58 - mmengine - INFO - Epoch(train) [48][ 7/15] lr: 1.0000e-06 eta: 0:13:32 time: 0.4089 data_time: 0.0886 memory: 31481 loss: 0.0877 loss_ce: 0.0877 2023/03/03 14:13:58 - mmengine - INFO - Epoch(train) [48][ 8/15] lr: 1.0000e-06 eta: 0:13:31 time: 0.3856 data_time: 0.0886 memory: 17272 loss: 0.0914 loss_ce: 0.0914 2023/03/03 14:13:59 - mmengine - INFO - Epoch(train) [48][ 9/15] lr: 1.0000e-06 eta: 0:13:30 time: 0.3927 data_time: 0.0886 memory: 16976 loss: 0.0953 loss_ce: 0.0953 2023/03/03 14:13:59 - mmengine - INFO - Epoch(train) [48][10/15] lr: 1.0000e-06 eta: 0:13:29 time: 0.4005 data_time: 0.0886 memory: 16212 loss: 0.0857 loss_ce: 0.0857 2023/03/03 14:13:59 - mmengine - INFO - Epoch(train) [48][11/15] lr: 1.0000e-06 eta: 0:13:29 time: 0.3391 data_time: 0.0016 memory: 16223 loss: 0.0944 loss_ce: 0.0944 2023/03/03 14:14:00 - mmengine - INFO - Epoch(train) [48][12/15] lr: 1.0000e-06 eta: 0:13:28 time: 0.2935 data_time: 0.0016 memory: 17421 loss: 0.0953 loss_ce: 0.0953 2023/03/03 14:14:00 - mmengine - INFO - Epoch(train) [48][13/15] lr: 1.0000e-06 eta: 0:13:28 time: 0.2881 data_time: 0.0016 memory: 17421 loss: 0.0980 loss_ce: 0.0980 2023/03/03 14:14:00 - mmengine - INFO - Epoch(train) [48][14/15] lr: 1.0000e-06 eta: 0:13:28 time: 0.3211 data_time: 0.0016 memory: 38334 loss: 0.0972 loss_ce: 0.0972 2023/03/03 14:14:00 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:14:00 - mmengine - INFO - Epoch(train) [48][15/15] lr: 1.0000e-06 eta: 0:13:27 time: 0.2990 data_time: 0.0016 memory: 4906 loss: 0.0997 loss_ce: 0.0997 2023/03/03 14:14:02 - mmengine - INFO - Epoch(train) [49][ 1/15] lr: 1.0000e-06 eta: 0:13:29 time: 0.4046 data_time: 0.0782 memory: 28254 loss: 0.0978 loss_ce: 0.0978 2023/03/03 14:14:02 - mmengine - INFO - Epoch(train) [49][ 2/15] lr: 1.0000e-06 eta: 0:13:29 time: 0.3744 data_time: 0.0783 memory: 16654 loss: 0.0937 loss_ce: 0.0937 2023/03/03 14:14:02 - mmengine - INFO - Epoch(train) [49][ 3/15] lr: 1.0000e-06 eta: 0:13:28 time: 0.3784 data_time: 0.0783 memory: 17619 loss: 0.0897 loss_ce: 0.0897 2023/03/03 14:14:02 - mmengine - INFO - Epoch(train) [49][ 4/15] lr: 1.0000e-06 eta: 0:13:27 time: 0.3825 data_time: 0.0783 memory: 18070 loss: 0.0841 loss_ce: 0.0841 2023/03/03 14:14:03 - mmengine - INFO - Epoch(train) [49][ 5/15] lr: 1.0000e-06 eta: 0:13:27 time: 0.3823 data_time: 0.0783 memory: 15911 loss: 0.0836 loss_ce: 0.0836 2023/03/03 14:14:03 - mmengine - INFO - Epoch(train) [49][ 6/15] lr: 1.0000e-06 eta: 0:13:26 time: 0.3738 data_time: 0.0783 memory: 15175 loss: 0.0784 loss_ce: 0.0784 2023/03/03 14:14:03 - mmengine - INFO - Epoch(train) [49][ 7/15] lr: 1.0000e-06 eta: 0:13:26 time: 0.3748 data_time: 0.0783 memory: 15175 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:14:04 - mmengine - INFO - Epoch(train) [49][ 8/15] lr: 1.0000e-06 eta: 0:13:26 time: 0.3941 data_time: 0.0783 memory: 15560 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:14:04 - mmengine - INFO - Epoch(train) [49][ 9/15] lr: 1.0000e-06 eta: 0:13:25 time: 0.3609 data_time: 0.0783 memory: 17120 loss: 0.0846 loss_ce: 0.0846 2023/03/03 14:14:04 - mmengine - INFO - Epoch(train) [49][10/15] lr: 1.0000e-06 eta: 0:13:24 time: 0.3763 data_time: 0.0782 memory: 16654 loss: 0.0831 loss_ce: 0.0831 2023/03/03 14:14:04 - mmengine - INFO - Epoch(train) [49][11/15] lr: 1.0000e-06 eta: 0:13:23 time: 0.2749 data_time: 0.0016 memory: 17120 loss: 0.0822 loss_ce: 0.0822 2023/03/03 14:14:05 - mmengine - INFO - Epoch(train) [49][12/15] lr: 1.0000e-06 eta: 0:13:23 time: 0.2947 data_time: 0.0016 memory: 18409 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:14:05 - mmengine - INFO - Epoch(train) [49][13/15] lr: 1.0000e-06 eta: 0:13:22 time: 0.2910 data_time: 0.0015 memory: 17730 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:14:06 - mmengine - INFO - Epoch(train) [49][14/15] lr: 1.0000e-06 eta: 0:13:22 time: 0.3060 data_time: 0.0015 memory: 19860 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:14:06 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:14:06 - mmengine - INFO - Epoch(train) [49][15/15] lr: 1.0000e-06 eta: 0:13:21 time: 0.2983 data_time: 0.0015 memory: 5085 loss: 0.0915 loss_ce: 0.0915 2023/03/03 14:14:07 - mmengine - INFO - Epoch(train) [50][ 1/15] lr: 1.0000e-06 eta: 0:13:23 time: 0.3519 data_time: 0.0732 memory: 17120 loss: 0.0917 loss_ce: 0.0917 2023/03/03 14:14:07 - mmengine - INFO - Epoch(train) [50][ 2/15] lr: 1.0000e-06 eta: 0:13:22 time: 0.3513 data_time: 0.0733 memory: 17223 loss: 0.0932 loss_ce: 0.0932 2023/03/03 14:14:07 - mmengine - INFO - Epoch(train) [50][ 3/15] lr: 1.0000e-06 eta: 0:13:22 time: 0.3452 data_time: 0.0733 memory: 16056 loss: 0.0894 loss_ce: 0.0894 2023/03/03 14:14:07 - mmengine - INFO - Epoch(train) [50][ 4/15] lr: 1.0000e-06 eta: 0:13:21 time: 0.3481 data_time: 0.0734 memory: 16508 loss: 0.0867 loss_ce: 0.0867 2023/03/03 14:14:08 - mmengine - INFO - Epoch(train) [50][ 5/15] lr: 1.0000e-06 eta: 0:13:20 time: 0.3481 data_time: 0.0734 memory: 18766 loss: 0.0845 loss_ce: 0.0845 2023/03/03 14:14:08 - mmengine - INFO - Epoch(train) [50][ 6/15] lr: 1.0000e-06 eta: 0:13:20 time: 0.3500 data_time: 0.0734 memory: 15586 loss: 0.0883 loss_ce: 0.0883 2023/03/03 14:14:08 - mmengine - INFO - Epoch(train) [50][ 7/15] lr: 1.0000e-06 eta: 0:13:19 time: 0.3378 data_time: 0.0734 memory: 18616 loss: 0.0898 loss_ce: 0.0898 2023/03/03 14:14:08 - mmengine - INFO - Epoch(train) [50][ 8/15] lr: 1.0000e-06 eta: 0:13:18 time: 0.3403 data_time: 0.0734 memory: 16654 loss: 0.0911 loss_ce: 0.0911 2023/03/03 14:14:09 - mmengine - INFO - Epoch(train) [50][ 9/15] lr: 1.0000e-06 eta: 0:13:18 time: 0.3318 data_time: 0.0734 memory: 20872 loss: 0.0929 loss_ce: 0.0929 2023/03/03 14:14:09 - mmengine - INFO - Epoch(train) [50][10/15] lr: 1.0000e-06 eta: 0:13:17 time: 0.3408 data_time: 0.0734 memory: 17122 loss: 0.0810 loss_ce: 0.0810 2023/03/03 14:14:09 - mmengine - INFO - Epoch(train) [50][11/15] lr: 1.0000e-06 eta: 0:13:17 time: 0.2750 data_time: 0.0017 memory: 16212 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:14:10 - mmengine - INFO - Epoch(train) [50][12/15] lr: 1.0000e-06 eta: 0:13:16 time: 0.2894 data_time: 0.0016 memory: 18338 loss: 0.0785 loss_ce: 0.0785 2023/03/03 14:14:10 - mmengine - INFO - Epoch(train) [50][13/15] lr: 1.0000e-06 eta: 0:13:16 time: 0.2866 data_time: 0.0016 memory: 21019 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:14:10 - mmengine - INFO - Epoch(train) [50][14/15] lr: 1.0000e-06 eta: 0:13:15 time: 0.2860 data_time: 0.0016 memory: 16212 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:14:11 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:14:11 - mmengine - INFO - Epoch(train) [50][15/15] lr: 1.0000e-06 eta: 0:13:15 time: 0.2788 data_time: 0.0015 memory: 7143 loss: 0.0848 loss_ce: 0.0848 2023/03/03 14:14:12 - mmengine - INFO - Epoch(val) [50][ 1/59] eta: 0:01:29 time: 1.1493 data_time: 0.0034 memory: 981 2023/03/03 14:14:13 - mmengine - INFO - Epoch(val) [50][ 2/59] eta: 0:01:07 time: 1.0651 data_time: 0.0034 memory: 981 2023/03/03 14:14:14 - mmengine - INFO - Epoch(val) [50][ 3/59] eta: 0:01:12 time: 1.1023 data_time: 0.0035 memory: 1003 2023/03/03 14:14:15 - mmengine - INFO - Epoch(val) [50][ 4/59] eta: 0:00:58 time: 1.0527 data_time: 0.0035 memory: 981 2023/03/03 14:14:18 - mmengine - INFO - Epoch(val) [50][ 5/59] eta: 0:01:20 time: 1.3103 data_time: 0.0035 memory: 1016 2023/03/03 14:14:21 - mmengine - INFO - Epoch(val) [50][ 6/59] eta: 0:01:29 time: 1.5066 data_time: 0.0034 memory: 981 2023/03/03 14:14:21 - mmengine - INFO - Epoch(val) [50][ 7/59] eta: 0:01:16 time: 1.4400 data_time: 0.0035 memory: 1043 2023/03/03 14:14:22 - mmengine - INFO - Epoch(val) [50][ 8/59] eta: 0:01:12 time: 1.3142 data_time: 0.0034 memory: 1016 2023/03/03 14:14:23 - mmengine - INFO - Epoch(val) [50][ 9/59] eta: 0:01:08 time: 1.2796 data_time: 0.0034 memory: 981 2023/03/03 14:14:24 - mmengine - INFO - Epoch(val) [50][10/59] eta: 0:01:05 time: 1.3451 data_time: 0.0034 memory: 981 2023/03/03 14:14:24 - mmengine - INFO - Epoch(val) [50][11/59] eta: 0:01:00 time: 1.2407 data_time: 0.0008 memory: 981 2023/03/03 14:14:28 - mmengine - INFO - Epoch(val) [50][12/59] eta: 0:01:08 time: 1.5030 data_time: 0.0008 memory: 1016 2023/03/03 14:14:30 - mmengine - INFO - Epoch(val) [50][13/59] eta: 0:01:08 time: 1.5560 data_time: 0.0009 memory: 981 2023/03/03 14:14:31 - mmengine - INFO - Epoch(val) [50][14/59] eta: 0:01:06 time: 1.6398 data_time: 0.0009 memory: 890 2023/03/03 14:14:31 - mmengine - INFO - Epoch(val) [50][15/59] eta: 0:01:00 time: 1.3170 data_time: 0.0009 memory: 981 2023/03/03 14:14:32 - mmengine - INFO - Epoch(val) [50][16/59] eta: 0:00:56 time: 1.1037 data_time: 0.0009 memory: 981 2023/03/03 14:14:32 - mmengine - INFO - Epoch(val) [50][17/59] eta: 0:00:53 time: 1.1197 data_time: 0.0009 memory: 981 2023/03/03 14:14:32 - mmengine - INFO - Epoch(val) [50][18/59] eta: 0:00:49 time: 1.0564 data_time: 0.0009 memory: 981 2023/03/03 14:14:33 - mmengine - INFO - Epoch(val) [50][19/59] eta: 0:00:48 time: 1.0570 data_time: 0.0010 memory: 981 2023/03/03 14:14:34 - mmengine - INFO - Epoch(val) [50][20/59] eta: 0:00:45 time: 0.9746 data_time: 0.0010 memory: 981 2023/03/03 14:14:36 - mmengine - INFO - Epoch(val) [50][21/59] eta: 0:00:46 time: 1.1472 data_time: 0.0010 memory: 981 2023/03/03 14:14:36 - mmengine - INFO - Epoch(val) [50][22/59] eta: 0:00:43 time: 0.8184 data_time: 0.0011 memory: 981 2023/03/03 14:14:37 - mmengine - INFO - Epoch(val) [50][23/59] eta: 0:00:41 time: 0.6814 data_time: 0.0010 memory: 981 2023/03/03 14:14:37 - mmengine - INFO - Epoch(val) [50][24/59] eta: 0:00:38 time: 0.5979 data_time: 0.0010 memory: 962 2023/03/03 14:14:37 - mmengine - INFO - Epoch(val) [50][25/59] eta: 0:00:36 time: 0.6300 data_time: 0.0009 memory: 981 2023/03/03 14:14:38 - mmengine - INFO - Epoch(val) [50][26/59] eta: 0:00:34 time: 0.6146 data_time: 0.0010 memory: 981 2023/03/03 14:14:38 - mmengine - INFO - Epoch(val) [50][27/59] eta: 0:00:32 time: 0.6159 data_time: 0.0010 memory: 981 2023/03/03 14:14:39 - mmengine - INFO - Epoch(val) [50][28/59] eta: 0:00:31 time: 0.6149 data_time: 0.0010 memory: 981 2023/03/03 14:14:40 - mmengine - INFO - Epoch(val) [50][29/59] eta: 0:00:30 time: 0.6526 data_time: 0.0009 memory: 981 2023/03/03 14:14:41 - mmengine - INFO - Epoch(val) [50][30/59] eta: 0:00:29 time: 0.7039 data_time: 0.0009 memory: 999 2023/03/03 14:14:41 - mmengine - INFO - Epoch(val) [50][31/59] eta: 0:00:27 time: 0.5473 data_time: 0.0010 memory: 981 2023/03/03 14:14:43 - mmengine - INFO - Epoch(val) [50][32/59] eta: 0:00:27 time: 0.6469 data_time: 0.0009 memory: 981 2023/03/03 14:14:43 - mmengine - INFO - Epoch(val) [50][33/59] eta: 0:00:25 time: 0.5958 data_time: 0.0009 memory: 981 2023/03/03 14:14:43 - mmengine - INFO - Epoch(val) [50][34/59] eta: 0:00:23 time: 0.5786 data_time: 0.0009 memory: 981 2023/03/03 14:14:43 - mmengine - INFO - Epoch(val) [50][35/59] eta: 0:00:22 time: 0.5608 data_time: 0.0009 memory: 981 2023/03/03 14:14:44 - mmengine - INFO - Epoch(val) [50][36/59] eta: 0:00:21 time: 0.5758 data_time: 0.0009 memory: 981 2023/03/03 14:14:44 - mmengine - INFO - Epoch(val) [50][37/59] eta: 0:00:19 time: 0.5584 data_time: 0.0009 memory: 981 2023/03/03 14:14:44 - mmengine - INFO - Epoch(val) [50][38/59] eta: 0:00:18 time: 0.5907 data_time: 0.0009 memory: 981 2023/03/03 14:14:45 - mmengine - INFO - Epoch(val) [50][39/59] eta: 0:00:17 time: 0.5036 data_time: 0.0009 memory: 987 2023/03/03 14:14:46 - mmengine - INFO - Epoch(val) [50][40/59] eta: 0:00:16 time: 0.5024 data_time: 0.0009 memory: 981 2023/03/03 14:14:47 - mmengine - INFO - Epoch(val) [50][41/59] eta: 0:00:15 time: 0.5537 data_time: 0.0009 memory: 986 2023/03/03 14:14:48 - mmengine - INFO - Epoch(val) [50][42/59] eta: 0:00:15 time: 0.5046 data_time: 0.0009 memory: 981 2023/03/03 14:14:48 - mmengine - INFO - Epoch(val) [50][43/59] eta: 0:00:14 time: 0.5720 data_time: 0.0009 memory: 976 2023/03/03 14:14:49 - mmengine - INFO - Epoch(val) [50][44/59] eta: 0:00:13 time: 0.6059 data_time: 0.0009 memory: 1003 2023/03/03 14:14:51 - mmengine - INFO - Epoch(val) [50][45/59] eta: 0:00:12 time: 0.7794 data_time: 0.0009 memory: 981 2023/03/03 14:14:52 - mmengine - INFO - Epoch(val) [50][46/59] eta: 0:00:11 time: 0.8111 data_time: 0.0009 memory: 981 2023/03/03 14:14:52 - mmengine - INFO - Epoch(val) [50][47/59] eta: 0:00:10 time: 0.8459 data_time: 0.0009 memory: 936 2023/03/03 14:14:53 - mmengine - INFO - Epoch(val) [50][48/59] eta: 0:00:09 time: 0.8312 data_time: 0.0010 memory: 1000 2023/03/03 14:14:54 - mmengine - INFO - Epoch(val) [50][49/59] eta: 0:00:08 time: 0.8841 data_time: 0.0010 memory: 981 2023/03/03 14:14:55 - mmengine - INFO - Epoch(val) [50][50/59] eta: 0:00:07 time: 0.8854 data_time: 0.0009 memory: 987 2023/03/03 14:14:56 - mmengine - INFO - Epoch(val) [50][51/59] eta: 0:00:07 time: 0.9375 data_time: 0.0010 memory: 981 2023/03/03 14:14:58 - mmengine - INFO - Epoch(val) [50][52/59] eta: 0:00:06 time: 0.9870 data_time: 0.0010 memory: 981 2023/03/03 14:14:58 - mmengine - INFO - Epoch(val) [50][53/59] eta: 0:00:05 time: 0.9701 data_time: 0.0010 memory: 962 2023/03/03 14:14:59 - mmengine - INFO - Epoch(val) [50][54/59] eta: 0:00:04 time: 0.9864 data_time: 0.0010 memory: 981 2023/03/03 14:15:00 - mmengine - INFO - Epoch(val) [50][55/59] eta: 0:00:03 time: 0.8637 data_time: 0.0010 memory: 981 2023/03/03 14:15:00 - mmengine - INFO - Epoch(val) [50][56/59] eta: 0:00:02 time: 0.8664 data_time: 0.0010 memory: 981 2023/03/03 14:15:03 - mmengine - INFO - Epoch(val) [50][57/59] eta: 0:00:01 time: 1.0412 data_time: 0.0010 memory: 981 2023/03/03 14:15:04 - mmengine - INFO - Epoch(val) [50][58/59] eta: 0:00:00 time: 1.1245 data_time: 0.0010 memory: 1016 2023/03/03 14:15:04 - mmengine - INFO - Epoch(val) [50][59/59] eta: 0:00:00 time: 1.0551 data_time: 0.0010 memory: 981 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.80, recall: 0.8146, precision: 0.8313, hmean: 0.8229 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.81, recall: 0.8146, precision: 0.8321, hmean: 0.8233 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.82, recall: 0.8137, precision: 0.8335, hmean: 0.8235 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.83, recall: 0.8137, precision: 0.8351, hmean: 0.8242 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.84, recall: 0.8110, precision: 0.8354, hmean: 0.8230 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.85, recall: 0.8110, precision: 0.8401, hmean: 0.8253 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.86, recall: 0.8110, precision: 0.8449, hmean: 0.8276 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.87, recall: 0.8082, precision: 0.8477, hmean: 0.8275 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.88, recall: 0.8073, precision: 0.8516, hmean: 0.8289 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.89, recall: 0.8055, precision: 0.8530, hmean: 0.8286 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.90, recall: 0.8037, precision: 0.8560, hmean: 0.8290 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.91, recall: 0.7991, precision: 0.8604, hmean: 0.8286 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.92, recall: 0.7918, precision: 0.8601, hmean: 0.8245 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.93, recall: 0.7826, precision: 0.8613, hmean: 0.8201 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.94, recall: 0.7735, precision: 0.8661, hmean: 0.8172 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.95, recall: 0.7662, precision: 0.8676, hmean: 0.8138 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.96, recall: 0.7571, precision: 0.8745, hmean: 0.8116 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.97, recall: 0.7452, precision: 0.8774, hmean: 0.8059 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.98, recall: 0.7342, precision: 0.8816, hmean: 0.8012 2023/03/03 14:15:33 - mmengine - INFO - text score threshold: 0.99, recall: 0.7114, precision: 0.8893, hmean: 0.7905 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.80, recall: 0.8247, precision: 0.9039, hmean: 0.8625 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.81, recall: 0.8247, precision: 0.9039, hmean: 0.8625 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.82, recall: 0.8237, precision: 0.9047, hmean: 0.8623 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.83, recall: 0.8237, precision: 0.9047, hmean: 0.8623 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.84, recall: 0.8219, precision: 0.9054, hmean: 0.8617 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.85, recall: 0.8219, precision: 0.9091, hmean: 0.8633 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.86, recall: 0.8210, precision: 0.9118, hmean: 0.8640 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.87, recall: 0.8192, precision: 0.9125, hmean: 0.8633 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.88, recall: 0.8174, precision: 0.9142, hmean: 0.8631 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.89, recall: 0.8155, precision: 0.9159, hmean: 0.8628 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.90, recall: 0.8128, precision: 0.9166, hmean: 0.8616 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.91, recall: 0.8055, precision: 0.9178, hmean: 0.8580 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.92, recall: 0.7982, precision: 0.9181, hmean: 0.8539 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.93, recall: 0.7890, precision: 0.9182, hmean: 0.8487 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.94, recall: 0.7781, precision: 0.9221, hmean: 0.8440 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.95, recall: 0.7689, precision: 0.9212, hmean: 0.8382 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.96, recall: 0.7580, precision: 0.9263, hmean: 0.8338 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.97, recall: 0.7461, precision: 0.9263, hmean: 0.8265 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.98, recall: 0.7352, precision: 0.9274, hmean: 0.8202 2023/03/03 14:15:36 - mmengine - INFO - text score threshold: 0.99, recall: 0.7114, precision: 0.9307, hmean: 0.8064 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.80, recall: 0.7489, precision: 0.9546, hmean: 0.8393 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.81, recall: 0.7489, precision: 0.9546, hmean: 0.8393 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.82, recall: 0.7479, precision: 0.9557, hmean: 0.8391 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.83, recall: 0.7479, precision: 0.9557, hmean: 0.8391 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.84, recall: 0.7452, precision: 0.9555, hmean: 0.8374 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.85, recall: 0.7443, precision: 0.9577, hmean: 0.8376 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.86, recall: 0.7425, precision: 0.9587, hmean: 0.8369 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.87, recall: 0.7397, precision: 0.9586, hmean: 0.8351 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.88, recall: 0.7379, precision: 0.9596, hmean: 0.8343 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.89, recall: 0.7361, precision: 0.9607, hmean: 0.8335 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.90, recall: 0.7333, precision: 0.9617, hmean: 0.8321 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.91, recall: 0.7269, precision: 0.9614, hmean: 0.8279 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.92, recall: 0.7205, precision: 0.9622, hmean: 0.8240 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.93, recall: 0.7123, precision: 0.9618, hmean: 0.8185 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.94, recall: 0.7023, precision: 0.9625, hmean: 0.8120 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.95, recall: 0.6932, precision: 0.9620, hmean: 0.8057 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.96, recall: 0.6813, precision: 0.9626, hmean: 0.7979 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.97, recall: 0.6731, precision: 0.9659, hmean: 0.7933 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.98, recall: 0.6648, precision: 0.9655, hmean: 0.7875 2023/03/03 14:15:38 - mmengine - INFO - text score threshold: 0.99, recall: 0.6402, precision: 0.9656, hmean: 0.7699 2023/03/03 14:15:38 - mmengine - INFO - Epoch(val) [50][59/59] generic/precision: 0.8560 generic/recall: 0.8037 generic/hmean: 0.8290 weak/precision: 0.9118 weak/recall: 0.8210 weak/hmean: 0.8640 strong/precision: 0.9546 strong/recall: 0.7489 strong/hmean: 0.8393 2023/03/03 14:15:39 - mmengine - INFO - Epoch(train) [51][ 1/15] lr: 1.0000e-06 eta: 0:13:16 time: 0.3455 data_time: 0.0636 memory: 16955 loss: 0.0788 loss_ce: 0.0788 2023/03/03 14:15:39 - mmengine - INFO - Epoch(train) [51][ 2/15] lr: 1.0000e-06 eta: 0:13:15 time: 0.3348 data_time: 0.0637 memory: 17892 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:15:40 - mmengine - INFO - Epoch(train) [51][ 3/15] lr: 1.0000e-06 eta: 0:13:15 time: 0.3516 data_time: 0.0637 memory: 25476 loss: 0.0809 loss_ce: 0.0809 2023/03/03 14:15:40 - mmengine - INFO - Epoch(train) [51][ 4/15] lr: 1.0000e-06 eta: 0:13:14 time: 0.3384 data_time: 0.0638 memory: 16654 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:15:40 - mmengine - INFO - Epoch(train) [51][ 5/15] lr: 1.0000e-06 eta: 0:13:13 time: 0.3359 data_time: 0.0638 memory: 18586 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:15:41 - mmengine - INFO - Epoch(train) [51][ 6/15] lr: 1.0000e-06 eta: 0:13:14 time: 0.3726 data_time: 0.0638 memory: 14914 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:15:41 - mmengine - INFO - Epoch(train) [51][ 7/15] lr: 1.0000e-06 eta: 0:13:13 time: 0.3605 data_time: 0.0638 memory: 16370 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:15:41 - mmengine - INFO - Epoch(train) [51][ 8/15] lr: 1.0000e-06 eta: 0:13:12 time: 0.3461 data_time: 0.0638 memory: 14906 loss: 0.0816 loss_ce: 0.0816 2023/03/03 14:15:42 - mmengine - INFO - Epoch(train) [51][ 9/15] lr: 1.0000e-06 eta: 0:13:12 time: 0.3457 data_time: 0.0637 memory: 15631 loss: 0.0802 loss_ce: 0.0802 2023/03/03 14:15:42 - mmengine - INFO - Epoch(train) [51][10/15] lr: 1.0000e-06 eta: 0:13:11 time: 0.3540 data_time: 0.0638 memory: 16508 loss: 0.0786 loss_ce: 0.0786 2023/03/03 14:15:42 - mmengine - INFO - Epoch(train) [51][11/15] lr: 1.0000e-06 eta: 0:13:10 time: 0.2855 data_time: 0.0017 memory: 17730 loss: 0.0794 loss_ce: 0.0794 2023/03/03 14:15:42 - mmengine - INFO - Epoch(train) [51][12/15] lr: 1.0000e-06 eta: 0:13:10 time: 0.3052 data_time: 0.0016 memory: 13612 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:15:43 - mmengine - INFO - Epoch(train) [51][13/15] lr: 1.0000e-06 eta: 0:13:10 time: 0.2981 data_time: 0.0016 memory: 19459 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:15:43 - mmengine - INFO - Epoch(train) [51][14/15] lr: 1.0000e-06 eta: 0:13:09 time: 0.3002 data_time: 0.0016 memory: 14014 loss: 0.0793 loss_ce: 0.0793 2023/03/03 14:15:43 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:15:43 - mmengine - INFO - Epoch(train) [51][15/15] lr: 1.0000e-06 eta: 0:13:08 time: 0.2940 data_time: 0.0016 memory: 5386 loss: 0.0875 loss_ce: 0.0875 2023/03/03 14:15:44 - mmengine - INFO - Epoch(train) [52][ 1/15] lr: 1.0000e-06 eta: 0:13:10 time: 0.3277 data_time: 0.0685 memory: 15911 loss: 0.0894 loss_ce: 0.0894 2023/03/03 14:15:44 - mmengine - INFO - Epoch(train) [52][ 2/15] lr: 1.0000e-06 eta: 0:13:09 time: 0.3282 data_time: 0.0685 memory: 16508 loss: 0.0920 loss_ce: 0.0920 2023/03/03 14:15:45 - mmengine - INFO - Epoch(train) [52][ 3/15] lr: 1.0000e-06 eta: 0:13:08 time: 0.3400 data_time: 0.0686 memory: 30397 loss: 0.0869 loss_ce: 0.0869 2023/03/03 14:15:45 - mmengine - INFO - Epoch(train) [52][ 4/15] lr: 1.0000e-06 eta: 0:13:08 time: 0.3461 data_time: 0.0686 memory: 12302 loss: 0.0895 loss_ce: 0.0895 2023/03/03 14:15:45 - mmengine - INFO - Epoch(train) [52][ 5/15] lr: 1.0000e-06 eta: 0:13:07 time: 0.3470 data_time: 0.0686 memory: 17572 loss: 0.0871 loss_ce: 0.0871 2023/03/03 14:15:46 - mmengine - INFO - Epoch(train) [52][ 6/15] lr: 1.0000e-06 eta: 0:13:07 time: 0.3518 data_time: 0.0686 memory: 18154 loss: 0.0854 loss_ce: 0.0854 2023/03/03 14:15:46 - mmengine - INFO - Epoch(train) [52][ 7/15] lr: 1.0000e-06 eta: 0:13:07 time: 0.3613 data_time: 0.0686 memory: 16685 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:15:46 - mmengine - INFO - Epoch(train) [52][ 8/15] lr: 1.0000e-06 eta: 0:13:06 time: 0.3539 data_time: 0.0686 memory: 18681 loss: 0.0821 loss_ce: 0.0821 2023/03/03 14:15:47 - mmengine - INFO - Epoch(train) [52][ 9/15] lr: 1.0000e-06 eta: 0:13:06 time: 0.3779 data_time: 0.0686 memory: 14588 loss: 0.0808 loss_ce: 0.0808 2023/03/03 14:15:47 - mmengine - INFO - Epoch(train) [52][10/15] lr: 1.0000e-06 eta: 0:13:06 time: 0.3902 data_time: 0.0686 memory: 19598 loss: 0.0741 loss_ce: 0.0741 2023/03/03 14:15:47 - mmengine - INFO - Epoch(train) [52][11/15] lr: 1.0000e-06 eta: 0:13:05 time: 0.3159 data_time: 0.0017 memory: 16223 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:15:47 - mmengine - INFO - Epoch(train) [52][12/15] lr: 1.0000e-06 eta: 0:13:04 time: 0.3106 data_time: 0.0016 memory: 15346 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:15:48 - mmengine - INFO - Epoch(train) [52][13/15] lr: 1.0000e-06 eta: 0:13:04 time: 0.3372 data_time: 0.0015 memory: 19908 loss: 0.0784 loss_ce: 0.0784 2023/03/03 14:15:48 - mmengine - INFO - Epoch(train) [52][14/15] lr: 1.0000e-06 eta: 0:13:04 time: 0.3305 data_time: 0.0015 memory: 15175 loss: 0.0753 loss_ce: 0.0753 2023/03/03 14:15:49 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:15:49 - mmengine - INFO - Epoch(train) [52][15/15] lr: 1.0000e-06 eta: 0:13:03 time: 0.3253 data_time: 0.0015 memory: 5534 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:15:49 - mmengine - INFO - Epoch(train) [53][ 1/15] lr: 1.0000e-06 eta: 0:13:04 time: 0.3753 data_time: 0.0518 memory: 17339 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:15:50 - mmengine - INFO - Epoch(train) [53][ 2/15] lr: 1.0000e-06 eta: 0:13:04 time: 0.3761 data_time: 0.0518 memory: 17421 loss: 0.0787 loss_ce: 0.0787 2023/03/03 14:15:50 - mmengine - INFO - Epoch(train) [53][ 3/15] lr: 1.0000e-06 eta: 0:13:03 time: 0.3744 data_time: 0.0518 memory: 17122 loss: 0.0812 loss_ce: 0.0812 2023/03/03 14:15:50 - mmengine - INFO - Epoch(train) [53][ 4/15] lr: 1.0000e-06 eta: 0:13:03 time: 0.3625 data_time: 0.0519 memory: 20166 loss: 0.0820 loss_ce: 0.0820 2023/03/03 14:15:51 - mmengine - INFO - Epoch(train) [53][ 5/15] lr: 1.0000e-06 eta: 0:13:02 time: 0.3557 data_time: 0.0519 memory: 17120 loss: 0.0863 loss_ce: 0.0863 2023/03/03 14:15:51 - mmengine - INFO - Epoch(train) [53][ 6/15] lr: 1.0000e-06 eta: 0:13:02 time: 0.3668 data_time: 0.0519 memory: 17198 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:15:51 - mmengine - INFO - Epoch(train) [53][ 7/15] lr: 1.0000e-06 eta: 0:13:01 time: 0.3729 data_time: 0.0520 memory: 11637 loss: 0.0820 loss_ce: 0.0820 2023/03/03 14:15:51 - mmengine - INFO - Epoch(train) [53][ 8/15] lr: 1.0000e-06 eta: 0:13:00 time: 0.3371 data_time: 0.0520 memory: 17284 loss: 0.0778 loss_ce: 0.0778 2023/03/03 14:15:52 - mmengine - INFO - Epoch(train) [53][ 9/15] lr: 1.0000e-06 eta: 0:13:00 time: 0.3458 data_time: 0.0520 memory: 21030 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:15:52 - mmengine - INFO - Epoch(train) [53][10/15] lr: 1.0000e-06 eta: 0:13:00 time: 0.3532 data_time: 0.0520 memory: 17162 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:15:52 - mmengine - INFO - Epoch(train) [53][11/15] lr: 1.0000e-06 eta: 0:12:59 time: 0.2981 data_time: 0.0017 memory: 17120 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:15:53 - mmengine - INFO - Epoch(train) [53][12/15] lr: 1.0000e-06 eta: 0:12:58 time: 0.2822 data_time: 0.0017 memory: 17272 loss: 0.0826 loss_ce: 0.0826 2023/03/03 14:15:53 - mmengine - INFO - Epoch(train) [53][13/15] lr: 1.0000e-06 eta: 0:12:58 time: 0.2917 data_time: 0.0016 memory: 16887 loss: 0.0817 loss_ce: 0.0817 2023/03/03 14:15:53 - mmengine - INFO - Epoch(train) [53][14/15] lr: 1.0000e-06 eta: 0:12:57 time: 0.2772 data_time: 0.0016 memory: 16370 loss: 0.0854 loss_ce: 0.0854 2023/03/03 14:15:53 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:15:53 - mmengine - INFO - Epoch(train) [53][15/15] lr: 1.0000e-06 eta: 0:12:56 time: 0.2728 data_time: 0.0016 memory: 6210 loss: 0.0859 loss_ce: 0.0859 2023/03/03 14:15:54 - mmengine - INFO - Epoch(train) [54][ 1/15] lr: 1.0000e-06 eta: 0:12:57 time: 0.3137 data_time: 0.0575 memory: 11150 loss: 0.0908 loss_ce: 0.0908 2023/03/03 14:15:54 - mmengine - INFO - Epoch(train) [54][ 2/15] lr: 1.0000e-06 eta: 0:12:56 time: 0.3101 data_time: 0.0575 memory: 17120 loss: 0.0930 loss_ce: 0.0930 2023/03/03 14:15:55 - mmengine - INFO - Epoch(train) [54][ 3/15] lr: 1.0000e-06 eta: 0:12:56 time: 0.3265 data_time: 0.0576 memory: 16508 loss: 0.0942 loss_ce: 0.0942 2023/03/03 14:15:55 - mmengine - INFO - Epoch(train) [54][ 4/15] lr: 1.0000e-06 eta: 0:12:55 time: 0.3195 data_time: 0.0576 memory: 16508 loss: 0.0982 loss_ce: 0.0982 2023/03/03 14:15:55 - mmengine - INFO - Epoch(train) [54][ 5/15] lr: 1.0000e-06 eta: 0:12:56 time: 0.3351 data_time: 0.0576 memory: 16976 loss: 0.0992 loss_ce: 0.0992 2023/03/03 14:15:56 - mmengine - INFO - Epoch(train) [54][ 6/15] lr: 1.0000e-06 eta: 0:12:55 time: 0.3382 data_time: 0.0576 memory: 16697 loss: 0.0975 loss_ce: 0.0975 2023/03/03 14:15:56 - mmengine - INFO - Epoch(train) [54][ 7/15] lr: 1.0000e-06 eta: 0:12:54 time: 0.3383 data_time: 0.0576 memory: 14761 loss: 0.1005 loss_ce: 0.1005 2023/03/03 14:15:56 - mmengine - INFO - Epoch(train) [54][ 8/15] lr: 1.0000e-06 eta: 0:12:54 time: 0.3283 data_time: 0.0577 memory: 16804 loss: 0.0990 loss_ce: 0.0990 2023/03/03 14:15:57 - mmengine - INFO - Epoch(train) [54][ 9/15] lr: 1.0000e-06 eta: 0:12:54 time: 0.3478 data_time: 0.0577 memory: 17080 loss: 0.0981 loss_ce: 0.0981 2023/03/03 14:15:57 - mmengine - INFO - Epoch(train) [54][10/15] lr: 1.0000e-06 eta: 0:12:53 time: 0.3683 data_time: 0.0577 memory: 17463 loss: 0.0950 loss_ce: 0.0950 2023/03/03 14:15:57 - mmengine - INFO - Epoch(train) [54][11/15] lr: 1.0000e-06 eta: 0:12:53 time: 0.3197 data_time: 0.0018 memory: 15037 loss: 0.0926 loss_ce: 0.0926 2023/03/03 14:15:58 - mmengine - INFO - Epoch(train) [54][12/15] lr: 1.0000e-06 eta: 0:12:52 time: 0.3221 data_time: 0.0017 memory: 16804 loss: 0.0898 loss_ce: 0.0898 2023/03/03 14:15:58 - mmengine - INFO - Epoch(train) [54][13/15] lr: 1.0000e-06 eta: 0:12:51 time: 0.3103 data_time: 0.0016 memory: 15466 loss: 0.0906 loss_ce: 0.0906 2023/03/03 14:15:58 - mmengine - INFO - Epoch(train) [54][14/15] lr: 1.0000e-06 eta: 0:12:51 time: 0.3357 data_time: 0.0016 memory: 34652 loss: 0.0869 loss_ce: 0.0869 2023/03/03 14:15:58 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:15:58 - mmengine - INFO - Epoch(train) [54][15/15] lr: 1.0000e-06 eta: 0:12:50 time: 0.2963 data_time: 0.0016 memory: 6850 loss: 0.0954 loss_ce: 0.0954 2023/03/03 14:15:59 - mmengine - INFO - Epoch(train) [55][ 1/15] lr: 1.0000e-06 eta: 0:12:51 time: 0.3445 data_time: 0.0521 memory: 16976 loss: 0.0970 loss_ce: 0.0970 2023/03/03 14:16:00 - mmengine - INFO - Epoch(train) [55][ 2/15] lr: 1.0000e-06 eta: 0:12:51 time: 0.3710 data_time: 0.0522 memory: 15911 loss: 0.0947 loss_ce: 0.0947 2023/03/03 14:16:00 - mmengine - INFO - Epoch(train) [55][ 3/15] lr: 1.0000e-06 eta: 0:12:51 time: 0.3731 data_time: 0.0522 memory: 15559 loss: 0.0921 loss_ce: 0.0921 2023/03/03 14:16:00 - mmengine - INFO - Epoch(train) [55][ 4/15] lr: 1.0000e-06 eta: 0:12:51 time: 0.3699 data_time: 0.0523 memory: 20935 loss: 0.0888 loss_ce: 0.0888 2023/03/03 14:16:01 - mmengine - INFO - Epoch(train) [55][ 5/15] lr: 1.0000e-06 eta: 0:12:50 time: 0.3557 data_time: 0.0522 memory: 15733 loss: 0.0889 loss_ce: 0.0889 2023/03/03 14:16:01 - mmengine - INFO - Epoch(train) [55][ 6/15] lr: 1.0000e-06 eta: 0:12:50 time: 0.3911 data_time: 0.0522 memory: 15037 loss: 0.0908 loss_ce: 0.0908 2023/03/03 14:16:01 - mmengine - INFO - Epoch(train) [55][ 7/15] lr: 1.0000e-06 eta: 0:12:49 time: 0.3884 data_time: 0.0522 memory: 17730 loss: 0.0889 loss_ce: 0.0889 2023/03/03 14:16:02 - mmengine - INFO - Epoch(train) [55][ 8/15] lr: 1.0000e-06 eta: 0:12:49 time: 0.3843 data_time: 0.0522 memory: 18586 loss: 0.0834 loss_ce: 0.0834 2023/03/03 14:16:02 - mmengine - INFO - Epoch(train) [55][ 9/15] lr: 1.0000e-06 eta: 0:12:48 time: 0.3563 data_time: 0.0522 memory: 17421 loss: 0.0851 loss_ce: 0.0851 2023/03/03 14:16:02 - mmengine - INFO - Epoch(train) [55][10/15] lr: 1.0000e-06 eta: 0:12:47 time: 0.3710 data_time: 0.0522 memory: 15805 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:16:02 - mmengine - INFO - Epoch(train) [55][11/15] lr: 1.0000e-06 eta: 0:12:47 time: 0.3255 data_time: 0.0017 memory: 18899 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:16:03 - mmengine - INFO - Epoch(train) [55][12/15] lr: 1.0000e-06 eta: 0:12:46 time: 0.2911 data_time: 0.0017 memory: 16744 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:16:03 - mmengine - INFO - Epoch(train) [55][13/15] lr: 1.0000e-06 eta: 0:12:46 time: 0.2869 data_time: 0.0016 memory: 18586 loss: 0.0848 loss_ce: 0.0848 2023/03/03 14:16:03 - mmengine - INFO - Epoch(train) [55][14/15] lr: 1.0000e-06 eta: 0:12:46 time: 0.2964 data_time: 0.0016 memory: 24812 loss: 0.0860 loss_ce: 0.0860 2023/03/03 14:16:03 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:16:03 - mmengine - INFO - Epoch(train) [55][15/15] lr: 1.0000e-06 eta: 0:12:45 time: 0.2901 data_time: 0.0016 memory: 5493 loss: 0.0890 loss_ce: 0.0890 2023/03/03 14:16:05 - mmengine - INFO - Epoch(train) [56][ 1/15] lr: 1.0000e-06 eta: 0:12:46 time: 0.3404 data_time: 0.0753 memory: 22159 loss: 0.0924 loss_ce: 0.0924 2023/03/03 14:16:05 - mmengine - INFO - Epoch(train) [56][ 2/15] lr: 1.0000e-06 eta: 0:12:46 time: 0.3407 data_time: 0.0754 memory: 17572 loss: 0.0980 loss_ce: 0.0980 2023/03/03 14:16:05 - mmengine - INFO - Epoch(train) [56][ 3/15] lr: 1.0000e-06 eta: 0:12:45 time: 0.3419 data_time: 0.0754 memory: 15767 loss: 0.0991 loss_ce: 0.0991 2023/03/03 14:16:05 - mmengine - INFO - Epoch(train) [56][ 4/15] lr: 1.0000e-06 eta: 0:12:44 time: 0.3508 data_time: 0.0755 memory: 14925 loss: 0.0987 loss_ce: 0.0987 2023/03/03 14:16:06 - mmengine - INFO - Epoch(train) [56][ 5/15] lr: 1.0000e-06 eta: 0:12:44 time: 0.3556 data_time: 0.0755 memory: 13785 loss: 0.0954 loss_ce: 0.0954 2023/03/03 14:16:06 - mmengine - INFO - Epoch(train) [56][ 6/15] lr: 1.0000e-06 eta: 0:12:44 time: 0.3602 data_time: 0.0755 memory: 15123 loss: 0.0949 loss_ce: 0.0949 2023/03/03 14:16:06 - mmengine - INFO - Epoch(train) [56][ 7/15] lr: 1.0000e-06 eta: 0:12:43 time: 0.3617 data_time: 0.0755 memory: 16199 loss: 0.0911 loss_ce: 0.0911 2023/03/03 14:16:06 - mmengine - INFO - Epoch(train) [56][ 8/15] lr: 1.0000e-06 eta: 0:12:42 time: 0.3587 data_time: 0.0755 memory: 14322 loss: 0.0916 loss_ce: 0.0916 2023/03/03 14:16:07 - mmengine - INFO - Epoch(train) [56][ 9/15] lr: 1.0000e-06 eta: 0:12:42 time: 0.3390 data_time: 0.0755 memory: 14461 loss: 0.0895 loss_ce: 0.0895 2023/03/03 14:16:07 - mmengine - INFO - Epoch(train) [56][10/15] lr: 1.0000e-06 eta: 0:12:41 time: 0.3417 data_time: 0.0755 memory: 15547 loss: 0.0849 loss_ce: 0.0849 2023/03/03 14:16:08 - mmengine - INFO - Epoch(train) [56][11/15] lr: 1.0000e-06 eta: 0:12:41 time: 0.2885 data_time: 0.0017 memory: 36660 loss: 0.0785 loss_ce: 0.0785 2023/03/03 14:16:08 - mmengine - INFO - Epoch(train) [56][12/15] lr: 1.0000e-06 eta: 0:12:40 time: 0.2908 data_time: 0.0017 memory: 16654 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:16:08 - mmengine - INFO - Epoch(train) [56][13/15] lr: 1.0000e-06 eta: 0:12:40 time: 0.2893 data_time: 0.0016 memory: 16370 loss: 0.0809 loss_ce: 0.0809 2023/03/03 14:16:08 - mmengine - INFO - Epoch(train) [56][14/15] lr: 1.0000e-06 eta: 0:12:39 time: 0.2934 data_time: 0.0016 memory: 19152 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:16:08 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:16:08 - mmengine - INFO - Epoch(train) [56][15/15] lr: 1.0000e-06 eta: 0:12:39 time: 0.2752 data_time: 0.0015 memory: 6734 loss: 0.0893 loss_ce: 0.0893 2023/03/03 14:16:09 - mmengine - INFO - Epoch(train) [57][ 1/15] lr: 1.0000e-06 eta: 0:12:39 time: 0.3065 data_time: 0.0428 memory: 17120 loss: 0.0927 loss_ce: 0.0927 2023/03/03 14:16:09 - mmengine - INFO - Epoch(train) [57][ 2/15] lr: 1.0000e-06 eta: 0:12:39 time: 0.3156 data_time: 0.0429 memory: 17446 loss: 0.0906 loss_ce: 0.0906 2023/03/03 14:16:10 - mmengine - INFO - Epoch(train) [57][ 3/15] lr: 1.0000e-06 eta: 0:12:38 time: 0.3253 data_time: 0.0429 memory: 12867 loss: 0.0930 loss_ce: 0.0930 2023/03/03 14:16:10 - mmengine - INFO - Epoch(train) [57][ 4/15] lr: 1.0000e-06 eta: 0:12:38 time: 0.3258 data_time: 0.0430 memory: 17272 loss: 0.0953 loss_ce: 0.0953 2023/03/03 14:16:10 - mmengine - INFO - Epoch(train) [57][ 5/15] lr: 1.0000e-06 eta: 0:12:37 time: 0.3373 data_time: 0.0430 memory: 11878 loss: 0.0952 loss_ce: 0.0952 2023/03/03 14:16:11 - mmengine - INFO - Epoch(train) [57][ 6/15] lr: 1.0000e-06 eta: 0:12:37 time: 0.3108 data_time: 0.0431 memory: 18340 loss: 0.0984 loss_ce: 0.0984 2023/03/03 14:16:11 - mmengine - INFO - Epoch(train) [57][ 7/15] lr: 1.0000e-06 eta: 0:12:36 time: 0.3082 data_time: 0.0431 memory: 17272 loss: 0.0974 loss_ce: 0.0974 2023/03/03 14:16:11 - mmengine - INFO - Epoch(train) [57][ 8/15] lr: 1.0000e-06 eta: 0:12:36 time: 0.3213 data_time: 0.0431 memory: 21900 loss: 0.0951 loss_ce: 0.0951 2023/03/03 14:16:11 - mmengine - INFO - Epoch(train) [57][ 9/15] lr: 1.0000e-06 eta: 0:12:35 time: 0.3104 data_time: 0.0431 memory: 16056 loss: 0.0965 loss_ce: 0.0965 2023/03/03 14:16:12 - mmengine - INFO - Epoch(train) [57][10/15] lr: 1.0000e-06 eta: 0:12:34 time: 0.3272 data_time: 0.0432 memory: 18586 loss: 0.0868 loss_ce: 0.0868 2023/03/03 14:16:12 - mmengine - INFO - Epoch(train) [57][11/15] lr: 1.0000e-06 eta: 0:12:34 time: 0.2973 data_time: 0.0020 memory: 18584 loss: 0.0860 loss_ce: 0.0860 2023/03/03 14:16:12 - mmengine - INFO - Epoch(train) [57][12/15] lr: 1.0000e-06 eta: 0:12:33 time: 0.2834 data_time: 0.0019 memory: 17131 loss: 0.0835 loss_ce: 0.0835 2023/03/03 14:16:12 - mmengine - INFO - Epoch(train) [57][13/15] lr: 1.0000e-06 eta: 0:12:33 time: 0.2758 data_time: 0.0018 memory: 17272 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:16:13 - mmengine - INFO - Epoch(train) [57][14/15] lr: 1.0000e-06 eta: 0:12:32 time: 0.2734 data_time: 0.0017 memory: 17342 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:16:13 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:16:13 - mmengine - INFO - Epoch(train) [57][15/15] lr: 1.0000e-06 eta: 0:12:31 time: 0.2599 data_time: 0.0020 memory: 6504 loss: 0.0914 loss_ce: 0.0914 2023/03/03 14:16:14 - mmengine - INFO - Epoch(train) [58][ 1/15] lr: 1.0000e-06 eta: 0:12:32 time: 0.2956 data_time: 0.0270 memory: 16976 loss: 0.0928 loss_ce: 0.0928 2023/03/03 14:16:14 - mmengine - INFO - Epoch(train) [58][ 2/15] lr: 1.0000e-06 eta: 0:12:31 time: 0.3064 data_time: 0.0270 memory: 14504 loss: 0.0922 loss_ce: 0.0922 2023/03/03 14:16:14 - mmengine - INFO - Epoch(train) [58][ 3/15] lr: 1.0000e-06 eta: 0:12:31 time: 0.2958 data_time: 0.0271 memory: 16804 loss: 0.0894 loss_ce: 0.0894 2023/03/03 14:16:15 - mmengine - INFO - Epoch(train) [58][ 4/15] lr: 1.0000e-06 eta: 0:12:30 time: 0.3129 data_time: 0.0271 memory: 25369 loss: 0.0906 loss_ce: 0.0906 2023/03/03 14:16:15 - mmengine - INFO - Epoch(train) [58][ 5/15] lr: 1.0000e-06 eta: 0:12:30 time: 0.3158 data_time: 0.0270 memory: 17730 loss: 0.0928 loss_ce: 0.0928 2023/03/03 14:16:15 - mmengine - INFO - Epoch(train) [58][ 6/15] lr: 1.0000e-06 eta: 0:12:29 time: 0.3066 data_time: 0.0270 memory: 19747 loss: 0.0896 loss_ce: 0.0896 2023/03/03 14:16:15 - mmengine - INFO - Epoch(train) [58][ 7/15] lr: 1.0000e-06 eta: 0:12:29 time: 0.3217 data_time: 0.0270 memory: 16654 loss: 0.0893 loss_ce: 0.0893 2023/03/03 14:16:16 - mmengine - INFO - Epoch(train) [58][ 8/15] lr: 1.0000e-06 eta: 0:12:28 time: 0.3250 data_time: 0.0270 memory: 16848 loss: 0.0951 loss_ce: 0.0951 2023/03/03 14:16:16 - mmengine - INFO - Epoch(train) [58][ 9/15] lr: 1.0000e-06 eta: 0:12:28 time: 0.3462 data_time: 0.0270 memory: 17421 loss: 0.0948 loss_ce: 0.0948 2023/03/03 14:16:16 - mmengine - INFO - Epoch(train) [58][10/15] lr: 1.0000e-06 eta: 0:12:28 time: 0.3502 data_time: 0.0269 memory: 18070 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:16:17 - mmengine - INFO - Epoch(train) [58][11/15] lr: 1.0000e-06 eta: 0:12:27 time: 0.3207 data_time: 0.0020 memory: 21406 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:16:17 - mmengine - INFO - Epoch(train) [58][12/15] lr: 1.0000e-06 eta: 0:12:27 time: 0.3085 data_time: 0.0020 memory: 16303 loss: 0.0760 loss_ce: 0.0760 2023/03/03 14:16:17 - mmengine - INFO - Epoch(train) [58][13/15] lr: 1.0000e-06 eta: 0:12:26 time: 0.3227 data_time: 0.0019 memory: 14461 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:16:18 - mmengine - INFO - Epoch(train) [58][14/15] lr: 1.0000e-06 eta: 0:12:26 time: 0.3036 data_time: 0.0019 memory: 18409 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:16:18 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:16:18 - mmengine - INFO - Epoch(train) [58][15/15] lr: 1.0000e-06 eta: 0:12:25 time: 0.2957 data_time: 0.0018 memory: 5534 loss: 0.0937 loss_ce: 0.0937 2023/03/03 14:16:19 - mmengine - INFO - Epoch(train) [59][ 1/15] lr: 1.0000e-06 eta: 0:12:27 time: 0.4014 data_time: 0.1084 memory: 18766 loss: 0.0951 loss_ce: 0.0951 2023/03/03 14:16:19 - mmengine - INFO - Epoch(train) [59][ 2/15] lr: 1.0000e-06 eta: 0:12:26 time: 0.3862 data_time: 0.1085 memory: 16508 loss: 0.0956 loss_ce: 0.0956 2023/03/03 14:16:20 - mmengine - INFO - Epoch(train) [59][ 3/15] lr: 1.0000e-06 eta: 0:12:26 time: 0.3859 data_time: 0.1085 memory: 16370 loss: 0.0886 loss_ce: 0.0886 2023/03/03 14:16:20 - mmengine - INFO - Epoch(train) [59][ 4/15] lr: 1.0000e-06 eta: 0:12:26 time: 0.3962 data_time: 0.1086 memory: 17162 loss: 0.0929 loss_ce: 0.0929 2023/03/03 14:16:20 - mmengine - INFO - Epoch(train) [59][ 5/15] lr: 1.0000e-06 eta: 0:12:25 time: 0.3960 data_time: 0.1085 memory: 18409 loss: 0.0923 loss_ce: 0.0923 2023/03/03 14:16:21 - mmengine - INFO - Epoch(train) [59][ 6/15] lr: 1.0000e-06 eta: 0:12:25 time: 0.4006 data_time: 0.1084 memory: 16654 loss: 0.0920 loss_ce: 0.0920 2023/03/03 14:16:21 - mmengine - INFO - Epoch(train) [59][ 7/15] lr: 1.0000e-06 eta: 0:12:24 time: 0.4028 data_time: 0.1083 memory: 18530 loss: 0.0937 loss_ce: 0.0937 2023/03/03 14:16:21 - mmengine - INFO - Epoch(train) [59][ 8/15] lr: 1.0000e-06 eta: 0:12:24 time: 0.3856 data_time: 0.1084 memory: 15494 loss: 0.0956 loss_ce: 0.0956 2023/03/03 14:16:21 - mmengine - INFO - Epoch(train) [59][ 9/15] lr: 1.0000e-06 eta: 0:12:23 time: 0.3853 data_time: 0.1084 memory: 16976 loss: 0.1018 loss_ce: 0.1018 2023/03/03 14:16:22 - mmengine - INFO - Epoch(train) [59][10/15] lr: 1.0000e-06 eta: 0:12:22 time: 0.3843 data_time: 0.1084 memory: 17272 loss: 0.0857 loss_ce: 0.0857 2023/03/03 14:16:22 - mmengine - INFO - Epoch(train) [59][11/15] lr: 1.0000e-06 eta: 0:12:22 time: 0.2762 data_time: 0.0019 memory: 16976 loss: 0.0903 loss_ce: 0.0903 2023/03/03 14:16:22 - mmengine - INFO - Epoch(train) [59][12/15] lr: 1.0000e-06 eta: 0:12:21 time: 0.2818 data_time: 0.0018 memory: 17120 loss: 0.0898 loss_ce: 0.0898 2023/03/03 14:16:22 - mmengine - INFO - Epoch(train) [59][13/15] lr: 1.0000e-06 eta: 0:12:21 time: 0.2839 data_time: 0.0017 memory: 17938 loss: 0.0896 loss_ce: 0.0896 2023/03/03 14:16:23 - mmengine - INFO - Epoch(train) [59][14/15] lr: 1.0000e-06 eta: 0:12:20 time: 0.2524 data_time: 0.0017 memory: 17091 loss: 0.0883 loss_ce: 0.0883 2023/03/03 14:16:23 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:16:23 - mmengine - INFO - Epoch(train) [59][15/15] lr: 1.0000e-06 eta: 0:12:19 time: 0.2488 data_time: 0.0017 memory: 5299 loss: 0.0973 loss_ce: 0.0973 2023/03/03 14:16:24 - mmengine - INFO - Epoch(train) [60][ 1/15] lr: 1.0000e-06 eta: 0:12:21 time: 0.3098 data_time: 0.0658 memory: 23082 loss: 0.1000 loss_ce: 0.1000 2023/03/03 14:16:24 - mmengine - INFO - Epoch(train) [60][ 2/15] lr: 1.0000e-06 eta: 0:12:20 time: 0.3091 data_time: 0.0658 memory: 17421 loss: 0.1009 loss_ce: 0.1009 2023/03/03 14:16:25 - mmengine - INFO - Epoch(train) [60][ 3/15] lr: 1.0000e-06 eta: 0:12:20 time: 0.3402 data_time: 0.0658 memory: 18563 loss: 0.0989 loss_ce: 0.0989 2023/03/03 14:16:25 - mmengine - INFO - Epoch(train) [60][ 4/15] lr: 1.0000e-06 eta: 0:12:19 time: 0.3396 data_time: 0.0658 memory: 16719 loss: 0.0955 loss_ce: 0.0955 2023/03/03 14:16:25 - mmengine - INFO - Epoch(train) [60][ 5/15] lr: 1.0000e-06 eta: 0:12:19 time: 0.3699 data_time: 0.0658 memory: 37909 loss: 0.0924 loss_ce: 0.0924 2023/03/03 14:16:26 - mmengine - INFO - Epoch(train) [60][ 6/15] lr: 1.0000e-06 eta: 0:12:19 time: 0.3728 data_time: 0.0658 memory: 16508 loss: 0.0927 loss_ce: 0.0927 2023/03/03 14:16:26 - mmengine - INFO - Epoch(train) [60][ 7/15] lr: 1.0000e-06 eta: 0:12:18 time: 0.3809 data_time: 0.0659 memory: 20489 loss: 0.0919 loss_ce: 0.0919 2023/03/03 14:16:26 - mmengine - INFO - Epoch(train) [60][ 8/15] lr: 1.0000e-06 eta: 0:12:18 time: 0.3837 data_time: 0.0659 memory: 13392 loss: 0.0977 loss_ce: 0.0977 2023/03/03 14:16:27 - mmengine - INFO - Epoch(train) [60][ 9/15] lr: 1.0000e-06 eta: 0:12:17 time: 0.3812 data_time: 0.0659 memory: 17788 loss: 0.0939 loss_ce: 0.0939 2023/03/03 14:16:27 - mmengine - INFO - Epoch(train) [60][10/15] lr: 1.0000e-06 eta: 0:12:17 time: 0.3879 data_time: 0.0659 memory: 13610 loss: 0.0854 loss_ce: 0.0854 2023/03/03 14:16:27 - mmengine - INFO - Epoch(train) [60][11/15] lr: 1.0000e-06 eta: 0:12:17 time: 0.3378 data_time: 0.0018 memory: 33422 loss: 0.0810 loss_ce: 0.0810 2023/03/03 14:16:28 - mmengine - INFO - Epoch(train) [60][12/15] lr: 1.0000e-06 eta: 0:12:16 time: 0.3468 data_time: 0.0017 memory: 21395 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:16:28 - mmengine - INFO - Epoch(train) [60][13/15] lr: 1.0000e-06 eta: 0:12:16 time: 0.3152 data_time: 0.0017 memory: 16501 loss: 0.0772 loss_ce: 0.0772 2023/03/03 14:16:28 - mmengine - INFO - Epoch(train) [60][14/15] lr: 1.0000e-06 eta: 0:12:16 time: 0.3379 data_time: 0.0017 memory: 23672 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:16:29 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:16:29 - mmengine - INFO - Epoch(train) [60][15/15] lr: 1.0000e-06 eta: 0:12:17 time: 0.3768 data_time: 0.0017 memory: 5638 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:16:31 - mmengine - INFO - Epoch(val) [60][ 1/59] eta: 0:01:29 time: 1.1237 data_time: 0.0036 memory: 981 2023/03/03 14:16:32 - mmengine - INFO - Epoch(val) [60][ 2/59] eta: 0:01:07 time: 1.0374 data_time: 0.0036 memory: 981 2023/03/03 14:16:33 - mmengine - INFO - Epoch(val) [60][ 3/59] eta: 0:01:12 time: 1.0707 data_time: 0.0035 memory: 1003 2023/03/03 14:16:34 - mmengine - INFO - Epoch(val) [60][ 4/59] eta: 0:01:00 time: 1.0532 data_time: 0.0035 memory: 981 2023/03/03 14:16:37 - mmengine - INFO - Epoch(val) [60][ 5/59] eta: 0:01:19 time: 1.2874 data_time: 0.0035 memory: 1016 2023/03/03 14:16:39 - mmengine - INFO - Epoch(val) [60][ 6/59] eta: 0:01:28 time: 1.4811 data_time: 0.0035 memory: 981 2023/03/03 14:16:39 - mmengine - INFO - Epoch(val) [60][ 7/59] eta: 0:01:15 time: 1.4146 data_time: 0.0035 memory: 1043 2023/03/03 14:16:40 - mmengine - INFO - Epoch(val) [60][ 8/59] eta: 0:01:11 time: 1.2865 data_time: 0.0035 memory: 1016 2023/03/03 14:16:41 - mmengine - INFO - Epoch(val) [60][ 9/59] eta: 0:01:07 time: 1.2503 data_time: 0.0035 memory: 981 2023/03/03 14:16:43 - mmengine - INFO - Epoch(val) [60][10/59] eta: 0:01:06 time: 1.3492 data_time: 0.0035 memory: 981 2023/03/03 14:16:43 - mmengine - INFO - Epoch(val) [60][11/59] eta: 0:01:01 time: 1.2452 data_time: 0.0009 memory: 981 2023/03/03 14:16:47 - mmengine - INFO - Epoch(val) [60][12/59] eta: 0:01:08 time: 1.5050 data_time: 0.0008 memory: 1016 2023/03/03 14:16:49 - mmengine - INFO - Epoch(val) [60][13/59] eta: 0:01:08 time: 1.5590 data_time: 0.0008 memory: 981 2023/03/03 14:16:50 - mmengine - INFO - Epoch(val) [60][14/59] eta: 0:01:06 time: 1.6266 data_time: 0.0009 memory: 890 2023/03/03 14:16:50 - mmengine - INFO - Epoch(val) [60][15/59] eta: 0:01:00 time: 1.3277 data_time: 0.0009 memory: 981 2023/03/03 14:16:50 - mmengine - INFO - Epoch(val) [60][16/59] eta: 0:00:56 time: 1.1161 data_time: 0.0009 memory: 981 2023/03/03 14:16:51 - mmengine - INFO - Epoch(val) [60][17/59] eta: 0:00:53 time: 1.1321 data_time: 0.0008 memory: 981 2023/03/03 14:16:51 - mmengine - INFO - Epoch(val) [60][18/59] eta: 0:00:49 time: 1.0669 data_time: 0.0008 memory: 981 2023/03/03 14:16:52 - mmengine - INFO - Epoch(val) [60][19/59] eta: 0:00:48 time: 1.0666 data_time: 0.0008 memory: 981 2023/03/03 14:16:52 - mmengine - INFO - Epoch(val) [60][20/59] eta: 0:00:45 time: 0.9672 data_time: 0.0008 memory: 981 2023/03/03 14:16:54 - mmengine - INFO - Epoch(val) [60][21/59] eta: 0:00:45 time: 1.1029 data_time: 0.0008 memory: 981 2023/03/03 14:16:54 - mmengine - INFO - Epoch(val) [60][22/59] eta: 0:00:42 time: 0.7771 data_time: 0.0008 memory: 981 2023/03/03 14:16:55 - mmengine - INFO - Epoch(val) [60][23/59] eta: 0:00:40 time: 0.6389 data_time: 0.0008 memory: 981 2023/03/03 14:16:55 - mmengine - INFO - Epoch(val) [60][24/59] eta: 0:00:38 time: 0.5550 data_time: 0.0007 memory: 962 2023/03/03 14:16:56 - mmengine - INFO - Epoch(val) [60][25/59] eta: 0:00:36 time: 0.5860 data_time: 0.0007 memory: 981 2023/03/03 14:16:56 - mmengine - INFO - Epoch(val) [60][26/59] eta: 0:00:34 time: 0.5699 data_time: 0.0007 memory: 981 2023/03/03 14:16:56 - mmengine - INFO - Epoch(val) [60][27/59] eta: 0:00:32 time: 0.5698 data_time: 0.0007 memory: 981 2023/03/03 14:16:57 - mmengine - INFO - Epoch(val) [60][28/59] eta: 0:00:30 time: 0.5698 data_time: 0.0007 memory: 981 2023/03/03 14:16:58 - mmengine - INFO - Epoch(val) [60][29/59] eta: 0:00:29 time: 0.6041 data_time: 0.0007 memory: 981 2023/03/03 14:16:59 - mmengine - INFO - Epoch(val) [60][30/59] eta: 0:00:28 time: 0.6530 data_time: 0.0007 memory: 999 2023/03/03 14:17:00 - mmengine - INFO - Epoch(val) [60][31/59] eta: 0:00:27 time: 0.5337 data_time: 0.0007 memory: 981 2023/03/03 14:17:01 - mmengine - INFO - Epoch(val) [60][32/59] eta: 0:00:26 time: 0.6319 data_time: 0.0007 memory: 981 2023/03/03 14:17:01 - mmengine - INFO - Epoch(val) [60][33/59] eta: 0:00:24 time: 0.5831 data_time: 0.0007 memory: 981 2023/03/03 14:17:01 - mmengine - INFO - Epoch(val) [60][34/59] eta: 0:00:23 time: 0.5667 data_time: 0.0007 memory: 981 2023/03/03 14:17:01 - mmengine - INFO - Epoch(val) [60][35/59] eta: 0:00:21 time: 0.5505 data_time: 0.0007 memory: 981 2023/03/03 14:17:02 - mmengine - INFO - Epoch(val) [60][36/59] eta: 0:00:20 time: 0.5667 data_time: 0.0007 memory: 981 2023/03/03 14:17:02 - mmengine - INFO - Epoch(val) [60][37/59] eta: 0:00:19 time: 0.5505 data_time: 0.0007 memory: 981 2023/03/03 14:17:03 - mmengine - INFO - Epoch(val) [60][38/59] eta: 0:00:18 time: 0.5834 data_time: 0.0007 memory: 981 2023/03/03 14:17:03 - mmengine - INFO - Epoch(val) [60][39/59] eta: 0:00:17 time: 0.4999 data_time: 0.0007 memory: 987 2023/03/03 14:17:04 - mmengine - INFO - Epoch(val) [60][40/59] eta: 0:00:16 time: 0.5002 data_time: 0.0007 memory: 981 2023/03/03 14:17:05 - mmengine - INFO - Epoch(val) [60][41/59] eta: 0:00:15 time: 0.5506 data_time: 0.0007 memory: 986 2023/03/03 14:17:06 - mmengine - INFO - Epoch(val) [60][42/59] eta: 0:00:14 time: 0.5013 data_time: 0.0008 memory: 981 2023/03/03 14:17:07 - mmengine - INFO - Epoch(val) [60][43/59] eta: 0:00:13 time: 0.5667 data_time: 0.0008 memory: 976 2023/03/03 14:17:07 - mmengine - INFO - Epoch(val) [60][44/59] eta: 0:00:12 time: 0.5992 data_time: 0.0008 memory: 1003 2023/03/03 14:17:09 - mmengine - INFO - Epoch(val) [60][45/59] eta: 0:00:12 time: 0.7679 data_time: 0.0008 memory: 981 2023/03/03 14:17:10 - mmengine - INFO - Epoch(val) [60][46/59] eta: 0:00:11 time: 0.8010 data_time: 0.0008 memory: 981 2023/03/03 14:17:10 - mmengine - INFO - Epoch(val) [60][47/59] eta: 0:00:10 time: 0.8332 data_time: 0.0008 memory: 936 2023/03/03 14:17:11 - mmengine - INFO - Epoch(val) [60][48/59] eta: 0:00:09 time: 0.8166 data_time: 0.0008 memory: 1000 2023/03/03 14:17:12 - mmengine - INFO - Epoch(val) [60][49/59] eta: 0:00:08 time: 0.8662 data_time: 0.0008 memory: 981 2023/03/03 14:17:13 - mmengine - INFO - Epoch(val) [60][50/59] eta: 0:00:07 time: 0.8661 data_time: 0.0008 memory: 987 2023/03/03 14:17:14 - mmengine - INFO - Epoch(val) [60][51/59] eta: 0:00:07 time: 0.9180 data_time: 0.0008 memory: 981 2023/03/03 14:17:15 - mmengine - INFO - Epoch(val) [60][52/59] eta: 0:00:06 time: 0.9686 data_time: 0.0008 memory: 981 2023/03/03 14:17:16 - mmengine - INFO - Epoch(val) [60][53/59] eta: 0:00:05 time: 0.9522 data_time: 0.0008 memory: 962 2023/03/03 14:17:17 - mmengine - INFO - Epoch(val) [60][54/59] eta: 0:00:04 time: 0.9675 data_time: 0.0007 memory: 981 2023/03/03 14:17:17 - mmengine - INFO - Epoch(val) [60][55/59] eta: 0:00:03 time: 0.8468 data_time: 0.0008 memory: 981 2023/03/03 14:17:18 - mmengine - INFO - Epoch(val) [60][56/59] eta: 0:00:02 time: 0.8482 data_time: 0.0007 memory: 981 2023/03/03 14:17:20 - mmengine - INFO - Epoch(val) [60][57/59] eta: 0:00:01 time: 1.0263 data_time: 0.0007 memory: 981 2023/03/03 14:17:22 - mmengine - INFO - Epoch(val) [60][58/59] eta: 0:00:00 time: 1.1122 data_time: 0.0007 memory: 1016 2023/03/03 14:17:22 - mmengine - INFO - Epoch(val) [60][59/59] eta: 0:00:00 time: 1.0471 data_time: 0.0007 memory: 981 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.80, recall: 0.8183, precision: 0.8335, hmean: 0.8258 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.81, recall: 0.8164, precision: 0.8340, hmean: 0.8251 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.82, recall: 0.8164, precision: 0.8379, hmean: 0.8270 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.83, recall: 0.8137, precision: 0.8382, hmean: 0.8258 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.84, recall: 0.8128, precision: 0.8396, hmean: 0.8260 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.85, recall: 0.8119, precision: 0.8451, hmean: 0.8281 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.86, recall: 0.8119, precision: 0.8491, hmean: 0.8301 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.87, recall: 0.8091, precision: 0.8552, hmean: 0.8315 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.88, recall: 0.8064, precision: 0.8589, hmean: 0.8318 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.89, recall: 0.8046, precision: 0.8595, hmean: 0.8311 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.90, recall: 0.8027, precision: 0.8609, hmean: 0.8308 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.91, recall: 0.7963, precision: 0.8642, hmean: 0.8289 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.92, recall: 0.7936, precision: 0.8673, hmean: 0.8288 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.93, recall: 0.7854, precision: 0.8669, hmean: 0.8241 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.94, recall: 0.7790, precision: 0.8695, hmean: 0.8218 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.95, recall: 0.7671, precision: 0.8705, hmean: 0.8155 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.96, recall: 0.7553, precision: 0.8751, hmean: 0.8108 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.97, recall: 0.7470, precision: 0.8805, hmean: 0.8083 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.98, recall: 0.7370, precision: 0.8868, hmean: 0.8050 2023/03/03 14:17:51 - mmengine - INFO - text score threshold: 0.99, recall: 0.7142, precision: 0.8917, hmean: 0.7931 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.80, recall: 0.8292, precision: 0.9035, hmean: 0.8648 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.81, recall: 0.8274, precision: 0.9042, hmean: 0.8641 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.82, recall: 0.8274, precision: 0.9078, hmean: 0.8657 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.83, recall: 0.8256, precision: 0.9095, hmean: 0.8655 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.84, recall: 0.8247, precision: 0.9103, hmean: 0.8654 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.85, recall: 0.8237, precision: 0.9130, hmean: 0.8661 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.86, recall: 0.8228, precision: 0.9129, hmean: 0.8655 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.87, recall: 0.8201, precision: 0.9163, hmean: 0.8655 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.88, recall: 0.8155, precision: 0.9178, hmean: 0.8636 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.89, recall: 0.8137, precision: 0.9186, hmean: 0.8630 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.90, recall: 0.8119, precision: 0.9193, hmean: 0.8623 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.91, recall: 0.8027, precision: 0.9185, hmean: 0.8567 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.92, recall: 0.7991, precision: 0.9201, hmean: 0.8553 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.93, recall: 0.7918, precision: 0.9204, hmean: 0.8513 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.94, recall: 0.7845, precision: 0.9227, hmean: 0.8480 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.95, recall: 0.7726, precision: 0.9226, hmean: 0.8410 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.96, recall: 0.7589, precision: 0.9264, hmean: 0.8343 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.97, recall: 0.7489, precision: 0.9276, hmean: 0.8287 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.98, recall: 0.7388, precision: 0.9320, hmean: 0.8242 2023/03/03 14:17:54 - mmengine - INFO - text score threshold: 0.99, recall: 0.7160, precision: 0.9344, hmean: 0.8108 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.80, recall: 0.7507, precision: 0.9547, hmean: 0.8405 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.81, recall: 0.7489, precision: 0.9557, hmean: 0.8397 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.82, recall: 0.7479, precision: 0.9557, hmean: 0.8391 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.83, recall: 0.7470, precision: 0.9556, hmean: 0.8385 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.84, recall: 0.7452, precision: 0.9555, hmean: 0.8374 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.85, recall: 0.7443, precision: 0.9577, hmean: 0.8376 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.86, recall: 0.7434, precision: 0.9576, hmean: 0.8370 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.87, recall: 0.7397, precision: 0.9597, hmean: 0.8355 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.88, recall: 0.7352, precision: 0.9606, hmean: 0.8329 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.89, recall: 0.7333, precision: 0.9617, hmean: 0.8321 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.90, recall: 0.7315, precision: 0.9616, hmean: 0.8309 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.91, recall: 0.7224, precision: 0.9611, hmean: 0.8248 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.92, recall: 0.7187, precision: 0.9621, hmean: 0.8228 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.93, recall: 0.7142, precision: 0.9619, hmean: 0.8197 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.94, recall: 0.7068, precision: 0.9615, hmean: 0.8147 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.95, recall: 0.6968, precision: 0.9610, hmean: 0.8078 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.96, recall: 0.6840, precision: 0.9652, hmean: 0.8006 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.97, recall: 0.6740, precision: 0.9647, hmean: 0.7935 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.98, recall: 0.6639, precision: 0.9655, hmean: 0.7868 2023/03/03 14:17:56 - mmengine - INFO - text score threshold: 0.99, recall: 0.6438, precision: 0.9671, hmean: 0.7730 2023/03/03 14:17:56 - mmengine - INFO - Epoch(val) [60][59/59] generic/precision: 0.8589 generic/recall: 0.8064 generic/hmean: 0.8318 weak/precision: 0.9130 weak/recall: 0.8237 weak/hmean: 0.8661 strong/precision: 0.9547 strong/recall: 0.7507 strong/hmean: 0.8405 2023/03/03 14:17:56 - mmengine - INFO - The previous best checkpoint mmocr/projects/SPTS/work_dirs/spts_resnet50_350e_icdar2013/best_generic/hmean_epoch_40.pth is removed 2023/03/03 14:17:58 - mmengine - INFO - The best checkpoint with 0.8318 generic/hmean at 60 epoch is saved to best_generic/hmean_epoch_60.pth. 2023/03/03 14:17:59 - mmengine - INFO - Epoch(train) [61][ 1/15] lr: 1.0000e-06 eta: 0:12:17 time: 0.4145 data_time: 0.0377 memory: 16199 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:17:59 - mmengine - INFO - Epoch(train) [61][ 2/15] lr: 1.0000e-06 eta: 0:12:16 time: 0.4009 data_time: 0.0376 memory: 16955 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:17:59 - mmengine - INFO - Epoch(train) [61][ 3/15] lr: 1.0000e-06 eta: 0:12:15 time: 0.3931 data_time: 0.0377 memory: 16976 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:18:00 - mmengine - INFO - Epoch(train) [61][ 4/15] lr: 1.0000e-06 eta: 0:12:15 time: 0.3968 data_time: 0.0377 memory: 16508 loss: 0.0818 loss_ce: 0.0818 2023/03/03 14:18:00 - mmengine - INFO - Epoch(train) [61][ 5/15] lr: 1.0000e-06 eta: 0:12:14 time: 0.3937 data_time: 0.0378 memory: 17397 loss: 0.0841 loss_ce: 0.0841 2023/03/03 14:18:00 - mmengine - INFO - Epoch(train) [61][ 6/15] lr: 1.0000e-06 eta: 0:12:14 time: 0.3838 data_time: 0.0378 memory: 15315 loss: 0.0890 loss_ce: 0.0890 2023/03/03 14:18:01 - mmengine - INFO - Epoch(train) [61][ 7/15] lr: 1.0000e-06 eta: 0:12:13 time: 0.3746 data_time: 0.0378 memory: 17120 loss: 0.0904 loss_ce: 0.0904 2023/03/03 14:18:01 - mmengine - INFO - Epoch(train) [61][ 8/15] lr: 1.0000e-06 eta: 0:12:13 time: 0.3744 data_time: 0.0378 memory: 17368 loss: 0.0896 loss_ce: 0.0896 2023/03/03 14:18:01 - mmengine - INFO - Epoch(train) [61][ 9/15] lr: 1.0000e-06 eta: 0:12:12 time: 0.3523 data_time: 0.0378 memory: 16976 loss: 0.0889 loss_ce: 0.0889 2023/03/03 14:18:02 - mmengine - INFO - Epoch(train) [61][10/15] lr: 1.0000e-06 eta: 0:12:12 time: 0.3240 data_time: 0.0379 memory: 32446 loss: 0.0875 loss_ce: 0.0875 2023/03/03 14:18:02 - mmengine - INFO - Epoch(train) [61][11/15] lr: 1.0000e-06 eta: 0:12:12 time: 0.2834 data_time: 0.0019 memory: 16901 loss: 0.0873 loss_ce: 0.0873 2023/03/03 14:18:02 - mmengine - INFO - Epoch(train) [61][12/15] lr: 1.0000e-06 eta: 0:12:12 time: 0.3135 data_time: 0.0018 memory: 38600 loss: 0.0921 loss_ce: 0.0921 2023/03/03 14:18:03 - mmengine - INFO - Epoch(train) [61][13/15] lr: 1.0000e-06 eta: 0:12:11 time: 0.3256 data_time: 0.0018 memory: 15238 loss: 0.0888 loss_ce: 0.0888 2023/03/03 14:18:03 - mmengine - INFO - Epoch(train) [61][14/15] lr: 1.0000e-06 eta: 0:12:11 time: 0.3339 data_time: 0.0018 memory: 17120 loss: 0.0867 loss_ce: 0.0867 2023/03/03 14:18:03 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:18:03 - mmengine - INFO - Epoch(train) [61][15/15] lr: 1.0000e-06 eta: 0:12:10 time: 0.3301 data_time: 0.0017 memory: 3989 loss: 0.0917 loss_ce: 0.0917 2023/03/03 14:18:04 - mmengine - INFO - Epoch(train) [62][ 1/15] lr: 1.0000e-06 eta: 0:12:11 time: 0.3528 data_time: 0.0317 memory: 14057 loss: 0.0938 loss_ce: 0.0938 2023/03/03 14:18:04 - mmengine - INFO - Epoch(train) [62][ 2/15] lr: 1.0000e-06 eta: 0:12:11 time: 0.3881 data_time: 0.0659 memory: 17892 loss: 0.0911 loss_ce: 0.0911 2023/03/03 14:18:05 - mmengine - INFO - Epoch(train) [62][ 3/15] lr: 1.0000e-06 eta: 0:12:10 time: 0.3995 data_time: 0.0660 memory: 24748 loss: 0.0923 loss_ce: 0.0923 2023/03/03 14:18:05 - mmengine - INFO - Epoch(train) [62][ 4/15] lr: 1.0000e-06 eta: 0:12:10 time: 0.4157 data_time: 0.0660 memory: 26015 loss: 0.0904 loss_ce: 0.0904 2023/03/03 14:18:05 - mmengine - INFO - Epoch(train) [62][ 5/15] lr: 1.0000e-06 eta: 0:12:09 time: 0.3745 data_time: 0.0660 memory: 16316 loss: 0.0894 loss_ce: 0.0894 2023/03/03 14:18:06 - mmengine - INFO - Epoch(train) [62][ 6/15] lr: 1.0000e-06 eta: 0:12:09 time: 0.3860 data_time: 0.0660 memory: 22816 loss: 0.0840 loss_ce: 0.0840 2023/03/03 14:18:06 - mmengine - INFO - Epoch(train) [62][ 7/15] lr: 1.0000e-06 eta: 0:12:08 time: 0.3591 data_time: 0.0660 memory: 17788 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:18:06 - mmengine - INFO - Epoch(train) [62][ 8/15] lr: 1.0000e-06 eta: 0:12:08 time: 0.3490 data_time: 0.0660 memory: 15911 loss: 0.0783 loss_ce: 0.0783 2023/03/03 14:18:07 - mmengine - INFO - Epoch(train) [62][ 9/15] lr: 1.0000e-06 eta: 0:12:08 time: 0.3639 data_time: 0.0660 memory: 16370 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:18:07 - mmengine - INFO - Epoch(train) [62][10/15] lr: 1.0000e-06 eta: 0:12:07 time: 0.3704 data_time: 0.0660 memory: 20389 loss: 0.0756 loss_ce: 0.0756 2023/03/03 14:18:07 - mmengine - INFO - Epoch(train) [62][11/15] lr: 1.0000e-06 eta: 0:12:07 time: 0.3346 data_time: 0.0360 memory: 20564 loss: 0.0707 loss_ce: 0.0707 2023/03/03 14:18:07 - mmengine - INFO - Epoch(train) [62][12/15] lr: 1.0000e-06 eta: 0:12:06 time: 0.3013 data_time: 0.0017 memory: 15911 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:18:08 - mmengine - INFO - Epoch(train) [62][13/15] lr: 1.0000e-06 eta: 0:12:06 time: 0.2990 data_time: 0.0017 memory: 20327 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:18:08 - mmengine - INFO - Epoch(train) [62][14/15] lr: 1.0000e-06 eta: 0:12:05 time: 0.2851 data_time: 0.0016 memory: 16125 loss: 0.0723 loss_ce: 0.0723 2023/03/03 14:18:08 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:18:08 - mmengine - INFO - Epoch(train) [62][15/15] lr: 1.0000e-06 eta: 0:12:04 time: 0.2791 data_time: 0.0015 memory: 6858 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:18:09 - mmengine - INFO - Epoch(train) [63][ 1/15] lr: 1.0000e-06 eta: 0:12:05 time: 0.3373 data_time: 0.0691 memory: 13573 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:18:09 - mmengine - INFO - Epoch(train) [63][ 2/15] lr: 1.0000e-06 eta: 0:12:05 time: 0.3510 data_time: 0.0691 memory: 20557 loss: 0.0833 loss_ce: 0.0833 2023/03/03 14:18:10 - mmengine - INFO - Epoch(train) [63][ 3/15] lr: 1.0000e-06 eta: 0:12:04 time: 0.3520 data_time: 0.0691 memory: 16370 loss: 0.0830 loss_ce: 0.0830 2023/03/03 14:18:10 - mmengine - INFO - Epoch(train) [63][ 4/15] lr: 1.0000e-06 eta: 0:12:04 time: 0.3324 data_time: 0.0692 memory: 20222 loss: 0.0840 loss_ce: 0.0840 2023/03/03 14:18:10 - mmengine - INFO - Epoch(train) [63][ 5/15] lr: 1.0000e-06 eta: 0:12:03 time: 0.3286 data_time: 0.0692 memory: 13567 loss: 0.0800 loss_ce: 0.0800 2023/03/03 14:18:11 - mmengine - INFO - Epoch(train) [63][ 6/15] lr: 1.0000e-06 eta: 0:12:03 time: 0.3354 data_time: 0.0692 memory: 15037 loss: 0.0839 loss_ce: 0.0839 2023/03/03 14:18:11 - mmengine - INFO - Epoch(train) [63][ 7/15] lr: 1.0000e-06 eta: 0:12:03 time: 0.3546 data_time: 0.0692 memory: 31937 loss: 0.0841 loss_ce: 0.0841 2023/03/03 14:18:11 - mmengine - INFO - Epoch(train) [63][ 8/15] lr: 1.0000e-06 eta: 0:12:02 time: 0.3468 data_time: 0.0692 memory: 13030 loss: 0.0833 loss_ce: 0.0833 2023/03/03 14:18:11 - mmengine - INFO - Epoch(train) [63][ 9/15] lr: 1.0000e-06 eta: 0:12:01 time: 0.3467 data_time: 0.0693 memory: 15733 loss: 0.0883 loss_ce: 0.0883 2023/03/03 14:18:12 - mmengine - INFO - Epoch(train) [63][10/15] lr: 1.0000e-06 eta: 0:12:01 time: 0.3585 data_time: 0.0692 memory: 17284 loss: 0.0802 loss_ce: 0.0802 2023/03/03 14:18:12 - mmengine - INFO - Epoch(train) [63][11/15] lr: 1.0000e-06 eta: 0:12:01 time: 0.2982 data_time: 0.0018 memory: 19561 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:18:12 - mmengine - INFO - Epoch(train) [63][12/15] lr: 1.0000e-06 eta: 0:12:00 time: 0.2800 data_time: 0.0017 memory: 17892 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:18:12 - mmengine - INFO - Epoch(train) [63][13/15] lr: 1.0000e-06 eta: 0:11:59 time: 0.2770 data_time: 0.0017 memory: 16976 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:18:13 - mmengine - INFO - Epoch(train) [63][14/15] lr: 1.0000e-06 eta: 0:11:59 time: 0.2706 data_time: 0.0016 memory: 15911 loss: 0.0830 loss_ce: 0.0830 2023/03/03 14:18:13 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:18:13 - mmengine - INFO - Epoch(train) [63][15/15] lr: 1.0000e-06 eta: 0:11:58 time: 0.2666 data_time: 0.0016 memory: 4025 loss: 0.0873 loss_ce: 0.0873 2023/03/03 14:18:14 - mmengine - INFO - Epoch(train) [64][ 1/15] lr: 1.0000e-06 eta: 0:11:58 time: 0.3041 data_time: 0.0241 memory: 18801 loss: 0.0842 loss_ce: 0.0842 2023/03/03 14:18:14 - mmengine - INFO - Epoch(train) [64][ 2/15] lr: 1.0000e-06 eta: 0:11:58 time: 0.2923 data_time: 0.0242 memory: 13872 loss: 0.0835 loss_ce: 0.0835 2023/03/03 14:18:14 - mmengine - INFO - Epoch(train) [64][ 3/15] lr: 1.0000e-06 eta: 0:11:57 time: 0.2935 data_time: 0.0242 memory: 17892 loss: 0.0830 loss_ce: 0.0830 2023/03/03 14:18:14 - mmengine - INFO - Epoch(train) [64][ 4/15] lr: 1.0000e-06 eta: 0:11:57 time: 0.2986 data_time: 0.0243 memory: 25033 loss: 0.0798 loss_ce: 0.0798 2023/03/03 14:18:15 - mmengine - INFO - Epoch(train) [64][ 5/15] lr: 1.0000e-06 eta: 0:11:56 time: 0.2940 data_time: 0.0243 memory: 17421 loss: 0.0841 loss_ce: 0.0841 2023/03/03 14:18:15 - mmengine - INFO - Epoch(train) [64][ 6/15] lr: 1.0000e-06 eta: 0:11:56 time: 0.3020 data_time: 0.0243 memory: 22069 loss: 0.0845 loss_ce: 0.0845 2023/03/03 14:18:15 - mmengine - INFO - Epoch(train) [64][ 7/15] lr: 1.0000e-06 eta: 0:11:55 time: 0.2996 data_time: 0.0243 memory: 14251 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:18:16 - mmengine - INFO - Epoch(train) [64][ 8/15] lr: 1.0000e-06 eta: 0:11:55 time: 0.3058 data_time: 0.0243 memory: 17467 loss: 0.0784 loss_ce: 0.0784 2023/03/03 14:18:16 - mmengine - INFO - Epoch(train) [64][ 9/15] lr: 1.0000e-06 eta: 0:11:54 time: 0.3088 data_time: 0.0244 memory: 15464 loss: 0.0728 loss_ce: 0.0728 2023/03/03 14:18:16 - mmengine - INFO - Epoch(train) [64][10/15] lr: 1.0000e-06 eta: 0:11:54 time: 0.3423 data_time: 0.0244 memory: 19062 loss: 0.0717 loss_ce: 0.0717 2023/03/03 14:18:17 - mmengine - INFO - Epoch(train) [64][11/15] lr: 1.0000e-06 eta: 0:11:54 time: 0.2955 data_time: 0.0018 memory: 16199 loss: 0.0718 loss_ce: 0.0718 2023/03/03 14:18:17 - mmengine - INFO - Epoch(train) [64][12/15] lr: 1.0000e-06 eta: 0:11:53 time: 0.2869 data_time: 0.0018 memory: 14675 loss: 0.0803 loss_ce: 0.0803 2023/03/03 14:18:17 - mmengine - INFO - Epoch(train) [64][13/15] lr: 1.0000e-06 eta: 0:11:53 time: 0.2958 data_time: 0.0017 memory: 17185 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:18:17 - mmengine - INFO - Epoch(train) [64][14/15] lr: 1.0000e-06 eta: 0:11:52 time: 0.2913 data_time: 0.0016 memory: 16223 loss: 0.0808 loss_ce: 0.0808 2023/03/03 14:18:18 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:18:18 - mmengine - INFO - Epoch(train) [64][15/15] lr: 1.0000e-06 eta: 0:11:51 time: 0.2828 data_time: 0.0016 memory: 7262 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:18:18 - mmengine - INFO - Epoch(train) [65][ 1/15] lr: 1.0000e-06 eta: 0:11:52 time: 0.3296 data_time: 0.0631 memory: 16369 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:18:19 - mmengine - INFO - Epoch(train) [65][ 2/15] lr: 1.0000e-06 eta: 0:11:52 time: 0.3693 data_time: 0.0632 memory: 12430 loss: 0.0868 loss_ce: 0.0868 2023/03/03 14:18:19 - mmengine - INFO - Epoch(train) [65][ 3/15] lr: 1.0000e-06 eta: 0:11:52 time: 0.3715 data_time: 0.0632 memory: 16115 loss: 0.0874 loss_ce: 0.0874 2023/03/03 14:18:20 - mmengine - INFO - Epoch(train) [65][ 4/15] lr: 1.0000e-06 eta: 0:11:51 time: 0.3689 data_time: 0.0632 memory: 16089 loss: 0.0911 loss_ce: 0.0911 2023/03/03 14:18:20 - mmengine - INFO - Epoch(train) [65][ 5/15] lr: 1.0000e-06 eta: 0:11:51 time: 0.3473 data_time: 0.0632 memory: 19738 loss: 0.0922 loss_ce: 0.0922 2023/03/03 14:18:20 - mmengine - INFO - Epoch(train) [65][ 6/15] lr: 1.0000e-06 eta: 0:11:50 time: 0.3600 data_time: 0.0632 memory: 23999 loss: 0.0903 loss_ce: 0.0903 2023/03/03 14:18:21 - mmengine - INFO - Epoch(train) [65][ 7/15] lr: 1.0000e-06 eta: 0:11:50 time: 0.3783 data_time: 0.0633 memory: 28546 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:18:21 - mmengine - INFO - Epoch(train) [65][ 8/15] lr: 1.0000e-06 eta: 0:11:50 time: 0.3644 data_time: 0.0633 memory: 17572 loss: 0.0851 loss_ce: 0.0851 2023/03/03 14:18:21 - mmengine - INFO - Epoch(train) [65][ 9/15] lr: 1.0000e-06 eta: 0:11:49 time: 0.3636 data_time: 0.0633 memory: 15911 loss: 0.0831 loss_ce: 0.0831 2023/03/03 14:18:22 - mmengine - INFO - Epoch(train) [65][10/15] lr: 1.0000e-06 eta: 0:11:49 time: 0.4004 data_time: 0.0633 memory: 21740 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:18:22 - mmengine - INFO - Epoch(train) [65][11/15] lr: 1.0000e-06 eta: 0:11:48 time: 0.3393 data_time: 0.0017 memory: 16955 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:18:22 - mmengine - INFO - Epoch(train) [65][12/15] lr: 1.0000e-06 eta: 0:11:48 time: 0.3289 data_time: 0.0017 memory: 17120 loss: 0.0792 loss_ce: 0.0792 2023/03/03 14:18:22 - mmengine - INFO - Epoch(train) [65][13/15] lr: 1.0000e-06 eta: 0:11:48 time: 0.3226 data_time: 0.0017 memory: 15911 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:18:23 - mmengine - INFO - Epoch(train) [65][14/15] lr: 1.0000e-06 eta: 0:11:47 time: 0.3279 data_time: 0.0017 memory: 16530 loss: 0.0735 loss_ce: 0.0735 2023/03/03 14:18:23 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:18:23 - mmengine - INFO - Epoch(train) [65][15/15] lr: 1.0000e-06 eta: 0:11:46 time: 0.3133 data_time: 0.0016 memory: 5116 loss: 0.0843 loss_ce: 0.0843 2023/03/03 14:18:24 - mmengine - INFO - Epoch(train) [66][ 1/15] lr: 1.0000e-06 eta: 0:11:47 time: 0.3581 data_time: 0.0599 memory: 17421 loss: 0.0869 loss_ce: 0.0869 2023/03/03 14:18:24 - mmengine - INFO - Epoch(train) [66][ 2/15] lr: 1.0000e-06 eta: 0:11:47 time: 0.3401 data_time: 0.0599 memory: 18766 loss: 0.0867 loss_ce: 0.0867 2023/03/03 14:18:24 - mmengine - INFO - Epoch(train) [66][ 3/15] lr: 1.0000e-06 eta: 0:11:46 time: 0.3466 data_time: 0.0599 memory: 17730 loss: 0.0829 loss_ce: 0.0829 2023/03/03 14:18:24 - mmengine - INFO - Epoch(train) [66][ 4/15] lr: 1.0000e-06 eta: 0:11:45 time: 0.3477 data_time: 0.0599 memory: 16508 loss: 0.0852 loss_ce: 0.0852 2023/03/03 14:18:25 - mmengine - INFO - Epoch(train) [66][ 5/15] lr: 1.0000e-06 eta: 0:11:45 time: 0.3351 data_time: 0.0600 memory: 18804 loss: 0.0850 loss_ce: 0.0850 2023/03/03 14:18:25 - mmengine - INFO - Epoch(train) [66][ 6/15] lr: 1.0000e-06 eta: 0:11:45 time: 0.3348 data_time: 0.0600 memory: 18247 loss: 0.0848 loss_ce: 0.0848 2023/03/03 14:18:25 - mmengine - INFO - Epoch(train) [66][ 7/15] lr: 1.0000e-06 eta: 0:11:44 time: 0.3148 data_time: 0.0600 memory: 15989 loss: 0.0850 loss_ce: 0.0850 2023/03/03 14:18:26 - mmengine - INFO - Epoch(train) [66][ 8/15] lr: 1.0000e-06 eta: 0:11:43 time: 0.3155 data_time: 0.0600 memory: 16370 loss: 0.0895 loss_ce: 0.0895 2023/03/03 14:18:26 - mmengine - INFO - Epoch(train) [66][ 9/15] lr: 1.0000e-06 eta: 0:11:43 time: 0.3174 data_time: 0.0600 memory: 17272 loss: 0.0897 loss_ce: 0.0897 2023/03/03 14:18:26 - mmengine - INFO - Epoch(train) [66][10/15] lr: 1.0000e-06 eta: 0:11:42 time: 0.3275 data_time: 0.0600 memory: 16199 loss: 0.0816 loss_ce: 0.0816 2023/03/03 14:18:27 - mmengine - INFO - Epoch(train) [66][11/15] lr: 1.0000e-06 eta: 0:11:43 time: 0.3076 data_time: 0.0018 memory: 16804 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:18:27 - mmengine - INFO - Epoch(train) [66][12/15] lr: 1.0000e-06 eta: 0:11:42 time: 0.3124 data_time: 0.0017 memory: 17378 loss: 0.0816 loss_ce: 0.0816 2023/03/03 14:18:28 - mmengine - INFO - Epoch(train) [66][13/15] lr: 1.0000e-06 eta: 0:11:42 time: 0.3247 data_time: 0.0017 memory: 18241 loss: 0.0811 loss_ce: 0.0811 2023/03/03 14:18:28 - mmengine - INFO - Epoch(train) [66][14/15] lr: 1.0000e-06 eta: 0:11:42 time: 0.3327 data_time: 0.0016 memory: 18271 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:18:28 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:18:28 - mmengine - INFO - Epoch(train) [66][15/15] lr: 1.0000e-06 eta: 0:11:41 time: 0.3122 data_time: 0.0016 memory: 5757 loss: 0.0875 loss_ce: 0.0875 2023/03/03 14:18:29 - mmengine - INFO - Epoch(train) [67][ 1/15] lr: 1.0000e-06 eta: 0:11:42 time: 0.3751 data_time: 0.0640 memory: 16219 loss: 0.0929 loss_ce: 0.0929 2023/03/03 14:18:30 - mmengine - INFO - Epoch(train) [67][ 2/15] lr: 1.0000e-06 eta: 0:11:42 time: 0.4204 data_time: 0.0640 memory: 20343 loss: 0.0891 loss_ce: 0.0891 2023/03/03 14:18:30 - mmengine - INFO - Epoch(train) [67][ 3/15] lr: 1.0000e-06 eta: 0:11:41 time: 0.4206 data_time: 0.0641 memory: 16508 loss: 0.0890 loss_ce: 0.0890 2023/03/03 14:18:30 - mmengine - INFO - Epoch(train) [67][ 4/15] lr: 1.0000e-06 eta: 0:11:41 time: 0.4092 data_time: 0.0641 memory: 18070 loss: 0.0872 loss_ce: 0.0872 2023/03/03 14:18:30 - mmengine - INFO - Epoch(train) [67][ 5/15] lr: 1.0000e-06 eta: 0:11:40 time: 0.4089 data_time: 0.0641 memory: 15962 loss: 0.0837 loss_ce: 0.0837 2023/03/03 14:18:31 - mmengine - INFO - Epoch(train) [67][ 6/15] lr: 1.0000e-06 eta: 0:11:40 time: 0.3900 data_time: 0.0641 memory: 16199 loss: 0.0874 loss_ce: 0.0874 2023/03/03 14:18:31 - mmengine - INFO - Epoch(train) [67][ 7/15] lr: 1.0000e-06 eta: 0:11:39 time: 0.3842 data_time: 0.0640 memory: 18070 loss: 0.0863 loss_ce: 0.0863 2023/03/03 14:18:31 - mmengine - INFO - Epoch(train) [67][ 8/15] lr: 1.0000e-06 eta: 0:11:39 time: 0.3741 data_time: 0.0640 memory: 15911 loss: 0.0894 loss_ce: 0.0894 2023/03/03 14:18:31 - mmengine - INFO - Epoch(train) [67][ 9/15] lr: 1.0000e-06 eta: 0:11:38 time: 0.3658 data_time: 0.0641 