2022/08/29 23:01:08 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0] CUDA available: True numpy_random_seed: 387890681 GPU 0: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/cache/share/cuda-11.1 NVCC: Cuda compilation tools, release 11.1, V11.1.74 GCC: gcc (GCC) 5.4.0 PyTorch: 1.9.0+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.1 - 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_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 - CuDNN 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu111 OpenCV: 4.5.4 MMEngine: 0.0.1 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: slurm Distributed training: True GPU number: 1 ------------------------------------------------------------ 2022/08/29 23:01:09 - mmengine - INFO - Config: model = dict( type='DBNet', backbone=dict( type='mmdet.ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=-1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=False, style='pytorch', dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), stage_with_dcn=(False, True, True, True)), neck=dict( type='FPNC', in_channels=[256, 512, 1024, 2048], lateral_channels=256, asf_cfg=dict(attention_type='ScaleChannelSpatial')), det_head=dict( type='DBHead', in_channels=256, module_loss=dict(type='DBModuleLoss'), postprocessor=dict( type='DBPostprocessor', text_repr_type='quad', epsilon_ratio=0.002)), data_preprocessor=dict( type='TextDetDataPreprocessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True, pad_size_divisor=32)) train_pipeline = [ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), dict( type='LoadOCRAnnotations', with_bbox=True, with_polygon=True, with_label=True), dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.12549019607843137, saturation=0.5), dict( type='ImgAugWrapper', args=[['Fliplr', 0.5], { 'cls': 'Affine', 'rotate': [-10, 10] }, ['Resize', [0.5, 3.0]]]), dict(type='RandomCrop', min_side_ratio=0.1), dict(type='Resize', scale=(640, 640), keep_ratio=True), dict(type='Pad', size=(640, 640)), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape')) ] test_pipeline = [ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), dict(type='Resize', scale=(4068, 1024), keep_ratio=True), dict( type='LoadOCRAnnotations', with_polygon=True, with_bbox=True, with_label=True), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor', 'instances')) ] default_scope = 'mmocr' env_cfg = dict( cudnn_benchmark=True, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) randomness = dict(seed=None) default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=5), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict( type='CheckpointHook', interval=20, out_dir=''), 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 = '~/dbnet_pretrain/dbnetpp_r50dcnv2_fpnc_100k_iter_synthtext-20220502-db297554.pth' resume = False val_evaluator = dict(type='HmeanIOUMetric') test_evaluator = dict(type='HmeanIOUMetric') vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='TextDetLocalVisualizer', name='visualizer', vis_backends=[dict(type='LocalVisBackend')]) ic15_det_data_root = 'data/det/icdar2015' ic15_det_train = dict( type='OCRDataset', data_root='data/det/icdar2015', ann_file='instances_training.json', data_prefix=dict(img_path='imgs/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None) ic15_det_test = dict( type='OCRDataset', data_root='data/det/icdar2015', ann_file='instances_test.json', data_prefix=dict(img_path='imgs/'), test_mode=True, pipeline=None) optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='SGD', lr=0.007, momentum=0.9, weight_decay=0.0001)) train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1200, val_interval=20) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [dict(type='PolyLR', power=0.9, eta_min=1e-07, end=1200)] train_list = [ dict( type='OCRDataset', data_root='data/det/icdar2015', ann_file='instances_training.json', data_prefix=dict(img_path='imgs/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None) ] test_list = [ dict( type='OCRDataset', data_root='data/det/icdar2015', ann_file='instances_test.json', data_prefix=dict(img_path='imgs/'), test_mode=True, pipeline=None) ] train_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='ConcatDataset', datasets=[ dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/det/icdar2015', ann_file='instances_training.json', data_prefix=dict(img_path='imgs/'), filter_cfg=dict(filter_empty_gt=True, min_size=32), pipeline=None) ], pipeline=[ dict( type='LoadImageFromFile', color_type='color_ignore_orientation', file_client_args=dict(backend='petrel')), dict( type='LoadOCRAnnotations', with_bbox=True, with_polygon=True, with_label=True), dict( type='TorchVisionWrapper', op='ColorJitter', brightness=0.12549019607843137, saturation=0.5), dict( type='ImgAugWrapper', args=[['Fliplr', 0.5], { 'cls': 'Affine', 'rotate': [-10, 10] }, ['Resize', [0.5, 3.0]]]), dict(type='RandomCrop', min_side_ratio=0.1), dict(type='Resize', scale=(640, 640), keep_ratio=True), dict(type='Pad', size=(640, 640)), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape')) ])) val_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='ConcatDataset', datasets=[ dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/det/icdar2015', ann_file='instances_test.json', data_prefix=dict(img_path='imgs/'), test_mode=True, pipeline=None) ], pipeline=[ dict( type='LoadImageFromFile', color_type='color_ignore_orientation', file_client_args=dict(backend='petrel')), dict(type='Resize', scale=(4068, 1024), keep_ratio=True), dict( type='LoadOCRAnnotations', with_polygon=True, with_bbox=True, with_label=True), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor', 'instances')) ])) test_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='ConcatDataset', datasets=[ dict( type='OCRDataset', data_root='openmmlab:s3://openmmlab/datasets/ocr/det/icdar2015', ann_file='instances_test.json', data_prefix=dict(img_path='imgs/'), test_mode=True, pipeline=None) ], pipeline=[ dict( type='LoadImageFromFile', color_type='color_ignore_orientation', file_client_args=dict(backend='petrel')), dict(type='Resize', scale=(4068, 1024), keep_ratio=True), dict( type='LoadOCRAnnotations', with_polygon=True, with_bbox=True, with_label=True), dict( type='PackTextDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor', 'instances')) ])) launcher = 'slurm' work_dir = './work_dirs/dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015' 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.conv2.conv_offset.weight - torch.Size([27, 128, 3, 3]): The value is the same before and after calling `init_weights` of DBNet backbone.layer2.0.conv2.conv_offset.bias - torch.Size([27]): The value is the same before and after calling `init_weights` of DBNet 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.conv2.conv_offset.weight - torch.Size([27, 128, 3, 3]): The value is the same before and after calling `init_weights` of DBNet backbone.layer2.1.conv2.conv_offset.bias - torch.Size([27]): The value is the same before and after calling `init_weights` of DBNet 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.conv2.conv_offset.weight - torch.Size([27, 128, 3, 3]): The value is the same before and after calling `init_weights` of DBNet backbone.layer2.2.conv2.conv_offset.bias - torch.Size([27]): The value is the same before and after calling `init_weights` of DBNet 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.conv2.conv_offset.weight - torch.Size([27, 128, 3, 3]): The value is the same before and after calling `init_weights` of DBNet backbone.layer2.3.conv2.conv_offset.bias - torch.Size([27]): The value is the same before and after calling `init_weights` of DBNet 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.conv2.conv_offset.weight - torch.Size([27, 256, 3, 3]): The value is the same before and after calling `init_weights` of DBNet backbone.layer3.0.conv2.conv_offset.bias - torch.Size([27]): The value is the same before and after calling `init_weights` of DBNet 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.conv2.conv_offset.weight - torch.Size([27, 256, 3, 3]): The value is the same before and after calling `init_weights` of DBNet backbone.layer3.1.conv2.conv_offset.bias - torch.Size([27]): The value is the same before and after calling `init_weights` of DBNet 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.conv2.conv_offset.weight - torch.Size([27, 256, 3, 3]): The value is the same before and after calling `init_weights` of DBNet backbone.layer3.2.conv2.conv_offset.bias - torch.Size([27]): The value is the same before and after calling `init_weights` of DBNet 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.conv2.conv_offset.weight - torch.Size([27, 256, 3, 3]): The value is the same before and after calling `init_weights` of DBNet backbone.layer3.3.conv2.conv_offset.bias - torch.Size([27]): The value is the same before and after calling `init_weights` of DBNet 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.conv2.conv_offset.weight - torch.Size([27, 256, 3, 3]): The value is the same before and after calling `init_weights` of DBNet backbone.layer3.4.conv2.conv_offset.bias - torch.Size([27]): The value is the same before and after calling `init_weights` of DBNet 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.conv2.conv_offset.weight - torch.Size([27, 256, 3, 3]): The value is the same before and after calling `init_weights` of DBNet backbone.layer3.5.conv2.conv_offset.bias - torch.Size([27]): The value is the same before and after calling `init_weights` of DBNet 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.conv2.conv_offset.weight - torch.Size([27, 512, 3, 3]): The value is the same before and after calling `init_weights` of DBNet backbone.layer4.0.conv2.conv_offset.bias - torch.Size([27]): The value is the same before and after calling `init_weights` of DBNet 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.conv2.conv_offset.weight - torch.Size([27, 512, 3, 3]): The value is the same before and after calling `init_weights` of DBNet backbone.layer4.1.conv2.conv_offset.bias - torch.Size([27]): The value is the same before and after calling `init_weights` of DBNet 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.conv2.conv_offset.weight - torch.Size([27, 512, 3, 3]): The value is the same before and after calling `init_weights` of DBNet backbone.layer4.2.conv2.conv_offset.bias - torch.Size([27]): The value is the same before and after calling `init_weights` of DBNet 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 neck.lateral_convs.0.conv.weight - torch.Size([256, 256, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.lateral_convs.1.conv.weight - torch.Size([256, 512, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.lateral_convs.2.conv.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.lateral_convs.3.conv.weight - torch.Size([256, 2048, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.smooth_convs.0.conv.weight - torch.Size([64, 256, 3, 3]): Initialized by user-defined `init_weights` in ConvModule neck.smooth_convs.1.conv.weight - torch.Size([64, 256, 3, 3]): Initialized by user-defined `init_weights` in ConvModule neck.smooth_convs.2.conv.weight - torch.Size([64, 256, 3, 3]): Initialized by user-defined `init_weights` in ConvModule neck.smooth_convs.3.conv.weight - torch.Size([64, 256, 3, 3]): Initialized by user-defined `init_weights` in ConvModule neck.asf_conv.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in ConvModule neck.asf_conv.conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DBNet neck.asf_attn.channel_wise.0.conv.weight - torch.Size([64, 256, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.asf_attn.channel_wise.1.conv.weight - torch.Size([256, 64, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.asf_attn.spatial_wise.0.conv.weight - torch.Size([1, 1, 3, 3]): Initialized by user-defined `init_weights` in ConvModule neck.asf_attn.spatial_wise.1.conv.weight - torch.Size([1, 1, 1, 1]): Initialized by user-defined `init_weights` in ConvModule neck.asf_attn.attention_wise.conv.weight - torch.Size([4, 256, 1, 1]): Initialized by user-defined `init_weights` in ConvModule det_head.binarize.0.weight - torch.Size([64, 256, 3, 3]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.3.weight - torch.Size([64, 64, 2, 2]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.6.weight - torch.Size([64, 1, 2, 2]): The value is the same before and after calling `init_weights` of DBNet det_head.binarize.6.bias - torch.Size([1]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.0.weight - torch.Size([64, 256, 3, 3]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.3.weight - torch.Size([64, 64, 2, 2]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.6.weight - torch.Size([64, 1, 2, 2]): The value is the same before and after calling `init_weights` of DBNet det_head.threshold.6.bias - torch.Size([1]): The value is the same before and after calling `init_weights` of DBNet 2022/08/29 23:03:50 - mmengine - INFO - ModulatedDeformConvPack backbone.layer2.0.conv2 is upgraded to version 2. 2022/08/29 23:03:50 - mmengine - INFO - ModulatedDeformConvPack backbone.layer2.1.conv2 is upgraded to version 2. 2022/08/29 23:03:50 - mmengine - INFO - ModulatedDeformConvPack backbone.layer2.2.conv2 is upgraded to version 2. 2022/08/29 23:03:50 - mmengine - INFO - ModulatedDeformConvPack backbone.layer2.3.conv2 is upgraded to version 2. 2022/08/29 23:03:50 - mmengine - INFO - ModulatedDeformConvPack backbone.layer3.0.conv2 is upgraded to version 2. 2022/08/29 23:03:50 - mmengine - INFO - ModulatedDeformConvPack backbone.layer3.1.conv2 is upgraded to version 2. 2022/08/29 23:03:50 - mmengine - INFO - ModulatedDeformConvPack backbone.layer3.2.conv2 is upgraded to version 2. 2022/08/29 23:03:50 - mmengine - INFO - ModulatedDeformConvPack backbone.layer3.3.conv2 is upgraded to version 2. 2022/08/29 23:03:50 - mmengine - INFO - ModulatedDeformConvPack backbone.layer3.4.conv2 is upgraded to version 2. 2022/08/29 23:03:50 - mmengine - INFO - ModulatedDeformConvPack backbone.layer3.5.conv2 is upgraded to version 2. 2022/08/29 23:03:50 - mmengine - INFO - ModulatedDeformConvPack backbone.layer4.0.conv2 is upgraded to version 2. 2022/08/29 23:03:50 - mmengine - INFO - ModulatedDeformConvPack backbone.layer4.1.conv2 is upgraded to version 2. 2022/08/29 23:03:50 - mmengine - INFO - ModulatedDeformConvPack backbone.layer4.2.conv2 is upgraded to version 2. 2022/08/29 23:03:50 - mmengine - INFO - Load checkpoint from ~/dbnet_pretrain/dbnetpp_r50dcnv2_fpnc_100k_iter_synthtext-20220502-db297554.pth 2022/08/29 23:07:51 - mmengine - INFO - Epoch(train) [1][5/63] lr: 7.0000e-03 memory: 48191 data_time: 44.7305 loss_prob: 2.5510 loss_thr: 0.8340 loss_db: 0.3972 loss: 3.7822 time: 48.1235 2022/08/29 23:07:56 - mmengine - INFO - Epoch(train) [1][10/63] lr: 7.0000e-03 eta: 21 days, 12:14:40 time: 24.5863 data_time: 22.4611 memory: 16198 loss_prob: 2.5794 loss_thr: 0.8199 loss_db: 0.4274 loss: 3.8266 2022/08/29 23:08:00 - mmengine - INFO - Epoch(train) [1][15/63] lr: 7.0000e-03 eta: 21 days, 12:14:40 time: 0.9536 data_time: 0.1111 memory: 16198 loss_prob: 2.4690 loss_thr: 0.7896 loss_db: 0.4234 loss: 3.6821 2022/08/29 23:08:05 - mmengine - INFO - Epoch(train) [1][20/63] lr: 7.0000e-03 eta: 11 days, 3:54:09 time: 0.9350 data_time: 0.0347 memory: 16198 loss_prob: 2.4320 loss_thr: 0.7915 loss_db: 0.3954 loss: 3.6189 2022/08/29 23:08:11 - mmengine - INFO - Epoch(train) [1][25/63] lr: 7.0000e-03 eta: 11 days, 3:54:09 time: 1.0294 data_time: 0.0935 memory: 16198 loss_prob: 2.3868 loss_thr: 0.7945 loss_db: 0.3920 loss: 3.5733 2022/08/29 23:08:15 - mmengine - INFO - Epoch(train) [1][30/63] lr: 7.0000e-03 eta: 7 days, 17:29:11 time: 0.9873 data_time: 0.1020 memory: 16198 loss_prob: 2.2988 loss_thr: 0.8036 loss_db: 0.3862 loss: 3.4886 2022/08/29 23:08:20 - mmengine - INFO - Epoch(train) [1][35/63] lr: 7.0000e-03 eta: 7 days, 17:29:11 time: 0.9133 data_time: 0.0558 memory: 16198 loss_prob: 2.2099 loss_thr: 0.8021 loss_db: 0.3804 loss: 3.3924 2022/08/29 23:08:25 - mmengine - INFO - Epoch(train) [1][40/63] lr: 7.0000e-03 eta: 5 days, 23:57:58 time: 0.9281 data_time: 0.0947 memory: 16198 loss_prob: 2.1226 loss_thr: 0.7697 loss_db: 0.3763 loss: 3.2686 2022/08/29 23:08:31 - mmengine - INFO - Epoch(train) [1][45/63] lr: 7.0000e-03 eta: 5 days, 23:57:58 time: 1.0930 data_time: 0.1141 memory: 16198 loss_prob: 2.0017 loss_thr: 0.7527 loss_db: 0.3307 loss: 3.0850 2022/08/29 23:08:37 - mmengine - INFO - Epoch(train) [1][50/63] lr: 7.0000e-03 eta: 5 days, 0:16:59 time: 1.2212 data_time: 0.1276 memory: 16198 loss_prob: 1.9976 loss_thr: 0.7640 loss_db: 0.3226 loss: 3.0842 2022/08/29 23:08:43 - mmengine - INFO - Epoch(train) [1][55/63] lr: 7.0000e-03 eta: 5 days, 0:16:59 time: 1.1950 data_time: 0.1167 memory: 16198 loss_prob: 2.1050 loss_thr: 0.7759 loss_db: 0.3506 loss: 3.2315 2022/08/29 23:08:49 - mmengine - INFO - Epoch(train) [1][60/63] lr: 7.0000e-03 eta: 4 days, 8:21:49 time: 1.1841 data_time: 0.1050 memory: 16198 loss_prob: 1.9185 loss_thr: 0.7379 loss_db: 0.3187 loss: 2.9751 2022/08/29 23:09:01 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:09:10 - mmengine - INFO - Epoch(train) [2][5/63] lr: 6.9947e-03 eta: 4 days, 8:21:49 time: 2.3033 data_time: 0.3072 memory: 40923 loss_prob: 1.7161 loss_thr: 0.7264 loss_db: 0.2842 loss: 2.7267 2022/08/29 23:09:17 - mmengine - INFO - Epoch(train) [2][10/63] lr: 6.9947e-03 eta: 3 days, 18:03:43 time: 1.4956 data_time: 0.3597 memory: 16198 loss_prob: 1.6659 loss_thr: 0.7079 loss_db: 0.2765 loss: 2.6502 2022/08/29 23:09:23 - mmengine - INFO - Epoch(train) [2][15/63] lr: 6.9947e-03 eta: 3 days, 18:03:43 time: 1.2866 data_time: 0.1116 memory: 16198 loss_prob: 1.5919 loss_thr: 0.6617 loss_db: 0.2581 loss: 2.5117 2022/08/29 23:09:29 - mmengine - INFO - Epoch(train) [2][20/63] lr: 6.9947e-03 eta: 3 days, 10:19:50 time: 1.2384 data_time: 0.0998 memory: 16198 loss_prob: 1.7231 loss_thr: 0.6894 loss_db: 0.2881 loss: 2.7007 2022/08/29 23:09:34 - mmengine - INFO - Epoch(train) [2][25/63] lr: 6.9947e-03 eta: 3 days, 10:19:50 time: 1.0664 data_time: 0.1110 memory: 16198 loss_prob: 1.7726 loss_thr: 0.7073 loss_db: 0.3005 loss: 2.7804 2022/08/29 23:09:38 - mmengine - INFO - Epoch(train) [2][30/63] lr: 6.9947e-03 eta: 3 days, 3:37:23 time: 0.9555 data_time: 0.0926 memory: 16198 loss_prob: 1.8376 loss_thr: 0.7312 loss_db: 0.3085 loss: 2.8773 2022/08/29 23:09:43 - mmengine - INFO - Epoch(train) [2][35/63] lr: 6.9947e-03 eta: 3 days, 3:37:23 time: 0.9226 data_time: 0.0897 memory: 16198 loss_prob: 1.9728 loss_thr: 0.7469 loss_db: 0.3381 loss: 3.0577 2022/08/29 23:09:47 - mmengine - INFO - Epoch(train) [2][40/63] lr: 6.9947e-03 eta: 2 days, 22:03:45 time: 0.8794 data_time: 0.0531 memory: 16198 loss_prob: 1.9150 loss_thr: 0.7218 loss_db: 0.3226 loss: 2.9594 2022/08/29 23:09:52 - mmengine - INFO - Epoch(train) [2][45/63] lr: 6.9947e-03 eta: 2 days, 22:03:45 time: 0.9293 data_time: 0.0894 memory: 16198 loss_prob: 1.8196 loss_thr: 0.7170 loss_db: 0.3033 loss: 2.8399 2022/08/29 23:09:57 - mmengine - INFO - Epoch(train) [2][50/63] lr: 6.9947e-03 eta: 2 days, 17:39:19 time: 0.9708 data_time: 0.1040 memory: 16198 loss_prob: 1.8569 loss_thr: 0.7251 loss_db: 0.3179 loss: 2.8998 2022/08/29 23:10:02 - mmengine - INFO - Epoch(train) [2][55/63] lr: 6.9947e-03 eta: 2 days, 17:39:19 time: 0.9541 data_time: 0.0563 memory: 16198 loss_prob: 1.8856 loss_thr: 0.7250 loss_db: 0.3209 loss: 2.9314 2022/08/29 23:10:07 - mmengine - INFO - Epoch(train) [2][60/63] lr: 6.9947e-03 eta: 2 days, 14:01:07 time: 1.0028 data_time: 0.1063 memory: 16198 loss_prob: 1.8089 loss_thr: 0.7167 loss_db: 0.3122 loss: 2.8378 2022/08/29 23:10:10 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:10:16 - mmengine - INFO - Epoch(train) [3][5/63] lr: 6.9895e-03 eta: 2 days, 14:01:07 time: 1.0723 data_time: 0.2595 memory: 16198 loss_prob: 1.8164 loss_thr: 0.7218 loss_db: 0.3073 loss: 2.8454 2022/08/29 23:10:21 - mmengine - INFO - Epoch(train) [3][10/63] lr: 6.9895e-03 eta: 2 days, 9:49:20 time: 1.1299 data_time: 0.2911 memory: 16198 loss_prob: 1.8354 loss_thr: 0.7166 loss_db: 0.3004 loss: 2.8524 2022/08/29 23:10:26 - mmengine - INFO - Epoch(train) [3][15/63] lr: 6.9895e-03 eta: 2 days, 9:49:20 time: 1.0477 data_time: 0.0885 memory: 16198 loss_prob: 1.6503 loss_thr: 0.6683 loss_db: 0.2677 loss: 2.5863 2022/08/29 23:10:32 - mmengine - INFO - Epoch(train) [3][20/63] lr: 6.9895e-03 eta: 2 days, 7:24:07 time: 1.0778 data_time: 0.0970 memory: 16198 loss_prob: 1.5808 loss_thr: 0.6689 loss_db: 0.2608 loss: 2.5105 2022/08/29 23:10:36 - mmengine - INFO - Epoch(train) [3][25/63] lr: 6.9895e-03 eta: 2 days, 7:24:07 time: 0.9809 data_time: 0.0998 memory: 16198 loss_prob: 1.6704 loss_thr: 0.7071 loss_db: 0.2776 loss: 2.6551 2022/08/29 23:10:41 - mmengine - INFO - Epoch(train) [3][30/63] lr: 6.9895e-03 eta: 2 days, 5:03:54 time: 0.9091 data_time: 0.0545 memory: 16198 loss_prob: 1.7590 loss_thr: 0.6967 loss_db: 0.2858 loss: 2.7415 2022/08/29 23:10:46 - mmengine - INFO - Epoch(train) [3][35/63] lr: 6.9895e-03 eta: 2 days, 5:03:54 time: 0.9900 data_time: 0.0912 memory: 16198 loss_prob: 2.0377 loss_thr: 0.6853 loss_db: 0.3183 loss: 3.0413 2022/08/29 23:10:51 - mmengine - INFO - Epoch(train) [3][40/63] lr: 6.9895e-03 eta: 2 days, 3:05:27 time: 0.9737 data_time: 0.0896 memory: 16198 loss_prob: 2.1461 loss_thr: 0.7397 loss_db: 0.3482 loss: 3.2340 2022/08/29 23:10:55 - mmengine - INFO - Epoch(train) [3][45/63] lr: 6.9895e-03 eta: 2 days, 3:05:27 time: 0.9177 data_time: 0.0602 memory: 16198 loss_prob: 2.0166 loss_thr: 0.7598 loss_db: 0.3530 loss: 3.1294 2022/08/29 23:11:00 - mmengine - INFO - Epoch(train) [3][50/63] lr: 6.9895e-03 eta: 2 days, 1:20:13 time: 0.9707 data_time: 0.1078 memory: 16198 loss_prob: 1.9755 loss_thr: 0.7465 loss_db: 0.3431 loss: 3.0651 2022/08/29 23:11:05 - mmengine - INFO - Epoch(train) [3][55/63] lr: 6.9895e-03 eta: 2 days, 1:20:13 time: 0.9274 data_time: 0.0972 memory: 16198 loss_prob: 1.9026 loss_thr: 0.7595 loss_db: 0.3274 loss: 2.9895 2022/08/29 23:11:10 - mmengine - INFO - Epoch(train) [3][60/63] lr: 6.9895e-03 eta: 1 day, 23:46:45 time: 0.9775 data_time: 0.0514 memory: 16198 loss_prob: 1.7902 loss_thr: 0.7174 loss_db: 0.3026 loss: 2.8101 2022/08/29 23:11:13 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:11:21 - mmengine - INFO - Epoch(train) [4][5/63] lr: 6.9842e-03 eta: 1 day, 23:46:45 time: 1.2237 data_time: 0.3591 memory: 16198 loss_prob: 1.6519 loss_thr: 0.7127 loss_db: 0.2730 loss: 2.6376 2022/08/29 23:11:27 - mmengine - INFO - Epoch(train) [4][10/63] lr: 6.9842e-03 eta: 1 day, 22:06:47 time: 1.3898 data_time: 0.3620 memory: 16198 loss_prob: 1.8620 loss_thr: 0.7395 loss_db: 0.3189 loss: 2.9205 2022/08/29 23:11:32 - mmengine - INFO - Epoch(train) [4][15/63] lr: 6.9842e-03 eta: 1 day, 22:06:47 time: 1.0783 data_time: 0.0502 memory: 16198 loss_prob: 1.8167 loss_thr: 0.7437 loss_db: 0.3151 loss: 2.8755 2022/08/29 23:11:37 - mmengine - INFO - Epoch(train) [4][20/63] lr: 6.9842e-03 eta: 1 day, 20:54:39 time: 1.0080 data_time: 0.0542 memory: 16198 loss_prob: 1.6880 loss_thr: 0.7151 loss_db: 0.2903 loss: 2.6934 2022/08/29 23:11:41 - mmengine - INFO - Epoch(train) [4][25/63] lr: 6.9842e-03 eta: 1 day, 20:54:39 time: 0.9714 data_time: 0.0567 memory: 16198 loss_prob: 1.8697 loss_thr: 0.7282 loss_db: 0.3192 loss: 2.9172 2022/08/29 23:11:46 - mmengine - INFO - Epoch(train) [4][30/63] lr: 6.9842e-03 eta: 1 day, 19:43:47 time: 0.9153 data_time: 0.0327 memory: 16198 loss_prob: 2.1078 loss_thr: 0.7636 loss_db: 0.3554 loss: 3.2268 2022/08/29 23:11:51 - mmengine - INFO - Epoch(train) [4][35/63] lr: 6.9842e-03 eta: 1 day, 19:43:47 time: 0.9479 data_time: 0.0513 memory: 16198 loss_prob: 2.1182 loss_thr: 0.7604 loss_db: 0.3621 loss: 3.2408 2022/08/29 23:11:56 - mmengine - INFO - Epoch(train) [4][40/63] lr: 6.9842e-03 eta: 1 day, 18:43:42 time: 0.9994 data_time: 0.0555 memory: 16198 loss_prob: 1.9800 loss_thr: 0.7458 loss_db: 0.3438 loss: 3.0696 2022/08/29 23:12:01 - mmengine - INFO - Epoch(train) [4][45/63] lr: 6.9842e-03 eta: 1 day, 18:43:42 time: 1.0236 data_time: 0.0572 memory: 16198 loss_prob: 2.0124 loss_thr: 0.7545 loss_db: 0.3520 loss: 3.1190 2022/08/29 23:12:06 - mmengine - INFO - Epoch(train) [4][50/63] lr: 6.9842e-03 eta: 1 day, 17:47:50 time: 0.9845 data_time: 0.0556 memory: 16198 loss_prob: 2.0599 loss_thr: 0.7496 loss_db: 0.3508 loss: 3.1603 2022/08/29 23:12:10 - mmengine - INFO - Epoch(train) [4][55/63] lr: 6.9842e-03 eta: 1 day, 17:47:50 time: 0.9056 data_time: 0.0270 memory: 16198 loss_prob: 1.9499 loss_thr: 0.7479 loss_db: 0.3253 loss: 3.0232 2022/08/29 23:12:16 - mmengine - INFO - Epoch(train) [4][60/63] lr: 6.9842e-03 eta: 1 day, 16:57:26 time: 1.0039 data_time: 0.0434 memory: 16198 loss_prob: 1.9390 loss_thr: 0.7561 loss_db: 0.3307 loss: 3.0259 2022/08/29 23:12:18 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:12:26 - mmengine - INFO - Epoch(train) [5][5/63] lr: 6.9790e-03 eta: 1 day, 16:57:26 time: 1.2785 data_time: 0.3513 memory: 16198 loss_prob: 2.0483 loss_thr: 0.7495 loss_db: 0.3733 loss: 3.1710 2022/08/29 23:12:31 - mmengine - INFO - Epoch(train) [5][10/63] lr: 6.9790e-03 eta: 1 day, 15:55:30 time: 1.2605 data_time: 0.3665 memory: 16198 loss_prob: 1.8028 loss_thr: 0.7235 loss_db: 0.2999 loss: 2.8262 2022/08/29 23:12:35 - mmengine - INFO - Epoch(train) [5][15/63] lr: 6.9790e-03 eta: 1 day, 15:55:30 time: 0.9313 data_time: 0.0543 memory: 16198 loss_prob: 1.7258 loss_thr: 0.7128 loss_db: 0.2807 loss: 2.7192 2022/08/29 23:12:40 - mmengine - INFO - Epoch(train) [5][20/63] lr: 6.9790e-03 eta: 1 day, 15:11:48 time: 0.9677 data_time: 0.0373 memory: 16198 loss_prob: 1.8516 loss_thr: 0.6960 loss_db: 0.3155 loss: 2.8631 2022/08/29 23:12:46 - mmengine - INFO - Epoch(train) [5][25/63] lr: 6.9790e-03 eta: 1 day, 15:11:48 time: 1.0447 data_time: 0.0623 memory: 16198 loss_prob: 1.9584 loss_thr: 0.7399 loss_db: 0.3425 loss: 3.0408 2022/08/29 23:12:50 - mmengine - INFO - Epoch(train) [5][30/63] lr: 6.9790e-03 eta: 1 day, 14:30:38 time: 0.9557 data_time: 0.0555 memory: 16198 loss_prob: 1.8784 loss_thr: 0.7362 loss_db: 0.3247 loss: 2.9393 2022/08/29 23:12:55 - mmengine - INFO - Epoch(train) [5][35/63] lr: 6.9790e-03 eta: 1 day, 14:30:38 time: 0.8867 data_time: 0.0385 memory: 16198 loss_prob: 1.7812 loss_thr: 0.7191 loss_db: 0.3050 loss: 2.8053 2022/08/29 23:13:00 - mmengine - INFO - Epoch(train) [5][40/63] lr: 6.9790e-03 eta: 1 day, 13:53:42 time: 0.9884 data_time: 0.0509 memory: 16198 loss_prob: 1.8569 loss_thr: 0.7328 loss_db: 0.3244 loss: 2.9142 2022/08/29 23:13:04 - mmengine - INFO - Epoch(train) [5][45/63] lr: 6.9790e-03 eta: 1 day, 13:53:42 time: 0.9320 data_time: 0.0548 memory: 16198 loss_prob: 1.8428 loss_thr: 0.7208 loss_db: 0.3199 loss: 2.8834 2022/08/29 23:13:08 - mmengine - INFO - Epoch(train) [5][50/63] lr: 6.9790e-03 eta: 1 day, 13:13:17 time: 0.8464 data_time: 0.0385 memory: 16198 loss_prob: 1.8511 loss_thr: 0.7362 loss_db: 0.3206 loss: 2.9079 2022/08/29 23:13:13 - mmengine - INFO - Epoch(train) [5][55/63] lr: 6.9790e-03 eta: 1 day, 13:13:17 time: 0.8979 data_time: 0.0416 memory: 16198 loss_prob: 1.8113 loss_thr: 0.7327 loss_db: 0.3035 loss: 2.8475 2022/08/29 23:13:17 - mmengine - INFO - Epoch(train) [5][60/63] lr: 6.9790e-03 eta: 1 day, 12:35:37 time: 0.8502 data_time: 0.0462 memory: 16198 loss_prob: 1.6703 loss_thr: 0.7240 loss_db: 0.2735 loss: 2.6678 2022/08/29 23:13:19 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:13:28 - mmengine - INFO - Epoch(train) [6][5/63] lr: 6.9737e-03 eta: 1 day, 12:35:37 time: 1.2675 data_time: 0.3560 memory: 16198 loss_prob: 1.5761 loss_thr: 0.6771 loss_db: 0.2494 loss: 2.5026 2022/08/29 23:13:33 - mmengine - INFO - Epoch(train) [6][10/63] lr: 6.9737e-03 eta: 1 day, 12:02:54 time: 1.4370 data_time: 0.3838 memory: 16198 loss_prob: 1.6049 loss_thr: 0.6740 loss_db: 0.2611 loss: 2.5400 2022/08/29 23:13:39 - mmengine - INFO - Epoch(train) [6][15/63] lr: 6.9737e-03 eta: 1 day, 12:02:54 time: 1.0500 data_time: 0.0614 memory: 16198 loss_prob: 1.4917 loss_thr: 0.6548 loss_db: 0.2434 loss: 2.3899 2022/08/29 23:13:45 - mmengine - INFO - Epoch(train) [6][20/63] lr: 6.9737e-03 eta: 1 day, 11:40:28 time: 1.1324 data_time: 0.0552 memory: 16198 loss_prob: 1.4926 loss_thr: 0.6819 loss_db: 0.2377 loss: 2.4122 2022/08/29 23:13:49 - mmengine - INFO - Epoch(train) [6][25/63] lr: 6.9737e-03 eta: 1 day, 11:40:28 time: 1.0635 data_time: 0.0571 memory: 16198 loss_prob: 1.5110 loss_thr: 0.7140 loss_db: 0.2437 loss: 2.4688 2022/08/29 23:13:54 - mmengine - INFO - Epoch(train) [6][30/63] lr: 6.9737e-03 eta: 1 day, 11:12:18 time: 0.9398 data_time: 0.0562 memory: 16198 loss_prob: 1.6528 loss_thr: 0.7342 loss_db: 0.2711 loss: 2.6581 2022/08/29 23:13:59 - mmengine - INFO - Epoch(train) [6][35/63] lr: 6.9737e-03 eta: 1 day, 11:12:18 time: 0.9353 data_time: 0.0575 memory: 16198 loss_prob: 1.8470 loss_thr: 0.7270 loss_db: 0.3090 loss: 2.8830 2022/08/29 23:14:03 - mmengine - INFO - Epoch(train) [6][40/63] lr: 6.9737e-03 eta: 1 day, 10:45:38 time: 0.9373 data_time: 0.0403 memory: 16198 loss_prob: 1.7599 loss_thr: 0.6851 loss_db: 0.2976 loss: 2.7426 2022/08/29 23:14:08 - mmengine - INFO - Epoch(train) [6][45/63] lr: 6.9737e-03 eta: 1 day, 10:45:38 time: 0.9983 data_time: 0.0999 memory: 16198 loss_prob: 1.7787 loss_thr: 0.7036 loss_db: 0.3056 loss: 2.7879 2022/08/29 23:14:13 - mmengine - INFO - Epoch(train) [6][50/63] lr: 6.9737e-03 eta: 1 day, 10:22:45 time: 1.0047 data_time: 0.1040 memory: 16198 loss_prob: 1.8087 loss_thr: 0.7237 loss_db: 0.3102 loss: 2.8426 2022/08/29 23:14:19 - mmengine - INFO - Epoch(train) [6][55/63] lr: 6.9737e-03 eta: 1 day, 10:22:45 time: 1.0148 data_time: 0.0577 memory: 16198 loss_prob: 1.6324 loss_thr: 0.6672 loss_db: 0.2685 loss: 2.5680 2022/08/29 23:14:23 - mmengine - INFO - Epoch(train) [6][60/63] lr: 6.9737e-03 eta: 1 day, 10:00:59 time: 1.0024 data_time: 0.0591 memory: 16198 loss_prob: 1.7046 loss_thr: 0.6975 loss_db: 0.2861 loss: 2.6881 2022/08/29 23:14:26 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:14:34 - mmengine - INFO - Epoch(train) [7][5/63] lr: 6.9685e-03 eta: 1 day, 10:00:59 time: 1.2699 data_time: 0.3711 memory: 16198 loss_prob: 1.7762 loss_thr: 0.7304 loss_db: 0.2852 loss: 2.7918 2022/08/29 23:14:39 - mmengine - INFO - Epoch(train) [7][10/63] lr: 6.9685e-03 eta: 1 day, 9:33:20 time: 1.2712 data_time: 0.3777 memory: 16198 loss_prob: 1.7801 loss_thr: 0.7303 loss_db: 0.2894 loss: 2.7998 2022/08/29 23:14:43 - mmengine - INFO - Epoch(train) [7][15/63] lr: 6.9685e-03 eta: 1 day, 9:33:20 time: 0.9301 data_time: 0.0473 memory: 16198 loss_prob: 2.0237 loss_thr: 0.7553 loss_db: 0.3445 loss: 3.1235 2022/08/29 23:14:50 - mmengine - INFO - Epoch(train) [7][20/63] lr: 6.9685e-03 eta: 1 day, 9:16:13 time: 1.0712 data_time: 0.0385 memory: 16198 loss_prob: 1.9952 loss_thr: 0.7531 loss_db: 0.3444 loss: 3.0927 2022/08/29 23:14:55 - mmengine - INFO - Epoch(train) [7][25/63] lr: 6.9685e-03 eta: 1 day, 9:16:13 time: 1.1094 data_time: 0.0690 memory: 16198 loss_prob: 1.7586 loss_thr: 0.7048 loss_db: 0.2964 loss: 2.7598 2022/08/29 23:14:59 - mmengine - INFO - Epoch(train) [7][30/63] lr: 6.9685e-03 eta: 1 day, 8:55:46 time: 0.9354 data_time: 0.0528 memory: 16198 loss_prob: 1.7258 loss_thr: 0.6943 loss_db: 0.2871 loss: 2.7072 2022/08/29 23:15:03 - mmengine - INFO - Epoch(train) [7][35/63] lr: 6.9685e-03 eta: 1 day, 8:55:46 time: 0.8938 data_time: 0.0346 memory: 16198 loss_prob: 1.8205 loss_thr: 0.7045 loss_db: 0.3110 loss: 2.8361 2022/08/29 23:15:09 - mmengine - INFO - Epoch(train) [7][40/63] lr: 6.9685e-03 eta: 1 day, 8:37:10 time: 0.9648 data_time: 0.0541 memory: 16198 loss_prob: 1.7010 loss_thr: 0.6759 loss_db: 0.2837 loss: 2.6605 2022/08/29 23:15:13 - mmengine - INFO - Epoch(train) [7][45/63] lr: 6.9685e-03 eta: 1 day, 8:37:10 time: 0.9975 data_time: 0.0887 memory: 16198 loss_prob: 1.6478 loss_thr: 0.7066 loss_db: 0.2713 loss: 2.6257 2022/08/29 23:15:18 - mmengine - INFO - Epoch(train) [7][50/63] lr: 6.9685e-03 eta: 1 day, 8:18:37 time: 0.9375 data_time: 0.0870 memory: 16198 loss_prob: 1.6834 loss_thr: 0.7088 loss_db: 0.2800 loss: 2.6722 2022/08/29 23:15:23 - mmengine - INFO - Epoch(train) [7][55/63] lr: 6.9685e-03 eta: 1 day, 8:18:37 time: 0.9072 data_time: 0.0542 memory: 16198 loss_prob: 1.6445 loss_thr: 0.7071 loss_db: 0.2790 loss: 2.6306 2022/08/29 23:15:28 - mmengine - INFO - Epoch(train) [7][60/63] lr: 6.9685e-03 eta: 1 day, 8:03:20 time: 1.0218 data_time: 0.0411 memory: 16198 loss_prob: 1.6809 loss_thr: 0.7388 loss_db: 0.2812 loss: 2.7010 2022/08/29 23:15:31 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:15:39 - mmengine - INFO - Epoch(train) [8][5/63] lr: 6.9632e-03 eta: 1 day, 8:03:20 time: 1.3412 data_time: 0.3849 memory: 16198 loss_prob: 1.6864 loss_thr: 0.7085 loss_db: 0.2773 loss: 2.6722 2022/08/29 23:15:43 - mmengine - INFO - Epoch(train) [8][10/63] lr: 6.9632e-03 eta: 1 day, 7:42:43 time: 1.2653 data_time: 0.3904 memory: 16198 loss_prob: 1.6688 loss_thr: 0.6954 loss_db: 0.2739 loss: 2.6381 2022/08/29 23:15:48 - mmengine - INFO - Epoch(train) [8][15/63] lr: 6.9632e-03 eta: 1 day, 7:42:43 time: 0.9283 data_time: 0.0629 memory: 16198 loss_prob: 1.7950 loss_thr: 0.7125 loss_db: 0.2996 loss: 2.8071 2022/08/29 23:15:52 - mmengine - INFO - Epoch(train) [8][20/63] lr: 6.9632e-03 eta: 1 day, 7:25:45 time: 0.9042 data_time: 0.0377 memory: 16198 loss_prob: 1.8364 loss_thr: 0.7240 loss_db: 0.3071 loss: 2.8675 2022/08/29 23:15:58 - mmengine - INFO - Epoch(train) [8][25/63] lr: 6.9632e-03 eta: 1 day, 7:25:45 time: 0.9578 data_time: 0.0579 memory: 16198 loss_prob: 1.9262 loss_thr: 0.7092 loss_db: 0.3137 loss: 2.9490 2022/08/29 23:16:02 - mmengine - INFO - Epoch(train) [8][30/63] lr: 6.9632e-03 eta: 1 day, 7:10:39 time: 0.9470 data_time: 0.0539 memory: 16198 loss_prob: 1.9405 loss_thr: 0.6987 loss_db: 0.3264 loss: 2.9656 2022/08/29 23:16:07 - mmengine - INFO - Epoch(train) [8][35/63] lr: 6.9632e-03 eta: 1 day, 7:10:39 time: 0.9080 data_time: 0.0336 memory: 16198 loss_prob: 1.8212 loss_thr: 0.7099 loss_db: 0.3146 loss: 2.8457 2022/08/29 23:16:11 - mmengine - INFO - Epoch(train) [8][40/63] lr: 6.9632e-03 eta: 1 day, 6:55:09 time: 0.9082 data_time: 0.0499 memory: 16198 loss_prob: 1.6606 loss_thr: 0.6859 loss_db: 0.2783 loss: 2.6248 2022/08/29 23:16:16 - mmengine - INFO - Epoch(train) [8][45/63] lr: 6.9632e-03 eta: 1 day, 6:55:09 time: 0.9206 data_time: 0.0584 memory: 16198 loss_prob: 1.7681 loss_thr: 0.6961 loss_db: 0.3039 loss: 2.7681 2022/08/29 23:16:20 - mmengine - INFO - Epoch(train) [8][50/63] lr: 6.9632e-03 eta: 1 day, 6:40:37 time: 0.9211 data_time: 0.0425 memory: 16198 loss_prob: 1.7625 loss_thr: 0.7035 loss_db: 0.2962 loss: 2.7623 2022/08/29 23:16:25 - mmengine - INFO - Epoch(train) [8][55/63] lr: 6.9632e-03 eta: 1 day, 6:40:37 time: 0.9240 data_time: 0.0491 memory: 16198 loss_prob: 1.5882 loss_thr: 0.6795 loss_db: 0.2582 loss: 2.5258 2022/08/29 23:16:30 - mmengine - INFO - Epoch(train) [8][60/63] lr: 6.9632e-03 eta: 1 day, 6:27:48 time: 0.9674 data_time: 0.0623 memory: 16198 loss_prob: 1.6098 loss_thr: 0.6985 loss_db: 0.2654 loss: 2.5737 2022/08/29 23:16:32 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:16:41 - mmengine - INFO - Epoch(train) [9][5/63] lr: 6.9580e-03 eta: 1 day, 6:27:48 time: 1.2263 data_time: 0.3372 memory: 16198 loss_prob: 1.6166 loss_thr: 0.6861 loss_db: 0.2747 loss: 2.5775 2022/08/29 23:16:46 - mmengine - INFO - Epoch(train) [9][10/63] lr: 6.9580e-03 eta: 1 day, 6:15:37 time: 1.4111 data_time: 0.3658 memory: 16198 loss_prob: 1.6262 loss_thr: 0.6856 loss_db: 0.2760 loss: 2.5878 2022/08/29 23:16:51 - mmengine - INFO - Epoch(train) [9][15/63] lr: 6.9580e-03 eta: 1 day, 6:15:37 time: 1.0677 data_time: 0.0602 memory: 16198 loss_prob: 1.6613 loss_thr: 0.6982 loss_db: 0.2792 loss: 2.6387 2022/08/29 23:16:56 - mmengine - INFO - Epoch(train) [9][20/63] lr: 6.9580e-03 eta: 1 day, 6:04:14 time: 0.9845 data_time: 0.0482 memory: 16198 loss_prob: 1.6965 loss_thr: 0.6877 loss_db: 0.2882 loss: 2.6724 2022/08/29 23:17:00 - mmengine - INFO - Epoch(train) [9][25/63] lr: 6.9580e-03 eta: 1 day, 6:04:14 time: 0.9063 data_time: 0.0463 memory: 16198 loss_prob: 1.6346 loss_thr: 0.6859 loss_db: 0.2768 loss: 2.5973 2022/08/29 23:17:05 - mmengine - INFO - Epoch(train) [9][30/63] lr: 6.9580e-03 eta: 1 day, 5:51:33 time: 0.9107 data_time: 0.0301 memory: 16198 loss_prob: 1.5113 loss_thr: 0.6682 loss_db: 0.2458 loss: 2.4253 2022/08/29 23:17:09 - mmengine - INFO - Epoch(train) [9][35/63] lr: 6.9580e-03 eta: 1 day, 5:51:33 time: 0.9124 data_time: 0.0549 memory: 16198 loss_prob: 1.5140 loss_thr: 0.6474 loss_db: 0.2418 loss: 2.4032 2022/08/29 23:17:14 - mmengine - INFO - Epoch(train) [9][40/63] lr: 6.9580e-03 eta: 1 day, 5:37:55 time: 0.8495 data_time: 0.0493 memory: 16198 loss_prob: 1.5883 loss_thr: 0.6510 loss_db: 0.2568 loss: 2.4961 2022/08/29 23:17:18 - mmengine - INFO - Epoch(train) [9][45/63] lr: 6.9580e-03 eta: 1 day, 5:37:55 time: 0.8555 data_time: 0.0305 memory: 16198 loss_prob: 1.5566 loss_thr: 0.6782 loss_db: 0.2500 loss: 2.4849 2022/08/29 23:17:23 - mmengine - INFO - Epoch(train) [9][50/63] lr: 6.9580e-03 eta: 1 day, 5:26:11 time: 0.9123 data_time: 0.0609 memory: 16198 loss_prob: 1.5449 loss_thr: 0.6874 loss_db: 0.2496 loss: 2.4820 2022/08/29 23:17:27 - mmengine - INFO - Epoch(train) [9][55/63] lr: 6.9580e-03 eta: 1 day, 5:26:11 time: 0.9169 data_time: 0.0563 memory: 16198 loss_prob: 1.5898 loss_thr: 0.7011 loss_db: 0.2563 loss: 2.5472 2022/08/29 23:17:32 - mmengine - INFO - Epoch(train) [9][60/63] lr: 6.9580e-03 eta: 1 day, 5:14:59 time: 0.9173 data_time: 0.0327 memory: 16198 loss_prob: 1.6322 loss_thr: 0.6952 loss_db: 0.2743 loss: 2.6017 2022/08/29 23:17:35 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:17:43 - mmengine - INFO - Epoch(train) [10][5/63] lr: 6.9527e-03 eta: 1 day, 5:14:59 time: 1.3042 data_time: 0.3996 memory: 16198 loss_prob: 1.7111 loss_thr: 0.6903 loss_db: 0.2941 loss: 2.6955 2022/08/29 23:17:47 - mmengine - INFO - Epoch(train) [10][10/63] lr: 6.9527e-03 eta: 1 day, 5:02:48 time: 1.2765 data_time: 0.4018 memory: 16198 loss_prob: 1.6636 loss_thr: 0.7105 loss_db: 0.2777 loss: 2.6518 2022/08/29 23:17:52 - mmengine - INFO - Epoch(train) [10][15/63] lr: 6.9527e-03 eta: 1 day, 5:02:48 time: 0.8968 data_time: 0.0526 memory: 16198 loss_prob: 1.6590 loss_thr: 0.7156 loss_db: 0.2770 loss: 2.6516 2022/08/29 23:17:58 - mmengine - INFO - Epoch(train) [10][20/63] lr: 6.9527e-03 eta: 1 day, 4:55:00 time: 1.0382 data_time: 0.0346 memory: 16198 loss_prob: 1.6173 loss_thr: 0.6809 loss_db: 0.2722 loss: 2.5704 2022/08/29 23:18:02 - mmengine - INFO - Epoch(train) [10][25/63] lr: 6.9527e-03 eta: 1 day, 4:55:00 time: 1.0522 data_time: 0.0600 memory: 16198 loss_prob: 1.7876 loss_thr: 0.7095 loss_db: 0.3017 loss: 2.7989 2022/08/29 23:18:07 - mmengine - INFO - Epoch(train) [10][30/63] lr: 6.9527e-03 eta: 1 day, 4:44:40 time: 0.9050 data_time: 0.0452 memory: 16198 loss_prob: 1.7075 loss_thr: 0.6853 loss_db: 0.2864 loss: 2.6791 2022/08/29 23:18:11 - mmengine - INFO - Epoch(train) [10][35/63] lr: 6.9527e-03 eta: 1 day, 4:44:40 time: 0.8958 data_time: 0.0330 memory: 16198 loss_prob: 1.5049 loss_thr: 0.6402 loss_db: 0.2498 loss: 2.3949 2022/08/29 23:18:16 - mmengine - INFO - Epoch(train) [10][40/63] lr: 6.9527e-03 eta: 1 day, 4:34:25 time: 0.8932 data_time: 0.0527 memory: 16198 loss_prob: 1.6839 loss_thr: 0.6517 loss_db: 0.2822 loss: 2.6178 2022/08/29 23:18:20 - mmengine - INFO - Epoch(train) [10][45/63] lr: 6.9527e-03 eta: 1 day, 4:34:25 time: 0.9057 data_time: 0.0466 memory: 16198 loss_prob: 1.8664 loss_thr: 0.7008 loss_db: 0.3188 loss: 2.8860 2022/08/29 23:18:25 - mmengine - INFO - Epoch(train) [10][50/63] lr: 6.9527e-03 eta: 1 day, 4:25:33 time: 0.9449 data_time: 0.0420 memory: 16198 loss_prob: 1.9491 loss_thr: 0.7264 loss_db: 0.3342 loss: 3.0097 2022/08/29 23:18:30 - mmengine - INFO - Epoch(train) [10][55/63] lr: 6.9527e-03 eta: 1 day, 4:25:33 time: 0.9415 data_time: 0.0493 memory: 16198 loss_prob: 1.9471 loss_thr: 0.6986 loss_db: 0.3382 loss: 2.9839 2022/08/29 23:18:36 - mmengine - INFO - Epoch(train) [10][60/63] lr: 6.9527e-03 eta: 1 day, 4:19:08 time: 1.0547 data_time: 0.0543 memory: 16198 loss_prob: 1.8017 loss_thr: 0.7031 loss_db: 0.3078 loss: 2.8126 2022/08/29 23:18:39 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:18:48 - mmengine - INFO - Epoch(train) [11][5/63] lr: 6.9474e-03 eta: 1 day, 4:19:08 time: 1.4229 data_time: 0.4447 memory: 16198 loss_prob: 1.6887 loss_thr: 0.6954 loss_db: 0.2785 loss: 2.6626 2022/08/29 23:18:52 - mmengine - INFO - Epoch(train) [11][10/63] lr: 6.9474e-03 eta: 1 day, 4:09:07 time: 1.2700 data_time: 0.4595 memory: 16198 loss_prob: 2.0388 loss_thr: 0.7334 loss_db: 0.3599 loss: 3.1321 2022/08/29 23:18:57 - mmengine - INFO - Epoch(train) [11][15/63] lr: 6.9474e-03 eta: 1 day, 4:09:07 time: 0.9217 data_time: 0.0508 memory: 16198 loss_prob: 1.9680 loss_thr: 0.7238 loss_db: 0.3532 loss: 3.0450 2022/08/29 23:19:01 - mmengine - INFO - Epoch(train) [11][20/63] lr: 6.9474e-03 eta: 1 day, 4:00:37 time: 0.9211 data_time: 0.0343 memory: 16198 loss_prob: 1.5918 loss_thr: 0.6778 loss_db: 0.2696 loss: 2.5393 2022/08/29 23:19:06 - mmengine - INFO - Epoch(train) [11][25/63] lr: 6.9474e-03 eta: 1 day, 4:00:37 time: 0.8992 data_time: 0.0598 memory: 16198 loss_prob: 1.5812 loss_thr: 0.7082 loss_db: 0.2646 loss: 2.5539 2022/08/29 23:19:11 - mmengine - INFO - Epoch(train) [11][30/63] lr: 6.9474e-03 eta: 1 day, 3:52:59 time: 0.9537 data_time: 0.0474 memory: 16198 loss_prob: 1.7290 loss_thr: 0.7281 loss_db: 0.2942 loss: 2.7514 2022/08/29 23:19:16 - mmengine - INFO - Epoch(train) [11][35/63] lr: 6.9474e-03 eta: 1 day, 3:52:59 time: 0.9786 data_time: 0.0324 memory: 16198 loss_prob: 1.7000 loss_thr: 0.7314 loss_db: 0.2866 loss: 2.7180 2022/08/29 23:19:21 - mmengine - INFO - Epoch(train) [11][40/63] lr: 6.9474e-03 eta: 1 day, 3:46:21 time: 0.9960 data_time: 0.0610 memory: 16198 loss_prob: 1.6934 loss_thr: 0.7243 loss_db: 0.2856 loss: 2.7033 2022/08/29 23:19:27 - mmengine - INFO - Epoch(train) [11][45/63] lr: 6.9474e-03 eta: 1 day, 3:46:21 time: 1.1708 data_time: 0.0630 memory: 16198 loss_prob: 1.8387 loss_thr: 0.7015 loss_db: 0.3410 loss: 2.8812 2022/08/29 23:19:33 - mmengine - INFO - Epoch(train) [11][50/63] lr: 6.9474e-03 eta: 1 day, 3:45:01 time: 1.2733 data_time: 0.0649 memory: 16198 loss_prob: 2.0100 loss_thr: 0.7211 loss_db: 0.3764 loss: 3.1076 2022/08/29 23:19:40 - mmengine - INFO - Epoch(train) [11][55/63] lr: 6.9474e-03 eta: 1 day, 3:45:01 time: 1.2440 data_time: 0.0581 memory: 16198 loss_prob: 1.8532 loss_thr: 0.7214 loss_db: 0.3133 loss: 2.8880 2022/08/29 23:19:48 - mmengine - INFO - Epoch(train) [11][60/63] lr: 6.9474e-03 eta: 1 day, 3:47:08 time: 1.4628 data_time: 0.0536 memory: 16198 loss_prob: 1.7460 loss_thr: 0.6976 loss_db: 0.2959 loss: 2.7396 2022/08/29 23:19:52 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:20:04 - mmengine - INFO - Epoch(train) [12][5/63] lr: 6.9422e-03 eta: 1 day, 3:47:08 time: 1.9800 data_time: 0.3073 memory: 16198 loss_prob: 1.8895 loss_thr: 0.7526 loss_db: 0.3212 loss: 2.9633 2022/08/29 23:20:10 - mmengine - INFO - Epoch(train) [12][10/63] lr: 6.9422e-03 eta: 1 day, 3:48:33 time: 1.8326 data_time: 0.3285 memory: 16198 loss_prob: 1.7464 loss_thr: 0.7233 loss_db: 0.2929 loss: 2.7627 2022/08/29 23:20:16 - mmengine - INFO - Epoch(train) [12][15/63] lr: 6.9422e-03 eta: 1 day, 3:48:33 time: 1.1716 data_time: 0.0403 memory: 16198 loss_prob: 1.6816 loss_thr: 0.7042 loss_db: 0.2767 loss: 2.6625 2022/08/29 23:20:24 - mmengine - INFO - Epoch(train) [12][20/63] lr: 6.9422e-03 eta: 1 day, 3:48:54 time: 1.3688 data_time: 0.0437 memory: 16198 loss_prob: 1.7748 loss_thr: 0.7037 loss_db: 0.3010 loss: 2.7796 2022/08/29 23:20:31 - mmengine - INFO - Epoch(train) [12][25/63] lr: 6.9422e-03 eta: 1 day, 3:48:54 time: 1.4954 data_time: 0.0686 memory: 16198 loss_prob: 1.8046 loss_thr: 0.7051 loss_db: 0.3135 loss: 2.8231 2022/08/29 23:20:40 - mmengine - INFO - Epoch(train) [12][30/63] lr: 6.9422e-03 eta: 1 day, 3:52:56 time: 1.5843 data_time: 0.0598 memory: 16198 loss_prob: 1.8559 loss_thr: 0.7295 loss_db: 0.3139 loss: 2.8992 2022/08/29 23:20:47 - mmengine - INFO - Epoch(train) [12][35/63] lr: 6.9422e-03 eta: 1 day, 3:52:56 time: 1.6169 data_time: 0.0564 memory: 16198 loss_prob: 1.8242 loss_thr: 0.7026 loss_db: 0.3000 loss: 2.8268 2022/08/29 23:20:54 - mmengine - INFO - Epoch(train) [12][40/63] lr: 6.9422e-03 eta: 1 day, 3:53:18 time: 1.3744 data_time: 0.0555 memory: 16198 loss_prob: 1.7478 loss_thr: 0.6868 loss_db: 0.2924 loss: 2.7269 2022/08/29 23:20:58 - mmengine - INFO - Epoch(train) [12][45/63] lr: 6.9422e-03 eta: 1 day, 3:53:18 time: 1.0915 data_time: 0.0480 memory: 16198 loss_prob: 1.6796 loss_thr: 0.6865 loss_db: 0.2833 loss: 2.6494 2022/08/29 23:21:03 - mmengine - INFO - Epoch(train) [12][50/63] lr: 6.9422e-03 eta: 1 day, 3:45:51 time: 0.9108 data_time: 0.0612 memory: 16198 loss_prob: 1.6276 loss_thr: 0.6821 loss_db: 0.2662 loss: 2.5759 2022/08/29 23:21:07 - mmengine - INFO - Epoch(train) [12][55/63] lr: 6.9422e-03 eta: 1 day, 3:45:51 time: 0.9179 data_time: 0.0518 memory: 16198 loss_prob: 1.6453 loss_thr: 0.6825 loss_db: 0.2682 loss: 2.5960 2022/08/29 23:21:12 - mmengine - INFO - Epoch(train) [12][60/63] lr: 6.9422e-03 eta: 1 day, 3:38:42 time: 0.9172 data_time: 0.0301 memory: 16198 loss_prob: 1.7802 loss_thr: 0.6944 loss_db: 0.2947 loss: 2.7693 2022/08/29 23:21:14 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:21:22 - mmengine - INFO - Epoch(train) [13][5/63] lr: 6.9369e-03 eta: 1 day, 3:38:42 time: 1.2256 data_time: 0.3777 memory: 16198 loss_prob: 1.7924 loss_thr: 0.7243 loss_db: 0.2996 loss: 2.8163 2022/08/29 23:21:27 - mmengine - INFO - Epoch(train) [13][10/63] lr: 6.9369e-03 eta: 1 day, 3:31:55 time: 1.3299 data_time: 0.3933 memory: 16198 loss_prob: 1.9848 loss_thr: 0.7629 loss_db: 0.3428 loss: 3.0906 2022/08/29 23:21:31 - mmengine - INFO - Epoch(train) [13][15/63] lr: 6.9369e-03 eta: 1 day, 3:31:55 time: 0.9013 data_time: 0.0451 memory: 16198 loss_prob: 1.7959 loss_thr: 0.7255 loss_db: 0.3014 loss: 2.8229 2022/08/29 23:21:35 - mmengine - INFO - Epoch(train) [13][20/63] lr: 6.9369e-03 eta: 1 day, 3:23:10 time: 0.7937 data_time: 0.0233 memory: 16198 loss_prob: 1.5945 loss_thr: 0.6935 loss_db: 0.2615 loss: 2.5495 2022/08/29 23:21:40 - mmengine - INFO - Epoch(train) [13][25/63] lr: 6.9369e-03 eta: 1 day, 3:23:10 time: 0.8338 data_time: 0.0428 memory: 16198 loss_prob: 1.5551 loss_thr: 0.6772 loss_db: 0.2527 loss: 2.4849 2022/08/29 23:21:44 - mmengine - INFO - Epoch(train) [13][30/63] lr: 6.9369e-03 eta: 1 day, 3:15:12 time: 0.8295 data_time: 0.0303 memory: 16198 loss_prob: 1.4545 loss_thr: 0.6674 loss_db: 0.2339 loss: 2.3558 2022/08/29 23:21:48 - mmengine - INFO - Epoch(train) [13][35/63] lr: 6.9369e-03 eta: 1 day, 3:15:12 time: 0.8507 data_time: 0.0192 memory: 16198 loss_prob: 1.6421 loss_thr: 0.6998 loss_db: 0.2739 loss: 2.6159 2022/08/29 23:21:53 - mmengine - INFO - Epoch(train) [13][40/63] lr: 6.9369e-03 eta: 1 day, 3:08:44 time: 0.9129 data_time: 0.0323 memory: 16198 loss_prob: 1.8175 loss_thr: 0.7200 loss_db: 0.3011 loss: 2.8387 2022/08/29 23:21:57 - mmengine - INFO - Epoch(train) [13][45/63] lr: 6.9369e-03 eta: 1 day, 3:08:44 time: 0.8574 data_time: 0.0325 memory: 16198 loss_prob: 1.6473 loss_thr: 0.6841 loss_db: 0.2648 loss: 2.5962 2022/08/29 23:22:01 - mmengine - INFO - Epoch(train) [13][50/63] lr: 6.9369e-03 eta: 1 day, 3:01:17 time: 0.8380 data_time: 0.0293 memory: 16198 loss_prob: 1.6644 loss_thr: 0.6937 loss_db: 0.2788 loss: 2.6370 2022/08/29 23:22:05 - mmengine - INFO - Epoch(train) [13][55/63] lr: 6.9369e-03 eta: 1 day, 3:01:17 time: 0.8549 data_time: 0.0247 memory: 16198 loss_prob: 1.7575 loss_thr: 0.6969 loss_db: 0.3040 loss: 2.7583 2022/08/29 23:22:11 - mmengine - INFO - Epoch(train) [13][60/63] lr: 6.9369e-03 eta: 1 day, 2:55:45 time: 0.9531 data_time: 0.0352 memory: 16198 loss_prob: 1.8773 loss_thr: 0.6939 loss_db: 0.3370 loss: 2.9082 2022/08/29 23:22:13 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:22:19 - mmengine - INFO - Epoch(train) [14][5/63] lr: 6.9317e-03 eta: 1 day, 2:55:45 time: 0.9597 data_time: 0.1981 memory: 16198 loss_prob: 1.7350 loss_thr: 0.6756 loss_db: 0.3000 loss: 2.7106 2022/08/29 23:22:23 - mmengine - INFO - Epoch(train) [14][10/63] lr: 6.9317e-03 eta: 1 day, 2:45:47 time: 1.0405 data_time: 0.2183 memory: 16198 loss_prob: 1.5693 loss_thr: 0.6554 loss_db: 0.2545 loss: 2.4792 2022/08/29 23:22:28 - mmengine - INFO - Epoch(train) [14][15/63] lr: 6.9317e-03 eta: 1 day, 2:45:47 time: 0.8793 data_time: 0.0365 memory: 16198 loss_prob: 1.6430 loss_thr: 0.7006 loss_db: 0.2733 loss: 2.6169 2022/08/29 23:22:32 - mmengine - INFO - Epoch(train) [14][20/63] lr: 6.9317e-03 eta: 1 day, 2:39:31 time: 0.8818 data_time: 0.0277 memory: 16198 loss_prob: 1.6874 loss_thr: 0.7197 loss_db: 0.2795 loss: 2.6866 2022/08/29 23:22:37 - mmengine - INFO - Epoch(train) [14][25/63] lr: 6.9317e-03 eta: 1 day, 2:39:31 time: 0.9714 data_time: 0.0585 memory: 16198 loss_prob: 1.6925 loss_thr: 0.6889 loss_db: 0.2809 loss: 2.6623 2022/08/29 23:22:42 - mmengine - INFO - Epoch(train) [14][30/63] lr: 6.9317e-03 eta: 1 day, 2:35:02 time: 0.9917 data_time: 0.0519 memory: 16198 loss_prob: 1.6473 loss_thr: 0.6854 loss_db: 0.2743 loss: 2.6070 2022/08/29 23:22:47 - mmengine - INFO - Epoch(train) [14][35/63] lr: 6.9317e-03 eta: 1 day, 2:35:02 time: 0.9748 data_time: 0.0331 memory: 16198 loss_prob: 1.6176 loss_thr: 0.6931 loss_db: 0.2716 loss: 2.5824 2022/08/29 23:22:52 - mmengine - INFO - Epoch(train) [14][40/63] lr: 6.9317e-03 eta: 1 day, 2:30:27 time: 0.9796 data_time: 0.0530 memory: 16198 loss_prob: 1.6511 loss_thr: 0.6829 loss_db: 0.2751 loss: 2.6091 2022/08/29 23:22:56 - mmengine - INFO - Epoch(train) [14][45/63] lr: 6.9317e-03 eta: 1 day, 2:30:27 time: 0.9207 data_time: 0.0489 memory: 16198 loss_prob: 1.5938 loss_thr: 0.6654 loss_db: 0.2608 loss: 2.5201 2022/08/29 23:23:01 - mmengine - INFO - Epoch(train) [14][50/63] lr: 6.9317e-03 eta: 1 day, 2:25:02 time: 0.9138 data_time: 0.0458 memory: 16198 loss_prob: 1.7038 loss_thr: 0.6807 loss_db: 0.2772 loss: 2.6617 2022/08/29 23:23:05 - mmengine - INFO - Epoch(train) [14][55/63] lr: 6.9317e-03 eta: 1 day, 2:25:02 time: 0.9182 data_time: 0.0555 memory: 16198 loss_prob: 1.6084 loss_thr: 0.6576 loss_db: 0.2523 loss: 2.5182 2022/08/29 23:23:11 - mmengine - INFO - Epoch(train) [14][60/63] lr: 6.9317e-03 eta: 1 day, 2:21:16 time: 1.0214 data_time: 0.0475 memory: 16198 loss_prob: 1.3753 loss_thr: 0.6140 loss_db: 0.2110 loss: 2.2003 2022/08/29 23:23:14 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:23:21 - mmengine - INFO - Epoch(train) [15][5/63] lr: 6.9264e-03 eta: 1 day, 2:21:16 time: 1.2295 data_time: 0.3687 memory: 16198 loss_prob: 1.5359 loss_thr: 0.6441 loss_db: 0.2485 loss: 2.4286 2022/08/29 23:23:26 - mmengine - INFO - Epoch(train) [15][10/63] lr: 6.9264e-03 eta: 1 day, 2:14:45 time: 1.2039 data_time: 0.3696 memory: 16198 loss_prob: 1.5010 loss_thr: 0.6220 loss_db: 0.2407 loss: 2.3637 2022/08/29 23:23:31 - mmengine - INFO - Epoch(train) [15][15/63] lr: 6.9264e-03 eta: 1 day, 2:14:45 time: 0.9403 data_time: 0.0485 memory: 16198 loss_prob: 1.4203 loss_thr: 0.6472 loss_db: 0.2269 loss: 2.2944 2022/08/29 23:23:37 - mmengine - INFO - Epoch(train) [15][20/63] lr: 6.9264e-03 eta: 1 day, 2:12:40 time: 1.1284 data_time: 0.0367 memory: 16198 loss_prob: 1.5125 loss_thr: 0.6741 loss_db: 0.2505 loss: 2.4371 2022/08/29 23:23:44 - mmengine - INFO - Epoch(train) [15][25/63] lr: 6.9264e-03 eta: 1 day, 2:12:40 time: 1.3178 data_time: 0.0543 memory: 16198 loss_prob: 1.5731 loss_thr: 0.6783 loss_db: 0.2657 loss: 2.5171 2022/08/29 23:23:49 - mmengine - INFO - Epoch(train) [15][30/63] lr: 6.9264e-03 eta: 1 day, 2:11:43 time: 1.2093 data_time: 0.0541 memory: 16198 loss_prob: 1.5123 loss_thr: 0.7017 loss_db: 0.2507 loss: 2.4647 2022/08/29 23:23:54 - mmengine - INFO - Epoch(train) [15][35/63] lr: 6.9264e-03 eta: 1 day, 2:11:43 time: 0.9707 data_time: 0.0539 memory: 16198 loss_prob: 1.5792 loss_thr: 0.7142 loss_db: 0.2606 loss: 2.5540 2022/08/29 23:23:59 - mmengine - INFO - Epoch(train) [15][40/63] lr: 6.9264e-03 eta: 1 day, 2:07:22 time: 0.9559 data_time: 0.0527 memory: 16198 loss_prob: 1.7024 loss_thr: 0.6947 loss_db: 0.2822 loss: 2.6793 2022/08/29 23:24:03 - mmengine - INFO - Epoch(train) [15][45/63] lr: 6.9264e-03 eta: 1 day, 2:07:22 time: 0.9737 data_time: 0.0373 memory: 16198 loss_prob: 1.6721 loss_thr: 0.6994 loss_db: 0.2781 loss: 2.6496 2022/08/29 23:24:08 - mmengine - INFO - Epoch(train) [15][50/63] lr: 6.9264e-03 eta: 1 day, 2:02:54 time: 0.9410 data_time: 0.0502 memory: 16198 loss_prob: 1.5568 loss_thr: 0.7061 loss_db: 0.2590 loss: 2.5219 2022/08/29 23:24:13 - mmengine - INFO - Epoch(train) [15][55/63] lr: 6.9264e-03 eta: 1 day, 2:02:54 time: 0.9249 data_time: 0.0456 memory: 16198 loss_prob: 1.5901 loss_thr: 0.7022 loss_db: 0.2602 loss: 2.5525 2022/08/29 23:24:18 - mmengine - INFO - Epoch(train) [15][60/63] lr: 6.9264e-03 eta: 1 day, 1:59:04 time: 0.9805 data_time: 0.0505 memory: 16198 loss_prob: 1.6987 loss_thr: 0.7113 loss_db: 0.2840 loss: 2.6940 2022/08/29 23:24:20 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:24:28 - mmengine - INFO - Epoch(train) [16][5/63] lr: 6.9211e-03 eta: 1 day, 1:59:04 time: 1.2193 data_time: 0.3855 memory: 16198 loss_prob: 1.4916 loss_thr: 0.6385 loss_db: 0.2478 loss: 2.3779 2022/08/29 23:24:33 - mmengine - INFO - Epoch(train) [16][10/63] lr: 6.9211e-03 eta: 1 day, 1:54:15 time: 1.2801 data_time: 0.3916 memory: 16198 loss_prob: 1.5386 loss_thr: 0.6653 loss_db: 0.2551 loss: 2.4589 2022/08/29 23:24:38 - mmengine - INFO - Epoch(train) [16][15/63] lr: 6.9211e-03 eta: 1 day, 1:54:15 time: 0.9869 data_time: 0.0534 memory: 16198 loss_prob: 1.7507 loss_thr: 0.6650 loss_db: 0.2778 loss: 2.6935 2022/08/29 23:24:43 - mmengine - INFO - Epoch(train) [16][20/63] lr: 6.9211e-03 eta: 1 day, 1:50:08 time: 0.9460 data_time: 0.0344 memory: 16198 loss_prob: 1.7388 loss_thr: 0.6515 loss_db: 0.2748 loss: 2.6651 2022/08/29 23:24:47 - mmengine - INFO - Epoch(train) [16][25/63] lr: 6.9211e-03 eta: 1 day, 1:50:08 time: 0.9414 data_time: 0.0472 memory: 16198 loss_prob: 1.7012 loss_thr: 0.6684 loss_db: 0.2814 loss: 2.6510 2022/08/29 23:24:53 - mmengine - INFO - Epoch(train) [16][30/63] lr: 6.9211e-03 eta: 1 day, 1:46:51 time: 1.0062 data_time: 0.0451 memory: 16198 loss_prob: 1.5510 loss_thr: 0.6539 loss_db: 0.2494 loss: 2.4543 2022/08/29 23:24:58 - mmengine - INFO - Epoch(train) [16][35/63] lr: 6.9211e-03 eta: 1 day, 1:46:51 time: 1.0281 data_time: 0.0497 memory: 16198 loss_prob: 1.3264 loss_thr: 0.6474 loss_db: 0.2112 loss: 2.1850 2022/08/29 23:25:02 - mmengine - INFO - Epoch(train) [16][40/63] lr: 6.9211e-03 eta: 1 day, 1:43:15 time: 0.9748 data_time: 0.0482 memory: 16198 loss_prob: 1.3900 loss_thr: 0.6818 loss_db: 0.2255 loss: 2.2974 2022/08/29 23:25:07 - mmengine - INFO - Epoch(train) [16][45/63] lr: 6.9211e-03 eta: 1 day, 1:43:15 time: 0.9864 data_time: 0.0366 memory: 16198 loss_prob: 1.6192 loss_thr: 0.7051 loss_db: 0.2691 loss: 2.5935 2022/08/29 23:25:12 - mmengine - INFO - Epoch(train) [16][50/63] lr: 6.9211e-03 eta: 1 day, 1:39:45 time: 0.9772 data_time: 0.0574 memory: 16198 loss_prob: 1.6385 loss_thr: 0.6758 loss_db: 0.2711 loss: 2.5855 2022/08/29 23:25:17 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:25:17 - mmengine - INFO - Epoch(train) [16][55/63] lr: 6.9211e-03 eta: 1 day, 1:39:45 time: 0.9693 data_time: 0.0493 memory: 16198 loss_prob: 1.5889 loss_thr: 0.6847 loss_db: 0.2487 loss: 2.5223 2022/08/29 23:25:22 - mmengine - INFO - Epoch(train) [16][60/63] lr: 6.9211e-03 eta: 1 day, 1:35:56 time: 0.9469 data_time: 0.0325 memory: 16198 loss_prob: 1.5605 loss_thr: 0.6546 loss_db: 0.2426 loss: 2.4577 2022/08/29 23:25:24 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:25:32 - mmengine - INFO - Epoch(train) [17][5/63] lr: 6.9159e-03 eta: 1 day, 1:35:56 time: 1.2087 data_time: 0.3372 memory: 16198 loss_prob: 1.5927 loss_thr: 0.6541 loss_db: 0.2634 loss: 2.5102 2022/08/29 23:25:37 - mmengine - INFO - Epoch(train) [17][10/63] lr: 6.9159e-03 eta: 1 day, 1:31:16 time: 1.2455 data_time: 0.3524 memory: 16198 loss_prob: 1.6643 loss_thr: 0.6673 loss_db: 0.2739 loss: 2.6055 2022/08/29 23:25:41 - mmengine - INFO - Epoch(train) [17][15/63] lr: 6.9159e-03 eta: 1 day, 1:31:16 time: 0.9066 data_time: 0.0593 memory: 16198 loss_prob: 1.6110 loss_thr: 0.6689 loss_db: 0.2657 loss: 2.5456 2022/08/29 23:25:45 - mmengine - INFO - Epoch(train) [17][20/63] lr: 6.9159e-03 eta: 1 day, 1:26:31 time: 0.8561 data_time: 0.0284 memory: 16198 loss_prob: 1.4250 loss_thr: 0.6574 loss_db: 0.2334 loss: 2.3158 2022/08/29 23:25:49 - mmengine - INFO - Epoch(train) [17][25/63] lr: 6.9159e-03 eta: 1 day, 1:26:31 time: 0.8463 data_time: 0.0442 memory: 16198 loss_prob: 1.4632 loss_thr: 0.6461 loss_db: 0.2406 loss: 2.3499 2022/08/29 23:25:54 - mmengine - INFO - Epoch(train) [17][30/63] lr: 6.9159e-03 eta: 1 day, 1:22:26 time: 0.9038 data_time: 0.0506 memory: 16198 loss_prob: 1.5567 loss_thr: 0.6519 loss_db: 0.2552 loss: 2.4639 2022/08/29 23:25:58 - mmengine - INFO - Epoch(train) [17][35/63] lr: 6.9159e-03 eta: 1 day, 1:22:26 time: 0.8862 data_time: 0.0432 memory: 16198 loss_prob: 1.5438 loss_thr: 0.6506 loss_db: 0.2550 loss: 2.4494 2022/08/29 23:26:03 - mmengine - INFO - Epoch(train) [17][40/63] lr: 6.9159e-03 eta: 1 day, 1:18:06 time: 0.8776 data_time: 0.0489 memory: 16198 loss_prob: 1.4687 loss_thr: 0.6608 loss_db: 0.2451 loss: 2.3745 2022/08/29 23:26:08 - mmengine - INFO - Epoch(train) [17][45/63] lr: 6.9159e-03 eta: 1 day, 1:18:06 time: 0.9290 data_time: 0.0441 memory: 16198 loss_prob: 1.4982 loss_thr: 0.6608 loss_db: 0.2460 loss: 2.4049 2022/08/29 23:26:12 - mmengine - INFO - Epoch(train) [17][50/63] lr: 6.9159e-03 eta: 1 day, 1:14:34 time: 0.9369 data_time: 0.0386 memory: 16198 loss_prob: 1.4314 loss_thr: 0.6283 loss_db: 0.2340 loss: 2.2937 2022/08/29 23:26:18 - mmengine - INFO - Epoch(train) [17][55/63] lr: 6.9159e-03 eta: 1 day, 1:14:34 time: 1.0390 data_time: 0.0525 memory: 16198 loss_prob: 1.3673 loss_thr: 0.5969 loss_db: 0.2224 loss: 2.1867 2022/08/29 23:26:23 - mmengine - INFO - Epoch(train) [17][60/63] lr: 6.9159e-03 eta: 1 day, 1:12:21 time: 1.0468 data_time: 0.0465 memory: 16198 loss_prob: 1.3047 loss_thr: 0.6170 loss_db: 0.2082 loss: 2.1298 2022/08/29 23:26:25 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:26:33 - mmengine - INFO - Epoch(train) [18][5/63] lr: 6.9106e-03 eta: 1 day, 1:12:21 time: 1.2446 data_time: 0.3935 memory: 16198 loss_prob: 1.3011 loss_thr: 0.6306 loss_db: 0.2097 loss: 2.1414 2022/08/29 23:26:39 - mmengine - INFO - Epoch(train) [18][10/63] lr: 6.9106e-03 eta: 1 day, 1:09:47 time: 1.3822 data_time: 0.4184 memory: 16198 loss_prob: 1.4306 loss_thr: 0.6637 loss_db: 0.2360 loss: 2.3304 2022/08/29 23:26:44 - mmengine - INFO - Epoch(train) [18][15/63] lr: 6.9106e-03 eta: 1 day, 1:09:47 time: 1.0320 data_time: 0.0657 memory: 16198 loss_prob: 1.5539 loss_thr: 0.6920 loss_db: 0.2559 loss: 2.5018 2022/08/29 23:26:49 - mmengine - INFO - Epoch(train) [18][20/63] lr: 6.9106e-03 eta: 1 day, 1:06:44 time: 0.9661 data_time: 0.0544 memory: 16198 loss_prob: 1.5998 loss_thr: 0.6954 loss_db: 0.2710 loss: 2.5661 2022/08/29 23:26:53 - mmengine - INFO - Epoch(train) [18][25/63] lr: 6.9106e-03 eta: 1 day, 1:06:44 time: 0.9459 data_time: 0.0595 memory: 16198 loss_prob: 1.5841 loss_thr: 0.6647 loss_db: 0.2728 loss: 2.5215 2022/08/29 23:26:58 - mmengine - INFO - Epoch(train) [18][30/63] lr: 6.9106e-03 eta: 1 day, 1:02:59 time: 0.8980 data_time: 0.0332 memory: 16198 loss_prob: 1.5284 loss_thr: 0.6239 loss_db: 0.2524 loss: 2.4047 2022/08/29 23:27:02 - mmengine - INFO - Epoch(train) [18][35/63] lr: 6.9106e-03 eta: 1 day, 1:02:59 time: 0.8946 data_time: 0.0474 memory: 16198 loss_prob: 1.5464 loss_thr: 0.6595 loss_db: 0.2588 loss: 2.4648 2022/08/29 23:27:07 - mmengine - INFO - Epoch(train) [18][40/63] lr: 6.9106e-03 eta: 1 day, 0:59:40 time: 0.9312 data_time: 0.0546 memory: 16198 loss_prob: 1.4871 loss_thr: 0.6579 loss_db: 0.2513 loss: 2.3964 2022/08/29 23:27:11 - mmengine - INFO - Epoch(train) [18][45/63] lr: 6.9106e-03 eta: 1 day, 0:59:40 time: 0.9090 data_time: 0.0392 memory: 16198 loss_prob: 1.5298 loss_thr: 0.6738 loss_db: 0.2500 loss: 2.4536 2022/08/29 23:27:16 - mmengine - INFO - Epoch(train) [18][50/63] lr: 6.9106e-03 eta: 1 day, 0:56:23 time: 0.9296 data_time: 0.0627 memory: 16198 loss_prob: 1.6281 loss_thr: 0.6990 loss_db: 0.2675 loss: 2.5946 2022/08/29 23:27:21 - mmengine - INFO - Epoch(train) [18][55/63] lr: 6.9106e-03 eta: 1 day, 0:56:23 time: 0.9854 data_time: 0.0556 memory: 16198 loss_prob: 1.6516 loss_thr: 0.6897 loss_db: 0.2755 loss: 2.6168 2022/08/29 23:27:26 - mmengine - INFO - Epoch(train) [18][60/63] lr: 6.9106e-03 eta: 1 day, 0:53:24 time: 0.9526 data_time: 0.0312 memory: 16198 loss_prob: 1.5019 loss_thr: 0.6836 loss_db: 0.2484 loss: 2.4339 2022/08/29 23:27:29 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:27:37 - mmengine - INFO - Epoch(train) [19][5/63] lr: 6.9054e-03 eta: 1 day, 0:53:24 time: 1.3097 data_time: 0.3511 memory: 16198 loss_prob: 1.5171 loss_thr: 0.6715 loss_db: 0.2519 loss: 2.4405 2022/08/29 23:27:42 - mmengine - INFO - Epoch(train) [19][10/63] lr: 6.9054e-03 eta: 1 day, 0:50:07 time: 1.2853 data_time: 0.3568 memory: 16198 loss_prob: 1.5701 loss_thr: 0.6503 loss_db: 0.2602 loss: 2.4807 2022/08/29 23:27:46 - mmengine - INFO - Epoch(train) [19][15/63] lr: 6.9054e-03 eta: 1 day, 0:50:07 time: 0.9214 data_time: 0.0483 memory: 16198 loss_prob: 1.3375 loss_thr: 0.6225 loss_db: 0.2128 loss: 2.1728 2022/08/29 23:27:51 - mmengine - INFO - Epoch(train) [19][20/63] lr: 6.9054e-03 eta: 1 day, 0:47:26 time: 0.9704 data_time: 0.0904 memory: 16198 loss_prob: 1.3443 loss_thr: 0.6304 loss_db: 0.2150 loss: 2.1897 2022/08/29 23:27:56 - mmengine - INFO - Epoch(train) [19][25/63] lr: 6.9054e-03 eta: 1 day, 0:47:26 time: 0.9829 data_time: 0.1105 memory: 16198 loss_prob: 1.2820 loss_thr: 0.6076 loss_db: 0.2108 loss: 2.1004 2022/08/29 23:28:01 - mmengine - INFO - Epoch(train) [19][30/63] lr: 6.9054e-03 eta: 1 day, 0:44:52 time: 0.9771 data_time: 0.0550 memory: 16198 loss_prob: 1.3177 loss_thr: 0.6097 loss_db: 0.2188 loss: 2.1461 2022/08/29 23:28:06 - mmengine - INFO - Epoch(train) [19][35/63] lr: 6.9054e-03 eta: 1 day, 0:44:52 time: 0.9646 data_time: 0.0474 memory: 16198 loss_prob: 1.3779 loss_thr: 0.6159 loss_db: 0.2281 loss: 2.2219 2022/08/29 23:28:11 - mmengine - INFO - Epoch(train) [19][40/63] lr: 6.9054e-03 eta: 1 day, 0:42:06 time: 0.9538 data_time: 0.0481 memory: 16198 loss_prob: 1.3391 loss_thr: 0.6193 loss_db: 0.2185 loss: 2.1769 2022/08/29 23:28:15 - mmengine - INFO - Epoch(train) [19][45/63] lr: 6.9054e-03 eta: 1 day, 0:42:06 time: 0.9288 data_time: 0.0465 memory: 16198 loss_prob: 1.4039 loss_thr: 0.6281 loss_db: 0.2301 loss: 2.2621 2022/08/29 23:28:20 - mmengine - INFO - Epoch(train) [19][50/63] lr: 6.9054e-03 eta: 1 day, 0:38:43 time: 0.8896 data_time: 0.0377 memory: 16198 loss_prob: 1.4497 loss_thr: 0.6350 loss_db: 0.2377 loss: 2.3225 2022/08/29 23:28:25 - mmengine - INFO - Epoch(train) [19][55/63] lr: 6.9054e-03 eta: 1 day, 0:38:43 time: 0.9419 data_time: 0.0576 memory: 16198 loss_prob: 1.4866 loss_thr: 0.6608 loss_db: 0.2437 loss: 2.3910 2022/08/29 23:28:29 - mmengine - INFO - Epoch(train) [19][60/63] lr: 6.9054e-03 eta: 1 day, 0:35:35 time: 0.9102 data_time: 0.0563 memory: 16198 loss_prob: 1.4657 loss_thr: 0.6683 loss_db: 0.2425 loss: 2.3764 2022/08/29 23:28:31 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:28:40 - mmengine - INFO - Epoch(train) [20][5/63] lr: 6.9001e-03 eta: 1 day, 0:35:35 time: 1.3265 data_time: 0.4498 memory: 16198 loss_prob: 1.4597 loss_thr: 0.6595 loss_db: 0.2433 loss: 2.3625 2022/08/29 23:28:47 - mmengine - INFO - Epoch(train) [20][10/63] lr: 6.9001e-03 eta: 1 day, 0:35:35 time: 1.5728 data_time: 0.4766 memory: 16198 loss_prob: 1.4807 loss_thr: 0.6617 loss_db: 0.2442 loss: 2.3866 2022/08/29 23:28:52 - mmengine - INFO - Epoch(train) [20][15/63] lr: 6.9001e-03 eta: 1 day, 0:35:35 time: 1.1349 data_time: 0.0537 memory: 16198 loss_prob: 1.3303 loss_thr: 0.6761 loss_db: 0.2163 loss: 2.2227 2022/08/29 23:28:56 - mmengine - INFO - Epoch(train) [20][20/63] lr: 6.9001e-03 eta: 1 day, 0:33:07 time: 0.9665 data_time: 0.0518 memory: 16198 loss_prob: 1.4417 loss_thr: 0.6817 loss_db: 0.2373 loss: 2.3607 2022/08/29 23:29:01 - mmengine - INFO - Epoch(train) [20][25/63] lr: 6.9001e-03 eta: 1 day, 0:33:07 time: 0.9148 data_time: 0.0617 memory: 16198 loss_prob: 1.4121 loss_thr: 0.6446 loss_db: 0.2304 loss: 2.2872 2022/08/29 23:29:05 - mmengine - INFO - Epoch(train) [20][30/63] lr: 6.9001e-03 eta: 1 day, 0:30:00 time: 0.8999 data_time: 0.0295 memory: 16198 loss_prob: 1.3672 loss_thr: 0.6598 loss_db: 0.2204 loss: 2.2474 2022/08/29 23:29:10 - mmengine - INFO - Epoch(train) [20][35/63] lr: 6.9001e-03 eta: 1 day, 0:30:00 time: 0.9603 data_time: 0.0455 memory: 16198 loss_prob: 1.5281 loss_thr: 0.6898 loss_db: 0.2522 loss: 2.4701 2022/08/29 23:29:15 - mmengine - INFO - Epoch(train) [20][40/63] lr: 6.9001e-03 eta: 1 day, 0:27:32 time: 0.9601 data_time: 0.0520 memory: 16198 loss_prob: 1.6206 loss_thr: 0.6663 loss_db: 0.2667 loss: 2.5536 2022/08/29 23:29:19 - mmengine - INFO - Epoch(train) [20][45/63] lr: 6.9001e-03 eta: 1 day, 0:27:32 time: 0.8736 data_time: 0.0355 memory: 16198 loss_prob: 1.5646 loss_thr: 0.6719 loss_db: 0.2557 loss: 2.4922 2022/08/29 23:29:24 - mmengine - INFO - Epoch(train) [20][50/63] lr: 6.9001e-03 eta: 1 day, 0:24:37 time: 0.9103 data_time: 0.0645 memory: 16198 loss_prob: 1.4981 loss_thr: 0.6858 loss_db: 0.2459 loss: 2.4297 2022/08/29 23:29:28 - mmengine - INFO - Epoch(train) [20][55/63] lr: 6.9001e-03 eta: 1 day, 0:24:37 time: 0.8994 data_time: 0.0600 memory: 16198 loss_prob: 1.5106 loss_thr: 0.6774 loss_db: 0.2557 loss: 2.4437 2022/08/29 23:29:33 - mmengine - INFO - Epoch(train) [20][60/63] lr: 6.9001e-03 eta: 1 day, 0:21:11 time: 0.8531 data_time: 0.0399 memory: 16198 loss_prob: 1.3867 loss_thr: 0.6354 loss_db: 0.2317 loss: 2.2538 2022/08/29 23:29:36 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:29:36 - mmengine - INFO - Saving checkpoint at 20 epochs 2022/08/29 23:33:35 - mmengine - INFO - Epoch(val) [20][5/32] eta: 1 day, 0:21:11 time: 46.9137 data_time: 44.3905 memory: 40873 2022/08/29 23:33:39 - mmengine - INFO - Epoch(val) [20][10/32] eta: 0:08:42 time: 23.7697 data_time: 22.2234 memory: 15734 2022/08/29 23:33:42 - mmengine - INFO - Epoch(val) [20][15/32] eta: 0:08:42 time: 0.6335 data_time: 0.0721 memory: 15734 2022/08/29 23:33:45 - mmengine - INFO - Epoch(val) [20][20/32] eta: 0:00:07 time: 0.6387 data_time: 0.0710 memory: 15734 2022/08/29 23:33:49 - mmengine - INFO - Epoch(val) [20][25/32] eta: 0:00:07 time: 0.7234 data_time: 0.0536 memory: 15734 2022/08/29 23:33:52 - mmengine - INFO - Epoch(val) [20][30/32] eta: 0:00:01 time: 0.7059 data_time: 0.0436 memory: 15734 2022/08/29 23:34:01 - mmengine - INFO - Evaluating hmean-iou... 2022/08/29 23:34:01 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.7660, precision: 0.6336, hmean: 0.6935 2022/08/29 23:34:01 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.7655, precision: 0.7371, hmean: 0.7511 2022/08/29 23:34:01 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.7583, precision: 0.7955, hmean: 0.7764 2022/08/29 23:34:01 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7304, precision: 0.8317, hmean: 0.7777 2022/08/29 23:34:01 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.6716, precision: 0.8897, hmean: 0.7654 2022/08/29 23:34:01 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.2860, precision: 0.9690, hmean: 0.4416 2022/08/29 23:34:01 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/08/29 23:34:01 - mmengine - INFO - Epoch(val) [20][32/32] icdar/precision: 0.8317 icdar/recall: 0.7304 icdar/hmean: 0.7777 2022/08/29 23:34:09 - mmengine - INFO - Epoch(train) [21][5/63] lr: 6.8948e-03 eta: 0:00:01 time: 1.2915 data_time: 0.3363 memory: 35430 loss_prob: 1.4544 loss_thr: 0.6290 loss_db: 0.2390 loss: 2.3224 2022/08/29 23:34:13 - mmengine - INFO - Epoch(train) [21][10/63] lr: 6.8948e-03 eta: 1 day, 0:17:37 time: 1.1935 data_time: 0.3312 memory: 16202 loss_prob: 1.6650 loss_thr: 0.6455 loss_db: 0.2785 loss: 2.5890 2022/08/29 23:34:18 - mmengine - INFO - Epoch(train) [21][15/63] lr: 6.8948e-03 eta: 1 day, 0:17:37 time: 0.8585 data_time: 0.0413 memory: 16202 loss_prob: 1.5297 loss_thr: 0.6692 loss_db: 0.2558 loss: 2.4547 2022/08/29 23:34:22 - mmengine - INFO - Epoch(train) [21][20/63] lr: 6.8948e-03 eta: 1 day, 0:14:23 time: 0.8635 data_time: 0.0464 memory: 16202 loss_prob: 1.3933 loss_thr: 0.6406 loss_db: 0.2312 loss: 2.2651 2022/08/29 23:34:27 - mmengine - INFO - Epoch(train) [21][25/63] lr: 6.8948e-03 eta: 1 day, 0:14:23 time: 0.8814 data_time: 0.0388 memory: 16202 loss_prob: 1.4951 loss_thr: 0.6574 loss_db: 0.2457 loss: 2.3982 2022/08/29 23:34:32 - mmengine - INFO - Epoch(train) [21][30/63] lr: 6.8948e-03 eta: 1 day, 0:12:08 time: 0.9589 data_time: 0.0487 memory: 16202 loss_prob: 1.5122 loss_thr: 0.6810 loss_db: 0.2484 loss: 2.4417 2022/08/29 23:34:37 - mmengine - INFO - Epoch(train) [21][35/63] lr: 6.8948e-03 eta: 1 day, 0:12:08 time: 1.0102 data_time: 0.0600 memory: 16202 loss_prob: 1.4350 loss_thr: 0.6495 loss_db: 0.2403 loss: 2.3248 2022/08/29 23:34:41 - mmengine - INFO - Epoch(train) [21][40/63] lr: 6.8948e-03 eta: 1 day, 0:09:38 time: 0.9319 data_time: 0.0448 memory: 16202 loss_prob: 1.5186 loss_thr: 0.6477 loss_db: 0.2595 loss: 2.4258 2022/08/29 23:34:45 - mmengine - INFO - Epoch(train) [21][45/63] lr: 6.8948e-03 eta: 1 day, 0:09:38 time: 0.8674 data_time: 0.0453 memory: 16202 loss_prob: 1.5045 loss_thr: 0.6463 loss_db: 0.2504 loss: 2.4013 2022/08/29 23:34:50 - mmengine - INFO - Epoch(train) [21][50/63] lr: 6.8948e-03 eta: 1 day, 0:07:06 time: 0.9217 data_time: 0.0508 memory: 16202 loss_prob: 1.4817 loss_thr: 0.6732 loss_db: 0.2386 loss: 2.3936 2022/08/29 23:34:55 - mmengine - INFO - Epoch(train) [21][55/63] lr: 6.8948e-03 eta: 1 day, 0:07:06 time: 1.0112 data_time: 0.0670 memory: 16202 loss_prob: 1.5341 loss_thr: 0.6675 loss_db: 0.2483 loss: 2.4499 2022/08/29 23:35:00 - mmengine - INFO - Epoch(train) [21][60/63] lr: 6.8948e-03 eta: 1 day, 0:05:20 time: 1.0026 data_time: 0.0770 memory: 16202 loss_prob: 1.5022 loss_thr: 0.6482 loss_db: 0.2439 loss: 2.3942 2022/08/29 23:35:03 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:35:10 - mmengine - INFO - Epoch(train) [22][5/63] lr: 6.8896e-03 eta: 1 day, 0:05:20 time: 1.1106 data_time: 0.3408 memory: 16202 loss_prob: 1.5542 loss_thr: 0.6691 loss_db: 0.2537 loss: 2.4770 2022/08/29 23:35:14 - mmengine - INFO - Epoch(train) [22][10/63] lr: 6.8896e-03 eta: 1 day, 0:01:31 time: 1.1332 data_time: 0.3210 memory: 16202 loss_prob: 1.5305 loss_thr: 0.6777 loss_db: 0.2498 loss: 2.4580 2022/08/29 23:35:19 - mmengine - INFO - Epoch(train) [22][15/63] lr: 6.8896e-03 eta: 1 day, 0:01:31 time: 0.9209 data_time: 0.0960 memory: 16202 loss_prob: 1.4580 loss_thr: 0.6596 loss_db: 0.2433 loss: 2.3610 2022/08/29 23:35:24 - mmengine - INFO - Epoch(train) [22][20/63] lr: 6.8896e-03 eta: 23:59:19 time: 0.9475 data_time: 0.0910 memory: 16202 loss_prob: 1.3133 loss_thr: 0.6269 loss_db: 0.2174 loss: 2.1577 2022/08/29 23:35:28 - mmengine - INFO - Epoch(train) [22][25/63] lr: 6.8896e-03 eta: 23:59:19 time: 0.9396 data_time: 0.0828 memory: 16202 loss_prob: 1.4296 loss_thr: 0.6263 loss_db: 0.2375 loss: 2.2934 2022/08/29 23:35:33 - mmengine - INFO - Epoch(train) [22][30/63] lr: 6.8896e-03 eta: 23:57:35 time: 0.9948 data_time: 0.1119 memory: 16202 loss_prob: 1.6027 loss_thr: 0.6662 loss_db: 0.2672 loss: 2.5362 2022/08/29 23:35:38 - mmengine - INFO - Epoch(train) [22][35/63] lr: 6.8896e-03 eta: 23:57:35 time: 0.9809 data_time: 0.0882 memory: 16202 loss_prob: 1.5199 loss_thr: 0.6599 loss_db: 0.2494 loss: 2.4292 2022/08/29 23:35:42 - mmengine - INFO - Epoch(train) [22][40/63] lr: 6.8896e-03 eta: 23:55:01 time: 0.8990 data_time: 0.0506 memory: 16202 loss_prob: 1.4096 loss_thr: 0.6328 loss_db: 0.2301 loss: 2.2724 2022/08/29 23:35:47 - mmengine - INFO - Epoch(train) [22][45/63] lr: 6.8896e-03 eta: 23:55:01 time: 0.9068 data_time: 0.0923 memory: 16202 loss_prob: 1.3952 loss_thr: 0.6218 loss_db: 0.2281 loss: 2.2451 2022/08/29 23:35:52 - mmengine - INFO - Epoch(train) [22][50/63] lr: 6.8896e-03 eta: 23:52:48 time: 0.9365 data_time: 0.1087 memory: 16202 loss_prob: 1.4271 loss_thr: 0.6381 loss_db: 0.2296 loss: 2.2948 2022/08/29 23:35:57 - mmengine - INFO - Epoch(train) [22][55/63] lr: 6.8896e-03 eta: 23:52:48 time: 0.9886 data_time: 0.0701 memory: 16202 loss_prob: 1.3463 loss_thr: 0.6244 loss_db: 0.2142 loss: 2.1849 2022/08/29 23:36:02 - mmengine - INFO - Epoch(train) [22][60/63] lr: 6.8896e-03 eta: 23:51:13 time: 1.0016 data_time: 0.0907 memory: 16202 loss_prob: 1.3727 loss_thr: 0.6216 loss_db: 0.2224 loss: 2.2167 2022/08/29 23:36:04 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:36:11 - mmengine - INFO - Epoch(train) [23][5/63] lr: 6.8843e-03 eta: 23:51:13 time: 1.0755 data_time: 0.3039 memory: 16202 loss_prob: 1.5429 loss_thr: 0.6817 loss_db: 0.2515 loss: 2.4761 2022/08/29 23:36:17 - mmengine - INFO - Epoch(train) [23][10/63] lr: 6.8843e-03 eta: 23:48:32 time: 1.2307 data_time: 0.3347 memory: 16202 loss_prob: 1.4682 loss_thr: 0.6706 loss_db: 0.2375 loss: 2.3763 2022/08/29 23:36:22 - mmengine - INFO - Epoch(train) [23][15/63] lr: 6.8843e-03 eta: 23:48:32 time: 1.0872 data_time: 0.1027 memory: 16202 loss_prob: 1.4465 loss_thr: 0.6414 loss_db: 0.2372 loss: 2.3251 2022/08/29 23:36:27 - mmengine - INFO - Epoch(train) [23][20/63] lr: 6.8843e-03 eta: 23:46:57 time: 0.9967 data_time: 0.0978 memory: 16202 loss_prob: 1.4969 loss_thr: 0.6203 loss_db: 0.2481 loss: 2.3653 2022/08/29 23:36:31 - mmengine - INFO - Epoch(train) [23][25/63] lr: 6.8843e-03 eta: 23:46:57 time: 0.9382 data_time: 0.0844 memory: 16202 loss_prob: 1.4366 loss_thr: 0.6274 loss_db: 0.2350 loss: 2.2990 2022/08/29 23:36:36 - mmengine - INFO - Epoch(train) [23][30/63] lr: 6.8843e-03 eta: 23:44:49 time: 0.9318 data_time: 0.0443 memory: 16202 loss_prob: 1.3609 loss_thr: 0.6506 loss_db: 0.2199 loss: 2.2313 2022/08/29 23:36:41 - mmengine - INFO - Epoch(train) [23][35/63] lr: 6.8843e-03 eta: 23:44:49 time: 1.0023 data_time: 0.0990 memory: 16202 loss_prob: 1.3524 loss_thr: 0.6433 loss_db: 0.2209 loss: 2.2166 2022/08/29 23:36:46 - mmengine - INFO - Epoch(train) [23][40/63] lr: 6.8843e-03 eta: 23:43:13 time: 0.9905 data_time: 0.0976 memory: 16202 loss_prob: 1.2349 loss_thr: 0.6170 loss_db: 0.2019 loss: 2.0537 2022/08/29 23:36:50 - mmengine - INFO - Epoch(train) [23][45/63] lr: 6.8843e-03 eta: 23:43:13 time: 0.8880 data_time: 0.0549 memory: 16202 loss_prob: 1.2315 loss_thr: 0.6225 loss_db: 0.2014 loss: 2.0553 2022/08/29 23:36:55 - mmengine - INFO - Epoch(train) [23][50/63] lr: 6.8843e-03 eta: 23:41:16 time: 0.9459 data_time: 0.1053 memory: 16202 loss_prob: 1.2612 loss_thr: 0.6195 loss_db: 0.2047 loss: 2.0853 2022/08/29 23:37:00 - mmengine - INFO - Epoch(train) [23][55/63] lr: 6.8843e-03 eta: 23:41:16 time: 0.9629 data_time: 0.0943 memory: 16202 loss_prob: 1.3207 loss_thr: 0.6130 loss_db: 0.2150 loss: 2.1487 2022/08/29 23:37:05 - mmengine - INFO - Epoch(train) [23][60/63] lr: 6.8843e-03 eta: 23:39:12 time: 0.9304 data_time: 0.0557 memory: 16202 loss_prob: 1.3468 loss_thr: 0.6059 loss_db: 0.2217 loss: 2.1744 2022/08/29 23:37:07 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:37:15 - mmengine - INFO - Epoch(train) [24][5/63] lr: 6.8790e-03 eta: 23:39:12 time: 1.1961 data_time: 0.3325 memory: 16202 loss_prob: 1.3141 loss_thr: 0.6009 loss_db: 0.2110 loss: 2.1261 2022/08/29 23:37:20 - mmengine - INFO - Epoch(train) [24][10/63] lr: 6.8790e-03 eta: 23:37:14 time: 1.2903 data_time: 0.3642 memory: 16202 loss_prob: 1.2401 loss_thr: 0.6064 loss_db: 0.1977 loss: 2.0442 2022/08/29 23:37:25 - mmengine - INFO - Epoch(train) [24][15/63] lr: 6.8790e-03 eta: 23:37:14 time: 1.0114 data_time: 0.1142 memory: 16202 loss_prob: 1.3307 loss_thr: 0.6467 loss_db: 0.2165 loss: 2.1939 2022/08/29 23:37:30 - mmengine - INFO - Epoch(train) [24][20/63] lr: 6.8790e-03 eta: 23:35:22 time: 0.9472 data_time: 0.0785 memory: 16202 loss_prob: 1.4003 loss_thr: 0.6451 loss_db: 0.2302 loss: 2.2757 2022/08/29 23:37:35 - mmengine - INFO - Epoch(train) [24][25/63] lr: 6.8790e-03 eta: 23:35:22 time: 0.9814 data_time: 0.1090 memory: 16202 loss_prob: 1.3919 loss_thr: 0.6455 loss_db: 0.2277 loss: 2.2651 2022/08/29 23:37:40 - mmengine - INFO - Epoch(train) [24][30/63] lr: 6.8790e-03 eta: 23:34:03 time: 1.0113 data_time: 0.0776 memory: 16202 loss_prob: 1.4187 loss_thr: 0.6638 loss_db: 0.2354 loss: 2.3179 2022/08/29 23:37:45 - mmengine - INFO - Epoch(train) [24][35/63] lr: 6.8790e-03 eta: 23:34:03 time: 1.0115 data_time: 0.0938 memory: 16202 loss_prob: 1.3952 loss_thr: 0.6447 loss_db: 0.2374 loss: 2.2773 2022/08/29 23:37:49 - mmengine - INFO - Epoch(train) [24][40/63] lr: 6.8790e-03 eta: 23:32:04 time: 0.9292 data_time: 0.1018 memory: 16202 loss_prob: 1.3375 loss_thr: 0.6178 loss_db: 0.2174 loss: 2.1727 2022/08/29 23:37:53 - mmengine - INFO - Epoch(train) [24][45/63] lr: 6.8790e-03 eta: 23:32:04 time: 0.8627 data_time: 0.0594 memory: 16202 loss_prob: 1.4302 loss_thr: 0.6292 loss_db: 0.2269 loss: 2.2864 2022/08/29 23:38:01 - mmengine - INFO - Epoch(train) [24][50/63] lr: 6.8790e-03 eta: 23:32:20 time: 1.1979 data_time: 0.0942 memory: 16202 loss_prob: 1.3389 loss_thr: 0.6080 loss_db: 0.2213 loss: 2.1682 2022/08/29 23:38:06 - mmengine - INFO - Epoch(train) [24][55/63] lr: 6.8790e-03 eta: 23:32:20 time: 1.2615 data_time: 0.1022 memory: 16202 loss_prob: 1.3288 loss_thr: 0.6223 loss_db: 0.2189 loss: 2.1700 