memory: 16199 loss: 0.0904 loss_ce: 0.0904 2023/03/03 14:18:32 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:18:32 - mmengine - INFO - Epoch(train) [67][10/15] lr: 1.0000e-06 eta: 0:11:38 time: 0.3680 data_time: 0.0641 memory: 17421 loss: 0.0857 loss_ce: 0.0857 2023/03/03 14:18:32 - mmengine - INFO - Epoch(train) [67][11/15] lr: 1.0000e-06 eta: 0:11:37 time: 0.3139 data_time: 0.0017 memory: 18559 loss: 0.0838 loss_ce: 0.0838 2023/03/03 14:18:32 - mmengine - INFO - Epoch(train) [67][12/15] lr: 1.0000e-06 eta: 0:11:37 time: 0.2699 data_time: 0.0016 memory: 17622 loss: 0.0848 loss_ce: 0.0848 2023/03/03 14:18:33 - mmengine - INFO - Epoch(train) [67][13/15] lr: 1.0000e-06 eta: 0:11:36 time: 0.2729 data_time: 0.0016 memory: 19208 loss: 0.0830 loss_ce: 0.0830 2023/03/03 14:18:33 - mmengine - INFO - Epoch(train) [67][14/15] lr: 1.0000e-06 eta: 0:11:36 time: 0.2916 data_time: 0.0015 memory: 18528 loss: 0.0824 loss_ce: 0.0824 2023/03/03 14:18:33 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:18:33 - mmengine - INFO - Epoch(train) [67][15/15] lr: 1.0000e-06 eta: 0:11:35 time: 0.2846 data_time: 0.0015 memory: 3758 loss: 0.0890 loss_ce: 0.0890 2023/03/03 14:18:34 - mmengine - INFO - Epoch(train) [68][ 1/15] lr: 1.0000e-06 eta: 0:11:37 time: 0.3444 data_time: 0.0325 memory: 26442 loss: 0.0849 loss_ce: 0.0849 2023/03/03 14:18:34 - mmengine - INFO - Epoch(train) [68][ 2/15] lr: 1.0000e-06 eta: 0:11:36 time: 0.3447 data_time: 0.0326 memory: 17572 loss: 0.0847 loss_ce: 0.0847 2023/03/03 14:18:35 - mmengine - INFO - Epoch(train) [68][ 3/15] lr: 1.0000e-06 eta: 0:11:36 time: 0.3501 data_time: 0.0326 memory: 18409 loss: 0.0827 loss_ce: 0.0827 2023/03/03 14:18:35 - mmengine - INFO - Epoch(train) [68][ 4/15] lr: 1.0000e-06 eta: 0:11:35 time: 0.3647 data_time: 0.0326 memory: 21725 loss: 0.0804 loss_ce: 0.0804 2023/03/03 14:18:35 - mmengine - INFO - Epoch(train) [68][ 5/15] lr: 1.0000e-06 eta: 0:11:35 time: 0.3644 data_time: 0.0326 memory: 15353 loss: 0.0788 loss_ce: 0.0788 2023/03/03 14:18:36 - mmengine - INFO - Epoch(train) [68][ 6/15] lr: 1.0000e-06 eta: 0:11:34 time: 0.3546 data_time: 0.0326 memory: 15767 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:18:36 - mmengine - INFO - Epoch(train) [68][ 7/15] lr: 1.0000e-06 eta: 0:11:34 time: 0.3568 data_time: 0.0326 memory: 15614 loss: 0.0768 loss_ce: 0.0768 2023/03/03 14:18:36 - mmengine - INFO - Epoch(train) [68][ 8/15] lr: 1.0000e-06 eta: 0:11:33 time: 0.3527 data_time: 0.0326 memory: 15767 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:18:37 - mmengine - INFO - Epoch(train) [68][ 9/15] lr: 1.0000e-06 eta: 0:11:33 time: 0.3608 data_time: 0.0326 memory: 10296 loss: 0.0812 loss_ce: 0.0812 2023/03/03 14:18:37 - mmengine - INFO - Epoch(train) [68][10/15] lr: 1.0000e-06 eta: 0:11:32 time: 0.3658 data_time: 0.0326 memory: 18070 loss: 0.0705 loss_ce: 0.0705 2023/03/03 14:18:37 - mmengine - INFO - Epoch(train) [68][11/15] lr: 1.0000e-06 eta: 0:11:32 time: 0.2914 data_time: 0.0017 memory: 17421 loss: 0.0725 loss_ce: 0.0725 2023/03/03 14:18:37 - mmengine - INFO - Epoch(train) [68][12/15] lr: 1.0000e-06 eta: 0:11:31 time: 0.2886 data_time: 0.0017 memory: 12785 loss: 0.0742 loss_ce: 0.0742 2023/03/03 14:18:38 - mmengine - INFO - Epoch(train) [68][13/15] lr: 1.0000e-06 eta: 0:11:31 time: 0.3050 data_time: 0.0016 memory: 18070 loss: 0.0749 loss_ce: 0.0749 2023/03/03 14:18:38 - mmengine - INFO - Epoch(train) [68][14/15] lr: 1.0000e-06 eta: 0:11:31 time: 0.2897 data_time: 0.0016 memory: 15767 loss: 0.0754 loss_ce: 0.0754 2023/03/03 14:18:38 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:18:38 - mmengine - INFO - Epoch(train) [68][15/15] lr: 1.0000e-06 eta: 0:11:30 time: 0.2933 data_time: 0.0016 memory: 7028 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:18:39 - mmengine - INFO - Epoch(train) [69][ 1/15] lr: 1.0000e-06 eta: 0:11:31 time: 0.3583 data_time: 0.0540 memory: 19265 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:18:39 - mmengine - INFO - Epoch(train) [69][ 2/15] lr: 1.0000e-06 eta: 0:11:30 time: 0.3509 data_time: 0.0541 memory: 17968 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:18:40 - mmengine - INFO - Epoch(train) [69][ 3/15] lr: 1.0000e-06 eta: 0:11:30 time: 0.3522 data_time: 0.0542 memory: 16804 loss: 0.0772 loss_ce: 0.0772 2023/03/03 14:18:40 - mmengine - INFO - Epoch(train) [69][ 4/15] lr: 1.0000e-06 eta: 0:11:29 time: 0.3412 data_time: 0.0542 memory: 18274 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:18:40 - mmengine - INFO - Epoch(train) [69][ 5/15] lr: 1.0000e-06 eta: 0:11:29 time: 0.3420 data_time: 0.0543 memory: 18586 loss: 0.0761 loss_ce: 0.0761 2023/03/03 14:18:41 - mmengine - INFO - Epoch(train) [69][ 6/15] lr: 1.0000e-06 eta: 0:11:28 time: 0.3470 data_time: 0.0542 memory: 22834 loss: 0.0787 loss_ce: 0.0787 2023/03/03 14:18:41 - mmengine - INFO - Epoch(train) [69][ 7/15] lr: 1.0000e-06 eta: 0:11:28 time: 0.3494 data_time: 0.0542 memory: 16976 loss: 0.0831 loss_ce: 0.0831 2023/03/03 14:18:41 - mmengine - INFO - Epoch(train) [69][ 8/15] lr: 1.0000e-06 eta: 0:11:27 time: 0.3255 data_time: 0.0542 memory: 16370 loss: 0.0848 loss_ce: 0.0848 2023/03/03 14:18:41 - mmengine - INFO - Epoch(train) [69][ 9/15] lr: 1.0000e-06 eta: 0:11:27 time: 0.3325 data_time: 0.0542 memory: 16930 loss: 0.0827 loss_ce: 0.0827 2023/03/03 14:18:42 - mmengine - INFO - Epoch(train) [69][10/15] lr: 1.0000e-06 eta: 0:11:26 time: 0.3420 data_time: 0.0542 memory: 17512 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:18:42 - mmengine - INFO - Epoch(train) [69][11/15] lr: 1.0000e-06 eta: 0:11:26 time: 0.2762 data_time: 0.0018 memory: 15175 loss: 0.0855 loss_ce: 0.0855 2023/03/03 14:18:42 - mmengine - INFO - Epoch(train) [69][12/15] lr: 1.0000e-06 eta: 0:11:25 time: 0.2733 data_time: 0.0017 memory: 18070 loss: 0.0881 loss_ce: 0.0881 2023/03/03 14:18:42 - mmengine - INFO - Epoch(train) [69][13/15] lr: 1.0000e-06 eta: 0:11:25 time: 0.2729 data_time: 0.0016 memory: 16508 loss: 0.0884 loss_ce: 0.0884 2023/03/03 14:18:43 - mmengine - INFO - Epoch(train) [69][14/15] lr: 1.0000e-06 eta: 0:11:24 time: 0.2655 data_time: 0.0015 memory: 16654 loss: 0.0880 loss_ce: 0.0880 2023/03/03 14:18:43 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:18:43 - mmengine - INFO - Epoch(train) [69][15/15] lr: 1.0000e-06 eta: 0:11:23 time: 0.2553 data_time: 0.0015 memory: 5144 loss: 0.1044 loss_ce: 0.1044 2023/03/03 14:18:44 - mmengine - INFO - Epoch(train) [70][ 1/15] lr: 1.0000e-06 eta: 0:11:24 time: 0.3095 data_time: 0.0664 memory: 14516 loss: 0.1002 loss_ce: 0.1002 2023/03/03 14:18:44 - mmengine - INFO - Epoch(train) [70][ 2/15] lr: 1.0000e-06 eta: 0:11:24 time: 0.3129 data_time: 0.0664 memory: 16849 loss: 0.0932 loss_ce: 0.0932 2023/03/03 14:18:44 - mmengine - INFO - Epoch(train) [70][ 3/15] lr: 1.0000e-06 eta: 0:11:23 time: 0.3128 data_time: 0.0665 memory: 17120 loss: 0.0959 loss_ce: 0.0959 2023/03/03 14:18:45 - mmengine - INFO - Epoch(train) [70][ 4/15] lr: 1.0000e-06 eta: 0:11:23 time: 0.3268 data_time: 0.0665 memory: 21190 loss: 0.0996 loss_ce: 0.0996 2023/03/03 14:18:45 - mmengine - INFO - Epoch(train) [70][ 5/15] lr: 1.0000e-06 eta: 0:11:23 time: 0.3321 data_time: 0.0666 memory: 23672 loss: 0.0967 loss_ce: 0.0967 2023/03/03 14:18:45 - mmengine - INFO - Epoch(train) [70][ 6/15] lr: 1.0000e-06 eta: 0:11:22 time: 0.3356 data_time: 0.0666 memory: 18024 loss: 0.0907 loss_ce: 0.0907 2023/03/03 14:18:46 - mmengine - INFO - Epoch(train) [70][ 7/15] lr: 1.0000e-06 eta: 0:11:22 time: 0.3523 data_time: 0.0666 memory: 16976 loss: 0.0937 loss_ce: 0.0937 2023/03/03 14:18:46 - mmengine - INFO - Epoch(train) [70][ 8/15] lr: 1.0000e-06 eta: 0:11:21 time: 0.3496 data_time: 0.0666 memory: 17120 loss: 0.0963 loss_ce: 0.0963 2023/03/03 14:18:46 - mmengine - INFO - Epoch(train) [70][ 9/15] lr: 1.0000e-06 eta: 0:11:21 time: 0.3569 data_time: 0.0667 memory: 17171 loss: 0.0951 loss_ce: 0.0951 2023/03/03 14:18:46 - mmengine - INFO - Epoch(train) [70][10/15] lr: 1.0000e-06 eta: 0:11:20 time: 0.3688 data_time: 0.0666 memory: 16199 loss: 0.0861 loss_ce: 0.0861 2023/03/03 14:18:47 - mmengine - INFO - Epoch(train) [70][11/15] lr: 1.0000e-06 eta: 0:11:20 time: 0.3170 data_time: 0.0018 memory: 19296 loss: 0.0879 loss_ce: 0.0879 2023/03/03 14:18:47 - mmengine - INFO - Epoch(train) [70][12/15] lr: 1.0000e-06 eta: 0:11:19 time: 0.3156 data_time: 0.0017 memory: 15911 loss: 0.0910 loss_ce: 0.0910 2023/03/03 14:18:48 - mmengine - INFO - Epoch(train) [70][13/15] lr: 1.0000e-06 eta: 0:11:19 time: 0.3362 data_time: 0.0017 memory: 17421 loss: 0.0872 loss_ce: 0.0872 2023/03/03 14:18:48 - mmengine - INFO - Epoch(train) [70][14/15] lr: 1.0000e-06 eta: 0:11:19 time: 0.3159 data_time: 0.0016 memory: 16530 loss: 0.0845 loss_ce: 0.0845 2023/03/03 14:18:48 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:18:48 - mmengine - INFO - Epoch(train) [70][15/15] lr: 1.0000e-06 eta: 0:11:18 time: 0.2918 data_time: 0.0016 memory: 4523 loss: 0.0885 loss_ce: 0.0885 2023/03/03 14:18:50 - mmengine - INFO - Epoch(val) [70][ 1/59] eta: 0:01:30 time: 1.1207 data_time: 0.0033 memory: 981 2023/03/03 14:18:50 - mmengine - INFO - Epoch(val) [70][ 2/59] eta: 0:01:07 time: 1.0330 data_time: 0.0033 memory: 981 2023/03/03 14:18:52 - mmengine - INFO - Epoch(val) [70][ 3/59] eta: 0:01:12 time: 1.0685 data_time: 0.0033 memory: 1003 2023/03/03 14:18:52 - mmengine - INFO - Epoch(val) [70][ 4/59] eta: 0:01:00 time: 1.0528 data_time: 0.0034 memory: 981 2023/03/03 14:18:55 - mmengine - INFO - Epoch(val) [70][ 5/59] eta: 0:01:20 time: 1.2909 data_time: 0.0034 memory: 1016 2023/03/03 14:18:58 - mmengine - INFO - Epoch(val) [70][ 6/59] eta: 0:01:28 time: 1.4876 data_time: 0.0034 memory: 981 2023/03/03 14:18:58 - mmengine - INFO - Epoch(val) [70][ 7/59] eta: 0:01:16 time: 1.4212 data_time: 0.0035 memory: 1043 2023/03/03 14:18:59 - mmengine - INFO - Epoch(val) [70][ 8/59] eta: 0:01:10 time: 1.2765 data_time: 0.0035 memory: 1016 2023/03/03 14:19:00 - mmengine - INFO - Epoch(val) [70][ 9/59] eta: 0:01:06 time: 1.2402 data_time: 0.0035 memory: 981 2023/03/03 14:19:01 - mmengine - INFO - Epoch(val) [70][10/59] eta: 0:01:05 time: 1.3392 data_time: 0.0035 memory: 981 2023/03/03 14:19:02 - mmengine - INFO - Epoch(val) [70][11/59] eta: 0:01:00 time: 1.2329 data_time: 0.0009 memory: 981 2023/03/03 14:19:05 - mmengine - INFO - Epoch(val) [70][12/59] eta: 0:01:07 time: 1.4966 data_time: 0.0009 memory: 1016 2023/03/03 14:19:07 - mmengine - INFO - Epoch(val) [70][13/59] eta: 0:01:08 time: 1.5509 data_time: 0.0010 memory: 981 2023/03/03 14:19:09 - mmengine - INFO - Epoch(val) [70][14/59] eta: 0:01:06 time: 1.6175 data_time: 0.0009 memory: 890 2023/03/03 14:19:09 - mmengine - INFO - Epoch(val) [70][15/59] eta: 0:01:00 time: 1.3168 data_time: 0.0009 memory: 981 2023/03/03 14:19:09 - mmengine - INFO - Epoch(val) [70][16/59] eta: 0:00:56 time: 1.1049 data_time: 0.0009 memory: 981 2023/03/03 14:19:09 - mmengine - INFO - Epoch(val) [70][17/59] eta: 0:00:52 time: 1.1208 data_time: 0.0008 memory: 981 2023/03/03 14:19:10 - mmengine - INFO - Epoch(val) [70][18/59] eta: 0:00:49 time: 1.0713 data_time: 0.0008 memory: 981 2023/03/03 14:19:11 - mmengine - INFO - Epoch(val) [70][19/59] eta: 0:00:47 time: 1.0714 data_time: 0.0008 memory: 981 2023/03/03 14:19:11 - mmengine - INFO - Epoch(val) [70][20/59] eta: 0:00:45 time: 0.9722 data_time: 0.0008 memory: 981 2023/03/03 14:19:13 - mmengine - INFO - Epoch(val) [70][21/59] eta: 0:00:45 time: 1.1089 data_time: 0.0009 memory: 981 2023/03/03 14:19:13 - mmengine - INFO - Epoch(val) [70][22/59] eta: 0:00:42 time: 0.7819 data_time: 0.0009 memory: 981 2023/03/03 14:19:14 - mmengine - INFO - Epoch(val) [70][23/59] eta: 0:00:40 time: 0.6418 data_time: 0.0008 memory: 981 2023/03/03 14:19:14 - mmengine - INFO - Epoch(val) [70][24/59] eta: 0:00:38 time: 0.5581 data_time: 0.0008 memory: 962 2023/03/03 14:19:14 - mmengine - INFO - Epoch(val) [70][25/59] eta: 0:00:36 time: 0.5893 data_time: 0.0008 memory: 981 2023/03/03 14:19:15 - mmengine - INFO - Epoch(val) [70][26/59] eta: 0:00:34 time: 0.5730 data_time: 0.0008 memory: 981 2023/03/03 14:19:15 - mmengine - INFO - Epoch(val) [70][27/59] eta: 0:00:32 time: 0.5729 data_time: 0.0008 memory: 981 2023/03/03 14:19:15 - mmengine - INFO - Epoch(val) [70][28/59] eta: 0:00:30 time: 0.5732 data_time: 0.0008 memory: 981 2023/03/03 14:19:17 - mmengine - INFO - Epoch(val) [70][29/59] eta: 0:00:29 time: 0.6097 data_time: 0.0008 memory: 981 2023/03/03 14:19:18 - mmengine - INFO - Epoch(val) [70][30/59] eta: 0:00:28 time: 0.6585 data_time: 0.0008 memory: 999 2023/03/03 14:19:18 - mmengine - INFO - Epoch(val) [70][31/59] eta: 0:00:27 time: 0.5382 data_time: 0.0007 memory: 981 2023/03/03 14:19:19 - mmengine - INFO - Epoch(val) [70][32/59] eta: 0:00:26 time: 0.6369 data_time: 0.0007 memory: 981 2023/03/03 14:19:20 - mmengine - INFO - Epoch(val) [70][33/59] eta: 0:00:24 time: 0.5876 data_time: 0.0007 memory: 981 2023/03/03 14:19:20 - mmengine - INFO - Epoch(val) [70][34/59] eta: 0:00:23 time: 0.5714 data_time: 0.0007 memory: 981 2023/03/03 14:19:20 - mmengine - INFO - Epoch(val) [70][35/59] eta: 0:00:21 time: 0.5551 data_time: 0.0008 memory: 981 2023/03/03 14:19:21 - mmengine - INFO - Epoch(val) [70][36/59] eta: 0:00:20 time: 0.5712 data_time: 0.0008 memory: 981 2023/03/03 14:19:21 - mmengine - INFO - Epoch(val) [70][37/59] eta: 0:00:19 time: 0.5549 data_time: 0.0008 memory: 981 2023/03/03 14:19:21 - mmengine - INFO - Epoch(val) [70][38/59] eta: 0:00:18 time: 0.5873 data_time: 0.0008 memory: 981 2023/03/03 14:19:22 - mmengine - INFO - Epoch(val) [70][39/59] eta: 0:00:17 time: 0.5009 data_time: 0.0008 memory: 987 2023/03/03 14:19:23 - mmengine - INFO - Epoch(val) [70][40/59] eta: 0:00:16 time: 0.5007 data_time: 0.0008 memory: 981 2023/03/03 14:19:24 - mmengine - INFO - Epoch(val) [70][41/59] eta: 0:00:15 time: 0.5508 data_time: 0.0008 memory: 986 2023/03/03 14:19:24 - mmengine - INFO - Epoch(val) [70][42/59] eta: 0:00:14 time: 0.5009 data_time: 0.0008 memory: 981 2023/03/03 14:19:25 - mmengine - INFO - Epoch(val) [70][43/59] eta: 0:00:13 time: 0.5663 data_time: 0.0008 memory: 976 2023/03/03 14:19:26 - mmengine - INFO - Epoch(val) [70][44/59] eta: 0:00:12 time: 0.5990 data_time: 0.0008 memory: 1003 2023/03/03 14:19:28 - mmengine - INFO - Epoch(val) [70][45/59] eta: 0:00:12 time: 0.7672 data_time: 0.0008 memory: 981 2023/03/03 14:19:29 - mmengine - INFO - Epoch(val) [70][46/59] eta: 0:00:11 time: 0.8000 data_time: 0.0008 memory: 981 2023/03/03 14:19:29 - mmengine - INFO - Epoch(val) [70][47/59] eta: 0:00:10 time: 0.8322 data_time: 0.0008 memory: 936 2023/03/03 14:19:30 - mmengine - INFO - Epoch(val) [70][48/59] eta: 0:00:09 time: 0.8162 data_time: 0.0008 memory: 1000 2023/03/03 14:19:31 - mmengine - INFO - Epoch(val) [70][49/59] eta: 0:00:08 time: 0.8660 data_time: 0.0008 memory: 981 2023/03/03 14:19:31 - mmengine - INFO - Epoch(val) [70][50/59] eta: 0:00:07 time: 0.8662 data_time: 0.0008 memory: 987 2023/03/03 14:19:33 - mmengine - INFO - Epoch(val) [70][51/59] eta: 0:00:07 time: 0.9178 data_time: 0.0008 memory: 981 2023/03/03 14:19:34 - mmengine - INFO - Epoch(val) [70][52/59] eta: 0:00:06 time: 0.9681 data_time: 0.0008 memory: 981 2023/03/03 14:19:35 - mmengine - INFO - Epoch(val) [70][53/59] eta: 0:00:05 time: 0.9516 data_time: 0.0008 memory: 962 2023/03/03 14:19:36 - mmengine - INFO - Epoch(val) [70][54/59] eta: 0:00:04 time: 0.9680 data_time: 0.0008 memory: 981 2023/03/03 14:19:36 - mmengine - INFO - Epoch(val) [70][55/59] eta: 0:00:03 time: 0.8488 data_time: 0.0008 memory: 981 2023/03/03 14:19:37 - mmengine - INFO - Epoch(val) [70][56/59] eta: 0:00:02 time: 0.8488 data_time: 0.0007 memory: 981 2023/03/03 14:19:39 - mmengine - INFO - Epoch(val) [70][57/59] eta: 0:00:01 time: 1.0230 data_time: 0.0007 memory: 981 2023/03/03 14:19:41 - mmengine - INFO - Epoch(val) [70][58/59] eta: 0:00:00 time: 1.1064 data_time: 0.0007 memory: 1016 2023/03/03 14:19:41 - mmengine - INFO - Epoch(val) [70][59/59] eta: 0:00:00 time: 1.0404 data_time: 0.0007 memory: 981 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.80, recall: 0.8164, precision: 0.8363, hmean: 0.8262 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.81, recall: 0.8155, precision: 0.8369, hmean: 0.8261 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.82, recall: 0.8155, precision: 0.8385, hmean: 0.8269 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.83, recall: 0.8146, precision: 0.8399, hmean: 0.8271 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.84, recall: 0.8146, precision: 0.8439, hmean: 0.8290 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.85, recall: 0.8137, precision: 0.8470, hmean: 0.8300 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.86, recall: 0.8119, precision: 0.8515, hmean: 0.8312 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.87, recall: 0.8082, precision: 0.8551, hmean: 0.8310 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.88, recall: 0.8073, precision: 0.8583, hmean: 0.8320 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.89, recall: 0.8055, precision: 0.8622, hmean: 0.8329 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.90, recall: 0.8018, precision: 0.8642, hmean: 0.8318 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.91, recall: 0.7982, precision: 0.8697, hmean: 0.8324 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.92, recall: 0.7963, precision: 0.8711, hmean: 0.8321 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.93, recall: 0.7863, precision: 0.8706, hmean: 0.8263 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.94, recall: 0.7790, precision: 0.8722, hmean: 0.8230 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.95, recall: 0.7735, precision: 0.8750, hmean: 0.8211 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.96, recall: 0.7589, precision: 0.8775, hmean: 0.8139 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.97, recall: 0.7489, precision: 0.8798, hmean: 0.8091 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.98, recall: 0.7370, precision: 0.8839, hmean: 0.8038 2023/03/03 14:20:09 - mmengine - INFO - text score threshold: 0.99, recall: 0.7215, precision: 0.8927, hmean: 0.7980 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.80, recall: 0.8265, precision: 0.9059, hmean: 0.8644 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.81, recall: 0.8256, precision: 0.9067, hmean: 0.8642 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.82, recall: 0.8256, precision: 0.9076, hmean: 0.8647 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.83, recall: 0.8247, precision: 0.9085, hmean: 0.8645 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.84, recall: 0.8247, precision: 0.9103, hmean: 0.8654 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.85, recall: 0.8237, precision: 0.9130, hmean: 0.8661 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.86, recall: 0.8219, precision: 0.9128, hmean: 0.8650 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.87, recall: 0.8164, precision: 0.9132, hmean: 0.8621 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.88, recall: 0.8155, precision: 0.9150, hmean: 0.8624 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.89, recall: 0.8128, precision: 0.9175, hmean: 0.8620 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.90, recall: 0.8082, precision: 0.9190, hmean: 0.8601 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.91, recall: 0.8046, precision: 0.9206, hmean: 0.8587 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.92, recall: 0.8018, precision: 0.9213, hmean: 0.8574 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.93, recall: 0.7918, precision: 0.9204, hmean: 0.8513 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.94, recall: 0.7836, precision: 0.9216, hmean: 0.8470 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.95, recall: 0.7772, precision: 0.9240, hmean: 0.8442 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.96, recall: 0.7607, precision: 0.9245, hmean: 0.8347 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.97, recall: 0.7489, precision: 0.9255, hmean: 0.8279 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.98, recall: 0.7370, precision: 0.9276, hmean: 0.8214 2023/03/03 14:20:12 - mmengine - INFO - text score threshold: 0.99, recall: 0.7215, precision: 0.9327, hmean: 0.8136 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.80, recall: 0.7479, precision: 0.9545, hmean: 0.8387 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.81, recall: 0.7461, precision: 0.9544, hmean: 0.8375 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.82, recall: 0.7461, precision: 0.9544, hmean: 0.8375 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.83, recall: 0.7461, precision: 0.9544, hmean: 0.8375 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.84, recall: 0.7452, precision: 0.9544, hmean: 0.8369 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.85, recall: 0.7443, precision: 0.9555, hmean: 0.8368 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.86, recall: 0.7425, precision: 0.9553, hmean: 0.8356 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.87, recall: 0.7379, precision: 0.9562, hmean: 0.8330 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.88, recall: 0.7370, precision: 0.9573, hmean: 0.8328 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.89, recall: 0.7333, precision: 0.9582, hmean: 0.8308 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.90, recall: 0.7288, precision: 0.9580, hmean: 0.8278 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.91, recall: 0.7251, precision: 0.9601, hmean: 0.8262 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.92, recall: 0.7224, precision: 0.9611, hmean: 0.8248 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.93, recall: 0.7142, precision: 0.9607, hmean: 0.8193 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.94, recall: 0.7068, precision: 0.9603, hmean: 0.8143 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.95, recall: 0.7014, precision: 0.9624, hmean: 0.8114 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.96, recall: 0.6858, precision: 0.9641, hmean: 0.8015 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.97, recall: 0.6767, precision: 0.9648, hmean: 0.7955 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.98, recall: 0.6658, precision: 0.9656, hmean: 0.7881 2023/03/03 14:20:15 - mmengine - INFO - text score threshold: 0.99, recall: 0.6511, precision: 0.9661, hmean: 0.7780 2023/03/03 14:20:15 - mmengine - INFO - Epoch(val) [70][59/59] generic/precision: 0.8622 generic/recall: 0.8055 generic/hmean: 0.8329 weak/precision: 0.9130 weak/recall: 0.8237 weak/hmean: 0.8661 strong/precision: 0.9545 strong/recall: 0.7479 strong/hmean: 0.8387 2023/03/03 14:20:15 - mmengine - INFO - The previous best checkpoint mmocr/projects/SPTS/work_dirs/spts_resnet50_350e_icdar2013/best_generic/hmean_epoch_60.pth is removed 2023/03/03 14:20:17 - mmengine - INFO - The best checkpoint with 0.8329 generic/hmean at 70 epoch is saved to best_generic/hmean_epoch_70.pth. 2023/03/03 14:20:18 - mmengine - INFO - Epoch(train) [71][ 1/15] lr: 1.0000e-06 eta: 0:11:19 time: 0.3633 data_time: 0.0542 memory: 29451 loss: 0.0886 loss_ce: 0.0886 2023/03/03 14:20:18 - mmengine - INFO - Epoch(train) [71][ 2/15] lr: 1.0000e-06 eta: 0:11:18 time: 0.3490 data_time: 0.0543 memory: 15112 loss: 0.0827 loss_ce: 0.0827 2023/03/03 14:20:18 - mmengine - INFO - Epoch(train) [71][ 3/15] lr: 1.0000e-06 eta: 0:11:18 time: 0.3627 data_time: 0.0543 memory: 19280 loss: 0.0792 loss_ce: 0.0792 2023/03/03 14:20:19 - mmengine - INFO - Epoch(train) [71][ 4/15] lr: 1.0000e-06 eta: 0:11:17 time: 0.3512 data_time: 0.0543 memory: 17284 loss: 0.0782 loss_ce: 0.0782 2023/03/03 14:20:19 - mmengine - INFO - Epoch(train) [71][ 5/15] lr: 1.0000e-06 eta: 0:11:17 time: 0.3549 data_time: 0.0543 memory: 19454 loss: 0.0718 loss_ce: 0.0718 2023/03/03 14:20:19 - mmengine - INFO - Epoch(train) [71][ 6/15] lr: 1.0000e-06 eta: 0:11:16 time: 0.3345 data_time: 0.0543 memory: 15571 loss: 0.0694 loss_ce: 0.0694 2023/03/03 14:20:19 - mmengine - INFO - Epoch(train) [71][ 7/15] lr: 1.0000e-06 eta: 0:11:16 time: 0.3328 data_time: 0.0543 memory: 17572 loss: 0.0692 loss_ce: 0.0692 2023/03/03 14:20:20 - mmengine - INFO - Epoch(train) [71][ 8/15] lr: 1.0000e-06 eta: 0:11:16 time: 0.3311 data_time: 0.0543 memory: 17561 loss: 0.0665 loss_ce: 0.0665 2023/03/03 14:20:20 - mmengine - INFO - Epoch(train) [71][ 9/15] lr: 1.0000e-06 eta: 0:11:15 time: 0.3336 data_time: 0.0543 memory: 18477 loss: 0.0705 loss_ce: 0.0705 2023/03/03 14:20:21 - mmengine - INFO - Epoch(train) [71][10/15] lr: 1.0000e-06 eta: 0:11:15 time: 0.3637 data_time: 0.0543 memory: 16849 loss: 0.0673 loss_ce: 0.0673 2023/03/03 14:20:21 - mmengine - INFO - Epoch(train) [71][11/15] lr: 1.0000e-06 eta: 0:11:14 time: 0.2904 data_time: 0.0017 memory: 16654 loss: 0.0670 loss_ce: 0.0670 2023/03/03 14:20:21 - mmengine - INFO - Epoch(train) [71][12/15] lr: 1.0000e-06 eta: 0:11:14 time: 0.3042 data_time: 0.0016 memory: 16200 loss: 0.0721 loss_ce: 0.0721 2023/03/03 14:20:21 - mmengine - INFO - Epoch(train) [71][13/15] lr: 1.0000e-06 eta: 0:11:13 time: 0.2905 data_time: 0.0016 memory: 16976 loss: 0.0729 loss_ce: 0.0729 2023/03/03 14:20:22 - mmengine - INFO - Epoch(train) [71][14/15] lr: 1.0000e-06 eta: 0:11:13 time: 0.2967 data_time: 0.0015 memory: 16976 loss: 0.0778 loss_ce: 0.0778 2023/03/03 14:20:22 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:20:22 - mmengine - INFO - Epoch(train) [71][15/15] lr: 1.0000e-06 eta: 0:11:12 time: 0.2865 data_time: 0.0015 memory: 4493 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:20:23 - mmengine - INFO - Epoch(train) [72][ 1/15] lr: 1.0000e-06 eta: 0:11:13 time: 0.3666 data_time: 0.0394 memory: 21063 loss: 0.0849 loss_ce: 0.0849 2023/03/03 14:20:23 - mmengine - INFO - Epoch(train) [72][ 2/15] lr: 1.0000e-06 eta: 0:11:13 time: 0.3685 data_time: 0.0395 memory: 15631 loss: 0.0858 loss_ce: 0.0858 2023/03/03 14:20:23 - mmengine - INFO - Epoch(train) [72][ 3/15] lr: 1.0000e-06 eta: 0:11:12 time: 0.3442 data_time: 0.0396 memory: 17424 loss: 0.0899 loss_ce: 0.0899 2023/03/03 14:20:24 - mmengine - INFO - Epoch(train) [72][ 4/15] lr: 1.0000e-06 eta: 0:11:12 time: 0.3474 data_time: 0.0396 memory: 15966 loss: 0.0865 loss_ce: 0.0865 2023/03/03 14:20:24 - mmengine - INFO - Epoch(train) [72][ 5/15] lr: 1.0000e-06 eta: 0:11:11 time: 0.3316 data_time: 0.0396 memory: 27629 loss: 0.0881 loss_ce: 0.0881 2023/03/03 14:20:24 - mmengine - INFO - Epoch(train) [72][ 6/15] lr: 1.0000e-06 eta: 0:11:10 time: 0.3268 data_time: 0.0396 memory: 15346 loss: 0.0852 loss_ce: 0.0852 2023/03/03 14:20:25 - mmengine - INFO - Epoch(train) [72][ 7/15] lr: 1.0000e-06 eta: 0:11:11 time: 0.3476 data_time: 0.0397 memory: 17446 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:20:25 - mmengine - INFO - Epoch(train) [72][ 8/15] lr: 1.0000e-06 eta: 0:11:10 time: 0.3478 data_time: 0.0397 memory: 17572 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:20:25 - mmengine - INFO - Epoch(train) [72][ 9/15] lr: 1.0000e-06 eta: 0:11:10 time: 0.3735 data_time: 0.0397 memory: 16508 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:20:26 - mmengine - INFO - Epoch(train) [72][10/15] lr: 1.0000e-06 eta: 0:11:09 time: 0.3792 data_time: 0.0397 memory: 15494 loss: 0.0764 loss_ce: 0.0764 2023/03/03 14:20:26 - mmengine - INFO - Epoch(train) [72][11/15] lr: 1.0000e-06 eta: 0:11:09 time: 0.3176 data_time: 0.0018 memory: 17120 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:20:26 - mmengine - INFO - Epoch(train) [72][12/15] lr: 1.0000e-06 eta: 0:11:09 time: 0.3268 data_time: 0.0017 memory: 18394 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:20:27 - mmengine - INFO - Epoch(train) [72][13/15] lr: 1.0000e-06 eta: 0:11:08 time: 0.3259 data_time: 0.0017 memory: 18241 loss: 0.0760 loss_ce: 0.0760 2023/03/03 14:20:27 - mmengine - INFO - Epoch(train) [72][14/15] lr: 1.0000e-06 eta: 0:11:08 time: 0.3178 data_time: 0.0017 memory: 17095 loss: 0.0798 loss_ce: 0.0798 2023/03/03 14:20:27 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:20:27 - mmengine - INFO - Epoch(train) [72][15/15] lr: 1.0000e-06 eta: 0:11:07 time: 0.3167 data_time: 0.0017 memory: 5311 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:20:28 - mmengine - INFO - Epoch(train) [73][ 1/15] lr: 1.0000e-06 eta: 0:11:08 time: 0.3853 data_time: 0.0675 memory: 17572 loss: 0.0841 loss_ce: 0.0841 2023/03/03 14:20:28 - mmengine - INFO - Epoch(train) [73][ 2/15] lr: 1.0000e-06 eta: 0:11:07 time: 0.3686 data_time: 0.0675 memory: 17272 loss: 0.0873 loss_ce: 0.0873 2023/03/03 14:20:29 - mmengine - INFO - Epoch(train) [73][ 3/15] lr: 1.0000e-06 eta: 0:11:07 time: 0.3729 data_time: 0.0675 memory: 17777 loss: 0.0887 loss_ce: 0.0887 2023/03/03 14:20:29 - mmengine - INFO - Epoch(train) [73][ 4/15] lr: 1.0000e-06 eta: 0:11:06 time: 0.3435 data_time: 0.0676 memory: 17421 loss: 0.0900 loss_ce: 0.0900 2023/03/03 14:20:29 - mmengine - INFO - Epoch(train) [73][ 5/15] lr: 1.0000e-06 eta: 0:11:06 time: 0.3523 data_time: 0.0676 memory: 14918 loss: 0.0898 loss_ce: 0.0898 2023/03/03 14:20:29 - mmengine - INFO - Epoch(train) [73][ 6/15] lr: 1.0000e-06 eta: 0:11:05 time: 0.3376 data_time: 0.0676 memory: 15030 loss: 0.0881 loss_ce: 0.0881 2023/03/03 14:20:30 - mmengine - INFO - Epoch(train) [73][ 7/15] lr: 1.0000e-06 eta: 0:11:05 time: 0.3327 data_time: 0.0676 memory: 23694 loss: 0.0838 loss_ce: 0.0838 2023/03/03 14:20:30 - mmengine - INFO - Epoch(train) [73][ 8/15] lr: 1.0000e-06 eta: 0:11:05 time: 0.3632 data_time: 0.0676 memory: 16370 loss: 0.0865 loss_ce: 0.0865 2023/03/03 14:20:30 - mmengine - INFO - Epoch(train) [73][ 9/15] lr: 1.0000e-06 eta: 0:11:04 time: 0.3684 data_time: 0.0676 memory: 18337 loss: 0.0806 loss_ce: 0.0806 2023/03/03 14:20:31 - mmengine - INFO - Epoch(train) [73][10/15] lr: 1.0000e-06 eta: 0:11:04 time: 0.3858 data_time: 0.0676 memory: 16379 loss: 0.0812 loss_ce: 0.0812 2023/03/03 14:20:31 - mmengine - INFO - Epoch(train) [73][11/15] lr: 1.0000e-06 eta: 0:11:04 time: 0.3321 data_time: 0.0018 memory: 16601 loss: 0.0782 loss_ce: 0.0782 2023/03/03 14:20:32 - mmengine - INFO - Epoch(train) [73][12/15] lr: 1.0000e-06 eta: 0:11:04 time: 0.3443 data_time: 0.0017 memory: 34260 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:20:32 - mmengine - INFO - Epoch(train) [73][13/15] lr: 1.0000e-06 eta: 0:11:03 time: 0.3398 data_time: 0.0017 memory: 17421 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:20:32 - mmengine - INFO - Epoch(train) [73][14/15] lr: 1.0000e-06 eta: 0:11:03 time: 0.3649 data_time: 0.0016 memory: 15911 loss: 0.0732 loss_ce: 0.0732 2023/03/03 14:20:33 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:20:33 - mmengine - INFO - Epoch(train) [73][15/15] lr: 1.0000e-06 eta: 0:11:03 time: 0.3494 data_time: 0.0017 memory: 4469 loss: 0.0760 loss_ce: 0.0760 2023/03/03 14:20:34 - mmengine - INFO - Epoch(train) [74][ 1/15] lr: 1.0000e-06 eta: 0:11:04 time: 0.4554 data_time: 0.0795 memory: 37937 loss: 0.0808 loss_ce: 0.0808 2023/03/03 14:20:34 - mmengine - INFO - Epoch(train) [74][ 2/15] lr: 1.0000e-06 eta: 0:11:03 time: 0.4534 data_time: 0.0796 memory: 17788 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:20:34 - mmengine - INFO - Epoch(train) [74][ 3/15] lr: 1.0000e-06 eta: 0:11:03 time: 0.4265 data_time: 0.0796 memory: 17709 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:20:35 - mmengine - INFO - Epoch(train) [74][ 4/15] lr: 1.0000e-06 eta: 0:11:02 time: 0.4263 data_time: 0.0796 memory: 17181 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:20:35 - mmengine - INFO - Epoch(train) [74][ 5/15] lr: 1.0000e-06 eta: 0:11:02 time: 0.4058 data_time: 0.0796 memory: 17572 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:20:35 - mmengine - INFO - Epoch(train) [74][ 6/15] lr: 1.0000e-06 eta: 0:11:01 time: 0.3956 data_time: 0.0795 memory: 16370 loss: 0.0869 loss_ce: 0.0869 2023/03/03 14:20:36 - mmengine - INFO - Epoch(train) [74][ 7/15] lr: 1.0000e-06 eta: 0:11:01 time: 0.3998 data_time: 0.0796 memory: 23854 loss: 0.0874 loss_ce: 0.0874 2023/03/03 14:20:36 - mmengine - INFO - Epoch(train) [74][ 8/15] lr: 1.0000e-06 eta: 0:11:01 time: 0.4030 data_time: 0.0796 memory: 11784 loss: 0.0838 loss_ce: 0.0838 2023/03/03 14:20:36 - mmengine - INFO - Epoch(train) [74][ 9/15] lr: 1.0000e-06 eta: 0:11:00 time: 0.3827 data_time: 0.0797 memory: 16955 loss: 0.0835 loss_ce: 0.0835 2023/03/03 14:20:37 - mmengine - INFO - Epoch(train) [74][10/15] lr: 1.0000e-06 eta: 0:11:00 time: 0.3875 data_time: 0.0796 memory: 17120 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:20:37 - mmengine - INFO - Epoch(train) [74][11/15] lr: 1.0000e-06 eta: 0:10:59 time: 0.2938 data_time: 0.0017 memory: 17421 loss: 0.0782 loss_ce: 0.0782 2023/03/03 14:20:37 - mmengine - INFO - Epoch(train) [74][12/15] lr: 1.0000e-06 eta: 0:10:59 time: 0.3205 data_time: 0.0017 memory: 37937 loss: 0.0857 loss_ce: 0.0857 2023/03/03 14:20:38 - mmengine - INFO - Epoch(train) [74][13/15] lr: 1.0000e-06 eta: 0:10:59 time: 0.3303 data_time: 0.0016 memory: 20039 loss: 0.0837 loss_ce: 0.0837 2023/03/03 14:20:38 - mmengine - INFO - Epoch(train) [74][14/15] lr: 1.0000e-06 eta: 0:10:58 time: 0.3272 data_time: 0.0016 memory: 14622 loss: 0.0842 loss_ce: 0.0842 2023/03/03 14:20:38 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:20:38 - mmengine - INFO - Epoch(train) [74][15/15] lr: 1.0000e-06 eta: 0:10:58 time: 0.3195 data_time: 0.0016 memory: 6443 loss: 0.0873 loss_ce: 0.0873 2023/03/03 14:20:39 - mmengine - INFO - Epoch(train) [75][ 1/15] lr: 1.0000e-06 eta: 0:10:58 time: 0.3697 data_time: 0.0538 memory: 17421 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:20:40 - mmengine - INFO - Epoch(train) [75][ 2/15] lr: 1.0000e-06 eta: 0:10:58 time: 0.3891 data_time: 0.0541 memory: 15767 loss: 0.0841 loss_ce: 0.0841 2023/03/03 14:20:40 - mmengine - INFO - Epoch(train) [75][ 3/15] lr: 1.0000e-06 eta: 0:10:58 time: 0.3887 data_time: 0.0541 memory: 16508 loss: 0.0902 loss_ce: 0.0902 2023/03/03 14:20:40 - mmengine - INFO - Epoch(train) [75][ 4/15] lr: 1.0000e-06 eta: 0:10:57 time: 0.3834 data_time: 0.0541 memory: 16011 loss: 0.0923 loss_ce: 0.0923 2023/03/03 14:20:40 - mmengine - INFO - Epoch(train) [75][ 5/15] lr: 1.0000e-06 eta: 0:10:57 time: 0.3973 data_time: 0.0541 memory: 24789 loss: 0.0898 loss_ce: 0.0898 2023/03/03 14:20:41 - mmengine - INFO - Epoch(train) [75][ 6/15] lr: 1.0000e-06 eta: 0:10:57 time: 0.3918 data_time: 0.0541 memory: 16370 loss: 0.0878 loss_ce: 0.0878 2023/03/03 14:20:41 - mmengine - INFO - Epoch(train) [75][ 7/15] lr: 1.0000e-06 eta: 0:10:56 time: 0.3658 data_time: 0.0541 memory: 18154 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:20:41 - mmengine - INFO - Epoch(train) [75][ 8/15] lr: 1.0000e-06 eta: 0:10:56 time: 0.3718 data_time: 0.0541 memory: 24250 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:20:42 - mmengine - INFO - Epoch(train) [75][ 9/15] lr: 1.0000e-06 eta: 0:10:55 time: 0.3737 data_time: 0.0541 memory: 16958 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:20:42 - mmengine - INFO - Epoch(train) [75][10/15] lr: 1.0000e-06 eta: 0:10:55 time: 0.3962 data_time: 0.0543 memory: 13846 loss: 0.0745 loss_ce: 0.0745 2023/03/03 14:20:42 - mmengine - INFO - Epoch(train) [75][11/15] lr: 1.0000e-06 eta: 0:10:55 time: 0.3465 data_time: 0.0021 memory: 21259 loss: 0.0726 loss_ce: 0.0726 2023/03/03 14:20:43 - mmengine - INFO - Epoch(train) [75][12/15] lr: 1.0000e-06 eta: 0:10:54 time: 0.3084 data_time: 0.0018 memory: 24666 loss: 0.0736 loss_ce: 0.0736 2023/03/03 14:20:43 - mmengine - INFO - Epoch(train) [75][13/15] lr: 1.0000e-06 eta: 0:10:54 time: 0.3075 data_time: 0.0017 memory: 15767 loss: 0.0723 loss_ce: 0.0723 2023/03/03 14:20:43 - mmengine - INFO - Epoch(train) [75][14/15] lr: 1.0000e-06 eta: 0:10:53 time: 0.3039 data_time: 0.0017 memory: 16976 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:20:43 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:20:43 - mmengine - INFO - Epoch(train) [75][15/15] lr: 1.0000e-06 eta: 0:10:53 time: 0.2857 data_time: 0.0017 memory: 4759 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:20:44 - mmengine - INFO - Epoch(train) [76][ 1/15] lr: 1.0000e-06 eta: 0:10:53 time: 0.3465 data_time: 0.0499 memory: 18274 loss: 0.0758 loss_ce: 0.0758 2023/03/03 14:20:44 - mmengine - INFO - Epoch(train) [76][ 2/15] lr: 1.0000e-06 eta: 0:10:53 time: 0.3436 data_time: 0.0499 memory: 15911 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:20:45 - mmengine - INFO - Epoch(train) [76][ 3/15] lr: 1.0000e-06 eta: 0:10:52 time: 0.3454 data_time: 0.0500 memory: 26806 loss: 0.0817 loss_ce: 0.0817 2023/03/03 14:20:45 - mmengine - INFO - Epoch(train) [76][ 4/15] lr: 1.0000e-06 eta: 0:10:52 time: 0.3419 data_time: 0.0500 memory: 17424 loss: 0.0838 loss_ce: 0.0838 2023/03/03 14:20:46 - mmengine - INFO - Epoch(train) [76][ 5/15] lr: 1.0000e-06 eta: 0:10:52 time: 0.3410 data_time: 0.0498 memory: 28391 loss: 0.0837 loss_ce: 0.0837 2023/03/03 14:20:46 - mmengine - INFO - Epoch(train) [76][ 6/15] lr: 1.0000e-06 eta: 0:10:51 time: 0.3398 data_time: 0.0498 memory: 16056 loss: 0.0840 loss_ce: 0.0840 2023/03/03 14:20:46 - mmengine - INFO - Epoch(train) [76][ 7/15] lr: 1.0000e-06 eta: 0:10:50 time: 0.3250 data_time: 0.0498 memory: 17572 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:20:46 - mmengine - INFO - Epoch(train) [76][ 8/15] lr: 1.0000e-06 eta: 0:10:50 time: 0.3231 data_time: 0.0498 memory: 17421 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:20:47 - mmengine - INFO - Epoch(train) [76][ 9/15] lr: 1.0000e-06 eta: 0:10:50 time: 0.3395 data_time: 0.0498 memory: 22249 loss: 0.0716 loss_ce: 0.0716 2023/03/03 14:20:47 - mmengine - INFO - Epoch(train) [76][10/15] lr: 1.0000e-06 eta: 0:10:49 time: 0.3467 data_time: 0.0498 memory: 16508 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:20:47 - mmengine - INFO - Epoch(train) [76][11/15] lr: 1.0000e-06 eta: 0:10:49 time: 0.2805 data_time: 0.0016 memory: 17768 loss: 0.0741 loss_ce: 0.0741 2023/03/03 14:20:47 - mmengine - INFO - Epoch(train) [76][12/15] lr: 1.0000e-06 eta: 0:10:48 time: 0.2854 data_time: 0.0016 memory: 21238 loss: 0.0727 loss_ce: 0.0727 2023/03/03 14:20:48 - mmengine - INFO - Epoch(train) [76][13/15] lr: 1.0000e-06 eta: 0:10:48 time: 0.2795 data_time: 0.0015 memory: 17266 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:20:48 - mmengine - INFO - Epoch(train) [76][14/15] lr: 1.0000e-06 eta: 0:10:47 time: 0.2795 data_time: 0.0015 memory: 18070 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:20:48 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:20:48 - mmengine - INFO - Epoch(train) [76][15/15] lr: 1.0000e-06 eta: 0:10:47 time: 0.2704 data_time: 0.0015 memory: 4045 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:20:49 - mmengine - INFO - Epoch(train) [77][ 1/15] lr: 1.0000e-06 eta: 0:10:47 time: 0.3282 data_time: 0.0577 memory: 16370 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:20:49 - mmengine - INFO - Epoch(train) [77][ 2/15] lr: 1.0000e-06 eta: 0:10:47 time: 0.3328 data_time: 0.0578 memory: 16484 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:20:50 - mmengine - INFO - Epoch(train) [77][ 3/15] lr: 1.0000e-06 eta: 0:10:46 time: 0.3372 data_time: 0.0583 memory: 17024 loss: 0.0783 loss_ce: 0.0783 2023/03/03 14:20:50 - mmengine - INFO - Epoch(train) [77][ 4/15] lr: 1.0000e-06 eta: 0:10:46 time: 0.3308 data_time: 0.0584 memory: 17272 loss: 0.0798 loss_ce: 0.0798 2023/03/03 14:20:50 - mmengine - INFO - Epoch(train) [77][ 5/15] lr: 1.0000e-06 eta: 0:10:45 time: 0.3281 data_time: 0.0584 memory: 17272 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:20:51 - mmengine - INFO - Epoch(train) [77][ 6/15] lr: 1.0000e-06 eta: 0:10:45 time: 0.3423 data_time: 0.0584 memory: 25371 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:20:51 - mmengine - INFO - Epoch(train) [77][ 7/15] lr: 1.0000e-06 eta: 0:10:44 time: 0.3377 data_time: 0.0584 memory: 19747 loss: 0.0780 loss_ce: 0.0780 2023/03/03 14:20:51 - mmengine - INFO - Epoch(train) [77][ 8/15] lr: 1.0000e-06 eta: 0:10:44 time: 0.3262 data_time: 0.0584 memory: 17788 loss: 0.0733 loss_ce: 0.0733 2023/03/03 14:20:51 - mmengine - INFO - Epoch(train) [77][ 9/15] lr: 1.0000e-06 eta: 0:10:43 time: 0.3298 data_time: 0.0584 memory: 17446 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:20:52 - mmengine - INFO - Epoch(train) [77][10/15] lr: 1.0000e-06 eta: 0:10:43 time: 0.3500 data_time: 0.0584 memory: 36840 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:20:52 - mmengine - INFO - Epoch(train) [77][11/15] lr: 1.0000e-06 eta: 0:10:43 time: 0.2901 data_time: 0.0022 memory: 16976 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:20:53 - mmengine - INFO - Epoch(train) [77][12/15] lr: 1.0000e-06 eta: 0:10:43 time: 0.3178 data_time: 0.0021 memory: 15089 loss: 0.0737 loss_ce: 0.0737 2023/03/03 14:20:53 - mmengine - INFO - Epoch(train) [77][13/15] lr: 1.0000e-06 eta: 0:10:42 time: 0.3164 data_time: 0.0016 memory: 16562 loss: 0.0780 loss_ce: 0.0780 2023/03/03 14:20:53 - mmengine - INFO - Epoch(train) [77][14/15] lr: 1.0000e-06 eta: 0:10:42 time: 0.3420 data_time: 0.0016 memory: 21210 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:20:53 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:20:53 - mmengine - INFO - Epoch(train) [77][15/15] lr: 1.0000e-06 eta: 0:10:42 time: 0.3337 data_time: 0.0016 memory: 6267 loss: 0.0858 loss_ce: 0.0858 2023/03/03 14:20:55 - mmengine - INFO - Epoch(train) [78][ 1/15] lr: 1.0000e-06 eta: 0:10:43 time: 0.4161 data_time: 0.0476 memory: 18362 loss: 0.0839 loss_ce: 0.0839 2023/03/03 14:20:55 - mmengine - INFO - Epoch(train) [78][ 2/15] lr: 1.0000e-06 eta: 0:10:42 time: 0.4258 data_time: 0.0482 memory: 18616 loss: 0.0842 loss_ce: 0.0842 2023/03/03 14:20:55 - mmengine - INFO - Epoch(train) [78][ 3/15] lr: 1.0000e-06 eta: 0:10:42 time: 0.4354 data_time: 0.0483 memory: 15457 loss: 0.0852 loss_ce: 0.0852 2023/03/03 14:20:56 - mmengine - INFO - Epoch(train) [78][ 4/15] lr: 1.0000e-06 eta: 0:10:41 time: 0.4317 data_time: 0.0483 memory: 17421 loss: 0.0848 loss_ce: 0.0848 2023/03/03 14:20:56 - mmengine - INFO - Epoch(train) [78][ 5/15] lr: 1.0000e-06 eta: 0:10:41 time: 0.4152 data_time: 0.0483 memory: 16530 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:20:56 - mmengine - INFO - Epoch(train) [78][ 6/15] lr: 1.0000e-06 eta: 0:10:40 time: 0.4211 data_time: 0.0483 memory: 16958 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:20:56 - mmengine - INFO - Epoch(train) [78][ 7/15] lr: 1.0000e-06 eta: 0:10:40 time: 0.3852 data_time: 0.0483 memory: 15432 loss: 0.0818 loss_ce: 0.0818 2023/03/03 14:20:57 - mmengine - INFO - Epoch(train) [78][ 8/15] lr: 1.0000e-06 eta: 0:10:39 time: 0.3823 data_time: 0.0482 memory: 17421 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:20:57 - mmengine - INFO - Epoch(train) [78][ 9/15] lr: 1.0000e-06 eta: 0:10:39 time: 0.3715 data_time: 0.0482 memory: 32429 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:20:57 - mmengine - INFO - Epoch(train) [78][10/15] lr: 1.0000e-06 eta: 0:10:39 time: 0.3802 data_time: 0.0482 memory: 18241 loss: 0.0718 loss_ce: 0.0718 2023/03/03 14:20:58 - mmengine - INFO - Epoch(train) [78][11/15] lr: 1.0000e-06 eta: 0:10:38 time: 0.3086 data_time: 0.0022 memory: 17120 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:20:58 - mmengine - INFO - Epoch(train) [78][12/15] lr: 1.0000e-06 eta: 0:10:38 time: 0.2958 data_time: 0.0016 memory: 16404 loss: 0.0738 loss_ce: 0.0738 2023/03/03 14:20:58 - mmengine - INFO - Epoch(train) [78][13/15] lr: 1.0000e-06 eta: 0:10:38 time: 0.3039 data_time: 0.0015 memory: 14092 loss: 0.0744 loss_ce: 0.0744 2023/03/03 14:20:59 - mmengine - INFO - Epoch(train) [78][14/15] lr: 1.0000e-06 eta: 0:10:37 time: 0.3040 data_time: 0.0016 memory: 18070 loss: 0.0730 loss_ce: 0.0730 2023/03/03 14:20:59 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:20:59 - mmengine - INFO - Epoch(train) [78][15/15] lr: 1.0000e-06 eta: 0:10:36 time: 0.2913 data_time: 0.0015 memory: 9232 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:20:59 - mmengine - INFO - Epoch(train) [79][ 1/15] lr: 1.0000e-06 eta: 0:10:37 time: 0.3284 data_time: 0.0441 memory: 16976 loss: 0.0819 loss_ce: 0.0819 2023/03/03 14:21:00 - mmengine - INFO - Epoch(train) [79][ 2/15] lr: 1.0000e-06 eta: 0:10:37 time: 0.3619 data_time: 0.0704 memory: 16149 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:21:00 - mmengine - INFO - Epoch(train) [79][ 3/15] lr: 1.0000e-06 eta: 0:10:36 time: 0.3651 data_time: 0.0705 memory: 16955 loss: 0.0786 loss_ce: 0.0786 2023/03/03 14:21:01 - mmengine - INFO - Epoch(train) [79][ 4/15] lr: 1.0000e-06 eta: 0:10:36 time: 0.3482 data_time: 0.0706 memory: 17421 loss: 0.0780 loss_ce: 0.0780 2023/03/03 14:21:01 - mmengine - INFO - Epoch(train) [79][ 5/15] lr: 1.0000e-06 eta: 0:10:35 time: 0.3497 data_time: 0.0706 memory: 15701 loss: 0.0782 loss_ce: 0.0782 2023/03/03 14:21:01 - mmengine - INFO - Epoch(train) [79][ 6/15] lr: 1.0000e-06 eta: 0:10:35 time: 0.3304 data_time: 0.0706 memory: 17655 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:21:01 - mmengine - INFO - Epoch(train) [79][ 7/15] lr: 1.0000e-06 eta: 0:10:34 time: 0.3329 data_time: 0.0706 memory: 15767 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:21:02 - mmengine - INFO - Epoch(train) [79][ 8/15] lr: 1.0000e-06 eta: 0:10:34 time: 0.3323 data_time: 0.0706 memory: 20238 loss: 0.0783 loss_ce: 0.0783 2023/03/03 14:21:02 - mmengine - INFO - Epoch(train) [79][ 9/15] lr: 1.0000e-06 eta: 0:10:33 time: 0.3322 data_time: 0.0706 memory: 17892 loss: 0.0787 loss_ce: 0.0787 2023/03/03 14:21:02 - mmengine - INFO - Epoch(train) [79][10/15] lr: 1.0000e-06 eta: 0:10:33 time: 0.3455 data_time: 0.0706 memory: 17421 loss: 0.0718 loss_ce: 0.0718 2023/03/03 14:21:03 - mmengine - INFO - Epoch(train) [79][11/15] lr: 1.0000e-06 eta: 0:10:33 time: 0.3134 data_time: 0.0281 memory: 18082 loss: 0.0701 loss_ce: 0.0701 2023/03/03 14:21:03 - mmengine - INFO - Epoch(train) [79][12/15] lr: 1.0000e-06 eta: 0:10:32 time: 0.2909 data_time: 0.0017 memory: 19169 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:21:03 - mmengine - INFO - Epoch(train) [79][13/15] lr: 1.0000e-06 eta: 0:10:32 time: 0.2878 data_time: 0.0017 memory: 18409 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:21:04 - mmengine - INFO - Epoch(train) [79][14/15] lr: 1.0000e-06 eta: 0:10:32 time: 0.3177 data_time: 0.0016 memory: 16223 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:21:04 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:21:04 - mmengine - INFO - Epoch(train) [79][15/15] lr: 1.0000e-06 eta: 0:10:31 time: 0.3111 data_time: 0.0016 memory: 3945 loss: 0.0856 loss_ce: 0.0856 2023/03/03 14:21:05 - mmengine - INFO - Epoch(train) [80][ 1/15] lr: 1.0000e-06 eta: 0:10:32 time: 0.3753 data_time: 0.0534 memory: 16370 loss: 0.0837 loss_ce: 0.0837 2023/03/03 14:21:05 - mmengine - INFO - Epoch(train) [80][ 2/15] lr: 1.0000e-06 eta: 0:10:31 time: 0.3774 data_time: 0.0535 memory: 17619 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:21:06 - mmengine - INFO - Epoch(train) [80][ 3/15] lr: 1.0000e-06 eta: 0:10:31 time: 0.3908 data_time: 0.0536 memory: 15767 loss: 0.0809 loss_ce: 0.0809 2023/03/03 14:21:06 - mmengine - INFO - Epoch(train) [80][ 4/15] lr: 1.0000e-06 eta: 0:10:31 time: 0.4120 data_time: 0.0536 memory: 24709 loss: 0.0803 loss_ce: 0.0803 2023/03/03 14:21:06 - mmengine - INFO - Epoch(train) [80][ 5/15] lr: 1.0000e-06 eta: 0:10:30 time: 0.3977 data_time: 0.0536 memory: 18070 loss: 0.0798 loss_ce: 0.0798 2023/03/03 14:21:06 - mmengine - INFO - Epoch(train) [80][ 6/15] lr: 1.0000e-06 eta: 0:10:30 time: 0.3903 data_time: 0.0536 memory: 14187 loss: 0.0803 loss_ce: 0.0803 2023/03/03 14:21:07 - mmengine - INFO - Epoch(train) [80][ 7/15] lr: 1.0000e-06 eta: 0:10:29 time: 0.3904 data_time: 0.0536 memory: 19415 loss: 0.0720 loss_ce: 0.0720 2023/03/03 14:21:07 - mmengine - INFO - Epoch(train) [80][ 8/15] lr: 1.0000e-06 eta: 0:10:29 time: 0.3903 data_time: 0.0536 memory: 17421 loss: 0.0736 loss_ce: 0.0736 2023/03/03 14:21:07 - mmengine - INFO - Epoch(train) [80][ 9/15] lr: 1.0000e-06 eta: 0:10:28 time: 0.3666 data_time: 0.0536 memory: 15639 loss: 0.0717 loss_ce: 0.0717 2023/03/03 14:21:08 - mmengine - INFO - Epoch(train) [80][10/15] lr: 1.0000e-06 eta: 0:10:28 time: 0.3749 data_time: 0.0536 memory: 17619 loss: 0.0687 loss_ce: 0.0687 2023/03/03 14:21:08 - mmengine - INFO - Epoch(train) [80][11/15] lr: 1.0000e-06 eta: 0:10:28 time: 0.3084 data_time: 0.0018 memory: 16459 loss: 0.0704 loss_ce: 0.0704 2023/03/03 14:21:08 - mmengine - INFO - Epoch(train) [80][12/15] lr: 1.0000e-06 eta: 0:10:27 time: 0.3099 data_time: 0.0017 memory: 18792 loss: 0.0710 loss_ce: 0.0710 2023/03/03 14:21:09 - mmengine - INFO - Epoch(train) [80][13/15] lr: 1.0000e-06 eta: 0:10:27 time: 0.2967 data_time: 0.0016 memory: 22481 loss: 0.0715 loss_ce: 0.0715 2023/03/03 14:21:09 - mmengine - INFO - Epoch(train) [80][14/15] lr: 1.0000e-06 eta: 0:10:26 time: 0.2752 data_time: 0.0015 memory: 17120 loss: 0.0736 loss_ce: 0.0736 2023/03/03 14:21:09 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:21:09 - mmengine - INFO - Epoch(train) [80][15/15] lr: 1.0000e-06 eta: 0:10:26 time: 0.2752 data_time: 0.0015 memory: 6551 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:21:11 - mmengine - INFO - Epoch(val) [80][ 1/59] eta: 0:01:29 time: 1.1115 data_time: 0.0033 memory: 981 2023/03/03 14:21:11 - mmengine - INFO - Epoch(val) [80][ 2/59] eta: 0:01:07 time: 1.0264 data_time: 0.0033 memory: 981 2023/03/03 14:21:13 - mmengine - INFO - Epoch(val) [80][ 3/59] eta: 0:01:12 time: 1.0604 data_time: 0.0033 memory: 1003 2023/03/03 14:21:13 - mmengine - INFO - Epoch(val) [80][ 4/59] eta: 0:01:00 time: 1.0442 data_time: 0.0033 memory: 981 2023/03/03 14:21:16 - mmengine - INFO - Epoch(val) [80][ 5/59] eta: 0:01:19 time: 1.2795 data_time: 0.0033 memory: 1016 2023/03/03 14:21:19 - mmengine - INFO - Epoch(val) [80][ 6/59] eta: 0:01:28 time: 1.4748 data_time: 0.0033 memory: 981 2023/03/03 14:21:19 - mmengine - INFO - Epoch(val) [80][ 7/59] eta: 0:01:15 time: 1.4096 data_time: 0.0033 memory: 1043 2023/03/03 14:21:20 - mmengine - INFO - Epoch(val) [80][ 8/59] eta: 0:01:10 time: 1.2679 data_time: 0.0033 memory: 1016 2023/03/03 14:21:21 - mmengine - INFO - Epoch(val) [80][ 9/59] eta: 0:01:06 time: 1.2305 data_time: 0.0033 memory: 981 2023/03/03 14:21:22 - mmengine - INFO - Epoch(val) [80][10/59] eta: 0:01:01 time: 1.2606 data_time: 0.0033 memory: 981 2023/03/03 14:21:22 - mmengine - INFO - Epoch(val) [80][11/59] eta: 0:00:57 time: 1.1548 data_time: 0.0008 memory: 981 2023/03/03 14:21:26 - mmengine - INFO - Epoch(val) [80][12/59] eta: 0:01:04 time: 1.4200 data_time: 0.0008 memory: 1016 2023/03/03 14:21:27 - mmengine - INFO - Epoch(val) [80][13/59] eta: 0:01:05 time: 1.4565 data_time: 0.0008 memory: 981 2023/03/03 14:21:29 - mmengine - INFO - Epoch(val) [80][14/59] eta: 0:01:03 time: 1.5253 data_time: 0.0009 memory: 890 2023/03/03 14:21:29 - mmengine - INFO - Epoch(val) [80][15/59] eta: 0:00:57 time: 1.2261 data_time: 0.0009 memory: 981 2023/03/03 14:21:29 - mmengine - INFO - Epoch(val) [80][16/59] eta: 0:00:54 time: 1.0148 data_time: 0.0009 memory: 981 2023/03/03 14:21:29 - mmengine - INFO - Epoch(val) [80][17/59] eta: 0:00:50 time: 1.0314 data_time: 0.0009 memory: 981 2023/03/03 14:21:30 - mmengine - INFO - Epoch(val) [80][18/59] eta: 0:00:47 time: 0.9832 data_time: 0.0009 memory: 981 2023/03/03 14:21:31 - mmengine - INFO - Epoch(val) [80][19/59] eta: 0:00:45 time: 0.9874 data_time: 0.0009 memory: 981 2023/03/03 14:21:31 - mmengine - INFO - Epoch(val) [80][20/59] eta: 0:00:43 time: 0.9576 data_time: 0.0010 memory: 981 2023/03/03 14:21:33 - mmengine - INFO - Epoch(val) [80][21/59] eta: 0:00:43 time: 1.1177 data_time: 0.0010 memory: 981 2023/03/03 14:21:33 - mmengine - INFO - Epoch(val) [80][22/59] eta: 0:00:41 time: 0.7873 data_time: 0.0010 memory: 981 2023/03/03 14:21:34 - mmengine - INFO - Epoch(val) [80][23/59] eta: 0:00:39 time: 0.6677 data_time: 0.0010 memory: 981 2023/03/03 14:21:34 - mmengine - INFO - Epoch(val) [80][24/59] eta: 0:00:37 time: 0.5828 data_time: 0.0009 memory: 962 2023/03/03 14:21:35 - mmengine - INFO - Epoch(val) [80][25/59] eta: 0:00:35 time: 0.6143 data_time: 0.0009 memory: 981 2023/03/03 14:21:35 - mmengine - INFO - Epoch(val) [80][26/59] eta: 0:00:33 time: 0.5982 data_time: 0.0009 memory: 981 2023/03/03 14:21:35 - mmengine - INFO - Epoch(val) [80][27/59] eta: 0:00:31 time: 0.5984 data_time: 0.0009 memory: 981 2023/03/03 14:21:36 - mmengine - INFO - Epoch(val) [80][28/59] eta: 0:00:29 time: 0.5986 data_time: 0.0009 memory: 981 2023/03/03 14:21:37 - mmengine - INFO - Epoch(val) [80][29/59] eta: 0:00:29 time: 0.6333 data_time: 0.0009 memory: 981 2023/03/03 14:21:38 - mmengine - INFO - Epoch(val) [80][30/59] eta: 0:00:28 time: 0.6829 data_time: 0.0009 memory: 999 2023/03/03 14:21:39 - mmengine - INFO - Epoch(val) [80][31/59] eta: 0:00:26 time: 0.5418 data_time: 0.0008 memory: 981 2023/03/03 14:21:40 - mmengine - INFO - Epoch(val) [80][32/59] eta: 0:00:26 time: 0.6417 data_time: 0.0008 memory: 981 2023/03/03 14:21:40 - mmengine - INFO - Epoch(val) [80][33/59] eta: 0:00:24 time: 0.5921 data_time: 0.0009 memory: 981 2023/03/03 14:21:40 - mmengine - INFO - Epoch(val) [80][34/59] eta: 0:00:22 time: 0.5756 data_time: 0.0009 memory: 981 2023/03/03 14:21:40 - mmengine - INFO - Epoch(val) [80][35/59] eta: 0:00:21 time: 0.5590 data_time: 0.0009 memory: 981 2023/03/03 14:21:41 - mmengine - INFO - Epoch(val) [80][36/59] eta: 0:00:20 time: 0.5749 data_time: 0.0008 memory: 981 2023/03/03 14:21:41 - mmengine - INFO - Epoch(val) [80][37/59] eta: 0:00:19 time: 0.5581 data_time: 0.0008 memory: 981 2023/03/03 14:21:42 - mmengine - INFO - Epoch(val) [80][38/59] eta: 0:00:18 time: 0.5910 data_time: 0.0008 memory: 981 2023/03/03 14:21:42 - mmengine - INFO - Epoch(val) [80][39/59] eta: 0:00:17 time: 0.5062 data_time: 0.0008 memory: 987 2023/03/03 14:21:43 - mmengine - INFO - Epoch(val) [80][40/59] eta: 0:00:16 time: 0.5065 data_time: 0.0008 memory: 981 2023/03/03 14:21:44 - mmengine - INFO - Epoch(val) [80][41/59] eta: 0:00:15 time: 0.5573 data_time: 0.0008 memory: 986 2023/03/03 14:21:45 - mmengine - INFO - Epoch(val) [80][42/59] eta: 0:00:14 time: 0.5067 data_time: 0.0008 memory: 981 2023/03/03 14:21:46 - mmengine - INFO - Epoch(val) [80][43/59] eta: 0:00:13 time: 0.5728 data_time: 0.0008 memory: 976 2023/03/03 14:21:46 - mmengine - INFO - Epoch(val) [80][44/59] eta: 0:00:12 time: 0.6058 data_time: 0.0008 memory: 1003 2023/03/03 14:21:49 - mmengine - INFO - Epoch(val) [80][45/59] eta: 0:00:12 time: 0.8142 data_time: 0.0008 memory: 981 2023/03/03 14:21:49 - mmengine - INFO - Epoch(val) [80][46/59] eta: 0:00:11 time: 0.8477 data_time: 0.0008 memory: 981 2023/03/03 14:21:50 - mmengine - INFO - Epoch(val) [80][47/59] eta: 0:00:10 time: 0.8802 data_time: 0.0008 memory: 936 2023/03/03 14:21:50 - mmengine - INFO - Epoch(val) [80][48/59] eta: 0:00:09 time: 0.8637 data_time: 0.0008 memory: 1000 2023/03/03 14:21:51 - mmengine - INFO - Epoch(val) [80][49/59] eta: 0:00:08 time: 0.9139 data_time: 0.0008 memory: 981 2023/03/03 14:21:52 - mmengine - INFO - Epoch(val) [80][50/59] eta: 0:00:07 time: 0.9139 data_time: 0.0008 memory: 987 2023/03/03 14:21:54 - mmengine - INFO - Epoch(val) [80][51/59] eta: 0:00:07 time: 0.9658 data_time: 0.0008 memory: 981 2023/03/03 14:21:55 - mmengine - INFO - Epoch(val) [80][52/59] eta: 0:00:06 time: 1.0164 data_time: 0.0008 memory: 981 2023/03/03 14:21:56 - mmengine - INFO - Epoch(val) [80][53/59] eta: 0:00:05 time: 0.9995 data_time: 0.0008 memory: 962 2023/03/03 14:21:56 - mmengine - INFO - Epoch(val) [80][54/59] eta: 0:00:04 time: 1.0162 data_time: 0.0008 memory: 981 2023/03/03 14:21:57 - mmengine - INFO - Epoch(val) [80][55/59] eta: 0:00:03 time: 0.8576 data_time: 0.0008 memory: 981 2023/03/03 14:21:58 - mmengine - INFO - Epoch(val) [80][56/59] eta: 0:00:02 time: 0.8573 data_time: 0.0008 memory: 981 2023/03/03 14:22:00 - mmengine - INFO - Epoch(val) [80][57/59] eta: 0:00:01 time: 1.0327 data_time: 0.0008 memory: 981 2023/03/03 14:22:02 - mmengine - INFO - Epoch(val) [80][58/59] eta: 0:00:00 time: 1.1166 data_time: 0.0008 memory: 1016 2023/03/03 14:22:02 - mmengine - INFO - Epoch(val) [80][59/59] eta: 0:00:00 time: 1.0501 data_time: 0.0008 memory: 981 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.80, recall: 0.8155, precision: 0.8322, hmean: 0.8238 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.81, recall: 0.8146, precision: 0.8329, hmean: 0.8236 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.82, recall: 0.8137, precision: 0.8343, hmean: 0.8239 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.83, recall: 0.8128, precision: 0.8365, hmean: 0.8245 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.84, recall: 0.8119, precision: 0.8371, hmean: 0.8243 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.85, recall: 0.8091, precision: 0.8422, hmean: 0.8253 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.86, recall: 0.8082, precision: 0.8469, hmean: 0.8271 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.87, recall: 0.8082, precision: 0.8493, hmean: 0.8283 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.88, recall: 0.8055, precision: 0.8514, hmean: 0.8278 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.89, recall: 0.8037, precision: 0.8544, hmean: 0.8282 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.90, recall: 0.8018, precision: 0.8599, hmean: 0.8299 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.91, recall: 0.8000, precision: 0.8622, hmean: 0.8299 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.92, recall: 0.7954, precision: 0.8641, hmean: 0.8283 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.93, recall: 0.7881, precision: 0.8682, hmean: 0.8262 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.94, recall: 0.7808, precision: 0.8698, hmean: 0.8229 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.95, recall: 0.7717, precision: 0.8729, hmean: 0.8192 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.96, recall: 0.7598, precision: 0.8776, hmean: 0.8145 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.97, recall: 0.7479, precision: 0.8778, hmean: 0.8077 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.98, recall: 0.7370, precision: 0.8839, hmean: 0.8038 2023/03/03 14:22:30 - mmengine - INFO - text score threshold: 0.99, recall: 0.7151, precision: 0.8908, hmean: 0.7933 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.80, recall: 0.8283, precision: 0.9034, hmean: 0.8642 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.81, recall: 0.8274, precision: 0.9033, hmean: 0.8637 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.82, recall: 0.8265, precision: 0.9050, hmean: 0.8640 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.83, recall: 0.8256, precision: 0.9058, hmean: 0.8638 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.84, recall: 0.8247, precision: 0.9057, hmean: 0.8633 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.85, recall: 0.8210, precision: 0.9072, hmean: 0.8619 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.86, recall: 0.8192, precision: 0.9097, hmean: 0.8621 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.87, recall: 0.8183, precision: 0.9106, hmean: 0.8620 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.88, recall: 0.8146, precision: 0.9121, hmean: 0.8606 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.89, recall: 0.8119, precision: 0.9137, hmean: 0.8598 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.90, recall: 0.8091, precision: 0.9172, hmean: 0.8598 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.91, recall: 0.8055, precision: 0.9168, hmean: 0.8576 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.92, recall: 0.8018, precision: 0.9165, hmean: 0.8553 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.93, recall: 0.7927, precision: 0.9175, hmean: 0.8506 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.94, recall: 0.7845, precision: 0.9177, hmean: 0.8459 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.95, recall: 0.7744, precision: 0.9207, hmean: 0.8413 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.96, recall: 0.7616, precision: 0.9246, hmean: 0.8353 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.97, recall: 0.7479, precision: 0.9233, hmean: 0.8264 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.98, recall: 0.7379, precision: 0.9277, hmean: 0.8220 2023/03/03 14:22:33 - mmengine - INFO - text score threshold: 0.99, recall: 0.7142, precision: 0.9287, hmean: 0.8074 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.80, recall: 0.7507, precision: 0.9547, hmean: 0.8405 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.81, recall: 0.7498, precision: 0.9547, hmean: 0.8399 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.82, recall: 0.7489, precision: 0.9557, hmean: 0.8397 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.83, recall: 0.7479, precision: 0.9557, hmean: 0.8391 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.84, recall: 0.7470, precision: 0.9556, hmean: 0.8385 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.85, recall: 0.7425, precision: 0.9553, hmean: 0.8356 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.86, recall: 0.7406, precision: 0.9575, hmean: 0.8352 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.87, recall: 0.7388, precision: 0.9574, hmean: 0.8340 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.88, recall: 0.7352, precision: 0.9572, hmean: 0.8316 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.89, recall: 0.7324, precision: 0.9582, hmean: 0.8302 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.90, recall: 0.7288, precision: 0.9591, hmean: 0.8282 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.91, recall: 0.7251, precision: 0.9589, hmean: 0.8258 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.92, recall: 0.7215, precision: 0.9587, hmean: 0.8233 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.93, recall: 0.7142, precision: 0.9595, hmean: 0.8188 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.94, recall: 0.7068, precision: 0.9603, hmean: 0.8143 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.95, recall: 0.6977, precision: 0.9610, hmean: 0.8085 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.96, recall: 0.6849, precision: 0.9615, hmean: 0.8000 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.97, recall: 0.6749, precision: 0.9635, hmean: 0.7938 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.98, recall: 0.6667, precision: 0.9669, hmean: 0.7892 2023/03/03 14:22:36 - mmengine - INFO - text score threshold: 0.99, recall: 0.6447, precision: 0.9658, hmean: 0.7733 2023/03/03 14:22:36 - mmengine - INFO - Epoch(val) [80][59/59] generic/precision: 0.8622 generic/recall: 0.8000 generic/hmean: 0.8299 weak/precision: 0.9034 weak/recall: 0.8283 weak/hmean: 0.8642 strong/precision: 0.9547 strong/recall: 0.7507 strong/hmean: 0.8405 2023/03/03 14:22:36 - mmengine - INFO - Epoch(train) [81][ 1/15] lr: 1.0000e-06 eta: 0:10:26 time: 0.3283 data_time: 0.0477 memory: 15011 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:22:37 - mmengine - INFO - Epoch(train) [81][ 2/15] lr: 1.0000e-06 eta: 0:10:26 time: 0.3490 data_time: 0.0478 memory: 17421 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:22:37 - mmengine - INFO - Epoch(train) [81][ 3/15] lr: 1.0000e-06 eta: 0:10:25 time: 0.3513 data_time: 0.0479 memory: 15631 loss: 0.0827 loss_ce: 0.0827 2023/03/03 14:22:38 - mmengine - INFO - Epoch(train) [81][ 4/15] lr: 1.0000e-06 eta: 0:10:25 time: 0.3449 data_time: 0.0479 memory: 18409 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:22:38 - mmengine - INFO - Epoch(train) [81][ 5/15] lr: 1.0000e-06 eta: 0:10:25 time: 0.3723 data_time: 0.0479 memory: 32280 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:22:38 - mmengine - INFO - Epoch(train) [81][ 6/15] lr: 1.0000e-06 eta: 0:10:24 time: 0.3696 data_time: 0.0480 memory: 15539 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:22:39 - mmengine - INFO - Epoch(train) [81][ 7/15] lr: 1.0000e-06 eta: 0:10:24 time: 0.3679 data_time: 0.0480 memory: 17521 loss: 0.0788 loss_ce: 0.0788 2023/03/03 14:22:39 - mmengine - INFO - Epoch(train) [81][ 8/15] lr: 1.0000e-06 eta: 0:10:24 time: 0.3643 data_time: 0.0480 memory: 17730 loss: 0.0792 loss_ce: 0.0792 2023/03/03 14:22:39 - mmengine - INFO - Epoch(train) [81][ 9/15] lr: 1.0000e-06 eta: 0:10:23 time: 0.3773 data_time: 0.0480 memory: 16352 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:22:39 - mmengine - INFO - Epoch(train) [81][10/15] lr: 1.0000e-06 eta: 0:10:23 time: 0.3739 data_time: 0.0480 memory: 11719 loss: 0.0782 loss_ce: 0.0782 2023/03/03 14:22:40 - mmengine - INFO - Epoch(train) [81][11/15] lr: 1.0000e-06 eta: 0:10:22 time: 0.3454 data_time: 0.0018 memory: 34605 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:22:40 - mmengine - INFO - Epoch(train) [81][12/15] lr: 1.0000e-06 eta: 0:10:22 time: 0.3143 data_time: 0.0017 memory: 16508 loss: 0.0786 loss_ce: 0.0786 2023/03/03 14:22:40 - mmengine - INFO - Epoch(train) [81][13/15] lr: 1.0000e-06 eta: 0:10:21 time: 0.3143 data_time: 0.0016 memory: 16223 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:22:41 - mmengine - INFO - Epoch(train) [81][14/15] lr: 1.0000e-06 eta: 0:10:21 time: 0.3340 data_time: 0.0016 memory: 23191 loss: 0.0778 loss_ce: 0.0778 2023/03/03 14:22:41 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:22:41 - mmengine - INFO - Epoch(train) [81][15/15] lr: 1.0000e-06 eta: 0:10:21 time: 0.2979 data_time: 0.0016 memory: 3621 loss: 0.0811 loss_ce: 0.0811 2023/03/03 14:22:42 - mmengine - INFO - Epoch(train) [82][ 1/15] lr: 1.0000e-06 eta: 0:10:21 time: 0.3614 data_time: 0.0564 memory: 17120 loss: 0.0802 loss_ce: 0.0802 2023/03/03 14:22:42 - mmengine - INFO - Epoch(train) [82][ 2/15] lr: 1.0000e-06 eta: 0:10:21 time: 0.3579 data_time: 0.0565 memory: 17272 loss: 0.0812 loss_ce: 0.0812 2023/03/03 14:22:43 - mmengine - INFO - Epoch(train) [82][ 3/15] lr: 1.0000e-06 eta: 0:10:20 time: 0.3562 data_time: 0.0565 memory: 17120 loss: 0.0811 loss_ce: 0.0811 2023/03/03 14:22:43 - mmengine - INFO - Epoch(train) [82][ 4/15] lr: 1.0000e-06 eta: 0:10:20 time: 0.3503 data_time: 0.0565 memory: 19532 loss: 0.0825 loss_ce: 0.0825 2023/03/03 14:22:43 - mmengine - INFO - Epoch(train) [82][ 5/15] lr: 1.0000e-06 eta: 0:10:20 time: 0.3956 data_time: 0.0565 memory: 16264 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:22:44 - mmengine - INFO - Epoch(train) [82][ 6/15] lr: 1.0000e-06 eta: 0:10:19 time: 0.3703 data_time: 0.0566 memory: 16466 loss: 0.0754 loss_ce: 0.0754 2023/03/03 14:22:44 - mmengine - INFO - Epoch(train) [82][ 7/15] lr: 1.0000e-06 eta: 0:10:19 time: 0.4127 data_time: 0.0565 memory: 17167 loss: 0.0721 loss_ce: 0.0721 2023/03/03 14:22:45 - mmengine - INFO - Epoch(train) [82][ 8/15] lr: 1.0000e-06 eta: 0:10:19 time: 0.4154 data_time: 0.0566 memory: 14329 loss: 0.0715 loss_ce: 0.0715 2023/03/03 14:22:45 - mmengine - INFO - Epoch(train) [82][ 9/15] lr: 1.0000e-06 eta: 0:10:18 time: 0.3871 data_time: 0.0566 memory: 16804 loss: 0.0735 loss_ce: 0.0735 2023/03/03 14:22:45 - mmengine - INFO - Epoch(train) [82][10/15] lr: 1.0000e-06 eta: 0:10:18 time: 0.3920 data_time: 0.0566 memory: 16976 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:22:45 - mmengine - INFO - Epoch(train) [82][11/15] lr: 1.0000e-06 eta: 0:10:17 time: 0.3340 data_time: 0.0018 memory: 14680 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:22:46 - mmengine - INFO - Epoch(train) [82][12/15] lr: 1.0000e-06 eta: 0:10:17 time: 0.3419 data_time: 0.0017 memory: 16462 loss: 0.0768 loss_ce: 0.0768 2023/03/03 14:22:46 - mmengine - INFO - Epoch(train) [82][13/15] lr: 1.0000e-06 eta: 0:10:16 time: 0.3256 data_time: 0.0017 memory: 17572 loss: 0.0792 loss_ce: 0.0792 2023/03/03 14:22:46 - mmengine - INFO - Epoch(train) [82][14/15] lr: 1.0000e-06 eta: 0:10:16 time: 0.3253 data_time: 0.0016 memory: 19362 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:22:46 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:22:46 - mmengine - INFO - Epoch(train) [82][15/15] lr: 1.0000e-06 eta: 0:10:15 time: 0.2782 data_time: 0.0016 memory: 6071 loss: 0.0856 loss_ce: 0.0856 2023/03/03 14:22:47 - mmengine - INFO - Epoch(train) [83][ 1/15] lr: 1.0000e-06 eta: 0:10:16 time: 0.3482 data_time: 0.0605 memory: 19225 loss: 0.0898 loss_ce: 0.0898 2023/03/03 14:22:47 - mmengine - INFO - Epoch(train) [83][ 2/15] lr: 1.0000e-06 eta: 0:10:15 time: 0.3145 data_time: 0.0606 memory: 16370 loss: 0.0918 loss_ce: 0.0918 2023/03/03 14:22:48 - mmengine - INFO - Epoch(train) [83][ 3/15] lr: 1.0000e-06 eta: 0:10:15 time: 0.3106 data_time: 0.0606 memory: 15095 loss: 0.0906 loss_ce: 0.0906 2023/03/03 14:22:48 - mmengine - INFO - Epoch(train) [83][ 4/15] lr: 1.0000e-06 eta: 0:10:14 time: 0.3192 data_time: 0.0606 memory: 17999 loss: 0.0926 loss_ce: 0.0926 2023/03/03 14:22:48 - mmengine - INFO - Epoch(train) [83][ 5/15] lr: 1.0000e-06 eta: 0:10:14 time: 0.3387 data_time: 0.0607 memory: 24505 loss: 0.0864 loss_ce: 0.0864 2023/03/03 14:22:49 - mmengine - INFO - Epoch(train) [83][ 6/15] lr: 1.0000e-06 eta: 0:10:14 time: 0.3478 data_time: 0.0607 memory: 21960 loss: 0.0858 loss_ce: 0.0858 2023/03/03 14:22:49 - mmengine - INFO - Epoch(train) [83][ 7/15] lr: 1.0000e-06 eta: 0:10:13 time: 0.3455 data_time: 0.0607 memory: 17256 loss: 0.0852 loss_ce: 0.0852 2023/03/03 14:22:50 - mmengine - INFO - Epoch(train) [83][ 8/15] lr: 1.0000e-06 eta: 0:10:13 time: 0.3789 data_time: 0.0606 memory: 19340 loss: 0.0809 loss_ce: 0.0809 2023/03/03 14:22:50 - mmengine - INFO - Epoch(train) [83][ 9/15] lr: 1.0000e-06 eta: 0:10:13 time: 0.3720 data_time: 0.0607 memory: 16976 loss: 0.0806 loss_ce: 0.0806 2023/03/03 14:22:50 - mmengine - INFO - Epoch(train) [83][10/15] lr: 1.0000e-06 eta: 0:10:13 time: 0.4131 data_time: 0.0607 memory: 16685 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:22:51 - mmengine - INFO - Epoch(train) [83][11/15] lr: 1.0000e-06 eta: 0:10:12 time: 0.3426 data_time: 0.0018 memory: 15631 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:22:51 - mmengine - INFO - Epoch(train) [83][12/15] lr: 1.0000e-06 eta: 0:10:12 time: 0.3359 data_time: 0.0018 memory: 18085 loss: 0.0758 loss_ce: 0.0758 2023/03/03 14:22:51 - mmengine - INFO - Epoch(train) [83][13/15] lr: 1.0000e-06 eta: 0:10:12 time: 0.3657 data_time: 0.0017 memory: 31996 loss: 0.0742 loss_ce: 0.0742 2023/03/03 14:22:52 - mmengine - INFO - Epoch(train) [83][14/15] lr: 1.0000e-06 eta: 0:10:11 time: 0.3562 data_time: 0.0017 memory: 15911 loss: 0.0703 loss_ce: 0.0703 2023/03/03 14:22:52 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:22:52 - mmengine - INFO - Epoch(train) [83][15/15] lr: 1.0000e-06 eta: 0:10:10 time: 0.3304 data_time: 0.0016 memory: 6473 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:22:53 - mmengine - INFO - Epoch(train) [84][ 1/15] lr: 1.0000e-06 eta: 0:10:11 time: 0.3782 data_time: 0.0421 memory: 19984 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:22:53 - mmengine - INFO - Epoch(train) [84][ 2/15] lr: 1.0000e-06 eta: 0:10:10 time: 0.3742 data_time: 0.0421 memory: 13754 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:22:53 - mmengine - INFO - Epoch(train) [84][ 3/15] lr: 1.0000e-06 eta: 0:10:10 time: 0.3464 data_time: 0.0422 memory: 18759 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:22:53 - mmengine - INFO - Epoch(train) [84][ 4/15] lr: 1.0000e-06 eta: 0:10:09 time: 0.3489 data_time: 0.0422 memory: 15816 loss: 0.0735 loss_ce: 0.0735 2023/03/03 14:22:54 - mmengine - INFO - Epoch(train) [84][ 5/15] lr: 1.0000e-06 eta: 0:10:09 time: 0.3402 data_time: 0.0423 memory: 30043 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:22:54 - mmengine - INFO - Epoch(train) [84][ 6/15] lr: 1.0000e-06 eta: 0:10:09 time: 0.3346 data_time: 0.0423 memory: 10983 loss: 0.0723 loss_ce: 0.0723 2023/03/03 14:22:54 - mmengine - INFO - Epoch(train) [84][ 7/15] lr: 1.0000e-06 eta: 0:10:08 time: 0.3423 data_time: 0.0422 memory: 16976 loss: 0.0788 loss_ce: 0.0788 2023/03/03 14:22:54 - mmengine - INFO - Epoch(train) [84][ 8/15] lr: 1.0000e-06 eta: 0:10:08 time: 0.3132 data_time: 0.0422 memory: 15631 loss: 0.0833 loss_ce: 0.0833 2023/03/03 14:22:55 - mmengine - INFO - Epoch(train) [84][ 9/15] lr: 1.0000e-06 eta: 0:10:07 time: 0.3320 data_time: 0.0422 memory: 12501 loss: 0.0873 loss_ce: 0.0873 2023/03/03 14:22:55 - mmengine - INFO - Epoch(train) [84][10/15] lr: 1.0000e-06 eta: 0:10:07 time: 0.3409 data_time: 0.0422 memory: 16223 loss: 0.0831 loss_ce: 0.0831 2023/03/03 14:22:55 - mmengine - INFO - Epoch(train) [84][11/15] lr: 1.0000e-06 eta: 0:10:07 time: 0.2895 data_time: 0.0018 memory: 21515 loss: 0.0826 loss_ce: 0.0826 2023/03/03 14:22:56 - mmengine - INFO - Epoch(train) [84][12/15] lr: 1.0000e-06 eta: 0:10:06 time: 0.2885 data_time: 0.0017 memory: 15037 loss: 0.0830 loss_ce: 0.0830 2023/03/03 14:22:56 - mmengine - INFO - Epoch(train) [84][13/15] lr: 1.0000e-06 eta: 0:10:06 time: 0.3139 data_time: 0.0017 memory: 18828 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:22:56 - mmengine - INFO - Epoch(train) [84][14/15] lr: 1.0000e-06 eta: 0:10:05 time: 0.3199 data_time: 0.0016 memory: 16763 loss: 0.0879 loss_ce: 0.0879 2023/03/03 14:22:57 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:22:57 - mmengine - INFO - Epoch(train) [84][15/15] lr: 1.0000e-06 eta: 0:10:05 time: 0.2948 data_time: 0.0016 memory: 4435 loss: 0.0973 loss_ce: 0.0973 2023/03/03 14:22:57 - mmengine - INFO - Epoch(train) [85][ 1/15] lr: 1.0000e-06 eta: 0:10:05 time: 0.3434 data_time: 0.0370 memory: 16418 loss: 0.0943 loss_ce: 0.0943 2023/03/03 14:22:58 - mmengine - INFO - Epoch(train) [85][ 2/15] lr: 1.0000e-06 eta: 0:10:05 time: 0.3605 data_time: 0.0372 memory: 17948 loss: 0.0901 loss_ce: 0.0901 2023/03/03 14:22:58 - mmengine - INFO - Epoch(train) [85][ 3/15] lr: 1.0000e-06 eta: 0:10:04 time: 0.3597 data_time: 0.0372 memory: 17272 loss: 0.0897 loss_ce: 0.0897 2023/03/03 14:22:59 - mmengine - INFO - Epoch(train) [85][ 4/15] lr: 1.0000e-06 eta: 0:10:04 time: 0.3780 data_time: 0.0373 memory: 17775 loss: 0.0874 loss_ce: 0.0874 2023/03/03 14:22:59 - mmengine - INFO - Epoch(train) [85][ 5/15] lr: 1.0000e-06 eta: 0:10:04 time: 0.3755 data_time: 0.0373 memory: 17421 loss: 0.0917 loss_ce: 0.0917 2023/03/03 14:22:59 - mmengine - INFO - Epoch(train) [85][ 6/15] lr: 1.0000e-06 eta: 0:10:03 time: 0.3654 data_time: 0.0373 memory: 23740 loss: 0.0986 loss_ce: 0.0986 2023/03/03 14:22:59 - mmengine - INFO - Epoch(train) [85][ 7/15] lr: 1.0000e-06 eta: 0:10:03 time: 0.3814 data_time: 0.0373 memory: 35656 loss: 0.0968 loss_ce: 0.0968 2023/03/03 14:23:00 - mmengine - INFO - Epoch(train) [85][ 8/15] lr: 1.0000e-06 eta: 0:10:03 time: 0.3729 data_time: 0.0373 memory: 19700 loss: 0.1016 loss_ce: 0.1016 2023/03/03 14:23:00 - mmengine - INFO - Epoch(train) [85][ 9/15] lr: 1.0000e-06 eta: 0:10:02 time: 0.3723 data_time: 0.0373 memory: 17705 loss: 0.0981 loss_ce: 0.0981 2023/03/03 14:23:01 - mmengine - INFO - Epoch(train) [85][10/15] lr: 1.0000e-06 eta: 0:10:02 time: 0.3884 data_time: 0.0373 memory: 18070 loss: 0.0891 loss_ce: 0.0891 2023/03/03 14:23:01 - mmengine - INFO - Epoch(train) [85][11/15] lr: 1.0000e-06 eta: 0:10:01 time: 0.3441 data_time: 0.0018 memory: 18586 loss: 0.0922 loss_ce: 0.0922 2023/03/03 14:23:01 - mmengine - INFO - Epoch(train) [85][12/15] lr: 1.0000e-06 eta: 0:10:01 time: 0.3260 data_time: 0.0017 memory: 15781 loss: 0.0931 loss_ce: 0.0931 2023/03/03 14:23:01 - mmengine - INFO - Epoch(train) [85][13/15] lr: 1.0000e-06 eta: 0:10:00 time: 0.3249 data_time: 0.0016 memory: 17572 loss: 0.0902 loss_ce: 0.0902 2023/03/03 14:23:02 - mmengine - INFO - Epoch(train) [85][14/15] lr: 1.0000e-06 eta: 0:10:00 time: 0.3047 data_time: 0.0016 memory: 14563 loss: 0.0888 loss_ce: 0.0888 2023/03/03 14:23:02 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:23:02 - mmengine - INFO - Epoch(train) [85][15/15] lr: 1.0000e-06 eta: 0:10:00 time: 0.2973 data_time: 0.0016 memory: 5517 loss: 0.0838 loss_ce: 0.0838 2023/03/03 14:23:03 - mmengine - INFO - Epoch(train) [86][ 1/15] lr: 1.0000e-06 eta: 0:10:00 time: 0.3850 data_time: 0.0887 memory: 16955 loss: 0.0794 loss_ce: 0.0794 2023/03/03 14:23:03 - mmengine - INFO - Epoch(train) [86][ 2/15] lr: 1.0000e-06 eta: 0:10:00 time: 0.3658 data_time: 0.0887 memory: 11659 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:23:04 - mmengine - INFO - Epoch(train) [86][ 3/15] lr: 1.0000e-06 eta: 0:10:00 time: 0.3912 data_time: 0.0887 memory: 26513 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:23:04 - mmengine - INFO - Epoch(train) [86][ 4/15] lr: 1.0000e-06 eta: 0:09:59 time: 0.3859 data_time: 0.0887 memory: 16199 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:23:04 - mmengine - INFO - Epoch(train) [86][ 5/15] lr: 1.0000e-06 eta: 0:09:59 time: 0.3745 data_time: 0.0888 memory: 15911 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:23:05 - mmengine - INFO - Epoch(train) [86][ 6/15] lr: 1.0000e-06 eta: 0:09:58 time: 0.3768 data_time: 0.0888 memory: 16830 loss: 0.0783 loss_ce: 0.0783 2023/03/03 14:23:05 - mmengine - INFO - Epoch(train) [86][ 7/15] lr: 1.0000e-06 eta: 0:09:58 time: 0.3738 data_time: 0.0888 memory: 17122 loss: 0.0794 loss_ce: 0.0794 2023/03/03 14:23:05 - mmengine - INFO - Epoch(train) [86][ 8/15] lr: 1.0000e-06 eta: 0:09:57 time: 0.3783 data_time: 0.0889 memory: 18028 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:23:05 - mmengine - INFO - Epoch(train) [86][ 9/15] lr: 1.0000e-06 eta: 0:09:57 time: 0.3679 data_time: 0.0889 memory: 14675 loss: 0.0826 loss_ce: 0.0826 2023/03/03 14:23:06 - mmengine - INFO - Epoch(train) [86][10/15] lr: 1.0000e-06 eta: 0:09:56 time: 0.3754 data_time: 0.0888 memory: 18409 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:23:06 - mmengine - INFO - Epoch(train) [86][11/15] lr: 1.0000e-06 eta: 0:09:56 time: 0.2902 data_time: 0.0017 memory: 16953 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:23:06 - mmengine - INFO - Epoch(train) [86][12/15] lr: 1.0000e-06 eta: 0:09:55 time: 0.2909 data_time: 0.0017 memory: 15844 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:23:06 - mmengine - INFO - Epoch(train) [86][13/15] lr: 1.0000e-06 eta: 0:09:55 time: 0.2613 data_time: 0.0016 memory: 25575 loss: 0.0761 loss_ce: 0.0761 2023/03/03 14:23:07 - mmengine - INFO - Epoch(train) [86][14/15] lr: 1.0000e-06 eta: 0:09:55 time: 0.2588 data_time: 0.0016 memory: 17543 loss: 0.0727 loss_ce: 0.0727 2023/03/03 14:23:07 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:23:07 - mmengine - INFO - Epoch(train) [86][15/15] lr: 1.0000e-06 eta: 0:09:54 time: 0.2488 data_time: 0.0015 memory: 5401 loss: 0.0780 loss_ce: 0.0780 2023/03/03 14:23:08 - mmengine - INFO - Epoch(train) [87][ 1/15] lr: 1.0000e-06 eta: 0:09:54 time: 0.2966 data_time: 0.0486 memory: 17284 loss: 0.0778 loss_ce: 0.0778 2023/03/03 14:23:08 - mmengine - INFO - Epoch(train) [87][ 2/15] lr: 1.0000e-06 eta: 0:09:54 time: 0.3425 data_time: 0.0487 memory: 17572 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:23:08 - mmengine - INFO - Epoch(train) [87][ 3/15] lr: 1.0000e-06 eta: 0:09:54 time: 0.3380 data_time: 0.0487 memory: 16976 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:23:09 - mmengine - INFO - Epoch(train) [87][ 4/15] lr: 1.0000e-06 eta: 0:09:53 time: 0.3435 data_time: 0.0488 memory: 17284 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:23:09 - mmengine - INFO - Epoch(train) [87][ 5/15] lr: 1.0000e-06 eta: 0:09:53 time: 0.3625 data_time: 0.0488 memory: 18366 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:23:09 - mmengine - INFO - Epoch(train) [87][ 6/15] lr: 1.0000e-06 eta: 0:09:53 time: 0.3569 data_time: 0.0488 memory: 20182 loss: 0.0759 loss_ce: 0.0759 2023/03/03 14:23:10 - mmengine - INFO - Epoch(train) [87][ 7/15] lr: 1.0000e-06 eta: 0:09:52 time: 0.3582 data_time: 0.0488 memory: 17281 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:23:10 - mmengine - INFO - Epoch(train) [87][ 8/15] lr: 1.0000e-06 eta: 0:09:52 time: 0.3514 data_time: 0.0489 memory: 17049 loss: 0.0802 loss_ce: 0.0802 2023/03/03 14:23:10 - mmengine - INFO - Epoch(train) [87][ 9/15] lr: 1.0000e-06 eta: 0:09:51 time: 0.3539 data_time: 0.0489 memory: 16370 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:23:11 - mmengine - INFO - Epoch(train) [87][10/15] lr: 1.0000e-06 eta: 0:09:51 time: 0.3736 data_time: 0.0489 memory: 20982 loss: 0.0758 loss_ce: 0.0758 2023/03/03 14:23:11 - mmengine - INFO - Epoch(train) [87][11/15] lr: 1.0000e-06 eta: 0:09:50 time: 0.3245 data_time: 0.0018 memory: 19340 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:23:11 - mmengine - INFO - Epoch(train) [87][12/15] lr: 1.0000e-06 eta: 0:09:50 time: 0.2892 data_time: 0.0017 memory: 17120 loss: 0.0758 loss_ce: 0.0758 2023/03/03 14:23:11 - mmengine - INFO - Epoch(train) [87][13/15] lr: 1.0000e-06 eta: 0:09:50 time: 0.2996 data_time: 0.0017 memory: 17776 loss: 0.0774 loss_ce: 0.0774 2023/03/03 14:23:12 - mmengine - INFO - Epoch(train) [87][14/15] lr: 1.0000e-06 eta: 0:09:49 time: 0.2886 data_time: 0.0016 memory: 16223 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:23:12 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:23:12 - mmengine - INFO - Epoch(train) [87][15/15] lr: 1.0000e-06 eta: 0:09:49 time: 0.2618 data_time: 0.0016 memory: 6274 loss: 0.0843 loss_ce: 0.0843 2023/03/03 14:23:13 - mmengine - INFO - Epoch(train) [88][ 1/15] lr: 1.0000e-06 eta: 0:09:49 time: 0.3172 data_time: 0.0551 memory: 15268 loss: 0.0869 loss_ce: 0.0869 2023/03/03 14:23:13 - mmengine - INFO - Epoch(train) [88][ 2/15] lr: 1.0000e-06 eta: 0:09:48 time: 0.3176 data_time: 0.0552 memory: 17421 loss: 0.0866 loss_ce: 0.0866 2023/03/03 14:23:13 - mmengine - INFO - Epoch(train) [88][ 3/15] lr: 1.0000e-06 eta: 0:09:48 time: 0.3212 data_time: 0.0553 memory: 15251 loss: 0.0889 loss_ce: 0.0889 2023/03/03 14:23:13 - mmengine - INFO - Epoch(train) [88][ 4/15] lr: 1.0000e-06 eta: 0:09:47 time: 0.3267 data_time: 0.0553 memory: 16253 loss: 0.0920 loss_ce: 0.0920 2023/03/03 14:23:14 - mmengine - INFO - Epoch(train) [88][ 5/15] lr: 1.0000e-06 eta: 0:09:47 time: 0.3246 data_time: 0.0553 memory: 16223 loss: 0.0936 loss_ce: 0.0936 2023/03/03 14:23:14 - mmengine - INFO - Epoch(train) [88][ 6/15] lr: 1.0000e-06 eta: 0:09:47 time: 0.3226 data_time: 0.0553 memory: 16976 loss: 0.0945 loss_ce: 0.0945 2023/03/03 14:23:14 - mmengine - INFO - Epoch(train) [88][ 7/15] lr: 1.0000e-06 eta: 0:09:46 time: 0.3116 data_time: 0.0553 memory: 15803 loss: 0.0927 loss_ce: 0.0927 2023/03/03 14:23:14 - mmengine - INFO - Epoch(train) [88][ 8/15] lr: 1.0000e-06 eta: 0:09:46 time: 0.3034 data_time: 0.0553 memory: 15274 loss: 0.0937 loss_ce: 0.0937 2023/03/03 14:23:15 - mmengine - INFO - Epoch(train) [88][ 9/15] lr: 1.0000e-06 eta: 0:09:45 time: 0.3215 data_time: 0.0553 memory: 25401 loss: 0.0904 loss_ce: 0.0904 2023/03/03 14:23:15 - mmengine - INFO - Epoch(train) [88][10/15] lr: 1.0000e-06 eta: 0:09:45 time: 0.3318 data_time: 0.0553 memory: 16804 loss: 0.0880 loss_ce: 0.0880 2023/03/03 14:23:16 - mmengine - INFO - Epoch(train) [88][11/15] lr: 1.0000e-06 eta: 0:09:45 time: 0.2980 data_time: 0.0017 memory: 28254 loss: 0.0860 loss_ce: 0.0860 2023/03/03 14:23:16 - mmengine - INFO - Epoch(train) [88][12/15] lr: 1.0000e-06 eta: 0:09:44 time: 0.3207 data_time: 0.0017 memory: 26370 loss: 0.0831 loss_ce: 0.0831 2023/03/03 14:23:16 - mmengine - INFO - Epoch(train) [88][13/15] lr: 1.0000e-06 eta: 0:09:44 time: 0.3075 data_time: 0.0016 memory: 13754 loss: 0.0831 loss_ce: 0.0831 2023/03/03 14:23:17 - mmengine - INFO - Epoch(train) [88][14/15] lr: 1.0000e-06 eta: 0:09:44 time: 0.3098 data_time: 0.0016 memory: 17469 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:23:17 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:23:17 - mmengine - INFO - Epoch(train) [88][15/15] lr: 1.0000e-06 eta: 0:09:43 time: 0.2885 data_time: 0.0016 memory: 5539 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:23:18 - mmengine - INFO - Epoch(train) [89][ 1/15] lr: 1.0000e-06 eta: 0:09:43 time: 0.3554 data_time: 0.0679 memory: 17730 loss: 0.0728 loss_ce: 0.0728 2023/03/03 14:23:18 - mmengine - INFO - Epoch(train) [89][ 2/15] lr: 1.0000e-06 eta: 0:09:43 time: 0.3687 data_time: 0.0680 memory: 15457 loss: 0.0698 loss_ce: 0.0698 2023/03/03 14:23:18 - mmengine - INFO - Epoch(train) [89][ 3/15] lr: 1.0000e-06 eta: 0:09:42 time: 0.3704 data_time: 0.0680 memory: 17446 loss: 0.0635 loss_ce: 0.0635 2023/03/03 14:23:19 - mmengine - INFO - Epoch(train) [89][ 4/15] lr: 1.0000e-06 eta: 0:09:42 time: 0.3686 data_time: 0.0680 memory: 17272 loss: 0.0660 loss_ce: 0.0660 2023/03/03 14:23:19 - mmengine - INFO - Epoch(train) [89][ 5/15] lr: 1.0000e-06 eta: 0:09:42 time: 0.3763 data_time: 0.0681 memory: 17951 loss: 0.0653 loss_ce: 0.0653 2023/03/03 14:23:19 - mmengine - INFO - Epoch(train) [89][ 6/15] lr: 1.0000e-06 eta: 0:09:41 time: 0.3683 data_time: 0.0681 memory: 28534 loss: 0.0676 loss_ce: 0.0676 2023/03/03 14:23:20 - mmengine - INFO - Epoch(train) [89][ 7/15] lr: 1.0000e-06 eta: 0:09:41 time: 0.3504 data_time: 0.0681 memory: 18131 loss: 0.0660 loss_ce: 0.0660 2023/03/03 14:23:20 - mmengine - INFO - Epoch(train) [89][ 8/15] lr: 1.0000e-06 eta: 0:09:41 time: 0.3780 data_time: 0.0681 memory: 17730 loss: 0.0646 loss_ce: 0.0646 2023/03/03 14:23:20 - mmengine - INFO - Epoch(train) [89][ 9/15] lr: 1.0000e-06 eta: 0:09:40 time: 0.3698 data_time: 0.0681 memory: 16345 loss: 0.0662 loss_ce: 0.0662 2023/03/03 14:23:21 - mmengine - INFO - Epoch(train) [89][10/15] lr: 1.0000e-06 eta: 0:09:40 time: 0.3877 data_time: 0.0680 memory: 14883 loss: 0.0656 loss_ce: 0.0656 2023/03/03 14:23:21 - mmengine - INFO - Epoch(train) [89][11/15] lr: 1.0000e-06 eta: 0:09:39 time: 0.3258 data_time: 0.0017 memory: 15004 loss: 0.0674 loss_ce: 0.0674 2023/03/03 14:23:21 - mmengine - INFO - Epoch(train) [89][12/15] lr: 1.0000e-06 eta: 0:09:39 time: 0.3076 data_time: 0.0017 memory: 16976 loss: 0.0749 loss_ce: 0.0749 2023/03/03 14:23:21 - mmengine - INFO - Epoch(train) [89][13/15] lr: 1.0000e-06 eta: 0:09:38 time: 0.3057 data_time: 0.0016 memory: 15911 loss: 0.0746 loss_ce: 0.0746 2023/03/03 14:23:22 - mmengine - INFO - Epoch(train) [89][14/15] lr: 1.0000e-06 eta: 0:09:38 time: 0.2973 data_time: 0.0016 memory: 15428 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:23:22 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:23:22 - mmengine - INFO - Epoch(train) [89][15/15] lr: 1.0000e-06 eta: 0:09:37 time: 0.2819 data_time: 0.0016 memory: 4603 loss: 0.0826 loss_ce: 0.0826 2023/03/03 14:23:23 - mmengine - INFO - Epoch(train) [90][ 1/15] lr: 1.0000e-06 eta: 0:09:38 time: 0.3351 data_time: 0.0450 memory: 17272 loss: 0.0806 loss_ce: 0.0806 2023/03/03 14:23:23 - mmengine - INFO - Epoch(train) [90][ 2/15] lr: 1.0000e-06 eta: 0:09:37 time: 0.3379 data_time: 0.0451 memory: 20433 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:23:23 - mmengine - INFO - Epoch(train) [90][ 3/15] lr: 1.0000e-06 eta: 0:09:37 time: 0.3091 data_time: 0.0451 memory: 17572 loss: 0.0800 loss_ce: 0.0800 2023/03/03 14:23:23 - mmengine - INFO - Epoch(train) [90][ 4/15] lr: 1.0000e-06 eta: 0:09:36 time: 0.3071 data_time: 0.0452 memory: 17120 loss: 0.0804 loss_ce: 0.0804 2023/03/03 14:23:24 - mmengine - INFO - Epoch(train) [90][ 5/15] lr: 1.0000e-06 eta: 0:09:36 time: 0.3150 data_time: 0.0452 memory: 27756 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:23:24 - mmengine - INFO - Epoch(train) [90][ 6/15] lr: 1.0000e-06 eta: 0:09:36 time: 0.3139 data_time: 0.0452 memory: 17619 loss: 0.0759 loss_ce: 0.0759 2023/03/03 14:23:24 - mmengine - INFO - Epoch(train) [90][ 7/15] lr: 1.0000e-06 eta: 0:09:35 time: 0.3243 data_time: 0.0453 memory: 17272 loss: 0.0732 loss_ce: 0.0732 2023/03/03 14:23:24 - mmengine - INFO - Epoch(train) [90][ 8/15] lr: 1.0000e-06 eta: 0:09:35 time: 0.3223 data_time: 0.0453 memory: 17272 loss: 0.0746 loss_ce: 0.0746 2023/03/03 14:23:25 - mmengine - INFO - Epoch(train) [90][ 9/15] lr: 1.0000e-06 eta: 0:09:34 time: 0.3301 data_time: 0.0452 memory: 16094 loss: 0.0725 loss_ce: 0.0725 2023/03/03 14:23:25 - mmengine - INFO - Epoch(train) [90][10/15] lr: 1.0000e-06 eta: 0:09:34 time: 0.3428 data_time: 0.0453 memory: 13783 loss: 0.0687 loss_ce: 0.0687 2023/03/03 14:23:25 - mmengine - INFO - Epoch(train) [90][11/15] lr: 1.0000e-06 eta: 0:09:34 time: 0.2809 data_time: 0.0018 memory: 16654 loss: 0.0698 loss_ce: 0.0698 2023/03/03 14:23:26 - mmengine - INFO - Epoch(train) [90][12/15] lr: 1.0000e-06 eta: 0:09:33 time: 0.2757 data_time: 0.0017 memory: 16804 loss: 0.0714 loss_ce: 0.0714 2023/03/03 14:23:26 - mmengine - INFO - Epoch(train) [90][13/15] lr: 1.0000e-06 eta: 0:09:33 time: 0.2927 data_time: 0.0017 memory: 21805 loss: 0.0721 loss_ce: 0.0721 2023/03/03 14:23:26 - mmengine - INFO - Epoch(train) [90][14/15] lr: 1.0000e-06 eta: 0:09:32 time: 0.2869 data_time: 0.0016 memory: 11368 loss: 0.0733 loss_ce: 0.0733 2023/03/03 14:23:26 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:23:26 - mmengine - INFO - Epoch(train) [90][15/15] lr: 1.0000e-06 eta: 0:09:32 time: 0.2752 data_time: 0.0016 memory: 5211 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:23:28 - mmengine - INFO - Epoch(val) [90][ 1/59] eta: 0:01:29 time: 1.1210 data_time: 0.0036 memory: 981 2023/03/03 14:23:29 - mmengine - INFO - Epoch(val) [90][ 2/59] eta: 0:01:07 time: 1.0344 data_time: 0.0036 memory: 981 2023/03/03 14:23:30 - mmengine - INFO - Epoch(val) [90][ 3/59] eta: 0:01:12 time: 1.0682 data_time: 0.0036 memory: 1003 2023/03/03 14:23:31 - mmengine - INFO - Epoch(val) [90][ 4/59] eta: 0:00:58 time: 1.0349 data_time: 0.0035 memory: 981 2023/03/03 14:23:34 - mmengine - INFO - Epoch(val) [90][ 5/59] eta: 0:01:18 time: 1.2712 data_time: 0.0035 memory: 1016 2023/03/03 14:23:36 - mmengine - INFO - Epoch(val) [90][ 6/59] eta: 0:01:27 time: 1.4666 data_time: 0.0035 memory: 981 2023/03/03 14:23:37 - mmengine - INFO - Epoch(val) [90][ 7/59] eta: 0:01:14 time: 1.4007 data_time: 0.0035 memory: 1043 2023/03/03 14:23:37 - mmengine - INFO - Epoch(val) [90][ 8/59] eta: 0:01:09 time: 1.2586 data_time: 0.0035 memory: 1016 2023/03/03 14:23:38 - mmengine - INFO - Epoch(val) [90][ 9/59] eta: 0:01:06 time: 1.2239 data_time: 0.0035 memory: 981 2023/03/03 14:23:39 - mmengine - INFO - Epoch(val) [90][10/59] eta: 0:01:01 time: 1.2566 data_time: 0.0035 memory: 981 2023/03/03 14:23:39 - mmengine - INFO - Epoch(val) [90][11/59] eta: 0:00:56 time: 1.1357 data_time: 0.0007 memory: 981 2023/03/03 14:23:43 - mmengine - INFO - Epoch(val) [90][12/59] eta: 0:01:03 time: 1.3763 data_time: 0.0007 memory: 1016 2023/03/03 14:23:45 - mmengine - INFO - Epoch(val) [90][13/59] eta: 0:01:04 time: 1.4308 data_time: 0.0007 memory: 981 2023/03/03 14:23:46 - mmengine - INFO - Epoch(val) [90][14/59] eta: 0:01:02 time: 1.5146 data_time: 0.0008 memory: 890 2023/03/03 14:23:46 - mmengine - INFO - Epoch(val) [90][15/59] eta: 0:00:56 time: 1.2137 data_time: 0.0008 memory: 981 2023/03/03 14:23:46 - mmengine - INFO - Epoch(val) [90][16/59] eta: 0:00:53 time: 1.0008 data_time: 0.0008 memory: 981 2023/03/03 14:23:47 - mmengine - INFO - Epoch(val) [90][17/59] eta: 0:00:50 time: 1.0169 data_time: 0.0008 memory: 981 2023/03/03 14:23:47 - mmengine - INFO - Epoch(val) [90][18/59] eta: 0:00:46 time: 0.9671 data_time: 0.0008 memory: 981 2023/03/03 14:23:48 - mmengine - INFO - Epoch(val) [90][19/59] eta: 0:00:45 time: 0.9669 data_time: 0.0008 memory: 981 2023/03/03 14:23:48 - mmengine - INFO - Epoch(val) [90][20/59] eta: 0:00:42 time: 0.9340 data_time: 0.0008 memory: 981 2023/03/03 14:23:50 - mmengine - INFO - Epoch(val) [90][21/59] eta: 0:00:43 time: 1.1044 data_time: 0.0008 memory: 981 2023/03/03 14:23:51 - mmengine - INFO - Epoch(val) [90][22/59] eta: 0:00:40 time: 0.7979 data_time: 0.0008 memory: 981 2023/03/03 14:23:51 - mmengine - INFO - Epoch(val) [90][23/59] eta: 0:00:38 time: 0.6582 data_time: 0.0008 memory: 981 2023/03/03 14:23:52 - mmengine - INFO - Epoch(val) [90][24/59] eta: 0:00:36 time: 0.5742 data_time: 0.0007 memory: 962 2023/03/03 14:23:52 - mmengine - INFO - Epoch(val) [90][25/59] eta: 0:00:34 time: 0.6054 data_time: 0.0007 memory: 981 2023/03/03 14:23:52 - mmengine - INFO - Epoch(val) [90][26/59] eta: 0:00:32 time: 0.5895 data_time: 0.0007 memory: 981 2023/03/03 14:23:53 - mmengine - INFO - Epoch(val) [90][27/59] eta: 0:00:30 time: 0.5896 data_time: 0.0008 memory: 981 2023/03/03 14:23:53 - mmengine - INFO - Epoch(val) [90][28/59] eta: 0:00:29 time: 0.5898 data_time: 0.0007 memory: 981 2023/03/03 14:23:54 - mmengine - INFO - Epoch(val) [90][29/59] eta: 0:00:28 time: 0.6238 data_time: 0.0007 memory: 981 2023/03/03 14:23:55 - mmengine - INFO - Epoch(val) [90][30/59] eta: 0:00:27 time: 0.6729 data_time: 0.0007 memory: 999 2023/03/03 14:23:56 - mmengine - INFO - Epoch(val) [90][31/59] eta: 0:00:26 time: 0.5351 data_time: 0.0007 memory: 981 2023/03/03 14:23:57 - mmengine - INFO - Epoch(val) [90][32/59] eta: 0:00:25 time: 0.6340 data_time: 0.0007 memory: 981 2023/03/03 14:23:57 - mmengine - INFO - Epoch(val) [90][33/59] eta: 0:00:24 time: 0.5851 data_time: 0.0007 memory: 981 2023/03/03 14:23:57 - mmengine - INFO - Epoch(val) [90][34/59] eta: 0:00:22 time: 0.5690 data_time: 0.0007 memory: 981 2023/03/03 14:23:57 - mmengine - INFO - Epoch(val) [90][35/59] eta: 0:00:21 time: 0.5527 data_time: 0.0007 memory: 981 2023/03/03 14:23:58 - mmengine - INFO - Epoch(val) [90][36/59] eta: 0:00:20 time: 0.5687 data_time: 0.0007 memory: 981 2023/03/03 14:23:58 - mmengine - INFO - Epoch(val) [90][37/59] eta: 0:00:18 time: 0.5524 data_time: 0.0007 memory: 981 2023/03/03 14:23:59 - mmengine - INFO - Epoch(val) [90][38/59] eta: 0:00:17 time: 0.5845 data_time: 0.0007 memory: 981 2023/03/03 14:23:59 - mmengine - INFO - Epoch(val) [90][39/59] eta: 0:00:16 time: 0.5007 data_time: 0.0007 memory: 987 2023/03/03 14:24:00 - mmengine - INFO - Epoch(val) [90][40/59] eta: 0:00:15 time: 0.5012 data_time: 0.0007 memory: 981 2023/03/03 14:24:01 - mmengine - INFO - Epoch(val) [90][41/59] eta: 0:00:15 time: 0.5526 data_time: 0.0007 memory: 986 2023/03/03 14:24:02 - mmengine - INFO - Epoch(val) [90][42/59] eta: 0:00:14 time: 0.5032 data_time: 0.0008 memory: 981 2023/03/03 14:24:03 - mmengine - INFO - Epoch(val) [90][43/59] eta: 0:00:13 time: 0.5693 data_time: 0.0008 memory: 976 2023/03/03 14:24:03 - mmengine - INFO - Epoch(val) [90][44/59] eta: 0:00:12 time: 0.6025 data_time: 0.0008 memory: 1003 2023/03/03 14:24:06 - mmengine - INFO - Epoch(val) [90][45/59] eta: 0:00:12 time: 0.8090 data_time: 0.0009 memory: 981 2023/03/03 14:24:06 - mmengine - INFO - Epoch(val) [90][46/59] eta: 0:00:11 time: 0.8424 data_time: 0.0009 memory: 981 2023/03/03 14:24:07 - mmengine - INFO - Epoch(val) [90][47/59] eta: 0:00:10 time: 0.8751 data_time: 0.0009 memory: 936 2023/03/03 14:24:07 - mmengine - INFO - Epoch(val) [90][48/59] eta: 0:00:09 time: 0.8595 data_time: 0.0009 memory: 1000 2023/03/03 14:24:08 - mmengine - INFO - Epoch(val) [90][49/59] eta: 0:00:08 time: 0.9095 data_time: 0.0009 memory: 981 2023/03/03 14:24:09 - mmengine - INFO - Epoch(val) [90][50/59] eta: 0:00:07 time: 0.9093 data_time: 0.0009 memory: 987 2023/03/03 14:24:11 - mmengine - INFO - Epoch(val) [90][51/59] eta: 0:00:06 time: 0.9603 data_time: 0.0009 memory: 981 2023/03/03 14:24:12 - mmengine - INFO - Epoch(val) [90][52/59] eta: 0:00:06 time: 1.0103 data_time: 0.0009 memory: 981 2023/03/03 14:24:13 - mmengine - INFO - Epoch(val) [90][53/59] eta: 0:00:05 time: 0.9930 data_time: 0.0009 memory: 962 2023/03/03 14:24:13 - mmengine - INFO - Epoch(val) [90][54/59] eta: 0:00:04 time: 1.0091 data_time: 0.0009 memory: 981 2023/03/03 14:24:14 - mmengine - INFO - Epoch(val) [90][55/59] eta: 0:00:03 time: 0.8517 data_time: 0.0008 memory: 981 2023/03/03 14:24:15 - mmengine - INFO - Epoch(val) [90][56/59] eta: 0:00:02 time: 0.8514 data_time: 0.0008 memory: 981 2023/03/03 14:24:17 - mmengine - INFO - Epoch(val) [90][57/59] eta: 0:00:01 time: 1.0247 data_time: 0.0008 memory: 981 2023/03/03 14:24:18 - mmengine - INFO - Epoch(val) [90][58/59] eta: 0:00:00 time: 1.1080 data_time: 0.0008 memory: 1016 2023/03/03 14:24:19 - mmengine - INFO - Epoch(val) [90][59/59] eta: 0:00:00 time: 1.0419 data_time: 0.0008 memory: 981 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.80, recall: 0.8174, precision: 0.8310, hmean: 0.8241 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.81, recall: 0.8164, precision: 0.8324, hmean: 0.8243 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.82, recall: 0.8137, precision: 0.8358, hmean: 0.8246 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.83, recall: 0.8119, precision: 0.8379, hmean: 0.8247 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.84, recall: 0.8110, precision: 0.8401, hmean: 0.8253 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.85, recall: 0.8091, precision: 0.8422, hmean: 0.8253 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.86, recall: 0.8082, precision: 0.8485, hmean: 0.8279 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.87, recall: 0.8064, precision: 0.8499, hmean: 0.8276 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.88, recall: 0.8064, precision: 0.8540, hmean: 0.8295 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.89, recall: 0.8064, precision: 0.8565, hmean: 0.8307 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.90, recall: 0.8037, precision: 0.8594, hmean: 0.8306 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.91, recall: 0.8000, precision: 0.8605, hmean: 0.8292 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.92, recall: 0.7936, precision: 0.8638, hmean: 0.8272 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.93, recall: 0.7845, precision: 0.8677, hmean: 0.8240 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.94, recall: 0.7772, precision: 0.8701, hmean: 0.8210 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.95, recall: 0.7717, precision: 0.8729, hmean: 0.8192 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.96, recall: 0.7598, precision: 0.8776, hmean: 0.8145 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.97, recall: 0.7470, precision: 0.8805, hmean: 0.8083 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.98, recall: 0.7324, precision: 0.8852, hmean: 0.8016 2023/03/03 14:24:47 - mmengine - INFO - text score threshold: 0.99, recall: 0.7142, precision: 0.8917, hmean: 0.7931 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.80, recall: 0.8283, precision: 0.8998, hmean: 0.8626 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.81, recall: 0.8274, precision: 0.9015, hmean: 0.8629 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.82, recall: 0.8247, precision: 0.9039, hmean: 0.8625 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.83, recall: 0.8228, precision: 0.9046, hmean: 0.8618 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.84, recall: 0.8219, precision: 0.9054, hmean: 0.8617 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.85, recall: 0.8201, precision: 0.9052, hmean: 0.8606 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.86, recall: 0.8174, precision: 0.9086, hmean: 0.8606 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.87, recall: 0.8146, precision: 0.9084, hmean: 0.8589 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.88, recall: 0.8146, precision: 0.9111, hmean: 0.8602 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.89, recall: 0.8146, precision: 0.9130, hmean: 0.8610 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.90, recall: 0.8110, precision: 0.9145, hmean: 0.8596 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.91, recall: 0.8064, precision: 0.9150, hmean: 0.8573 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.92, recall: 0.8009, precision: 0.9174, hmean: 0.8552 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.93, recall: 0.7890, precision: 0.9172, hmean: 0.8483 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.94, recall: 0.7817, precision: 0.9194, hmean: 0.8450 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.95, recall: 0.7753, precision: 0.9218, hmean: 0.8423 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.96, recall: 0.7616, precision: 0.9246, hmean: 0.8353 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.97, recall: 0.7479, precision: 0.9244, hmean: 0.8269 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.98, recall: 0.7324, precision: 0.9282, hmean: 0.8188 2023/03/03 14:24:50 - mmengine - INFO - text score threshold: 0.99, recall: 0.7132, precision: 0.9298, hmean: 0.8072 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.80, recall: 0.7507, precision: 0.9558, hmean: 0.8409 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.81, recall: 0.7498, precision: 0.9558, hmean: 0.8403 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.82, recall: 0.7470, precision: 0.9567, hmean: 0.8390 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.83, recall: 0.7452, precision: 0.9566, hmean: 0.8378 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.84, recall: 0.7434, precision: 0.9565, hmean: 0.8366 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.85, recall: 0.7416, precision: 0.9564, hmean: 0.8354 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.86, recall: 0.7379, precision: 0.9585, hmean: 0.8338 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.87, recall: 0.7352, precision: 0.9583, hmean: 0.8320 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.88, recall: 0.7352, precision: 0.9583, hmean: 0.8320 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.89, recall: 0.7352, precision: 0.9583, hmean: 0.8320 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.90, recall: 0.7315, precision: 0.9593, hmean: 0.8301 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.91, recall: 0.7269, precision: 0.9590, hmean: 0.8270 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.92, recall: 0.7215, precision: 0.9599, hmean: 0.8238 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.93, recall: 0.7105, precision: 0.9593, hmean: 0.8164 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.94, recall: 0.7041, precision: 0.9601, hmean: 0.8124 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.95, recall: 0.6977, precision: 0.9610, hmean: 0.8085 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.96, recall: 0.6868, precision: 0.9629, hmean: 0.8017 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.97, recall: 0.6749, precision: 0.9648, hmean: 0.7942 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.98, recall: 0.6621, precision: 0.9667, hmean: 0.7859 2023/03/03 14:24:53 - mmengine - INFO - text score threshold: 0.99, recall: 0.6438, precision: 0.9671, hmean: 0.7730 2023/03/03 14:24:53 - mmengine - INFO - Epoch(val) [90][59/59] generic/precision: 0.8565 generic/recall: 0.8064 generic/hmean: 0.8307 weak/precision: 0.9015 weak/recall: 0.8274 weak/hmean: 0.8629 strong/precision: 0.9558 strong/recall: 0.7507 strong/hmean: 0.8409 2023/03/03 14:24:53 - mmengine - INFO - Epoch(train) [91][ 1/15] lr: 1.0000e-06 eta: 0:09:32 time: 0.3199 data_time: 0.0466 memory: 18953 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:24:54 - mmengine - INFO - Epoch(train) [91][ 2/15] lr: 1.0000e-06 eta: 0:09:32 time: 0.3340 data_time: 0.0467 memory: 12675 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:24:54 - mmengine - INFO - Epoch(train) [91][ 3/15] lr: 1.0000e-06 eta: 0:09:31 time: 0.3415 data_time: 0.0467 memory: 17353 loss: 0.0787 loss_ce: 0.0787 2023/03/03 14:24:54 - mmengine - INFO - Epoch(train) [91][ 4/15] lr: 1.0000e-06 eta: 0:09:31 time: 0.3292 data_time: 0.0468 memory: 14056 loss: 0.0812 loss_ce: 0.0812 2023/03/03 14:24:55 - mmengine - INFO - Epoch(train) [91][ 5/15] lr: 1.0000e-06 eta: 0:09:30 time: 0.3211 data_time: 0.0468 memory: 17272 loss: 0.0784 loss_ce: 0.0784 2023/03/03 14:24:55 - mmengine - INFO - Epoch(train) [91][ 6/15] lr: 1.0000e-06 eta: 0:09:30 time: 0.3263 data_time: 0.0468 memory: 17120 loss: 0.0777 loss_ce: 0.0777 2023/03/03 14:24:55 - mmengine - INFO - Epoch(train) [91][ 7/15] lr: 1.0000e-06 eta: 0:09:29 time: 0.3264 data_time: 0.0468 memory: 16370 loss: 0.0821 loss_ce: 0.0821 2023/03/03 14:24:55 - mmengine - INFO - Epoch(train) [91][ 8/15] lr: 1.0000e-06 eta: 0:09:29 time: 0.3127 data_time: 0.0467 memory: 15911 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:24:56 - mmengine - INFO - Epoch(train) [91][ 9/15] lr: 1.0000e-06 eta: 0:09:28 time: 0.3183 data_time: 0.0467 memory: 17572 loss: 0.0784 loss_ce: 0.0784 2023/03/03 14:24:56 - mmengine - INFO - Epoch(train) [91][10/15] lr: 1.0000e-06 eta: 0:09:28 time: 0.3255 data_time: 0.0467 memory: 20638 loss: 0.0718 loss_ce: 0.0718 2023/03/03 14:24:56 - mmengine - INFO - Epoch(train) [91][11/15] lr: 1.0000e-06 eta: 0:09:27 time: 0.2788 data_time: 0.0017 memory: 15494 loss: 0.0772 loss_ce: 0.0772 2023/03/03 14:24:57 - mmengine - INFO - Epoch(train) [91][12/15] lr: 1.0000e-06 eta: 0:09:27 time: 0.2865 data_time: 0.0016 memory: 21168 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:24:57 - mmengine - INFO - Epoch(train) [91][13/15] lr: 1.0000e-06 eta: 0:09:27 time: 0.2816 data_time: 0.0015 memory: 16223 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:24:57 - mmengine - INFO - Epoch(train) [91][14/15] lr: 1.0000e-06 eta: 0:09:27 time: 0.2916 data_time: 0.0015 memory: 18131 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:24:57 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:24:57 - mmengine - INFO - Epoch(train) [91][15/15] lr: 1.0000e-06 eta: 0:09:26 time: 0.2833 data_time: 0.0014 memory: 6267 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:24:59 - mmengine - INFO - Epoch(train) [92][ 1/15] lr: 1.0000e-06 eta: 0:09:26 time: 0.3492 data_time: 0.0613 memory: 14771 loss: 0.0793 loss_ce: 0.0793 2023/03/03 14:24:59 - mmengine - INFO - Epoch(train) [92][ 2/15] lr: 1.0000e-06 eta: 0:09:26 time: 0.3570 data_time: 0.0614 memory: 21683 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:24:59 - mmengine - INFO - Epoch(train) [92][ 3/15] lr: 1.0000e-06 eta: 0:09:25 time: 0.3544 data_time: 0.0615 memory: 18070 loss: 0.0744 loss_ce: 0.0744 2023/03/03 14:25:00 - mmengine - INFO - Epoch(train) [92][ 4/15] lr: 1.0000e-06 eta: 0:09:25 time: 0.3888 data_time: 0.0615 memory: 33956 loss: 0.0764 loss_ce: 0.0764 2023/03/03 14:25:00 - mmengine - INFO - Epoch(train) [92][ 5/15] lr: 1.0000e-06 eta: 0:09:25 time: 0.3878 data_time: 0.0616 memory: 19579 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:25:00 - mmengine - INFO - Epoch(train) [92][ 6/15] lr: 1.0000e-06 eta: 0:09:24 time: 0.3862 data_time: 0.0616 memory: 17272 loss: 0.0727 loss_ce: 0.0727 2023/03/03 14:25:00 - mmengine - INFO - Epoch(train) [92][ 7/15] lr: 1.0000e-06 eta: 0:09:24 time: 0.3649 data_time: 0.0616 memory: 16654 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:25:01 - mmengine - INFO - Epoch(train) [92][ 8/15] lr: 1.0000e-06 eta: 0:09:24 time: 0.3660 data_time: 0.0615 memory: 17619 loss: 0.0727 loss_ce: 0.0727 2023/03/03 14:25:01 - mmengine - INFO - Epoch(train) [92][ 9/15] lr: 1.0000e-06 eta: 0:09:23 time: 0.3695 data_time: 0.0616 memory: 16804 loss: 0.0735 loss_ce: 0.0735 2023/03/03 14:25:01 - mmengine - INFO - Epoch(train) [92][10/15] lr: 1.0000e-06 eta: 0:09:23 time: 0.3807 data_time: 0.0615 memory: 16804 loss: 0.0693 loss_ce: 0.0693 2023/03/03 14:25:02 - mmengine - INFO - Epoch(train) [92][11/15] lr: 1.0000e-06 eta: 0:09:22 time: 0.3093 data_time: 0.0017 memory: 15432 loss: 0.0679 loss_ce: 0.0679 2023/03/03 14:25:02 - mmengine - INFO - Epoch(train) [92][12/15] lr: 1.0000e-06 eta: 0:09:22 time: 0.3302 data_time: 0.0016 memory: 37937 loss: 0.0673 loss_ce: 0.0673 2023/03/03 14:25:02 - mmengine - INFO - Epoch(train) [92][13/15] lr: 1.0000e-06 eta: 0:09:22 time: 0.3318 data_time: 0.0015 memory: 16654 loss: 0.0720 loss_ce: 0.0720 2023/03/03 14:25:03 - mmengine - INFO - Epoch(train) [92][14/15] lr: 1.0000e-06 eta: 0:09:21 time: 0.3001 data_time: 0.0015 memory: 16654 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:25:03 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:25:03 - mmengine - INFO - Epoch(train) [92][15/15] lr: 1.0000e-06 eta: 0:09:21 time: 0.2967 data_time: 0.0014 memory: 9701 loss: 0.0833 loss_ce: 0.0833 2023/03/03 14:25:04 - mmengine - INFO - Epoch(train) [93][ 1/15] lr: 1.0000e-06 eta: 0:09:21 time: 0.3598 data_time: 0.0623 memory: 15767 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:25:04 - mmengine - INFO - Epoch(train) [93][ 2/15] lr: 1.0000e-06 eta: 0:09:21 time: 0.3508 data_time: 0.0623 memory: 16849 loss: 0.0812 loss_ce: 0.0812 2023/03/03 14:25:04 - mmengine - INFO - Epoch(train) [93][ 3/15] lr: 1.0000e-06 eta: 0:09:20 time: 0.3492 data_time: 0.0624 memory: 15767 loss: 0.0853 loss_ce: 0.0853 2023/03/03 14:25:05 - mmengine - INFO - Epoch(train) [93][ 4/15] lr: 1.0000e-06 eta: 0:09:20 time: 0.3679 data_time: 0.0625 memory: 36574 loss: 0.0869 loss_ce: 0.0869 2023/03/03 14:25:05 - mmengine - INFO - Epoch(train) [93][ 5/15] lr: 1.0000e-06 eta: 0:09:20 time: 0.3678 data_time: 0.0625 memory: 16654 loss: 0.0891 loss_ce: 0.0891 2023/03/03 14:25:05 - mmengine - INFO - Epoch(train) [93][ 6/15] lr: 1.0000e-06 eta: 0:09:19 time: 0.3714 data_time: 0.0625 memory: 16976 loss: 0.0920 loss_ce: 0.0920 2023/03/03 14:25:06 - mmengine - INFO - Epoch(train) [93][ 7/15] lr: 1.0000e-06 eta: 0:09:19 time: 0.3416 data_time: 0.0626 memory: 15494 loss: 0.0957 loss_ce: 0.0957 2023/03/03 14:25:06 - mmengine - INFO - Epoch(train) [93][ 8/15] lr: 1.0000e-06 eta: 0:09:18 time: 0.3410 data_time: 0.0627 memory: 17380 loss: 0.0930 loss_ce: 0.0930 2023/03/03 14:25:06 - mmengine - INFO - Epoch(train) [93][ 9/15] lr: 1.0000e-06 eta: 0:09:18 time: 0.3410 data_time: 0.0627 memory: 16425 loss: 0.0916 loss_ce: 0.0916 2023/03/03 14:25:06 - mmengine - INFO - Epoch(train) [93][10/15] lr: 1.0000e-06 eta: 0:09:18 time: 0.3600 data_time: 0.0627 memory: 16778 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:25:07 - mmengine - INFO - Epoch(train) [93][11/15] lr: 1.0000e-06 eta: 0:09:17 time: 0.3147 data_time: 0.0019 memory: 22468 loss: 0.0803 loss_ce: 0.0803 2023/03/03 14:25:07 - mmengine - INFO - Epoch(train) [93][12/15] lr: 1.0000e-06 eta: 0:09:17 time: 0.3071 data_time: 0.0018 memory: 14509 loss: 0.0843 loss_ce: 0.0843 2023/03/03 14:25:07 - mmengine - INFO - Epoch(train) [93][13/15] lr: 1.0000e-06 eta: 0:09:16 time: 0.3050 data_time: 0.0017 memory: 17572 loss: 0.0832 loss_ce: 0.0832 2023/03/03 14:25:08 - mmengine - INFO - Epoch(train) [93][14/15] lr: 1.0000e-06 eta: 0:09:16 time: 0.2784 data_time: 0.0016 memory: 17284 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:25:08 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:25:08 - mmengine - INFO - Epoch(train) [93][15/15] lr: 1.0000e-06 eta: 0:09:15 time: 0.2710 data_time: 0.0016 memory: 4547 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:25:08 - mmengine - INFO - Epoch(train) [94][ 1/15] lr: 1.0000e-06 eta: 0:09:15 time: 0.3160 data_time: 0.0541 memory: 13315 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:25:09 - mmengine - INFO - Epoch(train) [94][ 2/15] lr: 1.0000e-06 eta: 0:09:15 time: 0.3258 data_time: 0.0540 memory: 14605 loss: 0.0692 loss_ce: 0.0692 2023/03/03 14:25:09 - mmengine - INFO - Epoch(train) [94][ 3/15] lr: 1.0000e-06 eta: 0:09:15 time: 0.3422 data_time: 0.0541 memory: 22625 loss: 0.0696 loss_ce: 0.0696 2023/03/03 14:25:09 - mmengine - INFO - Epoch(train) [94][ 4/15] lr: 1.0000e-06 eta: 0:09:14 time: 0.3503 data_time: 0.0541 memory: 14837 loss: 0.0766 loss_ce: 0.0766 2023/03/03 14:25:10 - mmengine - INFO - Epoch(train) [94][ 5/15] lr: 1.0000e-06 eta: 0:09:14 time: 0.3246 data_time: 0.0541 memory: 15847 loss: 0.0809 loss_ce: 0.0809 2023/03/03 14:25:10 - mmengine - INFO - Epoch(train) [94][ 6/15] lr: 1.0000e-06 eta: 0:09:13 time: 0.3151 data_time: 0.0542 memory: 15877 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:25:11 - mmengine - INFO - Epoch(train) [94][ 7/15] lr: 1.0000e-06 eta: 0:09:13 time: 0.3549 data_time: 0.0541 memory: 17421 loss: 0.0777 loss_ce: 0.0777 2023/03/03 14:25:11 - mmengine - INFO - Epoch(train) [94][ 8/15] lr: 1.0000e-06 eta: 0:09:13 time: 0.3624 data_time: 0.0542 memory: 17574 loss: 0.0738 loss_ce: 0.0738 2023/03/03 14:25:11 - mmengine - INFO - Epoch(train) [94][ 9/15] lr: 1.0000e-06 eta: 0:09:13 time: 0.3533 data_time: 0.0542 memory: 16199 loss: 0.0788 loss_ce: 0.0788 2023/03/03 14:25:11 - mmengine - INFO - Epoch(train) [94][10/15] lr: 1.0000e-06 eta: 0:09:12 time: 0.3592 data_time: 0.0542 memory: 13892 loss: 0.0800 loss_ce: 0.0800 2023/03/03 14:25:12 - mmengine - INFO - Epoch(train) [94][11/15] lr: 1.0000e-06 eta: 0:09:12 time: 0.3064 data_time: 0.0018 memory: 17434 loss: 0.0831 loss_ce: 0.0831 2023/03/03 14:25:12 - mmengine - INFO - Epoch(train) [94][12/15] lr: 1.0000e-06 eta: 0:09:11 time: 0.3035 data_time: 0.0017 memory: 17029 loss: 0.0864 loss_ce: 0.0864 2023/03/03 14:25:12 - mmengine - INFO - Epoch(train) [94][13/15] lr: 1.0000e-06 eta: 0:09:11 time: 0.3106 data_time: 0.0017 memory: 17284 loss: 0.0821 loss_ce: 0.0821 2023/03/03 14:25:13 - mmengine - INFO - Epoch(train) [94][14/15] lr: 1.0000e-06 eta: 0:09:11 time: 0.3111 data_time: 0.0016 memory: 22841 loss: 0.0744 loss_ce: 0.0744 2023/03/03 14:25:13 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:25:13 - mmengine - INFO - Epoch(train) [94][15/15] lr: 1.0000e-06 eta: 0:09:10 time: 0.3026 data_time: 0.0016 memory: 6247 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:25:14 - mmengine - INFO - Epoch(train) [95][ 1/15] lr: 1.0000e-06 eta: 0:09:10 time: 0.3509 data_time: 0.0576 memory: 17120 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:25:14 - mmengine - INFO - Epoch(train) [95][ 2/15] lr: 1.0000e-06 eta: 0:09:10 time: 0.3439 data_time: 0.0577 memory: 16804 loss: 0.0792 loss_ce: 0.0792 2023/03/03 14:25:14 - mmengine - INFO - Epoch(train) [95][ 3/15] lr: 1.0000e-06 eta: 0:09:09 time: 0.3367 data_time: 0.0578 memory: 17421 loss: 0.0830 loss_ce: 0.0830 2023/03/03 14:25:15 - mmengine - INFO - Epoch(train) [95][ 4/15] lr: 1.0000e-06 eta: 0:09:09 time: 0.3476 data_time: 0.0578 memory: 15983 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:25:15 - mmengine - INFO - Epoch(train) [95][ 5/15] lr: 1.0000e-06 eta: 0:09:09 time: 0.3490 data_time: 0.0578 memory: 16530 loss: 0.0780 loss_ce: 0.0780 2023/03/03 14:25:15 - mmengine - INFO - Epoch(train) [95][ 6/15] lr: 1.0000e-06 eta: 0:09:08 time: 0.3517 data_time: 0.0578 memory: 17122 loss: 0.0774 loss_ce: 0.0774 2023/03/03 14:25:15 - mmengine - INFO - Epoch(train) [95][ 7/15] lr: 1.0000e-06 eta: 0:09:08 time: 0.3454 data_time: 0.0578 memory: 16370 loss: 0.0756 loss_ce: 0.0756 2023/03/03 14:25:16 - mmengine - INFO - Epoch(train) [95][ 8/15] lr: 1.0000e-06 eta: 0:09:07 time: 0.3293 data_time: 0.0578 memory: 15962 loss: 0.0824 loss_ce: 0.0824 2023/03/03 14:25:16 - mmengine - INFO - Epoch(train) [95][ 9/15] lr: 1.0000e-06 eta: 0:09:07 time: 0.3208 data_time: 0.0578 memory: 16819 loss: 0.0865 loss_ce: 0.0865 2023/03/03 14:25:16 - mmengine - INFO - Epoch(train) [95][10/15] lr: 1.0000e-06 eta: 0:09:07 time: 0.3538 data_time: 0.0578 memory: 33562 loss: 0.0828 loss_ce: 0.0828 2023/03/03 14:25:16 - mmengine - INFO - Epoch(train) [95][11/15] lr: 1.0000e-06 eta: 0:09:06 time: 0.2971 data_time: 0.0017 memory: 17421 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:25:17 - mmengine - INFO - Epoch(train) [95][12/15] lr: 1.0000e-06 eta: 0:09:06 time: 0.2917 data_time: 0.0016 memory: 14493 loss: 0.0803 loss_ce: 0.0803 2023/03/03 14:25:17 - mmengine - INFO - Epoch(train) [95][13/15] lr: 1.0000e-06 eta: 0:09:05 time: 0.2970 data_time: 0.0016 memory: 10653 loss: 0.0809 loss_ce: 0.0809 2023/03/03 14:25:17 - mmengine - INFO - Epoch(train) [95][14/15] lr: 1.0000e-06 eta: 0:09:05 time: 0.2878 data_time: 0.0015 memory: 17446 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:25:18 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:25:18 - mmengine - INFO - Epoch(train) [95][15/15] lr: 1.0000e-06 eta: 0:09:04 time: 0.2774 data_time: 0.0015 memory: 6849 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:25:18 - mmengine - INFO - Epoch(train) [96][ 1/15] lr: 1.0000e-06 eta: 0:09:04 time: 0.3233 data_time: 0.0262 memory: 13144 loss: 0.0851 loss_ce: 0.0851 2023/03/03 14:25:19 - mmengine - INFO - Epoch(train) [96][ 2/15] lr: 1.0000e-06 eta: 0:09:04 time: 0.3253 data_time: 0.0263 memory: 17788 loss: 0.0846 loss_ce: 0.0846 2023/03/03 14:25:19 - mmengine - INFO - Epoch(train) [96][ 3/15] lr: 1.0000e-06 eta: 0:09:04 time: 0.3298 data_time: 0.0264 memory: 17619 loss: 0.0804 loss_ce: 0.0804 2023/03/03 14:25:19 - mmengine - INFO - Epoch(train) [96][ 4/15] lr: 1.0000e-06 eta: 0:09:03 time: 0.3298 data_time: 0.0264 memory: 16508 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:25:20 - mmengine - INFO - Epoch(train) [96][ 5/15] lr: 1.0000e-06 eta: 0:09:03 time: 0.3204 data_time: 0.0264 memory: 22553 loss: 0.0822 loss_ce: 0.0822 2023/03/03 14:25:20 - mmengine - INFO - Epoch(train) [96][ 6/15] lr: 1.0000e-06 eta: 0:09:02 time: 0.3275 data_time: 0.0265 memory: 19512 loss: 0.0824 loss_ce: 0.0824 2023/03/03 14:25:20 - mmengine - INFO - Epoch(train) [96][ 7/15] lr: 1.0000e-06 eta: 0:09:02 time: 0.3048 data_time: 0.0265 memory: 17421 loss: 0.0831 loss_ce: 0.0831 2023/03/03 14:25:20 - mmengine - INFO - Epoch(train) [96][ 8/15] lr: 1.0000e-06 eta: 0:09:01 time: 0.2992 data_time: 0.0265 memory: 18070 loss: 0.0800 loss_ce: 0.0800 2023/03/03 14:25:21 - mmengine - INFO - Epoch(train) [96][ 9/15] lr: 1.0000e-06 eta: 0:09:01 time: 0.3110 data_time: 0.0265 memory: 17272 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:25:21 - mmengine - INFO - Epoch(train) [96][10/15] lr: 1.0000e-06 eta: 0:09:01 time: 0.3199 data_time: 0.0265 memory: 12335 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:25:21 - mmengine - INFO - Epoch(train) [96][11/15] lr: 1.0000e-06 eta: 0:09:00 time: 0.2719 data_time: 0.0018 memory: 16199 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:25:21 - mmengine - INFO - Epoch(train) [96][12/15] lr: 1.0000e-06 eta: 0:09:00 time: 0.2835 data_time: 0.0017 memory: 21340 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:25:22 - mmengine - INFO - Epoch(train) [96][13/15] lr: 1.0000e-06 eta: 0:08:59 time: 0.2720 data_time: 0.0016 memory: 16508 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:25:22 - mmengine - INFO - Epoch(train) [96][14/15] lr: 1.0000e-06 eta: 0:08:59 time: 0.2700 data_time: 0.0016 memory: 18586 loss: 0.0766 loss_ce: 0.0766 2023/03/03 14:25:22 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:25:22 - mmengine - INFO - Epoch(train) [96][15/15] lr: 1.0000e-06 eta: 0:08:58 time: 0.2627 data_time: 0.0015 memory: 5741 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:25:23 - mmengine - INFO - Epoch(train) [97][ 1/15] lr: 1.0000e-06 eta: 0:08:59 time: 0.3225 data_time: 0.0678 memory: 17572 loss: 0.0751 loss_ce: 0.0751 2023/03/03 14:25:23 - mmengine - INFO - Epoch(train) [97][ 2/15] lr: 1.0000e-06 eta: 0:08:58 time: 0.3296 data_time: 0.0679 memory: 21038 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:25:23 - mmengine - INFO - Epoch(train) [97][ 3/15] lr: 1.0000e-06 eta: 0:08:58 time: 0.3296 data_time: 0.0679 memory: 17120 loss: 0.0835 loss_ce: 0.0835 2023/03/03 14:25:24 - mmengine - INFO - Epoch(train) [97][ 4/15] lr: 1.0000e-06 eta: 0:08:57 time: 0.3267 data_time: 0.0680 memory: 17080 loss: 0.0882 loss_ce: 0.0882 2023/03/03 14:25:24 - mmengine - INFO - Epoch(train) [97][ 5/15] lr: 1.0000e-06 eta: 0:08:57 time: 0.3307 data_time: 0.0680 memory: 18337 loss: 0.0874 loss_ce: 0.0874 2023/03/03 14:25:25 - mmengine - INFO - Epoch(train) [97][ 6/15] lr: 1.0000e-06 eta: 0:08:57 time: 0.3500 data_time: 0.0680 memory: 17120 loss: 0.0850 loss_ce: 0.0850 2023/03/03 14:25:25 - mmengine - INFO - Epoch(train) [97][ 7/15] lr: 1.0000e-06 eta: 0:08:56 time: 0.3371 data_time: 0.0681 memory: 17122 loss: 0.0837 loss_ce: 0.0837 2023/03/03 14:25:25 - mmengine - INFO - Epoch(train) [97][ 8/15] lr: 1.0000e-06 eta: 0:08:56 time: 0.3445 data_time: 0.0681 memory: 16976 loss: 0.0866 loss_ce: 0.0866 2023/03/03 14:25:25 - mmengine - INFO - Epoch(train) [97][ 9/15] lr: 1.0000e-06 eta: 0:08:55 time: 0.3456 data_time: 0.0681 memory: 15767 loss: 0.0883 loss_ce: 0.0883 2023/03/03 14:25:26 - mmengine - INFO - Epoch(train) [97][10/15] lr: 1.0000e-06 eta: 0:08:55 time: 0.3363 data_time: 0.0681 memory: 15747 loss: 0.0845 loss_ce: 0.0845 2023/03/03 14:25:26 - mmengine - INFO - Epoch(train) [97][11/15] lr: 1.0000e-06 eta: 0:08:54 time: 0.2722 data_time: 0.0017 memory: 16654 loss: 0.0860 loss_ce: 0.0860 2023/03/03 14:25:26 - mmengine - INFO - Epoch(train) [97][12/15] lr: 1.0000e-06 eta: 0:08:54 time: 0.2727 data_time: 0.0017 memory: 17272 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:25:26 - mmengine - INFO - Epoch(train) [97][13/15] lr: 1.0000e-06 eta: 0:08:54 time: 0.2808 data_time: 0.0016 memory: 15523 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:25:27 - mmengine - INFO - Epoch(train) [97][14/15] lr: 1.0000e-06 eta: 0:08:53 time: 0.2817 data_time: 0.0015 memory: 17730 loss: 0.0735 loss_ce: 0.0735 2023/03/03 14:25:27 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:25:27 - mmengine - INFO - Epoch(train) [97][15/15] lr: 1.0000e-06 eta: 0:08:53 time: 0.2717 data_time: 0.0015 memory: 3425 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:25:28 - mmengine - INFO - Epoch(train) [98][ 1/15] lr: 1.0000e-06 eta: 0:08:53 time: 0.3352 data_time: 0.0798 memory: 17120 loss: 0.0811 loss_ce: 0.0811 2023/03/03 14:25:28 - mmengine - INFO - Epoch(train) [98][ 2/15] lr: 1.0000e-06 eta: 0:08:53 time: 0.3349 data_time: 0.0798 memory: 16223 loss: 0.0828 loss_ce: 0.0828 2023/03/03 14:25:29 - mmengine - INFO - Epoch(train) [98][ 3/15] lr: 1.0000e-06 eta: 0:08:52 time: 0.3480 data_time: 0.0799 memory: 17120 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:25:29 - mmengine - INFO - Epoch(train) [98][ 4/15] lr: 1.0000e-06 eta: 0:08:52 time: 0.3463 data_time: 0.0799 memory: 17572 loss: 0.0787 loss_ce: 0.0787 2023/03/03 14:25:29 - mmengine - INFO - Epoch(train) [98][ 5/15] lr: 1.0000e-06 eta: 0:08:52 time: 0.3701 data_time: 0.0799 memory: 17272 loss: 0.0777 loss_ce: 0.0777 2023/03/03 14:25:30 - mmengine - INFO - Epoch(train) [98][ 6/15] lr: 1.0000e-06 eta: 0:08:51 time: 0.3832 data_time: 0.0799 memory: 26214 loss: 0.0751 loss_ce: 0.0751 2023/03/03 14:25:30 - mmengine - INFO - Epoch(train) [98][ 7/15] lr: 1.0000e-06 eta: 0:08:51 time: 0.3746 data_time: 0.0799 memory: 18241 loss: 0.0727 loss_ce: 0.0727 2023/03/03 14:25:30 - mmengine - INFO - Epoch(train) [98][ 8/15] lr: 1.0000e-06 eta: 0:08:50 time: 0.3665 data_time: 0.0799 memory: 17730 loss: 0.0742 loss_ce: 0.0742 2023/03/03 14:25:30 - mmengine - INFO - Epoch(train) [98][ 9/15] lr: 1.0000e-06 eta: 0:08:50 time: 0.3669 data_time: 0.0799 memory: 14229 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:25:31 - mmengine - INFO - Epoch(train) [98][10/15] lr: 1.0000e-06 eta: 0:08:50 time: 0.3738 data_time: 0.0799 memory: 15911 loss: 0.0734 loss_ce: 0.0734 2023/03/03 14:25:31 - mmengine - INFO - Epoch(train) [98][11/15] lr: 1.0000e-06 eta: 0:08:49 time: 0.2999 data_time: 0.0017 memory: 18586 loss: 0.0698 loss_ce: 0.0698 2023/03/03 14:25:31 - mmengine - INFO - Epoch(train) [98][12/15] lr: 1.0000e-06 eta: 0:08:49 time: 0.2968 data_time: 0.0016 memory: 17133 loss: 0.0677 loss_ce: 0.0677 2023/03/03 14:25:32 - mmengine - INFO - Epoch(train) [98][13/15] lr: 1.0000e-06 eta: 0:08:49 time: 0.3023 data_time: 0.0015 memory: 14575 loss: 0.0660 loss_ce: 0.0660 2023/03/03 14:25:32 - mmengine - INFO - Epoch(train) [98][14/15] lr: 1.0000e-06 eta: 0:08:48 time: 0.3023 data_time: 0.0015 memory: 18070 loss: 0.0648 loss_ce: 0.0648 2023/03/03 14:25:32 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:25:32 - mmengine - INFO - Epoch(train) [98][15/15] lr: 1.0000e-06 eta: 0:08:48 time: 0.2920 data_time: 0.0015 memory: 5836 loss: 0.0707 loss_ce: 0.0707 2023/03/03 14:25:33 - mmengine - INFO - Epoch(train) [99][ 1/15] lr: 1.0000e-06 eta: 0:08:48 time: 0.3581 data_time: 0.0724 memory: 18109 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:25:33 - mmengine - INFO - Epoch(train) [99][ 2/15] lr: 1.0000e-06 eta: 0:08:48 time: 0.3655 data_time: 0.0724 memory: 17788 loss: 0.0710 loss_ce: 0.0710 2023/03/03 14:25:34 - mmengine - INFO - Epoch(train) [99][ 3/15] lr: 1.0000e-06 eta: 0:08:47 time: 0.3656 data_time: 0.0725 memory: 17572 loss: 0.0711 loss_ce: 0.0711 2023/03/03 14:25:34 - mmengine - INFO - Epoch(train) [99][ 4/15] lr: 1.0000e-06 eta: 0:08:47 time: 0.3944 data_time: 0.0725 memory: 25838 loss: 0.0711 loss_ce: 0.0711 2023/03/03 14:25:34 - mmengine - INFO - Epoch(train) [99][ 5/15] lr: 1.0000e-06 eta: 0:08:47 time: 0.3952 data_time: 0.0725 memory: 16508 loss: 0.0681 loss_ce: 0.0681 2023/03/03 14:25:35 - mmengine - INFO - Epoch(train) [99][ 6/15] lr: 1.0000e-06 eta: 0:08:46 time: 0.3855 data_time: 0.0725 memory: 17343 loss: 0.0705 loss_ce: 0.0705 2023/03/03 14:25:35 - mmengine - INFO - Epoch(train) [99][ 7/15] lr: 1.0000e-06 eta: 0:08:46 time: 0.3873 data_time: 0.0725 memory: 15494 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:25:35 - mmengine - INFO - Epoch(train) [99][ 8/15] lr: 1.0000e-06 eta: 0:08:45 time: 0.3765 data_time: 0.0725 memory: 14370 loss: 0.0819 loss_ce: 0.0819 2023/03/03 14:25:36 - mmengine - INFO - Epoch(train) [99][ 9/15] lr: 1.0000e-06 eta: 0:08:45 time: 0.3903 data_time: 0.0725 memory: 16206 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:25:36 - mmengine - INFO - Epoch(train) [99][10/15] lr: 1.0000e-06 eta: 0:08:45 time: 0.3782 data_time: 0.0725 memory: 15968 loss: 0.0786 loss_ce: 0.0786 2023/03/03 14:25:36 - mmengine - INFO - Epoch(train) [99][11/15] lr: 1.0000e-06 eta: 0:08:44 time: 0.2964 data_time: 0.0016 memory: 14326 loss: 0.0820 loss_ce: 0.0820 2023/03/03 14:25:37 - mmengine - INFO - Epoch(train) [99][12/15] lr: 1.0000e-06 eta: 0:08:44 time: 0.3205 data_time: 0.0016 memory: 16976 loss: 0.0853 loss_ce: 0.0853 2023/03/03 14:25:37 - mmengine - INFO - Epoch(train) [99][13/15] lr: 1.0000e-06 eta: 0:08:43 time: 0.3247 data_time: 0.0015 memory: 15653 loss: 0.0898 loss_ce: 0.0898 2023/03/03 14:25:37 - mmengine - INFO - Epoch(train) [99][14/15] lr: 1.0000e-06 eta: 0:08:43 time: 0.2857 data_time: 0.0015 memory: 17024 loss: 0.0875 loss_ce: 0.0875 2023/03/03 14:25:37 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:25:37 - mmengine - INFO - Epoch(train) [99][15/15] lr: 1.0000e-06 eta: 0:08:42 time: 0.2770 data_time: 0.0015 memory: 7085 loss: 0.0879 loss_ce: 0.0879 2023/03/03 14:25:39 - mmengine - INFO - Epoch(train) [100][ 1/15] lr: 1.0000e-06 eta: 0:08:43 time: 0.3844 data_time: 0.0611 memory: 14831 loss: 0.0891 loss_ce: 0.0891 2023/03/03 14:25:39 - mmengine - INFO - Epoch(train) [100][ 2/15] lr: 1.0000e-06 eta: 0:08:43 time: 0.3862 data_time: 0.0611 memory: 17284 loss: 0.0844 loss_ce: 0.0844 2023/03/03 14:25:39 - mmengine - INFO - Epoch(train) [100][ 3/15] lr: 1.0000e-06 eta: 0:08:42 time: 0.3905 data_time: 0.0612 memory: 30477 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:25:39 - mmengine - INFO - Epoch(train) [100][ 4/15] lr: 1.0000e-06 eta: 0:08:42 time: 0.3766 data_time: 0.0612 memory: 17421 loss: 0.0803 loss_ce: 0.0803 2023/03/03 14:25:40 - mmengine - INFO - Epoch(train) [100][ 5/15] lr: 1.0000e-06 eta: 0:08:42 time: 0.4016 data_time: 0.0612 memory: 14201 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:25:40 - mmengine - INFO - Epoch(train) [100][ 6/15] lr: 1.0000e-06 eta: 0:08:41 time: 0.4027 data_time: 0.0612 memory: 15457 loss: 0.0745 loss_ce: 0.0745 2023/03/03 14:25:41 - mmengine - INFO - Epoch(train) [100][ 7/15] lr: 1.0000e-06 eta: 0:08:41 time: 0.4146 data_time: 0.0612 memory: 15767 loss: 0.0745 loss_ce: 0.0745 2023/03/03 14:25:41 - mmengine - INFO - Epoch(train) [100][ 8/15] lr: 1.0000e-06 eta: 0:08:41 time: 0.4129 data_time: 0.0612 memory: 16508 loss: 0.0706 loss_ce: 0.0706 2023/03/03 14:25:41 - mmengine - INFO - Epoch(train) [100][ 9/15] lr: 1.0000e-06 eta: 0:08:40 time: 0.4189 data_time: 0.0612 memory: 17284 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:25:42 - mmengine - INFO - Epoch(train) [100][10/15] lr: 1.0000e-06 eta: 0:08:40 time: 0.4267 data_time: 0.0612 memory: 15767 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:25:42 - mmengine - INFO - Epoch(train) [100][11/15] lr: 1.0000e-06 eta: 0:08:39 time: 0.3222 data_time: 0.0016 memory: 16370 loss: 0.0736 loss_ce: 0.0736 2023/03/03 14:25:42 - mmengine - INFO - Epoch(train) [100][12/15] lr: 1.0000e-06 eta: 0:08:39 time: 0.3212 data_time: 0.0015 memory: 13360 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:25:42 - mmengine - INFO - Epoch(train) [100][13/15] lr: 1.0000e-06 eta: 0:08:39 time: 0.3041 data_time: 0.0015 memory: 17572 loss: 0.0732 loss_ce: 0.0732 2023/03/03 14:25:43 - mmengine - INFO - Epoch(train) [100][14/15] lr: 1.0000e-06 eta: 0:08:38 time: 0.3099 data_time: 0.0015 memory: 19278 loss: 0.0718 loss_ce: 0.0718 2023/03/03 14:25:43 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:25:43 - mmengine - INFO - Epoch(train) [100][15/15] lr: 1.0000e-06 eta: 0:08:38 time: 0.2765 data_time: 0.0015 memory: 6201 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:25:44 - mmengine - INFO - Epoch(val) [100][ 1/59] eta: 0:01:29 time: 1.1125 data_time: 0.0033 memory: 981 2023/03/03 14:25:45 - mmengine - INFO - Epoch(val) [100][ 2/59] eta: 0:01:07 time: 1.0264 data_time: 0.0033 memory: 981 2023/03/03 14:25:47 - mmengine - INFO - Epoch(val) [100][ 3/59] eta: 0:01:12 time: 1.0604 data_time: 0.0033 memory: 1003 2023/03/03 14:25:47 - mmengine - INFO - Epoch(val) [100][ 4/59] eta: 0:00:57 time: 1.0277 data_time: 0.0033 memory: 981 2023/03/03 14:25:50 - mmengine - INFO - Epoch(val) [100][ 5/59] eta: 0:01:18 time: 1.2630 data_time: 0.0033 memory: 1016 2023/03/03 14:25:53 - mmengine - INFO - Epoch(val) [100][ 6/59] eta: 0:01:26 time: 1.4581 data_time: 0.0033 memory: 981 2023/03/03 14:25:53 - mmengine - INFO - Epoch(val) [100][ 7/59] eta: 0:01:14 time: 1.3925 data_time: 0.0033 memory: 1043 2023/03/03 14:25:54 - mmengine - INFO - Epoch(val) [100][ 8/59] eta: 0:01:09 time: 1.2519 data_time: 0.0033 memory: 1016 2023/03/03 14:25:55 - mmengine - INFO - Epoch(val) [100][ 9/59] eta: 0:01:05 time: 1.2178 data_time: 0.0033 memory: 981 2023/03/03 14:25:55 - mmengine - INFO - Epoch(val) [100][10/59] eta: 0:01:01 time: 1.2502 data_time: 0.0033 memory: 981 2023/03/03 14:25:56 - mmengine - INFO - Epoch(val) [100][11/59] eta: 0:00:56 time: 1.1302 data_time: 0.0008 memory: 981 2023/03/03 14:25:59 - mmengine - INFO - Epoch(val) [100][12/59] eta: 0:01:02 time: 1.3695 data_time: 0.0008 memory: 1016 2023/03/03 14:26:01 - mmengine - INFO - Epoch(val) [100][13/59] eta: 0:01:04 time: 1.4233 data_time: 0.0008 memory: 981 2023/03/03 14:26:02 - mmengine - INFO - Epoch(val) [100][14/59] eta: 0:01:01 time: 1.5067 data_time: 0.0008 memory: 890 2023/03/03 14:26:02 - mmengine - INFO - Epoch(val) [100][15/59] eta: 0:00:56 time: 1.2072 data_time: 0.0008 memory: 981 2023/03/03 14:26:03 - mmengine - INFO - Epoch(val) [100][16/59] eta: 0:00:53 time: 0.9953 data_time: 0.0007 memory: 981 2023/03/03 14:26:03 - mmengine - INFO - Epoch(val) [100][17/59] eta: 0:00:49 time: 1.0113 data_time: 0.0007 memory: 981 2023/03/03 14:26:03 - mmengine - INFO - Epoch(val) [100][18/59] eta: 0:00:46 time: 0.9620 data_time: 0.0007 memory: 981 2023/03/03 14:26:04 - mmengine - INFO - Epoch(val) [100][19/59] eta: 0:00:45 time: 0.9621 data_time: 0.0007 memory: 981 2023/03/03 14:26:05 - mmengine - INFO - Epoch(val) [100][20/59] eta: 0:00:42 time: 0.9296 data_time: 0.0007 memory: 981 2023/03/03 14:26:07 - mmengine - INFO - Epoch(val) [100][21/59] eta: 0:00:43 time: 1.1188 data_time: 0.0007 memory: 981 2023/03/03 14:26:07 - mmengine - INFO - Epoch(val) [100][22/59] eta: 0:00:40 time: 0.8141 data_time: 0.0007 memory: 981 2023/03/03 14:26:08 - mmengine - INFO - Epoch(val) [100][23/59] eta: 0:00:38 time: 0.6757 data_time: 0.0007 memory: 981 2023/03/03 14:26:08 - mmengine - INFO - Epoch(val) [100][24/59] eta: 0:00:36 time: 0.5924 data_time: 0.0007 memory: 962 2023/03/03 14:26:08 - mmengine - INFO - Epoch(val) [100][25/59] eta: 0:00:34 time: 0.6236 data_time: 0.0007 memory: 981 2023/03/03 14:26:09 - mmengine - INFO - Epoch(val) [100][26/59] eta: 0:00:32 time: 0.6074 data_time: 0.0007 memory: 981 2023/03/03 14:26:09 - mmengine - INFO - Epoch(val) [100][27/59] eta: 0:00:31 time: 0.6074 data_time: 0.0007 memory: 981 2023/03/03 14:26:09 - mmengine - INFO - Epoch(val) [100][28/59] eta: 0:00:29 time: 0.6074 data_time: 0.0007 memory: 981 2023/03/03 14:26:11 - mmengine - INFO - Epoch(val) [100][29/59] eta: 0:00:28 time: 0.6415 data_time: 0.0007 memory: 981 2023/03/03 14:26:11 - mmengine - INFO - Epoch(val) [100][30/59] eta: 0:00:27 time: 0.6906 data_time: 0.0007 memory: 999 2023/03/03 14:26:12 - mmengine - INFO - Epoch(val) [100][31/59] eta: 0:00:26 time: 0.5341 data_time: 0.0007 memory: 981 2023/03/03 14:26:13 - mmengine - INFO - Epoch(val) [100][32/59] eta: 0:00:25 time: 0.6330 data_time: 0.0007 memory: 981 2023/03/03 14:26:13 - mmengine - INFO - Epoch(val) [100][33/59] eta: 0:00:24 time: 0.5841 data_time: 0.0007 memory: 981 2023/03/03 14:26:14 - mmengine - INFO - Epoch(val) [100][34/59] eta: 0:00:22 time: 0.5679 data_time: 0.0007 memory: 981 2023/03/03 14:26:14 - mmengine - INFO - Epoch(val) [100][35/59] eta: 0:00:21 time: 0.5516 data_time: 0.0007 memory: 981 2023/03/03 14:26:14 - mmengine - INFO - Epoch(val) [100][36/59] eta: 0:00:20 time: 0.5680 data_time: 0.0007 memory: 981 2023/03/03 14:26:14 - mmengine - INFO - Epoch(val) [100][37/59] eta: 0:00:18 time: 0.5517 data_time: 0.0007 memory: 981 2023/03/03 14:26:15 - mmengine - INFO - Epoch(val) [100][38/59] eta: 0:00:17 time: 0.5844 data_time: 0.0007 memory: 981 2023/03/03 14:26:16 - mmengine - INFO - Epoch(val) [100][39/59] eta: 0:00:16 time: 0.5007 data_time: 0.0007 memory: 987 2023/03/03 14:26:16 - mmengine - INFO - Epoch(val) [100][40/59] eta: 0:00:16 time: 0.5010 data_time: 0.0007 memory: 981 2023/03/03 14:26:18 - mmengine - INFO - Epoch(val) [100][41/59] eta: 0:00:15 time: 0.5512 data_time: 0.0007 memory: 986 2023/03/03 14:26:18 - mmengine - INFO - Epoch(val) [100][42/59] eta: 0:00:14 time: 0.5012 data_time: 0.0007 memory: 981 2023/03/03 14:26:19 - mmengine - INFO - Epoch(val) [100][43/59] eta: 0:00:13 time: 0.5667 data_time: 0.0007 memory: 976 2023/03/03 14:26:20 - mmengine - INFO - Epoch(val) [100][44/59] eta: 0:00:12 time: 0.5991 data_time: 0.0007 memory: 1003 2023/03/03 14:26:21 - mmengine - INFO - Epoch(val) [100][45/59] eta: 0:00:12 time: 0.7681 data_time: 0.0007 memory: 981 2023/03/03 14:26:22 - mmengine - INFO - Epoch(val) [100][46/59] eta: 0:00:11 time: 0.8010 data_time: 0.0007 memory: 981 2023/03/03 14:26:23 - mmengine - INFO - Epoch(val) [100][47/59] eta: 0:00:10 time: 0.8333 data_time: 0.0007 memory: 936 2023/03/03 14:26:23 - mmengine - INFO - Epoch(val) [100][48/59] eta: 0:00:09 time: 0.8171 data_time: 0.0007 memory: 1000 2023/03/03 14:26:24 - mmengine - INFO - Epoch(val) [100][49/59] eta: 0:00:08 time: 0.8673 data_time: 0.0007 memory: 981 2023/03/03 14:26:25 - mmengine - INFO - Epoch(val) [100][50/59] eta: 0:00:07 time: 0.8672 data_time: 0.0007 memory: 987 2023/03/03 14:26:27 - mmengine - INFO - Epoch(val) [100][51/59] eta: 0:00:06 time: 0.9194 data_time: 0.0007 memory: 981 2023/03/03 14:26:28 - mmengine - INFO - Epoch(val) [100][52/59] eta: 0:00:06 time: 0.9697 data_time: 0.0007 memory: 981 2023/03/03 14:26:29 - mmengine - INFO - Epoch(val) [100][53/59] eta: 0:00:05 time: 0.9532 data_time: 0.0007 memory: 962 2023/03/03 14:26:29 - mmengine - INFO - Epoch(val) [100][54/59] eta: 0:00:04 time: 0.9697 data_time: 0.0007 memory: 981 2023/03/03 14:26:30 - mmengine - INFO - Epoch(val) [100][55/59] eta: 0:00:03 time: 0.8502 data_time: 0.0007 memory: 981 2023/03/03 14:26:31 - mmengine - INFO - Epoch(val) [100][56/59] eta: 0:00:02 time: 0.8504 data_time: 0.0007 memory: 981 2023/03/03 14:26:33 - mmengine - INFO - Epoch(val) [100][57/59] eta: 0:00:01 time: 1.0249 data_time: 0.0007 memory: 981 2023/03/03 14:26:34 - mmengine - INFO - Epoch(val) [100][58/59] eta: 0:00:00 time: 1.1086 data_time: 0.0007 memory: 1016 2023/03/03 14:26:35 - mmengine - INFO - Epoch(val) [100][59/59] eta: 0:00:00 time: 1.0423 data_time: 0.0007 memory: 981 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.80, recall: 0.8228, precision: 0.8381, hmean: 0.8304 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.81, recall: 0.8210, precision: 0.8394, hmean: 0.8301 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.82, recall: 0.8183, precision: 0.8405, hmean: 0.8292 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.83, recall: 0.8155, precision: 0.8425, hmean: 0.8288 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.84, recall: 0.8146, precision: 0.8471, hmean: 0.8305 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.85, recall: 0.8128, precision: 0.8484, hmean: 0.8302 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.86, recall: 0.8100, precision: 0.8496, hmean: 0.8294 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.87, recall: 0.8091, precision: 0.8527, hmean: 0.8304 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.88, recall: 0.8082, precision: 0.8559, hmean: 0.8314 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.89, recall: 0.8073, precision: 0.8599, hmean: 0.8328 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.90, recall: 0.8055, precision: 0.8622, hmean: 0.8329 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.91, recall: 0.8009, precision: 0.8640, hmean: 0.8313 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.92, recall: 0.7954, precision: 0.8675, hmean: 0.8299 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.93, recall: 0.7863, precision: 0.8706, hmean: 0.8263 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.94, recall: 0.7826, precision: 0.8736, hmean: 0.8256 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.95, recall: 0.7744, precision: 0.8769, hmean: 0.8225 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.96, recall: 0.7635, precision: 0.8809, hmean: 0.8180 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.97, recall: 0.7507, precision: 0.8820, hmean: 0.8111 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.98, recall: 0.7361, precision: 0.8877, hmean: 0.8048 2023/03/03 14:27:03 - mmengine - INFO - text score threshold: 0.99, recall: 0.7187, precision: 0.8943, hmean: 0.7970 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.80, recall: 0.8320, precision: 0.9038, hmean: 0.8664 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.81, recall: 0.8301, precision: 0.9054, hmean: 0.8661 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.82, recall: 0.8274, precision: 0.9051, hmean: 0.8645 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.83, recall: 0.8247, precision: 0.9066, hmean: 0.8637 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.84, recall: 0.8237, precision: 0.9093, hmean: 0.8644 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.85, recall: 0.8219, precision: 0.9100, hmean: 0.8637 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.86, recall: 0.8192, precision: 0.9107, hmean: 0.8625 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.87, recall: 0.8174, precision: 0.9123, hmean: 0.8622 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.88, recall: 0.8164, precision: 0.9141, hmean: 0.8625 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.89, recall: 0.8146, precision: 0.9168, hmean: 0.8627 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.90, recall: 0.8128, precision: 0.9185, hmean: 0.8624 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.91, recall: 0.8073, precision: 0.9199, hmean: 0.8599 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.92, recall: 0.8000, precision: 0.9202, hmean: 0.8559 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.93, recall: 0.7918, precision: 0.9214, hmean: 0.8517 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.94, recall: 0.7863, precision: 0.9218, hmean: 0.8487 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.95, recall: 0.7763, precision: 0.9219, hmean: 0.8428 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.96, recall: 0.7644, precision: 0.9238, hmean: 0.8366 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.97, recall: 0.7516, precision: 0.9247, hmean: 0.8292 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.98, recall: 0.7370, precision: 0.9297, hmean: 0.8222 2023/03/03 14:27:06 - mmengine - INFO - text score threshold: 0.99, recall: 0.7178, precision: 0.9313, hmean: 0.8107 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.80, recall: 0.7534, precision: 0.9571, hmean: 0.8431 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.81, recall: 0.7516, precision: 0.9570, hmean: 0.8419 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.82, recall: 0.7498, precision: 0.9569, hmean: 0.8408 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.83, recall: 0.7470, precision: 0.9578, hmean: 0.8394 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.84, recall: 0.7452, precision: 0.9577, hmean: 0.8382 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.85, recall: 0.7434, precision: 0.9576, hmean: 0.8370 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.86, recall: 0.7406, precision: 0.9575, hmean: 0.8352 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.87, recall: 0.7379, precision: 0.9573, hmean: 0.8334 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.88, recall: 0.7361, precision: 0.9584, hmean: 0.8326 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.89, recall: 0.7342, precision: 0.9583, hmean: 0.8314 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.90, recall: 0.7324, precision: 0.9582, hmean: 0.8302 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.91, recall: 0.7269, precision: 0.9590, hmean: 0.8270 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.92, recall: 0.7196, precision: 0.9586, hmean: 0.8221 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.93, recall: 0.7123, precision: 0.9594, hmean: 0.8176 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.94, recall: 0.7068, precision: 0.9603, hmean: 0.8143 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.95, recall: 0.6986, precision: 0.9598, hmean: 0.8087 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.96, recall: 0.6895, precision: 0.9618, hmean: 0.8032 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.97, recall: 0.6795, precision: 0.9637, hmean: 0.7970 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.98, recall: 0.6658, precision: 0.9668, hmean: 0.7885 2023/03/03 14:27:09 - mmengine - INFO - text score threshold: 0.99, recall: 0.6484, precision: 0.9673, hmean: 0.7764 2023/03/03 14:27:09 - mmengine - INFO - Epoch(val) [100][59/59] generic/precision: 0.8622 generic/recall: 0.8055 generic/hmean: 0.8329 weak/precision: 0.9038 weak/recall: 0.8320 weak/hmean: 0.8664 strong/precision: 0.9571 strong/recall: 0.7534 strong/hmean: 0.8431 2023/03/03 14:27:09 - mmengine - INFO - Epoch(train) [101][ 1/15] lr: 1.0000e-06 eta: 0:08:37 time: 0.3125 data_time: 0.0356 memory: 15175 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:27:10 - mmengine - INFO - Epoch(train) [101][ 2/15] lr: 1.0000e-06 eta: 0:08:37 time: 0.3147 data_time: 0.0361 memory: 16948 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:27:10 - mmengine - INFO - Epoch(train) [101][ 3/15] lr: 1.0000e-06 eta: 0:08:37 time: 0.3149 data_time: 0.0361 memory: 16654 loss: 0.0793 loss_ce: 0.0793 2023/03/03 14:27:10 - mmengine - INFO - Epoch(train) [101][ 4/15] lr: 1.0000e-06 eta: 0:08:37 time: 0.3163 data_time: 0.0362 memory: 18766 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:27:11 - mmengine - INFO - Epoch(train) [101][ 5/15] lr: 1.0000e-06 eta: 0:08:36 time: 0.3275 data_time: 0.0362 memory: 23595 loss: 0.0819 loss_ce: 0.0819 2023/03/03 14:27:11 - mmengine - INFO - Epoch(train) [101][ 6/15] lr: 1.0000e-06 eta: 0:08:36 time: 0.3204 data_time: 0.0362 memory: 15671 loss: 0.0764 loss_ce: 0.0764 2023/03/03 14:27:11 - mmengine - INFO - Epoch(train) [101][ 7/15] lr: 1.0000e-06 eta: 0:08:35 time: 0.3176 data_time: 0.0362 memory: 17120 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:27:11 - mmengine - INFO - Epoch(train) [101][ 8/15] lr: 1.0000e-06 eta: 0:08:35 time: 0.3180 data_time: 0.0361 memory: 17377 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:27:12 - mmengine - INFO - Epoch(train) [101][ 9/15] lr: 1.0000e-06 eta: 0:08:34 time: 0.3158 data_time: 0.0361 memory: 17488 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:27:12 - mmengine - INFO - Epoch(train) [101][10/15] lr: 1.0000e-06 eta: 0:08:34 time: 0.3499 data_time: 0.0362 memory: 14326 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:27:12 - mmengine - INFO - Epoch(train) [101][11/15] lr: 1.0000e-06 eta: 0:08:34 time: 0.3183 data_time: 0.0020 memory: 16782 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:27:13 - mmengine - INFO - Epoch(train) [101][12/15] lr: 1.0000e-06 eta: 0:08:33 time: 0.2714 data_time: 0.0015 memory: 17421 loss: 0.0723 loss_ce: 0.0723 2023/03/03 14:27:13 - mmengine - INFO - Epoch(train) [101][13/15] lr: 1.0000e-06 eta: 0:08:33 time: 0.2781 data_time: 0.0015 memory: 20660 loss: 0.0723 loss_ce: 0.0723 2023/03/03 14:27:13 - mmengine - INFO - Epoch(train) [101][14/15] lr: 1.0000e-06 eta: 0:08:32 time: 0.2750 data_time: 0.0014 memory: 17046 loss: 0.0710 loss_ce: 0.0710 2023/03/03 14:27:13 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:27:13 - mmengine - INFO - Epoch(train) [101][15/15] lr: 1.0000e-06 eta: 0:08:32 time: 0.2582 data_time: 0.0014 memory: 6355 loss: 0.0710 loss_ce: 0.0710 2023/03/03 14:27:14 - mmengine - INFO - Epoch(train) [102][ 1/15] lr: 1.0000e-06 eta: 0:08:32 time: 0.3346 data_time: 0.0723 memory: 16370 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:27:14 - mmengine - INFO - Epoch(train) [102][ 2/15] lr: 1.0000e-06 eta: 0:08:32 time: 0.3380 data_time: 0.0724 memory: 16804 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:27:15 - mmengine - INFO - Epoch(train) [102][ 3/15] lr: 1.0000e-06 eta: 0:08:31 time: 0.3379 data_time: 0.0724 memory: 17892 loss: 0.0736 loss_ce: 0.0736 2023/03/03 14:27:15 - mmengine - INFO - Epoch(train) [102][ 4/15] lr: 1.0000e-06 eta: 0:08:31 time: 0.3370 data_time: 0.0725 memory: 16370 loss: 0.0744 loss_ce: 0.0744 2023/03/03 14:27:15 - mmengine - INFO - Epoch(train) [102][ 5/15] lr: 1.0000e-06 eta: 0:08:30 time: 0.3250 data_time: 0.0725 memory: 16370 loss: 0.0751 loss_ce: 0.0751 2023/03/03 14:27:16 - mmengine - INFO - Epoch(train) [102][ 6/15] lr: 1.0000e-06 eta: 0:08:30 time: 0.3364 data_time: 0.0725 memory: 21896 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:27:16 - mmengine - INFO - Epoch(train) [102][ 7/15] lr: 1.0000e-06 eta: 0:08:30 time: 0.3503 data_time: 0.0725 memory: 18070 loss: 0.0756 loss_ce: 0.0756 2023/03/03 14:27:16 - mmengine - INFO - Epoch(train) [102][ 8/15] lr: 1.0000e-06 eta: 0:08:29 time: 0.3433 data_time: 0.0725 memory: 16508 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:27:17 - mmengine - INFO - Epoch(train) [102][ 9/15] lr: 1.0000e-06 eta: 0:08:29 time: 0.3661 data_time: 0.0725 memory: 37843 loss: 0.0862 loss_ce: 0.0862 2023/03/03 14:27:17 - mmengine - INFO - Epoch(train) [102][10/15] lr: 1.0000e-06 eta: 0:08:29 time: 0.3698 data_time: 0.0725 memory: 17162 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:27:17 - mmengine - INFO - Epoch(train) [102][11/15] lr: 1.0000e-06 eta: 0:08:28 time: 0.2989 data_time: 0.0017 memory: 18409 loss: 0.0808 loss_ce: 0.0808 2023/03/03 14:27:17 - mmengine - INFO - Epoch(train) [102][12/15] lr: 1.0000e-06 eta: 0:08:28 time: 0.2957 data_time: 0.0016 memory: 17120 loss: 0.0825 loss_ce: 0.0825 2023/03/03 14:27:18 - mmengine - INFO - Epoch(train) [102][13/15] lr: 1.0000e-06 eta: 0:08:28 time: 0.3222 data_time: 0.0016 memory: 13330 loss: 0.0821 loss_ce: 0.0821 2023/03/03 14:27:18 - mmengine - INFO - Epoch(train) [102][14/15] lr: 1.0000e-06 eta: 0:08:27 time: 0.3225 data_time: 0.0016 memory: 16840 loss: 0.0824 loss_ce: 0.0824 2023/03/03 14:27:18 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:27:18 - mmengine - INFO - Epoch(train) [102][15/15] lr: 1.0000e-06 eta: 0:08:27 time: 0.3036 data_time: 0.0016 memory: 3732 loss: 0.0876 loss_ce: 0.0876 2023/03/03 14:27:19 - mmengine - INFO - Epoch(train) [103][ 1/15] lr: 1.0000e-06 eta: 0:08:27 time: 0.3610 data_time: 0.0519 memory: 19451 loss: 0.0860 loss_ce: 0.0860 2023/03/03 14:27:20 - mmengine - INFO - Epoch(train) [103][ 2/15] lr: 1.0000e-06 eta: 0:08:26 time: 0.3532 data_time: 0.0519 memory: 18490 loss: 0.0844 loss_ce: 0.0844 2023/03/03 14:27:20 - mmengine - INFO - Epoch(train) [103][ 3/15] lr: 1.0000e-06 eta: 0:08:26 time: 0.3535 data_time: 0.0520 memory: 16654 loss: 0.0832 loss_ce: 0.0832 2023/03/03 14:27:20 - mmengine - INFO - Epoch(train) [103][ 4/15] lr: 1.0000e-06 eta: 0:08:26 time: 0.3426 data_time: 0.0521 memory: 18803 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:27:20 - mmengine - INFO - Epoch(train) [103][ 5/15] lr: 1.0000e-06 eta: 0:08:25 time: 0.3430 data_time: 0.0521 memory: 17892 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:27:21 - mmengine - INFO - Epoch(train) [103][ 6/15] lr: 1.0000e-06 eta: 0:08:25 time: 0.3588 data_time: 0.0522 memory: 15730 loss: 0.0745 loss_ce: 0.0745 2023/03/03 14:27:21 - mmengine - INFO - Epoch(train) [103][ 7/15] lr: 1.0000e-06 eta: 0:08:24 time: 0.3636 data_time: 0.0522 memory: 15457 loss: 0.0717 loss_ce: 0.0717 2023/03/03 14:27:22 - mmengine - INFO - Epoch(train) [103][ 8/15] lr: 1.0000e-06 eta: 0:08:24 time: 0.3521 data_time: 0.0521 memory: 16370 loss: 0.0753 loss_ce: 0.0753 2023/03/03 14:27:22 - mmengine - INFO - Epoch(train) [103][ 9/15] lr: 1.0000e-06 eta: 0:08:24 time: 0.3511 data_time: 0.0522 memory: 12365 loss: 0.0733 loss_ce: 0.0733 2023/03/03 14:27:22 - mmengine - INFO - Epoch(train) [103][10/15] lr: 1.0000e-06 eta: 0:08:23 time: 0.3640 data_time: 0.0521 memory: 18020 loss: 0.0661 loss_ce: 0.0661 2023/03/03 14:27:22 - mmengine - INFO - Epoch(train) [103][11/15] lr: 1.0000e-06 eta: 0:08:23 time: 0.2920 data_time: 0.0018 memory: 16370 loss: 0.0691 loss_ce: 0.0691 2023/03/03 14:27:23 - mmengine - INFO - Epoch(train) [103][12/15] lr: 1.0000e-06 eta: 0:08:23 time: 0.3004 data_time: 0.0018 memory: 18589 loss: 0.0682 loss_ce: 0.0682 2023/03/03 14:27:23 - mmengine - INFO - Epoch(train) [103][13/15] lr: 1.0000e-06 eta: 0:08:22 time: 0.2975 data_time: 0.0017 memory: 18070 loss: 0.0679 loss_ce: 0.0679 2023/03/03 14:27:23 - mmengine - INFO - Epoch(train) [103][14/15] lr: 1.0000e-06 eta: 0:08:22 time: 0.2919 data_time: 0.0016 memory: 15422 loss: 0.0661 loss_ce: 0.0661 2023/03/03 14:27:23 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:27:23 - mmengine - INFO - Epoch(train) [103][15/15] lr: 1.0000e-06 eta: 0:08:21 time: 0.2829 data_time: 0.0016 memory: 5979 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:27:24 - mmengine - INFO - Epoch(train) [104][ 1/15] lr: 1.0000e-06 eta: 0:08:21 time: 0.3355 data_time: 0.0430 memory: 18070 loss: 0.0751 loss_ce: 0.0751 2023/03/03 14:27:24 - mmengine - INFO - Epoch(train) [104][ 2/15] lr: 1.0000e-06 eta: 0:08:21 time: 0.3317 data_time: 0.0431 memory: 17120 loss: 0.0753 loss_ce: 0.0753 2023/03/03 14:27:25 - mmengine - INFO - Epoch(train) [104][ 3/15] lr: 1.0000e-06 eta: 0:08:21 time: 0.3303 data_time: 0.0431 memory: 22912 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:27:25 - mmengine - INFO - Epoch(train) [104][ 4/15] lr: 1.0000e-06 eta: 0:08:20 time: 0.3323 data_time: 0.0432 memory: 12419 loss: 0.0772 loss_ce: 0.0772 2023/03/03 14:27:25 - mmengine - INFO - Epoch(train) [104][ 5/15] lr: 1.0000e-06 eta: 0:08:20 time: 0.3237 data_time: 0.0433 memory: 15767 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:27:26 - mmengine - INFO - Epoch(train) [104][ 6/15] lr: 1.0000e-06 eta: 0:08:19 time: 0.3306 data_time: 0.0434 memory: 16109 loss: 0.0793 loss_ce: 0.0793 2023/03/03 14:27:26 - mmengine - INFO - Epoch(train) [104][ 7/15] lr: 1.0000e-06 eta: 0:08:19 time: 0.3361 data_time: 0.0436 memory: 28670 loss: 0.0810 loss_ce: 0.0810 2023/03/03 14:27:26 - mmengine - INFO - Epoch(train) [104][ 8/15] lr: 1.0000e-06 eta: 0:08:19 time: 0.3564 data_time: 0.0436 memory: 26048 loss: 0.0869 loss_ce: 0.0869 2023/03/03 14:27:27 - mmengine - INFO - Epoch(train) [104][ 9/15] lr: 1.0000e-06 eta: 0:08:19 time: 0.3686 data_time: 0.0437 memory: 17049 loss: 0.0881 loss_ce: 0.0881 2023/03/03 14:27:27 - mmengine - INFO - Epoch(train) [104][10/15] lr: 1.0000e-06 eta: 0:08:18 time: 0.3805 data_time: 0.0437 memory: 17081 loss: 0.0845 loss_ce: 0.0845 2023/03/03 14:27:27 - mmengine - INFO - Epoch(train) [104][11/15] lr: 1.0000e-06 eta: 0:08:18 time: 0.3195 data_time: 0.0022 memory: 15911 loss: 0.0861 loss_ce: 0.0861 2023/03/03 14:27:28 - mmengine - INFO - Epoch(train) [104][12/15] lr: 1.0000e-06 eta: 0:08:17 time: 0.3230 data_time: 0.0021 memory: 17676 loss: 0.0905 loss_ce: 0.0905 2023/03/03 14:27:28 - mmengine - INFO - Epoch(train) [104][13/15] lr: 1.0000e-06 eta: 0:08:17 time: 0.3066 data_time: 0.0020 memory: 16370 loss: 0.0903 loss_ce: 0.0903 2023/03/03 14:27:28 - mmengine - INFO - Epoch(train) [104][14/15] lr: 1.0000e-06 eta: 0:08:16 time: 0.3025 data_time: 0.0019 memory: 17120 loss: 0.0879 loss_ce: 0.0879 2023/03/03 14:27:28 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:27:28 - mmengine - INFO - Epoch(train) [104][15/15] lr: 1.0000e-06 eta: 0:08:16 time: 0.2967 data_time: 0.0018 memory: 3405 loss: 0.0899 loss_ce: 0.0899 2023/03/03 14:27:29 - mmengine - INFO - Epoch(train) [105][ 1/15] lr: 1.0000e-06 eta: 0:08:16 time: 0.3749 data_time: 0.0845 memory: 17968 loss: 0.0857 loss_ce: 0.0857 2023/03/03 14:27:30 - mmengine - INFO - Epoch(train) [105][ 2/15] lr: 1.0000e-06 eta: 0:08:16 time: 0.3575 data_time: 0.0844 memory: 15911 loss: 0.0828 loss_ce: 0.0828 2023/03/03 14:27:30 - mmengine - INFO - Epoch(train) [105][ 3/15] lr: 1.0000e-06 eta: 0:08:15 time: 0.3407 data_time: 0.0844 memory: 17284 loss: 0.0772 loss_ce: 0.0772 2023/03/03 14:27:30 - mmengine - INFO - Epoch(train) [105][ 4/15] lr: 1.0000e-06 eta: 0:08:15 time: 0.3134 data_time: 0.0844 memory: 17120 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:27:30 - mmengine - INFO - Epoch(train) [105][ 5/15] lr: 1.0000e-06 eta: 0:08:14 time: 0.3149 data_time: 0.0844 memory: 18182 loss: 0.0760 loss_ce: 0.0760 2023/03/03 14:27:30 - mmengine - INFO - Epoch(train) [105][ 6/15] lr: 1.0000e-06 eta: 0:08:14 time: 0.3086 data_time: 0.0844 memory: 16654 loss: 0.0768 loss_ce: 0.0768 2023/03/03 14:27:31 - mmengine - INFO - Epoch(train) [105][ 7/15] lr: 1.0000e-06 eta: 0:08:13 time: 0.3068 data_time: 0.0845 memory: 16199 loss: 0.0768 loss_ce: 0.0768 2023/03/03 14:27:31 - mmengine - INFO - Epoch(train) [105][ 8/15] lr: 1.0000e-06 eta: 0:08:13 time: 0.3392 data_time: 0.0845 memory: 32400 loss: 0.0764 loss_ce: 0.0764 2023/03/03 14:27:31 - mmengine - INFO - Epoch(train) [105][ 9/15] lr: 1.0000e-06 eta: 0:08:13 time: 0.3394 data_time: 0.0845 memory: 17272 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:27:32 - mmengine - INFO - Epoch(train) [105][10/15] lr: 1.0000e-06 eta: 0:08:13 time: 0.3613 data_time: 0.0845 memory: 22912 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:27:32 - mmengine - INFO - Epoch(train) [105][11/15] lr: 1.0000e-06 eta: 0:08:12 time: 0.2761 data_time: 0.0018 memory: 16530 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:27:32 - mmengine - INFO - Epoch(train) [105][12/15] lr: 1.0000e-06 eta: 0:08:12 time: 0.2944 