2022/08/29 23:38:12 - mmengine - INFO - Epoch(train) [24][60/63] lr: 6.8790e-03 eta: 23:31:47 time: 1.0997 data_time: 0.1013 memory: 16202 loss_prob: 1.3610 loss_thr: 0.6599 loss_db: 0.2203 loss: 2.2412 2022/08/29 23:38:14 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:38:21 - mmengine - INFO - Epoch(train) [25][5/63] lr: 6.8738e-03 eta: 23:31:47 time: 1.0148 data_time: 0.2831 memory: 16202 loss_prob: 1.2056 loss_thr: 0.5945 loss_db: 0.1967 loss: 1.9969 2022/08/29 23:38:25 - mmengine - INFO - Epoch(train) [25][10/63] lr: 6.8738e-03 eta: 23:28:18 time: 1.0871 data_time: 0.3201 memory: 16202 loss_prob: 1.2697 loss_thr: 0.6143 loss_db: 0.2072 loss: 2.0913 2022/08/29 23:38:30 - mmengine - INFO - Epoch(train) [25][15/63] lr: 6.8738e-03 eta: 23:28:18 time: 0.9233 data_time: 0.0903 memory: 16202 loss_prob: 1.3601 loss_thr: 0.6011 loss_db: 0.2228 loss: 2.1841 2022/08/29 23:38:34 - mmengine - INFO - Epoch(train) [25][20/63] lr: 6.8738e-03 eta: 23:26:25 time: 0.9301 data_time: 0.0479 memory: 16202 loss_prob: 1.4354 loss_thr: 0.6170 loss_db: 0.2380 loss: 2.2904 2022/08/29 23:38:40 - mmengine - INFO - Epoch(train) [25][25/63] lr: 6.8738e-03 eta: 23:26:25 time: 0.9667 data_time: 0.0573 memory: 16202 loss_prob: 1.5013 loss_thr: 0.6700 loss_db: 0.2475 loss: 2.4188 2022/08/29 23:38:44 - mmengine - INFO - Epoch(train) [25][30/63] lr: 6.8738e-03 eta: 23:24:51 time: 0.9683 data_time: 0.0487 memory: 16202 loss_prob: 1.4672 loss_thr: 0.6610 loss_db: 0.2359 loss: 2.3641 2022/08/29 23:38:49 - mmengine - INFO - Epoch(train) [25][35/63] lr: 6.8738e-03 eta: 23:24:51 time: 0.9340 data_time: 0.0342 memory: 16202 loss_prob: 1.4565 loss_thr: 0.6385 loss_db: 0.2324 loss: 2.3275 2022/08/29 23:38:54 - mmengine - INFO - Epoch(train) [25][40/63] lr: 6.8738e-03 eta: 23:23:24 time: 0.9787 data_time: 0.0535 memory: 16202 loss_prob: 1.4812 loss_thr: 0.6687 loss_db: 0.2465 loss: 2.3964 2022/08/29 23:38:59 - mmengine - INFO - Epoch(train) [25][45/63] lr: 6.8738e-03 eta: 23:23:24 time: 0.9978 data_time: 0.0514 memory: 16202 loss_prob: 1.4717 loss_thr: 0.6886 loss_db: 0.2490 loss: 2.4092 2022/08/29 23:39:03 - mmengine - INFO - Epoch(train) [25][50/63] lr: 6.8738e-03 eta: 23:21:44 time: 0.9504 data_time: 0.0480 memory: 16202 loss_prob: 1.5265 loss_thr: 0.6819 loss_db: 0.2484 loss: 2.4567 2022/08/29 23:39:09 - mmengine - INFO - Epoch(train) [25][55/63] lr: 6.8738e-03 eta: 23:21:44 time: 1.0071 data_time: 0.0605 memory: 16202 loss_prob: 1.4930 loss_thr: 0.6385 loss_db: 0.2397 loss: 2.3712 2022/08/29 23:39:14 - mmengine - INFO - Epoch(train) [25][60/63] lr: 6.8738e-03 eta: 23:20:40 time: 1.0258 data_time: 0.0614 memory: 16202 loss_prob: 1.4415 loss_thr: 0.6097 loss_db: 0.2303 loss: 2.2815 2022/08/29 23:39:16 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:39:24 - mmengine - INFO - Epoch(train) [26][5/63] lr: 6.8685e-03 eta: 23:20:40 time: 1.2210 data_time: 0.4128 memory: 16202 loss_prob: 1.3926 loss_thr: 0.6300 loss_db: 0.2211 loss: 2.2436 2022/08/29 23:39:29 - mmengine - INFO - Epoch(train) [26][10/63] lr: 6.8685e-03 eta: 23:18:49 time: 1.2694 data_time: 0.4135 memory: 16202 loss_prob: 1.3718 loss_thr: 0.6300 loss_db: 0.2257 loss: 2.2274 2022/08/29 23:39:33 - mmengine - INFO - Epoch(train) [26][15/63] lr: 6.8685e-03 eta: 23:18:49 time: 0.9033 data_time: 0.0440 memory: 16202 loss_prob: 1.2906 loss_thr: 0.6173 loss_db: 0.2138 loss: 2.1217 2022/08/29 23:39:38 - mmengine - INFO - Epoch(train) [26][20/63] lr: 6.8685e-03 eta: 23:17:04 time: 0.9315 data_time: 0.0334 memory: 16202 loss_prob: 1.3621 loss_thr: 0.6206 loss_db: 0.2248 loss: 2.2075 2022/08/29 23:39:43 - mmengine - INFO - Epoch(train) [26][25/63] lr: 6.8685e-03 eta: 23:17:04 time: 0.9575 data_time: 0.0609 memory: 16202 loss_prob: 1.3299 loss_thr: 0.6132 loss_db: 0.2186 loss: 2.1618 2022/08/29 23:39:48 - mmengine - INFO - Epoch(train) [26][30/63] lr: 6.8685e-03 eta: 23:15:23 time: 0.9386 data_time: 0.0533 memory: 16202 loss_prob: 1.3300 loss_thr: 0.6223 loss_db: 0.2178 loss: 2.1702 2022/08/29 23:39:52 - mmengine - INFO - Epoch(train) [26][35/63] lr: 6.8685e-03 eta: 23:15:23 time: 0.9121 data_time: 0.0353 memory: 16202 loss_prob: 1.3815 loss_thr: 0.6322 loss_db: 0.2275 loss: 2.2412 2022/08/29 23:39:57 - mmengine - INFO - Epoch(train) [26][40/63] lr: 6.8685e-03 eta: 23:13:33 time: 0.9162 data_time: 0.0535 memory: 16202 loss_prob: 1.3226 loss_thr: 0.6218 loss_db: 0.2190 loss: 2.1635 2022/08/29 23:40:02 - mmengine - INFO - Epoch(train) [26][45/63] lr: 6.8685e-03 eta: 23:13:33 time: 1.0409 data_time: 0.0574 memory: 16202 loss_prob: 1.3246 loss_thr: 0.6055 loss_db: 0.2208 loss: 2.1509 2022/08/29 23:40:07 - mmengine - INFO - Epoch(train) [26][50/63] lr: 6.8685e-03 eta: 23:12:39 time: 1.0368 data_time: 0.0737 memory: 16202 loss_prob: 1.3383 loss_thr: 0.6058 loss_db: 0.2178 loss: 2.1619 2022/08/29 23:40:12 - mmengine - INFO - Epoch(train) [26][55/63] lr: 6.8685e-03 eta: 23:12:39 time: 0.9217 data_time: 0.0604 memory: 16202 loss_prob: 1.3760 loss_thr: 0.6351 loss_db: 0.2254 loss: 2.2365 2022/08/29 23:40:17 - mmengine - INFO - Epoch(train) [26][60/63] lr: 6.8685e-03 eta: 23:11:22 time: 0.9828 data_time: 0.0414 memory: 16202 loss_prob: 1.4548 loss_thr: 0.6657 loss_db: 0.2437 loss: 2.3642 2022/08/29 23:40:19 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:40:27 - mmengine - INFO - Epoch(train) [27][5/63] lr: 6.8632e-03 eta: 23:11:22 time: 1.2206 data_time: 0.3522 memory: 16202 loss_prob: 1.5268 loss_thr: 0.7125 loss_db: 0.2503 loss: 2.4896 2022/08/29 23:40:32 - mmengine - INFO - Epoch(train) [27][10/63] lr: 6.8632e-03 eta: 23:09:31 time: 1.2523 data_time: 0.3616 memory: 16202 loss_prob: 1.4546 loss_thr: 0.6939 loss_db: 0.2422 loss: 2.3907 2022/08/29 23:40:37 - mmengine - INFO - Epoch(train) [27][15/63] lr: 6.8632e-03 eta: 23:09:31 time: 0.9915 data_time: 0.0513 memory: 16202 loss_prob: 1.3903 loss_thr: 0.6584 loss_db: 0.2333 loss: 2.2820 2022/08/29 23:40:42 - mmengine - INFO - Epoch(train) [27][20/63] lr: 6.8632e-03 eta: 23:08:16 time: 0.9856 data_time: 0.0399 memory: 16202 loss_prob: 1.3184 loss_thr: 0.6372 loss_db: 0.2213 loss: 2.1769 2022/08/29 23:40:47 - mmengine - INFO - Epoch(train) [27][25/63] lr: 6.8632e-03 eta: 23:08:16 time: 0.9406 data_time: 0.0701 memory: 16202 loss_prob: 1.4093 loss_thr: 0.6334 loss_db: 0.2360 loss: 2.2786 2022/08/29 23:40:51 - mmengine - INFO - Epoch(train) [27][30/63] lr: 6.8632e-03 eta: 23:06:41 time: 0.9372 data_time: 0.0533 memory: 16202 loss_prob: 1.4155 loss_thr: 0.6455 loss_db: 0.2368 loss: 2.2978 2022/08/29 23:40:56 - mmengine - INFO - Epoch(train) [27][35/63] lr: 6.8632e-03 eta: 23:06:41 time: 0.9102 data_time: 0.0421 memory: 16202 loss_prob: 1.3128 loss_thr: 0.6193 loss_db: 0.2168 loss: 2.1489 2022/08/29 23:41:01 - mmengine - INFO - Epoch(train) [27][40/63] lr: 6.8632e-03 eta: 23:05:20 time: 0.9674 data_time: 0.0569 memory: 16202 loss_prob: 1.2900 loss_thr: 0.5994 loss_db: 0.2150 loss: 2.1045 2022/08/29 23:41:06 - mmengine - INFO - Epoch(train) [27][45/63] lr: 6.8632e-03 eta: 23:05:20 time: 1.0121 data_time: 0.0444 memory: 16202 loss_prob: 1.2571 loss_thr: 0.5996 loss_db: 0.2070 loss: 2.0637 2022/08/29 23:41:10 - mmengine - INFO - Epoch(train) [27][50/63] lr: 6.8632e-03 eta: 23:03:59 time: 0.9657 data_time: 0.0590 memory: 16202 loss_prob: 1.2557 loss_thr: 0.6139 loss_db: 0.2033 loss: 2.0729 2022/08/29 23:41:15 - mmengine - INFO - Epoch(train) [27][55/63] lr: 6.8632e-03 eta: 23:03:59 time: 0.9076 data_time: 0.0532 memory: 16202 loss_prob: 1.3980 loss_thr: 0.6310 loss_db: 0.2271 loss: 2.2561 2022/08/29 23:41:20 - mmengine - INFO - Epoch(train) [27][60/63] lr: 6.8632e-03 eta: 23:02:16 time: 0.9119 data_time: 0.0374 memory: 16202 loss_prob: 1.6040 loss_thr: 0.6678 loss_db: 0.2677 loss: 2.5396 2022/08/29 23:41:22 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:41:30 - mmengine - INFO - Epoch(train) [28][5/63] lr: 6.8580e-03 eta: 23:02:16 time: 1.1797 data_time: 0.3363 memory: 16202 loss_prob: 1.3829 loss_thr: 0.6481 loss_db: 0.2360 loss: 2.2671 2022/08/29 23:41:35 - mmengine - INFO - Epoch(train) [28][10/63] lr: 6.8580e-03 eta: 23:00:35 time: 1.2590 data_time: 0.3376 memory: 16202 loss_prob: 1.2674 loss_thr: 0.5967 loss_db: 0.2075 loss: 2.0717 2022/08/29 23:41:40 - mmengine - INFO - Epoch(train) [28][15/63] lr: 6.8580e-03 eta: 23:00:35 time: 1.0139 data_time: 0.0472 memory: 16202 loss_prob: 1.3822 loss_thr: 0.6038 loss_db: 0.2284 loss: 2.2144 2022/08/29 23:41:44 - mmengine - INFO - Epoch(train) [28][20/63] lr: 6.8580e-03 eta: 22:59:02 time: 0.9299 data_time: 0.0521 memory: 16202 loss_prob: 1.2886 loss_thr: 0.6052 loss_db: 0.2136 loss: 2.1074 2022/08/29 23:41:48 - mmengine - INFO - Epoch(train) [28][25/63] lr: 6.8580e-03 eta: 22:59:02 time: 0.8666 data_time: 0.0460 memory: 16202 loss_prob: 1.3496 loss_thr: 0.6587 loss_db: 0.2276 loss: 2.2359 2022/08/29 23:41:53 - mmengine - INFO - Epoch(train) [28][30/63] lr: 6.8580e-03 eta: 22:57:19 time: 0.9057 data_time: 0.0421 memory: 16202 loss_prob: 1.4394 loss_thr: 0.6828 loss_db: 0.2428 loss: 2.3649 2022/08/29 23:41:58 - mmengine - INFO - Epoch(train) [28][35/63] lr: 6.8580e-03 eta: 22:57:19 time: 0.9806 data_time: 0.0810 memory: 16202 loss_prob: 1.3698 loss_thr: 0.6541 loss_db: 0.2229 loss: 2.2468 2022/08/29 23:42:06 - mmengine - INFO - Epoch(train) [28][40/63] lr: 6.8580e-03 eta: 22:58:07 time: 1.2580 data_time: 0.0687 memory: 16202 loss_prob: 1.2952 loss_thr: 0.6396 loss_db: 0.2100 loss: 2.1448 2022/08/29 23:42:14 - mmengine - INFO - Epoch(train) [28][45/63] lr: 6.8580e-03 eta: 22:58:07 time: 1.6210 data_time: 0.0554 memory: 16202 loss_prob: 1.3920 loss_thr: 0.6340 loss_db: 0.2256 loss: 2.2516 2022/08/29 23:42:20 - mmengine - INFO - Epoch(train) [28][50/63] lr: 6.8580e-03 eta: 23:00:13 time: 1.4439 data_time: 0.0538 memory: 16202 loss_prob: 1.3923 loss_thr: 0.6507 loss_db: 0.2229 loss: 2.2659 2022/08/29 23:42:26 - mmengine - INFO - Epoch(train) [28][55/63] lr: 6.8580e-03 eta: 23:00:13 time: 1.1528 data_time: 0.0459 memory: 16202 loss_prob: 1.3430 loss_thr: 0.6410 loss_db: 0.2186 loss: 2.2026 2022/08/29 23:42:31 - mmengine - INFO - Epoch(train) [28][60/63] lr: 6.8580e-03 eta: 23:00:06 time: 1.1332 data_time: 0.0474 memory: 16202 loss_prob: 1.4438 loss_thr: 0.6474 loss_db: 0.2361 loss: 2.3274 2022/08/29 23:42:35 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:42:44 - mmengine - INFO - Epoch(train) [29][5/63] lr: 6.8527e-03 eta: 23:00:06 time: 1.5128 data_time: 0.3234 memory: 16202 loss_prob: 1.3603 loss_thr: 0.6188 loss_db: 0.2146 loss: 2.1937 2022/08/29 23:42:52 - mmengine - INFO - Epoch(train) [29][10/63] lr: 6.8527e-03 eta: 23:01:29 time: 1.6915 data_time: 0.3319 memory: 16202 loss_prob: 1.4870 loss_thr: 0.6521 loss_db: 0.2362 loss: 2.3753 2022/08/29 23:42:59 - mmengine - INFO - Epoch(train) [29][15/63] lr: 6.8527e-03 eta: 23:01:29 time: 1.4523 data_time: 0.0605 memory: 16202 loss_prob: 1.5748 loss_thr: 0.6629 loss_db: 0.2550 loss: 2.4928 2022/08/29 23:43:07 - mmengine - INFO - Epoch(train) [29][20/63] lr: 6.8527e-03 eta: 23:04:27 time: 1.5793 data_time: 0.0533 memory: 16202 loss_prob: 1.5532 loss_thr: 0.6487 loss_db: 0.2574 loss: 2.4592 2022/08/29 23:43:15 - mmengine - INFO - Epoch(train) [29][25/63] lr: 6.8527e-03 eta: 23:04:27 time: 1.6704 data_time: 0.0573 memory: 16202 loss_prob: 1.3645 loss_thr: 0.5989 loss_db: 0.2267 loss: 2.1901 2022/08/29 23:43:21 - mmengine - INFO - Epoch(train) [29][30/63] lr: 6.8527e-03 eta: 23:05:37 time: 1.3239 data_time: 0.0585 memory: 16202 loss_prob: 1.2634 loss_thr: 0.5818 loss_db: 0.2109 loss: 2.0561 2022/08/29 23:43:25 - mmengine - INFO - Epoch(train) [29][35/63] lr: 6.8527e-03 eta: 23:05:37 time: 0.9442 data_time: 0.0525 memory: 16202 loss_prob: 1.3035 loss_thr: 0.6176 loss_db: 0.2162 loss: 2.1373 2022/08/29 23:43:29 - mmengine - INFO - Epoch(train) [29][40/63] lr: 6.8527e-03 eta: 23:03:34 time: 0.8519 data_time: 0.0316 memory: 16202 loss_prob: 1.3446 loss_thr: 0.6223 loss_db: 0.2203 loss: 2.1871 2022/08/29 23:43:34 - mmengine - INFO - Epoch(train) [29][45/63] lr: 6.8527e-03 eta: 23:03:34 time: 0.8890 data_time: 0.0467 memory: 16202 loss_prob: 1.3288 loss_thr: 0.6002 loss_db: 0.2198 loss: 2.1489 2022/08/29 23:43:38 - mmengine - INFO - Epoch(train) [29][50/63] lr: 6.8527e-03 eta: 23:02:04 time: 0.9311 data_time: 0.0496 memory: 16202 loss_prob: 1.2793 loss_thr: 0.6272 loss_db: 0.2130 loss: 2.1195 2022/08/29 23:43:43 - mmengine - INFO - Epoch(train) [29][55/63] lr: 6.8527e-03 eta: 23:02:04 time: 0.9449 data_time: 0.0369 memory: 16202 loss_prob: 1.3673 loss_thr: 0.6419 loss_db: 0.2272 loss: 2.2364 2022/08/29 23:43:47 - mmengine - INFO - Epoch(train) [29][60/63] lr: 6.8527e-03 eta: 23:00:17 time: 0.8888 data_time: 0.0507 memory: 16202 loss_prob: 1.4505 loss_thr: 0.6271 loss_db: 0.2389 loss: 2.3166 2022/08/29 23:43:50 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:43:59 - mmengine - INFO - Epoch(train) [30][5/63] lr: 6.8474e-03 eta: 23:00:17 time: 1.2849 data_time: 0.4120 memory: 16202 loss_prob: 1.4327 loss_thr: 0.6536 loss_db: 0.2341 loss: 2.3204 2022/08/29 23:44:03 - mmengine - INFO - Epoch(train) [30][10/63] lr: 6.8474e-03 eta: 22:59:05 time: 1.3161 data_time: 0.4291 memory: 16202 loss_prob: 1.2360 loss_thr: 0.6295 loss_db: 0.1990 loss: 2.0645 2022/08/29 23:44:08 - mmengine - INFO - Epoch(train) [30][15/63] lr: 6.8474e-03 eta: 22:59:05 time: 0.8986 data_time: 0.0531 memory: 16202 loss_prob: 1.2160 loss_thr: 0.6145 loss_db: 0.1954 loss: 2.0259 2022/08/29 23:44:12 - mmengine - INFO - Epoch(train) [30][20/63] lr: 6.8474e-03 eta: 22:57:35 time: 0.9251 data_time: 0.0441 memory: 16202 loss_prob: 1.2483 loss_thr: 0.6072 loss_db: 0.2033 loss: 2.0589 2022/08/29 23:44:17 - mmengine - INFO - Epoch(train) [30][25/63] lr: 6.8474e-03 eta: 22:57:35 time: 0.9281 data_time: 0.0604 memory: 16202 loss_prob: 1.3026 loss_thr: 0.5940 loss_db: 0.2130 loss: 2.1096 2022/08/29 23:44:21 - mmengine - INFO - Epoch(train) [30][30/63] lr: 6.8474e-03 eta: 22:55:54 time: 0.8937 data_time: 0.0455 memory: 16202 loss_prob: 1.3886 loss_thr: 0.6285 loss_db: 0.2298 loss: 2.2469 2022/08/29 23:44:26 - mmengine - INFO - Epoch(train) [30][35/63] lr: 6.8474e-03 eta: 22:55:54 time: 0.9371 data_time: 0.0364 memory: 16202 loss_prob: 1.5463 loss_thr: 0.6797 loss_db: 0.2641 loss: 2.4901 2022/08/29 23:44:32 - mmengine - INFO - Epoch(train) [30][40/63] lr: 6.8474e-03 eta: 22:55:30 time: 1.0871 data_time: 0.0579 memory: 16202 loss_prob: 1.4753 loss_thr: 0.6638 loss_db: 0.2525 loss: 2.3917 2022/08/29 23:44:37 - mmengine - INFO - Epoch(train) [30][45/63] lr: 6.8474e-03 eta: 22:55:30 time: 1.0638 data_time: 0.0586 memory: 16202 loss_prob: 1.3389 loss_thr: 0.6394 loss_db: 0.2196 loss: 2.1979 2022/08/29 23:44:41 - mmengine - INFO - Epoch(train) [30][50/63] lr: 6.8474e-03 eta: 22:53:59 time: 0.9169 data_time: 0.0545 memory: 16202 loss_prob: 1.3547 loss_thr: 0.6164 loss_db: 0.2194 loss: 2.1904 2022/08/29 23:44:46 - mmengine - INFO - Epoch(train) [30][55/63] lr: 6.8474e-03 eta: 22:53:59 time: 0.9093 data_time: 0.0518 memory: 16202 loss_prob: 1.4141 loss_thr: 0.5965 loss_db: 0.2306 loss: 2.2411 2022/08/29 23:44:52 - mmengine - INFO - Epoch(train) [30][60/63] lr: 6.8474e-03 eta: 22:53:25 time: 1.0583 data_time: 0.0403 memory: 16202 loss_prob: 1.4723 loss_thr: 0.5948 loss_db: 0.2341 loss: 2.3013 2022/08/29 23:44:54 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:45:03 - mmengine - INFO - Epoch(train) [31][5/63] lr: 6.8422e-03 eta: 22:53:25 time: 1.2760 data_time: 0.4134 memory: 16202 loss_prob: 1.6943 loss_thr: 0.6344 loss_db: 0.2823 loss: 2.6110 2022/08/29 23:45:08 - mmengine - INFO - Epoch(train) [31][10/63] lr: 6.8422e-03 eta: 22:52:21 time: 1.3266 data_time: 0.4175 memory: 16202 loss_prob: 1.6351 loss_thr: 0.6112 loss_db: 0.2742 loss: 2.5206 2022/08/29 23:45:12 - mmengine - INFO - Epoch(train) [31][15/63] lr: 6.8422e-03 eta: 22:52:21 time: 0.9824 data_time: 0.0514 memory: 16202 loss_prob: 1.4172 loss_thr: 0.6361 loss_db: 0.2307 loss: 2.2839 2022/08/29 23:45:17 - mmengine - INFO - Epoch(train) [31][20/63] lr: 6.8422e-03 eta: 22:50:49 time: 0.9080 data_time: 0.0366 memory: 16202 loss_prob: 1.3883 loss_thr: 0.6527 loss_db: 0.2335 loss: 2.2744 2022/08/29 23:45:22 - mmengine - INFO - Epoch(train) [31][25/63] lr: 6.8422e-03 eta: 22:50:49 time: 0.9079 data_time: 0.0568 memory: 16202 loss_prob: 1.2676 loss_thr: 0.6038 loss_db: 0.2117 loss: 2.0831 2022/08/29 23:45:27 - mmengine - INFO - Epoch(train) [31][30/63] lr: 6.8422e-03 eta: 22:50:03 time: 1.0239 data_time: 0.0459 memory: 16202 loss_prob: 1.2210 loss_thr: 0.6010 loss_db: 0.1978 loss: 2.0198 2022/08/29 23:45:32 - mmengine - INFO - Epoch(train) [31][35/63] lr: 6.8422e-03 eta: 22:50:03 time: 1.0117 data_time: 0.0533 memory: 16202 loss_prob: 1.2412 loss_thr: 0.6061 loss_db: 0.1983 loss: 2.0456 2022/08/29 23:45:36 - mmengine - INFO - Epoch(train) [31][40/63] lr: 6.8422e-03 eta: 22:48:32 time: 0.9081 data_time: 0.0521 memory: 16202 loss_prob: 1.3920 loss_thr: 0.6429 loss_db: 0.2260 loss: 2.2608 2022/08/29 23:45:42 - mmengine - INFO - Epoch(train) [31][45/63] lr: 6.8422e-03 eta: 22:48:32 time: 1.0084 data_time: 0.0881 memory: 16202 loss_prob: 1.3342 loss_thr: 0.6225 loss_db: 0.2170 loss: 2.1736 2022/08/29 23:45:47 - mmengine - INFO - Epoch(train) [31][50/63] lr: 6.8422e-03 eta: 22:47:58 time: 1.0534 data_time: 0.1090 memory: 16202 loss_prob: 1.3594 loss_thr: 0.5944 loss_db: 0.2219 loss: 2.1757 2022/08/29 23:45:51 - mmengine - INFO - Epoch(train) [31][55/63] lr: 6.8422e-03 eta: 22:47:58 time: 0.9177 data_time: 0.0490 memory: 16202 loss_prob: 1.4296 loss_thr: 0.6039 loss_db: 0.2323 loss: 2.2658 2022/08/29 23:45:55 - mmengine - INFO - Epoch(train) [31][60/63] lr: 6.8422e-03 eta: 22:46:16 time: 0.8737 data_time: 0.0338 memory: 16202 loss_prob: 1.3488 loss_thr: 0.6095 loss_db: 0.2170 loss: 2.1753 2022/08/29 23:45:58 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:46:06 - mmengine - INFO - Epoch(train) [32][5/63] lr: 6.8369e-03 eta: 22:46:16 time: 1.2065 data_time: 0.3482 memory: 16202 loss_prob: 1.2733 loss_thr: 0.5834 loss_db: 0.2101 loss: 2.0667 2022/08/29 23:46:10 - mmengine - INFO - Epoch(train) [32][10/63] lr: 6.8369e-03 eta: 22:44:54 time: 1.2683 data_time: 0.3668 memory: 16202 loss_prob: 1.2865 loss_thr: 0.6159 loss_db: 0.2160 loss: 2.1185 2022/08/29 23:46:15 - mmengine - INFO - Epoch(train) [32][15/63] lr: 6.8369e-03 eta: 22:44:54 time: 0.9088 data_time: 0.0583 memory: 16202 loss_prob: 1.2833 loss_thr: 0.6176 loss_db: 0.2141 loss: 2.1150 2022/08/29 23:46:20 - mmengine - INFO - Epoch(train) [32][20/63] lr: 6.8369e-03 eta: 22:43:29 time: 0.9132 data_time: 0.0421 memory: 16202 loss_prob: 1.2990 loss_thr: 0.6207 loss_db: 0.2107 loss: 2.1304 2022/08/29 23:46:24 - mmengine - INFO - Epoch(train) [32][25/63] lr: 6.8369e-03 eta: 22:43:29 time: 0.9212 data_time: 0.0587 memory: 16202 loss_prob: 1.3200 loss_thr: 0.6192 loss_db: 0.2115 loss: 2.1506 2022/08/29 23:46:29 - mmengine - INFO - Epoch(train) [32][30/63] lr: 6.8369e-03 eta: 22:42:03 time: 0.9103 data_time: 0.0539 memory: 16202 loss_prob: 1.4430 loss_thr: 0.6329 loss_db: 0.2348 loss: 2.3106 2022/08/29 23:46:34 - mmengine - INFO - Epoch(train) [32][35/63] lr: 6.8369e-03 eta: 22:42:03 time: 1.0309 data_time: 0.0462 memory: 16202 loss_prob: 1.4533 loss_thr: 0.6280 loss_db: 0.2402 loss: 2.3215 2022/08/29 23:46:39 - mmengine - INFO - Epoch(train) [32][40/63] lr: 6.8369e-03 eta: 22:41:16 time: 1.0128 data_time: 0.0526 memory: 16202 loss_prob: 1.3816 loss_thr: 0.5986 loss_db: 0.2262 loss: 2.2063 2022/08/29 23:46:43 - mmengine - INFO - Epoch(train) [32][45/63] lr: 6.8369e-03 eta: 22:41:16 time: 0.8735 data_time: 0.0472 memory: 16202 loss_prob: 1.3895 loss_thr: 0.6068 loss_db: 0.2199 loss: 2.2162 2022/08/29 23:46:45 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:46:47 - mmengine - INFO - Epoch(train) [32][50/63] lr: 6.8369e-03 eta: 22:39:25 time: 0.8358 data_time: 0.0352 memory: 16202 loss_prob: 1.3819 loss_thr: 0.6315 loss_db: 0.2165 loss: 2.2299 2022/08/29 23:46:52 - mmengine - INFO - Epoch(train) [32][55/63] lr: 6.8369e-03 eta: 22:39:25 time: 0.8648 data_time: 0.0444 memory: 16202 loss_prob: 1.5124 loss_thr: 0.6596 loss_db: 0.2454 loss: 2.4174 2022/08/29 23:46:56 - mmengine - INFO - Epoch(train) [32][60/63] lr: 6.8369e-03 eta: 22:37:55 time: 0.8935 data_time: 0.0489 memory: 16202 loss_prob: 1.4359 loss_thr: 0.6398 loss_db: 0.2354 loss: 2.3111 2022/08/29 23:46:58 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:47:06 - mmengine - INFO - Epoch(train) [33][5/63] lr: 6.8316e-03 eta: 22:37:55 time: 1.1897 data_time: 0.3241 memory: 16202 loss_prob: 1.3760 loss_thr: 0.6320 loss_db: 0.2302 loss: 2.2382 2022/08/29 23:47:12 - mmengine - INFO - Epoch(train) [33][10/63] lr: 6.8316e-03 eta: 22:37:06 time: 1.3452 data_time: 0.3481 memory: 16202 loss_prob: 1.3288 loss_thr: 0.6244 loss_db: 0.2138 loss: 2.1670 2022/08/29 23:47:16 - mmengine - INFO - Epoch(train) [33][15/63] lr: 6.8316e-03 eta: 22:37:06 time: 0.9785 data_time: 0.0504 memory: 16202 loss_prob: 1.3545 loss_thr: 0.6195 loss_db: 0.2190 loss: 2.1929 2022/08/29 23:47:22 - mmengine - INFO - Epoch(train) [33][20/63] lr: 6.8316e-03 eta: 22:36:16 time: 0.9974 data_time: 0.0401 memory: 16202 loss_prob: 1.4003 loss_thr: 0.6237 loss_db: 0.2317 loss: 2.2557 2022/08/29 23:47:26 - mmengine - INFO - Epoch(train) [33][25/63] lr: 6.8316e-03 eta: 22:36:16 time: 1.0332 data_time: 0.0510 memory: 16202 loss_prob: 1.3642 loss_thr: 0.6295 loss_db: 0.2238 loss: 2.2175 2022/08/29 23:47:31 - mmengine - INFO - Epoch(train) [33][30/63] lr: 6.8316e-03 eta: 22:34:55 time: 0.9116 data_time: 0.0421 memory: 16202 loss_prob: 1.3775 loss_thr: 0.6380 loss_db: 0.2237 loss: 2.2392 2022/08/29 23:47:35 - mmengine - INFO - Epoch(train) [33][35/63] lr: 6.8316e-03 eta: 22:34:55 time: 0.8845 data_time: 0.0372 memory: 16202 loss_prob: 1.4016 loss_thr: 0.6340 loss_db: 0.2249 loss: 2.2605 2022/08/29 23:47:41 - mmengine - INFO - Epoch(train) [33][40/63] lr: 6.8316e-03 eta: 22:34:08 time: 1.0040 data_time: 0.0507 memory: 16202 loss_prob: 1.3269 loss_thr: 0.6300 loss_db: 0.2154 loss: 2.1723 2022/08/29 23:47:45 - mmengine - INFO - Epoch(train) [33][45/63] lr: 6.8316e-03 eta: 22:34:08 time: 1.0240 data_time: 0.0497 memory: 16202 loss_prob: 1.2702 loss_thr: 0.6366 loss_db: 0.2094 loss: 2.1162 2022/08/29 23:47:50 - mmengine - INFO - Epoch(train) [33][50/63] lr: 6.8316e-03 eta: 22:33:07 time: 0.9660 data_time: 0.0496 memory: 16202 loss_prob: 1.4812 loss_thr: 0.6444 loss_db: 0.2508 loss: 2.3764 2022/08/29 23:47:55 - mmengine - INFO - Epoch(train) [33][55/63] lr: 6.8316e-03 eta: 22:33:07 time: 0.9568 data_time: 0.0526 memory: 16202 loss_prob: 1.5806 loss_thr: 0.6535 loss_db: 0.2656 loss: 2.4997 2022/08/29 23:48:00 - mmengine - INFO - Epoch(train) [33][60/63] lr: 6.8316e-03 eta: 22:32:17 time: 0.9942 data_time: 0.0439 memory: 16202 loss_prob: 1.4907 loss_thr: 0.6552 loss_db: 0.2349 loss: 2.3809 2022/08/29 23:48:03 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:48:13 - mmengine - INFO - Epoch(train) [34][5/63] lr: 6.8264e-03 eta: 22:32:17 time: 1.5192 data_time: 0.3688 memory: 16202 loss_prob: 1.3879 loss_thr: 0.6579 loss_db: 0.2256 loss: 2.2714 2022/08/29 23:48:19 - mmengine - INFO - Epoch(train) [34][10/63] lr: 6.8264e-03 eta: 22:33:14 time: 1.6381 data_time: 0.3816 memory: 16202 loss_prob: 1.4344 loss_thr: 0.6626 loss_db: 0.2332 loss: 2.3302 2022/08/29 23:48:24 - mmengine - INFO - Epoch(train) [34][15/63] lr: 6.8264e-03 eta: 22:33:14 time: 1.0829 data_time: 0.0676 memory: 16202 loss_prob: 1.3704 loss_thr: 0.6398 loss_db: 0.2216 loss: 2.2317 2022/08/29 23:48:28 - mmengine - INFO - Epoch(train) [34][20/63] lr: 6.8264e-03 eta: 22:32:04 time: 0.9341 data_time: 0.0598 memory: 16202 loss_prob: 1.2400 loss_thr: 0.6050 loss_db: 0.2018 loss: 2.0467 2022/08/29 23:48:33 - mmengine - INFO - Epoch(train) [34][25/63] lr: 6.8264e-03 eta: 22:32:04 time: 0.9817 data_time: 0.0580 memory: 16202 loss_prob: 1.3231 loss_thr: 0.6231 loss_db: 0.2148 loss: 2.1609 2022/08/29 23:48:38 - mmengine - INFO - Epoch(train) [34][30/63] lr: 6.8264e-03 eta: 22:30:49 time: 0.9222 data_time: 0.0275 memory: 16202 loss_prob: 1.4441 loss_thr: 0.6841 loss_db: 0.2391 loss: 2.3673 2022/08/29 23:48:43 - mmengine - INFO - Epoch(train) [34][35/63] lr: 6.8264e-03 eta: 22:30:49 time: 0.9611 data_time: 0.0507 memory: 16202 loss_prob: 1.5061 loss_thr: 0.6758 loss_db: 0.2526 loss: 2.4345 2022/08/29 23:48:48 - mmengine - INFO - Epoch(train) [34][40/63] lr: 6.8264e-03 eta: 22:30:04 time: 1.0052 data_time: 0.0592 memory: 16202 loss_prob: 1.5053 loss_thr: 0.6386 loss_db: 0.2527 loss: 2.3966 2022/08/29 23:48:52 - mmengine - INFO - Epoch(train) [34][45/63] lr: 6.8264e-03 eta: 22:30:04 time: 0.9136 data_time: 0.0499 memory: 16202 loss_prob: 1.4934 loss_thr: 0.6131 loss_db: 0.2495 loss: 2.3560 2022/08/29 23:48:56 - mmengine - INFO - Epoch(train) [34][50/63] lr: 6.8264e-03 eta: 22:28:33 time: 0.8699 data_time: 0.0507 memory: 16202 loss_prob: 1.4715 loss_thr: 0.6305 loss_db: 0.2448 loss: 2.3468 2022/08/29 23:49:02 - mmengine - INFO - Epoch(train) [34][55/63] lr: 6.8264e-03 eta: 22:28:33 time: 0.9619 data_time: 0.0357 memory: 16202 loss_prob: 1.4137 loss_thr: 0.6286 loss_db: 0.2336 loss: 2.2759 2022/08/29 23:49:06 - mmengine - INFO - Epoch(train) [34][60/63] lr: 6.8264e-03 eta: 22:27:49 time: 1.0059 data_time: 0.0525 memory: 16202 loss_prob: 1.3801 loss_thr: 0.6044 loss_db: 0.2226 loss: 2.2072 2022/08/29 23:49:09 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:49:17 - mmengine - INFO - Epoch(train) [35][5/63] lr: 6.8211e-03 eta: 22:27:49 time: 1.2086 data_time: 0.3395 memory: 16202 loss_prob: 1.3732 loss_thr: 0.5956 loss_db: 0.2280 loss: 2.1968 2022/08/29 23:49:22 - mmengine - INFO - Epoch(train) [35][10/63] lr: 6.8211e-03 eta: 22:26:44 time: 1.2810 data_time: 0.3773 memory: 16202 loss_prob: 1.2393 loss_thr: 0.5841 loss_db: 0.2049 loss: 2.0283 2022/08/29 23:49:27 - mmengine - INFO - Epoch(train) [35][15/63] lr: 6.8211e-03 eta: 22:26:44 time: 0.9536 data_time: 0.0600 memory: 16202 loss_prob: 1.3350 loss_thr: 0.6263 loss_db: 0.2112 loss: 2.1725 2022/08/29 23:49:32 - mmengine - INFO - Epoch(train) [35][20/63] lr: 6.8211e-03 eta: 22:26:01 time: 1.0057 data_time: 0.0335 memory: 16202 loss_prob: 1.2625 loss_thr: 0.6083 loss_db: 0.2019 loss: 2.0727 2022/08/29 23:49:36 - mmengine - INFO - Epoch(train) [35][25/63] lr: 6.8211e-03 eta: 22:26:01 time: 0.9726 data_time: 0.0605 memory: 16202 loss_prob: 1.1357 loss_thr: 0.5710 loss_db: 0.1851 loss: 1.8918 2022/08/29 23:49:41 - mmengine - INFO - Epoch(train) [35][30/63] lr: 6.8211e-03 eta: 22:24:58 time: 0.9469 data_time: 0.0548 memory: 16202 loss_prob: 1.2074 loss_thr: 0.6156 loss_db: 0.1977 loss: 2.0207 2022/08/29 23:49:46 - mmengine - INFO - Epoch(train) [35][35/63] lr: 6.8211e-03 eta: 22:24:58 time: 0.9681 data_time: 0.0367 memory: 16202 loss_prob: 1.2131 loss_thr: 0.6207 loss_db: 0.1971 loss: 2.0310 2022/08/29 23:49:51 - mmengine - INFO - Epoch(train) [35][40/63] lr: 6.8211e-03 eta: 22:23:58 time: 0.9528 data_time: 0.0494 memory: 16202 loss_prob: 1.3423 loss_thr: 0.6001 loss_db: 0.2096 loss: 2.1519 2022/08/29 23:49:55 - mmengine - INFO - Epoch(train) [35][45/63] lr: 6.8211e-03 eta: 22:23:58 time: 0.9237 data_time: 0.0509 memory: 16202 loss_prob: 1.3766 loss_thr: 0.6100 loss_db: 0.2200 loss: 2.2066 2022/08/29 23:50:00 - mmengine - INFO - Epoch(train) [35][50/63] lr: 6.8211e-03 eta: 22:23:03 time: 0.9666 data_time: 0.0438 memory: 16202 loss_prob: 1.2406 loss_thr: 0.6046 loss_db: 0.2027 loss: 2.0479 2022/08/29 23:50:05 - mmengine - INFO - Epoch(train) [35][55/63] lr: 6.8211e-03 eta: 22:23:03 time: 0.9493 data_time: 0.0443 memory: 16202 loss_prob: 1.2736 loss_thr: 0.6031 loss_db: 0.2044 loss: 2.0810 2022/08/29 23:50:09 - mmengine - INFO - Epoch(train) [35][60/63] lr: 6.8211e-03 eta: 22:21:45 time: 0.8978 data_time: 0.0470 memory: 16202 loss_prob: 1.2418 loss_thr: 0.5934 loss_db: 0.2015 loss: 2.0366 2022/08/29 23:50:12 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:50:19 - mmengine - INFO - Epoch(train) [36][5/63] lr: 6.8158e-03 eta: 22:21:45 time: 1.2154 data_time: 0.3633 memory: 16202 loss_prob: 1.2595 loss_thr: 0.6025 loss_db: 0.2105 loss: 2.0725 2022/08/29 23:50:25 - mmengine - INFO - Epoch(train) [36][10/63] lr: 6.8158e-03 eta: 22:20:43 time: 1.2820 data_time: 0.3906 memory: 16202 loss_prob: 1.3651 loss_thr: 0.6335 loss_db: 0.2182 loss: 2.2168 2022/08/29 23:50:30 - mmengine - INFO - Epoch(train) [36][15/63] lr: 6.8158e-03 eta: 22:20:43 time: 1.0252 data_time: 0.0584 memory: 16202 loss_prob: 1.3534 loss_thr: 0.6231 loss_db: 0.2141 loss: 2.1906 2022/08/29 23:50:35 - mmengine - INFO - Epoch(train) [36][20/63] lr: 6.8158e-03 eta: 22:20:14 time: 1.0413 data_time: 0.1083 memory: 16202 loss_prob: 1.2178 loss_thr: 0.6167 loss_db: 0.1964 loss: 2.0308 2022/08/29 23:50:40 - mmengine - INFO - Epoch(train) [36][25/63] lr: 6.8158e-03 eta: 22:20:14 time: 1.0110 data_time: 0.1069 memory: 16202 loss_prob: 1.1772 loss_thr: 0.6045 loss_db: 0.1918 loss: 1.9735 2022/08/29 23:50:45 - mmengine - INFO - Epoch(train) [36][30/63] lr: 6.8158e-03 eta: 22:19:20 time: 0.9652 data_time: 0.0537 memory: 16202 loss_prob: 1.1730 loss_thr: 0.5845 loss_db: 0.1894 loss: 1.9469 2022/08/29 23:50:49 - mmengine - INFO - Epoch(train) [36][35/63] lr: 6.8158e-03 eta: 22:19:20 time: 0.8870 data_time: 0.0544 memory: 16202 loss_prob: 1.1003 loss_thr: 0.5847 loss_db: 0.1796 loss: 1.8646 2022/08/29 23:50:53 - mmengine - INFO - Epoch(train) [36][40/63] lr: 6.8158e-03 eta: 22:17:37 time: 0.8129 data_time: 0.0286 memory: 16202 loss_prob: 1.1150 loss_thr: 0.5835 loss_db: 0.1845 loss: 1.8830 2022/08/29 23:50:57 - mmengine - INFO - Epoch(train) [36][45/63] lr: 6.8158e-03 eta: 22:17:37 time: 0.8267 data_time: 0.0477 memory: 16202 loss_prob: 1.2166 loss_thr: 0.5938 loss_db: 0.1990 loss: 2.0094 2022/08/29 23:51:01 - mmengine - INFO - Epoch(train) [36][50/63] lr: 6.8158e-03 eta: 22:16:04 time: 0.8420 data_time: 0.0605 memory: 16202 loss_prob: 1.2814 loss_thr: 0.6134 loss_db: 0.2067 loss: 2.1015 2022/08/29 23:51:05 - mmengine - INFO - Epoch(train) [36][55/63] lr: 6.8158e-03 eta: 22:16:04 time: 0.8261 data_time: 0.0346 memory: 16202 loss_prob: 1.2258 loss_thr: 0.6057 loss_db: 0.1965 loss: 2.0280 2022/08/29 23:51:09 - mmengine - INFO - Epoch(train) [36][60/63] lr: 6.8158e-03 eta: 22:14:30 time: 0.8366 data_time: 0.0292 memory: 16202 loss_prob: 1.3836 loss_thr: 0.6220 loss_db: 0.2164 loss: 2.2221 2022/08/29 23:51:12 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:51:19 - mmengine - INFO - Epoch(train) [37][5/63] lr: 6.8106e-03 eta: 22:14:30 time: 1.1352 data_time: 0.3255 memory: 16202 loss_prob: 1.4028 loss_thr: 0.6207 loss_db: 0.2137 loss: 2.2371 2022/08/29 23:51:24 - mmengine - INFO - Epoch(train) [37][10/63] lr: 6.8106e-03 eta: 22:13:13 time: 1.2230 data_time: 0.3459 memory: 16202 loss_prob: 1.2696 loss_thr: 0.6296 loss_db: 0.2029 loss: 2.1021 2022/08/29 23:51:28 - mmengine - INFO - Epoch(train) [37][15/63] lr: 6.8106e-03 eta: 22:13:13 time: 0.9069 data_time: 0.0462 memory: 16202 loss_prob: 1.3097 loss_thr: 0.6256 loss_db: 0.2114 loss: 2.1467 2022/08/29 23:51:33 - mmengine - INFO - Epoch(train) [37][20/63] lr: 6.8106e-03 eta: 22:12:15 time: 0.9460 data_time: 0.0327 memory: 16202 loss_prob: 1.2755 loss_thr: 0.6150 loss_db: 0.2005 loss: 2.0910 2022/08/29 23:51:38 - mmengine - INFO - Epoch(train) [37][25/63] lr: 6.8106e-03 eta: 22:12:15 time: 0.9819 data_time: 0.0585 memory: 16202 loss_prob: 1.1746 loss_thr: 0.5740 loss_db: 0.1857 loss: 1.9343 2022/08/29 23:51:42 - mmengine - INFO - Epoch(train) [37][30/63] lr: 6.8106e-03 eta: 22:11:04 time: 0.9004 data_time: 0.0553 memory: 16202 loss_prob: 1.1476 loss_thr: 0.5739 loss_db: 0.1858 loss: 1.9073 2022/08/29 23:51:47 - mmengine - INFO - Epoch(train) [37][35/63] lr: 6.8106e-03 eta: 22:11:04 time: 0.8650 data_time: 0.0383 memory: 16202 loss_prob: 1.1910 loss_thr: 0.5723 loss_db: 0.1951 loss: 1.9584 2022/08/29 23:51:52 - mmengine - INFO - Epoch(train) [37][40/63] lr: 6.8106e-03 eta: 22:10:07 time: 0.9447 data_time: 0.0372 memory: 16202 loss_prob: 1.3112 loss_thr: 0.5922 loss_db: 0.2143 loss: 2.1177 2022/08/29 23:51:56 - mmengine - INFO - Epoch(train) [37][45/63] lr: 6.8106e-03 eta: 22:10:07 time: 0.9589 data_time: 0.0356 memory: 16202 loss_prob: 1.2800 loss_thr: 0.6148 loss_db: 0.2085 loss: 2.1033 2022/08/29 23:52:00 - mmengine - INFO - Epoch(train) [37][50/63] lr: 6.8106e-03 eta: 22:08:42 time: 0.8536 data_time: 0.0285 memory: 16202 loss_prob: 1.2064 loss_thr: 0.6059 loss_db: 0.2011 loss: 2.0134 2022/08/29 23:52:05 - mmengine - INFO - Epoch(train) [37][55/63] lr: 6.8106e-03 eta: 22:08:42 time: 0.8297 data_time: 0.0265 memory: 16202 loss_prob: 1.2534 loss_thr: 0.6157 loss_db: 0.2058 loss: 2.0749 2022/08/29 23:52:09 - mmengine - INFO - Epoch(train) [37][60/63] lr: 6.8106e-03 eta: 22:07:16 time: 0.8510 data_time: 0.0340 memory: 16202 loss_prob: 1.2100 loss_thr: 0.5987 loss_db: 0.1992 loss: 2.0079 2022/08/29 23:52:11 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:52:17 - mmengine - INFO - Epoch(train) [38][5/63] lr: 6.8053e-03 eta: 22:07:16 time: 1.0235 data_time: 0.2366 memory: 16202 loss_prob: 1.0906 loss_thr: 0.5743 loss_db: 0.1776 loss: 1.8425 2022/08/29 23:52:22 - mmengine - INFO - Epoch(train) [38][10/63] lr: 6.8053e-03 eta: 22:05:06 time: 1.0415 data_time: 0.2391 memory: 16202 loss_prob: 1.1208 loss_thr: 0.5769 loss_db: 0.1830 loss: 1.8807 2022/08/29 23:52:26 - mmengine - INFO - Epoch(train) [38][15/63] lr: 6.8053e-03 eta: 22:05:06 time: 0.8697 data_time: 0.0248 memory: 16202 loss_prob: 1.2158 loss_thr: 0.5909 loss_db: 0.1973 loss: 2.0039 2022/08/29 23:52:30 - mmengine - INFO - Epoch(train) [38][20/63] lr: 6.8053e-03 eta: 22:03:48 time: 0.8700 data_time: 0.0238 memory: 16202 loss_prob: 1.3373 loss_thr: 0.6445 loss_db: 0.2184 loss: 2.2002 2022/08/29 23:52:35 - mmengine - INFO - Epoch(train) [38][25/63] lr: 6.8053e-03 eta: 22:03:48 time: 0.8723 data_time: 0.0389 memory: 16202 loss_prob: 1.2129 loss_thr: 0.5971 loss_db: 0.2003 loss: 2.0103 2022/08/29 23:52:40 - mmengine - INFO - Epoch(train) [38][30/63] lr: 6.8053e-03 eta: 22:03:13 time: 1.0070 data_time: 0.0335 memory: 16202 loss_prob: 1.0919 loss_thr: 0.5578 loss_db: 0.1758 loss: 1.8256 2022/08/29 23:52:45 - mmengine - INFO - Epoch(train) [38][35/63] lr: 6.8053e-03 eta: 22:03:13 time: 1.0169 data_time: 0.0602 memory: 16202 loss_prob: 1.1327 loss_thr: 0.5744 loss_db: 0.1808 loss: 1.8879 2022/08/29 23:52:50 - mmengine - INFO - Epoch(train) [38][40/63] lr: 6.8053e-03 eta: 22:02:13 time: 0.9231 data_time: 0.0593 memory: 16202 loss_prob: 1.1991 loss_thr: 0.5809 loss_db: 0.1932 loss: 1.9733 2022/08/29 23:52:54 - mmengine - INFO - Epoch(train) [38][45/63] lr: 6.8053e-03 eta: 22:02:13 time: 0.8901 data_time: 0.0319 memory: 16202 loss_prob: 1.2057 loss_thr: 0.5954 loss_db: 0.1915 loss: 1.9926 2022/08/29 23:53:00 - mmengine - INFO - Epoch(train) [38][50/63] lr: 6.8053e-03 eta: 22:01:54 time: 1.0567 data_time: 0.0571 memory: 16202 loss_prob: 1.1076 loss_thr: 0.5768 loss_db: 0.1760 loss: 1.8604 2022/08/29 23:53:06 - mmengine - INFO - Epoch(train) [38][55/63] lr: 6.8053e-03 eta: 22:01:54 time: 1.2406 data_time: 0.0501 memory: 16202 loss_prob: 1.0593 loss_thr: 0.5694 loss_db: 0.1704 loss: 1.7990 2022/08/29 23:53:15 - mmengine - INFO - Epoch(train) [38][60/63] lr: 6.8053e-03 eta: 22:03:32 time: 1.4399 data_time: 0.0729 memory: 16202 loss_prob: 1.0760 loss_thr: 0.6005 loss_db: 0.1738 loss: 1.8503 2022/08/29 23:53:20 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:53:30 - mmengine - INFO - Epoch(train) [39][5/63] lr: 6.8000e-03 eta: 22:03:32 time: 1.8493 data_time: 0.3570 memory: 16202 loss_prob: 1.3493 loss_thr: 0.6363 loss_db: 0.2138 loss: 2.1995 2022/08/29 23:53:38 - mmengine - INFO - Epoch(train) [39][10/63] lr: 6.8000e-03 eta: 22:05:13 time: 1.7873 data_time: 0.3954 memory: 16202 loss_prob: 1.2573 loss_thr: 0.6109 loss_db: 0.1988 loss: 2.0669 2022/08/29 23:53:47 - mmengine - INFO - Epoch(train) [39][15/63] lr: 6.8000e-03 eta: 22:05:13 time: 1.7057 data_time: 0.0582 memory: 16202 loss_prob: 1.2374 loss_thr: 0.5944 loss_db: 0.1938 loss: 2.0256 2022/08/29 23:53:55 - mmengine - INFO - Epoch(train) [39][20/63] lr: 6.8000e-03 eta: 22:08:09 time: 1.7027 data_time: 0.0494 memory: 16202 loss_prob: 1.1503 loss_thr: 0.5791 loss_db: 0.1868 loss: 1.9163 2022/08/29 23:54:02 - mmengine - INFO - Epoch(train) [39][25/63] lr: 6.8000e-03 eta: 22:08:09 time: 1.5208 data_time: 0.0587 memory: 16202 loss_prob: 1.1566 loss_thr: 0.5966 loss_db: 0.1907 loss: 1.9440 2022/08/29 23:54:10 - mmengine - INFO - Epoch(train) [39][30/63] lr: 6.8000e-03 eta: 22:09:49 time: 1.4581 data_time: 0.0482 memory: 16202 loss_prob: 1.2416 loss_thr: 0.6104 loss_db: 0.2008 loss: 2.0528 2022/08/29 23:54:18 - mmengine - INFO - Epoch(train) [39][35/63] lr: 6.8000e-03 eta: 22:09:49 time: 1.6168 data_time: 0.0743 memory: 16202 loss_prob: 1.1194 loss_thr: 0.5634 loss_db: 0.1816 loss: 1.8644 2022/08/29 23:54:27 - mmengine - INFO - Epoch(train) [39][40/63] lr: 6.8000e-03 eta: 22:12:40 time: 1.6942 data_time: 0.0752 memory: 16202 loss_prob: 1.2200 loss_thr: 0.5922 loss_db: 0.1981 loss: 2.0104 2022/08/29 23:54:31 - mmengine - INFO - Epoch(train) [39][45/63] lr: 6.8000e-03 eta: 22:12:40 time: 1.2673 data_time: 0.0531 memory: 16202 loss_prob: 1.3091 loss_thr: 0.6227 loss_db: 0.2122 loss: 2.1439 2022/08/29 23:54:36 - mmengine - INFO - Epoch(train) [39][50/63] lr: 6.8000e-03 eta: 22:11:45 time: 0.9458 data_time: 0.0591 memory: 16202 loss_prob: 1.1448 loss_thr: 0.5784 loss_db: 0.1885 loss: 1.9118 2022/08/29 23:54:41 - mmengine - INFO - Epoch(train) [39][55/63] lr: 6.8000e-03 eta: 22:11:45 time: 0.9799 data_time: 0.0490 memory: 16202 loss_prob: 1.1581 loss_thr: 0.6151 loss_db: 0.1962 loss: 1.9694 2022/08/29 23:54:46 - mmengine - INFO - Epoch(train) [39][60/63] lr: 6.8000e-03 eta: 22:10:48 time: 0.9369 data_time: 0.0359 memory: 16202 loss_prob: 1.2757 loss_thr: 0.6635 loss_db: 0.2147 loss: 2.1539 2022/08/29 23:54:48 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:54:56 - mmengine - INFO - Epoch(train) [40][5/63] lr: 6.7947e-03 eta: 22:10:48 time: 1.1860 data_time: 0.3785 memory: 16202 loss_prob: 1.1779 loss_thr: 0.6211 loss_db: 0.1955 loss: 1.9945 2022/08/29 23:55:01 - mmengine - INFO - Epoch(train) [40][10/63] lr: 6.7947e-03 eta: 22:09:49 time: 1.2693 data_time: 0.3952 memory: 16202 loss_prob: 1.1377 loss_thr: 0.5908 loss_db: 0.1851 loss: 1.9136 2022/08/29 23:55:05 - mmengine - INFO - Epoch(train) [40][15/63] lr: 6.7947e-03 eta: 22:09:49 time: 0.9213 data_time: 0.0584 memory: 16202 loss_prob: 1.0650 loss_thr: 0.5581 loss_db: 0.1711 loss: 1.7942 2022/08/29 23:55:09 - mmengine - INFO - Epoch(train) [40][20/63] lr: 6.7947e-03 eta: 22:08:33 time: 0.8687 data_time: 0.0366 memory: 16202 loss_prob: 1.1349 loss_thr: 0.5720 loss_db: 0.1861 loss: 1.8930 2022/08/29 23:55:14 - mmengine - INFO - Epoch(train) [40][25/63] lr: 6.7947e-03 eta: 22:08:33 time: 0.8380 data_time: 0.0567 memory: 16202 loss_prob: 1.2093 loss_thr: 0.5898 loss_db: 0.1946 loss: 1.9937 2022/08/29 23:55:18 - mmengine - INFO - Epoch(train) [40][30/63] lr: 6.7947e-03 eta: 22:07:16 time: 0.8659 data_time: 0.0629 memory: 16202 loss_prob: 1.1712 loss_thr: 0.5819 loss_db: 0.1872 loss: 1.9403 2022/08/29 23:55:22 - mmengine - INFO - Epoch(train) [40][35/63] lr: 6.7947e-03 eta: 22:07:16 time: 0.8474 data_time: 0.0409 memory: 16202 loss_prob: 1.2893 loss_thr: 0.5842 loss_db: 0.2078 loss: 2.0812 2022/08/29 23:55:26 - mmengine - INFO - Epoch(train) [40][40/63] lr: 6.7947e-03 eta: 22:05:49 time: 0.8311 data_time: 0.0417 memory: 16202 loss_prob: 1.3249 loss_thr: 0.5895 loss_db: 0.2112 loss: 2.1255 2022/08/29 23:55:31 - mmengine - INFO - Epoch(train) [40][45/63] lr: 6.7947e-03 eta: 22:05:49 time: 0.8886 data_time: 0.0529 memory: 16202 loss_prob: 1.2021 loss_thr: 0.5922 loss_db: 0.1943 loss: 1.9886 2022/08/29 23:55:35 - mmengine - INFO - Epoch(train) [40][50/63] lr: 6.7947e-03 eta: 22:04:47 time: 0.9130 data_time: 0.0460 memory: 16202 loss_prob: 1.3862 loss_thr: 0.6297 loss_db: 0.2258 loss: 2.2416 2022/08/29 23:55:40 - mmengine - INFO - Epoch(train) [40][55/63] lr: 6.7947e-03 eta: 22:04:47 time: 0.9328 data_time: 0.0502 memory: 16202 loss_prob: 1.3957 loss_thr: 0.6050 loss_db: 0.2254 loss: 2.2261 2022/08/29 23:55:44 - mmengine - INFO - Epoch(train) [40][60/63] lr: 6.7947e-03 eta: 22:03:45 time: 0.9091 data_time: 0.0546 memory: 16202 loss_prob: 1.2661 loss_thr: 0.5741 loss_db: 0.1989 loss: 2.0391 2022/08/29 23:55:47 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:55:47 - mmengine - INFO - Saving checkpoint at 40 epochs 2022/08/29 23:55:56 - mmengine - INFO - Epoch(val) [40][5/32] eta: 22:03:45 time: 1.5989 data_time: 0.2370 memory: 16202 2022/08/29 23:56:00 - mmengine - INFO - Epoch(val) [40][10/32] eta: 0:00:19 time: 0.8976 data_time: 0.2735 memory: 15734 2022/08/29 23:56:03 - mmengine - INFO - Epoch(val) [40][15/32] eta: 0:00:19 time: 0.6789 data_time: 0.0570 memory: 15734 2022/08/29 23:56:06 - mmengine - INFO - Epoch(val) [40][20/32] eta: 0:00:07 time: 0.6164 data_time: 0.0591 memory: 15734 2022/08/29 23:56:10 - mmengine - INFO - Epoch(val) [40][25/32] eta: 0:00:07 time: 0.7176 data_time: 0.0577 memory: 15734 2022/08/29 23:56:13 - mmengine - INFO - Epoch(val) [40][30/32] eta: 0:00:01 time: 0.6953 data_time: 0.0301 memory: 15734 2022/08/29 23:56:14 - mmengine - INFO - Evaluating hmean-iou... 2022/08/29 23:56:14 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8021, precision: 0.6819, hmean: 0.7372 2022/08/29 23:56:14 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8016, precision: 0.7737, hmean: 0.7874 2022/08/29 23:56:14 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.7867, precision: 0.8178, hmean: 0.8020 2022/08/29 23:56:14 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7631, precision: 0.8605, hmean: 0.8089 2022/08/29 23:56:14 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.6933, precision: 0.9017, hmean: 0.7839 2022/08/29 23:56:14 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.3067, precision: 0.9830, hmean: 0.4675 2022/08/29 23:56:14 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/08/29 23:56:14 - mmengine - INFO - Epoch(val) [40][32/32] icdar/precision: 0.8605 icdar/recall: 0.7631 icdar/hmean: 0.8089 2022/08/29 23:56:22 - mmengine - INFO - Epoch(train) [41][5/63] lr: 6.7895e-03 eta: 0:00:01 time: 1.2125 data_time: 0.4010 memory: 16201 loss_prob: 1.2038 loss_thr: 0.5831 loss_db: 0.1933 loss: 1.9802 2022/08/29 23:56:27 - mmengine - INFO - Epoch(train) [41][10/63] lr: 6.7895e-03 eta: 22:03:02 time: 1.3144 data_time: 0.4038 memory: 16201 loss_prob: 1.1212 loss_thr: 0.5876 loss_db: 0.1886 loss: 1.8974 2022/08/29 23:56:33 - mmengine - INFO - Epoch(train) [41][15/63] lr: 6.7895e-03 eta: 22:03:02 time: 1.0674 data_time: 0.0538 memory: 16201 loss_prob: 1.1808 loss_thr: 0.5801 loss_db: 0.1969 loss: 1.9578 2022/08/29 23:56:37 - mmengine - INFO - Epoch(train) [41][20/63] lr: 6.7895e-03 eta: 22:02:32 time: 1.0185 data_time: 0.0561 memory: 16201 loss_prob: 1.3913 loss_thr: 0.6075 loss_db: 0.2246 loss: 2.2234 2022/08/29 23:56:42 - mmengine - INFO - Epoch(train) [41][25/63] lr: 6.7895e-03 eta: 22:02:32 time: 0.8974 data_time: 0.0553 memory: 16201 loss_prob: 1.4570 loss_thr: 0.6510 loss_db: 0.2344 loss: 2.3424 2022/08/29 23:56:46 - mmengine - INFO - Epoch(train) [41][30/63] lr: 6.7895e-03 eta: 22:01:33 time: 0.9175 data_time: 0.0490 memory: 16201 loss_prob: 1.3453 loss_thr: 0.6148 loss_db: 0.2187 loss: 2.1788 2022/08/29 23:56:52 - mmengine - INFO - Epoch(train) [41][35/63] lr: 6.7895e-03 eta: 22:01:33 time: 1.0237 data_time: 0.0381 memory: 16201 loss_prob: 1.2098 loss_thr: 0.5700 loss_db: 0.2034 loss: 1.9832 2022/08/29 23:56:56 - mmengine - INFO - Epoch(train) [41][40/63] lr: 6.7895e-03 eta: 22:00:57 time: 1.0000 data_time: 0.0520 memory: 16201 loss_prob: 1.2118 loss_thr: 0.5762 loss_db: 0.1950 loss: 1.9830 2022/08/29 23:57:01 - mmengine - INFO - Epoch(train) [41][45/63] lr: 6.7895e-03 eta: 22:00:57 time: 0.8964 data_time: 0.0736 memory: 16201 loss_prob: 1.2626 loss_thr: 0.5805 loss_db: 0.1951 loss: 2.0382 2022/08/29 23:57:05 - mmengine - INFO - Epoch(train) [41][50/63] lr: 6.7895e-03 eta: 21:59:53 time: 0.8966 data_time: 0.0632 memory: 16201 loss_prob: 1.2409 loss_thr: 0.5758 loss_db: 0.2016 loss: 2.0184 2022/08/29 23:57:11 - mmengine - INFO - Epoch(train) [41][55/63] lr: 6.7895e-03 eta: 21:59:53 time: 1.0329 data_time: 0.0546 memory: 16201 loss_prob: 1.3229 loss_thr: 0.6093 loss_db: 0.2140 loss: 2.1461 2022/08/29 23:57:16 - mmengine - INFO - Epoch(train) [41][60/63] lr: 6.7895e-03 eta: 21:59:28 time: 1.0352 data_time: 0.0638 memory: 16201 loss_prob: 1.3464 loss_thr: 0.6323 loss_db: 0.2163 loss: 2.1951 2022/08/29 23:57:18 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:57:26 - mmengine - INFO - Epoch(train) [42][5/63] lr: 6.7842e-03 eta: 21:59:28 time: 1.1858 data_time: 0.3887 memory: 16201 loss_prob: 1.1590 loss_thr: 0.5843 loss_db: 0.1861 loss: 1.9295 2022/08/29 23:57:31 - mmengine - INFO - Epoch(train) [42][10/63] lr: 6.7842e-03 eta: 21:58:35 time: 1.2720 data_time: 0.3923 memory: 16201 loss_prob: 1.0933 loss_thr: 0.5562 loss_db: 0.1790 loss: 1.8285 2022/08/29 23:57:35 - mmengine - INFO - Epoch(train) [42][15/63] lr: 6.7842e-03 eta: 21:58:35 time: 0.9315 data_time: 0.0520 memory: 16201 loss_prob: 1.2053 loss_thr: 0.5821 loss_db: 0.1914 loss: 1.9788 2022/08/29 23:57:40 - mmengine - INFO - Epoch(train) [42][20/63] lr: 6.7842e-03 eta: 21:57:37 time: 0.9143 data_time: 0.0356 memory: 16201 loss_prob: 1.1825 loss_thr: 0.5918 loss_db: 0.1860 loss: 1.9603 2022/08/29 23:57:44 - mmengine - INFO - Epoch(train) [42][25/63] lr: 6.7842e-03 eta: 21:57:37 time: 0.8805 data_time: 0.0329 memory: 16201 loss_prob: 1.1289 loss_thr: 0.5922 loss_db: 0.1826 loss: 1.9038 2022/08/29 23:57:48 - mmengine - INFO - Epoch(train) [42][30/63] lr: 6.7842e-03 eta: 21:56:20 time: 0.8452 data_time: 0.0325 memory: 16201 loss_prob: 1.2262 loss_thr: 0.6537 loss_db: 0.1991 loss: 2.0790 2022/08/29 23:57:53 - mmengine - INFO - Epoch(train) [42][35/63] lr: 6.7842e-03 eta: 21:56:20 time: 0.8854 data_time: 0.0441 memory: 16201 loss_prob: 1.1612 loss_thr: 0.6171 loss_db: 0.1840 loss: 1.9623 2022/08/29 23:57:57 - mmengine - INFO - Epoch(train) [42][40/63] lr: 6.7842e-03 eta: 21:55:19 time: 0.9035 data_time: 0.0497 memory: 16201 loss_prob: 1.1475 loss_thr: 0.5810 loss_db: 0.1822 loss: 1.9107 2022/08/29 23:58:03 - mmengine - INFO - Epoch(train) [42][45/63] lr: 6.7842e-03 eta: 21:55:19 time: 0.9607 data_time: 0.0494 memory: 16201 loss_prob: 1.2050 loss_thr: 0.5986 loss_db: 0.1983 loss: 2.0020 2022/08/29 23:58:07 - mmengine - INFO - Epoch(train) [42][50/63] lr: 6.7842e-03 eta: 21:54:36 time: 0.9637 data_time: 0.0440 memory: 16201 loss_prob: 1.2516 loss_thr: 0.6073 loss_db: 0.2075 loss: 2.0665 2022/08/29 23:58:12 - mmengine - INFO - Epoch(train) [42][55/63] lr: 6.7842e-03 eta: 21:54:36 time: 0.8861 data_time: 0.0481 memory: 16201 loss_prob: 1.2444 loss_thr: 0.5942 loss_db: 0.2010 loss: 2.0397 2022/08/29 23:58:18 - mmengine - INFO - Epoch(train) [42][60/63] lr: 6.7842e-03 eta: 21:54:21 time: 1.0658 data_time: 0.0545 memory: 16201 loss_prob: 1.2342 loss_thr: 0.5953 loss_db: 0.1948 loss: 2.0244 2022/08/29 23:58:21 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:58:29 - mmengine - INFO - Epoch(train) [43][5/63] lr: 6.7789e-03 eta: 21:54:21 time: 1.3404 data_time: 0.4040 memory: 16201 loss_prob: 1.2206 loss_thr: 0.5863 loss_db: 0.1995 loss: 2.0065 2022/08/29 23:58:33 - mmengine - INFO - Epoch(train) [43][10/63] lr: 6.7789e-03 eta: 21:53:22 time: 1.2426 data_time: 0.4156 memory: 16201 loss_prob: 1.2517 loss_thr: 0.5830 loss_db: 0.2074 loss: 2.0421 2022/08/29 23:58:38 - mmengine - INFO - Epoch(train) [43][15/63] lr: 6.7789e-03 eta: 21:53:22 time: 0.9001 data_time: 0.0606 memory: 16201 loss_prob: 1.2387 loss_thr: 0.5893 loss_db: 0.2040 loss: 2.0319 2022/08/29 23:58:43 - mmengine - INFO - Epoch(train) [43][20/63] lr: 6.7789e-03 eta: 21:52:25 time: 0.9096 data_time: 0.0376 memory: 16201 loss_prob: 1.2687 loss_thr: 0.6369 loss_db: 0.2130 loss: 2.1185 2022/08/29 23:58:47 - mmengine - INFO - Epoch(train) [43][25/63] lr: 6.7789e-03 eta: 21:52:25 time: 0.9333 data_time: 0.0547 memory: 16201 loss_prob: 1.3004 loss_thr: 0.6411 loss_db: 0.2149 loss: 2.1564 2022/08/29 23:58:52 - mmengine - INFO - Epoch(train) [43][30/63] lr: 6.7789e-03 eta: 21:51:27 time: 0.9096 data_time: 0.0421 memory: 16201 loss_prob: 1.2365 loss_thr: 0.6002 loss_db: 0.2015 loss: 2.0382 2022/08/29 23:58:56 - mmengine - INFO - Epoch(train) [43][35/63] lr: 6.7789e-03 eta: 21:51:27 time: 0.8791 data_time: 0.0335 memory: 16201 loss_prob: 1.2281 loss_thr: 0.6057 loss_db: 0.2031 loss: 2.0369 2022/08/29 23:59:00 - mmengine - INFO - Epoch(train) [43][40/63] lr: 6.7789e-03 eta: 21:50:22 time: 0.8767 data_time: 0.0508 memory: 16201 loss_prob: 1.2226 loss_thr: 0.6244 loss_db: 0.2031 loss: 2.0502 2022/08/29 23:59:05 - mmengine - INFO - Epoch(train) [43][45/63] lr: 6.7789e-03 eta: 21:50:22 time: 0.9316 data_time: 0.0455 memory: 16201 loss_prob: 1.1390 loss_thr: 0.5936 loss_db: 0.1860 loss: 1.9187 2022/08/29 23:59:10 - mmengine - INFO - Epoch(train) [43][50/63] lr: 6.7789e-03 eta: 21:49:31 time: 0.9288 data_time: 0.0474 memory: 16201 loss_prob: 1.0846 loss_thr: 0.5568 loss_db: 0.1748 loss: 1.8162 2022/08/29 23:59:14 - mmengine - INFO - Epoch(train) [43][55/63] lr: 6.7789e-03 eta: 21:49:31 time: 0.8936 data_time: 0.0674 memory: 16201 loss_prob: 1.0695 loss_thr: 0.5591 loss_db: 0.1724 loss: 1.8011 2022/08/29 23:59:19 - mmengine - INFO - Epoch(train) [43][60/63] lr: 6.7789e-03 eta: 21:48:32 time: 0.9014 data_time: 0.0549 memory: 16201 loss_prob: 1.1583 loss_thr: 0.5780 loss_db: 0.1845 loss: 1.9209 2022/08/29 23:59:21 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/29 23:59:28 - mmengine - INFO - Epoch(train) [44][5/63] lr: 6.7737e-03 eta: 21:48:32 time: 1.1176 data_time: 0.2847 memory: 16201 loss_prob: 1.1674 loss_thr: 0.5735 loss_db: 0.1906 loss: 1.9316 2022/08/29 23:59:32 - mmengine - INFO - Epoch(train) [44][10/63] lr: 6.7737e-03 eta: 21:47:10 time: 1.1437 data_time: 0.3145 memory: 16201 loss_prob: 1.1983 loss_thr: 0.6175 loss_db: 0.1948 loss: 2.0106 2022/08/29 23:59:37 - mmengine - INFO - Epoch(train) [44][15/63] lr: 6.7737e-03 eta: 21:47:10 time: 0.8362 data_time: 0.0514 memory: 16201 loss_prob: 1.1694 loss_thr: 0.6262 loss_db: 0.1911 loss: 1.9867 2022/08/29 23:59:41 - mmengine - INFO - Epoch(train) [44][20/63] lr: 6.7737e-03 eta: 21:45:51 time: 0.8219 data_time: 0.0277 memory: 16201 loss_prob: 1.2344 loss_thr: 0.6507 loss_db: 0.2069 loss: 2.0920 2022/08/29 23:59:45 - mmengine - INFO - Epoch(train) [44][25/63] lr: 6.7737e-03 eta: 21:45:51 time: 0.8722 data_time: 0.0530 memory: 16201 loss_prob: 1.3064 loss_thr: 0.6320 loss_db: 0.2188 loss: 2.1572 2022/08/29 23:59:50 - mmengine - INFO - Epoch(train) [44][30/63] lr: 6.7737e-03 eta: 21:44:55 time: 0.9057 data_time: 0.0532 memory: 16201 loss_prob: 1.3376 loss_thr: 0.5966 loss_db: 0.2186 loss: 2.1528 2022/08/29 23:59:54 - mmengine - INFO - Epoch(train) [44][35/63] lr: 6.7737e-03 eta: 21:44:55 time: 0.8839 data_time: 0.0303 memory: 16201 loss_prob: 1.2534 loss_thr: 0.5672 loss_db: 0.1985 loss: 2.0191 2022/08/29 23:59:59 - mmengine - INFO - Epoch(train) [44][40/63] lr: 6.7737e-03 eta: 21:44:02 time: 0.9156 data_time: 0.0604 memory: 16201 loss_prob: 1.1247 loss_thr: 0.5660 loss_db: 0.1808 loss: 1.8714 2022/08/30 00:00:12 - mmengine - INFO - Epoch(train) [44][45/63] lr: 6.7737e-03 eta: 21:44:02 time: 1.7666 data_time: 0.0903 memory: 16201 loss_prob: 1.1769 loss_thr: 0.5887 loss_db: 0.1927 loss: 1.9582 2022/08/30 00:00:16 - mmengine - INFO - Epoch(train) [44][50/63] lr: 6.7737e-03 eta: 21:46:47 time: 1.7392 data_time: 0.0834 memory: 16201 loss_prob: 1.3639 loss_thr: 0.6444 loss_db: 0.2201 loss: 2.2283 2022/08/30 00:00:21 - mmengine - INFO - Epoch(train) [44][55/63] lr: 6.7737e-03 eta: 21:46:47 time: 0.8852 data_time: 0.0562 memory: 16201 loss_prob: 1.3146 loss_thr: 0.6336 loss_db: 0.2129 loss: 2.1612 2022/08/30 00:00:25 - mmengine - INFO - Epoch(train) [44][60/63] lr: 6.7737e-03 eta: 21:45:46 time: 0.8845 data_time: 0.0415 memory: 16201 loss_prob: 1.2637 loss_thr: 0.5866 loss_db: 0.2040 loss: 2.0543 2022/08/30 00:00:28 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:00:36 - mmengine - INFO - Epoch(train) [45][5/63] lr: 6.7684e-03 eta: 21:45:46 time: 1.2036 data_time: 0.3831 memory: 16201 loss_prob: 1.3096 loss_thr: 0.5969 loss_db: 0.2104 loss: 2.1169 2022/08/30 00:00:40 - mmengine - INFO - Epoch(train) [45][10/63] lr: 6.7684e-03 eta: 21:44:57 time: 1.2645 data_time: 0.3757 memory: 16201 loss_prob: 1.3402 loss_thr: 0.6198 loss_db: 0.2195 loss: 2.1794 2022/08/30 00:00:45 - mmengine - INFO - Epoch(train) [45][15/63] lr: 6.7684e-03 eta: 21:44:57 time: 0.9204 data_time: 0.0519 memory: 16201 loss_prob: 1.4571 loss_thr: 0.6432 loss_db: 0.2414 loss: 2.3417 2022/08/30 00:00:49 - mmengine - INFO - Epoch(train) [45][20/63] lr: 6.7684e-03 eta: 21:43:57 time: 0.8880 data_time: 0.0344 memory: 16201 loss_prob: 1.3743 loss_thr: 0.6078 loss_db: 0.2214 loss: 2.2035 2022/08/30 00:00:54 - mmengine - INFO - Epoch(train) [45][25/63] lr: 6.7684e-03 eta: 21:43:57 time: 0.9260 data_time: 0.0557 memory: 16201 loss_prob: 1.2253 loss_thr: 0.5946 loss_db: 0.1938 loss: 2.0138 2022/08/30 00:00:58 - mmengine - INFO - Epoch(train) [45][30/63] lr: 6.7684e-03 eta: 21:43:06 time: 0.9198 data_time: 0.0549 memory: 16201 loss_prob: 1.1944 loss_thr: 0.6155 loss_db: 0.1951 loss: 2.0050 2022/08/30 00:01:03 - mmengine - INFO - Epoch(train) [45][35/63] lr: 6.7684e-03 eta: 21:43:06 time: 0.9087 data_time: 0.0383 memory: 16201 loss_prob: 1.2171 loss_thr: 0.6317 loss_db: 0.2026 loss: 2.0514 2022/08/30 00:01:08 - mmengine - INFO - Epoch(train) [45][40/63] lr: 6.7684e-03 eta: 21:42:14 time: 0.9126 data_time: 0.0521 memory: 16201 loss_prob: 1.2286 loss_thr: 0.6345 loss_db: 0.2022 loss: 2.0654 2022/08/30 00:01:12 - mmengine - INFO - Epoch(train) [45][45/63] lr: 6.7684e-03 eta: 21:42:14 time: 0.8865 data_time: 0.0481 memory: 16201 loss_prob: 1.2310 loss_thr: 0.6134 loss_db: 0.2008 loss: 2.0453 2022/08/30 00:01:17 - mmengine - INFO - Epoch(train) [45][50/63] lr: 6.7684e-03 eta: 21:41:21 time: 0.9092 data_time: 0.0407 memory: 16201 loss_prob: 1.1662 loss_thr: 0.5801 loss_db: 0.1873 loss: 1.9336 2022/08/30 00:01:21 - mmengine - INFO - Epoch(train) [45][55/63] lr: 6.7684e-03 eta: 21:41:21 time: 0.9209 data_time: 0.0573 memory: 16201 loss_prob: 1.1890 loss_thr: 0.5844 loss_db: 0.1875 loss: 1.9610 2022/08/30 00:01:26 - mmengine - INFO - Epoch(train) [45][60/63] lr: 6.7684e-03 eta: 21:40:24 time: 0.8944 data_time: 0.0637 memory: 16201 loss_prob: 1.3078 loss_thr: 0.6311 loss_db: 0.2068 loss: 2.1458 2022/08/30 00:01:28 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:01:36 - mmengine - INFO - Epoch(train) [46][5/63] lr: 6.7631e-03 eta: 21:40:24 time: 1.2094 data_time: 0.3637 memory: 16201 loss_prob: 1.1431 loss_thr: 0.5922 loss_db: 0.1837 loss: 1.9189 2022/08/30 00:01:41 - mmengine - INFO - Epoch(train) [46][10/63] lr: 6.7631e-03 eta: 21:39:53 time: 1.3276 data_time: 0.3783 memory: 16201 loss_prob: 1.0792 loss_thr: 0.5727 loss_db: 0.1804 loss: 1.8324 2022/08/30 00:01:45 - mmengine - INFO - Epoch(train) [46][15/63] lr: 6.7631e-03 eta: 21:39:53 time: 0.9150 data_time: 0.0380 memory: 16201 loss_prob: 1.1145 loss_thr: 0.5781 loss_db: 0.1875 loss: 1.8801 2022/08/30 00:01:50 - mmengine - INFO - Epoch(train) [46][20/63] lr: 6.7631e-03 eta: 21:38:51 time: 0.8721 data_time: 0.0396 memory: 16201 loss_prob: 1.1853 loss_thr: 0.5610 loss_db: 0.1892 loss: 1.9354 2022/08/30 00:01:54 - mmengine - INFO - Epoch(train) [46][25/63] lr: 6.7631e-03 eta: 21:38:51 time: 0.9012 data_time: 0.0614 memory: 16201 loss_prob: 1.2346 loss_thr: 0.5594 loss_db: 0.1914 loss: 1.9854 2022/08/30 00:01:59 - mmengine - INFO - Epoch(train) [46][30/63] lr: 6.7631e-03 eta: 21:37:55 time: 0.8922 data_time: 0.0430 memory: 16201 loss_prob: 1.2219 loss_thr: 0.5762 loss_db: 0.1943 loss: 1.9924 2022/08/30 00:02:04 - mmengine - INFO - Epoch(train) [46][35/63] lr: 6.7631e-03 eta: 21:37:55 time: 0.9562 data_time: 0.0336 memory: 16201 loss_prob: 1.1634 loss_thr: 0.5650 loss_db: 0.1903 loss: 1.9186 2022/08/30 00:02:08 - mmengine - INFO - Epoch(train) [46][40/63] lr: 6.7631e-03 eta: 21:37:17 time: 0.9616 data_time: 0.0497 memory: 16201 loss_prob: 1.1817 loss_thr: 0.5760 loss_db: 0.1947 loss: 1.9524 2022/08/30 00:02:13 - mmengine - INFO - Epoch(train) [46][45/63] lr: 6.7631e-03 eta: 21:37:17 time: 0.8912 data_time: 0.0475 memory: 16201 loss_prob: 1.1808 loss_thr: 0.5848 loss_db: 0.1939 loss: 1.9595 2022/08/30 00:02:17 - mmengine - INFO - Epoch(train) [46][50/63] lr: 6.7631e-03 eta: 21:36:21 time: 0.8925 data_time: 0.0489 memory: 16201 loss_prob: 1.2956 loss_thr: 0.6354 loss_db: 0.2139 loss: 2.1449 2022/08/30 00:02:22 - mmengine - INFO - Epoch(train) [46][55/63] lr: 6.7631e-03 eta: 21:36:21 time: 0.9080 data_time: 0.0519 memory: 16201 loss_prob: 1.3109 loss_thr: 0.6404 loss_db: 0.2183 loss: 2.1697 2022/08/30 00:02:26 - mmengine - INFO - Epoch(train) [46][60/63] lr: 6.7631e-03 eta: 21:35:35 time: 0.9268 data_time: 0.0615 memory: 16201 loss_prob: 1.1821 loss_thr: 0.6080 loss_db: 0.1958 loss: 1.9859 2022/08/30 00:02:29 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:02:38 - mmengine - INFO - Epoch(train) [47][5/63] lr: 6.7578e-03 eta: 21:35:35 time: 1.3350 data_time: 0.5077 memory: 16201 loss_prob: 1.3541 loss_thr: 0.6435 loss_db: 0.2172 loss: 2.2148 2022/08/30 00:02:43 - mmengine - INFO - Epoch(train) [47][10/63] lr: 6.7578e-03 eta: 21:35:33 time: 1.4395 data_time: 0.5832 memory: 16201 loss_prob: 1.2212 loss_thr: 0.6038 loss_db: 0.1938 loss: 2.0188 2022/08/30 00:02:47 - mmengine - INFO - Epoch(train) [47][15/63] lr: 6.7578e-03 eta: 21:35:33 time: 0.9230 data_time: 0.0993 memory: 16201 loss_prob: 1.1938 loss_thr: 0.5679 loss_db: 0.1922 loss: 1.9539 2022/08/30 00:02:52 - mmengine - INFO - Epoch(train) [47][20/63] lr: 6.7578e-03 eta: 21:34:33 time: 0.8687 data_time: 0.0482 memory: 16201 loss_prob: 1.3274 loss_thr: 0.5784 loss_db: 0.2219 loss: 2.1277 2022/08/30 00:02:56 - mmengine - INFO - Epoch(train) [47][25/63] lr: 6.7578e-03 eta: 21:34:33 time: 0.9045 data_time: 0.1050 memory: 16201 loss_prob: 1.2935 loss_thr: 0.5803 loss_db: 0.2165 loss: 2.0902 2022/08/30 00:03:01 - mmengine - INFO - Epoch(train) [47][30/63] lr: 6.7578e-03 eta: 21:33:48 time: 0.9310 data_time: 0.0984 memory: 16201 loss_prob: 1.1830 loss_thr: 0.5954 loss_db: 0.1928 loss: 1.9712 2022/08/30 00:03:07 - mmengine - INFO - Epoch(train) [47][35/63] lr: 6.7578e-03 eta: 21:33:48 time: 1.0550 data_time: 0.0673 memory: 16201 loss_prob: 1.2305 loss_thr: 0.6286 loss_db: 0.2069 loss: 2.0661 2022/08/30 00:03:13 - mmengine - INFO - Epoch(train) [47][40/63] lr: 6.7578e-03 eta: 21:34:10 time: 1.2010 data_time: 0.1047 memory: 16201 loss_prob: 1.1899 loss_thr: 0.6153 loss_db: 0.2020 loss: 2.0072 2022/08/30 00:03:18 - mmengine - INFO - Epoch(train) [47][45/63] lr: 6.7578e-03 eta: 21:34:10 time: 1.0667 data_time: 0.1028 memory: 16201 loss_prob: 1.2509 loss_thr: 0.6361 loss_db: 0.2041 loss: 2.0911 2022/08/30 00:03:22 - mmengine - INFO - Epoch(train) [47][50/63] lr: 6.7578e-03 eta: 21:33:21 time: 0.9121 data_time: 0.1056 memory: 16201 loss_prob: 1.3486 loss_thr: 0.6390 loss_db: 0.2195 loss: 2.2071 2022/08/30 00:03:27 - mmengine - INFO - Epoch(train) [47][55/63] lr: 6.7578e-03 eta: 21:33:21 time: 0.9001 data_time: 0.0840 memory: 16201 loss_prob: 1.2157 loss_thr: 0.6046 loss_db: 0.2004 loss: 2.0207 2022/08/30 00:03:31 - mmengine - INFO - Epoch(train) [47][60/63] lr: 6.7578e-03 eta: 21:32:25 time: 0.8846 data_time: 0.0545 memory: 16201 loss_prob: 1.2655 loss_thr: 0.6156 loss_db: 0.2076 loss: 2.0888 2022/08/30 00:03:34 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:03:40 - mmengine - INFO - Epoch(train) [48][5/63] lr: 6.7526e-03 eta: 21:32:25 time: 1.0573 data_time: 0.3077 memory: 16201 loss_prob: 1.3244 loss_thr: 0.6424 loss_db: 0.2161 loss: 2.1829 2022/08/30 00:03:44 - mmengine - INFO - Epoch(train) [48][10/63] lr: 6.7526e-03 eta: 21:30:50 time: 1.0581 data_time: 0.2699 memory: 16201 loss_prob: 1.1929 loss_thr: 0.6209 loss_db: 0.1949 loss: 2.0087 2022/08/30 00:03:49 - mmengine - INFO - Epoch(train) [48][15/63] lr: 6.7526e-03 eta: 21:30:50 time: 0.9126 data_time: 0.0925 memory: 16201 loss_prob: 1.2047 loss_thr: 0.5971 loss_db: 0.1977 loss: 1.9995 2022/08/30 00:03:53 - mmengine - INFO - Epoch(train) [48][20/63] lr: 6.7526e-03 eta: 21:29:58 time: 0.8930 data_time: 0.0943 memory: 16201 loss_prob: 1.2203 loss_thr: 0.6021 loss_db: 0.2010 loss: 2.0234 2022/08/30 00:03:58 - mmengine - INFO - Epoch(train) [48][25/63] lr: 6.7526e-03 eta: 21:29:58 time: 0.8402 data_time: 0.0564 memory: 16201 loss_prob: 1.3498 loss_thr: 0.6205 loss_db: 0.2188 loss: 2.1891 2022/08/30 00:04:02 - mmengine - INFO - Epoch(train) [48][30/63] lr: 6.7526e-03 eta: 21:29:09 time: 0.9108 data_time: 0.0873 memory: 16201 loss_prob: 1.3033 loss_thr: 0.6176 loss_db: 0.2129 loss: 2.1338 2022/08/30 00:04:07 - mmengine - INFO - Epoch(train) [48][35/63] lr: 6.7526e-03 eta: 21:29:09 time: 0.9133 data_time: 0.0892 memory: 16201 loss_prob: 1.1545 loss_thr: 0.5959 loss_db: 0.1901 loss: 1.9405 2022/08/30 00:04:10 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:04:12 - mmengine - INFO - Epoch(train) [48][40/63] lr: 6.7526e-03 eta: 21:28:28 time: 0.9374 data_time: 0.0508 memory: 16201 loss_prob: 1.1812 loss_thr: 0.5930 loss_db: 0.1918 loss: 1.9660 2022/08/30 00:04:17 - mmengine - INFO - Epoch(train) [48][45/63] lr: 6.7526e-03 eta: 21:28:28 time: 1.0047 data_time: 0.0994 memory: 16201 loss_prob: 1.1984 loss_thr: 0.5900 loss_db: 0.1954 loss: 1.9839 2022/08/30 00:04:21 - mmengine - INFO - Epoch(train) [48][50/63] lr: 6.7526e-03 eta: 21:27:45 time: 0.9324 data_time: 0.1095 memory: 16201 loss_prob: 1.4066 loss_thr: 0.6273 loss_db: 0.2282 loss: 2.2621 2022/08/30 00:04:26 - mmengine - INFO - Epoch(train) [48][55/63] lr: 6.7526e-03 eta: 21:27:45 time: 0.9041 data_time: 0.0721 memory: 16201 loss_prob: 1.3221 loss_thr: 0.6144 loss_db: 0.2152 loss: 2.1517 2022/08/30 00:04:31 - mmengine - INFO - Epoch(train) [48][60/63] lr: 6.7526e-03 eta: 21:27:13 time: 0.9750 data_time: 0.1196 memory: 16201 loss_prob: 1.3756 loss_thr: 0.5955 loss_db: 0.2188 loss: 2.1899 2022/08/30 00:04:33 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:04:40 - mmengine - INFO - Epoch(train) [49][5/63] lr: 6.7473e-03 eta: 21:27:13 time: 1.0993 data_time: 0.2936 memory: 16201 loss_prob: 1.3676 loss_thr: 0.6130 loss_db: 0.2236 loss: 2.2042 2022/08/30 00:04:45 - mmengine - INFO - Epoch(train) [49][10/63] lr: 6.7473e-03 eta: 21:26:04 time: 1.1549 data_time: 0.3332 memory: 16201 loss_prob: 1.2092 loss_thr: 0.5831 loss_db: 0.2024 loss: 1.9947 2022/08/30 00:04:49 - mmengine - INFO - Epoch(train) [49][15/63] lr: 6.7473e-03 eta: 21:26:04 time: 0.9061 data_time: 0.0944 memory: 16201 loss_prob: 1.1490 loss_thr: 0.5588 loss_db: 0.1868 loss: 1.8946 2022/08/30 00:04:54 - mmengine - INFO - Epoch(train) [49][20/63] lr: 6.7473e-03 eta: 21:25:23 time: 0.9336 data_time: 0.0625 memory: 16201 loss_prob: 1.1480 loss_thr: 0.5970 loss_db: 0.1848 loss: 1.9298 2022/08/30 00:04:59 - mmengine - INFO - Epoch(train) [49][25/63] lr: 6.7473e-03 eta: 21:25:23 time: 0.9690 data_time: 0.0995 memory: 16201 loss_prob: 1.3175 loss_thr: 0.6264 loss_db: 0.2173 loss: 2.1612 2022/08/30 00:05:03 - mmengine - INFO - Epoch(train) [49][30/63] lr: 6.7473e-03 eta: 21:24:31 time: 0.8872 data_time: 0.0920 memory: 16201 loss_prob: 1.3400 loss_thr: 0.6051 loss_db: 0.2177 loss: 2.1628 2022/08/30 00:05:08 - mmengine - INFO - Epoch(train) [49][35/63] lr: 6.7473e-03 eta: 21:24:31 time: 0.8982 data_time: 0.0668 memory: 16201 loss_prob: 1.4991 loss_thr: 0.6185 loss_db: 0.2303 loss: 2.3479 2022/08/30 00:05:13 - mmengine - INFO - Epoch(train) [49][40/63] lr: 6.7473e-03 eta: 21:24:00 time: 0.9786 data_time: 0.1084 memory: 16201 loss_prob: 1.5217 loss_thr: 0.6285 loss_db: 0.2415 loss: 2.3917 2022/08/30 00:05:17 - mmengine - INFO - Epoch(train) [49][45/63] lr: 6.7473e-03 eta: 21:24:00 time: 0.9127 data_time: 0.1071 memory: 16201 loss_prob: 1.2195 loss_thr: 0.5863 loss_db: 0.2020 loss: 2.0078 2022/08/30 00:05:22 - mmengine - INFO - Epoch(train) [49][50/63] lr: 6.7473e-03 eta: 21:23:18 time: 0.9275 data_time: 0.0676 memory: 16201 loss_prob: 1.2456 loss_thr: 0.5723 loss_db: 0.2051 loss: 2.0230 2022/08/30 00:05:27 - mmengine - INFO - Epoch(train) [49][55/63] lr: 6.7473e-03 eta: 21:23:18 time: 0.9817 data_time: 0.0970 memory: 16201 loss_prob: 1.4920 loss_thr: 0.5889 loss_db: 0.2341 loss: 2.3150 2022/08/30 00:05:31 - mmengine - INFO - Epoch(train) [49][60/63] lr: 6.7473e-03 eta: 21:22:27 time: 0.8904 data_time: 0.0915 memory: 16201 loss_prob: 1.6971 loss_thr: 0.6098 loss_db: 0.2813 loss: 2.5882 2022/08/30 00:05:33 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:05:40 - mmengine - INFO - Epoch(train) [50][5/63] lr: 6.7420e-03 eta: 21:22:27 time: 1.0866 data_time: 0.2881 memory: 16201 loss_prob: 1.8137 loss_thr: 0.6655 loss_db: 0.3108 loss: 2.7900 2022/08/30 00:05:45 - mmengine - INFO - Epoch(train) [50][10/63] lr: 6.7420e-03 eta: 21:21:24 time: 1.1693 data_time: 0.3334 memory: 16201 loss_prob: 1.6508 loss_thr: 0.6663 loss_db: 0.2721 loss: 2.5892 2022/08/30 00:05:49 - mmengine - INFO - Epoch(train) [50][15/63] lr: 6.7420e-03 eta: 21:21:24 time: 0.8962 data_time: 0.0925 memory: 16201 loss_prob: 1.4254 loss_thr: 0.6185 loss_db: 0.2309 loss: 2.2748 2022/08/30 00:05:54 - mmengine - INFO - Epoch(train) [50][20/63] lr: 6.7420e-03 eta: 21:20:50 time: 0.9598 data_time: 0.0520 memory: 16201 loss_prob: 1.5014 loss_thr: 0.6278 loss_db: 0.2452 loss: 2.3743 2022/08/30 00:05:59 - mmengine - INFO - Epoch(train) [50][25/63] lr: 6.7420e-03 eta: 21:20:50 time: 1.0270 data_time: 0.0993 memory: 16201 loss_prob: 1.5088 loss_thr: 0.6367 loss_db: 0.2503 loss: 2.3958 2022/08/30 00:06:04 - mmengine - INFO - Epoch(train) [50][30/63] lr: 6.7420e-03 eta: 21:20:04 time: 0.9107 data_time: 0.0915 memory: 16201 loss_prob: 1.3580 loss_thr: 0.5902 loss_db: 0.2207 loss: 2.1690 2022/08/30 00:06:08 - mmengine - INFO - Epoch(train) [50][35/63] lr: 6.7420e-03 eta: 21:20:04 time: 0.8753 data_time: 0.0518 memory: 16201 loss_prob: 1.3517 loss_thr: 0.6055 loss_db: 0.2183 loss: 2.1756 2022/08/30 00:06:14 - mmengine - INFO - Epoch(train) [50][40/63] lr: 6.7420e-03 eta: 21:19:56 time: 1.0705 data_time: 0.0964 memory: 16201 loss_prob: 1.4770 loss_thr: 0.6432 loss_db: 0.2457 loss: 2.3659 2022/08/30 00:06:18 - mmengine - INFO - Epoch(train) [50][45/63] lr: 6.7420e-03 eta: 21:19:56 time: 1.0414 data_time: 0.0881 memory: 16201 loss_prob: 1.4470 loss_thr: 0.6397 loss_db: 0.2428 loss: 2.3295 2022/08/30 00:06:23 - mmengine - INFO - Epoch(train) [50][50/63] lr: 6.7420e-03 eta: 21:19:04 time: 0.8774 data_time: 0.0538 memory: 16201 loss_prob: 1.4066 loss_thr: 0.6258 loss_db: 0.2329 loss: 2.2653 2022/08/30 00:06:27 - mmengine - INFO - Epoch(train) [50][55/63] lr: 6.7420e-03 eta: 21:19:04 time: 0.8857 data_time: 0.0422 memory: 16201 loss_prob: 1.4891 loss_thr: 0.6268 loss_db: 0.2487 loss: 2.3646 2022/08/30 00:06:33 - mmengine - INFO - Epoch(train) [50][60/63] lr: 6.7420e-03 eta: 21:18:40 time: 1.0004 data_time: 0.0287 memory: 16201 loss_prob: 1.5016 loss_thr: 0.6363 loss_db: 0.2532 loss: 2.3912 2022/08/30 00:06:35 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:06:43 - mmengine - INFO - Epoch(train) [51][5/63] lr: 6.7367e-03 eta: 21:18:40 time: 1.2412 data_time: 0.3102 memory: 16201 loss_prob: 1.3742 loss_thr: 0.6257 loss_db: 0.2258 loss: 2.2256 2022/08/30 00:06:47 - mmengine - INFO - Epoch(train) [51][10/63] lr: 6.7367e-03 eta: 21:17:32 time: 1.1424 data_time: 0.3138 memory: 16201 loss_prob: 1.3320 loss_thr: 0.6099 loss_db: 0.2159 loss: 2.1578 2022/08/30 00:06:52 - mmengine - INFO - Epoch(train) [51][15/63] lr: 6.7367e-03 eta: 21:17:32 time: 0.9436 data_time: 0.0528 memory: 16201 loss_prob: 1.3446 loss_thr: 0.6226 loss_db: 0.2155 loss: 2.1828 2022/08/30 00:06:57 - mmengine - INFO - Epoch(train) [51][20/63] lr: 6.7367e-03 eta: 21:17:01 time: 0.9692 data_time: 0.0543 memory: 16201 loss_prob: 1.1770 loss_thr: 0.5735 loss_db: 0.1849 loss: 1.9353 2022/08/30 00:07:01 - mmengine - INFO - Epoch(train) [51][25/63] lr: 6.7367e-03 eta: 21:17:01 time: 0.8934 data_time: 0.0436 memory: 16201 loss_prob: 1.2140 loss_thr: 0.5873 loss_db: 0.1940 loss: 1.9953 2022/08/30 00:07:06 - mmengine - INFO - Epoch(train) [51][30/63] lr: 6.7367e-03 eta: 21:16:16 time: 0.9040 data_time: 0.0467 memory: 16201 loss_prob: 1.3331 loss_thr: 0.6093 loss_db: 0.2183 loss: 2.1607 2022/08/30 00:07:10 - mmengine - INFO - Epoch(train) [51][35/63] lr: 6.7367e-03 eta: 21:16:16 time: 0.9237 data_time: 0.0538 memory: 16201 loss_prob: 1.3611 loss_thr: 0.6055 loss_db: 0.2209 loss: 2.1875 2022/08/30 00:07:14 - mmengine - INFO - Epoch(train) [51][40/63] lr: 6.7367e-03 eta: 21:15:24 time: 0.8750 data_time: 0.0405 memory: 16201 loss_prob: 1.3766 loss_thr: 0.6493 loss_db: 0.2186 loss: 2.2445 2022/08/30 00:07:19 - mmengine - INFO - Epoch(train) [51][45/63] lr: 6.7367e-03 eta: 21:15:24 time: 0.8823 data_time: 0.0443 memory: 16201 loss_prob: 1.2926 loss_thr: 0.6399 loss_db: 0.2061 loss: 2.1385 2022/08/30 00:07:25 - mmengine - INFO - Epoch(train) [51][50/63] lr: 6.7367e-03 eta: 21:15:11 time: 1.0475 data_time: 0.0603 memory: 16201 loss_prob: 1.2811 loss_thr: 0.6331 loss_db: 0.2106 loss: 2.1248 2022/08/30 00:07:29 - mmengine - INFO - Epoch(train) [51][55/63] lr: 6.7367e-03 eta: 21:15:11 time: 1.0332 data_time: 0.0621 memory: 16201 loss_prob: 1.2946 loss_thr: 0.6493 loss_db: 0.2146 loss: 2.1585 2022/08/30 00:07:33 - mmengine - INFO - Epoch(train) [51][60/63] lr: 6.7367e-03 eta: 21:14:18 time: 0.8674 data_time: 0.0489 memory: 16201 loss_prob: 1.2594 loss_thr: 0.6263 loss_db: 0.2061 loss: 2.0918 2022/08/30 00:07:36 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:07:44 - mmengine - INFO - Epoch(train) [52][5/63] lr: 6.7315e-03 eta: 21:14:18 time: 1.1739 data_time: 0.3723 memory: 16201 loss_prob: 1.0860 loss_thr: 0.5750 loss_db: 0.1753 loss: 1.8363 2022/08/30 00:07:49 - mmengine - INFO - Epoch(train) [52][10/63] lr: 6.7315e-03 eta: 21:13:59 time: 1.3533 data_time: 0.3918 memory: 16201 loss_prob: 1.1777 loss_thr: 0.5890 loss_db: 0.1921 loss: 1.9588 2022/08/30 00:07:55 - mmengine - INFO - Epoch(train) [52][15/63] lr: 6.7315e-03 eta: 21:13:59 time: 1.1507 data_time: 0.0559 memory: 16201 loss_prob: 1.2010 loss_thr: 0.5835 loss_db: 0.1900 loss: 1.9744 2022/08/30 00:08:01 - mmengine - INFO - Epoch(train) [52][20/63] lr: 6.7315e-03 eta: 21:14:03 time: 1.1207 data_time: 0.0609 memory: 16201 loss_prob: 1.2534 loss_thr: 0.6104 loss_db: 0.1997 loss: 2.0635 2022/08/30 00:08:05 - mmengine - INFO - Epoch(train) [52][25/63] lr: 6.7315e-03 eta: 21:14:03 time: 0.9678 data_time: 0.0565 memory: 16201 loss_prob: 1.4206 loss_thr: 0.6134 loss_db: 0.2295 loss: 2.2635 2022/08/30 00:08:09 - mmengine - INFO - Epoch(train) [52][30/63] lr: 6.7315e-03 eta: 21:12:57 time: 0.8075 data_time: 0.0366 memory: 16201 loss_prob: 1.3840 loss_thr: 0.6171 loss_db: 0.2232 loss: 2.2243 2022/08/30 00:08:13 - mmengine - INFO - Epoch(train) [52][35/63] lr: 6.7315e-03 eta: 21:12:57 time: 0.7986 data_time: 0.0443 memory: 16201 loss_prob: 1.2678 loss_thr: 0.6285 loss_db: 0.2030 loss: 2.0993 2022/08/30 00:08:17 - mmengine - INFO - Epoch(train) [52][40/63] lr: 6.7315e-03 eta: 21:12:03 time: 0.8594 data_time: 0.0529 memory: 16201 loss_prob: 1.3091 loss_thr: 0.6156 loss_db: 0.2084 loss: 2.1331 2022/08/30 00:08:22 - mmengine - INFO - Epoch(train) [52][45/63] lr: 6.7315e-03 eta: 21:12:03 time: 0.8989 data_time: 0.0533 memory: 16201 loss_prob: 1.2736 loss_thr: 0.6221 loss_db: 0.2079 loss: 2.1037 2022/08/30 00:08:27 - mmengine - INFO - Epoch(train) [52][50/63] lr: 6.7315e-03 eta: 21:11:41 time: 1.0016 data_time: 0.0567 memory: 16201 loss_prob: 1.2258 loss_thr: 0.6260 loss_db: 0.2021 loss: 2.0539 2022/08/30 00:08:32 - mmengine - INFO - Epoch(train) [52][55/63] lr: 6.7315e-03 eta: 21:11:41 time: 0.9896 data_time: 0.0492 memory: 16201 loss_prob: 1.3187 loss_thr: 0.6227 loss_db: 0.2178 loss: 2.1591 2022/08/30 00:08:36 - mmengine - INFO - Epoch(train) [52][60/63] lr: 6.7315e-03 eta: 21:10:51 time: 0.8793 data_time: 0.0418 memory: 16201 loss_prob: 1.3023 loss_thr: 0.6025 loss_db: 0.2164 loss: 2.1212 2022/08/30 00:08:39 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:08:46 - mmengine - INFO - Epoch(train) [53][5/63] lr: 6.7262e-03 eta: 21:10:51 time: 1.2088 data_time: 0.3529 memory: 16201 loss_prob: 1.1923 loss_thr: 0.6230 loss_db: 0.1954 loss: 2.0107 2022/08/30 00:08:55 - mmengine - INFO - Epoch(train) [53][10/63] lr: 6.7262e-03 eta: 21:11:32 time: 1.6186 data_time: 0.3699 memory: 16201 loss_prob: 1.1699 loss_thr: 0.5913 loss_db: 0.1861 loss: 1.9473 2022/08/30 00:09:01 - mmengine - INFO - Epoch(train) [53][15/63] lr: 6.7262e-03 eta: 21:11:32 time: 1.4767 data_time: 0.0672 memory: 16201 loss_prob: 1.3785 loss_thr: 0.5742 loss_db: 0.2068 loss: 2.1594 2022/08/30 00:09:09 - mmengine - INFO - Epoch(train) [53][20/63] lr: 6.7262e-03 eta: 21:12:32 time: 1.3738 data_time: 0.0615 memory: 16201 loss_prob: 1.3440 loss_thr: 0.5940 loss_db: 0.2033 loss: 2.1412 2022/08/30 00:09:16 - mmengine - INFO - Epoch(train) [53][25/63] lr: 6.7262e-03 eta: 21:12:32 time: 1.5061 data_time: 0.0762 memory: 16201 loss_prob: 1.1557 loss_thr: 0.5714 loss_db: 0.1858 loss: 1.9130 2022/08/30 00:09:24 - mmengine - INFO - Epoch(train) [53][30/63] lr: 6.7262e-03 eta: 21:14:02 time: 1.5168 data_time: 0.0624 memory: 16201 loss_prob: 1.1834 loss_thr: 0.5942 loss_db: 0.1941 loss: 1.9717 2022/08/30 00:09:31 - mmengine - INFO - Epoch(train) [53][35/63] lr: 6.7262e-03 eta: 21:14:02 time: 1.4881 data_time: 0.0591 memory: 16201 loss_prob: 1.2910 loss_thr: 0.6521 loss_db: 0.2148 loss: 2.1579 2022/08/30 00:09:38 - mmengine - INFO - Epoch(train) [53][40/63] lr: 6.7262e-03 eta: 21:15:08 time: 1.4108 data_time: 0.0753 memory: 16201 loss_prob: 1.3314 loss_thr: 0.6505 loss_db: 0.2237 loss: 2.2056 2022/08/30 00:09:45 - mmengine - INFO - Epoch(train) [53][45/63] lr: 6.7262e-03 eta: 21:15:08 time: 1.3804 data_time: 0.0703 memory: 16201 loss_prob: 1.2608 loss_thr: 0.6474 loss_db: 0.2109 loss: 2.1190 2022/08/30 00:09:53 - mmengine - INFO - Epoch(train) [53][50/63] lr: 6.7262e-03 eta: 21:16:35 time: 1.5054 data_time: 0.0724 memory: 16201 loss_prob: 1.1756 loss_thr: 0.6308 loss_db: 0.1947 loss: 2.0010 2022/08/30 00:10:00 - mmengine - INFO - Epoch(train) [53][55/63] lr: 6.7262e-03 eta: 21:16:35 time: 1.5356 data_time: 0.0589 memory: 16201 loss_prob: 1.1533 loss_thr: 0.5673 loss_db: 0.1908 loss: 1.9114 2022/08/30 00:10:07 - mmengine - INFO - Epoch(train) [53][60/63] lr: 6.7262e-03 eta: 21:17:50 time: 1.4568 data_time: 0.0552 memory: 16201 loss_prob: 1.3169 loss_thr: 0.5994 loss_db: 0.2130 loss: 2.1293 2022/08/30 00:10:11 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:10:22 - mmengine - INFO - Epoch(train) [54][5/63] lr: 6.7209e-03 eta: 21:17:50 time: 1.7242 data_time: 0.3954 memory: 16201 loss_prob: 1.2254 loss_thr: 0.6141 loss_db: 0.1986 loss: 2.0381 2022/08/30 00:10:30 - mmengine - INFO - Epoch(train) [54][10/63] lr: 6.7209e-03 eta: 21:19:21 time: 1.8632 data_time: 0.4479 memory: 16201 loss_prob: 1.1535 loss_thr: 0.5968 loss_db: 0.1882 loss: 1.9384 2022/08/30 00:10:38 - mmengine - INFO - Epoch(train) [54][15/63] lr: 6.7209e-03 eta: 21:19:21 time: 1.5175 data_time: 0.0748 memory: 16201 loss_prob: 1.1751 loss_thr: 0.5786 loss_db: 0.1890 loss: 1.9427 2022/08/30 00:10:44 - mmengine - INFO - Epoch(train) [54][20/63] lr: 6.7209e-03 eta: 21:20:42 time: 1.4906 data_time: 0.0582 memory: 16201 loss_prob: 1.1326 loss_thr: 0.5849 loss_db: 0.1844 loss: 1.9019 2022/08/30 00:10:53 - mmengine - INFO - Epoch(train) [54][25/63] lr: 6.7209e-03 eta: 21:20:42 time: 1.5321 data_time: 0.0775 memory: 16201 loss_prob: 1.1692 loss_thr: 0.6274 loss_db: 0.1919 loss: 1.9885 2022/08/30 00:11:00 - mmengine - INFO - Epoch(train) [54][30/63] lr: 6.7209e-03 eta: 21:22:20 time: 1.5697 data_time: 0.0610 memory: 16201 loss_prob: 1.2687 loss_thr: 0.6369 loss_db: 0.2087 loss: 2.1143 2022/08/30 00:11:08 - mmengine - INFO - Epoch(train) [54][35/63] lr: 6.7209e-03 eta: 21:22:20 time: 1.5355 data_time: 0.0568 memory: 16201 loss_prob: 1.2380 loss_thr: 0.6157 loss_db: 0.2043 loss: 2.0579 2022/08/30 00:11:16 - mmengine - INFO - Epoch(train) [54][40/63] lr: 6.7209e-03 eta: 21:23:50 time: 1.5349 data_time: 0.0613 memory: 16201 loss_prob: 1.1263 loss_thr: 0.5946 loss_db: 0.1814 loss: 1.9024 2022/08/30 00:11:22 - mmengine - INFO - Epoch(train) [54][45/63] lr: 6.7209e-03 eta: 21:23:50 time: 1.3995 data_time: 0.0591 memory: 16201 loss_prob: 1.1416 loss_thr: 0.5977 loss_db: 0.1850 loss: 1.9243 2022/08/30 00:11:29 - mmengine - INFO - Epoch(train) [54][50/63] lr: 6.7209e-03 eta: 21:24:38 time: 1.3430 data_time: 0.0669 memory: 16201 loss_prob: 1.1227 loss_thr: 0.5918 loss_db: 0.1837 loss: 1.8982 2022/08/30 00:11:36 - mmengine - INFO - Epoch(train) [54][55/63] lr: 6.7209e-03 eta: 21:24:38 time: 1.3708 data_time: 0.0516 memory: 16201 loss_prob: 1.1500 loss_thr: 0.5858 loss_db: 0.1822 loss: 1.9179 2022/08/30 00:11:43 - mmengine - INFO - Epoch(train) [54][60/63] lr: 6.7209e-03 eta: 21:25:29 time: 1.3585 data_time: 0.0523 memory: 16201 loss_prob: 1.2927 loss_thr: 0.5813 loss_db: 0.2104 loss: 2.0844 2022/08/30 00:11:46 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:11:57 - mmengine - INFO - Epoch(train) [55][5/63] lr: 6.7156e-03 eta: 21:25:29 time: 1.6776 data_time: 0.3559 memory: 16201 loss_prob: 1.2838 loss_thr: 0.5934 loss_db: 0.2103 loss: 2.0876 2022/08/30 00:12:05 - mmengine - INFO - Epoch(train) [55][10/63] lr: 6.7156e-03 eta: 21:27:03 time: 1.8979 data_time: 0.4323 memory: 16201 loss_prob: 1.2130 loss_thr: 0.5987 loss_db: 0.2012 loss: 2.0129 2022/08/30 00:12:12 - mmengine - INFO - Epoch(train) [55][15/63] lr: 6.7156e-03 eta: 21:27:03 time: 1.5705 data_time: 0.1040 memory: 16201 loss_prob: 