data_time: 0.0016 memory: 17272 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:27:33 - mmengine - INFO - Epoch(train) [105][13/15] lr: 1.0000e-06 eta: 0:08:11 time: 0.3041 data_time: 0.0016 memory: 19511 loss: 0.0803 loss_ce: 0.0803 2023/03/03 14:27:33 - mmengine - INFO - Epoch(train) [105][14/15] lr: 1.0000e-06 eta: 0:08:11 time: 0.3102 data_time: 0.0015 memory: 13907 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:27:33 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:27:33 - mmengine - INFO - Epoch(train) [105][15/15] lr: 1.0000e-06 eta: 0:08:10 time: 0.3017 data_time: 0.0015 memory: 5756 loss: 0.0893 loss_ce: 0.0893 2023/03/03 14:27:34 - mmengine - INFO - Epoch(train) [106][ 1/15] lr: 1.0000e-06 eta: 0:08:11 time: 0.3551 data_time: 0.0410 memory: 18241 loss: 0.0868 loss_ce: 0.0868 2023/03/03 14:27:34 - mmengine - INFO - Epoch(train) [106][ 2/15] lr: 1.0000e-06 eta: 0:08:10 time: 0.3646 data_time: 0.0542 memory: 15560 loss: 0.0886 loss_ce: 0.0886 2023/03/03 14:27:35 - mmengine - INFO - Epoch(train) [106][ 3/15] lr: 1.0000e-06 eta: 0:08:10 time: 0.3467 data_time: 0.0543 memory: 17120 loss: 0.0899 loss_ce: 0.0899 2023/03/03 14:27:35 - mmengine - INFO - Epoch(train) [106][ 4/15] lr: 1.0000e-06 eta: 0:08:09 time: 0.3522 data_time: 0.0543 memory: 18757 loss: 0.0873 loss_ce: 0.0873 2023/03/03 14:27:35 - mmengine - INFO - Epoch(train) [106][ 5/15] lr: 1.0000e-06 eta: 0:08:09 time: 0.3394 data_time: 0.0544 memory: 17272 loss: 0.0847 loss_ce: 0.0847 2023/03/03 14:27:36 - mmengine - INFO - Epoch(train) [106][ 6/15] lr: 1.0000e-06 eta: 0:08:09 time: 0.3545 data_time: 0.0543 memory: 26470 loss: 0.0863 loss_ce: 0.0863 2023/03/03 14:27:36 - mmengine - INFO - Epoch(train) [106][ 7/15] lr: 1.0000e-06 eta: 0:08:08 time: 0.3420 data_time: 0.0544 memory: 20798 loss: 0.0844 loss_ce: 0.0844 2023/03/03 14:27:36 - mmengine - INFO - Epoch(train) [106][ 8/15] lr: 1.0000e-06 eta: 0:08:08 time: 0.3288 data_time: 0.0544 memory: 17572 loss: 0.0835 loss_ce: 0.0835 2023/03/03 14:27:36 - mmengine - INFO - Epoch(train) [106][ 9/15] lr: 1.0000e-06 eta: 0:08:08 time: 0.3375 data_time: 0.0544 memory: 17172 loss: 0.0856 loss_ce: 0.0856 2023/03/03 14:27:37 - mmengine - INFO - Epoch(train) [106][10/15] lr: 1.0000e-06 eta: 0:08:07 time: 0.3439 data_time: 0.0544 memory: 16654 loss: 0.0728 loss_ce: 0.0728 2023/03/03 14:27:37 - mmengine - INFO - Epoch(train) [106][11/15] lr: 1.0000e-06 eta: 0:08:07 time: 0.2986 data_time: 0.0148 memory: 16212 loss: 0.0746 loss_ce: 0.0746 2023/03/03 14:27:37 - mmengine - INFO - Epoch(train) [106][12/15] lr: 1.0000e-06 eta: 0:08:06 time: 0.2865 data_time: 0.0016 memory: 17272 loss: 0.0682 loss_ce: 0.0682 2023/03/03 14:27:38 - mmengine - INFO - Epoch(train) [106][13/15] lr: 1.0000e-06 eta: 0:08:06 time: 0.2941 data_time: 0.0016 memory: 17421 loss: 0.0676 loss_ce: 0.0676 2023/03/03 14:27:38 - mmengine - INFO - Epoch(train) [106][14/15] lr: 1.0000e-06 eta: 0:08:05 time: 0.2885 data_time: 0.0015 memory: 17421 loss: 0.0681 loss_ce: 0.0681 2023/03/03 14:27:38 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:27:38 - mmengine - INFO - Epoch(train) [106][15/15] lr: 1.0000e-06 eta: 0:08:05 time: 0.2807 data_time: 0.0015 memory: 5999 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:27:39 - mmengine - INFO - Epoch(train) [107][ 1/15] lr: 1.0000e-06 eta: 0:08:05 time: 0.3160 data_time: 0.0542 memory: 16976 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:27:39 - mmengine - INFO - Epoch(train) [107][ 2/15] lr: 1.0000e-06 eta: 0:08:05 time: 0.3226 data_time: 0.0543 memory: 15911 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:27:39 - mmengine - INFO - Epoch(train) [107][ 3/15] lr: 1.0000e-06 eta: 0:08:04 time: 0.3228 data_time: 0.0543 memory: 17572 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:27:40 - mmengine - INFO - Epoch(train) [107][ 4/15] lr: 1.0000e-06 eta: 0:08:04 time: 0.3243 data_time: 0.0544 memory: 14275 loss: 0.0780 loss_ce: 0.0780 2023/03/03 14:27:40 - mmengine - INFO - Epoch(train) [107][ 5/15] lr: 1.0000e-06 eta: 0:08:03 time: 0.3291 data_time: 0.0545 memory: 17989 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:27:40 - mmengine - INFO - Epoch(train) [107][ 6/15] lr: 1.0000e-06 eta: 0:08:03 time: 0.3442 data_time: 0.0546 memory: 14061 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:27:41 - mmengine - INFO - Epoch(train) [107][ 7/15] lr: 1.0000e-06 eta: 0:08:03 time: 0.3442 data_time: 0.0546 memory: 17416 loss: 0.0804 loss_ce: 0.0804 2023/03/03 14:27:41 - mmengine - INFO - Epoch(train) [107][ 8/15] lr: 1.0000e-06 eta: 0:08:02 time: 0.3336 data_time: 0.0546 memory: 19928 loss: 0.0798 loss_ce: 0.0798 2023/03/03 14:27:41 - mmengine - INFO - Epoch(train) [107][ 9/15] lr: 1.0000e-06 eta: 0:08:02 time: 0.3337 data_time: 0.0546 memory: 17272 loss: 0.0847 loss_ce: 0.0847 2023/03/03 14:27:42 - mmengine - INFO - Epoch(train) [107][10/15] lr: 1.0000e-06 eta: 0:08:02 time: 0.3544 data_time: 0.0546 memory: 13440 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:27:42 - mmengine - INFO - Epoch(train) [107][11/15] lr: 1.0000e-06 eta: 0:08:01 time: 0.3069 data_time: 0.0019 memory: 14122 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:27:42 - mmengine - INFO - Epoch(train) [107][12/15] lr: 1.0000e-06 eta: 0:08:01 time: 0.2988 data_time: 0.0018 memory: 14658 loss: 0.0759 loss_ce: 0.0759 2023/03/03 14:27:42 - mmengine - INFO - Epoch(train) [107][13/15] lr: 1.0000e-06 eta: 0:08:00 time: 0.2984 data_time: 0.0017 memory: 16976 loss: 0.0806 loss_ce: 0.0806 2023/03/03 14:27:43 - mmengine - INFO - Epoch(train) [107][14/15] lr: 1.0000e-06 eta: 0:08:00 time: 0.2895 data_time: 0.0017 memory: 23578 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:27:43 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:27:43 - mmengine - INFO - Epoch(train) [107][15/15] lr: 1.0000e-06 eta: 0:07:59 time: 0.2752 data_time: 0.0016 memory: 5916 loss: 0.0821 loss_ce: 0.0821 2023/03/03 14:27:44 - mmengine - INFO - Epoch(train) [108][ 1/15] lr: 1.0000e-06 eta: 0:08:00 time: 0.3202 data_time: 0.0479 memory: 13761 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:27:44 - mmengine - INFO - Epoch(train) [108][ 2/15] lr: 1.0000e-06 eta: 0:07:59 time: 0.3212 data_time: 0.0479 memory: 14120 loss: 0.0844 loss_ce: 0.0844 2023/03/03 14:27:44 - mmengine - INFO - Epoch(train) [108][ 3/15] lr: 1.0000e-06 eta: 0:07:59 time: 0.3096 data_time: 0.0481 memory: 15911 loss: 0.0851 loss_ce: 0.0851 2023/03/03 14:27:44 - mmengine - INFO - Epoch(train) [108][ 4/15] lr: 1.0000e-06 eta: 0:07:58 time: 0.3117 data_time: 0.0482 memory: 15767 loss: 0.0803 loss_ce: 0.0803 2023/03/03 14:27:45 - mmengine - INFO - Epoch(train) [108][ 5/15] lr: 1.0000e-06 eta: 0:07:58 time: 0.3250 data_time: 0.0483 memory: 15163 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:27:45 - mmengine - INFO - Epoch(train) [108][ 6/15] lr: 1.0000e-06 eta: 0:07:58 time: 0.3340 data_time: 0.0484 memory: 20636 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:27:45 - mmengine - INFO - Epoch(train) [108][ 7/15] lr: 1.0000e-06 eta: 0:07:57 time: 0.3339 data_time: 0.0484 memory: 16508 loss: 0.0794 loss_ce: 0.0794 2023/03/03 14:27:46 - mmengine - INFO - Epoch(train) [108][ 8/15] lr: 1.0000e-06 eta: 0:07:57 time: 0.3340 data_time: 0.0484 memory: 17120 loss: 0.0768 loss_ce: 0.0768 2023/03/03 14:27:46 - mmengine - INFO - Epoch(train) [108][ 9/15] lr: 1.0000e-06 eta: 0:07:56 time: 0.3342 data_time: 0.0484 memory: 16808 loss: 0.0792 loss_ce: 0.0792 2023/03/03 14:27:46 - mmengine - INFO - Epoch(train) [108][10/15] lr: 1.0000e-06 eta: 0:07:56 time: 0.3438 data_time: 0.0484 memory: 16370 loss: 0.0745 loss_ce: 0.0745 2023/03/03 14:27:46 - mmengine - INFO - Epoch(train) [108][11/15] lr: 1.0000e-06 eta: 0:07:56 time: 0.2802 data_time: 0.0020 memory: 17421 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:27:47 - mmengine - INFO - Epoch(train) [108][12/15] lr: 1.0000e-06 eta: 0:07:55 time: 0.2927 data_time: 0.0020 memory: 17642 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:27:47 - mmengine - INFO - Epoch(train) [108][13/15] lr: 1.0000e-06 eta: 0:07:55 time: 0.2937 data_time: 0.0019 memory: 13976 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:27:47 - mmengine - INFO - Epoch(train) [108][14/15] lr: 1.0000e-06 eta: 0:07:54 time: 0.2908 data_time: 0.0018 memory: 16177 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:27:47 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:27:47 - mmengine - INFO - Epoch(train) [108][15/15] lr: 1.0000e-06 eta: 0:07:54 time: 0.2589 data_time: 0.0016 memory: 6626 loss: 0.0721 loss_ce: 0.0721 2023/03/03 14:27:48 - mmengine - INFO - Epoch(train) [109][ 1/15] lr: 1.0000e-06 eta: 0:07:54 time: 0.3174 data_time: 0.0607 memory: 23527 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:27:49 - mmengine - INFO - Epoch(train) [109][ 2/15] lr: 1.0000e-06 eta: 0:07:54 time: 0.3215 data_time: 0.0608 memory: 17222 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:27:49 - mmengine - INFO - Epoch(train) [109][ 3/15] lr: 1.0000e-06 eta: 0:07:53 time: 0.3288 data_time: 0.0608 memory: 19511 loss: 0.0734 loss_ce: 0.0734 2023/03/03 14:27:49 - mmengine - INFO - Epoch(train) [109][ 4/15] lr: 1.0000e-06 eta: 0:07:53 time: 0.3290 data_time: 0.0610 memory: 19983 loss: 0.0695 loss_ce: 0.0695 2023/03/03 14:27:50 - mmengine - INFO - Epoch(train) [109][ 5/15] lr: 1.0000e-06 eta: 0:07:53 time: 0.3558 data_time: 0.0610 memory: 26437 loss: 0.0692 loss_ce: 0.0692 2023/03/03 14:27:50 - mmengine - INFO - Epoch(train) [109][ 6/15] lr: 1.0000e-06 eta: 0:07:52 time: 0.3461 data_time: 0.0611 memory: 16976 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:27:51 - mmengine - INFO - Epoch(train) [109][ 7/15] lr: 1.0000e-06 eta: 0:07:52 time: 0.3701 data_time: 0.0611 memory: 23994 loss: 0.0777 loss_ce: 0.0777 2023/03/03 14:27:51 - mmengine - INFO - Epoch(train) [109][ 8/15] lr: 1.0000e-06 eta: 0:07:52 time: 0.3690 data_time: 0.0611 memory: 16369 loss: 0.0787 loss_ce: 0.0787 2023/03/03 14:27:51 - mmengine - INFO - Epoch(train) [109][ 9/15] lr: 1.0000e-06 eta: 0:07:51 time: 0.3806 data_time: 0.0611 memory: 18182 loss: 0.0783 loss_ce: 0.0783 2023/03/03 14:27:52 - mmengine - INFO - Epoch(train) [109][10/15] lr: 1.0000e-06 eta: 0:07:51 time: 0.4128 data_time: 0.0612 memory: 16329 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:27:52 - mmengine - INFO - Epoch(train) [109][11/15] lr: 1.0000e-06 eta: 0:07:51 time: 0.3399 data_time: 0.0022 memory: 17572 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:27:52 - mmengine - INFO - Epoch(train) [109][12/15] lr: 1.0000e-06 eta: 0:07:50 time: 0.3591 data_time: 0.0021 memory: 16223 loss: 0.0809 loss_ce: 0.0809 2023/03/03 14:27:53 - mmengine - INFO - Epoch(train) [109][13/15] lr: 1.0000e-06 eta: 0:07:50 time: 0.3536 data_time: 0.0020 memory: 15631 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:27:53 - mmengine - INFO - Epoch(train) [109][14/15] lr: 1.0000e-06 eta: 0:07:49 time: 0.3515 data_time: 0.0019 memory: 16223 loss: 0.0832 loss_ce: 0.0832 2023/03/03 14:27:53 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:27:53 - mmengine - INFO - Epoch(train) [109][15/15] lr: 1.0000e-06 eta: 0:07:49 time: 0.3130 data_time: 0.0019 memory: 5847 loss: 0.0858 loss_ce: 0.0858 2023/03/03 14:27:54 - mmengine - INFO - Epoch(train) [110][ 1/15] lr: 1.0000e-06 eta: 0:07:49 time: 0.3970 data_time: 0.0830 memory: 16258 loss: 0.0742 loss_ce: 0.0742 2023/03/03 14:27:54 - mmengine - INFO - Epoch(train) [110][ 2/15] lr: 1.0000e-06 eta: 0:07:49 time: 0.3598 data_time: 0.0830 memory: 17421 loss: 0.0739 loss_ce: 0.0739 2023/03/03 14:27:54 - mmengine - INFO - Epoch(train) [110][ 3/15] lr: 1.0000e-06 eta: 0:07:48 time: 0.3706 data_time: 0.0831 memory: 18070 loss: 0.0725 loss_ce: 0.0725 2023/03/03 14:27:55 - mmengine - INFO - Epoch(train) [110][ 4/15] lr: 1.0000e-06 eta: 0:07:48 time: 0.3643 data_time: 0.0831 memory: 17968 loss: 0.0702 loss_ce: 0.0702 2023/03/03 14:27:55 - mmengine - INFO - Epoch(train) [110][ 5/15] lr: 1.0000e-06 eta: 0:07:48 time: 0.3361 data_time: 0.0830 memory: 16370 loss: 0.0739 loss_ce: 0.0739 2023/03/03 14:27:55 - mmengine - INFO - Epoch(train) [110][ 6/15] lr: 1.0000e-06 eta: 0:07:47 time: 0.3554 data_time: 0.0829 memory: 17400 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:27:56 - mmengine - INFO - Epoch(train) [110][ 7/15] lr: 1.0000e-06 eta: 0:07:47 time: 0.3390 data_time: 0.0829 memory: 17272 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:27:56 - mmengine - INFO - Epoch(train) [110][ 8/15] lr: 1.0000e-06 eta: 0:07:46 time: 0.3402 data_time: 0.0829 memory: 16704 loss: 0.0734 loss_ce: 0.0734 2023/03/03 14:27:56 - mmengine - INFO - Epoch(train) [110][ 9/15] lr: 1.0000e-06 eta: 0:07:46 time: 0.3437 data_time: 0.0829 memory: 16895 loss: 0.0710 loss_ce: 0.0710 2023/03/03 14:27:56 - mmengine - INFO - Epoch(train) [110][10/15] lr: 1.0000e-06 eta: 0:07:46 time: 0.3528 data_time: 0.0829 memory: 17421 loss: 0.0698 loss_ce: 0.0698 2023/03/03 14:27:57 - mmengine - INFO - Epoch(train) [110][11/15] lr: 1.0000e-06 eta: 0:07:45 time: 0.2897 data_time: 0.0017 memory: 17730 loss: 0.0705 loss_ce: 0.0705 2023/03/03 14:27:57 - mmengine - INFO - Epoch(train) [110][12/15] lr: 1.0000e-06 eta: 0:07:45 time: 0.2893 data_time: 0.0016 memory: 17421 loss: 0.0736 loss_ce: 0.0736 2023/03/03 14:27:57 - mmengine - INFO - Epoch(train) [110][13/15] lr: 1.0000e-06 eta: 0:07:44 time: 0.2837 data_time: 0.0016 memory: 20008 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:27:57 - mmengine - INFO - Epoch(train) [110][14/15] lr: 1.0000e-06 eta: 0:07:44 time: 0.2791 data_time: 0.0015 memory: 17421 loss: 0.0774 loss_ce: 0.0774 2023/03/03 14:27:58 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:27:58 - mmengine - INFO - Epoch(train) [110][15/15] lr: 1.0000e-06 eta: 0:07:43 time: 0.2714 data_time: 0.0015 memory: 5903 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:27:59 - mmengine - INFO - Epoch(val) [110][ 1/59] eta: 0:01:30 time: 1.1149 data_time: 0.0033 memory: 981 2023/03/03 14:28:00 - mmengine - INFO - Epoch(val) [110][ 2/59] eta: 0:01:08 time: 1.0304 data_time: 0.0033 memory: 981 2023/03/03 14:28:01 - mmengine - INFO - Epoch(val) [110][ 3/59] eta: 0:01:09 time: 1.0486 data_time: 0.0033 memory: 1003 2023/03/03 14:28:02 - mmengine - INFO - Epoch(val) [110][ 4/59] eta: 0:00:56 time: 1.0164 data_time: 0.0033 memory: 981 2023/03/03 14:28:05 - mmengine - INFO - Epoch(val) [110][ 5/59] eta: 0:01:17 time: 1.2544 data_time: 0.0033 memory: 1016 2023/03/03 14:28:07 - mmengine - INFO - Epoch(val) [110][ 6/59] eta: 0:01:26 time: 1.4514 data_time: 0.0034 memory: 981 2023/03/03 14:28:08 - mmengine - INFO - Epoch(val) [110][ 7/59] eta: 0:01:13 time: 1.3861 data_time: 0.0034 memory: 1043 2023/03/03 14:28:08 - mmengine - INFO - Epoch(val) [110][ 8/59] eta: 0:01:07 time: 1.2286 data_time: 0.0034 memory: 1016 2023/03/03 14:28:09 - mmengine - INFO - Epoch(val) [110][ 9/59] eta: 0:01:04 time: 1.1950 data_time: 0.0034 memory: 981 2023/03/03 14:28:10 - mmengine - INFO - Epoch(val) [110][10/59] eta: 0:01:00 time: 1.2282 data_time: 0.0034 memory: 981 2023/03/03 14:28:10 - mmengine - INFO - Epoch(val) [110][11/59] eta: 0:00:55 time: 1.1065 data_time: 0.0009 memory: 981 2023/03/03 14:28:14 - mmengine - INFO - Epoch(val) [110][12/59] eta: 0:01:02 time: 1.3472 data_time: 0.0009 memory: 1016 2023/03/03 14:28:16 - mmengine - INFO - Epoch(val) [110][13/59] eta: 0:01:03 time: 1.4178 data_time: 0.0008 memory: 981 2023/03/03 14:28:17 - mmengine - INFO - Epoch(val) [110][14/59] eta: 0:01:01 time: 1.5019 data_time: 0.0008 memory: 890 2023/03/03 14:28:17 - mmengine - INFO - Epoch(val) [110][15/59] eta: 0:00:56 time: 1.1999 data_time: 0.0008 memory: 981 2023/03/03 14:28:17 - mmengine - INFO - Epoch(val) [110][16/59] eta: 0:00:52 time: 0.9864 data_time: 0.0008 memory: 981 2023/03/03 14:28:18 - mmengine - INFO - Epoch(val) [110][17/59] eta: 0:00:49 time: 1.0028 data_time: 0.0008 memory: 981 2023/03/03 14:28:18 - mmengine - INFO - Epoch(val) [110][18/59] eta: 0:00:46 time: 0.9700 data_time: 0.0008 memory: 981 2023/03/03 14:28:19 - mmengine - INFO - Epoch(val) [110][19/59] eta: 0:00:44 time: 0.9700 data_time: 0.0008 memory: 981 2023/03/03 14:28:19 - mmengine - INFO - Epoch(val) [110][20/59] eta: 0:00:42 time: 0.9369 data_time: 0.0008 memory: 981 2023/03/03 14:28:22 - mmengine - INFO - Epoch(val) [110][21/59] eta: 0:00:43 time: 1.1272 data_time: 0.0008 memory: 981 2023/03/03 14:28:22 - mmengine - INFO - Epoch(val) [110][22/59] eta: 0:00:40 time: 0.8199 data_time: 0.0008 memory: 981 2023/03/03 14:28:22 - mmengine - INFO - Epoch(val) [110][23/59] eta: 0:00:38 time: 0.6813 data_time: 0.0008 memory: 981 2023/03/03 14:28:23 - mmengine - INFO - Epoch(val) [110][24/59] eta: 0:00:36 time: 0.5967 data_time: 0.0008 memory: 962 2023/03/03 14:28:23 - mmengine - INFO - Epoch(val) [110][25/59] eta: 0:00:34 time: 0.6280 data_time: 0.0008 memory: 981 2023/03/03 14:28:23 - mmengine - INFO - Epoch(val) [110][26/59] eta: 0:00:32 time: 0.6116 data_time: 0.0008 memory: 981 2023/03/03 14:28:24 - mmengine - INFO - Epoch(val) [110][27/59] eta: 0:00:30 time: 0.6115 data_time: 0.0008 memory: 981 2023/03/03 14:28:24 - mmengine - INFO - Epoch(val) [110][28/59] eta: 0:00:29 time: 0.6115 data_time: 0.0007 memory: 981 2023/03/03 14:28:25 - mmengine - INFO - Epoch(val) [110][29/59] eta: 0:00:28 time: 0.6455 data_time: 0.0007 memory: 981 2023/03/03 14:28:26 - mmengine - INFO - Epoch(val) [110][30/59] eta: 0:00:27 time: 0.6949 data_time: 0.0007 memory: 999 2023/03/03 14:28:27 - mmengine - INFO - Epoch(val) [110][31/59] eta: 0:00:26 time: 0.5376 data_time: 0.0007 memory: 981 2023/03/03 14:28:28 - mmengine - INFO - Epoch(val) [110][32/59] eta: 0:00:25 time: 0.6368 data_time: 0.0007 memory: 981 2023/03/03 14:28:28 - mmengine - INFO - Epoch(val) [110][33/59] eta: 0:00:24 time: 0.5875 data_time: 0.0007 memory: 981 2023/03/03 14:28:28 - mmengine - INFO - Epoch(val) [110][34/59] eta: 0:00:22 time: 0.5712 data_time: 0.0007 memory: 981 2023/03/03 14:28:29 - mmengine - INFO - Epoch(val) [110][35/59] eta: 0:00:21 time: 0.5548 data_time: 0.0007 memory: 981 2023/03/03 14:28:29 - mmengine - INFO - Epoch(val) [110][36/59] eta: 0:00:20 time: 0.5712 data_time: 0.0007 memory: 981 2023/03/03 14:28:29 - mmengine - INFO - Epoch(val) [110][37/59] eta: 0:00:18 time: 0.5547 data_time: 0.0007 memory: 981 2023/03/03 14:28:30 - mmengine - INFO - Epoch(val) [110][38/59] eta: 0:00:17 time: 0.5876 data_time: 0.0007 memory: 981 2023/03/03 14:28:30 - mmengine - INFO - Epoch(val) [110][39/59] eta: 0:00:16 time: 0.5034 data_time: 0.0007 memory: 987 2023/03/03 14:28:31 - mmengine - INFO - Epoch(val) [110][40/59] eta: 0:00:15 time: 0.5036 data_time: 0.0007 memory: 981 2023/03/03 14:28:32 - mmengine - INFO - Epoch(val) [110][41/59] eta: 0:00:15 time: 0.5539 data_time: 0.0007 memory: 986 2023/03/03 14:28:33 - mmengine - INFO - Epoch(val) [110][42/59] eta: 0:00:14 time: 0.5040 data_time: 0.0007 memory: 981 2023/03/03 14:28:34 - mmengine - INFO - Epoch(val) [110][43/59] eta: 0:00:13 time: 0.5702 data_time: 0.0007 memory: 976 2023/03/03 14:28:34 - mmengine - INFO - Epoch(val) [110][44/59] eta: 0:00:12 time: 0.6031 data_time: 0.0007 memory: 1003 2023/03/03 14:28:36 - mmengine - INFO - Epoch(val) [110][45/59] eta: 0:00:12 time: 0.7734 data_time: 0.0007 memory: 981 2023/03/03 14:28:37 - mmengine - INFO - Epoch(val) [110][46/59] eta: 0:00:11 time: 0.8067 data_time: 0.0008 memory: 981 2023/03/03 14:28:38 - mmengine - INFO - Epoch(val) [110][47/59] eta: 0:00:10 time: 0.8392 data_time: 0.0008 memory: 936 2023/03/03 14:28:38 - mmengine - INFO - Epoch(val) [110][48/59] eta: 0:00:09 time: 0.8230 data_time: 0.0008 memory: 1000 2023/03/03 14:28:39 - mmengine - INFO - Epoch(val) [110][49/59] eta: 0:00:08 time: 0.8730 data_time: 0.0008 memory: 981 2023/03/03 14:28:40 - mmengine - INFO - Epoch(val) [110][50/59] eta: 0:00:07 time: 0.8732 data_time: 0.0008 memory: 987 2023/03/03 14:28:42 - mmengine - INFO - Epoch(val) [110][51/59] eta: 0:00:06 time: 0.9257 data_time: 0.0008 memory: 981 2023/03/03 14:28:43 - mmengine - INFO - Epoch(val) [110][52/59] eta: 0:00:06 time: 0.9768 data_time: 0.0008 memory: 981 2023/03/03 14:28:43 - mmengine - INFO - Epoch(val) [110][53/59] eta: 0:00:05 time: 0.9431 data_time: 0.0008 memory: 962 2023/03/03 14:28:44 - mmengine - INFO - Epoch(val) [110][54/59] eta: 0:00:04 time: 0.9598 data_time: 0.0008 memory: 981 2023/03/03 14:28:45 - mmengine - INFO - Epoch(val) [110][55/59] eta: 0:00:03 time: 0.8390 data_time: 0.0008 memory: 981 2023/03/03 14:28:45 - mmengine - INFO - Epoch(val) [110][56/59] eta: 0:00:02 time: 0.8225 data_time: 0.0007 memory: 981 2023/03/03 14:28:48 - mmengine - INFO - Epoch(val) [110][57/59] eta: 0:00:01 time: 0.9983 data_time: 0.0007 memory: 981 2023/03/03 14:28:49 - mmengine - INFO - Epoch(val) [110][58/59] eta: 0:00:00 time: 1.0827 data_time: 0.0007 memory: 1016 2023/03/03 14:28:49 - mmengine - INFO - Epoch(val) [110][59/59] eta: 0:00:00 time: 1.0166 data_time: 0.0007 memory: 981 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.80, recall: 0.8155, precision: 0.8246, hmean: 0.8200 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.81, recall: 0.8155, precision: 0.8269, hmean: 0.8211 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.82, recall: 0.8137, precision: 0.8288, hmean: 0.8212 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.83, recall: 0.8110, precision: 0.8338, hmean: 0.8222 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.84, recall: 0.8100, precision: 0.8368, hmean: 0.8232 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.85, recall: 0.8091, precision: 0.8398, hmean: 0.8242 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.86, recall: 0.8082, precision: 0.8413, hmean: 0.8244 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.87, recall: 0.8055, precision: 0.8448, hmean: 0.8247 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.88, recall: 0.8046, precision: 0.8479, hmean: 0.8257 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.89, recall: 0.8027, precision: 0.8534, hmean: 0.8273 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.90, recall: 0.8000, precision: 0.8546, hmean: 0.8264 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.91, recall: 0.7973, precision: 0.8567, hmean: 0.8259 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.92, recall: 0.7918, precision: 0.8635, hmean: 0.8261 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.93, recall: 0.7845, precision: 0.8686, hmean: 0.8244 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.94, recall: 0.7772, precision: 0.8710, hmean: 0.8214 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.95, recall: 0.7699, precision: 0.8727, hmean: 0.8180 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.96, recall: 0.7607, precision: 0.8787, hmean: 0.8155 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.97, recall: 0.7479, precision: 0.8844, hmean: 0.8105 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.98, recall: 0.7370, precision: 0.8897, hmean: 0.8062 2023/03/03 14:29:18 - mmengine - INFO - text score threshold: 0.99, recall: 0.7160, precision: 0.8940, hmean: 0.7951 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.80, recall: 0.8265, precision: 0.8952, hmean: 0.8594 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.81, recall: 0.8265, precision: 0.8978, hmean: 0.8607 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.82, recall: 0.8247, precision: 0.8985, hmean: 0.8600 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.83, recall: 0.8219, precision: 0.9000, hmean: 0.8592 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.84, recall: 0.8210, precision: 0.9017, hmean: 0.8595 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.85, recall: 0.8192, precision: 0.9042, hmean: 0.8596 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.86, recall: 0.8183, precision: 0.9051, hmean: 0.8595 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.87, recall: 0.8155, precision: 0.9075, hmean: 0.8591 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.88, recall: 0.8128, precision: 0.9082, hmean: 0.8578 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.89, recall: 0.8100, precision: 0.9107, hmean: 0.8574 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.90, recall: 0.8073, precision: 0.9113, hmean: 0.8562 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.91, recall: 0.8037, precision: 0.9119, hmean: 0.8544 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.92, recall: 0.7973, precision: 0.9189, hmean: 0.8538 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.93, recall: 0.7890, precision: 0.9211, hmean: 0.8500 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.94, recall: 0.7808, precision: 0.9213, hmean: 0.8453 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.95, recall: 0.7726, precision: 0.9216, hmean: 0.8405 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.96, recall: 0.7626, precision: 0.9267, hmean: 0.8367 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.97, recall: 0.7498, precision: 0.9308, hmean: 0.8306 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.98, recall: 0.7370, precision: 0.9319, hmean: 0.8230 2023/03/03 14:29:21 - mmengine - INFO - text score threshold: 0.99, recall: 0.7142, precision: 0.9321, hmean: 0.8087 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.80, recall: 0.7507, precision: 0.9569, hmean: 0.8414 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.81, recall: 0.7507, precision: 0.9569, hmean: 0.8414 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.82, recall: 0.7489, precision: 0.9568, hmean: 0.8402 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.83, recall: 0.7461, precision: 0.9567, hmean: 0.8384 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.84, recall: 0.7452, precision: 0.9577, hmean: 0.8382 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.85, recall: 0.7425, precision: 0.9576, hmean: 0.8364 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.86, recall: 0.7416, precision: 0.9575, hmean: 0.8358 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.87, recall: 0.7388, precision: 0.9574, hmean: 0.8340 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.88, recall: 0.7352, precision: 0.9572, hmean: 0.8316 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.89, recall: 0.7324, precision: 0.9570, hmean: 0.8298 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.90, recall: 0.7297, precision: 0.9580, hmean: 0.8284 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.91, recall: 0.7269, precision: 0.9579, hmean: 0.8266 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.92, recall: 0.7205, precision: 0.9587, hmean: 0.8227 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.93, recall: 0.7132, precision: 0.9595, hmean: 0.8182 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.94, recall: 0.7050, precision: 0.9590, hmean: 0.8126 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.95, recall: 0.6986, precision: 0.9586, hmean: 0.8082 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.96, recall: 0.6895, precision: 0.9606, hmean: 0.8028 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.97, recall: 0.6785, precision: 0.9674, hmean: 0.7976 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.98, recall: 0.6658, precision: 0.9668, hmean: 0.7885 2023/03/03 14:29:24 - mmengine - INFO - text score threshold: 0.99, recall: 0.6447, precision: 0.9671, hmean: 0.7737 2023/03/03 14:29:24 - mmengine - INFO - Epoch(val) [110][59/59] generic/precision: 0.8534 generic/recall: 0.8027 generic/hmean: 0.8273 weak/precision: 0.8978 weak/recall: 0.8265 weak/hmean: 0.8607 strong/precision: 0.9569 strong/recall: 0.7507 strong/hmean: 0.8414 2023/03/03 14:29:25 - mmengine - INFO - Epoch(train) [111][ 1/15] lr: 1.0000e-06 eta: 0:07:44 time: 0.3166 data_time: 0.0634 memory: 18126 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:29:25 - mmengine - INFO - Epoch(train) [111][ 2/15] lr: 1.0000e-06 eta: 0:07:43 time: 0.3172 data_time: 0.0635 memory: 19576 loss: 0.0759 loss_ce: 0.0759 2023/03/03 14:29:25 - mmengine - INFO - Epoch(train) [111][ 3/15] lr: 1.0000e-06 eta: 0:07:43 time: 0.3159 data_time: 0.0635 memory: 18953 loss: 0.0728 loss_ce: 0.0728 2023/03/03 14:29:25 - mmengine - INFO - Epoch(train) [111][ 4/15] lr: 1.0000e-06 eta: 0:07:42 time: 0.3100 data_time: 0.0636 memory: 16507 loss: 0.0739 loss_ce: 0.0739 2023/03/03 14:29:26 - mmengine - INFO - Epoch(train) [111][ 5/15] lr: 1.0000e-06 eta: 0:07:42 time: 0.3101 data_time: 0.0636 memory: 18070 loss: 0.0751 loss_ce: 0.0751 2023/03/03 14:29:26 - mmengine - INFO - Epoch(train) [111][ 6/15] lr: 1.0000e-06 eta: 0:07:42 time: 0.3080 data_time: 0.0636 memory: 16860 loss: 0.0761 loss_ce: 0.0761 2023/03/03 14:29:26 - mmengine - INFO - Epoch(train) [111][ 7/15] lr: 1.0000e-06 eta: 0:07:41 time: 0.3091 data_time: 0.0636 memory: 15315 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:29:27 - mmengine - INFO - Epoch(train) [111][ 8/15] lr: 1.0000e-06 eta: 0:07:41 time: 0.3082 data_time: 0.0636 memory: 17122 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:29:27 - mmengine - INFO - Epoch(train) [111][ 9/15] lr: 1.0000e-06 eta: 0:07:40 time: 0.3263 data_time: 0.0636 memory: 22600 loss: 0.0726 loss_ce: 0.0726 2023/03/03 14:29:27 - mmengine - INFO - Epoch(train) [111][10/15] lr: 1.0000e-06 eta: 0:07:40 time: 0.3588 data_time: 0.0636 memory: 25688 loss: 0.0690 loss_ce: 0.0690 2023/03/03 14:29:28 - mmengine - INFO - Epoch(train) [111][11/15] lr: 1.0000e-06 eta: 0:07:40 time: 0.2988 data_time: 0.0017 memory: 17693 loss: 0.0695 loss_ce: 0.0695 2023/03/03 14:29:28 - mmengine - INFO - Epoch(train) [111][12/15] lr: 1.0000e-06 eta: 0:07:40 time: 0.3161 data_time: 0.0017 memory: 16370 loss: 0.0699 loss_ce: 0.0699 2023/03/03 14:29:28 - mmengine - INFO - Epoch(train) [111][13/15] lr: 1.0000e-06 eta: 0:07:39 time: 0.3145 data_time: 0.0016 memory: 17572 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:29:29 - mmengine - INFO - Epoch(train) [111][14/15] lr: 1.0000e-06 eta: 0:07:39 time: 0.3449 data_time: 0.0015 memory: 23593 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:29:29 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:29:29 - mmengine - INFO - Epoch(train) [111][15/15] lr: 1.0000e-06 eta: 0:07:38 time: 0.3396 data_time: 0.0015 memory: 3647 loss: 0.0735 loss_ce: 0.0735 2023/03/03 14:29:30 - mmengine - INFO - Epoch(train) [112][ 1/15] lr: 1.0000e-06 eta: 0:07:38 time: 0.3887 data_time: 0.0550 memory: 17730 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:29:30 - mmengine - INFO - Epoch(train) [112][ 2/15] lr: 1.0000e-06 eta: 0:07:38 time: 0.3944 data_time: 0.0551 memory: 20004 loss: 0.0728 loss_ce: 0.0728 2023/03/03 14:29:30 - mmengine - INFO - Epoch(train) [112][ 3/15] lr: 1.0000e-06 eta: 0:07:38 time: 0.3943 data_time: 0.0551 memory: 17968 loss: 0.0723 loss_ce: 0.0723 2023/03/03 14:29:31 - mmengine - INFO - Epoch(train) [112][ 4/15] lr: 1.0000e-06 eta: 0:07:37 time: 0.3800 data_time: 0.0552 memory: 12952 loss: 0.0718 loss_ce: 0.0718 2023/03/03 14:29:31 - mmengine - INFO - Epoch(train) [112][ 5/15] lr: 1.0000e-06 eta: 0:07:37 time: 0.3581 data_time: 0.0551 memory: 24516 loss: 0.0717 loss_ce: 0.0717 2023/03/03 14:29:31 - mmengine - INFO - Epoch(train) [112][ 6/15] lr: 1.0000e-06 eta: 0:07:36 time: 0.3573 data_time: 0.0551 memory: 17446 loss: 0.0705 loss_ce: 0.0705 2023/03/03 14:29:32 - mmengine - INFO - Epoch(train) [112][ 7/15] lr: 1.0000e-06 eta: 0:07:36 time: 0.3518 data_time: 0.0551 memory: 14671 loss: 0.0729 loss_ce: 0.0729 2023/03/03 14:29:32 - mmengine - INFO - Epoch(train) [112][ 8/15] lr: 1.0000e-06 eta: 0:07:36 time: 0.3508 data_time: 0.0551 memory: 16008 loss: 0.0786 loss_ce: 0.0786 2023/03/03 14:29:32 - mmengine - INFO - Epoch(train) [112][ 9/15] lr: 1.0000e-06 eta: 0:07:35 time: 0.3346 data_time: 0.0551 memory: 18789 loss: 0.0754 loss_ce: 0.0754 2023/03/03 14:29:32 - mmengine - INFO - Epoch(train) [112][10/15] lr: 1.0000e-06 eta: 0:07:35 time: 0.3450 data_time: 0.0551 memory: 15087 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:29:33 - mmengine - INFO - Epoch(train) [112][11/15] lr: 1.0000e-06 eta: 0:07:35 time: 0.3014 data_time: 0.0017 memory: 18632 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:29:33 - mmengine - INFO - Epoch(train) [112][12/15] lr: 1.0000e-06 eta: 0:07:34 time: 0.3125 data_time: 0.0016 memory: 17578 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:29:34 - mmengine - INFO - Epoch(train) [112][13/15] lr: 1.0000e-06 eta: 0:07:34 time: 0.3175 data_time: 0.0016 memory: 21040 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:29:34 - mmengine - INFO - Epoch(train) [112][14/15] lr: 1.0000e-06 eta: 0:07:34 time: 0.3161 data_time: 0.0015 memory: 16223 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:29:34 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:29:34 - mmengine - INFO - Epoch(train) [112][15/15] lr: 1.0000e-06 eta: 0:07:33 time: 0.3007 data_time: 0.0015 memory: 5710 loss: 0.0852 loss_ce: 0.0852 2023/03/03 14:29:35 - mmengine - INFO - Epoch(train) [113][ 1/15] lr: 1.0000e-06 eta: 0:07:33 time: 0.3682 data_time: 0.0699 memory: 16508 loss: 0.0890 loss_ce: 0.0890 2023/03/03 14:29:35 - mmengine - INFO - Epoch(train) [113][ 2/15] lr: 1.0000e-06 eta: 0:07:33 time: 0.3560 data_time: 0.0700 memory: 19434 loss: 0.0850 loss_ce: 0.0850 2023/03/03 14:29:36 - mmengine - INFO - Epoch(train) [113][ 3/15] lr: 1.0000e-06 eta: 0:07:32 time: 0.3702 data_time: 0.0701 memory: 23504 loss: 0.0804 loss_ce: 0.0804 2023/03/03 14:29:36 - mmengine - INFO - Epoch(train) [113][ 4/15] lr: 1.0000e-06 eta: 0:07:32 time: 0.3754 data_time: 0.0703 memory: 15562 loss: 0.0844 loss_ce: 0.0844 2023/03/03 14:29:36 - mmengine - INFO - Epoch(train) [113][ 5/15] lr: 1.0000e-06 eta: 0:07:32 time: 0.3705 data_time: 0.0704 memory: 17730 loss: 0.0835 loss_ce: 0.0835 2023/03/03 14:29:37 - mmengine - INFO - Epoch(train) [113][ 6/15] lr: 1.0000e-06 eta: 0:07:31 time: 0.3600 data_time: 0.0703 memory: 16895 loss: 0.0848 loss_ce: 0.0848 2023/03/03 14:29:37 - mmengine - INFO - Epoch(train) [113][ 7/15] lr: 1.0000e-06 eta: 0:07:31 time: 0.3467 data_time: 0.0703 memory: 15295 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:29:37 - mmengine - INFO - Epoch(train) [113][ 8/15] lr: 1.0000e-06 eta: 0:07:31 time: 0.3630 data_time: 0.0703 memory: 15936 loss: 0.0802 loss_ce: 0.0802 2023/03/03 14:29:38 - mmengine - INFO - Epoch(train) [113][ 9/15] lr: 1.0000e-06 eta: 0:07:30 time: 0.3648 data_time: 0.0703 memory: 17742 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:29:38 - mmengine - INFO - Epoch(train) [113][10/15] lr: 1.0000e-06 eta: 0:07:30 time: 0.3832 data_time: 0.0703 memory: 15432 loss: 0.0837 loss_ce: 0.0837 2023/03/03 14:29:38 - mmengine - INFO - Epoch(train) [113][11/15] lr: 1.0000e-06 eta: 0:07:29 time: 0.3148 data_time: 0.0019 memory: 16804 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:29:38 - mmengine - INFO - Epoch(train) [113][12/15] lr: 1.0000e-06 eta: 0:07:29 time: 0.3037 data_time: 0.0018 memory: 16223 loss: 0.0859 loss_ce: 0.0859 2023/03/03 14:29:38 - mmengine - INFO - Epoch(train) [113][13/15] lr: 1.0000e-06 eta: 0:07:29 time: 0.2915 data_time: 0.0017 memory: 15125 loss: 0.0846 loss_ce: 0.0846 2023/03/03 14:29:39 - mmengine - INFO - Epoch(train) [113][14/15] lr: 1.0000e-06 eta: 0:07:28 time: 0.2872 data_time: 0.0015 memory: 18944 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:29:39 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:29:39 - mmengine - INFO - Epoch(train) [113][15/15] lr: 1.0000e-06 eta: 0:07:28 time: 0.2768 data_time: 0.0014 memory: 6067 loss: 0.0886 loss_ce: 0.0886 2023/03/03 14:29:40 - mmengine - INFO - Epoch(train) [114][ 1/15] lr: 1.0000e-06 eta: 0:07:28 time: 0.3420 data_time: 0.0666 memory: 17421 loss: 0.0900 loss_ce: 0.0900 2023/03/03 14:29:40 - mmengine - INFO - Epoch(train) [114][ 2/15] lr: 1.0000e-06 eta: 0:07:27 time: 0.3381 data_time: 0.0667 memory: 18070 loss: 0.0901 loss_ce: 0.0901 2023/03/03 14:29:41 - mmengine - INFO - Epoch(train) [114][ 3/15] lr: 1.0000e-06 eta: 0:07:27 time: 0.3290 data_time: 0.0669 memory: 17102 loss: 0.0891 loss_ce: 0.0891 2023/03/03 14:29:41 - mmengine - INFO - Epoch(train) [114][ 4/15] lr: 1.0000e-06 eta: 0:07:27 time: 0.3288 data_time: 0.0670 memory: 17446 loss: 0.0875 loss_ce: 0.0875 2023/03/03 14:29:41 - mmengine - INFO - Epoch(train) [114][ 5/15] lr: 1.0000e-06 eta: 0:07:26 time: 0.3309 data_time: 0.0671 memory: 15136 loss: 0.0841 loss_ce: 0.0841 2023/03/03 14:29:41 - mmengine - INFO - Epoch(train) [114][ 6/15] lr: 1.0000e-06 eta: 0:07:26 time: 0.3293 data_time: 0.0672 memory: 15175 loss: 0.0873 loss_ce: 0.0873 2023/03/03 14:29:42 - mmengine - INFO - Epoch(train) [114][ 7/15] lr: 1.0000e-06 eta: 0:07:26 time: 0.3325 data_time: 0.0673 memory: 17272 loss: 0.0812 loss_ce: 0.0812 2023/03/03 14:29:42 - mmengine - INFO - Epoch(train) [114][ 8/15] lr: 1.0000e-06 eta: 0:07:25 time: 0.3331 data_time: 0.0673 memory: 15431 loss: 0.0857 loss_ce: 0.0857 2023/03/03 14:29:42 - mmengine - INFO - Epoch(train) [114][ 9/15] lr: 1.0000e-06 eta: 0:07:25 time: 0.3326 data_time: 0.0673 memory: 22472 loss: 0.0846 loss_ce: 0.0846 2023/03/03 14:29:42 - mmengine - INFO - Epoch(train) [114][10/15] lr: 1.0000e-06 eta: 0:07:24 time: 0.3405 data_time: 0.0673 memory: 14322 loss: 0.0785 loss_ce: 0.0785 2023/03/03 14:29:43 - mmengine - INFO - Epoch(train) [114][11/15] lr: 1.0000e-06 eta: 0:07:24 time: 0.3134 data_time: 0.0021 memory: 14675 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:29:43 - mmengine - INFO - Epoch(train) [114][12/15] lr: 1.0000e-06 eta: 0:07:24 time: 0.3265 data_time: 0.0021 memory: 20858 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:29:44 - mmengine - INFO - Epoch(train) [114][13/15] lr: 1.0000e-06 eta: 0:07:24 time: 0.3146 data_time: 0.0020 memory: 19384 loss: 0.0808 loss_ce: 0.0808 2023/03/03 14:29:44 - mmengine - INFO - Epoch(train) [114][14/15] lr: 1.0000e-06 eta: 0:07:23 time: 0.3133 data_time: 0.0019 memory: 16370 loss: 0.0876 loss_ce: 0.0876 2023/03/03 14:29:44 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:29:44 - mmengine - INFO - Epoch(train) [114][15/15] lr: 1.0000e-06 eta: 0:07:23 time: 0.3144 data_time: 0.0018 memory: 4380 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:29:45 - mmengine - INFO - Epoch(train) [115][ 1/15] lr: 1.0000e-06 eta: 0:07:23 time: 0.3929 data_time: 0.0795 memory: 15494 loss: 0.0843 loss_ce: 0.0843 2023/03/03 14:29:46 - mmengine - INFO - Epoch(train) [115][ 2/15] lr: 1.0000e-06 eta: 0:07:23 time: 0.3967 data_time: 0.0796 memory: 15432 loss: 0.0866 loss_ce: 0.0866 2023/03/03 14:29:46 - mmengine - INFO - Epoch(train) [115][ 3/15] lr: 1.0000e-06 eta: 0:07:22 time: 0.4061 data_time: 0.0796 memory: 18319 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:29:46 - mmengine - INFO - Epoch(train) [115][ 4/15] lr: 1.0000e-06 eta: 0:07:22 time: 0.3933 data_time: 0.0797 memory: 18268 loss: 0.0827 loss_ce: 0.0827 2023/03/03 14:29:46 - mmengine - INFO - Epoch(train) [115][ 5/15] lr: 1.0000e-06 eta: 0:07:21 time: 0.3959 data_time: 0.0797 memory: 17572 loss: 0.0826 loss_ce: 0.0826 2023/03/03 14:29:47 - mmengine - INFO - Epoch(train) [115][ 6/15] lr: 1.0000e-06 eta: 0:07:21 time: 0.3614 data_time: 0.0798 memory: 17572 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:29:47 - mmengine - INFO - Epoch(train) [115][ 7/15] lr: 1.0000e-06 eta: 0:07:21 time: 0.3507 data_time: 0.0798 memory: 16804 loss: 0.0804 loss_ce: 0.0804 2023/03/03 14:29:47 - mmengine - INFO - Epoch(train) [115][ 8/15] lr: 1.0000e-06 eta: 0:07:20 time: 0.3532 data_time: 0.0798 memory: 20155 loss: 0.0794 loss_ce: 0.0794 2023/03/03 14:29:47 - mmengine - INFO - Epoch(train) [115][ 9/15] lr: 1.0000e-06 eta: 0:07:20 time: 0.3516 data_time: 0.0798 memory: 15268 loss: 0.0745 loss_ce: 0.0745 2023/03/03 14:29:48 - mmengine - INFO - Epoch(train) [115][10/15] lr: 1.0000e-06 eta: 0:07:19 time: 0.3522 data_time: 0.0798 memory: 17272 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:29:48 - mmengine - INFO - Epoch(train) [115][11/15] lr: 1.0000e-06 eta: 0:07:19 time: 0.2782 data_time: 0.0019 memory: 16020 loss: 0.0694 loss_ce: 0.0694 2023/03/03 14:29:48 - mmengine - INFO - Epoch(train) [115][12/15] lr: 1.0000e-06 eta: 0:07:19 time: 0.2847 data_time: 0.0018 memory: 18139 loss: 0.0653 loss_ce: 0.0653 2023/03/03 14:29:49 - mmengine - INFO - Epoch(train) [115][13/15] lr: 1.0000e-06 eta: 0:07:18 time: 0.2736 data_time: 0.0018 memory: 17162 loss: 0.0698 loss_ce: 0.0698 2023/03/03 14:29:49 - mmengine - INFO - Epoch(train) [115][14/15] lr: 1.0000e-06 eta: 0:07:18 time: 0.2904 data_time: 0.0017 memory: 18730 loss: 0.0735 loss_ce: 0.0735 2023/03/03 14:29:49 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:29:49 - mmengine - INFO - Epoch(train) [115][15/15] lr: 1.0000e-06 eta: 0:07:17 time: 0.2875 data_time: 0.0017 memory: 6361 loss: 0.0768 loss_ce: 0.0768 2023/03/03 14:29:50 - mmengine - INFO - Epoch(train) [116][ 1/15] lr: 1.0000e-06 eta: 0:07:18 time: 0.3616 data_time: 0.0604 memory: 37937 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:29:51 - mmengine - INFO - Epoch(train) [116][ 2/15] lr: 1.0000e-06 eta: 0:07:17 time: 0.3611 data_time: 0.0604 memory: 15631 loss: 0.0782 loss_ce: 0.0782 2023/03/03 14:29:51 - mmengine - INFO - Epoch(train) [116][ 3/15] lr: 1.0000e-06 eta: 0:07:17 time: 0.3763 data_time: 0.0605 memory: 16976 loss: 0.0824 loss_ce: 0.0824 2023/03/03 14:29:51 - mmengine - INFO - Epoch(train) [116][ 4/15] lr: 1.0000e-06 eta: 0:07:17 time: 0.3912 data_time: 0.0605 memory: 21037 loss: 0.0844 loss_ce: 0.0844 2023/03/03 14:29:52 - mmengine - INFO - Epoch(train) [116][ 5/15] lr: 1.0000e-06 eta: 0:07:16 time: 0.3934 data_time: 0.0606 memory: 15631 loss: 0.0854 loss_ce: 0.0854 2023/03/03 14:29:52 - mmengine - INFO - Epoch(train) [116][ 6/15] lr: 1.0000e-06 eta: 0:07:16 time: 0.4047 data_time: 0.0606 memory: 21723 loss: 0.0897 loss_ce: 0.0897 2023/03/03 14:29:52 - mmengine - INFO - Epoch(train) [116][ 7/15] lr: 1.0000e-06 eta: 0:07:16 time: 0.4015 data_time: 0.0607 memory: 20222 loss: 0.0925 loss_ce: 0.0925 2023/03/03 14:29:53 - mmengine - INFO - Epoch(train) [116][ 8/15] lr: 1.0000e-06 eta: 0:07:15 time: 0.3999 data_time: 0.0607 memory: 15858 loss: 0.0884 loss_ce: 0.0884 2023/03/03 14:29:53 - mmengine - INFO - Epoch(train) [116][ 9/15] lr: 1.0000e-06 eta: 0:07:15 time: 0.3897 data_time: 0.0607 memory: 18737 loss: 0.0849 loss_ce: 0.0849 2023/03/03 14:29:53 - mmengine - INFO - Epoch(train) [116][10/15] lr: 1.0000e-06 eta: 0:07:14 time: 0.3980 data_time: 0.0607 memory: 14369 loss: 0.0824 loss_ce: 0.0824 2023/03/03 14:29:54 - mmengine - INFO - Epoch(train) [116][11/15] lr: 1.0000e-06 eta: 0:07:14 time: 0.3244 data_time: 0.0019 memory: 14761 loss: 0.0820 loss_ce: 0.0820 2023/03/03 14:29:54 - mmengine - INFO - Epoch(train) [116][12/15] lr: 1.0000e-06 eta: 0:07:14 time: 0.3219 data_time: 0.0018 memory: 17272 loss: 0.0809 loss_ce: 0.0809 2023/03/03 14:29:54 - mmengine - INFO - Epoch(train) [116][13/15] lr: 1.0000e-06 eta: 0:07:13 time: 0.3003 data_time: 0.0017 memory: 15555 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:29:54 - mmengine - INFO - Epoch(train) [116][14/15] lr: 1.0000e-06 eta: 0:07:13 time: 0.2865 data_time: 0.0017 memory: 16212 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:29:54 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:29:54 - mmengine - INFO - Epoch(train) [116][15/15] lr: 1.0000e-06 eta: 0:07:12 time: 0.2623 data_time: 0.0016 memory: 6229 loss: 0.0768 loss_ce: 0.0768 2023/03/03 14:29:55 - mmengine - INFO - Epoch(train) [117][ 1/15] lr: 1.0000e-06 eta: 0:07:12 time: 0.3007 data_time: 0.0542 memory: 15494 loss: 0.0759 loss_ce: 0.0759 2023/03/03 14:29:56 - mmengine - INFO - Epoch(train) [117][ 2/15] lr: 1.0000e-06 eta: 0:07:12 time: 0.3097 data_time: 0.0542 memory: 17079 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:29:56 - mmengine - INFO - Epoch(train) [117][ 3/15] lr: 1.0000e-06 eta: 0:07:12 time: 0.3169 data_time: 0.0543 memory: 18582 loss: 0.0827 loss_ce: 0.0827 2023/03/03 14:29:56 - mmengine - INFO - Epoch(train) [117][ 4/15] lr: 1.0000e-06 eta: 0:07:11 time: 0.3225 data_time: 0.0543 memory: 13006 loss: 0.0887 loss_ce: 0.0887 2023/03/03 14:29:56 - mmengine - INFO - Epoch(train) [117][ 5/15] lr: 1.0000e-06 eta: 0:07:11 time: 0.3217 data_time: 0.0544 memory: 18311 loss: 0.0882 loss_ce: 0.0882 2023/03/03 14:29:57 - mmengine - INFO - Epoch(train) [117][ 6/15] lr: 1.0000e-06 eta: 0:07:10 time: 0.3142 data_time: 0.0543 memory: 16199 loss: 0.0952 loss_ce: 0.0952 2023/03/03 14:29:57 - mmengine - INFO - Epoch(train) [117][ 7/15] lr: 1.0000e-06 eta: 0:07:10 time: 0.3160 data_time: 0.0544 memory: 15631 loss: 0.0951 loss_ce: 0.0951 2023/03/03 14:29:57 - mmengine - INFO - Epoch(train) [117][ 8/15] lr: 1.0000e-06 eta: 0:07:10 time: 0.3333 data_time: 0.0544 memory: 21363 loss: 0.0954 loss_ce: 0.0954 2023/03/03 14:29:58 - mmengine - INFO - Epoch(train) [117][ 9/15] lr: 1.0000e-06 eta: 0:07:09 time: 0.3472 data_time: 0.0544 memory: 17541 loss: 0.0975 loss_ce: 0.0975 2023/03/03 14:29:58 - mmengine - INFO - Epoch(train) [117][10/15] lr: 1.0000e-06 eta: 0:07:09 time: 0.3680 data_time: 0.0545 memory: 16883 loss: 0.0941 loss_ce: 0.0941 2023/03/03 14:29:58 - mmengine - INFO - Epoch(train) [117][11/15] lr: 1.0000e-06 eta: 0:07:09 time: 0.3123 data_time: 0.0019 memory: 17120 loss: 0.0922 loss_ce: 0.0922 2023/03/03 14:29:59 - mmengine - INFO - Epoch(train) [117][12/15] lr: 1.0000e-06 eta: 0:07:08 time: 0.3234 data_time: 0.0018 memory: 38088 loss: 0.0872 loss_ce: 0.0872 2023/03/03 14:29:59 - mmengine - INFO - Epoch(train) [117][13/15] lr: 1.0000e-06 eta: 0:07:08 time: 0.3173 data_time: 0.0018 memory: 16656 loss: 0.0830 loss_ce: 0.0830 2023/03/03 14:29:59 - mmengine - INFO - Epoch(train) [117][14/15] lr: 1.0000e-06 eta: 0:07:08 time: 0.3072 data_time: 0.0017 memory: 15994 loss: 0.0785 loss_ce: 0.0785 2023/03/03 14:29:59 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:29:59 - mmengine - INFO - Epoch(train) [117][15/15] lr: 1.0000e-06 eta: 0:07:07 time: 0.2979 data_time: 0.0017 memory: 4063 loss: 0.0830 loss_ce: 0.0830 2023/03/03 14:30:01 - mmengine - INFO - Epoch(train) [118][ 1/15] lr: 1.0000e-06 eta: 0:07:07 time: 0.3874 data_time: 0.0980 memory: 17284 loss: 0.0725 loss_ce: 0.0725 2023/03/03 14:30:01 - mmengine - INFO - Epoch(train) [118][ 2/15] lr: 1.0000e-06 eta: 0:07:07 time: 0.3857 data_time: 0.0980 memory: 16976 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:30:01 - mmengine - INFO - Epoch(train) [118][ 3/15] lr: 1.0000e-06 eta: 0:07:06 time: 0.3770 data_time: 0.0981 memory: 18649 loss: 0.0802 loss_ce: 0.0802 2023/03/03 14:30:01 - mmengine - INFO - Epoch(train) [118][ 4/15] lr: 1.0000e-06 eta: 0:07:06 time: 0.3604 data_time: 0.0981 memory: 16588 loss: 0.0783 loss_ce: 0.0783 2023/03/03 14:30:02 - mmengine - INFO - Epoch(train) [118][ 5/15] lr: 1.0000e-06 eta: 0:07:06 time: 0.3624 data_time: 0.0980 memory: 16566 loss: 0.0744 loss_ce: 0.0744 2023/03/03 14:30:02 - mmengine - INFO - Epoch(train) [118][ 6/15] lr: 1.0000e-06 eta: 0:07:05 time: 0.3627 data_time: 0.0980 memory: 18070 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:30:02 - mmengine - INFO - Epoch(train) [118][ 7/15] lr: 1.0000e-06 eta: 0:07:05 time: 0.3396 data_time: 0.0980 memory: 17855 loss: 0.0800 loss_ce: 0.0800 2023/03/03 14:30:02 - mmengine - INFO - Epoch(train) [118][ 8/15] lr: 1.0000e-06 eta: 0:07:04 time: 0.3469 data_time: 0.0980 memory: 16703 loss: 0.0812 loss_ce: 0.0812 2023/03/03 14:30:03 - mmengine - INFO - Epoch(train) [118][ 9/15] lr: 1.0000e-06 eta: 0:07:04 time: 0.3687 data_time: 0.0981 memory: 17572 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:30:03 - mmengine - INFO - Epoch(train) [118][10/15] lr: 1.0000e-06 eta: 0:07:04 time: 0.3790 data_time: 0.0981 memory: 19036 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:30:03 - mmengine - INFO - Epoch(train) [118][11/15] lr: 1.0000e-06 eta: 0:07:03 time: 0.2841 data_time: 0.0018 memory: 17421 loss: 0.0759 loss_ce: 0.0759 2023/03/03 14:30:04 - mmengine - INFO - Epoch(train) [118][12/15] lr: 1.0000e-06 eta: 0:07:03 time: 0.3089 data_time: 0.0018 memory: 20696 loss: 0.0711 loss_ce: 0.0711 2023/03/03 14:30:04 - mmengine - INFO - Epoch(train) [118][13/15] lr: 1.0000e-06 eta: 0:07:03 time: 0.3025 data_time: 0.0017 memory: 17284 loss: 0.0658 loss_ce: 0.0658 2023/03/03 14:30:04 - mmengine - INFO - Epoch(train) [118][14/15] lr: 1.0000e-06 eta: 0:07:02 time: 0.3024 data_time: 0.0017 memory: 16549 loss: 0.0683 loss_ce: 0.0683 2023/03/03 14:30:05 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:30:05 - mmengine - INFO - Epoch(train) [118][15/15] lr: 1.0000e-06 eta: 0:07:02 time: 0.2986 data_time: 0.0016 memory: 4383 loss: 0.0714 loss_ce: 0.0714 2023/03/03 14:30:05 - mmengine - INFO - Epoch(train) [119][ 1/15] lr: 1.0000e-06 eta: 0:07:02 time: 0.3474 data_time: 0.0509 memory: 16126 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:30:06 - mmengine - INFO - Epoch(train) [119][ 2/15] lr: 1.0000e-06 eta: 0:07:01 time: 0.3474 data_time: 0.0509 memory: 17968 loss: 0.0691 loss_ce: 0.0691 2023/03/03 14:30:06 - mmengine - INFO - Epoch(train) [119][ 3/15] lr: 1.0000e-06 eta: 0:07:01 time: 0.3427 data_time: 0.0510 memory: 15037 loss: 0.0687 loss_ce: 0.0687 2023/03/03 14:30:06 - mmengine - INFO - Epoch(train) [119][ 4/15] lr: 1.0000e-06 eta: 0:07:01 time: 0.3206 data_time: 0.0511 memory: 17494 loss: 0.0686 loss_ce: 0.0686 2023/03/03 14:30:06 - mmengine - INFO - Epoch(train) [119][ 5/15] lr: 1.0000e-06 eta: 0:07:00 time: 0.3152 data_time: 0.0512 memory: 18409 loss: 0.0697 loss_ce: 0.0697 2023/03/03 14:30:07 - mmengine - INFO - Epoch(train) [119][ 6/15] lr: 1.0000e-06 eta: 0:07:00 time: 0.3209 data_time: 0.0512 memory: 16056 loss: 0.0676 loss_ce: 0.0676 2023/03/03 14:30:07 - mmengine - INFO - Epoch(train) [119][ 7/15] lr: 1.0000e-06 eta: 0:06:59 time: 0.2981 data_time: 0.0512 memory: 15767 loss: 0.0686 loss_ce: 0.0686 2023/03/03 14:30:07 - mmengine - INFO - Epoch(train) [119][ 8/15] lr: 1.0000e-06 eta: 0:06:59 time: 0.3223 data_time: 0.0513 memory: 13471 loss: 0.0689 loss_ce: 0.0689 2023/03/03 14:30:08 - mmengine - INFO - Epoch(train) [119][ 9/15] lr: 1.0000e-06 eta: 0:06:59 time: 0.3259 data_time: 0.0513 memory: 16804 loss: 0.0684 loss_ce: 0.0684 2023/03/03 14:30:08 - mmengine - INFO - Epoch(train) [119][10/15] lr: 1.0000e-06 eta: 0:06:58 time: 0.3360 data_time: 0.0513 memory: 22337 loss: 0.0659 loss_ce: 0.0659 2023/03/03 14:30:08 - mmengine - INFO - Epoch(train) [119][11/15] lr: 1.0000e-06 eta: 0:06:58 time: 0.2855 data_time: 0.0021 memory: 15561 loss: 0.0630 loss_ce: 0.0630 2023/03/03 14:30:09 - mmengine - INFO - Epoch(train) [119][12/15] lr: 1.0000e-06 eta: 0:06:58 time: 0.2853 data_time: 0.0020 memory: 15067 loss: 0.0678 loss_ce: 0.0678 2023/03/03 14:30:09 - mmengine - INFO - Epoch(train) [119][13/15] lr: 1.0000e-06 eta: 0:06:57 time: 0.2862 data_time: 0.0019 memory: 16659 loss: 0.0686 loss_ce: 0.0686 2023/03/03 14:30:09 - mmengine - INFO - Epoch(train) [119][14/15] lr: 1.0000e-06 eta: 0:06:57 time: 0.2926 data_time: 0.0018 memory: 17272 loss: 0.0698 loss_ce: 0.0698 2023/03/03 14:30:09 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:30:09 - mmengine - INFO - Epoch(train) [119][15/15] lr: 1.0000e-06 eta: 0:06:56 time: 0.2882 data_time: 0.0017 memory: 5092 loss: 0.0723 loss_ce: 0.0723 2023/03/03 14:30:11 - mmengine - INFO - Epoch(train) [120][ 1/15] lr: 1.0000e-06 eta: 0:06:57 time: 0.3877 data_time: 0.0661 memory: 36638 loss: 0.0727 loss_ce: 0.0727 2023/03/03 14:30:11 - mmengine - INFO - Epoch(train) [120][ 2/15] lr: 1.0000e-06 eta: 0:06:56 time: 0.3966 data_time: 0.0661 memory: 17952 loss: 0.0717 loss_ce: 0.0717 2023/03/03 14:30:11 - mmengine - INFO - Epoch(train) [120][ 3/15] lr: 1.0000e-06 eta: 0:06:56 time: 0.3964 data_time: 0.0662 memory: 17421 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:30:12 - mmengine - INFO - Epoch(train) [120][ 4/15] lr: 1.0000e-06 eta: 0:06:56 time: 0.4034 data_time: 0.0663 memory: 16259 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:30:12 - mmengine - INFO - Epoch(train) [120][ 5/15] lr: 1.0000e-06 eta: 0:06:55 time: 0.4019 data_time: 0.0663 memory: 22666 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:30:12 - mmengine - INFO - Epoch(train) [120][ 6/15] lr: 1.0000e-06 eta: 0:06:55 time: 0.4106 data_time: 0.0663 memory: 20190 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:30:13 - mmengine - INFO - Epoch(train) [120][ 7/15] lr: 1.0000e-06 eta: 0:06:55 time: 0.4104 data_time: 0.0663 memory: 15736 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:30:13 - mmengine - INFO - Epoch(train) [120][ 8/15] lr: 1.0000e-06 eta: 0:06:54 time: 0.4134 data_time: 0.0664 memory: 16720 loss: 0.0726 loss_ce: 0.0726 2023/03/03 14:30:13 - mmengine - INFO - Epoch(train) [120][ 9/15] lr: 1.0000e-06 eta: 0:06:54 time: 0.4049 data_time: 0.0664 memory: 17120 loss: 0.0733 loss_ce: 0.0733 2023/03/03 14:30:13 - mmengine - INFO - Epoch(train) [120][10/15] lr: 1.0000e-06 eta: 0:06:53 time: 0.4118 data_time: 0.0664 memory: 16804 loss: 0.0695 loss_ce: 0.0695 2023/03/03 14:30:14 - mmengine - INFO - Epoch(train) [120][11/15] lr: 1.0000e-06 eta: 0:06:53 time: 0.3063 data_time: 0.0020 memory: 11316 loss: 0.0693 loss_ce: 0.0693 2023/03/03 14:30:14 - mmengine - INFO - Epoch(train) [120][12/15] lr: 1.0000e-06 eta: 0:06:53 time: 0.3014 data_time: 0.0019 memory: 17377 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:30:14 - mmengine - INFO - Epoch(train) [120][13/15] lr: 1.0000e-06 eta: 0:06:52 time: 0.2800 data_time: 0.0017 memory: 17120 loss: 0.0730 loss_ce: 0.0730 2023/03/03 14:30:14 - mmengine - INFO - Epoch(train) [120][14/15] lr: 1.0000e-06 eta: 0:06:52 time: 0.2780 data_time: 0.0016 memory: 16052 loss: 0.0766 loss_ce: 0.0766 2023/03/03 14:30:15 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:30:15 - mmengine - INFO - Epoch(train) [120][15/15] lr: 1.0000e-06 eta: 0:06:51 time: 0.2620 data_time: 0.0016 memory: 5242 loss: 0.0788 loss_ce: 0.0788 2023/03/03 14:30:16 - mmengine - INFO - Epoch(val) [120][ 1/59] eta: 0:01:30 time: 1.0885 data_time: 0.0034 memory: 981 2023/03/03 14:30:17 - mmengine - INFO - Epoch(val) [120][ 2/59] eta: 0:01:08 time: 1.0050 data_time: 0.0035 memory: 981 2023/03/03 14:30:18 - mmengine - INFO - Epoch(val) [120][ 3/59] eta: 0:01:10 time: 1.0253 data_time: 0.0035 memory: 1003 2023/03/03 14:30:19 - mmengine - INFO - Epoch(val) [120][ 4/59] eta: 0:00:56 time: 1.0100 data_time: 0.0036 memory: 981 2023/03/03 14:30:22 - mmengine - INFO - Epoch(val) [120][ 5/59] eta: 0:01:18 time: 1.2536 data_time: 0.0036 memory: 1016 2023/03/03 14:30:25 - mmengine - INFO - Epoch(val) [120][ 6/59] eta: 0:01:27 time: 1.4550 data_time: 0.0037 memory: 981 2023/03/03 14:30:25 - mmengine - INFO - Epoch(val) [120][ 7/59] eta: 0:01:15 time: 1.4061 data_time: 0.0037 memory: 1043 2023/03/03 14:30:25 - mmengine - INFO - Epoch(val) [120][ 8/59] eta: 0:01:08 time: 1.2479 data_time: 0.0037 memory: 1016 2023/03/03 14:30:26 - mmengine - INFO - Epoch(val) [120][ 9/59] eta: 0:01:05 time: 1.2144 data_time: 0.0037 memory: 981 2023/03/03 14:30:27 - mmengine - INFO - Epoch(val) [120][10/59] eta: 0:01:01 time: 1.2481 data_time: 0.0037 memory: 981 2023/03/03 14:30:28 - mmengine - INFO - Epoch(val) [120][11/59] eta: 0:00:55 time: 1.1273 data_time: 0.0010 memory: 981 2023/03/03 14:30:31 - mmengine - INFO - Epoch(val) [120][12/59] eta: 0:01:03 time: 1.3699 data_time: 0.0010 memory: 1016 2023/03/03 14:30:33 - mmengine - INFO - Epoch(val) [120][13/59] eta: 0:01:05 time: 1.4587 data_time: 0.0009 memory: 981 2023/03/03 14:30:34 - mmengine - INFO - Epoch(val) [120][14/59] eta: 0:01:02 time: 1.5261 data_time: 0.0009 memory: 890 2023/03/03 14:30:34 - mmengine - INFO - Epoch(val) [120][15/59] eta: 0:00:56 time: 1.2180 data_time: 0.0009 memory: 981 2023/03/03 14:30:35 - mmengine - INFO - Epoch(val) [120][16/59] eta: 0:00:53 time: 1.0004 data_time: 0.0009 memory: 981 2023/03/03 14:30:35 - mmengine - INFO - Epoch(val) [120][17/59] eta: 0:00:50 time: 1.0166 data_time: 0.0009 memory: 981 2023/03/03 14:30:35 - mmengine - INFO - Epoch(val) [120][18/59] eta: 0:00:46 time: 0.9835 data_time: 0.0009 memory: 981 2023/03/03 14:30:36 - mmengine - INFO - Epoch(val) [120][19/59] eta: 0:00:45 time: 0.9834 data_time: 0.0009 memory: 981 2023/03/03 14:30:37 - mmengine - INFO - Epoch(val) [120][20/59] eta: 0:00:42 time: 0.9498 data_time: 0.0009 memory: 981 2023/03/03 14:30:39 - mmengine - INFO - Epoch(val) [120][21/59] eta: 0:00:43 time: 1.1424 data_time: 0.0009 memory: 981 2023/03/03 14:30:39 - mmengine - INFO - Epoch(val) [120][22/59] eta: 0:00:41 time: 0.8314 data_time: 0.0009 memory: 981 2023/03/03 14:30:40 - mmengine - INFO - Epoch(val) [120][23/59] eta: 0:00:39 time: 0.6719 data_time: 0.0009 memory: 981 2023/03/03 14:30:40 - mmengine - INFO - Epoch(val) [120][24/59] eta: 0:00:37 time: 0.6038 data_time: 0.0009 memory: 962 2023/03/03 14:30:40 - mmengine - INFO - Epoch(val) [120][25/59] eta: 0:00:35 time: 0.6355 data_time: 0.0009 memory: 981 2023/03/03 14:30:41 - mmengine - INFO - Epoch(val) [120][26/59] eta: 0:00:33 time: 0.6190 data_time: 0.0008 memory: 981 2023/03/03 14:30:41 - mmengine - INFO - Epoch(val) [120][27/59] eta: 0:00:31 time: 0.6189 data_time: 0.0008 memory: 981 2023/03/03 14:30:41 - mmengine - INFO - Epoch(val) [120][28/59] eta: 0:00:29 time: 0.6188 data_time: 0.0008 memory: 981 2023/03/03 14:30:43 - mmengine - INFO - Epoch(val) [120][29/59] eta: 0:00:29 time: 0.6529 data_time: 0.0007 memory: 981 2023/03/03 14:30:44 - mmengine - INFO - Epoch(val) [120][30/59] eta: 0:00:28 time: 0.7028 data_time: 0.0007 memory: 999 2023/03/03 14:30:44 - mmengine - INFO - Epoch(val) [120][31/59] eta: 0:00:26 time: 0.5431 data_time: 0.0007 memory: 981 2023/03/03 14:30:46 - mmengine - INFO - Epoch(val) [120][32/59] eta: 0:00:26 time: 0.6439 data_time: 0.0007 memory: 981 2023/03/03 14:30:46 - mmengine - INFO - Epoch(val) [120][33/59] eta: 0:00:24 time: 0.5788 data_time: 0.0008 memory: 981 2023/03/03 14:30:46 - mmengine - INFO - Epoch(val) [120][34/59] eta: 0:00:22 time: 0.5624 data_time: 0.0007 memory: 981 2023/03/03 14:30:46 - mmengine - INFO - Epoch(val) [120][35/59] eta: 0:00:21 time: 0.5458 data_time: 0.0007 memory: 981 2023/03/03 14:30:46 - mmengine - INFO - Epoch(val) [120][36/59] eta: 0:00:20 time: 0.5625 data_time: 0.0007 memory: 981 2023/03/03 14:30:47 - mmengine - INFO - Epoch(val) [120][37/59] eta: 0:00:18 time: 0.5461 data_time: 0.0007 memory: 981 2023/03/03 14:30:47 - mmengine - INFO - Epoch(val) [120][38/59] eta: 0:00:18 time: 0.5795 data_time: 0.0007 memory: 981 2023/03/03 14:30:48 - mmengine - INFO - Epoch(val) [120][39/59] eta: 0:00:16 time: 0.4946 data_time: 0.0008 memory: 987 2023/03/03 14:30:49 - mmengine - INFO - Epoch(val) [120][40/59] eta: 0:00:16 time: 0.4948 data_time: 0.0008 memory: 981 2023/03/03 14:30:50 - mmengine - INFO - Epoch(val) [120][41/59] eta: 0:00:15 time: 0.5459 data_time: 0.0008 memory: 986 2023/03/03 14:30:50 - mmengine - INFO - Epoch(val) [120][42/59] eta: 0:00:14 time: 0.4949 data_time: 0.0008 memory: 981 2023/03/03 14:30:51 - mmengine - INFO - Epoch(val) [120][43/59] eta: 0:00:13 time: 0.5769 data_time: 0.0008 memory: 976 2023/03/03 14:30:52 - mmengine - INFO - Epoch(val) [120][44/59] eta: 0:00:12 time: 0.6099 data_time: 0.0008 memory: 1003 2023/03/03 14:30:54 - mmengine - INFO - Epoch(val) [120][45/59] eta: 0:00:12 time: 0.8008 data_time: 0.0008 memory: 981 2023/03/03 14:30:55 - mmengine - INFO - Epoch(val) [120][46/59] eta: 0:00:11 time: 0.8342 data_time: 0.0008 memory: 981 2023/03/03 14:30:55 - mmengine - INFO - Epoch(val) [120][47/59] eta: 0:00:10 time: 0.8673 data_time: 0.0008 memory: 936 2023/03/03 14:30:56 - mmengine - INFO - Epoch(val) [120][48/59] eta: 0:00:09 time: 0.8505 data_time: 0.0008 memory: 1000 2023/03/03 14:30:57 - mmengine - INFO - Epoch(val) [120][49/59] eta: 0:00:08 time: 0.9012 data_time: 0.0008 memory: 981 2023/03/03 14:30:58 - mmengine - INFO - Epoch(val) [120][50/59] eta: 0:00:07 time: 0.9013 data_time: 0.0008 memory: 987 2023/03/03 14:30:59 - mmengine - INFO - Epoch(val) [120][51/59] eta: 0:00:07 time: 0.9541 data_time: 0.0008 memory: 981 2023/03/03 14:31:01 - mmengine - INFO - Epoch(val) [120][52/59] eta: 0:00:06 time: 1.0052 data_time: 0.0008 memory: 981 2023/03/03 14:31:01 - mmengine - INFO - Epoch(val) [120][53/59] eta: 0:00:05 time: 0.9714 data_time: 0.0008 memory: 962 2023/03/03 14:31:02 - mmengine - INFO - Epoch(val) [120][54/59] eta: 0:00:04 time: 0.9885 data_time: 0.0008 memory: 981 2023/03/03 14:31:02 - mmengine - INFO - Epoch(val) [120][55/59] eta: 0:00:03 time: 0.8473 data_time: 0.0007 memory: 981 2023/03/03 14:31:03 - mmengine - INFO - Epoch(val) [120][56/59] eta: 0:00:02 time: 0.8309 data_time: 0.0007 memory: 981 2023/03/03 14:31:05 - mmengine - INFO - Epoch(val) [120][57/59] eta: 0:00:01 time: 1.0079 data_time: 0.0007 memory: 981 2023/03/03 14:31:07 - mmengine - INFO - Epoch(val) [120][58/59] eta: 0:00:00 time: 1.0753 data_time: 0.0007 memory: 1016 2023/03/03 14:31:07 - mmengine - INFO - Epoch(val) [120][59/59] eta: 0:00:00 time: 1.0086 data_time: 0.0007 memory: 981 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.80, recall: 0.8192, precision: 0.8368, hmean: 0.8279 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.81, recall: 0.8183, precision: 0.8374, hmean: 0.8277 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.82, recall: 0.8183, precision: 0.8390, hmean: 0.8285 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.83, recall: 0.8155, precision: 0.8432, hmean: 0.8292 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.84, recall: 0.8146, precision: 0.8439, hmean: 0.8290 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.85, recall: 0.8128, precision: 0.8460, hmean: 0.8291 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.86, recall: 0.8110, precision: 0.8489, hmean: 0.8295 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.87, recall: 0.8091, precision: 0.8519, hmean: 0.8300 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.88, recall: 0.8091, precision: 0.8527, hmean: 0.8304 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.89, recall: 0.8064, precision: 0.8548, hmean: 0.8299 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.90, recall: 0.8018, precision: 0.8599, hmean: 0.8299 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.91, recall: 0.8009, precision: 0.8615, hmean: 0.8301 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.92, recall: 0.7954, precision: 0.8641, hmean: 0.8283 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.93, recall: 0.7900, precision: 0.8667, hmean: 0.8266 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.94, recall: 0.7808, precision: 0.8733, hmean: 0.8245 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.95, recall: 0.7717, precision: 0.8747, hmean: 0.8200 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.96, recall: 0.7635, precision: 0.8772, hmean: 0.8164 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.97, recall: 0.7525, precision: 0.8822, hmean: 0.8122 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.98, recall: 0.7397, precision: 0.8833, hmean: 0.8052 2023/03/03 14:31:35 - mmengine - INFO - text score threshold: 0.99, recall: 0.7205, precision: 0.8935, hmean: 0.7978 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.80, recall: 0.8292, precision: 0.9044, hmean: 0.8652 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.81, recall: 0.8283, precision: 0.9043, hmean: 0.8646 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.82, recall: 0.8283, precision: 0.9052, hmean: 0.8650 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.83, recall: 0.8256, precision: 0.9076, hmean: 0.8647 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.84, recall: 0.8247, precision: 0.9085, hmean: 0.8645 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.85, recall: 0.8228, precision: 0.9083, hmean: 0.8634 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.86, recall: 0.8210, precision: 0.9108, hmean: 0.8636 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.87, recall: 0.8192, precision: 0.9134, hmean: 0.8637 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.88, recall: 0.8183, precision: 0.9134, hmean: 0.8632 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.89, recall: 0.8146, precision: 0.9149, hmean: 0.8618 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.90, recall: 0.8100, precision: 0.9192, hmean: 0.8612 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.91, recall: 0.8082, precision: 0.9190, hmean: 0.8601 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.92, recall: 0.8018, precision: 0.9203, hmean: 0.8570 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.93, recall: 0.7945, precision: 0.9197, hmean: 0.8525 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.94, recall: 0.7817, precision: 0.9214, hmean: 0.8458 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.95, recall: 0.7744, precision: 0.9237, hmean: 0.8425 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.96, recall: 0.7653, precision: 0.9249, hmean: 0.8376 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.97, recall: 0.7534, precision: 0.9270, hmean: 0.8312 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.98, recall: 0.7397, precision: 0.9278, hmean: 0.8232 2023/03/03 14:31:38 - mmengine - INFO - text score threshold: 0.99, recall: 0.7196, precision: 0.9314, hmean: 0.8120 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.80, recall: 0.7534, precision: 0.9571, hmean: 0.8431 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.81, recall: 0.7525, precision: 0.9570, hmean: 0.8425 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.82, recall: 0.7525, precision: 0.9570, hmean: 0.8425 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.83, recall: 0.7498, precision: 0.9580, hmean: 0.8412 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.84, recall: 0.7489, precision: 0.9579, hmean: 0.8406 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.85, recall: 0.7470, precision: 0.9578, hmean: 0.8394 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.86, recall: 0.7443, precision: 0.9588, hmean: 0.8380 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.87, recall: 0.7416, precision: 0.9587, hmean: 0.8363 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.88, recall: 0.7406, precision: 0.9586, hmean: 0.8357 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.89, recall: 0.7370, precision: 0.9584, hmean: 0.8332 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.90, recall: 0.7315, precision: 0.9593, hmean: 0.8301 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.91, recall: 0.7297, precision: 0.9592, hmean: 0.8288 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.92, recall: 0.7233, precision: 0.9600, hmean: 0.8250 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.93, recall: 0.7169, precision: 0.9597, hmean: 0.8207 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.94, recall: 0.7059, precision: 0.9602, hmean: 0.8137 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.95, recall: 0.7005, precision: 0.9612, hmean: 0.8104 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.96, recall: 0.6922, precision: 0.9632, hmean: 0.8055 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.97, recall: 0.6822, precision: 0.9664, hmean: 0.7998 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.98, recall: 0.6685, precision: 0.9657, hmean: 0.7901 2023/03/03 14:31:41 - mmengine - INFO - text score threshold: 0.99, recall: 0.6484, precision: 0.9673, hmean: 0.7764 2023/03/03 14:31:41 - mmengine - INFO - Epoch(val) [120][59/59] generic/precision: 0.8527 generic/recall: 0.8091 generic/hmean: 0.8304 weak/precision: 0.9044 weak/recall: 0.8292 weak/hmean: 0.8652 strong/precision: 0.9571 strong/recall: 0.7534 strong/hmean: 0.8431 2023/03/03 14:31:41 - mmengine - INFO - Epoch(train) [121][ 1/15] lr: 1.0000e-06 eta: 0:06:51 time: 0.3004 data_time: 0.0452 memory: 16886 loss: 0.0847 loss_ce: 0.0847 2023/03/03 14:31:42 - mmengine - INFO - Epoch(train) [121][ 2/15] lr: 1.0000e-06 eta: 0:06:51 time: 0.3209 data_time: 0.0453 memory: 17421 loss: 0.0871 loss_ce: 0.0871 2023/03/03 14:31:42 - mmengine - INFO - Epoch(train) [121][ 3/15] lr: 1.0000e-06 eta: 0:06:51 time: 0.3247 data_time: 0.0454 memory: 20155 loss: 0.0845 loss_ce: 0.0845 2023/03/03 14:31:42 - mmengine - INFO - Epoch(train) [121][ 4/15] lr: 1.0000e-06 eta: 0:06:50 time: 0.3216 data_time: 0.0453 memory: 18241 loss: 0.0810 loss_ce: 0.0810 2023/03/03 14:31:43 - mmengine - INFO - Epoch(train) [121][ 5/15] lr: 1.0000e-06 eta: 0:06:50 time: 0.3444 data_time: 0.0454 memory: 21002 loss: 0.0852 loss_ce: 0.0852 2023/03/03 14:31:43 - mmengine - INFO - Epoch(train) [121][ 6/15] lr: 1.0000e-06 eta: 0:06:50 time: 0.3439 data_time: 0.0454 memory: 17881 loss: 0.0926 loss_ce: 0.0926 2023/03/03 14:31:43 - mmengine - INFO - Epoch(train) [121][ 7/15] lr: 1.0000e-06 eta: 0:06:49 time: 0.3397 data_time: 0.0454 memory: 15631 loss: 0.0908 loss_ce: 0.0908 2023/03/03 14:31:44 - mmengine - INFO - Epoch(train) [121][ 8/15] lr: 1.0000e-06 eta: 0:06:49 time: 0.3453 data_time: 0.0454 memory: 17120 loss: 0.0901 loss_ce: 0.0901 2023/03/03 14:31:44 - mmengine - INFO - Epoch(train) [121][ 9/15] lr: 1.0000e-06 eta: 0:06:48 time: 0.3374 data_time: 0.0454 memory: 17572 loss: 0.0859 loss_ce: 0.0859 2023/03/03 14:31:44 - mmengine - INFO - Epoch(train) [121][10/15] lr: 1.0000e-06 eta: 0:06:48 time: 0.3516 data_time: 0.0454 memory: 18070 loss: 0.0819 loss_ce: 0.0819 2023/03/03 14:31:45 - mmengine - INFO - Epoch(train) [121][11/15] lr: 1.0000e-06 eta: 0:06:48 time: 0.3126 data_time: 0.0017 memory: 19733 loss: 0.0761 loss_ce: 0.0761 2023/03/03 14:31:45 - mmengine - INFO - Epoch(train) [121][12/15] lr: 1.0000e-06 eta: 0:06:47 time: 0.2908 data_time: 0.0016 memory: 17120 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:31:45 - mmengine - INFO - Epoch(train) [121][13/15] lr: 1.0000e-06 eta: 0:06:47 time: 0.2870 data_time: 0.0016 memory: 15633 loss: 0.0760 loss_ce: 0.0760 2023/03/03 14:31:45 - mmengine - INFO - Epoch(train) [121][14/15] lr: 1.0000e-06 eta: 0:06:47 time: 0.2932 data_time: 0.0015 memory: 18311 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:31:46 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:31:46 - mmengine - INFO - Epoch(train) [121][15/15] lr: 1.0000e-06 eta: 0:06:46 time: 0.2658 data_time: 0.0015 memory: 5766 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:31:47 - mmengine - INFO - Epoch(train) [122][ 1/15] lr: 1.0000e-06 eta: 0:06:46 time: 0.3934 data_time: 0.0617 memory: 16191 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:31:47 - mmengine - INFO - Epoch(train) [122][ 2/15] lr: 1.0000e-06 eta: 0:06:46 time: 0.3953 data_time: 0.0617 memory: 14209 loss: 0.0824 loss_ce: 0.0824 2023/03/03 14:31:48 - mmengine - INFO - Epoch(train) [122][ 3/15] lr: 1.0000e-06 eta: 0:06:46 time: 0.4216 data_time: 0.0618 memory: 19401 loss: 0.0824 loss_ce: 0.0824 2023/03/03 14:31:48 - mmengine - INFO - Epoch(train) [122][ 4/15] lr: 1.0000e-06 eta: 0:06:45 time: 0.4268 data_time: 0.0618 memory: 18479 loss: 0.0816 loss_ce: 0.0816 2023/03/03 14:31:49 - mmengine - INFO - Epoch(train) [122][ 5/15] lr: 1.0000e-06 eta: 0:06:45 time: 0.4224 data_time: 0.0619 memory: 15654 loss: 0.0854 loss_ce: 0.0854 2023/03/03 14:31:49 - mmengine - INFO - Epoch(train) [122][ 6/15] lr: 1.0000e-06 eta: 0:06:45 time: 0.4184 data_time: 0.0620 memory: 16370 loss: 0.0858 loss_ce: 0.0858 2023/03/03 14:31:49 - mmengine - INFO - Epoch(train) [122][ 7/15] lr: 1.0000e-06 eta: 0:06:44 time: 0.4243 data_time: 0.0620 memory: 20120 loss: 0.0855 loss_ce: 0.0855 2023/03/03 14:31:49 - mmengine - INFO - Epoch(train) [122][ 8/15] lr: 1.0000e-06 eta: 0:06:44 time: 0.4315 data_time: 0.0620 memory: 19171 loss: 0.0896 loss_ce: 0.0896 2023/03/03 14:31:50 - mmengine - INFO - Epoch(train) [122][ 9/15] lr: 1.0000e-06 eta: 0:06:44 time: 0.4344 data_time: 0.0621 memory: 15279 loss: 0.0909 loss_ce: 0.0909 2023/03/03 14:31:50 - mmengine - INFO - Epoch(train) [122][10/15] lr: 1.0000e-06 eta: 0:06:43 time: 0.4552 data_time: 0.0621 memory: 18271 loss: 0.0842 loss_ce: 0.0842 2023/03/03 14:31:50 - mmengine - INFO - Epoch(train) [122][11/15] lr: 1.0000e-06 eta: 0:06:43 time: 0.3273 data_time: 0.0020 memory: 16199 loss: 0.0856 loss_ce: 0.0856 2023/03/03 14:31:51 - mmengine - INFO - Epoch(train) [122][12/15] lr: 1.0000e-06 eta: 0:06:43 time: 0.3242 data_time: 0.0020 memory: 18032 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:31:51 - mmengine - INFO - Epoch(train) [122][13/15] lr: 1.0000e-06 eta: 0:06:42 time: 0.2997 data_time: 0.0021 memory: 16976 loss: 0.0766 loss_ce: 0.0766 2023/03/03 14:31:51 - mmengine - INFO - Epoch(train) [122][14/15] lr: 1.0000e-06 eta: 0:06:42 time: 0.2945 data_time: 0.0020 memory: 17239 loss: 0.0778 loss_ce: 0.0778 2023/03/03 14:31:51 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:31:51 - mmengine - INFO - Epoch(train) [122][15/15] lr: 1.0000e-06 eta: 0:06:41 time: 0.2834 data_time: 0.0019 memory: 4212 loss: 0.0819 loss_ce: 0.0819 2023/03/03 14:31:52 - mmengine - INFO - Epoch(train) [123][ 1/15] lr: 1.0000e-06 eta: 0:06:41 time: 0.3439 data_time: 0.0585 memory: 17406 loss: 0.0838 loss_ce: 0.0838 2023/03/03 14:31:53 - mmengine - INFO - Epoch(train) [123][ 2/15] lr: 1.0000e-06 eta: 0:06:41 time: 0.3520 data_time: 0.0586 memory: 18241 loss: 0.0809 loss_ce: 0.0809 2023/03/03 14:31:53 - mmengine - INFO - Epoch(train) [123][ 3/15] lr: 1.0000e-06 eta: 0:06:41 time: 0.3414 data_time: 0.0586 memory: 13573 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:31:53 - mmengine - INFO - Epoch(train) [123][ 4/15] lr: 1.0000e-06 eta: 0:06:40 time: 0.3460 data_time: 0.0585 memory: 16223 loss: 0.0816 loss_ce: 0.0816 2023/03/03 14:31:53 - mmengine - INFO - Epoch(train) [123][ 5/15] lr: 1.0000e-06 eta: 0:06:40 time: 0.3333 data_time: 0.0586 memory: 14719 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:31:54 - mmengine - INFO - Epoch(train) [123][ 6/15] lr: 1.0000e-06 eta: 0:06:40 time: 0.3556 data_time: 0.0585 memory: 22652 loss: 0.0798 loss_ce: 0.0798 2023/03/03 14:31:54 - mmengine - INFO - Epoch(train) [123][ 7/15] lr: 1.0000e-06 eta: 0:06:39 time: 0.3578 data_time: 0.0584 memory: 16199 loss: 0.0862 loss_ce: 0.0862 2023/03/03 14:31:54 - mmengine - INFO - Epoch(train) [123][ 8/15] lr: 1.0000e-06 eta: 0:06:39 time: 0.3460 data_time: 0.0583 memory: 14474 loss: 0.0853 loss_ce: 0.0853 2023/03/03 14:31:55 - mmengine - INFO - Epoch(train) [123][ 9/15] lr: 1.0000e-06 eta: 0:06:38 time: 0.3480 data_time: 0.0583 memory: 12204 loss: 0.0847 loss_ce: 0.0847 2023/03/03 14:31:55 - mmengine - INFO - Epoch(train) [123][10/15] lr: 1.0000e-06 eta: 0:06:38 time: 0.3827 data_time: 0.0583 memory: 22899 loss: 0.0833 loss_ce: 0.0833 2023/03/03 14:31:55 - mmengine - INFO - Epoch(train) [123][11/15] lr: 1.0000e-06 eta: 0:06:38 time: 0.3222 data_time: 0.0017 memory: 16508 loss: 0.0838 loss_ce: 0.0838 2023/03/03 14:31:56 - mmengine - INFO - Epoch(train) [123][12/15] lr: 1.0000e-06 eta: 0:06:37 time: 0.3263 data_time: 0.0016 memory: 15037 loss: 0.0847 loss_ce: 0.0847 2023/03/03 14:31:56 - mmengine - INFO - Epoch(train) [123][13/15] lr: 1.0000e-06 eta: 0:06:37 time: 0.3320 data_time: 0.0016 memory: 15174 loss: 0.0879 loss_ce: 0.0879 2023/03/03 14:31:56 - mmengine - INFO - Epoch(train) [123][14/15] lr: 1.0000e-06 eta: 0:06:37 time: 0.3301 data_time: 0.0016 memory: 17421 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:31:57 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:31:57 - mmengine - INFO - Epoch(train) [123][15/15] lr: 1.0000e-06 eta: 0:06:36 time: 0.3152 data_time: 0.0015 memory: 6297 loss: 0.0839 loss_ce: 0.0839 2023/03/03 14:31:58 - mmengine - INFO - Epoch(train) [124][ 1/15] lr: 1.0000e-06 eta: 0:06:36 time: 0.3610 data_time: 0.0356 memory: 15494 loss: 0.0843 loss_ce: 0.0843 2023/03/03 14:31:58 - mmengine - INFO - Epoch(train) [124][ 2/15] lr: 1.0000e-06 eta: 0:06:36 time: 0.3605 data_time: 0.0357 memory: 15767 loss: 0.0808 loss_ce: 0.0808 2023/03/03 14:31:58 - mmengine - INFO - Epoch(train) [124][ 3/15] lr: 1.0000e-06 eta: 0:06:35 time: 0.3725 data_time: 0.0357 memory: 16976 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:31:58 - mmengine - INFO - Epoch(train) [124][ 4/15] lr: 1.0000e-06 eta: 0:06:35 time: 0.3706 data_time: 0.0358 memory: 17421 loss: 0.0816 loss_ce: 0.0816 2023/03/03 14:31:59 - mmengine - INFO - Epoch(train) [124][ 5/15] lr: 1.0000e-06 eta: 0:06:35 time: 0.3491 data_time: 0.0358 memory: 17284 loss: 0.0766 loss_ce: 0.0766 2023/03/03 14:31:59 - mmengine - INFO - Epoch(train) [124][ 6/15] lr: 1.0000e-06 eta: 0:06:34 time: 0.3508 data_time: 0.0359 memory: 17788 loss: 0.0736 loss_ce: 0.0736 2023/03/03 14:31:59 - mmengine - INFO - Epoch(train) [124][ 7/15] lr: 1.0000e-06 eta: 0:06:34 time: 0.3536 data_time: 0.0359 memory: 26319 loss: 0.0761 loss_ce: 0.0761 2023/03/03 14:32:00 - mmengine - INFO - Epoch(train) [124][ 8/15] lr: 1.0000e-06 eta: 0:06:34 time: 0.3482 data_time: 0.0359 memory: 16849 loss: 0.0697 loss_ce: 0.0697 2023/03/03 14:32:00 - mmengine - INFO - Epoch(train) [124][ 9/15] lr: 1.0000e-06 eta: 0:06:33 time: 0.3505 data_time: 0.0359 memory: 17892 loss: 0.0715 loss_ce: 0.0715 2023/03/03 14:32:00 - mmengine - INFO - Epoch(train) [124][10/15] lr: 1.0000e-06 eta: 0:06:33 time: 0.3588 data_time: 0.0359 memory: 16976 loss: 0.0741 loss_ce: 0.0741 2023/03/03 14:32:00 - mmengine - INFO - Epoch(train) [124][11/15] lr: 1.0000e-06 eta: 0:06:33 time: 0.2937 data_time: 0.0018 memory: 16703 loss: 0.0732 loss_ce: 0.0732 2023/03/03 14:32:01 - mmengine - INFO - Epoch(train) [124][12/15] lr: 1.0000e-06 eta: 0:06:32 time: 0.2915 data_time: 0.0018 memory: 17272 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:32:01 - mmengine - INFO - Epoch(train) [124][13/15] lr: 1.0000e-06 eta: 0:06:32 time: 0.2918 data_time: 0.0017 memory: 19741 loss: 0.0720 loss_ce: 0.0720 2023/03/03 14:32:01 - mmengine - INFO - Epoch(train) [124][14/15] lr: 1.0000e-06 eta: 0:06:31 time: 0.2944 data_time: 0.0016 memory: 16804 loss: 0.0710 loss_ce: 0.0710 2023/03/03 14:32:02 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:32:02 - mmengine - INFO - Epoch(train) [124][15/15] lr: 1.0000e-06 eta: 0:06:31 time: 0.2881 data_time: 0.0016 memory: 6883 loss: 0.0761 loss_ce: 0.0761 2023/03/03 14:32:03 - mmengine - INFO - Epoch(train) [125][ 1/15] lr: 1.0000e-06 eta: 0:06:31 time: 0.3590 data_time: 0.0730 memory: 17619 loss: 0.0753 loss_ce: 0.0753 2023/03/03 14:32:03 - mmengine - INFO - Epoch(train) [125][ 2/15] lr: 1.0000e-06 eta: 0:06:31 time: 0.3590 data_time: 0.0730 memory: 18121 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:32:03 - mmengine - INFO - Epoch(train) [125][ 3/15] lr: 1.0000e-06 eta: 0:06:30 time: 0.3561 data_time: 0.0730 memory: 17120 loss: 0.0817 loss_ce: 0.0817 2023/03/03 14:32:04 - mmengine - INFO - Epoch(train) [125][ 4/15] lr: 1.0000e-06 eta: 0:06:30 time: 0.3617 data_time: 0.0730 memory: 16530 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:32:04 - mmengine - INFO - Epoch(train) [125][ 5/15] lr: 1.0000e-06 eta: 0:06:30 time: 0.3677 data_time: 0.0730 memory: 17162 loss: 0.0744 loss_ce: 0.0744 2023/03/03 14:32:04 - mmengine - INFO - Epoch(train) [125][ 6/15] lr: 1.0000e-06 eta: 0:06:29 time: 0.3633 data_time: 0.0730 memory: 16654 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:32:04 - mmengine - INFO - Epoch(train) [125][ 7/15] lr: 1.0000e-06 eta: 0:06:29 time: 0.3635 data_time: 0.0730 memory: 17572 loss: 0.0742 loss_ce: 0.0742 2023/03/03 14:32:05 - mmengine - INFO - Epoch(train) [125][ 8/15] lr: 1.0000e-06 eta: 0:06:28 time: 0.3671 data_time: 0.0731 memory: 18681 loss: 0.0738 loss_ce: 0.0738 2023/03/03 14:32:05 - mmengine - INFO - Epoch(train) [125][ 9/15] lr: 1.0000e-06 eta: 0:06:28 time: 0.3667 data_time: 0.0731 memory: 16223 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:32:05 - mmengine - INFO - Epoch(train) [125][10/15] lr: 1.0000e-06 eta: 0:06:28 time: 0.3610 data_time: 0.0731 memory: 16199 loss: 0.0758 loss_ce: 0.0758 2023/03/03 14:32:05 - mmengine - INFO - Epoch(train) [125][11/15] lr: 1.0000e-06 eta: 0:06:27 time: 0.2859 data_time: 0.0017 memory: 18070 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:32:06 - mmengine - INFO - Epoch(train) [125][12/15] lr: 1.0000e-06 eta: 0:06:27 time: 0.2726 data_time: 0.0016 memory: 20913 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:32:06 - mmengine - INFO - Epoch(train) [125][13/15] lr: 1.0000e-06 eta: 0:06:26 time: 0.2818 data_time: 0.0016 memory: 17380 loss: 0.0701 loss_ce: 0.0701 2023/03/03 14:32:06 - mmengine - INFO - Epoch(train) [125][14/15] lr: 1.0000e-06 eta: 0:06:26 time: 0.2704 data_time: 0.0016 memory: 16654 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:32:06 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:32:06 - mmengine - INFO - Epoch(train) [125][15/15] lr: 1.0000e-06 eta: 0:06:26 time: 0.2586 data_time: 0.0015 memory: 6169 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:32:08 - mmengine - INFO - Epoch(train) [126][ 1/15] lr: 1.0000e-06 eta: 0:06:26 time: 0.3532 data_time: 0.0966 memory: 15631 loss: 0.0793 loss_ce: 0.0793 2023/03/03 14:32:08 - mmengine - INFO - Epoch(train) [126][ 2/15] lr: 1.0000e-06 eta: 0:06:25 time: 0.3533 data_time: 0.0967 memory: 17272 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:32:08 - mmengine - INFO - Epoch(train) [126][ 3/15] lr: 1.0000e-06 eta: 0:06:25 time: 0.3390 data_time: 0.0967 memory: 15631 loss: 0.0802 loss_ce: 0.0802 2023/03/03 14:32:08 - mmengine - INFO - Epoch(train) [126][ 4/15] lr: 1.0000e-06 eta: 0:06:25 time: 0.3459 data_time: 0.0968 memory: 21484 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:32:09 - mmengine - INFO - Epoch(train) [126][ 5/15] lr: 1.0000e-06 eta: 0:06:24 time: 0.3459 data_time: 0.0968 memory: 16508 loss: 0.0751 loss_ce: 0.0751 2023/03/03 14:32:09 - mmengine - INFO - Epoch(train) [126][ 6/15] lr: 1.0000e-06 eta: 0:06:24 time: 0.3704 data_time: 0.0968 memory: 20275 loss: 0.0764 loss_ce: 0.0764 2023/03/03 14:32:09 - mmengine - INFO - Epoch(train) [126][ 7/15] lr: 1.0000e-06 eta: 0:06:23 time: 0.3549 data_time: 0.0968 memory: 18409 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:32:10 - mmengine - INFO - Epoch(train) [126][ 8/15] lr: 1.0000e-06 eta: 0:06:23 time: 0.3696 data_time: 0.0968 memory: 26839 loss: 0.0760 loss_ce: 0.0760 2023/03/03 14:32:10 - mmengine - INFO - Epoch(train) [126][ 9/15] lr: 1.0000e-06 eta: 0:06:23 time: 0.3663 data_time: 0.0969 memory: 15020 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:32:10 - mmengine - INFO - Epoch(train) [126][10/15] lr: 1.0000e-06 eta: 0:06:22 time: 0.3756 data_time: 0.0969 memory: 17024 loss: 0.0652 loss_ce: 0.0652 2023/03/03 14:32:11 - mmengine - INFO - Epoch(train) [126][11/15] lr: 1.0000e-06 eta: 0:06:22 time: 0.2893 data_time: 0.0018 memory: 17120 loss: 0.0662 loss_ce: 0.0662 2023/03/03 14:32:11 - mmengine - INFO - Epoch(train) [126][12/15] lr: 1.0000e-06 eta: 0:06:22 time: 0.2956 data_time: 0.0017 memory: 24051 loss: 0.0676 loss_ce: 0.0676 2023/03/03 14:32:11 - mmengine - INFO - Epoch(train) [126][13/15] lr: 1.0000e-06 eta: 0:06:21 time: 0.3082 data_time: 0.0016 memory: 18070 loss: 0.0654 loss_ce: 0.0654 2023/03/03 14:32:11 - mmengine - INFO - Epoch(train) [126][14/15] lr: 1.0000e-06 eta: 0:06:21 time: 0.2956 data_time: 0.0016 memory: 11564 loss: 0.0673 loss_ce: 0.0673 2023/03/03 14:32:12 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:32:12 - mmengine - INFO - Epoch(train) [126][15/15] lr: 1.0000e-06 eta: 0:06:20 time: 0.2921 data_time: 0.0016 memory: 4369 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:32:12 - mmengine - INFO - Epoch(train) [127][ 1/15] lr: 1.0000e-06 eta: 0:06:20 time: 0.3140 data_time: 0.0457 memory: 15911 loss: 0.0749 loss_ce: 0.0749 2023/03/03 14:32:13 - mmengine - INFO - Epoch(train) [127][ 2/15] lr: 1.0000e-06 eta: 0:06:20 time: 0.3709 data_time: 0.0457 memory: 14589 loss: 0.0806 loss_ce: 0.0806 2023/03/03 14:32:13 - mmengine - INFO - Epoch(train) [127][ 3/15] lr: 1.0000e-06 eta: 0:06:20 time: 0.3531 data_time: 0.0458 memory: 17006 loss: 0.0844 loss_ce: 0.0844 2023/03/03 14:32:14 - mmengine - INFO - Epoch(train) [127][ 4/15] lr: 1.0000e-06 eta: 0:06:19 time: 0.3614 data_time: 0.0458 memory: 31356 loss: 0.0854 loss_ce: 0.0854 2023/03/03 14:32:14 - mmengine - INFO - Epoch(train) [127][ 5/15] lr: 1.0000e-06 eta: 0:06:19 time: 0.3581 data_time: 0.0457 memory: 17272 loss: 0.0860 loss_ce: 0.0860 2023/03/03 14:32:14 - mmengine - INFO - Epoch(train) [127][ 6/15] lr: 1.0000e-06 eta: 0:06:19 time: 0.3571 data_time: 0.0457 memory: 17272 loss: 0.0828 loss_ce: 0.0828 2023/03/03 14:32:15 - mmengine - INFO - Epoch(train) [127][ 7/15] lr: 1.0000e-06 eta: 0:06:18 time: 0.3822 data_time: 0.0457 memory: 37937 loss: 0.0836 loss_ce: 0.0836 2023/03/03 14:32:15 - mmengine - INFO - Epoch(train) [127][ 8/15] lr: 1.0000e-06 eta: 0:06:18 time: 0.3679 data_time: 0.0457 memory: 17284 loss: 0.0834 loss_ce: 0.0834 2023/03/03 14:32:15 - mmengine - INFO - Epoch(train) [127][ 9/15] lr: 1.0000e-06 eta: 0:06:18 time: 0.3772 data_time: 0.0457 memory: 13646 loss: 0.0854 loss_ce: 0.0854 2023/03/03 14:32:15 - mmengine - INFO - Epoch(train) [127][10/15] lr: 1.0000e-06 eta: 0:06:17 time: 0.3823 data_time: 0.0457 memory: 17881 loss: 0.0793 loss_ce: 0.0793 2023/03/03 14:32:16 - mmengine - INFO - Epoch(train) [127][11/15] lr: 1.0000e-06 eta: 0:06:17 time: 0.3380 data_time: 0.0016 memory: 16111 loss: 0.0798 loss_ce: 0.0798 2023/03/03 14:32:16 - mmengine - INFO - Epoch(train) [127][12/15] lr: 1.0000e-06 eta: 0:06:16 time: 0.2917 data_time: 0.0016 memory: 16426 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:32:16 - mmengine - INFO - Epoch(train) [127][13/15] lr: 1.0000e-06 eta: 0:06:16 time: 0.2857 data_time: 0.0015 memory: 17568 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:32:17 - mmengine - INFO - Epoch(train) [127][14/15] lr: 1.0000e-06 eta: 0:06:16 time: 0.2891 data_time: 0.0015 memory: 17120 loss: 0.0741 loss_ce: 0.0741 2023/03/03 14:32:17 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:32:17 - mmengine - INFO - Epoch(train) [127][15/15] lr: 1.0000e-06 eta: 0:06:15 time: 0.2788 data_time: 0.0015 memory: 5705 loss: 0.0913 loss_ce: 0.0913 2023/03/03 14:32:17 - mmengine - INFO - Epoch(train) [128][ 1/15] lr: 1.0000e-06 eta: 0:06:15 time: 0.3371 data_time: 0.0589 memory: 16508 loss: 0.0940 loss_ce: 0.0940 2023/03/03 14:32:18 - mmengine - INFO - Epoch(train) [128][ 2/15] lr: 1.0000e-06 eta: 0:06:15 time: 0.3025 data_time: 0.0589 memory: 13308 loss: 0.0929 loss_ce: 0.0929 2023/03/03 14:32:18 - mmengine - INFO - Epoch(train) [128][ 3/15] lr: 1.0000e-06 eta: 0:06:14 time: 0.3188 data_time: 0.0590 memory: 15510 loss: 0.0936 loss_ce: 0.0936 2023/03/03 14:32:18 - mmengine - INFO - Epoch(train) [128][ 4/15] lr: 1.0000e-06 eta: 0:06:14 time: 0.3127 data_time: 0.0591 memory: 16976 loss: 0.0921 loss_ce: 0.0921 2023/03/03 14:32:19 - mmengine - INFO - Epoch(train) [128][ 5/15] lr: 1.0000e-06 eta: 0:06:14 time: 0.3484 data_time: 0.0592 memory: 25004 loss: 0.0874 loss_ce: 0.0874 2023/03/03 14:32:19 - mmengine - INFO - Epoch(train) [128][ 6/15] lr: 1.0000e-06 eta: 0:06:13 time: 0.3466 data_time: 0.0592 memory: 18241 loss: 0.0879 loss_ce: 0.0879 2023/03/03 14:32:19 - mmengine - INFO - Epoch(train) [128][ 7/15] lr: 1.0000e-06 eta: 0:06:13 time: 0.3490 data_time: 0.0591 memory: 27489 loss: 0.0896 loss_ce: 0.0896 2023/03/03 14:32:20 - mmengine - INFO - Epoch(train) [128][ 8/15] lr: 1.0000e-06 eta: 0:06:13 time: 0.3535 data_time: 0.0592 memory: 17968 loss: 0.0865 loss_ce: 0.0865 2023/03/03 14:32:20 - mmengine - INFO - Epoch(train) [128][ 9/15] lr: 1.0000e-06 eta: 0:06:12 time: 0.3401 data_time: 0.0593 memory: 16661 loss: 0.0881 loss_ce: 0.0881 2023/03/03 14:32:20 - mmengine - INFO - Epoch(train) [128][10/15] lr: 1.0000e-06 eta: 0:06:12 time: 0.3503 data_time: 0.0593 memory: 17272 loss: 0.0708 loss_ce: 0.0708 2023/03/03 14:32:20 - mmengine - INFO - Epoch(train) [128][11/15] lr: 1.0000e-06 eta: 0:06:12 time: 0.2993 data_time: 0.0019 memory: 27058 loss: 0.0677 loss_ce: 0.0677 2023/03/03 14:32:21 - mmengine - INFO - Epoch(train) [128][12/15] lr: 1.0000e-06 eta: 0:06:11 time: 0.3023 data_time: 0.0018 memory: 18070 loss: 0.0641 loss_ce: 0.0641 2023/03/03 14:32:21 - mmengine - INFO - Epoch(train) [128][13/15] lr: 1.0000e-06 eta: 0:06:11 time: 0.2874 data_time: 0.0017 memory: 17572 loss: 0.0685 loss_ce: 0.0685 2023/03/03 14:32:21 - mmengine - INFO - Epoch(train) [128][14/15] lr: 1.0000e-06 eta: 0:06:10 time: 0.2901 data_time: 0.0017 memory: 16508 loss: 0.0680 loss_ce: 0.0680 2023/03/03 14:32:22 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:32:22 - mmengine - INFO - Epoch(train) [128][15/15] lr: 1.0000e-06 eta: 0:06:10 time: 0.2752 data_time: 0.0017 memory: 6850 loss: 0.0718 loss_ce: 0.0718 2023/03/03 14:32:23 - mmengine - INFO - Epoch(train) [129][ 1/15] lr: 1.0000e-06 eta: 0:06:10 time: 0.3518 data_time: 0.0789 memory: 13483 loss: 0.0685 loss_ce: 0.0685 2023/03/03 14:32:23 - mmengine - INFO - Epoch(train) [129][ 2/15] lr: 1.0000e-06 eta: 0:06:10 time: 0.3532 data_time: 0.0790 memory: 17272 loss: 0.0678 loss_ce: 0.0678 2023/03/03 14:32:23 - mmengine - INFO - Epoch(train) [129][ 3/15] lr: 1.0000e-06 eta: 0:06:09 time: 0.3487 data_time: 0.0790 memory: 16976 loss: 0.0712 loss_ce: 0.0712 2023/03/03 14:32:23 - mmengine - INFO - Epoch(train) [129][ 4/15] lr: 1.0000e-06 eta: 0:06:09 time: 0.3527 data_time: 0.0790 memory: 17619 loss: 0.0659 loss_ce: 0.0659 2023/03/03 14:32:24 - mmengine - INFO - Epoch(train) [129][ 5/15] lr: 1.0000e-06 eta: 0:06:08 time: 0.3526 data_time: 0.0790 memory: 17120 loss: 0.0737 loss_ce: 0.0737 2023/03/03 14:32:24 - mmengine - INFO - Epoch(train) [129][ 6/15] lr: 1.0000e-06 eta: 0:06:08 time: 0.3382 data_time: 0.0790 memory: 16223 loss: 0.0780 loss_ce: 0.0780 2023/03/03 14:32:24 - mmengine - INFO - Epoch(train) [129][ 7/15] lr: 1.0000e-06 eta: 0:06:08 time: 0.3497 data_time: 0.0790 memory: 18649 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:32:25 - mmengine - INFO - Epoch(train) [129][ 8/15] lr: 1.0000e-06 eta: 0:06:07 time: 0.3601 data_time: 0.0791 memory: 17272 loss: 0.0782 loss_ce: 0.0782 2023/03/03 14:32:25 - mmengine - INFO - Epoch(train) [129][ 9/15] lr: 1.0000e-06 eta: 0:06:07 time: 0.3654 data_time: 0.0791 memory: 18653 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:32:25 - mmengine - INFO - Epoch(train) [129][10/15] lr: 1.0000e-06 eta: 0:06:07 time: 0.3480 data_time: 0.0790 memory: 19747 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:32:25 - mmengine - INFO - Epoch(train) [129][11/15] lr: 1.0000e-06 eta: 0:06:06 time: 0.2744 data_time: 0.0018 memory: 17284 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:32:26 - mmengine - INFO - Epoch(train) [129][12/15] lr: 1.0000e-06 eta: 0:06:06 time: 0.2710 data_time: 0.0017 memory: 20745 loss: 0.0748 loss_ce: 0.0748 2023/03/03 14:32:26 - mmengine - INFO - Epoch(train) [129][13/15] lr: 1.0000e-06 eta: 0:06:05 time: 0.2709 data_time: 0.0017 memory: 13964 loss: 0.0738 loss_ce: 0.0738 2023/03/03 14:32:26 - mmengine - INFO - Epoch(train) [129][14/15] lr: 1.0000e-06 eta: 0:06:05 time: 0.2873 data_time: 0.0016 memory: 16212 loss: 0.0778 loss_ce: 0.0778 2023/03/03 14:32:26 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:32:26 - mmengine - INFO - Epoch(train) [129][15/15] lr: 1.0000e-06 eta: 0:06:05 time: 0.2781 data_time: 0.0016 memory: 6910 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:32:27 - mmengine - INFO - Epoch(train) [130][ 1/15] lr: 1.0000e-06 eta: 0:06:05 time: 0.3569 data_time: 0.0644 memory: 16029 loss: 0.0728 loss_ce: 0.0728 2023/03/03 14:32:28 - mmengine - INFO - Epoch(train) [130][ 2/15] lr: 1.0000e-06 eta: 0:06:04 time: 0.3460 data_time: 0.0645 memory: 17892 loss: 0.0729 loss_ce: 0.0729 2023/03/03 14:32:28 - mmengine - INFO - Epoch(train) [130][ 3/15] lr: 1.0000e-06 eta: 0:06:04 time: 0.3588 data_time: 0.0646 memory: 15766 loss: 0.0718 loss_ce: 0.0718 2023/03/03 14:32:29 - mmengine - INFO - Epoch(train) [130][ 4/15] lr: 1.0000e-06 eta: 0:06:04 time: 0.3724 data_time: 0.0647 memory: 30584 loss: 0.0690 loss_ce: 0.0690 2023/03/03 14:32:29 - mmengine - INFO - Epoch(train) [130][ 5/15] lr: 1.0000e-06 eta: 0:06:03 time: 0.3648 data_time: 0.0648 memory: 17120 loss: 0.0746 loss_ce: 0.0746 2023/03/03 14:32:29 - mmengine - INFO - Epoch(train) [130][ 6/15] lr: 1.0000e-06 eta: 0:06:03 time: 0.3613 data_time: 0.0648 memory: 17272 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:32:29 - mmengine - INFO - Epoch(train) [130][ 7/15] lr: 1.0000e-06 eta: 0:06:03 time: 0.3673 data_time: 0.0648 memory: 15682 loss: 0.0783 loss_ce: 0.0783 2023/03/03 14:32:30 - mmengine - INFO - Epoch(train) [130][ 8/15] lr: 1.0000e-06 eta: 0:06:02 time: 0.3676 data_time: 0.0648 memory: 17572 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:32:30 - mmengine - INFO - Epoch(train) [130][ 9/15] lr: 1.0000e-06 eta: 0:06:02 time: 0.3472 data_time: 0.0648 memory: 18004 loss: 0.0758 loss_ce: 0.0758 2023/03/03 14:32:30 - mmengine - INFO - Epoch(train) [130][10/15] lr: 1.0000e-06 eta: 0:06:01 time: 0.3664 data_time: 0.0648 memory: 16531 loss: 0.0699 loss_ce: 0.0699 2023/03/03 14:32:30 - mmengine - INFO - Epoch(train) [130][11/15] lr: 1.0000e-06 eta: 0:06:01 time: 0.2936 data_time: 0.0020 memory: 16223 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:32:31 - mmengine - INFO - Epoch(train) [130][12/15] lr: 1.0000e-06 eta: 0:06:01 time: 0.3046 data_time: 0.0019 memory: 15478 loss: 0.0733 loss_ce: 0.0733 2023/03/03 14:32:31 - mmengine - INFO - Epoch(train) [130][13/15] lr: 1.0000e-06 eta: 0:06:00 time: 0.2850 data_time: 0.0019 memory: 16137 loss: 0.0705 loss_ce: 0.0705 2023/03/03 14:32:31 - mmengine - INFO - Epoch(train) [130][14/15] lr: 1.0000e-06 eta: 0:06:00 time: 0.2654 data_time: 0.0018 memory: 15985 loss: 0.0700 loss_ce: 0.0700 2023/03/03 14:32:31 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:32:31 - mmengine - INFO - Epoch(train) [130][15/15] lr: 1.0000e-06 eta: 0:05:59 time: 0.2608 data_time: 0.0017 memory: 5255 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:32:33 - mmengine - INFO - Epoch(val) [130][ 1/59] eta: 0:01:31 time: 1.0824 data_time: 0.0033 memory: 981 2023/03/03 14:32:34 - mmengine - INFO - Epoch(val) [130][ 2/59] eta: 0:01:09 time: 0.9960 data_time: 0.0034 memory: 981 2023/03/03 14:32:35 - mmengine - INFO - Epoch(val) [130][ 3/59] eta: 0:01:14 time: 1.0314 data_time: 0.0034 memory: 1003 2023/03/03 14:32:36 - mmengine - INFO - Epoch(val) [130][ 4/59] eta: 0:00:59 time: 1.0151 data_time: 0.0034 memory: 981 2023/03/03 14:32:39 - mmengine - INFO - Epoch(val) [130][ 5/59] eta: 0:01:19 time: 1.2564 data_time: 0.0034 memory: 1016 2023/03/03 14:32:41 - mmengine - INFO - Epoch(val) [130][ 6/59] eta: 0:01:28 time: 1.4560 data_time: 0.0035 memory: 981 2023/03/03 14:32:42 - mmengine - INFO - Epoch(val) [130][ 7/59] eta: 0:01:16 time: 1.4064 data_time: 0.0035 memory: 1043 2023/03/03 14:32:42 - mmengine - INFO - Epoch(val) [130][ 8/59] eta: 0:01:09 time: 1.2458 data_time: 0.0035 memory: 1016 2023/03/03 14:32:43 - mmengine - INFO - Epoch(val) [130][ 9/59] eta: 0:01:06 time: 1.2289 data_time: 0.0035 memory: 981 2023/03/03 14:32:44 - mmengine - INFO - Epoch(val) [130][10/59] eta: 0:01:01 time: 1.2621 data_time: 0.0035 memory: 981 2023/03/03 14:32:44 - mmengine - INFO - Epoch(val) [130][11/59] eta: 0:00:56 time: 1.1381 data_time: 0.0009 memory: 981 2023/03/03 14:32:48 - mmengine - INFO - Epoch(val) [130][12/59] eta: 0:01:03 time: 1.3824 data_time: 0.0009 memory: 1016 2023/03/03 14:32:50 - mmengine - INFO - Epoch(val) [130][13/59] eta: 0:01:05 time: 1.4560 data_time: 0.0009 memory: 981 2023/03/03 14:32:51 - mmengine - INFO - Epoch(val) [130][14/59] eta: 0:01:02 time: 1.5241 data_time: 0.0009 memory: 890 2023/03/03 14:32:51 - mmengine - INFO - Epoch(val) [130][15/59] eta: 0:00:57 time: 1.2178 data_time: 0.0009 memory: 981 2023/03/03 14:32:51 - mmengine - INFO - Epoch(val) [130][16/59] eta: 0:00:53 time: 1.0017 data_time: 0.0009 memory: 981 2023/03/03 14:32:52 - mmengine - INFO - Epoch(val) [130][17/59] eta: 0:00:50 time: 1.0177 data_time: 0.0009 memory: 981 2023/03/03 14:32:52 - mmengine - INFO - Epoch(val) [130][18/59] eta: 0:00:47 time: 0.9847 data_time: 0.0008 memory: 981 2023/03/03 14:32:53 - mmengine - INFO - Epoch(val) [130][19/59] eta: 0:00:45 time: 0.9845 data_time: 0.0008 memory: 981 2023/03/03 14:32:54 - mmengine - INFO - Epoch(val) [130][20/59] eta: 0:00:43 time: 0.9512 data_time: 0.0008 memory: 981 2023/03/03 14:32:54 - mmengine - INFO - Epoch(val) [130][21/59] eta: 0:00:40 time: 0.9677 data_time: 0.0008 memory: 981 2023/03/03 14:32:54 - mmengine - INFO - Epoch(val) [130][22/59] eta: 0:00:38 time: 0.6561 data_time: 0.0008 memory: 981 2023/03/03 14:32:55 - mmengine - INFO - Epoch(val) [130][23/59] eta: 0:00:36 time: 0.4961 data_time: 0.0008 memory: 981 2023/03/03 14:32:55 - mmengine - INFO - Epoch(val) [130][24/59] eta: 0:00:34 time: 0.4278 data_time: 0.0008 memory: 962 2023/03/03 14:32:56 - mmengine - INFO - Epoch(val) [130][25/59] eta: 0:00:32 time: 0.4594 data_time: 0.0008 memory: 981 2023/03/03 14:32:56 - mmengine - INFO - Epoch(val) [130][26/59] eta: 0:00:31 time: 0.4425 data_time: 0.0008 memory: 981 2023/03/03 14:32:56 - mmengine - INFO - Epoch(val) [130][27/59] eta: 0:00:29 time: 0.4427 data_time: 0.0008 memory: 981 2023/03/03 14:32:57 - mmengine - INFO - Epoch(val) [130][28/59] eta: 0:00:27 time: 0.4427 data_time: 0.0008 memory: 981 2023/03/03 14:32:58 - mmengine - INFO - Epoch(val) [130][29/59] eta: 0:00:27 time: 0.4769 data_time: 0.0007 memory: 981 2023/03/03 14:32:59 - mmengine - INFO - Epoch(val) [130][30/59] eta: 0:00:26 time: 0.5268 data_time: 0.0008 memory: 999 2023/03/03 14:32:59 - mmengine - INFO - Epoch(val) [130][31/59] eta: 0:00:25 time: 0.5433 data_time: 0.0007 memory: 981 2023/03/03 14:33:01 - mmengine - INFO - Epoch(val) [130][32/59] eta: 0:00:24 time: 0.6439 data_time: 0.0007 memory: 981 2023/03/03 14:33:01 - mmengine - INFO - Epoch(val) [130][33/59] eta: 0:00:23 time: 0.5940 data_time: 0.0007 memory: 981 2023/03/03 14:33:01 - mmengine - INFO - Epoch(val) [130][34/59] eta: 0:00:21 time: 0.5777 data_time: 0.0007 memory: 981 2023/03/03 14:33:01 - mmengine - INFO - Epoch(val) [130][35/59] eta: 0:00:20 time: 0.5610 data_time: 0.0007 memory: 981 2023/03/03 14:33:02 - mmengine - INFO - Epoch(val) [130][36/59] eta: 0:00:19 time: 0.5777 data_time: 0.0007 memory: 981 2023/03/03 14:33:02 - mmengine - INFO - Epoch(val) [130][37/59] eta: 0:00:18 time: 0.5611 data_time: 0.0007 memory: 981 2023/03/03 14:33:03 - mmengine - INFO - Epoch(val) [130][38/59] eta: 0:00:17 time: 0.5944 data_time: 0.0007 memory: 981 2023/03/03 14:33:03 - mmengine - INFO - Epoch(val) [130][39/59] eta: 0:00:16 time: 0.5095 data_time: 0.0008 memory: 987 2023/03/03 14:33:04 - mmengine - INFO - Epoch(val) [130][40/59] eta: 0:00:15 time: 0.5099 data_time: 0.0007 memory: 981 2023/03/03 14:33:05 - mmengine - INFO - Epoch(val) [130][41/59] eta: 0:00:14 time: 0.5614 data_time: 0.0008 memory: 986 2023/03/03 14:33:06 - mmengine - INFO - Epoch(val) [130][42/59] eta: 0:00:13 time: 0.5104 data_time: 0.0008 memory: 981 2023/03/03 14:33:07 - mmengine - INFO - Epoch(val) [130][43/59] eta: 0:00:13 time: 0.5768 data_time: 0.0008 memory: 976 2023/03/03 14:33:07 - mmengine - INFO - Epoch(val) [130][44/59] eta: 0:00:12 time: 0.6096 data_time: 0.0008 memory: 1003 2023/03/03 14:33:09 - mmengine - INFO - Epoch(val) [130][45/59] eta: 0:00:11 time: 0.7813 data_time: 0.0008 memory: 981 2023/03/03 14:33:10 - mmengine - INFO - Epoch(val) [130][46/59] eta: 0:00:10 time: 0.8147 data_time: 0.0008 memory: 981 2023/03/03 14:33:10 - mmengine - INFO - Epoch(val) [130][47/59] eta: 0:00:09 time: 0.8478 data_time: 0.0008 memory: 936 2023/03/03 14:33:11 - mmengine - INFO - Epoch(val) [130][48/59] eta: 0:00:09 time: 0.8309 data_time: 0.0009 memory: 1000 2023/03/03 14:33:12 - mmengine - INFO - Epoch(val) [130][49/59] eta: 0:00:08 time: 0.8814 data_time: 0.0008 memory: 981 2023/03/03 14:33:13 - mmengine - INFO - Epoch(val) [130][50/59] eta: 0:00:07 time: 0.8809 data_time: 0.0008 memory: 987 2023/03/03 14:33:14 - mmengine - INFO - Epoch(val) [130][51/59] eta: 0:00:06 time: 0.9332 data_time: 0.0008 memory: 981 2023/03/03 14:33:16 - mmengine - INFO - Epoch(val) [130][52/59] eta: 0:00:05 time: 0.9845 data_time: 0.0008 memory: 981 2023/03/03 14:33:16 - mmengine - INFO - Epoch(val) [130][53/59] eta: 0:00:05 time: 0.9678 data_time: 0.0008 memory: 962 2023/03/03 14:33:17 - mmengine - INFO - Epoch(val) [130][54/59] eta: 0:00:04 time: 0.9850 data_time: 0.0008 memory: 981 2023/03/03 14:33:18 - mmengine - INFO - Epoch(val) [130][55/59] eta: 0:00:03 time: 0.8631 data_time: 0.0007 memory: 981 2023/03/03 14:33:18 - mmengine - INFO - Epoch(val) [130][56/59] eta: 0:00:02 time: 0.8466 data_time: 0.0007 memory: 981 2023/03/03 14:33:21 - mmengine - INFO - Epoch(val) [130][57/59] eta: 0:00:01 time: 1.0233 data_time: 0.0007 memory: 981 2023/03/03 14:33:22 - mmengine - INFO - Epoch(val) [130][58/59] eta: 0:00:00 time: 1.0913 data_time: 0.0007 memory: 1016 2023/03/03 14:33:22 - mmengine - INFO - Epoch(val) [130][59/59] eta: 0:00:00 time: 1.0243 data_time: 0.0007 memory: 981 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.80, recall: 0.8192, precision: 0.8375, hmean: 0.8283 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.81, recall: 0.8174, precision: 0.8380, hmean: 0.8276 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.82, recall: 0.8174, precision: 0.8404, hmean: 0.8287 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.83, recall: 0.8155, precision: 0.8417, hmean: 0.8284 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.84, recall: 0.8137, precision: 0.8430, hmean: 0.8281 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.85, recall: 0.8119, precision: 0.8475, hmean: 0.8293 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.86, recall: 0.8100, precision: 0.8504, hmean: 0.8297 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.87, recall: 0.8091, precision: 0.8536, hmean: 0.8308 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.88, recall: 0.8073, precision: 0.8533, hmean: 0.8297 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.89, recall: 0.8037, precision: 0.8535, hmean: 0.8278 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.90, recall: 0.8018, precision: 0.8574, hmean: 0.8287 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.91, recall: 0.7991, precision: 0.8604, hmean: 0.8286 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.92, recall: 0.7954, precision: 0.8624, hmean: 0.8276 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.93, recall: 0.7863, precision: 0.8653, hmean: 0.8239 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.94, recall: 0.7799, precision: 0.8697, hmean: 0.8223 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.95, recall: 0.7726, precision: 0.8731, hmean: 0.8198 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.96, recall: 0.7616, precision: 0.8751, hmean: 0.8145 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.97, recall: 0.7534, precision: 0.8795, hmean: 0.8116 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.98, recall: 0.7379, precision: 0.8831, hmean: 0.8040 2023/03/03 14:33:52 - mmengine - INFO - text score threshold: 0.99, recall: 0.7151, precision: 0.8928, hmean: 0.7941 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.80, recall: 0.8292, precision: 0.9062, hmean: 0.8660 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.81, recall: 0.8274, precision: 0.9069, hmean: 0.8653 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.82, recall: 0.8274, precision: 0.9078, hmean: 0.8657 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.83, recall: 0.8256, precision: 0.9095, hmean: 0.8655 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.84, recall: 0.8237, precision: 0.9102, hmean: 0.8648 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.85, recall: 0.8219, precision: 0.9128, hmean: 0.8650 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.86, recall: 0.8201, precision: 0.9154, hmean: 0.8651 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.87, recall: 0.8192, precision: 0.9172, hmean: 0.8654 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.88, recall: 0.8174, precision: 0.9170, hmean: 0.8643 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.89, recall: 0.8137, precision: 0.9176, hmean: 0.8625 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.90, recall: 0.8100, precision: 0.9173, hmean: 0.8603 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.91, recall: 0.8064, precision: 0.9198, hmean: 0.8594 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.92, recall: 0.8018, precision: 0.9203, hmean: 0.8570 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.93, recall: 0.7918, precision: 0.9214, hmean: 0.8517 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.94, recall: 0.7836, precision: 0.9236, hmean: 0.8478 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.95, recall: 0.7753, precision: 0.9228, hmean: 0.8427 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.96, recall: 0.7635, precision: 0.9238, hmean: 0.8360 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.97, recall: 0.7543, precision: 0.9270, hmean: 0.8318 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.98, recall: 0.7397, precision: 0.9278, hmean: 0.8232 2023/03/03 14:33:55 - mmengine - INFO - text score threshold: 0.99, recall: 0.7142, precision: 0.9321, hmean: 0.8087 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.80, recall: 0.7507, precision: 0.9536, hmean: 0.8401 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.81, recall: 0.7498, precision: 0.9535, hmean: 0.8395 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.82, recall: 0.7498, precision: 0.9535, hmean: 0.8395 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.83, recall: 0.7479, precision: 0.9545, hmean: 0.8387 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.84, recall: 0.7461, precision: 0.9544, hmean: 0.8375 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.85, recall: 0.7443, precision: 0.9555, hmean: 0.8368 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.86, recall: 0.7416, precision: 0.9564, hmean: 0.8354 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.87, recall: 0.7397, precision: 0.9574, hmean: 0.8346 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.88, recall: 0.7379, precision: 0.9573, hmean: 0.8334 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.89, recall: 0.7342, precision: 0.9571, hmean: 0.8310 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.90, recall: 0.7306, precision: 0.9569, hmean: 0.8286 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.91, recall: 0.7269, precision: 0.9590, hmean: 0.8270 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.92, recall: 0.7242, precision: 0.9589, hmean: 0.8252 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.93, recall: 0.7142, precision: 0.9583, hmean: 0.8184 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.94, recall: 0.7087, precision: 0.9604, hmean: 0.8156 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.95, recall: 0.7014, precision: 0.9612, hmean: 0.8110 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.96, recall: 0.6913, precision: 0.9643, hmean: 0.8053 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.97, recall: 0.6822, precision: 0.9664, hmean: 0.7998 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.98, recall: 0.6685, precision: 0.9657, hmean: 0.7901 2023/03/03 14:33:57 - mmengine - INFO - text score threshold: 0.99, recall: 0.6438, precision: 0.9671, hmean: 0.7730 2023/03/03 14:33:57 - mmengine - INFO - Epoch(val) [130][59/59] generic/precision: 0.8536 generic/recall: 0.8091 generic/hmean: 0.8308 weak/precision: 0.9062 weak/recall: 0.8292 weak/hmean: 0.8660 strong/precision: 0.9536 strong/recall: 0.7507 strong/hmean: 0.8401 2023/03/03 14:33:58 - mmengine - INFO - Epoch(train) [131][ 1/15] lr: 1.0000e-06 eta: 0:05:59 time: 0.3138 data_time: 0.0494 memory: 14893 loss: 0.0761 loss_ce: 0.0761 2023/03/03 14:33:58 - mmengine - INFO - Epoch(train) [131][ 2/15] lr: 1.0000e-06 eta: 0:05:59 time: 0.3148 data_time: 0.0546 memory: 18766 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:33:58 - mmengine - INFO - Epoch(train) [131][ 3/15] lr: 1.0000e-06 eta: 0:05:59 time: 0.3172 data_time: 0.0546 memory: 16199 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:33:59 - mmengine - INFO - Epoch(train) [131][ 4/15] lr: 1.0000e-06 eta: 0:05:58 time: 0.3361 data_time: 0.0547 memory: 17750 loss: 0.0721 loss_ce: 0.0721 2023/03/03 14:33:59 - mmengine - INFO - Epoch(train) [131][ 5/15] lr: 1.0000e-06 eta: 0:05:58 time: 0.3270 data_time: 0.0547 memory: 14653 loss: 0.0792 loss_ce: 0.0792 2023/03/03 14:33:59 - mmengine - INFO - Epoch(train) [131][ 6/15] lr: 1.0000e-06 eta: 0:05:58 time: 0.3315 data_time: 0.0547 memory: 13034 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:34:00 - mmengine - INFO - Epoch(train) [131][ 7/15] lr: 1.0000e-06 eta: 0:05:57 time: 0.3326 data_time: 0.0546 memory: 16573 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:34:00 - mmengine - INFO - Epoch(train) [131][ 8/15] lr: 1.0000e-06 eta: 0:05:57 time: 0.3272 data_time: 0.0546 memory: 16370 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:34:00 - mmengine - INFO - Epoch(train) [131][ 9/15] lr: 1.0000e-06 eta: 0:05:56 time: 0.3491 data_time: 0.0546 memory: 28298 loss: 0.0850 loss_ce: 0.0850 2023/03/03 14:34:01 - mmengine - INFO - Epoch(train) [131][10/15] lr: 1.0000e-06 eta: 0:05:56 time: 0.3537 data_time: 0.0546 memory: 17272 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:34:01 - mmengine - INFO - Epoch(train) [131][11/15] lr: 1.0000e-06 eta: 0:05:56 time: 0.3101 data_time: 0.0069 memory: 21508 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:34:01 - mmengine - INFO - Epoch(train) [131][12/15] lr: 1.0000e-06 eta: 0:05:55 time: 0.2926 data_time: 0.0017 memory: 17892 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:34:01 - mmengine - INFO - Epoch(train) [131][13/15] lr: 1.0000e-06 eta: 0:05:55 time: 0.2901 data_time: 0.0017 memory: 14530 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:34:02 - mmengine - INFO - Epoch(train) [131][14/15] lr: 1.0000e-06 eta: 0:05:55 time: 0.2866 data_time: 0.0016 memory: 16111 loss: 0.0816 loss_ce: 0.0816 2023/03/03 14:34:02 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:34:02 - mmengine - INFO - Epoch(train) [131][15/15] lr: 1.0000e-06 eta: 0:05:54 time: 0.2859 data_time: 0.0016 memory: 10377 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:34:03 - mmengine - INFO - Epoch(train) [132][ 1/15] lr: 1.0000e-06 eta: 0:05:54 time: 0.3935 data_time: 0.0785 memory: 26469 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:34:03 - mmengine - INFO - Epoch(train) [132][ 2/15] lr: 1.0000e-06 eta: 0:05:54 time: 0.3812 data_time: 0.0785 memory: 17272 loss: 0.0766 loss_ce: 0.0766 2023/03/03 14:34:04 - mmengine - INFO - Epoch(train) [132][ 3/15] lr: 1.0000e-06 eta: 0:05:54 time: 0.3843 data_time: 0.0785 memory: 17331 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:34:04 - mmengine - INFO - Epoch(train) [132][ 4/15] lr: 1.0000e-06 eta: 0:05:53 time: 0.3760 data_time: 0.0785 memory: 21232 loss: 0.0705 loss_ce: 0.0705 2023/03/03 14:34:04 - mmengine - INFO - Epoch(train) [132][ 5/15] lr: 1.0000e-06 eta: 0:05:53 time: 0.3815 data_time: 0.0785 memory: 18722 loss: 0.0693 loss_ce: 0.0693 2023/03/03 14:34:05 - mmengine - INFO - Epoch(train) [132][ 6/15] lr: 1.0000e-06 eta: 0:05:52 time: 0.3721 data_time: 0.0785 memory: 17421 loss: 0.0687 loss_ce: 0.0687 2023/03/03 14:34:05 - mmengine - INFO - Epoch(train) [132][ 7/15] lr: 1.0000e-06 eta: 0:05:52 time: 0.4080 data_time: 0.0785 memory: 16370 loss: 0.0712 loss_ce: 0.0712 2023/03/03 14:34:05 - mmengine - INFO - Epoch(train) [132][ 8/15] lr: 1.0000e-06 eta: 0:05:52 time: 0.4080 data_time: 0.0785 memory: 17572 loss: 0.0693 loss_ce: 0.0693 2023/03/03 14:34:06 - mmengine - INFO - Epoch(train) [132][ 9/15] lr: 1.0000e-06 eta: 0:05:51 time: 0.3950 data_time: 0.0785 memory: 16804 loss: 0.0677 loss_ce: 0.0677 2023/03/03 14:34:06 - mmengine - INFO - Epoch(train) [132][10/15] lr: 1.0000e-06 eta: 0:05:51 time: 0.3934 data_time: 0.0786 memory: 15596 loss: 0.0676 loss_ce: 0.0676 2023/03/03 14:34:06 - mmengine - INFO - Epoch(train) [132][11/15] lr: 1.0000e-06 eta: 0:05:51 time: 0.2813 data_time: 0.0017 memory: 16804 loss: 0.0655 loss_ce: 0.0655 2023/03/03 14:34:06 - mmengine - INFO - Epoch(train) [132][12/15] lr: 1.0000e-06 eta: 0:05:50 time: 0.2923 data_time: 0.0016 memory: 18556 loss: 0.0694 loss_ce: 0.0694 2023/03/03 14:34:07 - mmengine - INFO - Epoch(train) [132][13/15] lr: 1.0000e-06 eta: 0:05:50 time: 0.2987 data_time: 0.0016 memory: 11138 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:34:07 - mmengine - INFO - Epoch(train) [132][14/15] lr: 1.0000e-06 eta: 0:05:49 time: 0.2827 data_time: 0.0016 memory: 17421 loss: 0.0728 loss_ce: 0.0728 2023/03/03 14:34:07 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:34:07 - mmengine - INFO - Epoch(train) [132][15/15] lr: 1.0000e-06 eta: 0:05:49 time: 0.2789 data_time: 0.0016 memory: 4913 loss: 0.0737 loss_ce: 0.0737 2023/03/03 14:34:08 - mmengine - INFO - Epoch(train) [133][ 1/15] lr: 1.0000e-06 eta: 0:05:49 time: 0.3462 data_time: 0.0692 memory: 15561 loss: 0.0748 loss_ce: 0.0748 2023/03/03 14:34:08 - mmengine - INFO - Epoch(train) [133][ 2/15] lr: 1.0000e-06 eta: 0:05:49 time: 0.3105 data_time: 0.0693 memory: 14273 loss: 0.0732 loss_ce: 0.0732 2023/03/03 14:34:08 - mmengine - INFO - Epoch(train) [133][ 3/15] lr: 1.0000e-06 eta: 0:05:48 time: 0.3114 data_time: 0.0693 memory: 18766 loss: 0.0730 loss_ce: 0.0730 2023/03/03 14:34:09 - mmengine - INFO - Epoch(train) [133][ 4/15] lr: 1.0000e-06 eta: 0:05:48 time: 0.3258 data_time: 0.0693 memory: 14059 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:34:09 - mmengine - INFO - Epoch(train) [133][ 5/15] lr: 1.0000e-06 eta: 0:05:47 time: 0.3314 data_time: 0.0693 memory: 17788 loss: 0.0761 loss_ce: 0.0761 2023/03/03 14:34:09 - mmengine - INFO - Epoch(train) [133][ 6/15] lr: 1.0000e-06 eta: 0:05:47 time: 0.3307 data_time: 0.0693 memory: 13842 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:34:10 - mmengine - INFO - Epoch(train) [133][ 7/15] lr: 1.0000e-06 eta: 0:05:47 time: 0.3193 data_time: 0.0693 memory: 17892 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:34:10 - mmengine - INFO - Epoch(train) [133][ 8/15] lr: 1.0000e-06 eta: 0:05:46 time: 0.3248 data_time: 0.0693 memory: 17215 loss: 0.0745 loss_ce: 0.0745 2023/03/03 14:34:10 - mmengine - INFO - Epoch(train) [133][ 9/15] lr: 1.0000e-06 eta: 0:05:46 time: 0.3277 data_time: 0.0693 memory: 16804 loss: 0.0733 loss_ce: 0.0733 2023/03/03 14:34:11 - mmengine - INFO - Epoch(train) [133][10/15] lr: 1.0000e-06 eta: 0:05:46 time: 0.3535 data_time: 0.0693 memory: 16804 loss: 0.0742 loss_ce: 0.0742 2023/03/03 14:34:11 - mmengine - INFO - Epoch(train) [133][11/15] lr: 1.0000e-06 eta: 0:05:45 time: 0.2918 data_time: 0.0017 memory: 14236 loss: 0.0772 loss_ce: 0.0772 2023/03/03 14:34:11 - mmengine - INFO - Epoch(train) [133][12/15] lr: 1.0000e-06 eta: 0:05:45 time: 0.2937 data_time: 0.0017 memory: 17892 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:34:11 - mmengine - INFO - Epoch(train) [133][13/15] lr: 1.0000e-06 eta: 0:05:45 time: 0.2911 data_time: 0.0016 memory: 15691 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:34:12 - mmengine - INFO - Epoch(train) [133][14/15] lr: 1.0000e-06 eta: 0:05:44 time: 0.3061 data_time: 0.0015 memory: 35519 loss: 0.0739 loss_ce: 0.0739 2023/03/03 14:34:12 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:34:12 - mmengine - INFO - Epoch(train) [133][15/15] lr: 1.0000e-06 eta: 0:05:44 time: 0.2993 data_time: 0.0015 memory: 3091 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:34:13 - mmengine - INFO - Epoch(train) [134][ 1/15] lr: 1.0000e-06 eta: 0:05:44 time: 0.3731 data_time: 0.0661 memory: 16223 loss: 0.0726 loss_ce: 0.0726 2023/03/03 14:34:13 - mmengine - INFO - Epoch(train) [134][ 2/15] lr: 1.0000e-06 eta: 0:05:43 time: 0.3835 data_time: 0.0662 memory: 28695 loss: 0.0707 loss_ce: 0.0707 2023/03/03 14:34:14 - mmengine - INFO - Epoch(train) [134][ 3/15] lr: 1.0000e-06 eta: 0:05:43 time: 0.3752 data_time: 0.0662 memory: 18250 loss: 0.0723 loss_ce: 0.0723 2023/03/03 14:34:14 - mmengine - INFO - Epoch(train) [134][ 4/15] lr: 1.0000e-06 eta: 0:05:43 time: 0.3723 data_time: 0.0662 memory: 16870 loss: 0.0751 loss_ce: 0.0751 2023/03/03 14:34:14 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:34:14 - mmengine - INFO - Epoch(train) [134][ 5/15] lr: 1.0000e-06 eta: 0:05:42 time: 0.3735 data_time: 0.0662 memory: 19434 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:34:15 - mmengine - INFO - Epoch(train) [134][ 6/15] lr: 1.0000e-06 eta: 0:05:42 time: 0.3812 data_time: 0.0663 memory: 16204 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:34:15 - mmengine - INFO - Epoch(train) [134][ 7/15] lr: 1.0000e-06 eta: 0:05:42 time: 0.3879 data_time: 0.0663 memory: 15631 loss: 0.0741 loss_ce: 0.0741 2023/03/03 14:34:15 - mmengine - INFO - Epoch(train) [134][ 8/15] lr: 1.0000e-06 eta: 0:05:41 time: 0.3916 data_time: 0.0663 memory: 16369 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:34:16 - mmengine - INFO - Epoch(train) [134][ 9/15] lr: 1.0000e-06 eta: 0:05:41 time: 0.3593 data_time: 0.0663 memory: 17968 loss: 0.0761 loss_ce: 0.0761 2023/03/03 14:34:16 - mmengine - INFO - Epoch(train) [134][10/15] lr: 1.0000e-06 eta: 0:05:41 time: 0.3625 data_time: 0.0662 memory: 14780 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:34:16 - mmengine - INFO - Epoch(train) [134][11/15] lr: 1.0000e-06 eta: 0:05:40 time: 0.2961 data_time: 0.0016 memory: 12346 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:34:16 - mmengine - INFO - Epoch(train) [134][12/15] lr: 1.0000e-06 eta: 0:05:40 time: 0.2876 data_time: 0.0015 memory: 15934 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:34:17 - mmengine - INFO - Epoch(train) [134][13/15] lr: 1.0000e-06 eta: 0:05:39 time: 0.2824 data_time: 0.0015 memory: 16369 loss: 0.0766 loss_ce: 0.0766 2023/03/03 14:34:17 - mmengine - INFO - Epoch(train) [134][14/15] lr: 1.0000e-06 eta: 0:05:39 time: 0.2849 data_time: 0.0015 memory: 16530 loss: 0.0741 loss_ce: 0.0741 2023/03/03 14:34:17 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:34:17 - mmengine - INFO - Epoch(train) [134][15/15] lr: 1.0000e-06 eta: 0:05:39 time: 0.2707 data_time: 0.0015 memory: 5460 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:34:18 - mmengine - INFO - Epoch(train) [135][ 1/15] lr: 1.0000e-06 eta: 0:05:39 time: 0.3144 data_time: 0.0573 memory: 16976 loss: 0.0744 loss_ce: 0.0744 2023/03/03 14:34:18 - mmengine - INFO - Epoch(train) [135][ 2/15] lr: 1.0000e-06 eta: 0:05:38 time: 0.3200 data_time: 0.0574 memory: 19131 loss: 0.0732 loss_ce: 0.0732 2023/03/03 14:34:18 - mmengine - INFO - Epoch(train) [135][ 3/15] lr: 1.0000e-06 eta: 0:05:38 time: 0.3179 data_time: 0.0574 memory: 17421 loss: 0.0766 loss_ce: 0.0766 2023/03/03 14:34:19 - mmengine - INFO - Epoch(train) [135][ 4/15] lr: 1.0000e-06 eta: 0:05:37 time: 0.3242 data_time: 0.0575 memory: 15494 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:34:19 - mmengine - INFO - Epoch(train) [135][ 5/15] lr: 1.0000e-06 eta: 0:05:37 time: 0.3233 data_time: 0.0575 memory: 17120 loss: 0.0778 loss_ce: 0.0778 2023/03/03 14:34:20 - mmengine - INFO - Epoch(train) [135][ 6/15] lr: 1.0000e-06 eta: 0:05:37 time: 0.3418 data_time: 0.0575 memory: 17421 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:34:20 - mmengine - INFO - Epoch(train) [135][ 7/15] lr: 1.0000e-06 eta: 0:05:36 time: 0.3536 data_time: 0.0576 memory: 19860 loss: 0.0753 loss_ce: 0.0753 2023/03/03 14:34:20 - mmengine - INFO - Epoch(train) [135][ 8/15] lr: 1.0000e-06 eta: 0:05:36 time: 0.3606 data_time: 0.0576 memory: 15911 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:34:20 - mmengine - INFO - Epoch(train) [135][ 9/15] lr: 1.0000e-06 eta: 0:05:36 time: 0.3582 data_time: 0.0576 memory: 17730 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:34:21 - mmengine - INFO - Epoch(train) [135][10/15] lr: 1.0000e-06 eta: 0:05:35 time: 0.3674 data_time: 0.0576 memory: 15221 loss: 0.0785 loss_ce: 0.0785 2023/03/03 14:34:21 - mmengine - INFO - Epoch(train) [135][11/15] lr: 1.0000e-06 eta: 0:05:35 time: 0.3139 data_time: 0.0017 memory: 13610 loss: 0.0764 loss_ce: 0.0764 2023/03/03 14:34:21 - mmengine - INFO - Epoch(train) [135][12/15] lr: 1.0000e-06 eta: 0:05:35 time: 0.3024 data_time: 0.0016 memory: 17446 loss: 0.0746 loss_ce: 0.0746 2023/03/03 14:34:21 - mmengine - INFO - Epoch(train) [135][13/15] lr: 1.0000e-06 eta: 0:05:34 time: 0.3030 data_time: 0.0016 memory: 14761 loss: 0.0730 loss_ce: 0.0730 2023/03/03 14:34:22 - mmengine - INFO - Epoch(train) [135][14/15] lr: 1.0000e-06 eta: 0:05:34 time: 0.3314 data_time: 0.0016 memory: 36778 loss: 0.0671 loss_ce: 0.0671 2023/03/03 14:34:22 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:34:22 - mmengine - INFO - Epoch(train) [135][15/15] lr: 1.0000e-06 eta: 0:05:33 time: 0.3262 data_time: 0.0015 memory: 5555 loss: 0.0668 loss_ce: 0.0668 2023/03/03 14:34:24 - mmengine - INFO - Epoch(train) [136][ 1/15] lr: 1.0000e-06 eta: 0:05:34 time: 0.3985 data_time: 0.0642 memory: 16654 loss: 0.0664 loss_ce: 0.0664 2023/03/03 14:34:24 - mmengine - INFO - Epoch(train) [136][ 2/15] lr: 1.0000e-06 eta: 0:05:33 time: 0.3904 data_time: 0.0642 memory: 18561 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:34:24 - mmengine - INFO - Epoch(train) [136][ 3/15] lr: 1.0000e-06 eta: 0:05:33 time: 0.3922 data_time: 0.0643 memory: 16976 loss: 0.0716 loss_ce: 0.0716 2023/03/03 14:34:24 - mmengine - INFO - Epoch(train) [136][ 4/15] lr: 1.0000e-06 eta: 0:05:32 time: 0.3978 data_time: 0.0643 memory: 14540 loss: 0.0716 loss_ce: 0.0716 2023/03/03 14:34:25 - mmengine - INFO - Epoch(train) [136][ 5/15] lr: 1.0000e-06 eta: 0:05:32 time: 0.3947 data_time: 0.0643 memory: 25798 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:34:25 - mmengine - INFO - Epoch(train) [136][ 6/15] lr: 1.0000e-06 eta: 0:05:32 time: 0.3912 data_time: 0.0643 memory: 16976 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:34:25 - mmengine - INFO - Epoch(train) [136][ 7/15] lr: 1.0000e-06 eta: 0:05:31 time: 0.3949 data_time: 0.0644 memory: 16223 loss: 0.0788 loss_ce: 0.0788 2023/03/03 14:34:25 - mmengine - INFO - Epoch(train) [136][ 8/15] lr: 1.0000e-06 eta: 0:05:31 time: 0.3967 data_time: 0.0644 memory: 16370 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:34:26 - mmengine - INFO - Epoch(train) [136][ 9/15] lr: 1.0000e-06 eta: 0:05:31 time: 0.3774 data_time: 0.0644 memory: 17272 loss: 0.0830 loss_ce: 0.0830 2023/03/03 14:34:26 - mmengine - INFO - Epoch(train) [136][10/15] lr: 1.0000e-06 eta: 0:05:30 time: 0.3940 data_time: 0.0644 memory: 18691 loss: 0.0820 loss_ce: 0.0820 2023/03/03 14:34:27 - mmengine - INFO - Epoch(train) [136][11/15] lr: 1.0000e-06 eta: 0:05:30 time: 0.3046 data_time: 0.0017 memory: 21806 loss: 0.0834 loss_ce: 0.0834 2023/03/03 14:34:27 - mmengine - INFO - Epoch(train) [136][12/15] lr: 1.0000e-06 eta: 0:05:30 time: 0.3002 data_time: 0.0016 memory: 12048 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:34:27 - mmengine - INFO - Epoch(train) [136][13/15] lr: 1.0000e-06 eta: 0:05:29 time: 0.2913 data_time: 0.0016 memory: 16370 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:34:27 - mmengine - INFO - Epoch(train) [136][14/15] lr: 1.0000e-06 eta: 0:05:29 time: 0.2881 data_time: 0.0016 memory: 16171 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:34:28 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:34:28 - mmengine - INFO - Epoch(train) [136][15/15] lr: 1.0000e-06 eta: 0:05:28 time: 0.2725 data_time: 0.0016 memory: 5432 loss: 0.0862 loss_ce: 0.0862 2023/03/03 14:34:29 - mmengine - INFO - Epoch(train) [137][ 1/15] lr: 1.0000e-06 eta: 0:05:28 time: 0.3554 data_time: 0.0812 memory: 16804 loss: 0.0831 loss_ce: 0.0831 2023/03/03 14:34:29 - mmengine - INFO - Epoch(train) [137][ 2/15] lr: 1.0000e-06 eta: 0:05:28 time: 0.3703 data_time: 0.0812 memory: 16370 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:34:29 - mmengine - INFO - Epoch(train) [137][ 3/15] lr: 1.0000e-06 eta: 0:05:28 time: 0.3817 data_time: 0.0813 memory: 19005 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:34:30 - mmengine - INFO - Epoch(train) [137][ 4/15] lr: 1.0000e-06 eta: 0:05:27 time: 0.3615 data_time: 0.0813 memory: 16314 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:34:30 - mmengine - INFO - Epoch(train) [137][ 5/15] lr: 1.0000e-06 eta: 0:05:27 time: 0.3503 data_time: 0.0813 memory: 17421 loss: 0.0787 loss_ce: 0.0787 2023/03/03 14:34:30 - mmengine - INFO - Epoch(train) [137][ 6/15] lr: 1.0000e-06 eta: 0:05:27 time: 0.3428 data_time: 0.0812 memory: 16396 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:34:30 - mmengine - INFO - Epoch(train) [137][ 7/15] lr: 1.0000e-06 eta: 0:05:26 time: 0.3444 data_time: 0.0813 memory: 16685 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:34:31 - mmengine - INFO - Epoch(train) [137][ 8/15] lr: 1.0000e-06 eta: 0:05:26 time: 0.3699 data_time: 0.0813 memory: 17272 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:34:31 - mmengine - INFO - Epoch(train) [137][ 9/15] lr: 1.0000e-06 eta: 0:05:25 time: 0.3673 data_time: 0.0813 memory: 17421 loss: 0.0720 loss_ce: 0.0720 2023/03/03 14:34:31 - mmengine - INFO - Epoch(train) [137][10/15] lr: 1.0000e-06 eta: 0:05:25 time: 0.3797 data_time: 0.0813 memory: 22249 loss: 0.0654 loss_ce: 0.0654 2023/03/03 14:34:31 - mmengine - INFO - Epoch(train) [137][11/15] lr: 1.0000e-06 eta: 0:05:25 time: 0.2946 data_time: 0.0016 memory: 14198 loss: 0.0671 loss_ce: 0.0671 2023/03/03 14:34:32 - mmengine - INFO - Epoch(train) [137][12/15] lr: 1.0000e-06 eta: 0:05:24 time: 0.2883 data_time: 0.0015 memory: 18070 loss: 0.0661 loss_ce: 0.0661 2023/03/03 14:34:32 - mmengine - INFO - Epoch(train) [137][13/15] lr: 1.0000e-06 eta: 0:05:24 time: 0.2741 data_time: 0.0015 memory: 17120 loss: 0.0679 loss_ce: 0.0679 2023/03/03 14:34:32 - mmengine - INFO - Epoch(train) [137][14/15] lr: 1.0000e-06 eta: 0:05:24 time: 0.2773 data_time: 0.0015 memory: 17421 loss: 0.0727 loss_ce: 0.0727 2023/03/03 14:34:32 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:34:32 - mmengine - INFO - Epoch(train) [137][15/15] lr: 1.0000e-06 eta: 0:05:23 time: 0.2726 data_time: 0.0015 memory: 5183 loss: 0.0857 loss_ce: 0.0857 2023/03/03 14:34:33 - mmengine - INFO - Epoch(train) [138][ 1/15] lr: 1.0000e-06 eta: 0:05:23 time: 0.3351 data_time: 0.0312 memory: 18652 loss: 0.0835 loss_ce: 0.0835 2023/03/03 14:34:34 - mmengine - INFO - Epoch(train) [138][ 2/15] lr: 1.0000e-06 eta: 0:05:23 time: 0.3357 data_time: 0.0312 memory: 16955 loss: 0.0907 loss_ce: 0.0907 2023/03/03 14:34:34 - mmengine - INFO - Epoch(train) [138][ 3/15] lr: 1.0000e-06 eta: 0:05:22 time: 0.3269 data_time: 0.0313 memory: 20849 loss: 0.0965 loss_ce: 0.0965 2023/03/03 14:34:34 - mmengine - INFO - Epoch(train) [138][ 4/15] lr: 1.0000e-06 eta: 0:05:22 time: 0.3269 data_time: 0.0313 memory: 17272 loss: 0.1008 loss_ce: 0.1008 2023/03/03 14:34:35 - mmengine - INFO - Epoch(train) [138][ 5/15] lr: 1.0000e-06 eta: 0:05:22 time: 0.3233 data_time: 0.0313 memory: 16056 loss: 0.1010 loss_ce: 0.1010 2023/03/03 14:34:35 - mmengine - INFO - Epoch(train) [138][ 6/15] lr: 1.0000e-06 eta: 0:05:21 time: 0.3324 data_time: 0.0313 memory: 19327 loss: 0.1018 loss_ce: 0.1018 2023/03/03 14:34:35 - mmengine - INFO - Epoch(train) [138][ 7/15] lr: 1.0000e-06 eta: 0:05:21 time: 0.3210 data_time: 0.0313 memory: 17272 loss: 0.1068 loss_ce: 0.1068 2023/03/03 14:34:35 - mmengine - INFO - Epoch(train) [138][ 8/15] lr: 1.0000e-06 eta: 0:05:21 time: 0.3294 data_time: 0.0313 memory: 16459 loss: 0.1038 loss_ce: 0.1038 2023/03/03 14:34:36 - mmengine - INFO - Epoch(train) [138][ 9/15] lr: 1.0000e-06 eta: 0:05:20 time: 0.3483 data_time: 0.0314 memory: 17284 loss: 0.0996 loss_ce: 0.0996 2023/03/03 14:34:36 - mmengine - INFO - Epoch(train) [138][10/15] lr: 1.0000e-06 eta: 0:05:20 time: 0.3531 data_time: 0.0314 memory: 17892 loss: 0.0847 loss_ce: 0.0847 2023/03/03 14:34:36 - mmengine - INFO - Epoch(train) [138][11/15] lr: 1.0000e-06 eta: 0:05:19 time: 0.2987 data_time: 0.0017 memory: 19485 loss: 0.0849 loss_ce: 0.0849 2023/03/03 14:34:37 - mmengine - INFO - Epoch(train) [138][12/15] lr: 1.0000e-06 eta: 0:05:19 time: 0.2992 data_time: 0.0016 memory: 17446 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:34:37 - mmengine - INFO - Epoch(train) [138][13/15] lr: 1.0000e-06 eta: 0:05:19 time: 0.2827 data_time: 0.0015 memory: 16223 loss: 0.0717 loss_ce: 0.0717 2023/03/03 14:34:37 - mmengine - INFO - Epoch(train) [138][14/15] lr: 1.0000e-06 eta: 0:05:18 time: 0.2830 data_time: 0.0015 memory: 17544 loss: 0.0664 loss_ce: 0.0664 2023/03/03 14:34:37 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:34:37 - mmengine - INFO - Epoch(train) [138][15/15] lr: 1.0000e-06 eta: 0:05:18 time: 0.2616 data_time: 0.0016 memory: 6483 loss: 0.0758 loss_ce: 0.0758 2023/03/03 14:34:38 - mmengine - INFO - Epoch(train) [139][ 1/15] lr: 1.0000e-06 eta: 0:05:18 time: 0.3145 data_time: 0.0529 memory: 20817 loss: 0.0732 loss_ce: 0.0732 2023/03/03 14:34:38 - mmengine - INFO - Epoch(train) [139][ 2/15] lr: 1.0000e-06 eta: 0:05:17 time: 0.3173 data_time: 0.0530 memory: 16212 loss: 0.0678 loss_ce: 0.0678 2023/03/03 14:34:38 - mmengine - INFO - Epoch(train) [139][ 3/15] lr: 1.0000e-06 eta: 0:05:17 time: 0.3093 data_time: 0.0530 memory: 17421 loss: 0.0667 loss_ce: 0.0667 2023/03/03 14:34:39 - mmengine - INFO - Epoch(train) [139][ 4/15] lr: 1.0000e-06 eta: 0:05:17 time: 0.2952 data_time: 0.0531 memory: 16530 loss: 0.0672 loss_ce: 0.0672 2023/03/03 14:34:39 - mmengine - INFO - Epoch(train) [139][ 5/15] lr: 1.0000e-06 eta: 0:05:16 time: 0.2978 data_time: 0.0532 memory: 16508 loss: 0.0715 loss_ce: 0.0715 2023/03/03 14:34:39 - mmengine - INFO - Epoch(train) [139][ 6/15] lr: 1.0000e-06 eta: 0:05:16 time: 0.2828 data_time: 0.0532 memory: 16370 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:34:39 - mmengine - INFO - Epoch(train) [139][ 7/15] lr: 1.0000e-06 eta: 0:05:15 time: 0.2884 data_time: 0.0532 memory: 19996 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:34:40 - mmengine - INFO - Epoch(train) [139][ 8/15] lr: 1.0000e-06 eta: 0:05:15 time: 0.2866 data_time: 0.0532 memory: 15781 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:34:40 - mmengine - INFO - Epoch(train) [139][ 9/15] lr: 1.0000e-06 eta: 0:05:15 time: 0.2912 data_time: 0.0532 memory: 12303 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:34:40 - mmengine - INFO - Epoch(train) [139][10/15] lr: 1.0000e-06 eta: 0:05:14 time: 0.3211 data_time: 0.0531 memory: 16255 loss: 0.0697 loss_ce: 0.0697 2023/03/03 14:34:41 - mmengine - INFO - Epoch(train) [139][11/15] lr: 1.0000e-06 eta: 0:05:14 time: 0.2719 data_time: 0.0018 memory: 17999 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:34:41 - mmengine - INFO - Epoch(train) [139][12/15] lr: 1.0000e-06 eta: 0:05:14 time: 0.2825 data_time: 0.0017 memory: 17284 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:34:41 - mmengine - INFO - Epoch(train) [139][13/15] lr: 1.0000e-06 eta: 0:05:13 time: 0.2848 data_time: 0.0016 memory: 16370 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:34:42 - mmengine - INFO - Epoch(train) [139][14/15] lr: 1.0000e-06 eta: 0:05:13 time: 0.2808 data_time: 0.0016 memory: 17421 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:34:42 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:34:42 - mmengine - INFO - Epoch(train) [139][15/15] lr: 1.0000e-06 eta: 0:05:12 time: 0.2727 data_time: 0.0015 memory: 5797 loss: 0.0761 loss_ce: 0.0761 2023/03/03 14:34:43 - mmengine - INFO - Epoch(train) [140][ 1/15] lr: 1.0000e-06 eta: 0:05:12 time: 0.3448 data_time: 0.0453 memory: 15107 loss: 0.0703 loss_ce: 0.0703 2023/03/03 14:34:43 - mmengine - INFO - Epoch(train) [140][ 2/15] lr: 1.0000e-06 eta: 0:05:12 time: 0.3391 data_time: 0.0454 memory: 17122 loss: 0.0725 loss_ce: 0.0725 2023/03/03 14:34:43 - mmengine - INFO - Epoch(train) [140][ 3/15] lr: 1.0000e-06 eta: 0:05:12 time: 0.3415 data_time: 0.0455 memory: 21260 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:34:43 - mmengine - INFO - Epoch(train) [140][ 4/15] lr: 1.0000e-06 eta: 0:05:11 time: 0.3369 data_time: 0.0455 memory: 17276 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:34:44 - mmengine - INFO - Epoch(train) [140][ 5/15] lr: 1.0000e-06 eta: 0:05:11 time: 0.3221 data_time: 0.0455 memory: 18766 loss: 0.0695 loss_ce: 0.0695 2023/03/03 14:34:44 - mmengine - INFO - Epoch(train) [140][ 6/15] lr: 1.0000e-06 eta: 0:05:10 time: 0.3118 data_time: 0.0455 memory: 17272 loss: 0.0658 loss_ce: 0.0658 2023/03/03 14:34:44 - mmengine - INFO - Epoch(train) [140][ 7/15] lr: 1.0000e-06 eta: 0:05:10 time: 0.3126 data_time: 0.0456 memory: 16370 loss: 0.0677 loss_ce: 0.0677 2023/03/03 14:34:44 - mmengine - INFO - Epoch(train) [140][ 8/15] lr: 1.0000e-06 eta: 0:05:10 time: 0.3129 data_time: 0.0456 memory: 16804 loss: 0.0673 loss_ce: 0.0673 2023/03/03 14:34:45 - mmengine - INFO - Epoch(train) [140][ 9/15] lr: 1.0000e-06 eta: 0:05:09 time: 0.3129 data_time: 0.0456 memory: 17122 loss: 0.0661 loss_ce: 0.0661 2023/03/03 14:34:45 - mmengine - INFO - Epoch(train) [140][10/15] lr: 1.0000e-06 eta: 0:05:09 time: 0.3198 data_time: 0.0457 memory: 15494 loss: 0.0675 loss_ce: 0.0675 2023/03/03 14:34:45 - mmengine - INFO - Epoch(train) [140][11/15] lr: 1.0000e-06 eta: 0:05:09 time: 0.2689 data_time: 0.0018 memory: 17120 loss: 0.0682 loss_ce: 0.0682 2023/03/03 14:34:46 - mmengine - INFO - Epoch(train) [140][12/15] lr: 1.0000e-06 eta: 0:05:08 time: 0.2670 data_time: 0.0018 memory: 15767 loss: 0.0679 loss_ce: 0.0679 2023/03/03 14:34:46 - mmengine - INFO - Epoch(train) [140][13/15] lr: 1.0000e-06 eta: 0:05:08 time: 0.2688 data_time: 0.0017 memory: 15175 loss: 0.0727 loss_ce: 0.0727 2023/03/03 14:34:46 - mmengine - INFO - Epoch(train) [140][14/15] lr: 1.0000e-06 eta: 0:05:07 time: 0.2704 data_time: 0.0017 memory: 15767 loss: 0.0707 loss_ce: 0.0707 2023/03/03 14:34:46 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:34:46 - mmengine - INFO - Epoch(train) [140][15/15] lr: 1.0000e-06 eta: 0:05:07 time: 0.2586 data_time: 0.0016 memory: 4895 loss: 0.0739 loss_ce: 0.0739 2023/03/03 14:34:48 - mmengine - INFO - Epoch(val) [140][ 1/59] eta: 0:01:29 time: 1.0939 data_time: 0.0034 memory: 981 2023/03/03 14:34:49 - mmengine - INFO - Epoch(val) [140][ 2/59] eta: 0:01:07 time: 1.0062 data_time: 0.0034 memory: 981 2023/03/03 14:34:50 - mmengine - INFO - Epoch(val) [140][ 3/59] eta: 0:01:12 time: 1.0379 data_time: 0.0034 memory: 1003 2023/03/03 14:34:50 - mmengine - INFO - Epoch(val) [140][ 4/59] eta: 0:00:57 time: 1.0040 data_time: 0.0034 memory: 981 2023/03/03 14:34:53 - mmengine - INFO - Epoch(val) [140][ 5/59] eta: 0:01:18 time: 1.2379 data_time: 0.0034 memory: 1016 2023/03/03 14:34:56 - mmengine - INFO - Epoch(val) [140][ 6/59] eta: 0:01:26 time: 1.4318 data_time: 0.0034 memory: 981 2023/03/03 14:34:56 - mmengine - INFO - Epoch(val) [140][ 7/59] eta: 0:01:14 time: 1.3817 data_time: 0.0034 memory: 1043 2023/03/03 14:34:57 - mmengine - INFO - Epoch(val) [140][ 8/59] eta: 0:01:08 time: 1.2202 data_time: 0.0034 memory: 1016 2023/03/03 14:34:58 - mmengine - INFO - Epoch(val) [140][ 9/59] eta: 0:01:04 time: 1.2005 data_time: 0.0034 memory: 981 2023/03/03 14:34:59 - mmengine - INFO - Epoch(val) [140][10/59] eta: 0:01:00 time: 1.2326 data_time: 0.0034 memory: 981 2023/03/03 14:34:59 - mmengine - INFO - Epoch(val) [140][11/59] eta: 0:00:55 time: 1.1123 data_time: 0.0008 memory: 981 2023/03/03 14:35:02 - mmengine - INFO - Epoch(val) [140][12/59] eta: 0:01:02 time: 1.3512 data_time: 0.0007 memory: 1016 2023/03/03 14:35:04 - mmengine - INFO - Epoch(val) [140][13/59] eta: 0:01:04 time: 1.4239 data_time: 0.0007 memory: 981 2023/03/03 14:35:05 - mmengine - INFO - Epoch(val) [140][14/59] eta: 0:01:01 time: 1.4903 data_time: 0.0007 memory: 890 2023/03/03 14:35:05 - mmengine - INFO - Epoch(val) [140][15/59] eta: 0:00:56 time: 1.1912 data_time: 0.0007 memory: 981 2023/03/03 14:35:06 - mmengine - INFO - Epoch(val) [140][16/59] eta: 0:00:52 time: 0.9798 data_time: 0.0007 memory: 981 2023/03/03 14:35:06 - mmengine - INFO - Epoch(val) [140][17/59] eta: 0:00:49 time: 0.9960 data_time: 0.0007 memory: 981 2023/03/03 14:35:06 - mmengine - INFO - Epoch(val) [140][18/59] eta: 0:00:46 time: 0.9636 data_time: 0.0007 memory: 981 2023/03/03 14:35:07 - mmengine - INFO - Epoch(val) [140][19/59] eta: 0:00:44 time: 0.9642 data_time: 0.0007 memory: 981 2023/03/03 14:35:08 - mmengine - INFO - Epoch(val) [140][20/59] eta: 0:00:42 time: 0.9316 data_time: 0.0008 memory: 981 2023/03/03 14:35:08 - mmengine - INFO - Epoch(val) [140][21/59] eta: 0:00:40 time: 0.9479 data_time: 0.0008 memory: 981 2023/03/03 14:35:09 - mmengine - INFO - Epoch(val) [140][22/59] eta: 0:00:37 time: 0.6434 data_time: 0.0008 memory: 981 2023/03/03 14:35:09 - mmengine - INFO - Epoch(val) [140][23/59] eta: 0:00:35 time: 0.4865 data_time: 0.0008 memory: 981 2023/03/03 14:35:10 - mmengine - INFO - Epoch(val) [140][24/59] eta: 0:00:33 time: 0.4199 data_time: 0.0008 memory: 962 2023/03/03 14:35:10 - mmengine - INFO - Epoch(val) [140][25/59] eta: 0:00:32 time: 0.4511 data_time: 0.0007 memory: 981 2023/03/03 14:35:10 - mmengine - INFO - Epoch(val) [140][26/59] eta: 0:00:30 time: 0.4349 data_time: 0.0007 memory: 981 2023/03/03 14:35:11 - mmengine - INFO - Epoch(val) [140][27/59] eta: 0:00:28 time: 0.4350 data_time: 0.0007 memory: 981 2023/03/03 14:35:11 - mmengine - INFO - Epoch(val) [140][28/59] eta: 0:00:27 time: 0.4352 data_time: 0.0008 memory: 981 2023/03/03 14:35:12 - mmengine - INFO - Epoch(val) [140][29/59] eta: 0:00:26 time: 0.4690 data_time: 0.0008 memory: 981 2023/03/03 14:35:13 - mmengine - INFO - Epoch(val) [140][30/59] eta: 0:00:25 time: 0.5181 data_time: 0.0008 memory: 999 2023/03/03 14:35:14 - mmengine - INFO - Epoch(val) [140][31/59] eta: 0:00:24 time: 0.5344 data_time: 0.0008 memory: 981 2023/03/03 14:35:15 - mmengine - INFO - Epoch(val) [140][32/59] eta: 0:00:24 time: 0.6332 data_time: 0.0008 memory: 981 2023/03/03 14:35:15 - mmengine - INFO - Epoch(val) [140][33/59] eta: 0:00:22 time: 0.5844 data_time: 0.0008 memory: 981 2023/03/03 14:35:15 - mmengine - INFO - Epoch(val) [140][34/59] eta: 0:00:21 time: 0.5683 data_time: 0.0008 memory: 981 2023/03/03 14:35:15 - mmengine - INFO - Epoch(val) [140][35/59] eta: 0:00:20 time: 0.5519 data_time: 0.0008 memory: 981 2023/03/03 14:35:16 - mmengine - INFO - Epoch(val) [140][36/59] eta: 0:00:18 time: 0.5681 data_time: 0.0007 memory: 981 2023/03/03 14:35:16 - mmengine - INFO - Epoch(val) [140][37/59] eta: 0:00:17 time: 0.5518 data_time: 0.0007 memory: 981 2023/03/03 14:35:17 - mmengine - INFO - Epoch(val) [140][38/59] eta: 0:00:16 time: 0.5842 data_time: 0.0007 memory: 981 2023/03/03 14:35:17 - mmengine - INFO - Epoch(val) [140][39/59] eta: 0:00:15 time: 0.5006 data_time: 0.0007 memory: 987 2023/03/03 14:35:18 - mmengine - INFO - Epoch(val) [140][40/59] eta: 0:00:15 time: 0.5006 data_time: 0.0007 memory: 981 2023/03/03 14:35:19 - mmengine - INFO - Epoch(val) [140][41/59] eta: 0:00:14 time: 0.5506 data_time: 0.0007 memory: 986 2023/03/03 14:35:20 - mmengine - INFO - Epoch(val) [140][42/59] eta: 0:00:13 time: 0.5007 data_time: 0.0007 memory: 981 2023/03/03 14:35:21 - mmengine - INFO - Epoch(val) [140][43/59] eta: 0:00:12 time: 0.5662 data_time: 0.0007 memory: 976 2023/03/03 14:35:21 - mmengine - INFO - Epoch(val) [140][44/59] eta: 0:00:11 time: 0.5987 data_time: 0.0007 memory: 1003 2023/03/03 14:35:23 - mmengine - INFO - Epoch(val) [140][45/59] eta: 0:00:11 time: 0.7676 data_time: 0.0007 memory: 981 2023/03/03 14:35:24 - mmengine - INFO - Epoch(val) [140][46/59] eta: 0:00:10 time: 0.8009 data_time: 0.0007 memory: 981 2023/03/03 14:35:24 - mmengine - INFO - Epoch(val) [140][47/59] eta: 0:00:09 time: 0.8332 data_time: 0.0007 memory: 936 2023/03/03 14:35:25 - mmengine - INFO - Epoch(val) [140][48/59] eta: 0:00:08 time: 0.8172 data_time: 0.0007 memory: 1000 2023/03/03 14:35:26 - mmengine - INFO - Epoch(val) [140][49/59] eta: 0:00:08 time: 0.8671 data_time: 0.0007 memory: 981 2023/03/03 14:35:27 - mmengine - INFO - Epoch(val) [140][50/59] eta: 0:00:07 time: 0.8671 data_time: 0.0007 memory: 987 2023/03/03 14:35:28 - mmengine - INFO - Epoch(val) [140][51/59] eta: 0:00:06 time: 0.9189 data_time: 0.0008 memory: 981 2023/03/03 14:35:30 - mmengine - INFO - Epoch(val) [140][52/59] eta: 0:00:05 time: 0.9695 data_time: 0.0007 memory: 981 2023/03/03 14:35:30 - mmengine - INFO - Epoch(val) [140][53/59] eta: 0:00:04 time: 0.9531 data_time: 0.0007 memory: 962 2023/03/03 14:35:31 - mmengine - INFO - Epoch(val) [140][54/59] eta: 0:00:04 time: 0.9700 data_time: 0.0008 memory: 981 2023/03/03 14:35:32 - mmengine - INFO - Epoch(val) [140][55/59] eta: 0:00:03 time: 0.8503 data_time: 0.0008 memory: 981 2023/03/03 14:35:32 - mmengine - INFO - Epoch(val) [140][56/59] eta: 0:00:02 time: 0.8338 data_time: 0.0008 memory: 981 2023/03/03 14:35:34 - mmengine - INFO - Epoch(val) [140][57/59] eta: 0:00:01 time: 1.0077 data_time: 0.0007 memory: 981 2023/03/03 14:35:36 - mmengine - INFO - Epoch(val) [140][58/59] eta: 0:00:00 time: 1.0742 data_time: 0.0008 memory: 1016 2023/03/03 14:35:36 - mmengine - INFO - Epoch(val) [140][59/59] eta: 0:00:00 time: 1.0081 data_time: 0.0008 memory: 981 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.80, recall: 0.8237, precision: 0.8367, hmean: 0.8302 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.81, recall: 0.8237, precision: 0.8375, hmean: 0.8306 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.82, recall: 0.8219, precision: 0.8396, hmean: 0.8306 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.83, recall: 0.8210, precision: 0.8402, hmean: 0.8305 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.84, recall: 0.8183, precision: 0.8429, hmean: 0.8304 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.85, recall: 0.8137, precision: 0.8445, hmean: 0.8288 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.86, recall: 0.8128, precision: 0.8460, hmean: 0.8291 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.87, recall: 0.8110, precision: 0.8481, hmean: 0.8291 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.88, recall: 0.8100, precision: 0.8504, hmean: 0.8297 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.89, recall: 0.8064, precision: 0.8531, hmean: 0.8291 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.90, recall: 0.8037, precision: 0.8569, hmean: 0.8294 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.91, recall: 0.8027, precision: 0.8609, hmean: 0.8308 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.92, recall: 0.7982, precision: 0.8628, hmean: 0.8292 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.93, recall: 0.7881, precision: 0.8673, hmean: 0.8258 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.94, recall: 0.7817, precision: 0.8690, hmean: 0.8231 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.95, recall: 0.7744, precision: 0.8724, hmean: 0.8205 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.96, recall: 0.7635, precision: 0.8727, hmean: 0.8144 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.97, recall: 0.7580, precision: 0.8792, hmean: 0.8141 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.98, recall: 0.7425, precision: 0.8847, hmean: 0.8073 2023/03/03 14:36:07 - mmengine - INFO - text score threshold: 0.99, recall: 0.7196, precision: 0.8924, hmean: 0.7968 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.80, recall: 0.8347, precision: 0.9041, hmean: 0.8680 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.81, recall: 0.8347, precision: 0.9050, hmean: 0.8684 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.82, recall: 0.8329, precision: 0.9057, hmean: 0.8677 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.83, recall: 0.8320, precision: 0.9065, hmean: 0.8676 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.84, recall: 0.8292, precision: 0.9089, hmean: 0.8672 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.85, recall: 0.8247, precision: 0.9094, hmean: 0.8649 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.86, recall: 0.8237, precision: 0.9102, hmean: 0.8648 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.87, recall: 0.8219, precision: 0.9119, hmean: 0.8646 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.88, recall: 0.8210, precision: 0.9136, hmean: 0.8648 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.89, recall: 0.8164, precision: 0.9141, hmean: 0.8625 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.90, recall: 0.8128, precision: 0.9147, hmean: 0.8607 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.91, recall: 0.8110, precision: 0.9174, hmean: 0.8609 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.92, recall: 0.8046, precision: 0.9177, hmean: 0.8574 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.93, recall: 0.7909, precision: 0.9193, hmean: 0.8503 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.94, recall: 0.7836, precision: 0.9186, hmean: 0.8457 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.95, recall: 0.7744, precision: 0.9207, hmean: 0.8413 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.96, recall: 0.7644, precision: 0.9198, hmean: 0.8349 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.97, recall: 0.7589, precision: 0.9254, hmean: 0.8339 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.98, recall: 0.7425, precision: 0.9260, hmean: 0.8241 2023/03/03 14:36:10 - mmengine - INFO - text score threshold: 0.99, recall: 0.7187, precision: 0.9292, hmean: 0.8105 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.80, recall: 0.7543, precision: 0.9582, hmean: 0.8441 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.81, recall: 0.7543, precision: 0.9582, hmean: 0.8441 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.82, recall: 0.7525, precision: 0.9581, hmean: 0.8430 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.83, recall: 0.7516, precision: 0.9581, hmean: 0.8424 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.84, recall: 0.7498, precision: 0.9580, hmean: 0.8412 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.85, recall: 0.7461, precision: 0.9578, hmean: 0.8388 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.86, recall: 0.7443, precision: 0.9577, hmean: 0.8376 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.87, recall: 0.7416, precision: 0.9575, hmean: 0.8358 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.88, recall: 0.7406, precision: 0.9575, hmean: 0.8352 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.89, recall: 0.7361, precision: 0.9572, hmean: 0.8322 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.90, recall: 0.7324, precision: 0.9582, hmean: 0.8302 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.91, recall: 0.7306, precision: 0.9581, hmean: 0.8290 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.92, recall: 0.7251, precision: 0.9589, hmean: 0.8258 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.93, recall: 0.7142, precision: 0.9607, hmean: 0.8193 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.94, recall: 0.7078, precision: 0.9603, hmean: 0.8149 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.95, recall: 0.7005, precision: 0.9624, hmean: 0.8108 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.96, recall: 0.6913, precision: 0.9631, hmean: 0.8049 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.97, recall: 0.6849, precision: 0.9665, hmean: 0.8017 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.98, recall: 0.6694, precision: 0.9657, hmean: 0.7907 2023/03/03 14:36:13 - mmengine - INFO - text score threshold: 0.99, recall: 0.6475, precision: 0.9673, hmean: 0.7757 2023/03/03 14:36:13 - mmengine - INFO - Epoch(val) [140][59/59] generic/precision: 0.8609 generic/recall: 0.8027 generic/hmean: 0.8308 weak/precision: 0.9050 weak/recall: 0.8347 weak/hmean: 0.8684 strong/precision: 0.9582 strong/recall: 0.7543 strong/hmean: 0.8441 2023/03/03 14:36:13 - mmengine - INFO - Epoch(train) [141][ 1/15] lr: 1.0000e-06 eta: 0:05:07 time: 0.3240 data_time: 0.0629 memory: 16037 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:36:14 - mmengine - INFO - Epoch(train) [141][ 2/15] lr: 1.0000e-06 eta: 0:05:07 time: 0.3246 data_time: 0.0629 memory: 22666 loss: 0.0788 loss_ce: 0.0788 2023/03/03 14:36:14 - mmengine - INFO - Epoch(train) [141][ 3/15] lr: 1.0000e-06 eta: 0:05:06 time: 0.3221 data_time: 0.0630 memory: 17892 loss: 0.0812 loss_ce: 0.0812 2023/03/03 14:36:14 - mmengine - INFO - Epoch(train) [141][ 4/15] lr: 1.0000e-06 eta: 0:05:06 time: 0.3370 data_time: 0.0630 memory: 15432 loss: 0.0841 loss_ce: 0.0841 2023/03/03 14:36:15 - mmengine - INFO - Epoch(train) [141][ 5/15] lr: 1.0000e-06 eta: 0:05:05 time: 0.3345 data_time: 0.0630 memory: 16137 loss: 0.0843 loss_ce: 0.0843 2023/03/03 14:36:15 - mmengine - INFO - Epoch(train) [141][ 6/15] lr: 1.0000e-06 eta: 0:05:05 time: 0.3232 data_time: 0.0630 memory: 17892 loss: 0.0872 loss_ce: 0.0872 2023/03/03 14:36:15 - mmengine - INFO - Epoch(train) [141][ 7/15] lr: 1.0000e-06 eta: 0:05:05 time: 0.3213 data_time: 0.0630 memory: 17120 loss: 0.0859 loss_ce: 0.0859 2023/03/03 14:36:15 - mmengine - INFO - Epoch(train) [141][ 8/15] lr: 1.0000e-06 eta: 0:05:04 time: 0.3300 data_time: 0.0630 memory: 15968 loss: 0.0818 loss_ce: 0.0818 2023/03/03 14:36:16 - mmengine - INFO - Epoch(train) [141][ 9/15] lr: 1.0000e-06 eta: 0:05:04 time: 0.3316 data_time: 0.0630 memory: 17198 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:36:16 - mmengine - INFO - Epoch(train) [141][10/15] lr: 1.0000e-06 eta: 0:05:04 time: 0.3583 data_time: 0.0630 memory: 17361 loss: 0.0808 loss_ce: 0.0808 2023/03/03 14:36:16 - mmengine - INFO - Epoch(train) [141][11/15] lr: 1.0000e-06 eta: 0:05:03 time: 0.2929 data_time: 0.0018 memory: 17572 loss: 0.0754 loss_ce: 0.0754 2023/03/03 14:36:17 - mmengine - INFO - Epoch(train) [141][12/15] lr: 1.0000e-06 eta: 0:05:03 time: 0.2883 data_time: 0.0017 memory: 17878 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:36:17 - mmengine - INFO - Epoch(train) [141][13/15] lr: 1.0000e-06 eta: 0:05:03 time: 0.2995 data_time: 0.0017 memory: 18959 loss: 0.0746 loss_ce: 0.0746 2023/03/03 14:36:17 - mmengine - INFO - Epoch(train) [141][14/15] lr: 1.0000e-06 eta: 0:05:02 time: 0.2890 data_time: 0.0016 memory: 13904 loss: 0.0787 loss_ce: 0.0787 2023/03/03 14:36:17 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:36:17 - mmengine - INFO - Epoch(train) [141][15/15] lr: 1.0000e-06 eta: 0:05:02 time: 0.2853 data_time: 0.0016 memory: 5421 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:36:18 - mmengine - INFO - Epoch(train) [142][ 1/15] lr: 1.0000e-06 eta: 0:05:02 time: 0.3474 data_time: 0.0512 memory: 19073 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:36:19 - mmengine - INFO - Epoch(train) [142][ 2/15] lr: 1.0000e-06 eta: 0:05:01 time: 0.3479 data_time: 0.0512 memory: 17272 loss: 0.0800 loss_ce: 0.0800 2023/03/03 14:36:19 - mmengine - INFO - Epoch(train) [142][ 3/15] lr: 1.0000e-06 eta: 0:05:01 time: 0.3475 data_time: 0.0513 memory: 14372 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:36:19 - mmengine - INFO - Epoch(train) [142][ 4/15] lr: 1.0000e-06 eta: 0:05:01 time: 0.3467 data_time: 0.0513 memory: 16370 loss: 0.0870 loss_ce: 0.0870 2023/03/03 14:36:20 - mmengine - INFO - Epoch(train) [142][ 5/15] lr: 1.0000e-06 eta: 0:05:00 time: 0.3394 data_time: 0.0513 memory: 15037 loss: 0.0889 loss_ce: 0.0889 2023/03/03 14:36:20 - mmengine - INFO - Epoch(train) [142][ 6/15] lr: 1.0000e-06 eta: 0:05:00 time: 0.3393 data_time: 0.0513 memory: 17421 loss: 0.0927 loss_ce: 0.0927 2023/03/03 14:36:20 - mmengine - INFO - Epoch(train) [142][ 7/15] lr: 1.0000e-06 eta: 0:04:59 time: 0.3312 data_time: 0.0513 memory: 14788 loss: 0.0904 loss_ce: 0.0904 2023/03/03 14:36:20 - mmengine - INFO - Epoch(train) [142][ 8/15] lr: 1.0000e-06 eta: 0:04:59 time: 0.3224 data_time: 0.0513 memory: 16827 loss: 0.0879 loss_ce: 0.0879 2023/03/03 14:36:21 - mmengine - INFO - Epoch(train) [142][ 9/15] lr: 1.0000e-06 eta: 0:04:59 time: 0.3247 data_time: 0.0513 memory: 16976 loss: 0.0846 loss_ce: 0.0846 2023/03/03 14:36:21 - mmengine - INFO - Epoch(train) [142][10/15] lr: 1.0000e-06 eta: 0:04:58 time: 0.3293 data_time: 0.0513 memory: 17421 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:36:21 - mmengine - INFO - Epoch(train) [142][11/15] lr: 1.0000e-06 eta: 0:04:58 time: 0.2812 data_time: 0.0018 memory: 17421 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:36:21 - mmengine - INFO - Epoch(train) [142][12/15] lr: 1.0000e-06 eta: 0:04:58 time: 0.2826 data_time: 0.0017 memory: 15494 loss: 0.0784 loss_ce: 0.0784 2023/03/03 14:36:22 - mmengine - INFO - Epoch(train) [142][13/15] lr: 1.0000e-06 eta: 0:04:57 time: 0.2724 data_time: 0.0017 memory: 18409 loss: 0.0772 loss_ce: 0.0772 2023/03/03 14:36:22 - mmengine - INFO - Epoch(train) [142][14/15] lr: 1.0000e-06 eta: 0:04:57 time: 0.2710 data_time: 0.0016 memory: 18953 loss: 0.0689 loss_ce: 0.0689 2023/03/03 14:36:22 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:36:22 - mmengine - INFO - Epoch(train) [142][15/15] lr: 1.0000e-06 eta: 0:04:56 time: 0.2501 data_time: 0.0016 memory: 6121 loss: 0.0699 loss_ce: 0.0699 2023/03/03 14:36:23 - mmengine - INFO - Epoch(train) [143][ 1/15] lr: 1.0000e-06 eta: 0:04:56 time: 0.2941 data_time: 0.0447 memory: 17421 loss: 0.0702 loss_ce: 0.0702 2023/03/03 14:36:23 - mmengine - INFO - Epoch(train) [143][ 2/15] lr: 1.0000e-06 eta: 0:04:56 time: 0.3131 data_time: 0.0447 memory: 16223 loss: 0.0707 loss_ce: 0.0707 2023/03/03 14:36:23 - mmengine - INFO - Epoch(train) [143][ 3/15] lr: 1.0000e-06 eta: 0:04:56 time: 0.3106 data_time: 0.0448 memory: 16976 loss: 0.0738 loss_ce: 0.0738 2023/03/03 14:36:24 - mmengine - INFO - Epoch(train) [143][ 4/15] lr: 1.0000e-06 eta: 0:04:55 time: 0.3125 data_time: 0.0448 memory: 13550 loss: 0.0698 loss_ce: 0.0698 2023/03/03 14:36:24 - mmengine - INFO - Epoch(train) [143][ 5/15] lr: 1.0000e-06 eta: 0:04:55 time: 0.3150 data_time: 0.0448 memory: 16223 loss: 0.0751 loss_ce: 0.0751 2023/03/03 14:36:24 - mmengine - INFO - Epoch(train) [143][ 6/15] lr: 1.0000e-06 eta: 0:04:55 time: 0.3070 data_time: 0.0448 memory: 16976 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:36:25 - mmengine - INFO - Epoch(train) [143][ 7/15] lr: 1.0000e-06 eta: 0:04:54 time: 0.3113 data_time: 0.0448 memory: 17029 loss: 0.0764 loss_ce: 0.0764 2023/03/03 14:36:25 - mmengine - INFO - Epoch(train) [143][ 8/15] lr: 1.0000e-06 eta: 0:04:54 time: 0.3111 data_time: 0.0448 memory: 15888 loss: 0.0800 loss_ce: 0.0800 2023/03/03 14:36:25 - mmengine - INFO - Epoch(train) [143][ 9/15] lr: 1.0000e-06 eta: 0:04:53 time: 0.3213 data_time: 0.0448 memory: 18740 loss: 0.0824 loss_ce: 0.0824 2023/03/03 14:36:25 - mmengine - INFO - Epoch(train) [143][10/15] lr: 1.0000e-06 eta: 0:04:53 time: 0.3367 data_time: 0.0448 memory: 17122 loss: 0.0788 loss_ce: 0.0788 2023/03/03 14:36:26 - mmengine - INFO - Epoch(train) [143][11/15] lr: 1.0000e-06 eta: 0:04:53 time: 0.2927 data_time: 0.0017 memory: 17327 loss: 0.0766 loss_ce: 0.0766 2023/03/03 14:36:26 - mmengine - INFO - Epoch(train) [143][12/15] lr: 1.0000e-06 eta: 0:04:52 time: 0.2886 data_time: 0.0016 memory: 18070 loss: 0.0736 loss_ce: 0.0736 2023/03/03 14:36:26 - mmengine - INFO - Epoch(train) [143][13/15] lr: 1.0000e-06 eta: 0:04:52 time: 0.2885 data_time: 0.0016 