1.3762 loss_thr: 0.6128 loss_db: 0.2287 loss: 2.2177 2022/08/30 00:12:19 - mmengine - INFO - Epoch(train) [55][20/63] lr: 6.7156e-03 eta: 21:28:07 time: 1.4229 data_time: 0.0632 memory: 16201 loss_prob: 1.5021 loss_thr: 0.6252 loss_db: 0.2425 loss: 2.3698 2022/08/30 00:12:26 - mmengine - INFO - Epoch(train) [55][25/63] lr: 6.7156e-03 eta: 21:28:07 time: 1.3398 data_time: 0.0736 memory: 16201 loss_prob: 1.4277 loss_thr: 0.6357 loss_db: 0.2288 loss: 2.2922 2022/08/30 00:12:32 - mmengine - INFO - Epoch(train) [55][30/63] lr: 6.7156e-03 eta: 21:28:37 time: 1.2656 data_time: 0.0570 memory: 16201 loss_prob: 1.3170 loss_thr: 0.6398 loss_db: 0.2164 loss: 2.1731 2022/08/30 00:12:37 - mmengine - INFO - Epoch(train) [55][35/63] lr: 6.7156e-03 eta: 21:28:37 time: 1.1730 data_time: 0.0579 memory: 16201 loss_prob: 1.2252 loss_thr: 0.6044 loss_db: 0.2075 loss: 2.0371 2022/08/30 00:12:43 - mmengine - INFO - Epoch(train) [55][40/63] lr: 6.7156e-03 eta: 21:28:34 time: 1.1048 data_time: 0.0540 memory: 16201 loss_prob: 1.1326 loss_thr: 0.5815 loss_db: 0.1891 loss: 1.9032 2022/08/30 00:12:48 - mmengine - INFO - Epoch(train) [55][45/63] lr: 6.7156e-03 eta: 21:28:34 time: 1.0386 data_time: 0.0481 memory: 16201 loss_prob: 1.1828 loss_thr: 0.6271 loss_db: 0.1874 loss: 1.9974 2022/08/30 00:12:54 - mmengine - INFO - Epoch(train) [55][50/63] lr: 6.7156e-03 eta: 21:28:23 time: 1.0702 data_time: 0.0622 memory: 16201 loss_prob: 1.3911 loss_thr: 0.6404 loss_db: 0.2170 loss: 2.2485 2022/08/30 00:12:59 - mmengine - INFO - Epoch(train) [55][55/63] lr: 6.7156e-03 eta: 21:28:23 time: 1.1510 data_time: 0.0555 memory: 16201 loss_prob: 1.4065 loss_thr: 0.6101 loss_db: 0.2231 loss: 2.2397 2022/08/30 00:13:05 - mmengine - INFO - Epoch(train) [55][60/63] lr: 6.7156e-03 eta: 21:28:26 time: 1.1386 data_time: 0.0635 memory: 16201 loss_prob: 1.2033 loss_thr: 0.6044 loss_db: 0.1947 loss: 2.0023 2022/08/30 00:13:08 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:13:17 - mmengine - INFO - Epoch(train) [56][5/63] lr: 6.7103e-03 eta: 21:28:26 time: 1.4267 data_time: 0.3681 memory: 16201 loss_prob: 1.2475 loss_thr: 0.5996 loss_db: 0.1996 loss: 2.0467 2022/08/30 00:13:23 - mmengine - INFO - Epoch(train) [56][10/63] lr: 6.7103e-03 eta: 21:28:38 time: 1.5190 data_time: 0.3905 memory: 16201 loss_prob: 1.2362 loss_thr: 0.5899 loss_db: 0.1988 loss: 2.0249 2022/08/30 00:13:28 - mmengine - INFO - Epoch(train) [56][15/63] lr: 6.7103e-03 eta: 21:28:38 time: 1.1215 data_time: 0.0567 memory: 16201 loss_prob: 1.1759 loss_thr: 0.6102 loss_db: 0.1915 loss: 1.9776 2022/08/30 00:13:35 - mmengine - INFO - Epoch(train) [56][20/63] lr: 6.7103e-03 eta: 21:28:50 time: 1.1810 data_time: 0.0605 memory: 16201 loss_prob: 1.1274 loss_thr: 0.5863 loss_db: 0.1844 loss: 1.8981 2022/08/30 00:13:41 - mmengine - INFO - Epoch(train) [56][25/63] lr: 6.7103e-03 eta: 21:28:50 time: 1.2419 data_time: 0.0724 memory: 16201 loss_prob: 1.2718 loss_thr: 0.6118 loss_db: 0.2156 loss: 2.0993 2022/08/30 00:13:46 - mmengine - INFO - Epoch(train) [56][30/63] lr: 6.7103e-03 eta: 21:28:56 time: 1.1545 data_time: 0.0565 memory: 16201 loss_prob: 1.5010 loss_thr: 0.6733 loss_db: 0.2514 loss: 2.4258 2022/08/30 00:13:52 - mmengine - INFO - Epoch(train) [56][35/63] lr: 6.7103e-03 eta: 21:28:56 time: 1.1522 data_time: 0.0603 memory: 16201 loss_prob: 1.4173 loss_thr: 0.6364 loss_db: 0.2267 loss: 2.2804 2022/08/30 00:14:01 - mmengine - INFO - Epoch(train) [56][40/63] lr: 6.7103e-03 eta: 21:30:11 time: 1.4901 data_time: 0.0551 memory: 16201 loss_prob: 1.2683 loss_thr: 0.5984 loss_db: 0.2113 loss: 2.0780 2022/08/30 00:14:12 - mmengine - INFO - Epoch(train) [56][45/63] lr: 6.7103e-03 eta: 21:30:11 time: 1.9832 data_time: 0.0507 memory: 16201 loss_prob: 1.3026 loss_thr: 0.6157 loss_db: 0.2208 loss: 2.1391 2022/08/30 00:14:26 - mmengine - INFO - Epoch(train) [56][50/63] lr: 6.7103e-03 eta: 21:34:35 time: 2.4117 data_time: 0.0739 memory: 16201 loss_prob: 1.2307 loss_thr: 0.6001 loss_db: 0.2018 loss: 2.0326 2022/08/30 00:14:37 - mmengine - INFO - Epoch(train) [56][55/63] lr: 6.7103e-03 eta: 21:34:35 time: 2.5281 data_time: 0.0999 memory: 16201 loss_prob: 1.1932 loss_thr: 0.6004 loss_db: 0.1952 loss: 1.9887 2022/08/30 00:14:50 - mmengine - INFO - Epoch(train) [56][60/63] lr: 6.7103e-03 eta: 21:39:09 time: 2.4700 data_time: 0.0901 memory: 16201 loss_prob: 1.2329 loss_thr: 0.6004 loss_db: 0.2077 loss: 2.0410 2022/08/30 00:14:54 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:15:12 - mmengine - INFO - Epoch(train) [57][5/63] lr: 6.7051e-03 eta: 21:39:09 time: 2.6568 data_time: 0.5786 memory: 16201 loss_prob: 1.3100 loss_thr: 0.5972 loss_db: 0.2067 loss: 2.1140 2022/08/30 00:15:24 - mmengine - INFO - Epoch(train) [57][10/63] lr: 6.7051e-03 eta: 21:44:12 time: 2.9658 data_time: 0.5835 memory: 16201 loss_prob: 1.3369 loss_thr: 0.5971 loss_db: 0.2165 loss: 2.1505 2022/08/30 00:15:34 - mmengine - INFO - Epoch(train) [57][15/63] lr: 6.7051e-03 eta: 21:44:12 time: 2.1432 data_time: 0.0986 memory: 16201 loss_prob: 1.6099 loss_thr: 0.6322 loss_db: 0.2768 loss: 2.5189 2022/08/30 00:15:43 - mmengine - INFO - Epoch(train) [57][20/63] lr: 6.7051e-03 eta: 21:46:50 time: 1.9139 data_time: 0.0823 memory: 16201 loss_prob: 1.7767 loss_thr: 0.6486 loss_db: 0.3041 loss: 2.7294 2022/08/30 00:15:55 - mmengine - INFO - Epoch(train) [57][25/63] lr: 6.7051e-03 eta: 21:46:50 time: 2.1595 data_time: 0.0626 memory: 16201 loss_prob: 1.5296 loss_thr: 0.6262 loss_db: 0.2494 loss: 2.4052 2022/08/30 00:16:07 - mmengine - INFO - Epoch(train) [57][30/63] lr: 6.7051e-03 eta: 21:51:10 time: 2.4294 data_time: 0.0693 memory: 16201 loss_prob: 1.5776 loss_thr: 0.6560 loss_db: 0.2560 loss: 2.4895 2022/08/30 00:16:18 - mmengine - INFO - Epoch(train) [57][35/63] lr: 6.7051e-03 eta: 21:51:10 time: 2.2698 data_time: 0.0978 memory: 16201 loss_prob: 1.5927 loss_thr: 0.6670 loss_db: 0.2654 loss: 2.5251 2022/08/30 00:16:29 - mmengine - INFO - Epoch(train) [57][40/63] lr: 6.7051e-03 eta: 21:54:44 time: 2.2065 data_time: 0.0966 memory: 16201 loss_prob: 1.5316 loss_thr: 0.6874 loss_db: 0.2541 loss: 2.4731 2022/08/30 00:16:40 - mmengine - INFO - Epoch(train) [57][45/63] lr: 6.7051e-03 eta: 21:54:44 time: 2.1469 data_time: 0.0950 memory: 16201 loss_prob: 1.5740 loss_thr: 0.7124 loss_db: 0.2572 loss: 2.5437 2022/08/30 00:16:49 - mmengine - INFO - Epoch(train) [57][50/63] lr: 6.7051e-03 eta: 21:57:31 time: 1.9759 data_time: 0.0962 memory: 16201 loss_prob: 1.4045 loss_thr: 0.6618 loss_db: 0.2323 loss: 2.2985 2022/08/30 00:16:59 - mmengine - INFO - Epoch(train) [57][55/63] lr: 6.7051e-03 eta: 21:57:31 time: 1.9513 data_time: 0.0697 memory: 16201 loss_prob: 1.3400 loss_thr: 0.6193 loss_db: 0.2161 loss: 2.1754 2022/08/30 00:17:09 - mmengine - INFO - Epoch(train) [57][60/63] lr: 6.7051e-03 eta: 22:00:17 time: 1.9782 data_time: 0.0680 memory: 16201 loss_prob: 1.4091 loss_thr: 0.6203 loss_db: 0.2256 loss: 2.2550 2022/08/30 00:17:14 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:17:24 - mmengine - INFO - Epoch(train) [58][5/63] lr: 6.6998e-03 eta: 22:00:17 time: 1.8222 data_time: 0.3397 memory: 16201 loss_prob: 1.2897 loss_thr: 0.6135 loss_db: 0.2115 loss: 2.1146 2022/08/30 00:17:31 - mmengine - INFO - Epoch(train) [58][10/63] lr: 6.6998e-03 eta: 22:00:47 time: 1.6549 data_time: 0.3666 memory: 16201 loss_prob: 1.2023 loss_thr: 0.5917 loss_db: 0.1962 loss: 1.9902 2022/08/30 00:17:38 - mmengine - INFO - Epoch(train) [58][15/63] lr: 6.6998e-03 eta: 22:00:47 time: 1.4211 data_time: 0.0659 memory: 16201 loss_prob: 1.0993 loss_thr: 0.6000 loss_db: 0.1773 loss: 1.8766 2022/08/30 00:17:44 - mmengine - INFO - Epoch(train) [58][20/63] lr: 6.6998e-03 eta: 22:01:15 time: 1.2941 data_time: 0.0612 memory: 16201 loss_prob: 1.1322 loss_thr: 0.6091 loss_db: 0.1844 loss: 1.9257 2022/08/30 00:17:49 - mmengine - INFO - Epoch(train) [58][25/63] lr: 6.6998e-03 eta: 22:01:15 time: 1.1135 data_time: 0.0640 memory: 16201 loss_prob: 1.2380 loss_thr: 0.6303 loss_db: 0.2017 loss: 2.0700 2022/08/30 00:17:55 - mmengine - INFO - Epoch(train) [58][30/63] lr: 6.6998e-03 eta: 22:01:03 time: 1.0991 data_time: 0.0643 memory: 16201 loss_prob: 1.3835 loss_thr: 0.6541 loss_db: 0.2245 loss: 2.2620 2022/08/30 00:18:00 - mmengine - INFO - Epoch(train) [58][35/63] lr: 6.6998e-03 eta: 22:01:03 time: 1.1318 data_time: 0.0677 memory: 16201 loss_prob: 1.2810 loss_thr: 0.6118 loss_db: 0.2093 loss: 2.1022 2022/08/30 00:18:06 - mmengine - INFO - Epoch(train) [58][40/63] lr: 6.6998e-03 eta: 22:01:00 time: 1.1396 data_time: 0.0539 memory: 16201 loss_prob: 1.1676 loss_thr: 0.5794 loss_db: 0.1890 loss: 1.9360 2022/08/30 00:18:11 - mmengine - INFO - Epoch(train) [58][45/63] lr: 6.6998e-03 eta: 22:01:00 time: 1.1225 data_time: 0.0561 memory: 16201 loss_prob: 1.2194 loss_thr: 0.5883 loss_db: 0.1977 loss: 2.0054 2022/08/30 00:18:18 - mmengine - INFO - Epoch(train) [58][50/63] lr: 6.6998e-03 eta: 22:00:59 time: 1.1525 data_time: 0.0624 memory: 16201 loss_prob: 1.2276 loss_thr: 0.5896 loss_db: 0.2004 loss: 2.0176 2022/08/30 00:18:24 - mmengine - INFO - Epoch(train) [58][55/63] lr: 6.6998e-03 eta: 22:00:59 time: 1.2134 data_time: 0.0550 memory: 16201 loss_prob: 1.3532 loss_thr: 0.6026 loss_db: 0.2193 loss: 2.1751 2022/08/30 00:18:29 - mmengine - INFO - Epoch(train) [58][60/63] lr: 6.6998e-03 eta: 22:00:55 time: 1.1349 data_time: 0.0587 memory: 16201 loss_prob: 1.3591 loss_thr: 0.6143 loss_db: 0.2192 loss: 2.1925 2022/08/30 00:18:32 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:18:41 - mmengine - INFO - Epoch(train) [59][5/63] lr: 6.6945e-03 eta: 22:00:55 time: 1.4126 data_time: 0.4022 memory: 16201 loss_prob: 1.4979 loss_thr: 0.6068 loss_db: 0.2400 loss: 2.3446 2022/08/30 00:18:47 - mmengine - INFO - Epoch(train) [59][10/63] lr: 6.6945e-03 eta: 22:00:53 time: 1.4964 data_time: 0.4422 memory: 16201 loss_prob: 1.4444 loss_thr: 0.6177 loss_db: 0.2299 loss: 2.2920 2022/08/30 00:18:52 - mmengine - INFO - Epoch(train) [59][15/63] lr: 6.6945e-03 eta: 22:00:53 time: 1.1197 data_time: 0.0650 memory: 16201 loss_prob: 1.1436 loss_thr: 0.5806 loss_db: 0.1859 loss: 1.9101 2022/08/30 00:18:58 - mmengine - INFO - Epoch(train) [59][20/63] lr: 6.6945e-03 eta: 22:00:48 time: 1.1314 data_time: 0.0554 memory: 16201 loss_prob: 1.1570 loss_thr: 0.5613 loss_db: 0.1879 loss: 1.9062 2022/08/30 00:19:03 - mmengine - INFO - Epoch(train) [59][25/63] lr: 6.6945e-03 eta: 22:00:48 time: 1.1228 data_time: 0.0724 memory: 16201 loss_prob: 1.3566 loss_thr: 0.6048 loss_db: 0.2151 loss: 2.1765 2022/08/30 00:19:09 - mmengine - INFO - Epoch(train) [59][30/63] lr: 6.6945e-03 eta: 22:00:41 time: 1.1247 data_time: 0.0519 memory: 16201 loss_prob: 1.3715 loss_thr: 0.6144 loss_db: 0.2244 loss: 2.2102 2022/08/30 00:19:14 - mmengine - INFO - Epoch(train) [59][35/63] lr: 6.6945e-03 eta: 22:00:41 time: 1.1022 data_time: 0.0533 memory: 16201 loss_prob: 1.1772 loss_thr: 0.5841 loss_db: 0.1965 loss: 1.9578 2022/08/30 00:19:20 - mmengine - INFO - Epoch(train) [59][40/63] lr: 6.6945e-03 eta: 22:00:28 time: 1.0933 data_time: 0.0568 memory: 16201 loss_prob: 1.2018 loss_thr: 0.6051 loss_db: 0.1955 loss: 2.0024 2022/08/30 00:19:26 - mmengine - INFO - Epoch(train) [59][45/63] lr: 6.6945e-03 eta: 22:00:28 time: 1.1502 data_time: 0.0486 memory: 16201 loss_prob: 1.2236 loss_thr: 0.5648 loss_db: 0.2048 loss: 1.9932 2022/08/30 00:19:32 - mmengine - INFO - Epoch(train) [59][50/63] lr: 6.6945e-03 eta: 22:00:37 time: 1.2021 data_time: 0.0645 memory: 16201 loss_prob: 1.2635 loss_thr: 0.5785 loss_db: 0.2160 loss: 2.0580 2022/08/30 00:19:38 - mmengine - INFO - Epoch(train) [59][55/63] lr: 6.6945e-03 eta: 22:00:37 time: 1.1583 data_time: 0.0622 memory: 16201 loss_prob: 1.2606 loss_thr: 0.6291 loss_db: 0.2029 loss: 2.0926 2022/08/30 00:19:43 - mmengine - INFO - Epoch(train) [59][60/63] lr: 6.6945e-03 eta: 22:00:22 time: 1.0809 data_time: 0.0581 memory: 16201 loss_prob: 1.1741 loss_thr: 0.6024 loss_db: 0.1870 loss: 1.9635 2022/08/30 00:19:46 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:19:54 - mmengine - INFO - Epoch(train) [60][5/63] lr: 6.6892e-03 eta: 22:00:22 time: 1.3385 data_time: 0.2976 memory: 16201 loss_prob: 1.4014 loss_thr: 0.5989 loss_db: 0.2208 loss: 2.2212 2022/08/30 00:20:00 - mmengine - INFO - Epoch(train) [60][10/63] lr: 6.6892e-03 eta: 21:59:56 time: 1.3714 data_time: 0.3197 memory: 16201 loss_prob: 1.1408 loss_thr: 0.5881 loss_db: 0.1848 loss: 1.9136 2022/08/30 00:20:06 - mmengine - INFO - Epoch(train) [60][15/63] lr: 6.6892e-03 eta: 21:59:56 time: 1.1659 data_time: 0.0599 memory: 16201 loss_prob: 1.0609 loss_thr: 0.5572 loss_db: 0.1717 loss: 1.7897 2022/08/30 00:20:11 - mmengine - INFO - Epoch(train) [60][20/63] lr: 6.6892e-03 eta: 21:59:56 time: 1.1629 data_time: 0.0648 memory: 16201 loss_prob: 1.2021 loss_thr: 0.5631 loss_db: 0.1898 loss: 1.9551 2022/08/30 00:20:17 - mmengine - INFO - Epoch(train) [60][25/63] lr: 6.6892e-03 eta: 21:59:56 time: 1.0936 data_time: 0.0581 memory: 16201 loss_prob: 1.2615 loss_thr: 0.5872 loss_db: 0.2017 loss: 2.0505 2022/08/30 00:20:23 - mmengine - INFO - Epoch(train) [60][30/63] lr: 6.6892e-03 eta: 21:59:57 time: 1.1603 data_time: 0.0597 memory: 16201 loss_prob: 1.1504 loss_thr: 0.5888 loss_db: 0.1836 loss: 1.9228 2022/08/30 00:20:29 - mmengine - INFO - Epoch(train) [60][35/63] lr: 6.6892e-03 eta: 21:59:57 time: 1.2170 data_time: 0.0733 memory: 16201 loss_prob: 1.1759 loss_thr: 0.6122 loss_db: 0.1888 loss: 1.9769 2022/08/30 00:20:34 - mmengine - INFO - Epoch(train) [60][40/63] lr: 6.6892e-03 eta: 21:59:50 time: 1.1272 data_time: 0.0582 memory: 16201 loss_prob: 1.1351 loss_thr: 0.6002 loss_db: 0.1902 loss: 1.9255 2022/08/30 00:20:40 - mmengine - INFO - Epoch(train) [60][45/63] lr: 6.6892e-03 eta: 21:59:50 time: 1.1376 data_time: 0.0498 memory: 16201 loss_prob: 1.1329 loss_thr: 0.5816 loss_db: 0.1854 loss: 1.8999 2022/08/30 00:20:46 - mmengine - INFO - Epoch(train) [60][50/63] lr: 6.6892e-03 eta: 21:59:58 time: 1.1983 data_time: 0.0576 memory: 16201 loss_prob: 1.1134 loss_thr: 0.5799 loss_db: 0.1749 loss: 1.8683 2022/08/30 00:20:52 - mmengine - INFO - Epoch(train) [60][55/63] lr: 6.6892e-03 eta: 21:59:58 time: 1.1635 data_time: 0.0483 memory: 16201 loss_prob: 1.1190 loss_thr: 0.5888 loss_db: 0.1821 loss: 1.8899 2022/08/30 00:20:58 - mmengine - INFO - Epoch(train) [60][60/63] lr: 6.6892e-03 eta: 22:00:03 time: 1.1892 data_time: 0.0644 memory: 16201 loss_prob: 1.1469 loss_thr: 0.5797 loss_db: 0.1890 loss: 1.9156 2022/08/30 00:21:01 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:21:01 - mmengine - INFO - Saving checkpoint at 60 epochs 2022/08/30 00:21:10 - mmengine - INFO - Epoch(val) [60][5/32] eta: 22:00:03 time: 0.6581 data_time: 0.1305 memory: 16201 2022/08/30 00:21:13 - mmengine - INFO - Epoch(val) [60][10/32] eta: 0:00:15 time: 0.7122 data_time: 0.1362 memory: 15734 2022/08/30 00:21:16 - mmengine - INFO - Epoch(val) [60][15/32] eta: 0:00:15 time: 0.6155 data_time: 0.0484 memory: 15734 2022/08/30 00:21:20 - mmengine - INFO - Epoch(val) [60][20/32] eta: 0:00:08 time: 0.7413 data_time: 0.0766 memory: 15734 2022/08/30 00:21:23 - mmengine - INFO - Epoch(val) [60][25/32] eta: 0:00:08 time: 0.7483 data_time: 0.0711 memory: 15734 2022/08/30 00:21:26 - mmengine - INFO - Epoch(val) [60][30/32] eta: 0:00:01 time: 0.6265 data_time: 0.0503 memory: 15734 2022/08/30 00:21:27 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 00:21:27 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8093, precision: 0.7494, hmean: 0.7782 2022/08/30 00:21:27 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8093, precision: 0.8062, hmean: 0.8078 2022/08/30 00:21:27 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8021, precision: 0.8470, hmean: 0.8239 2022/08/30 00:21:27 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7790, precision: 0.8713, hmean: 0.8226 2022/08/30 00:21:27 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7130, precision: 0.9080, hmean: 0.7988 2022/08/30 00:21:27 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.3207, precision: 0.9652, hmean: 0.4814 2022/08/30 00:21:27 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/08/30 00:21:27 - mmengine - INFO - Epoch(val) [60][32/32] icdar/precision: 0.8470 icdar/recall: 0.8021 icdar/hmean: 0.8239 2022/08/30 00:21:39 - mmengine - INFO - Epoch(train) [61][5/63] lr: 6.6839e-03 eta: 0:00:01 time: 1.7004 data_time: 0.2742 memory: 16201 loss_prob: 1.2316 loss_thr: 0.6015 loss_db: 0.1999 loss: 2.0330 2022/08/30 00:21:49 - mmengine - INFO - Epoch(train) [61][10/63] lr: 6.6839e-03 eta: 22:02:07 time: 2.1619 data_time: 0.3069 memory: 16201 loss_prob: 1.1867 loss_thr: 0.5892 loss_db: 0.1983 loss: 1.9742 2022/08/30 00:21:56 - mmengine - INFO - Epoch(train) [61][15/63] lr: 6.6839e-03 eta: 22:02:07 time: 1.6666 data_time: 0.0546 memory: 16201 loss_prob: 1.1164 loss_thr: 0.5684 loss_db: 0.1831 loss: 1.8679 2022/08/30 00:22:07 - mmengine - INFO - Epoch(train) [61][20/63] lr: 6.6839e-03 eta: 22:04:12 time: 1.8252 data_time: 0.0468 memory: 16201 loss_prob: 1.1817 loss_thr: 0.5631 loss_db: 0.1889 loss: 1.9337 2022/08/30 00:22:18 - mmengine - INFO - Epoch(train) [61][25/63] lr: 6.6839e-03 eta: 22:04:12 time: 2.1933 data_time: 0.1017 memory: 16201 loss_prob: 1.3060 loss_thr: 0.6039 loss_db: 0.2112 loss: 2.1211 2022/08/30 00:22:29 - mmengine - INFO - Epoch(train) [61][30/63] lr: 6.6839e-03 eta: 22:07:25 time: 2.1919 data_time: 0.1006 memory: 16201 loss_prob: 1.1777 loss_thr: 0.5839 loss_db: 0.1955 loss: 1.9571 2022/08/30 00:22:39 - mmengine - INFO - Epoch(train) [61][35/63] lr: 6.6839e-03 eta: 22:07:25 time: 2.0700 data_time: 0.0872 memory: 16201 loss_prob: 1.0562 loss_thr: 0.5401 loss_db: 0.1732 loss: 1.7695 2022/08/30 00:22:48 - mmengine - INFO - Epoch(train) [61][40/63] lr: 6.6839e-03 eta: 22:09:36 time: 1.8625 data_time: 0.0686 memory: 16201 loss_prob: 1.1696 loss_thr: 0.5754 loss_db: 0.1870 loss: 1.9320 2022/08/30 00:22:58 - mmengine - INFO - Epoch(train) [61][45/63] lr: 6.6839e-03 eta: 22:09:36 time: 1.8974 data_time: 0.0662 memory: 16201 loss_prob: 1.2329 loss_thr: 0.6090 loss_db: 0.1971 loss: 2.0390 2022/08/30 00:23:08 - mmengine - INFO - Epoch(train) [61][50/63] lr: 6.6839e-03 eta: 22:12:14 time: 2.0165 data_time: 0.0844 memory: 16201 loss_prob: 1.1415 loss_thr: 0.6058 loss_db: 0.1847 loss: 1.9319 2022/08/30 00:23:18 - mmengine - INFO - Epoch(train) [61][55/63] lr: 6.6839e-03 eta: 22:12:14 time: 2.0783 data_time: 0.0741 memory: 16201 loss_prob: 1.2110 loss_thr: 0.6157 loss_db: 0.1987 loss: 2.0254 2022/08/30 00:23:28 - mmengine - INFO - Epoch(train) [61][60/63] lr: 6.6839e-03 eta: 22:14:58 time: 2.0516 data_time: 0.0925 memory: 16201 loss_prob: 1.2433 loss_thr: 0.6042 loss_db: 0.2045 loss: 2.0520 2022/08/30 00:23:34 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:23:47 - mmengine - INFO - Epoch(train) [62][5/63] lr: 6.6787e-03 eta: 22:14:58 time: 2.3116 data_time: 0.3531 memory: 16201 loss_prob: 1.1329 loss_thr: 0.5947 loss_db: 0.1876 loss: 1.9153 2022/08/30 00:23:59 - mmengine - INFO - Epoch(train) [62][10/63] lr: 6.6787e-03 eta: 22:17:54 time: 2.4705 data_time: 0.3941 memory: 16201 loss_prob: 1.1741 loss_thr: 0.6032 loss_db: 0.1884 loss: 1.9657 2022/08/30 00:24:08 - mmengine - INFO - Epoch(train) [62][15/63] lr: 6.6787e-03 eta: 22:17:54 time: 2.0483 data_time: 0.1035 memory: 16201 loss_prob: 1.2693 loss_thr: 0.6082 loss_db: 0.2066 loss: 2.0841 2022/08/30 00:24:18 - mmengine - INFO - Epoch(train) [62][20/63] lr: 6.6787e-03 eta: 22:20:17 time: 1.9527 data_time: 0.0682 memory: 16201 loss_prob: 1.2689 loss_thr: 0.6049 loss_db: 0.2081 loss: 2.0819 2022/08/30 00:24:29 - mmengine - INFO - Epoch(train) [62][25/63] lr: 6.6787e-03 eta: 22:20:17 time: 2.1171 data_time: 0.0810 memory: 16201 loss_prob: 1.2280 loss_thr: 0.5878 loss_db: 0.1981 loss: 2.0140 2022/08/30 00:24:40 - mmengine - INFO - Epoch(train) [62][30/63] lr: 6.6787e-03 eta: 22:23:20 time: 2.1690 data_time: 0.0794 memory: 16201 loss_prob: 1.1458 loss_thr: 0.5693 loss_db: 0.1865 loss: 1.9017 2022/08/30 00:24:52 - mmengine - INFO - Epoch(train) [62][35/63] lr: 6.6787e-03 eta: 22:23:20 time: 2.2879 data_time: 0.0989 memory: 16201 loss_prob: 1.1246 loss_thr: 0.5736 loss_db: 0.1835 loss: 1.8818 2022/08/30 00:25:03 - mmengine - INFO - Epoch(train) [62][40/63] lr: 6.6787e-03 eta: 22:26:42 time: 2.2806 data_time: 0.1181 memory: 16201 loss_prob: 1.1510 loss_thr: 0.5943 loss_db: 0.1833 loss: 1.9285 2022/08/30 00:25:15 - mmengine - INFO - Epoch(train) [62][45/63] lr: 6.6787e-03 eta: 22:26:42 time: 2.3308 data_time: 0.1180 memory: 16201 loss_prob: 1.1402 loss_thr: 0.5960 loss_db: 0.1862 loss: 1.9224 2022/08/30 00:25:28 - mmengine - INFO - Epoch(train) [62][50/63] lr: 6.6787e-03 eta: 22:30:48 time: 2.5215 data_time: 0.1253 memory: 16201 loss_prob: 1.1806 loss_thr: 0.5899 loss_db: 0.1941 loss: 1.9647 2022/08/30 00:25:41 - mmengine - INFO - Epoch(train) [62][55/63] lr: 6.6787e-03 eta: 22:30:48 time: 2.5709 data_time: 0.1059 memory: 16201 loss_prob: 1.1475 loss_thr: 0.5753 loss_db: 0.1852 loss: 1.9079 2022/08/30 00:25:52 - mmengine - INFO - Epoch(train) [62][60/63] lr: 6.6787e-03 eta: 22:34:21 time: 2.3484 data_time: 0.1020 memory: 16201 loss_prob: 1.0615 loss_thr: 0.5587 loss_db: 0.1761 loss: 1.7963 2022/08/30 00:25:57 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:26:08 - mmengine - INFO - Epoch(train) [63][5/63] lr: 6.6734e-03 eta: 22:34:21 time: 1.9847 data_time: 0.3036 memory: 16201 loss_prob: 1.2749 loss_thr: 0.6163 loss_db: 0.2085 loss: 2.0996 2022/08/30 00:26:19 - mmengine - INFO - Epoch(train) [63][10/63] lr: 6.6734e-03 eta: 22:36:15 time: 2.1775 data_time: 0.3295 memory: 16201 loss_prob: 1.1456 loss_thr: 0.6069 loss_db: 0.1911 loss: 1.9437 2022/08/30 00:26:28 - mmengine - INFO - Epoch(train) [63][15/63] lr: 6.6734e-03 eta: 22:36:15 time: 2.0642 data_time: 0.0983 memory: 16201 loss_prob: 1.0179 loss_thr: 0.5547 loss_db: 0.1686 loss: 1.7412 2022/08/30 00:26:40 - mmengine - INFO - Epoch(train) [63][20/63] lr: 6.6734e-03 eta: 22:39:04 time: 2.1219 data_time: 0.0807 memory: 16201 loss_prob: 1.0798 loss_thr: 0.5654 loss_db: 0.1719 loss: 1.8170 2022/08/30 00:26:50 - mmengine - INFO - Epoch(train) [63][25/63] lr: 6.6734e-03 eta: 22:39:04 time: 2.2003 data_time: 0.0977 memory: 16201 loss_prob: 1.1508 loss_thr: 0.5962 loss_db: 0.1849 loss: 1.9319 2022/08/30 00:27:03 - mmengine - INFO - Epoch(train) [63][30/63] lr: 6.6734e-03 eta: 22:42:25 time: 2.3067 data_time: 0.1039 memory: 16201 loss_prob: 1.1266 loss_thr: 0.5881 loss_db: 0.1861 loss: 1.9007 2022/08/30 00:27:15 - mmengine - INFO - Epoch(train) [63][35/63] lr: 6.6734e-03 eta: 22:42:25 time: 2.5100 data_time: 0.1144 memory: 16201 loss_prob: 1.1503 loss_thr: 0.5823 loss_db: 0.1860 loss: 1.9186 2022/08/30 00:27:26 - mmengine - INFO - Epoch(train) [63][40/63] lr: 6.6734e-03 eta: 22:45:37 time: 2.2602 data_time: 0.1064 memory: 16201 loss_prob: 1.2021 loss_thr: 0.5700 loss_db: 0.1888 loss: 1.9609 2022/08/30 00:27:37 - mmengine - INFO - Epoch(train) [63][45/63] lr: 6.6734e-03 eta: 22:45:37 time: 2.1781 data_time: 0.0744 memory: 16201 loss_prob: 1.1423 loss_thr: 0.5666 loss_db: 0.1813 loss: 1.8901 2022/08/30 00:27:48 - mmengine - INFO - Epoch(train) [63][50/63] lr: 6.6734e-03 eta: 22:48:34 time: 2.1868 data_time: 0.0850 memory: 16201 loss_prob: 1.1012 loss_thr: 0.5723 loss_db: 0.1812 loss: 1.8547 2022/08/30 00:28:01 - mmengine - INFO - Epoch(train) [63][55/63] lr: 6.6734e-03 eta: 22:48:34 time: 2.3123 data_time: 0.0947 memory: 16201 loss_prob: 1.1147 loss_thr: 0.5734 loss_db: 0.1836 loss: 1.8717 2022/08/30 00:28:10 - mmengine - INFO - Epoch(train) [63][60/63] lr: 6.6734e-03 eta: 22:51:49 time: 2.2877 data_time: 0.1080 memory: 16201 loss_prob: 1.1501 loss_thr: 0.5958 loss_db: 0.1903 loss: 1.9362 2022/08/30 00:28:16 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:28:30 - mmengine - INFO - Epoch(train) [64][5/63] lr: 6.6681e-03 eta: 22:51:49 time: 2.3318 data_time: 0.3841 memory: 16201 loss_prob: 1.2760 loss_thr: 0.6359 loss_db: 0.2118 loss: 2.1237 2022/08/30 00:28:40 - mmengine - INFO - Epoch(train) [64][10/63] lr: 6.6681e-03 eta: 22:54:16 time: 2.3930 data_time: 0.4197 memory: 16201 loss_prob: 1.3070 loss_thr: 0.6176 loss_db: 0.2119 loss: 2.1364 2022/08/30 00:28:50 - mmengine - INFO - Epoch(train) [64][15/63] lr: 6.6681e-03 eta: 22:54:16 time: 2.0426 data_time: 0.0810 memory: 16201 loss_prob: 1.1992 loss_thr: 0.6072 loss_db: 0.1968 loss: 2.0032 2022/08/30 00:29:01 - mmengine - INFO - Epoch(train) [64][20/63] lr: 6.6681e-03 eta: 22:57:03 time: 2.1472 data_time: 0.0787 memory: 16201 loss_prob: 1.1036 loss_thr: 0.5998 loss_db: 0.1832 loss: 1.8866 2022/08/30 00:29:14 - mmengine - INFO - Epoch(train) [64][25/63] lr: 6.6681e-03 eta: 22:57:03 time: 2.3520 data_time: 0.0987 memory: 16201 loss_prob: 1.0839 loss_thr: 0.5710 loss_db: 0.1800 loss: 1.8349 2022/08/30 00:29:25 - mmengine - INFO - Epoch(train) [64][30/63] lr: 6.6681e-03 eta: 23:00:36 time: 2.4083 data_time: 0.0959 memory: 16201 loss_prob: 1.0907 loss_thr: 0.5780 loss_db: 0.1785 loss: 1.8472 2022/08/30 00:29:28 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:29:37 - mmengine - INFO - Epoch(train) [64][35/63] lr: 6.6681e-03 eta: 23:00:36 time: 2.3211 data_time: 0.0980 memory: 16201 loss_prob: 1.0857 loss_thr: 0.5752 loss_db: 0.1732 loss: 1.8341 2022/08/30 00:29:48 - mmengine - INFO - Epoch(train) [64][40/63] lr: 6.6681e-03 eta: 23:03:49 time: 2.2998 data_time: 0.0809 memory: 16201 loss_prob: 1.0847 loss_thr: 0.5663 loss_db: 0.1762 loss: 1.8272 2022/08/30 00:29:59 - mmengine - INFO - Epoch(train) [64][45/63] lr: 6.6681e-03 eta: 23:03:49 time: 2.2300 data_time: 0.0877 memory: 16201 loss_prob: 1.1606 loss_thr: 0.5473 loss_db: 0.1883 loss: 1.8961 2022/08/30 00:30:10 - mmengine - INFO - Epoch(train) [64][50/63] lr: 6.6681e-03 eta: 23:06:37 time: 2.1703 data_time: 0.0978 memory: 16201 loss_prob: 1.2566 loss_thr: 0.5913 loss_db: 0.2005 loss: 2.0483 2022/08/30 00:30:18 - mmengine - INFO - Epoch(train) [64][55/63] lr: 6.6681e-03 eta: 23:06:37 time: 1.9323 data_time: 0.0757 memory: 16201 loss_prob: 1.2107 loss_thr: 0.6066 loss_db: 0.1941 loss: 2.0114 2022/08/30 00:30:28 - mmengine - INFO - Epoch(train) [64][60/63] lr: 6.6681e-03 eta: 23:08:13 time: 1.7684 data_time: 0.0645 memory: 16201 loss_prob: 1.0732 loss_thr: 0.5553 loss_db: 0.1752 loss: 1.8037 2022/08/30 00:30:33 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:30:46 - mmengine - INFO - Epoch(train) [65][5/63] lr: 6.6628e-03 eta: 23:08:13 time: 2.2146 data_time: 0.3753 memory: 16201 loss_prob: 1.1895 loss_thr: 0.5934 loss_db: 0.1924 loss: 1.9754 2022/08/30 00:30:55 - mmengine - INFO - Epoch(train) [65][10/63] lr: 6.6628e-03 eta: 23:10:02 time: 2.2124 data_time: 0.4269 memory: 16201 loss_prob: 1.2309 loss_thr: 0.6228 loss_db: 0.2046 loss: 2.0583 2022/08/30 00:31:06 - mmengine - INFO - Epoch(train) [65][15/63] lr: 6.6628e-03 eta: 23:10:02 time: 2.0101 data_time: 0.1142 memory: 16201 loss_prob: 1.1688 loss_thr: 0.5990 loss_db: 0.1967 loss: 1.9644 2022/08/30 00:31:15 - mmengine - INFO - Epoch(train) [65][20/63] lr: 6.6628e-03 eta: 23:12:27 time: 2.0514 data_time: 0.1123 memory: 16201 loss_prob: 1.1596 loss_thr: 0.5893 loss_db: 0.1935 loss: 1.9424 2022/08/30 00:31:27 - mmengine - INFO - Epoch(train) [65][25/63] lr: 6.6628e-03 eta: 23:12:27 time: 2.0469 data_time: 0.1318 memory: 16201 loss_prob: 1.2432 loss_thr: 0.5917 loss_db: 0.2096 loss: 2.0445 2022/08/30 00:31:36 - mmengine - INFO - Epoch(train) [65][30/63] lr: 6.6628e-03 eta: 23:14:53 time: 2.0657 data_time: 0.1250 memory: 16201 loss_prob: 1.2413 loss_thr: 0.5958 loss_db: 0.2075 loss: 2.0447 2022/08/30 00:31:46 - mmengine - INFO - Epoch(train) [65][35/63] lr: 6.6628e-03 eta: 23:14:53 time: 1.9114 data_time: 0.1185 memory: 16201 loss_prob: 1.1201 loss_thr: 0.6009 loss_db: 0.1859 loss: 1.9069 2022/08/30 00:31:55 - mmengine - INFO - Epoch(train) [65][40/63] lr: 6.6628e-03 eta: 23:16:50 time: 1.9001 data_time: 0.1183 memory: 16201 loss_prob: 1.0850 loss_thr: 0.5626 loss_db: 0.1788 loss: 1.8264 2022/08/30 00:32:05 - mmengine - INFO - Epoch(train) [65][45/63] lr: 6.6628e-03 eta: 23:16:50 time: 1.9793 data_time: 0.1164 memory: 16201 loss_prob: 1.1322 loss_thr: 0.5574 loss_db: 0.1853 loss: 1.8750 2022/08/30 00:32:16 - mmengine - INFO - Epoch(train) [65][50/63] lr: 6.6628e-03 eta: 23:19:21 time: 2.1026 data_time: 0.1270 memory: 16201 loss_prob: 1.0826 loss_thr: 0.5601 loss_db: 0.1783 loss: 1.8211 2022/08/30 00:32:26 - mmengine - INFO - Epoch(train) [65][55/63] lr: 6.6628e-03 eta: 23:19:21 time: 2.0954 data_time: 0.1563 memory: 16201 loss_prob: 1.2053 loss_thr: 0.5829 loss_db: 0.1939 loss: 1.9821 2022/08/30 00:32:37 - mmengine - INFO - Epoch(train) [65][60/63] lr: 6.6628e-03 eta: 23:22:01 time: 2.1577 data_time: 0.1524 memory: 16201 loss_prob: 1.3371 loss_thr: 0.6217 loss_db: 0.2112 loss: 2.1701 2022/08/30 00:32:42 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:32:55 - mmengine - INFO - Epoch(train) [66][5/63] lr: 6.6575e-03 eta: 23:22:01 time: 2.1758 data_time: 0.3965 memory: 16201 loss_prob: 1.2209 loss_thr: 0.5684 loss_db: 0.2024 loss: 1.9916 2022/08/30 00:33:05 - mmengine - INFO - Epoch(train) [66][10/63] lr: 6.6575e-03 eta: 23:24:08 time: 2.3465 data_time: 0.4218 memory: 16201 loss_prob: 1.2439 loss_thr: 0.5842 loss_db: 0.2014 loss: 2.0295 2022/08/30 00:33:16 - mmengine - INFO - Epoch(train) [66][15/63] lr: 6.6575e-03 eta: 23:24:08 time: 2.0754 data_time: 0.1250 memory: 16201 loss_prob: 1.1596 loss_thr: 0.5677 loss_db: 0.1899 loss: 1.9172 2022/08/30 00:33:26 - mmengine - INFO - Epoch(train) [66][20/63] lr: 6.6575e-03 eta: 23:26:33 time: 2.0778 data_time: 0.1166 memory: 16201 loss_prob: 1.1074 loss_thr: 0.5711 loss_db: 0.1865 loss: 1.8650 2022/08/30 00:33:38 - mmengine - INFO - Epoch(train) [66][25/63] lr: 6.6575e-03 eta: 23:26:33 time: 2.2022 data_time: 0.1313 memory: 16201 loss_prob: 1.0926 loss_thr: 0.5805 loss_db: 0.1760 loss: 1.8491 2022/08/30 00:33:49 - mmengine - INFO - Epoch(train) [66][30/63] lr: 6.6575e-03 eta: 23:29:34 time: 2.2933 data_time: 0.1118 memory: 16201 loss_prob: 1.1697 loss_thr: 0.5869 loss_db: 0.1882 loss: 1.9448 2022/08/30 00:34:01 - mmengine - INFO - Epoch(train) [66][35/63] lr: 6.6575e-03 eta: 23:29:34 time: 2.3243 data_time: 0.1596 memory: 16201 loss_prob: 1.2454 loss_thr: 0.5800 loss_db: 0.2061 loss: 2.0315 2022/08/30 00:34:12 - mmengine - INFO - Epoch(train) [66][40/63] lr: 6.6575e-03 eta: 23:32:39 time: 2.3252 data_time: 0.1589 memory: 16201 loss_prob: 1.1864 loss_thr: 0.5937 loss_db: 0.1943 loss: 1.9744 2022/08/30 00:34:24 - mmengine - INFO - Epoch(train) [66][45/63] lr: 6.6575e-03 eta: 23:32:39 time: 2.2858 data_time: 0.1916 memory: 16201 loss_prob: 1.1159 loss_thr: 0.6073 loss_db: 0.1858 loss: 1.9090 2022/08/30 00:34:36 - mmengine - INFO - Epoch(train) [66][50/63] lr: 6.6575e-03 eta: 23:35:45 time: 2.3310 data_time: 0.1751 memory: 16201 loss_prob: 1.0796 loss_thr: 0.5854 loss_db: 0.1755 loss: 1.8405 2022/08/30 00:34:47 - mmengine - INFO - Epoch(train) [66][55/63] lr: 6.6575e-03 eta: 23:35:45 time: 2.3163 data_time: 0.0893 memory: 16201 loss_prob: 1.1119 loss_thr: 0.5793 loss_db: 0.1784 loss: 1.8696 2022/08/30 00:34:57 - mmengine - INFO - Epoch(train) [66][60/63] lr: 6.6575e-03 eta: 23:38:09 time: 2.0969 data_time: 0.0945 memory: 16201 loss_prob: 1.1753 loss_thr: 0.5837 loss_db: 0.1935 loss: 1.9526 2022/08/30 00:35:02 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:35:19 - mmengine - INFO - Epoch(train) [67][5/63] lr: 6.6522e-03 eta: 23:38:09 time: 2.5569 data_time: 0.5101 memory: 16201 loss_prob: 1.2461 loss_thr: 0.5869 loss_db: 0.1970 loss: 2.0299 2022/08/30 00:35:30 - mmengine - INFO - Epoch(train) [67][10/63] lr: 6.6522e-03 eta: 23:41:29 time: 2.8019 data_time: 0.5663 memory: 16201 loss_prob: 1.1646 loss_thr: 0.5866 loss_db: 0.1797 loss: 1.9309 2022/08/30 00:35:40 - mmengine - INFO - Epoch(train) [67][15/63] lr: 6.6522e-03 eta: 23:41:29 time: 2.1398 data_time: 0.0996 memory: 16201 loss_prob: 1.1845 loss_thr: 0.6012 loss_db: 0.1893 loss: 1.9749 2022/08/30 00:35:50 - mmengine - INFO - Epoch(train) [67][20/63] lr: 6.6522e-03 eta: 23:43:26 time: 1.9505 data_time: 0.0642 memory: 16201 loss_prob: 1.1582 loss_thr: 0.5792 loss_db: 0.1920 loss: 1.9294 2022/08/30 00:35:59 - mmengine - INFO - Epoch(train) [67][25/63] lr: 6.6522e-03 eta: 23:43:26 time: 1.8522 data_time: 0.0915 memory: 16201 loss_prob: 1.1207 loss_thr: 0.5852 loss_db: 0.1853 loss: 1.8912 2022/08/30 00:36:07 - mmengine - INFO - Epoch(train) [67][30/63] lr: 6.6522e-03 eta: 23:44:52 time: 1.7710 data_time: 0.0767 memory: 16201 loss_prob: 1.1292 loss_thr: 0.5989 loss_db: 0.1896 loss: 1.9176 2022/08/30 00:36:19 - mmengine - INFO - Epoch(train) [67][35/63] lr: 6.6522e-03 eta: 23:44:52 time: 2.0776 data_time: 0.0617 memory: 16201 loss_prob: 1.0720 loss_thr: 0.5939 loss_db: 0.1777 loss: 1.8435 2022/08/30 00:36:31 - mmengine - INFO - Epoch(train) [67][40/63] lr: 6.6522e-03 eta: 23:47:52 time: 2.3263 data_time: 0.1109 memory: 16201 loss_prob: 1.2359 loss_thr: 0.6065 loss_db: 0.1951 loss: 2.0374 2022/08/30 00:36:42 - mmengine - INFO - Epoch(train) [67][45/63] lr: 6.6522e-03 eta: 23:47:52 time: 2.2435 data_time: 0.1173 memory: 16201 loss_prob: 1.1826 loss_thr: 0.5776 loss_db: 0.1900 loss: 1.9502 2022/08/30 00:36:53 - mmengine - INFO - Epoch(train) [67][50/63] lr: 6.6522e-03 eta: 23:50:31 time: 2.2088 data_time: 0.1020 memory: 16201 loss_prob: 1.1165 loss_thr: 0.5694 loss_db: 0.1838 loss: 1.8696 2022/08/30 00:37:04 - mmengine - INFO - Epoch(train) [67][55/63] lr: 6.6522e-03 eta: 23:50:31 time: 2.1829 data_time: 0.0960 memory: 16201 loss_prob: 1.1766 loss_thr: 0.5989 loss_db: 0.1920 loss: 1.9676 2022/08/30 00:37:15 - mmengine - INFO - Epoch(train) [67][60/63] lr: 6.6522e-03 eta: 23:53:11 time: 2.2196 data_time: 0.1074 memory: 16201 loss_prob: 1.0933 loss_thr: 0.5978 loss_db: 0.1795 loss: 1.8705 2022/08/30 00:37:21 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:37:39 - mmengine - INFO - Epoch(train) [68][5/63] lr: 6.6470e-03 eta: 23:53:11 time: 2.7982 data_time: 0.5830 memory: 16201 loss_prob: 1.0863 loss_thr: 0.5924 loss_db: 0.1774 loss: 1.8561 2022/08/30 00:37:51 - mmengine - INFO - Epoch(train) [68][10/63] lr: 6.6470e-03 eta: 23:56:54 time: 2.9764 data_time: 0.6692 memory: 16201 loss_prob: 1.0278 loss_thr: 0.5810 loss_db: 0.1715 loss: 1.7803 2022/08/30 00:38:02 - mmengine - INFO - Epoch(train) [68][15/63] lr: 6.6470e-03 eta: 23:56:54 time: 2.3291 data_time: 0.1670 memory: 16201 loss_prob: 1.0791 loss_thr: 0.5892 loss_db: 0.1766 loss: 1.8449 2022/08/30 00:38:14 - mmengine - INFO - Epoch(train) [68][20/63] lr: 6.6470e-03 eta: 23:59:43 time: 2.2884 data_time: 0.0798 memory: 16201 loss_prob: 1.0525 loss_thr: 0.5787 loss_db: 0.1702 loss: 1.8015 2022/08/30 00:38:26 - mmengine - INFO - Epoch(train) [68][25/63] lr: 6.6470e-03 eta: 23:59:43 time: 2.3650 data_time: 0.1072 memory: 16201 loss_prob: 1.0652 loss_thr: 0.5619 loss_db: 0.1726 loss: 1.7997 2022/08/30 00:38:37 - mmengine - INFO - Epoch(train) [68][30/63] lr: 6.6470e-03 eta: 1 day, 0:02:39 time: 2.3303 data_time: 0.1030 memory: 16201 loss_prob: 1.0877 loss_thr: 0.5586 loss_db: 0.1766 loss: 1.8229 2022/08/30 00:38:48 - mmengine - INFO - Epoch(train) [68][35/63] lr: 6.6470e-03 eta: 1 day, 0:02:39 time: 2.2338 data_time: 0.0911 memory: 16201 loss_prob: 1.0683 loss_thr: 0.5688 loss_db: 0.1769 loss: 1.8140 2022/08/30 00:38:59 - mmengine - INFO - Epoch(train) [68][40/63] lr: 6.6470e-03 eta: 1 day, 0:05:15 time: 2.2183 data_time: 0.0725 memory: 16201 loss_prob: 1.1414 loss_thr: 0.5964 loss_db: 0.1912 loss: 1.9290 2022/08/30 00:39:10 - mmengine - INFO - Epoch(train) [68][45/63] lr: 6.6470e-03 eta: 1 day, 0:05:15 time: 2.2340 data_time: 0.0830 memory: 16201 loss_prob: 1.2172 loss_thr: 0.6142 loss_db: 0.2020 loss: 2.0333 2022/08/30 00:39:20 - mmengine - INFO - Epoch(train) [68][50/63] lr: 6.6470e-03 eta: 1 day, 0:07:32 time: 2.1046 data_time: 0.1065 memory: 16201 loss_prob: 1.1487 loss_thr: 0.5878 loss_db: 0.1885 loss: 1.9250 2022/08/30 00:39:30 - mmengine - INFO - Epoch(train) [68][55/63] lr: 6.6470e-03 eta: 1 day, 0:07:32 time: 1.9836 data_time: 0.0774 memory: 16201 loss_prob: 1.1238 loss_thr: 0.5750 loss_db: 0.1841 loss: 1.8829 2022/08/30 00:39:41 - mmengine - INFO - Epoch(train) [68][60/63] lr: 6.6470e-03 eta: 1 day, 0:09:39 time: 2.0542 data_time: 0.0670 memory: 16201 loss_prob: 1.1494 loss_thr: 0.5901 loss_db: 0.1898 loss: 1.9292 2022/08/30 00:39:46 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:40:01 - mmengine - INFO - Epoch(train) [69][5/63] lr: 6.6417e-03 eta: 1 day, 0:09:39 time: 2.4061 data_time: 0.3306 memory: 16201 loss_prob: 1.1492 loss_thr: 0.5980 loss_db: 0.1976 loss: 1.9448 2022/08/30 00:40:10 - mmengine - INFO - Epoch(train) [69][10/63] lr: 6.6417e-03 eta: 1 day, 0:11:36 time: 2.3885 data_time: 0.3412 memory: 16201 loss_prob: 1.1990 loss_thr: 0.6012 loss_db: 0.1985 loss: 1.9987 2022/08/30 00:40:17 - mmengine - INFO - Epoch(train) [69][15/63] lr: 6.6417e-03 eta: 1 day, 0:11:36 time: 1.6449 data_time: 0.0694 memory: 16201 loss_prob: 1.1800 loss_thr: 0.6057 loss_db: 0.1942 loss: 1.9799 2022/08/30 00:40:27 - mmengine - INFO - Epoch(train) [69][20/63] lr: 6.6417e-03 eta: 1 day, 0:12:50 time: 1.7410 data_time: 0.0665 memory: 16201 loss_prob: 1.1988 loss_thr: 0.6263 loss_db: 0.1947 loss: 2.0197 2022/08/30 00:40:37 - mmengine - INFO - Epoch(train) [69][25/63] lr: 6.6417e-03 eta: 1 day, 0:12:50 time: 1.9646 data_time: 0.0690 memory: 16201 loss_prob: 1.2640 loss_thr: 0.6392 loss_db: 0.2032 loss: 2.1063 2022/08/30 00:40:49 - mmengine - INFO - Epoch(train) [69][30/63] lr: 6.6417e-03 eta: 1 day, 0:15:25 time: 2.2302 data_time: 0.0673 memory: 16201 loss_prob: 1.1718 loss_thr: 0.6061 loss_db: 0.1880 loss: 1.9659 2022/08/30 00:40:59 - mmengine - INFO - Epoch(train) [69][35/63] lr: 6.6417e-03 eta: 1 day, 0:15:25 time: 2.1863 data_time: 0.0903 memory: 16201 loss_prob: 1.1682 loss_thr: 0.5871 loss_db: 0.1861 loss: 1.9415 2022/08/30 00:41:09 - mmengine - INFO - Epoch(train) [69][40/63] lr: 6.6417e-03 eta: 1 day, 0:17:18 time: 1.9860 data_time: 0.0715 memory: 16201 loss_prob: 1.1630 loss_thr: 0.5794 loss_db: 0.1911 loss: 1.9334 2022/08/30 00:41:20 - mmengine - INFO - Epoch(train) [69][45/63] lr: 6.6417e-03 eta: 1 day, 0:17:18 time: 2.0765 data_time: 0.0784 memory: 16201 loss_prob: 1.1206 loss_thr: 0.5646 loss_db: 0.1841 loss: 1.8692 2022/08/30 00:41:29 - mmengine - INFO - Epoch(train) [69][50/63] lr: 6.6417e-03 eta: 1 day, 0:19:03 time: 1.9417 data_time: 0.0956 memory: 16201 loss_prob: 1.1929 loss_thr: 0.5703 loss_db: 0.1902 loss: 1.9535 2022/08/30 00:41:40 - mmengine - INFO - Epoch(train) [69][55/63] lr: 6.6417e-03 eta: 1 day, 0:19:03 time: 2.0209 data_time: 0.0736 memory: 16201 loss_prob: 1.2436 loss_thr: 0.6039 loss_db: 0.2018 loss: 2.0493 2022/08/30 00:41:51 - mmengine - INFO - Epoch(train) [69][60/63] lr: 6.6417e-03 eta: 1 day, 0:21:34 time: 2.2220 data_time: 0.0866 memory: 16201 loss_prob: 1.1911 loss_thr: 0.5994 loss_db: 0.1953 loss: 1.9857 2022/08/30 00:41:56 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:42:11 - mmengine - INFO - Epoch(train) [70][5/63] lr: 6.6364e-03 eta: 1 day, 0:21:34 time: 2.4344 data_time: 0.5541 memory: 16201 loss_prob: 1.1730 loss_thr: 0.5941 loss_db: 0.1928 loss: 1.9599 2022/08/30 00:42:22 - mmengine - INFO - Epoch(train) [70][10/63] lr: 6.6364e-03 eta: 1 day, 0:24:04 time: 2.6191 data_time: 0.5637 memory: 16201 loss_prob: 1.1620 loss_thr: 0.5848 loss_db: 0.1935 loss: 1.9404 2022/08/30 00:42:34 - mmengine - INFO - Epoch(train) [70][15/63] lr: 6.6364e-03 eta: 1 day, 0:24:04 time: 2.3673 data_time: 0.0958 memory: 16201 loss_prob: 1.1106 loss_thr: 0.5639 loss_db: 0.1866 loss: 1.8611 2022/08/30 00:42:45 - mmengine - INFO - Epoch(train) [70][20/63] lr: 6.6364e-03 eta: 1 day, 0:26:48 time: 2.3099 data_time: 0.0844 memory: 16201 loss_prob: 1.0288 loss_thr: 0.5441 loss_db: 0.1708 loss: 1.7437 2022/08/30 00:42:57 - mmengine - INFO - Epoch(train) [70][25/63] lr: 6.6364e-03 eta: 1 day, 0:26:48 time: 2.2309 data_time: 0.1188 memory: 16201 loss_prob: 1.0339 loss_thr: 0.5472 loss_db: 0.1710 loss: 1.7521 2022/08/30 00:43:07 - mmengine - INFO - Epoch(train) [70][30/63] lr: 6.6364e-03 eta: 1 day, 0:29:14 time: 2.2123 data_time: 0.0913 memory: 16201 loss_prob: 1.1471 loss_thr: 0.5715 loss_db: 0.1859 loss: 1.9044 2022/08/30 00:43:19 - mmengine - INFO - Epoch(train) [70][35/63] lr: 6.6364e-03 eta: 1 day, 0:29:14 time: 2.2325 data_time: 0.0817 memory: 16201 loss_prob: 1.2076 loss_thr: 0.5673 loss_db: 0.1948 loss: 1.9698 2022/08/30 00:43:29 - mmengine - INFO - Epoch(train) [70][40/63] lr: 6.6364e-03 eta: 1 day, 0:31:23 time: 2.1062 data_time: 0.0844 memory: 16201 loss_prob: 1.1392 loss_thr: 0.5642 loss_db: 0.1873 loss: 1.8907 2022/08/30 00:43:39 - mmengine - INFO - Epoch(train) [70][45/63] lr: 6.6364e-03 eta: 1 day, 0:31:23 time: 2.0000 data_time: 0.0757 memory: 16201 loss_prob: 1.1308 loss_thr: 0.6095 loss_db: 0.1856 loss: 1.9260 2022/08/30 00:43:49 - mmengine - INFO - Epoch(train) [70][50/63] lr: 6.6364e-03 eta: 1 day, 0:33:23 time: 2.0612 data_time: 0.0956 memory: 16201 loss_prob: 1.1379 loss_thr: 0.6198 loss_db: 0.1855 loss: 1.9432 2022/08/30 00:43:59 - mmengine - INFO - Epoch(train) [70][55/63] lr: 6.6364e-03 eta: 1 day, 0:33:23 time: 1.9563 data_time: 0.0841 memory: 16201 loss_prob: 1.1319 loss_thr: 0.6033 loss_db: 0.1828 loss: 1.9180 2022/08/30 00:44:09 - mmengine - INFO - Epoch(train) [70][60/63] lr: 6.6364e-03 eta: 1 day, 0:35:14 time: 2.0004 data_time: 0.0578 memory: 16201 loss_prob: 1.1090 loss_thr: 0.5755 loss_db: 0.1784 loss: 1.8629 2022/08/30 00:44:14 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:44:30 - mmengine - INFO - Epoch(train) [71][5/63] lr: 6.6311e-03 eta: 1 day, 0:35:14 time: 2.5053 data_time: 0.4270 memory: 16201 loss_prob: 1.0756 loss_thr: 0.5885 loss_db: 0.1763 loss: 1.8404 2022/08/30 00:44:43 - mmengine - INFO - Epoch(train) [71][10/63] lr: 6.6311e-03 eta: 1 day, 0:38:12 time: 2.8213 data_time: 0.4533 memory: 16201 loss_prob: 1.0940 loss_thr: 0.5802 loss_db: 0.1795 loss: 1.8537 2022/08/30 00:44:52 - mmengine - INFO - Epoch(train) [71][15/63] lr: 6.6311e-03 eta: 1 day, 0:38:12 time: 2.2052 data_time: 0.0978 memory: 16201 loss_prob: 1.1452 loss_thr: 0.5807 loss_db: 0.1852 loss: 1.9111 2022/08/30 00:45:03 - mmengine - INFO - Epoch(train) [71][20/63] lr: 6.6311e-03 eta: 1 day, 0:39:57 time: 1.9821 data_time: 0.0682 memory: 16201 loss_prob: 1.1617 loss_thr: 0.5923 loss_db: 0.1851 loss: 1.9391 2022/08/30 00:45:12 - mmengine - INFO - Epoch(train) [71][25/63] lr: 6.6311e-03 eta: 1 day, 0:39:57 time: 2.0520 data_time: 0.0867 memory: 16201 loss_prob: 1.0745 loss_thr: 0.5776 loss_db: 0.1759 loss: 1.8279 2022/08/30 00:45:22 - mmengine - INFO - Epoch(train) [71][30/63] lr: 6.6311e-03 eta: 1 day, 0:41:45 time: 1.9943 data_time: 0.0838 memory: 16201 loss_prob: 0.9888 loss_thr: 0.5425 loss_db: 0.1671 loss: 1.6983 2022/08/30 00:45:32 - mmengine - INFO - Epoch(train) [71][35/63] lr: 6.6311e-03 eta: 1 day, 0:41:45 time: 2.0183 data_time: 0.0795 memory: 16201 loss_prob: 1.0965 loss_thr: 0.5545 loss_db: 0.1819 loss: 1.8329 2022/08/30 00:45:44 - mmengine - INFO - Epoch(train) [71][40/63] lr: 6.6311e-03 eta: 1 day, 0:43:50 time: 2.1127 data_time: 0.0733 memory: 16201 loss_prob: 1.1454 loss_thr: 0.5754 loss_db: 0.1888 loss: 1.9096 2022/08/30 00:45:54 - mmengine - INFO - Epoch(train) [71][45/63] lr: 6.6311e-03 eta: 1 day, 0:43:50 time: 2.1281 data_time: 0.0743 memory: 16201 loss_prob: 1.1337 loss_thr: 0.5851 loss_db: 0.1856 loss: 1.9044 2022/08/30 00:46:05 - mmengine - INFO - Epoch(train) [71][50/63] lr: 6.6311e-03 eta: 1 day, 0:45:57 time: 2.1230 data_time: 0.0970 memory: 16201 loss_prob: 1.2052 loss_thr: 0.5955 loss_db: 0.1930 loss: 1.9937 2022/08/30 00:46:16 - mmengine - INFO - Epoch(train) [71][55/63] lr: 6.6311e-03 eta: 1 day, 0:45:57 time: 2.1869 data_time: 0.0760 memory: 16201 loss_prob: 1.2225 loss_thr: 0.5786 loss_db: 0.2007 loss: 2.0018 2022/08/30 00:46:25 - mmengine - INFO - Epoch(train) [71][60/63] lr: 6.6311e-03 eta: 1 day, 0:47:51 time: 2.0523 data_time: 0.0762 memory: 16201 loss_prob: 1.1942 loss_thr: 0.5768 loss_db: 0.1979 loss: 1.9690 2022/08/30 00:46:30 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:46:44 - mmengine - INFO - Epoch(train) [72][5/63] lr: 6.6258e-03 eta: 1 day, 0:47:51 time: 2.2159 data_time: 0.3856 memory: 16201 loss_prob: 1.2688 loss_thr: 0.6168 loss_db: 0.2053 loss: 2.0909 2022/08/30 00:46:56 - mmengine - INFO - Epoch(train) [72][10/63] lr: 6.6258e-03 eta: 1 day, 0:50:07 time: 2.5905 data_time: 0.5611 memory: 16201 loss_prob: 1.1953 loss_thr: 0.5907 loss_db: 0.1937 loss: 1.9797 2022/08/30 00:47:06 - mmengine - INFO - Epoch(train) [72][15/63] lr: 6.6258e-03 eta: 1 day, 0:50:07 time: 2.2139 data_time: 0.2518 memory: 16201 loss_prob: 1.1644 loss_thr: 0.5730 loss_db: 0.1880 loss: 1.9254 2022/08/30 00:47:17 - mmengine - INFO - Epoch(train) [72][20/63] lr: 6.6258e-03 eta: 1 day, 0:52:06 time: 2.0879 data_time: 0.1064 memory: 16201 loss_prob: 1.0652 loss_thr: 0.5707 loss_db: 0.1697 loss: 1.8056 2022/08/30 00:47:29 - mmengine - INFO - Epoch(train) [72][25/63] lr: 6.6258e-03 eta: 1 day, 0:52:06 time: 2.2573 data_time: 0.1140 memory: 16201 loss_prob: 1.1714 loss_thr: 0.5727 loss_db: 0.1873 loss: 1.9313 2022/08/30 00:47:39 - mmengine - INFO - Epoch(train) [72][30/63] lr: 6.6258e-03 eta: 1 day, 0:54:22 time: 2.2007 data_time: 0.0961 memory: 16201 loss_prob: 1.2769 loss_thr: 0.5875 loss_db: 0.2065 loss: 2.0710 2022/08/30 00:47:51 - mmengine - INFO - Epoch(train) [72][35/63] lr: 6.6258e-03 eta: 1 day, 0:54:22 time: 2.2634 data_time: 0.0620 memory: 16201 loss_prob: 1.2554 loss_thr: 0.5890 loss_db: 0.2063 loss: 2.0506 2022/08/30 00:48:02 - mmengine - INFO - Epoch(train) [72][40/63] lr: 6.6258e-03 eta: 1 day, 0:56:56 time: 2.3213 data_time: 0.0821 memory: 16201 loss_prob: 1.1648 loss_thr: 0.5754 loss_db: 0.1915 loss: 1.9317 2022/08/30 00:48:13 - mmengine - INFO - Epoch(train) [72][45/63] lr: 6.6258e-03 eta: 1 day, 0:56:56 time: 2.1382 data_time: 0.1070 memory: 16201 loss_prob: 1.1828 loss_thr: 0.5955 loss_db: 0.1934 loss: 1.9717 2022/08/30 00:48:22 - mmengine - INFO - Epoch(train) [72][50/63] lr: 6.6258e-03 eta: 1 day, 0:58:43 time: 2.0211 data_time: 0.0822 memory: 16201 loss_prob: 1.1956 loss_thr: 0.6148 loss_db: 0.1981 loss: 2.0085 2022/08/30 00:48:34 - mmengine - INFO - Epoch(train) [72][55/63] lr: 6.6258e-03 eta: 1 day, 0:58:43 time: 2.1130 data_time: 0.0723 memory: 16201 loss_prob: 1.3377 loss_thr: 0.6334 loss_db: 0.2142 loss: 2.1853 2022/08/30 00:48:44 - mmengine - INFO - Epoch(train) [72][60/63] lr: 6.6258e-03 eta: 1 day, 1:00:56 time: 2.1982 data_time: 0.0772 memory: 16201 loss_prob: 1.3062 loss_thr: 0.6002 loss_db: 0.2094 loss: 2.1158 2022/08/30 00:48:49 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:49:05 - mmengine - INFO - Epoch(train) [73][5/63] lr: 6.6205e-03 eta: 1 day, 1:00:56 time: 2.4743 data_time: 0.4630 memory: 16201 loss_prob: 1.0661 loss_thr: 0.5580 loss_db: 0.1759 loss: 1.8000 2022/08/30 00:49:15 - mmengine - INFO - Epoch(train) [73][10/63] lr: 6.6205e-03 eta: 1 day, 1:03:14 time: 2.6335 data_time: 0.5077 memory: 16201 loss_prob: 1.1524 loss_thr: 0.6129 loss_db: 0.1919 loss: 1.9571 2022/08/30 00:49:25 - mmengine - INFO - Epoch(train) [73][15/63] lr: 6.6205e-03 eta: 1 day, 1:03:14 time: 2.1040 data_time: 0.1139 memory: 16201 loss_prob: 1.2538 loss_thr: 0.6438 loss_db: 0.2052 loss: 2.1028 2022/08/30 00:49:35 - mmengine - INFO - Epoch(train) [73][20/63] lr: 6.6205e-03 eta: 1 day, 1:04:56 time: 2.0052 data_time: 0.0775 memory: 16201 loss_prob: 1.2200 loss_thr: 0.6081 loss_db: 0.1995 loss: 2.0277 2022/08/30 00:49:45 - mmengine - INFO - Epoch(train) [73][25/63] lr: 6.6205e-03 eta: 1 day, 1:04:56 time: 1.9398 data_time: 0.0653 memory: 16201 loss_prob: 1.1524 loss_thr: 0.5728 loss_db: 0.1923 loss: 1.9174 2022/08/30 00:49:53 - mmengine - INFO - Epoch(train) [73][30/63] lr: 6.6205e-03 eta: 1 day, 1:06:09 time: 1.8185 data_time: 0.0667 memory: 16201 loss_prob: 1.1797 loss_thr: 0.5867 loss_db: 0.1909 loss: 1.9573 2022/08/30 00:50:05 - mmengine - INFO - Epoch(train) [73][35/63] lr: 6.6205e-03 eta: 1 day, 1:06:09 time: 2.0650 data_time: 0.0698 memory: 16201 loss_prob: 1.1971 loss_thr: 0.5848 loss_db: 0.1930 loss: 1.9749 2022/08/30 00:50:16 - mmengine - INFO - Epoch(train) [73][40/63] lr: 6.6205e-03 eta: 1 day, 1:08:33 time: 2.2836 data_time: 0.0721 memory: 16201 loss_prob: 1.1578 loss_thr: 0.5517 loss_db: 0.1865 loss: 1.8960 2022/08/30 00:50:27 - mmengine - INFO - Epoch(train) [73][45/63] lr: 6.6205e-03 eta: 1 day, 1:08:33 time: 2.1240 data_time: 0.0871 memory: 16201 loss_prob: 1.2155 loss_thr: 0.5615 loss_db: 0.1946 loss: 1.9716 2022/08/30 00:50:37 - mmengine - INFO - Epoch(train) [73][50/63] lr: 6.6205e-03 eta: 1 day, 1:10:27 time: 2.0943 data_time: 0.0936 memory: 16201 loss_prob: 1.1339 loss_thr: 0.5558 loss_db: 0.1847 loss: 1.8743 2022/08/30 00:50:47 - mmengine - INFO - Epoch(train) [73][55/63] lr: 6.6205e-03 eta: 1 day, 1:10:27 time: 2.0845 data_time: 0.1039 memory: 16201 loss_prob: 1.0816 loss_thr: 0.5703 loss_db: 0.1751 loss: 1.8271 2022/08/30 00:50:58 - mmengine - INFO - Epoch(train) [73][60/63] lr: 6.6205e-03 eta: 1 day, 1:12:16 time: 2.0639 data_time: 0.0921 memory: 16201 loss_prob: 1.0925 loss_thr: 0.5786 loss_db: 0.1784 loss: 1.8495 2022/08/30 00:51:04 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:51:18 - mmengine - INFO - Epoch(train) [74][5/63] lr: 6.6152e-03 eta: 1 day, 1:12:16 time: 2.5271 data_time: 0.3695 memory: 16201 loss_prob: 1.0952 loss_thr: 0.5739 loss_db: 0.1804 loss: 1.8495 2022/08/30 00:51:30 - mmengine - INFO - Epoch(train) [74][10/63] lr: 6.6152e-03 eta: 1 day, 1:14:13 time: 2.5289 data_time: 0.3654 memory: 16201 loss_prob: 1.0536 loss_thr: 0.5654 loss_db: 0.1710 loss: 1.7901 2022/08/30 00:51:39 - mmengine - INFO - Epoch(train) [74][15/63] lr: 6.6152e-03 eta: 1 day, 1:14:13 time: 2.0975 data_time: 0.0920 memory: 16201 loss_prob: 0.9964 loss_thr: 0.5484 loss_db: 0.1624 loss: 1.7072 2022/08/30 00:51:49 - mmengine - INFO - Epoch(train) [74][20/63] lr: 6.6152e-03 eta: 1 day, 1:15:39 time: 1.9248 data_time: 0.0746 memory: 16201 loss_prob: 1.0548 loss_thr: 0.5635 loss_db: 0.1699 loss: 1.7882 2022/08/30 00:51:59 - mmengine - INFO - Epoch(train) [74][25/63] lr: 6.6152e-03 eta: 1 day, 1:15:39 time: 1.9474 data_time: 0.0552 memory: 16201 loss_prob: 1.1081 loss_thr: 0.5704 loss_db: 0.1755 loss: 1.8540 2022/08/30 00:52:09 - mmengine - INFO - Epoch(train) [74][30/63] lr: 6.6152e-03 eta: 1 day, 1:17:26 time: 2.0601 data_time: 0.0639 memory: 16201 loss_prob: 1.0717 loss_thr: 0.5560 loss_db: 0.1761 loss: 1.8038 2022/08/30 00:52:19 - mmengine - INFO - Epoch(train) [74][35/63] lr: 6.6152e-03 eta: 1 day, 1:17:26 time: 2.0210 data_time: 0.0641 memory: 16201 loss_prob: 1.1021 loss_thr: 0.5666 loss_db: 0.1834 loss: 1.8521 2022/08/30 00:52:28 - mmengine - INFO - Epoch(train) [74][40/63] lr: 6.6152e-03 eta: 1 day, 1:18:43 time: 1.8726 data_time: 0.0417 memory: 16201 loss_prob: 1.1493 loss_thr: 0.5998 loss_db: 0.1880 loss: 1.9371 2022/08/30 00:52:38 - mmengine - INFO - Epoch(train) [74][45/63] lr: 6.6152e-03 eta: 1 day, 1:18:43 time: 1.8459 data_time: 0.0517 memory: 16201 loss_prob: 1.1381 loss_thr: 0.6010 loss_db: 0.1830 loss: 1.9221 2022/08/30 00:52:48 - mmengine - INFO - Epoch(train) [74][50/63] lr: 6.6152e-03 eta: 1 day, 1:20:18 time: 1.9869 data_time: 0.0545 memory: 16201 loss_prob: 1.0810 loss_thr: 0.5726 loss_db: 0.1735 loss: 1.8272 2022/08/30 00:53:00 - mmengine - INFO - Epoch(train) [74][55/63] lr: 6.6152e-03 eta: 1 day, 1:20:18 time: 2.2397 data_time: 0.0713 memory: 16201 loss_prob: 1.0876 loss_thr: 0.5760 loss_db: 0.1753 loss: 1.8389 2022/08/30 00:53:11 - mmengine - INFO - Epoch(train) [74][60/63] lr: 6.6152e-03 eta: 1 day, 1:22:44 time: 2.3321 data_time: 0.1099 memory: 16201 loss_prob: 1.0621 loss_thr: 0.5743 loss_db: 0.1747 loss: 1.8110 2022/08/30 00:53:16 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:53:30 - mmengine - INFO - Epoch(train) [75][5/63] lr: 6.6100e-03 eta: 1 day, 1:22:44 time: 2.3133 data_time: 0.3987 memory: 16201 loss_prob: 1.0405 loss_thr: 0.5467 loss_db: 0.1722 loss: 1.7594 2022/08/30 00:53:42 - mmengine - INFO - Epoch(train) [75][10/63] lr: 6.6100e-03 eta: 1 day, 1:24:42 time: 2.5575 data_time: 0.4226 memory: 16201 loss_prob: 1.0558 loss_thr: 0.5683 loss_db: 0.1700 loss: 1.7942 2022/08/30 00:53:53 - mmengine - INFO - Epoch(train) [75][15/63] lr: 6.6100e-03 eta: 1 day, 1:24:42 time: 2.2937 data_time: 0.1032 memory: 16201 loss_prob: 1.0692 loss_thr: 0.5738 loss_db: 0.1735 loss: 1.8166 2022/08/30 00:54:04 - mmengine - INFO - Epoch(train) [75][20/63] lr: 6.6100e-03 eta: 1 day, 1:26:50 time: 2.2205 data_time: 0.0966 memory: 16201 loss_prob: 1.1829 loss_thr: 0.5888 loss_db: 0.1954 loss: 1.9670 2022/08/30 00:54:14 - mmengine - INFO - Epoch(train) [75][25/63] lr: 6.6100e-03 eta: 1 day, 1:26:50 time: 2.0979 data_time: 0.0972 memory: 16201 loss_prob: 1.1071 loss_thr: 0.5805 loss_db: 0.1820 loss: 1.8696 2022/08/30 00:54:23 - mmengine - INFO - Epoch(train) [75][30/63] lr: 6.6100e-03 eta: 1 day, 1:28:04 time: 1.8705 data_time: 0.0807 memory: 16201 loss_prob: 1.1680 loss_thr: 0.5809 loss_db: 0.1803 loss: 1.9292 2022/08/30 00:54:33 - mmengine - INFO - Epoch(train) [75][35/63] lr: 6.6100e-03 eta: 1 day, 1:28:04 time: 1.8845 data_time: 0.0667 memory: 16201 loss_prob: 1.1811 loss_thr: 0.5625 loss_db: 0.1827 loss: 1.9264 2022/08/30 00:54:45 - mmengine - INFO - Epoch(train) [75][40/63] lr: 6.6100e-03 eta: 1 day, 1:30:08 time: 2.1990 data_time: 0.0716 memory: 16201 loss_prob: 1.0424 loss_thr: 0.5681 loss_db: 0.1731 loss: 1.7836 2022/08/30 00:54:55 - mmengine - INFO - Epoch(train) [75][45/63] lr: 6.6100e-03 eta: 1 day, 1:30:08 time: 2.2230 data_time: 0.0987 memory: 16201 loss_prob: 1.1141 loss_thr: 0.5918 loss_db: 0.1839 loss: 1.8898 2022/08/30 00:55:06 - mmengine - INFO - Epoch(train) [75][50/63] lr: 6.6100e-03 eta: 1 day, 1:31:55 time: 2.0909 data_time: 0.1220 memory: 16201 loss_prob: 1.1000 loss_thr: 0.5756 loss_db: 0.1839 loss: 1.8596 2022/08/30 00:55:16 - mmengine - INFO - Epoch(train) [75][55/63] lr: 6.6100e-03 eta: 1 day, 1:31:55 time: 2.0678 data_time: 0.1130 memory: 16201 loss_prob: 1.2921 loss_thr: 0.6073 loss_db: 0.2142 loss: 2.1136 2022/08/30 00:55:26 - mmengine - INFO - Epoch(train) [75][60/63] lr: 6.6100e-03 eta: 1 day, 1:33:31 time: 2.0235 data_time: 0.1112 memory: 16201 loss_prob: 1.3405 loss_thr: 0.6038 loss_db: 0.2160 loss: 2.1602 2022/08/30 00:55:31 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:55:46 - mmengine - INFO - Epoch(train) [76][5/63] lr: 6.6047e-03 eta: 1 day, 1:33:31 time: 2.3040 data_time: 0.3949 memory: 16201 loss_prob: 1.2143 loss_thr: 0.5931 loss_db: 0.2076 loss: 2.0151 2022/08/30 00:55:57 - mmengine - INFO - Epoch(train) [76][10/63] lr: 6.6047e-03 eta: 1 day, 1:35:22 time: 2.5457 data_time: 0.4347 memory: 16201 loss_prob: 1.1582 loss_thr: 0.5899 loss_db: 0.1899 loss: 1.9381 2022/08/30 00:56:06 - mmengine - INFO - Epoch(train) [76][15/63] lr: 6.6047e-03 eta: 1 day, 1:35:22 time: 2.0291 data_time: 0.0916 memory: 16201 loss_prob: 1.0887 loss_thr: 0.5681 loss_db: 0.1802 loss: 1.8370 2022/08/30 00:56:17 - mmengine - INFO - Epoch(train) [76][20/63] lr: 6.6047e-03 eta: 1 day, 1:36:56 time: 2.0144 data_time: 0.0689 memory: 16201 loss_prob: 1.2752 loss_thr: 0.6124 loss_db: 0.2059 loss: 2.0935 2022/08/30 00:56:27 - mmengine - INFO - Epoch(train) [76][25/63] lr: 6.6047e-03 eta: 1 day, 1:36:56 time: 2.1091 data_time: 0.0815 memory: 16201 loss_prob: 1.2578 loss_thr: 0.5998 loss_db: 0.1973 loss: 2.0550 2022/08/30 00:56:39 - mmengine - INFO - Epoch(train) [76][30/63] lr: 6.6047e-03 eta: 1 day, 1:39:04 time: 2.2461 data_time: 0.0870 memory: 16201 loss_prob: 1.0230 loss_thr: 0.5472 loss_db: 0.1634 loss: 1.7337 2022/08/30 00:56:52 - mmengine - INFO - Epoch(train) [76][35/63] lr: 6.6047e-03 eta: 1 day, 1:39:04 time: 2.4627 data_time: 0.1044 memory: 16201 loss_prob: 1.0796 loss_thr: 0.5648 loss_db: 0.1761 loss: 1.8205 2022/08/30 00:57:02 - mmengine - INFO - Epoch(train) [76][40/63] lr: 6.6047e-03 eta: 1 day, 1:41:13 time: 2.2577 data_time: 0.0932 memory: 16201 loss_prob: 1.1152 loss_thr: 0.5861 loss_db: 0.1829 loss: 1.8842 2022/08/30 00:57:13 - mmengine - INFO - Epoch(train) [76][45/63] lr: 6.6047e-03 eta: 1 day, 1:41:13 time: 2.1270 data_time: 0.0746 memory: 16201 loss_prob: 1.0270 loss_thr: 0.5760 loss_db: 0.1665 loss: 1.7695 2022/08/30 00:57:23 - mmengine - INFO - Epoch(train) [76][50/63] lr: 6.6047e-03 eta: 1 day, 1:42:57 time: 2.1008 data_time: 0.0799 memory: 16201 loss_prob: 1.0423 loss_thr: 0.5868 loss_db: 0.1701 loss: 1.7993 2022/08/30 00:57:31 - mmengine - INFO - Epoch(train) [76][55/63] lr: 6.6047e-03 eta: 1 day, 1:42:57 time: 1.8072 data_time: 0.0452 memory: 16201 loss_prob: 1.0679 loss_thr: 0.5866 loss_db: 0.1771 loss: 1.8316 2022/08/30 00:57:40 - mmengine - INFO - Epoch(train) [76][60/63] lr: 6.6047e-03 eta: 1 day, 1:43:46 time: 1.7241 data_time: 0.0421 memory: 16201 loss_prob: 0.9482 loss_thr: 0.5412 loss_db: 0.1598 loss: 1.6491 2022/08/30 00:57:45 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:57:57 - mmengine - INFO - Epoch(train) [77][5/63] lr: 6.5994e-03 eta: 1 day, 1:43:46 time: 1.9683 data_time: 0.2685 memory: 16201 loss_prob: 1.0205 loss_thr: 0.5310 loss_db: 0.1661 loss: 1.7177 2022/08/30 00:58:06 - mmengine - INFO - Epoch(train) [77][10/63] lr: 6.5994e-03 eta: 1 day, 1:44:23 time: 2.0622 data_time: 0.2985 memory: 16201 loss_prob: 1.0688 loss_thr: 0.5656 loss_db: 0.1769 loss: 1.8114 2022/08/30 00:58:15 - mmengine - INFO - Epoch(train) [77][15/63] lr: 6.5994e-03 eta: 1 day, 1:44:23 time: 1.8120 data_time: 0.0507 memory: 16201 loss_prob: 1.0591 loss_thr: 0.5767 loss_db: 0.1770 loss: 1.8127 2022/08/30 00:58:23 - mmengine - INFO - Epoch(train) [77][20/63] lr: 6.5994e-03 eta: 1 day, 1:45:12 time: 1.7356 data_time: 0.0456 memory: 16201 loss_prob: 1.2321 loss_thr: 0.5981 loss_db: 0.1990 loss: 2.0292 2022/08/30 00:58:32 - mmengine - INFO - Epoch(train) [77][25/63] lr: 6.5994e-03 eta: 1 day, 1:45:12 time: 1.7766 data_time: 0.0456 memory: 16201 loss_prob: 1.2422 loss_thr: 0.5705 loss_db: 0.1991 loss: 2.0118 2022/08/30 00:58:40 - mmengine - INFO - Epoch(train) [77][30/63] lr: 6.5994e-03 eta: 1 day, 1:45:51 time: 1.6592 data_time: 0.0312 memory: 16201 loss_prob: 1.1447 loss_thr: 0.5469 loss_db: 0.1897 loss: 1.8813 2022/08/30 00:58:47 - mmengine - INFO - Epoch(train) [77][35/63] lr: 6.5994e-03 eta: 1 day, 1:45:51 time: 1.4087 data_time: 0.0352 memory: 16201 loss_prob: 1.1472 loss_thr: 0.5744 loss_db: 0.1862 loss: 1.9077 2022/08/30 00:58:55 - mmengine - INFO - Epoch(train) [77][40/63] lr: 6.5994e-03 eta: 1 day, 1:46:02 time: 1.4811 data_time: 0.0396 memory: 16201 loss_prob: 1.0224 loss_thr: 0.5713 loss_db: 0.1676 loss: 1.7613 2022/08/30 00:59:02 - mmengine - INFO - Epoch(train) [77][45/63] lr: 6.5994e-03 eta: 1 day, 1:46:02 time: 1.5768 data_time: 0.0422 memory: 16201 loss_prob: 1.0000 loss_thr: 0.5557 loss_db: 0.1635 loss: 1.7193 2022/08/30 00:59:11 - mmengine - INFO - Epoch(train) [77][50/63] lr: 6.5994e-03 eta: 1 day, 1:46:40 time: 1.6547 data_time: 0.0531 memory: 16201 loss_prob: 1.1725 loss_thr: 0.5650 loss_db: 0.1874 loss: 1.9248 2022/08/30 00:59:20 - mmengine - INFO - Epoch(train) [77][55/63] lr: 6.5994e-03 eta: 1 day, 1:46:40 time: 1.7579 data_time: 0.0465 memory: 16201 loss_prob: 1.1615 loss_thr: 0.5563 loss_db: 0.1840 loss: 1.9018 2022/08/30 00:59:28 - mmengine - INFO - Epoch(train) [77][60/63] lr: 6.5994e-03 eta: 1 day, 1:47:20 time: 1.6751 data_time: 0.0490 memory: 16201 loss_prob: 1.1699 loss_thr: 0.5610 loss_db: 0.1863 loss: 1.9172 2022/08/30 00:59:32 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 00:59:44 - mmengine - INFO - Epoch(train) [78][5/63] lr: 6.5941e-03 eta: 1 day, 1:47:20 time: 1.9252 data_time: 0.3481 memory: 16201 loss_prob: 1.1286 loss_thr: 0.5873 loss_db: 0.1831 loss: 1.8990 2022/08/30 00:59:54 - mmengine - INFO - Epoch(train) [78][10/63] lr: 6.5941e-03 eta: 1 day, 1:48:10 time: 2.1718 data_time: 0.3848 memory: 16201 loss_prob: 1.1021 loss_thr: 0.5783 loss_db: 0.1778 loss: 1.8582 2022/08/30 01:00:04 - mmengine - INFO - Epoch(train) [78][15/63] lr: 6.5941e-03 eta: 1 day, 1:48:10 time: 2.0048 data_time: 0.0907 memory: 16201 loss_prob: 1.1799 loss_thr: 0.5823 loss_db: 0.1891 loss: 1.9512 2022/08/30 01:00:14 - mmengine - INFO - Epoch(train) [78][20/63] lr: 6.5941e-03 eta: 1 day, 1:49:30 time: 1.9511 data_time: 0.0864 memory: 16201 loss_prob: 1.1419 loss_thr: 0.5821 loss_db: 0.1822 loss: 1.9062 2022/08/30 01:00:23 - mmengine - INFO - Epoch(train) [78][25/63] lr: 6.5941e-03 eta: 1 day, 1:49:30 time: 1.9055 data_time: 0.0892 memory: 16201 loss_prob: 1.1785 loss_thr: 0.5580 loss_db: 0.1954 loss: 1.9320 2022/08/30 01:00:33 - mmengine - INFO - Epoch(train) [78][30/63] lr: 6.5941e-03 eta: 1 day, 1:50:54 time: 1.9867 data_time: 0.0983 memory: 16201 loss_prob: 1.2395 loss_thr: 0.5620 loss_db: 0.1998 loss: 2.0013 2022/08/30 01:00:44 - mmengine - INFO - Epoch(train) [78][35/63] lr: 6.5941e-03 eta: 1 day, 1:50:54 time: 2.0326 data_time: 0.1040 memory: 16201 loss_prob: 1.2095 loss_thr: 0.5929 loss_db: 0.1878 loss: 1.9903 2022/08/30 01:00:54 - mmengine - INFO - Epoch(train) [78][40/63] lr: 6.5941e-03 eta: 1 day, 1:52:36 time: 2.1091 data_time: 0.0936 memory: 16201 loss_prob: 1.1699 loss_thr: 0.5779 loss_db: 0.1873 loss: 1.9352 2022/08/30 01:01:04 - mmengine - INFO - Epoch(train) [78][45/63] lr: 6.5941e-03 eta: 1 day, 1:52:36 time: 2.0306 data_time: 0.1340 memory: 16201 loss_prob: 1.1132 loss_thr: 0.5787 loss_db: 0.1837 loss: 1.8757 2022/08/30 01:01:14 - mmengine - INFO - Epoch(train) [78][50/63] lr: 6.5941e-03 eta: 1 day, 1:54:00 time: 1.9934 data_time: 0.1082 memory: 16201 loss_prob: 1.1328 loss_thr: 0.5778 loss_db: 0.1893 loss: 1.8999 2022/08/30 01:01:22 - mmengine - INFO - Epoch(train) [78][55/63] lr: 6.5941e-03 eta: 1 day, 1:54:00 time: 1.8391 data_time: 0.0965 memory: 16201 loss_prob: 1.2202 loss_thr: 0.6161 loss_db: 0.1995 loss: 2.0358 2022/08/30 01:01:32 - mmengine - INFO - Epoch(train) [78][60/63] lr: 6.5941e-03 eta: 1 day, 1:54:52 time: 1.7707 data_time: 0.1009 memory: 16201 loss_prob: 1.2650 loss_thr: 0.6421 loss_db: 0.2056 loss: 2.1126 2022/08/30 01:01:38 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:01:49 - mmengine - INFO - Epoch(train) [79][5/63] lr: 6.5888e-03 eta: 1 day, 1:54:52 time: 2.0233 data_time: 0.2971 memory: 16201 loss_prob: 1.0373 loss_thr: 0.5432 loss_db: 0.1673 loss: 1.7477 2022/08/30 01:01:58 - mmengine - INFO - Epoch(train) [79][10/63] lr: 6.5888e-03 eta: 1 day, 1:55:08 time: 1.9474 data_time: 0.3249 memory: 16201 loss_prob: 1.0529 loss_thr: 0.5657 loss_db: 0.1768 loss: 1.7954 2022/08/30 01:02:06 - mmengine - INFO - Epoch(train) [79][15/63] lr: 6.5888e-03 eta: 1 day, 1:55:08 time: 1.7417 data_time: 0.0992 memory: 16201 loss_prob: 1.0593 loss_thr: 0.5889 loss_db: 0.1795 loss: 1.8276 2022/08/30 01:02:16 - mmengine - INFO - Epoch(train) [79][20/63] lr: 6.5888e-03 eta: 1 day, 1:56:12 time: 1.8601 data_time: 0.0478 memory: 16201 loss_prob: 1.0849 loss_thr: 0.5940 loss_db: 0.1795 loss: 1.8584 2022/08/30 01:02:24 - mmengine - INFO - Epoch(train) [79][25/63] lr: 6.5888e-03 eta: 1 day, 1:56:12 time: 1.8415 data_time: 0.0681 memory: 16201 loss_prob: 1.0643 loss_thr: 0.5687 loss_db: 0.1737 loss: 1.8067 2022/08/30 01:02:34 - mmengine - INFO - Epoch(train) [79][30/63] lr: 6.5888e-03 eta: 1 day, 1:57:07 time: 1.8012 data_time: 0.0499 memory: 16201 loss_prob: 1.0089 loss_thr: 0.5696 loss_db: 0.1655 loss: 1.7441 2022/08/30 01:02:44 - mmengine - INFO - Epoch(train) [79][35/63] lr: 6.5888e-03 eta: 1 day, 1:57:07 time: 1.9315 data_time: 0.0366 memory: 16201 loss_prob: 1.0221 loss_thr: 0.5757 loss_db: 0.1699 loss: 1.7678 2022/08/30 01:02:53 - mmengine - INFO - Epoch(train) [79][40/63] lr: 6.5888e-03 eta: 1 day, 1:58:14 time: 1.8840 data_time: 0.0421 memory: 16201 loss_prob: 1.0532 loss_thr: 0.5577 loss_db: 0.1756 loss: 1.7865 2022/08/30 01:03:02 - mmengine - INFO - Epoch(train) [79][45/63] lr: 6.5888e-03 eta: 1 day, 1:58:14 time: 1.8639 data_time: 0.0344 memory: 16201 loss_prob: 1.0298 loss_thr: 0.5430 loss_db: 0.1717 loss: 1.7445 2022/08/30 01:03:10 - mmengine - INFO - Epoch(train) [79][50/63] lr: 6.5888e-03 eta: 1 day, 1:58:59 time: 1.7309 data_time: 0.0473 memory: 16201 loss_prob: 0.9728 loss_thr: 0.5374 loss_db: 0.1618 loss: 1.6721 2022/08/30 01:03:18 - mmengine - INFO - Epoch(train) [79][55/63] lr: 6.5888e-03 eta: 1 day, 1:58:59 time: 1.5878 data_time: 0.0471 memory: 16201 loss_prob: 1.0488 loss_thr: 0.5621 loss_db: 0.1692 loss: 1.7801 2022/08/30 01:03:25 - mmengine - INFO - Epoch(train) [79][60/63] lr: 6.5888e-03 eta: 1 day, 1:59:12 time: 1.5088 data_time: 0.0438 memory: 16201 loss_prob: 1.1486 loss_thr: 0.5893 loss_db: 0.1823 loss: 1.9202 2022/08/30 01:03:29 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:03:40 - mmengine - INFO - Epoch(train) [80][5/63] lr: 6.5835e-03 eta: 1 day, 1:59:12 time: 1.7153 data_time: 0.2694 memory: 16201 loss_prob: 1.0775 loss_thr: 0.5695 loss_db: 0.1738 loss: 1.8208 2022/08/30 01:03:49 - mmengine - INFO - Epoch(train) [80][10/63] lr: 6.5835e-03 eta: 1 day, 1:59:34 time: 1.9967 data_time: 0.2804 memory: 16201 loss_prob: 1.0246 loss_thr: 0.5532 loss_db: 0.1681 loss: 1.7459 2022/08/30 01:03:57 - mmengine - INFO - Epoch(train) [80][15/63] lr: 6.5835e-03 eta: 1 day, 1:59:34 time: 1.6682 data_time: 0.0386 memory: 16201 loss_prob: 0.9948 loss_thr: 0.5498 loss_db: 0.1674 loss: 1.7120 2022/08/30 01:04:07 - mmengine - INFO - Epoch(train) [80][20/63] lr: 6.5835e-03 eta: 1 day, 2:00:20 time: 1.7494 data_time: 0.0492 memory: 16201 loss_prob: 0.9935 loss_thr: 0.5584 loss_db: 0.1658 loss: 1.7178 2022/08/30 01:04:12 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:04:15 - mmengine - INFO - Epoch(train) [80][25/63] lr: 6.5835e-03 eta: 1 day, 2:00:20 time: 1.8617 data_time: 0.0484 memory: 16201 loss_prob: 1.0870 loss_thr: 0.5803 loss_db: 0.1775 loss: 1.8448 2022/08/30 01:04:24 - mmengine - INFO - Epoch(train) [80][30/63] lr: 6.5835e-03 eta: 1 day, 2:01:10 time: 1.7695 data_time: 0.0369 memory: 16201 loss_prob: 1.1636 loss_thr: 0.5802 loss_db: 0.1887 loss: 1.9325 2022/08/30 01:04:35 - mmengine - INFO - Epoch(train) [80][35/63] lr: 6.5835e-03 eta: 1 day, 2:01:10 time: 1.9328 data_time: 0.0567 memory: 16201 loss_prob: 1.0855 loss_thr: 0.5708 loss_db: 0.1757 loss: 1.8321 2022/08/30 01:04:43 - mmengine - INFO - Epoch(train) [80][40/63] lr: 6.5835e-03 eta: 1 day, 2:02:21 time: 1.9273 data_time: 0.0484 memory: 16201 loss_prob: 1.0290 loss_thr: 0.5691 loss_db: 0.1668 loss: 1.7649 2022/08/30 01:04:53 - mmengine - INFO - Epoch(train) [80][45/63] lr: 6.5835e-03 eta: 1 day, 2:02:21 time: 1.8536 data_time: 0.0435 memory: 16201 loss_prob: 1.0071 loss_thr: 0.5730 loss_db: 0.1627 loss: 1.7427 2022/08/30 01:05:02 - mmengine - INFO - Epoch(train) [80][50/63] lr: 6.5835e-03 eta: 1 day, 2:03:20 time: 1.8412 data_time: 0.0523 memory: 16201 loss_prob: 1.0336 loss_thr: 0.5777 loss_db: 0.1717 loss: 1.7830 2022/08/30 01:05:11 - mmengine - INFO - Epoch(train) [80][55/63] lr: 6.5835e-03 eta: 1 day, 2:03:20 time: 1.8124 data_time: 0.0361 memory: 16201 loss_prob: 1.0501 loss_thr: 0.5490 loss_db: 0.1738 loss: 1.7729 2022/08/30 01:05:20 - mmengine - INFO - Epoch(train) [80][60/63] lr: 6.5835e-03 eta: 1 day, 2:04:08 time: 1.7715 data_time: 0.0392 memory: 16201 loss_prob: 1.0926 loss_thr: 0.5650 loss_db: 0.1800 loss: 1.8376 2022/08/30 01:05:24 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:05:24 - mmengine - INFO - Saving checkpoint at 80 epochs 2022/08/30 01:05:34 - mmengine - INFO - Epoch(val) [80][5/32] eta: 1 day, 2:04:08 time: 0.7534 data_time: 0.1612 memory: 16201 2022/08/30 01:05:37 - mmengine - INFO - Epoch(val) [80][10/32] eta: 0:00:18 time: 0.8501 data_time: 0.2045 memory: 15734 2022/08/30 01:05:40 - mmengine - INFO - Epoch(val) [80][15/32] eta: 0:00:18 time: 0.6679 data_time: 0.0631 memory: 15734 2022/08/30 01:05:44 - mmengine - INFO - Epoch(val) [80][20/32] eta: 0:00:08 time: 0.6928 data_time: 0.0796 memory: 15734 2022/08/30 01:05:48 - mmengine - INFO - Epoch(val) [80][25/32] eta: 0:00:08 time: 0.7959 data_time: 0.0877 memory: 15734 2022/08/30 01:05:51 - mmengine - INFO - Epoch(val) [80][30/32] eta: 0:00:01 time: 0.7439 data_time: 0.0434 memory: 15734 2022/08/30 01:05:52 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 01:05:52 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8267, precision: 0.7127, hmean: 0.7655 2022/08/30 01:05:52 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8267, precision: 0.7934, hmean: 0.8097 2022/08/30 01:05:52 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8185, precision: 0.8466, hmean: 0.8323 2022/08/30 01:05:52 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7939, precision: 0.8866, hmean: 0.8377 2022/08/30 01:05:52 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7130, precision: 0.9320, hmean: 0.8080 2022/08/30 01:05:52 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.3356, precision: 0.9667, hmean: 0.4982 2022/08/30 01:05:52 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0010, precision: 1.0000, hmean: 0.0019 2022/08/30 01:05:52 - mmengine - INFO - Epoch(val) [80][32/32] icdar/precision: 0.8866 icdar/recall: 0.7939 icdar/hmean: 0.8377 2022/08/30 01:06:04 - mmengine - INFO - Epoch(train) [81][5/63] lr: 6.5782e-03 eta: 0:00:01 time: 1.8780 data_time: 0.2541 memory: 16201 loss_prob: 1.0494 loss_thr: 0.5506 loss_db: 0.1757 loss: 1.7758 2022/08/30 01:06:14 - mmengine - INFO - Epoch(train) [81][10/63] lr: 6.5782e-03 eta: 1 day, 2:04:51 time: 2.1556 data_time: 0.2640 memory: 16201 loss_prob: 1.0710 loss_thr: 0.5644 loss_db: 0.1797 loss: 1.8151 2022/08/30 01:06:23 - mmengine - INFO - Epoch(train) [81][15/63] lr: 6.5782e-03 eta: 1 day, 2:04:51 time: 1.9250 data_time: 0.0446 memory: 16201 loss_prob: 1.0901 loss_thr: 0.5750 loss_db: 0.1769 loss: 1.8420 2022/08/30 01:06:32 - mmengine - INFO - Epoch(train) [81][20/63] lr: 6.5782e-03 eta: 1 day, 2:05:46 time: 1.8221 data_time: 0.0574 memory: 16201 loss_prob: 0.9824 loss_thr: 0.5349 loss_db: 0.1595 loss: 1.6768 2022/08/30 01:06:40 - mmengine - INFO - Epoch(train) [81][25/63] lr: 6.5782e-03 eta: 1 day, 2:05:46 time: 1.6500 data_time: 0.0483 memory: 16201 loss_prob: 0.9930 loss_thr: 0.5314 loss_db: 0.1606 loss: 1.6850 2022/08/30 01:06:49 - mmengine - INFO - Epoch(train) [81][30/63] lr: 6.5782e-03 eta: 1 day, 2:06:25 time: 1.7080 data_time: 0.0473 memory: 16201 loss_prob: 0.9902 loss_thr: 0.5453 loss_db: 0.1614 loss: 1.6969 2022/08/30 01:06:58 - mmengine - INFO - Epoch(train) [81][35/63] lr: 6.5782e-03 eta: 1 day, 2:06:25 time: 1.8393 data_time: 0.0539 memory: 16201 loss_prob: 0.9677 loss_thr: 0.5293 loss_db: 0.1585 loss: 1.6555 2022/08/30 01:07:08 - mmengine - INFO - Epoch(train) [81][40/63] lr: 6.5782e-03 eta: 1 day, 2:07:21 time: 1.8332 data_time: 0.0376 memory: 16201 loss_prob: 0.9618 loss_thr: 0.5234 loss_db: 0.1580 loss: 1.6432 2022/08/30 01:07:17 - mmengine - INFO - Epoch(train) [81][45/63] lr: 6.5782e-03 eta: 1 day, 2:07:21 time: 1.8362 data_time: 0.0412 memory: 16201 loss_prob: 1.0309 loss_thr: 0.5534 loss_db: 0.1676 loss: 1.7520 2022/08/30 01:07:26 - mmengine - INFO - Epoch(train) [81][50/63] lr: 6.5782e-03 eta: 1 day, 2:08:12 time: 1.7972 data_time: 0.0494 memory: 16201 loss_prob: 1.0799 loss_thr: 0.5688 loss_db: 0.1738 loss: 1.8225 2022/08/30 01:07:35 - mmengine - INFO - Epoch(train) [81][55/63] lr: 6.5782e-03 eta: 1 day, 2:08:12 time: 1.8251 data_time: 0.0432 memory: 16201 loss_prob: 1.0330 loss_thr: 0.5516 loss_db: 0.1698 loss: 1.7544 2022/08/30 01:07:43 - mmengine - INFO - Epoch(train) [81][60/63] lr: 6.5782e-03 eta: 1 day, 2:08:57 time: 1.7563 data_time: 0.0483 memory: 16201 loss_prob: 1.0880 loss_thr: 0.5788 loss_db: 0.1781 loss: 1.8449 2022/08/30 01:07:48 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:07:59 - mmengine - INFO - Epoch(train) [82][5/63] lr: 6.5729e-03 eta: 1 day, 2:08:57 time: 1.8344 data_time: 0.2418 memory: 16201 loss_prob: 1.1133 loss_thr: 0.5748 loss_db: 0.1826 loss: 1.8707 2022/08/30 01:08:07 - mmengine - INFO - Epoch(train) [82][10/63] lr: 6.5729e-03 eta: 1 day, 2:09:03 time: 1.9057 data_time: 0.2618 memory: 16201 loss_prob: 1.0384 loss_thr: 0.5417 loss_db: 0.1724 loss: 1.7525 2022/08/30 01:08:16 - mmengine - INFO - Epoch(train) [82][15/63] lr: 6.5729e-03 eta: 1 day, 2:09:03 time: 1.7823 data_time: 0.0367 memory: 16201 loss_prob: 1.1568 loss_thr: 0.5756 loss_db: 0.1866 loss: 1.9190 2022/08/30 01:08:26 - mmengine - INFO - Epoch(train) [82][20/63] lr: 6.5729e-03 eta: 1 day, 2:10:12 time: 1.9357 data_time: 0.0435 memory: 16201 loss_prob: 1.1506 loss_thr: 0.5733 loss_db: 0.1834 loss: 1.9073 2022/08/30 01:08:35 - mmengine - INFO - Epoch(train) [82][25/63] lr: 6.5729e-03 eta: 1 day, 2:10:12 time: 1.9054 data_time: 0.0578 memory: 16201 loss_prob: 1.1288 loss_thr: 0.5696 loss_db: 0.1818 loss: 1.8802 2022/08/30 01:08:44 - mmengine - INFO - Epoch(train) [82][30/63] lr: 6.5729e-03 eta: 1 day, 2:11:04 time: 1.8107 data_time: 0.0345 memory: 16201 loss_prob: 1.0951 loss_thr: 0.5721 loss_db: 0.1818 loss: 1.8491 2022/08/30 01:08:54 - mmengine - INFO - Epoch(train) [82][35/63] lr: 6.5729e-03 eta: 1 day, 2:11:04 time: 1.9107 data_time: 0.0362 memory: 16201 loss_prob: 0.9736 loss_thr: 0.5349 loss_db: 0.1618 loss: 1.6703 2022/08/30 01:09:04 - mmengine - INFO - Epoch(train) [82][40/63] lr: 6.5729e-03 eta: 1 day, 2:12:18 time: 1.9783 data_time: 0.0424 memory: 16201 loss_prob: 0.9580 loss_thr: 0.5260 loss_db: 0.1553 loss: 1.6393 2022/08/30 01:09:14 - mmengine - INFO - Epoch(train) [82][45/63] lr: 6.5729e-03 eta: 1 day, 2:12:18 time: 1.9543 data_time: 0.0383 memory: 16201 loss_prob: 1.0958 loss_thr: 0.5576 loss_db: 0.1751 loss: 1.8285 2022/08/30 01:09:22 - mmengine - INFO - Epoch(train) [82][50/63] lr: 6.5729e-03 eta: 1 day, 2:13:07 time: 1.7942 data_time: 0.0451 memory: 16201 loss_prob: 1.1566 loss_thr: 0.6003 loss_db: 0.1868 loss: 1.9437 2022/08/30 01:09:32 - mmengine - INFO - Epoch(train) [82][55/63] lr: 6.5729e-03 eta: 1 day, 2:13:07 time: 1.7329 data_time: 0.0362 memory: 16201 loss_prob: 1.0843 loss_thr: 0.5928 loss_db: 0.1792 loss: 1.8563 2022/08/30 01:09:41 - mmengine - INFO - Epoch(train) [82][60/63] lr: 6.5729e-03 eta: 1 day, 2:14:11 time: 1.9090 data_time: 0.0578 memory: 16201 loss_prob: 1.0763 loss_thr: 0.5744 loss_db: 0.1760 loss: 1.8267 2022/08/30 01:09:45 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:09:57 - mmengine - INFO - Epoch(train) [83][5/63] lr: 6.5676e-03 eta: 1 day, 2:14:11 time: 2.0155 data_time: 0.2475 memory: 16201 loss_prob: 1.0236 loss_thr: 0.5730 loss_db: 0.1700 loss: 1.7666 2022/08/30 01:10:07 - mmengine - INFO - Epoch(train) [83][10/63] lr: 6.5676e-03 eta: 1 day, 2:14:44 time: 2.1132 data_time: 0.2809 memory: 16201 loss_prob: 1.0505 loss_thr: 0.5642 loss_db: 0.1706 loss: 1.7853 2022/08/30 01:10:15 - mmengine - INFO - Epoch(train) [83][15/63] lr: 6.5676e-03 eta: 1 day, 2:14:44 time: 1.7594 data_time: 0.0482 memory: 16201 loss_prob: 0.9980 loss_thr: 0.5404 loss_db: 0.1593 loss: 1.6977 2022/08/30 01:10:24 - mmengine - INFO - Epoch(train) [83][20/63] lr: 6.5676e-03 eta: 1 day, 2:15:31 time: 1.7853 data_time: 0.0608 memory: 16201 loss_prob: 0.9737 loss_thr: 0.5345 loss_db: 0.1572 loss: 1.6654 2022/08/30 01:10:34 - mmengine - INFO - Epoch(train) [83][25/63] lr: 6.5676e-03 eta: 1 day, 2:15:31 time: 1.9329 data_time: 0.0680 memory: 16201 loss_prob: 1.0654 loss_thr: 0.5489 loss_db: 0.1705 loss: 1.7847 2022/08/30 01:10:43 - mmengine - INFO - Epoch(train) [83][30/63] lr: 6.5676e-03 eta: 1 day, 2:16:26 time: 1.8454 data_time: 0.0436 memory: 16201 loss_prob: 1.1226 loss_thr: 0.5500 loss_db: 0.1773 loss: 1.8499 2022/08/30 01:10:53 - mmengine - INFO - Epoch(train) [83][35/63] lr: 6.5676e-03 eta: 1 day, 2:16:26 time: 1.8634 data_time: 0.0459 memory: 16201 loss_prob: 1.0854 loss_thr: 0.5499 loss_db: 0.1701 loss: 1.8055 2022/08/30 01:11:02 - mmengine - INFO - Epoch(train) [83][40/63] lr: 6.5676e-03 eta: 1 day, 2:17:23 time: 1.8652 data_time: 0.0443 memory: 16201 loss_prob: 1.0713 loss_thr: 0.5587 loss_db: 0.1718 loss: 1.8018 2022/08/30 01:11:11 - mmengine - INFO - Epoch(train) [83][45/63] lr: 6.5676e-03 eta: 1 day, 2:17:23 time: 1.7970 data_time: 0.0520 memory: 16201 loss_prob: 1.0579 loss_thr: 0.5584 loss_db: 0.1750 loss: 1.7914 2022/08/30 01:11:22 - mmengine - INFO - Epoch(train) [83][50/63] lr: 6.5676e-03 eta: 1 day, 2:18:43 time: 2.0353 data_time: 0.0562 memory: 16201 loss_prob: 1.0558 loss_thr: 0.5746 loss_db: 0.1785 loss: 1.8089 2022/08/30 01:11:31 - mmengine - INFO - Epoch(train) [83][55/63] lr: 6.5676e-03 eta: 1 day, 2:18:43 time: 2.0371 data_time: 0.0506 memory: 16201 loss_prob: 1.0743 loss_thr: 0.5843 loss_db: 0.1837 loss: 1.8423 2022/08/30 01:11:41 - mmengine - INFO - Epoch(train) [83][60/63] lr: 6.5676e-03 eta: 1 day, 2:19:50 time: 1.9445 data_time: 0.0858 memory: 16201 loss_prob: 1.1281 loss_thr: 0.5912 loss_db: 0.1865 loss: 1.9058 2022/08/30 01:11:46 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:11:57 - mmengine - INFO - Epoch(train) [84][5/63] lr: 6.5623e-03 eta: 1 day, 2:19:50 time: 1.8973 data_time: 0.2451 memory: 16201 loss_prob: 0.9793 loss_thr: 0.5899 loss_db: 0.1614 loss: 1.7306 2022/08/30 01:12:06 - mmengine - INFO - Epoch(train) [84][10/63] lr: 6.5623e-03 eta: 1 day, 2:20:13 time: 2.0584 data_time: 0.2640 memory: 16201 loss_prob: 1.0788 loss_thr: 0.5678 loss_db: 0.1808 loss: 1.8274 2022/08/30 01:12:15 - mmengine - INFO - Epoch(train) [84][15/63] lr: 6.5623e-03 eta: 1 day, 2:20:13 time: 1.7732 data_time: 0.0479 memory: 16201 loss_prob: 1.1746 loss_thr: 0.5514 loss_db: 0.1968 loss: 1.9228 2022/08/30 01:12:22 - mmengine - INFO - Epoch(train) [84][20/63] lr: 6.5623e-03 eta: 1 day, 2:20:31 time: 1.5782 data_time: 0.0403 memory: 16201 loss_prob: 1.2229 loss_thr: 0.5826 loss_db: 0.2032 loss: 2.0087 2022/08/30 01:12:31 - mmengine - INFO - Epoch(train) [84][25/63] lr: 6.5623e-03 eta: 1 day, 2:20:31 time: 1.5884 data_time: 0.0455 memory: 16201 loss_prob: 1.2529 loss_thr: 0.6034 loss_db: 0.2055 loss: 2.0617 2022/08/30 01:12:39 - mmengine - INFO - Epoch(train) [84][30/63] lr: 6.5623e-03 eta: 1 day, 2:21:06 time: 1.7124 data_time: 0.0461 memory: 16201 loss_prob: 1.1667 loss_thr: 0.5838 loss_db: 0.1902 loss: 1.9407 2022/08/30 01:12:48 - mmengine - INFO - Epoch(train) [84][35/63] lr: 6.5623e-03 eta: 1 day, 2:21:06 time: 1.7095 data_time: 0.0478 memory: 16201 loss_prob: 1.2521 loss_thr: 0.5729 loss_db: 0.2020 loss: 2.0269 2022/08/30 01:12:56 - mmengine - INFO - Epoch(train) [84][40/63] lr: 6.5623e-03 eta: 1 day, 2:21:40 time: 1.7049 data_time: 0.0413 memory: 16201 loss_prob: 1.3401 loss_thr: 0.6039 loss_db: 0.2106 loss: 2.1547 2022/08/30 01:13:05 - mmengine - INFO - Epoch(train) [84][45/63] lr: 6.5623e-03 eta: 1 day, 2:21:40 time: 1.7345 data_time: 0.0421 memory: 16201 loss_prob: 1.3781 loss_thr: 0.6044 loss_db: 0.2146 loss: 2.1971 2022/08/30 01:13:14 - mmengine - INFO - Epoch(train) [84][50/63] lr: 6.5623e-03 eta: 1 day, 2:22:16 time: 1.7209 data_time: 0.0560 memory: 16201 loss_prob: 1.3196 loss_thr: 0.5857 loss_db: 0.2079 loss: 2.1132 2022/08/30 01:13:20 - mmengine - INFO - Epoch(train) [84][55/63] lr: 6.5623e-03 eta: 1 day, 2:22:16 time: 1.5309 data_time: 0.0428 memory: 16201 loss_prob: 1.0943 loss_thr: 0.5822 loss_db: 0.1774 loss: 1.8540 2022/08/30 01:13:29 - mmengine - INFO - Epoch(train) [84][60/63] lr: 6.5623e-03 eta: 1 day, 2:22:29 time: 1.5451 data_time: 0.0337 memory: 16201 loss_prob: 1.1246 loss_thr: 0.5886 loss_db: 0.1805 loss: 1.8937 2022/08/30 01:13:33 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:13:44 - mmengine - INFO - Epoch(train) [85][5/63] lr: 6.5571e-03 eta: 1 day, 2:22:29 time: 1.7380 data_time: 0.2420 memory: 16201 loss_prob: 1.1270 loss_thr: 0.5365 loss_db: 0.1762 loss: 1.8396 2022/08/30 01:13:55 - mmengine - INFO - Epoch(train) [85][10/63] lr: 6.5571e-03 eta: 1 day, 2:22:59 time: 2.1190 data_time: 0.2623 memory: 16201 loss_prob: 1.0372 loss_thr: 0.5180 loss_db: 0.1587 loss: 1.7139 2022/08/30 01:14:04 - mmengine - INFO - Epoch(train) [85][15/63] lr: 6.5571e-03 eta: 1 day, 2:22:59 time: 2.0249 data_time: 0.0519 memory: 16201 loss_prob: 1.1022 loss_thr: 0.5776 loss_db: 0.1800 loss: 1.8598 2022/08/30 01:14:13 - mmengine - INFO - Epoch(train) [85][20/63] lr: 6.5571e-03 eta: 1 day, 2:23:44 time: 1.7888 data_time: 0.0441 memory: 16201 loss_prob: 1.0306 loss_thr: 0.5676 loss_db: 0.1737 loss: 1.7719 2022/08/30 01:14:21 - mmengine - INFO - Epoch(train) [85][25/63] lr: 6.5571e-03 eta: 1 day, 2:23:44 time: 1.6889 data_time: 0.0497 memory: 16201 loss_prob: 1.0975 loss_thr: 0.5659 loss_db: 0.1806 loss: 1.8440 2022/08/30 01:14:31 - mmengine - INFO - Epoch(train) [85][30/63] lr: 6.5571e-03 eta: 1 day, 2:24:31 time: 1.8122 data_time: 0.0401 memory: 16201 loss_prob: 1.1926 loss_thr: 0.6031 loss_db: 0.1940 loss: 1.9897 2022/08/30 01:14:39 - mmengine - INFO - Epoch(train) [85][35/63] lr: 6.5571e-03 eta: 1 day, 2:24:31 time: 1.8213 data_time: 0.0532 memory: 16201 loss_prob: 1.2053 loss_thr: 0.6064 loss_db: 0.1966 loss: 2.0083 2022/08/30 01:14:48 - mmengine - INFO - Epoch(train) [85][40/63] lr: 6.5571e-03 eta: 1 day, 2:25:02 time: 1.6909 data_time: 0.0548 memory: 16201 loss_prob: 1.2483 loss_thr: 0.5882 loss_db: 0.1980 loss: 2.0344 2022/08/30 01:14:55 - mmengine - INFO - Epoch(train) [85][45/63] lr: 6.5571e-03 eta: 1 day, 2:25:02 time: 1.6371 data_time: 0.0447 memory: 16201 loss_prob: 1.1817 loss_thr: 0.5739 loss_db: 0.1878 loss: 1.9434 2022/08/30 01:15:04 - mmengine - INFO - Epoch(train) [85][50/63] lr: 6.5571e-03 eta: 1 day, 2:25:22 time: 1.6064 data_time: 0.0524 memory: 16201 loss_prob: 1.1505 loss_thr: 0.5811 loss_db: 0.1883 loss: 1.9200 2022/08/30 01:15:12 - mmengine - INFO - Epoch(train) [85][55/63] lr: 6.5571e-03 eta: 1 day, 2:25:22 time: 1.6078 data_time: 0.0446 memory: 16201 loss_prob: 1.0965 loss_thr: 0.5661 loss_db: 0.1812 loss: 1.8438 2022/08/30 01:15:19 - mmengine - INFO - Epoch(train) [85][60/63] lr: 6.5571e-03 eta: 1 day, 2:25:33 time: 1.5446 data_time: 0.0414 memory: 16201 loss_prob: 1.0507 loss_thr: 0.5736 loss_db: 0.1705 loss: 1.7948 2022/08/30 01:15:23 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:15:35 - mmengine - INFO - Epoch(train) [86][5/63] lr: 6.5518e-03 eta: 1 day, 2:25:33 time: 1.9029 data_time: 0.2688 memory: 16201 loss_prob: 1.0110 loss_thr: 0.5438 loss_db: 0.1670 loss: 1.7218 2022/08/30 01:15:44 - mmengine - INFO - Epoch(train) [86][10/63] lr: 6.5518e-03 eta: 1 day, 2:25:55 time: 2.0594 data_time: 0.2727 memory: 16201 loss_prob: 1.1375 loss_thr: 0.5692 loss_db: 0.1852 loss: 1.8919 2022/08/30 01:15:52 - mmengine - INFO - Epoch(train) [86][15/63] lr: 6.5518e-03 eta: 1 day, 2:25:55 time: 1.6514 data_time: 0.0399 memory: 16201 loss_prob: 1.1503 loss_thr: 0.5670 loss_db: 0.1867 loss: 1.9040 2022/08/30 01:16:02 - mmengine - INFO - Epoch(train) [86][20/63] lr: 6.5518e-03 eta: 1 day, 2:26:39 time: 1.7982 data_time: 0.0439 memory: 16201 loss_prob: 1.0564 loss_thr: 0.5536 loss_db: 0.1746 loss: 1.7846 2022/08/30 01:16:11 - mmengine - INFO - Epoch(train) [86][25/63] lr: 6.5518e-03 eta: 1 day, 2:26:39 time: 1.9525 data_time: 0.0429 memory: 16201 loss_prob: 1.3227 loss_thr: 0.6055 loss_db: 0.2107 loss: 2.1389 2022/08/30 01:16:20 - mmengine - INFO - Epoch(train) [86][30/63] lr: 6.5518e-03 eta: 1 day, 2:27:34 time: 1.8823 data_time: 0.0398 memory: 16201 loss_prob: 1.2282 loss_thr: 0.5851 loss_db: 0.1959 loss: 2.0092 2022/08/30 01:16:29 - mmengine - INFO - Epoch(train) [86][35/63] lr: 6.5518e-03 eta: 1 day, 2:27:34 time: 1.7429 data_time: 0.0549 memory: 16201 loss_prob: 0.9520 loss_thr: 0.5451 loss_db: 0.1583 loss: 1.6555 2022/08/30 01:16:38 - mmengine - INFO - Epoch(train) [86][40/63] lr: 6.5518e-03 eta: 1 day, 2:28:09 time: 1.7247 data_time: 0.0399 memory: 16201 loss_prob: 1.0308 loss_thr: 0.5592 loss_db: 0.1699 loss: 1.7598 2022/08/30 01:16:47 - mmengine - INFO - Epoch(train) [86][45/63] lr: 6.5518e-03 eta: 1 day, 2:28:09 time: 1.8042 data_time: 0.0414 memory: 16201 loss_prob: 0.9934 loss_thr: 0.5378 loss_db: 0.1604 loss: 1.6916 2022/08/30 01:16:54 - mmengine - INFO - Epoch(train) [86][50/63] lr: 6.5518e-03 eta: 1 day, 2:28:32 time: 1.6400 data_time: 0.0556 memory: 16201 loss_prob: 0.9787 loss_thr: 0.5417 loss_db: 0.1564 loss: 1.6768 2022/08/30 01:17:01 - mmengine - INFO - Epoch(train) [86][55/63] lr: 6.5518e-03 eta: 1 day, 2:28:32 time: 1.4057 data_time: 0.0331 memory: 16201 loss_prob: 1.0015 loss_thr: 0.5510 loss_db: 0.1628 loss: 1.7153 2022/08/30 01:17:09 - mmengine - INFO - Epoch(train) [86][60/63] lr: 6.5518e-03 eta: 1 day, 2:28:35 time: 1.4872 data_time: 0.0347 memory: 16201 loss_prob: 0.9673 loss_thr: 0.5263 loss_db: 0.1580 loss: 1.6517 2022/08/30 01:17:12 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:17:22 - mmengine - INFO - Epoch(train) [87][5/63] lr: 6.5465e-03 eta: 1 day, 2:28:35 time: 1.6340 data_time: 0.2525 memory: 16201 loss_prob: 1.1542 loss_thr: 0.5768 loss_db: 0.1953 loss: 1.9263 2022/08/30 01:17:32 - mmengine - INFO - Epoch(train) [87][10/63] lr: 6.5465e-03 eta: 1 day, 2:28:46 time: 1.9862 data_time: 0.2648 memory: 16201 loss_prob: 1.0916 loss_thr: 0.5427 loss_db: 0.1825 loss: 1.8168 2022/08/30 01:17:41 - mmengine - INFO - Epoch(train) [87][15/63] lr: 6.5465e-03 eta: 1 day, 2:28:46 time: 1.8481 data_time: 0.0457 memory: 16201 loss_prob: 1.0285 loss_thr: 0.5398 loss_db: 0.1702 loss: 1.7385 2022/08/30 01:17:48 - mmengine - INFO - Epoch(train) [87][20/63] lr: 6.5465e-03 eta: 1 day, 2:29:04 time: 1.6034 data_time: 0.0560 memory: 16201 loss_prob: 1.0927 loss_thr: 0.5701 loss_db: 0.1777 loss: 1.8405 2022/08/30 01:17:56 - mmengine - INFO - Epoch(train) [87][25/63] lr: 6.5465e-03 eta: 1 day, 2:29:04 time: 1.5202 data_time: 0.0556 memory: 16201 loss_prob: 1.0738 loss_thr: 0.5691 loss_db: 0.1722 loss: 1.8152 2022/08/30 01:18:04 - mmengine - INFO - Epoch(train) [87][30/63] lr: 6.5465e-03 eta: 1 day, 2:29:20 time: 1.5938 data_time: 0.0428 memory: 16201 loss_prob: 1.0639 loss_thr: 0.5692 loss_db: 0.1712 loss: 1.8042 2022/08/30 01:18:13 - mmengine - INFO - Epoch(train) [87][35/63] lr: 6.5465e-03 eta: 1 day, 2:29:20 time: 1.7327 data_time: 0.0359 memory: 16201 loss_prob: 1.0902 loss_thr: 0.5631 loss_db: 0.1751 loss: 1.8284 2022/08/30 01:18:22 - mmengine - INFO - Epoch(train) [87][40/63] lr: 6.5465e-03 eta: 1 day, 2:30:05 time: 1.8150 data_time: 0.0361 memory: 16201 loss_prob: 1.1299 loss_thr: 0.5579 loss_db: 0.1854 loss: 1.8732 2022/08/30 01:18:31 - mmengine - INFO - Epoch(train) [87][45/63] lr: 6.5465e-03 eta: 1 day, 2:30:05 time: 1.7701 data_time: 0.0491 memory: 16201 loss_prob: 1.2739 loss_thr: 0.5630 loss_db: 0.2045 loss: 2.0414 2022/08/30 01:18:39 - mmengine - INFO - Epoch(train) [87][50/63] lr: 6.5465e-03 eta: 1 day, 2:30:37 time: 1.7145 data_time: 0.0570 memory: 16201 loss_prob: 1.2711 loss_thr: 0.5600 loss_db: 0.2016 loss: 2.0326 2022/08/30 01:18:49 - mmengine - INFO - Epoch(train) [87][55/63] lr: 6.5465e-03 eta: 1 day, 2:30:37 time: 1.7620 data_time: 0.0437 memory: 16201 loss_prob: 1.1407 loss_thr: 0.5744 loss_db: 0.1854 loss: 1.9005 2022/08/30 01:18:58 - mmengine - INFO - Epoch(train) [87][60/63] lr: 6.5465e-03 eta: 1 day, 2:31:23 time: 1.8210 data_time: 0.0527 memory: 16201 loss_prob: 1.0333 loss_thr: 0.5729 loss_db: 0.1734 loss: 1.7796 2022/08/30 01:19:03 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:19:13 - mmengine - INFO - Epoch(train) [88][5/63] lr: 6.5412e-03 eta: 1 day, 2:31:23 time: 1.8818 data_time: 0.2541 memory: 16201 loss_prob: 1.1119 loss_thr: 0.5961 loss_db: 0.1811 loss: 1.8892 2022/08/30 01:19:21 - mmengine - INFO - Epoch(train) [88][10/63] lr: 6.5412e-03 eta: 1 day, 2:31:12 time: 1.8261 data_time: 0.2772 memory: 16201 loss_prob: 0.9847 loss_thr: 0.5465 loss_db: 0.1594 loss: 1.6907 2022/08/30 01:19:30 - mmengine - INFO - Epoch(train) [88][15/63] lr: 6.5412e-03 eta: 1 day, 2:31:12 time: 1.7848 data_time: 0.0458 memory: 16201 loss_prob: 1.0598 loss_thr: 0.5487 loss_db: 0.1646 loss: 1.7731 2022/08/30 01:19:39 - mmengine - INFO - Epoch(train) [88][20/63] lr: 6.5412e-03 eta: 1 day, 2:31:58 time: 1.8303 data_time: 0.0361 memory: 16201 loss_prob: 1.2140 loss_thr: 0.5673 loss_db: 0.1950 loss: 1.9763 2022/08/30 01:19:48 - mmengine - INFO - Epoch(train) [88][25/63] lr: 6.5412e-03 eta: 1 day, 2:31:58 time: 1.7720 data_time: 0.0546 memory: 16201 loss_prob: 1.1328 loss_thr: 0.5713 loss_db: 0.1879 loss: 1.8920 2022/08/30 01:19:57 - mmengine - INFO - Epoch(train) [88][30/63] lr: 6.5412e-03 eta: 1 day, 2:32:36 time: 1.7707 data_time: 0.0371 memory: 16201 loss_prob: 1.1021 loss_thr: 0.5966 loss_db: 0.1802 loss: 1.8790 2022/08/30 01:20:06 - mmengine - INFO - Epoch(train) [88][35/63] lr: 6.5412e-03 eta: 1 day, 2:32:36 time: 1.8073 data_time: 0.0452 memory: 16201 loss_prob: 1.1551 loss_thr: 0.5869 loss_db: 0.1902 loss: 1.9322 2022/08/30 01:20:16 - mmengine - INFO - Epoch(train) [88][40/63] lr: 6.5412e-03 eta: 1 day, 2:33:30 time: 1.8949 data_time: 0.0459 memory: 16201 loss_prob: 1.1189 loss_thr: 0.5383 loss_db: 0.1822 loss: 1.8394 2022/08/30 01:20:25 - mmengine - INFO - Epoch(train) [88][45/63] lr: 6.5412e-03 eta: 1 day, 2:33:30 time: 1.8689 data_time: 0.0323 memory: 16201 loss_prob: 1.2054 loss_thr: 0.5705 loss_db: 0.1922 loss: 1.9680 2022/08/30 01:20:34 - mmengine - INFO - Epoch(train) [88][50/63] lr: 6.5412e-03 eta: 1 day, 2:34:15 time: 1.8276 data_time: 0.0560 memory: 16201 loss_prob: 1.2148 loss_thr: 0.6072 loss_db: 0.1967 loss: 2.0187 2022/08/30 01:20:44 - mmengine - INFO - Epoch(train) [88][55/63] lr: 6.5412e-03 eta: 1 day, 2:34:15 time: 1.9320 data_time: 0.0416 memory: 16201 loss_prob: 1.1695 loss_thr: 0.5907 loss_db: 0.1905 loss: 1.9507 2022/08/30 01:20:54 - mmengine - INFO - Epoch(train) [88][60/63] lr: 6.5412e-03 eta: 1 day, 2:35:21 time: 1.9970 data_time: 0.0325 memory: 16201 loss_prob: 1.1533 loss_thr: 0.5826 loss_db: 0.1847 loss: 1.9206 2022/08/30 01:20:58 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:21:10 - mmengine - INFO - Epoch(train) [89][5/63] lr: 6.5359e-03 eta: 1 day, 2:35:21 time: 1.9508 data_time: 0.2846 memory: 16201 loss_prob: 1.0180 loss_thr: 0.5307 loss_db: 0.1642 loss: 1.7129 2022/08/30 01:21:18 - mmengine - INFO - Epoch(train) [89][10/63] lr: 6.5359e-03 eta: 1 day, 2:35:28 time: 1.9676 data_time: 0.3030 memory: 16201 loss_prob: 1.0422 loss_thr: 0.5382 loss_db: 0.1686 loss: 1.7490 2022/08/30 01:21:26 - mmengine - INFO - Epoch(train) [89][15/63] lr: 6.5359e-03 eta: 1 day, 2:35:28 time: 1.5675 data_time: 0.0372 memory: 16201 loss_prob: 1.1226 loss_thr: 0.5760 loss_db: 0.1812 loss: 1.8797 2022/08/30 01:21:33 - mmengine - INFO - Epoch(train) [89][20/63] lr: 6.5359e-03 eta: 1 day, 2:35:31 time: 1.5031 data_time: 0.0428 memory: 16201 loss_prob: 1.1236 loss_thr: 0.5725 loss_db: 0.1845 loss: 1.8806 2022/08/30 01:21:41 - mmengine - INFO - Epoch(train) [89][25/63] lr: 6.5359e-03 eta: 1 day, 2:35:31 time: 1.5007 data_time: 0.0476 memory: 16201 loss_prob: 1.0253 loss_thr: 0.5492 loss_db: 0.1711 loss: 1.7456 2022/08/30 01:21:50 - mmengine - INFO - Epoch(train) [89][30/63] lr: 6.5359e-03 eta: 1 day, 2:36:01 time: 1.7150 data_time: 0.0330 memory: 16201 loss_prob: 1.0123 loss_thr: 0.5325 loss_db: 0.1629 loss: 1.7076 2022/08/30 01:21:58 - mmengine - INFO - Epoch(train) [89][35/63] lr: 6.5359e-03 eta: 1 day, 2:36:01 time: 1.7975 data_time: 0.0458 memory: 16201 loss_prob: 1.0236 loss_thr: 0.5477 loss_db: 0.1648 loss: 1.7361 2022/08/30 01:22:08 - mmengine - INFO - Epoch(train) [89][40/63] lr: 6.5359e-03 eta: 1 day, 2:36:40 time: 1.7853 data_time: 0.0370 memory: 16201 loss_prob: 1.0654 loss_thr: 0.5616 loss_db: 0.1753 loss: 1.8023 2022/08/30 01:22:17 - mmengine - INFO - Epoch(train) [89][45/63] lr: 6.5359e-03 eta: 1 day, 2:36:40 time: 1.8776 data_time: 0.0412 memory: 16201 loss_prob: 1.1454 loss_thr: 0.5619 loss_db: 0.1848 loss: 1.8921 2022/08/30 01:22:26 - mmengine - INFO - Epoch(train) [89][50/63] lr: 6.5359e-03 eta: 1 day, 2:37:21 time: 1.8046 data_time: 0.0520 memory: 16201 loss_prob: 1.1541 loss_thr: 0.5733 loss_db: 0.1852 loss: 1.9126 2022/08/30 01:22:35 - mmengine - INFO - Epoch(train) [89][55/63] lr: 6.5359e-03 eta: 1 day, 2:37:21 time: 1.7404 data_time: 0.0460 memory: 16201 loss_prob: 1.0163 loss_thr: 0.5381 loss_db: 0.1686 loss: 1.7229 2022/08/30 01:22:44 - mmengine - INFO - Epoch(train) [89][60/63] lr: 6.5359e-03 eta: 1 day, 2:38:01 time: 1.7979 data_time: 0.0491 memory: 16201 loss_prob: 0.9415 loss_thr: 0.5400 loss_db: 0.1556 loss: 1.6372 2022/08/30 01:22:49 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:23:00 - mmengine - INFO - Epoch(train) [90][5/63] lr: 6.5306e-03 eta: 1 day, 2:38:01 time: 1.9231 data_time: 0.2693 memory: 16201 loss_prob: 0.9997 loss_thr: 0.5536 loss_db: 0.1634 loss: 1.7166 2022/08/30 01:23:10 - mmengine - INFO - Epoch(train) [90][10/63] lr: 6.5306e-03 eta: 1 day, 2:38:19 time: 2.0702 data_time: 0.2781 memory: 16201 loss_prob: 1.0633 loss_thr: 0.5640 loss_db: 0.1763 loss: 1.8036 2022/08/30 01:23:18 - mmengine - INFO - Epoch(train) [90][15/63] lr: 6.5306e-03 eta: 1 day, 2:38:19 time: 1.8623 data_time: 0.0497 memory: 16201 loss_prob: 1.0095 loss_thr: 0.5541 loss_db: 0.1669 loss: 1.7305 2022/08/30 01:23:27 - mmengine - INFO - Epoch(train) [90][20/63] lr: 6.5306e-03 eta: 1 day, 2:38:55 time: 1.7670 data_time: 0.0515 memory: 16201 loss_prob: 0.9686 loss_thr: 0.5513 loss_db: 0.1581 loss: 1.6780 2022/08/30 01:23:34 - mmengine - INFO - Epoch(train) [90][25/63] lr: 6.5306e-03 eta: 1 day, 2:38:55 time: 1.6108 data_time: 0.0514 memory: 16201 loss_prob: 0.9604 loss_thr: 0.5519 loss_db: 0.1562 loss: 1.6684 2022/08/30 01:23:43 - mmengine - INFO - Epoch(train) [90][30/63] lr: 6.5306e-03 eta: 1 day, 2:39:11 time: 1.6112 data_time: 0.0418 memory: 16201 loss_prob: 0.9310 loss_thr: 0.5417 loss_db: 0.1535 loss: 1.6262 2022/08/30 01:23:52 - mmengine - INFO - Epoch(train) [90][35/63] lr: 6.5306e-03 eta: 1 day, 2:39:11 time: 1.7433 data_time: 0.0486 memory: 16201 loss_prob: 0.9491 loss_thr: 0.5385 loss_db: 0.1556 loss: 1.6432 2022/08/30 01:24:00 - mmengine - INFO - Epoch(train) [90][40/63] lr: 6.5306e-03 eta: 1 day, 2:39:31 time: 1.6426 data_time: 0.0418 memory: 16201 loss_prob: 0.9775 loss_thr: 0.5178 loss_db: 0.1574 loss: 1.6527 2022/08/30 01:24:09 - mmengine - INFO - Epoch(train) [90][45/63] lr: 6.5306e-03 eta: 1 day, 2:39:31 time: 1.7012 data_time: 0.0320 memory: 16201 loss_prob: 0.9746 loss_thr: 0.5044 loss_db: 0.1591 loss: 1.6381 2022/08/30 01:24:17 - mmengine - INFO - Epoch(train) [90][50/63] lr: 6.5306e-03 eta: 1 day, 2:40:04 time: 1.7493 data_time: 0.0557 memory: 16201 loss_prob: 0.9486 loss_thr: 0.5256 loss_db: 0.1544 loss: 1.6285 2022/08/30 01:24:26 - mmengine - INFO - Epoch(train) [90][55/63] lr: 6.5306e-03 eta: 1 day, 2:40:04 time: 1.7255 data_time: 0.0513 memory: 16201 loss_prob: 0.9843 loss_thr: 0.5704 loss_db: 0.1651 loss: 1.7199 2022/08/30 01:24:37 - mmengine - INFO - Epoch(train) [90][60/63] lr: 6.5306e-03 eta: 1 day, 2:41:01 time: 1.9499 data_time: 0.0497 memory: 16201 loss_prob: 1.0072 loss_thr: 0.5819 loss_db: 0.1688 loss: 1.7580 2022/08/30 01:24:41 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:24:50 - mmengine - INFO - Epoch(train) [91][5/63] lr: 6.5253e-03 eta: 1 day, 2:41:01 time: 1.7361 data_time: 0.2691 memory: 16201 loss_prob: 1.2196 loss_thr: 0.5813 loss_db: 0.1937 loss: 1.9946 2022/08/30 01:25:00 - mmengine - INFO - Epoch(train) [91][10/63] lr: 6.5253e-03 eta: 1 day, 2:41:01 time: 1.9288 data_time: 0.2907 memory: 16201 loss_prob: 1.2695 loss_thr: 0.5824 loss_db: 0.2074 loss: 2.0594 2022/08/30 01:25:09 - mmengine - INFO - Epoch(train) [91][15/63] lr: 6.5253e-03 eta: 1 day, 2:41:01 time: 1.8080 data_time: 0.0561 memory: 16201 loss_prob: 1.1636 loss_thr: 0.5592 loss_db: 0.1949 loss: 1.9176 2022/08/30 01:25:18 - mmengine - INFO - Epoch(train) [91][20/63] lr: 6.5253e-03 eta: 1 day, 2:41:40 time: 1.8063 data_time: 0.0514 memory: 16201 loss_prob: 1.0800 loss_thr: 0.5569 loss_db: 0.1752 loss: 1.8122 2022/08/30 01:25:26 - mmengine - INFO - Epoch(train) [91][25/63] lr: 6.5253e-03 eta: 1 day, 2:41:40 time: 1.7133 data_time: 0.0490 memory: 16201 loss_prob: 1.0296 loss_thr: 0.5652 loss_db: 0.1655 loss: 1.7603 2022/08/30 01:25:34 - mmengine - INFO - Epoch(train) [91][30/63] lr: 6.5253e-03 eta: 1 day, 2:41:54 time: 1.6002 data_time: 0.0384 memory: 16201 loss_prob: 1.0025 loss_thr: 0.5259 loss_db: 0.1668 loss: 1.6951 2022/08/30 01:25:43 - mmengine - INFO - Epoch(train) [91][35/63] lr: 6.5253e-03 eta: 1 day, 2:41:54 time: 1.7085 data_time: 0.0423 memory: 16201 loss_prob: 0.9796 loss_thr: 0.5260 loss_db: 0.1618 loss: 1.6675 2022/08/30 01:25:52 - mmengine - INFO - Epoch(train) [91][40/63] lr: 6.5253e-03 eta: 1 day, 2:42:28 time: 1.7590 data_time: 0.0359 memory: 16201 loss_prob: 0.9648 loss_thr: 0.5395 loss_db: 0.1592 loss: 1.6634 2022/08/30 01:26:00 - mmengine - INFO - Epoch(train) [91][45/63] lr: 6.5253e-03 eta: 1 day, 2:42:28 time: 1.7433 data_time: 0.0444 memory: 16201 loss_prob: 0.9476 loss_thr: 0.5367 loss_db: 0.1563 loss: 1.6406 2022/08/30 01:26:09 - mmengine - INFO - Epoch(train) [91][50/63] lr: 6.5253e-03 eta: 1 day, 2:42:58 time: 1.7412 data_time: 0.0491 memory: 16201 loss_prob: 1.0232 loss_thr: 0.5547 loss_db: 0.1670 loss: 1.7449 2022/08/30 01:26:17 - mmengine - INFO - Epoch(train) [91][55/63] lr: 6.5253e-03 eta: 1 day, 2:42:58 time: 1.7037 data_time: 0.0504 memory: 16201 loss_prob: 1.0639 loss_thr: 0.5565 loss_db: 0.1791 loss: 1.7996 2022/08/30 01:26:27 - mmengine - INFO - Epoch(train) [91][60/63] lr: 6.5253e-03 eta: 1 day, 2:43:32 time: 1.7632 data_time: 0.0944 memory: 16201 loss_prob: 1.0552 loss_thr: 0.5697 loss_db: 0.1732 loss: 1.7981 2022/08/30 01:26:32 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:26:45 - mmengine - INFO - Epoch(train) [92][5/63] lr: 6.5200e-03 eta: 1 day, 2:43:32 time: 2.1642 data_time: 0.2940 memory: 16201 loss_prob: 0.9600 loss_thr: 0.5528 loss_db: 0.1611 loss: 1.6740 2022/08/30 01:26:56 - mmengine - INFO - Epoch(train) [92][10/63] lr: 6.5200e-03 eta: 1 day, 2:44:20 time: 2.3361 data_time: 0.3400 memory: 16201 loss_prob: 1.2238 loss_thr: 0.6096 loss_db: 0.1988 loss: 2.0322 2022/08/30 01:27:05 - mmengine - INFO - Epoch(train) [92][15/63] lr: 6.5200e-03 eta: 1 day, 2:44:20 time: 2.0050 data_time: 0.1342 memory: 16201 loss_prob: 1.2788 loss_thr: 0.6004 loss_db: 0.2088 loss: 2.0880 2022/08/30 01:27:16 - mmengine - INFO - Epoch(train) [92][20/63] lr: 6.5200e-03 eta: 1 day, 2:45:29 time: 2.0552 data_time: 0.0982 memory: 16201 loss_prob: 1.0931 loss_thr: 0.5649 loss_db: 0.1826 loss: 1.8407 2022/08/30 01:27:26 - mmengine - INFO - Epoch(train) [92][25/63] lr: 6.5200e-03 eta: 1 day, 2:45:29 time: 2.1181 data_time: 0.0917 memory: 16201 loss_prob: 1.1143 loss_thr: 0.5921 loss_db: 0.1833 loss: 1.8897 2022/08/30 01:27:36 - mmengine - INFO - Epoch(train) [92][30/63] lr: 6.5200e-03 eta: 1 day, 2:46:22 time: 1.9349 data_time: 0.0881 memory: 16201 loss_prob: 1.1346 loss_thr: 0.5986 loss_db: 0.1878 loss: 1.9209 2022/08/30 01:27:44 - mmengine - INFO - Epoch(train) [92][35/63] lr: 6.5200e-03 eta: 1 day, 2:46:22 time: 1.7408 data_time: 0.0995 memory: 16201 loss_prob: 0.9770 loss_thr: 0.5410 loss_db: 0.1581 loss: 1.6761 2022/08/30 01:27:54 - mmengine - INFO - Epoch(train) [92][40/63] lr: 6.5200e-03 eta: 1 day, 2:47:01 time: 1.8105 data_time: 0.0929 memory: 16201 loss_prob: 0.9607 loss_thr: 0.5279 loss_db: 0.1512 loss: 1.6398 2022/08/30 01:28:04 - mmengine - INFO - Epoch(train) [92][45/63] lr: 6.5200e-03 eta: 1 day, 2:47:01 time: 2.0053 data_time: 0.0984 memory: 16201 loss_prob: 1.0250 loss_thr: 0.5608 loss_db: 0.1677 loss: 1.7534 2022/08/30 01:28:12 - mmengine - INFO - Epoch(train) [92][50/63] lr: 6.5200e-03 eta: 1 day, 2:47:41 time: 1.8297 data_time: 0.0987 memory: 16201 loss_prob: 1.0255 loss_thr: 0.5758 loss_db: 0.1702 loss: 1.7715 2022/08/30 01:28:22 - mmengine - INFO - Epoch(train) [92][55/63] lr: 6.5200e-03 eta: 1 day, 2:47:41 time: 1.8629 data_time: 0.1005 memory: 16201 loss_prob: 1.1047 loss_thr: 0.5700 loss_db: 0.1716 loss: 1.8462 2022/08/30 01:28:30 - mmengine - INFO - Epoch(train) [92][60/63] lr: 6.5200e-03 eta: 1 day, 2:48:19 time: 1.8103 data_time: 0.1312 memory: 16201 loss_prob: 1.1840 loss_thr: 0.5531 loss_db: 0.1836 loss: 1.9208 2022/08/30 01:28:35 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:28:46 - mmengine - INFO - Epoch(train) [93][5/63] lr: 6.5147e-03 eta: 1 day, 2:48:19 time: 1.9680 data_time: 0.2910 memory: 16201 loss_prob: 1.1513 loss_thr: 0.5721 loss_db: 0.1927 loss: 1.9160 2022/08/30 01:28:55 - mmengine - INFO - Epoch(train) [93][10/63] lr: 6.5147e-03 eta: 1 day, 2:48:25 time: 1.9948 data_time: 0.2833 memory: 16201 loss_prob: 1.1466 loss_thr: 0.5727 loss_db: 0.1888 loss: 1.9081 2022/08/30 01:29:04 - mmengine - INFO - Epoch(train) [93][15/63] lr: 6.5147e-03 eta: 1 day, 2:48:25 time: 1.7855 data_time: 0.0465 memory: 16201 loss_prob: 1.2006 loss_thr: 0.5434 loss_db: 0.1957 loss: 1.9396 2022/08/30 01:29:12 - mmengine - INFO - Epoch(train) [93][20/63] lr: 6.5147e-03 eta: 1 day, 2:48:54 time: 1.7420 data_time: 0.0483 memory: 16201 loss_prob: 1.0978 loss_thr: 0.5073 loss_db: 0.1740 loss: 1.7791 2022/08/30 01:29:21 - mmengine - INFO - Epoch(train) [93][25/63] lr: 6.5147e-03 eta: 1 day, 2:48:54 time: 1.7204 data_time: 0.0436 memory: 16201 loss_prob: 1.0455 loss_thr: 0.5257 loss_db: 0.1655 loss: 1.7368 2022/08/30 01:29:30 - mmengine - INFO - Epoch(train) [93][30/63] lr: 6.5147e-03 eta: 1 day, 2:49:31 time: 1.8115 data_time: 0.0456 memory: 16201 loss_prob: 1.0834 loss_thr: 0.5627 loss_db: 0.1755 loss: 1.8216 2022/08/30 01:29:38 - mmengine - INFO - Epoch(train) [93][35/63] lr: 6.5147e-03 eta: 1 day, 2:49:31 time: 1.6489 data_time: 0.0517 memory: 16201 loss_prob: 1.1153 loss_thr: 0.5733 loss_db: 0.1816 loss: 1.8702 2022/08/30 01:29:46 - mmengine - INFO - Epoch(train) [93][40/63] lr: 6.5147e-03 eta: 1 day, 2:49:45 time: 1.6148 data_time: 0.0464 memory: 16201 loss_prob: 1.0616 loss_thr: 0.5551 loss_db: 0.1727 loss: 1.7894 2022/08/30 01:29:56 - mmengine - INFO - Epoch(train) [93][45/63] lr: 6.5147e-03 eta: 1 day, 2:49:45 time: 1.7733 data_time: 0.0544 memory: 16201 loss_prob: 0.9602 loss_thr: 0.5303 loss_db: 0.1543 loss: 1.6448 2022/08/30 01:30:04 - mmengine - INFO - Epoch(train) [93][50/63] lr: 6.5147e-03 eta: 1 day, 2:50:14 time: 1.7428 data_time: 0.0540 memory: 16201 loss_prob: 0.9183 loss_thr: 0.5202 loss_db: 0.1502 loss: 1.5887 2022/08/30 01:30:14 - mmengine - INFO - Epoch(train) [93][55/63] lr: 6.5147e-03 eta: 1 day, 2:50:14 time: 1.7995 data_time: 0.0419 memory: 16201 loss_prob: 0.8962 loss_thr: 0.5240 loss_db: 0.1460 loss: 1.5662 2022/08/30 01:30:21 - mmengine - INFO - Epoch(train) [93][60/63] lr: 6.5147e-03 eta: 1 day, 2:50:45 time: 1.7583 data_time: 0.0468 memory: 16201 loss_prob: 0.9662 loss_thr: 0.5340 loss_db: 0.1552 loss: 1.6554 2022/08/30 01:30:26 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:30:36 - mmengine - INFO - Epoch(train) [94][5/63] lr: 6.5094e-03 eta: 1 day, 2:50:45 time: 1.7083 data_time: 0.2708 memory: 16201 loss_prob: 0.9744 loss_thr: 0.5530 loss_db: 0.1572 loss: 1.6846 2022/08/30 01:30:44 - mmengine - INFO - Epoch(train) [94][10/63] lr: 6.5094e-03 eta: 1 day, 2:50:34 time: 1.8635 data_time: 0.2900 memory: 16201 loss_prob: 0.9279 loss_thr: 0.5443 loss_db: 0.1552 loss: 1.6275 2022/08/30 01:30:52 - mmengine - INFO - Epoch(train) [94][15/63] lr: 6.5094e-03 eta: 1 day, 2:50:34 time: 1.6615 data_time: 0.0369 memory: 16201 loss_prob: 0.9506 loss_thr: 0.5358 loss_db: 0.1553 loss: 1.6417 2022/08/30 01:31:01 - mmengine - INFO - Epoch(train) [94][20/63] lr: 6.5094e-03 eta: 1 day, 2:50:53 time: 1.6595 data_time: 0.0396 memory: 16201 loss_prob: 0.9956 loss_thr: 0.5454 loss_db: 0.1620 loss: 1.7031 2022/08/30 01:31:10 - mmengine - INFO - Epoch(train) [94][25/63] lr: 6.5094e-03 eta: 1 day, 2:50:53 time: 1.7197 data_time: 0.0539 memory: 16201 loss_prob: 1.0437 loss_thr: 0.5753 loss_db: 0.1739 loss: 1.7930 2022/08/30 01:31:19 - mmengine - INFO - Epoch(train) [94][30/63] lr: 6.5094e-03 eta: 1 day, 2:51:26 time: 1.7868 data_time: 0.0380 memory: 16201 loss_prob: 1.0197 loss_thr: 0.5489 loss_db: 0.1676 loss: 1.7363 2022/08/30 01:31:26 - mmengine - INFO - Epoch(train) [94][35/63] lr: 6.5094e-03 eta: 1 day, 2:51:26 time: 1.6683 data_time: 0.0490 memory: 16201 loss_prob: 0.9884 loss_thr: 0.5482 loss_db: 0.1606 loss: 1.6972 2022/08/30 01:31:35 - mmengine - INFO - Epoch(train) [94][40/63] lr: 6.5094e-03 eta: 1 day, 2:51:42 time: 1.6357 data_time: 0.0480 memory: 16201 loss_prob: 1.0674 loss_thr: 0.5922 loss_db: 0.1737 loss: 1.8332 2022/08/30 01:31:43 - mmengine - INFO - Epoch(train) [94][45/63] lr: 6.5094e-03 eta: 1 day, 2:51:42 time: 1.7244 data_time: 0.0366 memory: 16201 loss_prob: 1.0984 loss_thr: 0.5677 loss_db: 0.1774 loss: 1.8436 2022/08/30 01:31:53 - mmengine - INFO - Epoch(train) [94][50/63] lr: 6.5094e-03 eta: 1 day, 2:52:18 time: 1.8112 data_time: 0.0533 memory: 16201 loss_prob: 0.9990 loss_thr: 0.5440 loss_db: 0.1627 loss: 1.7057 2022/08/30 01:32:04 - mmengine - INFO - Epoch(train) [94][55/63] lr: 6.5094e-03 eta: 1 day, 2:52:18 time: 2.0061 data_time: 0.0408 memory: 16201 loss_prob: 1.1144 loss_thr: 0.5786 loss_db: 0.1769 loss: 1.8700 2022/08/30 01:32:13 - mmengine - INFO - Epoch(train) [94][60/63] lr: 6.5094e-03 eta: 1 day, 2:53:12 time: 1.9668 data_time: 0.0431 memory: 16201 loss_prob: 1.2357 loss_thr: 0.6030 loss_db: 0.1976 loss: 2.0363 2022/08/30 01:32:17 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:32:28 - mmengine - INFO - Epoch(train) [95][5/63] lr: 6.5041e-03 eta: 1 day, 2:53:12 time: 1.8862 data_time: 0.2639 memory: 16201 loss_prob: 1.0849 loss_thr: 0.5593 loss_db: 0.1803 loss: 1.8245 2022/08/30 01:32:37 - mmengine - INFO - Epoch(train) [95][10/63] lr: 6.5041e-03 eta: 1 day, 2:53:12 time: 1.9572 data_time: 0.3039 memory: 16201 loss_prob: 1.0879 loss_thr: 0.5433 loss_db: 0.1835 loss: 1.8147 2022/08/30 01:32:47 - mmengine - INFO - Epoch(train) [95][15/63] lr: 6.5041e-03 eta: 1 day, 2:53:12 time: 1.8674 data_time: 0.0604 memory: 16201 loss_prob: 1.1396 loss_thr: 0.5522 loss_db: 0.1946 loss: 1.8864 2022/08/30 01:32:56 - mmengine - INFO - Epoch(train) [95][20/63] lr: 6.5041e-03 eta: 1 day, 2:53:55 time: 1.8793 data_time: 0.0496 memory: 16201 loss_prob: 1.2727 loss_thr: 0.6055 loss_db: 0.2030 loss: 2.0812 2022/08/30 01:33:05 - mmengine - INFO - Epoch(train) [95][25/63] lr: 6.5041e-03 eta: 1 day, 2:53:55 time: 1.7823 data_time: 0.0571 memory: 16201 loss_prob: 1.2381 loss_thr: 0.6078 loss_db: 0.1964 loss: 2.0424 2022/08/30 01:33:14 - mmengine - INFO - Epoch(train) [95][30/63] lr: 6.5041e-03 eta: 1 day, 2:54:33 time: 1.8352 data_time: 0.0352 memory: 16201 loss_prob: 1.0691 loss_thr: 0.5616 loss_db: 0.1810 loss: 1.8117 2022/08/30 01:33:22 - mmengine - INFO - Epoch(train) [95][35/63] lr: 6.5041e-03 eta: 1 day, 2:54:33 time: 1.6912 data_time: 0.0476 memory: 16201 loss_prob: 1.0936 loss_thr: 0.5727 loss_db: 0.1856 loss: 1.8519 2022/08/30 01:33:30 - mmengine - INFO - Epoch(train) [95][40/63] lr: 6.5041e-03 eta: 1 day, 2:54:41 time: 1.5788 data_time: 0.0430 memory: 16201 loss_prob: 1.1468 loss_thr: 0.5739 loss_db: 0.1911 loss: 1.9117 2022/08/30 01:33:39 - mmengine - INFO - Epoch(train) [95][45/63] lr: 6.5041e-03 eta: 1 day, 2:54:41 time: 1.7503 data_time: 0.0427 memory: 16201 loss_prob: 1.0780 loss_thr: 0.5598 loss_db: 0.1759 loss: 1.8137 2022/08/30 01:33:47 - mmengine - INFO - Epoch(train) [95][50/63] lr: 6.5041e-03 eta: 1 day, 2:55:08 time: 1.7415 data_time: 0.0568 memory: 16201 loss_prob: 1.0575 loss_thr: 0.5505 loss_db: 0.1692 loss: 1.7772 2022/08/30 01:33:57 - mmengine - INFO - Epoch(train) [95][55/63] lr: 6.5041e-03 eta: 1 day, 2:55:08 time: 1.7474 data_time: 0.0327 memory: 16201 loss_prob: 1.1055 loss_thr: 0.5677 loss_db: 0.1776 loss: 1.8508 2022/08/30 01:34:05 - mmengine - INFO - Epoch(train) [95][60/63] lr: 6.5041e-03 eta: 1 day, 2:55:45 time: 1.8242 data_time: 0.0396 memory: 16201 loss_prob: 1.0623 loss_thr: 0.5793 loss_db: 0.1721 loss: 1.8137 2022/08/30 01:34:10 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:34:23 - mmengine - INFO - Epoch(train) [96][5/63] lr: 6.4988e-03 eta: 1 day, 2:55:45 time: 2.1646 data_time: 0.2871 memory: 16201 loss_prob: 0.9263 loss_thr: 0.5321 loss_db: 0.1543 loss: 1.6127 2022/08/30 01:34:33 - mmengine - INFO - Epoch(train) [96][10/63] lr: 6.4988e-03 eta: 1 day, 2:56:23 time: 2.2964 data_time: 0.2979 memory: 16201 loss_prob: 1.0343 loss_thr: 0.5388 loss_db: 0.1703 loss: 1.7434 2022/08/30 01:34:42 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:34:42 - mmengine - INFO - Epoch(train) [96][15/63] lr: 6.4988e-03 eta: 1 day, 2:56:23 time: 1.9420 data_time: 0.0538 memory: 16201 loss_prob: 0.9652 loss_thr: 0.5105 loss_db: 0.1548 loss: 1.6306 2022/08/30 01:34:50 - mmengine - INFO - Epoch(train) [96][20/63] lr: 6.4988e-03 eta: 1 day, 2:56:43 time: 1.6810 data_time: 0.0545 memory: 16201 loss_prob: 0.9299 loss_thr: 0.5168 loss_db: 0.1500 loss: 1.5968 2022/08/30 01:34:59 - mmengine - INFO - Epoch(train) [96][25/63] lr: 6.4988e-03 eta: 1 day, 2:56:43 time: 1.6562 data_time: 0.0465 memory: 16201 loss_prob: 1.0666 loss_thr: 0.5583 loss_db: 0.1715 loss: 1.7964 2022/08/30 01:35:07 - mmengine - INFO - Epoch(train) [96][30/63] lr: 6.4988e-03 eta: 1 day, 2:57:02 time: 1.6803 data_time: 0.0395 memory: 16201 loss_prob: 1.0689 loss_thr: 0.5646 loss_db: 0.1755 loss: 1.8089 2022/08/30 01:35:15 - mmengine - INFO - Epoch(train) [96][35/63] lr: 6.4988e-03 eta: 1 day, 2:57:02 time: 1.6424 data_time: 0.0499 memory: 16201 loss_prob: 1.0565 loss_thr: 0.5546 loss_db: 0.1704 loss: 1.7815 2022/08/30 01:35:23 - mmengine - INFO - Epoch(train) [96][40/63] lr: 6.4988e-03 eta: 1 day, 2:57:18 time: 1.6533 data_time: 0.0483 memory: 16201 loss_prob: 1.0267 loss_thr: 0.5143 loss_db: 0.1648 loss: 1.7057 2022/08/30 01:35:32 - mmengine - INFO - Epoch(train) [96][45/63] lr: 6.4988e-03 eta: 1 day, 2:57:18 time: 1.6949 data_time: 0.0458 memory: 16201 loss_prob: 1.0226 loss_thr: 0.5524 loss_db: 0.1675 loss: 1.7425 2022/08/30 01:35:40 - mmengine - INFO - Epoch(train) [96][50/63] lr: 6.4988e-03 eta: 1 day, 2:57:34 time: 1.6579 data_time: 0.0602 memory: 16201 loss_prob: 0.9571 loss_thr: 0.5674 loss_db: 0.1570 loss: 1.6815 2022/08/30 01:35:50 - mmengine - INFO - Epoch(train) [96][55/63] lr: 6.4988e-03 eta: 1 day, 2:57:34 time: 1.8369 data_time: 0.0545 memory: 16201 loss_prob: 0.9923 loss_thr: 0.5245 loss_db: 0.1574 loss: 1.6743 2022/08/30 01:35:59 - mmengine - INFO - Epoch(train) [96][60/63] lr: 6.4988e-03 eta: 1 day, 2:58:16 time: 1.8842 data_time: 0.0521 memory: 16201 loss_prob: 1.0364 loss_thr: 0.5324 loss_db: 0.1646 loss: 1.7334 2022/08/30 01:36:04 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:36:16 - mmengine - INFO - Epoch(train) [97][5/63] lr: 6.4935e-03 eta: 1 day, 2:58:16 time: 1.9963 data_time: 0.2695 memory: 16201 loss_prob: 0.9310 loss_thr: 0.5458 loss_db: 0.1534 loss: 1.6302 2022/08/30 01:36:25 - mmengine - INFO - Epoch(train) [97][10/63] lr: 6.4935e-03 eta: 1 day, 2:58:30 time: 2.0911 data_time: 0.2732 memory: 16201 loss_prob: 1.1742 loss_thr: 0.5784 loss_db: 0.1979 loss: 1.9505 2022/08/30 01:36:34 - mmengine - INFO - Epoch(train) [97][15/63] lr: 6.4935e-03 eta: 1 day, 2:58:30 time: 1.7858 data_time: 0.0495 memory: 16201 loss_prob: 1.3274 loss_thr: 0.6072 loss_db: 0.2152 loss: 2.1499 2022/08/30 01:36:43 - mmengine - INFO - Epoch(train) [97][20/63] lr: 6.4935e-03 eta: 1 day, 2:59:05 time: 1.8274 data_time: 0.0390 memory: 16201 loss_prob: 1.0859 loss_thr: 0.5783 loss_db: 0.1713 loss: 1.8355 2022/08/30 01:36:52 - mmengine - INFO - Epoch(train) [97][25/63] lr: 6.4935e-03 eta: 1 day, 2:59:05 time: 1.7757 data_time: 0.0466 memory: 16201 loss_prob: 1.2167 loss_thr: 0.5833 loss_db: 0.1965 loss: 1.9965 2022/08/30 01:37:01 - mmengine - INFO - Epoch(train) [97][30/63] lr: 6.4935e-03 eta: 1 day, 2:59:35 time: 1.7773 data_time: 0.0381 memory: 16201 loss_prob: 1.4453 loss_thr: 0.6157 loss_db: 0.2358 loss: 2.2968 2022/08/30 01:37:10 - mmengine - INFO - Epoch(train) [97][35/63] lr: 6.4935e-03 eta: 1 day, 2:59:35 time: 1.8270 data_time: 0.0520 memory: 16201 loss_prob: 1.2837 loss_thr: 0.5955 loss_db: 0.2117 loss: 2.0909 2022/08/30 01:37:19 - mmengine - INFO - Epoch(train) [97][40/63] lr: 6.4935e-03 eta: 1 day, 3:00:05 time: 1.7814 data_time: 0.0462 memory: 16201 loss_prob: 1.1649 loss_thr: 0.5688 loss_db: 0.1892 loss: 1.9229 2022/08/30 01:37:28 - mmengine - INFO - Epoch(train) [97][45/63] lr: 6.4935e-03 eta: 1 day, 3:00:05 time: 1.8001 data_time: 0.0595 memory: 16201 loss_prob: 1.0555 loss_thr: 0.5252 loss_db: 0.1729 loss: 1.7536 2022/08/30 01:37:36 - mmengine - INFO - Epoch(train) [97][50/63] lr: 6.4935e-03 eta: 1 day, 3:00:34 time: 1.7756 data_time: 0.0661 memory: 16201 loss_prob: 1.0173 loss_thr: 0.5334 loss_db: 0.1699 loss: 1.7206 2022/08/30 01:37:46 - mmengine - INFO - Epoch(train) [97][55/63] lr: 6.4935e-03 eta: 1 day, 3:00:34 time: 1.7581 data_time: 0.0365 memory: 16201 loss_prob: 1.2329 loss_thr: 0.5939 loss_db: 0.1933 loss: 2.0200 2022/08/30 01:37:55 - mmengine - INFO - Epoch(train) [97][60/63] lr: 6.4935e-03 eta: 1 day, 3:01:11 time: 1.8485 data_time: 0.0403 memory: 16201 loss_prob: 1.1777 loss_thr: 0.5591 loss_db: 0.1848 loss: 1.9217 2022/08/30 01:38:01 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:38:13 - mmengine - INFO - Epoch(train) [98][5/63] lr: 6.4882e-03 eta: 1 day, 3:01:11 time: 2.1202 data_time: 0.2965 memory: 16201 loss_prob: 1.0202 loss_thr: 0.5262 loss_db: 0.1701 loss: 1.7165 2022/08/30 01:38:21 - mmengine - INFO - Epoch(train) [98][10/63] lr: 6.4882e-03 eta: 1 day, 3:01:24 time: 2.0921 data_time: 0.3197 memory: 16201 loss_prob: 1.0315 loss_thr: 0.5209 loss_db: 0.1761 loss: 1.7285 2022/08/30 01:38:31 - mmengine - INFO - Epoch(train) [98][15/63] lr: 6.4882e-03 eta: 1 day, 3:01:24 time: 1.7940 data_time: 0.0441 memory: 16201 loss_prob: 1.0112 loss_thr: 0.5207 loss_db: 0.1700 loss: 1.7019 2022/08/30 01:38:40 - mmengine - INFO - Epoch(train) [98][20/63] lr: 6.4882e-03 eta: 1 day, 3:01:58 time: 1.8274 data_time: 0.0768 memory: 16201 loss_prob: 1.0314 loss_thr: 0.5352 loss_db: 0.1645 loss: 1.7311 2022/08/30 01:38:49 - mmengine - INFO - Epoch(train) [98][25/63] lr: 6.4882e-03 eta: 1 day, 3:01:58 time: 1.7760 data_time: 0.0809 memory: 16201 loss_prob: 1.0947 loss_thr: 0.5476 loss_db: 0.1746 loss: 1.8169 2022/08/30 01:38:58 - mmengine - INFO - Epoch(train) [98][30/63] lr: 6.4882e-03 eta: 1 day, 3:02:27 time: 1.7812 data_time: 0.0411 memory: 16201 loss_prob: 1.1204 loss_thr: 0.5597 loss_db: 0.1800 loss: 1.8602 2022/08/30 01:39:05 - mmengine - INFO - Epoch(train) [98][35/63] lr: 6.4882e-03 eta: 1 day, 3:02:27 time: 1.6428 data_time: 0.0483 memory: 16201 loss_prob: 1.1416 loss_thr: 0.5800 loss_db: 0.1852 loss: 1.9068 2022/08/30 01:39:13 - mmengine - INFO - Epoch(train) [98][40/63] lr: 6.4882e-03 eta: 1 day, 3:02:30 time: 1.5546 data_time: 0.0482 memory: 16201 loss_prob: 1.2095 loss_thr: 0.5669 loss_db: 0.1946 loss: 1.9709 2022/08/30 01:39:20 - mmengine - INFO - Epoch(train) [98][45/63] lr: 6.4882e-03 eta: 1 day, 3:02:30 time: 1.4742 data_time: 0.0434 memory: 16201 loss_prob: 1.1849 loss_thr: 0.5690 loss_db: 0.1882 loss: 1.9421 2022/08/30 01:39:27 - mmengine - INFO - Epoch(train) [98][50/63] lr: 6.4882e-03 eta: 1 day, 3:02:19 time: 1.4229 data_time: 0.0335 memory: 16201 loss_prob: 1.0935 loss_thr: 0.5797 loss_db: 0.1816 loss: 1.8547 2022/08/30 01:39:35 - mmengine - INFO - Epoch(train) [98][55/63] lr: 6.4882e-03 eta: 1 day, 3:02:19 time: 1.4724 data_time: 0.0322 memory: 16201 loss_prob: 1.1328 loss_thr: 0.5640 loss_db: 0.1877 loss: 1.8846 2022/08/30 01:39:43 - mmengine - INFO - Epoch(train) [98][60/63] lr: 6.4882e-03 eta: 1 day, 3:02:23 time: 1.5687 data_time: 0.0436 memory: 16201 loss_prob: 1.1699 loss_thr: 0.5792 loss_db: 0.1896 loss: 1.9387 2022/08/30 01:39:47 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:39:59 - mmengine - INFO - Epoch(train) [99][5/63] lr: 6.4829e-03 eta: 1 day, 3:02:23 time: 1.8551 data_time: 0.2504 memory: 16201 loss_prob: 1.1249 loss_thr: 0.5793 loss_db: 0.1871 loss: 1.8913 2022/08/30 01:40:08 - mmengine - INFO - Epoch(train) [99][10/63] lr: 6.4829e-03 eta: 1 day, 3:02:41 time: 2.1422 data_time: 0.2639 memory: 16201 loss_prob: 1.1504 loss_thr: 0.5820 loss_db: 0.1913 loss: 1.9237 2022/08/30 01:40:18 - mmengine - INFO - Epoch(train) [99][15/63] lr: 6.4829e-03 eta: 1 day, 3:02:41 time: 1.9146 data_time: 0.0463 memory: 16201 loss_prob: 1.0131 loss_thr: 0.5435 loss_db: 0.1663 loss: 1.7229 2022/08/30 01:40:28 - mmengine - INFO - Epoch(train) [99][20/63] lr: 6.4829e-03 eta: 1 day, 3:03:35 time: 2.0120 data_time: 0.0513 memory: 16201 loss_prob: 1.0055 loss_thr: 0.5545 loss_db: 0.1653 loss: 1.7253 2022/08/30 01:40:37 - mmengine - INFO - Epoch(train) [99][25/63] lr: 6.4829e-03 eta: 1 day, 3:03:35 time: 1.9019 data_time: 0.0489 memory: 16201 loss_prob: 1.1322 loss_thr: 0.5848 loss_db: 0.1864 loss: 1.9034 2022/08/30 01:40:46 - mmengine - INFO - Epoch(train) [99][30/63] lr: 6.4829e-03 eta: 1 day, 3:03:58 time: 1.7307 data_time: 0.0417 memory: 16201 loss_prob: 1.1312 loss_thr: 0.5518 loss_db: 0.1837 loss: 1.8667 2022/08/30 01:40:55 - mmengine - INFO - Epoch(train) [99][35/63] lr: 6.4829e-03 eta: 1 day, 3:03:58 time: 1.7783 data_time: 0.0610 memory: 16201 loss_prob: 1.2562 loss_thr: 0.5725 loss_db: 0.2003 loss: 2.0291 2022/08/30 01:41:04 - mmengine - INFO - Epoch(train) [99][40/63] lr: 6.4829e-03 eta: 1 day, 3:04:29 time: 1.8069 data_time: 0.0475 memory: 16201 loss_prob: 1.2517 loss_thr: 0.6025 loss_db: 0.2005 loss: 2.0546 2022/08/30 01:41:13 - mmengine - INFO - Epoch(train) [99][45/63] lr: 6.4829e-03 eta: 1 day, 3:04:29 time: 1.8768 data_time: 0.0527 memory: 16201 loss_prob: 1.0422 loss_thr: 0.5878 loss_db: 0.1714 loss: 1.8014 2022/08/30 01:41:22 - mmengine - INFO - Epoch(train) [99][50/63] lr: 6.4829e-03 eta: 1 day, 3:04:57 time: 1.7873 data_time: 0.0465 memory: 16201 loss_prob: 0.9928 loss_thr: 0.5517 loss_db: 0.1637 loss: 1.7082 2022/08/30 01:41:30 - mmengine - INFO - Epoch(train) [99][55/63] lr: 6.4829e-03 eta: 1 day, 3:04:57 time: 1.7207 data_time: 0.0251 memory: 16201 loss_prob: 1.0297 loss_thr: 0.5277 loss_db: 0.1663 loss: 1.7237 2022/08/30 01:41:38 - mmengine - INFO - Epoch(train) [99][60/63] lr: 6.4829e-03 eta: 1 day, 3:05:09 time: 1.6339 data_time: 0.0370 memory: 16201 loss_prob: 1.0516 loss_thr: 0.5431 loss_db: 0.1706 loss: 1.7653 2022/08/30 01:41:43 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:41:54 - mmengine - INFO - Epoch(train) [100][5/63] lr: 6.4776e-03 eta: 1 day, 3:05:09 time: 1.9073 data_time: 0.2891 memory: 16201 loss_prob: 1.0658 loss_thr: 0.5574 loss_db: 0.1782 loss: 1.8014 2022/08/30 01:42:05 - mmengine - INFO - Epoch(train) [100][10/63] lr: 6.4776e-03 eta: 1 day, 3:05:31 time: 2.1909 data_time: 0.3003 memory: 16201 loss_prob: 1.1412 loss_thr: 0.5728 loss_db: 0.1915 loss: 1.9055 2022/08/30 01:42:13 - mmengine - INFO - Epoch(train) [100][15/63] lr: 6.4776e-03 eta: 1 day, 3:05:31 time: 1.8459 data_time: 0.0448 memory: 16201 loss_prob: 1.1696 loss_thr: 0.5432 loss_db: 0.1915 loss: 1.9042 2022/08/30 01:42:22 - mmengine - INFO - Epoch(train) [100][20/63] lr: 6.4776e-03 eta: 1 day, 3:05:53 time: 1.7332 data_time: 0.0406 memory: 16201 loss_prob: 1.2469 loss_thr: 0.5824 loss_db: 0.2025 loss: 2.0318 2022/08/30 01:42:32 - mmengine - INFO - Epoch(train) [100][25/63] lr: 6.4776e-03 eta: 1 day, 3:05:53 time: 1.8725 data_time: 0.0621 memory: 16201 loss_prob: 1.2561 loss_thr: 0.6188 loss_db: 0.2149 loss: 2.0897 2022/08/30 01:42:40 - mmengine - INFO - Epoch(train) [100][30/63] lr: 6.4776e-03 eta: 1 day, 3:06:23 time: 1.8083 data_time: 0.0455 memory: 16201 loss_prob: 1.3803 loss_thr: 0.6097 loss_db: 0.2408 loss: 2.2308 2022/08/30 01:42:50 - mmengine - INFO - Epoch(train) [100][35/63] lr: 6.4776e-03 eta: 1 day, 3:06:23 time: 1.8019 data_time: 0.0376 memory: 16201 loss_prob: 1.2607 loss_thr: 0.6014 loss_db: 0.2131 loss: 2.0752 2022/08/30 01:42:57 - mmengine - INFO - Epoch(train) [100][40/63] lr: 6.4776e-03 eta: 1 day, 3:06:44 time: 1.7174 data_time: 0.0465 memory: 16201 loss_prob: 1.2818 loss_thr: 0.6188 loss_db: 0.2158 loss: 2.1164 2022/08/30 01:43:08 - mmengine - INFO - Epoch(train) [100][45/63] lr: 6.4776e-03 eta: 1 day, 3:06:44 time: 1.8081 data_time: 0.0453 memory: 16201 loss_prob: 1.4282 loss_thr: 0.6214 loss_db: 0.2418 loss: 2.2914 2022/08/30 01:43:16 - mmengine - INFO - Epoch(train) [100][50/63] lr: 6.4776e-03 eta: 1 day, 3:07:21 time: 1.8728 data_time: 0.0566 memory: 16201 loss_prob: 1.3022 loss_thr: 0.5959 loss_db: 0.2124 loss: 2.1104 2022/08/30 01:43:25 - mmengine - INFO - Epoch(train) [100][55/63] lr: 6.4776e-03 eta: 1 day, 3:07:21 time: 1.7759 data_time: 0.0386 memory: 16201 loss_prob: 1.2445 loss_thr: 0.5862 loss_db: 0.2003 loss: 2.0310 2022/08/30 01:43:35 - mmengine - INFO - Epoch(train) [100][60/63] lr: 6.4776e-03 eta: 1 day, 3:08:01 time: 1.9049 data_time: 0.0439 memory: 16201 loss_prob: 1.2451 loss_thr: 0.5899 loss_db: 0.2043 loss: 2.0393 2022/08/30 01:43:40 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:43:40 - mmengine - INFO - Saving checkpoint at 100 epochs 2022/08/30 01:43:49 - mmengine - INFO - Epoch(val) [100][5/32] eta: 1 day, 3:08:01 time: 0.7891 data_time: 0.1552 memory: 16201 2022/08/30 01:43:52 - mmengine - INFO - Epoch(val) [100][10/32] eta: 0:00:18 time: 0.8560 data_time: 0.2067 memory: 15734 2022/08/30 01:43:55 - mmengine - INFO - Epoch(val) [100][15/32] eta: 0:00:18 time: 0.6480 data_time: 0.0715 memory: 15734 2022/08/30 01:43:59 - mmengine - INFO - Epoch(val) [100][20/32] eta: 0:00:08 time: 0.6672 data_time: 0.0659 memory: 15734 2022/08/30 01:44:03 - mmengine - INFO - Epoch(val) [100][25/32] eta: 0:00:08 time: 0.7698 data_time: 0.0831 memory: 15734 2022/08/30 01:44:06 - mmengine - INFO - Epoch(val) [100][30/32] eta: 0:00:01 time: 0.7155 data_time: 0.0454 memory: 15734 2022/08/30 01:44:07 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 01:44:07 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.7809, precision: 0.6615, hmean: 0.7163 2022/08/30 01:44:07 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.7800, precision: 0.7545, hmean: 0.7670 2022/08/30 01:44:07 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.7713, precision: 0.8022, hmean: 0.7865 2022/08/30 01:44:07 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7492, precision: 0.8447, hmean: 0.7941 2022/08/30 01:44:07 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.6986, precision: 0.8821, hmean: 0.7797 2022/08/30 01:44:07 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.4434, precision: 0.9341, hmean: 0.6014 2022/08/30 01:44:07 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0135, precision: 1.0000, hmean: 0.0266 2022/08/30 01:44:07 - mmengine - INFO - Epoch(val) [100][32/32] icdar/precision: 0.8447 icdar/recall: 0.7492 icdar/hmean: 0.7941 2022/08/30 01:44:19 - mmengine - INFO - Epoch(train) [101][5/63] lr: 6.4723e-03 eta: 0:00:01 time: 1.9680 data_time: 0.2713 memory: 16201 loss_prob: 1.3841 loss_thr: 0.6302 loss_db: 0.2336 loss: 2.2480 2022/08/30 01:44:28 - mmengine - INFO - Epoch(train) [101][10/63] lr: 6.4723e-03 eta: 1 day, 3:08:08 time: 2.0614 data_time: 0.2938 memory: 16201 loss_prob: 1.1434 loss_thr: 0.5709 loss_db: 0.1907 loss: 1.9049 2022/08/30 01:44:38 - mmengine - INFO - Epoch(train) [101][15/63] lr: 6.4723e-03 eta: 1 day, 3:08:08 time: 1.8514 data_time: 0.0403 memory: 16201 loss_prob: 1.2327 loss_thr: 0.5461 loss_db: 0.1997 loss: 1.9785 2022/08/30 01:44:47 - mmengine - INFO - Epoch(train) [101][20/63] lr: 6.4723e-03 eta: 1 day, 3:08:46 time: 1.8856 data_time: 0.0384 memory: 16201 loss_prob: 1.4333 loss_thr: 0.5961 loss_db: 0.2239 loss: 2.2533 2022/08/30 01:44:57 - mmengine - INFO - Epoch(train) [101][25/63] lr: 6.4723e-03 eta: 1 day, 3:08:46 time: 1.9802 data_time: 0.0558 memory: 16201 loss_prob: 1.4952 loss_thr: 0.6483 loss_db: 0.2318 loss: 2.3753 2022/08/30 01:45:05 - mmengine - INFO - Epoch(train) [101][30/63] lr: 6.4723e-03 eta: 1 day, 3:09:20 time: 1.8498 data_time: 0.0399 memory: 16201 loss_prob: 1.5861 loss_thr: 0.6678 loss_db: 0.2545 loss: 2.5083 2022/08/30 01:45:15 - mmengine - INFO - Epoch(train) [101][35/63] lr: 6.4723e-03 eta: 1 day, 3:09:20 time: 1.7831 data_time: 0.0404 memory: 16201 loss_prob: 1.4311 loss_thr: 0.6418 loss_db: 0.2339 loss: 2.3068 2022/08/30 01:45:23 - mmengine - INFO - Epoch(train) [101][40/63] lr: 6.4723e-03 eta: 1 day, 3:09:52 time: 1.8295 data_time: 0.0395 memory: 16201 loss_prob: 1.2239 loss_thr: 0.6181 loss_db: 0.1992 loss: 2.0412 2022/08/30 01:45:32 - mmengine - INFO - Epoch(train) [101][45/63] lr: 6.4723e-03 eta: 1 day, 3:09:52 time: 1.7283 data_time: 0.0343 memory: 16201 loss_prob: 1.1409 loss_thr: 0.5944 loss_db: 0.1791 loss: 1.9144 2022/08/30 01:45:41 - mmengine - INFO - Epoch(train) [101][50/63] lr: 6.4723e-03 eta: 1 day, 3:10:19 time: 1.7894 data_time: 0.0523 memory: 16201 loss_prob: 1.1190 loss_thr: 0.5737 loss_db: 0.1765 loss: 1.8692 2022/08/30 01:45:51 - mmengine - INFO - Epoch(train) [101][55/63] lr: 6.4723e-03 eta: 1 day, 3:10:19 time: 1.8536 data_time: 0.0431 memory: 16201 loss_prob: 1.1416 loss_thr: 0.5788 loss_db: 0.1847 loss: 1.9051 2022/08/30 01:46:00 - mmengine - INFO - Epoch(train) [101][60/63] lr: 6.4723e-03 eta: 1 day, 3:10:58 time: 1.9046 data_time: 0.0375 memory: 16201 loss_prob: 1.2735 loss_thr: 0.6026 loss_db: 0.2055 loss: 2.0816 2022/08/30 01:46:05 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:46:17 - mmengine - INFO - Epoch(train) [102][5/63] lr: 6.4670e-03 eta: 1 day, 3:10:58 time: 2.0680 data_time: 0.2452 memory: 16201 loss_prob: 1.1247 loss_thr: 0.5739 loss_db: 0.1825 loss: 1.8811 2022/08/30 01:46:26 - mmengine - INFO - Epoch(train) [102][10/63] lr: 6.4670e-03 eta: 1 day, 3:11:16 time: 2.1738 data_time: 0.2711 memory: 16201 loss_prob: 1.1486 loss_thr: 0.5753 loss_db: 0.1886 loss: 1.9126 2022/08/30 01:46:34 - mmengine - INFO - Epoch(train) [102][15/63] lr: 6.4670e-03 eta: 1 day, 3:11:16 time: 1.7442 data_time: 0.0482 memory: 16201 loss_prob: 1.1289 loss_thr: 0.5846 loss_db: 0.1809 loss: 1.8943 2022/08/30 01:46:44 - mmengine - INFO - Epoch(train) [102][20/63] lr: 6.4670e-03 eta: 1 day, 3:11:45 time: 1.8115 data_time: 0.0397 memory: 16201 loss_prob: 1.0679 loss_thr: 0.5649 loss_db: 0.1715 loss: 1.8044 2022/08/30 01:46:53 - mmengine - INFO - Epoch(train) [102][25/63] lr: 6.4670e-03 eta: 1 day, 3:11:45 time: 1.8540 data_time: 0.0513 memory: 16201 loss_prob: 1.0676 loss_thr: 0.5446 loss_db: 0.1728 loss: 1.7850 2022/08/30 01:47:01 - mmengine - INFO - Epoch(train) [102][30/63] lr: 6.4670e-03 eta: 1 day, 3:12:00 time: 1.6826 data_time: 0.0494 memory: 16201 loss_prob: 1.1938 loss_thr: 0.5568 loss_db: 0.1893 loss: 1.9399 2022/08/30 01:47:10 - mmengine - INFO - Epoch(train) [102][35/63] lr: 6.4670e-03 eta: 1 day, 3:12:00 time: 1.7114 data_time: 0.0629 memory: 16201 loss_prob: 1.1664 loss_thr: 0.5544 loss_db: 0.1839 loss: 1.9047 2022/08/30 01:47:18 - mmengine - INFO - Epoch(train) [102][40/63] lr: 6.4670e-03 eta: 1 day, 3:12:18 time: 1.7082 data_time: 0.0493 memory: 16201 loss_prob: 1.0750 loss_thr: 0.5630 loss_db: 0.1678 loss: 1.8059 2022/08/30 01:47:26 - mmengine - INFO - Epoch(train) [102][45/63] lr: 6.4670e-03 eta: 1 day, 3:12:18 time: 1.6326 data_time: 0.0414 memory: 16201 loss_prob: 1.0417 loss_thr: 0.5756 loss_db: 0.1668 loss: 1.7841 2022/08/30 01:47:34 - mmengine - INFO - Epoch(train) [102][50/63] lr: 6.4670e-03 eta: 1 day, 3:12:23 time: 1.5915 data_time: 0.0567 memory: 16201 loss_prob: 0.9893 loss_thr: 0.5582 loss_db: 0.1646 loss: 1.7121 2022/08/30 01:47:44 - mmengine - INFO - Epoch(train) [102][55/63] lr: 6.4670e-03 eta: 1 day, 3:12:23 time: 1.8012 data_time: 0.0448 memory: 16201 loss_prob: 1.0495 loss_thr: 0.5693 loss_db: 0.1727 loss: 1.7915 2022/08/30 01:47:52 - mmengine - INFO - Epoch(train) [102][60/63] lr: 6.4670e-03 eta: 1 day, 3:12:47 time: 1.7730 data_time: 0.0467 memory: 16201 loss_prob: 1.0358 loss_thr: 0.5638 loss_db: 0.1680 loss: 1.7677 2022/08/30 01:47:56 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:48:07 - mmengine - INFO - Epoch(train) [103][5/63] lr: 6.4617e-03 eta: 1 day, 3:12:47 time: 1.7590 data_time: 0.2531 memory: 16201 loss_prob: 1.0540 loss_thr: 0.5533 loss_db: 0.1748 loss: 1.7821 2022/08/30 01:48:16 - mmengine - INFO - Epoch(train) [103][10/63] lr: 6.4617e-03 eta: 1 day, 3:12:48 time: 2.0202 data_time: 0.2712 memory: 16201 loss_prob: 1.0572 loss_thr: 0.5520 loss_db: 0.1783 loss: 1.7875 2022/08/30 01:48:23 - mmengine - INFO - Epoch(train) [103][15/63] lr: 6.4617e-03 eta: 1 day, 3:12:48 time: 1.5764 data_time: 0.0383 memory: 16201 loss_prob: 1.0016 loss_thr: 0.5537 loss_db: 0.1641 loss: 1.7194 2022/08/30 01:48:31 - mmengine - INFO - Epoch(train) [103][20/63] lr: 6.4617e-03 eta: 1 day, 3:12:39 time: 1.4654 data_time: 0.0504 memory: 16201 loss_prob: 1.0958 loss_thr: 0.5762 loss_db: 0.1756 loss: 1.8476 2022/08/30 01:48:38 - mmengine - INFO - Epoch(train) [103][25/63] lr: 6.4617e-03 eta: 1 day, 3:12:39 time: 1.5031 data_time: 0.0513 memory: 16201 loss_prob: 1.0423 loss_thr: 0.5436 loss_db: 0.1738 loss: 1.7598 2022/08/30 01:48:46 - mmengine - INFO - Epoch(train) [103][30/63] lr: 6.4617e-03 eta: 1 day, 3:12:38 time: 1.5457 data_time: 0.0319 memory: 16201 loss_prob: 0.9826 loss_thr: 0.5169 loss_db: 0.1655 loss: 1.6649 2022/08/30 01:48:55 - mmengine - INFO - Epoch(train) [103][35/63] lr: 6.4617e-03 eta: 1 day, 3:12:38 time: 1.7206 data_time: 0.0463 memory: 16201 loss_prob: 1.0179 loss_thr: 0.5505 loss_db: 0.1663 loss: 1.7348 2022/08/30 01:49:05 - mmengine - INFO - Epoch(train) [103][40/63] lr: 6.4617e-03 eta: 1 day, 3:13:11 time: 1.8511 data_time: 0.0366 memory: 16201 loss_prob: 1.0357 loss_thr: 0.5876 loss_db: 0.1689 loss: 1.7921 2022/08/30 01:49:15 - mmengine - INFO - Epoch(train) [103][45/63] lr: 6.4617e-03 eta: 1 day, 3:13:11 time: 1.9355 data_time: 0.0491 memory: 16201 loss_prob: 1.0621 loss_thr: 0.5809 loss_db: 0.1740 loss: 1.8170 2022/08/30 01:49:23 - mmengine - INFO - Epoch(train) [103][50/63] lr: 6.4617e-03 eta: 1 day, 3:13:35 time: 1.7739 data_time: 0.0664 memory: 16201 loss_prob: 1.0852 loss_thr: 0.5535 loss_db: 0.1794 loss: 1.8180 2022/08/30 01:49:32 - mmengine - INFO - Epoch(train) [103][55/63] lr: 6.4617e-03 eta: 1 day, 3:13:35 time: 1.7314 data_time: 0.0382 memory: 16201 loss_prob: 1.1898 loss_thr: 0.5592 loss_db: 0.1928 loss: 1.9418 2022/08/30 01:49:41 - mmengine - INFO - Epoch(train) [103][60/63] lr: 6.4617e-03 eta: 1 day, 3:14:04 time: 1.8304 data_time: 0.0390 memory: 16201 loss_prob: 1.1736 loss_thr: 0.5773 loss_db: 0.1907 loss: 1.9416 2022/08/30 01:49:45 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:49:57 - mmengine - INFO - Epoch(train) [104][5/63] lr: 6.4564e-03 eta: 1 day, 3:14:04 time: 1.9100 data_time: 0.2839 memory: 16201 loss_prob: 1.0670 loss_thr: 0.5557 loss_db: 0.1777 loss: 1.8003 2022/08/30 01:50:07 - mmengine - INFO - Epoch(train) [104][10/63] lr: 6.4564e-03 eta: 1 day, 3:14:25 time: 2.2145 data_time: 0.3114 memory: 16201 loss_prob: 1.0182 loss_thr: 0.5224 loss_db: 0.1643 loss: 1.7049 2022/08/30 01:50:17 - mmengine - INFO - Epoch(train) [104][15/63] lr: 6.4564e-03 eta: 1 day, 3:14:25 time: 1.9392 data_time: 0.0484 memory: 16201 loss_prob: 0.9854 loss_thr: 0.5291 loss_db: 0.1620 loss: 1.6765 2022/08/30 01:50:26 - mmengine - INFO - Epoch(train) [104][20/63] lr: 6.4564e-03 eta: 1 day, 3:15:04 time: 1.9185 data_time: 0.0403 memory: 16201 loss_prob: 1.0555 loss_thr: 0.5755 loss_db: 0.1732 loss: 1.8041 2022/08/30 01:50:36 - mmengine - INFO - Epoch(train) [104][25/63] lr: 6.4564e-03 eta: 1 day, 3:15:04 time: 1.9079 data_time: 0.0591 memory: 16201 loss_prob: 1.0129 loss_thr: 0.5536 loss_db: 0.1661 loss: 1.7326 2022/08/30 01:50:45 - mmengine - INFO - Epoch(train) [104][30/63] lr: 6.4564e-03 eta: 1 day, 3:15:38 time: 1.8698 data_time: 0.0412 memory: 16201 loss_prob: 1.0317 loss_thr: 0.5546 loss_db: 0.1697 loss: 1.7561 2022/08/30 01:50:52 - mmengine - INFO - Epoch(train) [104][35/63] lr: 6.4564e-03 eta: 1 day, 3:15:38 time: 1.6639 data_time: 0.0448 memory: 16201 loss_prob: 1.0969 loss_thr: 0.5614 loss_db: 0.1805 loss: 1.8389 2022/08/30 01:51:02 - mmengine - INFO - Epoch(train) [104][40/63] lr: 6.4564e-03 eta: 1 day, 3:15:50 time: 1.6721 data_time: 0.0431 memory: 16201 loss_prob: 0.9903 loss_thr: 0.5371 loss_db: 0.1606 loss: 1.6880 2022/08/30 01:51:11 - mmengine - INFO - Epoch(train) [104][45/63] lr: 6.4564e-03 eta: 1 day, 3:15:50 time: 1.8524 data_time: 0.0369 memory: 16201 loss_prob: 0.9622 loss_thr: 0.5313 loss_db: 0.1542 loss: 1.6478 2022/08/30 01:51:21 - mmengine - INFO - Epoch(train) [104][50/63] lr: 6.4564e-03 eta: 1 day, 3:16:22 time: 1.8542 data_time: 0.0525 memory: 16201 loss_prob: 1.1398 loss_thr: 0.5457 loss_db: 0.1795 loss: 1.8650 2022/08/30 01:51:29 - mmengine - INFO - Epoch(train) [104][55/63] lr: 6.4564e-03 eta: 1 day, 3:16:22 time: 1.8059 data_time: 0.0468 memory: 16201 loss_prob: 1.2717 loss_thr: 0.5439 loss_db: 0.1952 loss: 2.0108 2022/08/30 01:51:37 - mmengine - INFO - Epoch(train) [104][60/63] lr: 6.4564e-03 eta: 1 day, 3:16:33 time: 1.6687 data_time: 0.0557 memory: 16201 loss_prob: 1.1044 loss_thr: 0.5422 loss_db: 0.1696 loss: 1.8162 2022/08/30 01:51:40 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:51:52 - mmengine - INFO - Epoch(train) [105][5/63] lr: 6.4511e-03 eta: 1 day, 3:16:33 time: 1.7516 data_time: 0.2543 memory: 16201 loss_prob: 1.0760 loss_thr: 0.5900 loss_db: 0.1790 loss: 1.8449 2022/08/30 01:52:00 - mmengine - INFO - Epoch(train) [105][10/63] lr: 6.4511e-03 eta: 1 day, 3:16:21 time: 1.9060 data_time: 0.2771 memory: 16201 loss_prob: 1.0746 loss_thr: 0.5843 loss_db: 0.1795 loss: 1.8384 2022/08/30 01:52:08 - mmengine - INFO - Epoch(train) [105][15/63] lr: 6.4511e-03 eta: 1 day, 3:16:21 time: 1.6383 data_time: 0.0498 memory: 16201 loss_prob: 0.9571 loss_thr: 0.5260 loss_db: 0.1594 loss: 1.6426 2022/08/30 01:52:17 - mmengine - INFO - Epoch(train) [105][20/63] lr: 6.4511e-03 eta: 1 day, 3:16:42 time: 1.7607 data_time: 0.0476 memory: 16201 loss_prob: 1.0024 loss_thr: 0.5409 loss_db: 0.1632 loss: 1.7065 2022/08/30 01:52:26 - mmengine - INFO - Epoch(train) [105][25/63] lr: 6.4511e-03 eta: 1 day, 3:16:42 time: 1.7705 data_time: 0.0453 memory: 16201 loss_prob: 1.0811 loss_thr: 0.5404 loss_db: 0.1704 loss: 1.7919 2022/08/30 01:52:35 - mmengine - INFO - Epoch(train) [105][30/63] lr: 6.4511e-03 eta: 1 day, 3:17:01 time: 1.7369 data_time: 0.0407 memory: 16201 loss_prob: 1.0634 loss_thr: 0.5434 loss_db: 0.1704 loss: 1.7773 2022/08/30 01:52:43 - mmengine - INFO - Epoch(train) [105][35/63] lr: 6.4511e-03 eta: 1 day, 3:17:01 time: 1.7401 data_time: 0.0528 memory: 16201 loss_prob: 0.9533 loss_thr: 0.5472 loss_db: 0.1606 loss: 1.6612 2022/08/30 01:52:52 - mmengine - INFO - Epoch(train) [105][40/63] lr: 6.4511e-03 eta: 1 day, 3:17:17 time: 1.7144 data_time: 0.0381 memory: 16201 loss_prob: 0.9694 loss_thr: 0.5382 loss_db: 0.1624 loss: 1.6700 2022/08/30 01:52:59 - mmengine - INFO - Epoch(train) [105][45/63] lr: 6.4511e-03 eta: 1 day, 3:17:17 time: 1.5375 data_time: 0.0326 memory: 16201 loss_prob: 1.2160 loss_thr: 0.5616 loss_db: 0.1999 loss: 1.9775 2022/08/30 01:53:06 - mmengine - INFO - Epoch(train) [105][50/63] lr: 6.4511e-03 eta: 1 day, 3:17:06 time: 1.4538 data_time: 0.0443 memory: 16201 loss_prob: 1.3148 loss_thr: 0.5783 loss_db: 0.2170 loss: 2.1102 2022/08/30 01:53:14 - mmengine - INFO - Epoch(train) [105][55/63] lr: 6.4511e-03 eta: 1 day, 3:17:06 time: 1.5021 data_time: 0.0446 memory: 16201 loss_prob: 1.1183 loss_thr: 0.5689 loss_db: 0.1829 loss: 1.8702 2022/08/30 01:53:23 - mmengine - INFO - Epoch(train) [105][60/63] lr: 6.4511e-03 eta: 1 day, 3:17:17 time: 1.6627 data_time: 0.0942 memory: 16201 loss_prob: 1.1404 loss_thr: 0.5778 loss_db: 0.1884 loss: 1.9065 2022/08/30 01:53:27 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:53:38 - mmengine - INFO - Epoch(train) [106][5/63] lr: 6.4458e-03 eta: 1 day, 3:17:17 time: 1.7625 data_time: 0.3153 memory: 16201 loss_prob: 1.2502 loss_thr: 0.5801 loss_db: 0.1963 loss: 2.0265 2022/08/30 01:53:47 - mmengine - INFO - Epoch(train) [106][10/63] lr: 6.4458e-03 eta: 1 day, 3:17:05 time: 1.9145 data_time: 0.3592 memory: 16201 loss_prob: 1.2527 loss_thr: 0.6039 loss_db: 0.1977 loss: 2.0542 2022/08/30 01:53:54 - mmengine - INFO - Epoch(train) [106][15/63] lr: 6.4458e-03 eta: 1 day, 3:17:05 time: 1.6412 data_time: 0.0925 memory: 16201 loss_prob: 1.1832 loss_thr: 0.6160 loss_db: 0.1926 loss: 1.9918 2022/08/30 01:54:05 - mmengine - INFO - Epoch(train) [106][20/63] lr: 6.4458e-03 eta: 1 day, 3:17:38 time: 1.8794 data_time: 0.0885 memory: 16201 loss_prob: 1.0291 loss_thr: 0.5834 loss_db: 0.1683 loss: 1.7807 2022/08/30 01:54:14 - mmengine - INFO - Epoch(train) [106][25/63] lr: 6.4458e-03 eta: 1 day, 3:17:38 time: 1.9534 data_time: 0.1257 memory: 16201 loss_prob: 1.0285 loss_thr: 0.5586 loss_db: 0.1701 loss: 1.7571 2022/08/30 01:54:22 - mmengine - INFO - Epoch(train) [106][30/63] lr: 6.4458e-03 eta: 1 day, 3:17:51 time: 1.6845 data_time: 0.0927 memory: 16201 loss_prob: 1.0910 loss_thr: 0.5474 loss_db: 0.1825 loss: 1.8209 2022/08/30 01:54:31 - mmengine - INFO - Epoch(train) [106][35/63] lr: 6.4458e-03 eta: 1 day, 3:17:51 time: 1.7688 data_time: 0.0865 memory: 16201 loss_prob: 1.4788 loss_thr: 0.5752 loss_db: 0.2387 loss: 2.2927 2022/08/30 01:54:39 - mmengine - INFO - Epoch(train) [106][40/63] lr: 6.4458e-03 eta: 1 day, 3:18:05 time: 1.7032 data_time: 0.0816 memory: 16201 loss_prob: 1.5197 loss_thr: 0.6059 loss_db: 0.2369 loss: 2.3625 2022/08/30 01:54:49 - mmengine - INFO - Epoch(train) [106][45/63] lr: 6.4458e-03 eta: 1 day, 3:18:05 time: 1.7525 data_time: 0.1061 memory: 16201 loss_prob: 1.3088 loss_thr: 0.5991 loss_db: 0.1984 loss: 2.1063 2022/08/30 01:54:58 - mmengine - INFO - Epoch(train) [106][50/63] lr: 6.4458e-03 eta: 1 day, 3:18:35 time: 1.8530 data_time: 0.1082 memory: 16201 loss_prob: 1.3051 loss_thr: 0.5888 loss_db: 0.2034 loss: 2.0973 2022/08/30 01:55:09 - mmengine - INFO - Epoch(train) [106][55/63] lr: 6.4458e-03 eta: 1 day, 3:18:35 time: 1.9837 data_time: 0.0809 memory: 16201 loss_prob: 1.2076 loss_thr: 0.5849 loss_db: 0.1988 loss: 1.9913 2022/08/30 01:55:19 - mmengine - INFO - Epoch(train) [106][60/63] lr: 6.4458e-03 eta: 1 day, 3:19:27 time: 2.0673 data_time: 0.1246 memory: 16201 loss_prob: 1.2227 loss_thr: 0.5883 loss_db: 0.1972 loss: 2.0083 2022/08/30 01:55:24 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:55:37 - mmengine - INFO - Epoch(train) [107][5/63] lr: 6.4405e-03 eta: 1 day, 3:19:27 time: 2.2503 data_time: 0.3096 memory: 16201 loss_prob: 1.2475 loss_thr: 0.5988 loss_db: 0.2005 loss: 2.0469 2022/08/30 01:55:47 - mmengine - INFO - Epoch(train) [107][10/63] lr: 6.4405e-03 eta: 1 day, 3:19:46 time: 2.2197 data_time: 0.3700 memory: 16201 loss_prob: 1.1455 loss_thr: 0.5743 loss_db: 0.1813 loss: 1.9010 2022/08/30 01:55:55 - mmengine - INFO - Epoch(train) [107][15/63] lr: 6.4405e-03 eta: 1 day, 3:19:46 time: 1.7852 data_time: 0.0884 memory: 16201 loss_prob: 1.0896 loss_thr: 0.5626 loss_db: 0.1721 loss: 1.8243 2022/08/30 01:56:05 - mmengine - INFO - Epoch(train) [107][20/63] lr: 6.4405e-03 eta: 1 day, 3:20:16 time: 1.8541 data_time: 0.0526 memory: 16201 loss_prob: 1.0487 loss_thr: 0.5605 loss_db: 0.1685 loss: 1.7777 2022/08/30 01:56:13 - mmengine - INFO - Epoch(train) [107][25/63] lr: 6.4405e-03 eta: 1 day, 3:20:16 time: 1.7866 data_time: 0.0698 memory: 16201 loss_prob: 1.1060 loss_thr: 0.5625 loss_db: 0.1779 loss: 1.8464 2022/08/30 01:56:22 - mmengine - INFO - Epoch(train) [107][30/63] lr: 6.4405e-03 eta: 1 day, 3:20:29 time: 1.7003 data_time: 0.0369 memory: 16201 loss_prob: 1.0687 loss_thr: 0.5674 loss_db: 0.1724 loss: 1.8085 2022/08/30 01:56:30 - mmengine - INFO - Epoch(train) [107][35/63] lr: 6.4405e-03 eta: 1 day, 3:20:29 time: 1.7213 data_time: 0.0379 memory: 16201 loss_prob: 1.0034 loss_thr: 0.5612 loss_db: 0.1662 loss: 1.7308 2022/08/30 01:56:38 - mmengine - INFO - Epoch(train) [107][40/63] lr: 6.4405e-03 eta: 1 day, 3:20:30 time: 1.5748 data_time: 0.0427 memory: 16201 loss_prob: 1.0005 loss_thr: 0.5478 loss_db: 0.1673 loss: 1.7156 2022/08/30 01:56:47 - mmengine - INFO - Epoch(train) [107][45/63] lr: 6.4405e-03 eta: 1 day, 3:20:30 time: 1.6377 data_time: 0.0464 memory: 16201 loss_prob: 1.0408 loss_thr: 0.5511 loss_db: 0.1684 loss: 1.7603 2022/08/30 01:56:56 - mmengine - INFO - Epoch(train) [107][50/63] lr: 6.4405e-03 eta: 1 day, 3:20:52 time: 1.7854 data_time: 0.0598 memory: 16201 loss_prob: 1.1626 loss_thr: 0.5669 loss_db: 0.1826 loss: 1.9120 2022/08/30 01:57:04 - mmengine - INFO - Epoch(train) [107][55/63] lr: 6.4405e-03 eta: 1 day, 3:20:52 time: 1.7126 data_time: 0.0402 memory: 16201 loss_prob: 1.1131 loss_thr: 0.5552 loss_db: 0.1779 loss: 1.8463 2022/08/30 01:57:11 - mmengine - INFO - Epoch(train) [107][60/63] lr: 6.4405e-03 eta: 1 day, 3:20:53 time: 1.5810 data_time: 0.0395 memory: 16201 loss_prob: 1.0293 loss_thr: 0.5406 loss_db: 0.1688 loss: 1.7387 2022/08/30 01:57:15 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:57:26 - mmengine - INFO - Epoch(train) [108][5/63] lr: 6.4352e-03 eta: 1 day, 3:20:53 time: 1.6773 data_time: 0.2647 memory: 16201 loss_prob: 0.9931 loss_thr: 0.5270 loss_db: 0.1597 loss: 1.6798 2022/08/30 01:57:34 - mmengine - INFO - Epoch(train) [108][10/63] lr: 6.4352e-03 eta: 1 day, 3:20:41 time: 1.9233 data_time: 0.2853 memory: 16201 loss_prob: 0.9647 loss_thr: 0.5319 loss_db: 0.1586 loss: 1.6552 2022/08/30 01:57:41 - mmengine - INFO - Epoch(train) [108][15/63] lr: 6.4352e-03 eta: 1 day, 3:20:41 time: 1.5279 data_time: 0.0435 memory: 16201 loss_prob: 0.9991 loss_thr: 0.5527 loss_db: 0.1648 loss: 1.7166 2022/08/30 01:57:49 - mmengine - INFO - Epoch(train) [108][20/63] lr: 6.4352e-03 eta: 1 day, 3:20:32 time: 1.4785 data_time: 0.0401 memory: 16201 loss_prob: 0.9918 loss_thr: 0.5540 loss_db: 0.1580 loss: 1.7038 2022/08/30 01:57:58 - mmengine - INFO - Epoch(train) [108][25/63] lr: 6.4352e-03 eta: 1 day, 3:20:32 time: 1.6807 data_time: 0.0401 memory: 16201 loss_prob: 0.9535 loss_thr: 0.5362 loss_db: 0.1531 loss: 1.6428 2022/08/30 01:58:06 - mmengine - INFO - Epoch(train) [108][30/63] lr: 6.4352e-03 eta: 1 day, 3:20:48 time: 1.7278 data_time: 0.0507 memory: 16201 loss_prob: 0.9143 loss_thr: 0.5411 loss_db: 0.1547 loss: 1.6101 2022/08/30 01:58:14 - mmengine - INFO - Epoch(train) [108][35/63] lr: 6.4352e-03 eta: 1 day, 3:20:48 time: 1.6426 data_time: 0.0635 memory: 16201 loss_prob: 1.0641 loss_thr: 0.5820 loss_db: 0.1767 loss: 1.8228 2022/08/30 01:58:22 - mmengine - INFO - Epoch(train) [108][40/63] lr: 6.4352e-03 eta: 1 day, 3:20:50 time: 1.5881 data_time: 0.0465 memory: 16201 loss_prob: 1.1364 loss_thr: 0.5918 loss_db: 0.1867 loss: 1.9149 2022/08/30 01:58:31 - mmengine - INFO - Epoch(train) [108][45/63] lr: 6.4352e-03 eta: 1 day, 3:20:50 time: 1.6788 data_time: 0.0454 memory: 16201 loss_prob: 1.1083 loss_thr: 0.5822 loss_db: 0.1835 loss: 1.8740 2022/08/30 01:58:40 - mmengine - INFO - Epoch(train) [108][50/63] lr: 6.4352e-03 eta: 1 day, 3:21:17 time: 1.8351 data_time: 0.0432 memory: 16201 loss_prob: 1.0508 loss_thr: 0.5648 loss_db: 0.1742 loss: 1.7899 2022/08/30 01:58:50 - mmengine - INFO - Epoch(train) [108][55/63] lr: 6.4352e-03 eta: 1 day, 3:21:17 time: 1.8644 data_time: 0.0469 memory: 16201 loss_prob: 0.9399 loss_thr: 0.5362 loss_db: 0.1573 loss: 1.6333 2022/08/30 01:58:58 - mmengine - INFO - Epoch(train) [108][60/63] lr: 6.4352e-03 eta: 1 day, 3:21:37 time: 1.7801 data_time: 0.0541 memory: 16201 loss_prob: 1.0321 loss_thr: 0.5723 loss_db: 0.1722 loss: 1.7766 2022/08/30 01:59:03 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 01:59:14 - mmengine - INFO - Epoch(train) [109][5/63] lr: 6.4299e-03 eta: 1 day, 3:21:37 time: 1.9418 data_time: 0.2827 memory: 16201 loss_prob: 1.0368 loss_thr: 0.5703 loss_db: 0.1719 loss: 1.7789 2022/08/30 01:59:22 - mmengine - INFO - Epoch(train) [109][10/63] lr: 6.4299e-03 eta: 1 day, 3:21:25 time: 1.9201 data_time: 0.2945 memory: 16201 loss_prob: 1.1274 loss_thr: 0.5659 loss_db: 0.1804 loss: 1.8737 2022/08/30 01:59:31 - mmengine - INFO - Epoch(train) [109][15/63] lr: 6.4299e-03 eta: 1 day, 3:21:25 time: 1.7160 data_time: 0.0390 memory: 16201 loss_prob: 1.1122 loss_thr: 0.5473 loss_db: 0.1792 loss: 1.8387 2022/08/30 01:59:40 - mmengine - INFO - Epoch(train) [109][20/63] lr: 6.4299e-03 eta: 1 day, 3:21:47 time: 1.7903 data_time: 0.0363 memory: 16201 loss_prob: 0.9467 loss_thr: 0.5419 loss_db: 0.1559 loss: 1.6445 2022/08/30 01:59:48 - mmengine - INFO - Epoch(train) [109][25/63] lr: 6.4299e-03 eta: 1 day, 3:21:47 time: 1.7096 data_time: 0.0405 memory: 16201 loss_prob: 0.9377 loss_thr: 0.5357 loss_db: 0.1550 loss: 1.6285 2022/08/30 01:59:57 - mmengine - INFO - Epoch(train) [109][30/63] lr: 6.4299e-03 eta: 1 day, 3:21:57 time: 1.6751 data_time: 0.0386 memory: 16201 loss_prob: 0.9861 loss_thr: 0.5394 loss_db: 0.1608 loss: 1.6863 2022/08/30 02:00:09 - mmengine - INFO - Epoch(train) [109][35/63] lr: 6.4299e-03 eta: 1 day, 3:21:57 time: 2.0985 data_time: 0.0573 memory: 16201 loss_prob: 0.9574 loss_thr: 0.5286 loss_db: 0.1519 loss: 1.6380 2022/08/30 02:00:18 - mmengine - INFO - Epoch(train) [109][40/63] lr: 6.4299e-03 eta: 1 day, 3:22:53 time: 2.1390 data_time: 0.0627 memory: 16201 loss_prob: 0.9405 loss_thr: 0.5118 loss_db: 0.1537 loss: 1.6060 2022/08/30 02:00:27 - mmengine - INFO - Epoch(train) [109][45/63] lr: 6.4299e-03 eta: 1 day, 3:22:53 time: 1.7920 data_time: 0.0601 memory: 16201 loss_prob: 1.0146 loss_thr: 0.5332 loss_db: 0.1692 loss: 1.7170 2022/08/30 02:00:35 - mmengine - INFO - Epoch(train) [109][50/63] lr: 6.4299e-03 eta: 1 day, 3:23:04 time: 1.6793 data_time: 0.0532 memory: 16201 loss_prob: 1.1645 loss_thr: 0.5989 loss_db: 0.1879 loss: 1.9513 2022/08/30 02:00:43 - mmengine - INFO - Epoch(train) [109][55/63] lr: 6.4299e-03 eta: 1 day, 3:23:04 time: 1.5833 data_time: 0.0438 memory: 16201 loss_prob: 1.2034 loss_thr: 0.6242 loss_db: 0.1986 loss: 2.0261 2022/08/30 02:00:52 - mmengine - INFO - Epoch(train) [109][60/63] lr: 6.4299e-03 eta: 1 day, 3:23:10 time: 1.6370 data_time: 0.0471 memory: 16201 loss_prob: 1.0772 loss_thr: 0.5862 loss_db: 0.1812 loss: 1.8446 2022/08/30 02:00:56 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:01:08 - mmengine - INFO - Epoch(train) [110][5/63] lr: 6.4246e-03 eta: 1 day, 3:23:10 time: 1.8743 data_time: 0.2648 memory: 16201 loss_prob: 1.0450 loss_thr: 0.5457 loss_db: 0.1647 loss: 1.7553 2022/08/30 02:01:17 - mmengine - INFO - Epoch(train) [110][10/63] lr: 6.4246e-03 eta: 1 day, 3:23:20 time: 2.1550 data_time: 0.2868 memory: 16201 loss_prob: 1.1159 loss_thr: 0.5963 loss_db: 0.1778 loss: 1.8900 2022/08/30 02:01:26 - mmengine - INFO - Epoch(train) [110][15/63] lr: 6.4246e-03 eta: 1 day, 3:23:20 time: 1.8146 data_time: 0.0569 memory: 16201 loss_prob: 1.0570 loss_thr: 0.5695 loss_db: 0.1721 loss: 1.7986 2022/08/30 02:01:37 - mmengine - INFO - Epoch(train) [110][20/63] lr: 6.4246e-03 eta: 1 day, 3:23:55 time: 1.9253 data_time: 0.0572 memory: 16201 loss_prob: 0.9676 loss_thr: 0.5346 loss_db: 0.1597 loss: 1.6618 2022/08/30 02:01:45 - mmengine - INFO - Epoch(train) [110][25/63] lr: 6.4246e-03 eta: 1 day, 3:23:55 time: 1.8831 data_time: 0.0516 memory: 16201 loss_prob: 1.0601 loss_thr: 0.5535 loss_db: 0.1698 loss: 1.7835 2022/08/30 02:01:53 - mmengine - INFO - Epoch(train) [110][30/63] lr: 6.4246e-03 eta: 1 day, 3:24:02 time: 1.6495 data_time: 0.0393 memory: 16201 loss_prob: 0.9933 loss_thr: 0.5249 loss_db: 0.1593 loss: 1.6775 2022/08/30 02:02:01 - mmengine - INFO - Epoch(train) [110][35/63] lr: 6.4246e-03 eta: 1 day, 3:24:02 time: 1.5972 data_time: 0.0473 memory: 16201 loss_prob: 1.0573 loss_thr: 0.5273 loss_db: 0.1749 loss: 1.7595 2022/08/30 02:02:08 - mmengine - INFO - Epoch(train) [110][40/63] lr: 6.4246e-03 eta: 1 day, 3:23:55 time: 1.5118 data_time: 0.0308 memory: 16201 loss_prob: 1.0350 loss_thr: 0.5420 loss_db: 0.1741 loss: 1.7510 2022/08/30 02:02:17 - mmengine - INFO - Epoch(train) [110][45/63] lr: 6.4246e-03 eta: 1 day, 3:23:55 time: 1.6150 data_time: 0.0333 memory: 16201 loss_prob: 1.0137 loss_thr: 0.5479 loss_db: 0.1691 loss: 1.7308 2022/08/30 02:02:25 - mmengine - INFO - Epoch(train) [110][50/63] lr: 6.4246e-03 eta: 1 day, 3:24:04 time: 1.6696 data_time: 0.0457 memory: 16201 loss_prob: 1.0993 loss_thr: 0.5689 loss_db: 0.1805 loss: 1.8486 2022/08/30 02:02:34 - mmengine - INFO - Epoch(train) [110][55/63] lr: 6.4246e-03 eta: 1 day, 3:24:04 time: 1.7368 data_time: 0.0370 memory: 16201 loss_prob: 1.0017 loss_thr: 0.5626 loss_db: 0.1653 loss: 1.7296 2022/08/30 02:02:43 - mmengine - INFO - Epoch(train) [110][60/63] lr: 6.4246e-03 eta: 1 day, 3:24:22 time: 1.7682 data_time: 0.0389 memory: 16201 loss_prob: 1.0160 loss_thr: 0.5661 loss_db: 0.1677 loss: 1.7498 2022/08/30 02:02:48 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:02:59 - mmengine - INFO - Epoch(train) [111][5/63] lr: 6.4193e-03 eta: 1 day, 3:24:22 time: 1.9694 data_time: 0.2941 memory: 16201 loss_prob: 0.9880 loss_thr: 0.5321 loss_db: 0.1606 loss: 1.6808 2022/08/30 02:03:08 - mmengine - INFO - Epoch(train) [111][10/63] lr: 6.4193e-03 eta: 1 day, 3:24:17 time: 2.0035 data_time: 0.3018 memory: 16201 loss_prob: 1.0050 loss_thr: 0.5387 loss_db: 0.1657 loss: 1.7095 2022/08/30 02:03:16 - mmengine - INFO - Epoch(train) [111][15/63] lr: 6.4193e-03 eta: 1 day, 3:24:17 time: 1.7090 data_time: 0.0406 memory: 16201 loss_prob: 0.9630 loss_thr: 0.5234 loss_db: 0.1569 loss: 1.6432 2022/08/30 02:03:25 - mmengine - INFO - Epoch(train) [111][20/63] lr: 6.4193e-03 eta: 1 day, 3:24:28 time: 1.6921 data_time: 0.0438 memory: 16201 loss_prob: 0.9262 loss_thr: 0.5344 loss_db: 0.1504 loss: 1.6110 2022/08/30 02:03:34 - mmengine - INFO - Epoch(train) [111][25/63] lr: 6.4193e-03 eta: 1 day, 3:24:28 time: 1.8275 data_time: 0.0494 memory: 16201 loss_prob: 0.9267 loss_thr: 0.5447 loss_db: 0.1529 loss: 1.6243 2022/08/30 02:03:44 - mmengine - INFO - Epoch(train) [111][30/63] lr: 6.4193e-03 eta: 1 day, 3:25:06 time: 1.9680 data_time: 0.0425 memory: 16201 loss_prob: 0.9229 loss_thr: 0.5286 loss_db: 0.1539 loss: 1.6054 2022/08/30 02:03:53 - mmengine - INFO - Epoch(train) [111][35/63] lr: 6.4193e-03 eta: 1 day, 3:25:06 time: 1.8337 data_time: 0.0433 memory: 16201 loss_prob: 0.9614 loss_thr: 0.5319 loss_db: 0.1600 loss: 1.6532 2022/08/30 02:04:00 - mmengine - INFO - Epoch(train) [111][40/63] lr: 6.4193e-03 eta: 1 day, 3:25:07 time: 1.5984 data_time: 0.0349 memory: 16201 loss_prob: 0.9256 loss_thr: 0.5131 loss_db: 0.1531 loss: 1.5918 2022/08/30 02:04:10 - mmengine - INFO - Epoch(train) [111][45/63] lr: 6.4193e-03 eta: 1 day, 3:25:07 time: 1.7501 data_time: 0.0444 memory: 16201 loss_prob: 0.9540 loss_thr: 0.5166 loss_db: 0.1569 loss: 1.6275 2022/08/30 02:04:20 - mmengine - INFO - Epoch(train) [111][50/63] lr: 6.4193e-03 eta: 1 day, 3:25:41 time: 1.9327 data_time: 0.0518 memory: 16201 loss_prob: 1.0835 loss_thr: 0.5466 loss_db: 0.1745 loss: 1.8046 2022/08/30 02:04:28 - mmengine - INFO - Epoch(train) [111][55/63] lr: 6.4193e-03 eta: 1 day, 3:25:41 time: 1.7995 data_time: 0.0384 memory: 16201 loss_prob: 1.0706 loss_thr: 0.5483 loss_db: 0.1736 loss: 1.7926 2022/08/30 02:04:37 - mmengine - INFO - Epoch(train) [111][60/63] lr: 6.4193e-03 eta: 1 day, 3:25:54 time: 1.7163 data_time: 0.0475 memory: 16201 loss_prob: 1.0508 loss_thr: 0.5440 loss_db: 0.1701 loss: 1.7650 2022/08/30 02:04:41 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:04:52 - mmengine - INFO - Epoch(train) [112][5/63] lr: 6.4140e-03 eta: 1 day, 3:25:54 time: 1.9099 data_time: 0.2706 memory: 16201 loss_prob: 0.9810 loss_thr: 0.5209 loss_db: 0.1632 loss: 1.6651 2022/08/30 02:04:56 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:05:01 - mmengine - INFO - Epoch(train) [112][10/63] lr: 6.4140e-03 eta: 1 day, 3:25:44 time: 1.9596 data_time: 0.2970 memory: 16201 loss_prob: 0.8834 loss_thr: 0.5080 loss_db: 0.1477 loss: 1.5390 2022/08/30 02:05:10 - mmengine - INFO - Epoch(train) [112][15/63] lr: 6.4140e-03 eta: 1 day, 3:25:44 time: 1.7377 data_time: 0.0486 memory: 16201 loss_prob: 0.9515 loss_thr: 0.5023 loss_db: 0.1555 loss: 1.6093 2022/08/30 02:05:19 - mmengine - INFO - Epoch(train) [112][20/63] lr: 6.4140e-03 eta: 1 day, 3:26:07 time: 1.8211 data_time: 0.0464 memory: 16201 loss_prob: 1.0006 loss_thr: 0.5117 loss_db: 0.1652 loss: 1.6774 2022/08/30 02:05:26 - mmengine - INFO - Epoch(train) [112][25/63] lr: 6.4140e-03 eta: 1 day, 3:26:07 time: 1.6180 data_time: 0.0932 memory: 16201 loss_prob: 0.9198 loss_thr: 0.5107 loss_db: 0.1538 loss: 1.5843 2022/08/30 02:05:35 - mmengine - INFO - Epoch(train) [112][30/63] lr: 6.4140e-03 eta: 1 day, 3:26:09 time: 1.6030 data_time: 0.0810 memory: 16201 loss_prob: 0.8898 loss_thr: 0.5145 loss_db: 0.1442 loss: 1.5485 2022/08/30 02:05:43 - mmengine - INFO - Epoch(train) [112][35/63] lr: 6.4140e-03 eta: 1 day, 3:26:09 time: 1.6566 data_time: 0.0490 memory: 16201 loss_prob: 0.9973 loss_thr: 0.5419 loss_db: 0.1565 loss: 1.6957 2022/08/30 02:05:51 - mmengine - INFO - Epoch(train) [112][40/63] lr: 6.4140e-03 eta: 1 day, 3:26:13 time: 1.6294 data_time: 0.0415 memory: 16201 loss_prob: 1.0955 loss_thr: 0.5718 loss_db: 0.1724 loss: 1.8396 2022/08/30 02:06:00 - mmengine - INFO - Epoch(train) [112][45/63] lr: 6.4140e-03 eta: 1 day, 3:26:13 time: 1.6975 data_time: 0.0372 memory: 16201 loss_prob: 1.0272 loss_thr: 0.5415 loss_db: 0.1653 loss: 1.7341 2022/08/30 02:06:10 - mmengine - INFO - Epoch(train) [112][50/63] lr: 6.4140e-03 eta: 1 day, 3:26:38 time: 1.8531 data_time: 0.0521 memory: 16201 loss_prob: 0.9690 loss_thr: 0.5184 loss_db: 0.1568 loss: 1.6441 2022/08/30 02:06:18 - mmengine - INFO - Epoch(train) [112][55/63] lr: 6.4140e-03 eta: 1 day, 3:26:38 time: 1.8159 data_time: 0.0459 memory: 16201 loss_prob: 1.0465 loss_thr: 0.5419 loss_db: 0.1760 loss: 1.7644 2022/08/30 02:06:27 - mmengine - INFO - Epoch(train) [112][60/63] lr: 6.4140e-03 eta: 1 day, 3:26:46 time: 1.6632 data_time: 0.0517 memory: 16201 loss_prob: 1.0403 loss_thr: 0.5409 loss_db: 0.1779 loss: 1.7591 2022/08/30 02:06:30 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:06:40 - mmengine - INFO - Epoch(train) [113][5/63] lr: 6.4087e-03 eta: 1 day, 3:26:46 time: 1.6382 data_time: 0.2768 memory: 16201 loss_prob: 1.0045 loss_thr: 0.5393 loss_db: 0.1661 loss: 1.7099 2022/08/30 02:06:47 - mmengine - INFO - Epoch(train) [113][10/63] lr: 6.4087e-03 eta: 1 day, 3:26:17 time: 1.7699 data_time: 0.2922 memory: 16201 loss_prob: 1.0011 loss_thr: 0.5547 loss_db: 0.1650 loss: 1.7209 2022/08/30 02:06:54 - mmengine - INFO - Epoch(train) [113][15/63] lr: 6.4087e-03 eta: 1 day, 3:26:17 time: 1.4410 data_time: 0.0388 memory: 16201 loss_prob: 1.0543 loss_thr: 0.5566 loss_db: 0.1731 loss: 1.7841 2022/08/30 02:07:03 - mmengine - INFO - Epoch(train) [113][20/63] lr: 6.4087e-03 eta: 1 day, 3:26:11 time: 1.5310 data_time: 0.0322 memory: 16201 loss_prob: 0.9616 loss_thr: 0.5234 loss_db: 0.1622 loss: 1.6472 2022/08/30 02:07:11 - mmengine - INFO - Epoch(train) [113][25/63] lr: 6.4087e-03 eta: 1 day, 3:26:11 time: 1.7471 data_time: 0.0404 memory: 16201 loss_prob: 0.9385 loss_thr: 0.5127 loss_db: 0.1583 loss: 1.6096 2022/08/30 02:07:20 - mmengine - INFO - Epoch(train) [113][30/63] lr: 6.4087e-03 eta: 1 day, 3:26:27 time: 1.7549 data_time: 0.0397 memory: 16201 loss_prob: 1.1012 loss_thr: 0.5652 loss_db: 0.1798 loss: 1.8463 2022/08/30 02:07:28 - mmengine - INFO - Epoch(train) [113][35/63] lr: 6.4087e-03 eta: 1 day, 3:26:27 time: 1.6783 data_time: 0.0469 memory: 16201 loss_prob: 1.1698 loss_thr: 0.5859 loss_db: 0.1896 loss: 1.9453 2022/08/30 02:07:37 - mmengine - INFO - Epoch(train) [113][40/63] lr: 6.4087e-03 eta: 1 day, 3:26:37 time: 1.6907 data_time: 0.0350 memory: 16201 loss_prob: 1.0401 loss_thr: 0.5623 loss_db: 0.1713 loss: 1.7737 2022/08/30 02:07:46 - mmengine - INFO - Epoch(train) [113][45/63] lr: 6.4087e-03 eta: 1 day, 3:26:37 time: 1.8143 data_time: 0.0363 memory: 16201 loss_prob: 0.9312 loss_thr: 0.5388 loss_db: 0.1542 loss: 1.6242 2022/08/30 02:07:54 - mmengine - INFO - Epoch(train) [113][50/63] lr: 6.4087e-03 eta: 1 day, 3:26:51 time: 1.7437 