memory: 17665 loss: 0.0744 loss_ce: 0.0744 2023/03/03 14:36:27 - mmengine - INFO - Epoch(train) [143][14/15] lr: 1.0000e-06 eta: 0:04:52 time: 0.2866 data_time: 0.0015 memory: 15044 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:36:27 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:36:27 - mmengine - INFO - Epoch(train) [143][15/15] lr: 1.0000e-06 eta: 0:04:51 time: 0.2797 data_time: 0.0015 memory: 5015 loss: 0.0741 loss_ce: 0.0741 2023/03/03 14:36:28 - mmengine - INFO - Epoch(train) [144][ 1/15] lr: 1.0000e-06 eta: 0:04:51 time: 0.3763 data_time: 0.0292 memory: 16508 loss: 0.0759 loss_ce: 0.0759 2023/03/03 14:36:28 - mmengine - INFO - Epoch(train) [144][ 2/15] lr: 1.0000e-06 eta: 0:04:51 time: 0.3704 data_time: 0.0292 memory: 17572 loss: 0.0784 loss_ce: 0.0784 2023/03/03 14:36:29 - mmengine - INFO - Epoch(train) [144][ 3/15] lr: 1.0000e-06 eta: 0:04:50 time: 0.3780 data_time: 0.0292 memory: 16804 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:36:29 - mmengine - INFO - Epoch(train) [144][ 4/15] lr: 1.0000e-06 eta: 0:04:50 time: 0.3696 data_time: 0.0293 memory: 16598 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:36:29 - mmengine - INFO - Epoch(train) [144][ 5/15] lr: 1.0000e-06 eta: 0:04:50 time: 0.3638 data_time: 0.0293 memory: 11703 loss: 0.0744 loss_ce: 0.0744 2023/03/03 14:36:29 - mmengine - INFO - Epoch(train) [144][ 6/15] lr: 1.0000e-06 eta: 0:04:49 time: 0.3670 data_time: 0.0293 memory: 13717 loss: 0.0811 loss_ce: 0.0811 2023/03/03 14:36:30 - mmengine - INFO - Epoch(train) [144][ 7/15] lr: 1.0000e-06 eta: 0:04:49 time: 0.3924 data_time: 0.0293 memory: 18131 loss: 0.0824 loss_ce: 0.0824 2023/03/03 14:36:30 - mmengine - INFO - Epoch(train) [144][ 8/15] lr: 1.0000e-06 eta: 0:04:49 time: 0.3952 data_time: 0.0293 memory: 16645 loss: 0.0825 loss_ce: 0.0825 2023/03/03 14:36:30 - mmengine - INFO - Epoch(train) [144][ 9/15] lr: 1.0000e-06 eta: 0:04:48 time: 0.3881 data_time: 0.0293 memory: 17730 loss: 0.0810 loss_ce: 0.0810 2023/03/03 14:36:31 - mmengine - INFO - Epoch(train) [144][10/15] lr: 1.0000e-06 eta: 0:04:48 time: 0.3941 data_time: 0.0293 memory: 15631 loss: 0.0787 loss_ce: 0.0787 2023/03/03 14:36:31 - mmengine - INFO - Epoch(train) [144][11/15] lr: 1.0000e-06 eta: 0:04:48 time: 0.3075 data_time: 0.0016 memory: 26295 loss: 0.0748 loss_ce: 0.0748 2023/03/03 14:36:31 - mmengine - INFO - Epoch(train) [144][12/15] lr: 1.0000e-06 eta: 0:04:47 time: 0.3092 data_time: 0.0015 memory: 15767 loss: 0.0725 loss_ce: 0.0725 2023/03/03 14:36:32 - mmengine - INFO - Epoch(train) [144][13/15] lr: 1.0000e-06 eta: 0:04:47 time: 0.3073 data_time: 0.0015 memory: 17272 loss: 0.0737 loss_ce: 0.0737 2023/03/03 14:36:32 - mmengine - INFO - Epoch(train) [144][14/15] lr: 1.0000e-06 eta: 0:04:47 time: 0.3157 data_time: 0.0015 memory: 18584 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:36:32 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:36:32 - mmengine - INFO - Epoch(train) [144][15/15] lr: 1.0000e-06 eta: 0:04:46 time: 0.3080 data_time: 0.0015 memory: 5451 loss: 0.0803 loss_ce: 0.0803 2023/03/03 14:36:33 - mmengine - INFO - Epoch(train) [145][ 1/15] lr: 1.0000e-06 eta: 0:04:46 time: 0.3595 data_time: 0.0504 memory: 18876 loss: 0.0742 loss_ce: 0.0742 2023/03/03 14:36:33 - mmengine - INFO - Epoch(train) [145][ 2/15] lr: 1.0000e-06 eta: 0:04:46 time: 0.3458 data_time: 0.0504 memory: 17272 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:36:34 - mmengine - INFO - Epoch(train) [145][ 3/15] lr: 1.0000e-06 eta: 0:04:45 time: 0.3412 data_time: 0.0505 memory: 14742 loss: 0.0751 loss_ce: 0.0751 2023/03/03 14:36:34 - mmengine - INFO - Epoch(train) [145][ 4/15] lr: 1.0000e-06 eta: 0:04:45 time: 0.3439 data_time: 0.0505 memory: 13221 loss: 0.0829 loss_ce: 0.0829 2023/03/03 14:36:34 - mmengine - INFO - Epoch(train) [145][ 5/15] lr: 1.0000e-06 eta: 0:04:45 time: 0.3534 data_time: 0.0505 memory: 15614 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:36:34 - mmengine - INFO - Epoch(train) [145][ 6/15] lr: 1.0000e-06 eta: 0:04:44 time: 0.3306 data_time: 0.0506 memory: 17421 loss: 0.0835 loss_ce: 0.0835 2023/03/03 14:36:35 - mmengine - INFO - Epoch(train) [145][ 7/15] lr: 1.0000e-06 eta: 0:04:44 time: 0.3317 data_time: 0.0506 memory: 16804 loss: 0.0842 loss_ce: 0.0842 2023/03/03 14:36:35 - mmengine - INFO - Epoch(train) [145][ 8/15] lr: 1.0000e-06 eta: 0:04:44 time: 0.3514 data_time: 0.0506 memory: 17619 loss: 0.0838 loss_ce: 0.0838 2023/03/03 14:36:35 - mmengine - INFO - Epoch(train) [145][ 9/15] lr: 1.0000e-06 eta: 0:04:43 time: 0.3481 data_time: 0.0506 memory: 16483 loss: 0.0844 loss_ce: 0.0844 2023/03/03 14:36:36 - mmengine - INFO - Epoch(train) [145][10/15] lr: 1.0000e-06 eta: 0:04:43 time: 0.3578 data_time: 0.0506 memory: 16804 loss: 0.0793 loss_ce: 0.0793 2023/03/03 14:36:36 - mmengine - INFO - Epoch(train) [145][11/15] lr: 1.0000e-06 eta: 0:04:42 time: 0.3034 data_time: 0.0018 memory: 18409 loss: 0.0810 loss_ce: 0.0810 2023/03/03 14:36:36 - mmengine - INFO - Epoch(train) [145][12/15] lr: 1.0000e-06 eta: 0:04:42 time: 0.2947 data_time: 0.0017 memory: 21271 loss: 0.0798 loss_ce: 0.0798 2023/03/03 14:36:37 - mmengine - INFO - Epoch(train) [145][13/15] lr: 1.0000e-06 eta: 0:04:42 time: 0.2979 data_time: 0.0016 memory: 15621 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:36:37 - mmengine - INFO - Epoch(train) [145][14/15] lr: 1.0000e-06 eta: 0:04:41 time: 0.2998 data_time: 0.0016 memory: 16429 loss: 0.0678 loss_ce: 0.0678 2023/03/03 14:36:37 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:36:37 - mmengine - INFO - Epoch(train) [145][15/15] lr: 1.0000e-06 eta: 0:04:41 time: 0.2863 data_time: 0.0015 memory: 10053 loss: 0.0732 loss_ce: 0.0732 2023/03/03 14:36:38 - mmengine - INFO - Epoch(train) [146][ 1/15] lr: 1.0000e-06 eta: 0:04:41 time: 0.3779 data_time: 0.0487 memory: 17120 loss: 0.0708 loss_ce: 0.0708 2023/03/03 14:36:38 - mmengine - INFO - Epoch(train) [146][ 2/15] lr: 1.0000e-06 eta: 0:04:41 time: 0.3759 data_time: 0.0487 memory: 17421 loss: 0.0720 loss_ce: 0.0720 2023/03/03 14:36:39 - mmengine - INFO - Epoch(train) [146][ 3/15] lr: 1.0000e-06 eta: 0:04:40 time: 0.3744 data_time: 0.0488 memory: 14761 loss: 0.0736 loss_ce: 0.0736 2023/03/03 14:36:39 - mmengine - INFO - Epoch(train) [146][ 4/15] lr: 1.0000e-06 eta: 0:04:40 time: 0.3785 data_time: 0.0488 memory: 19299 loss: 0.0726 loss_ce: 0.0726 2023/03/03 14:36:40 - mmengine - INFO - Epoch(train) [146][ 5/15] lr: 1.0000e-06 eta: 0:04:40 time: 0.3947 data_time: 0.0487 memory: 10827 loss: 0.0745 loss_ce: 0.0745 2023/03/03 14:36:40 - mmengine - INFO - Epoch(train) [146][ 6/15] lr: 1.0000e-06 eta: 0:04:39 time: 0.3958 data_time: 0.0488 memory: 19144 loss: 0.0703 loss_ce: 0.0703 2023/03/03 14:36:40 - mmengine - INFO - Epoch(train) [146][ 7/15] lr: 1.0000e-06 eta: 0:04:39 time: 0.3756 data_time: 0.0488 memory: 18070 loss: 0.0680 loss_ce: 0.0680 2023/03/03 14:36:40 - mmengine - INFO - Epoch(train) [146][ 8/15] lr: 1.0000e-06 eta: 0:04:38 time: 0.3747 data_time: 0.0488 memory: 12332 loss: 0.0702 loss_ce: 0.0702 2023/03/03 14:36:41 - mmengine - INFO - Epoch(train) [146][ 9/15] lr: 1.0000e-06 eta: 0:04:38 time: 0.4030 data_time: 0.0488 memory: 16508 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:36:41 - mmengine - INFO - Epoch(train) [146][10/15] lr: 1.0000e-06 eta: 0:04:38 time: 0.4079 data_time: 0.0489 memory: 16370 loss: 0.0694 loss_ce: 0.0694 2023/03/03 14:36:41 - mmengine - INFO - Epoch(train) [146][11/15] lr: 1.0000e-06 eta: 0:04:37 time: 0.3154 data_time: 0.0017 memory: 17572 loss: 0.0687 loss_ce: 0.0687 2023/03/03 14:36:42 - mmengine - INFO - Epoch(train) [146][12/15] lr: 1.0000e-06 eta: 0:04:37 time: 0.3193 data_time: 0.0016 memory: 18311 loss: 0.0673 loss_ce: 0.0673 2023/03/03 14:36:42 - mmengine - INFO - Epoch(train) [146][13/15] lr: 1.0000e-06 eta: 0:04:37 time: 0.2957 data_time: 0.0016 memory: 18474 loss: 0.0643 loss_ce: 0.0643 2023/03/03 14:36:42 - mmengine - INFO - Epoch(train) [146][14/15] lr: 1.0000e-06 eta: 0:04:36 time: 0.2861 data_time: 0.0016 memory: 16654 loss: 0.0645 loss_ce: 0.0645 2023/03/03 14:36:42 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:36:42 - mmengine - INFO - Epoch(train) [146][15/15] lr: 1.0000e-06 eta: 0:04:36 time: 0.2609 data_time: 0.0016 memory: 6263 loss: 0.0682 loss_ce: 0.0682 2023/03/03 14:36:43 - mmengine - INFO - Epoch(train) [147][ 1/15] lr: 1.0000e-06 eta: 0:04:36 time: 0.2932 data_time: 0.0361 memory: 12870 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:36:44 - mmengine - INFO - Epoch(train) [147][ 2/15] lr: 1.0000e-06 eta: 0:04:35 time: 0.3448 data_time: 0.0668 memory: 18923 loss: 0.0733 loss_ce: 0.0733 2023/03/03 14:36:44 - mmengine - INFO - Epoch(train) [147][ 3/15] lr: 1.0000e-06 eta: 0:04:35 time: 0.3443 data_time: 0.0669 memory: 16976 loss: 0.0756 loss_ce: 0.0756 2023/03/03 14:36:44 - mmengine - INFO - Epoch(train) [147][ 4/15] lr: 1.0000e-06 eta: 0:04:35 time: 0.3132 data_time: 0.0669 memory: 14675 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:36:44 - mmengine - INFO - Epoch(train) [147][ 5/15] lr: 1.0000e-06 eta: 0:04:34 time: 0.3265 data_time: 0.0669 memory: 19217 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:36:45 - mmengine - INFO - Epoch(train) [147][ 6/15] lr: 1.0000e-06 eta: 0:04:34 time: 0.3345 data_time: 0.0669 memory: 18489 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:36:45 - mmengine - INFO - Epoch(train) [147][ 7/15] lr: 1.0000e-06 eta: 0:04:34 time: 0.3742 data_time: 0.0669 memory: 29810 loss: 0.0803 loss_ce: 0.0803 2023/03/03 14:36:46 - mmengine - INFO - Epoch(train) [147][ 8/15] lr: 1.0000e-06 eta: 0:04:33 time: 0.3825 data_time: 0.0669 memory: 19530 loss: 0.0824 loss_ce: 0.0824 2023/03/03 14:36:46 - mmengine - INFO - Epoch(train) [147][ 9/15] lr: 1.0000e-06 eta: 0:04:33 time: 0.3797 data_time: 0.0669 memory: 17120 loss: 0.0828 loss_ce: 0.0828 2023/03/03 14:36:46 - mmengine - INFO - Epoch(train) [147][10/15] lr: 1.0000e-06 eta: 0:04:33 time: 0.3928 data_time: 0.0669 memory: 14951 loss: 0.0821 loss_ce: 0.0821 2023/03/03 14:36:47 - mmengine - INFO - Epoch(train) [147][11/15] lr: 1.0000e-06 eta: 0:04:32 time: 0.3714 data_time: 0.0324 memory: 15602 loss: 0.0793 loss_ce: 0.0793 2023/03/03 14:36:47 - mmengine - INFO - Epoch(train) [147][12/15] lr: 1.0000e-06 eta: 0:04:32 time: 0.3362 data_time: 0.0016 memory: 17272 loss: 0.0831 loss_ce: 0.0831 2023/03/03 14:36:47 - mmengine - INFO - Epoch(train) [147][13/15] lr: 1.0000e-06 eta: 0:04:32 time: 0.3379 data_time: 0.0016 memory: 16089 loss: 0.0778 loss_ce: 0.0778 2023/03/03 14:36:47 - mmengine - INFO - Epoch(train) [147][14/15] lr: 1.0000e-06 eta: 0:04:31 time: 0.3402 data_time: 0.0015 memory: 15682 loss: 0.0774 loss_ce: 0.0774 2023/03/03 14:36:48 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:36:48 - mmengine - INFO - Epoch(train) [147][15/15] lr: 1.0000e-06 eta: 0:04:31 time: 0.3191 data_time: 0.0015 memory: 3739 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:36:49 - mmengine - INFO - Epoch(train) [148][ 1/15] lr: 1.0000e-06 eta: 0:04:31 time: 0.3861 data_time: 0.0611 memory: 16508 loss: 0.0735 loss_ce: 0.0735 2023/03/03 14:36:49 - mmengine - INFO - Epoch(train) [148][ 2/15] lr: 1.0000e-06 eta: 0:04:30 time: 0.3420 data_time: 0.0611 memory: 17272 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:36:49 - mmengine - INFO - Epoch(train) [148][ 3/15] lr: 1.0000e-06 eta: 0:04:30 time: 0.3490 data_time: 0.0612 memory: 15631 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:36:49 - mmengine - INFO - Epoch(train) [148][ 4/15] lr: 1.0000e-06 eta: 0:04:30 time: 0.3471 data_time: 0.0612 memory: 14899 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:36:50 - mmengine - INFO - Epoch(train) [148][ 5/15] lr: 1.0000e-06 eta: 0:04:29 time: 0.3617 data_time: 0.0612 memory: 19941 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:36:50 - mmengine - INFO - Epoch(train) [148][ 6/15] lr: 1.0000e-06 eta: 0:04:29 time: 0.3527 data_time: 0.0613 memory: 17122 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:36:50 - mmengine - INFO - Epoch(train) [148][ 7/15] lr: 1.0000e-06 eta: 0:04:29 time: 0.3401 data_time: 0.0613 memory: 15491 loss: 0.0732 loss_ce: 0.0732 2023/03/03 14:36:51 - mmengine - INFO - Epoch(train) [148][ 8/15] lr: 1.0000e-06 eta: 0:04:28 time: 0.3412 data_time: 0.0613 memory: 16749 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:36:51 - mmengine - INFO - Epoch(train) [148][ 9/15] lr: 1.0000e-06 eta: 0:04:28 time: 0.3685 data_time: 0.0613 memory: 15937 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:36:51 - mmengine - INFO - Epoch(train) [148][10/15] lr: 1.0000e-06 eta: 0:04:28 time: 0.3737 data_time: 0.0613 memory: 17272 loss: 0.0736 loss_ce: 0.0736 2023/03/03 14:36:52 - mmengine - INFO - Epoch(train) [148][11/15] lr: 1.0000e-06 eta: 0:04:27 time: 0.3073 data_time: 0.0017 memory: 18110 loss: 0.0785 loss_ce: 0.0785 2023/03/03 14:36:52 - mmengine - INFO - Epoch(train) [148][12/15] lr: 1.0000e-06 eta: 0:04:27 time: 0.3079 data_time: 0.0016 memory: 15175 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:36:52 - mmengine - INFO - Epoch(train) [148][13/15] lr: 1.0000e-06 eta: 0:04:27 time: 0.3100 data_time: 0.0016 memory: 17788 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:36:53 - mmengine - INFO - Epoch(train) [148][14/15] lr: 1.0000e-06 eta: 0:04:26 time: 0.3178 data_time: 0.0015 memory: 19280 loss: 0.0758 loss_ce: 0.0758 2023/03/03 14:36:53 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:36:53 - mmengine - INFO - Epoch(train) [148][15/15] lr: 1.0000e-06 eta: 0:04:26 time: 0.3075 data_time: 0.0015 memory: 5640 loss: 0.0816 loss_ce: 0.0816 2023/03/03 14:36:54 - mmengine - INFO - Epoch(train) [149][ 1/15] lr: 1.0000e-06 eta: 0:04:26 time: 0.3690 data_time: 0.0623 memory: 17159 loss: 0.0802 loss_ce: 0.0802 2023/03/03 14:36:54 - mmengine - INFO - Epoch(train) [149][ 2/15] lr: 1.0000e-06 eta: 0:04:25 time: 0.3990 data_time: 0.0624 memory: 16223 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:36:55 - mmengine - INFO - Epoch(train) [149][ 3/15] lr: 1.0000e-06 eta: 0:04:25 time: 0.3961 data_time: 0.0625 memory: 17120 loss: 0.0834 loss_ce: 0.0834 2023/03/03 14:36:55 - mmengine - INFO - Epoch(train) [149][ 4/15] lr: 1.0000e-06 eta: 0:04:25 time: 0.3751 data_time: 0.0625 memory: 16165 loss: 0.0830 loss_ce: 0.0830 2023/03/03 14:36:55 - mmengine - INFO - Epoch(train) [149][ 5/15] lr: 1.0000e-06 eta: 0:04:24 time: 0.3771 data_time: 0.0625 memory: 16056 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:36:55 - mmengine - INFO - Epoch(train) [149][ 6/15] lr: 1.0000e-06 eta: 0:04:24 time: 0.3770 data_time: 0.0625 memory: 17120 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:36:56 - mmengine - INFO - Epoch(train) [149][ 7/15] lr: 1.0000e-06 eta: 0:04:24 time: 0.3761 data_time: 0.0625 memory: 17892 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:36:56 - mmengine - INFO - Epoch(train) [149][ 8/15] lr: 1.0000e-06 eta: 0:04:23 time: 0.4014 data_time: 0.0624 memory: 25575 loss: 0.0854 loss_ce: 0.0854 2023/03/03 14:36:57 - mmengine - INFO - Epoch(train) [149][ 9/15] lr: 1.0000e-06 eta: 0:04:23 time: 0.3984 data_time: 0.0624 memory: 16804 loss: 0.0848 loss_ce: 0.0848 2023/03/03 14:36:57 - mmengine - INFO - Epoch(train) [149][10/15] lr: 1.0000e-06 eta: 0:04:23 time: 0.4078 data_time: 0.0625 memory: 15346 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:36:57 - mmengine - INFO - Epoch(train) [149][11/15] lr: 1.0000e-06 eta: 0:04:22 time: 0.3588 data_time: 0.0017 memory: 16136 loss: 0.0817 loss_ce: 0.0817 2023/03/03 14:36:58 - mmengine - INFO - Epoch(train) [149][12/15] lr: 1.0000e-06 eta: 0:04:22 time: 0.3236 data_time: 0.0016 memory: 21215 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:36:58 - mmengine - INFO - Epoch(train) [149][13/15] lr: 1.0000e-06 eta: 0:04:22 time: 0.3266 data_time: 0.0016 memory: 16884 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:36:58 - mmengine - INFO - Epoch(train) [149][14/15] lr: 1.0000e-06 eta: 0:04:21 time: 0.3160 data_time: 0.0015 memory: 16370 loss: 0.0759 loss_ce: 0.0759 2023/03/03 14:36:58 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:36:58 - mmengine - INFO - Epoch(train) [149][15/15] lr: 1.0000e-06 eta: 0:04:21 time: 0.3061 data_time: 0.0015 memory: 5575 loss: 0.0772 loss_ce: 0.0772 2023/03/03 14:36:59 - mmengine - INFO - Epoch(train) [150][ 1/15] lr: 1.0000e-06 eta: 0:04:21 time: 0.3658 data_time: 0.0296 memory: 15783 loss: 0.0768 loss_ce: 0.0768 2023/03/03 14:36:59 - mmengine - INFO - Epoch(train) [150][ 2/15] lr: 1.0000e-06 eta: 0:04:20 time: 0.3696 data_time: 0.0297 memory: 17619 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:37:00 - mmengine - INFO - Epoch(train) [150][ 3/15] lr: 1.0000e-06 eta: 0:04:20 time: 0.3253 data_time: 0.0298 memory: 17120 loss: 0.0746 loss_ce: 0.0746 2023/03/03 14:37:00 - mmengine - INFO - Epoch(train) [150][ 4/15] lr: 1.0000e-06 eta: 0:04:19 time: 0.3227 data_time: 0.0298 memory: 17421 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:37:00 - mmengine - INFO - Epoch(train) [150][ 5/15] lr: 1.0000e-06 eta: 0:04:19 time: 0.3107 data_time: 0.0298 memory: 18586 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:37:00 - mmengine - INFO - Epoch(train) [150][ 6/15] lr: 1.0000e-06 eta: 0:04:19 time: 0.2950 data_time: 0.0298 memory: 17421 loss: 0.0673 loss_ce: 0.0673 2023/03/03 14:37:01 - mmengine - INFO - Epoch(train) [150][ 7/15] lr: 1.0000e-06 eta: 0:04:18 time: 0.3074 data_time: 0.0298 memory: 11772 loss: 0.0694 loss_ce: 0.0694 2023/03/03 14:37:01 - mmengine - INFO - Epoch(train) [150][ 8/15] lr: 1.0000e-06 eta: 0:04:18 time: 0.3061 data_time: 0.0298 memory: 15767 loss: 0.0705 loss_ce: 0.0705 2023/03/03 14:37:01 - mmengine - INFO - Epoch(train) [150][ 9/15] lr: 1.0000e-06 eta: 0:04:18 time: 0.3297 data_time: 0.0298 memory: 31202 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:37:02 - mmengine - INFO - Epoch(train) [150][10/15] lr: 1.0000e-06 eta: 0:04:17 time: 0.3528 data_time: 0.0298 memory: 25899 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:37:02 - mmengine - INFO - Epoch(train) [150][11/15] lr: 1.0000e-06 eta: 0:04:17 time: 0.2827 data_time: 0.0018 memory: 16830 loss: 0.0693 loss_ce: 0.0693 2023/03/03 14:37:02 - mmengine - INFO - Epoch(train) [150][12/15] lr: 1.0000e-06 eta: 0:04:17 time: 0.2805 data_time: 0.0017 memory: 13879 loss: 0.0678 loss_ce: 0.0678 2023/03/03 14:37:03 - mmengine - INFO - Epoch(train) [150][13/15] lr: 1.0000e-06 eta: 0:04:16 time: 0.2959 data_time: 0.0016 memory: 16370 loss: 0.0668 loss_ce: 0.0668 2023/03/03 14:37:03 - mmengine - INFO - Epoch(train) [150][14/15] lr: 1.0000e-06 eta: 0:04:16 time: 0.3018 data_time: 0.0016 memory: 19208 loss: 0.0751 loss_ce: 0.0751 2023/03/03 14:37:03 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:37:03 - mmengine - INFO - Epoch(train) [150][15/15] lr: 1.0000e-06 eta: 0:04:15 time: 0.2886 data_time: 0.0016 memory: 5406 loss: 0.0780 loss_ce: 0.0780 2023/03/03 14:37:05 - mmengine - INFO - Epoch(val) [150][ 1/59] eta: 0:01:29 time: 1.0801 data_time: 0.0033 memory: 981 2023/03/03 14:37:05 - mmengine - INFO - Epoch(val) [150][ 2/59] eta: 0:01:07 time: 0.9951 data_time: 0.0033 memory: 981 2023/03/03 14:37:07 - mmengine - INFO - Epoch(val) [150][ 3/59] eta: 0:01:09 time: 1.0123 data_time: 0.0033 memory: 1003 2023/03/03 14:37:07 - mmengine - INFO - Epoch(val) [150][ 4/59] eta: 0:00:55 time: 0.9796 data_time: 0.0033 memory: 981 2023/03/03 14:37:10 - mmengine - INFO - Epoch(val) [150][ 5/59] eta: 0:01:16 time: 1.2159 data_time: 0.0033 memory: 1016 2023/03/03 14:37:13 - mmengine - INFO - Epoch(val) [150][ 6/59] eta: 0:01:25 time: 1.4122 data_time: 0.0033 memory: 981 2023/03/03 14:37:13 - mmengine - INFO - Epoch(val) [150][ 7/59] eta: 0:01:13 time: 1.3630 data_time: 0.0033 memory: 1043 2023/03/03 14:37:14 - mmengine - INFO - Epoch(val) [150][ 8/59] eta: 0:01:07 time: 1.2062 data_time: 0.0033 memory: 1016 2023/03/03 14:37:15 - mmengine - INFO - Epoch(val) [150][ 9/59] eta: 0:01:04 time: 1.1895 data_time: 0.0034 memory: 981 2023/03/03 14:37:15 - mmengine - INFO - Epoch(val) [150][10/59] eta: 0:00:59 time: 1.2223 data_time: 0.0034 memory: 981 2023/03/03 14:37:16 - mmengine - INFO - Epoch(val) [150][11/59] eta: 0:00:54 time: 1.1014 data_time: 0.0008 memory: 981 2023/03/03 14:37:19 - mmengine - INFO - Epoch(val) [150][12/59] eta: 0:01:01 time: 1.3428 data_time: 0.0008 memory: 1016 2023/03/03 14:37:21 - mmengine - INFO - Epoch(val) [150][13/59] eta: 0:01:03 time: 1.4332 data_time: 0.0008 memory: 981 2023/03/03 14:37:22 - mmengine - INFO - Epoch(val) [150][14/59] eta: 0:01:01 time: 1.5001 data_time: 0.0008 memory: 890 2023/03/03 14:37:22 - mmengine - INFO - Epoch(val) [150][15/59] eta: 0:00:55 time: 1.1996 data_time: 0.0008 memory: 981 2023/03/03 14:37:23 - mmengine - INFO - Epoch(val) [150][16/59] eta: 0:00:52 time: 0.9867 data_time: 0.0008 memory: 981 2023/03/03 14:37:23 - mmengine - INFO - Epoch(val) [150][17/59] eta: 0:00:49 time: 1.0030 data_time: 0.0008 memory: 981 2023/03/03 14:37:23 - mmengine - INFO - Epoch(val) [150][18/59] eta: 0:00:46 time: 0.9700 data_time: 0.0008 memory: 981 2023/03/03 14:37:24 - mmengine - INFO - Epoch(val) [150][19/59] eta: 0:00:44 time: 0.9701 data_time: 0.0008 memory: 981 2023/03/03 14:37:25 - mmengine - INFO - Epoch(val) [150][20/59] eta: 0:00:42 time: 0.9375 data_time: 0.0008 memory: 981 2023/03/03 14:37:25 - mmengine - INFO - Epoch(val) [150][21/59] eta: 0:00:39 time: 0.9538 data_time: 0.0008 memory: 981 2023/03/03 14:37:25 - mmengine - INFO - Epoch(val) [150][22/59] eta: 0:00:37 time: 0.6467 data_time: 0.0008 memory: 981 2023/03/03 14:37:26 - mmengine - INFO - Epoch(val) [150][23/59] eta: 0:00:35 time: 0.4888 data_time: 0.0007 memory: 981 2023/03/03 14:37:26 - mmengine - INFO - Epoch(val) [150][24/59] eta: 0:00:33 time: 0.4218 data_time: 0.0007 memory: 962 2023/03/03 14:37:27 - mmengine - INFO - Epoch(val) [150][25/59] eta: 0:00:32 time: 0.4532 data_time: 0.0007 memory: 981 2023/03/03 14:37:27 - mmengine - INFO - Epoch(val) [150][26/59] eta: 0:00:30 time: 0.4369 data_time: 0.0007 memory: 981 2023/03/03 14:37:27 - mmengine - INFO - Epoch(val) [150][27/59] eta: 0:00:28 time: 0.4370 data_time: 0.0007 memory: 981 2023/03/03 14:37:28 - mmengine - INFO - Epoch(val) [150][28/59] eta: 0:00:27 time: 0.4370 data_time: 0.0007 memory: 981 2023/03/03 14:37:29 - mmengine - INFO - Epoch(val) [150][29/59] eta: 0:00:26 time: 0.4712 data_time: 0.0007 memory: 981 2023/03/03 14:37:30 - mmengine - INFO - Epoch(val) [150][30/59] eta: 0:00:25 time: 0.5203 data_time: 0.0007 memory: 999 2023/03/03 14:37:30 - mmengine - INFO - Epoch(val) [150][31/59] eta: 0:00:24 time: 0.5365 data_time: 0.0007 memory: 981 2023/03/03 14:37:32 - mmengine - INFO - Epoch(val) [150][32/59] eta: 0:00:24 time: 0.6351 data_time: 0.0007 memory: 981 2023/03/03 14:37:32 - mmengine - INFO - Epoch(val) [150][33/59] eta: 0:00:22 time: 0.5710 data_time: 0.0007 memory: 981 2023/03/03 14:37:32 - mmengine - INFO - Epoch(val) [150][34/59] eta: 0:00:21 time: 0.5547 data_time: 0.0007 memory: 981 2023/03/03 14:37:32 - mmengine - INFO - Epoch(val) [150][35/59] eta: 0:00:19 time: 0.5382 data_time: 0.0007 memory: 981 2023/03/03 14:37:33 - mmengine - INFO - Epoch(val) [150][36/59] eta: 0:00:18 time: 0.5545 data_time: 0.0007 memory: 981 2023/03/03 14:37:33 - mmengine - INFO - Epoch(val) [150][37/59] eta: 0:00:17 time: 0.5381 data_time: 0.0007 memory: 981 2023/03/03 14:37:33 - mmengine - INFO - Epoch(val) [150][38/59] eta: 0:00:16 time: 0.5707 data_time: 0.0007 memory: 981 2023/03/03 14:37:34 - mmengine - INFO - Epoch(val) [150][39/59] eta: 0:00:15 time: 0.4864 data_time: 0.0007 memory: 987 2023/03/03 14:37:35 - mmengine - INFO - Epoch(val) [150][40/59] eta: 0:00:15 time: 0.4863 data_time: 0.0007 memory: 981 2023/03/03 14:37:36 - mmengine - INFO - Epoch(val) [150][41/59] eta: 0:00:14 time: 0.5363 data_time: 0.0007 memory: 986 2023/03/03 14:37:37 - mmengine - INFO - Epoch(val) [150][42/59] eta: 0:00:13 time: 0.4865 data_time: 0.0007 memory: 981 2023/03/03 14:37:37 - mmengine - INFO - Epoch(val) [150][43/59] eta: 0:00:12 time: 0.5670 data_time: 0.0007 memory: 976 2023/03/03 14:37:38 - mmengine - INFO - Epoch(val) [150][44/59] eta: 0:00:11 time: 0.5996 data_time: 0.0007 memory: 1003 2023/03/03 14:37:40 - mmengine - INFO - Epoch(val) [150][45/59] eta: 0:00:11 time: 0.7687 data_time: 0.0007 memory: 981 2023/03/03 14:37:41 - mmengine - INFO - Epoch(val) [150][46/59] eta: 0:00:10 time: 0.8020 data_time: 0.0007 memory: 981 2023/03/03 14:37:41 - mmengine - INFO - Epoch(val) [150][47/59] eta: 0:00:09 time: 0.8342 data_time: 0.0007 memory: 936 2023/03/03 14:37:42 - mmengine - INFO - Epoch(val) [150][48/59] eta: 0:00:08 time: 0.8177 data_time: 0.0007 memory: 1000 2023/03/03 14:37:43 - mmengine - INFO - Epoch(val) [150][49/59] eta: 0:00:08 time: 0.8676 data_time: 0.0007 memory: 981 2023/03/03 14:37:43 - mmengine - INFO - Epoch(val) [150][50/59] eta: 0:00:07 time: 0.8676 data_time: 0.0008 memory: 987 2023/03/03 14:37:45 - mmengine - INFO - Epoch(val) [150][51/59] eta: 0:00:06 time: 0.9201 data_time: 0.0007 memory: 981 2023/03/03 14:37:46 - mmengine - INFO - Epoch(val) [150][52/59] eta: 0:00:05 time: 0.9705 data_time: 0.0007 memory: 981 2023/03/03 14:37:47 - mmengine - INFO - Epoch(val) [150][53/59] eta: 0:00:04 time: 0.9375 data_time: 0.0007 memory: 962 2023/03/03 14:37:47 - mmengine - INFO - Epoch(val) [150][54/59] eta: 0:00:04 time: 0.9542 data_time: 0.0007 memory: 981 2023/03/03 14:37:48 - mmengine - INFO - Epoch(val) [150][55/59] eta: 0:00:03 time: 0.8344 data_time: 0.0007 memory: 981 2023/03/03 14:37:49 - mmengine - INFO - Epoch(val) [150][56/59] eta: 0:00:02 time: 0.8177 data_time: 0.0007 memory: 981 2023/03/03 14:37:51 - mmengine - INFO - Epoch(val) [150][57/59] eta: 0:00:01 time: 0.9928 data_time: 0.0007 memory: 981 2023/03/03 14:37:52 - mmengine - INFO - Epoch(val) [150][58/59] eta: 0:00:00 time: 1.0590 data_time: 0.0007 memory: 1016 2023/03/03 14:37:52 - mmengine - INFO - Epoch(val) [150][59/59] eta: 0:00:00 time: 0.9929 data_time: 0.0007 memory: 981 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.80, recall: 0.8210, precision: 0.8347, hmean: 0.8278 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.81, recall: 0.8201, precision: 0.8377, hmean: 0.8288 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.82, recall: 0.8192, precision: 0.8391, hmean: 0.8290 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.83, recall: 0.8164, precision: 0.8442, hmean: 0.8301 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.84, recall: 0.8146, precision: 0.8447, hmean: 0.8294 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.85, recall: 0.8128, precision: 0.8460, hmean: 0.8291 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.86, recall: 0.8119, precision: 0.8491, hmean: 0.8301 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.87, recall: 0.8110, precision: 0.8506, hmean: 0.8303 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.88, recall: 0.8082, precision: 0.8518, hmean: 0.8294 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.89, recall: 0.8055, precision: 0.8514, hmean: 0.8278 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.90, recall: 0.8009, precision: 0.8556, hmean: 0.8274 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.91, recall: 0.7973, precision: 0.8593, hmean: 0.8271 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.92, recall: 0.7927, precision: 0.8637, hmean: 0.8267 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.93, recall: 0.7872, precision: 0.8690, hmean: 0.8261 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.94, recall: 0.7763, precision: 0.8700, hmean: 0.8205 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.95, recall: 0.7735, precision: 0.8732, hmean: 0.8203 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.96, recall: 0.7589, precision: 0.8757, hmean: 0.8131 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.97, recall: 0.7516, precision: 0.8793, hmean: 0.8104 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.98, recall: 0.7406, precision: 0.8854, hmean: 0.8066 2023/03/03 14:38:24 - mmengine - INFO - text score threshold: 0.99, recall: 0.7178, precision: 0.8912, hmean: 0.7951 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.80, recall: 0.8338, precision: 0.9058, hmean: 0.8683 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.81, recall: 0.8329, precision: 0.9075, hmean: 0.8686 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.82, recall: 0.8311, precision: 0.9073, hmean: 0.8675 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.83, recall: 0.8283, precision: 0.9106, hmean: 0.8675 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.84, recall: 0.8265, precision: 0.9114, hmean: 0.8669 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.85, recall: 0.8247, precision: 0.9121, hmean: 0.8662 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.86, recall: 0.8237, precision: 0.9130, hmean: 0.8661 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.87, recall: 0.8228, precision: 0.9138, hmean: 0.8659 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.88, recall: 0.8192, precision: 0.9144, hmean: 0.8642 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.89, recall: 0.8164, precision: 0.9141, hmean: 0.8625 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.90, recall: 0.8100, precision: 0.9154, hmean: 0.8595 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.91, recall: 0.8046, precision: 0.9177, hmean: 0.8574 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.92, recall: 0.7982, precision: 0.9200, hmean: 0.8548 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.93, recall: 0.7890, precision: 0.9201, hmean: 0.8496 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.94, recall: 0.7790, precision: 0.9212, hmean: 0.8441 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.95, recall: 0.7753, precision: 0.9218, hmean: 0.8423 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.96, recall: 0.7598, precision: 0.9234, hmean: 0.8337 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.97, recall: 0.7534, precision: 0.9249, hmean: 0.8304 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.98, recall: 0.7397, precision: 0.9257, hmean: 0.8223 2023/03/03 14:38:27 - mmengine - INFO - text score threshold: 0.99, recall: 0.7169, precision: 0.9301, hmean: 0.8097 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.80, recall: 0.7543, precision: 0.9560, hmean: 0.8433 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.81, recall: 0.7534, precision: 0.9571, hmean: 0.8431 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.82, recall: 0.7525, precision: 0.9570, hmean: 0.8425 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.83, recall: 0.7498, precision: 0.9580, hmean: 0.8412 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.84, recall: 0.7479, precision: 0.9579, hmean: 0.8400 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.85, recall: 0.7461, precision: 0.9578, hmean: 0.8388 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.86, recall: 0.7452, precision: 0.9577, hmean: 0.8382 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.87, recall: 0.7443, precision: 0.9577, hmean: 0.8376 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.88, recall: 0.7406, precision: 0.9575, hmean: 0.8352 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.89, recall: 0.7388, precision: 0.9574, hmean: 0.8340 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.90, recall: 0.7324, precision: 0.9582, hmean: 0.8302 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.91, recall: 0.7279, precision: 0.9579, hmean: 0.8272 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.92, recall: 0.7215, precision: 0.9587, hmean: 0.8233 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.93, recall: 0.7132, precision: 0.9606, hmean: 0.8187 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.94, recall: 0.7050, precision: 0.9614, hmean: 0.8135 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.95, recall: 0.7014, precision: 0.9624, hmean: 0.8114 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.96, recall: 0.6886, precision: 0.9654, hmean: 0.8038 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.97, recall: 0.6813, precision: 0.9663, hmean: 0.7991 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.98, recall: 0.6685, precision: 0.9657, hmean: 0.7901 2023/03/03 14:38:29 - mmengine - INFO - text score threshold: 0.99, recall: 0.6475, precision: 0.9673, hmean: 0.7757 2023/03/03 14:38:29 - mmengine - INFO - Epoch(val) [150][59/59] generic/precision: 0.8506 generic/recall: 0.8110 generic/hmean: 0.8303 weak/precision: 0.9075 weak/recall: 0.8329 weak/hmean: 0.8686 strong/precision: 0.9560 strong/recall: 0.7543 strong/hmean: 0.8433 2023/03/03 14:38:30 - mmengine - INFO - Epoch(train) [151][ 1/15] lr: 1.0000e-06 eta: 0:04:15 time: 0.3563 data_time: 0.0630 memory: 16574 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:38:30 - mmengine - INFO - Epoch(train) [151][ 2/15] lr: 1.0000e-06 eta: 0:04:15 time: 0.3631 data_time: 0.0631 memory: 22999 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:38:31 - mmengine - INFO - Epoch(train) [151][ 3/15] lr: 1.0000e-06 eta: 0:04:15 time: 0.3717 data_time: 0.0631 memory: 16240 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:38:31 - mmengine - INFO - Epoch(train) [151][ 4/15] lr: 1.0000e-06 eta: 0:04:14 time: 0.3657 data_time: 0.0632 memory: 15507 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:38:31 - mmengine - INFO - Epoch(train) [151][ 5/15] lr: 1.0000e-06 eta: 0:04:14 time: 0.3529 data_time: 0.0632 memory: 16530 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:38:32 - mmengine - INFO - Epoch(train) [151][ 6/15] lr: 1.0000e-06 eta: 0:04:14 time: 0.3820 data_time: 0.0632 memory: 17198 loss: 0.0742 loss_ce: 0.0742 2023/03/03 14:38:32 - mmengine - INFO - Epoch(train) [151][ 7/15] lr: 1.0000e-06 eta: 0:04:13 time: 0.3907 data_time: 0.0632 memory: 17722 loss: 0.0748 loss_ce: 0.0748 2023/03/03 14:38:32 - mmengine - INFO - Epoch(train) [151][ 8/15] lr: 1.0000e-06 eta: 0:04:13 time: 0.3788 data_time: 0.0632 memory: 18241 loss: 0.0734 loss_ce: 0.0734 2023/03/03 14:38:33 - mmengine - INFO - Epoch(train) [151][ 9/15] lr: 1.0000e-06 eta: 0:04:13 time: 0.3727 data_time: 0.0632 memory: 17272 loss: 0.0661 loss_ce: 0.0661 2023/03/03 14:38:33 - mmengine - INFO - Epoch(train) [151][10/15] lr: 1.0000e-06 eta: 0:04:12 time: 0.3841 data_time: 0.0632 memory: 16804 loss: 0.0670 loss_ce: 0.0670 2023/03/03 14:38:33 - mmengine - INFO - Epoch(train) [151][11/15] lr: 1.0000e-06 eta: 0:04:12 time: 0.3180 data_time: 0.0018 memory: 15767 loss: 0.0676 loss_ce: 0.0676 2023/03/03 14:38:33 - mmengine - INFO - Epoch(train) [151][12/15] lr: 1.0000e-06 eta: 0:04:12 time: 0.3146 data_time: 0.0018 memory: 16804 loss: 0.0668 loss_ce: 0.0668 2023/03/03 14:38:34 - mmengine - INFO - Epoch(train) [151][13/15] lr: 1.0000e-06 eta: 0:04:11 time: 0.3075 data_time: 0.0017 memory: 15165 loss: 0.0678 loss_ce: 0.0678 2023/03/03 14:38:34 - mmengine - INFO - Epoch(train) [151][14/15] lr: 1.0000e-06 eta: 0:04:11 time: 0.2950 data_time: 0.0017 memory: 16976 loss: 0.0698 loss_ce: 0.0698 2023/03/03 14:38:34 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:38:34 - mmengine - INFO - Epoch(train) [151][15/15] lr: 1.0000e-06 eta: 0:04:10 time: 0.2896 data_time: 0.0016 memory: 7277 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:38:35 - mmengine - INFO - Epoch(train) [152][ 1/15] lr: 1.0000e-06 eta: 0:04:10 time: 0.3213 data_time: 0.0446 memory: 17421 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:38:35 - mmengine - INFO - Epoch(train) [152][ 2/15] lr: 1.0000e-06 eta: 0:04:10 time: 0.3103 data_time: 0.0446 memory: 12851 loss: 0.0756 loss_ce: 0.0756 2023/03/03 14:38:36 - mmengine - INFO - Epoch(train) [152][ 3/15] lr: 1.0000e-06 eta: 0:04:10 time: 0.3558 data_time: 0.0447 memory: 15037 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:38:36 - mmengine - INFO - Epoch(train) [152][ 4/15] lr: 1.0000e-06 eta: 0:04:09 time: 0.3605 data_time: 0.0447 memory: 17750 loss: 0.0726 loss_ce: 0.0726 2023/03/03 14:38:36 - mmengine - INFO - Epoch(train) [152][ 5/15] lr: 1.0000e-06 eta: 0:04:09 time: 0.3540 data_time: 0.0447 memory: 16508 loss: 0.0704 loss_ce: 0.0704 2023/03/03 14:38:37 - mmengine - INFO - Epoch(train) [152][ 6/15] lr: 1.0000e-06 eta: 0:04:08 time: 0.3558 data_time: 0.0447 memory: 17619 loss: 0.0690 loss_ce: 0.0690 2023/03/03 14:38:37 - mmengine - INFO - Epoch(train) [152][ 7/15] lr: 1.0000e-06 eta: 0:04:08 time: 0.3403 data_time: 0.0447 memory: 16258 loss: 0.0689 loss_ce: 0.0689 2023/03/03 14:38:37 - mmengine - INFO - Epoch(train) [152][ 8/15] lr: 1.0000e-06 eta: 0:04:08 time: 0.3381 data_time: 0.0447 memory: 15175 loss: 0.0679 loss_ce: 0.0679 2023/03/03 14:38:37 - mmengine - INFO - Epoch(train) [152][ 9/15] lr: 1.0000e-06 eta: 0:04:07 time: 0.3495 data_time: 0.0447 memory: 17122 loss: 0.0654 loss_ce: 0.0654 2023/03/03 14:38:38 - mmengine - INFO - Epoch(train) [152][10/15] lr: 1.0000e-06 eta: 0:04:07 time: 0.3550 data_time: 0.0447 memory: 16654 loss: 0.0649 loss_ce: 0.0649 2023/03/03 14:38:38 - mmengine - INFO - Epoch(train) [152][11/15] lr: 1.0000e-06 eta: 0:04:07 time: 0.3261 data_time: 0.0017 memory: 15631 loss: 0.0647 loss_ce: 0.0647 2023/03/03 14:38:38 - mmengine - INFO - Epoch(train) [152][12/15] lr: 1.0000e-06 eta: 0:04:06 time: 0.3252 data_time: 0.0016 memory: 12779 loss: 0.0666 loss_ce: 0.0666 2023/03/03 14:38:39 - mmengine - INFO - Epoch(train) [152][13/15] lr: 1.0000e-06 eta: 0:04:06 time: 0.2951 data_time: 0.0016 memory: 17120 loss: 0.0715 loss_ce: 0.0715 2023/03/03 14:38:39 - mmengine - INFO - Epoch(train) [152][14/15] lr: 1.0000e-06 eta: 0:04:06 time: 0.3123 data_time: 0.0016 memory: 21636 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:38:39 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:38:39 - mmengine - INFO - Epoch(train) [152][15/15] lr: 1.0000e-06 eta: 0:04:05 time: 0.3047 data_time: 0.0016 memory: 3160 loss: 0.0849 loss_ce: 0.0849 2023/03/03 14:38:40 - mmengine - INFO - Epoch(train) [153][ 1/15] lr: 1.0000e-06 eta: 0:04:05 time: 0.3547 data_time: 0.0513 memory: 14592 loss: 0.0868 loss_ce: 0.0868 2023/03/03 14:38:41 - mmengine - INFO - Epoch(train) [153][ 2/15] lr: 1.0000e-06 eta: 0:04:05 time: 0.3689 data_time: 0.0514 memory: 16294 loss: 0.0890 loss_ce: 0.0890 2023/03/03 14:38:41 - mmengine - INFO - Epoch(train) [153][ 3/15] lr: 1.0000e-06 eta: 0:04:04 time: 0.3685 data_time: 0.0514 memory: 17120 loss: 0.0915 loss_ce: 0.0915 2023/03/03 14:38:41 - mmengine - INFO - Epoch(train) [153][ 4/15] lr: 1.0000e-06 eta: 0:04:04 time: 0.3649 data_time: 0.0515 memory: 18294 loss: 0.0941 loss_ce: 0.0941 2023/03/03 14:38:41 - mmengine - INFO - Epoch(train) [153][ 5/15] lr: 1.0000e-06 eta: 0:04:04 time: 0.3656 data_time: 0.0516 memory: 17284 loss: 0.0918 loss_ce: 0.0918 2023/03/03 14:38:42 - mmengine - INFO - Epoch(train) [153][ 6/15] lr: 1.0000e-06 eta: 0:04:03 time: 0.3459 data_time: 0.0516 memory: 17120 loss: 0.0939 loss_ce: 0.0939 2023/03/03 14:38:42 - mmengine - INFO - Epoch(train) [153][ 7/15] lr: 1.0000e-06 eta: 0:04:03 time: 0.3477 data_time: 0.0516 memory: 18070 loss: 0.0935 loss_ce: 0.0935 2023/03/03 14:38:42 - mmengine - INFO - Epoch(train) [153][ 8/15] lr: 1.0000e-06 eta: 0:04:03 time: 0.3595 data_time: 0.0516 memory: 16976 loss: 0.0915 loss_ce: 0.0915 2023/03/03 14:38:43 - mmengine - INFO - Epoch(train) [153][ 9/15] lr: 1.0000e-06 eta: 0:04:02 time: 0.3558 data_time: 0.0516 memory: 22651 loss: 0.0867 loss_ce: 0.0867 2023/03/03 14:38:43 - mmengine - INFO - Epoch(train) [153][10/15] lr: 1.0000e-06 eta: 0:04:02 time: 0.3723 data_time: 0.0516 memory: 17572 loss: 0.0777 loss_ce: 0.0777 2023/03/03 14:38:44 - mmengine - INFO - Epoch(train) [153][11/15] lr: 1.0000e-06 eta: 0:04:02 time: 0.3401 data_time: 0.0018 memory: 29773 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:38:44 - mmengine - INFO - Epoch(train) [153][12/15] lr: 1.0000e-06 eta: 0:04:01 time: 0.3269 data_time: 0.0018 memory: 18409 loss: 0.0726 loss_ce: 0.0726 2023/03/03 14:38:44 - mmengine - INFO - Epoch(train) [153][13/15] lr: 1.0000e-06 eta: 0:04:01 time: 0.3376 data_time: 0.0017 memory: 18496 loss: 0.0694 loss_ce: 0.0694 2023/03/03 14:38:45 - mmengine - INFO - Epoch(train) [153][14/15] lr: 1.0000e-06 eta: 0:04:01 time: 0.3394 data_time: 0.0016 memory: 23963 loss: 0.0661 loss_ce: 0.0661 2023/03/03 14:38:45 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:38:45 - mmengine - INFO - Epoch(train) [153][15/15] lr: 1.0000e-06 eta: 0:04:00 time: 0.3320 data_time: 0.0016 memory: 5818 loss: 0.0687 loss_ce: 0.0687 2023/03/03 14:38:46 - mmengine - INFO - Epoch(train) [154][ 1/15] lr: 1.0000e-06 eta: 0:04:00 time: 0.4131 data_time: 0.0597 memory: 36593 loss: 0.0691 loss_ce: 0.0691 2023/03/03 14:38:46 - mmengine - INFO - Epoch(train) [154][ 2/15] lr: 1.0000e-06 eta: 0:04:00 time: 0.4165 data_time: 0.0598 memory: 17284 loss: 0.0686 loss_ce: 0.0686 2023/03/03 14:38:47 - mmengine - INFO - Epoch(train) [154][ 3/15] lr: 1.0000e-06 eta: 0:03:59 time: 0.4080 data_time: 0.0599 memory: 17948 loss: 0.0691 loss_ce: 0.0691 2023/03/03 14:38:47 - mmengine - INFO - Epoch(train) [154][ 4/15] lr: 1.0000e-06 eta: 0:03:59 time: 0.3883 data_time: 0.0599 memory: 14589 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:38:47 - mmengine - INFO - Epoch(train) [154][ 5/15] lr: 1.0000e-06 eta: 0:03:59 time: 0.3986 data_time: 0.0599 memory: 18409 loss: 0.0712 loss_ce: 0.0712 2023/03/03 14:38:47 - mmengine - INFO - Epoch(train) [154][ 6/15] lr: 1.0000e-06 eta: 0:03:58 time: 0.3842 data_time: 0.0600 memory: 19689 loss: 0.0705 loss_ce: 0.0705 2023/03/03 14:38:48 - mmengine - INFO - Epoch(train) [154][ 7/15] lr: 1.0000e-06 eta: 0:03:58 time: 0.3975 data_time: 0.0600 memory: 26488 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:38:48 - mmengine - INFO - Epoch(train) [154][ 8/15] lr: 1.0000e-06 eta: 0:03:58 time: 0.3861 data_time: 0.0600 memory: 17272 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:38:48 - mmengine - INFO - Epoch(train) [154][ 9/15] lr: 1.0000e-06 eta: 0:03:57 time: 0.3823 data_time: 0.0601 memory: 15204 loss: 0.0825 loss_ce: 0.0825 2023/03/03 14:38:49 - mmengine - INFO - Epoch(train) [154][10/15] lr: 1.0000e-06 eta: 0:03:57 time: 0.3862 data_time: 0.0601 memory: 17272 loss: 0.0788 loss_ce: 0.0788 2023/03/03 14:38:49 - mmengine - INFO - Epoch(train) [154][11/15] lr: 1.0000e-06 eta: 0:03:57 time: 0.3007 data_time: 0.0020 memory: 24246 loss: 0.0783 loss_ce: 0.0783 2023/03/03 14:38:49 - mmengine - INFO - Epoch(train) [154][12/15] lr: 1.0000e-06 eta: 0:03:56 time: 0.2999 data_time: 0.0019 memory: 16849 loss: 0.0793 loss_ce: 0.0793 2023/03/03 14:38:49 - mmengine - INFO - Epoch(train) [154][13/15] lr: 1.0000e-06 eta: 0:03:56 time: 0.2769 data_time: 0.0018 memory: 16508 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:38:50 - mmengine - INFO - Epoch(train) [154][14/15] lr: 1.0000e-06 eta: 0:03:56 time: 0.2970 data_time: 0.0018 memory: 22715 loss: 0.0759 loss_ce: 0.0759 2023/03/03 14:38:50 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:38:50 - mmengine - INFO - Epoch(train) [154][15/15] lr: 1.0000e-06 eta: 0:03:55 time: 0.2662 data_time: 0.0019 memory: 6910 loss: 0.0786 loss_ce: 0.0786 2023/03/03 14:38:51 - mmengine - INFO - Epoch(train) [155][ 1/15] lr: 1.0000e-06 eta: 0:03:55 time: 0.3189 data_time: 0.0364 memory: 19289 loss: 0.0803 loss_ce: 0.0803 2023/03/03 14:38:51 - mmengine - INFO - Epoch(train) [155][ 2/15] lr: 1.0000e-06 eta: 0:03:55 time: 0.3270 data_time: 0.0365 memory: 15675 loss: 0.0808 loss_ce: 0.0808 2023/03/03 14:38:51 - mmengine - INFO - Epoch(train) [155][ 3/15] lr: 1.0000e-06 eta: 0:03:54 time: 0.3431 data_time: 0.0365 memory: 20935 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:38:52 - mmengine - INFO - Epoch(train) [155][ 4/15] lr: 1.0000e-06 eta: 0:03:54 time: 0.3319 data_time: 0.0365 memory: 16089 loss: 0.0704 loss_ce: 0.0704 2023/03/03 14:38:52 - mmengine - INFO - Epoch(train) [155][ 5/15] lr: 1.0000e-06 eta: 0:03:54 time: 0.3418 data_time: 0.0366 memory: 21094 loss: 0.0686 loss_ce: 0.0686 2023/03/03 14:38:52 - mmengine - INFO - Epoch(train) [155][ 6/15] lr: 1.0000e-06 eta: 0:03:53 time: 0.3311 data_time: 0.0366 memory: 17421 loss: 0.0684 loss_ce: 0.0684 2023/03/03 14:38:52 - mmengine - INFO - Epoch(train) [155][ 7/15] lr: 1.0000e-06 eta: 0:03:53 time: 0.3284 data_time: 0.0366 memory: 17572 loss: 0.0666 loss_ce: 0.0666 2023/03/03 14:38:53 - mmengine - INFO - Epoch(train) [155][ 8/15] lr: 1.0000e-06 eta: 0:03:52 time: 0.3429 data_time: 0.0366 memory: 18095 loss: 0.0667 loss_ce: 0.0667 2023/03/03 14:38:53 - mmengine - INFO - Epoch(train) [155][ 9/15] lr: 1.0000e-06 eta: 0:03:52 time: 0.3270 data_time: 0.0366 memory: 16223 loss: 0.0669 loss_ce: 0.0669 2023/03/03 14:38:53 - mmengine - INFO - Epoch(train) [155][10/15] lr: 1.0000e-06 eta: 0:03:52 time: 0.3479 data_time: 0.0364 memory: 20375 loss: 0.0644 loss_ce: 0.0644 2023/03/03 14:38:54 - mmengine - INFO - Epoch(train) [155][11/15] lr: 1.0000e-06 eta: 0:03:51 time: 0.2930 data_time: 0.0019 memory: 18271 loss: 0.0636 loss_ce: 0.0636 2023/03/03 14:38:54 - mmengine - INFO - Epoch(train) [155][12/15] lr: 1.0000e-06 eta: 0:03:51 time: 0.2804 data_time: 0.0018 memory: 16199 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:38:54 - mmengine - INFO - Epoch(train) [155][13/15] lr: 1.0000e-06 eta: 0:03:51 time: 0.2687 data_time: 0.0018 memory: 17788 loss: 0.0694 loss_ce: 0.0694 2023/03/03 14:38:55 - mmengine - INFO - Epoch(train) [155][14/15] lr: 1.0000e-06 eta: 0:03:50 time: 0.2922 data_time: 0.0017 memory: 33492 loss: 0.0721 loss_ce: 0.0721 2023/03/03 14:38:55 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:38:55 - mmengine - INFO - Epoch(train) [155][15/15] lr: 1.0000e-06 eta: 0:03:50 time: 0.2732 data_time: 0.0017 memory: 6850 loss: 0.0840 loss_ce: 0.0840 2023/03/03 14:38:56 - mmengine - INFO - Epoch(train) [156][ 1/15] lr: 1.0000e-06 eta: 0:03:50 time: 0.3565 data_time: 0.0302 memory: 15804 loss: 0.0826 loss_ce: 0.0826 2023/03/03 14:38:56 - mmengine - INFO - Epoch(train) [156][ 2/15] lr: 1.0000e-06 eta: 0:03:49 time: 0.3575 data_time: 0.0303 memory: 18586 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:38:56 - mmengine - INFO - Epoch(train) [156][ 3/15] lr: 1.0000e-06 eta: 0:03:49 time: 0.3483 data_time: 0.0303 memory: 16976 loss: 0.0846 loss_ce: 0.0846 2023/03/03 14:38:56 - mmengine - INFO - Epoch(train) [156][ 4/15] lr: 1.0000e-06 eta: 0:03:49 time: 0.3486 data_time: 0.0303 memory: 16654 loss: 0.0853 loss_ce: 0.0853 2023/03/03 14:38:57 - mmengine - INFO - Epoch(train) [156][ 5/15] lr: 1.0000e-06 eta: 0:03:48 time: 0.3525 data_time: 0.0303 memory: 24937 loss: 0.0866 loss_ce: 0.0866 2023/03/03 14:38:57 - mmengine - INFO - Epoch(train) [156][ 6/15] lr: 1.0000e-06 eta: 0:03:48 time: 0.3558 data_time: 0.0303 memory: 16399 loss: 0.0871 loss_ce: 0.0871 2023/03/03 14:38:57 - mmengine - INFO - Epoch(train) [156][ 7/15] lr: 1.0000e-06 eta: 0:03:48 time: 0.3467 data_time: 0.0303 memory: 16976 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:38:58 - mmengine - INFO - Epoch(train) [156][ 8/15] lr: 1.0000e-06 eta: 0:03:47 time: 0.3426 data_time: 0.0303 memory: 18241 loss: 0.0829 loss_ce: 0.0829 2023/03/03 14:38:58 - mmengine - INFO - Epoch(train) [156][ 9/15] lr: 1.0000e-06 eta: 0:03:47 time: 0.3319 data_time: 0.0303 memory: 17093 loss: 0.0832 loss_ce: 0.0832 2023/03/03 14:38:58 - mmengine - INFO - Epoch(train) [156][10/15] lr: 1.0000e-06 eta: 0:03:47 time: 0.3447 data_time: 0.0303 memory: 17619 loss: 0.0726 loss_ce: 0.0726 2023/03/03 14:38:59 - mmengine - INFO - Epoch(train) [156][11/15] lr: 1.0000e-06 eta: 0:03:46 time: 0.2804 data_time: 0.0017 memory: 16125 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:38:59 - mmengine - INFO - Epoch(train) [156][12/15] lr: 1.0000e-06 eta: 0:03:46 time: 0.2793 data_time: 0.0017 memory: 16976 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:38:59 - mmengine - INFO - Epoch(train) [156][13/15] lr: 1.0000e-06 eta: 0:03:45 time: 0.2832 data_time: 0.0016 memory: 17272 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:38:59 - mmengine - INFO - Epoch(train) [156][14/15] lr: 1.0000e-06 eta: 0:03:45 time: 0.2808 data_time: 0.0016 memory: 18241 loss: 0.0717 loss_ce: 0.0717 2023/03/03 14:39:00 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:39:00 - mmengine - INFO - Epoch(train) [156][15/15] lr: 1.0000e-06 eta: 0:03:45 time: 0.2672 data_time: 0.0016 memory: 6456 loss: 0.0734 loss_ce: 0.0734 2023/03/03 14:39:00 - mmengine - INFO - Epoch(train) [157][ 1/15] lr: 1.0000e-06 eta: 0:03:44 time: 0.2975 data_time: 0.0389 memory: 15011 loss: 0.0726 loss_ce: 0.0726 2023/03/03 14:39:01 - mmengine - INFO - Epoch(train) [157][ 2/15] lr: 1.0000e-06 eta: 0:03:44 time: 0.3371 data_time: 0.0390 memory: 17284 loss: 0.0723 loss_ce: 0.0723 2023/03/03 14:39:01 - mmengine - INFO - Epoch(train) [157][ 3/15] lr: 1.0000e-06 eta: 0:03:44 time: 0.3404 data_time: 0.0391 memory: 17036 loss: 0.0728 loss_ce: 0.0728 2023/03/03 14:39:01 - mmengine - INFO - Epoch(train) [157][ 4/15] lr: 1.0000e-06 eta: 0:03:43 time: 0.3398 data_time: 0.0392 memory: 17421 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:39:02 - mmengine - INFO - Epoch(train) [157][ 5/15] lr: 1.0000e-06 eta: 0:03:43 time: 0.3361 data_time: 0.0392 memory: 17120 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:39:02 - mmengine - INFO - Epoch(train) [157][ 6/15] lr: 1.0000e-06 eta: 0:03:43 time: 0.3585 data_time: 0.0393 memory: 14965 loss: 0.0759 loss_ce: 0.0759 2023/03/03 14:39:02 - mmengine - INFO - Epoch(train) [157][ 7/15] lr: 1.0000e-06 eta: 0:03:42 time: 0.3590 data_time: 0.0393 memory: 18046 loss: 0.0732 loss_ce: 0.0732 2023/03/03 14:39:03 - mmengine - INFO - Epoch(train) [157][ 8/15] lr: 1.0000e-06 eta: 0:03:42 time: 0.3554 data_time: 0.0393 memory: 15911 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:39:03 - mmengine - INFO - Epoch(train) [157][ 9/15] lr: 1.0000e-06 eta: 0:03:42 time: 0.3779 data_time: 0.0393 memory: 26319 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:39:03 - mmengine - INFO - Epoch(train) [157][10/15] lr: 1.0000e-06 eta: 0:03:41 time: 0.3864 data_time: 0.0394 memory: 15805 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:39:04 - mmengine - INFO - Epoch(train) [157][11/15] lr: 1.0000e-06 eta: 0:03:41 time: 0.3579 data_time: 0.0020 memory: 27893 loss: 0.0777 loss_ce: 0.0777 2023/03/03 14:39:04 - mmengine - INFO - Epoch(train) [157][12/15] lr: 1.0000e-06 eta: 0:03:41 time: 0.3163 data_time: 0.0020 memory: 15175 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:39:04 - mmengine - INFO - Epoch(train) [157][13/15] lr: 1.0000e-06 eta: 0:03:40 time: 0.3125 data_time: 0.0019 memory: 16948 loss: 0.0759 loss_ce: 0.0759 2023/03/03 14:39:04 - mmengine - INFO - Epoch(train) [157][14/15] lr: 1.0000e-06 eta: 0:03:40 time: 0.3057 data_time: 0.0019 memory: 15911 loss: 0.0694 loss_ce: 0.0694 2023/03/03 14:39:05 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:39:05 - mmengine - INFO - Epoch(train) [157][15/15] lr: 1.0000e-06 eta: 0:03:40 time: 0.3011 data_time: 0.0019 memory: 3639 loss: 0.0704 loss_ce: 0.0704 2023/03/03 14:39:06 - mmengine - INFO - Epoch(train) [158][ 1/15] lr: 1.0000e-06 eta: 0:03:39 time: 0.3377 data_time: 0.0602 memory: 17113 loss: 0.0745 loss_ce: 0.0745 2023/03/03 14:39:06 - mmengine - INFO - Epoch(train) [158][ 2/15] lr: 1.0000e-06 eta: 0:03:39 time: 0.3425 data_time: 0.0603 memory: 13282 loss: 0.0749 loss_ce: 0.0749 2023/03/03 14:39:06 - mmengine - INFO - Epoch(train) [158][ 3/15] lr: 1.0000e-06 eta: 0:03:39 time: 0.3360 data_time: 0.0603 memory: 15494 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:39:06 - mmengine - INFO - Epoch(train) [158][ 4/15] lr: 1.0000e-06 eta: 0:03:38 time: 0.3159 data_time: 0.0604 memory: 16654 loss: 0.0764 loss_ce: 0.0764 2023/03/03 14:39:07 - mmengine - INFO - Epoch(train) [158][ 5/15] lr: 1.0000e-06 eta: 0:03:38 time: 0.3231 data_time: 0.0603 memory: 22227 loss: 0.0748 loss_ce: 0.0748 2023/03/03 14:39:07 - mmengine - INFO - Epoch(train) [158][ 6/15] lr: 1.0000e-06 eta: 0:03:38 time: 0.3244 data_time: 0.0603 memory: 15615 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:39:07 - mmengine - INFO - Epoch(train) [158][ 7/15] lr: 1.0000e-06 eta: 0:03:37 time: 0.3284 data_time: 0.0603 memory: 16976 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:39:07 - mmengine - INFO - Epoch(train) [158][ 8/15] lr: 1.0000e-06 eta: 0:03:37 time: 0.3303 data_time: 0.0602 memory: 15911 loss: 0.0802 loss_ce: 0.0802 2023/03/03 14:39:08 - mmengine - INFO - Epoch(train) [158][ 9/15] lr: 1.0000e-06 eta: 0:03:37 time: 0.3566 data_time: 0.0602 memory: 17665 loss: 0.0828 loss_ce: 0.0828 2023/03/03 14:39:08 - mmengine - INFO - Epoch(train) [158][10/15] lr: 1.0000e-06 eta: 0:03:36 time: 0.3714 data_time: 0.0602 memory: 17584 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:39:08 - mmengine - INFO - Epoch(train) [158][11/15] lr: 1.0000e-06 eta: 0:03:36 time: 0.2939 data_time: 0.0018 memory: 18586 loss: 0.0764 loss_ce: 0.0764 2023/03/03 14:39:09 - mmengine - INFO - Epoch(train) [158][12/15] lr: 1.0000e-06 eta: 0:03:35 time: 0.2886 data_time: 0.0017 memory: 16976 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:39:09 - mmengine - INFO - Epoch(train) [158][13/15] lr: 1.0000e-06 eta: 0:03:35 time: 0.3004 data_time: 0.0016 memory: 16370 loss: 0.0772 loss_ce: 0.0772 2023/03/03 14:39:09 - mmengine - INFO - Epoch(train) [158][14/15] lr: 1.0000e-06 eta: 0:03:35 time: 0.2987 data_time: 0.0016 memory: 15131 loss: 0.0739 loss_ce: 0.0739 2023/03/03 14:39:09 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:39:09 - mmengine - INFO - Epoch(train) [158][15/15] lr: 1.0000e-06 eta: 0:03:34 time: 0.2782 data_time: 0.0016 memory: 4129 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:39:10 - mmengine - INFO - Epoch(train) [159][ 1/15] lr: 1.0000e-06 eta: 0:03:34 time: 0.3146 data_time: 0.0462 memory: 16049 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:39:11 - mmengine - INFO - Epoch(train) [159][ 2/15] lr: 1.0000e-06 eta: 0:03:34 time: 0.3667 data_time: 0.0463 memory: 17421 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:39:11 - mmengine - INFO - Epoch(train) [159][ 3/15] lr: 1.0000e-06 eta: 0:03:34 time: 0.3654 data_time: 0.0466 memory: 17272 loss: 0.0714 loss_ce: 0.0714 2023/03/03 14:39:12 - mmengine - INFO - Epoch(train) [159][ 4/15] lr: 1.0000e-06 eta: 0:03:33 time: 0.3609 data_time: 0.0467 memory: 17572 loss: 0.0706 loss_ce: 0.0706 2023/03/03 14:39:12 - mmengine - INFO - Epoch(train) [159][ 5/15] lr: 1.0000e-06 eta: 0:03:33 time: 0.3569 data_time: 0.0467 memory: 18923 loss: 0.0694 loss_ce: 0.0694 2023/03/03 14:39:12 - mmengine - INFO - Epoch(train) [159][ 6/15] lr: 1.0000e-06 eta: 0:03:33 time: 0.3704 data_time: 0.0468 memory: 17682 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:39:12 - mmengine - INFO - Epoch(train) [159][ 7/15] lr: 1.0000e-06 eta: 0:03:32 time: 0.3704 data_time: 0.0468 memory: 17120 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:39:13 - mmengine - INFO - Epoch(train) [159][ 8/15] lr: 1.0000e-06 eta: 0:03:32 time: 0.3652 data_time: 0.0468 memory: 17064 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:39:13 - mmengine - INFO - Epoch(train) [159][ 9/15] lr: 1.0000e-06 eta: 0:03:32 time: 0.4059 data_time: 0.0468 memory: 28520 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:39:13 - mmengine - INFO - Epoch(train) [159][10/15] lr: 1.0000e-06 eta: 0:03:31 time: 0.4086 data_time: 0.0468 memory: 17272 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:39:14 - mmengine - INFO - Epoch(train) [159][11/15] lr: 1.0000e-06 eta: 0:03:31 time: 0.3596 data_time: 0.0021 memory: 15831 loss: 0.0774 loss_ce: 0.0774 2023/03/03 14:39:14 - mmengine - INFO - Epoch(train) [159][12/15] lr: 1.0000e-06 eta: 0:03:30 time: 0.3152 data_time: 0.0020 memory: 15631 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:39:14 - mmengine - INFO - Epoch(train) [159][13/15] lr: 1.0000e-06 eta: 0:03:30 time: 0.3148 data_time: 0.0017 memory: 16976 loss: 0.0845 loss_ce: 0.0845 2023/03/03 14:39:15 - mmengine - INFO - Epoch(train) [159][14/15] lr: 1.0000e-06 eta: 0:03:30 time: 0.2981 data_time: 0.0017 memory: 17446 loss: 0.0844 loss_ce: 0.0844 2023/03/03 14:39:15 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:39:15 - mmengine - INFO - Epoch(train) [159][15/15] lr: 1.0000e-06 eta: 0:03:29 time: 0.2822 data_time: 0.0017 memory: 5325 loss: 0.0876 loss_ce: 0.0876 2023/03/03 14:39:16 - mmengine - INFO - Epoch(train) [160][ 1/15] lr: 1.0000e-06 eta: 0:03:29 time: 0.3455 data_time: 0.0708 memory: 16212 loss: 0.0854 loss_ce: 0.0854 2023/03/03 14:39:16 - mmengine - INFO - Epoch(train) [160][ 2/15] lr: 1.0000e-06 eta: 0:03:29 time: 0.3555 data_time: 0.0708 memory: 20240 loss: 0.0841 loss_ce: 0.0841 2023/03/03 14:39:16 - mmengine - INFO - Epoch(train) [160][ 3/15] lr: 1.0000e-06 eta: 0:03:28 time: 0.3473 data_time: 0.0709 memory: 17272 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:39:16 - mmengine - INFO - Epoch(train) [160][ 4/15] lr: 1.0000e-06 eta: 0:03:28 time: 0.3055 data_time: 0.0710 memory: 17272 loss: 0.0759 loss_ce: 0.0759 2023/03/03 14:39:17 - mmengine - INFO - Epoch(train) [160][ 5/15] lr: 1.0000e-06 eta: 0:03:28 time: 0.3325 data_time: 0.0709 memory: 17272 loss: 0.0751 loss_ce: 0.0751 2023/03/03 14:39:17 - mmengine - INFO - Epoch(train) [160][ 6/15] lr: 1.0000e-06 eta: 0:03:27 time: 0.3473 data_time: 0.0709 memory: 16347 loss: 0.0742 loss_ce: 0.0742 2023/03/03 14:39:17 - mmengine - INFO - Epoch(train) [160][ 7/15] lr: 1.0000e-06 eta: 0:03:27 time: 0.3411 data_time: 0.0710 memory: 15457 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:39:18 - mmengine - INFO - Epoch(train) [160][ 8/15] lr: 1.0000e-06 eta: 0:03:27 time: 0.3383 data_time: 0.0710 memory: 13824 loss: 0.0667 loss_ce: 0.0667 2023/03/03 14:39:18 - mmengine - INFO - Epoch(train) [160][ 9/15] lr: 1.0000e-06 eta: 0:03:26 time: 0.3473 data_time: 0.0710 memory: 17272 loss: 0.0692 loss_ce: 0.0692 2023/03/03 14:39:18 - mmengine - INFO - Epoch(train) [160][10/15] lr: 1.0000e-06 eta: 0:03:26 time: 0.3662 data_time: 0.0710 memory: 27486 loss: 0.0692 loss_ce: 0.0692 2023/03/03 14:39:19 - mmengine - INFO - Epoch(train) [160][11/15] lr: 1.0000e-06 eta: 0:03:26 time: 0.2967 data_time: 0.0019 memory: 14717 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:39:19 - mmengine - INFO - Epoch(train) [160][12/15] lr: 1.0000e-06 eta: 0:03:25 time: 0.2913 data_time: 0.0018 memory: 18005 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:39:19 - mmengine - INFO - Epoch(train) [160][13/15] lr: 1.0000e-06 eta: 0:03:25 time: 0.3048 data_time: 0.0017 memory: 18081 loss: 0.0749 loss_ce: 0.0749 2023/03/03 14:39:20 - mmengine - INFO - Epoch(train) [160][14/15] lr: 1.0000e-06 eta: 0:03:25 time: 0.3189 data_time: 0.0017 memory: 16823 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:39:20 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:39:20 - mmengine - INFO - Epoch(train) [160][15/15] lr: 1.0000e-06 eta: 0:03:24 time: 0.2871 data_time: 0.0017 memory: 4197 loss: 0.0927 loss_ce: 0.0927 2023/03/03 14:39:21 - mmengine - INFO - Epoch(val) [160][ 1/59] eta: 0:01:30 time: 1.0664 data_time: 0.0033 memory: 981 2023/03/03 14:39:22 - mmengine - INFO - Epoch(val) [160][ 2/59] eta: 0:01:08 time: 0.9814 data_time: 0.0033 memory: 981 2023/03/03 14:39:23 - mmengine - INFO - Epoch(val) [160][ 3/59] eta: 0:01:10 time: 1.0002 data_time: 0.0034 memory: 1003 2023/03/03 14:39:24 - mmengine - INFO - Epoch(val) [160][ 4/59] eta: 0:00:56 time: 0.9845 data_time: 0.0034 memory: 981 2023/03/03 14:39:27 - mmengine - INFO - Epoch(val) [160][ 5/59] eta: 0:01:17 time: 1.2234 data_time: 0.0034 memory: 1016 2023/03/03 14:39:30 - mmengine - INFO - Epoch(val) [160][ 6/59] eta: 0:01:26 time: 1.4221 data_time: 0.0034 memory: 981 2023/03/03 14:39:30 - mmengine - INFO - Epoch(val) [160][ 7/59] eta: 0:01:14 time: 1.3732 data_time: 0.0034 memory: 1043 2023/03/03 14:39:30 - mmengine - INFO - Epoch(val) [160][ 8/59] eta: 0:01:07 time: 1.2156 data_time: 0.0034 memory: 1016 2023/03/03 14:39:31 - mmengine - INFO - Epoch(val) [160][ 9/59] eta: 0:01:04 time: 1.2003 data_time: 0.0035 memory: 981 2023/03/03 14:39:32 - mmengine - INFO - Epoch(val) [160][10/59] eta: 0:01:00 time: 1.2336 data_time: 0.0035 memory: 981 2023/03/03 14:39:32 - mmengine - INFO - Epoch(val) [160][11/59] eta: 0:00:55 time: 1.1113 data_time: 0.0009 memory: 981 2023/03/03 14:39:36 - mmengine - INFO - Epoch(val) [160][12/59] eta: 0:01:02 time: 1.3521 data_time: 0.0009 memory: 1016 2023/03/03 14:39:38 - mmengine - INFO - Epoch(val) [160][13/59] eta: 0:01:04 time: 1.4424 data_time: 0.0009 memory: 981 2023/03/03 14:39:39 - mmengine - INFO - Epoch(val) [160][14/59] eta: 0:01:01 time: 1.5092 data_time: 0.0009 memory: 890 2023/03/03 14:39:39 - mmengine - INFO - Epoch(val) [160][15/59] eta: 0:00:56 time: 1.2062 data_time: 0.0010 memory: 981 2023/03/03 14:39:39 - mmengine - INFO - Epoch(val) [160][16/59] eta: 0:00:52 time: 0.9908 data_time: 0.0009 memory: 981 2023/03/03 14:39:40 - mmengine - INFO - Epoch(val) [160][17/59] eta: 0:00:49 time: 1.0069 data_time: 0.0009 memory: 981 2023/03/03 14:39:40 - mmengine - INFO - Epoch(val) [160][18/59] eta: 0:00:46 time: 0.9735 data_time: 0.0009 memory: 981 2023/03/03 14:39:41 - mmengine - INFO - Epoch(val) [160][19/59] eta: 0:00:45 time: 0.9733 data_time: 0.0009 memory: 981 2023/03/03 14:39:41 - mmengine - INFO - Epoch(val) [160][20/59] eta: 0:00:42 time: 0.9405 data_time: 0.0009 memory: 981 2023/03/03 14:39:42 - mmengine - INFO - Epoch(val) [160][21/59] eta: 0:00:40 time: 0.9567 data_time: 0.0009 memory: 981 2023/03/03 14:39:42 - mmengine - INFO - Epoch(val) [160][22/59] eta: 0:00:37 time: 0.6495 data_time: 0.0009 memory: 981 2023/03/03 14:39:43 - mmengine - INFO - Epoch(val) [160][23/59] eta: 0:00:36 time: 0.4907 data_time: 0.0009 memory: 981 2023/03/03 14:39:43 - mmengine - INFO - Epoch(val) [160][24/59] eta: 0:00:34 time: 0.4235 data_time: 0.0008 memory: 962 2023/03/03 14:39:43 - mmengine - INFO - Epoch(val) [160][25/59] eta: 0:00:32 time: 0.4548 data_time: 0.0008 memory: 981 2023/03/03 14:39:44 - mmengine - INFO - Epoch(val) [160][26/59] eta: 0:00:30 time: 0.4390 data_time: 0.0008 memory: 981 2023/03/03 14:39:44 - mmengine - INFO - Epoch(val) [160][27/59] eta: 0:00:28 time: 0.4392 data_time: 0.0008 memory: 981 2023/03/03 14:39:44 - mmengine - INFO - Epoch(val) [160][28/59] eta: 0:00:27 time: 0.4391 data_time: 0.0008 memory: 981 2023/03/03 14:39:46 - mmengine - INFO - Epoch(val) [160][29/59] eta: 0:00:27 time: 0.4725 data_time: 0.0008 memory: 981 2023/03/03 14:39:47 - mmengine - INFO - Epoch(val) [160][30/59] eta: 0:00:26 time: 0.5219 data_time: 0.0008 memory: 999 2023/03/03 14:39:47 - mmengine - INFO - Epoch(val) [160][31/59] eta: 0:00:24 time: 0.5382 data_time: 0.0008 memory: 981 2023/03/03 14:39:48 - mmengine - INFO - Epoch(val) [160][32/59] eta: 0:00:24 time: 0.6378 data_time: 0.0008 memory: 981 2023/03/03 14:39:49 - mmengine - INFO - Epoch(val) [160][33/59] eta: 0:00:22 time: 0.5733 data_time: 0.0008 memory: 981 2023/03/03 14:39:49 - mmengine - INFO - Epoch(val) [160][34/59] eta: 0:00:21 time: 0.5569 data_time: 0.0008 memory: 981 2023/03/03 14:39:49 - mmengine - INFO - Epoch(val) [160][35/59] eta: 0:00:20 time: 0.5405 data_time: 0.0008 memory: 981 2023/03/03 14:39:49 - mmengine - INFO - Epoch(val) [160][36/59] eta: 0:00:18 time: 0.5566 data_time: 0.0008 memory: 981 2023/03/03 14:39:50 - mmengine - INFO - Epoch(val) [160][37/59] eta: 0:00:17 time: 0.5400 data_time: 0.0008 memory: 981 2023/03/03 14:39:50 - mmengine - INFO - Epoch(val) [160][38/59] eta: 0:00:16 time: 0.5730 data_time: 0.0008 memory: 981 2023/03/03 14:39:51 - mmengine - INFO - Epoch(val) [160][39/59] eta: 0:00:15 time: 0.4891 data_time: 0.0008 memory: 987 2023/03/03 14:39:52 - mmengine - INFO - Epoch(val) [160][40/59] eta: 0:00:15 time: 0.4888 data_time: 0.0008 memory: 981 2023/03/03 14:39:53 - mmengine - INFO - Epoch(val) [160][41/59] eta: 0:00:14 time: 0.5390 data_time: 0.0008 memory: 986 2023/03/03 14:39:53 - mmengine - INFO - Epoch(val) [160][42/59] eta: 0:00:13 time: 0.4887 data_time: 0.0007 memory: 981 2023/03/03 14:39:54 - mmengine - INFO - Epoch(val) [160][43/59] eta: 0:00:12 time: 0.5692 data_time: 0.0007 memory: 976 2023/03/03 14:39:55 - mmengine - INFO - Epoch(val) [160][44/59] eta: 0:00:11 time: 0.6020 data_time: 0.0008 memory: 1003 2023/03/03 14:39:57 - mmengine - INFO - Epoch(val) [160][45/59] eta: 0:00:11 time: 0.7719 data_time: 0.0008 memory: 981 2023/03/03 14:39:57 - mmengine - INFO - Epoch(val) [160][46/59] eta: 0:00:10 time: 0.8050 data_time: 0.0008 memory: 981 2023/03/03 14:39:58 - mmengine - INFO - Epoch(val) [160][47/59] eta: 0:00:09 time: 0.8377 data_time: 0.0008 memory: 936 2023/03/03 14:39:58 - mmengine - INFO - Epoch(val) [160][48/59] eta: 0:00:08 time: 0.8213 data_time: 0.0008 memory: 1000 2023/03/03 14:39:59 - mmengine - INFO - Epoch(val) [160][49/59] eta: 0:00:08 time: 0.8711 data_time: 0.0008 memory: 981 2023/03/03 14:40:00 - mmengine - INFO - Epoch(val) [160][50/59] eta: 0:00:07 time: 0.8717 data_time: 0.0009 memory: 987 2023/03/03 14:40:02 - mmengine - INFO - Epoch(val) [160][51/59] eta: 0:00:06 time: 0.9243 data_time: 0.0008 memory: 981 2023/03/03 14:40:03 - mmengine - INFO - Epoch(val) [160][52/59] eta: 0:00:05 time: 0.9742 data_time: 0.0008 memory: 981 2023/03/03 14:40:04 - mmengine - INFO - Epoch(val) [160][53/59] eta: 0:00:04 time: 0.9409 data_time: 0.0008 memory: 962 2023/03/03 14:40:04 - mmengine - INFO - Epoch(val) [160][54/59] eta: 0:00:04 time: 0.9572 data_time: 0.0008 memory: 981 2023/03/03 14:40:05 - mmengine - INFO - Epoch(val) [160][55/59] eta: 0:00:03 time: 0.8365 data_time: 0.0008 memory: 981 2023/03/03 14:40:06 - mmengine - INFO - Epoch(val) [160][56/59] eta: 0:00:02 time: 0.8196 data_time: 0.0007 memory: 981 2023/03/03 14:40:08 - mmengine - INFO - Epoch(val) [160][57/59] eta: 0:00:01 time: 0.9932 data_time: 0.0007 memory: 981 2023/03/03 14:40:09 - mmengine - INFO - Epoch(val) [160][58/59] eta: 0:00:00 time: 1.0590 data_time: 0.0007 memory: 1016 2023/03/03 14:40:09 - mmengine - INFO - Epoch(val) [160][59/59] eta: 0:00:00 time: 0.9928 data_time: 0.0007 memory: 981 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.80, recall: 0.8201, precision: 0.8353, hmean: 0.8276 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.81, recall: 0.8192, precision: 0.8375, hmean: 0.8283 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.82, recall: 0.8164, precision: 0.8394, hmean: 0.8278 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.83, recall: 0.8155, precision: 0.8425, hmean: 0.8288 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.84, recall: 0.8128, precision: 0.8436, hmean: 0.8279 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.85, recall: 0.8110, precision: 0.8473, hmean: 0.8287 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.86, recall: 0.8091, precision: 0.8478, hmean: 0.8280 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.87, recall: 0.8073, precision: 0.8476, hmean: 0.8269 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.88, recall: 0.8055, precision: 0.8514, hmean: 0.8278 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.89, recall: 0.8055, precision: 0.8547, hmean: 0.8293 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.90, recall: 0.8009, precision: 0.8573, hmean: 0.8281 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.91, recall: 0.7991, precision: 0.8612, hmean: 0.8290 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.92, recall: 0.7945, precision: 0.8657, hmean: 0.8286 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.93, recall: 0.7890, precision: 0.8683, hmean: 0.8268 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.94, recall: 0.7836, precision: 0.8711, hmean: 0.8250 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.95, recall: 0.7772, precision: 0.8755, hmean: 0.8234 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.96, recall: 0.7644, precision: 0.8746, hmean: 0.8158 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.97, recall: 0.7580, precision: 0.8792, hmean: 0.8141 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.98, recall: 0.7416, precision: 0.8874, hmean: 0.8080 2023/03/03 14:40:39 - mmengine - INFO - text score threshold: 0.99, recall: 0.7215, precision: 0.8916, hmean: 0.7976 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.80, recall: 0.8320, precision: 0.9056, hmean: 0.8672 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.81, recall: 0.8311, precision: 0.9064, hmean: 0.8671 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.82, recall: 0.8283, precision: 0.9079, hmean: 0.8663 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.83, recall: 0.8274, precision: 0.9096, hmean: 0.8666 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.84, recall: 0.8247, precision: 0.9112, hmean: 0.8658 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.85, recall: 0.8228, precision: 0.9119, hmean: 0.8651 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.86, recall: 0.8210, precision: 0.9127, hmean: 0.8644 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.87, recall: 0.8192, precision: 0.9125, hmean: 0.8633 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.88, recall: 0.8164, precision: 0.9150, hmean: 0.8629 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.89, recall: 0.8155, precision: 0.9159, hmean: 0.8628 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.90, recall: 0.8100, precision: 0.9163, hmean: 0.8599 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.91, recall: 0.8064, precision: 0.9188, hmean: 0.8589 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.92, recall: 0.7991, precision: 0.9201, hmean: 0.8553 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.93, recall: 0.7918, precision: 0.9194, hmean: 0.8508 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.94, recall: 0.7854, precision: 0.9208, hmean: 0.8477 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.95, recall: 0.7772, precision: 0.9210, hmean: 0.8430 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.96, recall: 0.7644, precision: 0.9208, hmean: 0.8353 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.97, recall: 0.7589, precision: 0.9233, hmean: 0.8331 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.98, recall: 0.7406, precision: 0.9269, hmean: 0.8234 2023/03/03 14:40:42 - mmengine - INFO - text score threshold: 0.99, recall: 0.7205, precision: 0.9293, hmean: 0.8117 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.80, recall: 0.7525, precision: 0.9559, hmean: 0.8421 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.81, recall: 0.7516, precision: 0.9559, hmean: 0.8415 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.82, recall: 0.7489, precision: 0.9557, hmean: 0.8397 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.83, recall: 0.7489, precision: 0.9568, hmean: 0.8402 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.84, recall: 0.7461, precision: 0.9567, hmean: 0.8384 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.85, recall: 0.7443, precision: 0.9566, hmean: 0.8372 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.86, recall: 0.7425, precision: 0.9576, hmean: 0.8364 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.87, recall: 0.7406, precision: 0.9575, hmean: 0.8352 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.88, recall: 0.7370, precision: 0.9573, hmean: 0.8328 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.89, recall: 0.7361, precision: 0.9584, hmean: 0.8326 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.90, recall: 0.7315, precision: 0.9581, hmean: 0.8296 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.91, recall: 0.7288, precision: 0.9591, hmean: 0.8282 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.92, recall: 0.7215, precision: 0.9599, hmean: 0.8238 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.93, recall: 0.7160, precision: 0.9608, hmean: 0.8205 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.94, recall: 0.7105, precision: 0.9617, hmean: 0.8172 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.95, recall: 0.7023, precision: 0.9613, hmean: 0.8116 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.96, recall: 0.6913, precision: 0.9631, hmean: 0.8049 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.97, recall: 0.6868, precision: 0.9653, hmean: 0.8026 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.98, recall: 0.6694, precision: 0.9670, hmean: 0.7911 2023/03/03 14:40:45 - mmengine - INFO - text score threshold: 0.99, recall: 0.6511, precision: 0.9661, hmean: 0.7780 2023/03/03 14:40:45 - mmengine - INFO - Epoch(val) [160][59/59] generic/precision: 0.8547 generic/recall: 0.8055 generic/hmean: 0.8293 weak/precision: 0.9056 weak/recall: 0.8320 weak/hmean: 0.8672 strong/precision: 0.9559 strong/recall: 0.7525 strong/hmean: 0.8421 2023/03/03 14:40:46 - mmengine - INFO - Epoch(train) [161][ 1/15] lr: 1.0000e-06 eta: 0:03:24 time: 0.3285 data_time: 0.0548 memory: 17192 loss: 0.0939 loss_ce: 0.0939 2023/03/03 14:40:46 - mmengine - INFO - Epoch(train) [161][ 2/15] lr: 1.0000e-06 eta: 0:03:24 time: 0.3437 data_time: 0.0549 memory: 17006 loss: 0.0966 loss_ce: 0.0966 2023/03/03 14:40:46 - mmengine - INFO - Epoch(train) [161][ 3/15] lr: 1.0000e-06 eta: 0:03:23 time: 0.3497 data_time: 0.0549 memory: 16897 loss: 0.0953 loss_ce: 0.0953 2023/03/03 14:40:47 - mmengine - INFO - Epoch(train) [161][ 4/15] lr: 1.0000e-06 eta: 0:03:23 time: 0.3306 data_time: 0.0549 memory: 16976 loss: 0.0969 loss_ce: 0.0969 2023/03/03 14:40:47 - mmengine - INFO - Epoch(train) [161][ 5/15] lr: 1.0000e-06 eta: 0:03:23 time: 0.3327 data_time: 0.0549 memory: 18392 loss: 0.0975 loss_ce: 0.0975 2023/03/03 14:40:47 - mmengine - INFO - Epoch(train) [161][ 6/15] lr: 1.0000e-06 eta: 0:03:22 time: 0.3293 data_time: 0.0549 memory: 18090 loss: 0.0930 loss_ce: 0.0930 2023/03/03 14:40:47 - mmengine - INFO - Epoch(train) [161][ 7/15] lr: 1.0000e-06 eta: 0:03:22 time: 0.3227 data_time: 0.0549 memory: 14392 loss: 0.0920 loss_ce: 0.0920 2023/03/03 14:40:48 - mmengine - INFO - Epoch(train) [161][ 8/15] lr: 1.0000e-06 eta: 0:03:21 time: 0.3270 data_time: 0.0549 memory: 24810 loss: 0.0910 loss_ce: 0.0910 2023/03/03 14:40:48 - mmengine - INFO - Epoch(train) [161][ 9/15] lr: 1.0000e-06 eta: 0:03:21 time: 0.3158 data_time: 0.0549 memory: 16804 loss: 0.0908 loss_ce: 0.0908 2023/03/03 14:40:48 - mmengine - INFO - Epoch(train) [161][10/15] lr: 1.0000e-06 eta: 0:03:21 time: 0.3421 data_time: 0.0549 memory: 20873 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:40:49 - mmengine - INFO - Epoch(train) [161][11/15] lr: 1.0000e-06 eta: 0:03:20 time: 0.3183 data_time: 0.0018 memory: 37937 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:40:49 - mmengine - INFO - Epoch(train) [161][12/15] lr: 1.0000e-06 eta: 0:03:20 time: 0.2953 data_time: 0.0017 memory: 16976 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:40:49 - mmengine - INFO - Epoch(train) [161][13/15] lr: 1.0000e-06 eta: 0:03:20 time: 0.3035 data_time: 0.0016 memory: 18482 loss: 0.0832 loss_ce: 0.0832 2023/03/03 14:40:50 - mmengine - INFO - Epoch(train) [161][14/15] lr: 1.0000e-06 eta: 0:03:19 time: 0.3067 data_time: 0.0016 memory: 19111 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:40:50 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:40:50 - mmengine - INFO - Epoch(train) [161][15/15] lr: 1.0000e-06 eta: 0:03:19 time: 0.2912 data_time: 0.0016 memory: 5458 loss: 0.0772 loss_ce: 0.0772 2023/03/03 14:40:51 - mmengine - INFO - Epoch(train) [162][ 1/15] lr: 1.0000e-06 eta: 0:03:19 time: 0.3775 data_time: 0.0656 memory: 16955 loss: 0.0761 loss_ce: 0.0761 2023/03/03 14:40:51 - mmengine - INFO - Epoch(train) [162][ 2/15] lr: 1.0000e-06 eta: 0:03:18 time: 0.3799 data_time: 0.0657 memory: 17272 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:40:51 - mmengine - INFO - Epoch(train) [162][ 3/15] lr: 1.0000e-06 eta: 0:03:18 time: 0.3677 data_time: 0.0657 memory: 16955 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:40:52 - mmengine - INFO - Epoch(train) [162][ 4/15] lr: 1.0000e-06 eta: 0:03:18 time: 0.3629 data_time: 0.0657 memory: 14731 loss: 0.0734 loss_ce: 0.0734 2023/03/03 14:40:52 - mmengine - INFO - Epoch(train) [162][ 5/15] lr: 1.0000e-06 eta: 0:03:17 time: 0.3828 data_time: 0.0657 memory: 16508 loss: 0.0672 loss_ce: 0.0672 2023/03/03 14:40:53 - mmengine - INFO - Epoch(train) [162][ 6/15] lr: 1.0000e-06 eta: 0:03:17 time: 0.3650 data_time: 0.0657 memory: 23895 loss: 0.0658 loss_ce: 0.0658 2023/03/03 14:40:53 - mmengine - INFO - Epoch(train) [162][ 7/15] lr: 1.0000e-06 eta: 0:03:17 time: 0.3668 data_time: 0.0657 memory: 17434 loss: 0.0656 loss_ce: 0.0656 2023/03/03 14:40:53 - mmengine - INFO - Epoch(train) [162][ 8/15] lr: 1.0000e-06 eta: 0:03:16 time: 0.3527 data_time: 0.0657 memory: 14074 loss: 0.0644 loss_ce: 0.0644 2023/03/03 14:40:53 - mmengine - INFO - Epoch(train) [162][ 9/15] lr: 1.0000e-06 eta: 0:03:16 time: 0.3576 data_time: 0.0658 memory: 17421 loss: 0.0651 loss_ce: 0.0651 2023/03/03 14:40:54 - mmengine - INFO - Epoch(train) [162][10/15] lr: 1.0000e-06 eta: 0:03:16 time: 0.3696 data_time: 0.0658 memory: 15214 loss: 0.0644 loss_ce: 0.0644 2023/03/03 14:40:54 - mmengine - INFO - Epoch(train) [162][11/15] lr: 1.0000e-06 eta: 0:03:15 time: 0.2850 data_time: 0.0018 memory: 18953 loss: 0.0634 loss_ce: 0.0634 2023/03/03 14:40:54 - mmengine - INFO - Epoch(train) [162][12/15] lr: 1.0000e-06 eta: 0:03:15 time: 0.2872 data_time: 0.0017 memory: 16223 loss: 0.0669 loss_ce: 0.0669 2023/03/03 14:40:54 - mmengine - INFO - Epoch(train) [162][13/15] lr: 1.0000e-06 eta: 0:03:15 time: 0.2990 data_time: 0.0017 memory: 17892 loss: 0.0679 loss_ce: 0.0679 2023/03/03 14:40:55 - mmengine - INFO - Epoch(train) [162][14/15] lr: 1.0000e-06 eta: 0:03:14 time: 0.3067 data_time: 0.0017 memory: 15305 loss: 0.0721 loss_ce: 0.0721 2023/03/03 14:40:55 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:40:55 - mmengine - INFO - Epoch(train) [162][15/15] lr: 1.0000e-06 eta: 0:03:14 time: 0.2642 data_time: 0.0017 memory: 5962 loss: 0.0760 loss_ce: 0.0760 2023/03/03 14:40:56 - mmengine - INFO - Epoch(train) [163][ 1/15] lr: 1.0000e-06 eta: 0:03:14 time: 0.3144 data_time: 0.0650 memory: 17120 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:40:56 - mmengine - INFO - Epoch(train) [163][ 2/15] lr: 1.0000e-06 eta: 0:03:13 time: 0.3302 data_time: 0.0651 memory: 16976 loss: 0.0798 loss_ce: 0.0798 2023/03/03 14:40:56 - mmengine - INFO - Epoch(train) [163][ 3/15] lr: 1.0000e-06 eta: 0:03:13 time: 0.3407 data_time: 0.0651 memory: 15111 loss: 0.0780 loss_ce: 0.0780 2023/03/03 14:40:57 - mmengine - INFO - Epoch(train) [163][ 4/15] lr: 1.0000e-06 eta: 0:03:13 time: 0.3430 data_time: 0.0651 memory: 20152 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:40:57 - mmengine - INFO - Epoch(train) [163][ 5/15] lr: 1.0000e-06 eta: 0:03:12 time: 0.3378 data_time: 0.0651 memory: 16508 loss: 0.0817 loss_ce: 0.0817 2023/03/03 14:40:57 - mmengine - INFO - Epoch(train) [163][ 6/15] lr: 1.0000e-06 eta: 0:03:12 time: 0.3446 data_time: 0.0651 memory: 17730 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:40:57 - mmengine - INFO - Epoch(train) [163][ 7/15] lr: 1.0000e-06 eta: 0:03:11 time: 0.3421 data_time: 0.0651 memory: 18070 loss: 0.0778 loss_ce: 0.0778 2023/03/03 14:40:58 - mmengine - INFO - Epoch(train) [163][ 8/15] lr: 1.0000e-06 eta: 0:03:11 time: 0.3399 data_time: 0.0651 memory: 15432 loss: 0.0810 loss_ce: 0.0810 2023/03/03 14:40:58 - mmengine - INFO - Epoch(train) [163][ 9/15] lr: 1.0000e-06 eta: 0:03:11 time: 0.3459 data_time: 0.0651 memory: 23304 loss: 0.0772 loss_ce: 0.0772 2023/03/03 14:40:58 - mmengine - INFO - Epoch(train) [163][10/15] lr: 1.0000e-06 eta: 0:03:10 time: 0.3508 data_time: 0.0651 memory: 18241 loss: 0.0761 loss_ce: 0.0761 2023/03/03 14:40:59 - mmengine - INFO - Epoch(train) [163][11/15] lr: 1.0000e-06 eta: 0:03:10 time: 0.2898 data_time: 0.0018 memory: 16530 loss: 0.0726 loss_ce: 0.0726 2023/03/03 14:40:59 - mmengine - INFO - Epoch(train) [163][12/15] lr: 1.0000e-06 eta: 0:03:10 time: 0.2796 data_time: 0.0017 memory: 17002 loss: 0.0695 loss_ce: 0.0695 2023/03/03 14:40:59 - mmengine - INFO - Epoch(train) [163][13/15] lr: 1.0000e-06 eta: 0:03:09 time: 0.2751 data_time: 0.0017 memory: 17122 loss: 0.0686 loss_ce: 0.0686 2023/03/03 14:40:59 - mmengine - INFO - Epoch(train) [163][14/15] lr: 1.0000e-06 eta: 0:03:09 time: 0.2741 data_time: 0.0016 memory: 15494 loss: 0.0679 loss_ce: 0.0679 2023/03/03 14:41:00 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:41:00 - mmengine - INFO - Epoch(train) [163][15/15] lr: 1.0000e-06 eta: 0:03:09 time: 0.2621 data_time: 0.0016 memory: 5592 loss: 0.0689 loss_ce: 0.0689 2023/03/03 14:41:01 - mmengine - INFO - Epoch(train) [164][ 1/15] lr: 1.0000e-06 eta: 0:03:09 time: 0.3599 data_time: 0.0552 memory: 16955 loss: 0.0692 loss_ce: 0.0692 2023/03/03 14:41:01 - mmengine - INFO - Epoch(train) [164][ 2/15] lr: 1.0000e-06 eta: 0:03:08 time: 0.3601 data_time: 0.0552 memory: 17120 loss: 0.0716 loss_ce: 0.0716 2023/03/03 14:41:01 - mmengine - INFO - Epoch(train) [164][ 3/15] lr: 1.0000e-06 eta: 0:03:08 time: 0.3579 data_time: 0.0552 memory: 17272 loss: 0.0728 loss_ce: 0.0728 2023/03/03 14:41:02 - mmengine - INFO - Epoch(train) [164][ 4/15] lr: 1.0000e-06 eta: 0:03:07 time: 0.3462 data_time: 0.0553 memory: 17421 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:41:02 - mmengine - INFO - Epoch(train) [164][ 5/15] lr: 1.0000e-06 eta: 0:03:07 time: 0.3611 data_time: 0.0553 memory: 15767 loss: 0.0737 loss_ce: 0.0737 2023/03/03 14:41:02 - mmengine - INFO - Epoch(train) [164][ 6/15] lr: 1.0000e-06 eta: 0:03:07 time: 0.3612 data_time: 0.0553 memory: 16370 loss: 0.0741 loss_ce: 0.0741 2023/03/03 14:41:03 - mmengine - INFO - Epoch(train) [164][ 7/15] lr: 1.0000e-06 eta: 0:03:06 time: 0.3804 data_time: 0.0552 memory: 19452 loss: 0.0784 loss_ce: 0.0784 2023/03/03 14:41:03 - mmengine - INFO - Epoch(train) [164][ 8/15] lr: 1.0000e-06 eta: 0:03:06 time: 0.3749 data_time: 0.0552 memory: 14322 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:41:03 - mmengine - INFO - Epoch(train) [164][ 9/15] lr: 1.0000e-06 eta: 0:03:06 time: 0.3684 data_time: 0.0552 memory: 19217 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:41:03 - mmengine - INFO - Epoch(train) [164][10/15] lr: 1.0000e-06 eta: 0:03:05 time: 0.3848 data_time: 0.0552 memory: 15405 loss: 0.0732 loss_ce: 0.0732 2023/03/03 14:41:04 - mmengine - INFO - Epoch(train) [164][11/15] lr: 1.0000e-06 eta: 0:03:05 time: 0.2845 data_time: 0.0017 memory: 17513 loss: 0.0756 loss_ce: 0.0756 2023/03/03 14:41:04 - mmengine - INFO - Epoch(train) [164][12/15] lr: 1.0000e-06 eta: 0:03:05 time: 0.2868 data_time: 0.0017 memory: 16508 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:41:04 - mmengine - INFO - Epoch(train) [164][13/15] lr: 1.0000e-06 eta: 0:03:04 time: 0.2827 data_time: 0.0016 memory: 21203 loss: 0.0714 loss_ce: 0.0714 2023/03/03 14:41:04 - mmengine - INFO - Epoch(train) [164][14/15] lr: 1.0000e-06 eta: 0:03:04 time: 0.2827 data_time: 0.0016 memory: 18070 loss: 0.0703 loss_ce: 0.0703 2023/03/03 14:41:05 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:41:05 - mmengine - INFO - Epoch(train) [164][15/15] lr: 1.0000e-06 eta: 0:03:04 time: 0.2609 data_time: 0.0016 memory: 5780 loss: 0.0833 loss_ce: 0.0833 2023/03/03 14:41:05 - mmengine - INFO - Epoch(train) [165][ 1/15] lr: 1.0000e-06 eta: 0:03:03 time: 0.3270 data_time: 0.0578 memory: 19007 loss: 0.0830 loss_ce: 0.0830 2023/03/03 14:41:06 - mmengine - INFO - Epoch(train) [165][ 2/15] lr: 1.0000e-06 eta: 0:03:03 time: 0.3149 data_time: 0.0579 memory: 23595 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:41:06 - mmengine - INFO - Epoch(train) [165][ 3/15] lr: 1.0000e-06 eta: 0:03:03 time: 0.3280 data_time: 0.0580 memory: 17951 loss: 0.0817 loss_ce: 0.0817 2023/03/03 14:41:06 - mmengine - INFO - Epoch(train) [165][ 4/15] lr: 1.0000e-06 eta: 0:03:02 time: 0.3253 data_time: 0.0580 memory: 16370 loss: 0.0827 loss_ce: 0.0827 2023/03/03 14:41:07 - mmengine - INFO - Epoch(train) [165][ 5/15] lr: 1.0000e-06 eta: 0:03:02 time: 0.3241 data_time: 0.0580 memory: 17873 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:41:07 - mmengine - INFO - Epoch(train) [165][ 6/15] lr: 1.0000e-06 eta: 0:03:02 time: 0.3175 data_time: 0.0580 memory: 16654 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:41:07 - mmengine - INFO - Epoch(train) [165][ 7/15] lr: 1.0000e-06 eta: 0:03:01 time: 0.3150 data_time: 0.0580 memory: 17730 loss: 0.0817 loss_ce: 0.0817 2023/03/03 14:41:07 - mmengine - INFO - Epoch(train) [165][ 8/15] lr: 1.0000e-06 eta: 0:03:01 time: 0.3257 data_time: 0.0580 memory: 19043 loss: 0.0784 loss_ce: 0.0784 2023/03/03 14:41:08 - mmengine - INFO - Epoch(train) [165][ 9/15] lr: 1.0000e-06 eta: 0:03:00 time: 0.3287 data_time: 0.0580 memory: 20682 loss: 0.0780 loss_ce: 0.0780 2023/03/03 14:41:08 - mmengine - INFO - Epoch(train) [165][10/15] lr: 1.0000e-06 eta: 0:03:00 time: 0.3395 data_time: 0.0580 memory: 17272 loss: 0.0649 loss_ce: 0.0649 2023/03/03 14:41:08 - mmengine - INFO - Epoch(train) [165][11/15] lr: 1.0000e-06 eta: 0:03:00 time: 0.2708 data_time: 0.0018 memory: 17272 loss: 0.0637 loss_ce: 0.0637 2023/03/03 14:41:08 - mmengine - INFO - Epoch(train) [165][12/15] lr: 1.0000e-06 eta: 0:02:59 time: 0.2674 data_time: 0.0017 memory: 20390 loss: 0.0632 loss_ce: 0.0632 2023/03/03 14:41:09 - mmengine - INFO - Epoch(train) [165][13/15] lr: 1.0000e-06 eta: 0:02:59 time: 0.2620 data_time: 0.0017 memory: 18740 loss: 0.0632 loss_ce: 0.0632 2023/03/03 14:41:09 - mmengine - INFO - Epoch(train) [165][14/15] lr: 1.0000e-06 eta: 0:02:59 time: 0.2771 data_time: 0.0016 memory: 21749 loss: 0.0640 loss_ce: 0.0640 2023/03/03 14:41:09 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:41:09 - mmengine - INFO - Epoch(train) [165][15/15] lr: 1.0000e-06 eta: 0:02:58 time: 0.2633 data_time: 0.0016 memory: 5862 loss: 0.0704 loss_ce: 0.0704 2023/03/03 14:41:10 - mmengine - INFO - Epoch(train) [166][ 1/15] lr: 1.0000e-06 eta: 0:02:58 time: 0.3516 data_time: 0.0739 memory: 15477 loss: 0.0690 loss_ce: 0.0690 2023/03/03 14:41:11 - mmengine - INFO - Epoch(train) [166][ 2/15] lr: 1.0000e-06 eta: 0:02:58 time: 0.3550 data_time: 0.0739 memory: 13233 loss: 0.0673 loss_ce: 0.0673 2023/03/03 14:41:11 - mmengine - INFO - Epoch(train) [166][ 3/15] lr: 1.0000e-06 eta: 0:02:57 time: 0.3552 data_time: 0.0740 memory: 25642 loss: 0.0700 loss_ce: 0.0700 2023/03/03 14:41:11 - mmengine - INFO - Epoch(train) [166][ 4/15] lr: 1.0000e-06 eta: 0:02:57 time: 0.3552 data_time: 0.0740 memory: 16955 loss: 0.0695 loss_ce: 0.0695 2023/03/03 14:41:12 - mmengine - INFO - Epoch(train) [166][ 5/15] lr: 1.0000e-06 eta: 0:02:57 time: 0.3564 data_time: 0.0740 memory: 15499 loss: 0.0706 loss_ce: 0.0706 2023/03/03 14:41:12 - mmengine - INFO - Epoch(train) [166][ 6/15] lr: 1.0000e-06 eta: 0:02:56 time: 0.3660 data_time: 0.0740 memory: 20488 loss: 0.0700 loss_ce: 0.0700 2023/03/03 14:41:12 - mmengine - INFO - Epoch(train) [166][ 7/15] lr: 1.0000e-06 eta: 0:02:56 time: 0.3821 data_time: 0.0740 memory: 22848 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:41:13 - mmengine - INFO - Epoch(train) [166][ 8/15] lr: 1.0000e-06 eta: 0:02:56 time: 0.3795 data_time: 0.0740 memory: 16515 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:41:13 - mmengine - INFO - Epoch(train) [166][ 9/15] lr: 1.0000e-06 eta: 0:02:55 time: 0.3912 data_time: 0.0740 memory: 19117 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:41:13 - mmengine - INFO - Epoch(train) [166][10/15] lr: 1.0000e-06 eta: 0:02:55 time: 0.3984 data_time: 0.0740 memory: 17129 loss: 0.0714 loss_ce: 0.0714 2023/03/03 14:41:14 - mmengine - INFO - Epoch(train) [166][11/15] lr: 1.0000e-06 eta: 0:02:55 time: 0.3379 data_time: 0.0018 memory: 16744 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:41:14 - mmengine - INFO - Epoch(train) [166][12/15] lr: 1.0000e-06 eta: 0:02:54 time: 0.3734 data_time: 0.0017 memory: 37744 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:41:15 - mmengine - INFO - Epoch(train) [166][13/15] lr: 1.0000e-06 eta: 0:02:54 time: 0.3554 data_time: 0.0017 memory: 15911 loss: 0.0829 loss_ce: 0.0829 2023/03/03 14:41:15 - mmengine - INFO - Epoch(train) [166][14/15] lr: 1.0000e-06 eta: 0:02:54 time: 0.3568 data_time: 0.0016 memory: 15740 loss: 0.0828 loss_ce: 0.0828 2023/03/03 14:41:15 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:41:15 - mmengine - INFO - Epoch(train) [166][15/15] lr: 1.0000e-06 eta: 0:02:53 time: 0.3582 data_time: 0.0016 memory: 4355 loss: 0.0841 loss_ce: 0.0841 2023/03/03 14:41:16 - mmengine - INFO - Epoch(train) [167][ 1/15] lr: 1.0000e-06 eta: 0:02:53 time: 0.4109 data_time: 0.0601 memory: 17284 loss: 0.0843 loss_ce: 0.0843 2023/03/03 14:41:16 - mmengine - INFO - Epoch(train) [167][ 2/15] lr: 1.0000e-06 eta: 0:02:53 time: 0.3956 data_time: 0.0602 memory: 16654 loss: 0.0823 loss_ce: 0.0823 2023/03/03 14:41:16 - mmengine - INFO - Epoch(train) [167][ 3/15] lr: 1.0000e-06 eta: 0:02:52 time: 0.3909 data_time: 0.0602 memory: 15346 loss: 0.0785 loss_ce: 0.0785 2023/03/03 14:41:17 - mmengine - INFO - Epoch(train) [167][ 4/15] lr: 1.0000e-06 eta: 0:02:52 time: 0.3704 data_time: 0.0603 memory: 18586 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:41:17 - mmengine - INFO - Epoch(train) [167][ 5/15] lr: 1.0000e-06 eta: 0:02:52 time: 0.3710 data_time: 0.0603 memory: 16976 loss: 0.0815 loss_ce: 0.0815 2023/03/03 14:41:17 - mmengine - INFO - Epoch(train) [167][ 6/15] lr: 1.0000e-06 eta: 0:02:51 time: 0.3690 data_time: 0.0603 memory: 14906 loss: 0.0810 loss_ce: 0.0810 2023/03/03 14:41:18 - mmengine - INFO - Epoch(train) [167][ 7/15] lr: 1.0000e-06 eta: 0:02:51 time: 0.3464 data_time: 0.0604 memory: 19227 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:41:18 - mmengine - INFO - Epoch(train) [167][ 8/15] lr: 1.0000e-06 eta: 0:02:51 time: 0.3620 data_time: 0.0603 memory: 16258 loss: 0.0709 loss_ce: 0.0709 2023/03/03 14:41:18 - mmengine - INFO - Epoch(train) [167][ 9/15] lr: 1.0000e-06 eta: 0:02:50 time: 0.3678 data_time: 0.0604 memory: 21466 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:41:19 - mmengine - INFO - Epoch(train) [167][10/15] lr: 1.0000e-06 eta: 0:02:50 time: 0.3718 data_time: 0.0604 memory: 25600 loss: 0.0714 loss_ce: 0.0714 2023/03/03 14:41:19 - mmengine - INFO - Epoch(train) [167][11/15] lr: 1.0000e-06 eta: 0:02:50 time: 0.3097 data_time: 0.0020 memory: 17572 loss: 0.0708 loss_ce: 0.0708 2023/03/03 14:41:20 - mmengine - INFO - Epoch(train) [167][12/15] lr: 1.0000e-06 eta: 0:02:49 time: 0.3280 data_time: 0.0019 memory: 17029 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:41:20 - mmengine - INFO - Epoch(train) [167][13/15] lr: 1.0000e-06 eta: 0:02:49 time: 0.3298 data_time: 0.0019 memory: 17272 loss: 0.0721 loss_ce: 0.0721 2023/03/03 14:41:20 - mmengine - INFO - Epoch(train) [167][14/15] lr: 1.0000e-06 eta: 0:02:49 time: 0.3322 data_time: 0.0018 memory: 12459 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:41:20 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:41:20 - mmengine - INFO - Epoch(train) [167][15/15] lr: 1.0000e-06 eta: 0:02:48 time: 0.3242 data_time: 0.0018 memory: 6551 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:41:21 - mmengine - INFO - Epoch(train) [168][ 1/15] lr: 1.0000e-06 eta: 0:02:48 time: 0.4011 data_time: 0.0514 memory: 16370 loss: 0.0796 loss_ce: 0.0796 2023/03/03 14:41:22 - mmengine - INFO - Epoch(train) [168][ 2/15] lr: 1.0000e-06 eta: 0:02:48 time: 0.3821 data_time: 0.0514 memory: 9866 loss: 0.0810 loss_ce: 0.0810 2023/03/03 14:41:22 - mmengine - INFO - Epoch(train) [168][ 3/15] lr: 1.0000e-06 eta: 0:02:47 time: 0.3886 data_time: 0.0515 memory: 17176 loss: 0.0816 loss_ce: 0.0816 2023/03/03 14:41:22 - mmengine - INFO - Epoch(train) [168][ 4/15] lr: 1.0000e-06 eta: 0:02:47 time: 0.3765 data_time: 0.0514 memory: 15190 loss: 0.0804 loss_ce: 0.0804 2023/03/03 14:41:23 - mmengine - INFO - Epoch(train) [168][ 5/15] lr: 1.0000e-06 eta: 0:02:47 time: 0.3690 data_time: 0.0514 memory: 16456 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:41:23 - mmengine - INFO - Epoch(train) [168][ 6/15] lr: 1.0000e-06 eta: 0:02:46 time: 0.3693 data_time: 0.0514 memory: 18156 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:41:23 - mmengine - INFO - Epoch(train) [168][ 7/15] lr: 1.0000e-06 eta: 0:02:46 time: 0.3687 data_time: 0.0514 memory: 16947 loss: 0.0780 loss_ce: 0.0780 2023/03/03 14:41:23 - mmengine - INFO - Epoch(train) [168][ 8/15] lr: 1.0000e-06 eta: 0:02:46 time: 0.3686 data_time: 0.0514 memory: 17120 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:41:24 - mmengine - INFO - Epoch(train) [168][ 9/15] lr: 1.0000e-06 eta: 0:02:45 time: 0.3671 data_time: 0.0514 memory: 15631 loss: 0.0800 loss_ce: 0.0800 2023/03/03 14:41:24 - mmengine - INFO - Epoch(train) [168][10/15] lr: 1.0000e-06 eta: 0:02:45 time: 0.3751 data_time: 0.0514 memory: 17120 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:41:24 - mmengine - INFO - Epoch(train) [168][11/15] lr: 1.0000e-06 eta: 0:02:45 time: 0.2830 data_time: 0.0018 memory: 16508 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:41:24 - mmengine - INFO - Epoch(train) [168][12/15] lr: 1.0000e-06 eta: 0:02:44 time: 0.2856 data_time: 0.0018 memory: 17120 loss: 0.0751 loss_ce: 0.0751 2023/03/03 14:41:25 - mmengine - INFO - Epoch(train) [168][13/15] lr: 1.0000e-06 eta: 0:02:44 time: 0.2840 data_time: 0.0017 memory: 16508 loss: 0.0770 loss_ce: 0.0770 2023/03/03 14:41:25 - mmengine - INFO - Epoch(train) [168][14/15] lr: 1.0000e-06 eta: 0:02:44 time: 0.2992 data_time: 0.0017 memory: 23869 loss: 0.0760 loss_ce: 0.0760 2023/03/03 14:41:25 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:41:25 - mmengine - INFO - Epoch(train) [168][15/15] lr: 1.0000e-06 eta: 0:02:43 time: 0.2885 data_time: 0.0017 memory: 4172 loss: 0.0842 loss_ce: 0.0842 2023/03/03 14:41:27 - mmengine - INFO - Epoch(train) [169][ 1/15] lr: 1.0000e-06 eta: 0:02:43 time: 0.4108 data_time: 0.0977 memory: 22636 loss: 0.0860 loss_ce: 0.0860 2023/03/03 14:41:27 - mmengine - INFO - Epoch(train) [169][ 2/15] lr: 1.0000e-06 eta: 0:02:43 time: 0.3833 data_time: 0.0977 memory: 13497 loss: 0.0829 loss_ce: 0.0829 2023/03/03 14:41:27 - mmengine - INFO - Epoch(train) [169][ 3/15] lr: 1.0000e-06 eta: 0:02:42 time: 0.3835 data_time: 0.0977 memory: 17421 loss: 0.0785 loss_ce: 0.0785 2023/03/03 14:41:28 - mmengine - INFO - Epoch(train) [169][ 4/15] lr: 1.0000e-06 eta: 0:02:42 time: 0.3822 data_time: 0.0978 memory: 16370 loss: 0.0812 loss_ce: 0.0812 2023/03/03 14:41:28 - mmengine - INFO - Epoch(train) [169][ 5/15] lr: 1.0000e-06 eta: 0:02:42 time: 0.3824 data_time: 0.0978 memory: 17892 loss: 0.0758 loss_ce: 0.0758 2023/03/03 14:41:28 - mmengine - INFO - Epoch(train) [169][ 6/15] lr: 1.0000e-06 eta: 0:02:41 time: 0.3835 data_time: 0.0978 memory: 23760 loss: 0.0730 loss_ce: 0.0730 2023/03/03 14:41:28 - mmengine - INFO - Epoch(train) [169][ 7/15] lr: 1.0000e-06 eta: 0:02:41 time: 0.3835 data_time: 0.0978 memory: 17120 loss: 0.0708 loss_ce: 0.0708 2023/03/03 14:41:29 - mmengine - INFO - Epoch(train) [169][ 8/15] lr: 1.0000e-06 eta: 0:02:41 time: 0.4159 data_time: 0.0978 memory: 18586 loss: 0.0673 loss_ce: 0.0673 2023/03/03 14:41:29 - mmengine - INFO - Epoch(train) [169][ 9/15] lr: 1.0000e-06 eta: 0:02:40 time: 0.4024 data_time: 0.0979 memory: 16976 loss: 0.0729 loss_ce: 0.0729 2023/03/03 14:41:30 - mmengine - INFO - Epoch(train) [169][10/15] lr: 1.0000e-06 eta: 0:02:40 time: 0.4219 data_time: 0.0978 memory: 16223 loss: 0.0663 loss_ce: 0.0663 2023/03/03 14:41:30 - mmengine - INFO - Epoch(train) [169][11/15] lr: 1.0000e-06 eta: 0:02:40 time: 0.3094 data_time: 0.0019 memory: 16374 loss: 0.0670 loss_ce: 0.0670 2023/03/03 14:41:30 - mmengine - INFO - Epoch(train) [169][12/15] lr: 1.0000e-06 eta: 0:02:39 time: 0.3141 data_time: 0.0018 memory: 16508 loss: 0.0666 loss_ce: 0.0666 2023/03/03 14:41:30 - mmengine - INFO - Epoch(train) [169][13/15] lr: 1.0000e-06 eta: 0:02:39 time: 0.3126 data_time: 0.0018 memory: 13034 loss: 0.0691 loss_ce: 0.0691 2023/03/03 14:41:31 - mmengine - INFO - Epoch(train) [169][14/15] lr: 1.0000e-06 eta: 0:02:38 time: 0.3309 data_time: 0.0017 memory: 15507 loss: 0.0682 loss_ce: 0.0682 2023/03/03 14:41:31 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:41:31 - mmengine - INFO - Epoch(train) [169][15/15] lr: 1.0000e-06 eta: 0:02:38 time: 0.3264 data_time: 0.0017 memory: 4317 loss: 0.0733 loss_ce: 0.0733 2023/03/03 14:41:32 - mmengine - INFO - Epoch(train) [170][ 1/15] lr: 1.0000e-06 eta: 0:02:38 time: 0.4016 data_time: 0.0571 memory: 18797 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:41:32 - mmengine - INFO - Epoch(train) [170][ 2/15] lr: 1.0000e-06 eta: 0:02:38 time: 0.4024 data_time: 0.0572 memory: 17397 loss: 0.0721 loss_ce: 0.0721 2023/03/03 14:41:33 - mmengine - INFO - Epoch(train) [170][ 3/15] lr: 1.0000e-06 eta: 0:02:37 time: 0.3975 data_time: 0.0572 memory: 25378 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:41:33 - mmengine - INFO - Epoch(train) [170][ 4/15] lr: 1.0000e-06 eta: 0:02:37 time: 0.4133 data_time: 0.0572 memory: 16453 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:41:34 - mmengine - INFO - Epoch(train) [170][ 5/15] lr: 1.0000e-06 eta: 0:02:37 time: 0.4108 data_time: 0.0573 memory: 18952 loss: 0.0735 loss_ce: 0.0735 2023/03/03 14:41:34 - mmengine - INFO - Epoch(train) [170][ 6/15] lr: 1.0000e-06 eta: 0:02:36 time: 0.4201 data_time: 0.0572 memory: 24556 loss: 0.0742 loss_ce: 0.0742 2023/03/03 14:41:34 - mmengine - INFO - Epoch(train) [170][ 7/15] lr: 1.0000e-06 eta: 0:02:36 time: 0.4157 data_time: 0.0572 memory: 16212 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:41:35 - mmengine - INFO - Epoch(train) [170][ 8/15] lr: 1.0000e-06 eta: 0:02:36 time: 0.4169 data_time: 0.0572 memory: 17421 loss: 0.0733 loss_ce: 0.0733 2023/03/03 14:41:35 - mmengine - INFO - Epoch(train) [170][ 9/15] lr: 1.0000e-06 eta: 0:02:35 time: 0.3970 data_time: 0.0572 memory: 17272 loss: 0.0725 loss_ce: 0.0725 2023/03/03 14:41:35 - mmengine - INFO - Epoch(train) [170][10/15] lr: 1.0000e-06 eta: 0:02:35 time: 0.4038 data_time: 0.0572 memory: 16530 loss: 0.0705 loss_ce: 0.0705 2023/03/03 14:41:35 - mmengine - INFO - Epoch(train) [170][11/15] lr: 1.0000e-06 eta: 0:02:34 time: 0.3262 data_time: 0.0018 memory: 16056 loss: 0.0684 loss_ce: 0.0684 2023/03/03 14:41:36 - mmengine - INFO - Epoch(train) [170][12/15] lr: 1.0000e-06 eta: 0:02:34 time: 0.3255 data_time: 0.0018 memory: 17272 loss: 0.0699 loss_ce: 0.0699 2023/03/03 14:41:36 - mmengine - INFO - Epoch(train) [170][13/15] lr: 1.0000e-06 eta: 0:02:34 time: 0.2782 data_time: 0.0017 memory: 16976 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:41:36 - mmengine - INFO - Epoch(train) [170][14/15] lr: 1.0000e-06 eta: 0:02:33 time: 0.2661 data_time: 0.0017 memory: 17333 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:41:36 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:41:36 - mmengine - INFO - Epoch(train) [170][15/15] lr: 1.0000e-06 eta: 0:02:33 time: 0.2451 data_time: 0.0017 memory: 6027 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:41:38 - mmengine - INFO - Epoch(val) [170][ 1/59] eta: 0:01:30 time: 1.0649 data_time: 0.0033 memory: 981 2023/03/03 14:41:39 - mmengine - INFO - Epoch(val) [170][ 2/59] eta: 0:01:08 time: 0.9792 data_time: 0.0033 memory: 981 2023/03/03 14:41:40 - mmengine - INFO - Epoch(val) [170][ 3/59] eta: 0:01:09 time: 0.9941 data_time: 0.0033 memory: 1003 2023/03/03 14:41:40 - mmengine - INFO - Epoch(val) [170][ 4/59] eta: 0:00:53 time: 0.9621 data_time: 0.0033 memory: 981 2023/03/03 14:41:43 - mmengine - INFO - Epoch(val) [170][ 5/59] eta: 0:01:15 time: 1.2055 data_time: 0.0033 memory: 1016 2023/03/03 14:41:46 - mmengine - INFO - Epoch(val) [170][ 6/59] eta: 0:01:25 time: 1.4076 data_time: 0.0034 memory: 981 2023/03/03 14:41:46 - mmengine - INFO - Epoch(val) [170][ 7/59] eta: 0:01:13 time: 1.3591 data_time: 0.0034 memory: 1043 2023/03/03 14:41:47 - mmengine - INFO - Epoch(val) [170][ 8/59] eta: 0:01:07 time: 1.2031 data_time: 0.0034 memory: 1016 2023/03/03 14:41:48 - mmengine - INFO - Epoch(val) [170][ 9/59] eta: 0:01:04 time: 1.1889 data_time: 0.0034 memory: 981 2023/03/03 14:41:48 - mmengine - INFO - Epoch(val) [170][10/59] eta: 0:00:59 time: 1.2232 data_time: 0.0035 memory: 981 2023/03/03 14:41:49 - mmengine - INFO - Epoch(val) [170][11/59] eta: 0:00:54 time: 1.1017 data_time: 0.0010 memory: 981 2023/03/03 14:41:52 - mmengine - INFO - Epoch(val) [170][12/59] eta: 0:01:02 time: 1.3473 data_time: 0.0010 memory: 1016 2023/03/03 14:41:54 - mmengine - INFO - Epoch(val) [170][13/59] eta: 0:01:04 time: 1.4397 data_time: 0.0009 memory: 981 2023/03/03 14:41:55 - mmengine - INFO - Epoch(val) [170][14/59] eta: 0:01:01 time: 1.5241 data_time: 0.0010 memory: 890 2023/03/03 14:41:55 - mmengine - INFO - Epoch(val) [170][15/59] eta: 0:00:56 time: 1.2169 data_time: 0.0010 memory: 981 2023/03/03 14:41:56 - mmengine - INFO - Epoch(val) [170][16/59] eta: 0:00:52 time: 0.9990 data_time: 0.0009 memory: 981 2023/03/03 14:41:56 - mmengine - INFO - Epoch(val) [170][17/59] eta: 0:00:49 time: 1.0158 data_time: 0.0009 memory: 981 2023/03/03 14:41:57 - mmengine - INFO - Epoch(val) [170][18/59] eta: 0:00:46 time: 0.9828 data_time: 0.0009 memory: 981 2023/03/03 14:41:58 - mmengine - INFO - Epoch(val) [170][19/59] eta: 0:00:44 time: 0.9823 data_time: 0.0009 memory: 981 2023/03/03 14:41:58 - mmengine - INFO - Epoch(val) [170][20/59] eta: 0:00:42 time: 0.9484 data_time: 0.0009 memory: 981 2023/03/03 14:41:58 - mmengine - INFO - Epoch(val) [170][21/59] eta: 0:00:40 time: 0.9650 data_time: 0.0009 memory: 981 2023/03/03 14:41:59 - mmengine - INFO - Epoch(val) [170][22/59] eta: 0:00:37 time: 0.6535 data_time: 0.0009 memory: 981 2023/03/03 14:41:59 - mmengine - INFO - Epoch(val) [170][23/59] eta: 0:00:36 time: 0.4973 data_time: 0.0009 memory: 981 2023/03/03 14:42:00 - mmengine - INFO - Epoch(val) [170][24/59] eta: 0:00:34 time: 0.4295 data_time: 0.0009 memory: 962 2023/03/03 14:42:00 - mmengine - INFO - Epoch(val) [170][25/59] eta: 0:00:32 time: 0.4612 data_time: 0.0009 memory: 981 2023/03/03 14:42:00 - mmengine - INFO - Epoch(val) [170][26/59] eta: 0:00:30 time: 0.4446 data_time: 0.0009 memory: 981 2023/03/03 14:42:01 - mmengine - INFO - Epoch(val) [170][27/59] eta: 0:00:28 time: 0.4443 data_time: 0.0009 memory: 981 2023/03/03 14:42:01 - mmengine - INFO - Epoch(val) [170][28/59] eta: 0:00:27 time: 0.4441 data_time: 0.0008 memory: 981 2023/03/03 14:42:02 - mmengine - INFO - Epoch(val) [170][29/59] eta: 0:00:27 time: 0.4788 data_time: 0.0008 memory: 981 2023/03/03 14:42:03 - mmengine - INFO - Epoch(val) [170][30/59] eta: 0:00:26 time: 0.5287 data_time: 0.0009 memory: 999 2023/03/03 14:42:04 - mmengine - INFO - Epoch(val) [170][31/59] eta: 0:00:25 time: 0.5455 data_time: 0.0009 memory: 981 2023/03/03 14:42:05 - mmengine - INFO - Epoch(val) [170][32/59] eta: 0:00:24 time: 0.6457 data_time: 0.0009 memory: 981 2023/03/03 14:42:05 - mmengine - INFO - Epoch(val) [170][33/59] eta: 0:00:22 time: 0.5804 data_time: 0.0008 memory: 981 2023/03/03 14:42:05 - mmengine - INFO - Epoch(val) [170][34/59] eta: 0:00:21 time: 0.5637 data_time: 0.0008 memory: 981 2023/03/03 14:42:05 - mmengine - INFO - Epoch(val) [170][35/59] eta: 0:00:20 time: 0.5470 data_time: 0.0008 memory: 981 2023/03/03 14:42:06 - mmengine - INFO - Epoch(val) [170][36/59] eta: 0:00:19 time: 0.5634 data_time: 0.0008 memory: 981 2023/03/03 14:42:06 - mmengine - INFO - Epoch(val) [170][37/59] eta: 0:00:17 time: 0.5467 data_time: 0.0008 memory: 981 2023/03/03 14:42:07 - mmengine - INFO - Epoch(val) [170][38/59] eta: 0:00:16 time: 0.5798 data_time: 0.0008 memory: 981 2023/03/03 14:42:07 - mmengine - INFO - Epoch(val) [170][39/59] eta: 0:00:15 time: 0.4943 data_time: 0.0008 memory: 987 2023/03/03 14:42:08 - mmengine - INFO - Epoch(val) [170][40/59] eta: 0:00:15 time: 0.4945 data_time: 0.0007 memory: 981 2023/03/03 14:42:09 - mmengine - INFO - Epoch(val) [170][41/59] eta: 0:00:14 time: 0.5454 data_time: 0.0008 memory: 986 2023/03/03 14:42:10 - mmengine - INFO - Epoch(val) [170][42/59] eta: 0:00:13 time: 0.4948 data_time: 0.0008 memory: 981 2023/03/03 14:42:11 - mmengine - INFO - Epoch(val) [170][43/59] eta: 0:00:12 time: 0.5765 data_time: 0.0008 memory: 976 2023/03/03 14:42:11 - mmengine - INFO - Epoch(val) [170][44/59] eta: 0:00:11 time: 0.6094 data_time: 0.0008 memory: 1003 2023/03/03 14:42:13 - mmengine - INFO - Epoch(val) [170][45/59] eta: 0:00:11 time: 0.7809 data_time: 0.0008 memory: 981 2023/03/03 14:42:14 - mmengine - INFO - Epoch(val) [170][46/59] eta: 0:00:10 time: 0.8143 data_time: 0.0008 memory: 981 2023/03/03 14:42:15 - mmengine - INFO - Epoch(val) [170][47/59] eta: 0:00:09 time: 0.8470 data_time: 0.0008 memory: 936 2023/03/03 14:42:15 - mmengine - INFO - Epoch(val) [170][48/59] eta: 0:00:08 time: 0.8303 data_time: 0.0008 memory: 1000 2023/03/03 14:42:16 - mmengine - INFO - Epoch(val) [170][49/59] eta: 0:00:08 time: 0.8810 data_time: 0.0008 memory: 981 2023/03/03 14:42:17 - mmengine - INFO - Epoch(val) [170][50/59] eta: 0:00:07 time: 0.8808 data_time: 0.0008 memory: 987 2023/03/03 14:42:19 - mmengine - INFO - Epoch(val) [170][51/59] eta: 0:00:06 time: 0.9319 data_time: 0.0009 memory: 981 2023/03/03 14:42:20 - mmengine - INFO - Epoch(val) [170][52/59] eta: 0:00:05 time: 0.9830 data_time: 0.0009 memory: 981 2023/03/03 14:42:20 - mmengine - INFO - Epoch(val) [170][53/59] eta: 0:00:04 time: 0.9492 data_time: 0.0009 memory: 962 2023/03/03 14:42:21 - mmengine - INFO - Epoch(val) [170][54/59] eta: 0:00:04 time: 0.9662 data_time: 0.0009 memory: 981 2023/03/03 14:42:22 - mmengine - INFO - Epoch(val) [170][55/59] eta: 0:00:03 time: 0.8445 data_time: 0.0009 memory: 981 2023/03/03 14:42:22 - mmengine - INFO - Epoch(val) [170][56/59] eta: 0:00:02 time: 0.8275 data_time: 0.0009 memory: 981 2023/03/03 14:42:25 - mmengine - INFO - Epoch(val) [170][57/59] eta: 0:00:01 time: 1.0118 data_time: 0.0009 memory: 981 2023/03/03 14:42:26 - mmengine - INFO - Epoch(val) [170][58/59] eta: 0:00:00 time: 1.0787 data_time: 0.0010 memory: 1016 2023/03/03 14:42:26 - mmengine - INFO - Epoch(val) [170][59/59] eta: 0:00:00 time: 1.0113 data_time: 0.0010 memory: 981 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.80, recall: 0.8183, precision: 0.8358, hmean: 0.8269 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.81, recall: 0.8164, precision: 0.8371, hmean: 0.8266 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.82, recall: 0.8164, precision: 0.8386, hmean: 0.8274 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.83, recall: 0.8137, precision: 0.8438, hmean: 0.8285 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.84, recall: 0.8128, precision: 0.8468, hmean: 0.8295 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.85, recall: 0.8100, precision: 0.8472, hmean: 0.8282 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.86, recall: 0.8091, precision: 0.8478, hmean: 0.8280 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.87, recall: 0.8082, precision: 0.8477, hmean: 0.8275 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.88, recall: 0.8064, precision: 0.8490, hmean: 0.8272 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.89, recall: 0.8027, precision: 0.8526, hmean: 0.8269 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.90, recall: 0.8000, precision: 0.8563, hmean: 0.8272 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.91, recall: 0.7963, precision: 0.8608, hmean: 0.8273 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.92, recall: 0.7927, precision: 0.8654, hmean: 0.8275 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.93, recall: 0.7881, precision: 0.8682, hmean: 0.8262 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.94, recall: 0.7790, precision: 0.8704, hmean: 0.8222 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.95, recall: 0.7726, precision: 0.8740, hmean: 0.8202 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.96, recall: 0.7653, precision: 0.8747, hmean: 0.8164 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.97, recall: 0.7525, precision: 0.8785, hmean: 0.8106 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.98, recall: 0.7388, precision: 0.8861, hmean: 0.8058 2023/03/03 14:42:56 - mmengine - INFO - text score threshold: 0.99, recall: 0.7205, precision: 0.8925, hmean: 0.7974 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.80, recall: 0.8301, precision: 0.9054, hmean: 0.8661 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.81, recall: 0.8283, precision: 0.9052, hmean: 0.8650 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.82, recall: 0.8283, precision: 0.9061, hmean: 0.8655 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.83, recall: 0.8256, precision: 0.9095, hmean: 0.8655 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.84, recall: 0.8247, precision: 0.9112, hmean: 0.8658 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.85, recall: 0.8219, precision: 0.9119, hmean: 0.8646 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.86, recall: 0.8210, precision: 0.9118, hmean: 0.8640 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.87, recall: 0.8201, precision: 0.9117, hmean: 0.8635 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.88, recall: 0.8183, precision: 0.9134, hmean: 0.8632 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.89, recall: 0.8128, precision: 0.9147, hmean: 0.8607 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.90, recall: 0.8082, precision: 0.9152, hmean: 0.8584 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.91, recall: 0.8018, precision: 0.9165, hmean: 0.8553 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.92, recall: 0.7973, precision: 0.9199, hmean: 0.8542 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.93, recall: 0.7909, precision: 0.9193, hmean: 0.8503 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.94, recall: 0.7808, precision: 0.9194, hmean: 0.8444 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.95, recall: 0.7726, precision: 0.9196, hmean: 0.8397 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.96, recall: 0.7653, precision: 0.9209, hmean: 0.8359 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.97, recall: 0.7534, precision: 0.9239, hmean: 0.8300 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.98, recall: 0.7388, precision: 0.9278, hmean: 0.8226 2023/03/03 14:42:59 - mmengine - INFO - text score threshold: 0.99, recall: 0.7196, precision: 0.9292, hmean: 0.8111 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.80, recall: 0.7507, precision: 0.9547, hmean: 0.8405 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.81, recall: 0.7489, precision: 0.9546, hmean: 0.8393 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.82, recall: 0.7479, precision: 0.9545, hmean: 0.8387 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.83, recall: 0.7461, precision: 0.9567, hmean: 0.8384 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.84, recall: 0.7452, precision: 0.9566, hmean: 0.8378 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.85, recall: 0.7425, precision: 0.9565, hmean: 0.8360 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.86, recall: 0.7425, precision: 0.9565, hmean: 0.8360 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.87, recall: 0.7416, precision: 0.9564, hmean: 0.8354 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.88, recall: 0.7406, precision: 0.9564, hmean: 0.8348 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.89, recall: 0.7342, precision: 0.9560, hmean: 0.8306 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.90, recall: 0.7306, precision: 0.9569, hmean: 0.8286 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.91, recall: 0.7242, precision: 0.9577, hmean: 0.8248 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.92, recall: 0.7205, precision: 0.9599, hmean: 0.8232 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.93, recall: 0.7151, precision: 0.9607, hmean: 0.8199 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.94, recall: 0.7068, precision: 0.9615, hmean: 0.8147 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.95, recall: 0.7005, precision: 0.9624, hmean: 0.8108 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.96, recall: 0.6932, precision: 0.9644, hmean: 0.8066 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.97, recall: 0.6804, precision: 0.9650, hmean: 0.7981 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.98, recall: 0.6667, precision: 0.9669, hmean: 0.7892 2023/03/03 14:43:02 - mmengine - INFO - text score threshold: 0.99, recall: 0.6493, precision: 0.9660, hmean: 0.7766 2023/03/03 14:43:02 - mmengine - INFO - Epoch(val) [170][59/59] generic/precision: 0.8468 generic/recall: 0.8128 generic/hmean: 0.8295 weak/precision: 0.9054 weak/recall: 0.8301 weak/hmean: 0.8661 strong/precision: 0.9547 strong/recall: 0.7507 strong/hmean: 0.8405 2023/03/03 14:43:03 - mmengine - INFO - Epoch(train) [171][ 1/15] lr: 1.0000e-06 eta: 0:02:33 time: 0.3195 data_time: 0.0867 memory: 17728 loss: 0.0720 loss_ce: 0.0720 2023/03/03 14:43:03 - mmengine - INFO - Epoch(train) [171][ 2/15] lr: 1.0000e-06 eta: 0:02:32 time: 0.3317 data_time: 0.0867 memory: 13987 loss: 0.0753 loss_ce: 0.0753 2023/03/03 14:43:03 - mmengine - INFO - Epoch(train) [171][ 3/15] lr: 1.0000e-06 eta: 0:02:32 time: 0.3336 data_time: 0.0868 memory: 15494 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:43:04 - mmengine - INFO - Epoch(train) [171][ 4/15] lr: 1.0000e-06 eta: 0:02:32 time: 0.3334 data_time: 0.0869 memory: 23762 loss: 0.0810 loss_ce: 0.0810 2023/03/03 14:43:04 - mmengine - INFO - Epoch(train) [171][ 5/15] lr: 1.0000e-06 eta: 0:02:31 time: 0.3310 data_time: 0.0869 memory: 17421 loss: 0.0785 loss_ce: 0.0785 2023/03/03 14:43:04 - mmengine - INFO - Epoch(train) [171][ 6/15] lr: 1.0000e-06 eta: 0:02:31 time: 0.3200 data_time: 0.0869 memory: 16212 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:43:04 - mmengine - INFO - Epoch(train) [171][ 7/15] lr: 1.0000e-06 eta: 0:02:31 time: 0.3219 data_time: 0.0869 memory: 15747 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:43:05 - mmengine - INFO - Epoch(train) [171][ 8/15] lr: 1.0000e-06 eta: 0:02:30 time: 0.3242 data_time: 0.0869 memory: 17421 loss: 0.0725 loss_ce: 0.0725 2023/03/03 14:43:05 - mmengine - INFO - Epoch(train) [171][ 9/15] lr: 1.0000e-06 eta: 0:02:30 time: 0.3429 data_time: 0.0869 memory: 30416 loss: 0.0734 loss_ce: 0.0734 2023/03/03 14:43:05 - mmengine - INFO - Epoch(train) [171][10/15] lr: 1.0000e-06 eta: 0:02:30 time: 0.3511 data_time: 0.0870 memory: 17572 loss: 0.0727 loss_ce: 0.0727 2023/03/03 14:43:05 - mmengine - INFO - Epoch(train) [171][11/15] lr: 1.0000e-06 eta: 0:02:29 time: 0.2550 data_time: 0.0021 memory: 13169 loss: 0.0739 loss_ce: 0.0739 2023/03/03 14:43:06 - mmengine - INFO - Epoch(train) [171][12/15] lr: 1.0000e-06 eta: 0:02:29 time: 0.2597 data_time: 0.0021 memory: 16546 loss: 0.0746 loss_ce: 0.0746 2023/03/03 14:43:06 - mmengine - INFO - Epoch(train) [171][13/15] lr: 1.0000e-06 eta: 0:02:29 time: 0.2579 data_time: 0.0020 memory: 17421 loss: 0.0718 loss_ce: 0.0718 2023/03/03 14:43:06 - mmengine - INFO - Epoch(train) [171][14/15] lr: 1.0000e-06 eta: 0:02:28 time: 0.2654 data_time: 0.0019 memory: 17272 loss: 0.0677 loss_ce: 0.0677 2023/03/03 14:43:06 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:43:06 - mmengine - INFO - Epoch(train) [171][15/15] lr: 1.0000e-06 eta: 0:02:28 time: 0.2574 data_time: 0.0019 memory: 6622 loss: 0.0811 loss_ce: 0.0811 2023/03/03 14:43:07 - mmengine - INFO - Epoch(train) [172][ 1/15] lr: 1.0000e-06 eta: 0:02:28 time: 0.3398 data_time: 0.0633 memory: 17161 loss: 0.0817 loss_ce: 0.0817 2023/03/03 14:43:08 - mmengine - INFO - Epoch(train) [172][ 2/15] lr: 1.0000e-06 eta: 0:02:27 time: 0.3409 data_time: 0.0634 memory: 16370 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:43:08 - mmengine - INFO - Epoch(train) [172][ 3/15] lr: 1.0000e-06 eta: 0:02:27 time: 0.3574 data_time: 0.0634 memory: 17272 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:43:09 - mmengine - INFO - Epoch(train) [172][ 4/15] lr: 1.0000e-06 eta: 0:02:27 time: 0.3742 data_time: 0.0635 memory: 27342 loss: 0.0786 loss_ce: 0.0786 2023/03/03 14:43:09 - mmengine - INFO - Epoch(train) [172][ 5/15] lr: 1.0000e-06 eta: 0:02:26 time: 0.3777 data_time: 0.0635 memory: 12792 loss: 0.0788 loss_ce: 0.0788 2023/03/03 14:43:09 - mmengine - INFO - Epoch(train) [172][ 6/15] lr: 1.0000e-06 eta: 0:02:26 time: 0.3798 data_time: 0.0634 memory: 17120 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:43:09 - mmengine - INFO - Epoch(train) [172][ 7/15] lr: 1.0000e-06 eta: 0:02:26 time: 0.3710 data_time: 0.0634 memory: 17122 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:43:10 - mmengine - INFO - Epoch(train) [172][ 8/15] lr: 1.0000e-06 eta: 0:02:25 time: 0.3712 data_time: 0.0634 memory: 17730 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:43:10 - mmengine - INFO - Epoch(train) [172][ 9/15] lr: 1.0000e-06 eta: 0:02:25 time: 0.3632 data_time: 0.0634 memory: 15479 loss: 0.0792 loss_ce: 0.0792 2023/03/03 14:43:10 - mmengine - INFO - Epoch(train) [172][10/15] lr: 1.0000e-06 eta: 0:02:24 time: 0.3907 data_time: 0.0634 memory: 27565 loss: 0.0691 loss_ce: 0.0691 2023/03/03 14:43:11 - mmengine - INFO - Epoch(train) [172][11/15] lr: 1.0000e-06 eta: 0:02:24 time: 0.3060 data_time: 0.0019 memory: 17272 loss: 0.0670 loss_ce: 0.0670 2023/03/03 14:43:11 - mmengine - INFO - Epoch(train) [172][12/15] lr: 1.0000e-06 eta: 0:02:24 time: 0.3032 data_time: 0.0019 memory: 17572 loss: 0.0654 loss_ce: 0.0654 2023/03/03 14:43:11 - mmengine - INFO - Epoch(train) [172][13/15] lr: 1.0000e-06 eta: 0:02:23 time: 0.2938 data_time: 0.0019 memory: 17325 loss: 0.0665 loss_ce: 0.0665 2023/03/03 14:43:11 - mmengine - INFO - Epoch(train) [172][14/15] lr: 1.0000e-06 eta: 0:02:23 time: 0.2570 data_time: 0.0018 memory: 19747 loss: 0.0665 loss_ce: 0.0665 2023/03/03 14:43:12 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:43:12 - mmengine - INFO - Epoch(train) [172][15/15] lr: 1.0000e-06 eta: 0:02:23 time: 0.2578 data_time: 0.0017 memory: 3707 loss: 0.0664 loss_ce: 0.0664 2023/03/03 14:43:13 - mmengine - INFO - Epoch(train) [173][ 1/15] lr: 1.0000e-06 eta: 0:02:22 time: 0.3447 data_time: 0.0878 memory: 17120 loss: 0.0676 loss_ce: 0.0676 2023/03/03 14:43:13 - mmengine - INFO - Epoch(train) [173][ 2/15] lr: 1.0000e-06 eta: 0:02:22 time: 0.3353 data_time: 0.0879 memory: 18241 loss: 0.0659 loss_ce: 0.0659 2023/03/03 14:43:13 - mmengine - INFO - Epoch(train) [173][ 3/15] lr: 1.0000e-06 eta: 0:02:22 time: 0.3363 data_time: 0.0880 memory: 12824 loss: 0.0649 loss_ce: 0.0649 2023/03/03 14:43:13 - mmengine - INFO - Epoch(train) [173][ 4/15] lr: 1.0000e-06 eta: 0:02:21 time: 0.3364 data_time: 0.0882 memory: 17284 loss: 0.0623 loss_ce: 0.0623 2023/03/03 14:43:14 - mmengine - INFO - Epoch(train) [173][ 5/15] lr: 1.0000e-06 eta: 0:02:21 time: 0.3191 data_time: 0.0883 memory: 15911 loss: 0.0601 loss_ce: 0.0601 2023/03/03 14:43:14 - mmengine - INFO - Epoch(train) [173][ 6/15] lr: 1.0000e-06 eta: 0:02:21 time: 0.3350 data_time: 0.0884 memory: 20872 loss: 0.0629 loss_ce: 0.0629 2023/03/03 14:43:14 - mmengine - INFO - Epoch(train) [173][ 7/15] lr: 1.0000e-06 eta: 0:02:20 time: 0.3369 data_time: 0.0884 memory: 16056 loss: 0.0618 loss_ce: 0.0618 2023/03/03 14:43:15 - mmengine - INFO - Epoch(train) [173][ 8/15] lr: 1.0000e-06 eta: 0:02:20 time: 0.3573 data_time: 0.0884 memory: 17120 loss: 0.0610 loss_ce: 0.0610 2023/03/03 14:43:15 - mmengine - INFO - Epoch(train) [173][ 9/15] lr: 1.0000e-06 eta: 0:02:20 time: 0.3611 data_time: 0.0884 memory: 17498 loss: 0.0609 loss_ce: 0.0609 2023/03/03 14:43:15 - mmengine - INFO - Epoch(train) [173][10/15] lr: 1.0000e-06 eta: 0:02:19 time: 0.3754 data_time: 0.0885 memory: 16508 loss: 0.0607 loss_ce: 0.0607 2023/03/03 14:43:16 - mmengine - INFO - Epoch(train) [173][11/15] lr: 1.0000e-06 eta: 0:02:19 time: 0.2942 data_time: 0.0024 memory: 18722 loss: 0.0588 loss_ce: 0.0588 2023/03/03 14:43:16 - mmengine - INFO - Epoch(train) [173][12/15] lr: 1.0000e-06 eta: 0:02:19 time: 0.3158 data_time: 0.0023 memory: 16804 loss: 0.0596 loss_ce: 0.0596 2023/03/03 14:43:16 - mmengine - INFO - Epoch(train) [173][13/15] lr: 1.0000e-06 eta: 0:02:18 time: 0.3162 data_time: 0.0022 memory: 15990 loss: 0.0621 loss_ce: 0.0621 2023/03/03 14:43:17 - mmengine - INFO - Epoch(train) [173][14/15] lr: 1.0000e-06 eta: 0:02:18 time: 0.3218 data_time: 0.0021 memory: 17730 loss: 0.0628 loss_ce: 0.0628 2023/03/03 14:43:17 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:43:17 - mmengine - INFO - Epoch(train) [173][15/15] lr: 1.0000e-06 eta: 0:02:18 time: 0.3105 data_time: 0.0019 memory: 5638 loss: 0.0667 loss_ce: 0.0667 2023/03/03 14:43:18 - mmengine - INFO - Epoch(train) [174][ 1/15] lr: 1.0000e-06 eta: 0:02:17 time: 0.3866 data_time: 0.0822 memory: 17590 loss: 0.0688 loss_ce: 0.0688 2023/03/03 14:43:18 - mmengine - INFO - Epoch(train) [174][ 2/15] lr: 1.0000e-06 eta: 0:02:17 time: 0.3894 data_time: 0.0822 memory: 14921 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:43:18 - mmengine - INFO - Epoch(train) [174][ 3/15] lr: 1.0000e-06 eta: 0:02:17 time: 0.3637 data_time: 0.0823 memory: 16804 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:43:18 - mmengine - INFO - Epoch(train) [174][ 4/15] lr: 1.0000e-06 eta: 0:02:16 time: 0.3608 data_time: 0.0823 memory: 16287 loss: 0.0780 loss_ce: 0.0780 2023/03/03 14:43:19 - mmengine - INFO - Epoch(train) [174][ 5/15] lr: 1.0000e-06 eta: 0:02:16 time: 0.3661 data_time: 0.0823 memory: 26298 loss: 0.0818 loss_ce: 0.0818 2023/03/03 14:43:19 - mmengine - INFO - Epoch(train) [174][ 6/15] lr: 1.0000e-06 eta: 0:02:16 time: 0.3634 data_time: 0.0823 memory: 16849 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:43:20 - mmengine - INFO - Epoch(train) [174][ 7/15] lr: 1.0000e-06 eta: 0:02:15 time: 0.3538 data_time: 0.0823 memory: 22242 loss: 0.0781 loss_ce: 0.0781 2023/03/03 14:43:20 - mmengine - INFO - Epoch(train) [174][ 8/15] lr: 1.0000e-06 eta: 0:02:15 time: 0.3551 data_time: 0.0823 memory: 16779 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:43:20 - mmengine - INFO - Epoch(train) [174][ 9/15] lr: 1.0000e-06 eta: 0:02:15 time: 0.3589 data_time: 0.0824 memory: 17421 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:43:20 - mmengine - INFO - Epoch(train) [174][10/15] lr: 1.0000e-06 eta: 0:02:14 time: 0.3844 data_time: 0.0824 memory: 15237 loss: 0.0776 loss_ce: 0.0776 2023/03/03 14:43:21 - mmengine - INFO - Epoch(train) [174][11/15] lr: 1.0000e-06 eta: 0:02:14 time: 0.3046 data_time: 0.0020 memory: 19117 loss: 0.0728 loss_ce: 0.0728 2023/03/03 14:43:21 - mmengine - INFO - Epoch(train) [174][12/15] lr: 1.0000e-06 eta: 0:02:14 time: 0.2999 data_time: 0.0020 memory: 17424 loss: 0.0685 loss_ce: 0.0685 2023/03/03 14:43:21 - mmengine - INFO - Epoch(train) [174][13/15] lr: 1.0000e-06 eta: 0:02:13 time: 0.3068 data_time: 0.0019 memory: 16644 loss: 0.0687 loss_ce: 0.0687 2023/03/03 14:43:22 - mmengine - INFO - Epoch(train) [174][14/15] lr: 1.0000e-06 eta: 0:02:13 time: 0.3096 data_time: 0.0018 memory: 16860 loss: 0.0671 loss_ce: 0.0671 2023/03/03 14:43:22 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:43:22 - mmengine - INFO - Epoch(train) [174][15/15] lr: 1.0000e-06 eta: 0:02:12 time: 0.2886 data_time: 0.0018 memory: 6830 loss: 0.0664 loss_ce: 0.0664 2023/03/03 14:43:23 - mmengine - INFO - Epoch(train) [175][ 1/15] lr: 1.0000e-06 eta: 0:02:12 time: 0.3408 data_time: 0.0422 memory: 16991 loss: 0.0672 loss_ce: 0.0672 2023/03/03 14:43:23 - mmengine - INFO - Epoch(train) [175][ 2/15] lr: 1.0000e-06 eta: 0:02:12 time: 0.3346 data_time: 0.0422 memory: 18681 loss: 0.0704 loss_ce: 0.0704 2023/03/03 14:43:23 - mmengine - INFO - Epoch(train) [175][ 3/15] lr: 1.0000e-06 eta: 0:02:11 time: 0.3297 data_time: 0.0422 memory: 14963 loss: 0.0679 loss_ce: 0.0679 2023/03/03 14:43:23 - mmengine - INFO - Epoch(train) [175][ 4/15] lr: 1.0000e-06 eta: 0:02:11 time: 0.3257 data_time: 0.0422 memory: 18923 loss: 0.0665 loss_ce: 0.0665 2023/03/03 14:43:24 - mmengine - INFO - Epoch(train) [175][ 5/15] lr: 1.0000e-06 eta: 0:02:11 time: 0.3092 data_time: 0.0422 memory: 17120 loss: 0.0633 loss_ce: 0.0633 2023/03/03 14:43:24 - mmengine - INFO - Epoch(train) [175][ 6/15] lr: 1.0000e-06 eta: 0:02:10 time: 0.3081 data_time: 0.0423 memory: 21508 loss: 0.0663 loss_ce: 0.0663 2023/03/03 14:43:24 - mmengine - INFO - Epoch(train) [175][ 7/15] lr: 1.0000e-06 eta: 0:02:10 time: 0.3115 data_time: 0.0423 memory: 17619 loss: 0.0666 loss_ce: 0.0666 2023/03/03 14:43:25 - mmengine - INFO - Epoch(train) [175][ 8/15] lr: 1.0000e-06 eta: 0:02:10 time: 0.3344 data_time: 0.0423 memory: 22091 loss: 0.0652 loss_ce: 0.0652 2023/03/03 14:43:25 - mmengine - INFO - Epoch(train) [175][ 9/15] lr: 1.0000e-06 eta: 0:02:09 time: 0.3287 data_time: 0.0423 memory: 17418 loss: 0.0642 loss_ce: 0.0642 2023/03/03 14:43:25 - mmengine - INFO - Epoch(train) [175][10/15] lr: 1.0000e-06 eta: 0:02:09 time: 0.3324 data_time: 0.0422 memory: 17272 loss: 0.0637 loss_ce: 0.0637 2023/03/03 14:43:25 - mmengine - INFO - Epoch(train) [175][11/15] lr: 1.0000e-06 eta: 0:02:09 time: 0.2801 data_time: 0.0019 memory: 16644 loss: 0.0653 loss_ce: 0.0653 2023/03/03 14:43:26 - mmengine - INFO - Epoch(train) [175][12/15] lr: 1.0000e-06 eta: 0:02:08 time: 0.3147 data_time: 0.0018 memory: 16370 loss: 0.0650 loss_ce: 0.0650 2023/03/03 14:43:26 - mmengine - INFO - Epoch(train) [175][13/15] lr: 1.0000e-06 eta: 0:02:08 time: 0.3193 data_time: 0.0018 memory: 16370 loss: 0.0675 loss_ce: 0.0675 2023/03/03 14:43:27 - mmengine - INFO - Epoch(train) [175][14/15] lr: 1.0000e-06 eta: 0:02:08 time: 0.3269 data_time: 0.0018 memory: 19238 loss: 0.0763 loss_ce: 0.0763 2023/03/03 14:43:27 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:43:27 - mmengine - INFO - Epoch(train) [175][15/15] lr: 1.0000e-06 eta: 0:02:07 time: 0.3234 data_time: 0.0018 memory: 5889 loss: 0.0812 loss_ce: 0.0812 2023/03/03 14:43:28 - mmengine - INFO - Epoch(train) [176][ 1/15] lr: 1.0000e-06 eta: 0:02:07 time: 0.3998 data_time: 0.0753 memory: 19755 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:43:28 - mmengine - INFO - Epoch(train) [176][ 2/15] lr: 1.0000e-06 eta: 0:02:07 time: 0.3998 data_time: 0.0753 memory: 16212 loss: 0.0798 loss_ce: 0.0798 2023/03/03 14:43:28 - mmengine - INFO - Epoch(train) [176][ 3/15] lr: 1.0000e-06 eta: 0:02:06 time: 0.3714 data_time: 0.0754 memory: 17892 loss: 0.0801 loss_ce: 0.0801 2023/03/03 14:43:29 - mmengine - INFO - Epoch(train) [176][ 4/15] lr: 1.0000e-06 eta: 0:02:06 time: 0.3731 data_time: 0.0755 memory: 14367 loss: 0.0788 loss_ce: 0.0788 2023/03/03 14:43:29 - mmengine - INFO - Epoch(train) [176][ 5/15] lr: 1.0000e-06 eta: 0:02:06 time: 0.3976 data_time: 0.0756 memory: 16654 loss: 0.0745 loss_ce: 0.0745 2023/03/03 14:43:29 - mmengine - INFO - Epoch(train) [176][ 6/15] lr: 1.0000e-06 eta: 0:02:05 time: 0.3977 data_time: 0.0757 memory: 16804 loss: 0.0727 loss_ce: 0.0727 2023/03/03 14:43:30 - mmengine - INFO - Epoch(train) [176][ 7/15] lr: 1.0000e-06 eta: 0:02:05 time: 0.3664 data_time: 0.0757 memory: 19036 loss: 0.0759 loss_ce: 0.0759 2023/03/03 14:43:30 - mmengine - INFO - Epoch(train) [176][ 8/15] lr: 1.0000e-06 eta: 0:02:05 time: 0.3751 data_time: 0.0757 memory: 22989 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:43:30 - mmengine - INFO - Epoch(train) [176][ 9/15] lr: 1.0000e-06 eta: 0:02:04 time: 0.3851 data_time: 0.0757 memory: 16056 loss: 0.0705 loss_ce: 0.0705 2023/03/03 14:43:31 - mmengine - INFO - Epoch(train) [176][10/15] lr: 1.0000e-06 eta: 0:02:04 time: 0.3889 data_time: 0.0758 memory: 18409 loss: 0.0639 loss_ce: 0.0639 2023/03/03 14:43:31 - mmengine - INFO - Epoch(train) [176][11/15] lr: 1.0000e-06 eta: 0:02:04 time: 0.3224 data_time: 0.0023 memory: 15767 loss: 0.0652 loss_ce: 0.0652 2023/03/03 14:43:31 - mmengine - INFO - Epoch(train) [176][12/15] lr: 1.0000e-06 eta: 0:02:03 time: 0.3187 data_time: 0.0023 memory: 17120 loss: 0.0655 loss_ce: 0.0655 2023/03/03 14:43:32 - mmengine - INFO - Epoch(train) [176][13/15] lr: 1.0000e-06 eta: 0:02:03 time: 0.3205 data_time: 0.0021 memory: 16863 loss: 0.0652 loss_ce: 0.0652 2023/03/03 14:43:32 - mmengine - INFO - Epoch(train) [176][14/15] lr: 1.0000e-06 eta: 0:02:03 time: 0.3184 data_time: 0.0020 memory: 16976 loss: 0.0696 loss_ce: 0.0696 2023/03/03 14:43:32 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:43:32 - mmengine - INFO - Epoch(train) [176][15/15] lr: 1.0000e-06 eta: 0:02:02 time: 0.2805 data_time: 0.0019 memory: 6551 loss: 0.0785 loss_ce: 0.0785 2023/03/03 14:43:33 - mmengine - INFO - Epoch(train) [177][ 1/15] lr: 1.0000e-06 eta: 0:02:02 time: 0.3372 data_time: 0.0579 memory: 20234 loss: 0.0802 loss_ce: 0.0802 2023/03/03 14:43:33 - mmengine - INFO - Epoch(train) [177][ 2/15] lr: 1.0000e-06 eta: 0:02:02 time: 0.3624 data_time: 0.0579 memory: 14748 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:43:34 - mmengine - INFO - Epoch(train) [177][ 3/15] lr: 1.0000e-06 eta: 0:02:01 time: 0.3531 data_time: 0.0579 memory: 15631 loss: 0.0810 loss_ce: 0.0810 2023/03/03 14:43:34 - mmengine - INFO - Epoch(train) [177][ 4/15] lr: 1.0000e-06 eta: 0:02:01 time: 0.3282 data_time: 0.0580 memory: 14266 loss: 0.0856 loss_ce: 0.0856 2023/03/03 14:43:34 - mmengine - INFO - Epoch(train) [177][ 5/15] lr: 1.0000e-06 eta: 0:02:01 time: 0.3277 data_time: 0.0579 memory: 17120 loss: 0.0896 loss_ce: 0.0896 2023/03/03 14:43:34 - mmengine - INFO - Epoch(train) [177][ 6/15] lr: 1.0000e-06 eta: 0:02:00 time: 0.3184 data_time: 0.0579 memory: 16849 loss: 0.0871 loss_ce: 0.0871 2023/03/03 14:43:35 - mmengine - INFO - Epoch(train) [177][ 7/15] lr: 1.0000e-06 eta: 0:02:00 time: 0.3340 data_time: 0.0579 memory: 25798 loss: 0.0878 loss_ce: 0.0878 2023/03/03 14:43:35 - mmengine - INFO - Epoch(train) [177][ 8/15] lr: 1.0000e-06 eta: 0:02:00 time: 0.3403 data_time: 0.0580 memory: 21555 loss: 0.0891 loss_ce: 0.0891 2023/03/03 14:43:36 - mmengine - INFO - Epoch(train) [177][ 9/15] lr: 1.0000e-06 eta: 0:01:59 time: 0.3767 data_time: 0.0580 memory: 26780 loss: 0.0855 loss_ce: 0.0855 2023/03/03 14:43:36 - mmengine - INFO - Epoch(train) [177][10/15] lr: 1.0000e-06 eta: 0:01:59 time: 0.3854 data_time: 0.0580 memory: 16530 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:43:36 - mmengine - INFO - Epoch(train) [177][11/15] lr: 1.0000e-06 eta: 0:01:59 time: 0.3407 data_time: 0.0019 memory: 20758 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:43:36 - mmengine - INFO - Epoch(train) [177][12/15] lr: 1.0000e-06 eta: 0:01:58 time: 0.3144 data_time: 0.0019 memory: 16976 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:43:37 - mmengine - INFO - Epoch(train) [177][13/15] lr: 1.0000e-06 eta: 0:01:58 time: 0.3151 data_time: 0.0018 memory: 16370 loss: 0.0744 loss_ce: 0.0744 2023/03/03 14:43:37 - mmengine - INFO - Epoch(train) [177][14/15] lr: 1.0000e-06 eta: 0:01:58 time: 0.3195 data_time: 0.0018 memory: 17572 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:43:37 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:43:37 - mmengine - INFO - Epoch(train) [177][15/15] lr: 1.0000e-06 eta: 0:01:57 time: 0.3105 data_time: 0.0018 memory: 7028 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:43:38 - mmengine - INFO - Epoch(train) [178][ 1/15] lr: 1.0000e-06 eta: 0:01:57 time: 0.3879 data_time: 0.0403 memory: 22168 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:43:38 - mmengine - INFO - Epoch(train) [178][ 2/15] lr: 1.0000e-06 eta: 0:01:57 time: 0.3733 data_time: 0.0404 memory: 18409 loss: 0.0730 loss_ce: 0.0730 2023/03/03 14:43:39 - mmengine - INFO - Epoch(train) [178][ 3/15] lr: 1.0000e-06 eta: 0:01:56 time: 0.3744 data_time: 0.0406 memory: 15222 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:43:39 - mmengine - INFO - Epoch(train) [178][ 4/15] lr: 1.0000e-06 eta: 0:01:56 time: 0.3394 data_time: 0.0407 memory: 14675 loss: 0.0753 loss_ce: 0.0753 2023/03/03 14:43:39 - mmengine - INFO - Epoch(train) [178][ 5/15] lr: 1.0000e-06 eta: 0:01:55 time: 0.3415 data_time: 0.0408 memory: 16026 loss: 0.0794 loss_ce: 0.0794 2023/03/03 14:43:40 - mmengine - INFO - Epoch(train) [178][ 6/15] lr: 1.0000e-06 eta: 0:01:55 time: 0.3401 data_time: 0.0409 memory: 19754 loss: 0.0818 loss_ce: 0.0818 2023/03/03 14:43:40 - mmengine - INFO - Epoch(train) [178][ 7/15] lr: 1.0000e-06 eta: 0:01:55 time: 0.3316 data_time: 0.0409 memory: 17421 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:43:40 - mmengine - INFO - Epoch(train) [178][ 8/15] lr: 1.0000e-06 eta: 0:01:54 time: 0.3392 data_time: 0.0410 memory: 16232 loss: 0.0802 loss_ce: 0.0802 2023/03/03 14:43:40 - mmengine - INFO - Epoch(train) [178][ 9/15] lr: 1.0000e-06 eta: 0:01:54 time: 0.3286 data_time: 0.0410 memory: 15579 loss: 0.0751 loss_ce: 0.0751 2023/03/03 14:43:40 - mmengine - INFO - Epoch(train) [178][10/15] lr: 1.0000e-06 eta: 0:01:54 time: 0.3406 data_time: 0.0411 memory: 16654 loss: 0.0697 loss_ce: 0.0697 2023/03/03 14:43:41 - mmengine - INFO - Epoch(train) [178][11/15] lr: 1.0000e-06 eta: 0:01:53 time: 0.2529 data_time: 0.0026 memory: 12512 loss: 0.0738 loss_ce: 0.0738 2023/03/03 14:43:41 - mmengine - INFO - Epoch(train) [178][12/15] lr: 1.0000e-06 eta: 0:01:53 time: 0.2699 data_time: 0.0025 memory: 35654 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:43:41 - mmengine - INFO - Epoch(train) [178][13/15] lr: 1.0000e-06 eta: 0:01:53 time: 0.2619 data_time: 0.0025 memory: 17046 loss: 0.0736 loss_ce: 0.0736 2023/03/03 14:43:42 - mmengine - INFO - Epoch(train) [178][14/15] lr: 1.0000e-06 eta: 0:01:52 time: 0.2639 data_time: 0.0025 memory: 16661 loss: 0.0694 loss_ce: 0.0694 2023/03/03 14:43:42 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:43:42 - mmengine - INFO - Epoch(train) [178][15/15] lr: 1.0000e-06 eta: 0:01:52 time: 0.2553 data_time: 0.0025 memory: 6329 loss: 0.0686 loss_ce: 0.0686 2023/03/03 14:43:43 - mmengine - INFO - Epoch(train) [179][ 1/15] lr: 1.0000e-06 eta: 0:01:52 time: 0.3479 data_time: 0.0668 memory: 27522 loss: 0.0720 loss_ce: 0.0720 2023/03/03 14:43:43 - mmengine - INFO - Epoch(train) [179][ 2/15] lr: 1.0000e-06 eta: 0:01:51 time: 0.3444 data_time: 0.0669 memory: 13061 loss: 0.0711 loss_ce: 0.0711 2023/03/03 14:43:43 - mmengine - INFO - Epoch(train) [179][ 3/15] lr: 1.0000e-06 eta: 0:01:51 time: 0.3345 data_time: 0.0670 memory: 17421 loss: 0.0710 loss_ce: 0.0710 2023/03/03 14:43:44 - mmengine - INFO - Epoch(train) [179][ 4/15] lr: 1.0000e-06 eta: 0:01:51 time: 0.3559 data_time: 0.0669 memory: 16986 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:43:44 - mmengine - INFO - Epoch(train) [179][ 5/15] lr: 1.0000e-06 eta: 0:01:50 time: 0.3551 data_time: 0.0669 memory: 16056 loss: 0.0711 loss_ce: 0.0711 2023/03/03 14:43:44 - mmengine - INFO - Epoch(train) [179][ 6/15] lr: 1.0000e-06 eta: 0:01:50 time: 0.3718 data_time: 0.0669 memory: 19354 loss: 0.0682 loss_ce: 0.0682 2023/03/03 14:43:45 - mmengine - INFO - Epoch(train) [179][ 7/15] lr: 1.0000e-06 eta: 0:01:50 time: 0.3569 data_time: 0.0669 memory: 16370 loss: 0.0708 loss_ce: 0.0708 2023/03/03 14:43:45 - mmengine - INFO - Epoch(train) [179][ 8/15] lr: 1.0000e-06 eta: 0:01:49 time: 0.3692 data_time: 0.0667 memory: 16849 loss: 0.0698 loss_ce: 0.0698 2023/03/03 14:43:45 - mmengine - INFO - Epoch(train) [179][ 9/15] lr: 1.0000e-06 eta: 0:01:49 time: 0.3688 data_time: 0.0667 memory: 16508 loss: 0.0702 loss_ce: 0.0702 2023/03/03 14:43:46 - mmengine - INFO - Epoch(train) [179][10/15] lr: 1.0000e-06 eta: 0:01:49 time: 0.3838 data_time: 0.0666 memory: 11734 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:43:46 - mmengine - INFO - Epoch(train) [179][11/15] lr: 1.0000e-06 eta: 0:01:48 time: 0.2776 data_time: 0.0022 memory: 17120 loss: 0.0709 loss_ce: 0.0709 2023/03/03 14:43:46 - mmengine - INFO - Epoch(train) [179][12/15] lr: 1.0000e-06 eta: 0:01:48 time: 0.2926 data_time: 0.0021 memory: 15494 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:43:46 - mmengine - INFO - Epoch(train) [179][13/15] lr: 1.0000e-06 eta: 0:01:48 time: 0.3049 data_time: 0.0021 memory: 19387 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:43:47 - mmengine - INFO - Epoch(train) [179][14/15] lr: 1.0000e-06 eta: 0:01:47 time: 0.2934 data_time: 0.0021 memory: 16002 loss: 0.0803 loss_ce: 0.0803 2023/03/03 14:43:47 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:43:47 - mmengine - INFO - Epoch(train) [179][15/15] lr: 1.0000e-06 eta: 0:01:47 time: 0.2824 data_time: 0.0022 memory: 6850 loss: 0.0865 loss_ce: 0.0865 2023/03/03 14:43:48 - mmengine - INFO - Epoch(train) [180][ 1/15] lr: 1.0000e-06 eta: 0:01:47 time: 0.3513 data_time: 0.0469 memory: 11915 loss: 0.0873 loss_ce: 0.0873 2023/03/03 14:43:48 - mmengine - INFO - Epoch(train) [180][ 2/15] lr: 1.0000e-06 eta: 0:01:46 time: 0.3528 data_time: 0.0468 memory: 17370 loss: 0.0830 loss_ce: 0.0830 2023/03/03 14:43:49 - mmengine - INFO - Epoch(train) [180][ 3/15] lr: 1.0000e-06 eta: 0:01:46 time: 0.3621 data_time: 0.0472 memory: 22209 loss: 0.0834 loss_ce: 0.0834 2023/03/03 14:43:49 - mmengine - INFO - Epoch(train) [180][ 4/15] lr: 1.0000e-06 eta: 0:01:46 time: 0.3616 data_time: 0.0472 memory: 15767 loss: 0.0837 loss_ce: 0.0837 2023/03/03 14:43:49 - mmengine - INFO - Epoch(train) [180][ 5/15] lr: 1.0000e-06 eta: 0:01:45 time: 0.3736 data_time: 0.0473 memory: 17120 loss: 0.0799 loss_ce: 0.0799 2023/03/03 14:43:50 - mmengine - INFO - Epoch(train) [180][ 6/15] lr: 1.0000e-06 eta: 0:01:45 time: 0.3781 data_time: 0.0473 memory: 15311 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:43:50 - mmengine - INFO - Epoch(train) [180][ 7/15] lr: 1.0000e-06 eta: 0:01:45 time: 0.3830 data_time: 0.0473 memory: 14075 loss: 0.0744 loss_ce: 0.0744 2023/03/03 14:43:50 - mmengine - INFO - Epoch(train) [180][ 8/15] lr: 1.0000e-06 eta: 0:01:44 time: 0.3807 data_time: 0.0473 memory: 28264 loss: 0.0762 loss_ce: 0.0762 2023/03/03 14:43:51 - mmengine - INFO - Epoch(train) [180][ 9/15] lr: 1.0000e-06 eta: 0:01:44 time: 0.3767 data_time: 0.0473 memory: 16703 loss: 0.0683 loss_ce: 0.0683 2023/03/03 14:43:51 - mmengine - INFO - Epoch(train) [180][10/15] lr: 1.0000e-06 eta: 0:01:44 time: 0.4239 data_time: 0.0472 memory: 25516 loss: 0.0623 loss_ce: 0.0623 2023/03/03 14:43:51 - mmengine - INFO - Epoch(train) [180][11/15] lr: 1.0000e-06 eta: 0:01:43 time: 0.3372 data_time: 0.0026 memory: 18070 loss: 0.0623 loss_ce: 0.0623 2023/03/03 14:43:52 - mmengine - INFO - Epoch(train) [180][12/15] lr: 1.0000e-06 eta: 0:01:43 time: 0.3331 data_time: 0.0025 memory: 18070 loss: 0.0627 loss_ce: 0.0627 2023/03/03 14:43:52 - mmengine - INFO - Epoch(train) [180][13/15] lr: 1.0000e-06 eta: 0:01:42 time: 0.3122 data_time: 0.0022 memory: 13689 loss: 0.0675 loss_ce: 0.0675 2023/03/03 14:43:52 - mmengine - INFO - Epoch(train) [180][14/15] lr: 1.0000e-06 eta: 0:01:42 time: 0.3103 data_time: 0.0022 memory: 17272 loss: 0.0694 loss_ce: 0.0694 2023/03/03 14:43:52 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:43:52 - mmengine - INFO - Epoch(train) [180][15/15] lr: 1.0000e-06 eta: 0:01:42 time: 0.2825 data_time: 0.0022 memory: 6551 loss: 0.0748 loss_ce: 0.0748 2023/03/03 14:43:54 - mmengine - INFO - Epoch(val) [180][ 1/59] eta: 0:01:30 time: 1.0840 data_time: 0.0036 memory: 981 2023/03/03 14:43:55 - mmengine - INFO - Epoch(val) [180][ 2/59] eta: 0:01:08 time: 0.9990 data_time: 0.0036 memory: 981 2023/03/03 14:43:56 - mmengine - INFO - Epoch(val) [180][ 3/59] eta: 0:01:10 time: 1.0166 data_time: 0.0036 memory: 1003 2023/03/03 14:43:56 - mmengine - INFO - Epoch(val) [180][ 4/59] eta: 0:00:56 time: 1.0004 data_time: 0.0036 memory: 981 2023/03/03 14:43:59 - mmengine - INFO - Epoch(val) [180][ 5/59] eta: 0:01:17 time: 1.2374 data_time: 0.0036 memory: 1016 2023/03/03 14:44:02 - mmengine - INFO - Epoch(val) [180][ 6/59] eta: 0:01:27 time: 1.4393 data_time: 0.0036 memory: 981 2023/03/03 14:44:02 - mmengine - INFO - Epoch(val) [180][ 7/59] eta: 0:01:14 time: 1.3901 data_time: 0.0037 memory: 1043 2023/03/03 14:44:03 - mmengine - INFO - Epoch(val) [180][ 8/59] eta: 0:01:08 time: 1.2227 data_time: 0.0037 memory: 1016 2023/03/03 14:44:04 - mmengine - INFO - Epoch(val) [180][ 9/59] eta: 0:01:05 time: 1.2064 data_time: 0.0036 memory: 981 2023/03/03 14:44:05 - mmengine - INFO - Epoch(val) [180][10/59] eta: 0:01:00 time: 1.2406 data_time: 0.0036 memory: 981 2023/03/03 14:44:05 - mmengine - INFO - Epoch(val) [180][11/59] eta: 0:00:55 time: 1.1181 data_time: 0.0011 memory: 981 2023/03/03 14:44:08 - mmengine - INFO - Epoch(val) [180][12/59] eta: 0:01:03 time: 1.3829 data_time: 0.0010 memory: 1016 2023/03/03 14:44:11 - mmengine - INFO - Epoch(val) [180][13/59] eta: 0:01:05 time: 1.4712 data_time: 0.0010 memory: 981 2023/03/03 14:44:12 - mmengine - INFO - Epoch(val) [180][14/59] eta: 0:01:02 time: 1.5396 data_time: 0.0011 memory: 890 2023/03/03 14:44:12 - mmengine - INFO - Epoch(val) [180][15/59] eta: 0:00:57 time: 1.2376 data_time: 0.0011 memory: 981 2023/03/03 14:44:12 - mmengine - INFO - Epoch(val) [180][16/59] eta: 0:00:53 time: 1.0188 data_time: 0.0010 memory: 981 2023/03/03 14:44:13 - mmengine - INFO - Epoch(val) [180][17/59] eta: 0:00:50 time: 1.0350 data_time: 0.0010 memory: 981 2023/03/03 14:44:13 - mmengine - INFO - Epoch(val) [180][18/59] eta: 0:00:47 time: 1.0023 data_time: 0.0010 memory: 981 2023/03/03 14:44:14 - mmengine - INFO - Epoch(val) [180][19/59] eta: 0:00:45 time: 1.0023 data_time: 0.0010 memory: 981 2023/03/03 14:44:14 - mmengine - INFO - Epoch(val) [180][20/59] eta: 0:00:43 time: 0.9683 data_time: 0.0010 memory: 981 2023/03/03 14:44:15 - mmengine - INFO - Epoch(val) [180][21/59] eta: 0:00:40 time: 0.9845 data_time: 0.0010 memory: 981 2023/03/03 14:44:15 - mmengine - INFO - Epoch(val) [180][22/59] eta: 0:00:38 time: 0.6524 data_time: 0.0010 memory: 981 2023/03/03 14:44:16 - mmengine - INFO - Epoch(val) [180][23/59] eta: 0:00:36 time: 0.4961 data_time: 0.0010 memory: 981 2023/03/03 14:44:16 - mmengine - INFO - Epoch(val) [180][24/59] eta: 0:00:34 time: 0.4274 data_time: 0.0009 memory: 962 2023/03/03 14:44:16 - mmengine - INFO - Epoch(val) [180][25/59] eta: 0:00:32 time: 0.4591 data_time: 0.0008 memory: 981 2023/03/03 14:44:17 - mmengine - INFO - Epoch(val) [180][26/59] eta: 0:00:31 time: 0.4429 data_time: 0.0008 memory: 981 2023/03/03 14:44:17 - mmengine - INFO - Epoch(val) [180][27/59] eta: 0:00:29 time: 0.4432 data_time: 0.0009 memory: 981 2023/03/03 14:44:17 - mmengine - INFO - Epoch(val) [180][28/59] eta: 0:00:27 time: 0.4425 data_time: 0.0009 memory: 981 2023/03/03 14:44:19 - mmengine - INFO - Epoch(val) [180][29/59] eta: 0:00:27 time: 0.4765 data_time: 0.0009 memory: 981 2023/03/03 14:44:20 - mmengine - INFO - Epoch(val) [180][30/59] eta: 0:00:26 time: 0.5246 data_time: 0.0008 memory: 999 2023/03/03 14:44:20 - mmengine - INFO - Epoch(val) [180][31/59] eta: 0:00:25 time: 0.5417 data_time: 0.0008 memory: 981 2023/03/03 14:44:21 - mmengine - INFO - Epoch(val) [180][32/59] eta: 0:00:24 time: 0.6457 data_time: 0.0008 memory: 981 2023/03/03 14:44:21 - mmengine - INFO - Epoch(val) [180][33/59] eta: 0:00:23 time: 0.5802 data_time: 0.0009 memory: 981 2023/03/03 14:44:22 - mmengine - INFO - Epoch(val) [180][34/59] eta: 0:00:21 time: 0.5637 data_time: 0.0009 memory: 981 2023/03/03 14:44:22 - mmengine - INFO - Epoch(val) [180][35/59] eta: 0:00:20 time: 0.5468 data_time: 0.0009 memory: 981 2023/03/03 14:44:22 - mmengine - INFO - Epoch(val) [180][36/59] eta: 0:00:19 time: 0.5628 data_time: 0.0009 memory: 981 2023/03/03 14:44:22 - mmengine - INFO - Epoch(val) [180][37/59] eta: 0:00:18 time: 0.5461 data_time: 0.0008 memory: 981 2023/03/03 14:44:23 - mmengine - INFO - Epoch(val) [180][38/59] eta: 0:00:17 time: 0.5794 data_time: 0.0008 memory: 981 2023/03/03 14:44:24 - mmengine - INFO - Epoch(val) [180][39/59] eta: 0:00:16 time: 0.4941 data_time: 0.0009 memory: 987 2023/03/03 14:44:25 - mmengine - INFO - Epoch(val) [180][40/59] eta: 0:00:15 time: 0.4984 data_time: 0.0009 memory: 981 2023/03/03 14:44:26 - mmengine - INFO - Epoch(val) [180][41/59] eta: 0:00:14 time: 0.5477 data_time: 0.0009 memory: 986 2023/03/03 14:44:26 - mmengine - INFO - Epoch(val) [180][42/59] eta: 0:00:13 time: 0.4942 data_time: 0.0009 memory: 981 2023/03/03 14:44:27 - mmengine - INFO - Epoch(val) [180][43/59] eta: 0:00:13 time: 0.5765 data_time: 0.0009 memory: 976 2023/03/03 14:44:28 - mmengine - INFO - Epoch(val) [180][44/59] eta: 0:00:12 time: 0.6100 data_time: 0.0009 memory: 1003 2023/03/03 14:44:31 - mmengine - INFO - Epoch(val) [180][45/59] eta: 0:00:12 time: 0.9010 data_time: 0.0009 memory: 981 2023/03/03 14:44:32 - mmengine - INFO - Epoch(val) [180][46/59] eta: 0:00:11 time: 0.9357 data_time: 0.0009 memory: 981 2023/03/03 14:44:32 - mmengine - INFO - Epoch(val) [180][47/59] eta: 0:00:10 time: 0.9686 data_time: 0.0010 memory: 936 2023/03/03 14:44:33 - mmengine - INFO - Epoch(val) [180][48/59] eta: 0:00:09 time: 0.9521 data_time: 0.0010 memory: 1000 2023/03/03 14:44:34 - mmengine - INFO - Epoch(val) [180][49/59] eta: 0:00:08 time: 1.0031 data_time: 0.0009 memory: 981 2023/03/03 14:44:35 - mmengine - INFO - Epoch(val) [180][50/59] eta: 0:00:07 time: 0.9989 data_time: 0.0009 memory: 987 2023/03/03 14:44:36 - mmengine - INFO - Epoch(val) [180][51/59] eta: 0:00:06 time: 1.0538 data_time: 0.0010 memory: 981 2023/03/03 14:44:37 - mmengine - INFO - Epoch(val) [180][52/59] eta: 0:00:06 time: 1.1012 data_time: 0.0010 memory: 981 2023/03/03 14:44:38 - mmengine - INFO - Epoch(val) [180][53/59] eta: 0:00:05 time: 1.0667 data_time: 0.0010 memory: 962 2023/03/03 14:44:39 - mmengine - INFO - Epoch(val) [180][54/59] eta: 0:00:04 time: 1.0831 data_time: 0.0010 memory: 981 2023/03/03 14:44:39 - mmengine - INFO - Epoch(val) [180][55/59] eta: 0:00:03 time: 0.8418 data_time: 0.0009 memory: 981 2023/03/03 14:44:40 - mmengine - INFO - Epoch(val) [180][56/59] eta: 0:00:02 time: 0.8237 data_time: 0.0009 memory: 981 2023/03/03 14:44:42 - mmengine - INFO - Epoch(val) [180][57/59] eta: 0:00:01 time: 0.9984 data_time: 0.0009 memory: 981 2023/03/03 14:44:43 - mmengine - INFO - Epoch(val) [180][58/59] eta: 0:00:00 time: 1.0642 data_time: 0.0009 memory: 1016 2023/03/03 14:44:44 - mmengine - INFO - Epoch(val) [180][59/59] eta: 0:00:00 time: 0.9969 data_time: 0.0009 memory: 981 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.80, recall: 0.8228, precision: 0.8389, hmean: 0.8308 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.81, recall: 0.8219, precision: 0.8403, hmean: 0.8310 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.82, recall: 0.8201, precision: 0.8416, hmean: 0.8307 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.83, recall: 0.8192, precision: 0.8430, hmean: 0.8309 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.84, recall: 0.8164, precision: 0.8474, hmean: 0.8316 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.85, recall: 0.8164, precision: 0.8506, hmean: 0.8332 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.86, recall: 0.8164, precision: 0.8514, hmean: 0.8336 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.87, recall: 0.8137, precision: 0.8518, hmean: 0.8323 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.88, recall: 0.8100, precision: 0.8529, hmean: 0.8309 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.89, recall: 0.8064, precision: 0.8540, hmean: 0.8295 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.90, recall: 0.8018, precision: 0.8566, hmean: 0.8283 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.91, recall: 0.7982, precision: 0.8569, hmean: 0.8265 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.92, recall: 0.7963, precision: 0.8634, hmean: 0.8285 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.93, recall: 0.7881, precision: 0.8647, hmean: 0.8247 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.94, recall: 0.7781, precision: 0.8667, hmean: 0.8200 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.95, recall: 0.7708, precision: 0.8683, hmean: 0.8166 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.96, recall: 0.7635, precision: 0.8745, hmean: 0.8152 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.97, recall: 0.7553, precision: 0.8761, hmean: 0.8112 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.98, recall: 0.7397, precision: 0.8843, hmean: 0.8056 2023/03/03 14:45:13 - mmengine - INFO - text score threshold: 0.99, recall: 0.7242, precision: 0.8940, hmean: 0.8002 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.80, recall: 0.8338, precision: 0.9076, hmean: 0.8691 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.81, recall: 0.8329, precision: 0.9093, hmean: 0.8694 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.82, recall: 0.8311, precision: 0.9091, hmean: 0.8683 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.83, recall: 0.8301, precision: 0.9108, hmean: 0.8686 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.84, recall: 0.8274, precision: 0.9124, hmean: 0.8678 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.85, recall: 0.8274, precision: 0.9142, hmean: 0.8686 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.86, recall: 0.8274, precision: 0.9142, hmean: 0.8686 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.87, recall: 0.8247, precision: 0.9140, hmean: 0.8670 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.88, recall: 0.8201, precision: 0.9145, hmean: 0.8647 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.89, recall: 0.8164, precision: 0.9160, hmean: 0.8634 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.90, recall: 0.8100, precision: 0.9163, hmean: 0.8599 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.91, recall: 0.8064, precision: 0.9169, hmean: 0.8581 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.92, recall: 0.8009, precision: 0.9174, hmean: 0.8552 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.93, recall: 0.7927, precision: 0.9195, hmean: 0.8514 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.94, recall: 0.7808, precision: 0.9194, hmean: 0.8444 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.95, recall: 0.7726, precision: 0.9186, hmean: 0.8393 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.96, recall: 0.7644, precision: 0.9198, hmean: 0.8349 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.97, recall: 0.7571, precision: 0.9211, hmean: 0.8311 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.98, recall: 0.7406, precision: 0.9279, hmean: 0.8238 2023/03/03 14:45:16 - mmengine - INFO - text score threshold: 0.99, recall: 0.7224, precision: 0.9295, hmean: 0.8129 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.80, recall: 0.7543, precision: 0.9560, hmean: 0.8433 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.81, recall: 0.7534, precision: 0.9571, hmean: 0.8431 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.82, recall: 0.7516, precision: 0.9570, hmean: 0.8419 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.83, recall: 0.7498, precision: 0.9569, hmean: 0.8408 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.84, recall: 0.7470, precision: 0.9567, hmean: 0.8390 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.85, recall: 0.7470, precision: 0.9578, hmean: 0.8394 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.86, recall: 0.7470, precision: 0.9578, hmean: 0.8394 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.87, recall: 0.7452, precision: 0.9577, hmean: 0.8382 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.88, recall: 0.7406, precision: 0.9575, hmean: 0.8352 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.89, recall: 0.7379, precision: 0.9585, hmean: 0.8338 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.90, recall: 0.7324, precision: 0.9593, hmean: 0.8307 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.91, recall: 0.7288, precision: 0.9591, hmean: 0.8282 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.92, recall: 0.7233, precision: 0.9600, hmean: 0.8250 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.93, recall: 0.7169, precision: 0.9597, hmean: 0.8207 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.94, recall: 0.7068, precision: 0.9615, hmean: 0.8147 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.95, recall: 0.6986, precision: 0.9611, hmean: 0.8091 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.96, recall: 0.6913, precision: 0.9631, hmean: 0.8049 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.97, recall: 0.6849, precision: 0.9640, hmean: 0.8009 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.98, recall: 0.6694, precision: 0.9670, hmean: 0.7911 2023/03/03 14:45:18 - mmengine - INFO - text score threshold: 0.99, recall: 0.6521, precision: 0.9662, hmean: 0.7786 2023/03/03 14:45:18 - mmengine - INFO - Epoch(val) [180][59/59] generic/precision: 0.8514 generic/recall: 0.8164 generic/hmean: 0.8336 weak/precision: 0.9093 weak/recall: 0.8329 weak/hmean: 0.8694 strong/precision: 0.9560 strong/recall: 0.7543 strong/hmean: 0.8433 2023/03/03 14:45:18 - mmengine - INFO - The previous best checkpoint mmocr/projects/SPTS/work_dirs/spts_resnet50_350e_icdar2013/best_generic/hmean_epoch_70.pth is removed 2023/03/03 14:45:20 - mmengine - INFO - The best checkpoint with 0.8336 generic/hmean at 180 epoch is saved to best_generic/hmean_epoch_180.pth. 2023/03/03 14:45:21 - mmengine - INFO - Epoch(train) [181][ 1/15] lr: 1.0000e-06 eta: 0:01:41 time: 0.3401 data_time: 0.0611 memory: 17421 loss: 0.0772 loss_ce: 0.0772 2023/03/03 14:45:21 - mmengine - INFO - Epoch(train) [181][ 2/15] lr: 1.0000e-06 eta: 0:01:41 time: 0.3241 data_time: 0.0611 memory: 17272 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:45:22 - mmengine - INFO - Epoch(train) [181][ 3/15] lr: 1.0000e-06 eta: 0:01:41 time: 0.3146 data_time: 0.0611 memory: 17120 loss: 0.0701 loss_ce: 0.0701 2023/03/03 14:45:22 - mmengine - INFO - Epoch(train) [181][ 4/15] lr: 1.0000e-06 eta: 0:01:40 time: 0.3150 data_time: 0.0610 memory: 17122 loss: 0.0707 loss_ce: 0.0707 2023/03/03 14:45:22 - mmengine - INFO - Epoch(train) [181][ 5/15] lr: 1.0000e-06 eta: 0:01:40 time: 0.2766 data_time: 0.0610 memory: 17120 loss: 0.0704 loss_ce: 0.0704 2023/03/03 14:45:23 - mmengine - INFO - Epoch(train) [181][ 6/15] lr: 1.0000e-06 eta: 0:01:40 time: 0.2956 data_time: 0.0609 memory: 16339 loss: 0.0711 loss_ce: 0.0711 2023/03/03 14:45:23 - mmengine - INFO - Epoch(train) [181][ 7/15] lr: 1.0000e-06 eta: 0:01:39 time: 0.3008 data_time: 0.0610 memory: 18740 loss: 0.0742 loss_ce: 0.0742 2023/03/03 14:45:23 - mmengine - INFO - Epoch(train) [181][ 8/15] lr: 1.0000e-06 eta: 0:01:39 time: 0.3009 data_time: 0.0609 memory: 13941 loss: 0.0748 loss_ce: 0.0748 2023/03/03 14:45:23 - mmengine - INFO - Epoch(train) [181][ 9/15] lr: 1.0000e-06 eta: 0:01:39 time: 0.3030 data_time: 0.0609 memory: 12442 loss: 0.0738 loss_ce: 0.0738 2023/03/03 14:45:24 - mmengine - INFO - Epoch(train) [181][10/15] lr: 1.0000e-06 eta: 0:01:38 time: 0.3087 data_time: 0.0609 memory: 18070 loss: 0.0690 loss_ce: 0.0690 2023/03/03 14:45:24 - mmengine - INFO - Epoch(train) [181][11/15] lr: 1.0000e-06 eta: 0:01:38 time: 0.2650 data_time: 0.0020 memory: 22644 loss: 0.0697 loss_ce: 0.0697 2023/03/03 14:45:24 - mmengine - INFO - Epoch(train) [181][12/15] lr: 1.0000e-06 eta: 0:01:38 time: 0.2742 data_time: 0.0019 memory: 15532 loss: 0.0676 loss_ce: 0.0676 2023/03/03 14:45:24 - mmengine - INFO - Epoch(train) [181][13/15] lr: 1.0000e-06 eta: 0:01:37 time: 0.2751 data_time: 0.0019 memory: 15771 loss: 0.0680 loss_ce: 0.0680 2023/03/03 14:45:25 - mmengine - INFO - Epoch(train) [181][14/15] lr: 1.0000e-06 eta: 0:01:37 time: 0.2806 data_time: 0.0018 memory: 13350 loss: 0.0686 loss_ce: 0.0686 2023/03/03 14:45:25 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:45:25 - mmengine - INFO - Epoch(train) [181][15/15] lr: 1.0000e-06 eta: 0:01:37 time: 0.2704 data_time: 0.0018 memory: 4981 loss: 0.0718 loss_ce: 0.0718 2023/03/03 14:45:26 - mmengine - INFO - Epoch(train) [182][ 1/15] lr: 1.0000e-06 eta: 0:01:36 time: 0.3450 data_time: 0.0743 memory: 16654 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:45:26 - mmengine - INFO - Epoch(train) [182][ 2/15] lr: 1.0000e-06 eta: 0:01:36 time: 0.3404 data_time: 0.0744 memory: 17730 loss: 0.0717 loss_ce: 0.0717 2023/03/03 14:45:26 - mmengine - INFO - Epoch(train) [182][ 3/15] lr: 1.0000e-06 eta: 0:01:36 time: 0.3399 data_time: 0.0745 memory: 19624 loss: 0.0677 loss_ce: 0.0677 2023/03/03 14:45:27 - mmengine - INFO - Epoch(train) [182][ 4/15] lr: 1.0000e-06 eta: 0:01:35 time: 0.3406 data_time: 0.0745 memory: 16955 loss: 0.0668 loss_ce: 0.0668 2023/03/03 14:45:27 - mmengine - INFO - Epoch(train) [182][ 5/15] lr: 1.0000e-06 eta: 0:01:35 time: 0.3455 data_time: 0.0745 memory: 17120 loss: 0.0694 loss_ce: 0.0694 2023/03/03 14:45:27 - mmengine - INFO - Epoch(train) [182][ 6/15] lr: 1.0000e-06 eta: 0:01:35 time: 0.3271 data_time: 0.0745 memory: 17272 loss: 0.0715 loss_ce: 0.0715 2023/03/03 14:45:27 - mmengine - INFO - Epoch(train) [182][ 7/15] lr: 1.0000e-06 eta: 0:01:34 time: 0.3202 data_time: 0.0744 memory: 16508 loss: 0.0726 loss_ce: 0.0726 2023/03/03 14:45:28 - mmengine - INFO - Epoch(train) [182][ 8/15] lr: 1.0000e-06 eta: 0:01:34 time: 0.3299 data_time: 0.0744 memory: 15136 loss: 0.0745 loss_ce: 0.0745 2023/03/03 14:45:28 - mmengine - INFO - Epoch(train) [182][ 9/15] lr: 1.0000e-06 eta: 0:01:34 time: 0.3272 data_time: 0.0744 memory: 18641 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:45:28 - mmengine - INFO - Epoch(train) [182][10/15] lr: 1.0000e-06 eta: 0:01:33 time: 0.3403 data_time: 0.0744 memory: 16804 loss: 0.0723 loss_ce: 0.0723 2023/03/03 14:45:29 - mmengine - INFO - Epoch(train) [182][11/15] lr: 1.0000e-06 eta: 0:01:33 time: 0.2827 data_time: 0.0019 memory: 18070 loss: 0.0725 loss_ce: 0.0725 2023/03/03 14:45:29 - mmengine - INFO - Epoch(train) [182][12/15] lr: 1.0000e-06 eta: 0:01:33 time: 0.2944 data_time: 0.0019 memory: 16622 loss: 0.0705 loss_ce: 0.0705 2023/03/03 14:45:29 - mmengine - INFO - Epoch(train) [182][13/15] lr: 1.0000e-06 eta: 0:01:32 time: 0.2971 data_time: 0.0019 memory: 11729 loss: 0.0711 loss_ce: 0.0711 2023/03/03 14:45:30 - mmengine - INFO - Epoch(train) [182][14/15] lr: 1.0000e-06 eta: 0:01:32 time: 0.2944 data_time: 0.0019 memory: 18409 loss: 0.0710 loss_ce: 0.0710 2023/03/03 14:45:30 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:45:30 - mmengine - INFO - Epoch(train) [182][15/15] lr: 1.0000e-06 eta: 0:01:31 time: 0.2914 data_time: 0.0019 memory: 5493 loss: 0.0730 loss_ce: 0.0730 2023/03/03 14:45:31 - mmengine - INFO - Epoch(train) [183][ 1/15] lr: 1.0000e-06 eta: 0:01:31 time: 0.3664 data_time: 0.0708 memory: 18797 loss: 0.0693 loss_ce: 0.0693 2023/03/03 14:45:31 - mmengine - INFO - Epoch(train) [183][ 2/15] lr: 1.0000e-06 eta: 0:01:31 time: 0.3756 data_time: 0.0710 memory: 19384 loss: 0.0706 loss_ce: 0.0706 2023/03/03 14:45:31 - mmengine - INFO - Epoch(train) [183][ 3/15] lr: 1.0000e-06 eta: 0:01:30 time: 0.3643 data_time: 0.0711 memory: 16976 loss: 0.0733 loss_ce: 0.0733 2023/03/03 14:45:32 - mmengine - INFO - Epoch(train) [183][ 4/15] lr: 1.0000e-06 eta: 0:01:30 time: 0.3610 data_time: 0.0711 memory: 18070 loss: 0.0720 loss_ce: 0.0720 2023/03/03 14:45:32 - mmengine - INFO - Epoch(train) [183][ 5/15] lr: 1.0000e-06 eta: 0:01:30 time: 0.3580 data_time: 0.0711 memory: 16976 loss: 0.0746 loss_ce: 0.0746 2023/03/03 14:45:32 - mmengine - INFO - Epoch(train) [183][ 6/15] lr: 1.0000e-06 eta: 0:01:29 time: 0.3437 data_time: 0.0710 memory: 15839 loss: 0.0718 loss_ce: 0.0718 2023/03/03 14:45:32 - mmengine - INFO - Epoch(train) [183][ 7/15] lr: 1.0000e-06 eta: 0:01:29 time: 0.3313 data_time: 0.0710 memory: 18409 loss: 0.0715 loss_ce: 0.0715 2023/03/03 14:45:33 - mmengine - INFO - Epoch(train) [183][ 8/15] lr: 1.0000e-06 eta: 0:01:29 time: 0.3478 data_time: 0.0709 memory: 16654 loss: 0.0708 loss_ce: 0.0708 2023/03/03 14:45:33 - mmengine - INFO - Epoch(train) [183][ 9/15] lr: 1.0000e-06 eta: 0:01:28 time: 0.3551 data_time: 0.0709 memory: 14047 loss: 0.0724 loss_ce: 0.0724 2023/03/03 14:45:33 - mmengine - INFO - Epoch(train) [183][10/15] lr: 1.0000e-06 eta: 0:01:28 time: 0.3533 data_time: 0.0709 memory: 14761 loss: 0.0707 loss_ce: 0.0707 2023/03/03 14:45:34 - mmengine - INFO - Epoch(train) [183][11/15] lr: 1.0000e-06 eta: 0:01:28 time: 0.2785 data_time: 0.0021 memory: 17272 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:45:34 - mmengine - INFO - Epoch(train) [183][12/15] lr: 1.0000e-06 eta: 0:01:27 time: 0.2876 data_time: 0.0019 memory: 15246 loss: 0.0700 loss_ce: 0.0700 2023/03/03 14:45:34 - mmengine - INFO - Epoch(train) [183][13/15] lr: 1.0000e-06 eta: 0:01:27 time: 0.2878 data_time: 0.0019 memory: 18070 loss: 0.0661 loss_ce: 0.0661 2023/03/03 14:45:35 - mmengine - INFO - Epoch(train) [183][14/15] lr: 1.0000e-06 eta: 0:01:27 time: 0.2969 data_time: 0.0019 memory: 17272 loss: 0.0665 loss_ce: 0.0665 2023/03/03 14:45:35 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:45:35 - mmengine - INFO - Epoch(train) [183][15/15] lr: 1.0000e-06 eta: 0:01:26 time: 0.2939 data_time: 0.0019 memory: 4510 loss: 0.0668 loss_ce: 0.0668 2023/03/03 14:45:36 - mmengine - INFO - Epoch(train) [184][ 1/15] lr: 1.0000e-06 eta: 0:01:26 time: 0.3359 data_time: 0.0480 memory: 24603 loss: 0.0687 loss_ce: 0.0687 2023/03/03 14:45:36 - mmengine - INFO - Epoch(train) [184][ 2/15] lr: 1.0000e-06 eta: 0:01:26 time: 0.3362 data_time: 0.0481 memory: 17572 loss: 0.0720 loss_ce: 0.0720 2023/03/03 14:45:36 - mmengine - INFO - Epoch(train) [184][ 3/15] lr: 1.0000e-06 eta: 0:01:25 time: 0.3199 data_time: 0.0482 memory: 17120 loss: 0.0730 loss_ce: 0.0730 2023/03/03 14:45:36 - mmengine - INFO - Epoch(train) [184][ 4/15] lr: 1.0000e-06 eta: 0:01:25 time: 0.3233 data_time: 0.0483 memory: 15045 loss: 0.0729 loss_ce: 0.0729 2023/03/03 14:45:37 - mmengine - INFO - Epoch(train) [184][ 5/15] lr: 1.0000e-06 eta: 0:01:25 time: 0.3417 data_time: 0.0484 memory: 15432 loss: 0.0758 loss_ce: 0.0758 2023/03/03 14:45:37 - mmengine - INFO - Epoch(train) [184][ 6/15] lr: 1.0000e-06 eta: 0:01:24 time: 0.3423 data_time: 0.0484 memory: 17272 loss: 0.0761 loss_ce: 0.0761 2023/03/03 14:45:37 - mmengine - INFO - Epoch(train) [184][ 7/15] lr: 1.0000e-06 eta: 0:01:24 time: 0.3258 data_time: 0.0486 memory: 17572 loss: 0.0758 loss_ce: 0.0758 2023/03/03 14:45:38 - mmengine - INFO - Epoch(train) [184][ 8/15] lr: 1.0000e-06 eta: 0:01:24 time: 0.3289 data_time: 0.0486 memory: 16955 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:45:38 - mmengine - INFO - Epoch(train) [184][ 9/15] lr: 1.0000e-06 eta: 0:01:23 time: 0.3374 data_time: 0.0487 memory: 13803 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:45:38 - mmengine - INFO - Epoch(train) [184][10/15] lr: 1.0000e-06 eta: 0:01:23 time: 0.3550 data_time: 0.0488 memory: 20206 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:45:39 - mmengine - INFO - Epoch(train) [184][11/15] lr: 1.0000e-06 eta: 0:01:23 time: 0.2913 data_time: 0.0028 memory: 17892 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:45:39 - mmengine - INFO - Epoch(train) [184][12/15] lr: 1.0000e-06 eta: 0:01:22 time: 0.2936 data_time: 0.0027 memory: 16508 loss: 0.0749 loss_ce: 0.0749 2023/03/03 14:45:39 - mmengine - INFO - Epoch(train) [184][13/15] lr: 1.0000e-06 eta: 0:01:22 time: 0.2914 data_time: 0.0026 memory: 17968 loss: 0.0716 loss_ce: 0.0716 2023/03/03 14:45:39 - mmengine - INFO - Epoch(train) [184][14/15] lr: 1.0000e-06 eta: 0:01:22 time: 0.2820 data_time: 0.0027 memory: 15037 loss: 0.0703 loss_ce: 0.0703 2023/03/03 14:45:39 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:45:39 - mmengine - INFO - Epoch(train) [184][15/15] lr: 1.0000e-06 eta: 0:01:21 time: 0.2643 data_time: 0.0031 memory: 5325 loss: 0.0693 loss_ce: 0.0693 2023/03/03 14:45:40 - mmengine - INFO - Epoch(train) [185][ 1/15] lr: 1.0000e-06 eta: 0:01:21 time: 0.3261 data_time: 0.0584 memory: 19932 loss: 0.0676 loss_ce: 0.0676 2023/03/03 14:45:41 - mmengine - INFO - Epoch(train) [185][ 2/15] lr: 1.0000e-06 eta: 0:01:21 time: 0.3410 data_time: 0.0583 memory: 17403 loss: 0.0693 loss_ce: 0.0693 2023/03/03 14:45:41 - mmengine - INFO - Epoch(train) [185][ 3/15] lr: 1.0000e-06 eta: 0:01:20 time: 0.3507 data_time: 0.0583 memory: 16601 loss: 0.0693 loss_ce: 0.0693 2023/03/03 14:45:41 - mmengine - INFO - Epoch(train) [185][ 4/15] lr: 1.0000e-06 eta: 0:01:20 time: 0.3436 data_time: 0.0583 memory: 16538 loss: 0.0672 loss_ce: 0.0672 2023/03/03 14:45:42 - mmengine - INFO - Epoch(train) [185][ 5/15] lr: 1.0000e-06 eta: 0:01:20 time: 0.3322 data_time: 0.0583 memory: 16654 loss: 0.0692 loss_ce: 0.0692 2023/03/03 14:45:42 - mmengine - INFO - Epoch(train) [185][ 6/15] lr: 1.0000e-06 eta: 0:01:19 time: 0.3478 data_time: 0.0582 memory: 16976 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:45:42 - mmengine - INFO - Epoch(train) [185][ 7/15] lr: 1.0000e-06 eta: 0:01:19 time: 0.3451 data_time: 0.0582 memory: 17272 loss: 0.0764 loss_ce: 0.0764 2023/03/03 14:45:43 - mmengine - INFO - Epoch(train) [185][ 8/15] lr: 1.0000e-06 eta: 0:01:19 time: 0.3800 data_time: 0.0581 memory: 19940 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:45:43 - mmengine - INFO - Epoch(train) [185][ 9/15] lr: 1.0000e-06 eta: 0:01:18 time: 0.3793 data_time: 0.0580 memory: 18556 loss: 0.0778 loss_ce: 0.0778 2023/03/03 14:45:43 - mmengine - INFO - Epoch(train) [185][10/15] lr: 1.0000e-06 eta: 0:01:18 time: 0.3958 data_time: 0.0576 memory: 17722 loss: 0.0753 loss_ce: 0.0753 2023/03/03 14:45:44 - mmengine - INFO - Epoch(train) [185][11/15] lr: 1.0000e-06 eta: 0:01:17 time: 0.3361 data_time: 0.0022 memory: 16223 loss: 0.0782 loss_ce: 0.0782 2023/03/03 14:45:44 - mmengine - INFO - Epoch(train) [185][12/15] lr: 1.0000e-06 eta: 0:01:17 time: 0.3350 data_time: 0.0023 memory: 16976 loss: 0.0809 loss_ce: 0.0809 2023/03/03 14:45:44 - mmengine - INFO - Epoch(train) [185][13/15] lr: 1.0000e-06 eta: 0:01:17 time: 0.3225 data_time: 0.0023 memory: 18409 loss: 0.0794 loss_ce: 0.0794 2023/03/03 14:45:45 - mmengine - INFO - Epoch(train) [185][14/15] lr: 1.0000e-06 eta: 0:01:16 time: 0.3325 data_time: 0.0022 memory: 15631 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:45:45 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:45:45 - mmengine - INFO - Epoch(train) [185][15/15] lr: 1.0000e-06 eta: 0:01:16 time: 0.3217 data_time: 0.0022 memory: 6551 loss: 0.0808 loss_ce: 0.0808 2023/03/03 14:45:46 - mmengine - INFO - Epoch(train) [186][ 1/15] lr: 1.0000e-06 eta: 0:01:16 time: 0.3581 data_time: 0.0355 memory: 17120 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:45:46 - mmengine - INFO - Epoch(train) [186][ 2/15] lr: 1.0000e-06 eta: 0:01:15 time: 0.3594 data_time: 0.0356 memory: 17093 loss: 0.0764 loss_ce: 0.0764 2023/03/03 14:45:46 - mmengine - INFO - Epoch(train) [186][ 3/15] lr: 1.0000e-06 eta: 0:01:15 time: 0.3262 data_time: 0.0357 memory: 16654 loss: 0.0760 loss_ce: 0.0760 2023/03/03 14:45:46 - mmengine - INFO - Epoch(train) [186][ 4/15] lr: 1.0000e-06 eta: 0:01:15 time: 0.3303 data_time: 0.0357 memory: 17948 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:45:47 - mmengine - INFO - Epoch(train) [186][ 5/15] lr: 1.0000e-06 eta: 0:01:14 time: 0.3118 data_time: 0.0358 memory: 16305 loss: 0.0765 loss_ce: 0.0765 2023/03/03 14:45:47 - mmengine - INFO - Epoch(train) [186][ 6/15] lr: 1.0000e-06 eta: 0:01:14 time: 0.3322 data_time: 0.0361 memory: 21209 loss: 0.0735 loss_ce: 0.0735 2023/03/03 14:45:47 - mmengine - INFO - Epoch(train) [186][ 7/15] lr: 1.0000e-06 eta: 0:01:14 time: 0.3127 data_time: 0.0361 memory: 16069 loss: 0.0677 loss_ce: 0.0677 2023/03/03 14:45:47 - mmengine - INFO - Epoch(train) [186][ 8/15] lr: 1.0000e-06 eta: 0:01:13 time: 0.3152 data_time: 0.0361 memory: 16508 loss: 0.0702 loss_ce: 0.0702 2023/03/03 14:45:48 - mmengine - INFO - Epoch(train) [186][ 9/15] lr: 1.0000e-06 eta: 0:01:13 time: 0.3027 data_time: 0.0362 memory: 20085 loss: 0.0707 loss_ce: 0.0707 2023/03/03 14:45:48 - mmengine - INFO - Epoch(train) [186][10/15] lr: 1.0000e-06 eta: 0:01:13 time: 0.3117 data_time: 0.0362 memory: 14906 loss: 0.0708 loss_ce: 0.0708 2023/03/03 14:45:48 - mmengine - INFO - Epoch(train) [186][11/15] lr: 1.0000e-06 eta: 0:01:12 time: 0.2702 data_time: 0.0030 memory: 17284 loss: 0.0704 loss_ce: 0.0704 2023/03/03 14:45:49 - mmengine - INFO - Epoch(train) [186][12/15] lr: 1.0000e-06 eta: 0:01:12 time: 0.2780 data_time: 0.0029 memory: 21161 loss: 0.0701 loss_ce: 0.0701 2023/03/03 14:45:49 - mmengine - INFO - Epoch(train) [186][13/15] lr: 1.0000e-06 eta: 0:01:12 time: 0.2710 data_time: 0.0029 memory: 15037 loss: 0.0720 loss_ce: 0.0720 2023/03/03 14:45:49 - mmengine - INFO - Epoch(train) [186][14/15] lr: 1.0000e-06 eta: 0:01:11 time: 0.2669 data_time: 0.0027 memory: 15037 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:45:50 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:45:50 - mmengine - INFO - Epoch(train) [186][15/15] lr: 1.0000e-06 eta: 0:01:11 time: 0.3012 data_time: 0.0026 memory: 4957 loss: 0.0790 loss_ce: 0.0790 2023/03/03 14:45:50 - mmengine - INFO - Epoch(train) [187][ 1/15] lr: 1.0000e-06 eta: 0:01:11 time: 0.3364 data_time: 0.0590 memory: 18490 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:45:51 - mmengine - INFO - Epoch(train) [187][ 2/15] lr: 1.0000e-06 eta: 0:01:10 time: 0.4048 data_time: 0.0589 memory: 19566 loss: 0.0804 loss_ce: 0.0804 2023/03/03 14:45:51 - mmengine - INFO - Epoch(train) [187][ 3/15] lr: 1.0000e-06 eta: 0:01:10 time: 0.4029 data_time: 0.0589 memory: 18766 loss: 0.0800 loss_ce: 0.0800 2023/03/03 14:45:52 - mmengine - INFO - Epoch(train) [187][ 4/15] lr: 1.0000e-06 eta: 0:01:10 time: 0.3991 data_time: 0.0589 memory: 18070 loss: 0.0747 loss_ce: 0.0747 2023/03/03 14:45:52 - mmengine - INFO - Epoch(train) [187][ 5/15] lr: 1.0000e-06 eta: 0:01:09 time: 0.4013 data_time: 0.0589 memory: 20719 loss: 0.0711 loss_ce: 0.0711 2023/03/03 14:45:52 - mmengine - INFO - Epoch(train) [187][ 6/15] lr: 1.0000e-06 eta: 0:01:09 time: 0.3928 data_time: 0.0589 memory: 15825 loss: 0.0749 loss_ce: 0.0749 2023/03/03 14:45:53 - mmengine - INFO - Epoch(train) [187][ 7/15] lr: 1.0000e-06 eta: 0:01:09 time: 0.3958 data_time: 0.0588 memory: 19196 loss: 0.0714 loss_ce: 0.0714 2023/03/03 14:45:53 - mmengine - INFO - Epoch(train) [187][ 8/15] lr: 1.0000e-06 eta: 0:01:08 time: 0.3953 data_time: 0.0589 memory: 16830 loss: 0.0725 loss_ce: 0.0725 2023/03/03 14:45:53 - mmengine - INFO - Epoch(train) [187][ 9/15] lr: 1.0000e-06 eta: 0:01:08 time: 0.3968 data_time: 0.0589 memory: 16199 loss: 0.0739 loss_ce: 0.0739 2023/03/03 14:45:53 - mmengine - INFO - Epoch(train) [187][10/15] lr: 1.0000e-06 eta: 0:01:08 time: 0.3695 data_time: 0.0589 memory: 16426 loss: 0.0696 loss_ce: 0.0696 2023/03/03 14:45:53 - mmengine - INFO - Epoch(train) [187][11/15] lr: 1.0000e-06 eta: 0:01:07 time: 0.3112 data_time: 0.0022 memory: 17421 loss: 0.0691 loss_ce: 0.0691 2023/03/03 14:45:54 - mmengine - INFO - Epoch(train) [187][12/15] lr: 1.0000e-06 eta: 0:01:07 time: 0.2514 data_time: 0.0022 memory: 16370 loss: 0.0704 loss_ce: 0.0704 2023/03/03 14:45:54 - mmengine - INFO - Epoch(train) [187][13/15] lr: 1.0000e-06 eta: 0:01:07 time: 0.2504 data_time: 0.0022 memory: 17421 loss: 0.0692 loss_ce: 0.0692 2023/03/03 14:45:55 - mmengine - INFO - Epoch(train) [187][14/15] lr: 1.0000e-06 eta: 0:01:06 time: 0.2846 data_time: 0.0022 memory: 17187 loss: 0.0714 loss_ce: 0.0714 2023/03/03 14:45:55 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:45:55 - mmengine - INFO - Epoch(train) [187][15/15] lr: 1.0000e-06 eta: 0:01:06 time: 0.2747 data_time: 0.0021 memory: 6027 loss: 0.0730 loss_ce: 0.0730 2023/03/03 14:45:56 - mmengine - INFO - Epoch(train) [188][ 1/15] lr: 1.0000e-06 eta: 0:01:06 time: 0.3725 data_time: 0.0451 memory: 17654 loss: 0.0726 loss_ce: 0.0726 2023/03/03 14:45:56 - mmengine - INFO - Epoch(train) [188][ 2/15] lr: 1.0000e-06 eta: 0:01:05 time: 0.3615 data_time: 0.0451 memory: 14906 loss: 0.0760 loss_ce: 0.0760 2023/03/03 14:45:57 - mmengine - INFO - Epoch(train) [188][ 3/15] lr: 1.0000e-06 eta: 0:01:05 time: 0.3753 data_time: 0.0451 memory: 17198 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:45:57 - mmengine - INFO - Epoch(train) [188][ 4/15] lr: 1.0000e-06 eta: 0:01:05 time: 0.3912 data_time: 0.0451 memory: 17484 loss: 0.0746 loss_ce: 0.0746 2023/03/03 14:45:57 - mmengine - INFO - Epoch(train) [188][ 5/15] lr: 1.0000e-06 eta: 0:01:04 time: 0.3936 data_time: 0.0452 memory: 15432 loss: 0.0766 loss_ce: 0.0766 2023/03/03 14:45:57 - mmengine - INFO - Epoch(train) [188][ 6/15] lr: 1.0000e-06 eta: 0:01:04 time: 0.3933 data_time: 0.0451 memory: 17272 loss: 0.0779 loss_ce: 0.0779 2023/03/03 14:45:58 - mmengine - INFO - Epoch(train) [188][ 7/15] lr: 1.0000e-06 eta: 0:01:04 time: 0.4072 data_time: 0.0451 memory: 15494 loss: 0.0791 loss_ce: 0.0791 2023/03/03 14:45:58 - mmengine - INFO - Epoch(train) [188][ 8/15] lr: 1.0000e-06 eta: 0:01:03 time: 0.4194 data_time: 0.0450 memory: 19251 loss: 0.0785 loss_ce: 0.0785 2023/03/03 14:45:58 - mmengine - INFO - Epoch(train) [188][ 9/15] lr: 1.0000e-06 eta: 0:01:03 time: 0.3806 data_time: 0.0450 memory: 17421 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:45:59 - mmengine - INFO - Epoch(train) [188][10/15] lr: 1.0000e-06 eta: 0:01:02 time: 0.3927 data_time: 0.0450 memory: 18759 loss: 0.0764 loss_ce: 0.0764 2023/03/03 14:45:59 - mmengine - INFO - Epoch(train) [188][11/15] lr: 1.0000e-06 eta: 0:01:02 time: 0.2970 data_time: 0.0019 memory: 17024 loss: 0.0738 loss_ce: 0.0738 2023/03/03 14:45:59 - mmengine - INFO - Epoch(train) [188][12/15] lr: 1.0000e-06 eta: 0:01:02 time: 0.2955 data_time: 0.0018 memory: 17272 loss: 0.0712 loss_ce: 0.0712 2023/03/03 14:45:59 - mmengine - INFO - Epoch(train) [188][13/15] lr: 1.0000e-06 eta: 0:01:01 time: 0.2931 data_time: 0.0018 memory: 16369 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:46:00 - mmengine - INFO - Epoch(train) [188][14/15] lr: 1.0000e-06 eta: 0:01:01 time: 0.2774 data_time: 0.0018 memory: 16223 loss: 0.0720 loss_ce: 0.0720 2023/03/03 14:46:00 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:46:00 - mmengine - INFO - Epoch(train) [188][15/15] lr: 1.0000e-06 eta: 0:01:01 time: 0.2646 data_time: 0.0019 memory: 6046 loss: 0.0792 loss_ce: 0.0792 2023/03/03 14:46:00 - mmengine - INFO - Epoch(train) [189][ 1/15] lr: 1.0000e-06 eta: 0:01:00 time: 0.3042 data_time: 0.0407 memory: 12258 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:46:01 - mmengine - INFO - Epoch(train) [189][ 2/15] lr: 1.0000e-06 eta: 0:01:00 time: 0.3028 data_time: 0.0443 memory: 16488 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:46:01 - mmengine - INFO - Epoch(train) [189][ 3/15] lr: 1.0000e-06 eta: 0:01:00 time: 0.2911 data_time: 0.0444 memory: 17421 loss: 0.0825 loss_ce: 0.0825 2023/03/03 14:46:02 - mmengine - INFO - Epoch(train) [189][ 4/15] lr: 1.0000e-06 eta: 0:00:59 time: 0.3231 data_time: 0.0445 memory: 16976 loss: 0.0845 loss_ce: 0.0845 2023/03/03 14:46:02 - mmengine - INFO - Epoch(train) [189][ 5/15] lr: 1.0000e-06 eta: 0:00:59 time: 0.3173 data_time: 0.0446 memory: 16719 loss: 0.0850 loss_ce: 0.0850 2023/03/03 14:46:02 - mmengine - INFO - Epoch(train) [189][ 6/15] lr: 1.0000e-06 eta: 0:00:59 time: 0.3305 data_time: 0.0447 memory: 16370 loss: 0.0849 loss_ce: 0.0849 2023/03/03 14:46:02 - mmengine - INFO - Epoch(train) [189][ 7/15] lr: 1.0000e-06 eta: 0:00:58 time: 0.3338 data_time: 0.0448 memory: 16628 loss: 0.0859 loss_ce: 0.0859 2023/03/03 14:46:03 - mmengine - INFO - Epoch(train) [189][ 8/15] lr: 1.0000e-06 eta: 0:00:58 time: 0.3274 data_time: 0.0448 memory: 21040 loss: 0.0869 loss_ce: 0.0869 2023/03/03 14:46:03 - mmengine - INFO - Epoch(train) [189][ 9/15] lr: 1.0000e-06 eta: 0:00:58 time: 0.3306 data_time: 0.0449 memory: 16808 loss: 0.0891 loss_ce: 0.0891 2023/03/03 14:46:03 - mmengine - INFO - Epoch(train) [189][10/15] lr: 1.0000e-06 eta: 0:00:57 time: 0.3365 data_time: 0.0448 memory: 16370 loss: 0.0804 loss_ce: 0.0804 2023/03/03 14:46:03 - mmengine - INFO - Epoch(train) [189][11/15] lr: 1.0000e-06 eta: 0:00:57 time: 0.2969 data_time: 0.0059 memory: 17572 loss: 0.0805 loss_ce: 0.0805 2023/03/03 14:46:04 - mmengine - INFO - Epoch(train) [189][12/15] lr: 1.0000e-06 eta: 0:00:57 time: 0.2812 data_time: 0.0023 memory: 16788 loss: 0.0793 loss_ce: 0.0793 2023/03/03 14:46:04 - mmengine - INFO - Epoch(train) [189][13/15] lr: 1.0000e-06 eta: 0:00:56 time: 0.2906 data_time: 0.0022 memory: 21466 loss: 0.0804 loss_ce: 0.0804 2023/03/03 14:46:04 - mmengine - INFO - Epoch(train) [189][14/15] lr: 1.0000e-06 eta: 0:00:56 time: 0.2808 data_time: 0.0021 memory: 16976 loss: 0.0819 loss_ce: 0.0819 2023/03/03 14:46:05 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:46:05 - mmengine - INFO - Epoch(train) [189][15/15] lr: 1.0000e-06 eta: 0:00:56 time: 0.2780 data_time: 0.0021 memory: 7249 loss: 0.0814 loss_ce: 0.0814 2023/03/03 14:46:05 - mmengine - INFO - Epoch(train) [190][ 1/15] lr: 1.0000e-06 eta: 0:00:55 time: 0.3138 data_time: 0.0247 memory: 17421 loss: 0.0833 loss_ce: 0.0833 2023/03/03 14:46:06 - mmengine - INFO - Epoch(train) [190][ 2/15] lr: 1.0000e-06 eta: 0:00:55 time: 0.3140 data_time: 0.0247 memory: 16654 loss: 0.0812 loss_ce: 0.0812 2023/03/03 14:46:06 - mmengine - INFO - Epoch(train) [190][ 3/15] lr: 1.0000e-06 eta: 0:00:55 time: 0.3110 data_time: 0.0246 memory: 16370 loss: 0.0819 loss_ce: 0.0819 2023/03/03 14:46:06 - mmengine - INFO - Epoch(train) [190][ 4/15] lr: 1.0000e-06 eta: 0:00:54 time: 0.3078 data_time: 0.0245 memory: 16223 loss: 0.0777 loss_ce: 0.0777 2023/03/03 14:46:06 - mmengine - INFO - Epoch(train) [190][ 5/15] lr: 1.0000e-06 eta: 0:00:54 time: 0.3198 data_time: 0.0245 memory: 17401 loss: 0.0732 loss_ce: 0.0732 2023/03/03 14:46:07 - mmengine - INFO - Epoch(train) [190][ 6/15] lr: 1.0000e-06 eta: 0:00:54 time: 0.3197 data_time: 0.0246 memory: 16976 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:46:07 - mmengine - INFO - Epoch(train) [190][ 7/15] lr: 1.0000e-06 eta: 0:00:53 time: 0.3353 data_time: 0.0247 memory: 17619 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:46:07 - mmengine - INFO - Epoch(train) [190][ 8/15] lr: 1.0000e-06 eta: 0:00:53 time: 0.3270 data_time: 0.0247 memory: 15911 loss: 0.0734 loss_ce: 0.0734 2023/03/03 14:46:08 - mmengine - INFO - Epoch(train) [190][ 9/15] lr: 1.0000e-06 eta: 0:00:53 time: 0.3072 data_time: 0.0247 memory: 17201 loss: 0.0698 loss_ce: 0.0698 2023/03/03 14:46:08 - mmengine - INFO - Epoch(train) [190][10/15] lr: 1.0000e-06 eta: 0:00:52 time: 0.3107 data_time: 0.0246 memory: 18137 loss: 0.0663 loss_ce: 0.0663 2023/03/03 14:46:08 - mmengine - INFO - Epoch(train) [190][11/15] lr: 1.0000e-06 eta: 0:00:52 time: 0.3010 data_time: 0.0020 memory: 18070 loss: 0.0644 loss_ce: 0.0644 2023/03/03 14:46:09 - mmengine - INFO - Epoch(train) [190][12/15] lr: 1.0000e-06 eta: 0:00:52 time: 0.3054 data_time: 0.0019 memory: 14661 loss: 0.0665 loss_ce: 0.0665 2023/03/03 14:46:09 - mmengine - INFO - Epoch(train) [190][13/15] lr: 1.0000e-06 eta: 0:00:51 time: 0.3241 data_time: 0.0019 memory: 20945 loss: 0.0652 loss_ce: 0.0652 2023/03/03 14:46:09 - mmengine - INFO - Epoch(train) [190][14/15] lr: 1.0000e-06 eta: 0:00:51 time: 0.3244 data_time: 0.0019 memory: 16562 loss: 0.0624 loss_ce: 0.0624 2023/03/03 14:46:10 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:46:10 - mmengine - INFO - Epoch(train) [190][15/15] lr: 1.0000e-06 eta: 0:00:51 time: 0.3348 data_time: 0.0019 memory: 5179 loss: 0.0687 loss_ce: 0.0687 2023/03/03 14:46:11 - mmengine - INFO - Epoch(val) [190][ 1/59] eta: 0:01:25 time: 1.0621 data_time: 0.0035 memory: 981 2023/03/03 14:46:12 - mmengine - INFO - Epoch(val) [190][ 2/59] eta: 0:01:04 time: 0.9694 data_time: 0.0035 memory: 981 2023/03/03 14:46:13 - mmengine - INFO - Epoch(val) [190][ 3/59] eta: 0:01:05 time: 0.9808 data_time: 0.0035 memory: 1003 2023/03/03 14:46:14 - mmengine - INFO - Epoch(val) [190][ 4/59] eta: 0:00:53 time: 0.9630 data_time: 0.0036 memory: 981 2023/03/03 14:46:17 - mmengine - INFO - Epoch(val) [190][ 5/59] eta: 0:01:14 time: 1.1968 data_time: 0.0036 memory: 1016 2023/03/03 14:46:19 - mmengine - INFO - Epoch(val) [190][ 6/59] eta: 0:01:23 time: 1.3910 data_time: 0.0036 memory: 981 2023/03/03 14:46:19 - mmengine - INFO - Epoch(val) [190][ 7/59] eta: 0:01:11 time: 1.3423 data_time: 0.0036 memory: 1043 2023/03/03 14:46:20 - mmengine - INFO - Epoch(val) [190][ 8/59] eta: 0:01:05 time: 1.1847 data_time: 0.0036 memory: 1016 2023/03/03 14:46:21 - mmengine - INFO - Epoch(val) [190][ 9/59] eta: 0:01:02 time: 1.1634 data_time: 0.0036 memory: 981 2023/03/03 14:46:22 - mmengine - INFO - Epoch(val) [190][10/59] eta: 0:00:58 time: 1.1936 data_time: 0.0036 memory: 981 2023/03/03 14:46:22 - mmengine - INFO - Epoch(val) [190][11/59] eta: 0:00:53 time: 1.0786 data_time: 0.0010 memory: 981 2023/03/03 14:46:25 - mmengine - INFO - Epoch(val) [190][12/59] eta: 0:01:00 time: 1.3117 data_time: 0.0010 memory: 1016 2023/03/03 14:46:27 - mmengine - INFO - Epoch(val) [190][13/59] eta: 0:01:02 time: 1.4067 data_time: 0.0011 memory: 981 2023/03/03 14:46:28 - mmengine - INFO - Epoch(val) [190][14/59] eta: 0:00:59 time: 1.4733 data_time: 0.0011 memory: 890 2023/03/03 14:46:28 - mmengine - INFO - Epoch(val) [190][15/59] eta: 0:00:54 time: 1.1747 data_time: 0.0011 memory: 981 2023/03/03 14:46:29 - mmengine - INFO - Epoch(val) [190][16/59] eta: 0:00:51 time: 0.9627 data_time: 0.0011 memory: 981 2023/03/03 14:46:29 - mmengine - INFO - Epoch(val) [190][17/59] eta: 0:00:48 time: 0.9776 data_time: 0.0011 memory: 981 2023/03/03 14:46:30 - mmengine - INFO - Epoch(val) [190][18/59] eta: 0:00:45 time: 0.9432 data_time: 0.0010 memory: 981 2023/03/03 14:46:31 - mmengine - INFO - Epoch(val) [190][19/59] eta: 0:00:43 time: 0.9479 data_time: 0.0010 memory: 981 2023/03/03 14:46:31 - mmengine - INFO - Epoch(val) [190][20/59] eta: 0:00:41 time: 0.9183 data_time: 0.0011 memory: 981 2023/03/03 14:46:31 - mmengine - INFO - Epoch(val) [190][21/59] eta: 0:00:39 time: 0.9363 data_time: 0.0010 memory: 981 2023/03/03 14:46:32 - mmengine - INFO - Epoch(val) [190][22/59] eta: 0:00:36 time: 0.6415 data_time: 0.0011 memory: 981 2023/03/03 14:46:32 - mmengine - INFO - Epoch(val) [190][23/59] eta: 0:00:35 time: 0.4852 data_time: 0.0010 memory: 981 2023/03/03 14:46:33 - mmengine - INFO - Epoch(val) [190][24/59] eta: 0:00:33 time: 0.4223 data_time: 0.0009 memory: 962 2023/03/03 14:46:33 - mmengine - INFO - Epoch(val) [190][25/59] eta: 0:00:31 time: 0.4555 data_time: 0.0012 memory: 981 2023/03/03 14:46:33 - mmengine - INFO - Epoch(val) [190][26/59] eta: 0:00:29 time: 0.4406 data_time: 0.0013 memory: 981 2023/03/03 14:46:34 - mmengine - INFO - Epoch(val) [190][27/59] eta: 0:00:28 time: 0.4420 data_time: 0.0013 memory: 981 2023/03/03 14:46:34 - mmengine - INFO - Epoch(val) [190][28/59] eta: 0:00:26 time: 0.4432 data_time: 0.0013 memory: 981 2023/03/03 14:46:35 - mmengine - INFO - Epoch(val) [190][29/59] eta: 0:00:26 time: 0.4758 data_time: 0.0013 memory: 981 2023/03/03 14:46:36 - mmengine - INFO - Epoch(val) [190][30/59] eta: 0:00:25 time: 0.5268 data_time: 0.0014 memory: 999 2023/03/03 14:46:37 - mmengine - INFO - Epoch(val) [190][31/59] eta: 0:00:24 time: 0.5439 data_time: 0.0014 memory: 981 2023/03/03 14:46:38 - mmengine - INFO - Epoch(val) [190][32/59] eta: 0:00:23 time: 0.6454 data_time: 0.0014 memory: 981 2023/03/03 14:46:38 - mmengine - INFO - Epoch(val) [190][33/59] eta: 0:00:22 time: 0.5822 data_time: 0.0014 memory: 981 2023/03/03 14:46:38 - mmengine - INFO - Epoch(val) [190][34/59] eta: 0:00:20 time: 0.5638 data_time: 0.0014 memory: 981 2023/03/03 14:46:38 - mmengine - INFO - Epoch(val) [190][35/59] eta: 0:00:19 time: 0.5458 data_time: 0.0011 memory: 981 2023/03/03 14:46:39 - mmengine - INFO - Epoch(val) [190][36/59] eta: 0:00:18 time: 0.5624 data_time: 0.0010 memory: 981 2023/03/03 14:46:39 - mmengine - INFO - Epoch(val) [190][37/59] eta: 0:00:17 time: 0.5456 data_time: 0.0010 memory: 981 2023/03/03 14:46:40 - mmengine - INFO - Epoch(val) [190][38/59] eta: 0:00:16 time: 0.5785 data_time: 0.0010 memory: 981 2023/03/03 14:46:40 - mmengine - INFO - Epoch(val) [190][39/59] eta: 0:00:15 time: 0.4964 data_time: 0.0010 memory: 987 2023/03/03 14:46:41 - mmengine - INFO - Epoch(val) [190][40/59] eta: 0:00:14 time: 0.4937 data_time: 0.0009 memory: 981 2023/03/03 14:46:42 - mmengine - INFO - Epoch(val) [190][41/59] eta: 0:00:14 time: 0.5461 data_time: 0.0009 memory: 986 2023/03/03 14:46:43 - mmengine - INFO - Epoch(val) [190][42/59] eta: 0:00:13 time: 0.4955 data_time: 0.0009 memory: 981 2023/03/03 14:46:44 - mmengine - INFO - Epoch(val) [190][43/59] eta: 0:00:12 time: 0.5755 data_time: 0.0008 memory: 976 2023/03/03 14:46:44 - mmengine - INFO - Epoch(val) [190][44/59] eta: 0:00:11 time: 0.6084 data_time: 0.0009 memory: 1003 2023/03/03 14:46:47 - mmengine - INFO - Epoch(val) [190][45/59] eta: 0:00:11 time: 0.9049 data_time: 0.0009 memory: 981 2023/03/03 14:46:48 - mmengine - INFO - Epoch(val) [190][46/59] eta: 0:00:10 time: 0.9381 data_time: 0.0010 memory: 981 2023/03/03 14:46:49 - mmengine - INFO - Epoch(val) [190][47/59] eta: 0:00:09 time: 0.9711 data_time: 0.0010 memory: 936 2023/03/03 14:46:49 - mmengine - INFO - Epoch(val) [190][48/59] eta: 0:00:09 time: 0.9548 data_time: 0.0010 memory: 1000 2023/03/03 14:46:50 - mmengine - INFO - Epoch(val) [190][49/59] eta: 0:00:08 time: 1.0052 data_time: 0.0010 memory: 981 2023/03/03 14:46:51 - mmengine - INFO - Epoch(val) [190][50/59] eta: 0:00:07 time: 1.0065 data_time: 0.0010 memory: 987 2023/03/03 14:46:53 - mmengine - INFO - Epoch(val) [190][51/59] eta: 0:00:06 time: 1.0549 data_time: 0.0010 memory: 981 2023/03/03 14:46:54 - mmengine - INFO - Epoch(val) [190][52/59] eta: 0:00:05 time: 1.1050 data_time: 0.0009 memory: 981 2023/03/03 14:46:55 - mmengine - INFO - Epoch(val) [190][53/59] eta: 0:00:05 time: 1.0729 data_time: 0.0010 memory: 962 2023/03/03 14:46:55 - mmengine - INFO - Epoch(val) [190][54/59] eta: 0:00:04 time: 1.0895 data_time: 0.0010 memory: 981 2023/03/03 14:46:56 - mmengine - INFO - Epoch(val) [190][55/59] eta: 0:00:03 time: 0.8434 data_time: 0.0010 memory: 981 2023/03/03 14:46:57 - mmengine - INFO - Epoch(val) [190][56/59] eta: 0:00:02 time: 0.8271 data_time: 0.0009 memory: 981 2023/03/03 14:46:59 - mmengine - INFO - Epoch(val) [190][57/59] eta: 0:00:01 time: 1.0041 data_time: 0.0009 memory: 981 2023/03/03 14:47:00 - mmengine - INFO - Epoch(val) [190][58/59] eta: 0:00:00 time: 1.0712 data_time: 0.0010 memory: 1016 2023/03/03 14:47:00 - mmengine - INFO - Epoch(val) [190][59/59] eta: 0:00:00 time: 1.0042 data_time: 0.0009 memory: 981 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.80, recall: 0.8174, precision: 0.8396, hmean: 0.8283 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.81, recall: 0.8174, precision: 0.8420, hmean: 0.8295 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.82, recall: 0.8174, precision: 0.8435, hmean: 0.8302 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.83, recall: 0.8164, precision: 0.8458, hmean: 0.8309 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.84, recall: 0.8146, precision: 0.8495, hmean: 0.8317 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.85, recall: 0.8146, precision: 0.8503, hmean: 0.8321 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.86, recall: 0.8137, precision: 0.8534, hmean: 0.8331 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.87, recall: 0.8110, precision: 0.8547, hmean: 0.8322 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.88, recall: 0.8073, precision: 0.8558, hmean: 0.8308 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.89, recall: 0.8027, precision: 0.8567, hmean: 0.8289 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.90, recall: 0.7991, precision: 0.8595, hmean: 0.8282 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.91, recall: 0.7954, precision: 0.8641, hmean: 0.8283 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.92, recall: 0.7918, precision: 0.8670, hmean: 0.8277 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.93, recall: 0.7872, precision: 0.8698, hmean: 0.8265 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.94, recall: 0.7772, precision: 0.8710, hmean: 0.8214 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.95, recall: 0.7699, precision: 0.8727, hmean: 0.8180 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.96, recall: 0.7616, precision: 0.8770, hmean: 0.8152 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.97, recall: 0.7534, precision: 0.8795, hmean: 0.8116 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.98, recall: 0.7416, precision: 0.8855, hmean: 0.8072 2023/03/03 14:47:30 - mmengine - INFO - text score threshold: 0.99, recall: 0.7233, precision: 0.8939, hmean: 0.7996 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.80, recall: 0.8292, precision: 0.9089, hmean: 0.8672 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.81, recall: 0.8292, precision: 0.9098, hmean: 0.8677 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.82, recall: 0.8283, precision: 0.9097, hmean: 0.8671 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.83, recall: 0.8274, precision: 0.9115, hmean: 0.8674 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.84, recall: 0.8256, precision: 0.9131, hmean: 0.8671 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.85, recall: 0.8256, precision: 0.9131, hmean: 0.8671 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.86, recall: 0.8247, precision: 0.9149, hmean: 0.8674 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.87, recall: 0.8210, precision: 0.9145, hmean: 0.8653 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.88, recall: 0.8174, precision: 0.9161, hmean: 0.8639 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.89, recall: 0.8119, precision: 0.9156, hmean: 0.8606 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.90, recall: 0.8082, precision: 0.9180, hmean: 0.8596 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.91, recall: 0.8037, precision: 0.9186, hmean: 0.8573 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.92, recall: 0.7982, precision: 0.9190, hmean: 0.8543 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.93, recall: 0.7918, precision: 0.9204, hmean: 0.8513 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.94, recall: 0.7799, precision: 0.9193, hmean: 0.8439 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.95, recall: 0.7726, precision: 0.9206, hmean: 0.8401 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.96, recall: 0.7635, precision: 0.9207, hmean: 0.8347 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.97, recall: 0.7543, precision: 0.9219, hmean: 0.8297 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.98, recall: 0.7416, precision: 0.9269, hmean: 0.8239 2023/03/03 14:47:33 - mmengine - INFO - text score threshold: 0.99, recall: 0.7224, precision: 0.9295, hmean: 0.8129 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.80, recall: 0.7498, precision: 0.9569, hmean: 0.8408 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.81, recall: 0.7489, precision: 0.9568, hmean: 0.8402 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.82, recall: 0.7479, precision: 0.9568, hmean: 0.8396 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.83, recall: 0.7470, precision: 0.9578, hmean: 0.8394 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.84, recall: 0.7452, precision: 0.9589, hmean: 0.8386 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.85, recall: 0.7452, precision: 0.9589, hmean: 0.8386 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.86, recall: 0.7443, precision: 0.9588, hmean: 0.8380 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.87, recall: 0.7425, precision: 0.9587, hmean: 0.8369 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.88, recall: 0.7388, precision: 0.9585, hmean: 0.8345 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.89, recall: 0.7352, precision: 0.9583, hmean: 0.8320 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.90, recall: 0.7315, precision: 0.9593, hmean: 0.8301 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.91, recall: 0.7269, precision: 0.9590, hmean: 0.8270 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.92, recall: 0.7224, precision: 0.9600, hmean: 0.8244 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.93, recall: 0.7169, precision: 0.9608, hmean: 0.8211 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.94, recall: 0.7059, precision: 0.9614, hmean: 0.8141 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.95, recall: 0.6995, precision: 0.9635, hmean: 0.8106 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.96, recall: 0.6913, precision: 0.9631, hmean: 0.8049 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.97, recall: 0.6831, precision: 0.9639, hmean: 0.7996 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.98, recall: 0.6712, precision: 0.9658, hmean: 0.7920 2023/03/03 14:47:36 - mmengine - INFO - text score threshold: 0.99, recall: 0.6521, precision: 0.9662, hmean: 0.7786 2023/03/03 14:47:36 - mmengine - INFO - Epoch(val) [190][59/59] generic/precision: 0.8534 generic/recall: 0.8137 generic/hmean: 0.8331 weak/precision: 0.9098 weak/recall: 0.8292 weak/hmean: 0.8677 strong/precision: 0.9569 strong/recall: 0.7498 strong/hmean: 0.8408 2023/03/03 14:47:37 - mmengine - INFO - Epoch(train) [191][ 1/15] lr: 1.0000e-06 eta: 0:00:50 time: 0.4283 data_time: 0.0926 memory: 16976 loss: 0.0677 loss_ce: 0.0677 2023/03/03 14:47:37 - mmengine - INFO - Epoch(train) [191][ 2/15] lr: 1.0000e-06 eta: 0:00:50 time: 0.4119 data_time: 0.0927 memory: 16804 loss: 0.0710 loss_ce: 0.0710 2023/03/03 14:47:37 - mmengine - INFO - Epoch(train) [191][ 3/15] lr: 1.0000e-06 eta: 0:00:50 time: 0.4172 data_time: 0.0927 memory: 19600 loss: 0.0684 loss_ce: 0.0684 2023/03/03 14:47:38 - mmengine - INFO - Epoch(train) [191][ 4/15] lr: 1.0000e-06 eta: 0:00:49 time: 0.4336 data_time: 0.0928 memory: 17572 loss: 0.0691 loss_ce: 0.0691 2023/03/03 14:47:38 - mmengine - INFO - Epoch(train) [191][ 5/15] lr: 1.0000e-06 eta: 0:00:49 time: 0.4333 data_time: 0.0928 memory: 17272 loss: 0.0743 loss_ce: 0.0743 2023/03/03 14:47:38 - mmengine - INFO - Epoch(train) [191][ 6/15] lr: 1.0000e-06 eta: 0:00:49 time: 0.3977 data_time: 0.0929 memory: 14546 loss: 0.0733 loss_ce: 0.0733 2023/03/03 14:47:39 - mmengine - INFO - Epoch(train) [191][ 7/15] lr: 1.0000e-06 eta: 0:00:48 time: 0.4082 data_time: 0.0929 memory: 22793 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:47:39 - mmengine - INFO - Epoch(train) [191][ 8/15] lr: 1.0000e-06 eta: 0:00:48 time: 0.3982 data_time: 0.0929 memory: 18738 loss: 0.0690 loss_ce: 0.0690 2023/03/03 14:47:39 - mmengine - INFO - Epoch(train) [191][ 9/15] lr: 1.0000e-06 eta: 0:00:47 time: 0.3954 data_time: 0.0929 memory: 17572 loss: 0.0681 loss_ce: 0.0681 2023/03/03 14:47:40 - mmengine - INFO - Epoch(train) [191][10/15] lr: 1.0000e-06 eta: 0:00:47 time: 0.3883 data_time: 0.0929 memory: 14906 loss: 0.0653 loss_ce: 0.0653 2023/03/03 14:47:40 - mmengine - INFO - Epoch(train) [191][11/15] lr: 1.0000e-06 eta: 0:00:47 time: 0.2983 data_time: 0.0022 memory: 17122 loss: 0.0612 loss_ce: 0.0612 2023/03/03 14:47:40 - mmengine - INFO - Epoch(train) [191][12/15] lr: 1.0000e-06 eta: 0:00:46 time: 0.2999 data_time: 0.0022 memory: 16370 loss: 0.0605 loss_ce: 0.0605 2023/03/03 14:47:40 - mmengine - INFO - Epoch(train) [191][13/15] lr: 1.0000e-06 eta: 0:00:46 time: 0.2928 data_time: 0.0022 memory: 16976 loss: 0.0665 loss_ce: 0.0665 2023/03/03 14:47:41 - mmengine - INFO - Epoch(train) [191][14/15] lr: 1.0000e-06 eta: 0:00:46 time: 0.2758 data_time: 0.0023 memory: 15767 loss: 0.0706 loss_ce: 0.0706 2023/03/03 14:47:41 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:47:41 - mmengine - INFO - Epoch(train) [191][15/15] lr: 1.0000e-06 eta: 0:00:45 time: 0.2688 data_time: 0.0022 memory: 6823 loss: 0.0704 loss_ce: 0.0704 2023/03/03 14:47:42 - mmengine - INFO - Epoch(train) [192][ 1/15] lr: 1.0000e-06 eta: 0:00:45 time: 0.3222 data_time: 0.0391 memory: 18528 loss: 0.0698 loss_ce: 0.0698 2023/03/03 14:47:42 - mmengine - INFO - Epoch(train) [192][ 2/15] lr: 1.0000e-06 eta: 0:00:45 time: 0.3064 data_time: 0.0391 memory: 16049 loss: 0.0713 loss_ce: 0.0713 2023/03/03 14:47:42 - mmengine - INFO - Epoch(train) [192][ 3/15] lr: 1.0000e-06 eta: 0:00:44 time: 0.2974 data_time: 0.0391 memory: 16223 loss: 0.0721 loss_ce: 0.0721 2023/03/03 14:47:43 - mmengine - INFO - Epoch(train) [192][ 4/15] lr: 1.0000e-06 eta: 0:00:44 time: 0.3176 data_time: 0.0391 memory: 19336 loss: 0.0729 loss_ce: 0.0729 2023/03/03 14:47:43 - mmengine - INFO - Epoch(train) [192][ 5/15] lr: 1.0000e-06 eta: 0:00:44 time: 0.3199 data_time: 0.0392 memory: 20913 loss: 0.0752 loss_ce: 0.0752 2023/03/03 14:47:43 - mmengine - INFO - Epoch(train) [192][ 6/15] lr: 1.0000e-06 eta: 0:00:43 time: 0.3183 data_time: 0.0392 memory: 15631 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:47:43 - mmengine - INFO - Epoch(train) [192][ 7/15] lr: 1.0000e-06 eta: 0:00:43 time: 0.3160 data_time: 0.0391 memory: 16199 loss: 0.0811 loss_ce: 0.0811 2023/03/03 14:47:44 - mmengine - INFO - Epoch(train) [192][ 8/15] lr: 1.0000e-06 eta: 0:00:43 time: 0.3192 data_time: 0.0391 memory: 16933 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:47:44 - mmengine - INFO - Epoch(train) [192][ 9/15] lr: 1.0000e-06 eta: 0:00:42 time: 0.3234 data_time: 0.0390 memory: 16370 loss: 0.0727 loss_ce: 0.0727 2023/03/03 14:47:44 - mmengine - INFO - Epoch(train) [192][10/15] lr: 1.0000e-06 eta: 0:00:42 time: 0.3484 data_time: 0.0389 memory: 22646 loss: 0.0728 loss_ce: 0.0728 2023/03/03 14:47:45 - mmengine - INFO - Epoch(train) [192][11/15] lr: 1.0000e-06 eta: 0:00:42 time: 0.2996 data_time: 0.0020 memory: 16976 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:47:45 - mmengine - INFO - Epoch(train) [192][12/15] lr: 1.0000e-06 eta: 0:00:41 time: 0.2966 data_time: 0.0019 memory: 16656 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:47:45 - mmengine - INFO - Epoch(train) [192][13/15] lr: 1.0000e-06 eta: 0:00:41 time: 0.3228 data_time: 0.0019 memory: 17272 loss: 0.0787 loss_ce: 0.0787 2023/03/03 14:47:46 - mmengine - INFO - Epoch(train) [192][14/15] lr: 1.0000e-06 eta: 0:00:41 time: 0.3067 data_time: 0.0018 memory: 17788 loss: 0.0789 loss_ce: 0.0789 2023/03/03 14:47:46 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:47:46 - mmengine - INFO - Epoch(train) [192][15/15] lr: 1.0000e-06 eta: 0:00:40 time: 0.2865 data_time: 0.0017 memory: 6267 loss: 0.0856 loss_ce: 0.0856 2023/03/03 14:47:47 - mmengine - INFO - Epoch(train) [193][ 1/15] lr: 1.0000e-06 eta: 0:00:40 time: 0.3530 data_time: 0.0679 memory: 15781 loss: 0.0857 loss_ce: 0.0857 2023/03/03 14:47:47 - mmengine - INFO - Epoch(train) [193][ 2/15] lr: 1.0000e-06 eta: 0:00:40 time: 0.3553 data_time: 0.0680 memory: 18586 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:47:47 - mmengine - INFO - Epoch(train) [193][ 3/15] lr: 1.0000e-06 eta: 0:00:39 time: 0.3528 data_time: 0.0680 memory: 16976 loss: 0.0874 loss_ce: 0.0874 2023/03/03 14:47:48 - mmengine - INFO - Epoch(train) [193][ 4/15] lr: 1.0000e-06 eta: 0:00:39 time: 0.3761 data_time: 0.0680 memory: 14215 loss: 0.0887 loss_ce: 0.0887 2023/03/03 14:47:48 - mmengine - INFO - Epoch(train) [193][ 5/15] lr: 1.0000e-06 eta: 0:00:39 time: 0.3604 data_time: 0.0681 memory: 15631 loss: 0.0885 loss_ce: 0.0885 2023/03/03 14:47:48 - mmengine - INFO - Epoch(train) [193][ 6/15] lr: 1.0000e-06 eta: 0:00:38 time: 0.3509 data_time: 0.0681 memory: 16223 loss: 0.0868 loss_ce: 0.0868 2023/03/03 14:47:48 - mmengine - INFO - Epoch(train) [193][ 7/15] lr: 1.0000e-06 eta: 0:00:38 time: 0.3541 data_time: 0.0681 memory: 15825 loss: 0.0888 loss_ce: 0.0888 2023/03/03 14:47:49 - mmengine - INFO - Epoch(train) [193][ 8/15] lr: 1.0000e-06 eta: 0:00:38 time: 0.3261 data_time: 0.0681 memory: 16171 loss: 0.0884 loss_ce: 0.0884 2023/03/03 14:47:49 - mmengine - INFO - Epoch(train) [193][ 9/15] lr: 1.0000e-06 eta: 0:00:37 time: 0.3255 data_time: 0.0681 memory: 16897 loss: 0.0913 loss_ce: 0.0913 2023/03/03 14:47:49 - mmengine - INFO - Epoch(train) [193][10/15] lr: 1.0000e-06 eta: 0:00:37 time: 0.3522 data_time: 0.0681 memory: 28412 loss: 0.0797 loss_ce: 0.0797 2023/03/03 14:47:50 - mmengine - INFO - Epoch(train) [193][11/15] lr: 1.0000e-06 eta: 0:00:37 time: 0.3016 data_time: 0.0019 memory: 22012 loss: 0.0788 loss_ce: 0.0788 2023/03/03 14:47:50 - mmengine - INFO - Epoch(train) [193][12/15] lr: 1.0000e-06 eta: 0:00:36 time: 0.3133 data_time: 0.0018 memory: 17323 loss: 0.0836 loss_ce: 0.0836 2023/03/03 14:47:50 - mmengine - INFO - Epoch(train) [193][13/15] lr: 1.0000e-06 eta: 0:00:36 time: 0.3132 data_time: 0.0018 memory: 17272 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:47:51 - mmengine - INFO - Epoch(train) [193][14/15] lr: 1.0000e-06 eta: 0:00:36 time: 0.2898 data_time: 0.0017 memory: 18070 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:47:51 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:47:51 - mmengine - INFO - Epoch(train) [193][15/15] lr: 1.0000e-06 eta: 0:00:35 time: 0.2835 data_time: 0.0017 memory: 6371 loss: 0.0820 loss_ce: 0.0820 2023/03/03 14:47:52 - mmengine - INFO - Epoch(train) [194][ 1/15] lr: 1.0000e-06 eta: 0:00:35 time: 0.3668 data_time: 0.0676 memory: 24418 loss: 0.0813 loss_ce: 0.0813 2023/03/03 14:47:52 - mmengine - INFO - Epoch(train) [194][ 2/15] lr: 1.0000e-06 eta: 0:00:35 time: 0.3678 data_time: 0.0677 memory: 16508 loss: 0.0807 loss_ce: 0.0807 2023/03/03 14:47:52 - mmengine - INFO - Epoch(train) [194][ 3/15] lr: 1.0000e-06 eta: 0:00:34 time: 0.3739 data_time: 0.0678 memory: 16804 loss: 0.0798 loss_ce: 0.0798 2023/03/03 14:47:53 - mmengine - INFO - Epoch(train) [194][ 4/15] lr: 1.0000e-06 eta: 0:00:34 time: 0.3870 data_time: 0.0679 memory: 21039 loss: 0.0792 loss_ce: 0.0792 2023/03/03 14:47:53 - mmengine - INFO - Epoch(train) [194][ 5/15] lr: 1.0000e-06 eta: 0:00:34 time: 0.3903 data_time: 0.0680 memory: 18953 loss: 0.0786 loss_ce: 0.0786 2023/03/03 14:47:54 - mmengine - INFO - Epoch(train) [194][ 6/15] lr: 1.0000e-06 eta: 0:00:33 time: 0.3866 data_time: 0.0681 memory: 23595 loss: 0.0773 loss_ce: 0.0773 2023/03/03 14:47:54 - mmengine - INFO - Epoch(train) [194][ 7/15] lr: 1.0000e-06 eta: 0:00:33 time: 0.3708 data_time: 0.0681 memory: 18241 loss: 0.0742 loss_ce: 0.0742 2023/03/03 14:47:54 - mmengine - INFO - Epoch(train) [194][ 8/15] lr: 1.0000e-06 eta: 0:00:33 time: 0.3756 data_time: 0.0682 memory: 18090 loss: 0.0750 loss_ce: 0.0750 2023/03/03 14:47:54 - mmengine - INFO - Epoch(train) [194][ 9/15] lr: 1.0000e-06 eta: 0:00:32 time: 0.3818 data_time: 0.0683 memory: 18239 loss: 0.0650 loss_ce: 0.0650 2023/03/03 14:47:55 - mmengine - INFO - Epoch(train) [194][10/15] lr: 1.0000e-06 eta: 0:00:32 time: 0.3897 data_time: 0.0682 memory: 20682 loss: 0.0616 loss_ce: 0.0616 2023/03/03 14:47:55 - mmengine - INFO - Epoch(train) [194][11/15] lr: 1.0000e-06 eta: 0:00:31 time: 0.3211 data_time: 0.0024 memory: 17421 loss: 0.0618 loss_ce: 0.0618 2023/03/03 14:47:55 - mmengine - INFO - Epoch(train) [194][12/15] lr: 1.0000e-06 eta: 0:00:31 time: 0.3206 data_time: 0.0024 memory: 16849 loss: 0.0585 loss_ce: 0.0585 2023/03/03 14:47:56 - mmengine - INFO - Epoch(train) [194][13/15] lr: 1.0000e-06 eta: 0:00:31 time: 0.3159 data_time: 0.0022 memory: 17120 loss: 0.0592 loss_ce: 0.0592 2023/03/03 14:47:56 - mmengine - INFO - Epoch(train) [194][14/15] lr: 1.0000e-06 eta: 0:00:30 time: 0.2993 data_time: 0.0022 memory: 17272 loss: 0.0587 loss_ce: 0.0587 2023/03/03 14:47:56 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:47:56 - mmengine - INFO - Epoch(train) [194][15/15] lr: 1.0000e-06 eta: 0:00:30 time: 0.2662 data_time: 0.0021 memory: 5420 loss: 0.0662 loss_ce: 0.0662 2023/03/03 14:47:57 - mmengine - INFO - Epoch(train) [195][ 1/15] lr: 1.0000e-06 eta: 0:00:30 time: 0.3027 data_time: 0.0349 memory: 16507 loss: 0.0698 loss_ce: 0.0698 2023/03/03 14:47:57 - mmengine - INFO - Epoch(train) [195][ 2/15] lr: 1.0000e-06 eta: 0:00:29 time: 0.3118 data_time: 0.0442 memory: 17120 loss: 0.0703 loss_ce: 0.0703 2023/03/03 14:47:57 - mmengine - INFO - Epoch(train) [195][ 3/15] lr: 1.0000e-06 eta: 0:00:29 time: 0.3071 data_time: 0.0442 memory: 17272 loss: 0.0709 loss_ce: 0.0709 2023/03/03 14:47:57 - mmengine - INFO - Epoch(train) [195][ 4/15] lr: 1.0000e-06 eta: 0:00:29 time: 0.2984 data_time: 0.0442 memory: 16681 loss: 0.0755 loss_ce: 0.0755 2023/03/03 14:47:58 - mmengine - INFO - Epoch(train) [195][ 5/15] lr: 1.0000e-06 eta: 0:00:28 time: 0.3054 data_time: 0.0443 memory: 22596 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:47:58 - mmengine - INFO - Epoch(train) [195][ 6/15] lr: 1.0000e-06 eta: 0:00:28 time: 0.3071 data_time: 0.0445 memory: 16056 loss: 0.0753 loss_ce: 0.0753 2023/03/03 14:47:58 - mmengine - INFO - Epoch(train) [195][ 7/15] lr: 1.0000e-06 eta: 0:00:28 time: 0.3124 data_time: 0.0445 memory: 16484 loss: 0.0795 loss_ce: 0.0795 2023/03/03 14:47:59 - mmengine - INFO - Epoch(train) [195][ 8/15] lr: 1.0000e-06 eta: 0:00:27 time: 0.3138 data_time: 0.0445 memory: 17969 loss: 0.0783 loss_ce: 0.0783 2023/03/03 14:47:59 - mmengine - INFO - Epoch(train) [195][ 9/15] lr: 1.0000e-06 eta: 0:00:27 time: 0.3427 data_time: 0.0445 memory: 21759 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:47:59 - mmengine - INFO - Epoch(train) [195][10/15] lr: 1.0000e-06 eta: 0:00:27 time: 0.3462 data_time: 0.0446 memory: 16947 loss: 0.0740 loss_ce: 0.0740 2023/03/03 14:48:00 - mmengine - INFO - Epoch(train) [195][11/15] lr: 1.0000e-06 eta: 0:00:26 time: 0.3078 data_time: 0.0119 memory: 18624 loss: 0.0699 loss_ce: 0.0699 2023/03/03 14:48:00 - mmengine - INFO - Epoch(train) [195][12/15] lr: 1.0000e-06 eta: 0:00:26 time: 0.3149 data_time: 0.0031 memory: 17120 loss: 0.0693 loss_ce: 0.0693 2023/03/03 14:48:00 - mmengine - INFO - Epoch(train) [195][13/15] lr: 1.0000e-06 eta: 0:00:26 time: 0.3148 data_time: 0.0032 memory: 17272 loss: 0.0678 loss_ce: 0.0678 2023/03/03 14:48:01 - mmengine - INFO - Epoch(train) [195][14/15] lr: 1.0000e-06 eta: 0:00:25 time: 0.3262 data_time: 0.0032 memory: 16753 loss: 0.0642 loss_ce: 0.0642 2023/03/03 14:48:01 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:48:01 - mmengine - INFO - Epoch(train) [195][15/15] lr: 1.0000e-06 eta: 0:00:25 time: 0.3111 data_time: 0.0031 memory: 5135 loss: 0.0767 loss_ce: 0.0767 2023/03/03 14:48:02 - mmengine - INFO - Epoch(train) [196][ 1/15] lr: 1.0000e-06 eta: 0:00:25 time: 0.3638 data_time: 0.0516 memory: 16512 loss: 0.0771 loss_ce: 0.0771 2023/03/03 14:48:02 - mmengine - INFO - Epoch(train) [196][ 2/15] lr: 1.0000e-06 eta: 0:00:24 time: 0.3580 data_time: 0.0516 memory: 16056 loss: 0.0736 loss_ce: 0.0736 2023/03/03 14:48:02 - mmengine - INFO - Epoch(train) [196][ 3/15] lr: 1.0000e-06 eta: 0:00:24 time: 0.3657 data_time: 0.0516 memory: 17122 loss: 0.0727 loss_ce: 0.0727 2023/03/03 14:48:03 - mmengine - INFO - Epoch(train) [196][ 4/15] lr: 1.0000e-06 eta: 0:00:24 time: 0.3490 data_time: 0.0518 memory: 15124 loss: 0.0735 loss_ce: 0.0735 2023/03/03 14:48:03 - mmengine - INFO - Epoch(train) [196][ 5/15] lr: 1.0000e-06 eta: 0:00:23 time: 0.3508 data_time: 0.0518 memory: 11956 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:48:03 - mmengine - INFO - Epoch(train) [196][ 6/15] lr: 1.0000e-06 eta: 0:00:23 time: 0.3467 data_time: 0.0517 memory: 20375 loss: 0.0712 loss_ce: 0.0712 2023/03/03 14:48:03 - mmengine - INFO - Epoch(train) [196][ 7/15] lr: 1.0000e-06 eta: 0:00:23 time: 0.3324 data_time: 0.0512 memory: 18766 loss: 0.0694 loss_ce: 0.0694 2023/03/03 14:48:04 - mmengine - INFO - Epoch(train) [196][ 8/15] lr: 1.0000e-06 eta: 0:00:22 time: 0.3318 data_time: 0.0510 memory: 17276 loss: 0.0706 loss_ce: 0.0706 2023/03/03 14:48:04 - mmengine - INFO - Epoch(train) [196][ 9/15] lr: 1.0000e-06 eta: 0:00:22 time: 0.3266 data_time: 0.0509 memory: 18070 loss: 0.0722 loss_ce: 0.0722 2023/03/03 14:48:04 - mmengine - INFO - Epoch(train) [196][10/15] lr: 1.0000e-06 eta: 0:00:22 time: 0.3310 data_time: 0.0509 memory: 18070 loss: 0.0566 loss_ce: 0.0566 2023/03/03 14:48:05 - mmengine - INFO - Epoch(train) [196][11/15] lr: 1.0000e-06 eta: 0:00:21 time: 0.2851 data_time: 0.0023 memory: 16508 loss: 0.0617 loss_ce: 0.0617 2023/03/03 14:48:05 - mmengine - INFO - Epoch(train) [196][12/15] lr: 1.0000e-06 eta: 0:00:21 time: 0.2867 data_time: 0.0023 memory: 17954 loss: 0.0635 loss_ce: 0.0635 2023/03/03 14:48:05 - mmengine - INFO - Epoch(train) [196][13/15] lr: 1.0000e-06 eta: 0:00:21 time: 0.2808 data_time: 0.0022 memory: 14761 loss: 0.0668 loss_ce: 0.0668 2023/03/03 14:48:05 - mmengine - INFO - Epoch(train) [196][14/15] lr: 1.0000e-06 eta: 0:00:20 time: 0.2662 data_time: 0.0021 memory: 14509 loss: 0.0669 loss_ce: 0.0669 2023/03/03 14:48:06 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:48:06 - mmengine - INFO - Epoch(train) [196][15/15] lr: 1.0000e-06 eta: 0:00:20 time: 0.2909 data_time: 0.0020 memory: 6031 loss: 0.0677 loss_ce: 0.0677 2023/03/03 14:48:07 - mmengine - INFO - Epoch(train) [197][ 1/15] lr: 1.0000e-06 eta: 0:00:20 time: 0.3520 data_time: 0.0642 memory: 16860 loss: 0.0696 loss_ce: 0.0696 2023/03/03 14:48:07 - mmengine - INFO - Epoch(train) [197][ 2/15] lr: 1.0000e-06 eta: 0:00:19 time: 0.3521 data_time: 0.0643 memory: 17272 loss: 0.0723 loss_ce: 0.0723 2023/03/03 14:48:07 - mmengine - INFO - Epoch(train) [197][ 3/15] lr: 1.0000e-06 eta: 0:00:19 time: 0.3556 data_time: 0.0644 memory: 16840 loss: 0.0757 loss_ce: 0.0757 2023/03/03 14:48:08 - mmengine - INFO - Epoch(train) [197][ 4/15] lr: 1.0000e-06 eta: 0:00:19 time: 0.3586 data_time: 0.0649 memory: 15457 loss: 0.0730 loss_ce: 0.0730 2023/03/03 14:48:08 - mmengine - INFO - Epoch(train) [197][ 5/15] lr: 1.0000e-06 eta: 0:00:18 time: 0.3714 data_time: 0.0650 memory: 17382 loss: 0.0746 loss_ce: 0.0746 2023/03/03 14:48:08 - mmengine - INFO - Epoch(train) [197][ 6/15] lr: 1.0000e-06 eta: 0:00:18 time: 0.3462 data_time: 0.0650 memory: 14906 loss: 0.0732 loss_ce: 0.0732 2023/03/03 14:48:08 - mmengine - INFO - Epoch(train) [197][ 7/15] lr: 1.0000e-06 eta: 0:00:18 time: 0.3414 data_time: 0.0649 memory: 13674 loss: 0.0720 loss_ce: 0.0720 2023/03/03 14:48:09 - mmengine - INFO - Epoch(train) [197][ 8/15] lr: 1.0000e-06 eta: 0:00:17 time: 0.3429 data_time: 0.0649 memory: 21861 loss: 0.0674 loss_ce: 0.0674 2023/03/03 14:48:09 - mmengine - INFO - Epoch(train) [197][ 9/15] lr: 1.0000e-06 eta: 0:00:17 time: 0.3479 data_time: 0.0649 memory: 16223 loss: 0.0688 loss_ce: 0.0688 2023/03/03 14:48:09 - mmengine - INFO - Epoch(train) [197][10/15] lr: 1.0000e-06 eta: 0:00:17 time: 0.3480 data_time: 0.0649 memory: 16056 loss: 0.0670 loss_ce: 0.0670 2023/03/03 14:48:10 - mmengine - INFO - Epoch(train) [197][11/15] lr: 1.0000e-06 eta: 0:00:16 time: 0.2893 data_time: 0.0027 memory: 24895 loss: 0.0655 loss_ce: 0.0655 2023/03/03 14:48:10 - mmengine - INFO - Epoch(train) [197][12/15] lr: 1.0000e-06 eta: 0:00:16 time: 0.2930 data_time: 0.0026 memory: 14589 loss: 0.0653 loss_ce: 0.0653 2023/03/03 14:48:10 - mmengine - INFO - Epoch(train) [197][13/15] lr: 1.0000e-06 eta: 0:00:15 time: 0.3060 data_time: 0.0026 memory: 21587 loss: 0.0624 loss_ce: 0.0624 2023/03/03 14:48:10 - mmengine - INFO - Epoch(train) [197][14/15] lr: 1.0000e-06 eta: 0:00:15 time: 0.2983 data_time: 0.0021 memory: 14041 loss: 0.0674 loss_ce: 0.0674 2023/03/03 14:48:11 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:48:11 - mmengine - INFO - Epoch(train) [197][15/15] lr: 1.0000e-06 eta: 0:00:15 time: 0.2799 data_time: 0.0019 memory: 4776 loss: 0.0709 loss_ce: 0.0709 2023/03/03 14:48:12 - mmengine - INFO - Epoch(train) [198][ 1/15] lr: 1.0000e-06 eta: 0:00:14 time: 0.3519 data_time: 0.0665 memory: 17572 loss: 0.0664 loss_ce: 0.0664 2023/03/03 14:48:12 - mmengine - INFO - Epoch(train) [198][ 2/15] lr: 1.0000e-06 eta: 0:00:14 time: 0.3529 data_time: 0.0666 memory: 16976 loss: 0.0715 loss_ce: 0.0715 2023/03/03 14:48:12 - mmengine - INFO - Epoch(train) [198][ 3/15] lr: 1.0000e-06 eta: 0:00:14 time: 0.3438 data_time: 0.0667 memory: 17272 loss: 0.0749 loss_ce: 0.0749 2023/03/03 14:48:12 - mmengine - INFO - Epoch(train) [198][ 4/15] lr: 1.0000e-06 eta: 0:00:13 time: 0.3480 data_time: 0.0669 memory: 18239 loss: 0.0710 loss_ce: 0.0710 2023/03/03 14:48:13 - mmengine - INFO - Epoch(train) [198][ 5/15] lr: 1.0000e-06 eta: 0:00:13 time: 0.3335 data_time: 0.0670 memory: 19046 loss: 0.0719 loss_ce: 0.0719 2023/03/03 14:48:13 - mmengine - INFO - Epoch(train) [198][ 6/15] lr: 1.0000e-06 eta: 0:00:13 time: 0.3252 data_time: 0.0670 memory: 15631 loss: 0.0775 loss_ce: 0.0775 2023/03/03 14:48:13 - mmengine - INFO - Epoch(train) [198][ 7/15] lr: 1.0000e-06 eta: 0:00:12 time: 0.3279 data_time: 0.0671 memory: 15175 loss: 0.0769 loss_ce: 0.0769 2023/03/03 14:48:13 - mmengine - INFO - Epoch(train) [198][ 8/15] lr: 1.0000e-06 eta: 0:00:12 time: 0.3138 data_time: 0.0671 memory: 16056 loss: 0.0772 loss_ce: 0.0772 2023/03/03 14:48:14 - mmengine - INFO - Epoch(train) [198][ 9/15] lr: 1.0000e-06 eta: 0:00:12 time: 0.3134 data_time: 0.0672 memory: 17224 loss: 0.0738 loss_ce: 0.0738 2023/03/03 14:48:14 - mmengine - INFO - Epoch(train) [198][10/15] lr: 1.0000e-06 eta: 0:00:11 time: 0.3175 data_time: 0.0672 memory: 16663 loss: 0.0690 loss_ce: 0.0690 2023/03/03 14:48:14 - mmengine - INFO - Epoch(train) [198][11/15] lr: 1.0000e-06 eta: 0:00:11 time: 0.2478 data_time: 0.0027 memory: 17892 loss: 0.0683 loss_ce: 0.0683 2023/03/03 14:48:14 - mmengine - INFO - Epoch(train) [198][12/15] lr: 1.0000e-06 eta: 0:00:11 time: 0.2453 data_time: 0.0025 memory: 14322 loss: 0.0655 loss_ce: 0.0655 2023/03/03 14:48:15 - mmengine - INFO - Epoch(train) [198][13/15] lr: 1.0000e-06 eta: 0:00:10 time: 0.2632 data_time: 0.0024 memory: 18239 loss: 0.0622 loss_ce: 0.0622 2023/03/03 14:48:15 - mmengine - INFO - Epoch(train) [198][14/15] lr: 1.0000e-06 eta: 0:00:10 time: 0.2568 data_time: 0.0023 memory: 18069 loss: 0.0613 loss_ce: 0.0613 2023/03/03 14:48:15 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:48:15 - mmengine - INFO - Epoch(train) [198][15/15] lr: 1.0000e-06 eta: 0:00:10 time: 0.2472 data_time: 0.0024 memory: 3584 loss: 0.0662 loss_ce: 0.0662 2023/03/03 14:48:16 - mmengine - INFO - Epoch(train) [199][ 1/15] lr: 1.0000e-06 eta: 0:00:09 time: 0.3006 data_time: 0.0559 memory: 17572 loss: 0.0608 loss_ce: 0.0608 2023/03/03 14:48:16 - mmengine - INFO - Epoch(train) [199][ 2/15] lr: 1.0000e-06 eta: 0:00:09 time: 0.3104 data_time: 0.0559 memory: 18070 loss: 0.0607 loss_ce: 0.0607 2023/03/03 14:48:16 - mmengine - INFO - Epoch(train) [199][ 3/15] lr: 1.0000e-06 eta: 0:00:09 time: 0.3100 data_time: 0.0559 memory: 19340 loss: 0.0568 loss_ce: 0.0568 2023/03/03 14:48:17 - mmengine - INFO - Epoch(train) [199][ 4/15] lr: 1.0000e-06 eta: 0:00:08 time: 0.3084 data_time: 0.0559 memory: 17029 loss: 0.0547 loss_ce: 0.0547 2023/03/03 14:48:17 - mmengine - INFO - Epoch(train) [199][ 5/15] lr: 1.0000e-06 eta: 0:00:08 time: 0.3085 data_time: 0.0560 memory: 14063 loss: 0.0564 loss_ce: 0.0564 2023/03/03 14:48:17 - mmengine - INFO - Epoch(train) [199][ 6/15] lr: 1.0000e-06 eta: 0:00:08 time: 0.3171 data_time: 0.0560 memory: 17572 loss: 0.0587 loss_ce: 0.0587 2023/03/03 14:48:18 - mmengine - INFO - Epoch(train) [199][ 7/15] lr: 1.0000e-06 eta: 0:00:07 time: 0.3325 data_time: 0.0561 memory: 19815 loss: 0.0557 loss_ce: 0.0557 2023/03/03 14:48:18 - mmengine - INFO - Epoch(train) [199][ 8/15] lr: 1.0000e-06 eta: 0:00:07 time: 0.3167 data_time: 0.0562 memory: 17120 loss: 0.0584 loss_ce: 0.0584 2023/03/03 14:48:18 - mmengine - INFO - Epoch(train) [199][ 9/15] lr: 1.0000e-06 eta: 0:00:07 time: 0.3163 data_time: 0.0562 memory: 17120 loss: 0.0605 loss_ce: 0.0605 2023/03/03 14:48:18 - mmengine - INFO - Epoch(train) [199][10/15] lr: 1.0000e-06 eta: 0:00:06 time: 0.3281 data_time: 0.0560 memory: 15207 loss: 0.0547 loss_ce: 0.0547 2023/03/03 14:48:19 - mmengine - INFO - Epoch(train) [199][11/15] lr: 1.0000e-06 eta: 0:00:06 time: 0.3080 data_time: 0.0024 memory: 34415 loss: 0.0555 loss_ce: 0.0555 2023/03/03 14:48:19 - mmengine - INFO - Epoch(train) [199][12/15] lr: 1.0000e-06 eta: 0:00:06 time: 0.2891 data_time: 0.0023 memory: 18070 loss: 0.0551 loss_ce: 0.0551 2023/03/03 14:48:19 - mmengine - INFO - Epoch(train) [199][13/15] lr: 1.0000e-06 eta: 0:00:05 time: 0.2900 data_time: 0.0022 memory: 16897 loss: 0.0595 loss_ce: 0.0595 2023/03/03 14:48:20 - mmengine - INFO - Epoch(train) [199][14/15] lr: 1.0000e-06 eta: 0:00:05 time: 0.2935 data_time: 0.0023 memory: 11826 loss: 0.0631 loss_ce: 0.0631 2023/03/03 14:48:20 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:48:20 - mmengine - INFO - Epoch(train) [199][15/15] lr: 1.0000e-06 eta: 0:00:05 time: 0.2847 data_time: 0.0022 memory: 5705 loss: 0.0687 loss_ce: 0.0687 2023/03/03 14:48:21 - mmengine - INFO - Epoch(train) [200][ 1/15] lr: 1.0000e-06 eta: 0:00:04 time: 0.3524 data_time: 0.0821 memory: 16191 loss: 0.0717 loss_ce: 0.0717 2023/03/03 14:48:21 - mmengine - INFO - Epoch(train) [200][ 2/15] lr: 1.0000e-06 eta: 0:00:04 time: 0.3492 data_time: 0.0821 memory: 20850 loss: 0.0746 loss_ce: 0.0746 2023/03/03 14:48:22 - mmengine - INFO - Epoch(train) [200][ 3/15] lr: 1.0000e-06 eta: 0:00:04 time: 0.3789 data_time: 0.0820 memory: 17446 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:48:22 - mmengine - INFO - Epoch(train) [200][ 4/15] lr: 1.0000e-06 eta: 0:00:03 time: 0.3792 data_time: 0.0820 memory: 18070 loss: 0.0712 loss_ce: 0.0712 2023/03/03 14:48:22 - mmengine - INFO - Epoch(train) [200][ 5/15] lr: 1.0000e-06 eta: 0:00:03 time: 0.3817 data_time: 0.0820 memory: 24261 loss: 0.0695 loss_ce: 0.0695 2023/03/03 14:48:22 - mmengine - INFO - Epoch(train) [200][ 6/15] lr: 1.0000e-06 eta: 0:00:03 time: 0.3574 data_time: 0.0820 memory: 22902 loss: 0.0734 loss_ce: 0.0734 2023/03/03 14:48:23 - mmengine - INFO - Epoch(train) [200][ 7/15] lr: 1.0000e-06 eta: 0:00:02 time: 0.3664 data_time: 0.0820 memory: 17572 loss: 0.0742 loss_ce: 0.0742 2023/03/03 14:48:23 - mmengine - INFO - Epoch(train) [200][ 8/15] lr: 1.0000e-06 eta: 0:00:02 time: 0.3634 data_time: 0.0820 memory: 17248 loss: 0.0731 loss_ce: 0.0731 2023/03/03 14:48:23 - mmengine - INFO - Epoch(train) [200][ 9/15] lr: 1.0000e-06 eta: 0:00:02 time: 0.3577 data_time: 0.0818 memory: 14722 loss: 0.0738 loss_ce: 0.0738 2023/03/03 14:48:23 - mmengine - INFO - Epoch(train) [200][10/15] lr: 1.0000e-06 eta: 0:00:01 time: 0.3676 data_time: 0.0818 memory: 17421 loss: 0.0677 loss_ce: 0.0677 2023/03/03 14:48:24 - mmengine - INFO - Epoch(train) [200][11/15] lr: 1.0000e-06 eta: 0:00:01 time: 0.3054 data_time: 0.0018 memory: 17572 loss: 0.0647 loss_ce: 0.0647 2023/03/03 14:48:24 - mmengine - INFO - Epoch(train) [200][12/15] lr: 1.0000e-06 eta: 0:00:01 time: 0.3030 data_time: 0.0018 memory: 14791 loss: 0.0643 loss_ce: 0.0643 2023/03/03 14:48:25 - mmengine - INFO - Epoch(train) [200][13/15] lr: 1.0000e-06 eta: 0:00:00 time: 0.2926 data_time: 0.0017 memory: 17878 loss: 0.0649 loss_ce: 0.0649 2023/03/03 14:48:25 - mmengine - INFO - Epoch(train) [200][14/15] lr: 1.0000e-06 eta: 0:00:00 time: 0.2950 data_time: 0.0018 memory: 16199 loss: 0.0736 loss_ce: 0.0736 2023/03/03 14:48:25 - mmengine - INFO - Exp name: spts_resnet50_350e_icdar2013_20230303_140316 2023/03/03 14:48:25 - mmengine - INFO - Epoch(train) [200][15/15] lr: 1.0000e-06 eta: 0:00:00 time: 0.2732 data_time: 0.0018 memory: 3969 loss: 0.0845 loss_ce: 0.0845 2023/03/03 14:48:25 - mmengine - INFO - Saving checkpoint at 200 epochs 2023/03/03 14:48:27 - mmengine - WARNING - `save_param_scheduler` is True but `self.param_schedulers` is None, so skip saving parameter schedulers 2023/03/03 14:48:29 - mmengine - INFO - Epoch(val) [200][ 1/59] eta: 0:01:31 time: 1.0777 data_time: 0.0038 memory: 981 2023/03/03 14:48:30 - mmengine - INFO - Epoch(val) [200][ 2/59] eta: 0:01:09 time: 0.9945 data_time: 0.0039 memory: 981 2023/03/03 14:48:31 - mmengine - INFO - Epoch(val) [200][ 3/59] eta: 0:01:10 time: 1.0135 data_time: 0.0039 memory: 1003 2023/03/03 14:48:32 - mmengine - INFO - Epoch(val) [200][ 4/59] eta: 0:00:56 time: 0.9976 data_time: 0.0039 memory: 981 2023/03/03 14:48:35 - mmengine - INFO - Epoch(val) [200][ 5/59] eta: 0:01:18 time: 1.2395 data_time: 0.0039 memory: 1016 2023/03/03 14:48:38 - mmengine - INFO - Epoch(val) [200][ 6/59] eta: 0:01:27 time: 1.4328 data_time: 0.0039 memory: 981 2023/03/03 14:48:38 - mmengine - INFO - Epoch(val) [200][ 7/59] eta: 0:01:14 time: 1.3831 data_time: 0.0040 memory: 1043 2023/03/03 14:48:38 - mmengine - INFO - Epoch(val) [200][ 8/59] eta: 0:01:08 time: 1.2231 data_time: 0.0039 memory: 1016 2023/03/03 14:48:39 - mmengine - INFO - Epoch(val) [200][ 9/59] eta: 0:01:05 time: 1.2067 data_time: 0.0039 memory: 981 2023/03/03 14:48:40 - mmengine - INFO - Epoch(val) [200][10/59] eta: 0:01:00 time: 1.2403 data_time: 0.0039 memory: 981 2023/03/03 14:48:40 - mmengine - INFO - Epoch(val) [200][11/59] eta: 0:00:55 time: 1.1171 data_time: 0.0011 memory: 981 2023/03/03 14:48:44 - mmengine - INFO - Epoch(val) [200][12/59] eta: 0:01:02 time: 1.3606 data_time: 0.0010 memory: 1016 2023/03/03 14:48:46 - mmengine - INFO - Epoch(val) [200][13/59] eta: 0:01:04 time: 1.4499 data_time: 0.0011 memory: 981 2023/03/03 14:48:47 - mmengine - INFO - Epoch(val) [200][14/59] eta: 0:01:02 time: 1.5169 data_time: 0.0010 memory: 890 2023/03/03 14:48:47 - mmengine - INFO - Epoch(val) [200][15/59] eta: 0:00:56 time: 1.2102 data_time: 0.0010 memory: 981 2023/03/03 14:48:48 - mmengine - INFO - Epoch(val) [200][16/59] eta: 0:00:53 time: 0.9996 data_time: 0.0010 memory: 981 2023/03/03 14:48:48 - mmengine - INFO - Epoch(val) [200][17/59] eta: 0:00:49 time: 1.0156 data_time: 0.0009 memory: 981 2023/03/03 14:48:48 - mmengine - INFO - Epoch(val) [200][18/59] eta: 0:00:46 time: 0.9821 data_time: 0.0009 memory: 981 2023/03/03 14:48:49 - mmengine - INFO - Epoch(val) [200][19/59] eta: 0:00:45 time: 0.9816 data_time: 0.0009 memory: 981 2023/03/03 14:48:50 - mmengine - INFO - Epoch(val) [200][20/59] eta: 0:00:42 time: 0.9484 data_time: 0.0009 memory: 981 2023/03/03 14:48:50 - mmengine - INFO - Epoch(val) [200][21/59] eta: 0:00:40 time: 0.9644 data_time: 0.0009 memory: 981 2023/03/03 14:48:50 - mmengine - INFO - Epoch(val) [200][22/59] eta: 0:00:37 time: 0.6536 data_time: 0.0009 memory: 981 2023/03/03 14:48:51 - mmengine - INFO - Epoch(val) [200][23/59] eta: 0:00:36 time: 0.4946 data_time: 0.0008 memory: 981 2023/03/03 14:48:51 - mmengine - INFO - Epoch(val) [200][24/59] eta: 0:00:34 time: 0.4270 data_time: 0.0008 memory: 962 2023/03/03 14:48:52 - mmengine - INFO - Epoch(val) [200][25/59] eta: 0:00:32 time: 0.4589 data_time: 0.0008 memory: 981 2023/03/03 14:48:52 - mmengine - INFO - Epoch(val) [200][26/59] eta: 0:00:30 time: 0.4421 data_time: 0.0008 memory: 981 2023/03/03 14:48:52 - mmengine - INFO - Epoch(val) [200][27/59] eta: 0:00:29 time: 0.4423 data_time: 0.0008 memory: 981 2023/03/03 14:48:53 - mmengine - INFO - Epoch(val) [200][28/59] eta: 0:00:27 time: 0.4423 data_time: 0.0008 memory: 981 2023/03/03 14:48:54 - mmengine - INFO - Epoch(val) [200][29/59] eta: 0:00:27 time: 0.4780 data_time: 0.0008 memory: 981 2023/03/03 14:48:55 - mmengine - INFO - Epoch(val) [200][30/59] eta: 0:00:26 time: 0.5273 data_time: 0.0007 memory: 999 2023/03/03 14:48:55 - mmengine - INFO - Epoch(val) [200][31/59] eta: 0:00:25 time: 0.5443 data_time: 0.0007 memory: 981 2023/03/03 14:48:57 - mmengine - INFO - Epoch(val) [200][32/59] eta: 0:00:24 time: 0.6452 data_time: 0.0007 memory: 981 2023/03/03 14:48:57 - mmengine - INFO - Epoch(val) [200][33/59] eta: 0:00:22 time: 0.5802 data_time: 0.0008 memory: 981 2023/03/03 14:48:57 - mmengine - INFO - Epoch(val) [200][34/59] eta: 0:00:21 time: 0.5639 data_time: 0.0007 memory: 981 2023/03/03 14:48:57 - mmengine - INFO - Epoch(val) [200][35/59] eta: 0:00:20 time: 0.5472 data_time: 0.0007 memory: 981 2023/03/03 14:48:58 - mmengine - INFO - Epoch(val) [200][36/59] eta: 0:00:19 time: 0.5640 data_time: 0.0007 memory: 981 2023/03/03 14:48:58 - mmengine - INFO - Epoch(val) [200][37/59] eta: 0:00:17 time: 0.5474 data_time: 0.0008 memory: 981 2023/03/03 14:48:58 - mmengine - INFO - Epoch(val) [200][38/59] eta: 0:00:16 time: 0.5808 data_time: 0.0008 memory: 981 2023/03/03 14:48:59 - mmengine - INFO - Epoch(val) [200][39/59] eta: 0:00:16 time: 0.4945 data_time: 0.0008 memory: 987 2023/03/03 14:49:00 - mmengine - INFO - Epoch(val) [200][40/59] eta: 0:00:15 time: 0.4958 data_time: 0.0008 memory: 981 2023/03/03 14:49:01 - mmengine - INFO - Epoch(val) [200][41/59] eta: 0:00:14 time: 0.5468 data_time: 0.0008 memory: 986 2023/03/03 14:49:02 - mmengine - INFO - Epoch(val) [200][42/59] eta: 0:00:13 time: 0.4930 data_time: 0.0008 memory: 981 2023/03/03 14:49:02 - mmengine - INFO - Epoch(val) [200][43/59] eta: 0:00:12 time: 0.5728 data_time: 0.0008 memory: 976 2023/03/03 14:49:03 - mmengine - INFO - Epoch(val) [200][44/59] eta: 0:00:12 time: 0.6055 data_time: 0.0008 memory: 1003 2023/03/03 14:49:05 - mmengine - INFO - Epoch(val) [200][45/59] eta: 0:00:11 time: 0.8145 data_time: 0.0008 memory: 981 2023/03/03 14:49:06 - mmengine - INFO - Epoch(val) [200][46/59] eta: 0:00:10 time: 0.8478 data_time: 0.0009 memory: 981 2023/03/03 14:49:07 - mmengine - INFO - Epoch(val) [200][47/59] eta: 0:00:09 time: 0.8804 data_time: 0.0009 memory: 936 2023/03/03 14:49:07 - mmengine - INFO - Epoch(val) [200][48/59] eta: 0:00:09 time: 0.8631 data_time: 0.0009 memory: 1000 2023/03/03 14:49:08 - mmengine - INFO - Epoch(val) [200][49/59] eta: 0:00:08 time: 0.9127 data_time: 0.0009 memory: 981 2023/03/03 14:49:09 - mmengine - INFO - Epoch(val) [200][50/59] eta: 0:00:07 time: 0.9114 data_time: 0.0009 memory: 987 2023/03/03 14:49:11 - mmengine - INFO - Epoch(val) [200][51/59] eta: 0:00:06 time: 0.9624 data_time: 0.0009 memory: 981 2023/03/03 14:49:12 - mmengine - INFO - Epoch(val) [200][52/59] eta: 0:00:05 time: 1.0143 data_time: 0.0009 memory: 981 2023/03/03 14:49:12 - mmengine - INFO - Epoch(val) [200][53/59] eta: 0:00:05 time: 0.9817 data_time: 0.0009 memory: 962 2023/03/03 14:49:13 - mmengine - INFO - Epoch(val) [200][54/59] eta: 0:00:04 time: 0.9976 data_time: 0.0009 memory: 981 2023/03/03 14:49:14 - mmengine - INFO - Epoch(val) [200][55/59] eta: 0:00:03 time: 0.8374 data_time: 0.0009 memory: 981 2023/03/03 14:49:14 - mmengine - INFO - Epoch(val) [200][56/59] eta: 0:00:02 time: 0.8199 data_time: 0.0008 memory: 981 2023/03/03 14:49:16 - mmengine - INFO - Epoch(val) [200][57/59] eta: 0:00:01 time: 0.9948 data_time: 0.0008 memory: 981 2023/03/03 14:49:18 - mmengine - INFO - Epoch(val) [200][58/59] eta: 0:00:00 time: 1.0602 data_time: 0.0008 memory: 1016 2023/03/03 14:49:18 - mmengine - INFO - Epoch(val) [200][59/59] eta: 0:00:00 time: 0.9946 data_time: 0.0008 memory: 981 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.80, recall: 0.8183, precision: 0.8437, hmean: 0.8308 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.81, recall: 0.8183, precision: 0.8437, hmean: 0.8308 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.82, recall: 0.8183, precision: 0.8477, hmean: 0.8327 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.83, recall: 0.8174, precision: 0.8491, hmean: 0.8329 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.84, recall: 0.8164, precision: 0.8506, hmean: 0.8332 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.85, recall: 0.8146, precision: 0.8528, hmean: 0.8333 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.86, recall: 0.8119, precision: 0.8556, hmean: 0.8332 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.87, recall: 0.8110, precision: 0.8571, hmean: 0.8334 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.88, recall: 0.8091, precision: 0.8594, hmean: 0.8335 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.89, recall: 0.8055, precision: 0.8630, hmean: 0.8333 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.90, recall: 0.8027, precision: 0.8643, hmean: 0.8324 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.91, recall: 0.7991, precision: 0.8638, hmean: 0.8302 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.92, recall: 0.7918, precision: 0.8670, hmean: 0.8277 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.93, recall: 0.7836, precision: 0.8684, hmean: 0.8238 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.94, recall: 0.7772, precision: 0.8701, hmean: 0.8210 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.95, recall: 0.7689, precision: 0.8725, hmean: 0.8175 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.96, recall: 0.7607, precision: 0.8732, hmean: 0.8131 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.97, recall: 0.7543, precision: 0.8797, hmean: 0.8122 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.98, recall: 0.7397, precision: 0.8852, hmean: 0.8060 2023/03/03 14:49:46 - mmengine - INFO - text score threshold: 0.99, recall: 0.7224, precision: 0.8918, hmean: 0.7982 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.80, recall: 0.8283, precision: 0.9125, hmean: 0.8684 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.81, recall: 0.8283, precision: 0.9125, hmean: 0.8684 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.82, recall: 0.8283, precision: 0.9134, hmean: 0.8688 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.83, recall: 0.8274, precision: 0.9152, hmean: 0.8691 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.84, recall: 0.8265, precision: 0.9151, hmean: 0.8685 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.85, recall: 0.8247, precision: 0.9168, hmean: 0.8683 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.86, recall: 0.8219, precision: 0.9174, hmean: 0.8671 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.87, recall: 0.8210, precision: 0.9183, hmean: 0.8669 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.88, recall: 0.8183, precision: 0.9190, hmean: 0.8657 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.89, recall: 0.8146, precision: 0.9186, hmean: 0.8635 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.90, recall: 0.8119, precision: 0.9203, hmean: 0.8627 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.91, recall: 0.8082, precision: 0.9200, hmean: 0.8605 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.92, recall: 0.7982, precision: 0.9200, hmean: 0.8548 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.93, recall: 0.7890, precision: 0.9211, hmean: 0.8500 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.94, recall: 0.7799, precision: 0.9203, hmean: 0.8443 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.95, recall: 0.7708, precision: 0.9194, hmean: 0.8385 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.96, recall: 0.7626, precision: 0.9196, hmean: 0.8337 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.97, recall: 0.7543, precision: 0.9229, hmean: 0.8302 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.98, recall: 0.7397, precision: 0.9278, hmean: 0.8232 2023/03/03 14:49:49 - mmengine - INFO - text score threshold: 0.99, recall: 0.7215, precision: 0.9305, hmean: 0.8128 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.80, recall: 0.7498, precision: 0.9569, hmean: 0.8408 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.81, recall: 0.7498, precision: 0.9569, hmean: 0.8408 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.82, recall: 0.7498, precision: 0.9580, hmean: 0.8412 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.83, recall: 0.7489, precision: 0.9591, hmean: 0.8410 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.84, recall: 0.7479, precision: 0.9590, hmean: 0.8404 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.85, recall: 0.7470, precision: 0.9590, hmean: 0.8398 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.86, recall: 0.7443, precision: 0.9588, hmean: 0.8380 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.87, recall: 0.7443, precision: 0.9588, hmean: 0.8380 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.88, recall: 0.7406, precision: 0.9586, hmean: 0.8357 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.89, recall: 0.7379, precision: 0.9585, hmean: 0.8338 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.90, recall: 0.7352, precision: 0.9595, hmean: 0.8325 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.91, recall: 0.7315, precision: 0.9593, hmean: 0.8301 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.92, recall: 0.7224, precision: 0.9588, hmean: 0.8240 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.93, recall: 0.7160, precision: 0.9631, hmean: 0.8214 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.94, recall: 0.7068, precision: 0.9627, hmean: 0.8152 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.95, recall: 0.6977, precision: 0.9622, hmean: 0.8089 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.96, recall: 0.6913, precision: 0.9631, hmean: 0.8049 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.97, recall: 0.6840, precision: 0.9640, hmean: 0.8002 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.98, recall: 0.6703, precision: 0.9645, hmean: 0.7909 2023/03/03 14:49:52 - mmengine - INFO - text score threshold: 0.99, recall: 0.6521, precision: 0.9662, hmean: 0.7786 2023/03/03 14:49:52 - mmengine - INFO - Epoch(val) [200][59/59] generic/precision: 0.8594 generic/recall: 0.8091 generic/hmean: 0.8335 weak/precision: 0.9152 weak/recall: 0.8274 weak/hmean: 0.8691 strong/precision: 0.9580 strong/recall: 0.7498 strong/hmean: 0.8412