data_time: 0.0570 memory: 16201 loss_prob: 0.9821 loss_thr: 0.5398 loss_db: 0.1622 loss: 1.6841 2022/08/30 02:08:03 - mmengine - INFO - Epoch(train) [113][55/63] lr: 6.4087e-03 eta: 1 day, 3:26:51 time: 1.6777 data_time: 0.0395 memory: 16201 loss_prob: 1.0156 loss_thr: 0.5561 loss_db: 0.1673 loss: 1.7390 2022/08/30 02:08:11 - mmengine - INFO - Epoch(train) [113][60/63] lr: 6.4087e-03 eta: 1 day, 3:26:56 time: 1.6428 data_time: 0.0335 memory: 16201 loss_prob: 1.0058 loss_thr: 0.5691 loss_db: 0.1661 loss: 1.7410 2022/08/30 02:08:15 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:08:26 - mmengine - INFO - Epoch(train) [114][5/63] lr: 6.4034e-03 eta: 1 day, 3:26:56 time: 1.7860 data_time: 0.2667 memory: 16201 loss_prob: 1.0222 loss_thr: 0.5626 loss_db: 0.1664 loss: 1.7512 2022/08/30 02:08:34 - mmengine - INFO - Epoch(train) [114][10/63] lr: 6.4034e-03 eta: 1 day, 3:26:34 time: 1.8403 data_time: 0.2769 memory: 16201 loss_prob: 0.9134 loss_thr: 0.5303 loss_db: 0.1481 loss: 1.5918 2022/08/30 02:08:43 - mmengine - INFO - Epoch(train) [114][15/63] lr: 6.4034e-03 eta: 1 day, 3:26:34 time: 1.6655 data_time: 0.0458 memory: 16201 loss_prob: 0.8227 loss_thr: 0.4988 loss_db: 0.1361 loss: 1.4576 2022/08/30 02:08:52 - mmengine - INFO - Epoch(train) [114][20/63] lr: 6.4034e-03 eta: 1 day, 3:26:53 time: 1.7907 data_time: 0.0566 memory: 16201 loss_prob: 0.9009 loss_thr: 0.5178 loss_db: 0.1522 loss: 1.5709 2022/08/30 02:09:00 - mmengine - INFO - Epoch(train) [114][25/63] lr: 6.4034e-03 eta: 1 day, 3:26:53 time: 1.7880 data_time: 0.0407 memory: 16201 loss_prob: 1.0030 loss_thr: 0.5509 loss_db: 0.1641 loss: 1.7180 2022/08/30 02:09:08 - mmengine - INFO - Epoch(train) [114][30/63] lr: 6.4034e-03 eta: 1 day, 3:27:01 time: 1.6755 data_time: 0.0373 memory: 16201 loss_prob: 1.0439 loss_thr: 0.5647 loss_db: 0.1691 loss: 1.7776 2022/08/30 02:09:17 - mmengine - INFO - Epoch(train) [114][35/63] lr: 6.4034e-03 eta: 1 day, 3:27:01 time: 1.6645 data_time: 0.0560 memory: 16201 loss_prob: 1.1182 loss_thr: 0.5496 loss_db: 0.1770 loss: 1.8448 2022/08/30 02:09:27 - mmengine - INFO - Epoch(train) [114][40/63] lr: 6.4034e-03 eta: 1 day, 3:27:22 time: 1.8161 data_time: 0.0458 memory: 16201 loss_prob: 1.1080 loss_thr: 0.5550 loss_db: 0.1724 loss: 1.8353 2022/08/30 02:09:34 - mmengine - INFO - Epoch(train) [114][45/63] lr: 6.4034e-03 eta: 1 day, 3:27:22 time: 1.7286 data_time: 0.0485 memory: 16201 loss_prob: 0.9685 loss_thr: 0.5396 loss_db: 0.1596 loss: 1.6677 2022/08/30 02:09:43 - mmengine - INFO - Epoch(train) [114][50/63] lr: 6.4034e-03 eta: 1 day, 3:27:30 time: 1.6836 data_time: 0.0504 memory: 16201 loss_prob: 0.9023 loss_thr: 0.5195 loss_db: 0.1502 loss: 1.5719 2022/08/30 02:09:53 - mmengine - INFO - Epoch(train) [114][55/63] lr: 6.4034e-03 eta: 1 day, 3:27:30 time: 1.8450 data_time: 0.0355 memory: 16201 loss_prob: 0.8919 loss_thr: 0.5269 loss_db: 0.1411 loss: 1.5599 2022/08/30 02:10:02 - mmengine - INFO - Epoch(train) [114][60/63] lr: 6.4034e-03 eta: 1 day, 3:27:51 time: 1.8171 data_time: 0.0507 memory: 16201 loss_prob: 0.9982 loss_thr: 0.5442 loss_db: 0.1611 loss: 1.7035 2022/08/30 02:10:07 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:10:19 - mmengine - INFO - Epoch(train) [115][5/63] lr: 6.3981e-03 eta: 1 day, 3:27:51 time: 2.1094 data_time: 0.2606 memory: 16201 loss_prob: 0.9690 loss_thr: 0.5479 loss_db: 0.1606 loss: 1.6775 2022/08/30 02:10:28 - mmengine - INFO - Epoch(train) [115][10/63] lr: 6.3981e-03 eta: 1 day, 3:27:52 time: 2.0850 data_time: 0.2819 memory: 16201 loss_prob: 0.9844 loss_thr: 0.5584 loss_db: 0.1633 loss: 1.7061 2022/08/30 02:10:35 - mmengine - INFO - Epoch(train) [115][15/63] lr: 6.3981e-03 eta: 1 day, 3:27:52 time: 1.5772 data_time: 0.0463 memory: 16201 loss_prob: 0.9664 loss_thr: 0.5258 loss_db: 0.1589 loss: 1.6510 2022/08/30 02:10:44 - mmengine - INFO - Epoch(train) [115][20/63] lr: 6.3981e-03 eta: 1 day, 3:27:54 time: 1.6218 data_time: 0.0328 memory: 16201 loss_prob: 0.9072 loss_thr: 0.4983 loss_db: 0.1456 loss: 1.5511 2022/08/30 02:10:53 - mmengine - INFO - Epoch(train) [115][25/63] lr: 6.3981e-03 eta: 1 day, 3:27:54 time: 1.8134 data_time: 0.0457 memory: 16201 loss_prob: 0.9339 loss_thr: 0.5212 loss_db: 0.1483 loss: 1.6034 2022/08/30 02:11:01 - mmengine - INFO - Epoch(train) [115][30/63] lr: 6.3981e-03 eta: 1 day, 3:28:07 time: 1.7293 data_time: 0.0392 memory: 16201 loss_prob: 0.9524 loss_thr: 0.5327 loss_db: 0.1531 loss: 1.6382 2022/08/30 02:11:09 - mmengine - INFO - Epoch(train) [115][35/63] lr: 6.3981e-03 eta: 1 day, 3:28:07 time: 1.5632 data_time: 0.0376 memory: 16201 loss_prob: 0.9680 loss_thr: 0.5216 loss_db: 0.1613 loss: 1.6509 2022/08/30 02:11:16 - mmengine - INFO - Epoch(train) [115][40/63] lr: 6.3981e-03 eta: 1 day, 3:27:56 time: 1.4890 data_time: 0.0347 memory: 16201 loss_prob: 0.9342 loss_thr: 0.5111 loss_db: 0.1551 loss: 1.6004 2022/08/30 02:11:23 - mmengine - INFO - Epoch(train) [115][45/63] lr: 6.3981e-03 eta: 1 day, 3:27:56 time: 1.3941 data_time: 0.0326 memory: 16201 loss_prob: 0.9761 loss_thr: 0.5438 loss_db: 0.1561 loss: 1.6760 2022/08/30 02:11:32 - mmengine - INFO - Epoch(train) [115][50/63] lr: 6.3981e-03 eta: 1 day, 3:27:55 time: 1.5855 data_time: 0.0452 memory: 16201 loss_prob: 1.0471 loss_thr: 0.5755 loss_db: 0.1708 loss: 1.7934 2022/08/30 02:11:41 - mmengine - INFO - Epoch(train) [115][55/63] lr: 6.3981e-03 eta: 1 day, 3:27:55 time: 1.8174 data_time: 0.0326 memory: 16201 loss_prob: 1.1310 loss_thr: 0.5933 loss_db: 0.1796 loss: 1.9039 2022/08/30 02:11:49 - mmengine - INFO - Epoch(train) [115][60/63] lr: 6.3981e-03 eta: 1 day, 3:28:06 time: 1.7127 data_time: 0.0418 memory: 16201 loss_prob: 1.1470 loss_thr: 0.5786 loss_db: 0.1824 loss: 1.9080 2022/08/30 02:11:54 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:12:06 - mmengine - INFO - Epoch(train) [116][5/63] lr: 6.3927e-03 eta: 1 day, 3:28:06 time: 1.9400 data_time: 0.2937 memory: 16201 loss_prob: 0.9712 loss_thr: 0.5409 loss_db: 0.1572 loss: 1.6693 2022/08/30 02:12:15 - mmengine - INFO - Epoch(train) [116][10/63] lr: 6.3927e-03 eta: 1 day, 3:28:12 time: 2.1450 data_time: 0.3192 memory: 16201 loss_prob: 0.8620 loss_thr: 0.5211 loss_db: 0.1420 loss: 1.5251 2022/08/30 02:12:23 - mmengine - INFO - Epoch(train) [116][15/63] lr: 6.3927e-03 eta: 1 day, 3:28:12 time: 1.7206 data_time: 0.0424 memory: 16201 loss_prob: 0.9159 loss_thr: 0.5519 loss_db: 0.1514 loss: 1.6192 2022/08/30 02:12:32 - mmengine - INFO - Epoch(train) [116][20/63] lr: 6.3927e-03 eta: 1 day, 3:28:16 time: 1.6435 data_time: 0.0334 memory: 16201 loss_prob: 0.9433 loss_thr: 0.5608 loss_db: 0.1577 loss: 1.6619 2022/08/30 02:12:40 - mmengine - INFO - Epoch(train) [116][25/63] lr: 6.3927e-03 eta: 1 day, 3:28:16 time: 1.6909 data_time: 0.0414 memory: 16201 loss_prob: 0.9247 loss_thr: 0.5307 loss_db: 0.1561 loss: 1.6114 2022/08/30 02:12:49 - mmengine - INFO - Epoch(train) [116][30/63] lr: 6.3927e-03 eta: 1 day, 3:28:28 time: 1.7303 data_time: 0.0464 memory: 16201 loss_prob: 1.0321 loss_thr: 0.5496 loss_db: 0.1688 loss: 1.7506 2022/08/30 02:12:57 - mmengine - INFO - Epoch(train) [116][35/63] lr: 6.3927e-03 eta: 1 day, 3:28:28 time: 1.7189 data_time: 0.0608 memory: 16201 loss_prob: 0.9976 loss_thr: 0.5324 loss_db: 0.1625 loss: 1.6926 2022/08/30 02:13:06 - mmengine - INFO - Epoch(train) [116][40/63] lr: 6.3927e-03 eta: 1 day, 3:28:41 time: 1.7402 data_time: 0.0429 memory: 16201 loss_prob: 0.9932 loss_thr: 0.5605 loss_db: 0.1638 loss: 1.7174 2022/08/30 02:13:15 - mmengine - INFO - Epoch(train) [116][45/63] lr: 6.3927e-03 eta: 1 day, 3:28:41 time: 1.7725 data_time: 0.0321 memory: 16201 loss_prob: 0.9922 loss_thr: 0.5731 loss_db: 0.1633 loss: 1.7286 2022/08/30 02:13:24 - mmengine - INFO - Epoch(train) [116][50/63] lr: 6.3927e-03 eta: 1 day, 3:28:57 time: 1.7782 data_time: 0.0469 memory: 16201 loss_prob: 0.9234 loss_thr: 0.5254 loss_db: 0.1531 loss: 1.6019 2022/08/30 02:13:33 - mmengine - INFO - Epoch(train) [116][55/63] lr: 6.3927e-03 eta: 1 day, 3:28:57 time: 1.8166 data_time: 0.0325 memory: 16201 loss_prob: 0.9644 loss_thr: 0.5390 loss_db: 0.1585 loss: 1.6619 2022/08/30 02:13:42 - mmengine - INFO - Epoch(train) [116][60/63] lr: 6.3927e-03 eta: 1 day, 3:29:11 time: 1.7497 data_time: 0.0391 memory: 16201 loss_prob: 0.9922 loss_thr: 0.5416 loss_db: 0.1633 loss: 1.6972 2022/08/30 02:13:45 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:13:57 - mmengine - INFO - Epoch(train) [117][5/63] lr: 6.3874e-03 eta: 1 day, 3:29:11 time: 1.8494 data_time: 0.2539 memory: 16201 loss_prob: 0.9920 loss_thr: 0.5495 loss_db: 0.1647 loss: 1.7062 2022/08/30 02:14:06 - mmengine - INFO - Epoch(train) [117][10/63] lr: 6.3874e-03 eta: 1 day, 3:29:04 time: 2.0117 data_time: 0.2844 memory: 16201 loss_prob: 1.0028 loss_thr: 0.5553 loss_db: 0.1649 loss: 1.7230 2022/08/30 02:14:14 - mmengine - INFO - Epoch(train) [117][15/63] lr: 6.3874e-03 eta: 1 day, 3:29:04 time: 1.7001 data_time: 0.0449 memory: 16201 loss_prob: 1.0088 loss_thr: 0.5589 loss_db: 0.1678 loss: 1.7354 2022/08/30 02:14:23 - mmengine - INFO - Epoch(train) [117][20/63] lr: 6.3874e-03 eta: 1 day, 3:29:14 time: 1.7089 data_time: 0.0349 memory: 16201 loss_prob: 0.9799 loss_thr: 0.5676 loss_db: 0.1642 loss: 1.7116 2022/08/30 02:14:32 - mmengine - INFO - Epoch(train) [117][25/63] lr: 6.3874e-03 eta: 1 day, 3:29:14 time: 1.8687 data_time: 0.0382 memory: 16201 loss_prob: 0.9393 loss_thr: 0.5483 loss_db: 0.1591 loss: 1.6466 2022/08/30 02:14:41 - mmengine - INFO - Epoch(train) [117][30/63] lr: 6.3874e-03 eta: 1 day, 3:29:38 time: 1.8586 data_time: 0.0375 memory: 16201 loss_prob: 0.8822 loss_thr: 0.5114 loss_db: 0.1477 loss: 1.5413 2022/08/30 02:14:50 - mmengine - INFO - Epoch(train) [117][35/63] lr: 6.3874e-03 eta: 1 day, 3:29:38 time: 1.7813 data_time: 0.0513 memory: 16201 loss_prob: 0.9105 loss_thr: 0.5272 loss_db: 0.1485 loss: 1.5861 2022/08/30 02:14:59 - mmengine - INFO - Epoch(train) [117][40/63] lr: 6.3874e-03 eta: 1 day, 3:29:56 time: 1.8096 data_time: 0.0385 memory: 16201 loss_prob: 0.9747 loss_thr: 0.5643 loss_db: 0.1625 loss: 1.7015 2022/08/30 02:15:08 - mmengine - INFO - Epoch(train) [117][45/63] lr: 6.3874e-03 eta: 1 day, 3:29:56 time: 1.7466 data_time: 0.0365 memory: 16201 loss_prob: 0.9394 loss_thr: 0.5403 loss_db: 0.1581 loss: 1.6378 2022/08/30 02:15:18 - mmengine - INFO - Epoch(train) [117][50/63] lr: 6.3874e-03 eta: 1 day, 3:30:23 time: 1.8977 data_time: 0.0539 memory: 16201 loss_prob: 0.9977 loss_thr: 0.5312 loss_db: 0.1632 loss: 1.6921 2022/08/30 02:15:25 - mmengine - INFO - Epoch(train) [117][55/63] lr: 6.3874e-03 eta: 1 day, 3:30:23 time: 1.7520 data_time: 0.0502 memory: 16201 loss_prob: 0.9680 loss_thr: 0.5134 loss_db: 0.1591 loss: 1.6404 2022/08/30 02:15:34 - mmengine - INFO - Epoch(train) [117][60/63] lr: 6.3874e-03 eta: 1 day, 3:30:21 time: 1.5843 data_time: 0.0407 memory: 16201 loss_prob: 0.9276 loss_thr: 0.5053 loss_db: 0.1564 loss: 1.5892 2022/08/30 02:15:38 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:15:48 - mmengine - INFO - Epoch(train) [118][5/63] lr: 6.3821e-03 eta: 1 day, 3:30:21 time: 1.7660 data_time: 0.2546 memory: 16201 loss_prob: 0.9659 loss_thr: 0.5446 loss_db: 0.1614 loss: 1.6720 2022/08/30 02:15:55 - mmengine - INFO - Epoch(train) [118][10/63] lr: 6.3821e-03 eta: 1 day, 3:29:48 time: 1.7294 data_time: 0.2606 memory: 16201 loss_prob: 0.9788 loss_thr: 0.5295 loss_db: 0.1615 loss: 1.6698 2022/08/30 02:16:04 - mmengine - INFO - Epoch(train) [118][15/63] lr: 6.3821e-03 eta: 1 day, 3:29:48 time: 1.5898 data_time: 0.0451 memory: 16201 loss_prob: 0.9619 loss_thr: 0.5111 loss_db: 0.1592 loss: 1.6322 2022/08/30 02:16:13 - mmengine - INFO - Epoch(train) [118][20/63] lr: 6.3821e-03 eta: 1 day, 3:30:06 time: 1.7992 data_time: 0.0443 memory: 16201 loss_prob: 1.0431 loss_thr: 0.5657 loss_db: 0.1718 loss: 1.7806 2022/08/30 02:16:22 - mmengine - INFO - Epoch(train) [118][25/63] lr: 6.3821e-03 eta: 1 day, 3:30:06 time: 1.8100 data_time: 0.0420 memory: 16201 loss_prob: 0.9853 loss_thr: 0.5512 loss_db: 0.1661 loss: 1.7026 2022/08/30 02:16:30 - mmengine - INFO - Epoch(train) [118][30/63] lr: 6.3821e-03 eta: 1 day, 3:30:14 time: 1.7051 data_time: 0.0375 memory: 16201 loss_prob: 0.8902 loss_thr: 0.5241 loss_db: 0.1507 loss: 1.5651 2022/08/30 02:16:38 - mmengine - INFO - Epoch(train) [118][35/63] lr: 6.3821e-03 eta: 1 day, 3:30:14 time: 1.6118 data_time: 0.0426 memory: 16201 loss_prob: 0.9772 loss_thr: 0.5568 loss_db: 0.1672 loss: 1.7011 2022/08/30 02:16:47 - mmengine - INFO - Epoch(train) [118][40/63] lr: 6.3821e-03 eta: 1 day, 3:30:19 time: 1.6588 data_time: 0.0456 memory: 16201 loss_prob: 1.0130 loss_thr: 0.5722 loss_db: 0.1744 loss: 1.7596 2022/08/30 02:16:55 - mmengine - INFO - Epoch(train) [118][45/63] lr: 6.3821e-03 eta: 1 day, 3:30:19 time: 1.7274 data_time: 0.0492 memory: 16201 loss_prob: 0.9927 loss_thr: 0.5471 loss_db: 0.1654 loss: 1.7052 2022/08/30 02:17:05 - mmengine - INFO - Epoch(train) [118][50/63] lr: 6.3821e-03 eta: 1 day, 3:30:38 time: 1.8162 data_time: 0.0481 memory: 16201 loss_prob: 1.0083 loss_thr: 0.5351 loss_db: 0.1700 loss: 1.7134 2022/08/30 02:17:14 - mmengine - INFO - Epoch(train) [118][55/63] lr: 6.3821e-03 eta: 1 day, 3:30:38 time: 1.8230 data_time: 0.0378 memory: 16201 loss_prob: 0.9946 loss_thr: 0.5491 loss_db: 0.1717 loss: 1.7154 2022/08/30 02:17:23 - mmengine - INFO - Epoch(train) [118][60/63] lr: 6.3821e-03 eta: 1 day, 3:30:53 time: 1.7788 data_time: 0.0385 memory: 16201 loss_prob: 1.0247 loss_thr: 0.5453 loss_db: 0.1702 loss: 1.7402 2022/08/30 02:17:27 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:17:39 - mmengine - INFO - Epoch(train) [119][5/63] lr: 6.3768e-03 eta: 1 day, 3:30:53 time: 1.9512 data_time: 0.2589 memory: 16201 loss_prob: 1.1612 loss_thr: 0.5544 loss_db: 0.1900 loss: 1.9056 2022/08/30 02:17:48 - mmengine - INFO - Epoch(train) [119][10/63] lr: 6.3768e-03 eta: 1 day, 3:30:57 time: 2.1365 data_time: 0.2872 memory: 16201 loss_prob: 1.1326 loss_thr: 0.5536 loss_db: 0.1900 loss: 1.8762 2022/08/30 02:17:57 - mmengine - INFO - Epoch(train) [119][15/63] lr: 6.3768e-03 eta: 1 day, 3:30:57 time: 1.7466 data_time: 0.0427 memory: 16201 loss_prob: 1.0863 loss_thr: 0.5644 loss_db: 0.1868 loss: 1.8374 2022/08/30 02:18:05 - mmengine - INFO - Epoch(train) [119][20/63] lr: 6.3768e-03 eta: 1 day, 3:31:00 time: 1.6483 data_time: 0.0328 memory: 16201 loss_prob: 1.1616 loss_thr: 0.5641 loss_db: 0.1915 loss: 1.9172 2022/08/30 02:18:14 - mmengine - INFO - Epoch(train) [119][25/63] lr: 6.3768e-03 eta: 1 day, 3:31:00 time: 1.7043 data_time: 0.0458 memory: 16201 loss_prob: 1.2347 loss_thr: 0.5807 loss_db: 0.1991 loss: 2.0145 2022/08/30 02:18:24 - mmengine - INFO - Epoch(train) [119][30/63] lr: 6.3768e-03 eta: 1 day, 3:31:22 time: 1.8560 data_time: 0.0365 memory: 16201 loss_prob: 1.0559 loss_thr: 0.5509 loss_db: 0.1743 loss: 1.7811 2022/08/30 02:18:33 - mmengine - INFO - Epoch(train) [119][35/63] lr: 6.3768e-03 eta: 1 day, 3:31:22 time: 1.9117 data_time: 0.0480 memory: 16201 loss_prob: 0.9876 loss_thr: 0.5474 loss_db: 0.1658 loss: 1.7008 2022/08/30 02:18:42 - mmengine - INFO - Epoch(train) [119][40/63] lr: 6.3768e-03 eta: 1 day, 3:31:43 time: 1.8415 data_time: 0.0446 memory: 16201 loss_prob: 1.0930 loss_thr: 0.5892 loss_db: 0.1800 loss: 1.8623 2022/08/30 02:18:51 - mmengine - INFO - Epoch(train) [119][45/63] lr: 6.3768e-03 eta: 1 day, 3:31:43 time: 1.7696 data_time: 0.0446 memory: 16201 loss_prob: 1.0239 loss_thr: 0.5747 loss_db: 0.1676 loss: 1.7662 2022/08/30 02:18:59 - mmengine - INFO - Epoch(train) [119][50/63] lr: 6.3768e-03 eta: 1 day, 3:31:54 time: 1.7364 data_time: 0.0613 memory: 16201 loss_prob: 1.1535 loss_thr: 0.5965 loss_db: 0.1798 loss: 1.9298 2022/08/30 02:19:09 - mmengine - INFO - Epoch(train) [119][55/63] lr: 6.3768e-03 eta: 1 day, 3:31:54 time: 1.8366 data_time: 0.0373 memory: 16201 loss_prob: 1.1606 loss_thr: 0.5959 loss_db: 0.1796 loss: 1.9361 2022/08/30 02:19:18 - mmengine - INFO - Epoch(train) [119][60/63] lr: 6.3768e-03 eta: 1 day, 3:32:14 time: 1.8319 data_time: 0.0302 memory: 16201 loss_prob: 1.0270 loss_thr: 0.5646 loss_db: 0.1669 loss: 1.7585 2022/08/30 02:19:22 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:19:34 - mmengine - INFO - Epoch(train) [120][5/63] lr: 6.3715e-03 eta: 1 day, 3:32:14 time: 1.9740 data_time: 0.2600 memory: 16201 loss_prob: 1.1232 loss_thr: 0.5865 loss_db: 0.1885 loss: 1.8982 2022/08/30 02:19:43 - mmengine - INFO - Epoch(train) [120][10/63] lr: 6.3715e-03 eta: 1 day, 3:32:09 time: 2.0440 data_time: 0.2815 memory: 16201 loss_prob: 1.0481 loss_thr: 0.5617 loss_db: 0.1752 loss: 1.7850 2022/08/30 02:19:51 - mmengine - INFO - Epoch(train) [120][15/63] lr: 6.3715e-03 eta: 1 day, 3:32:09 time: 1.6903 data_time: 0.0405 memory: 16201 loss_prob: 0.9310 loss_thr: 0.5526 loss_db: 0.1551 loss: 1.6387 2022/08/30 02:19:59 - mmengine - INFO - Epoch(train) [120][20/63] lr: 6.3715e-03 eta: 1 day, 3:32:10 time: 1.6283 data_time: 0.0390 memory: 16201 loss_prob: 0.9469 loss_thr: 0.5707 loss_db: 0.1577 loss: 1.6753 2022/08/30 02:20:08 - mmengine - INFO - Epoch(train) [120][25/63] lr: 6.3715e-03 eta: 1 day, 3:32:10 time: 1.6678 data_time: 0.0530 memory: 16201 loss_prob: 1.0903 loss_thr: 0.6050 loss_db: 0.1769 loss: 1.8722 2022/08/30 02:20:16 - mmengine - INFO - Epoch(train) [120][30/63] lr: 6.3715e-03 eta: 1 day, 3:32:12 time: 1.6412 data_time: 0.0386 memory: 16201 loss_prob: 1.1999 loss_thr: 0.6364 loss_db: 0.1953 loss: 2.0316 2022/08/30 02:20:23 - mmengine - INFO - Epoch(train) [120][35/63] lr: 6.3715e-03 eta: 1 day, 3:32:12 time: 1.5562 data_time: 0.0394 memory: 16201 loss_prob: 1.1283 loss_thr: 0.5912 loss_db: 0.1881 loss: 1.9076 2022/08/30 02:20:30 - mmengine - INFO - Epoch(train) [120][40/63] lr: 6.3715e-03 eta: 1 day, 3:31:59 time: 1.4690 data_time: 0.0375 memory: 16201 loss_prob: 1.0970 loss_thr: 0.5617 loss_db: 0.1787 loss: 1.8374 2022/08/30 02:20:37 - mmengine - INFO - Epoch(train) [120][45/63] lr: 6.3715e-03 eta: 1 day, 3:31:59 time: 1.4087 data_time: 0.0340 memory: 16201 loss_prob: 1.0436 loss_thr: 0.5517 loss_db: 0.1687 loss: 1.7641 2022/08/30 02:20:46 - mmengine - INFO - Epoch(train) [120][50/63] lr: 6.3715e-03 eta: 1 day, 3:31:58 time: 1.6114 data_time: 0.0461 memory: 16201 loss_prob: 1.0158 loss_thr: 0.5506 loss_db: 0.1699 loss: 1.7363 2022/08/30 02:20:55 - mmengine - INFO - Epoch(train) [120][55/63] lr: 6.3715e-03 eta: 1 day, 3:31:58 time: 1.8103 data_time: 0.0471 memory: 16201 loss_prob: 1.0458 loss_thr: 0.5435 loss_db: 0.1720 loss: 1.7613 2022/08/30 02:21:04 - mmengine - INFO - Epoch(train) [120][60/63] lr: 6.3715e-03 eta: 1 day, 3:32:13 time: 1.7825 data_time: 0.0506 memory: 16201 loss_prob: 1.1317 loss_thr: 0.5528 loss_db: 0.1846 loss: 1.8691 2022/08/30 02:21:07 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:21:07 - mmengine - INFO - Saving checkpoint at 120 epochs 2022/08/30 02:21:16 - mmengine - INFO - Epoch(val) [120][5/32] eta: 1 day, 3:32:13 time: 0.7142 data_time: 0.1427 memory: 16201 2022/08/30 02:21:20 - mmengine - INFO - Epoch(val) [120][10/32] eta: 0:00:17 time: 0.8168 data_time: 0.1921 memory: 15734 2022/08/30 02:21:23 - mmengine - INFO - Epoch(val) [120][15/32] eta: 0:00:17 time: 0.6782 data_time: 0.0740 memory: 15734 2022/08/30 02:21:27 - mmengine - INFO - Epoch(val) [120][20/32] eta: 0:00:08 time: 0.7359 data_time: 0.0780 memory: 15734 2022/08/30 02:21:30 - mmengine - INFO - Epoch(val) [120][25/32] eta: 0:00:08 time: 0.7683 data_time: 0.0861 memory: 15734 2022/08/30 02:21:34 - mmengine - INFO - Epoch(val) [120][30/32] eta: 0:00:01 time: 0.6613 data_time: 0.0329 memory: 15734 2022/08/30 02:21:34 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 02:21:34 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.7824, precision: 0.7162, hmean: 0.7478 2022/08/30 02:21:34 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.7805, precision: 0.8191, hmean: 0.7993 2022/08/30 02:21:34 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.7650, precision: 0.8740, hmean: 0.8159 2022/08/30 02:21:34 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7203, precision: 0.9072, hmean: 0.8030 2022/08/30 02:21:34 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.6052, precision: 0.9472, hmean: 0.7385 2022/08/30 02:21:34 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.1998, precision: 0.9834, hmean: 0.3321 2022/08/30 02:21:34 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0000, precision: 0.0000, hmean: 0.0000 2022/08/30 02:21:34 - mmengine - INFO - Epoch(val) [120][32/32] icdar/precision: 0.8740 icdar/recall: 0.7650 icdar/hmean: 0.8159 2022/08/30 02:21:47 - mmengine - INFO - Epoch(train) [121][5/63] lr: 6.3662e-03 eta: 0:00:01 time: 1.7774 data_time: 0.2386 memory: 16201 loss_prob: 1.4678 loss_thr: 0.6127 loss_db: 0.2342 loss: 2.3147 2022/08/30 02:21:54 - mmengine - INFO - Epoch(train) [121][10/63] lr: 6.3662e-03 eta: 1 day, 3:32:02 time: 1.9787 data_time: 0.2615 memory: 16201 loss_prob: 1.1597 loss_thr: 0.5701 loss_db: 0.1920 loss: 1.9219 2022/08/30 02:22:03 - mmengine - INFO - Epoch(train) [121][15/63] lr: 6.3662e-03 eta: 1 day, 3:32:02 time: 1.6550 data_time: 0.0416 memory: 16201 loss_prob: 1.1594 loss_thr: 0.5620 loss_db: 0.1866 loss: 1.9080 2022/08/30 02:22:12 - mmengine - INFO - Epoch(train) [121][20/63] lr: 6.3662e-03 eta: 1 day, 3:32:12 time: 1.7348 data_time: 0.0339 memory: 16201 loss_prob: 1.2408 loss_thr: 0.5786 loss_db: 0.1966 loss: 2.0160 2022/08/30 02:22:19 - mmengine - INFO - Epoch(train) [121][25/63] lr: 6.3662e-03 eta: 1 day, 3:32:12 time: 1.5585 data_time: 0.0487 memory: 16201 loss_prob: 1.1827 loss_thr: 0.5927 loss_db: 0.1925 loss: 1.9679 2022/08/30 02:22:27 - mmengine - INFO - Epoch(train) [121][30/63] lr: 6.3662e-03 eta: 1 day, 3:32:03 time: 1.5164 data_time: 0.0384 memory: 16201 loss_prob: 1.1479 loss_thr: 0.5891 loss_db: 0.1934 loss: 1.9304 2022/08/30 02:22:34 - mmengine - INFO - Epoch(train) [121][35/63] lr: 6.3662e-03 eta: 1 day, 3:32:03 time: 1.5634 data_time: 0.0392 memory: 16201 loss_prob: 1.1612 loss_thr: 0.5818 loss_db: 0.1913 loss: 1.9342 2022/08/30 02:22:44 - mmengine - INFO - Epoch(train) [121][40/63] lr: 6.3662e-03 eta: 1 day, 3:32:12 time: 1.7301 data_time: 0.0434 memory: 16201 loss_prob: 1.1603 loss_thr: 0.5750 loss_db: 0.1855 loss: 1.9208 2022/08/30 02:22:53 - mmengine - INFO - Epoch(train) [121][45/63] lr: 6.3662e-03 eta: 1 day, 3:32:12 time: 1.8895 data_time: 0.0357 memory: 16201 loss_prob: 1.1615 loss_thr: 0.5863 loss_db: 0.1856 loss: 1.9334 2022/08/30 02:23:02 - mmengine - INFO - Epoch(train) [121][50/63] lr: 6.3662e-03 eta: 1 day, 3:32:27 time: 1.7881 data_time: 0.0550 memory: 16201 loss_prob: 1.1432 loss_thr: 0.5967 loss_db: 0.1854 loss: 1.9253 2022/08/30 02:23:11 - mmengine - INFO - Epoch(train) [121][55/63] lr: 6.3662e-03 eta: 1 day, 3:32:27 time: 1.7773 data_time: 0.0451 memory: 16201 loss_prob: 1.0379 loss_thr: 0.5623 loss_db: 0.1707 loss: 1.7709 2022/08/30 02:23:21 - mmengine - INFO - Epoch(train) [121][60/63] lr: 6.3662e-03 eta: 1 day, 3:32:49 time: 1.8608 data_time: 0.0485 memory: 16201 loss_prob: 0.9757 loss_thr: 0.5259 loss_db: 0.1613 loss: 1.6629 2022/08/30 02:23:25 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:23:37 - mmengine - INFO - Epoch(train) [122][5/63] lr: 6.3609e-03 eta: 1 day, 3:32:49 time: 2.0488 data_time: 0.2589 memory: 16201 loss_prob: 1.0058 loss_thr: 0.5326 loss_db: 0.1663 loss: 1.7048 2022/08/30 02:23:46 - mmengine - INFO - Epoch(train) [122][10/63] lr: 6.3609e-03 eta: 1 day, 3:32:41 time: 2.0197 data_time: 0.2845 memory: 16201 loss_prob: 1.0392 loss_thr: 0.5438 loss_db: 0.1695 loss: 1.7525 2022/08/30 02:23:56 - mmengine - INFO - Epoch(train) [122][15/63] lr: 6.3609e-03 eta: 1 day, 3:32:41 time: 1.8102 data_time: 0.0408 memory: 16201 loss_prob: 1.1868 loss_thr: 0.5707 loss_db: 0.1988 loss: 1.9564 2022/08/30 02:24:05 - mmengine - INFO - Epoch(train) [122][20/63] lr: 6.3609e-03 eta: 1 day, 3:33:12 time: 1.9758 data_time: 0.0352 memory: 16201 loss_prob: 1.1204 loss_thr: 0.5745 loss_db: 0.1917 loss: 1.8866 2022/08/30 02:24:15 - mmengine - INFO - Epoch(train) [122][25/63] lr: 6.3609e-03 eta: 1 day, 3:33:12 time: 1.9640 data_time: 0.0401 memory: 16201 loss_prob: 1.0624 loss_thr: 0.5618 loss_db: 0.1762 loss: 1.8004 2022/08/30 02:24:25 - mmengine - INFO - Epoch(train) [122][30/63] lr: 6.3609e-03 eta: 1 day, 3:33:39 time: 1.9314 data_time: 0.0389 memory: 16201 loss_prob: 1.1165 loss_thr: 0.5544 loss_db: 0.1852 loss: 1.8560 2022/08/30 02:24:34 - mmengine - INFO - Epoch(train) [122][35/63] lr: 6.3609e-03 eta: 1 day, 3:33:39 time: 1.8622 data_time: 0.0483 memory: 16201 loss_prob: 1.1070 loss_thr: 0.5642 loss_db: 0.1867 loss: 1.8580 2022/08/30 02:24:44 - mmengine - INFO - Epoch(train) [122][40/63] lr: 6.3609e-03 eta: 1 day, 3:34:07 time: 1.9346 data_time: 0.0490 memory: 16201 loss_prob: 1.1071 loss_thr: 0.5548 loss_db: 0.1863 loss: 1.8482 2022/08/30 02:24:53 - mmengine - INFO - Epoch(train) [122][45/63] lr: 6.3609e-03 eta: 1 day, 3:34:07 time: 1.8744 data_time: 0.0504 memory: 16201 loss_prob: 1.1601 loss_thr: 0.5658 loss_db: 0.1959 loss: 1.9218 2022/08/30 02:25:01 - mmengine - INFO - Epoch(train) [122][50/63] lr: 6.3609e-03 eta: 1 day, 3:34:10 time: 1.6663 data_time: 0.0479 memory: 16201 loss_prob: 1.0822 loss_thr: 0.5372 loss_db: 0.1756 loss: 1.7950 2022/08/30 02:25:08 - mmengine - INFO - Epoch(train) [122][55/63] lr: 6.3609e-03 eta: 1 day, 3:34:10 time: 1.5753 data_time: 0.0441 memory: 16201 loss_prob: 0.9845 loss_thr: 0.5035 loss_db: 0.1561 loss: 1.6441 2022/08/30 02:25:17 - mmengine - INFO - Epoch(train) [122][60/63] lr: 6.3609e-03 eta: 1 day, 3:34:07 time: 1.5841 data_time: 0.0354 memory: 16201 loss_prob: 1.0010 loss_thr: 0.5296 loss_db: 0.1639 loss: 1.6945 2022/08/30 02:25:20 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:25:32 - mmengine - INFO - Epoch(train) [123][5/63] lr: 6.3556e-03 eta: 1 day, 3:34:07 time: 1.8791 data_time: 0.2521 memory: 16201 loss_prob: 0.9623 loss_thr: 0.5168 loss_db: 0.1588 loss: 1.6378 2022/08/30 02:25:42 - mmengine - INFO - Epoch(train) [123][10/63] lr: 6.3556e-03 eta: 1 day, 3:34:14 time: 2.1982 data_time: 0.2770 memory: 16201 loss_prob: 0.8899 loss_thr: 0.4908 loss_db: 0.1434 loss: 1.5241 2022/08/30 02:25:50 - mmengine - INFO - Epoch(train) [123][15/63] lr: 6.3556e-03 eta: 1 day, 3:34:14 time: 1.7949 data_time: 0.0514 memory: 16201 loss_prob: 0.8674 loss_thr: 0.4953 loss_db: 0.1386 loss: 1.5013 2022/08/30 02:25:59 - mmengine - INFO - Epoch(train) [123][20/63] lr: 6.3556e-03 eta: 1 day, 3:34:23 time: 1.7278 data_time: 0.0404 memory: 16201 loss_prob: 1.0881 loss_thr: 0.5398 loss_db: 0.1793 loss: 1.8073 2022/08/30 02:26:08 - mmengine - INFO - Epoch(train) [123][25/63] lr: 6.3556e-03 eta: 1 day, 3:34:23 time: 1.8650 data_time: 0.0451 memory: 16201 loss_prob: 1.1110 loss_thr: 0.5581 loss_db: 0.1829 loss: 1.8520 2022/08/30 02:26:17 - mmengine - INFO - Epoch(train) [123][30/63] lr: 6.3556e-03 eta: 1 day, 3:34:35 time: 1.7662 data_time: 0.0461 memory: 16201 loss_prob: 1.0390 loss_thr: 0.5559 loss_db: 0.1644 loss: 1.7593 2022/08/30 02:26:26 - mmengine - INFO - Epoch(train) [123][35/63] lr: 6.3556e-03 eta: 1 day, 3:34:35 time: 1.7498 data_time: 0.0554 memory: 16201 loss_prob: 1.0821 loss_thr: 0.5605 loss_db: 0.1768 loss: 1.8195 2022/08/30 02:26:35 - mmengine - INFO - Epoch(train) [123][40/63] lr: 6.3556e-03 eta: 1 day, 3:34:54 time: 1.8401 data_time: 0.0379 memory: 16201 loss_prob: 1.0726 loss_thr: 0.5788 loss_db: 0.1739 loss: 1.8253 2022/08/30 02:26:44 - mmengine - INFO - Epoch(train) [123][45/63] lr: 6.3556e-03 eta: 1 day, 3:34:54 time: 1.8036 data_time: 0.0392 memory: 16201 loss_prob: 0.9995 loss_thr: 0.5584 loss_db: 0.1589 loss: 1.7168 2022/08/30 02:26:53 - mmengine - INFO - Epoch(train) [123][50/63] lr: 6.3556e-03 eta: 1 day, 3:35:03 time: 1.7397 data_time: 0.0580 memory: 16201 loss_prob: 1.0188 loss_thr: 0.5420 loss_db: 0.1631 loss: 1.7239 2022/08/30 02:27:01 - mmengine - INFO - Epoch(train) [123][55/63] lr: 6.3556e-03 eta: 1 day, 3:35:03 time: 1.7178 data_time: 0.0410 memory: 16201 loss_prob: 1.0720 loss_thr: 0.5429 loss_db: 0.1697 loss: 1.7846 2022/08/30 02:27:11 - mmengine - INFO - Epoch(train) [123][60/63] lr: 6.3556e-03 eta: 1 day, 3:35:22 time: 1.8497 data_time: 0.0560 memory: 16201 loss_prob: 1.0911 loss_thr: 0.5611 loss_db: 0.1700 loss: 1.8221 2022/08/30 02:27:15 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:27:27 - mmengine - INFO - Epoch(train) [124][5/63] lr: 6.3503e-03 eta: 1 day, 3:35:22 time: 1.8932 data_time: 0.3019 memory: 16201 loss_prob: 0.9927 loss_thr: 0.5531 loss_db: 0.1609 loss: 1.7067 2022/08/30 02:27:35 - mmengine - INFO - Epoch(train) [124][10/63] lr: 6.3503e-03 eta: 1 day, 3:35:11 time: 1.9899 data_time: 0.3125 memory: 16201 loss_prob: 0.9846 loss_thr: 0.5440 loss_db: 0.1600 loss: 1.6887 2022/08/30 02:27:44 - mmengine - INFO - Epoch(train) [124][15/63] lr: 6.3503e-03 eta: 1 day, 3:35:11 time: 1.7537 data_time: 0.0402 memory: 16201 loss_prob: 1.0084 loss_thr: 0.5560 loss_db: 0.1635 loss: 1.7279 2022/08/30 02:27:54 - mmengine - INFO - Epoch(train) [124][20/63] lr: 6.3503e-03 eta: 1 day, 3:35:35 time: 1.9103 data_time: 0.0545 memory: 16201 loss_prob: 1.0330 loss_thr: 0.5536 loss_db: 0.1733 loss: 1.7599 2022/08/30 02:28:03 - mmengine - INFO - Epoch(train) [124][25/63] lr: 6.3503e-03 eta: 1 day, 3:35:35 time: 1.8731 data_time: 0.0590 memory: 16201 loss_prob: 0.9352 loss_thr: 0.5202 loss_db: 0.1602 loss: 1.6156 2022/08/30 02:28:13 - mmengine - INFO - Epoch(train) [124][30/63] lr: 6.3503e-03 eta: 1 day, 3:35:55 time: 1.8604 data_time: 0.0406 memory: 16201 loss_prob: 0.8726 loss_thr: 0.5076 loss_db: 0.1438 loss: 1.5240 2022/08/30 02:28:21 - mmengine - INFO - Epoch(train) [124][35/63] lr: 6.3503e-03 eta: 1 day, 3:35:55 time: 1.7566 data_time: 0.0464 memory: 16201 loss_prob: 0.9343 loss_thr: 0.5449 loss_db: 0.1584 loss: 1.6376 2022/08/30 02:28:30 - mmengine - INFO - Epoch(train) [124][40/63] lr: 6.3503e-03 eta: 1 day, 3:36:07 time: 1.7658 data_time: 0.0402 memory: 16201 loss_prob: 0.9458 loss_thr: 0.5493 loss_db: 0.1610 loss: 1.6561 2022/08/30 02:28:39 - mmengine - INFO - Epoch(train) [124][45/63] lr: 6.3503e-03 eta: 1 day, 3:36:07 time: 1.7954 data_time: 0.0371 memory: 16201 loss_prob: 0.9748 loss_thr: 0.5588 loss_db: 0.1606 loss: 1.6942 2022/08/30 02:28:47 - mmengine - INFO - Epoch(train) [124][50/63] lr: 6.3503e-03 eta: 1 day, 3:36:13 time: 1.7075 data_time: 0.0424 memory: 16201 loss_prob: 0.9900 loss_thr: 0.5703 loss_db: 0.1657 loss: 1.7260 2022/08/30 02:28:55 - mmengine - INFO - Epoch(train) [124][55/63] lr: 6.3503e-03 eta: 1 day, 3:36:13 time: 1.6653 data_time: 0.0424 memory: 16201 loss_prob: 1.0393 loss_thr: 0.5706 loss_db: 0.1760 loss: 1.7860 2022/08/30 02:29:05 - mmengine - INFO - Epoch(train) [124][60/63] lr: 6.3503e-03 eta: 1 day, 3:36:23 time: 1.7523 data_time: 0.0557 memory: 16201 loss_prob: 1.0405 loss_thr: 0.5567 loss_db: 0.1744 loss: 1.7716 2022/08/30 02:29:10 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:29:21 - mmengine - INFO - Epoch(train) [125][5/63] lr: 6.3450e-03 eta: 1 day, 3:36:23 time: 1.9967 data_time: 0.2722 memory: 16201 loss_prob: 0.9732 loss_thr: 0.5533 loss_db: 0.1651 loss: 1.6915 2022/08/30 02:29:30 - mmengine - INFO - Epoch(train) [125][10/63] lr: 6.3450e-03 eta: 1 day, 3:36:10 time: 1.9706 data_time: 0.2845 memory: 16201 loss_prob: 0.9929 loss_thr: 0.5402 loss_db: 0.1692 loss: 1.7024 2022/08/30 02:29:37 - mmengine - INFO - Epoch(train) [125][15/63] lr: 6.3450e-03 eta: 1 day, 3:36:10 time: 1.5860 data_time: 0.0421 memory: 16201 loss_prob: 0.9528 loss_thr: 0.5280 loss_db: 0.1586 loss: 1.6394 2022/08/30 02:29:44 - mmengine - INFO - Epoch(train) [125][20/63] lr: 6.3450e-03 eta: 1 day, 3:35:55 time: 1.4638 data_time: 0.0418 memory: 16201 loss_prob: 0.9013 loss_thr: 0.4955 loss_db: 0.1467 loss: 1.5435 2022/08/30 02:29:53 - mmengine - INFO - Epoch(train) [125][25/63] lr: 6.3450e-03 eta: 1 day, 3:35:55 time: 1.5883 data_time: 0.0418 memory: 16201 loss_prob: 0.9671 loss_thr: 0.5130 loss_db: 0.1569 loss: 1.6370 2022/08/30 02:30:01 - mmengine - INFO - Epoch(train) [125][30/63] lr: 6.3450e-03 eta: 1 day, 3:35:56 time: 1.6442 data_time: 0.0380 memory: 16201 loss_prob: 0.9750 loss_thr: 0.5322 loss_db: 0.1605 loss: 1.6677 2022/08/30 02:30:11 - mmengine - INFO - Epoch(train) [125][35/63] lr: 6.3450e-03 eta: 1 day, 3:35:56 time: 1.7798 data_time: 0.0501 memory: 16201 loss_prob: 0.9294 loss_thr: 0.5284 loss_db: 0.1553 loss: 1.6131 2022/08/30 02:30:18 - mmengine - INFO - Epoch(train) [125][40/63] lr: 6.3450e-03 eta: 1 day, 3:36:03 time: 1.7192 data_time: 0.0389 memory: 16201 loss_prob: 0.9655 loss_thr: 0.5492 loss_db: 0.1597 loss: 1.6743 2022/08/30 02:30:28 - mmengine - INFO - Epoch(train) [125][45/63] lr: 6.3450e-03 eta: 1 day, 3:36:03 time: 1.7674 data_time: 0.0491 memory: 16201 loss_prob: 0.9160 loss_thr: 0.5275 loss_db: 0.1504 loss: 1.5939 2022/08/30 02:30:38 - mmengine - INFO - Epoch(train) [125][50/63] lr: 6.3450e-03 eta: 1 day, 3:36:33 time: 1.9830 data_time: 0.0602 memory: 16201 loss_prob: 0.9298 loss_thr: 0.5191 loss_db: 0.1519 loss: 1.6008 2022/08/30 02:30:47 - mmengine - INFO - Epoch(train) [125][55/63] lr: 6.3450e-03 eta: 1 day, 3:36:33 time: 1.8716 data_time: 0.0416 memory: 16201 loss_prob: 1.0181 loss_thr: 0.5506 loss_db: 0.1663 loss: 1.7350 2022/08/30 02:30:57 - mmengine - INFO - Epoch(train) [125][60/63] lr: 6.3450e-03 eta: 1 day, 3:36:55 time: 1.8912 data_time: 0.0488 memory: 16201 loss_prob: 0.9350 loss_thr: 0.5367 loss_db: 0.1550 loss: 1.6267 2022/08/30 02:31:01 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:31:12 - mmengine - INFO - Epoch(train) [126][5/63] lr: 6.3396e-03 eta: 1 day, 3:36:55 time: 1.8807 data_time: 0.2644 memory: 16201 loss_prob: 0.8791 loss_thr: 0.5192 loss_db: 0.1495 loss: 1.5478 2022/08/30 02:31:21 - mmengine - INFO - Epoch(train) [126][10/63] lr: 6.3396e-03 eta: 1 day, 3:36:36 time: 1.9155 data_time: 0.2991 memory: 16201 loss_prob: 0.9121 loss_thr: 0.5401 loss_db: 0.1566 loss: 1.6088 2022/08/30 02:31:29 - mmengine - INFO - Epoch(train) [126][15/63] lr: 6.3396e-03 eta: 1 day, 3:36:36 time: 1.7035 data_time: 0.0529 memory: 16201 loss_prob: 1.0271 loss_thr: 0.5835 loss_db: 0.1737 loss: 1.7843 2022/08/30 02:31:38 - mmengine - INFO - Epoch(train) [126][20/63] lr: 6.3396e-03 eta: 1 day, 3:36:46 time: 1.7554 data_time: 0.0335 memory: 16201 loss_prob: 1.0020 loss_thr: 0.5417 loss_db: 0.1636 loss: 1.7073 2022/08/30 02:31:46 - mmengine - INFO - Epoch(train) [126][25/63] lr: 6.3396e-03 eta: 1 day, 3:36:46 time: 1.7250 data_time: 0.0468 memory: 16201 loss_prob: 1.0196 loss_thr: 0.5312 loss_db: 0.1669 loss: 1.7177 2022/08/30 02:31:55 - mmengine - INFO - Epoch(train) [126][30/63] lr: 6.3396e-03 eta: 1 day, 3:36:53 time: 1.7219 data_time: 0.0401 memory: 16201 loss_prob: 1.1942 loss_thr: 0.5697 loss_db: 0.1899 loss: 1.9538 2022/08/30 02:32:05 - mmengine - INFO - Epoch(train) [126][35/63] lr: 6.3396e-03 eta: 1 day, 3:36:53 time: 1.8977 data_time: 0.0478 memory: 16201 loss_prob: 1.1418 loss_thr: 0.5564 loss_db: 0.1803 loss: 1.8785 2022/08/30 02:32:15 - mmengine - INFO - Epoch(train) [126][40/63] lr: 6.3396e-03 eta: 1 day, 3:37:22 time: 1.9765 data_time: 0.0474 memory: 16201 loss_prob: 0.9270 loss_thr: 0.5220 loss_db: 0.1539 loss: 1.6028 2022/08/30 02:32:24 - mmengine - INFO - Epoch(train) [126][45/63] lr: 6.3396e-03 eta: 1 day, 3:37:22 time: 1.8340 data_time: 0.0456 memory: 16201 loss_prob: 0.8932 loss_thr: 0.4975 loss_db: 0.1499 loss: 1.5406 2022/08/30 02:32:34 - mmengine - INFO - Epoch(train) [126][50/63] lr: 6.3396e-03 eta: 1 day, 3:37:41 time: 1.8705 data_time: 0.0578 memory: 16201 loss_prob: 0.9167 loss_thr: 0.5124 loss_db: 0.1529 loss: 1.5820 2022/08/30 02:32:43 - mmengine - INFO - Epoch(train) [126][55/63] lr: 6.3396e-03 eta: 1 day, 3:37:41 time: 1.8781 data_time: 0.0562 memory: 16201 loss_prob: 0.9459 loss_thr: 0.5414 loss_db: 0.1562 loss: 1.6436 2022/08/30 02:32:51 - mmengine - INFO - Epoch(train) [126][60/63] lr: 6.3396e-03 eta: 1 day, 3:37:51 time: 1.7510 data_time: 0.0472 memory: 16201 loss_prob: 1.0466 loss_thr: 0.5606 loss_db: 0.1707 loss: 1.7779 2022/08/30 02:32:56 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:33:07 - mmengine - INFO - Epoch(train) [127][5/63] lr: 6.3343e-03 eta: 1 day, 3:37:51 time: 1.9007 data_time: 0.2827 memory: 16201 loss_prob: 1.1554 loss_thr: 0.5595 loss_db: 0.1863 loss: 1.9011 2022/08/30 02:33:18 - mmengine - INFO - Epoch(train) [127][10/63] lr: 6.3343e-03 eta: 1 day, 3:37:59 time: 2.2327 data_time: 0.2972 memory: 16201 loss_prob: 1.0905 loss_thr: 0.5571 loss_db: 0.1788 loss: 1.8265 2022/08/30 02:33:28 - mmengine - INFO - Epoch(train) [127][15/63] lr: 6.3343e-03 eta: 1 day, 3:37:59 time: 2.0445 data_time: 0.0530 memory: 16201 loss_prob: 1.0183 loss_thr: 0.5680 loss_db: 0.1699 loss: 1.7562 2022/08/30 02:33:38 - mmengine - INFO - Epoch(train) [127][20/63] lr: 6.3343e-03 eta: 1 day, 3:38:31 time: 2.0238 data_time: 0.0601 memory: 16201 loss_prob: 1.1470 loss_thr: 0.5961 loss_db: 0.1949 loss: 1.9379 2022/08/30 02:33:47 - mmengine - INFO - Epoch(train) [127][25/63] lr: 6.3343e-03 eta: 1 day, 3:38:31 time: 1.9426 data_time: 0.0596 memory: 16201 loss_prob: 1.1970 loss_thr: 0.5964 loss_db: 0.1975 loss: 1.9909 2022/08/30 02:33:55 - mmengine - INFO - Epoch(train) [127][30/63] lr: 6.3343e-03 eta: 1 day, 3:38:33 time: 1.6597 data_time: 0.0388 memory: 16201 loss_prob: 0.9944 loss_thr: 0.5447 loss_db: 0.1625 loss: 1.7016 2022/08/30 02:34:03 - mmengine - INFO - Epoch(train) [127][35/63] lr: 6.3343e-03 eta: 1 day, 3:38:33 time: 1.5809 data_time: 0.0338 memory: 16201 loss_prob: 0.9405 loss_thr: 0.5082 loss_db: 0.1571 loss: 1.6057 2022/08/30 02:34:11 - mmengine - INFO - Epoch(train) [127][40/63] lr: 6.3343e-03 eta: 1 day, 3:38:27 time: 1.5745 data_time: 0.0405 memory: 16201 loss_prob: 0.9900 loss_thr: 0.5261 loss_db: 0.1632 loss: 1.6794 2022/08/30 02:34:19 - mmengine - INFO - Epoch(train) [127][45/63] lr: 6.3343e-03 eta: 1 day, 3:38:27 time: 1.6103 data_time: 0.0435 memory: 16201 loss_prob: 1.0522 loss_thr: 0.5743 loss_db: 0.1721 loss: 1.7986 2022/08/30 02:34:28 - mmengine - INFO - Epoch(train) [127][50/63] lr: 6.3343e-03 eta: 1 day, 3:38:34 time: 1.7329 data_time: 0.0486 memory: 16201 loss_prob: 1.0600 loss_thr: 0.5775 loss_db: 0.1740 loss: 1.8114 2022/08/30 02:34:37 - mmengine - INFO - Epoch(train) [127][55/63] lr: 6.3343e-03 eta: 1 day, 3:38:34 time: 1.7867 data_time: 0.0471 memory: 16201 loss_prob: 1.0046 loss_thr: 0.5433 loss_db: 0.1666 loss: 1.7145 2022/08/30 02:34:45 - mmengine - INFO - Epoch(train) [127][60/63] lr: 6.3343e-03 eta: 1 day, 3:38:34 time: 1.6515 data_time: 0.0429 memory: 16201 loss_prob: 0.9544 loss_thr: 0.5177 loss_db: 0.1600 loss: 1.6321 2022/08/30 02:34:49 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:34:50 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:35:01 - mmengine - INFO - Epoch(train) [128][5/63] lr: 6.3290e-03 eta: 1 day, 3:38:34 time: 1.9451 data_time: 0.2607 memory: 16201 loss_prob: 1.0459 loss_thr: 0.5557 loss_db: 0.1716 loss: 1.7732 2022/08/30 02:35:11 - mmengine - INFO - Epoch(train) [128][10/63] lr: 6.3290e-03 eta: 1 day, 3:38:34 time: 2.1345 data_time: 0.2707 memory: 16201 loss_prob: 0.8779 loss_thr: 0.4903 loss_db: 0.1438 loss: 1.5120 2022/08/30 02:35:21 - mmengine - INFO - Epoch(train) [128][15/63] lr: 6.3290e-03 eta: 1 day, 3:38:34 time: 1.9550 data_time: 0.0557 memory: 16201 loss_prob: 0.8430 loss_thr: 0.5071 loss_db: 0.1374 loss: 1.4875 2022/08/30 02:35:30 - mmengine - INFO - Epoch(train) [128][20/63] lr: 6.3290e-03 eta: 1 day, 3:38:48 time: 1.8133 data_time: 0.0570 memory: 16201 loss_prob: 0.9203 loss_thr: 0.5347 loss_db: 0.1530 loss: 1.6080 2022/08/30 02:35:37 - mmengine - INFO - Epoch(train) [128][25/63] lr: 6.3290e-03 eta: 1 day, 3:38:48 time: 1.6709 data_time: 0.0446 memory: 16201 loss_prob: 0.9916 loss_thr: 0.5229 loss_db: 0.1638 loss: 1.6784 2022/08/30 02:35:46 - mmengine - INFO - Epoch(train) [128][30/63] lr: 6.3290e-03 eta: 1 day, 3:38:44 time: 1.6014 data_time: 0.0338 memory: 16201 loss_prob: 1.0064 loss_thr: 0.5548 loss_db: 0.1650 loss: 1.7262 2022/08/30 02:35:54 - mmengine - INFO - Epoch(train) [128][35/63] lr: 6.3290e-03 eta: 1 day, 3:38:44 time: 1.6795 data_time: 0.0434 memory: 16201 loss_prob: 0.9005 loss_thr: 0.5342 loss_db: 0.1485 loss: 1.5832 2022/08/30 02:36:03 - mmengine - INFO - Epoch(train) [128][40/63] lr: 6.3290e-03 eta: 1 day, 3:38:52 time: 1.7483 data_time: 0.0415 memory: 16201 loss_prob: 0.8497 loss_thr: 0.5093 loss_db: 0.1418 loss: 1.5008 2022/08/30 02:36:12 - mmengine - INFO - Epoch(train) [128][45/63] lr: 6.3290e-03 eta: 1 day, 3:38:52 time: 1.8187 data_time: 0.0464 memory: 16201 loss_prob: 0.9627 loss_thr: 0.5502 loss_db: 0.1567 loss: 1.6697 2022/08/30 02:36:22 - mmengine - INFO - Epoch(train) [128][50/63] lr: 6.3290e-03 eta: 1 day, 3:39:09 time: 1.8451 data_time: 0.0451 memory: 16201 loss_prob: 1.0118 loss_thr: 0.5614 loss_db: 0.1632 loss: 1.7365 2022/08/30 02:36:30 - mmengine - INFO - Epoch(train) [128][55/63] lr: 6.3290e-03 eta: 1 day, 3:39:09 time: 1.8148 data_time: 0.0826 memory: 16201 loss_prob: 1.0133 loss_thr: 0.5576 loss_db: 0.1678 loss: 1.7388 2022/08/30 02:36:41 - mmengine - INFO - Epoch(train) [128][60/63] lr: 6.3290e-03 eta: 1 day, 3:39:34 time: 1.9455 data_time: 0.1032 memory: 16201 loss_prob: 1.1600 loss_thr: 0.5752 loss_db: 0.1828 loss: 1.9180 2022/08/30 02:36:45 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:36:55 - mmengine - INFO - Epoch(train) [129][5/63] lr: 6.3237e-03 eta: 1 day, 3:39:34 time: 1.8176 data_time: 0.2731 memory: 16201 loss_prob: 1.0665 loss_thr: 0.5480 loss_db: 0.1670 loss: 1.7815 2022/08/30 02:37:04 - mmengine - INFO - Epoch(train) [129][10/63] lr: 6.3237e-03 eta: 1 day, 3:39:14 time: 1.9107 data_time: 0.2965 memory: 16201 loss_prob: 0.8886 loss_thr: 0.5161 loss_db: 0.1479 loss: 1.5526 2022/08/30 02:37:11 - mmengine - INFO - Epoch(train) [129][15/63] lr: 6.3237e-03 eta: 1 day, 3:39:14 time: 1.6035 data_time: 0.0486 memory: 16201 loss_prob: 0.8819 loss_thr: 0.5140 loss_db: 0.1491 loss: 1.5451 2022/08/30 02:37:20 - mmengine - INFO - Epoch(train) [129][20/63] lr: 6.3237e-03 eta: 1 day, 3:39:09 time: 1.5952 data_time: 0.0423 memory: 16201 loss_prob: 0.8821 loss_thr: 0.5073 loss_db: 0.1433 loss: 1.5327 2022/08/30 02:37:27 - mmengine - INFO - Epoch(train) [129][25/63] lr: 6.3237e-03 eta: 1 day, 3:39:09 time: 1.5504 data_time: 0.0477 memory: 16201 loss_prob: 0.9020 loss_thr: 0.5214 loss_db: 0.1454 loss: 1.5689 2022/08/30 02:37:36 - mmengine - INFO - Epoch(train) [129][30/63] lr: 6.3237e-03 eta: 1 day, 3:39:09 time: 1.6408 data_time: 0.0342 memory: 16201 loss_prob: 0.8812 loss_thr: 0.5138 loss_db: 0.1490 loss: 1.5441 2022/08/30 02:37:44 - mmengine - INFO - Epoch(train) [129][35/63] lr: 6.3237e-03 eta: 1 day, 3:39:09 time: 1.7007 data_time: 0.0449 memory: 16201 loss_prob: 0.9083 loss_thr: 0.5098 loss_db: 0.1554 loss: 1.5735 2022/08/30 02:37:52 - mmengine - INFO - Epoch(train) [129][40/63] lr: 6.3237e-03 eta: 1 day, 3:39:00 time: 1.5521 data_time: 0.0360 memory: 16201 loss_prob: 0.9361 loss_thr: 0.5363 loss_db: 0.1565 loss: 1.6289 2022/08/30 02:38:00 - mmengine - INFO - Epoch(train) [129][45/63] lr: 6.3237e-03 eta: 1 day, 3:39:00 time: 1.6458 data_time: 0.0355 memory: 16201 loss_prob: 0.9622 loss_thr: 0.5574 loss_db: 0.1594 loss: 1.6789 2022/08/30 02:38:08 - mmengine - INFO - Epoch(train) [129][50/63] lr: 6.3237e-03 eta: 1 day, 3:39:01 time: 1.6636 data_time: 0.0564 memory: 16201 loss_prob: 1.0031 loss_thr: 0.5422 loss_db: 0.1681 loss: 1.7134 2022/08/30 02:38:16 - mmengine - INFO - Epoch(train) [129][55/63] lr: 6.3237e-03 eta: 1 day, 3:39:01 time: 1.6190 data_time: 0.0407 memory: 16201 loss_prob: 0.9858 loss_thr: 0.5191 loss_db: 0.1628 loss: 1.6677 2022/08/30 02:38:23 - mmengine - INFO - Epoch(train) [129][60/63] lr: 6.3237e-03 eta: 1 day, 3:38:50 time: 1.5139 data_time: 0.0277 memory: 16201 loss_prob: 0.9865 loss_thr: 0.5322 loss_db: 0.1631 loss: 1.6819 2022/08/30 02:38:28 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:38:37 - mmengine - INFO - Epoch(train) [130][5/63] lr: 6.3184e-03 eta: 1 day, 3:38:50 time: 1.6191 data_time: 0.2538 memory: 16201 loss_prob: 0.9436 loss_thr: 0.5504 loss_db: 0.1624 loss: 1.6563 2022/08/30 02:38:46 - mmengine - INFO - Epoch(train) [130][10/63] lr: 6.3184e-03 eta: 1 day, 3:38:22 time: 1.8159 data_time: 0.2791 memory: 16201 loss_prob: 0.9061 loss_thr: 0.5072 loss_db: 0.1501 loss: 1.5633 2022/08/30 02:38:55 - mmengine - INFO - Epoch(train) [130][15/63] lr: 6.3184e-03 eta: 1 day, 3:38:22 time: 1.8229 data_time: 0.0418 memory: 16201 loss_prob: 1.0063 loss_thr: 0.5442 loss_db: 0.1652 loss: 1.7157 2022/08/30 02:39:02 - mmengine - INFO - Epoch(train) [130][20/63] lr: 6.3184e-03 eta: 1 day, 3:38:20 time: 1.6251 data_time: 0.0394 memory: 16201 loss_prob: 0.9322 loss_thr: 0.5283 loss_db: 0.1567 loss: 1.6172 2022/08/30 02:39:10 - mmengine - INFO - Epoch(train) [130][25/63] lr: 6.3184e-03 eta: 1 day, 3:38:20 time: 1.5046 data_time: 0.0440 memory: 16201 loss_prob: 0.8692 loss_thr: 0.4980 loss_db: 0.1448 loss: 1.5120 2022/08/30 02:39:19 - mmengine - INFO - Epoch(train) [130][30/63] lr: 6.3184e-03 eta: 1 day, 3:38:21 time: 1.6647 data_time: 0.0341 memory: 16201 loss_prob: 0.9833 loss_thr: 0.5364 loss_db: 0.1580 loss: 1.6777 2022/08/30 02:39:28 - mmengine - INFO - Epoch(train) [130][35/63] lr: 6.3184e-03 eta: 1 day, 3:38:21 time: 1.8318 data_time: 0.0516 memory: 16201 loss_prob: 0.9354 loss_thr: 0.5221 loss_db: 0.1531 loss: 1.6107 2022/08/30 02:39:37 - mmengine - INFO - Epoch(train) [130][40/63] lr: 6.3184e-03 eta: 1 day, 3:38:35 time: 1.8310 data_time: 0.0491 memory: 16201 loss_prob: 0.8310 loss_thr: 0.5049 loss_db: 0.1389 loss: 1.4748 2022/08/30 02:39:45 - mmengine - INFO - Epoch(train) [130][45/63] lr: 6.3184e-03 eta: 1 day, 3:38:35 time: 1.6725 data_time: 0.0355 memory: 16201 loss_prob: 0.9100 loss_thr: 0.5338 loss_db: 0.1511 loss: 1.5949 2022/08/30 02:39:54 - mmengine - INFO - Epoch(train) [130][50/63] lr: 6.3184e-03 eta: 1 day, 3:38:38 time: 1.6831 data_time: 0.0459 memory: 16201 loss_prob: 1.0057 loss_thr: 0.5418 loss_db: 0.1653 loss: 1.7128 2022/08/30 02:40:02 - mmengine - INFO - Epoch(train) [130][55/63] lr: 6.3184e-03 eta: 1 day, 3:38:38 time: 1.7223 data_time: 0.0368 memory: 16201 loss_prob: 0.9575 loss_thr: 0.5589 loss_db: 0.1555 loss: 1.6719 2022/08/30 02:40:11 - mmengine - INFO - Epoch(train) [130][60/63] lr: 6.3184e-03 eta: 1 day, 3:38:47 time: 1.7637 data_time: 0.0452 memory: 16201 loss_prob: 0.9381 loss_thr: 0.5641 loss_db: 0.1566 loss: 1.6589 2022/08/30 02:40:15 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:40:26 - mmengine - INFO - Epoch(train) [131][5/63] lr: 6.3131e-03 eta: 1 day, 3:38:47 time: 1.7527 data_time: 0.2331 memory: 16201 loss_prob: 0.8874 loss_thr: 0.5286 loss_db: 0.1513 loss: 1.5673 2022/08/30 02:40:34 - mmengine - INFO - Epoch(train) [131][10/63] lr: 6.3131e-03 eta: 1 day, 3:38:24 time: 1.8827 data_time: 0.2453 memory: 16201 loss_prob: 0.8660 loss_thr: 0.5203 loss_db: 0.1469 loss: 1.5332 2022/08/30 02:40:42 - mmengine - INFO - Epoch(train) [131][15/63] lr: 6.3131e-03 eta: 1 day, 3:38:24 time: 1.6377 data_time: 0.0399 memory: 16201 loss_prob: 0.8336 loss_thr: 0.5037 loss_db: 0.1375 loss: 1.4748 2022/08/30 02:40:51 - mmengine - INFO - Epoch(train) [131][20/63] lr: 6.3131e-03 eta: 1 day, 3:38:25 time: 1.6665 data_time: 0.0460 memory: 16201 loss_prob: 0.9128 loss_thr: 0.5223 loss_db: 0.1483 loss: 1.5835 2022/08/30 02:40:59 - mmengine - INFO - Epoch(train) [131][25/63] lr: 6.3131e-03 eta: 1 day, 3:38:25 time: 1.7368 data_time: 0.0420 memory: 16201 loss_prob: 0.8995 loss_thr: 0.5272 loss_db: 0.1489 loss: 1.5755 2022/08/30 02:41:08 - mmengine - INFO - Epoch(train) [131][30/63] lr: 6.3131e-03 eta: 1 day, 3:38:29 time: 1.6997 data_time: 0.0383 memory: 16201 loss_prob: 0.8661 loss_thr: 0.5177 loss_db: 0.1447 loss: 1.5285 2022/08/30 02:41:16 - mmengine - INFO - Epoch(train) [131][35/63] lr: 6.3131e-03 eta: 1 day, 3:38:29 time: 1.6646 data_time: 0.0479 memory: 16201 loss_prob: 0.9798 loss_thr: 0.5324 loss_db: 0.1642 loss: 1.6765 2022/08/30 02:41:24 - mmengine - INFO - Epoch(train) [131][40/63] lr: 6.3131e-03 eta: 1 day, 3:38:29 time: 1.6624 data_time: 0.0356 memory: 16201 loss_prob: 0.9709 loss_thr: 0.5291 loss_db: 0.1642 loss: 1.6642 2022/08/30 02:41:34 - mmengine - INFO - Epoch(train) [131][45/63] lr: 6.3131e-03 eta: 1 day, 3:38:29 time: 1.8094 data_time: 0.0373 memory: 16201 loss_prob: 0.9039 loss_thr: 0.5059 loss_db: 0.1503 loss: 1.5600 2022/08/30 02:41:44 - mmengine - INFO - Epoch(train) [131][50/63] lr: 6.3131e-03 eta: 1 day, 3:38:54 time: 1.9621 data_time: 0.0391 memory: 16201 loss_prob: 0.9373 loss_thr: 0.5139 loss_db: 0.1520 loss: 1.6032 2022/08/30 02:41:53 - mmengine - INFO - Epoch(train) [131][55/63] lr: 6.3131e-03 eta: 1 day, 3:38:54 time: 1.9280 data_time: 0.0379 memory: 16201 loss_prob: 0.9968 loss_thr: 0.5438 loss_db: 0.1636 loss: 1.7042 2022/08/30 02:42:04 - mmengine - INFO - Epoch(train) [131][60/63] lr: 6.3131e-03 eta: 1 day, 3:39:25 time: 2.0368 data_time: 0.0479 memory: 16201 loss_prob: 0.9888 loss_thr: 0.5499 loss_db: 0.1662 loss: 1.7049 2022/08/30 02:42:09 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:42:21 - mmengine - INFO - Epoch(train) [132][5/63] lr: 6.3078e-03 eta: 1 day, 3:39:25 time: 1.9862 data_time: 0.2609 memory: 16201 loss_prob: 1.0885 loss_thr: 0.5510 loss_db: 0.1678 loss: 1.8073 2022/08/30 02:42:31 - mmengine - INFO - Epoch(train) [132][10/63] lr: 6.3078e-03 eta: 1 day, 3:39:35 time: 2.2826 data_time: 0.2675 memory: 16201 loss_prob: 1.0704 loss_thr: 0.5400 loss_db: 0.1633 loss: 1.7736 2022/08/30 02:42:40 - mmengine - INFO - Epoch(train) [132][15/63] lr: 6.3078e-03 eta: 1 day, 3:39:35 time: 1.9023 data_time: 0.0508 memory: 16201 loss_prob: 0.9135 loss_thr: 0.5375 loss_db: 0.1510 loss: 1.6020 2022/08/30 02:42:48 - mmengine - INFO - Epoch(train) [132][20/63] lr: 6.3078e-03 eta: 1 day, 3:39:34 time: 1.6465 data_time: 0.0517 memory: 16201 loss_prob: 0.9330 loss_thr: 0.5394 loss_db: 0.1556 loss: 1.6281 2022/08/30 02:42:56 - mmengine - INFO - Epoch(train) [132][25/63] lr: 6.3078e-03 eta: 1 day, 3:39:34 time: 1.6106 data_time: 0.0564 memory: 16201 loss_prob: 0.9203 loss_thr: 0.5285 loss_db: 0.1541 loss: 1.6030 2022/08/30 02:43:03 - mmengine - INFO - Epoch(train) [132][30/63] lr: 6.3078e-03 eta: 1 day, 3:39:18 time: 1.4696 data_time: 0.0386 memory: 16201 loss_prob: 0.9667 loss_thr: 0.5313 loss_db: 0.1564 loss: 1.6544 2022/08/30 02:43:10 - mmengine - INFO - Epoch(train) [132][35/63] lr: 6.3078e-03 eta: 1 day, 3:39:18 time: 1.4068 data_time: 0.0381 memory: 16201 loss_prob: 1.0380 loss_thr: 0.5202 loss_db: 0.1643 loss: 1.7225 2022/08/30 02:43:18 - mmengine - INFO - Epoch(train) [132][40/63] lr: 6.3078e-03 eta: 1 day, 3:39:05 time: 1.4960 data_time: 0.0392 memory: 16201 loss_prob: 1.0900 loss_thr: 0.5363 loss_db: 0.1719 loss: 1.7982 2022/08/30 02:43:27 - mmengine - INFO - Epoch(train) [132][45/63] lr: 6.3078e-03 eta: 1 day, 3:39:05 time: 1.6799 data_time: 0.0410 memory: 16201 loss_prob: 1.0394 loss_thr: 0.5511 loss_db: 0.1669 loss: 1.7575 2022/08/30 02:43:36 - mmengine - INFO - Epoch(train) [132][50/63] lr: 6.3078e-03 eta: 1 day, 3:39:20 time: 1.8512 data_time: 0.0551 memory: 16201 loss_prob: 1.0206 loss_thr: 0.5466 loss_db: 0.1618 loss: 1.7290 2022/08/30 02:43:44 - mmengine - INFO - Epoch(train) [132][55/63] lr: 6.3078e-03 eta: 1 day, 3:39:20 time: 1.7042 data_time: 0.0436 memory: 16201 loss_prob: 1.0174 loss_thr: 0.5412 loss_db: 0.1631 loss: 1.7217 2022/08/30 02:43:53 - mmengine - INFO - Epoch(train) [132][60/63] lr: 6.3078e-03 eta: 1 day, 3:39:19 time: 1.6493 data_time: 0.0636 memory: 16201 loss_prob: 0.9729 loss_thr: 0.5530 loss_db: 0.1622 loss: 1.6881 2022/08/30 02:43:57 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:44:08 - mmengine - INFO - Epoch(train) [133][5/63] lr: 6.3025e-03 eta: 1 day, 3:39:19 time: 1.8216 data_time: 0.2986 memory: 16201 loss_prob: 1.0411 loss_thr: 0.5351 loss_db: 0.1734 loss: 1.7496 2022/08/30 02:44:18 - mmengine - INFO - Epoch(train) [133][10/63] lr: 6.3025e-03 eta: 1 day, 3:39:13 time: 2.0789 data_time: 0.3304 memory: 16201 loss_prob: 1.0286 loss_thr: 0.5380 loss_db: 0.1718 loss: 1.7384 2022/08/30 02:44:26 - mmengine - INFO - Epoch(train) [133][15/63] lr: 6.3025e-03 eta: 1 day, 3:39:13 time: 1.8391 data_time: 0.0661 memory: 16201 loss_prob: 1.0889 loss_thr: 0.5467 loss_db: 0.1760 loss: 1.8116 2022/08/30 02:44:35 - mmengine - INFO - Epoch(train) [133][20/63] lr: 6.3025e-03 eta: 1 day, 3:39:16 time: 1.7003 data_time: 0.0292 memory: 16201 loss_prob: 1.2985 loss_thr: 0.5537 loss_db: 0.2118 loss: 2.0639 2022/08/30 02:44:43 - mmengine - INFO - Epoch(train) [133][25/63] lr: 6.3025e-03 eta: 1 day, 3:39:16 time: 1.7300 data_time: 0.0312 memory: 16201 loss_prob: 1.2563 loss_thr: 0.5653 loss_db: 0.2056 loss: 2.0271 2022/08/30 02:44:52 - mmengine - INFO - Epoch(train) [133][30/63] lr: 6.3025e-03 eta: 1 day, 3:39:20 time: 1.7235 data_time: 0.0457 memory: 16201 loss_prob: 1.1343 loss_thr: 0.5411 loss_db: 0.1880 loss: 1.8633 2022/08/30 02:45:00 - mmengine - INFO - Epoch(train) [133][35/63] lr: 6.3025e-03 eta: 1 day, 3:39:20 time: 1.6816 data_time: 0.0601 memory: 16201 loss_prob: 1.0752 loss_thr: 0.5459 loss_db: 0.1810 loss: 1.8022 2022/08/30 02:45:07 - mmengine - INFO - Epoch(train) [133][40/63] lr: 6.3025e-03 eta: 1 day, 3:39:11 time: 1.5498 data_time: 0.0383 memory: 16201 loss_prob: 1.0399 loss_thr: 0.5556 loss_db: 0.1734 loss: 1.7689 2022/08/30 02:45:17 - mmengine - INFO - Epoch(train) [133][45/63] lr: 6.3025e-03 eta: 1 day, 3:39:11 time: 1.7225 data_time: 0.0405 memory: 16201 loss_prob: 1.0116 loss_thr: 0.5384 loss_db: 0.1699 loss: 1.7200 2022/08/30 02:45:26 - mmengine - INFO - Epoch(train) [133][50/63] lr: 6.3025e-03 eta: 1 day, 3:39:29 time: 1.8846 data_time: 0.0564 memory: 16201 loss_prob: 1.0831 loss_thr: 0.5704 loss_db: 0.1780 loss: 1.8315 2022/08/30 02:45:35 - mmengine - INFO - Epoch(train) [133][55/63] lr: 6.3025e-03 eta: 1 day, 3:39:29 time: 1.7995 data_time: 0.0382 memory: 16201 loss_prob: 1.2917 loss_thr: 0.5737 loss_db: 0.2054 loss: 2.0708 2022/08/30 02:45:44 - mmengine - INFO - Epoch(train) [133][60/63] lr: 6.3025e-03 eta: 1 day, 3:39:41 time: 1.8123 data_time: 0.0517 memory: 16201 loss_prob: 1.3282 loss_thr: 0.5663 loss_db: 0.2134 loss: 2.1080 2022/08/30 02:45:49 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:45:59 - mmengine - INFO - Epoch(train) [134][5/63] lr: 6.2971e-03 eta: 1 day, 3:39:41 time: 1.7982 data_time: 0.2619 memory: 16201 loss_prob: 1.1167 loss_thr: 0.5631 loss_db: 0.1740 loss: 1.8538 2022/08/30 02:46:07 - mmengine - INFO - Epoch(train) [134][10/63] lr: 6.2971e-03 eta: 1 day, 3:39:13 time: 1.8156 data_time: 0.2711 memory: 16201 loss_prob: 1.0974 loss_thr: 0.5387 loss_db: 0.1797 loss: 1.8158 2022/08/30 02:46:15 - mmengine - INFO - Epoch(train) [134][15/63] lr: 6.2971e-03 eta: 1 day, 3:39:13 time: 1.5453 data_time: 0.0371 memory: 16201 loss_prob: 1.0882 loss_thr: 0.5432 loss_db: 0.1789 loss: 1.8103 2022/08/30 02:46:23 - mmengine - INFO - Epoch(train) [134][20/63] lr: 6.2971e-03 eta: 1 day, 3:39:07 time: 1.5972 data_time: 0.0423 memory: 16201 loss_prob: 1.1034 loss_thr: 0.5740 loss_db: 0.1786 loss: 1.8560 2022/08/30 02:46:32 - mmengine - INFO - Epoch(train) [134][25/63] lr: 6.2971e-03 eta: 1 day, 3:39:07 time: 1.7884 data_time: 0.0441 memory: 16201 loss_prob: 1.1315 loss_thr: 0.5694 loss_db: 0.1820 loss: 1.8829 2022/08/30 02:46:41 - mmengine - INFO - Epoch(train) [134][30/63] lr: 6.2971e-03 eta: 1 day, 3:39:22 time: 1.8510 data_time: 0.0365 memory: 16201 loss_prob: 1.1551 loss_thr: 0.5554 loss_db: 0.1856 loss: 1.8960 2022/08/30 02:46:50 - mmengine - INFO - Epoch(train) [134][35/63] lr: 6.2971e-03 eta: 1 day, 3:39:22 time: 1.7977 data_time: 0.0479 memory: 16201 loss_prob: 1.1235 loss_thr: 0.5525 loss_db: 0.1851 loss: 1.8610 2022/08/30 02:46:59 - mmengine - INFO - Epoch(train) [134][40/63] lr: 6.2971e-03 eta: 1 day, 3:39:34 time: 1.8163 data_time: 0.0536 memory: 16201 loss_prob: 1.0628 loss_thr: 0.5666 loss_db: 0.1787 loss: 1.8081 2022/08/30 02:47:09 - mmengine - INFO - Epoch(train) [134][45/63] lr: 6.2971e-03 eta: 1 day, 3:39:34 time: 1.9015 data_time: 0.0465 memory: 16201 loss_prob: 1.0019 loss_thr: 0.5333 loss_db: 0.1666 loss: 1.7018 2022/08/30 02:47:17 - mmengine - INFO - Epoch(train) [134][50/63] lr: 6.2971e-03 eta: 1 day, 3:39:40 time: 1.7509 data_time: 0.0495 memory: 16201 loss_prob: 0.8614 loss_thr: 0.4787 loss_db: 0.1397 loss: 1.4798 2022/08/30 02:47:24 - mmengine - INFO - Epoch(train) [134][55/63] lr: 6.2971e-03 eta: 1 day, 3:39:40 time: 1.5047 data_time: 0.0375 memory: 16201 loss_prob: 0.9020 loss_thr: 0.5098 loss_db: 0.1454 loss: 1.5573 2022/08/30 02:47:33 - mmengine - INFO - Epoch(train) [134][60/63] lr: 6.2971e-03 eta: 1 day, 3:39:35 time: 1.5980 data_time: 0.0382 memory: 16201 loss_prob: 1.1307 loss_thr: 0.5672 loss_db: 0.1820 loss: 1.8799 2022/08/30 02:47:37 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:47:48 - mmengine - INFO - Epoch(train) [135][5/63] lr: 6.2918e-03 eta: 1 day, 3:39:35 time: 1.8622 data_time: 0.2665 memory: 16201 loss_prob: 1.0427 loss_thr: 0.5754 loss_db: 0.1674 loss: 1.7855 2022/08/30 02:47:57 - mmengine - INFO - Epoch(train) [135][10/63] lr: 6.2918e-03 eta: 1 day, 3:39:22 time: 2.0051 data_time: 0.2752 memory: 16201 loss_prob: 0.9972 loss_thr: 0.5644 loss_db: 0.1632 loss: 1.7248 2022/08/30 02:48:05 - mmengine - INFO - Epoch(train) [135][15/63] lr: 6.2918e-03 eta: 1 day, 3:39:22 time: 1.6295 data_time: 0.0376 memory: 16201 loss_prob: 1.0462 loss_thr: 0.5603 loss_db: 0.1689 loss: 1.7754 2022/08/30 02:48:14 - mmengine - INFO - Epoch(train) [135][20/63] lr: 6.2918e-03 eta: 1 day, 3:39:22 time: 1.6810 data_time: 0.0454 memory: 16201 loss_prob: 1.0496 loss_thr: 0.5511 loss_db: 0.1716 loss: 1.7723 2022/08/30 02:48:23 - mmengine - INFO - Epoch(train) [135][25/63] lr: 6.2918e-03 eta: 1 day, 3:39:22 time: 1.8580 data_time: 0.0457 memory: 16201 loss_prob: 1.0331 loss_thr: 0.5454 loss_db: 0.1736 loss: 1.7521 2022/08/30 02:48:32 - mmengine - INFO - Epoch(train) [135][30/63] lr: 6.2918e-03 eta: 1 day, 3:39:34 time: 1.8197 data_time: 0.0459 memory: 16201 loss_prob: 0.9223 loss_thr: 0.5176 loss_db: 0.1513 loss: 1.5912 2022/08/30 02:48:43 - mmengine - INFO - Epoch(train) [135][35/63] lr: 6.2918e-03 eta: 1 day, 3:39:34 time: 1.9313 data_time: 0.0589 memory: 16201 loss_prob: 0.9445 loss_thr: 0.5287 loss_db: 0.1522 loss: 1.6254 2022/08/30 02:48:51 - mmengine - INFO - Epoch(train) [135][40/63] lr: 6.2918e-03 eta: 1 day, 3:39:51 time: 1.8862 data_time: 0.0491 memory: 16201 loss_prob: 1.0264 loss_thr: 0.5383 loss_db: 0.1708 loss: 1.7355 2022/08/30 02:49:01 - mmengine - INFO - Epoch(train) [135][45/63] lr: 6.2918e-03 eta: 1 day, 3:39:51 time: 1.8716 data_time: 0.0541 memory: 16201 loss_prob: 0.9392 loss_thr: 0.5119 loss_db: 0.1570 loss: 1.6080 2022/08/30 02:49:11 - mmengine - INFO - Epoch(train) [135][50/63] lr: 6.2918e-03 eta: 1 day, 3:40:18 time: 2.0145 data_time: 0.0664 memory: 16201 loss_prob: 0.8959 loss_thr: 0.5058 loss_db: 0.1464 loss: 1.5481 2022/08/30 02:49:21 - mmengine - INFO - Epoch(train) [135][55/63] lr: 6.2918e-03 eta: 1 day, 3:40:18 time: 1.9771 data_time: 0.0433 memory: 16201 loss_prob: 0.9293 loss_thr: 0.5262 loss_db: 0.1553 loss: 1.6108 2022/08/30 02:49:30 - mmengine - INFO - Epoch(train) [135][60/63] lr: 6.2918e-03 eta: 1 day, 3:40:34 time: 1.8654 data_time: 0.0423 memory: 16201 loss_prob: 0.9843 loss_thr: 0.5433 loss_db: 0.1652 loss: 1.6928 2022/08/30 02:49:34 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:49:46 - mmengine - INFO - Epoch(train) [136][5/63] lr: 6.2865e-03 eta: 1 day, 3:40:34 time: 1.9758 data_time: 0.2600 memory: 16201 loss_prob: 1.0262 loss_thr: 0.5553 loss_db: 0.1702 loss: 1.7517 2022/08/30 02:49:56 - mmengine - INFO - Epoch(train) [136][10/63] lr: 6.2865e-03 eta: 1 day, 3:40:31 time: 2.1473 data_time: 0.2815 memory: 16201 loss_prob: 1.0273 loss_thr: 0.5535 loss_db: 0.1705 loss: 1.7513 2022/08/30 02:50:05 - mmengine - INFO - Epoch(train) [136][15/63] lr: 6.2865e-03 eta: 1 day, 3:40:31 time: 1.8606 data_time: 0.0462 memory: 16201 loss_prob: 1.0196 loss_thr: 0.5326 loss_db: 0.1732 loss: 1.7254 2022/08/30 02:50:14 - mmengine - INFO - Epoch(train) [136][20/63] lr: 6.2865e-03 eta: 1 day, 3:40:46 time: 1.8574 data_time: 0.0410 memory: 16201 loss_prob: 0.9984 loss_thr: 0.5448 loss_db: 0.1657 loss: 1.7089 2022/08/30 02:50:23 - mmengine - INFO - Epoch(train) [136][25/63] lr: 6.2865e-03 eta: 1 day, 3:40:46 time: 1.7970 data_time: 0.0556 memory: 16201 loss_prob: 0.9955 loss_thr: 0.5598 loss_db: 0.1636 loss: 1.7190 2022/08/30 02:50:31 - mmengine - INFO - Epoch(train) [136][30/63] lr: 6.2865e-03 eta: 1 day, 3:40:49 time: 1.7163 data_time: 0.0562 memory: 16201 loss_prob: 1.2002 loss_thr: 0.5600 loss_db: 0.1909 loss: 1.9511 2022/08/30 02:50:41 - mmengine - INFO - Epoch(train) [136][35/63] lr: 6.2865e-03 eta: 1 day, 3:40:49 time: 1.7813 data_time: 0.0551 memory: 16201 loss_prob: 1.2461 loss_thr: 0.5480 loss_db: 0.1964 loss: 1.9906 2022/08/30 02:50:51 - mmengine - INFO - Epoch(train) [136][40/63] lr: 6.2865e-03 eta: 1 day, 3:41:09 time: 1.9235 data_time: 0.0395 memory: 16201 loss_prob: 1.0665 loss_thr: 0.5315 loss_db: 0.1751 loss: 1.7731 2022/08/30 02:51:00 - mmengine - INFO - Epoch(train) [136][45/63] lr: 6.2865e-03 eta: 1 day, 3:41:09 time: 1.9508 data_time: 0.0439 memory: 16201 loss_prob: 1.0856 loss_thr: 0.5619 loss_db: 0.1812 loss: 1.8288 2022/08/30 02:51:10 - mmengine - INFO - Epoch(train) [136][50/63] lr: 6.2865e-03 eta: 1 day, 3:41:27 time: 1.9153 data_time: 0.0621 memory: 16201 loss_prob: 1.0680 loss_thr: 0.5469 loss_db: 0.1794 loss: 1.7943 2022/08/30 02:51:19 - mmengine - INFO - Epoch(train) [136][55/63] lr: 6.2865e-03 eta: 1 day, 3:41:27 time: 1.8911 data_time: 0.0429 memory: 16201 loss_prob: 0.9323 loss_thr: 0.4995 loss_db: 0.1558 loss: 1.5876 2022/08/30 02:51:28 - mmengine - INFO - Epoch(train) [136][60/63] lr: 6.2865e-03 eta: 1 day, 3:41:40 time: 1.8430 data_time: 0.0474 memory: 16201 loss_prob: 0.9658 loss_thr: 0.5125 loss_db: 0.1578 loss: 1.6361 2022/08/30 02:51:32 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:51:41 - mmengine - INFO - Epoch(train) [137][5/63] lr: 6.2812e-03 eta: 1 day, 3:41:40 time: 1.6862 data_time: 0.2617 memory: 16201 loss_prob: 0.9409 loss_thr: 0.5188 loss_db: 0.1550 loss: 1.6148 2022/08/30 02:51:50 - mmengine - INFO - Epoch(train) [137][10/63] lr: 6.2812e-03 eta: 1 day, 3:41:13 time: 1.8278 data_time: 0.2789 memory: 16201 loss_prob: 1.0871 loss_thr: 0.5400 loss_db: 0.1788 loss: 1.8059 2022/08/30 02:51:57 - mmengine - INFO - Epoch(train) [137][15/63] lr: 6.2812e-03 eta: 1 day, 3:41:13 time: 1.5463 data_time: 0.0329 memory: 16201 loss_prob: 1.0136 loss_thr: 0.5176 loss_db: 0.1696 loss: 1.7007 2022/08/30 02:52:09 - mmengine - INFO - Epoch(train) [137][20/63] lr: 6.2812e-03 eta: 1 day, 3:41:27 time: 1.8614 data_time: 0.0361 memory: 16201 loss_prob: 0.9648 loss_thr: 0.5151 loss_db: 0.1626 loss: 1.6425 2022/08/30 02:52:17 - mmengine - INFO - Epoch(train) [137][25/63] lr: 6.2812e-03 eta: 1 day, 3:41:27 time: 2.0288 data_time: 0.0559 memory: 16201 loss_prob: 1.2601 loss_thr: 0.5841 loss_db: 0.2027 loss: 2.0470 2022/08/30 02:52:27 - mmengine - INFO - Epoch(train) [137][30/63] lr: 6.2812e-03 eta: 1 day, 3:41:40 time: 1.8376 data_time: 0.0487 memory: 16201 loss_prob: 1.1724 loss_thr: 0.5601 loss_db: 0.1898 loss: 1.9223 2022/08/30 02:52:37 - mmengine - INFO - Epoch(train) [137][35/63] lr: 6.2812e-03 eta: 1 day, 3:41:40 time: 1.9707 data_time: 0.0579 memory: 16201 loss_prob: 0.9435 loss_thr: 0.5045 loss_db: 0.1572 loss: 1.6053 2022/08/30 02:52:45 - mmengine - INFO - Epoch(train) [137][40/63] lr: 6.2812e-03 eta: 1 day, 3:41:48 time: 1.7941 data_time: 0.0389 memory: 16201 loss_prob: 0.9335 loss_thr: 0.5112 loss_db: 0.1568 loss: 1.6016 2022/08/30 02:52:55 - mmengine - INFO - Epoch(train) [137][45/63] lr: 6.2812e-03 eta: 1 day, 3:41:48 time: 1.8238 data_time: 0.0440 memory: 16201 loss_prob: 1.0423 loss_thr: 0.5298 loss_db: 0.1724 loss: 1.7445 2022/08/30 02:53:04 - mmengine - INFO - Epoch(train) [137][50/63] lr: 6.2812e-03 eta: 1 day, 3:42:03 time: 1.8658 data_time: 0.0721 memory: 16201 loss_prob: 1.1109 loss_thr: 0.5645 loss_db: 0.1813 loss: 1.8567 2022/08/30 02:53:12 - mmengine - INFO - Epoch(train) [137][55/63] lr: 6.2812e-03 eta: 1 day, 3:42:03 time: 1.7031 data_time: 0.0409 memory: 16201 loss_prob: 0.9847 loss_thr: 0.5664 loss_db: 0.1624 loss: 1.7134 2022/08/30 02:53:22 - mmengine - INFO - Epoch(train) [137][60/63] lr: 6.2812e-03 eta: 1 day, 3:42:14 time: 1.8186 data_time: 0.0360 memory: 16201 loss_prob: 0.9576 loss_thr: 0.5324 loss_db: 0.1537 loss: 1.6438 2022/08/30 02:53:26 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:53:38 - mmengine - INFO - Epoch(train) [138][5/63] lr: 6.2759e-03 eta: 1 day, 3:42:14 time: 1.8917 data_time: 0.2435 memory: 16201 loss_prob: 1.1315 loss_thr: 0.5651 loss_db: 0.1836 loss: 1.8802 2022/08/30 02:53:48 - mmengine - INFO - Epoch(train) [138][10/63] lr: 6.2759e-03 eta: 1 day, 3:42:13 time: 2.1785 data_time: 0.2646 memory: 16201 loss_prob: 1.1270 loss_thr: 0.5741 loss_db: 0.1878 loss: 1.8890 2022/08/30 02:53:56 - mmengine - INFO - Epoch(train) [138][15/63] lr: 6.2759e-03 eta: 1 day, 3:42:13 time: 1.8685 data_time: 0.0465 memory: 16201 loss_prob: 1.0767 loss_thr: 0.5587 loss_db: 0.1801 loss: 1.8155 2022/08/30 02:54:04 - mmengine - INFO - Epoch(train) [138][20/63] lr: 6.2759e-03 eta: 1 day, 3:42:10 time: 1.6421 data_time: 0.0461 memory: 16201 loss_prob: 1.0748 loss_thr: 0.5657 loss_db: 0.1779 loss: 1.8184 2022/08/30 02:54:14 - mmengine - INFO - Epoch(train) [138][25/63] lr: 6.2759e-03 eta: 1 day, 3:42:10 time: 1.7516 data_time: 0.0378 memory: 16201 loss_prob: 0.9592 loss_thr: 0.5378 loss_db: 0.1582 loss: 1.6551 2022/08/30 02:54:22 - mmengine - INFO - Epoch(train) [138][30/63] lr: 6.2759e-03 eta: 1 day, 3:42:18 time: 1.7923 data_time: 0.0306 memory: 16201 loss_prob: 0.8839 loss_thr: 0.5113 loss_db: 0.1487 loss: 1.5439 2022/08/30 02:54:31 - mmengine - INFO - Epoch(train) [138][35/63] lr: 6.2759e-03 eta: 1 day, 3:42:18 time: 1.7240 data_time: 0.0437 memory: 16201 loss_prob: 1.0392 loss_thr: 0.5483 loss_db: 0.1703 loss: 1.7579 2022/08/30 02:54:39 - mmengine - INFO - Epoch(train) [138][40/63] lr: 6.2759e-03 eta: 1 day, 3:42:18 time: 1.6719 data_time: 0.0377 memory: 16201 loss_prob: 1.0828 loss_thr: 0.5499 loss_db: 0.1698 loss: 1.8025 2022/08/30 02:54:47 - mmengine - INFO - Epoch(train) [138][45/63] lr: 6.2759e-03 eta: 1 day, 3:42:18 time: 1.5621 data_time: 0.0354 memory: 16201 loss_prob: 0.9595 loss_thr: 0.5280 loss_db: 0.1533 loss: 1.6408 2022/08/30 02:54:55 - mmengine - INFO - Epoch(train) [138][50/63] lr: 6.2759e-03 eta: 1 day, 3:42:11 time: 1.5931 data_time: 0.0424 memory: 16201 loss_prob: 0.9779 loss_thr: 0.5392 loss_db: 0.1642 loss: 1.6813 2022/08/30 02:55:02 - mmengine - INFO - Epoch(train) [138][55/63] lr: 6.2759e-03 eta: 1 day, 3:42:11 time: 1.5538 data_time: 0.0339 memory: 16201 loss_prob: 1.1229 loss_thr: 0.5800 loss_db: 0.1822 loss: 1.8852 2022/08/30 02:55:10 - mmengine - INFO - Epoch(train) [138][60/63] lr: 6.2759e-03 eta: 1 day, 3:41:59 time: 1.5383 data_time: 0.0393 memory: 16201 loss_prob: 1.0971 loss_thr: 0.5881 loss_db: 0.1739 loss: 1.8590 2022/08/30 02:55:14 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:55:25 - mmengine - INFO - Epoch(train) [139][5/63] lr: 6.2705e-03 eta: 1 day, 3:41:59 time: 1.8411 data_time: 0.2591 memory: 16201 loss_prob: 0.9293 loss_thr: 0.5390 loss_db: 0.1534 loss: 1.6217 2022/08/30 02:55:35 - mmengine - INFO - Epoch(train) [139][10/63] lr: 6.2705e-03 eta: 1 day, 3:41:46 time: 2.0205 data_time: 0.2906 memory: 16201 loss_prob: 0.9621 loss_thr: 0.5300 loss_db: 0.1615 loss: 1.6536 2022/08/30 02:55:44 - mmengine - INFO - Epoch(train) [139][15/63] lr: 6.2705e-03 eta: 1 day, 3:41:46 time: 1.8086 data_time: 0.0498 memory: 16201 loss_prob: 0.9782 loss_thr: 0.5570 loss_db: 0.1626 loss: 1.6978 2022/08/30 02:55:52 - mmengine - INFO - Epoch(train) [139][20/63] lr: 6.2705e-03 eta: 1 day, 3:41:53 time: 1.7641 data_time: 0.0398 memory: 16201 loss_prob: 0.9744 loss_thr: 0.5429 loss_db: 0.1604 loss: 1.6778 2022/08/30 02:56:00 - mmengine - INFO - Epoch(train) [139][25/63] lr: 6.2705e-03 eta: 1 day, 3:41:53 time: 1.6457 data_time: 0.0602 memory: 16201 loss_prob: 0.9606 loss_thr: 0.5178 loss_db: 0.1605 loss: 1.6389 2022/08/30 02:56:08 - mmengine - INFO - Epoch(train) [139][30/63] lr: 6.2705e-03 eta: 1 day, 3:41:48 time: 1.6227 data_time: 0.0424 memory: 16201 loss_prob: 0.9797 loss_thr: 0.5294 loss_db: 0.1633 loss: 1.6724 2022/08/30 02:56:17 - mmengine - INFO - Epoch(train) [139][35/63] lr: 6.2705e-03 eta: 1 day, 3:41:48 time: 1.7167 data_time: 0.0437 memory: 16201 loss_prob: 0.9874 loss_thr: 0.5334 loss_db: 0.1600 loss: 1.6808 2022/08/30 02:56:25 - mmengine - INFO - Epoch(train) [139][40/63] lr: 6.2705e-03 eta: 1 day, 3:41:47 time: 1.6790 data_time: 0.0515 memory: 16201 loss_prob: 0.9825 loss_thr: 0.5379 loss_db: 0.1643 loss: 1.6847 2022/08/30 02:56:32 - mmengine - INFO - Epoch(train) [139][45/63] lr: 6.2705e-03 eta: 1 day, 3:41:47 time: 1.5191 data_time: 0.0383 memory: 16201 loss_prob: 0.9796 loss_thr: 0.5151 loss_db: 0.1670 loss: 1.6617 2022/08/30 02:56:41 - mmengine - INFO - Epoch(train) [139][50/63] lr: 6.2705e-03 eta: 1 day, 3:41:35 time: 1.5320 data_time: 0.0504 memory: 16201 loss_prob: 1.0324 loss_thr: 0.5099 loss_db: 0.1699 loss: 1.7121 2022/08/30 02:56:47 - mmengine - INFO - Epoch(train) [139][55/63] lr: 6.2705e-03 eta: 1 day, 3:41:35 time: 1.4686 data_time: 0.0422 memory: 16201 loss_prob: 1.1101 loss_thr: 0.5477 loss_db: 0.1782 loss: 1.8361 2022/08/30 02:56:57 - mmengine - INFO - Epoch(train) [139][60/63] lr: 6.2705e-03 eta: 1 day, 3:41:30 time: 1.6190 data_time: 0.0467 memory: 16201 loss_prob: 1.0590 loss_thr: 0.5657 loss_db: 0.1748 loss: 1.7994 2022/08/30 02:57:00 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:57:12 - mmengine - INFO - Epoch(train) [140][5/63] lr: 6.2652e-03 eta: 1 day, 3:41:30 time: 1.9132 data_time: 0.2719 memory: 16201 loss_prob: 0.9662 loss_thr: 0.5432 loss_db: 0.1558 loss: 1.6651 2022/08/30 02:57:22 - mmengine - INFO - Epoch(train) [140][10/63] lr: 6.2652e-03 eta: 1 day, 3:41:29 time: 2.1820 data_time: 0.3015 memory: 16201 loss_prob: 1.0366 loss_thr: 0.5720 loss_db: 0.1672 loss: 1.7759 2022/08/30 02:57:31 - mmengine - INFO - Epoch(train) [140][15/63] lr: 6.2652e-03 eta: 1 day, 3:41:29 time: 1.8303 data_time: 0.0446 memory: 16201 loss_prob: 1.1181 loss_thr: 0.5801 loss_db: 0.1778 loss: 1.8759 2022/08/30 02:57:41 - mmengine - INFO - Epoch(train) [140][20/63] lr: 6.2652e-03 eta: 1 day, 3:41:50 time: 1.9569 data_time: 0.0393 memory: 16201 loss_prob: 0.9504 loss_thr: 0.5149 loss_db: 0.1533 loss: 1.6186 2022/08/30 02:57:51 - mmengine - INFO - Epoch(train) [140][25/63] lr: 6.2652e-03 eta: 1 day, 3:41:50 time: 2.0349 data_time: 0.0646 memory: 16201 loss_prob: 0.8732 loss_thr: 0.4855 loss_db: 0.1454 loss: 1.5040 2022/08/30 02:57:59 - mmengine - INFO - Epoch(train) [140][30/63] lr: 6.2652e-03 eta: 1 day, 3:41:57 time: 1.7841 data_time: 0.0383 memory: 16201 loss_prob: 0.8753 loss_thr: 0.4866 loss_db: 0.1463 loss: 1.5083 2022/08/30 02:58:08 - mmengine - INFO - Epoch(train) [140][35/63] lr: 6.2652e-03 eta: 1 day, 3:41:57 time: 1.7318 data_time: 0.0409 memory: 16201 loss_prob: 0.9113 loss_thr: 0.5055 loss_db: 0.1500 loss: 1.5668 2022/08/30 02:58:16 - mmengine - INFO - Epoch(train) [140][40/63] lr: 6.2652e-03 eta: 1 day, 3:41:56 time: 1.6770 data_time: 0.0445 memory: 16201 loss_prob: 0.8713 loss_thr: 0.5094 loss_db: 0.1429 loss: 1.5236 2022/08/30 02:58:25 - mmengine - INFO - Epoch(train) [140][45/63] lr: 6.2652e-03 eta: 1 day, 3:41:56 time: 1.6214 data_time: 0.0340 memory: 16201 loss_prob: 0.8885 loss_thr: 0.5086 loss_db: 0.1494 loss: 1.5465 2022/08/30 02:58:33 - mmengine - INFO - Epoch(train) [140][50/63] lr: 6.2652e-03 eta: 1 day, 3:42:00 time: 1.7419 data_time: 0.0459 memory: 16201 loss_prob: 0.9433 loss_thr: 0.5176 loss_db: 0.1568 loss: 1.6177 2022/08/30 02:58:41 - mmengine - INFO - Epoch(train) [140][55/63] lr: 6.2652e-03 eta: 1 day, 3:42:00 time: 1.6540 data_time: 0.0321 memory: 16201 loss_prob: 0.8901 loss_thr: 0.5125 loss_db: 0.1457 loss: 1.5483 2022/08/30 02:58:51 - mmengine - INFO - Epoch(train) [140][60/63] lr: 6.2652e-03 eta: 1 day, 3:42:02 time: 1.7112 data_time: 0.0406 memory: 16201 loss_prob: 0.9563 loss_thr: 0.5298 loss_db: 0.1575 loss: 1.6437 2022/08/30 02:58:55 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 02:58:55 - mmengine - INFO - Saving checkpoint at 140 epochs 2022/08/30 02:59:05 - mmengine - INFO - Epoch(val) [140][5/32] eta: 1 day, 3:42:02 time: 0.7385 data_time: 0.1483 memory: 16201 2022/08/30 02:59:09 - mmengine - INFO - Epoch(val) [140][10/32] eta: 0:00:18 time: 0.8515 data_time: 0.2005 memory: 15734 2022/08/30 02:59:12 - mmengine - INFO - Epoch(val) [140][15/32] eta: 0:00:18 time: 0.7090 data_time: 0.0722 memory: 15734 2022/08/30 02:59:16 - mmengine - INFO - Epoch(val) [140][20/32] eta: 0:00:08 time: 0.6874 data_time: 0.0653 memory: 15734 2022/08/30 02:59:20 - mmengine - INFO - Epoch(val) [140][25/32] eta: 0:00:08 time: 0.8083 data_time: 0.0773 memory: 15734 2022/08/30 02:59:23 - mmengine - INFO - Epoch(val) [140][30/32] eta: 0:00:01 time: 0.7635 data_time: 0.0405 memory: 15734 2022/08/30 02:59:24 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 02:59:24 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8334, precision: 0.6617, hmean: 0.7377 2022/08/30 02:59:24 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8334, precision: 0.7325, hmean: 0.7797 2022/08/30 02:59:24 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8300, precision: 0.7826, hmean: 0.8056 2022/08/30 02:59:24 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8219, precision: 0.8319, hmean: 0.8268 2022/08/30 02:59:24 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7886, precision: 0.8745, hmean: 0.8294 2022/08/30 02:59:24 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.5797, precision: 0.9406, hmean: 0.7173 2022/08/30 02:59:24 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0058, precision: 1.0000, hmean: 0.0115 2022/08/30 02:59:24 - mmengine - INFO - Epoch(val) [140][32/32] icdar/precision: 0.8745 icdar/recall: 0.7886 icdar/hmean: 0.8294 2022/08/30 02:59:35 - mmengine - INFO - Epoch(train) [141][5/63] lr: 6.2599e-03 eta: 0:00:01 time: 1.8840 data_time: 0.2590 memory: 16201 loss_prob: 0.9438 loss_thr: 0.5264 loss_db: 0.1528 loss: 1.6230 2022/08/30 02:59:44 - mmengine - INFO - Epoch(train) [141][10/63] lr: 6.2599e-03 eta: 1 day, 3:41:47 time: 2.0025 data_time: 0.2893 memory: 16201 loss_prob: 0.9582 loss_thr: 0.5429 loss_db: 0.1533 loss: 1.6543 2022/08/30 02:59:52 - mmengine - INFO - Epoch(train) [141][15/63] lr: 6.2599e-03 eta: 1 day, 3:41:47 time: 1.7448 data_time: 0.0548 memory: 16201 loss_prob: 0.9479 loss_thr: 0.5387 loss_db: 0.1573 loss: 1.6439 2022/08/30 03:00:02 - mmengine - INFO - Epoch(train) [141][20/63] lr: 6.2599e-03 eta: 1 day, 3:41:52 time: 1.7602 data_time: 0.0527 memory: 16201 loss_prob: 0.9496 loss_thr: 0.5346 loss_db: 0.1566 loss: 1.6408 2022/08/30 03:00:09 - mmengine - INFO - Epoch(train) [141][25/63] lr: 6.2599e-03 eta: 1 day, 3:41:52 time: 1.6239 data_time: 0.0545 memory: 16201 loss_prob: 0.9351 loss_thr: 0.5384 loss_db: 0.1532 loss: 1.6266 2022/08/30 03:00:18 - mmengine - INFO - Epoch(train) [141][30/63] lr: 6.2599e-03 eta: 1 day, 3:41:43 time: 1.5666 data_time: 0.0366 memory: 16201 loss_prob: 0.9145 loss_thr: 0.5218 loss_db: 0.1556 loss: 1.5919 2022/08/30 03:00:27 - mmengine - INFO - Epoch(train) [141][35/63] lr: 6.2599e-03 eta: 1 day, 3:41:43 time: 1.8874 data_time: 0.0359 memory: 16201 loss_prob: 0.9136 loss_thr: 0.5016 loss_db: 0.1560 loss: 1.5712 2022/08/30 03:00:36 - mmengine - INFO - Epoch(train) [141][40/63] lr: 6.2599e-03 eta: 1 day, 3:41:54 time: 1.8381 data_time: 0.0387 memory: 16201 loss_prob: 0.9135 loss_thr: 0.5030 loss_db: 0.1498 loss: 1.5663 2022/08/30 03:00:45 - mmengine - INFO - Epoch(train) [141][45/63] lr: 6.2599e-03 eta: 1 day, 3:41:54 time: 1.7270 data_time: 0.0441 memory: 16201 loss_prob: 0.8720 loss_thr: 0.5042 loss_db: 0.1437 loss: 1.5199 2022/08/30 03:00:55 - mmengine - INFO - Epoch(train) [141][50/63] lr: 6.2599e-03 eta: 1 day, 3:42:07 time: 1.8654 data_time: 0.0431 memory: 16201 loss_prob: 0.8885 loss_thr: 0.5027 loss_db: 0.1477 loss: 1.5390 2022/08/30 03:01:02 - mmengine - INFO - Epoch(train) [141][55/63] lr: 6.2599e-03 eta: 1 day, 3:42:07 time: 1.7514 data_time: 0.0651 memory: 16201 loss_prob: 0.9366 loss_thr: 0.5186 loss_db: 0.1546 loss: 1.6098 2022/08/30 03:01:11 - mmengine - INFO - Epoch(train) [141][60/63] lr: 6.2599e-03 eta: 1 day, 3:42:03 time: 1.6434 data_time: 0.0654 memory: 16201 loss_prob: 0.9308 loss_thr: 0.5316 loss_db: 0.1530 loss: 1.6154 2022/08/30 03:01:15 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:01:27 - mmengine - INFO - Epoch(train) [142][5/63] lr: 6.2546e-03 eta: 1 day, 3:42:03 time: 1.9541 data_time: 0.2427 memory: 16201 loss_prob: 0.8712 loss_thr: 0.4954 loss_db: 0.1440 loss: 1.5106 2022/08/30 03:01:36 - mmengine - INFO - Epoch(train) [142][10/63] lr: 6.2546e-03 eta: 1 day, 3:41:54 time: 2.0853 data_time: 0.2720 memory: 16201 loss_prob: 0.9672 loss_thr: 0.5219 loss_db: 0.1593 loss: 1.6484 2022/08/30 03:01:45 - mmengine - INFO - Epoch(train) [142][15/63] lr: 6.2546e-03 eta: 1 day, 3:41:54 time: 1.8018 data_time: 0.0499 memory: 16201 loss_prob: 1.0403 loss_thr: 0.5715 loss_db: 0.1727 loss: 1.7845 2022/08/30 03:01:55 - mmengine - INFO - Epoch(train) [142][20/63] lr: 6.2546e-03 eta: 1 day, 3:42:07 time: 1.8643 data_time: 0.0265 memory: 16201 loss_prob: 1.0233 loss_thr: 0.5606 loss_db: 0.1687 loss: 1.7526 2022/08/30 03:02:03 - mmengine - INFO - Epoch(train) [142][25/63] lr: 6.2546e-03 eta: 1 day, 3:42:07 time: 1.8310 data_time: 0.0275 memory: 16201 loss_prob: 0.8997 loss_thr: 0.4953 loss_db: 0.1479 loss: 1.5430 2022/08/30 03:02:12 - mmengine - INFO - Epoch(train) [142][30/63] lr: 6.2546e-03 eta: 1 day, 3:42:09 time: 1.7159 data_time: 0.0340 memory: 16201 loss_prob: 0.8324 loss_thr: 0.4952 loss_db: 0.1384 loss: 1.4660 2022/08/30 03:02:19 - mmengine - INFO - Epoch(train) [142][35/63] lr: 6.2546e-03 eta: 1 day, 3:42:09 time: 1.6074 data_time: 0.0505 memory: 16201 loss_prob: 0.8481 loss_thr: 0.5023 loss_db: 0.1435 loss: 1.4939 2022/08/30 03:02:28 - mmengine - INFO - Epoch(train) [142][40/63] lr: 6.2546e-03 eta: 1 day, 3:42:04 time: 1.6392 data_time: 0.0372 memory: 16201 loss_prob: 0.9050 loss_thr: 0.5080 loss_db: 0.1486 loss: 1.5616 2022/08/30 03:02:38 - mmengine - INFO - Epoch(train) [142][45/63] lr: 6.2546e-03 eta: 1 day, 3:42:04 time: 1.8464 data_time: 0.0466 memory: 16201 loss_prob: 0.9645 loss_thr: 0.5275 loss_db: 0.1577 loss: 1.6497 2022/08/30 03:02:47 - mmengine - INFO - Epoch(train) [142][50/63] lr: 6.2546e-03 eta: 1 day, 3:42:14 time: 1.8208 data_time: 0.0711 memory: 16201 loss_prob: 0.9725 loss_thr: 0.5318 loss_db: 0.1603 loss: 1.6646 2022/08/30 03:02:56 - mmengine - INFO - Epoch(train) [142][55/63] lr: 6.2546e-03 eta: 1 day, 3:42:14 time: 1.7888 data_time: 0.0447 memory: 16201 loss_prob: 0.9702 loss_thr: 0.5323 loss_db: 0.1559 loss: 1.6583 2022/08/30 03:03:05 - mmengine - INFO - Epoch(train) [142][60/63] lr: 6.2546e-03 eta: 1 day, 3:42:24 time: 1.8292 data_time: 0.0386 memory: 16201 loss_prob: 0.9345 loss_thr: 0.5202 loss_db: 0.1518 loss: 1.6065 2022/08/30 03:03:09 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:03:21 - mmengine - INFO - Epoch(train) [143][5/63] lr: 6.2493e-03 eta: 1 day, 3:42:24 time: 1.9504 data_time: 0.2548 memory: 16201 loss_prob: 0.8701 loss_thr: 0.4979 loss_db: 0.1419 loss: 1.5100 2022/08/30 03:03:30 - mmengine - INFO - Epoch(train) [143][10/63] lr: 6.2493e-03 eta: 1 day, 3:42:12 time: 2.0478 data_time: 0.3086 memory: 16201 loss_prob: 0.7657 loss_thr: 0.4910 loss_db: 0.1271 loss: 1.3839 2022/08/30 03:03:38 - mmengine - INFO - Epoch(train) [143][15/63] lr: 6.2493e-03 eta: 1 day, 3:42:12 time: 1.7488 data_time: 0.0800 memory: 16201 loss_prob: 0.7802 loss_thr: 0.4981 loss_db: 0.1310 loss: 1.4093 2022/08/30 03:03:46 - mmengine - INFO - Epoch(train) [143][20/63] lr: 6.2493e-03 eta: 1 day, 3:42:07 time: 1.6292 data_time: 0.0455 memory: 16201 loss_prob: 0.8525 loss_thr: 0.5228 loss_db: 0.1445 loss: 1.5198 2022/08/30 03:03:57 - mmengine - INFO - Epoch(train) [143][25/63] lr: 6.2493e-03 eta: 1 day, 3:42:07 time: 1.8936 data_time: 0.0419 memory: 16201 loss_prob: 0.9172 loss_thr: 0.5325 loss_db: 0.1534 loss: 1.6031 2022/08/30 03:04:06 - mmengine - INFO - Epoch(train) [143][30/63] lr: 6.2493e-03 eta: 1 day, 3:42:30 time: 2.0145 data_time: 0.0455 memory: 16201 loss_prob: 0.8742 loss_thr: 0.5170 loss_db: 0.1449 loss: 1.5361 2022/08/30 03:04:16 - mmengine - INFO - Epoch(train) [143][35/63] lr: 6.2493e-03 eta: 1 day, 3:42:30 time: 1.8840 data_time: 0.0498 memory: 16201 loss_prob: 0.8168 loss_thr: 0.5169 loss_db: 0.1347 loss: 1.4684 2022/08/30 03:04:25 - mmengine - INFO - Epoch(train) [143][40/63] lr: 6.2493e-03 eta: 1 day, 3:42:41 time: 1.8468 data_time: 0.0430 memory: 16201 loss_prob: 0.8192 loss_thr: 0.5038 loss_db: 0.1386 loss: 1.4616 2022/08/30 03:04:34 - mmengine - INFO - Epoch(train) [143][45/63] lr: 6.2493e-03 eta: 1 day, 3:42:41 time: 1.7874 data_time: 0.0494 memory: 16201 loss_prob: 0.8279 loss_thr: 0.5041 loss_db: 0.1392 loss: 1.4712 2022/08/30 03:04:43 - mmengine - INFO - Epoch(train) [143][50/63] lr: 6.2493e-03 eta: 1 day, 3:42:50 time: 1.8188 data_time: 0.0484 memory: 16201 loss_prob: 1.0443 loss_thr: 0.5457 loss_db: 0.1655 loss: 1.7555 2022/08/30 03:04:51 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:04:52 - mmengine - INFO - Epoch(train) [143][55/63] lr: 6.2493e-03 eta: 1 day, 3:42:50 time: 1.8716 data_time: 0.0446 memory: 16201 loss_prob: 1.0318 loss_thr: 0.5393 loss_db: 0.1647 loss: 1.7358 2022/08/30 03:05:01 - mmengine - INFO - Epoch(train) [143][60/63] lr: 6.2493e-03 eta: 1 day, 3:42:55 time: 1.7676 data_time: 0.0712 memory: 16201 loss_prob: 0.8847 loss_thr: 0.4826 loss_db: 0.1465 loss: 1.5138 2022/08/30 03:05:05 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:05:18 - mmengine - INFO - Epoch(train) [144][5/63] lr: 6.2439e-03 eta: 1 day, 3:42:55 time: 2.0162 data_time: 0.3093 memory: 16201 loss_prob: 1.1075 loss_thr: 0.5177 loss_db: 0.1707 loss: 1.7959 2022/08/30 03:05:27 - mmengine - INFO - Epoch(train) [144][10/63] lr: 6.2439e-03 eta: 1 day, 3:42:52 time: 2.1699 data_time: 0.3377 memory: 16201 loss_prob: 1.0171 loss_thr: 0.5182 loss_db: 0.1622 loss: 1.6975 2022/08/30 03:05:34 - mmengine - INFO - Epoch(train) [144][15/63] lr: 6.2439e-03 eta: 1 day, 3:42:52 time: 1.6309 data_time: 0.0586 memory: 16201 loss_prob: 1.0355 loss_thr: 0.5328 loss_db: 0.1738 loss: 1.7420 2022/08/30 03:05:43 - mmengine - INFO - Epoch(train) [144][20/63] lr: 6.2439e-03 eta: 1 day, 3:42:44 time: 1.5918 data_time: 0.0400 memory: 16201 loss_prob: 1.2121 loss_thr: 0.5714 loss_db: 0.2031 loss: 1.9866 2022/08/30 03:05:51 - mmengine - INFO - Epoch(train) [144][25/63] lr: 6.2439e-03 eta: 1 day, 3:42:44 time: 1.6942 data_time: 0.0478 memory: 16201 loss_prob: 1.1099 loss_thr: 0.5698 loss_db: 0.1843 loss: 1.8640 2022/08/30 03:06:00 - mmengine - INFO - Epoch(train) [144][30/63] lr: 6.2439e-03 eta: 1 day, 3:42:48 time: 1.7652 data_time: 0.0405 memory: 16201 loss_prob: 1.0859 loss_thr: 0.5702 loss_db: 0.1765 loss: 1.8326 2022/08/30 03:06:10 - mmengine - INFO - Epoch(train) [144][35/63] lr: 6.2439e-03 eta: 1 day, 3:42:48 time: 1.9051 data_time: 0.0382 memory: 16201 loss_prob: 1.0893 loss_thr: 0.5706 loss_db: 0.1784 loss: 1.8382 2022/08/30 03:06:20 - mmengine - INFO - Epoch(train) [144][40/63] lr: 6.2439e-03 eta: 1 day, 3:43:07 time: 1.9495 data_time: 0.0384 memory: 16201 loss_prob: 1.0078 loss_thr: 0.5527 loss_db: 0.1690 loss: 1.7295 2022/08/30 03:06:28 - mmengine - INFO - Epoch(train) [144][45/63] lr: 6.2439e-03 eta: 1 day, 3:43:07 time: 1.7948 data_time: 0.0357 memory: 16201 loss_prob: 1.0007 loss_thr: 0.5666 loss_db: 0.1680 loss: 1.7352 2022/08/30 03:06:37 - mmengine - INFO - Epoch(train) [144][50/63] lr: 6.2439e-03 eta: 1 day, 3:43:08 time: 1.7208 data_time: 0.0432 memory: 16201 loss_prob: 0.9596 loss_thr: 0.5479 loss_db: 0.1579 loss: 1.6654 2022/08/30 03:06:47 - mmengine - INFO - Epoch(train) [144][55/63] lr: 6.2439e-03 eta: 1 day, 3:43:08 time: 1.8294 data_time: 0.0482 memory: 16201 loss_prob: 0.9567 loss_thr: 0.5165 loss_db: 0.1581 loss: 1.6314 2022/08/30 03:06:56 - mmengine - INFO - Epoch(train) [144][60/63] lr: 6.2439e-03 eta: 1 day, 3:43:21 time: 1.8868 data_time: 0.0490 memory: 16201 loss_prob: 1.0375 loss_thr: 0.5514 loss_db: 0.1715 loss: 1.7604 2022/08/30 03:07:02 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:07:13 - mmengine - INFO - Epoch(train) [145][5/63] lr: 6.2386e-03 eta: 1 day, 3:43:21 time: 2.0585 data_time: 0.2649 memory: 16201 loss_prob: 1.1181 loss_thr: 0.5705 loss_db: 0.1920 loss: 1.8806 2022/08/30 03:07:24 - mmengine - INFO - Epoch(train) [145][10/63] lr: 6.2386e-03 eta: 1 day, 3:43:17 time: 2.1537 data_time: 0.2758 memory: 16201 loss_prob: 1.0030 loss_thr: 0.5313 loss_db: 0.1711 loss: 1.7053 2022/08/30 03:07:31 - mmengine - INFO - Epoch(train) [145][15/63] lr: 6.2386e-03 eta: 1 day, 3:43:17 time: 1.8263 data_time: 0.0485 memory: 16201 loss_prob: 0.9938 loss_thr: 0.5225 loss_db: 0.1656 loss: 1.6818 2022/08/30 03:07:41 - mmengine - INFO - Epoch(train) [145][20/63] lr: 6.2386e-03 eta: 1 day, 3:43:19 time: 1.7357 data_time: 0.0377 memory: 16201 loss_prob: 0.9542 loss_thr: 0.5213 loss_db: 0.1579 loss: 1.6334 2022/08/30 03:07:49 - mmengine - INFO - Epoch(train) [145][25/63] lr: 6.2386e-03 eta: 1 day, 3:43:19 time: 1.7615 data_time: 0.0434 memory: 16201 loss_prob: 0.9875 loss_thr: 0.5276 loss_db: 0.1624 loss: 1.6776 2022/08/30 03:07:57 - mmengine - INFO - Epoch(train) [145][30/63] lr: 6.2386e-03 eta: 1 day, 3:43:15 time: 1.6505 data_time: 0.0431 memory: 16201 loss_prob: 1.0041 loss_thr: 0.5326 loss_db: 0.1688 loss: 1.7055 2022/08/30 03:08:06 - mmengine - INFO - Epoch(train) [145][35/63] lr: 6.2386e-03 eta: 1 day, 3:43:15 time: 1.7468 data_time: 0.0514 memory: 16201 loss_prob: 1.0773 loss_thr: 0.5575 loss_db: 0.1804 loss: 1.8153 2022/08/30 03:08:14 - mmengine - INFO - Epoch(train) [145][40/63] lr: 6.2386e-03 eta: 1 day, 3:43:08 time: 1.6169 data_time: 0.0629 memory: 16201 loss_prob: 1.2239 loss_thr: 0.5762 loss_db: 0.1913 loss: 1.9914 2022/08/30 03:08:22 - mmengine - INFO - Epoch(train) [145][45/63] lr: 6.2386e-03 eta: 1 day, 3:43:08 time: 1.5957 data_time: 0.0433 memory: 16201 loss_prob: 1.0441 loss_thr: 0.5349 loss_db: 0.1632 loss: 1.7422 2022/08/30 03:08:31 - mmengine - INFO - Epoch(train) [145][50/63] lr: 6.2386e-03 eta: 1 day, 3:43:13 time: 1.7694 data_time: 0.0418 memory: 16201 loss_prob: 0.9483 loss_thr: 0.5679 loss_db: 0.1612 loss: 1.6774 2022/08/30 03:08:40 - mmengine - INFO - Epoch(train) [145][55/63] lr: 6.2386e-03 eta: 1 day, 3:43:13 time: 1.7130 data_time: 0.0485 memory: 16201 loss_prob: 1.0406 loss_thr: 0.5839 loss_db: 0.1713 loss: 1.7958 2022/08/30 03:08:48 - mmengine - INFO - Epoch(train) [145][60/63] lr: 6.2386e-03 eta: 1 day, 3:43:11 time: 1.6842 data_time: 0.0446 memory: 16201 loss_prob: 0.9085 loss_thr: 0.5107 loss_db: 0.1470 loss: 1.5662 2022/08/30 03:08:53 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:09:04 - mmengine - INFO - Epoch(train) [146][5/63] lr: 6.2333e-03 eta: 1 day, 3:43:11 time: 1.9527 data_time: 0.2793 memory: 16201 loss_prob: 0.9497 loss_thr: 0.5335 loss_db: 0.1557 loss: 1.6390 2022/08/30 03:09:14 - mmengine - INFO - Epoch(train) [146][10/63] lr: 6.2333e-03 eta: 1 day, 3:43:09 time: 2.1944 data_time: 0.2946 memory: 16201 loss_prob: 0.9720 loss_thr: 0.5430 loss_db: 0.1589 loss: 1.6739 2022/08/30 03:09:22 - mmengine - INFO - Epoch(train) [146][15/63] lr: 6.2333e-03 eta: 1 day, 3:43:09 time: 1.8365 data_time: 0.0465 memory: 16201 loss_prob: 0.8393 loss_thr: 0.4908 loss_db: 0.1382 loss: 1.4682 2022/08/30 03:09:33 - mmengine - INFO - Epoch(train) [146][20/63] lr: 6.2333e-03 eta: 1 day, 3:43:19 time: 1.8370 data_time: 0.0339 memory: 16201 loss_prob: 0.8035 loss_thr: 0.4878 loss_db: 0.1301 loss: 1.4214 2022/08/30 03:09:41 - mmengine - INFO - Epoch(train) [146][25/63] lr: 6.2333e-03 eta: 1 day, 3:43:19 time: 1.8558 data_time: 0.0429 memory: 16201 loss_prob: 0.8071 loss_thr: 0.4895 loss_db: 0.1342 loss: 1.4308 2022/08/30 03:09:51 - mmengine - INFO - Epoch(train) [146][30/63] lr: 6.2333e-03 eta: 1 day, 3:43:30 time: 1.8614 data_time: 0.0419 memory: 16201 loss_prob: 0.8377 loss_thr: 0.4894 loss_db: 0.1397 loss: 1.4668 2022/08/30 03:10:00 - mmengine - INFO - Epoch(train) [146][35/63] lr: 6.2333e-03 eta: 1 day, 3:43:30 time: 1.8993 data_time: 0.0560 memory: 16201 loss_prob: 0.8756 loss_thr: 0.5052 loss_db: 0.1423 loss: 1.5231 2022/08/30 03:10:08 - mmengine - INFO - Epoch(train) [146][40/63] lr: 6.2333e-03 eta: 1 day, 3:43:23 time: 1.6221 data_time: 0.0431 memory: 16201 loss_prob: 0.8519 loss_thr: 0.5053 loss_db: 0.1403 loss: 1.4975 2022/08/30 03:10:15 - mmengine - INFO - Epoch(train) [146][45/63] lr: 6.2333e-03 eta: 1 day, 3:43:23 time: 1.4935 data_time: 0.0304 memory: 16201 loss_prob: 0.8633 loss_thr: 0.5133 loss_db: 0.1423 loss: 1.5189 2022/08/30 03:10:22 - mmengine - INFO - Epoch(train) [146][50/63] lr: 6.2333e-03 eta: 1 day, 3:43:07 time: 1.4798 data_time: 0.0368 memory: 16201 loss_prob: 0.8740 loss_thr: 0.5170 loss_db: 0.1438 loss: 1.5348 2022/08/30 03:10:31 - mmengine - INFO - Epoch(train) [146][55/63] lr: 6.2333e-03 eta: 1 day, 3:43:07 time: 1.6000 data_time: 0.0363 memory: 16201 loss_prob: 0.8947 loss_thr: 0.5348 loss_db: 0.1463 loss: 1.5758 2022/08/30 03:10:40 - mmengine - INFO - Epoch(train) [146][60/63] lr: 6.2333e-03 eta: 1 day, 3:43:06 time: 1.7062 data_time: 0.0548 memory: 16201 loss_prob: 0.9590 loss_thr: 0.5577 loss_db: 0.1554 loss: 1.6721 2022/08/30 03:10:44 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:10:54 - mmengine - INFO - Epoch(train) [147][5/63] lr: 6.2280e-03 eta: 1 day, 3:43:06 time: 1.8221 data_time: 0.2655 memory: 16201 loss_prob: 1.0147 loss_thr: 0.5317 loss_db: 0.1728 loss: 1.7193 2022/08/30 03:11:04 - mmengine - INFO - Epoch(train) [147][10/63] lr: 6.2280e-03 eta: 1 day, 3:42:54 time: 2.0545 data_time: 0.2656 memory: 16201 loss_prob: 0.9686 loss_thr: 0.5389 loss_db: 0.1642 loss: 1.6717 2022/08/30 03:11:13 - mmengine - INFO - Epoch(train) [147][15/63] lr: 6.2280e-03 eta: 1 day, 3:42:54 time: 1.8486 data_time: 0.0571 memory: 16201 loss_prob: 0.8750 loss_thr: 0.5285 loss_db: 0.1422 loss: 1.5458 2022/08/30 03:11:23 - mmengine - INFO - Epoch(train) [147][20/63] lr: 6.2280e-03 eta: 1 day, 3:43:05 time: 1.8652 data_time: 0.0494 memory: 16201 loss_prob: 0.9964 loss_thr: 0.5419 loss_db: 0.1585 loss: 1.6968 2022/08/30 03:11:32 - mmengine - INFO - Epoch(train) [147][25/63] lr: 6.2280e-03 eta: 1 day, 3:43:05 time: 1.9172 data_time: 0.0548 memory: 16201 loss_prob: 1.0668 loss_thr: 0.5490 loss_db: 0.1714 loss: 1.7872 2022/08/30 03:11:41 - mmengine - INFO - Epoch(train) [147][30/63] lr: 6.2280e-03 eta: 1 day, 3:43:11 time: 1.7915 data_time: 0.0431 memory: 16201 loss_prob: 0.9263 loss_thr: 0.5095 loss_db: 0.1499 loss: 1.5858 2022/08/30 03:11:50 - mmengine - INFO - Epoch(train) [147][35/63] lr: 6.2280e-03 eta: 1 day, 3:43:11 time: 1.8036 data_time: 0.0389 memory: 16201 loss_prob: 1.0537 loss_thr: 0.5639 loss_db: 0.1672 loss: 1.7847 2022/08/30 03:12:00 - mmengine - INFO - Epoch(train) [147][40/63] lr: 6.2280e-03 eta: 1 day, 3:43:23 time: 1.8858 data_time: 0.0397 memory: 16201 loss_prob: 1.1109 loss_thr: 0.5821 loss_db: 0.1812 loss: 1.8742 2022/08/30 03:12:09 - mmengine - INFO - Epoch(train) [147][45/63] lr: 6.2280e-03 eta: 1 day, 3:43:23 time: 1.9005 data_time: 0.0388 memory: 16201 loss_prob: 0.9988 loss_thr: 0.5361 loss_db: 0.1706 loss: 1.7055 2022/08/30 03:12:18 - mmengine - INFO - Epoch(train) [147][50/63] lr: 6.2280e-03 eta: 1 day, 3:43:32 time: 1.8403 data_time: 0.0588 memory: 16201 loss_prob: 0.9650 loss_thr: 0.5166 loss_db: 0.1607 loss: 1.6423 2022/08/30 03:12:26 - mmengine - INFO - Epoch(train) [147][55/63] lr: 6.2280e-03 eta: 1 day, 3:43:32 time: 1.7136 data_time: 0.0549 memory: 16201 loss_prob: 0.8951 loss_thr: 0.4979 loss_db: 0.1461 loss: 1.5392 2022/08/30 03:12:36 - mmengine - INFO - Epoch(train) [147][60/63] lr: 6.2280e-03 eta: 1 day, 3:43:40 time: 1.8257 data_time: 0.0413 memory: 16201 loss_prob: 0.9844 loss_thr: 0.5216 loss_db: 0.1603 loss: 1.6663 2022/08/30 03:12:40 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:12:51 - mmengine - INFO - Epoch(train) [148][5/63] lr: 6.2227e-03 eta: 1 day, 3:43:40 time: 1.8353 data_time: 0.2647 memory: 16201 loss_prob: 1.0211 loss_thr: 0.5482 loss_db: 0.1656 loss: 1.7348 2022/08/30 03:12:59 - mmengine - INFO - Epoch(train) [148][10/63] lr: 6.2227e-03 eta: 1 day, 3:43:14 time: 1.8668 data_time: 0.2722 memory: 16201 loss_prob: 0.9528 loss_thr: 0.5251 loss_db: 0.1554 loss: 1.6333 2022/08/30 03:13:07 - mmengine - INFO - Epoch(train) [148][15/63] lr: 6.2227e-03 eta: 1 day, 3:43:14 time: 1.6446 data_time: 0.0407 memory: 16201 loss_prob: 0.9497 loss_thr: 0.5225 loss_db: 0.1615 loss: 1.6337 2022/08/30 03:13:16 - mmengine - INFO - Epoch(train) [148][20/63] lr: 6.2227e-03 eta: 1 day, 3:43:12 time: 1.6780 data_time: 0.0512 memory: 16201 loss_prob: 0.9429 loss_thr: 0.5146 loss_db: 0.1630 loss: 1.6206 2022/08/30 03:13:24 - mmengine - INFO - Epoch(train) [148][25/63] lr: 6.2227e-03 eta: 1 day, 3:43:12 time: 1.6738 data_time: 0.0499 memory: 16201 loss_prob: 1.0346 loss_thr: 0.5298 loss_db: 0.1727 loss: 1.7371 2022/08/30 03:13:33 - mmengine - INFO - Epoch(train) [148][30/63] lr: 6.2227e-03 eta: 1 day, 3:43:13 time: 1.7324 data_time: 0.0461 memory: 16201 loss_prob: 1.1230 loss_thr: 0.5664 loss_db: 0.1818 loss: 1.8711 2022/08/30 03:13:41 - mmengine - INFO - Epoch(train) [148][35/63] lr: 6.2227e-03 eta: 1 day, 3:43:13 time: 1.7102 data_time: 0.0422 memory: 16201 loss_prob: 1.0347 loss_thr: 0.5612 loss_db: 0.1692 loss: 1.7651 2022/08/30 03:13:50 - mmengine - INFO - Epoch(train) [148][40/63] lr: 6.2227e-03 eta: 1 day, 3:43:14 time: 1.7269 data_time: 0.0391 memory: 16201 loss_prob: 1.0109 loss_thr: 0.5355 loss_db: 0.1648 loss: 1.7112 2022/08/30 03:13:59 - mmengine - INFO - Epoch(train) [148][45/63] lr: 6.2227e-03 eta: 1 day, 3:43:14 time: 1.8150 data_time: 0.0489 memory: 16201 loss_prob: 1.0409 loss_thr: 0.5458 loss_db: 0.1706 loss: 1.7573 2022/08/30 03:14:07 - mmengine - INFO - Epoch(train) [148][50/63] lr: 6.2227e-03 eta: 1 day, 3:43:09 time: 1.6507 data_time: 0.0417 memory: 16201 loss_prob: 0.9622 loss_thr: 0.5474 loss_db: 0.1622 loss: 1.6718 2022/08/30 03:14:16 - mmengine - INFO - Epoch(train) [148][55/63] lr: 6.2227e-03 eta: 1 day, 3:43:09 time: 1.7156 data_time: 0.0369 memory: 16201 loss_prob: 0.8766 loss_thr: 0.5200 loss_db: 0.1475 loss: 1.5441 2022/08/30 03:14:24 - mmengine - INFO - Epoch(train) [148][60/63] lr: 6.2227e-03 eta: 1 day, 3:43:11 time: 1.7507 data_time: 0.0491 memory: 16201 loss_prob: 0.8861 loss_thr: 0.5215 loss_db: 0.1450 loss: 1.5526 2022/08/30 03:14:28 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:14:38 - mmengine - INFO - Epoch(train) [149][5/63] lr: 6.2173e-03 eta: 1 day, 3:43:11 time: 1.7011 data_time: 0.2522 memory: 16201 loss_prob: 0.9132 loss_thr: 0.4879 loss_db: 0.1477 loss: 1.5488 2022/08/30 03:14:46 - mmengine - INFO - Epoch(train) [149][10/63] lr: 6.2173e-03 eta: 1 day, 3:42:42 time: 1.8186 data_time: 0.2579 memory: 16201 loss_prob: 0.9603 loss_thr: 0.5224 loss_db: 0.1552 loss: 1.6380 2022/08/30 03:14:54 - mmengine - INFO - Epoch(train) [149][15/63] lr: 6.2173e-03 eta: 1 day, 3:42:42 time: 1.5428 data_time: 0.0355 memory: 16201 loss_prob: 0.9378 loss_thr: 0.5335 loss_db: 0.1540 loss: 1.6254 2022/08/30 03:15:00 - mmengine - INFO - Epoch(train) [149][20/63] lr: 6.2173e-03 eta: 1 day, 3:42:20 time: 1.4025 data_time: 0.0352 memory: 16201 loss_prob: 0.8927 loss_thr: 0.5111 loss_db: 0.1469 loss: 1.5507 2022/08/30 03:15:10 - mmengine - INFO - Epoch(train) [149][25/63] lr: 6.2173e-03 eta: 1 day, 3:42:20 time: 1.5949 data_time: 0.0514 memory: 16201 loss_prob: 0.9025 loss_thr: 0.5225 loss_db: 0.1485 loss: 1.5735 2022/08/30 03:15:19 - mmengine - INFO - Epoch(train) [149][30/63] lr: 6.2173e-03 eta: 1 day, 3:42:29 time: 1.8512 data_time: 0.0470 memory: 16201 loss_prob: 0.9542 loss_thr: 0.5373 loss_db: 0.1591 loss: 1.6506 2022/08/30 03:15:27 - mmengine - INFO - Epoch(train) [149][35/63] lr: 6.2173e-03 eta: 1 day, 3:42:29 time: 1.7902 data_time: 0.0450 memory: 16201 loss_prob: 0.9125 loss_thr: 0.5062 loss_db: 0.1527 loss: 1.5713 2022/08/30 03:15:37 - mmengine - INFO - Epoch(train) [149][40/63] lr: 6.2173e-03 eta: 1 day, 3:42:33 time: 1.7681 data_time: 0.0465 memory: 16201 loss_prob: 0.9771 loss_thr: 0.5233 loss_db: 0.1586 loss: 1.6589 2022/08/30 03:15:45 - mmengine - INFO - Epoch(train) [149][45/63] lr: 6.2173e-03 eta: 1 day, 3:42:33 time: 1.7163 data_time: 0.0467 memory: 16201 loss_prob: 0.9468 loss_thr: 0.5373 loss_db: 0.1537 loss: 1.6377 2022/08/30 03:15:53 - mmengine - INFO - Epoch(train) [149][50/63] lr: 6.2173e-03 eta: 1 day, 3:42:28 time: 1.6553 data_time: 0.0478 memory: 16201 loss_prob: 0.8381 loss_thr: 0.5212 loss_db: 0.1427 loss: 1.5020 2022/08/30 03:16:04 - mmengine - INFO - Epoch(train) [149][55/63] lr: 6.2173e-03 eta: 1 day, 3:42:28 time: 1.9012 data_time: 0.0458 memory: 16201 loss_prob: 0.8925 loss_thr: 0.5317 loss_db: 0.1490 loss: 1.5733 2022/08/30 03:16:12 - mmengine - INFO - Epoch(train) [149][60/63] lr: 6.2173e-03 eta: 1 day, 3:42:41 time: 1.9102 data_time: 0.0566 memory: 16201 loss_prob: 0.9577 loss_thr: 0.5441 loss_db: 0.1557 loss: 1.6575 2022/08/30 03:16:17 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:16:28 - mmengine - INFO - Epoch(train) [150][5/63] lr: 6.2120e-03 eta: 1 day, 3:42:41 time: 1.8339 data_time: 0.2673 memory: 16201 loss_prob: 0.9331 loss_thr: 0.5146 loss_db: 0.1591 loss: 1.6068 2022/08/30 03:16:38 - mmengine - INFO - Epoch(train) [150][10/63] lr: 6.2120e-03 eta: 1 day, 3:42:31 time: 2.0854 data_time: 0.2921 memory: 16201 loss_prob: 0.8415 loss_thr: 0.4807 loss_db: 0.1430 loss: 1.4652 2022/08/30 03:16:45 - mmengine - INFO - Epoch(train) [150][15/63] lr: 6.2120e-03 eta: 1 day, 3:42:31 time: 1.7757 data_time: 0.0532 memory: 16201 loss_prob: 0.9016 loss_thr: 0.4993 loss_db: 0.1473 loss: 1.5482 2022/08/30 03:16:55 - mmengine - INFO - Epoch(train) [150][20/63] lr: 6.2120e-03 eta: 1 day, 3:42:33 time: 1.7581 data_time: 0.0444 memory: 16201 loss_prob: 0.9460 loss_thr: 0.5189 loss_db: 0.1564 loss: 1.6213 2022/08/30 03:17:03 - mmengine - INFO - Epoch(train) [150][25/63] lr: 6.2120e-03 eta: 1 day, 3:42:33 time: 1.7525 data_time: 0.0436 memory: 16201 loss_prob: 0.8431 loss_thr: 0.5048 loss_db: 0.1421 loss: 1.4900 2022/08/30 03:17:11 - mmengine - INFO - Epoch(train) [150][30/63] lr: 6.2120e-03 eta: 1 day, 3:42:27 time: 1.6247 data_time: 0.0423 memory: 16201 loss_prob: 0.8134 loss_thr: 0.4950 loss_db: 0.1363 loss: 1.4448 2022/08/30 03:17:20 - mmengine - INFO - Epoch(train) [150][35/63] lr: 6.2120e-03 eta: 1 day, 3:42:27 time: 1.7216 data_time: 0.0495 memory: 16201 loss_prob: 0.8798 loss_thr: 0.5025 loss_db: 0.1453 loss: 1.5276 2022/08/30 03:17:28 - mmengine - INFO - Epoch(train) [150][40/63] lr: 6.2120e-03 eta: 1 day, 3:42:26 time: 1.7106 data_time: 0.0320 memory: 16201 loss_prob: 0.9262 loss_thr: 0.5172 loss_db: 0.1511 loss: 1.5945 2022/08/30 03:17:38 - mmengine - INFO - Epoch(train) [150][45/63] lr: 6.2120e-03 eta: 1 day, 3:42:26 time: 1.7727 data_time: 0.0426 memory: 16201 loss_prob: 0.8856 loss_thr: 0.5089 loss_db: 0.1450 loss: 1.5395 2022/08/30 03:17:47 - mmengine - INFO - Epoch(train) [150][50/63] lr: 6.2120e-03 eta: 1 day, 3:42:34 time: 1.8389 data_time: 0.0630 memory: 16201 loss_prob: 0.8865 loss_thr: 0.5073 loss_db: 0.1502 loss: 1.5440 2022/08/30 03:17:56 - mmengine - INFO - Epoch(train) [150][55/63] lr: 6.2120e-03 eta: 1 day, 3:42:34 time: 1.7879 data_time: 0.0423 memory: 16201 loss_prob: 0.9002 loss_thr: 0.5234 loss_db: 0.1521 loss: 1.5757 2022/08/30 03:18:04 - mmengine - INFO - Epoch(train) [150][60/63] lr: 6.2120e-03 eta: 1 day, 3:42:36 time: 1.7478 data_time: 0.0415 memory: 16201 loss_prob: 0.8535 loss_thr: 0.5152 loss_db: 0.1440 loss: 1.5127 2022/08/30 03:18:09 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:18:21 - mmengine - INFO - Epoch(train) [151][5/63] lr: 6.2067e-03 eta: 1 day, 3:42:36 time: 1.9957 data_time: 0.2541 memory: 16201 loss_prob: 0.8685 loss_thr: 0.5197 loss_db: 0.1455 loss: 1.5338 2022/08/30 03:18:30 - mmengine - INFO - Epoch(train) [151][10/63] lr: 6.2067e-03 eta: 1 day, 3:42:22 time: 2.0488 data_time: 0.2700 memory: 16201 loss_prob: 0.7916 loss_thr: 0.4811 loss_db: 0.1329 loss: 1.4056 2022/08/30 03:18:39 - mmengine - INFO - Epoch(train) [151][15/63] lr: 6.2067e-03 eta: 1 day, 3:42:22 time: 1.7585 data_time: 0.0362 memory: 16201 loss_prob: 0.8449 loss_thr: 0.4939 loss_db: 0.1397 loss: 1.4785 2022/08/30 03:18:46 - mmengine - INFO - Epoch(train) [151][20/63] lr: 6.2067e-03 eta: 1 day, 3:42:14 time: 1.6006 data_time: 0.0382 memory: 16201 loss_prob: 0.9278 loss_thr: 0.5184 loss_db: 0.1495 loss: 1.5957 2022/08/30 03:18:55 - mmengine - INFO - Epoch(train) [151][25/63] lr: 6.2067e-03 eta: 1 day, 3:42:14 time: 1.6942 data_time: 0.0362 memory: 16201 loss_prob: 0.9136 loss_thr: 0.5169 loss_db: 0.1517 loss: 1.5823 2022/08/30 03:19:03 - mmengine - INFO - Epoch(train) [151][30/63] lr: 6.2067e-03 eta: 1 day, 3:42:15 time: 1.7373 data_time: 0.0387 memory: 16201 loss_prob: 0.8913 loss_thr: 0.5130 loss_db: 0.1517 loss: 1.5559 2022/08/30 03:19:10 - mmengine - INFO - Epoch(train) [151][35/63] lr: 6.2067e-03 eta: 1 day, 3:42:15 time: 1.4956 data_time: 0.0492 memory: 16201 loss_prob: 0.8786 loss_thr: 0.5100 loss_db: 0.1463 loss: 1.5349 2022/08/30 03:19:18 - mmengine - INFO - Epoch(train) [151][40/63] lr: 6.2067e-03 eta: 1 day, 3:41:54 time: 1.4289 data_time: 0.0347 memory: 16201 loss_prob: 0.8830 loss_thr: 0.5159 loss_db: 0.1435 loss: 1.5424 2022/08/30 03:19:25 - mmengine - INFO - Epoch(train) [151][45/63] lr: 6.2067e-03 eta: 1 day, 3:41:54 time: 1.5029 data_time: 0.0371 memory: 16201 loss_prob: 0.8978 loss_thr: 0.5274 loss_db: 0.1437 loss: 1.5689 2022/08/30 03:19:36 - mmengine - INFO - Epoch(train) [151][50/63] lr: 6.2067e-03 eta: 1 day, 3:42:00 time: 1.8049 data_time: 0.0497 memory: 16201 loss_prob: 0.9087 loss_thr: 0.5237 loss_db: 0.1468 loss: 1.5792 2022/08/30 03:19:43 - mmengine - INFO - Epoch(train) [151][55/63] lr: 6.2067e-03 eta: 1 day, 3:42:00 time: 1.7947 data_time: 0.0392 memory: 16201 loss_prob: 0.8417 loss_thr: 0.4838 loss_db: 0.1397 loss: 1.4652 2022/08/30 03:19:52 - mmengine - INFO - Epoch(train) [151][60/63] lr: 6.2067e-03 eta: 1 day, 3:41:56 time: 1.6709 data_time: 0.0519 memory: 16201 loss_prob: 0.8543 loss_thr: 0.4908 loss_db: 0.1413 loss: 1.4864 2022/08/30 03:19:56 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:20:07 - mmengine - INFO - Epoch(train) [152][5/63] lr: 6.2014e-03 eta: 1 day, 3:41:56 time: 1.7551 data_time: 0.2624 memory: 16201 loss_prob: 0.9178 loss_thr: 0.5310 loss_db: 0.1537 loss: 1.6024 2022/08/30 03:20:16 - mmengine - INFO - Epoch(train) [152][10/63] lr: 6.2014e-03 eta: 1 day, 3:41:38 time: 1.9858 data_time: 0.2713 memory: 16201 loss_prob: 0.8652 loss_thr: 0.5363 loss_db: 0.1469 loss: 1.5483 2022/08/30 03:20:24 - mmengine - INFO - Epoch(train) [152][15/63] lr: 6.2014e-03 eta: 1 day, 3:41:38 time: 1.7027 data_time: 0.0451 memory: 16201 loss_prob: 0.9237 loss_thr: 0.5320 loss_db: 0.1523 loss: 1.6081 2022/08/30 03:20:32 - mmengine - INFO - Epoch(train) [152][20/63] lr: 6.2014e-03 eta: 1 day, 3:41:32 time: 1.6402 data_time: 0.0521 memory: 16201 loss_prob: 0.9416 loss_thr: 0.5239 loss_db: 0.1530 loss: 1.6185 2022/08/30 03:20:40 - mmengine - INFO - Epoch(train) [152][25/63] lr: 6.2014e-03 eta: 1 day, 3:41:32 time: 1.6648 data_time: 0.0571 memory: 16201 loss_prob: 0.8791 loss_thr: 0.4972 loss_db: 0.1444 loss: 1.5208 2022/08/30 03:20:49 - mmengine - INFO - Epoch(train) [152][30/63] lr: 6.2014e-03 eta: 1 day, 3:41:31 time: 1.7129 data_time: 0.0406 memory: 16201 loss_prob: 0.9227 loss_thr: 0.5066 loss_db: 0.1512 loss: 1.5805 2022/08/30 03:20:59 - mmengine - INFO - Epoch(train) [152][35/63] lr: 6.2014e-03 eta: 1 day, 3:41:31 time: 1.8211 data_time: 0.0396 memory: 16201 loss_prob: 0.8525 loss_thr: 0.5116 loss_db: 0.1404 loss: 1.5044 2022/08/30 03:21:07 - mmengine - INFO - Epoch(train) [152][40/63] lr: 6.2014e-03 eta: 1 day, 3:41:34 time: 1.7695 data_time: 0.0310 memory: 16201 loss_prob: 0.8850 loss_thr: 0.5123 loss_db: 0.1363 loss: 1.5336 2022/08/30 03:21:17 - mmengine - INFO - Epoch(train) [152][45/63] lr: 6.2014e-03 eta: 1 day, 3:41:34 time: 1.7931 data_time: 0.0306 memory: 16201 loss_prob: 0.9403 loss_thr: 0.5281 loss_db: 0.1454 loss: 1.6138 2022/08/30 03:21:26 - mmengine - INFO - Epoch(train) [152][50/63] lr: 6.2014e-03 eta: 1 day, 3:41:42 time: 1.8472 data_time: 0.0428 memory: 16201 loss_prob: 0.8766 loss_thr: 0.5227 loss_db: 0.1460 loss: 1.5452 2022/08/30 03:21:35 - mmengine - INFO - Epoch(train) [152][55/63] lr: 6.2014e-03 eta: 1 day, 3:41:42 time: 1.8171 data_time: 0.0367 memory: 16201 loss_prob: 0.8363 loss_thr: 0.5077 loss_db: 0.1396 loss: 1.4835 2022/08/30 03:21:44 - mmengine - INFO - Epoch(train) [152][60/63] lr: 6.2014e-03 eta: 1 day, 3:41:48 time: 1.8185 data_time: 0.0476 memory: 16201 loss_prob: 0.8469 loss_thr: 0.5328 loss_db: 0.1411 loss: 1.5207 2022/08/30 03:21:47 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:21:58 - mmengine - INFO - Epoch(train) [153][5/63] lr: 6.1960e-03 eta: 1 day, 3:41:48 time: 1.7913 data_time: 0.2503 memory: 16201 loss_prob: 0.8734 loss_thr: 0.5390 loss_db: 0.1461 loss: 1.5586 2022/08/30 03:22:05 - mmengine - INFO - Epoch(train) [153][10/63] lr: 6.1960e-03 eta: 1 day, 3:41:17 time: 1.7960 data_time: 0.2588 memory: 16201 loss_prob: 0.8498 loss_thr: 0.5109 loss_db: 0.1439 loss: 1.5046 2022/08/30 03:22:13 - mmengine - INFO - Epoch(train) [153][15/63] lr: 6.1960e-03 eta: 1 day, 3:41:17 time: 1.5137 data_time: 0.0416 memory: 16201 loss_prob: 0.8736 loss_thr: 0.4881 loss_db: 0.1439 loss: 1.5056 2022/08/30 03:22:23 - mmengine - INFO - Epoch(train) [153][20/63] lr: 6.1960e-03 eta: 1 day, 3:41:20 time: 1.7671 data_time: 0.0497 memory: 16201 loss_prob: 0.8877 loss_thr: 0.5049 loss_db: 0.1482 loss: 1.5409 2022/08/30 03:22:30 - mmengine - INFO - Epoch(train) [153][25/63] lr: 6.1960e-03 eta: 1 day, 3:41:20 time: 1.7185 data_time: 0.0549 memory: 16201 loss_prob: 0.8583 loss_thr: 0.5071 loss_db: 0.1482 loss: 1.5136 2022/08/30 03:22:38 - mmengine - INFO - Epoch(train) [153][30/63] lr: 6.1960e-03 eta: 1 day, 3:41:01 time: 1.4593 data_time: 0.0416 memory: 16201 loss_prob: 0.8596 loss_thr: 0.5154 loss_db: 0.1442 loss: 1.5192 2022/08/30 03:22:47 - mmengine - INFO - Epoch(train) [153][35/63] lr: 6.1960e-03 eta: 1 day, 3:41:01 time: 1.6558 data_time: 0.0399 memory: 16201 loss_prob: 0.9170 loss_thr: 0.5368 loss_db: 0.1540 loss: 1.6078 2022/08/30 03:22:56 - mmengine - INFO - Epoch(train) [153][40/63] lr: 6.1960e-03 eta: 1 day, 3:41:05 time: 1.7940 data_time: 0.0380 memory: 16201 loss_prob: 0.8977 loss_thr: 0.5274 loss_db: 0.1501 loss: 1.5752 2022/08/30 03:23:04 - mmengine - INFO - Epoch(train) [153][45/63] lr: 6.1960e-03 eta: 1 day, 3:41:05 time: 1.7427 data_time: 0.0445 memory: 16201 loss_prob: 0.8833 loss_thr: 0.5034 loss_db: 0.1426 loss: 1.5292 2022/08/30 03:23:11 - mmengine - INFO - Epoch(train) [153][50/63] lr: 6.1960e-03 eta: 1 day, 3:40:53 time: 1.5548 data_time: 0.0471 memory: 16201 loss_prob: 0.9268 loss_thr: 0.5020 loss_db: 0.1529 loss: 1.5816 2022/08/30 03:23:21 - mmengine - INFO - Epoch(train) [153][55/63] lr: 6.1960e-03 eta: 1 day, 3:40:53 time: 1.6365 data_time: 0.0375 memory: 16201 loss_prob: 0.9153 loss_thr: 0.5001 loss_db: 0.1578 loss: 1.5732 2022/08/30 03:23:30 - mmengine - INFO - Epoch(train) [153][60/63] lr: 6.1960e-03 eta: 1 day, 3:41:03 time: 1.8698 data_time: 0.0451 memory: 16201 loss_prob: 0.9643 loss_thr: 0.5093 loss_db: 0.1642 loss: 1.6378 2022/08/30 03:23:33 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:23:42 - mmengine - INFO - Epoch(train) [154][5/63] lr: 6.1907e-03 eta: 1 day, 3:41:03 time: 1.6329 data_time: 0.2572 memory: 16201 loss_prob: 0.9581 loss_thr: 0.5280 loss_db: 0.1610 loss: 1.6472 2022/08/30 03:23:51 - mmengine - INFO - Epoch(train) [154][10/63] lr: 6.1907e-03 eta: 1 day, 3:40:28 time: 1.7396 data_time: 0.2772 memory: 16201 loss_prob: 0.9736 loss_thr: 0.5171 loss_db: 0.1659 loss: 1.6566 2022/08/30 03:23:57 - mmengine - INFO - Epoch(train) [154][15/63] lr: 6.1907e-03 eta: 1 day, 3:40:28 time: 1.4821 data_time: 0.0403 memory: 16201 loss_prob: 0.9791 loss_thr: 0.5196 loss_db: 0.1627 loss: 1.6614 2022/08/30 03:24:04 - mmengine - INFO - Epoch(train) [154][20/63] lr: 6.1907e-03 eta: 1 day, 3:40:04 time: 1.3822 data_time: 0.0390 memory: 16201 loss_prob: 1.0243 loss_thr: 0.5572 loss_db: 0.1695 loss: 1.7510 2022/08/30 03:24:13 - mmengine - INFO - Epoch(train) [154][25/63] lr: 6.1907e-03 eta: 1 day, 3:40:04 time: 1.5542 data_time: 0.0572 memory: 16201 loss_prob: 1.0408 loss_thr: 0.5927 loss_db: 0.1737 loss: 1.8072 2022/08/30 03:24:21 - mmengine - INFO - Epoch(train) [154][30/63] lr: 6.1907e-03 eta: 1 day, 3:39:56 time: 1.6168 data_time: 0.0365 memory: 16201 loss_prob: 1.0606 loss_thr: 0.5790 loss_db: 0.1755 loss: 1.8151 2022/08/30 03:24:29 - mmengine - INFO - Epoch(train) [154][35/63] lr: 6.1907e-03 eta: 1 day, 3:39:56 time: 1.6193 data_time: 0.0432 memory: 16201 loss_prob: 1.0096 loss_thr: 0.5492 loss_db: 0.1646 loss: 1.7234 2022/08/30 03:24:38 - mmengine - INFO - Epoch(train) [154][40/63] lr: 6.1907e-03 eta: 1 day, 3:39:57 time: 1.7475 data_time: 0.0456 memory: 16201 loss_prob: 1.0249 loss_thr: 0.5481 loss_db: 0.1672 loss: 1.7402 2022/08/30 03:24:46 - mmengine - INFO - Epoch(train) [154][45/63] lr: 6.1907e-03 eta: 1 day, 3:39:57 time: 1.7028 data_time: 0.0336 memory: 16201 loss_prob: 1.0538 loss_thr: 0.5346 loss_db: 0.1748 loss: 1.7632 2022/08/30 03:24:55 - mmengine - INFO - Epoch(train) [154][50/63] lr: 6.1907e-03 eta: 1 day, 3:39:54 time: 1.6891 data_time: 0.0485 memory: 16201 loss_prob: 0.9701 loss_thr: 0.5277 loss_db: 0.1592 loss: 1.6571 2022/08/30 03:25:04 - mmengine - INFO - Epoch(train) [154][55/63] lr: 6.1907e-03 eta: 1 day, 3:39:54 time: 1.7510 data_time: 0.0360 memory: 16201 loss_prob: 0.9014 loss_thr: 0.5082 loss_db: 0.1464 loss: 1.5560 2022/08/30 03:25:13 - mmengine - INFO - Epoch(train) [154][60/63] lr: 6.1907e-03 eta: 1 day, 3:39:58 time: 1.7858 data_time: 0.0334 memory: 16201 loss_prob: 0.9196 loss_thr: 0.4863 loss_db: 0.1444 loss: 1.5503 2022/08/30 03:25:17 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:25:27 - mmengine - INFO - Epoch(train) [155][5/63] lr: 6.1854e-03 eta: 1 day, 3:39:58 time: 1.8264 data_time: 0.2413 memory: 16201 loss_prob: 0.9501 loss_thr: 0.5045 loss_db: 0.1500 loss: 1.6046 2022/08/30 03:25:37 - mmengine - INFO - Epoch(train) [155][10/63] lr: 6.1854e-03 eta: 1 day, 3:39:43 time: 2.0447 data_time: 0.2682 memory: 16201 loss_prob: 0.9480 loss_thr: 0.5162 loss_db: 0.1538 loss: 1.6180 2022/08/30 03:25:48 - mmengine - INFO - Epoch(train) [155][15/63] lr: 6.1854e-03 eta: 1 day, 3:39:43 time: 2.0373 data_time: 0.0520 memory: 16201 loss_prob: 1.0203 loss_thr: 0.5535 loss_db: 0.1728 loss: 1.7467 2022/08/30 03:25:56 - mmengine - INFO - Epoch(train) [155][20/63] lr: 6.1854e-03 eta: 1 day, 3:39:51 time: 1.8486 data_time: 0.0566 memory: 16201 loss_prob: 0.9524 loss_thr: 0.5382 loss_db: 0.1610 loss: 1.6516 2022/08/30 03:26:04 - mmengine - INFO - Epoch(train) [155][25/63] lr: 6.1854e-03 eta: 1 day, 3:39:51 time: 1.6799 data_time: 0.0581 memory: 16201 loss_prob: 0.9185 loss_thr: 0.5046 loss_db: 0.1516 loss: 1.5746 2022/08/30 03:26:15 - mmengine - INFO - Epoch(train) [155][30/63] lr: 6.1854e-03 eta: 1 day, 3:40:02 time: 1.8875 data_time: 0.0375 memory: 16201 loss_prob: 0.9888 loss_thr: 0.5217 loss_db: 0.1633 loss: 1.6739 2022/08/30 03:26:24 - mmengine - INFO - Epoch(train) [155][35/63] lr: 6.1854e-03 eta: 1 day, 3:40:02 time: 1.9119 data_time: 0.0487 memory: 16201 loss_prob: 0.9868 loss_thr: 0.5186 loss_db: 0.1605 loss: 1.6659 2022/08/30 03:26:33 - mmengine - INFO - Epoch(train) [155][40/63] lr: 6.1854e-03 eta: 1 day, 3:40:09 time: 1.8464 data_time: 0.0426 memory: 16201 loss_prob: 0.9830 loss_thr: 0.5097 loss_db: 0.1567 loss: 1.6494 2022/08/30 03:26:41 - mmengine - INFO - Epoch(train) [155][45/63] lr: 6.1854e-03 eta: 1 day, 3:40:09 time: 1.7710 data_time: 0.0453 memory: 16201 loss_prob: 0.9844 loss_thr: 0.5107 loss_db: 0.1598 loss: 1.6549 2022/08/30 03:26:51 - mmengine - INFO - Epoch(train) [155][50/63] lr: 6.1854e-03 eta: 1 day, 3:40:12 time: 1.7766 data_time: 0.0565 memory: 16201 loss_prob: 0.9947 loss_thr: 0.5505 loss_db: 0.1623 loss: 1.7075 2022/08/30 03:26:59 - mmengine - INFO - Epoch(train) [155][55/63] lr: 6.1854e-03 eta: 1 day, 3:40:12 time: 1.7958 data_time: 0.0470 memory: 16201 loss_prob: 0.9949 loss_thr: 0.5798 loss_db: 0.1622 loss: 1.7370 2022/08/30 03:27:07 - mmengine - INFO - Epoch(train) [155][60/63] lr: 6.1854e-03 eta: 1 day, 3:40:07 time: 1.6647 data_time: 0.0423 memory: 16201 loss_prob: 0.9600 loss_thr: 0.5240 loss_db: 0.1562 loss: 1.6402 2022/08/30 03:27:12 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:27:21 - mmengine - INFO - Epoch(train) [156][5/63] lr: 6.1801e-03 eta: 1 day, 3:40:07 time: 1.7461 data_time: 0.2607 memory: 16201 loss_prob: 0.9582 loss_thr: 0.5129 loss_db: 0.1550 loss: 1.6262 2022/08/30 03:27:32 - mmengine - INFO - Epoch(train) [156][10/63] lr: 6.1801e-03 eta: 1 day, 3:39:52 time: 2.0358 data_time: 0.2842 memory: 16201 loss_prob: 1.0318 loss_thr: 0.5349 loss_db: 0.1687 loss: 1.7354 2022/08/30 03:27:41 - mmengine - INFO - Epoch(train) [156][15/63] lr: 6.1801e-03 eta: 1 day, 3:39:52 time: 1.9245 data_time: 0.0532 memory: 16201 loss_prob: 0.9323 loss_thr: 0.5261 loss_db: 0.1564 loss: 1.6148 2022/08/30 03:27:49 - mmengine - INFO - Epoch(train) [156][20/63] lr: 6.1801e-03 eta: 1 day, 3:39:50 time: 1.7099 data_time: 0.0386 memory: 16201 loss_prob: 0.7828 loss_thr: 0.4803 loss_db: 0.1313 loss: 1.3944 2022/08/30 03:27:58 - mmengine - INFO - Epoch(train) [156][25/63] lr: 6.1801e-03 eta: 1 day, 3:39:50 time: 1.7226 data_time: 0.0531 memory: 16201 loss_prob: 0.7972 loss_thr: 0.4862 loss_db: 0.1328 loss: 1.4162 2022/08/30 03:28:06 - mmengine - INFO - Epoch(train) [156][30/63] lr: 6.1801e-03 eta: 1 day, 3:39:42 time: 1.6245 data_time: 0.0408 memory: 16201 loss_prob: 1.0828 loss_thr: 0.5347 loss_db: 0.1687 loss: 1.7863 2022/08/30 03:28:14 - mmengine - INFO - Epoch(train) [156][35/63] lr: 6.1801e-03 eta: 1 day, 3:39:42 time: 1.6125 data_time: 0.0404 memory: 16201 loss_prob: 1.0999 loss_thr: 0.5376 loss_db: 0.1704 loss: 1.8078 2022/08/30 03:28:21 - mmengine - INFO - Epoch(train) [156][40/63] lr: 6.1801e-03 eta: 1 day, 3:39:29 time: 1.5408 data_time: 0.0316 memory: 16201 loss_prob: 0.9826 loss_thr: 0.5578 loss_db: 0.1631 loss: 1.7035 2022/08/30 03:28:28 - mmengine - INFO - Epoch(train) [156][45/63] lr: 6.1801e-03 eta: 1 day, 3:39:29 time: 1.4038 data_time: 0.0322 memory: 16201 loss_prob: 0.9957 loss_thr: 0.5568 loss_db: 0.1654 loss: 1.7179 2022/08/30 03:28:36 - mmengine - INFO - Epoch(train) [156][50/63] lr: 6.1801e-03 eta: 1 day, 3:39:10 time: 1.4599 data_time: 0.0539 memory: 16201 loss_prob: 0.9645 loss_thr: 0.5366 loss_db: 0.1589 loss: 1.6599 2022/08/30 03:28:44 - mmengine - INFO - Epoch(train) [156][55/63] lr: 6.1801e-03 eta: 1 day, 3:39:10 time: 1.5998 data_time: 0.0411 memory: 16201 loss_prob: 0.9514 loss_thr: 0.5224 loss_db: 0.1562 loss: 1.6300 2022/08/30 03:28:53 - mmengine - INFO - Epoch(train) [156][60/63] lr: 6.1801e-03 eta: 1 day, 3:39:12 time: 1.7621 data_time: 0.0491 memory: 16201 loss_prob: 0.9841 loss_thr: 0.5168 loss_db: 0.1609 loss: 1.6618 2022/08/30 03:28:57 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:29:08 - mmengine - INFO - Epoch(train) [157][5/63] lr: 6.1747e-03 eta: 1 day, 3:39:12 time: 1.8458 data_time: 0.2486 memory: 16201 loss_prob: 0.9373 loss_thr: 0.5262 loss_db: 0.1515 loss: 1.6150 2022/08/30 03:29:17 - mmengine - INFO - Epoch(train) [157][10/63] lr: 6.1747e-03 eta: 1 day, 3:38:49 time: 1.9255 data_time: 0.2649 memory: 16201 loss_prob: 0.9309 loss_thr: 0.5202 loss_db: 0.1567 loss: 1.6078 2022/08/30 03:29:25 - mmengine - INFO - Epoch(train) [157][15/63] lr: 6.1747e-03 eta: 1 day, 3:38:49 time: 1.6799 data_time: 0.0391 memory: 16201 loss_prob: 0.9141 loss_thr: 0.5001 loss_db: 0.1511 loss: 1.5653 2022/08/30 03:29:34 - mmengine - INFO - Epoch(train) [157][20/63] lr: 6.1747e-03 eta: 1 day, 3:38:50 time: 1.7503 data_time: 0.0446 memory: 16201 loss_prob: 0.9092 loss_thr: 0.5033 loss_db: 0.1444 loss: 1.5569 2022/08/30 03:29:43 - mmengine - INFO - Epoch(train) [157][25/63] lr: 6.1747e-03 eta: 1 day, 3:38:50 time: 1.8251 data_time: 0.0511 memory: 16201 loss_prob: 0.9596 loss_thr: 0.5239 loss_db: 0.1623 loss: 1.6458 2022/08/30 03:29:51 - mmengine - INFO - Epoch(train) [157][30/63] lr: 6.1747e-03 eta: 1 day, 3:38:46 time: 1.6834 data_time: 0.0362 memory: 16201 loss_prob: 0.9644 loss_thr: 0.5304 loss_db: 0.1648 loss: 1.6596 2022/08/30 03:30:00 - mmengine - INFO - Epoch(train) [157][35/63] lr: 6.1747e-03 eta: 1 day, 3:38:46 time: 1.6152 data_time: 0.0409 memory: 16201 loss_prob: 0.8998 loss_thr: 0.5164 loss_db: 0.1468 loss: 1.5631 2022/08/30 03:30:08 - mmengine - INFO - Epoch(train) [157][40/63] lr: 6.1747e-03 eta: 1 day, 3:38:43 time: 1.7000 data_time: 0.0345 memory: 16201 loss_prob: 0.9054 loss_thr: 0.5527 loss_db: 0.1511 loss: 1.6092 2022/08/30 03:30:16 - mmengine - INFO - Epoch(train) [157][45/63] lr: 6.1747e-03 eta: 1 day, 3:38:43 time: 1.6409 data_time: 0.0351 memory: 16201 loss_prob: 0.9688 loss_thr: 0.5474 loss_db: 0.1656 loss: 1.6818 2022/08/30 03:30:25 - mmengine - INFO - Epoch(train) [157][50/63] lr: 6.1747e-03 eta: 1 day, 3:38:39 time: 1.6806 data_time: 0.0481 memory: 16201 loss_prob: 0.9085 loss_thr: 0.4911 loss_db: 0.1560 loss: 1.5556 2022/08/30 03:30:34 - mmengine - INFO - Epoch(train) [157][55/63] lr: 6.1747e-03 eta: 1 day, 3:38:39 time: 1.7940 data_time: 0.0450 memory: 16201 loss_prob: 0.8437 loss_thr: 0.4907 loss_db: 0.1416 loss: 1.4760 2022/08/30 03:30:43 - mmengine - INFO - Epoch(train) [157][60/63] lr: 6.1747e-03 eta: 1 day, 3:38:44 time: 1.8093 data_time: 0.0478 memory: 16201 loss_prob: 0.8933 loss_thr: 0.5002 loss_db: 0.1502 loss: 1.5436 2022/08/30 03:30:47 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:30:57 - mmengine - INFO - Epoch(train) [158][5/63] lr: 6.1694e-03 eta: 1 day, 3:38:44 time: 1.8032 data_time: 0.2681 memory: 16201 loss_prob: 0.9336 loss_thr: 0.5203 loss_db: 0.1534 loss: 1.6074 2022/08/30 03:31:05 - mmengine - INFO - Epoch(train) [158][10/63] lr: 6.1694e-03 eta: 1 day, 3:38:13 time: 1.8010 data_time: 0.2826 memory: 16201 loss_prob: 0.8923 loss_thr: 0.4908 loss_db: 0.1466 loss: 1.5297 2022/08/30 03:31:14 - mmengine - INFO - Epoch(train) [158][15/63] lr: 6.1694e-03 eta: 1 day, 3:38:13 time: 1.6587 data_time: 0.0490 memory: 16201 loss_prob: 0.8787 loss_thr: 0.5019 loss_db: 0.1446 loss: 1.5251 2022/08/30 03:31:24 - mmengine - INFO - Epoch(train) [158][20/63] lr: 6.1694e-03 eta: 1 day, 3:38:21 time: 1.8625 data_time: 0.1148 memory: 16201 loss_prob: 0.9661 loss_thr: 0.5349 loss_db: 0.1597 loss: 1.6607 2022/08/30 03:31:32 - mmengine - INFO - Epoch(train) [158][25/63] lr: 6.1694e-03 eta: 1 day, 3:38:21 time: 1.8434 data_time: 0.1112 memory: 16201 loss_prob: 0.9343 loss_thr: 0.5220 loss_db: 0.1516 loss: 1.6079 2022/08/30 03:31:43 - mmengine - INFO - Epoch(train) [158][30/63] lr: 6.1694e-03 eta: 1 day, 3:38:31 time: 1.8916 data_time: 0.0578 memory: 16201 loss_prob: 0.8435 loss_thr: 0.4926 loss_db: 0.1367 loss: 1.4728 2022/08/30 03:31:51 - mmengine - INFO - Epoch(train) [158][35/63] lr: 6.1694e-03 eta: 1 day, 3:38:31 time: 1.9147 data_time: 0.0725 memory: 16201 loss_prob: 0.8317 loss_thr: 0.4976 loss_db: 0.1416 loss: 1.4709 2022/08/30 03:32:01 - mmengine - INFO - Epoch(train) [158][40/63] lr: 6.1694e-03 eta: 1 day, 3:38:35 time: 1.8117 data_time: 0.0444 memory: 16201 loss_prob: 0.9232 loss_thr: 0.5236 loss_db: 0.1531 loss: 1.5999 2022/08/30 03:32:10 - mmengine - INFO - Epoch(train) [158][45/63] lr: 6.1694e-03 eta: 1 day, 3:38:35 time: 1.8722 data_time: 0.0369 memory: 16201 loss_prob: 0.9916 loss_thr: 0.5290 loss_db: 0.1535 loss: 1.6741 2022/08/30 03:32:18 - mmengine - INFO - Epoch(train) [158][50/63] lr: 6.1694e-03 eta: 1 day, 3:38:31 time: 1.6744 data_time: 0.0436 memory: 16201 loss_prob: 0.9148 loss_thr: 0.5064 loss_db: 0.1447 loss: 1.5659 2022/08/30 03:32:27 - mmengine - INFO - Epoch(train) [158][55/63] lr: 6.1694e-03 eta: 1 day, 3:38:31 time: 1.6666 data_time: 0.0388 memory: 16201 loss_prob: 0.8561 loss_thr: 0.4998 loss_db: 0.1406 loss: 1.4964 2022/08/30 03:32:35 - mmengine - INFO - Epoch(train) [158][60/63] lr: 6.1694e-03 eta: 1 day, 3:38:31 time: 1.7546 data_time: 0.0698 memory: 16201 loss_prob: 0.8685 loss_thr: 0.5327 loss_db: 0.1435 loss: 1.5447 2022/08/30 03:32:38 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:32:48 - mmengine - INFO - Epoch(train) [159][5/63] lr: 6.1641e-03 eta: 1 day, 3:38:31 time: 1.5782 data_time: 0.2632 memory: 16201 loss_prob: 0.8444 loss_thr: 0.5176 loss_db: 0.1367 loss: 1.4987 2022/08/30 03:32:55 - mmengine - INFO - Epoch(train) [159][10/63] lr: 6.1641e-03 eta: 1 day, 3:37:50 time: 1.6416 data_time: 0.2722 memory: 16201 loss_prob: 0.9691 loss_thr: 0.5476 loss_db: 0.1601 loss: 1.6768 2022/08/30 03:33:02 - mmengine - INFO - Epoch(train) [159][15/63] lr: 6.1641e-03 eta: 1 day, 3:37:50 time: 1.4537 data_time: 0.0372 memory: 16201 loss_prob: 0.9920 loss_thr: 0.5390 loss_db: 0.1655 loss: 1.6966 2022/08/30 03:33:12 - mmengine - INFO - Epoch(train) [159][20/63] lr: 6.1641e-03 eta: 1 day, 3:37:51 time: 1.7574 data_time: 0.0354 memory: 16201 loss_prob: 0.8495 loss_thr: 0.5025 loss_db: 0.1425 loss: 1.4946 2022/08/30 03:33:21 - mmengine - INFO - Epoch(train) [159][25/63] lr: 6.1641e-03 eta: 1 day, 3:37:51 time: 1.9056 data_time: 0.0470 memory: 16201 loss_prob: 0.8512 loss_thr: 0.5168 loss_db: 0.1457 loss: 1.5137 2022/08/30 03:33:29 - mmengine - INFO - Epoch(train) [159][30/63] lr: 6.1641e-03 eta: 1 day, 3:37:47 time: 1.6976 data_time: 0.0396 memory: 16201 loss_prob: 0.8812 loss_thr: 0.5218 loss_db: 0.1498 loss: 1.5528 2022/08/30 03:33:38 - mmengine - INFO - Epoch(train) [159][35/63] lr: 6.1641e-03 eta: 1 day, 3:37:47 time: 1.6475 data_time: 0.0389 memory: 16201 loss_prob: 0.8776 loss_thr: 0.5146 loss_db: 0.1423 loss: 1.5344 2022/08/30 03:33:47 - mmengine - INFO - Epoch(train) [159][40/63] lr: 6.1641e-03 eta: 1 day, 3:37:49 time: 1.7746 data_time: 0.0478 memory: 16201 loss_prob: 0.8681 loss_thr: 0.5180 loss_db: 0.1403 loss: 1.5264 2022/08/30 03:33:54 - mmengine - INFO - Epoch(train) [159][45/63] lr: 6.1641e-03 eta: 1 day, 3:37:49 time: 1.6000 data_time: 0.0506 memory: 16201 loss_prob: 0.8128 loss_thr: 0.5065 loss_db: 0.1367 loss: 1.4560 2022/08/30 03:33:56 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:34:02 - mmengine - INFO - Epoch(train) [159][50/63] lr: 6.1641e-03 eta: 1 day, 3:37:36 time: 1.5417 data_time: 0.0493 memory: 16201 loss_prob: 0.8161 loss_thr: 0.5041 loss_db: 0.1412 loss: 1.4614 2022/08/30 03:34:11 - mmengine - INFO - Epoch(train) [159][55/63] lr: 6.1641e-03 eta: 1 day, 3:37:36 time: 1.7046 data_time: 0.0377 memory: 16201 loss_prob: 0.8788 loss_thr: 0.5121 loss_db: 0.1477 loss: 1.5385 2022/08/30 03:34:20 - mmengine - INFO - Epoch(train) [159][60/63] lr: 6.1641e-03 eta: 1 day, 3:37:38 time: 1.7775 data_time: 0.0367 memory: 16201 loss_prob: 0.8641 loss_thr: 0.5031 loss_db: 0.1441 loss: 1.5113 2022/08/30 03:34:23 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:34:34 - mmengine - INFO - Epoch(train) [160][5/63] lr: 6.1587e-03 eta: 1 day, 3:37:38 time: 1.7888 data_time: 0.2693 memory: 16201 loss_prob: 0.8976 loss_thr: 0.5064 loss_db: 0.1575 loss: 1.5614 2022/08/30 03:34:44 - mmengine - INFO - Epoch(train) [160][10/63] lr: 6.1587e-03 eta: 1 day, 3:37:28 time: 2.1213 data_time: 0.2828 memory: 16201 loss_prob: 0.8420 loss_thr: 0.5137 loss_db: 0.1418 loss: 1.4974 2022/08/30 03:34:52 - mmengine - INFO - Epoch(train) [160][15/63] lr: 6.1587e-03 eta: 1 day, 3:37:28 time: 1.7749 data_time: 0.0468 memory: 16201 loss_prob: 0.8348 loss_thr: 0.5150 loss_db: 0.1377 loss: 1.4876 2022/08/30 03:35:01 - mmengine - INFO - Epoch(train) [160][20/63] lr: 6.1587e-03 eta: 1 day, 3:37:21 time: 1.6485 data_time: 0.0492 memory: 16201 loss_prob: 0.9368 loss_thr: 0.5282 loss_db: 0.1511 loss: 1.6161 2022/08/30 03:35:09 - mmengine - INFO - Epoch(train) [160][25/63] lr: 6.1587e-03 eta: 1 day, 3:37:21 time: 1.6977 data_time: 0.0496 memory: 16201 loss_prob: 0.9244 loss_thr: 0.5311 loss_db: 0.1540 loss: 1.6095 2022/08/30 03:35:19 - mmengine - INFO - Epoch(train) [160][30/63] lr: 6.1587e-03 eta: 1 day, 3:37:27 time: 1.8427 data_time: 0.0422 memory: 16201 loss_prob: 0.9619 loss_thr: 0.5358 loss_db: 0.1665 loss: 1.6642 2022/08/30 03:35:29 - mmengine - INFO - Epoch(train) [160][35/63] lr: 6.1587e-03 eta: 1 day, 3:37:27 time: 1.9877 data_time: 0.0549 memory: 16201 loss_prob: 0.9393 loss_thr: 0.5189 loss_db: 0.1545 loss: 1.6127 2022/08/30 03:35:38 - mmengine - INFO - Epoch(train) [160][40/63] lr: 6.1587e-03 eta: 1 day, 3:37:32 time: 1.8188 data_time: 0.0504 memory: 16201 loss_prob: 0.8489 loss_thr: 0.5095 loss_db: 0.1375 loss: 1.4960 2022/08/30 03:35:47 - mmengine - INFO - Epoch(train) [160][45/63] lr: 6.1587e-03 eta: 1 day, 3:37:32 time: 1.7699 data_time: 0.0479 memory: 16201 loss_prob: 0.8252 loss_thr: 0.5062 loss_db: 0.1403 loss: 1.4717 2022/08/30 03:35:58 - mmengine - INFO - Epoch(train) [160][50/63] lr: 6.1587e-03 eta: 1 day, 3:37:51 time: 2.0417 data_time: 0.0526 memory: 16201 loss_prob: 0.8642 loss_thr: 0.5132 loss_db: 0.1427 loss: 1.5201 2022/08/30 03:36:08 - mmengine - INFO - Epoch(train) [160][55/63] lr: 6.1587e-03 eta: 1 day, 3:37:51 time: 2.0947 data_time: 0.0604 memory: 16201 loss_prob: 0.9371 loss_thr: 0.5251 loss_db: 0.1524 loss: 1.6146 2022/08/30 03:36:18 - mmengine - INFO - Epoch(train) [160][60/63] lr: 6.1587e-03 eta: 1 day, 3:38:05 time: 1.9649 data_time: 0.0608 memory: 16201 loss_prob: 0.8968 loss_thr: 0.5178 loss_db: 0.1487 loss: 1.5633 2022/08/30 03:36:23 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:36:23 - mmengine - INFO - Saving checkpoint at 160 epochs 2022/08/30 03:36:32 - mmengine - INFO - Epoch(val) [160][5/32] eta: 1 day, 3:38:05 time: 0.7392 data_time: 0.1587 memory: 16201 2022/08/30 03:36:35 - mmengine - INFO - Epoch(val) [160][10/32] eta: 0:00:18 time: 0.8293 data_time: 0.1858 memory: 15734 2022/08/30 03:36:39 - mmengine - INFO - Epoch(val) [160][15/32] eta: 0:00:18 time: 0.7056 data_time: 0.0816 memory: 15734 2022/08/30 03:36:43 - mmengine - INFO - Epoch(val) [160][20/32] eta: 0:00:09 time: 0.7671 data_time: 0.0888 memory: 15734 2022/08/30 03:36:46 - mmengine - INFO - Epoch(val) [160][25/32] eta: 0:00:09 time: 0.7500 data_time: 0.0692 memory: 15734 2022/08/30 03:36:50 - mmengine - INFO - Epoch(val) [160][30/32] eta: 0:00:01 time: 0.7306 data_time: 0.0972 memory: 15734 2022/08/30 03:36:51 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 03:36:51 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8368, precision: 0.7534, hmean: 0.7929 2022/08/30 03:36:51 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8363, precision: 0.8124, hmean: 0.8242 2022/08/30 03:36:51 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8310, precision: 0.8490, hmean: 0.8399 2022/08/30 03:36:51 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8180, precision: 0.8726, hmean: 0.8444 2022/08/30 03:36:51 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7814, precision: 0.9007, hmean: 0.8368 2022/08/30 03:36:51 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.4983, precision: 0.9628, hmean: 0.6567 2022/08/30 03:36:51 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0024, precision: 1.0000, hmean: 0.0048 2022/08/30 03:36:51 - mmengine - INFO - Epoch(val) [160][32/32] icdar/precision: 0.8726 icdar/recall: 0.8180 icdar/hmean: 0.8444 2022/08/30 03:37:02 - mmengine - INFO - Epoch(train) [161][5/63] lr: 6.1534e-03 eta: 0:00:01 time: 2.0100 data_time: 0.3044 memory: 16201 loss_prob: 0.8332 loss_thr: 0.4871 loss_db: 0.1360 loss: 1.4564 2022/08/30 03:37:13 - mmengine - INFO - Epoch(train) [161][10/63] lr: 6.1534e-03 eta: 1 day, 3:37:57 time: 2.1619 data_time: 0.2917 memory: 16201 loss_prob: 0.7935 loss_thr: 0.4839 loss_db: 0.1335 loss: 1.4110 2022/08/30 03:37:21 - mmengine - INFO - Epoch(train) [161][15/63] lr: 6.1534e-03 eta: 1 day, 3:37:57 time: 1.9062 data_time: 0.0462 memory: 16201 loss_prob: 0.7910 loss_thr: 0.4800 loss_db: 0.1372 loss: 1.4082 2022/08/30 03:37:29 - mmengine - INFO - Epoch(train) [161][20/63] lr: 6.1534e-03 eta: 1 day, 3:37:50 time: 1.6403 data_time: 0.0391 memory: 16201 loss_prob: 0.8917 loss_thr: 0.4848 loss_db: 0.1442 loss: 1.5207 2022/08/30 03:37:38 - mmengine - INFO - Epoch(train) [161][25/63] lr: 6.1534e-03 eta: 1 day, 3:37:50 time: 1.6488 data_time: 0.0466 memory: 16201 loss_prob: 0.9403 loss_thr: 0.5160 loss_db: 0.1505 loss: 1.6068 2022/08/30 03:37:47 - mmengine - INFO - Epoch(train) [161][30/63] lr: 6.1534e-03 eta: 1 day, 3:37:52 time: 1.7864 data_time: 0.0332 memory: 16201 loss_prob: 0.9903 loss_thr: 0.5447 loss_db: 0.1686 loss: 1.7035 2022/08/30 03:37:58 - mmengine - INFO - Epoch(train) [161][35/63] lr: 6.1534e-03 eta: 1 day, 3:37:52 time: 1.9760 data_time: 0.0479 memory: 16201 loss_prob: 0.9815 loss_thr: 0.5282 loss_db: 0.1641 loss: 1.6737 2022/08/30 03:38:07 - mmengine - INFO - Epoch(train) [161][40/63] lr: 6.1534e-03 eta: 1 day, 3:38:04 time: 1.9370 data_time: 0.0560 memory: 16201 loss_prob: 0.9166 loss_thr: 0.5135 loss_db: 0.1459 loss: 1.5760 2022/08/30 03:38:16 - mmengine - INFO - Epoch(train) [161][45/63] lr: 6.1534e-03 eta: 1 day, 3:38:04 time: 1.8637 data_time: 0.0456 memory: 16201 loss_prob: 0.9418 loss_thr: 0.5280 loss_db: 0.1540 loss: 1.6238 2022/08/30 03:38:25 - mmengine - INFO - Epoch(train) [161][50/63] lr: 6.1534e-03 eta: 1 day, 3:38:08 time: 1.8203 data_time: 0.0532 memory: 16201 loss_prob: 0.8939 loss_thr: 0.5176 loss_db: 0.1502 loss: 1.5617 2022/08/30 03:38:34 - mmengine - INFO - Epoch(train) [161][55/63] lr: 6.1534e-03 eta: 1 day, 3:38:08 time: 1.7876 data_time: 0.0401 memory: 16201 loss_prob: 0.8897 loss_thr: 0.5182 loss_db: 0.1468 loss: 1.5547 2022/08/30 03:38:43 - mmengine - INFO - Epoch(train) [161][60/63] lr: 6.1534e-03 eta: 1 day, 3:38:14 time: 1.8531 data_time: 0.0438 memory: 16201 loss_prob: 0.8767 loss_thr: 0.5172 loss_db: 0.1470 loss: 1.5409 2022/08/30 03:38:48 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:39:01 - mmengine - INFO - Epoch(train) [162][5/63] lr: 6.1481e-03 eta: 1 day, 3:38:14 time: 2.0295 data_time: 0.2616 memory: 16201 loss_prob: 0.8059 loss_thr: 0.4897 loss_db: 0.1348 loss: 1.4304 2022/08/30 03:39:10 - mmengine - INFO - Epoch(train) [162][10/63] lr: 6.1481e-03 eta: 1 day, 3:38:06 time: 2.1549 data_time: 0.2899 memory: 16201 loss_prob: 0.8485 loss_thr: 0.5136 loss_db: 0.1442 loss: 1.5062 2022/08/30 03:39:17 - mmengine - INFO - Epoch(train) [162][15/63] lr: 6.1481e-03 eta: 1 day, 3:38:06 time: 1.6372 data_time: 0.0588 memory: 16201 loss_prob: 0.8426 loss_thr: 0.5181 loss_db: 0.1433 loss: 1.5040 2022/08/30 03:39:26 - mmengine - INFO - Epoch(train) [162][20/63] lr: 6.1481e-03 eta: 1 day, 3:37:57 time: 1.6169 data_time: 0.0511 memory: 16201 loss_prob: 0.7714 loss_thr: 0.4715 loss_db: 0.1294 loss: 1.3723 2022/08/30 03:39:33 - mmengine - INFO - Epoch(train) [162][25/63] lr: 6.1481e-03 eta: 1 day, 3:37:57 time: 1.6135 data_time: 0.0485 memory: 16201 loss_prob: 0.9058 loss_thr: 0.4936 loss_db: 0.1496 loss: 1.5491 2022/08/30 03:39:42 - mmengine - INFO - Epoch(train) [162][30/63] lr: 6.1481e-03 eta: 1 day, 3:37:49 time: 1.6317 data_time: 0.0345 memory: 16201 loss_prob: 0.9619 loss_thr: 0.5406 loss_db: 0.1602 loss: 1.6627 2022/08/30 03:39:51 - mmengine - INFO - Epoch(train) [162][35/63] lr: 6.1481e-03 eta: 1 day, 3:37:49 time: 1.7848 data_time: 0.0428 memory: 16201 loss_prob: 0.8333 loss_thr: 0.5164 loss_db: 0.1467 loss: 1.4963 2022/08/30 03:40:00 - mmengine - INFO - Epoch(train) [162][40/63] lr: 6.1481e-03 eta: 1 day, 3:37:49 time: 1.7544 data_time: 0.0392 memory: 16201 loss_prob: 0.8834 loss_thr: 0.5092 loss_db: 0.1525 loss: 1.5451 2022/08/30 03:40:09 - mmengine - INFO - Epoch(train) [162][45/63] lr: 6.1481e-03 eta: 1 day, 3:37:49 time: 1.7530 data_time: 0.0462 memory: 16201 loss_prob: 0.9000 loss_thr: 0.5201 loss_db: 0.1472 loss: 1.5673 2022/08/30 03:40:17 - mmengine - INFO - Epoch(train) [162][50/63] lr: 6.1481e-03 eta: 1 day, 3:37:46 time: 1.7065 data_time: 0.0449 memory: 16201 loss_prob: 0.8488 loss_thr: 0.5124 loss_db: 0.1400 loss: 1.5013 2022/08/30 03:40:25 - mmengine - INFO - Epoch(train) [162][55/63] lr: 6.1481e-03 eta: 1 day, 3:37:46 time: 1.6893 data_time: 0.0345 memory: 16201 loss_prob: 0.8382 loss_thr: 0.4914 loss_db: 0.1439 loss: 1.4735 2022/08/30 03:40:33 - mmengine - INFO - Epoch(train) [162][60/63] lr: 6.1481e-03 eta: 1 day, 3:37:39 time: 1.6533 data_time: 0.0473 memory: 16201 loss_prob: 0.8410 loss_thr: 0.4966 loss_db: 0.1400 loss: 1.4777 2022/08/30 03:40:37 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:40:48 - mmengine - INFO - Epoch(train) [163][5/63] lr: 6.1427e-03 eta: 1 day, 3:37:39 time: 1.8145 data_time: 0.2870 memory: 16201 loss_prob: 0.8631 loss_thr: 0.4909 loss_db: 0.1421 loss: 1.4961 2022/08/30 03:40:58 - mmengine - INFO - Epoch(train) [163][10/63] lr: 6.1427e-03 eta: 1 day, 3:37:24 time: 2.0463 data_time: 0.3072 memory: 16201 loss_prob: 0.8654 loss_thr: 0.4658 loss_db: 0.1409 loss: 1.4721 2022/08/30 03:41:06 - mmengine - INFO - Epoch(train) [163][15/63] lr: 6.1427e-03 eta: 1 day, 3:37:24 time: 1.7755 data_time: 0.0448 memory: 16201 loss_prob: 0.8915 loss_thr: 0.4912 loss_db: 0.1465 loss: 1.5292 2022/08/30 03:41:15 - mmengine - INFO - Epoch(train) [163][20/63] lr: 6.1427e-03 eta: 1 day, 3:37:20 time: 1.7039 data_time: 0.0357 memory: 16201 loss_prob: 0.8762 loss_thr: 0.4995 loss_db: 0.1454 loss: 1.5210 2022/08/30 03:41:24 - mmengine - INFO - Epoch(train) [163][25/63] lr: 6.1427e-03 eta: 1 day, 3:37:20 time: 1.7499 data_time: 0.0483 memory: 16201 loss_prob: 0.8488 loss_thr: 0.4947 loss_db: 0.1411 loss: 1.4846 2022/08/30 03:41:32 - mmengine - INFO - Epoch(train) [163][30/63] lr: 6.1427e-03 eta: 1 day, 3:37:16 time: 1.6912 data_time: 0.0334 memory: 16201 loss_prob: 0.8142 loss_thr: 0.4904 loss_db: 0.1381 loss: 1.4427 2022/08/30 03:41:39 - mmengine - INFO - Epoch(train) [163][35/63] lr: 6.1427e-03 eta: 1 day, 3:37:16 time: 1.5139 data_time: 0.0363 memory: 16201 loss_prob: 0.8549 loss_thr: 0.5011 loss_db: 0.1431 loss: 1.4991 2022/08/30 03:41:46 - mmengine - INFO - Epoch(train) [163][40/63] lr: 6.1427e-03 eta: 1 day, 3:36:54 time: 1.4110 data_time: 0.0380 memory: 16201 loss_prob: 0.8897 loss_thr: 0.5108 loss_db: 0.1484 loss: 1.5489 2022/08/30 03:41:53 - mmengine - INFO - Epoch(train) [163][45/63] lr: 6.1427e-03 eta: 1 day, 3:36:54 time: 1.4334 data_time: 0.0349 memory: 16201 loss_prob: 0.8917 loss_thr: 0.5142 loss_db: 0.1461 loss: 1.5520 2022/08/30 03:42:02 - mmengine - INFO - Epoch(train) [163][50/63] lr: 6.1427e-03 eta: 1 day, 3:36:42 time: 1.5849 data_time: 0.0556 memory: 16201 loss_prob: 0.8417 loss_thr: 0.4980 loss_db: 0.1378 loss: 1.4776 2022/08/30 03:42:10 - mmengine - INFO - Epoch(train) [163][55/63] lr: 6.1427e-03 eta: 1 day, 3:36:42 time: 1.6786 data_time: 0.0394 memory: 16201 loss_prob: 0.8352 loss_thr: 0.5160 loss_db: 0.1400 loss: 1.4912 2022/08/30 03:42:19 - mmengine - INFO - Epoch(train) [163][60/63] lr: 6.1427e-03 eta: 1 day, 3:36:39 time: 1.7022 data_time: 0.0421 memory: 16201 loss_prob: 0.9515 loss_thr: 0.5519 loss_db: 0.1588 loss: 1.6622 2022/08/30 03:42:23 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:42:34 - mmengine - INFO - Epoch(train) [164][5/63] lr: 6.1374e-03 eta: 1 day, 3:36:39 time: 1.8722 data_time: 0.2834 memory: 16201 loss_prob: 0.8509 loss_thr: 0.5064 loss_db: 0.1452 loss: 1.5025 2022/08/30 03:42:43 - mmengine - INFO - Epoch(train) [164][10/63] lr: 6.1374e-03 eta: 1 day, 3:36:20 time: 1.9897 data_time: 0.2953 memory: 16201 loss_prob: 0.8929 loss_thr: 0.5252 loss_db: 0.1486 loss: 1.5667 2022/08/30 03:42:51 - mmengine - INFO - Epoch(train) [164][15/63] lr: 6.1374e-03 eta: 1 day, 3:36:20 time: 1.7433 data_time: 0.0362 memory: 16201 loss_prob: 0.8174 loss_thr: 0.5054 loss_db: 0.1347 loss: 1.4575 2022/08/30 03:43:00 - mmengine - INFO - Epoch(train) [164][20/63] lr: 6.1374e-03 eta: 1 day, 3:36:19 time: 1.7561 data_time: 0.0359 memory: 16201 loss_prob: 1.0064 loss_thr: 0.5270 loss_db: 0.1555 loss: 1.6889 2022/08/30 03:43:10 - mmengine - INFO - Epoch(train) [164][25/63] lr: 6.1374e-03 eta: 1 day, 3:36:19 time: 1.8186 data_time: 0.0358 memory: 16201 loss_prob: 1.0179 loss_thr: 0.5263 loss_db: 0.1599 loss: 1.7040 2022/08/30 03:43:18 - mmengine - INFO - Epoch(train) [164][30/63] lr: 6.1374e-03 eta: 1 day, 3:36:23 time: 1.8260 data_time: 0.0495 memory: 16201 loss_prob: 0.8664 loss_thr: 0.5126 loss_db: 0.1454 loss: 1.5243 2022/08/30 03:43:28 - mmengine - INFO - Epoch(train) [164][35/63] lr: 6.1374e-03 eta: 1 day, 3:36:23 time: 1.8763 data_time: 0.0652 memory: 16201 loss_prob: 0.9078 loss_thr: 0.5544 loss_db: 0.1514 loss: 1.6136 2022/08/30 03:43:37 - mmengine - INFO - Epoch(train) [164][40/63] lr: 6.1374e-03 eta: 1 day, 3:36:27 time: 1.8126 data_time: 0.0367 memory: 16201 loss_prob: 0.8844 loss_thr: 0.5215 loss_db: 0.1505 loss: 1.5565 2022/08/30 03:43:45 - mmengine - INFO - Epoch(train) [164][45/63] lr: 6.1374e-03 eta: 1 day, 3:36:27 time: 1.6654 data_time: 0.0383 memory: 16201 loss_prob: 0.8617 loss_thr: 0.4911 loss_db: 0.1412 loss: 1.4940 2022/08/30 03:43:54 - mmengine - INFO - Epoch(train) [164][50/63] lr: 6.1374e-03 eta: 1 day, 3:36:28 time: 1.7850 data_time: 0.0610 memory: 16201 loss_prob: 0.8057 loss_thr: 0.4869 loss_db: 0.1307 loss: 1.4234 2022/08/30 03:44:02 - mmengine - INFO - Epoch(train) [164][55/63] lr: 6.1374e-03 eta: 1 day, 3:36:28 time: 1.7227 data_time: 0.0416 memory: 16201 loss_prob: 0.8266 loss_thr: 0.4814 loss_db: 0.1376 loss: 1.4456 2022/08/30 03:44:12 - mmengine - INFO - Epoch(train) [164][60/63] lr: 6.1374e-03 eta: 1 day, 3:36:27 time: 1.7470 data_time: 0.0542 memory: 16201 loss_prob: 0.8498 loss_thr: 0.4883 loss_db: 0.1409 loss: 1.4790 2022/08/30 03:44:17 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:44:28 - mmengine - INFO - Epoch(train) [165][5/63] lr: 6.1321e-03 eta: 1 day, 3:36:27 time: 1.9904 data_time: 0.2301 memory: 16201 loss_prob: 0.9387 loss_thr: 0.5221 loss_db: 0.1576 loss: 1.6184 2022/08/30 03:44:38 - mmengine - INFO - Epoch(train) [165][10/63] lr: 6.1321e-03 eta: 1 day, 3:36:11 time: 2.0447 data_time: 0.2567 memory: 16201 loss_prob: 0.9816 loss_thr: 0.5394 loss_db: 0.1592 loss: 1.6802 2022/08/30 03:44:45 - mmengine - INFO - Epoch(train) [165][15/63] lr: 6.1321e-03 eta: 1 day, 3:36:11 time: 1.7132 data_time: 0.0432 memory: 16201 loss_prob: 0.8837 loss_thr: 0.5209 loss_db: 0.1401 loss: 1.5447 2022/08/30 03:44:54 - mmengine - INFO - Epoch(train) [165][20/63] lr: 6.1321e-03 eta: 1 day, 3:36:02 time: 1.6228 data_time: 0.0467 memory: 16201 loss_prob: 0.8961 loss_thr: 0.5214 loss_db: 0.1475 loss: 1.5650 2022/08/30 03:45:04 - mmengine - INFO - Epoch(train) [165][25/63] lr: 6.1321e-03 eta: 1 day, 3:36:02 time: 1.8419 data_time: 0.0596 memory: 16201 loss_prob: 0.8858 loss_thr: 0.5088 loss_db: 0.1479 loss: 1.5425 2022/08/30 03:45:13 - mmengine - INFO - Epoch(train) [165][30/63] lr: 6.1321e-03 eta: 1 day, 3:36:10 time: 1.8854 data_time: 0.0367 memory: 16201 loss_prob: 0.8256 loss_thr: 0.4692 loss_db: 0.1387 loss: 1.4335 2022/08/30 03:45:22 - mmengine - INFO - Epoch(train) [165][35/63] lr: 6.1321e-03 eta: 1 day, 3:36:10 time: 1.7913 data_time: 0.0437 memory: 16201 loss_prob: 0.8114 loss_thr: 0.4661 loss_db: 0.1401 loss: 1.4176 2022/08/30 03:45:31 - mmengine - INFO - Epoch(train) [165][40/63] lr: 6.1321e-03 eta: 1 day, 3:36:14 time: 1.8310 data_time: 0.0454 memory: 16201 loss_prob: 0.8874 loss_thr: 0.5028 loss_db: 0.1498 loss: 1.5400 2022/08/30 03:45:38 - mmengine - INFO - Epoch(train) [165][45/63] lr: 6.1321e-03 eta: 1 day, 3:36:14 time: 1.6625 data_time: 0.0430 memory: 16201 loss_prob: 0.9016 loss_thr: 0.5107 loss_db: 0.1507 loss: 1.5630 2022/08/30 03:45:47 - mmengine - INFO - Epoch(train) [165][50/63] lr: 6.1321e-03 eta: 1 day, 3:36:03 time: 1.5937 data_time: 0.0534 memory: 16201 loss_prob: 0.8273 loss_thr: 0.4960 loss_db: 0.1395 loss: 1.4628 2022/08/30 03:45:55 - mmengine - INFO - Epoch(train) [165][55/63] lr: 6.1321e-03 eta: 1 day, 3:36:03 time: 1.6534 data_time: 0.0467 memory: 16201 loss_prob: 0.8070 loss_thr: 0.5014 loss_db: 0.1350 loss: 1.4433 2022/08/30 03:46:04 - mmengine - INFO - Epoch(train) [165][60/63] lr: 6.1321e-03 eta: 1 day, 3:35:59 time: 1.6962 data_time: 0.0461 memory: 16201 loss_prob: 0.8320 loss_thr: 0.5152 loss_db: 0.1399 loss: 1.4870 2022/08/30 03:46:07 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:46:17 - mmengine - INFO - Epoch(train) [166][5/63] lr: 6.1267e-03 eta: 1 day, 3:35:59 time: 1.6498 data_time: 0.2519 memory: 16201 loss_prob: 0.8601 loss_thr: 0.5000 loss_db: 0.1436 loss: 1.5038 2022/08/30 03:46:24 - mmengine - INFO - Epoch(train) [166][10/63] lr: 6.1267e-03 eta: 1 day, 3:35:21 time: 1.6956 data_time: 0.2705 memory: 16201 loss_prob: 0.7823 loss_thr: 0.4796 loss_db: 0.1291 loss: 1.3910 2022/08/30 03:46:31 - mmengine - INFO - Epoch(train) [166][15/63] lr: 6.1267e-03 eta: 1 day, 3:35:21 time: 1.3913 data_time: 0.0360 memory: 16201 loss_prob: 0.8424 loss_thr: 0.5006 loss_db: 0.1458 loss: 1.4889 2022/08/30 03:46:39 - mmengine - INFO - Epoch(train) [166][20/63] lr: 6.1267e-03 eta: 1 day, 3:35:01 time: 1.4410 data_time: 0.0404 memory: 16201 loss_prob: 0.8722 loss_thr: 0.4992 loss_db: 0.1490 loss: 1.5205 2022/08/30 03:46:48 - mmengine - INFO - Epoch(train) [166][25/63] lr: 6.1267e-03 eta: 1 day, 3:35:01 time: 1.7365 data_time: 0.0559 memory: 16201 loss_prob: 0.8039 loss_thr: 0.4837 loss_db: 0.1333 loss: 1.4209 2022/08/30 03:46:57 - mmengine - INFO - Epoch(train) [166][30/63] lr: 6.1267e-03 eta: 1 day, 3:35:02 time: 1.7924 data_time: 0.0385 memory: 16201 loss_prob: 0.8037 loss_thr: 0.4933 loss_db: 0.1359 loss: 1.4329 2022/08/30 03:47:07 - mmengine - INFO - Epoch(train) [166][35/63] lr: 6.1267e-03 eta: 1 day, 3:35:02 time: 1.8759 data_time: 0.0448 memory: 16201 loss_prob: 0.9899 loss_thr: 0.5124 loss_db: 0.1575 loss: 1.6598 2022/08/30 03:47:16 - mmengine - INFO - Epoch(train) [166][40/63] lr: 6.1267e-03 eta: 1 day, 3:35:14 time: 1.9461 data_time: 0.0395 memory: 16201 loss_prob: 1.0008 loss_thr: 0.5251 loss_db: 0.1613 loss: 1.6872 2022/08/30 03:47:25 - mmengine - INFO - Epoch(train) [166][45/63] lr: 6.1267e-03 eta: 1 day, 3:35:14 time: 1.8440 data_time: 0.0402 memory: 16201 loss_prob: 0.8671 loss_thr: 0.5101 loss_db: 0.1450 loss: 1.5222 2022/08/30 03:47:33 - mmengine - INFO - Epoch(train) [166][50/63] lr: 6.1267e-03 eta: 1 day, 3:35:11 time: 1.7320 data_time: 0.0616 memory: 16201 loss_prob: 0.8446 loss_thr: 0.5080 loss_db: 0.1357 loss: 1.4883 2022/08/30 03:47:41 - mmengine - INFO - Epoch(train) [166][55/63] lr: 6.1267e-03 eta: 1 day, 3:35:11 time: 1.6142 data_time: 0.0394 memory: 16201 loss_prob: 0.8040 loss_thr: 0.4880 loss_db: 0.1294 loss: 1.4214 2022/08/30 03:47:51 - mmengine - INFO - Epoch(train) [166][60/63] lr: 6.1267e-03 eta: 1 day, 3:35:09 time: 1.7261 data_time: 0.0456 memory: 16201 loss_prob: 0.8986 loss_thr: 0.4929 loss_db: 0.1442 loss: 1.5357 2022/08/30 03:47:55 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:48:06 - mmengine - INFO - Epoch(train) [167][5/63] lr: 6.1214e-03 eta: 1 day, 3:35:09 time: 1.8955 data_time: 0.2352 memory: 16201 loss_prob: 0.8358 loss_thr: 0.5149 loss_db: 0.1369 loss: 1.4876 2022/08/30 03:48:15 - mmengine - INFO - Epoch(train) [167][10/63] lr: 6.1214e-03 eta: 1 day, 3:34:49 time: 1.9865 data_time: 0.2559 memory: 16201 loss_prob: 0.8068 loss_thr: 0.5064 loss_db: 0.1362 loss: 1.4495 2022/08/30 03:48:24 - mmengine - INFO - Epoch(train) [167][15/63] lr: 6.1214e-03 eta: 1 day, 3:34:49 time: 1.7587 data_time: 0.0487 memory: 16201 loss_prob: 0.8193 loss_thr: 0.5011 loss_db: 0.1397 loss: 1.4601 2022/08/30 03:48:32 - mmengine - INFO - Epoch(train) [167][20/63] lr: 6.1214e-03 eta: 1 day, 3:34:46 time: 1.7225 data_time: 0.0424 memory: 16201 loss_prob: 0.8352 loss_thr: 0.5025 loss_db: 0.1389 loss: 1.4766 2022/08/30 03:48:41 - mmengine - INFO - Epoch(train) [167][25/63] lr: 6.1214e-03 eta: 1 day, 3:34:46 time: 1.6957 data_time: 0.0515 memory: 16201 loss_prob: 0.8842 loss_thr: 0.5049 loss_db: 0.1488 loss: 1.5380 2022/08/30 03:48:49 - mmengine - INFO - Epoch(train) [167][30/63] lr: 6.1214e-03 eta: 1 day, 3:34:42 time: 1.7012 data_time: 0.0413 memory: 16201 loss_prob: 0.8818 loss_thr: 0.4860 loss_db: 0.1478 loss: 1.5156 2022/08/30 03:48:58 - mmengine - INFO - Epoch(train) [167][35/63] lr: 6.1214e-03 eta: 1 day, 3:34:42 time: 1.7423 data_time: 0.0390 memory: 16201 loss_prob: 0.8424 loss_thr: 0.4834 loss_db: 0.1370 loss: 1.4628 2022/08/30 03:49:07 - mmengine - INFO - Epoch(train) [167][40/63] lr: 6.1214e-03 eta: 1 day, 3:34:41 time: 1.7499 data_time: 0.0503 memory: 16201 loss_prob: 0.8537 loss_thr: 0.5157 loss_db: 0.1407 loss: 1.5101 2022/08/30 03:49:16 - mmengine - INFO - Epoch(train) [167][45/63] lr: 6.1214e-03 eta: 1 day, 3:34:41 time: 1.7293 data_time: 0.0442 memory: 16201 loss_prob: 0.8907 loss_thr: 0.5243 loss_db: 0.1463 loss: 1.5614 2022/08/30 03:49:24 - mmengine - INFO - Epoch(train) [167][50/63] lr: 6.1214e-03 eta: 1 day, 3:34:39 time: 1.7378 data_time: 0.0442 memory: 16201 loss_prob: 0.8577 loss_thr: 0.4980 loss_db: 0.1401 loss: 1.4959 2022/08/30 03:49:33 - mmengine - INFO - Epoch(train) [167][55/63] lr: 6.1214e-03 eta: 1 day, 3:34:39 time: 1.7151 data_time: 0.0382 memory: 16201 loss_prob: 0.7966 loss_thr: 0.5035 loss_db: 0.1305 loss: 1.4307 2022/08/30 03:49:42 - mmengine - INFO - Epoch(train) [167][60/63] lr: 6.1214e-03 eta: 1 day, 3:34:38 time: 1.7630 data_time: 0.0421 memory: 16201 loss_prob: 0.8716 loss_thr: 0.5059 loss_db: 0.1423 loss: 1.5198 2022/08/30 03:49:46 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:49:57 - mmengine - INFO - Epoch(train) [168][5/63] lr: 6.1161e-03 eta: 1 day, 3:34:38 time: 1.8597 data_time: 0.2738 memory: 16201 loss_prob: 0.8909 loss_thr: 0.5225 loss_db: 0.1474 loss: 1.5608 2022/08/30 03:50:05 - mmengine - INFO - Epoch(train) [168][10/63] lr: 6.1161e-03 eta: 1 day, 3:34:14 time: 1.9098 data_time: 0.2801 memory: 16201 loss_prob: 0.8527 loss_thr: 0.5104 loss_db: 0.1404 loss: 1.5034 2022/08/30 03:50:13 - mmengine - INFO - Epoch(train) [168][15/63] lr: 6.1161e-03 eta: 1 day, 3:34:14 time: 1.6563 data_time: 0.0337 memory: 16201 loss_prob: 0.8254 loss_thr: 0.4781 loss_db: 0.1372 loss: 1.4407 2022/08/30 03:50:22 - mmengine - INFO - Epoch(train) [168][20/63] lr: 6.1161e-03 eta: 1 day, 3:34:12 time: 1.7408 data_time: 0.0340 memory: 16201 loss_prob: 0.8496 loss_thr: 0.4947 loss_db: 0.1441 loss: 1.4885 2022/08/30 03:50:31 - mmengine - INFO - Epoch(train) [168][25/63] lr: 6.1161e-03 eta: 1 day, 3:34:12 time: 1.7783 data_time: 0.0460 memory: 16201 loss_prob: 0.8292 loss_thr: 0.4979 loss_db: 0.1410 loss: 1.4682 2022/08/30 03:50:40 - mmengine - INFO - Epoch(train) [168][30/63] lr: 6.1161e-03 eta: 1 day, 3:34:13 time: 1.7999 data_time: 0.0385 memory: 16201 loss_prob: 0.8253 loss_thr: 0.5089 loss_db: 0.1397 loss: 1.4739 2022/08/30 03:50:48 - mmengine - INFO - Epoch(train) [168][35/63] lr: 6.1161e-03 eta: 1 day, 3:34:13 time: 1.6825 data_time: 0.0434 memory: 16201 loss_prob: 0.8157 loss_thr: 0.4950 loss_db: 0.1356 loss: 1.4462 2022/08/30 03:50:54 - mmengine - INFO - Epoch(train) [168][40/63] lr: 6.1161e-03 eta: 1 day, 3:33:49 time: 1.3806 data_time: 0.0364 memory: 16201 loss_prob: 0.8525 loss_thr: 0.4855 loss_db: 0.1413 loss: 1.4793 2022/08/30 03:51:01 - mmengine - INFO - Epoch(train) [168][45/63] lr: 6.1161e-03 eta: 1 day, 3:33:49 time: 1.3067 data_time: 0.0333 memory: 16201 loss_prob: 0.8566 loss_thr: 0.4934 loss_db: 0.1444 loss: 1.4944 2022/08/30 03:51:10 - mmengine - INFO - Epoch(train) [168][50/63] lr: 6.1161e-03 eta: 1 day, 3:33:35 time: 1.5480 data_time: 0.0484 memory: 16201 loss_prob: 0.8347 loss_thr: 0.4977 loss_db: 0.1407 loss: 1.4731 2022/08/30 03:51:18 - mmengine - INFO - Epoch(train) [168][55/63] lr: 6.1161e-03 eta: 1 day, 3:33:35 time: 1.7146 data_time: 0.0320 memory: 16201 loss_prob: 0.8641 loss_thr: 0.4944 loss_db: 0.1437 loss: 1.5023 2022/08/30 03:51:27 - mmengine - INFO - Epoch(train) [168][60/63] lr: 6.1161e-03 eta: 1 day, 3:33:35 time: 1.7623 data_time: 0.0440 memory: 16201 loss_prob: 0.9601 loss_thr: 0.5069 loss_db: 0.1532 loss: 1.6202 2022/08/30 03:51:32 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:51:43 - mmengine - INFO - Epoch(train) [169][5/63] lr: 6.1107e-03 eta: 1 day, 3:33:35 time: 1.8554 data_time: 0.2252 memory: 16201 loss_prob: 0.8591 loss_thr: 0.5142 loss_db: 0.1443 loss: 1.5177 2022/08/30 03:51:51 - mmengine - INFO - Epoch(train) [169][10/63] lr: 6.1107e-03 eta: 1 day, 3:33:14 time: 1.9722 data_time: 0.2439 memory: 16201 loss_prob: 0.8318 loss_thr: 0.5127 loss_db: 0.1425 loss: 1.4870 2022/08/30 03:52:00 - mmengine - INFO - Epoch(train) [169][15/63] lr: 6.1107e-03 eta: 1 day, 3:33:14 time: 1.7083 data_time: 0.0504 memory: 16201 loss_prob: 0.7870 loss_thr: 0.4854 loss_db: 0.1337 loss: 1.4061 2022/08/30 03:52:08 - mmengine - INFO - Epoch(train) [169][20/63] lr: 6.1107e-03 eta: 1 day, 3:33:10 time: 1.7162 data_time: 0.0549 memory: 16201 loss_prob: 0.8142 loss_thr: 0.4983 loss_db: 0.1365 loss: 1.4490 2022/08/30 03:52:18 - mmengine - INFO - Epoch(train) [169][25/63] lr: 6.1107e-03 eta: 1 day, 3:33:10 time: 1.7958 data_time: 0.0461 memory: 16201 loss_prob: 0.8585 loss_thr: 0.5315 loss_db: 0.1425 loss: 1.5326 2022/08/30 03:52:27 - mmengine - INFO - Epoch(train) [169][30/63] lr: 6.1107e-03 eta: 1 day, 3:33:12 time: 1.8058 data_time: 0.0438 memory: 16201 loss_prob: 0.7704 loss_thr: 0.4993 loss_db: 0.1301 loss: 1.3998 2022/08/30 03:52:36 - mmengine - INFO - Epoch(train) [169][35/63] lr: 6.1107e-03 eta: 1 day, 3:33:12 time: 1.8227 data_time: 0.0468 memory: 16201 loss_prob: 0.8224 loss_thr: 0.4882 loss_db: 0.1372 loss: 1.4478 2022/08/30 03:52:44 - mmengine - INFO - Epoch(train) [169][40/63] lr: 6.1107e-03 eta: 1 day, 3:33:13 time: 1.7914 data_time: 0.0401 memory: 16201 loss_prob: 0.8558 loss_thr: 0.5014 loss_db: 0.1413 loss: 1.4984 2022/08/30 03:52:52 - mmengine - INFO - Epoch(train) [169][45/63] lr: 6.1107e-03 eta: 1 day, 3:33:13 time: 1.6443 data_time: 0.0472 memory: 16201 loss_prob: 0.8710 loss_thr: 0.5078 loss_db: 0.1445 loss: 1.5233 2022/08/30 03:53:01 - mmengine - INFO - Epoch(train) [169][50/63] lr: 6.1107e-03 eta: 1 day, 3:33:07 time: 1.6781 data_time: 0.0492 memory: 16201 loss_prob: 0.9143 loss_thr: 0.5308 loss_db: 0.1535 loss: 1.5986 2022/08/30 03:53:10 - mmengine - INFO - Epoch(train) [169][55/63] lr: 6.1107e-03 eta: 1 day, 3:33:07 time: 1.7478 data_time: 0.0524 memory: 16201 loss_prob: 0.8476 loss_thr: 0.5065 loss_db: 0.1427 loss: 1.4968 2022/08/30 03:53:18 - mmengine - INFO - Epoch(train) [169][60/63] lr: 6.1107e-03 eta: 1 day, 3:33:04 time: 1.7188 data_time: 0.0629 memory: 16201 loss_prob: 0.8304 loss_thr: 0.4722 loss_db: 0.1336 loss: 1.4362 2022/08/30 03:53:22 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:53:33 - mmengine - INFO - Epoch(train) [170][5/63] lr: 6.1054e-03 eta: 1 day, 3:33:04 time: 1.7916 data_time: 0.2705 memory: 16201 loss_prob: 0.8836 loss_thr: 0.5060 loss_db: 0.1470 loss: 1.5365 2022/08/30 03:53:41 - mmengine - INFO - Epoch(train) [170][10/63] lr: 6.1054e-03 eta: 1 day, 3:32:40 time: 1.9199 data_time: 0.2672 memory: 16201 loss_prob: 0.8130 loss_thr: 0.5077 loss_db: 0.1360 loss: 1.4567 2022/08/30 03:53:50 - mmengine - INFO - Epoch(train) [170][15/63] lr: 6.1054e-03 eta: 1 day, 3:32:40 time: 1.7609 data_time: 0.0357 memory: 16201 loss_prob: 0.8813 loss_thr: 0.5346 loss_db: 0.1433 loss: 1.5592 2022/08/30 03:53:58 - mmengine - INFO - Epoch(train) [170][20/63] lr: 6.1054e-03 eta: 1 day, 3:32:33 time: 1.6660 data_time: 0.0410 memory: 16201 loss_prob: 0.9123 loss_thr: 0.5214 loss_db: 0.1501 loss: 1.5839 2022/08/30 03:54:06 - mmengine - INFO - Epoch(train) [170][25/63] lr: 6.1054e-03 eta: 1 day, 3:32:33 time: 1.5517 data_time: 0.0416 memory: 16201 loss_prob: 0.8073 loss_thr: 0.4880 loss_db: 0.1369 loss: 1.4322 2022/08/30 03:54:15 - mmengine - INFO - Epoch(train) [170][30/63] lr: 6.1054e-03 eta: 1 day, 3:32:31 time: 1.7508 data_time: 0.0401 memory: 16201 loss_prob: 0.7759 loss_thr: 0.4725 loss_db: 0.1277 loss: 1.3762 2022/08/30 03:54:23 - mmengine - INFO - Epoch(train) [170][35/63] lr: 6.1054e-03 eta: 1 day, 3:32:31 time: 1.6972 data_time: 0.0577 memory: 16201 loss_prob: 0.8196 loss_thr: 0.4821 loss_db: 0.1315 loss: 1.4332 2022/08/30 03:54:31 - mmengine - INFO - Epoch(train) [170][40/63] lr: 6.1054e-03 eta: 1 day, 3:32:20 time: 1.5842 data_time: 0.0478 memory: 16201 loss_prob: 0.8011 loss_thr: 0.4902 loss_db: 0.1338 loss: 1.4251 2022/08/30 03:54:41 - mmengine - INFO - Epoch(train) [170][45/63] lr: 6.1054e-03 eta: 1 day, 3:32:20 time: 1.7809 data_time: 0.0409 memory: 16201 loss_prob: 0.8089 loss_thr: 0.5139 loss_db: 0.1372 loss: 1.4600 2022/08/30 03:54:50 - mmengine - INFO - Epoch(train) [170][50/63] lr: 6.1054e-03 eta: 1 day, 3:32:24 time: 1.8518 data_time: 0.0518 memory: 16201 loss_prob: 0.8108 loss_thr: 0.5056 loss_db: 0.1344 loss: 1.4508 2022/08/30 03:54:59 - mmengine - INFO - Epoch(train) [170][55/63] lr: 6.1054e-03 eta: 1 day, 3:32:24 time: 1.7965 data_time: 0.0397 memory: 16201 loss_prob: 0.7985 loss_thr: 0.4918 loss_db: 0.1330 loss: 1.4233 2022/08/30 03:55:06 - mmengine - INFO - Epoch(train) [170][60/63] lr: 6.1054e-03 eta: 1 day, 3:32:16 time: 1.6500 data_time: 0.0458 memory: 16201 loss_prob: 0.8418 loss_thr: 0.5068 loss_db: 0.1438 loss: 1.4924 2022/08/30 03:55:10 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:55:19 - mmengine - INFO - Epoch(train) [171][5/63] lr: 6.1001e-03 eta: 1 day, 3:32:16 time: 1.6534 data_time: 0.2535 memory: 16201 loss_prob: 0.8053 loss_thr: 0.4899 loss_db: 0.1339 loss: 1.4291 2022/08/30 03:55:28 - mmengine - INFO - Epoch(train) [171][10/63] lr: 6.1001e-03 eta: 1 day, 3:31:50 time: 1.8862 data_time: 0.2736 memory: 16201 loss_prob: 0.8149 loss_thr: 0.4785 loss_db: 0.1338 loss: 1.4272 2022/08/30 03:55:38 - mmengine - INFO - Epoch(train) [171][15/63] lr: 6.1001e-03 eta: 1 day, 3:31:50 time: 1.8040 data_time: 0.0393 memory: 16201 loss_prob: 0.9432 loss_thr: 0.5149 loss_db: 0.1591 loss: 1.6173 2022/08/30 03:55:47 - mmengine - INFO - Epoch(train) [171][20/63] lr: 6.1001e-03 eta: 1 day, 3:31:57 time: 1.8933 data_time: 0.0393 memory: 16201 loss_prob: 0.8989 loss_thr: 0.5072 loss_db: 0.1493 loss: 1.5554 2022/08/30 03:55:57 - mmengine - INFO - Epoch(train) [171][25/63] lr: 6.1001e-03 eta: 1 day, 3:31:57 time: 1.9647 data_time: 0.0483 memory: 16201 loss_prob: 0.7617 loss_thr: 0.4692 loss_db: 0.1252 loss: 1.3561 2022/08/30 03:56:07 - mmengine - INFO - Epoch(train) [171][30/63] lr: 6.1001e-03 eta: 1 day, 3:32:08 time: 1.9590 data_time: 0.0465 memory: 16201 loss_prob: 0.8173 loss_thr: 0.5024 loss_db: 0.1383 loss: 1.4580 2022/08/30 03:56:15 - mmengine - INFO - Epoch(train) [171][35/63] lr: 6.1001e-03 eta: 1 day, 3:32:08 time: 1.7898 data_time: 0.0481 memory: 16201 loss_prob: 0.8602 loss_thr: 0.5292 loss_db: 0.1483 loss: 1.5378 2022/08/30 03:56:25 - mmengine - INFO - Epoch(train) [171][40/63] lr: 6.1001e-03 eta: 1 day, 3:32:07 time: 1.7619 data_time: 0.0409 memory: 16201 loss_prob: 0.8266 loss_thr: 0.5251 loss_db: 0.1424 loss: 1.4941 2022/08/30 03:56:33 - mmengine - INFO - Epoch(train) [171][45/63] lr: 6.1001e-03 eta: 1 day, 3:32:07 time: 1.7457 data_time: 0.0366 memory: 16201 loss_prob: 0.8347 loss_thr: 0.5025 loss_db: 0.1384 loss: 1.4756 2022/08/30 03:56:41 - mmengine - INFO - Epoch(train) [171][50/63] lr: 6.1001e-03 eta: 1 day, 3:31:59 time: 1.6495 data_time: 0.0500 memory: 16201 loss_prob: 0.8410 loss_thr: 0.4826 loss_db: 0.1386 loss: 1.4622 2022/08/30 03:56:50 - mmengine - INFO - Epoch(train) [171][55/63] lr: 6.1001e-03 eta: 1 day, 3:31:59 time: 1.7346 data_time: 0.0573 memory: 16201 loss_prob: 0.8211 loss_thr: 0.4810 loss_db: 0.1354 loss: 1.4376 2022/08/30 03:56:58 - mmengine - INFO - Epoch(train) [171][60/63] lr: 6.1001e-03 eta: 1 day, 3:31:53 time: 1.6951 data_time: 0.0467 memory: 16201 loss_prob: 0.8961 loss_thr: 0.4871 loss_db: 0.1444 loss: 1.5276 2022/08/30 03:57:03 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:57:15 - mmengine - INFO - Epoch(train) [172][5/63] lr: 6.0947e-03 eta: 1 day, 3:31:53 time: 2.0715 data_time: 0.2810 memory: 16201 loss_prob: 0.8375 loss_thr: 0.4787 loss_db: 0.1372 loss: 1.4534 2022/08/30 03:57:24 - mmengine - INFO - Epoch(train) [172][10/63] lr: 6.0947e-03 eta: 1 day, 3:31:40 time: 2.0893 data_time: 0.3100 memory: 16201 loss_prob: 0.8299 loss_thr: 0.5084 loss_db: 0.1379 loss: 1.4762 2022/08/30 03:57:32 - mmengine - INFO - Epoch(train) [172][15/63] lr: 6.0947e-03 eta: 1 day, 3:31:40 time: 1.6719 data_time: 0.0458 memory: 16201 loss_prob: 0.8518 loss_thr: 0.5141 loss_db: 0.1439 loss: 1.5097 2022/08/30 03:57:40 - mmengine - INFO - Epoch(train) [172][20/63] lr: 6.0947e-03 eta: 1 day, 3:31:32 time: 1.6500 data_time: 0.0398 memory: 16201 loss_prob: 0.9053 loss_thr: 0.5173 loss_db: 0.1491 loss: 1.5717 2022/08/30 03:57:50 - mmengine - INFO - Epoch(train) [172][25/63] lr: 6.0947e-03 eta: 1 day, 3:31:32 time: 1.8635 data_time: 0.0579 memory: 16201 loss_prob: 0.8523 loss_thr: 0.4951 loss_db: 0.1405 loss: 1.4878 2022/08/30 03:57:59 - mmengine - INFO - Epoch(train) [172][30/63] lr: 6.0947e-03 eta: 1 day, 3:31:38 time: 1.8937 data_time: 0.0399 memory: 16201 loss_prob: 0.7732 loss_thr: 0.4769 loss_db: 0.1329 loss: 1.3830 2022/08/30 03:58:09 - mmengine - INFO - Epoch(train) [172][35/63] lr: 6.0947e-03 eta: 1 day, 3:31:38 time: 1.8628 data_time: 0.0392 memory: 16201 loss_prob: 0.7727 loss_thr: 0.4941 loss_db: 0.1319 loss: 1.3987 2022/08/30 03:58:17 - mmengine - INFO - Epoch(train) [172][40/63] lr: 6.0947e-03 eta: 1 day, 3:31:37 time: 1.7688 data_time: 0.0454 memory: 16201 loss_prob: 0.8810 loss_thr: 0.5263 loss_db: 0.1413 loss: 1.5485 2022/08/30 03:58:28 - mmengine - INFO - Epoch(train) [172][45/63] lr: 6.0947e-03 eta: 1 day, 3:31:37 time: 1.9171 data_time: 0.0444 memory: 16201 loss_prob: 0.8816 loss_thr: 0.5159 loss_db: 0.1409 loss: 1.5385 2022/08/30 03:58:37 - mmengine - INFO - Epoch(train) [172][50/63] lr: 6.0947e-03 eta: 1 day, 3:31:48 time: 1.9601 data_time: 0.0580 memory: 16201 loss_prob: 0.8040 loss_thr: 0.4848 loss_db: 0.1361 loss: 1.4249 2022/08/30 03:58:45 - mmengine - INFO - Epoch(train) [172][55/63] lr: 6.0947e-03 eta: 1 day, 3:31:48 time: 1.6534 data_time: 0.0433 memory: 16201 loss_prob: 0.8030 loss_thr: 0.4639 loss_db: 0.1331 loss: 1.3999 2022/08/30 03:58:53 - mmengine - INFO - Epoch(train) [172][60/63] lr: 6.0947e-03 eta: 1 day, 3:31:40 time: 1.6548 data_time: 0.0503 memory: 16201 loss_prob: 0.8517 loss_thr: 0.4738 loss_db: 0.1384 loss: 1.4639 2022/08/30 03:58:58 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 03:59:08 - mmengine - INFO - Epoch(train) [173][5/63] lr: 6.0894e-03 eta: 1 day, 3:31:40 time: 1.8595 data_time: 0.2724 memory: 16201 loss_prob: 0.8560 loss_thr: 0.5116 loss_db: 0.1420 loss: 1.5096 2022/08/30 03:59:17 - mmengine - INFO - Epoch(train) [173][10/63] lr: 6.0894e-03 eta: 1 day, 3:31:15 time: 1.9044 data_time: 0.2898 memory: 16201 loss_prob: 0.8565 loss_thr: 0.5372 loss_db: 0.1400 loss: 1.5336 2022/08/30 03:59:24 - mmengine - INFO - Epoch(train) [173][15/63] lr: 6.0894e-03 eta: 1 day, 3:31:15 time: 1.5719 data_time: 0.0391 memory: 16201 loss_prob: 0.8540 loss_thr: 0.5224 loss_db: 0.1436 loss: 1.5199 2022/08/30 03:59:32 - mmengine - INFO - Epoch(train) [173][20/63] lr: 6.0894e-03 eta: 1 day, 3:31:01 time: 1.5620 data_time: 0.0297 memory: 16201 loss_prob: 0.7859 loss_thr: 0.4716 loss_db: 0.1327 loss: 1.3902 2022/08/30 03:59:40 - mmengine - INFO - Epoch(train) [173][25/63] lr: 6.0894e-03 eta: 1 day, 3:31:01 time: 1.5805 data_time: 0.0447 memory: 16201 loss_prob: 0.7526 loss_thr: 0.4527 loss_db: 0.1246 loss: 1.3298 2022/08/30 03:59:46 - mmengine - INFO - Epoch(train) [173][30/63] lr: 6.0894e-03 eta: 1 day, 3:30:39 time: 1.4041 data_time: 0.0351 memory: 16201 loss_prob: 0.8191 loss_thr: 0.4906 loss_db: 0.1386 loss: 1.4483 2022/08/30 03:59:55 - mmengine - INFO - Epoch(train) [173][35/63] lr: 6.0894e-03 eta: 1 day, 3:30:39 time: 1.5093 data_time: 0.0381 memory: 16201 loss_prob: 0.8489 loss_thr: 0.4839 loss_db: 0.1415 loss: 1.4743 2022/08/30 04:00:03 - mmengine - INFO - Epoch(train) [173][40/63] lr: 6.0894e-03 eta: 1 day, 3:30:31 time: 1.6553 data_time: 0.0454 memory: 16201 loss_prob: 0.8146 loss_thr: 0.4808 loss_db: 0.1355 loss: 1.4309 2022/08/30 04:00:11 - mmengine - INFO - Epoch(train) [173][45/63] lr: 6.0894e-03 eta: 1 day, 3:30:31 time: 1.6507 data_time: 0.0670 memory: 16201 loss_prob: 0.7675 loss_thr: 0.4772 loss_db: 0.1323 loss: 1.3770 2022/08/30 04:00:19 - mmengine - INFO - Epoch(train) [173][50/63] lr: 6.0894e-03 eta: 1 day, 3:30:20 time: 1.6021 data_time: 0.0731 memory: 16201 loss_prob: 0.8354 loss_thr: 0.4838 loss_db: 0.1416 loss: 1.4607 2022/08/30 04:00:28 - mmengine - INFO - Epoch(train) [173][55/63] lr: 6.0894e-03 eta: 1 day, 3:30:20 time: 1.6447 data_time: 0.0462 memory: 16201 loss_prob: 0.8870 loss_thr: 0.5003 loss_db: 0.1459 loss: 1.5332 2022/08/30 04:00:37 - mmengine - INFO - Epoch(train) [173][60/63] lr: 6.0894e-03 eta: 1 day, 3:30:19 time: 1.7676 data_time: 0.0441 memory: 16201 loss_prob: 0.8556 loss_thr: 0.5026 loss_db: 0.1430 loss: 1.5011 2022/08/30 04:00:41 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:00:54 - mmengine - INFO - Epoch(train) [174][5/63] lr: 6.0841e-03 eta: 1 day, 3:30:19 time: 1.9928 data_time: 0.2196 memory: 16201 loss_prob: 0.7653 loss_thr: 0.4988 loss_db: 0.1296 loss: 1.3937 2022/08/30 04:01:03 - mmengine - INFO - Epoch(train) [174][10/63] lr: 6.0841e-03 eta: 1 day, 3:30:13 time: 2.2251 data_time: 0.2672 memory: 16201 loss_prob: 0.7725 loss_thr: 0.4862 loss_db: 0.1301 loss: 1.3888 2022/08/30 04:01:11 - mmengine - INFO - Epoch(train) [174][15/63] lr: 6.0841e-03 eta: 1 day, 3:30:13 time: 1.7861 data_time: 0.0643 memory: 16201 loss_prob: 0.8201 loss_thr: 0.4883 loss_db: 0.1391 loss: 1.4475 2022/08/30 04:01:20 - mmengine - INFO - Epoch(train) [174][20/63] lr: 6.0841e-03 eta: 1 day, 3:30:09 time: 1.7239 data_time: 0.0353 memory: 16201 loss_prob: 0.8151 loss_thr: 0.4893 loss_db: 0.1371 loss: 1.4415 2022/08/30 04:01:30 - mmengine - INFO - Epoch(train) [174][25/63] lr: 6.0841e-03 eta: 1 day, 3:30:09 time: 1.8417 data_time: 0.0447 memory: 16201 loss_prob: 0.7578 loss_thr: 0.4528 loss_db: 0.1269 loss: 1.3375 2022/08/30 04:01:38 - mmengine - INFO - Epoch(train) [174][30/63] lr: 6.0841e-03 eta: 1 day, 3:30:07 time: 1.7665 data_time: 0.0390 memory: 16201 loss_prob: 0.7793 loss_thr: 0.4771 loss_db: 0.1294 loss: 1.3858 2022/08/30 04:01:46 - mmengine - INFO - Epoch(train) [174][35/63] lr: 6.0841e-03 eta: 1 day, 3:30:07 time: 1.6381 data_time: 0.0369 memory: 16201 loss_prob: 0.7866 loss_thr: 0.4990 loss_db: 0.1314 loss: 1.4170 2022/08/30 04:01:55 - mmengine - INFO - Epoch(train) [174][40/63] lr: 6.0841e-03 eta: 1 day, 3:30:05 time: 1.7537 data_time: 0.0396 memory: 16201 loss_prob: 0.7929 loss_thr: 0.4860 loss_db: 0.1348 loss: 1.4136 2022/08/30 04:02:04 - mmengine - INFO - Epoch(train) [174][45/63] lr: 6.0841e-03 eta: 1 day, 3:30:05 time: 1.7768 data_time: 0.0367 memory: 16201 loss_prob: 0.8864 loss_thr: 0.5155 loss_db: 0.1508 loss: 1.5527 2022/08/30 04:02:13 - mmengine - INFO - Epoch(train) [174][50/63] lr: 6.0841e-03 eta: 1 day, 3:30:01 time: 1.7212 data_time: 0.0477 memory: 16201 loss_prob: 0.8706 loss_thr: 0.5159 loss_db: 0.1451 loss: 1.5317 2022/08/30 04:02:21 - mmengine - INFO - Epoch(train) [174][55/63] lr: 6.0841e-03 eta: 1 day, 3:30:01 time: 1.6889 data_time: 0.0452 memory: 16201 loss_prob: 0.9511 loss_thr: 0.5149 loss_db: 0.1554 loss: 1.6214 2022/08/30 04:02:29 - mmengine - INFO - Epoch(train) [174][60/63] lr: 6.0841e-03 eta: 1 day, 3:29:51 time: 1.6107 data_time: 0.0348 memory: 16201 loss_prob: 0.9734 loss_thr: 0.5163 loss_db: 0.1620 loss: 1.6517 2022/08/30 04:02:33 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:02:45 - mmengine - INFO - Epoch(train) [175][5/63] lr: 6.0787e-03 eta: 1 day, 3:29:51 time: 1.9683 data_time: 0.2840 memory: 16201 loss_prob: 0.9942 loss_thr: 0.5290 loss_db: 0.1630 loss: 1.6862 2022/08/30 04:02:54 - mmengine - INFO - Epoch(train) [175][10/63] lr: 6.0787e-03 eta: 1 day, 3:29:33 time: 2.0341 data_time: 0.3052 memory: 16201 loss_prob: 0.9727 loss_thr: 0.5321 loss_db: 0.1599 loss: 1.6648 2022/08/30 04:03:03 - mmengine - INFO - Epoch(train) [175][15/63] lr: 6.0787e-03 eta: 1 day, 3:29:33 time: 1.7941 data_time: 0.0437 memory: 16201 loss_prob: 0.9577 loss_thr: 0.5247 loss_db: 0.1615 loss: 1.6439 2022/08/30 04:03:12 - mmengine - INFO - Epoch(train) [175][20/63] lr: 6.0787e-03 eta: 1 day, 3:29:37 time: 1.8520 data_time: 0.0554 memory: 16201 loss_prob: 0.8254 loss_thr: 0.4859 loss_db: 0.1409 loss: 1.4522 2022/08/30 04:03:20 - mmengine - INFO - Epoch(train) [175][25/63] lr: 6.0787e-03 eta: 1 day, 3:29:37 time: 1.6786 data_time: 0.0604 memory: 16201 loss_prob: 0.7826 loss_thr: 0.4859 loss_db: 0.1321 loss: 1.4007 2022/08/30 04:03:28 - mmengine - INFO - Epoch(train) [175][30/63] lr: 6.0787e-03 eta: 1 day, 3:29:24 time: 1.5659 data_time: 0.0304 memory: 16201 loss_prob: 0.9563 loss_thr: 0.5231 loss_db: 0.1529 loss: 1.6323 2022/08/30 04:03:35 - mmengine - INFO - Epoch(train) [175][35/63] lr: 6.0787e-03 eta: 1 day, 3:29:24 time: 1.5522 data_time: 0.0310 memory: 16201 loss_prob: 0.9584 loss_thr: 0.5262 loss_db: 0.1527 loss: 1.6373 2022/08/30 04:03:40 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:03:43 - mmengine - INFO - Epoch(train) [175][40/63] lr: 6.0787e-03 eta: 1 day, 3:29:05 time: 1.4693 data_time: 0.0323 memory: 16201 loss_prob: 0.8098 loss_thr: 0.4998 loss_db: 0.1365 loss: 1.4461 2022/08/30 04:03:49 - mmengine - INFO - Epoch(train) [175][45/63] lr: 6.0787e-03 eta: 1 day, 3:29:05 time: 1.3710 data_time: 0.0343 memory: 16201 loss_prob: 0.7945 loss_thr: 0.4940 loss_db: 0.1349 loss: 1.4233 2022/08/30 04:03:56 - mmengine - INFO - Epoch(train) [175][50/63] lr: 6.0787e-03 eta: 1 day, 3:28:39 time: 1.3645 data_time: 0.0429 memory: 16201 loss_prob: 0.8024 loss_thr: 0.4871 loss_db: 0.1317 loss: 1.4212 2022/08/30 04:04:04 - mmengine - INFO - Epoch(train) [175][55/63] lr: 6.0787e-03 eta: 1 day, 3:28:39 time: 1.4550 data_time: 0.0386 memory: 16201 loss_prob: 0.8486 loss_thr: 0.5018 loss_db: 0.1388 loss: 1.4892 2022/08/30 04:04:12 - mmengine - INFO - Epoch(train) [175][60/63] lr: 6.0787e-03 eta: 1 day, 3:28:27 time: 1.5761 data_time: 0.0378 memory: 16201 loss_prob: 0.8317 loss_thr: 0.4978 loss_db: 0.1404 loss: 1.4699 2022/08/30 04:04:16 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:04:27 - mmengine - INFO - Epoch(train) [176][5/63] lr: 6.0734e-03 eta: 1 day, 3:28:27 time: 1.8427 data_time: 0.2504 memory: 16201 loss_prob: 1.2178 loss_thr: 0.5238 loss_db: 0.1829 loss: 1.9245 2022/08/30 04:04:35 - mmengine - INFO - Epoch(train) [176][10/63] lr: 6.0734e-03 eta: 1 day, 3:28:03 time: 1.9256 data_time: 0.2810 memory: 16201 loss_prob: 0.9694 loss_thr: 0.5192 loss_db: 0.1649 loss: 1.6535 2022/08/30 04:04:43 - mmengine - INFO - Epoch(train) [176][15/63] lr: 6.0734e-03 eta: 1 day, 3:28:03 time: 1.5565 data_time: 0.0469 memory: 16201 loss_prob: 1.0804 loss_thr: 0.5280 loss_db: 0.1818 loss: 1.7901 2022/08/30 04:04:51 - mmengine - INFO - Epoch(train) [176][20/63] lr: 6.0734e-03 eta: 1 day, 3:27:48 time: 1.5367 data_time: 0.0429 memory: 16201 loss_prob: 1.0996 loss_thr: 0.5349 loss_db: 0.1811 loss: 1.8156 2022/08/30 04:04:58 - mmengine - INFO - Epoch(train) [176][25/63] lr: 6.0734e-03 eta: 1 day, 3:27:48 time: 1.5382 data_time: 0.0590 memory: 16201 loss_prob: 0.9899 loss_thr: 0.5395 loss_db: 0.1635 loss: 1.6928 2022/08/30 04:05:06 - mmengine - INFO - Epoch(train) [176][30/63] lr: 6.0734e-03 eta: 1 day, 3:27:30 time: 1.4879 data_time: 0.0393 memory: 16201 loss_prob: 0.9314 loss_thr: 0.5154 loss_db: 0.1534 loss: 1.6001 2022/08/30 04:05:13 - mmengine - INFO - Epoch(train) [176][35/63] lr: 6.0734e-03 eta: 1 day, 3:27:30 time: 1.4296 data_time: 0.0378 memory: 16201 loss_prob: 1.0471 loss_thr: 0.5152 loss_db: 0.1669 loss: 1.7292 2022/08/30 04:05:20 - mmengine - INFO - Epoch(train) [176][40/63] lr: 6.0734e-03 eta: 1 day, 3:27:10 time: 1.4498 data_time: 0.0345 memory: 16201 loss_prob: 1.0225 loss_thr: 0.5089 loss_db: 0.1642 loss: 1.6955 2022/08/30 04:05:28 - mmengine - INFO - Epoch(train) [176][45/63] lr: 6.0734e-03 eta: 1 day, 3:27:10 time: 1.5225 data_time: 0.0311 memory: 16201 loss_prob: 1.0189 loss_thr: 0.5211 loss_db: 0.1678 loss: 1.7078 2022/08/30 04:05:35 - mmengine - INFO - Epoch(train) [176][50/63] lr: 6.0734e-03 eta: 1 day, 3:26:50 time: 1.4570 data_time: 0.0421 memory: 16201 loss_prob: 1.0297 loss_thr: 0.5399 loss_db: 0.1643 loss: 1.7340 2022/08/30 04:05:42 - mmengine - INFO - Epoch(train) [176][55/63] lr: 6.0734e-03 eta: 1 day, 3:26:50 time: 1.4389 data_time: 0.0348 memory: 16201 loss_prob: 0.9916 loss_thr: 0.5331 loss_db: 0.1594 loss: 1.6841 2022/08/30 04:05:50 - mmengine - INFO - Epoch(train) [176][60/63] lr: 6.0734e-03 eta: 1 day, 3:26:34 time: 1.5072 data_time: 0.0368 memory: 16201 loss_prob: 0.9783 loss_thr: 0.5302 loss_db: 0.1627 loss: 1.6712 2022/08/30 04:05:54 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:06:04 - mmengine - INFO - Epoch(train) [177][5/63] lr: 6.0681e-03 eta: 1 day, 3:26:34 time: 1.7374 data_time: 0.2547 memory: 16201 loss_prob: 1.3344 loss_thr: 0.5552 loss_db: 0.2078 loss: 2.0974 2022/08/30 04:06:12 - mmengine - INFO - Epoch(train) [177][10/63] lr: 6.0681e-03 eta: 1 day, 3:26:02 time: 1.7963 data_time: 0.2753 memory: 16201 loss_prob: 1.0681 loss_thr: 0.5691 loss_db: 0.1738 loss: 1.8110 2022/08/30 04:06:20 - mmengine - INFO - Epoch(train) [177][15/63] lr: 6.0681e-03 eta: 1 day, 3:26:02 time: 1.5821 data_time: 0.0359 memory: 16201 loss_prob: 1.0118 loss_thr: 0.5569 loss_db: 0.1666 loss: 1.7354 2022/08/30 04:06:27 - mmengine - INFO - Epoch(train) [177][20/63] lr: 6.0681e-03 eta: 1 day, 3:25:48 time: 1.5450 data_time: 0.0339 memory: 16201 loss_prob: 1.0111 loss_thr: 0.5520 loss_db: 0.1702 loss: 1.7333 2022/08/30 04:06:35 - mmengine - INFO - Epoch(train) [177][25/63] lr: 6.0681e-03 eta: 1 day, 3:25:48 time: 1.4818 data_time: 0.0398 memory: 16201 loss_prob: 0.9314 loss_thr: 0.5172 loss_db: 0.1576 loss: 1.6063 2022/08/30 04:06:43 - mmengine - INFO - Epoch(train) [177][30/63] lr: 6.0681e-03 eta: 1 day, 3:25:33 time: 1.5444 data_time: 0.0359 memory: 16201 loss_prob: 0.9693 loss_thr: 0.5242 loss_db: 0.1718 loss: 1.6653 2022/08/30 04:06:50 - mmengine - INFO - Epoch(train) [177][35/63] lr: 6.0681e-03 eta: 1 day, 3:25:33 time: 1.5814 data_time: 0.0429 memory: 16201 loss_prob: 1.2300 loss_thr: 0.5438 loss_db: 0.1930 loss: 1.9668 2022/08/30 04:06:57 - mmengine - INFO - Epoch(train) [177][40/63] lr: 6.0681e-03 eta: 1 day, 3:25:13 time: 1.4501 data_time: 0.0340 memory: 16201 loss_prob: 1.2220 loss_thr: 0.5276 loss_db: 0.1789 loss: 1.9285 2022/08/30 04:07:04 - mmengine - INFO - Epoch(train) [177][45/63] lr: 6.0681e-03 eta: 1 day, 3:25:13 time: 1.3348 data_time: 0.0337 memory: 16201 loss_prob: 1.1374 loss_thr: 0.5267 loss_db: 0.1794 loss: 1.8434 2022/08/30 04:07:11 - mmengine - INFO - Epoch(train) [177][50/63] lr: 6.0681e-03 eta: 1 day, 3:24:50 time: 1.3841 data_time: 0.0423 memory: 16201 loss_prob: 1.1008 loss_thr: 0.5343 loss_db: 0.1756 loss: 1.8107 2022/08/30 04:07:18 - mmengine - INFO - Epoch(train) [177][55/63] lr: 6.0681e-03 eta: 1 day, 3:24:50 time: 1.4127 data_time: 0.0296 memory: 16201 loss_prob: 1.0155 loss_thr: 0.5404 loss_db: 0.1664 loss: 1.7223 2022/08/30 04:07:25 - mmengine - INFO - Epoch(train) [177][60/63] lr: 6.0681e-03 eta: 1 day, 3:24:29 time: 1.4414 data_time: 0.0397 memory: 16201 loss_prob: 1.2575 loss_thr: 0.5701 loss_db: 0.1997 loss: 2.0274 2022/08/30 04:07:29 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:07:40 - mmengine - INFO - Epoch(train) [178][5/63] lr: 6.0627e-03 eta: 1 day, 3:24:29 time: 1.7240 data_time: 0.2419 memory: 16201 loss_prob: 0.9952 loss_thr: 0.5139 loss_db: 0.1624 loss: 1.6715 2022/08/30 04:07:48 - mmengine - INFO - Epoch(train) [178][10/63] lr: 6.0627e-03 eta: 1 day, 3:24:00 time: 1.8319 data_time: 0.2620 memory: 16201 loss_prob: 0.9260 loss_thr: 0.5121 loss_db: 0.1523 loss: 1.5903 2022/08/30 04:07:55 - mmengine - INFO - Epoch(train) [178][15/63] lr: 6.0627e-03 eta: 1 day, 3:24:00 time: 1.5537 data_time: 0.0422 memory: 16201 loss_prob: 1.0292 loss_thr: 0.5269 loss_db: 0.1634 loss: 1.7195 2022/08/30 04:08:03 - mmengine - INFO - Epoch(train) [178][20/63] lr: 6.0627e-03 eta: 1 day, 3:23:44 time: 1.5194 data_time: 0.0296 memory: 16201 loss_prob: 1.1574 loss_thr: 0.5571 loss_db: 0.1842 loss: 1.8987 2022/08/30 04:08:10 - mmengine - INFO - Epoch(train) [178][25/63] lr: 6.0627e-03 eta: 1 day, 3:23:44 time: 1.5215 data_time: 0.0450 memory: 16201 loss_prob: 1.0434 loss_thr: 0.5445 loss_db: 0.1757 loss: 1.7636 2022/08/30 04:08:18 - mmengine - INFO - Epoch(train) [178][30/63] lr: 6.0627e-03 eta: 1 day, 3:23:28 time: 1.5153 data_time: 0.0452 memory: 16201 loss_prob: 0.9575 loss_thr: 0.5023 loss_db: 0.1597 loss: 1.6194 2022/08/30 04:08:25 - mmengine - INFO - Epoch(train) [178][35/63] lr: 6.0627e-03 eta: 1 day, 3:23:28 time: 1.4252 data_time: 0.0340 memory: 16201 loss_prob: 0.9977 loss_thr: 0.5301 loss_db: 0.1618 loss: 1.6895 2022/08/30 04:08:32 - mmengine - INFO - Epoch(train) [178][40/63] lr: 6.0627e-03 eta: 1 day, 3:23:06 time: 1.4172 data_time: 0.0327 memory: 16201 loss_prob: 0.9263 loss_thr: 0.5215 loss_db: 0.1504 loss: 1.5983 2022/08/30 04:08:40 - mmengine - INFO - Epoch(train) [178][45/63] lr: 6.0627e-03 eta: 1 day, 3:23:06 time: 1.5011 data_time: 0.0368 memory: 16201 loss_prob: 0.8902 loss_thr: 0.4917 loss_db: 0.1451 loss: 1.5270 2022/08/30 04:08:47 - mmengine - INFO - Epoch(train) [178][50/63] lr: 6.0627e-03 eta: 1 day, 3:22:47 time: 1.4603 data_time: 0.0389 memory: 16201 loss_prob: 0.9004 loss_thr: 0.5071 loss_db: 0.1479 loss: 1.5554 2022/08/30 04:08:54 - mmengine - INFO - Epoch(train) [178][55/63] lr: 6.0627e-03 eta: 1 day, 3:22:47 time: 1.3925 data_time: 0.0308 memory: 16201 loss_prob: 0.9741 loss_thr: 0.5509 loss_db: 0.1593 loss: 1.6842 2022/08/30 04:09:01 - mmengine - INFO - Epoch(train) [178][60/63] lr: 6.0627e-03 eta: 1 day, 3:22:27 time: 1.4607 data_time: 0.0294 memory: 16201 loss_prob: 0.9623 loss_thr: 0.5299 loss_db: 0.1555 loss: 1.6477 2022/08/30 04:09:05 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:09:15 - mmengine - INFO - Epoch(train) [179][5/63] lr: 6.0574e-03 eta: 1 day, 3:22:27 time: 1.6454 data_time: 0.2443 memory: 16201 loss_prob: 0.8783 loss_thr: 0.4919 loss_db: 0.1432 loss: 1.5135 2022/08/30 04:09:22 - mmengine - INFO - Epoch(train) [179][10/63] lr: 6.0574e-03 eta: 1 day, 3:21:49 time: 1.6622 data_time: 0.2624 memory: 16201 loss_prob: 0.9552 loss_thr: 0.5249 loss_db: 0.1548 loss: 1.6349 2022/08/30 04:09:30 - mmengine - INFO - Epoch(train) [179][15/63] lr: 6.0574e-03 eta: 1 day, 3:21:49 time: 1.5221 data_time: 0.0359 memory: 16201 loss_prob: 0.9888 loss_thr: 0.5389 loss_db: 0.1606 loss: 1.6883 2022/08/30 04:09:37 - mmengine - INFO - Epoch(train) [179][20/63] lr: 6.0574e-03 eta: 1 day, 3:21:30 time: 1.4714 data_time: 0.0373 memory: 16201 loss_prob: 0.9853 loss_thr: 0.5381 loss_db: 0.1620 loss: 1.6854 2022/08/30 04:09:44 - mmengine - INFO - Epoch(train) [179][25/63] lr: 6.0574e-03 eta: 1 day, 3:21:30 time: 1.4532 data_time: 0.0361 memory: 16201 loss_prob: 0.9458 loss_thr: 0.5232 loss_db: 0.1550 loss: 1.6240 2022/08/30 04:09:52 - mmengine - INFO - Epoch(train) [179][30/63] lr: 6.0574e-03 eta: 1 day, 3:21:12 time: 1.4908 data_time: 0.0350 memory: 16201 loss_prob: 0.8666 loss_thr: 0.5036 loss_db: 0.1461 loss: 1.5162 2022/08/30 04:09:59 - mmengine - INFO - Epoch(train) [179][35/63] lr: 6.0574e-03 eta: 1 day, 3:21:12 time: 1.4821 data_time: 0.0456 memory: 16201 loss_prob: 0.8042 loss_thr: 0.4829 loss_db: 0.1348 loss: 1.4219 2022/08/30 04:10:06 - mmengine - INFO - Epoch(train) [179][40/63] lr: 6.0574e-03 eta: 1 day, 3:20:49 time: 1.3941 data_time: 0.0370 memory: 16201 loss_prob: 0.7861 loss_thr: 0.4903 loss_db: 0.1298 loss: 1.4062 2022/08/30 04:10:13 - mmengine - INFO - Epoch(train) [179][45/63] lr: 6.0574e-03 eta: 1 day, 3:20:49 time: 1.3776 data_time: 0.0351 memory: 16201 loss_prob: 0.8751 loss_thr: 0.5213 loss_db: 0.1488 loss: 1.5452 2022/08/30 04:10:19 - mmengine - INFO - Epoch(train) [179][50/63] lr: 6.0574e-03 eta: 1 day, 3:20:26 time: 1.3819 data_time: 0.0509 memory: 16201 loss_prob: 0.8556 loss_thr: 0.5013 loss_db: 0.1447 loss: 1.5016 2022/08/30 04:10:27 - mmengine - INFO - Epoch(train) [179][55/63] lr: 6.0574e-03 eta: 1 day, 3:20:26 time: 1.4245 data_time: 0.0451 memory: 16201 loss_prob: 0.8075 loss_thr: 0.4851 loss_db: 0.1305 loss: 1.4230 2022/08/30 04:10:34 - mmengine - INFO - Epoch(train) [179][60/63] lr: 6.0574e-03 eta: 1 day, 3:20:09 time: 1.5013 data_time: 0.0365 memory: 16201 loss_prob: 0.8606 loss_thr: 0.5000 loss_db: 0.1429 loss: 1.5034 2022/08/30 04:10:38 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:10:47 - mmengine - INFO - Epoch(train) [180][5/63] lr: 6.0520e-03 eta: 1 day, 3:20:09 time: 1.5429 data_time: 0.2420 memory: 16201 loss_prob: 0.9250 loss_thr: 0.5235 loss_db: 0.1534 loss: 1.6019 2022/08/30 04:10:55 - mmengine - INFO - Epoch(train) [180][10/63] lr: 6.0520e-03 eta: 1 day, 3:19:33 time: 1.7102 data_time: 0.2518 memory: 16201 loss_prob: 0.8656 loss_thr: 0.5028 loss_db: 0.1441 loss: 1.5124 2022/08/30 04:11:01 - mmengine - INFO - Epoch(train) [180][15/63] lr: 6.0520e-03 eta: 1 day, 3:19:33 time: 1.4633 data_time: 0.0342 memory: 16201 loss_prob: 0.8836 loss_thr: 0.5184 loss_db: 0.1495 loss: 1.5515 2022/08/30 04:11:09 - mmengine - INFO - Epoch(train) [180][20/63] lr: 6.0520e-03 eta: 1 day, 3:19:14 time: 1.4708 data_time: 0.0298 memory: 16201 loss_prob: 0.8536 loss_thr: 0.5203 loss_db: 0.1417 loss: 1.5156 2022/08/30 04:11:18 - mmengine - INFO - Epoch(train) [180][25/63] lr: 6.0520e-03 eta: 1 day, 3:19:14 time: 1.6178 data_time: 0.0384 memory: 16201 loss_prob: 0.7611 loss_thr: 0.4753 loss_db: 0.1266 loss: 1.3630 2022/08/30 04:11:25 - mmengine - INFO - Epoch(train) [180][30/63] lr: 6.0520e-03 eta: 1 day, 3:19:01 time: 1.5599 data_time: 0.0311 memory: 16201 loss_prob: 0.7925 loss_thr: 0.4860 loss_db: 0.1309 loss: 1.4094 2022/08/30 04:11:32 - mmengine - INFO - Epoch(train) [180][35/63] lr: 6.0520e-03 eta: 1 day, 3:19:01 time: 1.4552 data_time: 0.0429 memory: 16201 loss_prob: 0.8857 loss_thr: 0.5296 loss_db: 0.1459 loss: 1.5611 2022/08/30 04:11:39 - mmengine - INFO - Epoch(train) [180][40/63] lr: 6.0520e-03 eta: 1 day, 3:18:40 time: 1.4348 data_time: 0.0369 memory: 16201 loss_prob: 0.9020 loss_thr: 0.5170 loss_db: 0.1498 loss: 1.5688 2022/08/30 04:11:46 - mmengine - INFO - Epoch(train) [180][45/63] lr: 6.0520e-03 eta: 1 day, 3:18:40 time: 1.4088 data_time: 0.0366 memory: 16201 loss_prob: 0.8687 loss_thr: 0.4812 loss_db: 0.1440 loss: 1.4938 2022/08/30 04:11:53 - mmengine - INFO - Epoch(train) [180][50/63] lr: 6.0520e-03 eta: 1 day, 3:18:13 time: 1.3224 data_time: 0.0561 memory: 16201 loss_prob: 0.9794 loss_thr: 0.4954 loss_db: 0.1531 loss: 1.6278 2022/08/30 04:12:00 - mmengine - INFO - Epoch(train) [180][55/63] lr: 6.0520e-03 eta: 1 day, 3:18:13 time: 1.3446 data_time: 0.0361 memory: 16201 loss_prob: 0.9619 loss_thr: 0.5132 loss_db: 0.1490 loss: 1.6241 2022/08/30 04:12:07 - mmengine - INFO - Epoch(train) [180][60/63] lr: 6.0520e-03 eta: 1 day, 3:17:50 time: 1.3927 data_time: 0.0330 memory: 16201 loss_prob: 0.9025 loss_thr: 0.5135 loss_db: 0.1440 loss: 1.5600 2022/08/30 04:12:09 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:12:09 - mmengine - INFO - Saving checkpoint at 180 epochs 2022/08/30 04:12:18 - mmengine - INFO - Epoch(val) [180][5/32] eta: 1 day, 3:17:50 time: 0.7148 data_time: 0.1446 memory: 16201 2022/08/30 04:12:22 - mmengine - INFO - Epoch(val) [180][10/32] eta: 0:00:17 time: 0.7918 data_time: 0.1819 memory: 15734 2022/08/30 04:12:25 - mmengine - INFO - Epoch(val) [180][15/32] eta: 0:00:17 time: 0.6570 data_time: 0.0633 memory: 15734 2022/08/30 04:12:28 - mmengine - INFO - Epoch(val) [180][20/32] eta: 0:00:07 time: 0.6638 data_time: 0.0696 memory: 15734 2022/08/30 04:12:32 - mmengine - INFO - Epoch(val) [180][25/32] eta: 0:00:07 time: 0.7250 data_time: 0.0792 memory: 15734 2022/08/30 04:12:35 - mmengine - INFO - Epoch(val) [180][30/32] eta: 0:00:01 time: 0.6968 data_time: 0.0379 memory: 15734 2022/08/30 04:12:36 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 04:12:36 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8377, precision: 0.7529, hmean: 0.7931 2022/08/30 04:12:36 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8373, precision: 0.8077, hmean: 0.8222 2022/08/30 04:12:36 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8334, precision: 0.8456, hmean: 0.8395 2022/08/30 04:12:36 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8252, precision: 0.8692, hmean: 0.8466 2022/08/30 04:12:36 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7906, precision: 0.9007, hmean: 0.8421 2022/08/30 04:12:36 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.5455, precision: 0.9457, hmean: 0.6919 2022/08/30 04:12:36 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0154, precision: 1.0000, hmean: 0.0303 2022/08/30 04:12:36 - mmengine - INFO - Epoch(val) [180][32/32] icdar/precision: 0.8692 icdar/recall: 0.8252 icdar/hmean: 0.8466 2022/08/30 04:12:46 - mmengine - INFO - Epoch(train) [181][5/63] lr: 6.0467e-03 eta: 0:00:01 time: 1.4950 data_time: 0.2344 memory: 16201 loss_prob: 0.8493 loss_thr: 0.4749 loss_db: 0.1426 loss: 1.4668 2022/08/30 04:12:53 - mmengine - INFO - Epoch(train) [181][10/63] lr: 6.0467e-03 eta: 1 day, 3:17:14 time: 1.7071 data_time: 0.2417 memory: 16201 loss_prob: 0.8274 loss_thr: 0.4823 loss_db: 0.1392 loss: 1.4489 2022/08/30 04:13:01 - mmengine - INFO - Epoch(train) [181][15/63] lr: 6.0467e-03 eta: 1 day, 3:17:14 time: 1.5142 data_time: 0.0337 memory: 16201 loss_prob: 0.7238 loss_thr: 0.4730 loss_db: 0.1240 loss: 1.3207 2022/08/30 04:13:08 - mmengine - INFO - Epoch(train) [181][20/63] lr: 6.0467e-03 eta: 1 day, 3:16:52 time: 1.4045 data_time: 0.0273 memory: 16201 loss_prob: 0.7345 loss_thr: 0.4735 loss_db: 0.1265 loss: 1.3345 2022/08/30 04:13:14 - mmengine - INFO - Epoch(train) [181][25/63] lr: 6.0467e-03 eta: 1 day, 3:16:52 time: 1.2486 data_time: 0.0346 memory: 16201 loss_prob: 0.8152 loss_thr: 0.5034 loss_db: 0.1396 loss: 1.4582 2022/08/30 04:13:21 - mmengine - INFO - Epoch(train) [181][30/63] lr: 6.0467e-03 eta: 1 day, 3:16:28 time: 1.3721 data_time: 0.0465 memory: 16201 loss_prob: 0.8437 loss_thr: 0.5039 loss_db: 0.1411 loss: 1.4887 2022/08/30 04:13:28 - mmengine - INFO - Epoch(train) [181][35/63] lr: 6.0467e-03 eta: 1 day, 3:16:28 time: 1.4217 data_time: 0.0370 memory: 16201 loss_prob: 0.8441 loss_thr: 0.4932 loss_db: 0.1395 loss: 1.4768 2022/08/30 04:13:35 - mmengine - INFO - Epoch(train) [181][40/63] lr: 6.0467e-03 eta: 1 day, 3:16:03 time: 1.3632 data_time: 0.0360 memory: 16201 loss_prob: 0.8525 loss_thr: 0.5022 loss_db: 0.1407 loss: 1.4954 2022/08/30 04:13:42 - mmengine - INFO - Epoch(train) [181][45/63] lr: 6.0467e-03 eta: 1 day, 3:16:03 time: 1.4421 data_time: 0.0386 memory: 16201 loss_prob: 0.8635 loss_thr: 0.5116 loss_db: 0.1437 loss: 1.5187 2022/08/30 04:13:50 - mmengine - INFO - Epoch(train) [181][50/63] lr: 6.0467e-03 eta: 1 day, 3:15:46 time: 1.5048 data_time: 0.0382 memory: 16201 loss_prob: 0.8617 loss_thr: 0.5003 loss_db: 0.1430 loss: 1.5050 2022/08/30 04:13:57 - mmengine - INFO - Epoch(train) [181][55/63] lr: 6.0467e-03 eta: 1 day, 3:15:46 time: 1.4812 data_time: 0.0430 memory: 16201 loss_prob: 0.7853 loss_thr: 0.4755 loss_db: 0.1294 loss: 1.3903 2022/08/30 04:14:04 - mmengine - INFO - Epoch(train) [181][60/63] lr: 6.0467e-03 eta: 1 day, 3:15:26 time: 1.4396 data_time: 0.0400 memory: 16201 loss_prob: 0.7692 loss_thr: 0.4701 loss_db: 0.1299 loss: 1.3692 2022/08/30 04:14:08 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:14:18 - mmengine - INFO - Epoch(train) [182][5/63] lr: 6.0414e-03 eta: 1 day, 3:15:26 time: 1.5992 data_time: 0.2517 memory: 16201 loss_prob: 0.8406 loss_thr: 0.4979 loss_db: 0.1431 loss: 1.4816 2022/08/30 04:14:26 - mmengine - INFO - Epoch(train) [182][10/63] lr: 6.0414e-03 eta: 1 day, 3:14:56 time: 1.8079 data_time: 0.2803 memory: 16201 loss_prob: 0.8697 loss_thr: 0.5347 loss_db: 0.1478 loss: 1.5523 2022/08/30 04:14:33 - mmengine - INFO - Epoch(train) [182][15/63] lr: 6.0414e-03 eta: 1 day, 3:14:56 time: 1.5650 data_time: 0.0608 memory: 16201 loss_prob: 0.8947 loss_thr: 0.5465 loss_db: 0.1491 loss: 1.5903 2022/08/30 04:14:42 - mmengine - INFO - Epoch(train) [182][20/63] lr: 6.0414e-03 eta: 1 day, 3:14:42 time: 1.5529 data_time: 0.0470 memory: 16201 loss_prob: 0.7921 loss_thr: 0.4950 loss_db: 0.1332 loss: 1.4203 2022/08/30 04:14:48 - mmengine - INFO - Epoch(train) [182][25/63] lr: 6.0414e-03 eta: 1 day, 3:14:42 time: 1.5170 data_time: 0.0405 memory: 16201 loss_prob: 0.8610 loss_thr: 0.4944 loss_db: 0.1450 loss: 1.5004 2022/08/30 04:14:56 - mmengine - INFO - Epoch(train) [182][30/63] lr: 6.0414e-03 eta: 1 day, 3:14:21 time: 1.4151 data_time: 0.0332 memory: 16201 loss_prob: 0.8554 loss_thr: 0.5133 loss_db: 0.1401 loss: 1.5088 2022/08/30 04:15:02 - mmengine - INFO - Epoch(train) [182][35/63] lr: 6.0414e-03 eta: 1 day, 3:14:21 time: 1.3966 data_time: 0.0518 memory: 16201 loss_prob: 0.7481 loss_thr: 0.4995 loss_db: 0.1247 loss: 1.3723 2022/08/30 04:15:10 - mmengine - INFO - Epoch(train) [182][40/63] lr: 6.0414e-03 eta: 1 day, 3:13:57 time: 1.3868 data_time: 0.0430 memory: 16201 loss_prob: 0.7715 loss_thr: 0.4858 loss_db: 0.1323 loss: 1.3896 2022/08/30 04:15:16 - mmengine - INFO - Epoch(train) [182][45/63] lr: 6.0414e-03 eta: 1 day, 3:13:57 time: 1.3851 data_time: 0.0398 memory: 16201 loss_prob: 0.8094 loss_thr: 0.4898 loss_db: 0.1387 loss: 1.4380 2022/08/30 04:15:23 - mmengine - INFO - Epoch(train) [182][50/63] lr: 6.0414e-03 eta: 1 day, 3:13:30 time: 1.3016 data_time: 0.0457 memory: 16201 loss_prob: 0.7609 loss_thr: 0.4684 loss_db: 0.1291 loss: 1.3585 2022/08/30 04:15:30 - mmengine - INFO - Epoch(train) [182][55/63] lr: 6.0414e-03 eta: 1 day, 3:13:30 time: 1.4034 data_time: 0.0336 memory: 16201 loss_prob: 0.7526 loss_thr: 0.4622 loss_db: 0.1267 loss: 1.3415 2022/08/30 04:15:38 - mmengine - INFO - Epoch(train) [182][60/63] lr: 6.0414e-03 eta: 1 day, 3:13:13 time: 1.5032 data_time: 0.0400 memory: 16201 loss_prob: 0.8289 loss_thr: 0.4966 loss_db: 0.1381 loss: 1.4636 2022/08/30 04:15:41 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:15:51 - mmengine - INFO - Epoch(train) [183][5/63] lr: 6.0360e-03 eta: 1 day, 3:13:13 time: 1.5675 data_time: 0.2423 memory: 16201 loss_prob: 0.8899 loss_thr: 0.5121 loss_db: 0.1532 loss: 1.5552 2022/08/30 04:15:58 - mmengine - INFO - Epoch(train) [183][10/63] lr: 6.0360e-03 eta: 1 day, 3:12:38 time: 1.7109 data_time: 0.2515 memory: 16201 loss_prob: 0.8240 loss_thr: 0.4917 loss_db: 0.1390 loss: 1.4547 2022/08/30 04:16:05 - mmengine - INFO - Epoch(train) [183][15/63] lr: 6.0360e-03 eta: 1 day, 3:12:38 time: 1.4853 data_time: 0.0344 memory: 16201 loss_prob: 0.7919 loss_thr: 0.4767 loss_db: 0.1289 loss: 1.3975 2022/08/30 04:16:12 - mmengine - INFO - Epoch(train) [183][20/63] lr: 6.0360e-03 eta: 1 day, 3:12:18 time: 1.4446 data_time: 0.0332 memory: 16201 loss_prob: 0.8613 loss_thr: 0.5092 loss_db: 0.1408 loss: 1.5114 2022/08/30 04:16:19 - mmengine - INFO - Epoch(train) [183][25/63] lr: 6.0360e-03 eta: 1 day, 3:12:18 time: 1.3972 data_time: 0.0452 memory: 16201 loss_prob: 0.8928 loss_thr: 0.5315 loss_db: 0.1467 loss: 1.5710 2022/08/30 04:16:26 - mmengine - INFO - Epoch(train) [183][30/63] lr: 6.0360e-03 eta: 1 day, 3:11:52 time: 1.3414 data_time: 0.0366 memory: 16201 loss_prob: 0.8475 loss_thr: 0.5109 loss_db: 0.1405 loss: 1.4989 2022/08/30 04:16:33 - mmengine - INFO - Epoch(train) [183][35/63] lr: 6.0360e-03 eta: 1 day, 3:11:52 time: 1.3466 data_time: 0.0344 memory: 16201 loss_prob: 0.8215 loss_thr: 0.4800 loss_db: 0.1349 loss: 1.4364 2022/08/30 04:16:39 - mmengine - INFO - Epoch(train) [183][40/63] lr: 6.0360e-03 eta: 1 day, 3:11:28 time: 1.3729 data_time: 0.0330 memory: 16201 loss_prob: 0.7891 loss_thr: 0.4617 loss_db: 0.1272 loss: 1.3781 2022/08/30 04:16:46 - mmengine - INFO - Epoch(train) [183][45/63] lr: 6.0360e-03 eta: 1 day, 3:11:28 time: 1.3262 data_time: 0.0327 memory: 16201 loss_prob: 0.7243 loss_thr: 0.4531 loss_db: 0.1244 loss: 1.3018 2022/08/30 04:16:54 - mmengine - INFO - Epoch(train) [183][50/63] lr: 6.0360e-03 eta: 1 day, 3:11:07 time: 1.4153 data_time: 0.0420 memory: 16201 loss_prob: 0.8095 loss_thr: 0.4802 loss_db: 0.1370 loss: 1.4266 2022/08/30 04:17:00 - mmengine - INFO - Epoch(train) [183][55/63] lr: 6.0360e-03 eta: 1 day, 3:11:07 time: 1.4207 data_time: 0.0283 memory: 16201 loss_prob: 0.8443 loss_thr: 0.4989 loss_db: 0.1413 loss: 1.4844 2022/08/30 04:17:08 - mmengine - INFO - Epoch(train) [183][60/63] lr: 6.0360e-03 eta: 1 day, 3:10:45 time: 1.4179 data_time: 0.0377 memory: 16201 loss_prob: 0.8452 loss_thr: 0.4833 loss_db: 0.1433 loss: 1.4719 2022/08/30 04:17:11 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:17:21 - mmengine - INFO - Epoch(train) [184][5/63] lr: 6.0307e-03 eta: 1 day, 3:10:45 time: 1.5811 data_time: 0.2513 memory: 16201 loss_prob: 0.8558 loss_thr: 0.4898 loss_db: 0.1419 loss: 1.4875 2022/08/30 04:17:28 - mmengine - INFO - Epoch(train) [184][10/63] lr: 6.0307e-03 eta: 1 day, 3:10:09 time: 1.6835 data_time: 0.2817 memory: 16201 loss_prob: 0.8659 loss_thr: 0.4898 loss_db: 0.1477 loss: 1.5034 2022/08/30 04:17:35 - mmengine - INFO - Epoch(train) [184][15/63] lr: 6.0307e-03 eta: 1 day, 3:10:09 time: 1.4599 data_time: 0.0526 memory: 16201 loss_prob: 0.8856 loss_thr: 0.4687 loss_db: 0.1446 loss: 1.4988 2022/08/30 04:17:43 - mmengine - INFO - Epoch(train) [184][20/63] lr: 6.0307e-03 eta: 1 day, 3:09:54 time: 1.5393 data_time: 0.0513 memory: 16201 loss_prob: 0.8669 loss_thr: 0.4934 loss_db: 0.1381 loss: 1.4985 2022/08/30 04:17:50 - mmengine - INFO - Epoch(train) [184][25/63] lr: 6.0307e-03 eta: 1 day, 3:09:54 time: 1.5030 data_time: 0.0532 memory: 16201 loss_prob: 0.8611 loss_thr: 0.5214 loss_db: 0.1464 loss: 1.5289 2022/08/30 04:17:58 - mmengine - INFO - Epoch(train) [184][30/63] lr: 6.0307e-03 eta: 1 day, 3:09:34 time: 1.4398 data_time: 0.0399 memory: 16201 loss_prob: 0.8463 loss_thr: 0.5061 loss_db: 0.1428 loss: 1.4952 2022/08/30 04:18:04 - mmengine - INFO - Epoch(train) [184][35/63] lr: 6.0307e-03 eta: 1 day, 3:09:34 time: 1.4245 data_time: 0.0345 memory: 16201 loss_prob: 0.8237 loss_thr: 0.5049 loss_db: 0.1346 loss: 1.4632 2022/08/30 04:18:11 - mmengine - INFO - Epoch(train) [184][40/63] lr: 6.0307e-03 eta: 1 day, 3:09:09 time: 1.3563 data_time: 0.0352 memory: 16201 loss_prob: 0.8120 loss_thr: 0.4908 loss_db: 0.1321 loss: 1.4349 2022/08/30 04:18:19 - mmengine - INFO - Epoch(train) [184][45/63] lr: 6.0307e-03 eta: 1 day, 3:09:09 time: 1.4641 data_time: 0.0384 memory: 16201 loss_prob: 0.7877 loss_thr: 0.4754 loss_db: 0.1294 loss: 1.3924 2022/08/30 04:18:26 - mmengine - INFO - Epoch(train) [184][50/63] lr: 6.0307e-03 eta: 1 day, 3:08:51 time: 1.4682 data_time: 0.0416 memory: 16201 loss_prob: 0.7955 loss_thr: 0.4742 loss_db: 0.1360 loss: 1.4056 2022/08/30 04:18:34 - mmengine - INFO - Epoch(train) [184][55/63] lr: 6.0307e-03 eta: 1 day, 3:08:51 time: 1.4973 data_time: 0.0373 memory: 16201 loss_prob: 0.8166 loss_thr: 0.4825 loss_db: 0.1364 loss: 1.4356 2022/08/30 04:18:41 - mmengine - INFO - Epoch(train) [184][60/63] lr: 6.0307e-03 eta: 1 day, 3:08:33 time: 1.4828 data_time: 0.0313 memory: 16201 loss_prob: 0.7737 loss_thr: 0.4933 loss_db: 0.1277 loss: 1.3947 2022/08/30 04:18:44 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:18:54 - mmengine - INFO - Epoch(train) [185][5/63] lr: 6.0253e-03 eta: 1 day, 3:08:33 time: 1.5764 data_time: 0.2865 memory: 16201 loss_prob: 0.9085 loss_thr: 0.5057 loss_db: 0.1552 loss: 1.5694 2022/08/30 04:19:01 - mmengine - INFO - Epoch(train) [185][10/63] lr: 6.0253e-03 eta: 1 day, 3:07:58 time: 1.6987 data_time: 0.2848 memory: 16201 loss_prob: 0.8238 loss_thr: 0.4914 loss_db: 0.1391 loss: 1.4543 2022/08/30 04:19:08 - mmengine - INFO - Epoch(train) [185][15/63] lr: 6.0253e-03 eta: 1 day, 3:07:58 time: 1.4780 data_time: 0.0340 memory: 16201 loss_prob: 0.8091 loss_thr: 0.4670 loss_db: 0.1374 loss: 1.4134 2022/08/30 04:19:17 - mmengine - INFO - Epoch(train) [185][20/63] lr: 6.0253e-03 eta: 1 day, 3:07:42 time: 1.5271 data_time: 0.0348 memory: 16201 loss_prob: 0.9044 loss_thr: 0.4838 loss_db: 0.1580 loss: 1.5462 2022/08/30 04:19:24 - mmengine - INFO - Epoch(train) [185][25/63] lr: 6.0253e-03 eta: 1 day, 3:07:42 time: 1.5205 data_time: 0.0378 memory: 16201 loss_prob: 0.9987 loss_thr: 0.5211 loss_db: 0.1672 loss: 1.6870 2022/08/30 04:19:31 - mmengine - INFO - Epoch(train) [185][30/63] lr: 6.0253e-03 eta: 1 day, 3:07:24 time: 1.4693 data_time: 0.0296 memory: 16201 loss_prob: 0.9395 loss_thr: 0.5112 loss_db: 0.1534 loss: 1.6041 2022/08/30 04:19:38 - mmengine - INFO - Epoch(train) [185][35/63] lr: 6.0253e-03 eta: 1 day, 3:07:24 time: 1.4614 data_time: 0.0395 memory: 16201 loss_prob: 0.8919 loss_thr: 0.4821 loss_db: 0.1491 loss: 1.5231 2022/08/30 04:19:45 - mmengine - INFO - Epoch(train) [185][40/63] lr: 6.0253e-03 eta: 1 day, 3:06:59 time: 1.3423 data_time: 0.0326 memory: 16201 loss_prob: 0.8955 loss_thr: 0.4783 loss_db: 0.1482 loss: 1.5220 2022/08/30 04:19:51 - mmengine - INFO - Epoch(train) [185][45/63] lr: 6.0253e-03 eta: 1 day, 3:06:59 time: 1.3159 data_time: 0.0318 memory: 16201 loss_prob: 0.8992 loss_thr: 0.5063 loss_db: 0.1455 loss: 1.5511 2022/08/30 04:19:59 - mmengine - INFO - Epoch(train) [185][50/63] lr: 6.0253e-03 eta: 1 day, 3:06:35 time: 1.3743 data_time: 0.0536 memory: 16201 loss_prob: 0.9101 loss_thr: 0.5252 loss_db: 0.1477 loss: 1.5830 2022/08/30 04:20:05 - mmengine - INFO - Epoch(train) [185][55/63] lr: 6.0253e-03 eta: 1 day, 3:06:35 time: 1.3535 data_time: 0.0380 memory: 16201 loss_prob: 0.8605 loss_thr: 0.4994 loss_db: 0.1426 loss: 1.5024 2022/08/30 04:20:12 - mmengine - INFO - Epoch(train) [185][60/63] lr: 6.0253e-03 eta: 1 day, 3:06:08 time: 1.3195 data_time: 0.0318 memory: 16201 loss_prob: 0.7536 loss_thr: 0.4576 loss_db: 0.1269 loss: 1.3382 2022/08/30 04:20:16 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:20:24 - mmengine - INFO - Epoch(train) [186][5/63] lr: 6.0200e-03 eta: 1 day, 3:06:08 time: 1.5273 data_time: 0.2330 memory: 16201 loss_prob: 0.8240 loss_thr: 0.4647 loss_db: 0.1367 loss: 1.4253 2022/08/30 04:20:31 - mmengine - INFO - Epoch(train) [186][10/63] lr: 6.0200e-03 eta: 1 day, 3:05:21 time: 1.4864 data_time: 0.2585 memory: 16201 loss_prob: 0.8886 loss_thr: 0.4869 loss_db: 0.1481 loss: 1.5236 2022/08/30 04:20:38 - mmengine - INFO - Epoch(train) [186][15/63] lr: 6.0200e-03 eta: 1 day, 3:05:21 time: 1.3426 data_time: 0.0338 memory: 16201 loss_prob: 0.8362 loss_thr: 0.4761 loss_db: 0.1430 loss: 1.4552 2022/08/30 04:20:44 - mmengine - INFO - Epoch(train) [186][20/63] lr: 6.0200e-03 eta: 1 day, 3:04:59 time: 1.3881 data_time: 0.0256 memory: 16201 loss_prob: 0.8367 loss_thr: 0.4728 loss_db: 0.1419 loss: 1.4514 2022/08/30 04:20:51 - mmengine - INFO - Epoch(train) [186][25/63] lr: 6.0200e-03 eta: 1 day, 3:04:59 time: 1.3580 data_time: 0.0393 memory: 16201 loss_prob: 0.9784 loss_thr: 0.5227 loss_db: 0.1588 loss: 1.6600 2022/08/30 04:20:58 - mmengine - INFO - Epoch(train) [186][30/63] lr: 6.0200e-03 eta: 1 day, 3:04:36 time: 1.4011 data_time: 0.0282 memory: 16201 loss_prob: 0.9256 loss_thr: 0.5218 loss_db: 0.1484 loss: 1.5959 2022/08/30 04:21:05 - mmengine - INFO - Epoch(train) [186][35/63] lr: 6.0200e-03 eta: 1 day, 3:04:36 time: 1.3974 data_time: 0.0280 memory: 16201 loss_prob: 0.8396 loss_thr: 0.4924 loss_db: 0.1391 loss: 1.4710 2022/08/30 04:21:12 - mmengine - INFO - Epoch(train) [186][40/63] lr: 6.0200e-03 eta: 1 day, 3:04:11 time: 1.3404 data_time: 0.0331 memory: 16201 loss_prob: 0.8943 loss_thr: 0.5071 loss_db: 0.1513 loss: 1.5527 2022/08/30 04:21:18 - mmengine - INFO - Epoch(train) [186][45/63] lr: 6.0200e-03 eta: 1 day, 3:04:11 time: 1.2800 data_time: 0.0320 memory: 16201 loss_prob: 0.7999 loss_thr: 0.5000 loss_db: 0.1354 loss: 1.4353 2022/08/30 04:21:25 - mmengine - INFO - Epoch(train) [186][50/63] lr: 6.0200e-03 eta: 1 day, 3:03:43 time: 1.2968 data_time: 0.0361 memory: 16201 loss_prob: 0.7688 loss_thr: 0.4838 loss_db: 0.1288 loss: 1.3814 2022/08/30 04:21:32 - mmengine - INFO - Epoch(train) [186][55/63] lr: 6.0200e-03 eta: 1 day, 3:03:43 time: 1.3398 data_time: 0.0360 memory: 16201 loss_prob: 0.8302 loss_thr: 0.4728 loss_db: 0.1373 loss: 1.4403 2022/08/30 04:21:39 - mmengine - INFO - Epoch(train) [186][60/63] lr: 6.0200e-03 eta: 1 day, 3:03:23 time: 1.4369 data_time: 0.0427 memory: 16201 loss_prob: 0.7778 loss_thr: 0.4708 loss_db: 0.1293 loss: 1.3780 2022/08/30 04:21:42 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:21:51 - mmengine - INFO - Epoch(train) [187][5/63] lr: 6.0146e-03 eta: 1 day, 3:03:23 time: 1.4945 data_time: 0.2421 memory: 16201 loss_prob: 0.8517 loss_thr: 0.5013 loss_db: 0.1434 loss: 1.4963 2022/08/30 04:21:58 - mmengine - INFO - Epoch(train) [187][10/63] lr: 6.0146e-03 eta: 1 day, 3:02:43 time: 1.6009 data_time: 0.2580 memory: 16201 loss_prob: 0.9407 loss_thr: 0.5234 loss_db: 0.1533 loss: 1.6173 2022/08/30 04:22:05 - mmengine - INFO - Epoch(train) [187][15/63] lr: 6.0146e-03 eta: 1 day, 3:02:43 time: 1.3885 data_time: 0.0357 memory: 16201 loss_prob: 0.8951 loss_thr: 0.5279 loss_db: 0.1490 loss: 1.5720 2022/08/30 04:22:12 - mmengine - INFO - Epoch(train) [187][20/63] lr: 6.0146e-03 eta: 1 day, 3:02:18 time: 1.3499 data_time: 0.0262 memory: 16201 loss_prob: 0.7761 loss_thr: 0.4811 loss_db: 0.1333 loss: 1.3905 2022/08/30 04:22:18 - mmengine - INFO - Epoch(train) [187][25/63] lr: 6.0146e-03 eta: 1 day, 3:02:18 time: 1.3358 data_time: 0.0290 memory: 16201 loss_prob: 0.7845 loss_thr: 0.4660 loss_db: 0.1308 loss: 1.3813 2022/08/30 04:22:25 - mmengine - INFO - Epoch(train) [187][30/63] lr: 6.0146e-03 eta: 1 day, 3:01:52 time: 1.3203 data_time: 0.0317 memory: 16201 loss_prob: 0.8373 loss_thr: 0.4905 loss_db: 0.1407 loss: 1.4684 2022/08/30 04:22:31 - mmengine - INFO - Epoch(train) [187][35/63] lr: 6.0146e-03 eta: 1 day, 3:01:52 time: 1.2657 data_time: 0.0379 memory: 16201 loss_prob: 0.8265 loss_thr: 0.4924 loss_db: 0.1359 loss: 1.4548 2022/08/30 04:22:38 - mmengine - INFO - Epoch(train) [187][40/63] lr: 6.0146e-03 eta: 1 day, 3:01:27 time: 1.3505 data_time: 0.0252 memory: 16201 loss_prob: 0.8462 loss_thr: 0.5111 loss_db: 0.1407 loss: 1.4979 2022/08/30 04:22:45 - mmengine - INFO - Epoch(train) [187][45/63] lr: 6.0146e-03 eta: 1 day, 3:01:27 time: 1.3992 data_time: 0.0311 memory: 16201 loss_prob: 1.3210 loss_thr: 0.5494 loss_db: 0.2004 loss: 2.0709 2022/08/30 04:22:52 - mmengine - INFO - Epoch(train) [187][50/63] lr: 6.0146e-03 eta: 1 day, 3:01:01 time: 1.3277 data_time: 0.0472 memory: 16201 loss_prob: 1.4075 loss_thr: 0.5355 loss_db: 0.2106 loss: 2.1536 2022/08/30 04:22:58 - mmengine - INFO - Epoch(train) [187][55/63] lr: 6.0146e-03 eta: 1 day, 3:01:01 time: 1.3028 data_time: 0.0322 memory: 16201 loss_prob: 1.0310 loss_thr: 0.4957 loss_db: 0.1616 loss: 1.6883 2022/08/30 04:23:05 - mmengine - INFO - Epoch(train) [187][60/63] lr: 6.0146e-03 eta: 1 day, 3:00:33 time: 1.2921 data_time: 0.0369 memory: 16201 loss_prob: 0.9339 loss_thr: 0.5001 loss_db: 0.1431 loss: 1.5772 2022/08/30 04:23:07 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:23:16 - mmengine - INFO - Epoch(train) [188][5/63] lr: 6.0093e-03 eta: 1 day, 3:00:33 time: 1.4182 data_time: 0.2199 memory: 16201 loss_prob: 0.8202 loss_thr: 0.5072 loss_db: 0.1399 loss: 1.4672 2022/08/30 04:23:24 - mmengine - INFO - Epoch(train) [188][10/63] lr: 6.0093e-03 eta: 1 day, 2:59:53 time: 1.6015 data_time: 0.2447 memory: 16201 loss_prob: 0.8463 loss_thr: 0.4891 loss_db: 0.1408 loss: 1.4762 2022/08/30 04:23:30 - mmengine - INFO - Epoch(train) [188][15/63] lr: 6.0093e-03 eta: 1 day, 2:59:53 time: 1.3333 data_time: 0.0388 memory: 16201 loss_prob: 0.9342 loss_thr: 0.5248 loss_db: 0.1521 loss: 1.6111 2022/08/30 04:23:36 - mmengine - INFO - Epoch(train) [188][20/63] lr: 6.0093e-03 eta: 1 day, 2:59:24 time: 1.2712 data_time: 0.0310 memory: 16201 loss_prob: 0.9164 loss_thr: 0.5315 loss_db: 0.1496 loss: 1.5974 2022/08/30 04:23:43 - mmengine - INFO - Epoch(train) [188][25/63] lr: 6.0093e-03 eta: 1 day, 2:59:24 time: 1.3050 data_time: 0.0378 memory: 16201 loss_prob: 0.7744 loss_thr: 0.4690 loss_db: 0.1292 loss: 1.3726 2022/08/30 04:23:49 - mmengine - INFO - Epoch(train) [188][30/63] lr: 6.0093e-03 eta: 1 day, 2:58:56 time: 1.2789 data_time: 0.0325 memory: 16201 loss_prob: 0.7272 loss_thr: 0.4443 loss_db: 0.1226 loss: 1.2941 2022/08/30 04:23:56 - mmengine - INFO - Epoch(train) [188][35/63] lr: 6.0093e-03 eta: 1 day, 2:58:56 time: 1.3406 data_time: 0.0315 memory: 16201 loss_prob: 0.8140 loss_thr: 0.4834 loss_db: 0.1367 loss: 1.4341 2022/08/30 04:24:02 - mmengine - INFO - Epoch(train) [188][40/63] lr: 6.0093e-03 eta: 1 day, 2:58:31 time: 1.3409 data_time: 0.0305 memory: 16201 loss_prob: 0.7977 loss_thr: 0.4604 loss_db: 0.1342 loss: 1.3923 2022/08/30 04:24:09 - mmengine - INFO - Epoch(train) [188][45/63] lr: 6.0093e-03 eta: 1 day, 2:58:31 time: 1.3114 data_time: 0.0334 memory: 16201 loss_prob: 0.8102 loss_thr: 0.4564 loss_db: 0.1365 loss: 1.4031 2022/08/30 04:24:16 - mmengine - INFO - Epoch(train) [188][50/63] lr: 6.0093e-03 eta: 1 day, 2:58:07 time: 1.3712 data_time: 0.0403 memory: 16201 loss_prob: 0.8385 loss_thr: 0.4912 loss_db: 0.1414 loss: 1.4711 2022/08/30 04:24:22 - mmengine - INFO - Epoch(train) [188][55/63] lr: 6.0093e-03 eta: 1 day, 2:58:07 time: 1.2902 data_time: 0.0344 memory: 16201 loss_prob: 0.8371 loss_thr: 0.4871 loss_db: 0.1399 loss: 1.4641 2022/08/30 04:24:28 - mmengine - INFO - Epoch(train) [188][60/63] lr: 6.0093e-03 eta: 1 day, 2:57:35 time: 1.1990 data_time: 0.0290 memory: 16201 loss_prob: 0.8450 loss_thr: 0.4946 loss_db: 0.1387 loss: 1.4784 2022/08/30 04:24:31 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:24:41 - mmengine - INFO - Epoch(train) [189][5/63] lr: 6.0040e-03 eta: 1 day, 2:57:35 time: 1.4759 data_time: 0.2175 memory: 16201 loss_prob: 0.7777 loss_thr: 0.4808 loss_db: 0.1274 loss: 1.3859 2022/08/30 04:24:48 - mmengine - INFO - Epoch(train) [189][10/63] lr: 6.0040e-03 eta: 1 day, 2:56:56 time: 1.6327 data_time: 0.2307 memory: 16201 loss_prob: 0.8516 loss_thr: 0.5130 loss_db: 0.1439 loss: 1.5084 2022/08/30 04:24:54 - mmengine - INFO - Epoch(train) [189][15/63] lr: 6.0040e-03 eta: 1 day, 2:56:56 time: 1.3668 data_time: 0.0392 memory: 16201 loss_prob: 0.9162 loss_thr: 0.5450 loss_db: 0.1554 loss: 1.6166 2022/08/30 04:25:01 - mmengine - INFO - Epoch(train) [189][20/63] lr: 6.0040e-03 eta: 1 day, 2:56:31 time: 1.3408 data_time: 0.0706 memory: 16201 loss_prob: 0.9031 loss_thr: 0.5243 loss_db: 0.1510 loss: 1.5784 2022/08/30 04:25:08 - mmengine - INFO - Epoch(train) [189][25/63] lr: 6.0040e-03 eta: 1 day, 2:56:31 time: 1.3939 data_time: 0.0656 memory: 16201 loss_prob: 0.8447 loss_thr: 0.4965 loss_db: 0.1409 loss: 1.4820 2022/08/30 04:25:15 - mmengine - INFO - Epoch(train) [189][30/63] lr: 6.0040e-03 eta: 1 day, 2:56:07 time: 1.3545 data_time: 0.0289 memory: 16201 loss_prob: 0.8368 loss_thr: 0.4740 loss_db: 0.1377 loss: 1.4485 2022/08/30 04:25:22 - mmengine - INFO - Epoch(train) [189][35/63] lr: 6.0040e-03 eta: 1 day, 2:56:07 time: 1.3680 data_time: 0.0313 memory: 16201 loss_prob: 0.8588 loss_thr: 0.4796 loss_db: 0.1412 loss: 1.4796 2022/08/30 04:25:29 - mmengine - INFO - Epoch(train) [189][40/63] lr: 6.0040e-03 eta: 1 day, 2:55:45 time: 1.3938 data_time: 0.0304 memory: 16201 loss_prob: 0.8750 loss_thr: 0.5148 loss_db: 0.1442 loss: 1.5340 2022/08/30 04:25:36 - mmengine - INFO - Epoch(train) [189][45/63] lr: 6.0040e-03 eta: 1 day, 2:55:45 time: 1.4097 data_time: 0.0306 memory: 16201 loss_prob: 0.9232 loss_thr: 0.5334 loss_db: 0.1562 loss: 1.6128 2022/08/30 04:25:43 - mmengine - INFO - Epoch(train) [189][50/63] lr: 6.0040e-03 eta: 1 day, 2:55:27 time: 1.4596 data_time: 0.0382 memory: 16201 loss_prob: 0.8736 loss_thr: 0.5143 loss_db: 0.1511 loss: 1.5390 2022/08/30 04:25:49 - mmengine - INFO - Epoch(train) [189][55/63] lr: 6.0040e-03 eta: 1 day, 2:55:27 time: 1.3405 data_time: 0.0341 memory: 16201 loss_prob: 0.8542 loss_thr: 0.5021 loss_db: 0.1466 loss: 1.5029 2022/08/30 04:25:56 - mmengine - INFO - Epoch(train) [189][60/63] lr: 6.0040e-03 eta: 1 day, 2:54:56 time: 1.2276 data_time: 0.0339 memory: 16201 loss_prob: 0.9346 loss_thr: 0.5244 loss_db: 0.1595 loss: 1.6186 2022/08/30 04:25:58 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:26:07 - mmengine - INFO - Epoch(train) [190][5/63] lr: 5.9986e-03 eta: 1 day, 2:54:56 time: 1.4334 data_time: 0.2400 memory: 16201 loss_prob: 0.9921 loss_thr: 0.5697 loss_db: 0.1689 loss: 1.7307 2022/08/30 04:26:14 - mmengine - INFO - Epoch(train) [190][10/63] lr: 5.9986e-03 eta: 1 day, 2:54:13 time: 1.5518 data_time: 0.2427 memory: 16201 loss_prob: 1.0081 loss_thr: 0.5528 loss_db: 0.1697 loss: 1.7306 2022/08/30 04:26:20 - mmengine - INFO - Epoch(train) [190][15/63] lr: 5.9986e-03 eta: 1 day, 2:54:13 time: 1.2794 data_time: 0.0361 memory: 16201 loss_prob: 1.1281 loss_thr: 0.5425 loss_db: 0.1825 loss: 1.8531 2022/08/30 04:26:28 - mmengine - INFO - Epoch(train) [190][20/63] lr: 5.9986e-03 eta: 1 day, 2:53:49 time: 1.3503 data_time: 0.0342 memory: 16201 loss_prob: 1.1216 loss_thr: 0.5505 loss_db: 0.1835 loss: 1.8555 2022/08/30 04:26:35 - mmengine - INFO - Epoch(train) [190][25/63] lr: 5.9986e-03 eta: 1 day, 2:53:49 time: 1.4541 data_time: 0.0358 memory: 16201 loss_prob: 1.1316 loss_thr: 0.5710 loss_db: 0.1918 loss: 1.8944 2022/08/30 04:26:41 - mmengine - INFO - Epoch(train) [190][30/63] lr: 5.9986e-03 eta: 1 day, 2:53:25 time: 1.3631 data_time: 0.0431 memory: 16201 loss_prob: 1.0957 loss_thr: 0.5652 loss_db: 0.1850 loss: 1.8459 2022/08/30 04:26:47 - mmengine - INFO - Epoch(train) [190][35/63] lr: 5.9986e-03 eta: 1 day, 2:53:25 time: 1.2615 data_time: 0.0399 memory: 16201 loss_prob: 0.9726 loss_thr: 0.5400 loss_db: 0.1585 loss: 1.6710 2022/08/30 04:26:54 - mmengine - INFO - Epoch(train) [190][40/63] lr: 5.9986e-03 eta: 1 day, 2:52:56 time: 1.2572 data_time: 0.0309 memory: 16201 loss_prob: 0.9728 loss_thr: 0.5077 loss_db: 0.1526 loss: 1.6331 2022/08/30 04:27:00 - mmengine - INFO - Epoch(train) [190][45/63] lr: 5.9986e-03 eta: 1 day, 2:52:56 time: 1.2804 data_time: 0.0378 memory: 16201 loss_prob: 1.0426 loss_thr: 0.5101 loss_db: 0.1586 loss: 1.7112 2022/08/30 04:27:07 - mmengine - INFO - Epoch(train) [190][50/63] lr: 5.9986e-03 eta: 1 day, 2:52:29 time: 1.3027 data_time: 0.0411 memory: 16201 loss_prob: 1.0123 loss_thr: 0.5231 loss_db: 0.1597 loss: 1.6951 2022/08/30 04:27:14 - mmengine - INFO - Epoch(train) [190][55/63] lr: 5.9986e-03 eta: 1 day, 2:52:29 time: 1.3259 data_time: 0.0282 memory: 16201 loss_prob: 0.8907 loss_thr: 0.5173 loss_db: 0.1523 loss: 1.5603 2022/08/30 04:27:21 - mmengine - INFO - Epoch(train) [190][60/63] lr: 5.9986e-03 eta: 1 day, 2:52:10 time: 1.4472 data_time: 0.0480 memory: 16201 loss_prob: 0.8733 loss_thr: 0.5173 loss_db: 0.1501 loss: 1.5407 2022/08/30 04:27:25 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:27:34 - mmengine - INFO - Epoch(train) [191][5/63] lr: 5.9933e-03 eta: 1 day, 2:52:10 time: 1.5677 data_time: 0.2325 memory: 16201 loss_prob: 0.9605 loss_thr: 0.5587 loss_db: 0.1631 loss: 1.6822 2022/08/30 04:27:42 - mmengine - INFO - Epoch(train) [191][10/63] lr: 5.9933e-03 eta: 1 day, 2:51:37 time: 1.7268 data_time: 0.2455 memory: 16201 loss_prob: 0.9149 loss_thr: 0.5230 loss_db: 0.1538 loss: 1.5918 2022/08/30 04:27:49 - mmengine - INFO - Epoch(train) [191][15/63] lr: 5.9933e-03 eta: 1 day, 2:51:37 time: 1.4524 data_time: 0.0326 memory: 16201 loss_prob: 0.8992 loss_thr: 0.5074 loss_db: 0.1502 loss: 1.5568 2022/08/30 04:27:55 - mmengine - INFO - Epoch(train) [191][20/63] lr: 5.9933e-03 eta: 1 day, 2:51:13 time: 1.3610 data_time: 0.0314 memory: 16201 loss_prob: 0.9140 loss_thr: 0.5106 loss_db: 0.1512 loss: 1.5757 2022/08/30 04:28:04 - mmengine - INFO - Epoch(train) [191][25/63] lr: 5.9933e-03 eta: 1 day, 2:51:13 time: 1.4871 data_time: 0.0415 memory: 16201 loss_prob: 0.8626 loss_thr: 0.4928 loss_db: 0.1417 loss: 1.4970 2022/08/30 04:28:10 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:28:10 - mmengine - INFO - Epoch(train) [191][30/63] lr: 5.9933e-03 eta: 1 day, 2:50:56 time: 1.4697 data_time: 0.0303 memory: 16201 loss_prob: 0.8760 loss_thr: 0.5019 loss_db: 0.1452 loss: 1.5231 2022/08/30 04:28:17 - mmengine - INFO - Epoch(train) [191][35/63] lr: 5.9933e-03 eta: 1 day, 2:50:56 time: 1.3091 data_time: 0.0288 memory: 16201 loss_prob: 0.8704 loss_thr: 0.5124 loss_db: 0.1458 loss: 1.5285 2022/08/30 04:28:24 - mmengine - INFO - Epoch(train) [191][40/63] lr: 5.9933e-03 eta: 1 day, 2:50:33 time: 1.3797 data_time: 0.0299 memory: 16201 loss_prob: 0.8510 loss_thr: 0.4939 loss_db: 0.1427 loss: 1.4876 2022/08/30 04:28:30 - mmengine - INFO - Epoch(train) [191][45/63] lr: 5.9933e-03 eta: 1 day, 2:50:33 time: 1.2983 data_time: 0.0319 memory: 16201 loss_prob: 0.8898 loss_thr: 0.5091 loss_db: 0.1530 loss: 1.5519 2022/08/30 04:28:37 - mmengine - INFO - Epoch(train) [191][50/63] lr: 5.9933e-03 eta: 1 day, 2:50:05 time: 1.2818 data_time: 0.0424 memory: 16201 loss_prob: 0.9052 loss_thr: 0.5342 loss_db: 0.1520 loss: 1.5914 2022/08/30 04:28:44 - mmengine - INFO - Epoch(train) [191][55/63] lr: 5.9933e-03 eta: 1 day, 2:50:05 time: 1.4043 data_time: 0.0319 memory: 16201 loss_prob: 0.8946 loss_thr: 0.5297 loss_db: 0.1461 loss: 1.5704 2022/08/30 04:28:50 - mmengine - INFO - Epoch(train) [191][60/63] lr: 5.9933e-03 eta: 1 day, 2:49:40 time: 1.3342 data_time: 0.0338 memory: 16201 loss_prob: 0.8397 loss_thr: 0.5001 loss_db: 0.1415 loss: 1.4813 2022/08/30 04:28:53 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:29:02 - mmengine - INFO - Epoch(train) [192][5/63] lr: 5.9879e-03 eta: 1 day, 2:49:40 time: 1.4280 data_time: 0.2114 memory: 16201 loss_prob: 0.8070 loss_thr: 0.4996 loss_db: 0.1378 loss: 1.4444 2022/08/30 04:29:09 - mmengine - INFO - Epoch(train) [192][10/63] lr: 5.9879e-03 eta: 1 day, 2:48:57 time: 1.5280 data_time: 0.2246 memory: 16201 loss_prob: 0.7984 loss_thr: 0.5018 loss_db: 0.1334 loss: 1.4336 2022/08/30 04:29:15 - mmengine - INFO - Epoch(train) [192][15/63] lr: 5.9879e-03 eta: 1 day, 2:48:57 time: 1.3473 data_time: 0.0321 memory: 16201 loss_prob: 0.7929 loss_thr: 0.4970 loss_db: 0.1313 loss: 1.4211 2022/08/30 04:29:21 - mmengine - INFO - Epoch(train) [192][20/63] lr: 5.9879e-03 eta: 1 day, 2:48:28 time: 1.2614 data_time: 0.0429 memory: 16201 loss_prob: 0.8078 loss_thr: 0.4898 loss_db: 0.1372 loss: 1.4347 2022/08/30 04:29:28 - mmengine - INFO - Epoch(train) [192][25/63] lr: 5.9879e-03 eta: 1 day, 2:48:28 time: 1.2987 data_time: 0.0434 memory: 16201 loss_prob: 0.8294 loss_thr: 0.4950 loss_db: 0.1375 loss: 1.4619 2022/08/30 04:29:35 - mmengine - INFO - Epoch(train) [192][30/63] lr: 5.9879e-03 eta: 1 day, 2:48:05 time: 1.3708 data_time: 0.0343 memory: 16201 loss_prob: 0.8402 loss_thr: 0.4916 loss_db: 0.1373 loss: 1.4692 2022/08/30 04:29:42 - mmengine - INFO - Epoch(train) [192][35/63] lr: 5.9879e-03 eta: 1 day, 2:48:05 time: 1.3768 data_time: 0.0309 memory: 16201 loss_prob: 0.8526 loss_thr: 0.4919 loss_db: 0.1431 loss: 1.4875 2022/08/30 04:29:49 - mmengine - INFO - Epoch(train) [192][40/63] lr: 5.9879e-03 eta: 1 day, 2:47:45 time: 1.4244 data_time: 0.0305 memory: 16201 loss_prob: 0.8263 loss_thr: 0.5032 loss_db: 0.1388 loss: 1.4682 2022/08/30 04:29:56 - mmengine - INFO - Epoch(train) [192][45/63] lr: 5.9879e-03 eta: 1 day, 2:47:45 time: 1.3803 data_time: 0.0381 memory: 16201 loss_prob: 0.8723 loss_thr: 0.5131 loss_db: 0.1448 loss: 1.5302 2022/08/30 04:30:02 - mmengine - INFO - Epoch(train) [192][50/63] lr: 5.9879e-03 eta: 1 day, 2:47:19 time: 1.3077 data_time: 0.0384 memory: 16201 loss_prob: 0.9973 loss_thr: 0.5387 loss_db: 0.1590 loss: 1.6951 2022/08/30 04:30:10 - mmengine - INFO - Epoch(train) [192][55/63] lr: 5.9879e-03 eta: 1 day, 2:47:19 time: 1.4075 data_time: 0.0321 memory: 16201 loss_prob: 0.9306 loss_thr: 0.5332 loss_db: 0.1506 loss: 1.6144 2022/08/30 04:30:18 - mmengine - INFO - Epoch(train) [192][60/63] lr: 5.9879e-03 eta: 1 day, 2:47:04 time: 1.5279 data_time: 0.0581 memory: 16201 loss_prob: 0.7774 loss_thr: 0.4796 loss_db: 0.1322 loss: 1.3891 2022/08/30 04:30:21 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:30:30 - mmengine - INFO - Epoch(train) [193][5/63] lr: 5.9826e-03 eta: 1 day, 2:47:04 time: 1.6115 data_time: 0.2418 memory: 16201 loss_prob: 0.9029 loss_thr: 0.5341 loss_db: 0.1497 loss: 1.5867 2022/08/30 04:30:37 - mmengine - INFO - Epoch(train) [193][10/63] lr: 5.9826e-03 eta: 1 day, 2:46:23 time: 1.5604 data_time: 0.2507 memory: 16201 loss_prob: 0.9395 loss_thr: 0.5421 loss_db: 0.1521 loss: 1.6336 2022/08/30 04:30:44 - mmengine - INFO - Epoch(train) [193][15/63] lr: 5.9826e-03 eta: 1 day, 2:46:23 time: 1.4030 data_time: 0.0360 memory: 16201 loss_prob: 0.9213 loss_thr: 0.5301 loss_db: 0.1519 loss: 1.6034 2022/08/30 04:30:51 - mmengine - INFO - Epoch(train) [193][20/63] lr: 5.9826e-03 eta: 1 day, 2:46:03 time: 1.4370 data_time: 0.0310 memory: 16201 loss_prob: 0.8150 loss_thr: 0.4874 loss_db: 0.1371 loss: 1.4395 2022/08/30 04:30:58 - mmengine - INFO - Epoch(train) [193][25/63] lr: 5.9826e-03 eta: 1 day, 2:46:03 time: 1.3352 data_time: 0.0435 memory: 16201 loss_prob: 0.7652 loss_thr: 0.4519 loss_db: 0.1275 loss: 1.3446 2022/08/30 04:31:05 - mmengine - INFO - Epoch(train) [193][30/63] lr: 5.9826e-03 eta: 1 day, 2:45:39 time: 1.3465 data_time: 0.0321 memory: 16201 loss_prob: 0.8164 loss_thr: 0.4856 loss_db: 0.1390 loss: 1.4409 2022/08/30 04:31:11 - mmengine - INFO - Epoch(train) [193][35/63] lr: 5.9826e-03 eta: 1 day, 2:45:39 time: 1.3387 data_time: 0.0289 memory: 16201 loss_prob: 0.8586 loss_thr: 0.4972 loss_db: 0.1452 loss: 1.5010 2022/08/30 04:31:18 - mmengine - INFO - Epoch(train) [193][40/63] lr: 5.9826e-03 eta: 1 day, 2:45:13 time: 1.3000 data_time: 0.0349 memory: 16201 loss_prob: 0.8650 loss_thr: 0.4895 loss_db: 0.1420 loss: 1.4964 2022/08/30 04:31:24 - mmengine - INFO - Epoch(train) [193][45/63] lr: 5.9826e-03 eta: 1 day, 2:45:13 time: 1.3050 data_time: 0.0311 memory: 16201 loss_prob: 0.8026 loss_thr: 0.4828 loss_db: 0.1341 loss: 1.4195 2022/08/30 04:31:31 - mmengine - INFO - Epoch(train) [193][50/63] lr: 5.9826e-03 eta: 1 day, 2:44:49 time: 1.3509 data_time: 0.0372 memory: 16201 loss_prob: 0.7821 loss_thr: 0.4789 loss_db: 0.1342 loss: 1.3951 2022/08/30 04:31:38 - mmengine - INFO - Epoch(train) [193][55/63] lr: 5.9826e-03 eta: 1 day, 2:44:49 time: 1.3708 data_time: 0.0302 memory: 16201 loss_prob: 0.8349 loss_thr: 0.4810 loss_db: 0.1383 loss: 1.4542 2022/08/30 04:31:44 - mmengine - INFO - Epoch(train) [193][60/63] lr: 5.9826e-03 eta: 1 day, 2:44:21 time: 1.2803 data_time: 0.0275 memory: 16201 loss_prob: 0.8009 loss_thr: 0.4793 loss_db: 0.1333 loss: 1.4136 2022/08/30 04:31:48 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:31:57 - mmengine - INFO - Epoch(train) [194][5/63] lr: 5.9772e-03 eta: 1 day, 2:44:21 time: 1.5763 data_time: 0.2332 memory: 16201 loss_prob: 0.8849 loss_thr: 0.5155 loss_db: 0.1476 loss: 1.5479 2022/08/30 04:32:04 - mmengine - INFO - Epoch(train) [194][10/63] lr: 5.9772e-03 eta: 1 day, 2:43:42 time: 1.6066 data_time: 0.2500 memory: 16201 loss_prob: 0.8895 loss_thr: 0.5250 loss_db: 0.1491 loss: 1.5637 2022/08/30 04:32:11 - mmengine - INFO - Epoch(train) [194][15/63] lr: 5.9772e-03 eta: 1 day, 2:43:42 time: 1.3090 data_time: 0.0318 memory: 16201 loss_prob: 0.8718 loss_thr: 0.5262 loss_db: 0.1472 loss: 1.5452 2022/08/30 04:32:17 - mmengine - INFO - Epoch(train) [194][20/63] lr: 5.9772e-03 eta: 1 day, 2:43:13 time: 1.2545 data_time: 0.0324 memory: 16201 loss_prob: 0.8342 loss_thr: 0.5268 loss_db: 0.1408 loss: 1.5019 2022/08/30 04:32:23 - mmengine - INFO - Epoch(train) [194][25/63] lr: 5.9772e-03 eta: 1 day, 2:43:13 time: 1.2348 data_time: 0.0375 memory: 16201 loss_prob: 0.8222 loss_thr: 0.5138 loss_db: 0.1380 loss: 1.4741 2022/08/30 04:32:31 - mmengine - INFO - Epoch(train) [194][30/63] lr: 5.9772e-03 eta: 1 day, 2:42:52 time: 1.4026 data_time: 0.0299 memory: 16201 loss_prob: 0.8563 loss_thr: 0.5170 loss_db: 0.1450 loss: 1.5183 2022/08/30 04:32:37 - mmengine - INFO - Epoch(train) [194][35/63] lr: 5.9772e-03 eta: 1 day, 2:42:52 time: 1.3592 data_time: 0.0385 memory: 16201 loss_prob: 0.8624 loss_thr: 0.5261 loss_db: 0.1432 loss: 1.5318 2022/08/30 04:32:43 - mmengine - INFO - Epoch(train) [194][40/63] lr: 5.9772e-03 eta: 1 day, 2:42:23 time: 1.2433 data_time: 0.0341 memory: 16201 loss_prob: 0.7962 loss_thr: 0.4929 loss_db: 0.1339 loss: 1.4231 2022/08/30 04:32:50 - mmengine - INFO - Epoch(train) [194][45/63] lr: 5.9772e-03 eta: 1 day, 2:42:23 time: 1.3464 data_time: 0.0302 memory: 16201 loss_prob: 0.8184 loss_thr: 0.4867 loss_db: 0.1402 loss: 1.4453 2022/08/30 04:32:56 - mmengine - INFO - Epoch(train) [194][50/63] lr: 5.9772e-03 eta: 1 day, 2:41:59 time: 1.3421 data_time: 0.0373 memory: 16201 loss_prob: 0.8400 loss_thr: 0.4942 loss_db: 0.1423 loss: 1.4765 2022/08/30 04:33:03 - mmengine - INFO - Epoch(train) [194][55/63] lr: 5.9772e-03 eta: 1 day, 2:41:59 time: 1.2667 data_time: 0.0308 memory: 16201 loss_prob: 0.7629 loss_thr: 0.4727 loss_db: 0.1268 loss: 1.3623 2022/08/30 04:33:10 - mmengine - INFO - Epoch(train) [194][60/63] lr: 5.9772e-03 eta: 1 day, 2:41:33 time: 1.3180 data_time: 0.0447 memory: 16201 loss_prob: 0.7254 loss_thr: 0.4625 loss_db: 0.1220 loss: 1.3099 2022/08/30 04:33:13 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:33:22 - mmengine - INFO - Epoch(train) [195][5/63] lr: 5.9719e-03 eta: 1 day, 2:41:33 time: 1.5239 data_time: 0.2392 memory: 16201 loss_prob: 0.8074 loss_thr: 0.4903 loss_db: 0.1353 loss: 1.4331 2022/08/30 04:33:28 - mmengine - INFO - Epoch(train) [195][10/63] lr: 5.9719e-03 eta: 1 day, 2:40:51 time: 1.5361 data_time: 0.2414 memory: 16201 loss_prob: 0.7744 loss_thr: 0.4713 loss_db: 0.1285 loss: 1.3741 2022/08/30 04:33:35 - mmengine - INFO - Epoch(train) [195][15/63] lr: 5.9719e-03 eta: 1 day, 2:40:51 time: 1.2938 data_time: 0.0315 memory: 16201 loss_prob: 0.7636 loss_thr: 0.4674 loss_db: 0.1291 loss: 1.3601 2022/08/30 04:33:41 - mmengine - INFO - Epoch(train) [195][20/63] lr: 5.9719e-03 eta: 1 day, 2:40:21 time: 1.2322 data_time: 0.0241 memory: 16201 loss_prob: 0.8219 loss_thr: 0.4765 loss_db: 0.1395 loss: 1.4380 2022/08/30 04:33:48 - mmengine - INFO - Epoch(train) [195][25/63] lr: 5.9719e-03 eta: 1 day, 2:40:21 time: 1.2820 data_time: 0.0344 memory: 16201 loss_prob: 0.8487 loss_thr: 0.4837 loss_db: 0.1443 loss: 1.4767 2022/08/30 04:33:54 - mmengine - INFO - Epoch(train) [195][30/63] lr: 5.9719e-03 eta: 1 day, 2:39:55 time: 1.3052 data_time: 0.0340 memory: 16201 loss_prob: 0.8708 loss_thr: 0.4879 loss_db: 0.1469 loss: 1.5056 2022/08/30 04:34:00 - mmengine - INFO - Epoch(train) [195][35/63] lr: 5.9719e-03 eta: 1 day, 2:39:55 time: 1.2854 data_time: 0.0344 memory: 16201 loss_prob: 0.8696 loss_thr: 0.4910 loss_db: 0.1448 loss: 1.5054 2022/08/30 04:34:07 - mmengine - INFO - Epoch(train) [195][40/63] lr: 5.9719e-03 eta: 1 day, 2:39:31 time: 1.3421 data_time: 0.0412 memory: 16201 loss_prob: 0.8127 loss_thr: 0.4909 loss_db: 0.1340 loss: 1.4377 2022/08/30 04:34:14 - mmengine - INFO - Epoch(train) [195][45/63] lr: 5.9719e-03 eta: 1 day, 2:39:31 time: 1.3838 data_time: 0.0407 memory: 16201 loss_prob: 0.7821 loss_thr: 0.4656 loss_db: 0.1294 loss: 1.3771 2022/08/30 04:34:21 - mmengine - INFO - Epoch(train) [195][50/63] lr: 5.9719e-03 eta: 1 day, 2:39:07 time: 1.3351 data_time: 0.0420 memory: 16201 loss_prob: 0.8009 loss_thr: 0.4717 loss_db: 0.1321 loss: 1.4048 2022/08/30 04:34:28 - mmengine - INFO - Epoch(train) [195][55/63] lr: 5.9719e-03 eta: 1 day, 2:39:07 time: 1.3209 data_time: 0.0296 memory: 16201 loss_prob: 0.8485 loss_thr: 0.5061 loss_db: 0.1420 loss: 1.4967 2022/08/30 04:34:35 - mmengine - INFO - Epoch(train) [195][60/63] lr: 5.9719e-03 eta: 1 day, 2:38:45 time: 1.3940 data_time: 0.0408 memory: 16201 loss_prob: 0.8099 loss_thr: 0.4843 loss_db: 0.1368 loss: 1.4311 2022/08/30 04:34:38 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:34:47 - mmengine - INFO - Epoch(train) [196][5/63] lr: 5.9665e-03 eta: 1 day, 2:38:45 time: 1.4534 data_time: 0.2165 memory: 16201 loss_prob: 0.8132 loss_thr: 0.4841 loss_db: 0.1363 loss: 1.4336 2022/08/30 04:34:53 - mmengine - INFO - Epoch(train) [196][10/63] lr: 5.9665e-03 eta: 1 day, 2:38:04 time: 1.5499 data_time: 0.2356 memory: 16201 loss_prob: 0.8349 loss_thr: 0.4635 loss_db: 0.1343 loss: 1.4327 2022/08/30 04:35:00 - mmengine - INFO - Epoch(train) [196][15/63] lr: 5.9665e-03 eta: 1 day, 2:38:04 time: 1.3524 data_time: 0.0321 memory: 16201 loss_prob: 0.8460 loss_thr: 0.4683 loss_db: 0.1365 loss: 1.4508 2022/08/30 04:35:07 - mmengine - INFO - Epoch(train) [196][20/63] lr: 5.9665e-03 eta: 1 day, 2:37:41 time: 1.3585 data_time: 0.0308 memory: 16201 loss_prob: 0.9238 loss_thr: 0.5020 loss_db: 0.1533 loss: 1.5792 2022/08/30 04:35:14 - mmengine - INFO - Epoch(train) [196][25/63] lr: 5.9665e-03 eta: 1 day, 2:37:41 time: 1.4032 data_time: 0.0364 memory: 16201 loss_prob: 0.9244 loss_thr: 0.4973 loss_db: 0.1551 loss: 1.5768 2022/08/30 04:35:21 - mmengine - INFO - Epoch(train) [196][30/63] lr: 5.9665e-03 eta: 1 day, 2:37:19 time: 1.3793 data_time: 0.0298 memory: 16201 loss_prob: 0.8168 loss_thr: 0.4607 loss_db: 0.1398 loss: 1.4173 2022/08/30 04:35:28 - mmengine - INFO - Epoch(train) [196][35/63] lr: 5.9665e-03 eta: 1 day, 2:37:19 time: 1.3927 data_time: 0.0359 memory: 16201 loss_prob: 0.7622 loss_thr: 0.4386 loss_db: 0.1265 loss: 1.3274 2022/08/30 04:35:35 - mmengine - INFO - Epoch(train) [196][40/63] lr: 5.9665e-03 eta: 1 day, 2:36:58 time: 1.4077 data_time: 0.0294 memory: 16201 loss_prob: 0.7834 loss_thr: 0.4680 loss_db: 0.1318 loss: 1.3832 2022/08/30 04:35:42 - mmengine - INFO - Epoch(train) [196][45/63] lr: 5.9665e-03 eta: 1 day, 2:36:58 time: 1.3845 data_time: 0.0317 memory: 16201 loss_prob: 0.8044 loss_thr: 0.4864 loss_db: 0.1360 loss: 1.4268 2022/08/30 04:35:49 - mmengine - INFO - Epoch(train) [196][50/63] lr: 5.9665e-03 eta: 1 day, 2:36:37 time: 1.4033 data_time: 0.0464 memory: 16201 loss_prob: 0.7998 loss_thr: 0.4828 loss_db: 0.1315 loss: 1.4141 2022/08/30 04:35:56 - mmengine - INFO - Epoch(train) [196][55/63] lr: 5.9665e-03 eta: 1 day, 2:36:37 time: 1.3637 data_time: 0.0300 memory: 16201 loss_prob: 0.8310 loss_thr: 0.5000 loss_db: 0.1360 loss: 1.4669 2022/08/30 04:36:02 - mmengine - INFO - Epoch(train) [196][60/63] lr: 5.9665e-03 eta: 1 day, 2:36:14 time: 1.3564 data_time: 0.0292 memory: 16201 loss_prob: 0.8238 loss_thr: 0.5093 loss_db: 0.1339 loss: 1.4670 2022/08/30 04:36:06 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:36:14 - mmengine - INFO - Epoch(train) [197][5/63] lr: 5.9612e-03 eta: 1 day, 2:36:14 time: 1.3910 data_time: 0.2228 memory: 16201 loss_prob: 0.8693 loss_thr: 0.5188 loss_db: 0.1460 loss: 1.5341 2022/08/30 04:36:21 - mmengine - INFO - Epoch(train) [197][10/63] lr: 5.9612e-03 eta: 1 day, 2:35:31 time: 1.5071 data_time: 0.2349 memory: 16201 loss_prob: 0.8665 loss_thr: 0.5128 loss_db: 0.1456 loss: 1.5249 2022/08/30 04:36:28 - mmengine - INFO - Epoch(train) [197][15/63] lr: 5.9612e-03 eta: 1 day, 2:35:31 time: 1.3809 data_time: 0.0477 memory: 16201 loss_prob: 0.9186 loss_thr: 0.5334 loss_db: 0.1505 loss: 1.6025 2022/08/30 04:36:35 - mmengine - INFO - Epoch(train) [197][20/63] lr: 5.9612e-03 eta: 1 day, 2:35:10 time: 1.4047 data_time: 0.0454 memory: 16201 loss_prob: 0.8454 loss_thr: 0.5089 loss_db: 0.1372 loss: 1.4915 2022/08/30 04:36:41 - mmengine - INFO - Epoch(train) [197][25/63] lr: 5.9612e-03 eta: 1 day, 2:35:10 time: 1.3322 data_time: 0.0309 memory: 16201 loss_prob: 0.8079 loss_thr: 0.5060 loss_db: 0.1345 loss: 1.4485 2022/08/30 04:36:48 - mmengine - INFO - Epoch(train) [197][30/63] lr: 5.9612e-03 eta: 1 day, 2:34:46 time: 1.3349 data_time: 0.0329 memory: 16201 loss_prob: 0.8026 loss_thr: 0.4950 loss_db: 0.1331 loss: 1.4307 2022/08/30 04:36:54 - mmengine - INFO - Epoch(train) [197][35/63] lr: 5.9612e-03 eta: 1 day, 2:34:46 time: 1.3367 data_time: 0.0320 memory: 16201 loss_prob: 0.8604 loss_thr: 0.4912 loss_db: 0.1377 loss: 1.4892 2022/08/30 04:37:01 - mmengine - INFO - Epoch(train) [197][40/63] lr: 5.9612e-03 eta: 1 day, 2:34:19 time: 1.2804 data_time: 0.0305 memory: 16201 loss_prob: 0.9862 loss_thr: 0.5127 loss_db: 0.1576 loss: 1.6565 2022/08/30 04:37:07 - mmengine - INFO - Epoch(train) [197][45/63] lr: 5.9612e-03 eta: 1 day, 2:34:19 time: 1.2725 data_time: 0.0328 memory: 16201 loss_prob: 0.9954 loss_thr: 0.5180 loss_db: 0.1607 loss: 1.6741 2022/08/30 04:37:14 - mmengine - INFO - Epoch(train) [197][50/63] lr: 5.9612e-03 eta: 1 day, 2:33:54 time: 1.3328 data_time: 0.0360 memory: 16201 loss_prob: 0.8439 loss_thr: 0.4766 loss_db: 0.1379 loss: 1.4584 2022/08/30 04:37:21 - mmengine - INFO - Epoch(train) [197][55/63] lr: 5.9612e-03 eta: 1 day, 2:33:54 time: 1.3976 data_time: 0.0355 memory: 16201 loss_prob: 0.7756 loss_thr: 0.4542 loss_db: 0.1323 loss: 1.3621 2022/08/30 04:37:28 - mmengine - INFO - Epoch(train) [197][60/63] lr: 5.9612e-03 eta: 1 day, 2:33:33 time: 1.3914 data_time: 0.0398 memory: 16201 loss_prob: 0.9244 loss_thr: 0.4996 loss_db: 0.1525 loss: 1.5765 2022/08/30 04:37:32 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:37:41 - mmengine - INFO - Epoch(train) [198][5/63] lr: 5.9558e-03 eta: 1 day, 2:33:33 time: 1.5851 data_time: 0.2295 memory: 16201 loss_prob: 0.7698 loss_thr: 0.4712 loss_db: 0.1335 loss: 1.3745 2022/08/30 04:37:48 - mmengine - INFO - Epoch(train) [198][10/63] lr: 5.9558e-03 eta: 1 day, 2:32:57 time: 1.6423 data_time: 0.2457 memory: 16201 loss_prob: 0.8455 loss_thr: 0.4974 loss_db: 0.1408 loss: 1.4836 2022/08/30 04:37:55 - mmengine - INFO - Epoch(train) [198][15/63] lr: 5.9558e-03 eta: 1 day, 2:32:57 time: 1.3676 data_time: 0.0341 memory: 16201 loss_prob: 0.8938 loss_thr: 0.5171 loss_db: 0.1476 loss: 1.5584 2022/08/30 04:38:01 - mmengine - INFO - Epoch(train) [198][20/63] lr: 5.9558e-03 eta: 1 day, 2:32:30 time: 1.2833 data_time: 0.0331 memory: 16201 loss_prob: 0.8171 loss_thr: 0.5150 loss_db: 0.1379 loss: 1.4700 2022/08/30 04:38:08 - mmengine - INFO - Epoch(train) [198][25/63] lr: 5.9558e-03 eta: 1 day, 2:32:30 time: 1.2677 data_time: 0.0380 memory: 16201 loss_prob: 0.8526 loss_thr: 0.5160 loss_db: 0.1402 loss: 1.5088 2022/08/30 04:38:14 - mmengine - INFO - Epoch(train) [198][30/63] lr: 5.9558e-03 eta: 1 day, 2:32:04 time: 1.3037 data_time: 0.0317 memory: 16201 loss_prob: 0.8802 loss_thr: 0.5083 loss_db: 0.1435 loss: 1.5320 2022/08/30 04:38:21 - mmengine - INFO - Epoch(train) [198][35/63] lr: 5.9558e-03 eta: 1 day, 2:32:04 time: 1.3199 data_time: 0.0399 memory: 16201 loss_prob: 0.8441 loss_thr: 0.4899 loss_db: 0.1411 loss: 1.4751 2022/08/30 04:38:27 - mmengine - INFO - Epoch(train) [198][40/63] lr: 5.9558e-03 eta: 1 day, 2:31:40 time: 1.3259 data_time: 0.0297 memory: 16201 loss_prob: 0.8495 loss_thr: 0.5062 loss_db: 0.1433 loss: 1.4990 2022/08/30 04:38:34 - mmengine - INFO - Epoch(train) [198][45/63] lr: 5.9558e-03 eta: 1 day, 2:31:40 time: 1.2967 data_time: 0.0326 memory: 16201 loss_prob: 0.8918 loss_thr: 0.5295 loss_db: 0.1497 loss: 1.5710 2022/08/30 04:38:40 - mmengine - INFO - Epoch(train) [198][50/63] lr: 5.9558e-03 eta: 1 day, 2:31:10 time: 1.2298 data_time: 0.0493 memory: 16201 loss_prob: 0.7770 loss_thr: 0.4732 loss_db: 0.1278 loss: 1.3780 2022/08/30 04:38:46 - mmengine - INFO - Epoch(train) [198][55/63] lr: 5.9558e-03 eta: 1 day, 2:31:10 time: 1.2501 data_time: 0.0334 memory: 16201 loss_prob: 0.8270 loss_thr: 0.4836 loss_db: 0.1336 loss: 1.4442 2022/08/30 04:38:53 - mmengine - INFO - Epoch(train) [198][60/63] lr: 5.9558e-03 eta: 1 day, 2:30:49 time: 1.3869 data_time: 0.0479 memory: 16201 loss_prob: 0.8920 loss_thr: 0.5001 loss_db: 0.1457 loss: 1.5378 2022/08/30 04:38:57 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:39:06 - mmengine - INFO - Epoch(train) [199][5/63] lr: 5.9505e-03 eta: 1 day, 2:30:49 time: 1.5175 data_time: 0.2272 memory: 16201 loss_prob: 0.8280 loss_thr: 0.4756 loss_db: 0.1404 loss: 1.4440 2022/08/30 04:39:13 - mmengine - INFO - Epoch(train) [199][10/63] lr: 5.9505e-03 eta: 1 day, 2:30:09 time: 1.5642 data_time: 0.2479 memory: 16201 loss_prob: 0.7317 loss_thr: 0.4474 loss_db: 0.1265 loss: 1.3055 2022/08/30 04:39:20 - mmengine - INFO - Epoch(train) [199][15/63] lr: 5.9505e-03 eta: 1 day, 2:30:09 time: 1.4117 data_time: 0.0412 memory: 16201 loss_prob: 0.7028 loss_thr: 0.4621 loss_db: 0.1196 loss: 1.2845 2022/08/30 04:39:26 - mmengine - INFO - Epoch(train) [199][20/63] lr: 5.9505e-03 eta: 1 day, 2:29:47 time: 1.3682 data_time: 0.0342 memory: 16201 loss_prob: 0.8749 loss_thr: 0.4984 loss_db: 0.1463 loss: 1.5197 2022/08/30 04:39:33 - mmengine - INFO - Epoch(train) [199][25/63] lr: 5.9505e-03 eta: 1 day, 2:29:47 time: 1.2297 data_time: 0.0441 memory: 16201 loss_prob: 0.8985 loss_thr: 0.5041 loss_db: 0.1480 loss: 1.5506 2022/08/30 04:39:39 - mmengine - INFO - Epoch(train) [199][30/63] lr: 5.9505e-03 eta: 1 day, 2:29:20 time: 1.2938 data_time: 0.0374 memory: 16201 loss_prob: 0.8141 loss_thr: 0.4958 loss_db: 0.1369 loss: 1.4467 2022/08/30 04:39:46 - mmengine - INFO - Epoch(train) [199][35/63] lr: 5.9505e-03 eta: 1 day, 2:29:20 time: 1.3050 data_time: 0.0305 memory: 16201 loss_prob: 0.7957 loss_thr: 0.4964 loss_db: 0.1342 loss: 1.4263 2022/08/30 04:39:52 - mmengine - INFO - Epoch(train) [199][40/63] lr: 5.9505e-03 eta: 1 day, 2:28:53 time: 1.2700 data_time: 0.0315 memory: 16201 loss_prob: 0.7807 loss_thr: 0.5156 loss_db: 0.1319 loss: 1.4282 2022/08/30 04:39:58 - mmengine - INFO - Epoch(train) [199][45/63] lr: 5.9505e-03 eta: 1 day, 2:28:53 time: 1.2749 data_time: 0.0339 memory: 16201 loss_prob: 0.8257 loss_thr: 0.5141 loss_db: 0.1410 loss: 1.4808 2022/08/30 04:40:05 - mmengine - INFO - Epoch(train) [199][50/63] lr: 5.9505e-03 eta: 1 day, 2:28:26 time: 1.2701 data_time: 0.0409 memory: 16201 loss_prob: 0.8119 loss_thr: 0.4848 loss_db: 0.1393 loss: 1.4360 2022/08/30 04:40:11 - mmengine - INFO - Epoch(train) [199][55/63] lr: 5.9505e-03 eta: 1 day, 2:28:26 time: 1.2716 data_time: 0.0282 memory: 16201 loss_prob: 0.8385 loss_thr: 0.4721 loss_db: 0.1330 loss: 1.4436 2022/08/30 04:40:18 - mmengine - INFO - Epoch(train) [199][60/63] lr: 5.9505e-03 eta: 1 day, 2:28:01 time: 1.3137 data_time: 0.0349 memory: 16201 loss_prob: 0.9038 loss_thr: 0.4896 loss_db: 0.1417 loss: 1.5352 2022/08/30 04:40:21 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:40:30 - mmengine - INFO - Epoch(train) [200][5/63] lr: 5.9451e-03 eta: 1 day, 2:28:01 time: 1.4818 data_time: 0.2296 memory: 16201 loss_prob: 0.8390 loss_thr: 0.5022 loss_db: 0.1415 loss: 1.4827 2022/08/30 04:40:36 - mmengine - INFO - Epoch(train) [200][10/63] lr: 5.9451e-03 eta: 1 day, 2:27:17 time: 1.4741 data_time: 0.2504 memory: 16201 loss_prob: 0.7861 loss_thr: 0.4736 loss_db: 0.1352 loss: 1.3949 2022/08/30 04:40:43 - mmengine - INFO - Epoch(train) [200][15/63] lr: 5.9451e-03 eta: 1 day, 2:27:17 time: 1.3615 data_time: 0.0342 memory: 16201 loss_prob: 0.7736 loss_thr: 0.4790 loss_db: 0.1298 loss: 1.3824 2022/08/30 04:40:49 - mmengine - INFO - Epoch(train) [200][20/63] lr: 5.9451e-03 eta: 1 day, 2:26:53 time: 1.3511 data_time: 0.0303 memory: 16201 loss_prob: 0.8534 loss_thr: 0.4873 loss_db: 0.1416 loss: 1.4822 2022/08/30 04:40:56 - mmengine - INFO - Epoch(train) [200][25/63] lr: 5.9451e-03 eta: 1 day, 2:26:53 time: 1.2718 data_time: 0.0429 memory: 16201 loss_prob: 0.8486 loss_thr: 0.4912 loss_db: 0.1422 loss: 1.4820 2022/08/30 04:41:03 - mmengine - INFO - Epoch(train) [200][30/63] lr: 5.9451e-03 eta: 1 day, 2:26:32 time: 1.3749 data_time: 0.0334 memory: 16201 loss_prob: 0.7708 loss_thr: 0.4733 loss_db: 0.1309 loss: 1.3750 2022/08/30 04:41:09 - mmengine - INFO - Epoch(train) [200][35/63] lr: 5.9451e-03 eta: 1 day, 2:26:32 time: 1.3329 data_time: 0.0284 memory: 16201 loss_prob: 0.8045 loss_thr: 0.4953 loss_db: 0.1339 loss: 1.4337 2022/08/30 04:41:16 - mmengine - INFO - Epoch(train) [200][40/63] lr: 5.9451e-03 eta: 1 day, 2:26:03 time: 1.2399 data_time: 0.0298 memory: 16201 loss_prob: 0.8312 loss_thr: 0.5198 loss_db: 0.1375 loss: 1.4885 2022/08/30 04:41:23 - mmengine - INFO - Epoch(train) [200][45/63] lr: 5.9451e-03 eta: 1 day, 2:26:03 time: 1.3389 data_time: 0.0264 memory: 16201 loss_prob: 0.7734 loss_thr: 0.4808 loss_db: 0.1299 loss: 1.3840 2022/08/30 04:41:29 - mmengine - INFO - Epoch(train) [200][50/63] lr: 5.9451e-03 eta: 1 day, 2:25:38 time: 1.3142 data_time: 0.0346 memory: 16201 loss_prob: 0.8119 loss_thr: 0.4806 loss_db: 0.1331 loss: 1.4256 2022/08/30 04:41:36 - mmengine - INFO - Epoch(train) [200][55/63] lr: 5.9451e-03 eta: 1 day, 2:25:38 time: 1.2775 data_time: 0.0322 memory: 16201 loss_prob: 0.8396 loss_thr: 0.4889 loss_db: 0.1393 loss: 1.4677 2022/08/30 04:41:42 - mmengine - INFO - Epoch(train) [200][60/63] lr: 5.9451e-03 eta: 1 day, 2:25:16 time: 1.3665 data_time: 0.0309 memory: 16201 loss_prob: 0.7981 loss_thr: 0.4723 loss_db: 0.1347 loss: 1.4051 2022/08/30 04:41:46 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:41:46 - mmengine - INFO - Saving checkpoint at 200 epochs 2022/08/30 04:41:54 - mmengine - INFO - Epoch(val) [200][5/32] eta: 1 day, 2:25:16 time: 0.7270 data_time: 0.1363 memory: 16201 2022/08/30 04:41:58 - mmengine - INFO - Epoch(val) [200][10/32] eta: 0:00:17 time: 0.7938 data_time: 0.1762 memory: 15734 2022/08/30 04:42:01 - mmengine - INFO - Epoch(val) [200][15/32] eta: 0:00:17 time: 0.6622 data_time: 0.0686 memory: 15734 2022/08/30 04:42:04 - mmengine - INFO - Epoch(val) [200][20/32] eta: 0:00:07 time: 0.6539 data_time: 0.0714 memory: 15734 2022/08/30 04:42:08 - mmengine - INFO - Epoch(val) [200][25/32] eta: 0:00:07 time: 0.7353 data_time: 0.0861 memory: 15734 2022/08/30 04:42:11 - mmengine - INFO - Epoch(val) [200][30/32] eta: 0:00:01 time: 0.6695 data_time: 0.0339 memory: 15734 2022/08/30 04:42:12 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 04:42:12 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8382, precision: 0.7758, hmean: 0.8058 2022/08/30 04:42:12 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8377, precision: 0.8294, hmean: 0.8335 2022/08/30 04:42:12 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8310, precision: 0.8587, hmean: 0.8446 2022/08/30 04:42:12 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8146, precision: 0.8836, hmean: 0.8477 2022/08/30 04:42:12 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7814, precision: 0.9118, hmean: 0.8416 2022/08/30 04:42:12 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.5816, precision: 0.9587, hmean: 0.7240 2022/08/30 04:42:12 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0226, precision: 1.0000, hmean: 0.0443 2022/08/30 04:42:12 - mmengine - INFO - Epoch(val) [200][32/32] icdar/precision: 0.8836 icdar/recall: 0.8146 icdar/hmean: 0.8477 2022/08/30 04:42:21 - mmengine - INFO - Epoch(train) [201][5/63] lr: 5.9398e-03 eta: 0:00:01 time: 1.5066 data_time: 0.2110 memory: 16201 loss_prob: 0.8414 loss_thr: 0.5015 loss_db: 0.1410 loss: 1.4839 2022/08/30 04:42:29 - mmengine - INFO - Epoch(train) [201][10/63] lr: 5.9398e-03 eta: 1 day, 2:24:41 time: 1.6495 data_time: 0.2243 memory: 16201 loss_prob: 0.7305 loss_thr: 0.4693 loss_db: 0.1219 loss: 1.3217 2022/08/30 04:42:35 - mmengine - INFO - Epoch(train) [201][15/63] lr: 5.9398e-03 eta: 1 day, 2:24:41 time: 1.4009 data_time: 0.0415 memory: 16201 loss_prob: 0.7244 loss_thr: 0.4645 loss_db: 0.1227 loss: 1.3117 2022/08/30 04:42:41 - mmengine - INFO - Epoch(train) [201][20/63] lr: 5.9398e-03 eta: 1 day, 2:24:15 time: 1.2937 data_time: 0.0343 memory: 16201 loss_prob: 0.7810 loss_thr: 0.4940 loss_db: 0.1323 loss: 1.4073 2022/08/30 04:42:48 - mmengine - INFO - Epoch(train) [201][25/63] lr: 5.9398e-03 eta: 1 day, 2:24:15 time: 1.3055 data_time: 0.0350 memory: 16201 loss_prob: 0.8700 loss_thr: 0.4987 loss_db: 0.1441 loss: 1.5128 2022/08/30 04:42:55 - mmengine - INFO - Epoch(train) [201][30/63] lr: 5.9398e-03 eta: 1 day, 2:23:52 time: 1.3567 data_time: 0.0373 memory: 16201 loss_prob: 0.8520 loss_thr: 0.4671 loss_db: 0.1413 loss: 1.4604 2022/08/30 04:43:02 - mmengine - INFO - Epoch(train) [201][35/63] lr: 5.9398e-03 eta: 1 day, 2:23:52 time: 1.3490 data_time: 0.0330 memory: 16201 loss_prob: 0.7623 loss_thr: 0.4593 loss_db: 0.1280 loss: 1.3497 2022/08/30 04:43:09 - mmengine - INFO - Epoch(train) [201][40/63] lr: 5.9398e-03 eta: 1 day, 2:23:31 time: 1.3845 data_time: 0.0330 memory: 16201 loss_prob: 0.7603 loss_thr: 0.4780 loss_db: 0.1290 loss: 1.3673 2022/08/30 04:43:16 - mmengine - INFO - Epoch(train) [201][45/63] lr: 5.9398e-03 eta: 1 day, 2:23:31 time: 1.3820 data_time: 0.0361 memory: 16201 loss_prob: 0.8059 loss_thr: 0.4951 loss_db: 0.1368 loss: 1.4379 2022/08/30 04:43:22 - mmengine - INFO - Epoch(train) [201][50/63] lr: 5.9398e-03 eta: 1 day, 2:23:04 time: 1.2870 data_time: 0.0447 memory: 16201 loss_prob: 0.7287 loss_thr: 0.4565 loss_db: 0.1229 loss: 1.3080 2022/08/30 04:43:28 - mmengine - INFO - Epoch(train) [201][55/63] lr: 5.9398e-03 eta: 1 day, 2:23:04 time: 1.2541 data_time: 0.0400 memory: 16201 loss_prob: 0.6789 loss_thr: 0.4475 loss_db: 0.1157 loss: 1.2421 2022/08/30 04:43:34 - mmengine - INFO - Epoch(train) [201][60/63] lr: 5.9398e-03 eta: 1 day, 2:22:38 time: 1.2697 data_time: 0.0318 memory: 16201 loss_prob: 0.7833 loss_thr: 0.5074 loss_db: 0.1313 loss: 1.4221 2022/08/30 04:43:38 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:43:47 - mmengine - INFO - Epoch(train) [202][5/63] lr: 5.9344e-03 eta: 1 day, 2:22:38 time: 1.4585 data_time: 0.2412 memory: 16201 loss_prob: 0.7409 loss_thr: 0.4845 loss_db: 0.1268 loss: 1.3522 2022/08/30 04:43:53 - mmengine - INFO - Epoch(train) [202][10/63] lr: 5.9344e-03 eta: 1 day, 2:21:58 time: 1.5588 data_time: 0.2485 memory: 16201 loss_prob: 0.7544 loss_thr: 0.4599 loss_db: 0.1267 loss: 1.3411 2022/08/30 04:44:00 - mmengine - INFO - Epoch(train) [202][15/63] lr: 5.9344e-03 eta: 1 day, 2:21:58 time: 1.2938 data_time: 0.0394 memory: 16201 loss_prob: 0.7573 loss_thr: 0.4575 loss_db: 0.1257 loss: 1.3405 2022/08/30 04:44:06 - mmengine - INFO - Epoch(train) [202][20/63] lr: 5.9344e-03 eta: 1 day, 2:21:32 time: 1.2944 data_time: 0.0430 memory: 16201 loss_prob: 0.7425 loss_thr: 0.4538 loss_db: 0.1268 loss: 1.3231 2022/08/30 04:44:13 - mmengine - INFO - Epoch(train) [202][25/63] lr: 5.9344e-03 eta: 1 day, 2:21:32 time: 1.3139 data_time: 0.0335 memory: 16201 loss_prob: 0.7819 loss_thr: 0.4748 loss_db: 0.1321 loss: 1.3888 2022/08/30 04:44:20 - mmengine - INFO - Epoch(train) [202][30/63] lr: 5.9344e-03 eta: 1 day, 2:21:09 time: 1.3367 data_time: 0.0321 memory: 16201 loss_prob: 0.8008 loss_thr: 0.5023 loss_db: 0.1331 loss: 1.4362 2022/08/30 04:44:27 - mmengine - INFO - Epoch(train) [202][35/63] lr: 5.9344e-03 eta: 1 day, 2:21:09 time: 1.3819 data_time: 0.0449 memory: 16201 loss_prob: 0.7689 loss_thr: 0.4877 loss_db: 0.1281 loss: 1.3847 2022/08/30 04:44:33 - mmengine - INFO - Epoch(train) [202][40/63] lr: 5.9344e-03 eta: 1 day, 2:20:47 time: 1.3730 data_time: 0.0304 memory: 16201 loss_prob: 0.7824 loss_thr: 0.4825 loss_db: 0.1312 loss: 1.3961 2022/08/30 04:44:40 - mmengine - INFO - Epoch(train) [202][45/63] lr: 5.9344e-03 eta: 1 day, 2:20:47 time: 1.3599 data_time: 0.0273 memory: 16201 loss_prob: 0.7693 loss_thr: 0.4662 loss_db: 0.1287 loss: 1.3643 2022/08/30 04:44:46 - mmengine - INFO - Epoch(train) [202][50/63] lr: 5.9344e-03 eta: 1 day, 2:20:21 time: 1.2996 data_time: 0.0387 memory: 16201 loss_prob: 0.7518 loss_thr: 0.4414 loss_db: 0.1257 loss: 1.3189 2022/08/30 04:44:52 - mmengine - INFO - Epoch(train) [202][55/63] lr: 5.9344e-03 eta: 1 day, 2:20:21 time: 1.2288 data_time: 0.0288 memory: 16201 loss_prob: 0.8619 loss_thr: 0.4903 loss_db: 0.1413 loss: 1.4936 2022/08/30 04:44:59 - mmengine - INFO - Epoch(train) [202][60/63] lr: 5.9344e-03 eta: 1 day, 2:19:55 time: 1.2843 data_time: 0.0276 memory: 16201 loss_prob: 0.8369 loss_thr: 0.5047 loss_db: 0.1385 loss: 1.4801 2022/08/30 04:45:02 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:45:11 - mmengine - INFO - Epoch(train) [203][5/63] lr: 5.9291e-03 eta: 1 day, 2:19:55 time: 1.5212 data_time: 0.2181 memory: 16201 loss_prob: 0.7651 loss_thr: 0.4631 loss_db: 0.1306 loss: 1.3588 2022/08/30 04:45:18 - mmengine - INFO - Epoch(train) [203][10/63] lr: 5.9291e-03 eta: 1 day, 2:19:18 time: 1.6045 data_time: 0.2320 memory: 16201 loss_prob: 0.7872 loss_thr: 0.4674 loss_db: 0.1337 loss: 1.3883 2022/08/30 04:45:25 - mmengine - INFO - Epoch(train) [203][15/63] lr: 5.9291e-03 eta: 1 day, 2:19:18 time: 1.3447 data_time: 0.0382 memory: 16201 loss_prob: 0.7775 loss_thr: 0.4641 loss_db: 0.1300 loss: 1.3716 2022/08/30 04:45:32 - mmengine - INFO - Epoch(train) [203][20/63] lr: 5.9291e-03 eta: 1 day, 2:18:56 time: 1.3698 data_time: 0.0358 memory: 16201 loss_prob: 0.7285 loss_thr: 0.4726 loss_db: 0.1238 loss: 1.3249 2022/08/30 04:45:39 - mmengine - INFO - Epoch(train) [203][25/63] lr: 5.9291e-03 eta: 1 day, 2:18:56 time: 1.3970 data_time: 0.0376 memory: 16201 loss_prob: 0.7301 loss_thr: 0.4756 loss_db: 0.1261 loss: 1.3318 2022/08/30 04:45:45 - mmengine - INFO - Epoch(train) [203][30/63] lr: 5.9291e-03 eta: 1 day, 2:18:35 time: 1.3710 data_time: 0.0375 memory: 16201 loss_prob: 0.7507 loss_thr: 0.4650 loss_db: 0.1253 loss: 1.3411 2022/08/30 04:45:51 - mmengine - INFO - Epoch(train) [203][35/63] lr: 5.9291e-03 eta: 1 day, 2:18:35 time: 1.2540 data_time: 0.0345 memory: 16201 loss_prob: 0.8373 loss_thr: 0.4932 loss_db: 0.1392 loss: 1.4697 2022/08/30 04:45:58 - mmengine - INFO - Epoch(train) [203][40/63] lr: 5.9291e-03 eta: 1 day, 2:18:09 time: 1.2990 data_time: 0.0267 memory: 16201 loss_prob: 0.8792 loss_thr: 0.5115 loss_db: 0.1503 loss: 1.5410 2022/08/30 04:46:05 - mmengine - INFO - Epoch(train) [203][45/63] lr: 5.9291e-03 eta: 1 day, 2:18:09 time: 1.3745 data_time: 0.0305 memory: 16201 loss_prob: 0.7726 loss_thr: 0.4781 loss_db: 0.1316 loss: 1.3823 2022/08/30 04:46:12 - mmengine - INFO - Epoch(train) [203][50/63] lr: 5.9291e-03 eta: 1 day, 2:17:45 time: 1.3172 data_time: 0.0364 memory: 16201 loss_prob: 0.8709 loss_thr: 0.4828 loss_db: 0.1355 loss: 1.4892 2022/08/30 04:46:18 - mmengine - INFO - Epoch(train) [203][55/63] lr: 5.9291e-03 eta: 1 day, 2:17:45 time: 1.2502 data_time: 0.0384 memory: 16201 loss_prob: 0.9332 loss_thr: 0.5075 loss_db: 0.1431 loss: 1.5837 2022/08/30 04:46:25 - mmengine - INFO - Epoch(train) [203][60/63] lr: 5.9291e-03 eta: 1 day, 2:17:20 time: 1.3052 data_time: 0.0431 memory: 16201 loss_prob: 0.7815 loss_thr: 0.4808 loss_db: 0.1293 loss: 1.3916 2022/08/30 04:46:28 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:46:37 - mmengine - INFO - Epoch(train) [204][5/63] lr: 5.9237e-03 eta: 1 day, 2:17:20 time: 1.5269 data_time: 0.2257 memory: 16201 loss_prob: 0.7453 loss_thr: 0.4759 loss_db: 0.1256 loss: 1.3468 2022/08/30 04:46:45 - mmengine - INFO - Epoch(train) [204][10/63] lr: 5.9237e-03 eta: 1 day, 2:16:47 time: 1.6790 data_time: 0.2361 memory: 16201 loss_prob: 0.7544 loss_thr: 0.4759 loss_db: 0.1330 loss: 1.3632 2022/08/30 04:46:51 - mmengine - INFO - Epoch(train) [204][15/63] lr: 5.9237e-03 eta: 1 day, 2:16:47 time: 1.3954 data_time: 0.0355 memory: 16201 loss_prob: 0.7033 loss_thr: 0.4531 loss_db: 0.1216 loss: 1.2780 2022/08/30 04:46:57 - mmengine - INFO - Epoch(train) [204][20/63] lr: 5.9237e-03 eta: 1 day, 2:16:19 time: 1.2611 data_time: 0.0391 memory: 16201 loss_prob: 0.7119 loss_thr: 0.4535 loss_db: 0.1189 loss: 1.2844 2022/08/30 04:47:04 - mmengine - INFO - Epoch(train) [204][25/63] lr: 5.9237e-03 eta: 1 day, 2:16:19 time: 1.2768 data_time: 0.0351 memory: 16201 loss_prob: 0.7773 loss_thr: 0.4726 loss_db: 0.1311 loss: 1.3810 2022/08/30 04:47:11 - mmengine - INFO - Epoch(train) [204][30/63] lr: 5.9237e-03 eta: 1 day, 2:15:56 time: 1.3296 data_time: 0.0379 memory: 16201 loss_prob: 0.7735 loss_thr: 0.4741 loss_db: 0.1306 loss: 1.3782 2022/08/30 04:47:17 - mmengine - INFO - Epoch(train) [204][35/63] lr: 5.9237e-03 eta: 1 day, 2:15:56 time: 1.3451 data_time: 0.0409 memory: 16201 loss_prob: 0.7506 loss_thr: 0.4771 loss_db: 0.1262 loss: 1.3540 2022/08/30 04:47:24 - mmengine - INFO - Epoch(train) [204][40/63] lr: 5.9237e-03 eta: 1 day, 2:15:34 time: 1.3637 data_time: 0.0303 memory: 16201 loss_prob: 0.9002 loss_thr: 0.4962 loss_db: 0.1494 loss: 1.5458 2022/08/30 04:47:31 - mmengine - INFO - Epoch(train) [204][45/63] lr: 5.9237e-03 eta: 1 day, 2:15:34 time: 1.3758 data_time: 0.0346 memory: 16201 loss_prob: 0.9606 loss_thr: 0.5152 loss_db: 0.1602 loss: 1.6360 2022/08/30 04:47:38 - mmengine - INFO - Epoch(train) [204][50/63] lr: 5.9237e-03 eta: 1 day, 2:15:11 time: 1.3454 data_time: 0.0341 memory: 16201 loss_prob: 0.9748 loss_thr: 0.4996 loss_db: 0.1602 loss: 1.6346 2022/08/30 04:47:44 - mmengine - INFO - Epoch(train) [204][55/63] lr: 5.9237e-03 eta: 1 day, 2:15:11 time: 1.2929 data_time: 0.0370 memory: 16201 loss_prob: 1.0422 loss_thr: 0.5125 loss_db: 0.1651 loss: 1.7199 2022/08/30 04:47:51 - mmengine - INFO - Epoch(train) [204][60/63] lr: 5.9237e-03 eta: 1 day, 2:14:47 time: 1.3331 data_time: 0.0418 memory: 16201 loss_prob: 0.9261 loss_thr: 0.5123 loss_db: 0.1512 loss: 1.5896 2022/08/30 04:47:55 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:48:03 - mmengine - INFO - Epoch(train) [205][5/63] lr: 5.9184e-03 eta: 1 day, 2:14:47 time: 1.5762 data_time: 0.2852 memory: 16201 loss_prob: 0.8068 loss_thr: 0.4576 loss_db: 0.1332 loss: 1.3975 2022/08/30 04:48:11 - mmengine - INFO - Epoch(train) [205][10/63] lr: 5.9184e-03 eta: 1 day, 2:14:11 time: 1.6148 data_time: 0.2449 memory: 16201 loss_prob: 0.7592 loss_thr: 0.4513 loss_db: 0.1266 loss: 1.3371 2022/08/30 04:48:17 - mmengine - INFO - Epoch(train) [205][15/63] lr: 5.9184e-03 eta: 1 day, 2:14:11 time: 1.3664 data_time: 0.0337 memory: 16201 loss_prob: 0.8720 loss_thr: 0.4844 loss_db: 0.1483 loss: 1.5048 2022/08/30 04:48:23 - mmengine - INFO - Epoch(train) [205][20/63] lr: 5.9184e-03 eta: 1 day, 2:13:44 time: 1.2627 data_time: 0.0309 memory: 16201 loss_prob: 0.8980 loss_thr: 0.5066 loss_db: 0.1513 loss: 1.5560 2022/08/30 04:48:30 - mmengine - INFO - Epoch(train) [205][25/63] lr: 5.9184e-03 eta: 1 day, 2:13:44 time: 1.3118 data_time: 0.0439 memory: 16201 loss_prob: 0.8442 loss_thr: 0.4989 loss_db: 0.1407 loss: 1.4837 2022/08/30 04:48:36 - mmengine - INFO - Epoch(train) [205][30/63] lr: 5.9184e-03 eta: 1 day, 2:13:19 time: 1.2992 data_time: 0.0307 memory: 16201 loss_prob: 0.8424 loss_thr: 0.4847 loss_db: 0.1403 loss: 1.4674 2022/08/30 04:48:43 - mmengine - INFO - Epoch(train) [205][35/63] lr: 5.9184e-03 eta: 1 day, 2:13:19 time: 1.3070 data_time: 0.0321 memory: 16201 loss_prob: 0.8516 loss_thr: 0.5047 loss_db: 0.1415 loss: 1.4978 2022/08/30 04:48:49 - mmengine - INFO - Epoch(train) [205][40/63] lr: 5.9184e-03 eta: 1 day, 2:12:55 time: 1.3147 data_time: 0.0372 memory: 16201 loss_prob: 0.9218 loss_thr: 0.5472 loss_db: 0.1531 loss: 1.6221 2022/08/30 04:48:56 - mmengine - INFO - Epoch(train) [205][45/63] lr: 5.9184e-03 eta: 1 day, 2:12:55 time: 1.2648 data_time: 0.0310 memory: 16201 loss_prob: 0.8920 loss_thr: 0.5067 loss_db: 0.1478 loss: 1.5465 2022/08/30 04:49:02 - mmengine - INFO - Epoch(train) [205][50/63] lr: 5.9184e-03 eta: 1 day, 2:12:29 time: 1.2955 data_time: 0.0380 memory: 16201 loss_prob: 0.8724 loss_thr: 0.4659 loss_db: 0.1433 loss: 1.4815 2022/08/30 04:49:09 - mmengine - INFO - Epoch(train) [205][55/63] lr: 5.9184e-03 eta: 1 day, 2:12:29 time: 1.3080 data_time: 0.0389 memory: 16201 loss_prob: 0.8722 loss_thr: 0.4833 loss_db: 0.1408 loss: 1.4963 2022/08/30 04:49:16 - mmengine - INFO - Epoch(train) [205][60/63] lr: 5.9184e-03 eta: 1 day, 2:12:07 time: 1.3562 data_time: 0.0389 memory: 16201 loss_prob: 0.8753 loss_thr: 0.5095 loss_db: 0.1395 loss: 1.5243 2022/08/30 04:49:19 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:49:28 - mmengine - INFO - Epoch(train) [206][5/63] lr: 5.9130e-03 eta: 1 day, 2:12:07 time: 1.4554 data_time: 0.2344 memory: 16201 loss_prob: 0.7727 loss_thr: 0.4835 loss_db: 0.1281 loss: 1.3843 2022/08/30 04:49:34 - mmengine - INFO - Epoch(train) [206][10/63] lr: 5.9130e-03 eta: 1 day, 2:11:24 time: 1.4669 data_time: 0.2590 memory: 16201 loss_prob: 0.6964 loss_thr: 0.4578 loss_db: 0.1184 loss: 1.2726 2022/08/30 04:49:40 - mmengine - INFO - Epoch(train) [206][15/63] lr: 5.9130e-03 eta: 1 day, 2:11:24 time: 1.2469 data_time: 0.0342 memory: 16201 loss_prob: 0.7520 loss_thr: 0.4581 loss_db: 0.1243 loss: 1.3343 2022/08/30 04:49:46 - mmengine - INFO - Epoch(train) [206][20/63] lr: 5.9130e-03 eta: 1 day, 2:10:56 time: 1.2468 data_time: 0.0294 memory: 16201 loss_prob: 0.7641 loss_thr: 0.4741 loss_db: 0.1276 loss: 1.3658 2022/08/30 04:49:53 - mmengine - INFO - Epoch(train) [206][25/63] lr: 5.9130e-03 eta: 1 day, 2:10:56 time: 1.2786 data_time: 0.0437 memory: 16201 loss_prob: 0.8311 loss_thr: 0.5094 loss_db: 0.1386 loss: 1.4791 2022/08/30 04:49:58 - mmengine - INFO - Epoch(train) [206][30/63] lr: 5.9130e-03 eta: 1 day, 2:10:28 time: 1.2320 data_time: 0.0297 memory: 16201 loss_prob: 0.9069 loss_thr: 0.5095 loss_db: 0.1470 loss: 1.5635 2022/08/30 04:50:05 - mmengine - INFO - Epoch(train) [206][35/63] lr: 5.9130e-03 eta: 1 day, 2:10:28 time: 1.2217 data_time: 0.0286 memory: 16201 loss_prob: 0.8367 loss_thr: 0.4799 loss_db: 0.1399 loss: 1.4565 2022/08/30 04:50:11 - mmengine - INFO - Epoch(train) [206][40/63] lr: 5.9130e-03 eta: 1 day, 2:10:01 time: 1.2491 data_time: 0.0307 memory: 16201 loss_prob: 0.7705 loss_thr: 0.4564 loss_db: 0.1320 loss: 1.3589 2022/08/30 04:50:18 - mmengine - INFO - Epoch(train) [206][45/63] lr: 5.9130e-03 eta: 1 day, 2:10:01 time: 1.3173 data_time: 0.0283 memory: 16201 loss_prob: 0.7906 loss_thr: 0.4688 loss_db: 0.1305 loss: 1.3899 2022/08/30 04:50:25 - mmengine - INFO - Epoch(train) [206][50/63] lr: 5.9130e-03 eta: 1 day, 2:09:43 time: 1.4383 data_time: 0.0404 memory: 16201 loss_prob: 0.7958 loss_thr: 0.4897 loss_db: 0.1297 loss: 1.4152 2022/08/30 04:50:32 - mmengine - INFO - Epoch(train) [206][55/63] lr: 5.9130e-03 eta: 1 day, 2:09:43 time: 1.3950 data_time: 0.0290 memory: 16201 loss_prob: 0.8054 loss_thr: 0.4716 loss_db: 0.1320 loss: 1.4090 2022/08/30 04:50:39 - mmengine - INFO - Epoch(train) [206][60/63] lr: 5.9130e-03 eta: 1 day, 2:09:20 time: 1.3454 data_time: 0.0268 memory: 16201 loss_prob: 0.8230 loss_thr: 0.4867 loss_db: 0.1366 loss: 1.4463 2022/08/30 04:50:42 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:50:51 - mmengine - INFO - Epoch(train) [207][5/63] lr: 5.9077e-03 eta: 1 day, 2:09:20 time: 1.4738 data_time: 0.2248 memory: 16201 loss_prob: 0.8360 loss_thr: 0.5263 loss_db: 0.1435 loss: 1.5058 2022/08/30 04:50:58 - mmengine - INFO - Epoch(train) [207][10/63] lr: 5.9077e-03 eta: 1 day, 2:08:41 time: 1.5470 data_time: 0.2432 memory: 16201 loss_prob: 0.8288 loss_thr: 0.4982 loss_db: 0.1414 loss: 1.4684 2022/08/30 04:51:05 - mmengine - INFO - Epoch(train) [207][15/63] lr: 5.9077e-03 eta: 1 day, 2:08:41 time: 1.4380 data_time: 0.0328 memory: 16201 loss_prob: 0.7901 loss_thr: 0.4783 loss_db: 0.1318 loss: 1.4002 2022/08/30 04:51:12 - mmengine - INFO - Epoch(train) [207][20/63] lr: 5.9077e-03 eta: 1 day, 2:08:21 time: 1.4067 data_time: 0.0342 memory: 16201 loss_prob: 0.8028 loss_thr: 0.4667 loss_db: 0.1264 loss: 1.3958 2022/08/30 04:51:14 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:51:19 - mmengine - INFO - Epoch(train) [207][25/63] lr: 5.9077e-03 eta: 1 day, 2:08:21 time: 1.3222 data_time: 0.0380 memory: 16201 loss_prob: 0.8795 loss_thr: 0.4901 loss_db: 0.1437 loss: 1.5133 2022/08/30 04:51:26 - mmengine - INFO - Epoch(train) [207][30/63] lr: 5.9077e-03 eta: 1 day, 2:08:00 time: 1.3839 data_time: 0.0292 memory: 16201 loss_prob: 0.8673 loss_thr: 0.5167 loss_db: 0.1480 loss: 1.5320 2022/08/30 04:51:32 - mmengine - INFO - Epoch(train) [207][35/63] lr: 5.9077e-03 eta: 1 day, 2:08:00 time: 1.3444 data_time: 0.0387 memory: 16201 loss_prob: 0.8022 loss_thr: 0.4859 loss_db: 0.1323 loss: 1.4204 2022/08/30 04:51:39 - mmengine - INFO - Epoch(train) [207][40/63] lr: 5.9077e-03 eta: 1 day, 2:07:37 time: 1.3290 data_time: 0.0338 memory: 16201 loss_prob: 0.8051 loss_thr: 0.4826 loss_db: 0.1299 loss: 1.4176 2022/08/30 04:51:46 - mmengine - INFO - Epoch(train) [207][45/63] lr: 5.9077e-03 eta: 1 day, 2:07:37 time: 1.3903 data_time: 0.0338 memory: 16201 loss_prob: 0.8311 loss_thr: 0.4833 loss_db: 0.1364 loss: 1.4508 2022/08/30 04:51:53 - mmengine - INFO - Epoch(train) [207][50/63] lr: 5.9077e-03 eta: 1 day, 2:07:16 time: 1.3811 data_time: 0.0422 memory: 16201 loss_prob: 0.8627 loss_thr: 0.4928 loss_db: 0.1442 loss: 1.4998 2022/08/30 04:51:59 - mmengine - INFO - Epoch(train) [207][55/63] lr: 5.9077e-03 eta: 1 day, 2:07:16 time: 1.3340 data_time: 0.0333 memory: 16201 loss_prob: 0.8240 loss_thr: 0.5018 loss_db: 0.1377 loss: 1.4635 2022/08/30 04:52:06 - mmengine - INFO - Epoch(train) [207][60/63] lr: 5.9077e-03 eta: 1 day, 2:06:54 time: 1.3626 data_time: 0.0396 memory: 16201 loss_prob: 0.7797 loss_thr: 0.4734 loss_db: 0.1305 loss: 1.3836 2022/08/30 04:52:09 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:52:18 - mmengine - INFO - Epoch(train) [208][5/63] lr: 5.9023e-03 eta: 1 day, 2:06:54 time: 1.4378 data_time: 0.2362 memory: 16201 loss_prob: 0.8835 loss_thr: 0.5151 loss_db: 0.1479 loss: 1.5465 2022/08/30 04:52:25 - mmengine - INFO - Epoch(train) [208][10/63] lr: 5.9023e-03 eta: 1 day, 2:06:17 time: 1.5751 data_time: 0.2540 memory: 16201 loss_prob: 0.8597 loss_thr: 0.5051 loss_db: 0.1443 loss: 1.5091 2022/08/30 04:52:31 - mmengine - INFO - Epoch(train) [208][15/63] lr: 5.9023e-03 eta: 1 day, 2:06:17 time: 1.3402 data_time: 0.0386 memory: 16201 loss_prob: 0.7856 loss_thr: 0.4740 loss_db: 0.1313 loss: 1.3908 2022/08/30 04:52:38 - mmengine - INFO - Epoch(train) [208][20/63] lr: 5.9023e-03 eta: 1 day, 2:05:53 time: 1.3243 data_time: 0.0325 memory: 16201 loss_prob: 0.7265 loss_thr: 0.4536 loss_db: 0.1224 loss: 1.3025 2022/08/30 04:52:46 - mmengine - INFO - Epoch(train) [208][25/63] lr: 5.9023e-03 eta: 1 day, 2:05:53 time: 1.4151 data_time: 0.0495 memory: 16201 loss_prob: 0.7384 loss_thr: 0.4627 loss_db: 0.1260 loss: 1.3271 2022/08/30 04:52:52 - mmengine - INFO - Epoch(train) [208][30/63] lr: 5.9023e-03 eta: 1 day, 2:05:32 time: 1.3716 data_time: 0.0361 memory: 16201 loss_prob: 0.8194 loss_thr: 0.5007 loss_db: 0.1390 loss: 1.4591 2022/08/30 04:52:59 - mmengine - INFO - Epoch(train) [208][35/63] lr: 5.9023e-03 eta: 1 day, 2:05:32 time: 1.3004 data_time: 0.0333 memory: 16201 loss_prob: 0.8145 loss_thr: 0.5067 loss_db: 0.1368 loss: 1.4580 2022/08/30 04:53:05 - mmengine - INFO - Epoch(train) [208][40/63] lr: 5.9023e-03 eta: 1 day, 2:05:10 time: 1.3605 data_time: 0.0386 memory: 16201 loss_prob: 0.7618 loss_thr: 0.4781 loss_db: 0.1276 loss: 1.3675 2022/08/30 04:53:12 - mmengine - INFO - Epoch(train) [208][45/63] lr: 5.9023e-03 eta: 1 day, 2:05:10 time: 1.3442 data_time: 0.0336 memory: 16201 loss_prob: 0.7919 loss_thr: 0.4776 loss_db: 0.1326 loss: 1.4021 2022/08/30 04:53:19 - mmengine - INFO - Epoch(train) [208][50/63] lr: 5.9023e-03 eta: 1 day, 2:04:48 time: 1.3537 data_time: 0.0418 memory: 16201 loss_prob: 0.7936 loss_thr: 0.4728 loss_db: 0.1333 loss: 1.3997 2022/08/30 04:53:25 - mmengine - INFO - Epoch(train) [208][55/63] lr: 5.9023e-03 eta: 1 day, 2:04:48 time: 1.3412 data_time: 0.0317 memory: 16201 loss_prob: 0.7553 loss_thr: 0.4698 loss_db: 0.1303 loss: 1.3555 2022/08/30 04:53:32 - mmengine - INFO - Epoch(train) [208][60/63] lr: 5.9023e-03 eta: 1 day, 2:04:25 time: 1.3404 data_time: 0.0325 memory: 16201 loss_prob: 0.7460 loss_thr: 0.4669 loss_db: 0.1278 loss: 1.3407 2022/08/30 04:53:35 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:53:44 - mmengine - INFO - Epoch(train) [209][5/63] lr: 5.8970e-03 eta: 1 day, 2:04:25 time: 1.4768 data_time: 0.2438 memory: 16201 loss_prob: 0.7965 loss_thr: 0.4824 loss_db: 0.1356 loss: 1.4144 2022/08/30 04:53:51 - mmengine - INFO - Epoch(train) [209][10/63] lr: 5.8970e-03 eta: 1 day, 2:03:49 time: 1.5963 data_time: 0.2681 memory: 16201 loss_prob: 0.7490 loss_thr: 0.4584 loss_db: 0.1296 loss: 1.3369 2022/08/30 04:53:58 - mmengine - INFO - Epoch(train) [209][15/63] lr: 5.8970e-03 eta: 1 day, 2:03:49 time: 1.3777 data_time: 0.0350 memory: 16201 loss_prob: 0.7241 loss_thr: 0.4471 loss_db: 0.1220 loss: 1.2931 2022/08/30 04:54:04 - mmengine - INFO - Epoch(train) [209][20/63] lr: 5.8970e-03 eta: 1 day, 2:03:25 time: 1.3262 data_time: 0.0298 memory: 16201 loss_prob: 0.7503 loss_thr: 0.4484 loss_db: 0.1318 loss: 1.3305 2022/08/30 04:54:10 - mmengine - INFO - Epoch(train) [209][25/63] lr: 5.8970e-03 eta: 1 day, 2:03:25 time: 1.2467 data_time: 0.0323 memory: 16201 loss_prob: 0.7729 loss_thr: 0.4639 loss_db: 0.1361 loss: 1.3729 2022/08/30 04:54:17 - mmengine - INFO - Epoch(train) [209][30/63] lr: 5.8970e-03 eta: 1 day, 2:03:00 time: 1.2940 data_time: 0.0304 memory: 16201 loss_prob: 0.8852 loss_thr: 0.5070 loss_db: 0.1496 loss: 1.5419 2022/08/30 04:54:24 - mmengine - INFO - Epoch(train) [209][35/63] lr: 5.8970e-03 eta: 1 day, 2:03:00 time: 1.3891 data_time: 0.0442 memory: 16201 loss_prob: 0.9544 loss_thr: 0.4954 loss_db: 0.1608 loss: 1.6106 2022/08/30 04:54:31 - mmengine - INFO - Epoch(train) [209][40/63] lr: 5.8970e-03 eta: 1 day, 2:02:40 time: 1.3887 data_time: 0.0319 memory: 16201 loss_prob: 1.0792 loss_thr: 0.5124 loss_db: 0.1736 loss: 1.7652 2022/08/30 04:54:38 - mmengine - INFO - Epoch(train) [209][45/63] lr: 5.8970e-03 eta: 1 day, 2:02:40 time: 1.3821 data_time: 0.0315 memory: 16201 loss_prob: 1.1503 loss_thr: 0.5568 loss_db: 0.1800 loss: 1.8870 2022/08/30 04:54:45 - mmengine - INFO - Epoch(train) [209][50/63] lr: 5.8970e-03 eta: 1 day, 2:02:18 time: 1.3550 data_time: 0.0462 memory: 16201 loss_prob: 1.0016 loss_thr: 0.5385 loss_db: 0.1636 loss: 1.7036 2022/08/30 04:54:52 - mmengine - INFO - Epoch(train) [209][55/63] lr: 5.8970e-03 eta: 1 day, 2:02:18 time: 1.3632 data_time: 0.0291 memory: 16201 loss_prob: 0.9426 loss_thr: 0.5079 loss_db: 0.1573 loss: 1.6078 2022/08/30 04:54:59 - mmengine - INFO - Epoch(train) [209][60/63] lr: 5.8970e-03 eta: 1 day, 2:01:59 time: 1.4061 data_time: 0.0280 memory: 16201 loss_prob: 0.9552 loss_thr: 0.5186 loss_db: 0.1574 loss: 1.6313 2022/08/30 04:55:02 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:55:11 - mmengine - INFO - Epoch(train) [210][5/63] lr: 5.8916e-03 eta: 1 day, 2:01:59 time: 1.5419 data_time: 0.2270 memory: 16201 loss_prob: 1.0587 loss_thr: 0.5351 loss_db: 0.1826 loss: 1.7763 2022/08/30 04:55:18 - mmengine - INFO - Epoch(train) [210][10/63] lr: 5.8916e-03 eta: 1 day, 2:01:23 time: 1.6103 data_time: 0.2401 memory: 16201 loss_prob: 1.0433 loss_thr: 0.5213 loss_db: 0.1713 loss: 1.7359 2022/08/30 04:55:26 - mmengine - INFO - Epoch(train) [210][15/63] lr: 5.8916e-03 eta: 1 day, 2:01:23 time: 1.4100 data_time: 0.0362 memory: 16201 loss_prob: 1.0463 loss_thr: 0.4830 loss_db: 0.1686 loss: 1.6978 2022/08/30 04:55:32 - mmengine - INFO - Epoch(train) [210][20/63] lr: 5.8916e-03 eta: 1 day, 2:01:01 time: 1.3594 data_time: 0.0407 memory: 16201 loss_prob: 1.0571 loss_thr: 0.4981 loss_db: 0.1707 loss: 1.7260 2022/08/30 04:55:38 - mmengine - INFO - Epoch(train) [210][25/63] lr: 5.8916e-03 eta: 1 day, 2:01:01 time: 1.2855 data_time: 0.0320 memory: 16201 loss_prob: 1.1285 loss_thr: 0.5446 loss_db: 0.1844 loss: 1.8574 2022/08/30 04:55:46 - mmengine - INFO - Epoch(train) [210][30/63] lr: 5.8916e-03 eta: 1 day, 2:00:45 time: 1.4786 data_time: 0.0362 memory: 16201 loss_prob: 1.0683 loss_thr: 0.5428 loss_db: 0.1760 loss: 1.7871 2022/08/30 04:55:53 - mmengine - INFO - Epoch(train) [210][35/63] lr: 5.8916e-03 eta: 1 day, 2:00:45 time: 1.4477 data_time: 0.0441 memory: 16201 loss_prob: 1.0725 loss_thr: 0.5324 loss_db: 0.1756 loss: 1.7805 2022/08/30 04:56:00 - mmengine - INFO - Epoch(train) [210][40/63] lr: 5.8916e-03 eta: 1 day, 2:00:25 time: 1.3932 data_time: 0.0317 memory: 16201 loss_prob: 1.1125 loss_thr: 0.5318 loss_db: 0.1909 loss: 1.8353 2022/08/30 04:56:06 - mmengine - INFO - Epoch(train) [210][45/63] lr: 5.8916e-03 eta: 1 day, 2:00:25 time: 1.3647 data_time: 0.0327 memory: 16201 loss_prob: 0.9348 loss_thr: 0.5134 loss_db: 0.1613 loss: 1.6095 2022/08/30 04:56:13 - mmengine - INFO - Epoch(train) [210][50/63] lr: 5.8916e-03 eta: 1 day, 2:00:00 time: 1.2811 data_time: 0.0372 memory: 16201 loss_prob: 0.8637 loss_thr: 0.5001 loss_db: 0.1452 loss: 1.5091 2022/08/30 04:56:20 - mmengine - INFO - Epoch(train) [210][55/63] lr: 5.8916e-03 eta: 1 day, 2:00:00 time: 1.3468 data_time: 0.0321 memory: 16201 loss_prob: 0.9493 loss_thr: 0.5041 loss_db: 0.1595 loss: 1.6128 2022/08/30 04:56:27 - mmengine - INFO - Epoch(train) [210][60/63] lr: 5.8916e-03 eta: 1 day, 1:59:37 time: 1.3386 data_time: 0.0352 memory: 16201 loss_prob: 1.0478 loss_thr: 0.5383 loss_db: 0.1689 loss: 1.7550 2022/08/30 04:56:30 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:56:39 - mmengine - INFO - Epoch(train) [211][5/63] lr: 5.8862e-03 eta: 1 day, 1:59:37 time: 1.4953 data_time: 0.2342 memory: 16201 loss_prob: 1.1474 loss_thr: 0.5614 loss_db: 0.1773 loss: 1.8862 2022/08/30 04:56:46 - mmengine - INFO - Epoch(train) [211][10/63] lr: 5.8862e-03 eta: 1 day, 1:58:58 time: 1.5449 data_time: 0.2485 memory: 16201 loss_prob: 1.0542 loss_thr: 0.5461 loss_db: 0.1703 loss: 1.7705 2022/08/30 04:56:52 - mmengine - INFO - Epoch(train) [211][15/63] lr: 5.8862e-03 eta: 1 day, 1:58:58 time: 1.3621 data_time: 0.0338 memory: 16201 loss_prob: 0.9593 loss_thr: 0.5250 loss_db: 0.1598 loss: 1.6441 2022/08/30 04:56:59 - mmengine - INFO - Epoch(train) [211][20/63] lr: 5.8862e-03 eta: 1 day, 1:58:37 time: 1.3655 data_time: 0.0352 memory: 16201 loss_prob: 0.9751 loss_thr: 0.5041 loss_db: 0.1554 loss: 1.6346 2022/08/30 04:57:06 - mmengine - INFO - Epoch(train) [211][25/63] lr: 5.8862e-03 eta: 1 day, 1:58:37 time: 1.3537 data_time: 0.0509 memory: 16201 loss_prob: 1.0714 loss_thr: 0.5165 loss_db: 0.1707 loss: 1.7586 2022/08/30 04:57:13 - mmengine - INFO - Epoch(train) [211][30/63] lr: 5.8862e-03 eta: 1 day, 1:58:14 time: 1.3370 data_time: 0.0349 memory: 16201 loss_prob: 1.0273 loss_thr: 0.5209 loss_db: 0.1668 loss: 1.7150 2022/08/30 04:57:20 - mmengine - INFO - Epoch(train) [211][35/63] lr: 5.8862e-03 eta: 1 day, 1:58:14 time: 1.4097 data_time: 0.0317 memory: 16201 loss_prob: 0.8793 loss_thr: 0.4922 loss_db: 0.1429 loss: 1.5145 2022/08/30 04:57:27 - mmengine - INFO - Epoch(train) [211][40/63] lr: 5.8862e-03 eta: 1 day, 1:57:55 time: 1.3952 data_time: 0.0337 memory: 16201 loss_prob: 0.8889 loss_thr: 0.5046 loss_db: 0.1459 loss: 1.5395 2022/08/30 04:57:34 - mmengine - INFO - Epoch(train) [211][45/63] lr: 5.8862e-03 eta: 1 day, 1:57:55 time: 1.4314 data_time: 0.0382 memory: 16201 loss_prob: 0.8712 loss_thr: 0.4983 loss_db: 0.1449 loss: 1.5145 2022/08/30 04:57:42 - mmengine - INFO - Epoch(train) [211][50/63] lr: 5.8862e-03 eta: 1 day, 1:57:39 time: 1.4805 data_time: 0.0517 memory: 16201 loss_prob: 0.7506 loss_thr: 0.4538 loss_db: 0.1272 loss: 1.3316 2022/08/30 04:57:49 - mmengine - INFO - Epoch(train) [211][55/63] lr: 5.8862e-03 eta: 1 day, 1:57:39 time: 1.4338 data_time: 0.0314 memory: 16201 loss_prob: 0.7495 loss_thr: 0.4827 loss_db: 0.1264 loss: 1.3587 2022/08/30 04:57:55 - mmengine - INFO - Epoch(train) [211][60/63] lr: 5.8862e-03 eta: 1 day, 1:57:17 time: 1.3587 data_time: 0.0273 memory: 16201 loss_prob: 0.8239 loss_thr: 0.5075 loss_db: 0.1383 loss: 1.4698 2022/08/30 04:57:58 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:58:08 - mmengine - INFO - Epoch(train) [212][5/63] lr: 5.8809e-03 eta: 1 day, 1:57:17 time: 1.4941 data_time: 0.2472 memory: 16201 loss_prob: 0.8273 loss_thr: 0.4825 loss_db: 0.1400 loss: 1.4498 2022/08/30 04:58:14 - mmengine - INFO - Epoch(train) [212][10/63] lr: 5.8809e-03 eta: 1 day, 1:56:41 time: 1.5998 data_time: 0.2686 memory: 16201 loss_prob: 0.7259 loss_thr: 0.4523 loss_db: 0.1214 loss: 1.2996 2022/08/30 04:58:20 - mmengine - INFO - Epoch(train) [212][15/63] lr: 5.8809e-03 eta: 1 day, 1:56:41 time: 1.2919 data_time: 0.0311 memory: 16201 loss_prob: 0.7185 loss_thr: 0.4709 loss_db: 0.1188 loss: 1.3082 2022/08/30 04:58:28 - mmengine - INFO - Epoch(train) [212][20/63] lr: 5.8809e-03 eta: 1 day, 1:56:19 time: 1.3381 data_time: 0.0320 memory: 16201 loss_prob: 0.7806 loss_thr: 0.4987 loss_db: 0.1316 loss: 1.4109 2022/08/30 04:58:35 - mmengine - INFO - Epoch(train) [212][25/63] lr: 5.8809e-03 eta: 1 day, 1:56:19 time: 1.4344 data_time: 0.0379 memory: 16201 loss_prob: 0.7742 loss_thr: 0.4841 loss_db: 0.1313 loss: 1.3896 2022/08/30 04:58:41 - mmengine - INFO - Epoch(train) [212][30/63] lr: 5.8809e-03 eta: 1 day, 1:55:57 time: 1.3483 data_time: 0.0277 memory: 16201 loss_prob: 0.7660 loss_thr: 0.4743 loss_db: 0.1314 loss: 1.3717 2022/08/30 04:58:48 - mmengine - INFO - Epoch(train) [212][35/63] lr: 5.8809e-03 eta: 1 day, 1:55:57 time: 1.2745 data_time: 0.0353 memory: 16201 loss_prob: 0.7765 loss_thr: 0.4743 loss_db: 0.1324 loss: 1.3832 2022/08/30 04:58:55 - mmengine - INFO - Epoch(train) [212][40/63] lr: 5.8809e-03 eta: 1 day, 1:55:34 time: 1.3344 data_time: 0.0314 memory: 16201 loss_prob: 0.8115 loss_thr: 0.4825 loss_db: 0.1348 loss: 1.4288 2022/08/30 04:59:01 - mmengine - INFO - Epoch(train) [212][45/63] lr: 5.8809e-03 eta: 1 day, 1:55:34 time: 1.3242 data_time: 0.0314 memory: 16201 loss_prob: 0.8740 loss_thr: 0.5100 loss_db: 0.1456 loss: 1.5295 2022/08/30 04:59:08 - mmengine - INFO - Epoch(train) [212][50/63] lr: 5.8809e-03 eta: 1 day, 1:55:11 time: 1.3378 data_time: 0.0465 memory: 16201 loss_prob: 0.8206 loss_thr: 0.4923 loss_db: 0.1385 loss: 1.4515 2022/08/30 04:59:14 - mmengine - INFO - Epoch(train) [212][55/63] lr: 5.8809e-03 eta: 1 day, 1:55:11 time: 1.3317 data_time: 0.0316 memory: 16201 loss_prob: 0.7981 loss_thr: 0.4742 loss_db: 0.1300 loss: 1.4023 2022/08/30 04:59:21 - mmengine - INFO - Epoch(train) [212][60/63] lr: 5.8809e-03 eta: 1 day, 1:54:48 time: 1.3172 data_time: 0.0335 memory: 16201 loss_prob: 0.8732 loss_thr: 0.4934 loss_db: 0.1426 loss: 1.5092 2022/08/30 04:59:24 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 04:59:33 - mmengine - INFO - Epoch(train) [213][5/63] lr: 5.8755e-03 eta: 1 day, 1:54:48 time: 1.5453 data_time: 0.2384 memory: 16201 loss_prob: 0.8151 loss_thr: 0.4883 loss_db: 0.1366 loss: 1.4400 2022/08/30 04:59:40 - mmengine - INFO - Epoch(train) [213][10/63] lr: 5.8755e-03 eta: 1 day, 1:54:10 time: 1.5585 data_time: 0.2589 memory: 16201 loss_prob: 0.8110 loss_thr: 0.4779 loss_db: 0.1374 loss: 1.4264 2022/08/30 04:59:47 - mmengine - INFO - Epoch(train) [213][15/63] lr: 5.8755e-03 eta: 1 day, 1:54:10 time: 1.3587 data_time: 0.0363 memory: 16201 loss_prob: 0.8255 loss_thr: 0.4981 loss_db: 0.1390 loss: 1.4627 2022/08/30 04:59:53 - mmengine - INFO - Epoch(train) [213][20/63] lr: 5.8755e-03 eta: 1 day, 1:53:48 time: 1.3349 data_time: 0.0292 memory: 16201 loss_prob: 0.8305 loss_thr: 0.4988 loss_db: 0.1381 loss: 1.4674 2022/08/30 05:00:00 - mmengine - INFO - Epoch(train) [213][25/63] lr: 5.8755e-03 eta: 1 day, 1:53:48 time: 1.2654 data_time: 0.0392 memory: 16201 loss_prob: 0.8312 loss_thr: 0.4940 loss_db: 0.1392 loss: 1.4645 2022/08/30 05:00:06 - mmengine - INFO - Epoch(train) [213][30/63] lr: 5.8755e-03 eta: 1 day, 1:53:23 time: 1.2975 data_time: 0.0304 memory: 16201 loss_prob: 0.8437 loss_thr: 0.4898 loss_db: 0.1392 loss: 1.4727 2022/08/30 05:00:12 - mmengine - INFO - Epoch(train) [213][35/63] lr: 5.8755e-03 eta: 1 day, 1:53:23 time: 1.2629 data_time: 0.0310 memory: 16201 loss_prob: 0.8608 loss_thr: 0.4815 loss_db: 0.1403 loss: 1.4826 2022/08/30 05:00:18 - mmengine - INFO - Epoch(train) [213][40/63] lr: 5.8755e-03 eta: 1 day, 1:52:57 time: 1.2410 data_time: 0.0303 memory: 16201 loss_prob: 0.7688 loss_thr: 0.4605 loss_db: 0.1295 loss: 1.3589 2022/08/30 05:00:25 - mmengine - INFO - Epoch(train) [213][45/63] lr: 5.8755e-03 eta: 1 day, 1:52:57 time: 1.3212 data_time: 0.0290 memory: 16201 loss_prob: 0.7008 loss_thr: 0.4472 loss_db: 0.1212 loss: 1.2691 2022/08/30 05:00:32 - mmengine - INFO - Epoch(train) [213][50/63] lr: 5.8755e-03 eta: 1 day, 1:52:34 time: 1.3295 data_time: 0.0437 memory: 16201 loss_prob: 0.7447 loss_thr: 0.4678 loss_db: 0.1280 loss: 1.3405 2022/08/30 05:00:38 - mmengine - INFO - Epoch(train) [213][55/63] lr: 5.8755e-03 eta: 1 day, 1:52:34 time: 1.3119 data_time: 0.0316 memory: 16201 loss_prob: 0.7372 loss_thr: 0.4634 loss_db: 0.1249 loss: 1.3254 2022/08/30 05:00:46 - mmengine - INFO - Epoch(train) [213][60/63] lr: 5.8755e-03 eta: 1 day, 1:52:13 time: 1.3735 data_time: 0.0345 memory: 16201 loss_prob: 0.7549 loss_thr: 0.4869 loss_db: 0.1283 loss: 1.3701 2022/08/30 05:00:49 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:00:58 - mmengine - INFO - Epoch(train) [214][5/63] lr: 5.8702e-03 eta: 1 day, 1:52:13 time: 1.4693 data_time: 0.2253 memory: 16201 loss_prob: 0.7828 loss_thr: 0.4907 loss_db: 0.1314 loss: 1.4049 2022/08/30 05:01:04 - mmengine - INFO - Epoch(train) [214][10/63] lr: 5.8702e-03 eta: 1 day, 1:51:35 time: 1.5439 data_time: 0.2373 memory: 16201 loss_prob: 0.7871 loss_thr: 0.4761 loss_db: 0.1260 loss: 1.3893 2022/08/30 05:01:11 - mmengine - INFO - Epoch(train) [214][15/63] lr: 5.8702e-03 eta: 1 day, 1:51:35 time: 1.3381 data_time: 0.0363 memory: 16201 loss_prob: 0.7711 loss_thr: 0.4647 loss_db: 0.1247 loss: 1.3605 2022/08/30 05:01:17 - mmengine - INFO - Epoch(train) [214][20/63] lr: 5.8702e-03 eta: 1 day, 1:51:11 time: 1.2989 data_time: 0.0368 memory: 16201 loss_prob: 0.7412 loss_thr: 0.4960 loss_db: 0.1254 loss: 1.3626 2022/08/30 05:01:25 - mmengine - INFO - Epoch(train) [214][25/63] lr: 5.8702e-03 eta: 1 day, 1:51:11 time: 1.3899 data_time: 0.0348 memory: 16201 loss_prob: 0.7920 loss_thr: 0.5144 loss_db: 0.1329 loss: 1.4393 2022/08/30 05:01:31 - mmengine - INFO - Epoch(train) [214][30/63] lr: 5.8702e-03 eta: 1 day, 1:50:51 time: 1.3989 data_time: 0.0401 memory: 16201 loss_prob: 0.8608 loss_thr: 0.5105 loss_db: 0.1393 loss: 1.5105 2022/08/30 05:01:38 - mmengine - INFO - Epoch(train) [214][35/63] lr: 5.8702e-03 eta: 1 day, 1:50:51 time: 1.2945 data_time: 0.0381 memory: 16201 loss_prob: 0.8544 loss_thr: 0.5094 loss_db: 0.1402 loss: 1.5039 2022/08/30 05:01:44 - mmengine - INFO - Epoch(train) [214][40/63] lr: 5.8702e-03 eta: 1 day, 1:50:26 time: 1.2824 data_time: 0.0310 memory: 16201 loss_prob: 0.8593 loss_thr: 0.5033 loss_db: 0.1458 loss: 1.5084 2022/08/30 05:01:51 - mmengine - INFO - Epoch(train) [214][45/63] lr: 5.8702e-03 eta: 1 day, 1:50:26 time: 1.3000 data_time: 0.0394 memory: 16201 loss_prob: 0.8625 loss_thr: 0.4973 loss_db: 0.1441 loss: 1.5038 2022/08/30 05:01:57 - mmengine - INFO - Epoch(train) [214][50/63] lr: 5.8702e-03 eta: 1 day, 1:50:02 time: 1.2873 data_time: 0.0376 memory: 16201 loss_prob: 0.7621 loss_thr: 0.4623 loss_db: 0.1269 loss: 1.3513 2022/08/30 05:02:04 - mmengine - INFO - Epoch(train) [214][55/63] lr: 5.8702e-03 eta: 1 day, 1:50:02 time: 1.3676 data_time: 0.0337 memory: 16201 loss_prob: 0.7179 loss_thr: 0.4497 loss_db: 0.1207 loss: 1.2883 2022/08/30 05:02:11 - mmengine - INFO - Epoch(train) [214][60/63] lr: 5.8702e-03 eta: 1 day, 1:49:42 time: 1.3850 data_time: 0.0378 memory: 16201 loss_prob: 0.7979 loss_thr: 0.4802 loss_db: 0.1382 loss: 1.4163 2022/08/30 05:02:15 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:02:23 - mmengine - INFO - Epoch(train) [215][5/63] lr: 5.8648e-03 eta: 1 day, 1:49:42 time: 1.5294 data_time: 0.2434 memory: 16201 loss_prob: 0.7477 loss_thr: 0.4768 loss_db: 0.1250 loss: 1.3495 2022/08/30 05:02:30 - mmengine - INFO - Epoch(train) [215][10/63] lr: 5.8648e-03 eta: 1 day, 1:49:04 time: 1.5551 data_time: 0.2527 memory: 16201 loss_prob: 0.7670 loss_thr: 0.4838 loss_db: 0.1295 loss: 1.3803 2022/08/30 05:02:37 - mmengine - INFO - Epoch(train) [215][15/63] lr: 5.8648e-03 eta: 1 day, 1:49:04 time: 1.3431 data_time: 0.0357 memory: 16201 loss_prob: 0.8303 loss_thr: 0.5207 loss_db: 0.1393 loss: 1.4903 2022/08/30 05:02:43 - mmengine - INFO - Epoch(train) [215][20/63] lr: 5.8648e-03 eta: 1 day, 1:48:38 time: 1.2536 data_time: 0.0323 memory: 16201 loss_prob: 0.7963 loss_thr: 0.4875 loss_db: 0.1335 loss: 1.4173 2022/08/30 05:02:49 - mmengine - INFO - Epoch(train) [215][25/63] lr: 5.8648e-03 eta: 1 day, 1:48:38 time: 1.2619 data_time: 0.0398 memory: 16201 loss_prob: 0.6874 loss_thr: 0.4477 loss_db: 0.1148 loss: 1.2499 2022/08/30 05:02:56 - mmengine - INFO - Epoch(train) [215][30/63] lr: 5.8648e-03 eta: 1 day, 1:48:16 time: 1.3370 data_time: 0.0388 memory: 16201 loss_prob: 0.7705 loss_thr: 0.4902 loss_db: 0.1230 loss: 1.3837 2022/08/30 05:03:03 - mmengine - INFO - Epoch(train) [215][35/63] lr: 5.8648e-03 eta: 1 day, 1:48:16 time: 1.3353 data_time: 0.0355 memory: 16201 loss_prob: 0.8195 loss_thr: 0.5040 loss_db: 0.1336 loss: 1.4570 2022/08/30 05:03:09 - mmengine - INFO - Epoch(train) [215][40/63] lr: 5.8648e-03 eta: 1 day, 1:47:52 time: 1.3123 data_time: 0.0353 memory: 16201 loss_prob: 0.8451 loss_thr: 0.5052 loss_db: 0.1397 loss: 1.4899 2022/08/30 05:03:16 - mmengine - INFO - Epoch(train) [215][45/63] lr: 5.8648e-03 eta: 1 day, 1:47:52 time: 1.3352 data_time: 0.0353 memory: 16201 loss_prob: 0.8826 loss_thr: 0.4971 loss_db: 0.1454 loss: 1.5251 2022/08/30 05:03:23 - mmengine - INFO - Epoch(train) [215][50/63] lr: 5.8648e-03 eta: 1 day, 1:47:32 time: 1.3879 data_time: 0.0388 memory: 16201 loss_prob: 0.8264 loss_thr: 0.4618 loss_db: 0.1402 loss: 1.4284 2022/08/30 05:03:30 - mmengine - INFO - Epoch(train) [215][55/63] lr: 5.8648e-03 eta: 1 day, 1:47:32 time: 1.3351 data_time: 0.0325 memory: 16201 loss_prob: 0.8191 loss_thr: 0.4653 loss_db: 0.1381 loss: 1.4225 2022/08/30 05:03:36 - mmengine - INFO - Epoch(train) [215][60/63] lr: 5.8648e-03 eta: 1 day, 1:47:07 time: 1.2706 data_time: 0.0294 memory: 16201 loss_prob: 0.7989 loss_thr: 0.4775 loss_db: 0.1329 loss: 1.4093 2022/08/30 05:03:39 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:03:48 - mmengine - INFO - Epoch(train) [216][5/63] lr: 5.8595e-03 eta: 1 day, 1:47:07 time: 1.4467 data_time: 0.2550 memory: 16201 loss_prob: 0.7744 loss_thr: 0.4837 loss_db: 0.1309 loss: 1.3890 2022/08/30 05:03:55 - mmengine - INFO - Epoch(train) [216][10/63] lr: 5.8595e-03 eta: 1 day, 1:46:34 time: 1.6431 data_time: 0.2756 memory: 16201 loss_prob: 0.8316 loss_thr: 0.4970 loss_db: 0.1434 loss: 1.4720 2022/08/30 05:04:01 - mmengine - INFO - Epoch(train) [216][15/63] lr: 5.8595e-03 eta: 1 day, 1:46:34 time: 1.3452 data_time: 0.0347 memory: 16201 loss_prob: 0.8437 loss_thr: 0.4856 loss_db: 0.1429 loss: 1.4723 2022/08/30 05:04:08 - mmengine - INFO - Epoch(train) [216][20/63] lr: 5.8595e-03 eta: 1 day, 1:46:12 time: 1.3441 data_time: 0.0277 memory: 16201 loss_prob: 0.8055 loss_thr: 0.4707 loss_db: 0.1320 loss: 1.4082 2022/08/30 05:04:15 - mmengine - INFO - Epoch(train) [216][25/63] lr: 5.8595e-03 eta: 1 day, 1:46:12 time: 1.3630 data_time: 0.0416 memory: 16201 loss_prob: 0.8487 loss_thr: 0.4969 loss_db: 0.1417 loss: 1.4873 2022/08/30 05:04:21 - mmengine - INFO - Epoch(train) [216][30/63] lr: 5.8595e-03 eta: 1 day, 1:45:46 time: 1.2470 data_time: 0.0322 memory: 16201 loss_prob: 0.8535 loss_thr: 0.4892 loss_db: 0.1461 loss: 1.4888 2022/08/30 05:04:27 - mmengine - INFO - Epoch(train) [216][35/63] lr: 5.8595e-03 eta: 1 day, 1:45:46 time: 1.2413 data_time: 0.0321 memory: 16201 loss_prob: 0.7472 loss_thr: 0.4652 loss_db: 0.1282 loss: 1.3405 2022/08/30 05:04:33 - mmengine - INFO - Epoch(train) [216][40/63] lr: 5.8595e-03 eta: 1 day, 1:45:19 time: 1.2486 data_time: 0.0352 memory: 16201 loss_prob: 0.6769 loss_thr: 0.4637 loss_db: 0.1146 loss: 1.2552 2022/08/30 05:04:41 - mmengine - INFO - Epoch(train) [216][45/63] lr: 5.8595e-03 eta: 1 day, 1:45:19 time: 1.3154 data_time: 0.0657 memory: 16201 loss_prob: 0.7305 loss_thr: 0.4561 loss_db: 0.1252 loss: 1.3118 2022/08/30 05:04:48 - mmengine - INFO - Epoch(train) [216][50/63] lr: 5.8595e-03 eta: 1 day, 1:45:01 time: 1.4236 data_time: 0.0722 memory: 16201 loss_prob: 0.8366 loss_thr: 0.4799 loss_db: 0.1403 loss: 1.4568 2022/08/30 05:04:54 - mmengine - INFO - Epoch(train) [216][55/63] lr: 5.8595e-03 eta: 1 day, 1:45:01 time: 1.3740 data_time: 0.0354 memory: 16201 loss_prob: 0.8208 loss_thr: 0.4828 loss_db: 0.1387 loss: 1.4423 2022/08/30 05:05:01 - mmengine - INFO - Epoch(train) [216][60/63] lr: 5.8595e-03 eta: 1 day, 1:44:39 time: 1.3293 data_time: 0.0492 memory: 16201 loss_prob: 0.8474 loss_thr: 0.4740 loss_db: 0.1442 loss: 1.4655 2022/08/30 05:05:04 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:05:13 - mmengine - INFO - Epoch(train) [217][5/63] lr: 5.8541e-03 eta: 1 day, 1:44:39 time: 1.4532 data_time: 0.2410 memory: 16201 loss_prob: 0.8976 loss_thr: 0.5093 loss_db: 0.1446 loss: 1.5515 2022/08/30 05:05:20 - mmengine - INFO - Epoch(train) [217][10/63] lr: 5.8541e-03 eta: 1 day, 1:44:04 time: 1.6035 data_time: 0.2697 memory: 16201 loss_prob: 0.8658 loss_thr: 0.5131 loss_db: 0.1452 loss: 1.5242 2022/08/30 05:05:27 - mmengine - INFO - Epoch(train) [217][15/63] lr: 5.8541e-03 eta: 1 day, 1:44:04 time: 1.3560 data_time: 0.0445 memory: 16201 loss_prob: 0.8107 loss_thr: 0.4943 loss_db: 0.1389 loss: 1.4440 2022/08/30 05:05:34 - mmengine - INFO - Epoch(train) [217][20/63] lr: 5.8541e-03 eta: 1 day, 1:43:42 time: 1.3552 data_time: 0.0319 memory: 16201 loss_prob: 0.8084 loss_thr: 0.4903 loss_db: 0.1331 loss: 1.4318 2022/08/30 05:05:40 - mmengine - INFO - Epoch(train) [217][25/63] lr: 5.8541e-03 eta: 1 day, 1:43:42 time: 1.3108 data_time: 0.0408 memory: 16201 loss_prob: 0.7890 loss_thr: 0.4789 loss_db: 0.1290 loss: 1.3969 2022/08/30 05:05:46 - mmengine - INFO - Epoch(train) [217][30/63] lr: 5.8541e-03 eta: 1 day, 1:43:16 time: 1.2388 data_time: 0.0310 memory: 16201 loss_prob: 0.8109 loss_thr: 0.4702 loss_db: 0.1333 loss: 1.4143 2022/08/30 05:05:53 - mmengine - INFO - Epoch(train) [217][35/63] lr: 5.8541e-03 eta: 1 day, 1:43:16 time: 1.3366 data_time: 0.0324 memory: 16201 loss_prob: 0.7983 loss_thr: 0.4755 loss_db: 0.1326 loss: 1.4065 2022/08/30 05:05:59 - mmengine - INFO - Epoch(train) [217][40/63] lr: 5.8541e-03 eta: 1 day, 1:42:53 time: 1.3221 data_time: 0.0325 memory: 16201 loss_prob: 0.7695 loss_thr: 0.4920 loss_db: 0.1278 loss: 1.3893 2022/08/30 05:06:06 - mmengine - INFO - Epoch(train) [217][45/63] lr: 5.8541e-03 eta: 1 day, 1:42:53 time: 1.2988 data_time: 0.0366 memory: 16201 loss_prob: 0.8183 loss_thr: 0.4979 loss_db: 0.1387 loss: 1.4549 2022/08/30 05:06:13 - mmengine - INFO - Epoch(train) [217][50/63] lr: 5.8541e-03 eta: 1 day, 1:42:33 time: 1.3866 data_time: 0.0444 memory: 16201 loss_prob: 0.8188 loss_thr: 0.4883 loss_db: 0.1383 loss: 1.4454 2022/08/30 05:06:20 - mmengine - INFO - Epoch(train) [217][55/63] lr: 5.8541e-03 eta: 1 day, 1:42:33 time: 1.3885 data_time: 0.0269 memory: 16201 loss_prob: 0.8062 loss_thr: 0.4813 loss_db: 0.1360 loss: 1.4235 2022/08/30 05:06:27 - mmengine - INFO - Epoch(train) [217][60/63] lr: 5.8541e-03 eta: 1 day, 1:42:13 time: 1.3786 data_time: 0.0333 memory: 16201 loss_prob: 0.7946 loss_thr: 0.4723 loss_db: 0.1359 loss: 1.4028 2022/08/30 05:06:29 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:06:38 - mmengine - INFO - Epoch(train) [218][5/63] lr: 5.8487e-03 eta: 1 day, 1:42:13 time: 1.4351 data_time: 0.2274 memory: 16201 loss_prob: 0.8694 loss_thr: 0.4998 loss_db: 0.1445 loss: 1.5136 2022/08/30 05:06:45 - mmengine - INFO - Epoch(train) [218][10/63] lr: 5.8487e-03 eta: 1 day, 1:41:38 time: 1.5966 data_time: 0.2372 memory: 16201 loss_prob: 0.8322 loss_thr: 0.4811 loss_db: 0.1380 loss: 1.4513 2022/08/30 05:06:52 - mmengine - INFO - Epoch(train) [218][15/63] lr: 5.8487e-03 eta: 1 day, 1:41:38 time: 1.3855 data_time: 0.0403 memory: 16201 loss_prob: 0.7735 loss_thr: 0.4762 loss_db: 0.1299 loss: 1.3796 2022/08/30 05:06:59 - mmengine - INFO - Epoch(train) [218][20/63] lr: 5.8487e-03 eta: 1 day, 1:41:15 time: 1.3177 data_time: 0.0423 memory: 16201 loss_prob: 0.7299 loss_thr: 0.4590 loss_db: 0.1242 loss: 1.3130 2022/08/30 05:07:06 - mmengine - INFO - Epoch(train) [218][25/63] lr: 5.8487e-03 eta: 1 day, 1:41:15 time: 1.3448 data_time: 0.0388 memory: 16201 loss_prob: 0.7614 loss_thr: 0.4655 loss_db: 0.1273 loss: 1.3543 2022/08/30 05:07:12 - mmengine - INFO - Epoch(train) [218][30/63] lr: 5.8487e-03 eta: 1 day, 1:40:53 time: 1.3380 data_time: 0.0368 memory: 16201 loss_prob: 0.8009 loss_thr: 0.4711 loss_db: 0.1332 loss: 1.4052 2022/08/30 05:07:18 - mmengine - INFO - Epoch(train) [218][35/63] lr: 5.8487e-03 eta: 1 day, 1:40:53 time: 1.2477 data_time: 0.0332 memory: 16201 loss_prob: 0.8204 loss_thr: 0.4789 loss_db: 0.1353 loss: 1.4346 2022/08/30 05:07:25 - mmengine - INFO - Epoch(train) [218][40/63] lr: 5.8487e-03 eta: 1 day, 1:40:30 time: 1.3157 data_time: 0.0371 memory: 16201 loss_prob: 0.8093 loss_thr: 0.4971 loss_db: 0.1362 loss: 1.4426 2022/08/30 05:07:31 - mmengine - INFO - Epoch(train) [218][45/63] lr: 5.8487e-03 eta: 1 day, 1:40:30 time: 1.3452 data_time: 0.0373 memory: 16201 loss_prob: 0.7693 loss_thr: 0.4894 loss_db: 0.1312 loss: 1.3898 2022/08/30 05:07:38 - mmengine - INFO - Epoch(train) [218][50/63] lr: 5.8487e-03 eta: 1 day, 1:40:06 time: 1.2820 data_time: 0.0339 memory: 16201 loss_prob: 0.7913 loss_thr: 0.4873 loss_db: 0.1324 loss: 1.4110 2022/08/30 05:07:46 - mmengine - INFO - Epoch(train) [218][55/63] lr: 5.8487e-03 eta: 1 day, 1:40:06 time: 1.4499 data_time: 0.0348 memory: 16201 loss_prob: 0.7924 loss_thr: 0.4981 loss_db: 0.1347 loss: 1.4252 2022/08/30 05:07:52 - mmengine - INFO - Epoch(train) [218][60/63] lr: 5.8487e-03 eta: 1 day, 1:39:47 time: 1.4143 data_time: 0.0415 memory: 16201 loss_prob: 0.8870 loss_thr: 0.5424 loss_db: 0.1519 loss: 1.5813 2022/08/30 05:07:56 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:08:04 - mmengine - INFO - Epoch(train) [219][5/63] lr: 5.8434e-03 eta: 1 day, 1:39:47 time: 1.4564 data_time: 0.2345 memory: 16201 loss_prob: 0.9247 loss_thr: 0.5181 loss_db: 0.1485 loss: 1.5914 2022/08/30 05:08:11 - mmengine - INFO - Epoch(train) [219][10/63] lr: 5.8434e-03 eta: 1 day, 1:39:09 time: 1.5120 data_time: 0.2418 memory: 16201 loss_prob: 0.7727 loss_thr: 0.4701 loss_db: 0.1293 loss: 1.3721 2022/08/30 05:08:17 - mmengine - INFO - Epoch(train) [219][15/63] lr: 5.8434e-03 eta: 1 day, 1:39:09 time: 1.3045 data_time: 0.0325 memory: 16201 loss_prob: 0.7319 loss_thr: 0.4800 loss_db: 0.1266 loss: 1.3385 2022/08/30 05:08:24 - mmengine - INFO - Epoch(train) [219][20/63] lr: 5.8434e-03 eta: 1 day, 1:38:46 time: 1.3212 data_time: 0.0339 memory: 16201 loss_prob: 0.7946 loss_thr: 0.4813 loss_db: 0.1349 loss: 1.4108 2022/08/30 05:08:31 - mmengine - INFO - Epoch(train) [219][25/63] lr: 5.8434e-03 eta: 1 day, 1:38:46 time: 1.3462 data_time: 0.0400 memory: 16201 loss_prob: 0.7613 loss_thr: 0.4435 loss_db: 0.1272 loss: 1.3320 2022/08/30 05:08:38 - mmengine - INFO - Epoch(train) [219][30/63] lr: 5.8434e-03 eta: 1 day, 1:38:25 time: 1.3620 data_time: 0.0326 memory: 16201 loss_prob: 0.7397 loss_thr: 0.4402 loss_db: 0.1252 loss: 1.3051 2022/08/30 05:08:44 - mmengine - INFO - Epoch(train) [219][35/63] lr: 5.8434e-03 eta: 1 day, 1:38:25 time: 1.3114 data_time: 0.0291 memory: 16201 loss_prob: 0.7371 loss_thr: 0.4412 loss_db: 0.1254 loss: 1.3038 2022/08/30 05:08:51 - mmengine - INFO - Epoch(train) [219][40/63] lr: 5.8434e-03 eta: 1 day, 1:38:02 time: 1.3204 data_time: 0.0283 memory: 16201 loss_prob: 0.7121 loss_thr: 0.4594 loss_db: 0.1194 loss: 1.2909 2022/08/30 05:08:58 - mmengine - INFO - Epoch(train) [219][45/63] lr: 5.8434e-03 eta: 1 day, 1:38:02 time: 1.3682 data_time: 0.0331 memory: 16201 loss_prob: 0.7344 loss_thr: 0.4667 loss_db: 0.1235 loss: 1.3246 2022/08/30 05:09:04 - mmengine - INFO - Epoch(train) [219][50/63] lr: 5.8434e-03 eta: 1 day, 1:37:40 time: 1.3153 data_time: 0.0402 memory: 16201 loss_prob: 0.7393 loss_thr: 0.4563 loss_db: 0.1234 loss: 1.3190 2022/08/30 05:09:10 - mmengine - INFO - Epoch(train) [219][55/63] lr: 5.8434e-03 eta: 1 day, 1:37:40 time: 1.2678 data_time: 0.0320 memory: 16201 loss_prob: 0.7564 loss_thr: 0.4618 loss_db: 0.1269 loss: 1.3451 2022/08/30 05:09:16 - mmengine - INFO - Epoch(train) [219][60/63] lr: 5.8434e-03 eta: 1 day, 1:37:13 time: 1.2334 data_time: 0.0322 memory: 16201 loss_prob: 0.8005 loss_thr: 0.4893 loss_db: 0.1368 loss: 1.4265 2022/08/30 05:09:19 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:09:28 - mmengine - INFO - Epoch(train) [220][5/63] lr: 5.8380e-03 eta: 1 day, 1:37:13 time: 1.4529 data_time: 0.2188 memory: 16201 loss_prob: 0.7725 loss_thr: 0.4777 loss_db: 0.1309 loss: 1.3810 2022/08/30 05:09:36 - mmengine - INFO - Epoch(train) [220][10/63] lr: 5.8380e-03 eta: 1 day, 1:36:40 time: 1.6398 data_time: 0.2366 memory: 16201 loss_prob: 0.7214 loss_thr: 0.4633 loss_db: 0.1214 loss: 1.3061 2022/08/30 05:09:43 - mmengine - INFO - Epoch(train) [220][15/63] lr: 5.8380e-03 eta: 1 day, 1:36:40 time: 1.4347 data_time: 0.0375 memory: 16201 loss_prob: 0.7698 loss_thr: 0.4796 loss_db: 0.1303 loss: 1.3797 2022/08/30 05:09:50 - mmengine - INFO - Epoch(train) [220][20/63] lr: 5.8380e-03 eta: 1 day, 1:36:21 time: 1.3868 data_time: 0.0333 memory: 16201 loss_prob: 0.8041 loss_thr: 0.4709 loss_db: 0.1339 loss: 1.4088 2022/08/30 05:09:57 - mmengine - INFO - Epoch(train) [220][25/63] lr: 5.8380e-03 eta: 1 day, 1:36:21 time: 1.4130 data_time: 0.0385 memory: 16201 loss_prob: 0.8117 loss_thr: 0.4710 loss_db: 0.1350 loss: 1.4177 2022/08/30 05:10:03 - mmengine - INFO - Epoch(train) [220][30/63] lr: 5.8380e-03 eta: 1 day, 1:35:58 time: 1.3300 data_time: 0.0389 memory: 16201 loss_prob: 0.7718 loss_thr: 0.4635 loss_db: 0.1301 loss: 1.3655 2022/08/30 05:10:10 - mmengine - INFO - Epoch(train) [220][35/63] lr: 5.8380e-03 eta: 1 day, 1:35:58 time: 1.2880 data_time: 0.0398 memory: 16201 loss_prob: 0.7492 loss_thr: 0.4589 loss_db: 0.1259 loss: 1.3341 2022/08/30 05:10:17 - mmengine - INFO - Epoch(train) [220][40/63] lr: 5.8380e-03 eta: 1 day, 1:35:37 time: 1.3467 data_time: 0.0318 memory: 16201 loss_prob: 0.7465 loss_thr: 0.4872 loss_db: 0.1254 loss: 1.3591 2022/08/30 05:10:24 - mmengine - INFO - Epoch(train) [220][45/63] lr: 5.8380e-03 eta: 1 day, 1:35:37 time: 1.4603 data_time: 0.0363 memory: 16201 loss_prob: 0.7711 loss_thr: 0.4911 loss_db: 0.1293 loss: 1.3915 2022/08/30 05:10:31 - mmengine - INFO - Epoch(train) [220][50/63] lr: 5.8380e-03 eta: 1 day, 1:35:22 time: 1.4837 data_time: 0.0469 memory: 16201 loss_prob: 0.7962 loss_thr: 0.4801 loss_db: 0.1360 loss: 1.4124 2022/08/30 05:10:38 - mmengine - INFO - Epoch(train) [220][55/63] lr: 5.8380e-03 eta: 1 day, 1:35:22 time: 1.3890 data_time: 0.0397 memory: 16201 loss_prob: 0.7328 loss_thr: 0.4704 loss_db: 0.1264 loss: 1.3297 2022/08/30 05:10:45 - mmengine - INFO - Epoch(train) [220][60/63] lr: 5.8380e-03 eta: 1 day, 1:35:01 time: 1.3602 data_time: 0.0319 memory: 16201 loss_prob: 0.7714 loss_thr: 0.4979 loss_db: 0.1286 loss: 1.3979 2022/08/30 05:10:48 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:10:48 - mmengine - INFO - Saving checkpoint at 220 epochs 2022/08/30 05:10:56 - mmengine - INFO - Epoch(val) [220][5/32] eta: 1 day, 1:35:01 time: 0.6818 data_time: 0.1435 memory: 16201 2022/08/30 05:11:00 - mmengine - INFO - Epoch(val) [220][10/32] eta: 0:00:17 time: 0.8021 data_time: 0.1829 memory: 15734 2022/08/30 05:11:03 - mmengine - INFO - Epoch(val) [220][15/32] eta: 0:00:17 time: 0.6669 data_time: 0.0571 memory: 15734 2022/08/30 05:11:07 - mmengine - INFO - Epoch(val) [220][20/32] eta: 0:00:08 time: 0.7150 data_time: 0.0688 memory: 15734 2022/08/30 05:11:10 - mmengine - INFO - Epoch(val) [220][25/32] eta: 0:00:08 time: 0.7350 data_time: 0.0690 memory: 15734 2022/08/30 05:11:13 - mmengine - INFO - Epoch(val) [220][30/32] eta: 0:00:01 time: 0.6158 data_time: 0.0269 memory: 15734 2022/08/30 05:11:14 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 05:11:14 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8219, precision: 0.7686, hmean: 0.7943 2022/08/30 05:11:14 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8214, precision: 0.8182, hmean: 0.8198 2022/08/30 05:11:14 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8185, precision: 0.8462, hmean: 0.8321 2022/08/30 05:11:14 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8031, precision: 0.8747, hmean: 0.8373 2022/08/30 05:11:14 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7689, precision: 0.9074, hmean: 0.8324 2022/08/30 05:11:14 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.5455, precision: 0.9634, hmean: 0.6966 2022/08/30 05:11:14 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0318, precision: 1.0000, hmean: 0.0616 2022/08/30 05:11:14 - mmengine - INFO - Epoch(val) [220][32/32] icdar/precision: 0.8747 icdar/recall: 0.8031 icdar/hmean: 0.8373 2022/08/30 05:11:24 - mmengine - INFO - Epoch(train) [221][5/63] lr: 5.8327e-03 eta: 0:00:01 time: 1.5442 data_time: 0.2215 memory: 16201 loss_prob: 0.8444 loss_thr: 0.5130 loss_db: 0.1360 loss: 1.4934 2022/08/30 05:11:31 - mmengine - INFO - Epoch(train) [221][10/63] lr: 5.8327e-03 eta: 1 day, 1:34:28 time: 1.6397 data_time: 0.2286 memory: 16201 loss_prob: 0.7536 loss_thr: 0.4742 loss_db: 0.1256 loss: 1.3535 2022/08/30 05:11:37 - mmengine - INFO - Epoch(train) [221][15/63] lr: 5.8327e-03 eta: 1 day, 1:34:28 time: 1.3522 data_time: 0.0298 memory: 16201 loss_prob: 0.7279 loss_thr: 0.4587 loss_db: 0.1251 loss: 1.3117 2022/08/30 05:11:45 - mmengine - INFO - Epoch(train) [221][20/63] lr: 5.8327e-03 eta: 1 day, 1:34:08 time: 1.3832 data_time: 0.0304 memory: 16201 loss_prob: 0.7398 loss_thr: 0.4621 loss_db: 0.1237 loss: 1.3255 2022/08/30 05:11:52 - mmengine - INFO - Epoch(train) [221][25/63] lr: 5.8327e-03 eta: 1 day, 1:34:08 time: 1.4255 data_time: 0.0355 memory: 16201 loss_prob: 0.7422 loss_thr: 0.4608 loss_db: 0.1243 loss: 1.3272 2022/08/30 05:11:58 - mmengine - INFO - Epoch(train) [221][30/63] lr: 5.8327e-03 eta: 1 day, 1:33:47 time: 1.3548 data_time: 0.0336 memory: 16201 loss_prob: 0.6894 loss_thr: 0.4370 loss_db: 0.1184 loss: 1.2448 2022/08/30 05:12:05 - mmengine - INFO - Epoch(train) [221][35/63] lr: 5.8327e-03 eta: 1 day, 1:33:47 time: 1.3181 data_time: 0.0358 memory: 16201 loss_prob: 0.7241 loss_thr: 0.4496 loss_db: 0.1236 loss: 1.2973 2022/08/30 05:12:11 - mmengine - INFO - Epoch(train) [221][40/63] lr: 5.8327e-03 eta: 1 day, 1:33:26 time: 1.3335 data_time: 0.0315 memory: 16201 loss_prob: 0.8288 loss_thr: 0.4899 loss_db: 0.1399 loss: 1.4586 2022/08/30 05:12:18 - mmengine - INFO - Epoch(train) [221][45/63] lr: 5.8327e-03 eta: 1 day, 1:33:26 time: 1.3107 data_time: 0.0317 memory: 16201 loss_prob: 0.7920 loss_thr: 0.4820 loss_db: 0.1309 loss: 1.4049 2022/08/30 05:12:24 - mmengine - INFO - Epoch(train) [221][50/63] lr: 5.8327e-03 eta: 1 day, 1:33:02 time: 1.2884 data_time: 0.0479 memory: 16201 loss_prob: 0.7343 loss_thr: 0.4630 loss_db: 0.1243 loss: 1.3216 2022/08/30 05:12:30 - mmengine - INFO - Epoch(train) [221][55/63] lr: 5.8327e-03 eta: 1 day, 1:33:02 time: 1.2467 data_time: 0.0426 memory: 16201 loss_prob: 0.8068 loss_thr: 0.4886 loss_db: 0.1416 loss: 1.4370 2022/08/30 05:12:37 - mmengine - INFO - Epoch(train) [221][60/63] lr: 5.8327e-03 eta: 1 day, 1:32:37 time: 1.2779 data_time: 0.0309 memory: 16201 loss_prob: 0.8204 loss_thr: 0.5020 loss_db: 0.1408 loss: 1.4631 2022/08/30 05:12:40 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:12:49 - mmengine - INFO - Epoch(train) [222][5/63] lr: 5.8273e-03 eta: 1 day, 1:32:37 time: 1.4050 data_time: 0.2315 memory: 16201 loss_prob: 0.9769 loss_thr: 0.4961 loss_db: 0.1470 loss: 1.6199 2022/08/30 05:12:56 - mmengine - INFO - Epoch(train) [222][10/63] lr: 5.8273e-03 eta: 1 day, 1:32:00 time: 1.5442 data_time: 0.2391 memory: 16201 loss_prob: 0.9854 loss_thr: 0.4827 loss_db: 0.1485 loss: 1.6167 2022/08/30 05:13:02 - mmengine - INFO - Epoch(train) [222][15/63] lr: 5.8273e-03 eta: 1 day, 1:32:00 time: 1.3572 data_time: 0.0330 memory: 16201 loss_prob: 0.8658 loss_thr: 0.4937 loss_db: 0.1481 loss: 1.5076 2022/08/30 05:13:08 - mmengine - INFO - Epoch(train) [222][20/63] lr: 5.8273e-03 eta: 1 day, 1:31:37 time: 1.2953 data_time: 0.0335 memory: 16201 loss_prob: 1.0493 loss_thr: 0.5134 loss_db: 0.1710 loss: 1.7338 2022/08/30 05:13:15 - mmengine - INFO - Epoch(train) [222][25/63] lr: 5.8273e-03 eta: 1 day, 1:31:37 time: 1.2286 data_time: 0.0323 memory: 16201 loss_prob: 1.1009 loss_thr: 0.5443 loss_db: 0.1754 loss: 1.8206 2022/08/30 05:13:21 - mmengine - INFO - Epoch(train) [222][30/63] lr: 5.8273e-03 eta: 1 day, 1:31:12 time: 1.2650 data_time: 0.0285 memory: 16201 loss_prob: 0.9539 loss_thr: 0.5392 loss_db: 0.1628 loss: 1.6558 2022/08/30 05:13:28 - mmengine - INFO - Epoch(train) [222][35/63] lr: 5.8273e-03 eta: 1 day, 1:31:12 time: 1.3074 data_time: 0.0353 memory: 16201 loss_prob: 0.9460 loss_thr: 0.5142 loss_db: 0.1598 loss: 1.6200 2022/08/30 05:13:34 - mmengine - INFO - Epoch(train) [222][40/63] lr: 5.8273e-03 eta: 1 day, 1:30:47 time: 1.2685 data_time: 0.0338 memory: 16201 loss_prob: 0.9618 loss_thr: 0.5246 loss_db: 0.1575 loss: 1.6439 2022/08/30 05:13:41 - mmengine - INFO - Epoch(train) [222][45/63] lr: 5.8273e-03 eta: 1 day, 1:30:47 time: 1.3119 data_time: 0.0309 memory: 16201 loss_prob: 0.9023 loss_thr: 0.5222 loss_db: 0.1503 loss: 1.5748 2022/08/30 05:13:48 - mmengine - INFO - Epoch(train) [222][50/63] lr: 5.8273e-03 eta: 1 day, 1:30:29 time: 1.4224 data_time: 0.0399 memory: 16201 loss_prob: 0.8754 loss_thr: 0.5199 loss_db: 0.1479 loss: 1.5432 2022/08/30 05:13:54 - mmengine - INFO - Epoch(train) [222][55/63] lr: 5.8273e-03 eta: 1 day, 1:30:29 time: 1.3659 data_time: 0.0386 memory: 16201 loss_prob: 0.8579 loss_thr: 0.5021 loss_db: 0.1433 loss: 1.5034 2022/08/30 05:14:01 - mmengine - INFO - Epoch(train) [222][60/63] lr: 5.8273e-03 eta: 1 day, 1:30:08 time: 1.3318 data_time: 0.0349 memory: 16201 loss_prob: 0.9102 loss_thr: 0.5131 loss_db: 0.1508 loss: 1.5742 2022/08/30 05:14:04 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:14:14 - mmengine - INFO - Epoch(train) [223][5/63] lr: 5.8219e-03 eta: 1 day, 1:30:08 time: 1.5164 data_time: 0.2277 memory: 16201 loss_prob: 0.8736 loss_thr: 0.5016 loss_db: 0.1483 loss: 1.5236 2022/08/30 05:14:21 - mmengine - INFO - Epoch(train) [223][10/63] lr: 5.8219e-03 eta: 1 day, 1:29:35 time: 1.6417 data_time: 0.2309 memory: 16201 loss_prob: 0.8655 loss_thr: 0.5132 loss_db: 0.1450 loss: 1.5237 2022/08/30 05:14:26 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:14:28 - mmengine - INFO - Epoch(train) [223][15/63] lr: 5.8219e-03 eta: 1 day, 1:29:35 time: 1.4309 data_time: 0.0358 memory: 16201 loss_prob: 0.8106 loss_thr: 0.4944 loss_db: 0.1337 loss: 1.4388 2022/08/30 05:14:34 - mmengine - INFO - Epoch(train) [223][20/63] lr: 5.8219e-03 eta: 1 day, 1:29:14 time: 1.3411 data_time: 0.0426 memory: 16201 loss_prob: 0.8225 loss_thr: 0.4743 loss_db: 0.1387 loss: 1.4355 2022/08/30 05:14:41 - mmengine - INFO - Epoch(train) [223][25/63] lr: 5.8219e-03 eta: 1 day, 1:29:14 time: 1.3062 data_time: 0.0313 memory: 16201 loss_prob: 0.8719 loss_thr: 0.4850 loss_db: 0.1490 loss: 1.5059 2022/08/30 05:14:47 - mmengine - INFO - Epoch(train) [223][30/63] lr: 5.8219e-03 eta: 1 day, 1:28:51 time: 1.3161 data_time: 0.0320 memory: 16201 loss_prob: 0.8988 loss_thr: 0.5017 loss_db: 0.1489 loss: 1.5494 2022/08/30 05:14:54 - mmengine - INFO - Epoch(train) [223][35/63] lr: 5.8219e-03 eta: 1 day, 1:28:51 time: 1.2943 data_time: 0.0376 memory: 16201 loss_prob: 0.8823 loss_thr: 0.5097 loss_db: 0.1433 loss: 1.5352 2022/08/30 05:15:01 - mmengine - INFO - Epoch(train) [223][40/63] lr: 5.8219e-03 eta: 1 day, 1:28:29 time: 1.3275 data_time: 0.0297 memory: 16201 loss_prob: 0.8968 loss_thr: 0.5310 loss_db: 0.1469 loss: 1.5747 2022/08/30 05:15:08 - mmengine - INFO - Epoch(train) [223][45/63] lr: 5.8219e-03 eta: 1 day, 1:28:29 time: 1.3553 data_time: 0.0387 memory: 16201 loss_prob: 0.8904 loss_thr: 0.5236 loss_db: 0.1496 loss: 1.5636 2022/08/30 05:15:14 - mmengine - INFO - Epoch(train) [223][50/63] lr: 5.8219e-03 eta: 1 day, 1:28:06 time: 1.3090 data_time: 0.0358 memory: 16201 loss_prob: 0.8169 loss_thr: 0.4898 loss_db: 0.1372 loss: 1.4439 2022/08/30 05:15:22 - mmengine - INFO - Epoch(train) [223][55/63] lr: 5.8219e-03 eta: 1 day, 1:28:06 time: 1.4671 data_time: 0.0276 memory: 16201 loss_prob: 0.7904 loss_thr: 0.4737 loss_db: 0.1285 loss: 1.3926 2022/08/30 05:15:30 - mmengine - INFO - Epoch(train) [223][60/63] lr: 5.8219e-03 eta: 1 day, 1:27:57 time: 1.6121 data_time: 0.0470 memory: 16201 loss_prob: 0.7387 loss_thr: 0.4540 loss_db: 0.1239 loss: 1.3165 2022/08/30 05:15:34 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:15:43 - mmengine - INFO - Epoch(train) [224][5/63] lr: 5.8166e-03 eta: 1 day, 1:27:57 time: 1.6115 data_time: 0.2546 memory: 16201 loss_prob: 0.7306 loss_thr: 0.4598 loss_db: 0.1243 loss: 1.3147 2022/08/30 05:15:51 - mmengine - INFO - Epoch(train) [224][10/63] lr: 5.8166e-03 eta: 1 day, 1:27:30 time: 1.7704 data_time: 0.2661 memory: 16201 loss_prob: 0.8383 loss_thr: 0.4677 loss_db: 0.1321 loss: 1.4381 2022/08/30 05:15:58 - mmengine - INFO - Epoch(train) [224][15/63] lr: 5.8166e-03 eta: 1 day, 1:27:30 time: 1.5229 data_time: 0.0370 memory: 16201 loss_prob: 0.8908 loss_thr: 0.4854 loss_db: 0.1400 loss: 1.5162 2022/08/30 05:16:05 - mmengine - INFO - Epoch(train) [224][20/63] lr: 5.8166e-03 eta: 1 day, 1:27:11 time: 1.3965 data_time: 0.0371 memory: 16201 loss_prob: 0.7807 loss_thr: 0.4606 loss_db: 0.1278 loss: 1.3691 2022/08/30 05:16:13 - mmengine - INFO - Epoch(train) [224][25/63] lr: 5.8166e-03 eta: 1 day, 1:27:11 time: 1.4570 data_time: 0.0469 memory: 16201 loss_prob: 0.7948 loss_thr: 0.4885 loss_db: 0.1318 loss: 1.4151 2022/08/30 05:16:20 - mmengine - INFO - Epoch(train) [224][30/63] lr: 5.8166e-03 eta: 1 day, 1:26:55 time: 1.4592 data_time: 0.0342 memory: 16201 loss_prob: 0.8374 loss_thr: 0.5109 loss_db: 0.1415 loss: 1.4898 2022/08/30 05:16:26 - mmengine - INFO - Epoch(train) [224][35/63] lr: 5.8166e-03 eta: 1 day, 1:26:55 time: 1.2864 data_time: 0.0274 memory: 16201 loss_prob: 0.8206 loss_thr: 0.4947 loss_db: 0.1365 loss: 1.4519 2022/08/30 05:16:32 - mmengine - INFO - Epoch(train) [224][40/63] lr: 5.8166e-03 eta: 1 day, 1:26:30 time: 1.2578 data_time: 0.0292 memory: 16201 loss_prob: 0.7561 loss_thr: 0.4802 loss_db: 0.1251 loss: 1.3614 2022/08/30 05:16:39 - mmengine - INFO - Epoch(train) [224][45/63] lr: 5.8166e-03 eta: 1 day, 1:26:30 time: 1.2957 data_time: 0.0312 memory: 16201 loss_prob: 0.7423 loss_thr: 0.4629 loss_db: 0.1227 loss: 1.3279 2022/08/30 05:16:45 - mmengine - INFO - Epoch(train) [224][50/63] lr: 5.8166e-03 eta: 1 day, 1:26:07 time: 1.2894 data_time: 0.0382 memory: 16201 loss_prob: 0.8034 loss_thr: 0.4819 loss_db: 0.1350 loss: 1.4203 2022/08/30 05:16:53 - mmengine - INFO - Epoch(train) [224][55/63] lr: 5.8166e-03 eta: 1 day, 1:26:07 time: 1.3979 data_time: 0.0296 memory: 16201 loss_prob: 0.8209 loss_thr: 0.4935 loss_db: 0.1401 loss: 1.4546 2022/08/30 05:16:59 - mmengine - INFO - Epoch(train) [224][60/63] lr: 5.8166e-03 eta: 1 day, 1:25:48 time: 1.4121 data_time: 0.0306 memory: 16201 loss_prob: 0.7857 loss_thr: 0.4732 loss_db: 0.1323 loss: 1.3911 2022/08/30 05:17:02 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:17:11 - mmengine - INFO - Epoch(train) [225][5/63] lr: 5.8112e-03 eta: 1 day, 1:25:48 time: 1.3965 data_time: 0.2246 memory: 16201 loss_prob: 0.8371 loss_thr: 0.4971 loss_db: 0.1427 loss: 1.4769 2022/08/30 05:17:18 - mmengine - INFO - Epoch(train) [225][10/63] lr: 5.8112e-03 eta: 1 day, 1:25:15 time: 1.6091 data_time: 0.2437 memory: 16201 loss_prob: 0.8440 loss_thr: 0.5089 loss_db: 0.1441 loss: 1.4970 2022/08/30 05:17:25 - mmengine - INFO - Epoch(train) [225][15/63] lr: 5.8112e-03 eta: 1 day, 1:25:15 time: 1.4411 data_time: 0.0306 memory: 16201 loss_prob: 0.8152 loss_thr: 0.5051 loss_db: 0.1362 loss: 1.4565 2022/08/30 05:17:32 - mmengine - INFO - Epoch(train) [225][20/63] lr: 5.8112e-03 eta: 1 day, 1:24:55 time: 1.3777 data_time: 0.0350 memory: 16201 loss_prob: 0.7903 loss_thr: 0.4913 loss_db: 0.1287 loss: 1.4103 2022/08/30 05:17:39 - mmengine - INFO - Epoch(train) [225][25/63] lr: 5.8112e-03 eta: 1 day, 1:24:55 time: 1.4008 data_time: 0.0410 memory: 16201 loss_prob: 0.8502 loss_thr: 0.5111 loss_db: 0.1422 loss: 1.5035 2022/08/30 05:17:46 - mmengine - INFO - Epoch(train) [225][30/63] lr: 5.8112e-03 eta: 1 day, 1:24:36 time: 1.3981 data_time: 0.0273 memory: 16201 loss_prob: 0.8043 loss_thr: 0.4888 loss_db: 0.1375 loss: 1.4305 2022/08/30 05:17:53 - mmengine - INFO - Epoch(train) [225][35/63] lr: 5.8112e-03 eta: 1 day, 1:24:36 time: 1.3585 data_time: 0.0381 memory: 16201 loss_prob: 0.8330 loss_thr: 0.5003 loss_db: 0.1419 loss: 1.4753 2022/08/30 05:18:00 - mmengine - INFO - Epoch(train) [225][40/63] lr: 5.8112e-03 eta: 1 day, 1:24:16 time: 1.3546 data_time: 0.0312 memory: 16201 loss_prob: 0.8725 loss_thr: 0.5175 loss_db: 0.1483 loss: 1.5383 2022/08/30 05:18:07 - mmengine - INFO - Epoch(train) [225][45/63] lr: 5.8112e-03 eta: 1 day, 1:24:16 time: 1.3695 data_time: 0.0285 memory: 16201 loss_prob: 1.0288 loss_thr: 0.5322 loss_db: 0.1641 loss: 1.7251 2022/08/30 05:18:14 - mmengine - INFO - Epoch(train) [225][50/63] lr: 5.8112e-03 eta: 1 day, 1:23:57 time: 1.4031 data_time: 0.0415 memory: 16201 loss_prob: 0.9632 loss_thr: 0.5040 loss_db: 0.1535 loss: 1.6207 2022/08/30 05:18:20 - mmengine - INFO - Epoch(train) [225][55/63] lr: 5.8112e-03 eta: 1 day, 1:23:57 time: 1.3409 data_time: 0.0277 memory: 16201 loss_prob: 0.7521 loss_thr: 0.4675 loss_db: 0.1267 loss: 1.3463 2022/08/30 05:18:27 - mmengine - INFO - Epoch(train) [225][60/63] lr: 5.8112e-03 eta: 1 day, 1:23:35 time: 1.3204 data_time: 0.0282 memory: 16201 loss_prob: 0.8139 loss_thr: 0.4985 loss_db: 0.1347 loss: 1.4471 2022/08/30 05:18:30 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:18:40 - mmengine - INFO - Epoch(train) [226][5/63] lr: 5.8058e-03 eta: 1 day, 1:23:35 time: 1.5211 data_time: 0.2483 memory: 16201 loss_prob: 0.7358 loss_thr: 0.4443 loss_db: 0.1202 loss: 1.3003 2022/08/30 05:18:47 - mmengine - INFO - Epoch(train) [226][10/63] lr: 5.8058e-03 eta: 1 day, 1:23:06 time: 1.7217 data_time: 0.2709 memory: 16201 loss_prob: 0.7254 loss_thr: 0.4526 loss_db: 0.1228 loss: 1.3007 2022/08/30 05:18:54 - mmengine - INFO - Epoch(train) [226][15/63] lr: 5.8058e-03 eta: 1 day, 1:23:06 time: 1.4206 data_time: 0.0339 memory: 16201 loss_prob: 0.8423 loss_thr: 0.4935 loss_db: 0.1431 loss: 1.4788 2022/08/30 05:19:00 - mmengine - INFO - Epoch(train) [226][20/63] lr: 5.8058e-03 eta: 1 day, 1:22:43 time: 1.3026 data_time: 0.0324 memory: 16201 loss_prob: 0.8650 loss_thr: 0.5083 loss_db: 0.1434 loss: 1.5167 2022/08/30 05:19:07 - mmengine - INFO - Epoch(train) [226][25/63] lr: 5.8058e-03 eta: 1 day, 1:22:43 time: 1.2704 data_time: 0.0540 memory: 16201 loss_prob: 0.7957 loss_thr: 0.4871 loss_db: 0.1334 loss: 1.4162 2022/08/30 05:19:13 - mmengine - INFO - Epoch(train) [226][30/63] lr: 5.8058e-03 eta: 1 day, 1:22:20 time: 1.2998 data_time: 0.0375 memory: 16201 loss_prob: 0.8304 loss_thr: 0.4711 loss_db: 0.1362 loss: 1.4377 2022/08/30 05:19:20 - mmengine - INFO - Epoch(train) [226][35/63] lr: 5.8058e-03 eta: 1 day, 1:22:20 time: 1.3022 data_time: 0.0246 memory: 16201 loss_prob: 0.8903 loss_thr: 0.4755 loss_db: 0.1460 loss: 1.5118 2022/08/30 05:19:26 - mmengine - INFO - Epoch(train) [226][40/63] lr: 5.8058e-03 eta: 1 day, 1:21:57 time: 1.2893 data_time: 0.0310 memory: 16201 loss_prob: 0.9255 loss_thr: 0.5060 loss_db: 0.1507 loss: 1.5822 2022/08/30 05:19:34 - mmengine - INFO - Epoch(train) [226][45/63] lr: 5.8058e-03 eta: 1 day, 1:21:57 time: 1.4011 data_time: 0.0307 memory: 16201 loss_prob: 0.8691 loss_thr: 0.5168 loss_db: 0.1399 loss: 1.5259 2022/08/30 05:19:41 - mmengine - INFO - Epoch(train) [226][50/63] lr: 5.8058e-03 eta: 1 day, 1:21:40 time: 1.4367 data_time: 0.0427 memory: 16201 loss_prob: 0.8181 loss_thr: 0.4961 loss_db: 0.1370 loss: 1.4512 2022/08/30 05:19:47 - mmengine - INFO - Epoch(train) [226][55/63] lr: 5.8058e-03 eta: 1 day, 1:21:40 time: 1.3801 data_time: 0.0377 memory: 16201 loss_prob: 0.7725 loss_thr: 0.4760 loss_db: 0.1295 loss: 1.3780 2022/08/30 05:19:54 - mmengine - INFO - Epoch(train) [226][60/63] lr: 5.8058e-03 eta: 1 day, 1:21:20 time: 1.3810 data_time: 0.0320 memory: 16201 loss_prob: 0.7668 loss_thr: 0.4844 loss_db: 0.1275 loss: 1.3787 2022/08/30 05:19:58 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:20:07 - mmengine - INFO - Epoch(train) [227][5/63] lr: 5.8005e-03 eta: 1 day, 1:21:20 time: 1.5671 data_time: 0.2425 memory: 16201 loss_prob: 0.8754 loss_thr: 0.4914 loss_db: 0.1434 loss: 1.5103 2022/08/30 05:20:15 - mmengine - INFO - Epoch(train) [227][10/63] lr: 5.8005e-03 eta: 1 day, 1:20:50 time: 1.6883 data_time: 0.2703 memory: 16201 loss_prob: 0.8161 loss_thr: 0.4573 loss_db: 0.1316 loss: 1.4051 2022/08/30 05:20:22 - mmengine - INFO - Epoch(train) [227][15/63] lr: 5.8005e-03 eta: 1 day, 1:20:50 time: 1.4932 data_time: 0.0424 memory: 16201 loss_prob: 0.7851 loss_thr: 0.4616 loss_db: 0.1296 loss: 1.3762 2022/08/30 05:20:29 - mmengine - INFO - Epoch(train) [227][20/63] lr: 5.8005e-03 eta: 1 day, 1:20:34 time: 1.4478 data_time: 0.0311 memory: 16201 loss_prob: 0.7868 loss_thr: 0.4821 loss_db: 0.1339 loss: 1.4029 2022/08/30 05:20:36 - mmengine - INFO - Epoch(train) [227][25/63] lr: 5.8005e-03 eta: 1 day, 1:20:34 time: 1.4013 data_time: 0.0387 memory: 16201 loss_prob: 0.7770 loss_thr: 0.4754 loss_db: 0.1310 loss: 1.3834 2022/08/30 05:20:43 - mmengine - INFO - Epoch(train) [227][30/63] lr: 5.8005e-03 eta: 1 day, 1:20:15 time: 1.3955 data_time: 0.0304 memory: 16201 loss_prob: 0.8033 loss_thr: 0.4823 loss_db: 0.1346 loss: 1.4202 2022/08/30 05:20:50 - mmengine - INFO - Epoch(train) [227][35/63] lr: 5.8005e-03 eta: 1 day, 1:20:15 time: 1.4123 data_time: 0.0349 memory: 16201 loss_prob: 0.8287 loss_thr: 0.5049 loss_db: 0.1392 loss: 1.4728 2022/08/30 05:20:57 - mmengine - INFO - Epoch(train) [227][40/63] lr: 5.8005e-03 eta: 1 day, 1:19:55 time: 1.3695 data_time: 0.0329 memory: 16201 loss_prob: 0.8401 loss_thr: 0.4993 loss_db: 0.1403 loss: 1.4796 2022/08/30 05:21:04 - mmengine - INFO - Epoch(train) [227][45/63] lr: 5.8005e-03 eta: 1 day, 1:19:55 time: 1.3517 data_time: 0.0316 memory: 16201 loss_prob: 0.8576 loss_thr: 0.4917 loss_db: 0.1459 loss: 1.4953 2022/08/30 05:21:11 - mmengine - INFO - Epoch(train) [227][50/63] lr: 5.8005e-03 eta: 1 day, 1:19:35 time: 1.3715 data_time: 0.0422 memory: 16201 loss_prob: 0.8530 loss_thr: 0.4997 loss_db: 0.1444 loss: 1.4971 2022/08/30 05:21:17 - mmengine - INFO - Epoch(train) [227][55/63] lr: 5.8005e-03 eta: 1 day, 1:19:35 time: 1.3287 data_time: 0.0287 memory: 16201 loss_prob: 0.8136 loss_thr: 0.4984 loss_db: 0.1368 loss: 1.4488 2022/08/30 05:21:24 - mmengine - INFO - Epoch(train) [227][60/63] lr: 5.8005e-03 eta: 1 day, 1:19:13 time: 1.3046 data_time: 0.0276 memory: 16201 loss_prob: 0.8225 loss_thr: 0.4927 loss_db: 0.1397 loss: 1.4549 2022/08/30 05:21:27 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:21:36 - mmengine - INFO - Epoch(train) [228][5/63] lr: 5.7951e-03 eta: 1 day, 1:19:13 time: 1.4820 data_time: 0.2301 memory: 16201 loss_prob: 0.8238 loss_thr: 0.4900 loss_db: 0.1402 loss: 1.4539 2022/08/30 05:21:43 - mmengine - INFO - Epoch(train) [228][10/63] lr: 5.7951e-03 eta: 1 day, 1:18:41 time: 1.6577 data_time: 0.2556 memory: 16201 loss_prob: 0.7293 loss_thr: 0.4418 loss_db: 0.1209 loss: 1.2921 2022/08/30 05:21:50 - mmengine - INFO - Epoch(train) [228][15/63] lr: 5.7951e-03 eta: 1 day, 1:18:41 time: 1.4545 data_time: 0.0364 memory: 16201 loss_prob: 0.7012 loss_thr: 0.4275 loss_db: 0.1173 loss: 1.2459 2022/08/30 05:21:57 - mmengine - INFO - Epoch(train) [228][20/63] lr: 5.7951e-03 eta: 1 day, 1:18:22 time: 1.3689 data_time: 0.0276 memory: 16201 loss_prob: 0.7338 loss_thr: 0.4504 loss_db: 0.1258 loss: 1.3099 2022/08/30 05:22:04 - mmengine - INFO - Epoch(train) [228][25/63] lr: 5.7951e-03 eta: 1 day, 1:18:22 time: 1.3832 data_time: 0.0408 memory: 16201 loss_prob: 0.7841 loss_thr: 0.4774 loss_db: 0.1311 loss: 1.3927 2022/08/30 05:22:11 - mmengine - INFO - Epoch(train) [228][30/63] lr: 5.7951e-03 eta: 1 day, 1:18:02 time: 1.3760 data_time: 0.0312 memory: 16201 loss_prob: 0.7815 loss_thr: 0.4696 loss_db: 0.1294 loss: 1.3805 2022/08/30 05:22:18 - mmengine - INFO - Epoch(train) [228][35/63] lr: 5.7951e-03 eta: 1 day, 1:18:02 time: 1.3705 data_time: 0.0340 memory: 16201 loss_prob: 0.7911 loss_thr: 0.4781 loss_db: 0.1365 loss: 1.4057 2022/08/30 05:22:24 - mmengine - INFO - Epoch(train) [228][40/63] lr: 5.7951e-03 eta: 1 day, 1:17:40 time: 1.3313 data_time: 0.0338 memory: 16201 loss_prob: 0.7884 loss_thr: 0.4955 loss_db: 0.1359 loss: 1.4198 2022/08/30 05:22:30 - mmengine - INFO - Epoch(train) [228][45/63] lr: 5.7951e-03 eta: 1 day, 1:17:40 time: 1.2473 data_time: 0.0292 memory: 16201 loss_prob: 0.7724 loss_thr: 0.4782 loss_db: 0.1297 loss: 1.3802 2022/08/30 05:22:37 - mmengine - INFO - Epoch(train) [228][50/63] lr: 5.7951e-03 eta: 1 day, 1:17:18 time: 1.3067 data_time: 0.0411 memory: 16201 loss_prob: 0.7941 loss_thr: 0.4911 loss_db: 0.1308 loss: 1.4160 2022/08/30 05:22:43 - mmengine - INFO - Epoch(train) [228][55/63] lr: 5.7951e-03 eta: 1 day, 1:17:18 time: 1.2967 data_time: 0.0293 memory: 16201 loss_prob: 0.8119 loss_thr: 0.5021 loss_db: 0.1355 loss: 1.4495 2022/08/30 05:22:51 - mmengine - INFO - Epoch(train) [228][60/63] lr: 5.7951e-03 eta: 1 day, 1:16:57 time: 1.3550 data_time: 0.0297 memory: 16201 loss_prob: 0.8002 loss_thr: 0.4847 loss_db: 0.1352 loss: 1.4201 2022/08/30 05:22:54 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:23:03 - mmengine - INFO - Epoch(train) [229][5/63] lr: 5.7897e-03 eta: 1 day, 1:16:57 time: 1.4705 data_time: 0.2418 memory: 16201 loss_prob: 0.7311 loss_thr: 0.4615 loss_db: 0.1210 loss: 1.3136 2022/08/30 05:23:10 - mmengine - INFO - Epoch(train) [229][10/63] lr: 5.7897e-03 eta: 1 day, 1:16:23 time: 1.5666 data_time: 0.2591 memory: 16201 loss_prob: 0.7454 loss_thr: 0.4631 loss_db: 0.1235 loss: 1.3321 2022/08/30 05:23:17 - mmengine - INFO - Epoch(train) [229][15/63] lr: 5.7897e-03 eta: 1 day, 1:16:23 time: 1.3943 data_time: 0.0326 memory: 16201 loss_prob: 0.7515 loss_thr: 0.4863 loss_db: 0.1284 loss: 1.3661 2022/08/30 05:23:23 - mmengine - INFO - Epoch(train) [229][20/63] lr: 5.7897e-03 eta: 1 day, 1:16:00 time: 1.2962 data_time: 0.0364 memory: 16201 loss_prob: 0.7627 loss_thr: 0.4940 loss_db: 0.1295 loss: 1.3862 2022/08/30 05:23:30 - mmengine - INFO - Epoch(train) [229][25/63] lr: 5.7897e-03 eta: 1 day, 1:16:00 time: 1.3176 data_time: 0.0360 memory: 16201 loss_prob: 0.8286 loss_thr: 0.4720 loss_db: 0.1342 loss: 1.4348 2022/08/30 05:23:37 - mmengine - INFO - Epoch(train) [229][30/63] lr: 5.7897e-03 eta: 1 day, 1:15:40 time: 1.3820 data_time: 0.0409 memory: 16201 loss_prob: 0.8820 loss_thr: 0.4925 loss_db: 0.1450 loss: 1.5195 2022/08/30 05:23:43 - mmengine - INFO - Epoch(train) [229][35/63] lr: 5.7897e-03 eta: 1 day, 1:15:40 time: 1.3336 data_time: 0.0503 memory: 16201 loss_prob: 0.8344 loss_thr: 0.4881 loss_db: 0.1405 loss: 1.4630 2022/08/30 05:23:50 - mmengine - INFO - Epoch(train) [229][40/63] lr: 5.7897e-03 eta: 1 day, 1:15:20 time: 1.3593 data_time: 0.0310 memory: 16201 loss_prob: 0.7309 loss_thr: 0.4564 loss_db: 0.1231 loss: 1.3104 2022/08/30 05:23:57 - mmengine - INFO - Epoch(train) [229][45/63] lr: 5.7897e-03 eta: 1 day, 1:15:20 time: 1.3201 data_time: 0.0372 memory: 16201 loss_prob: 0.7440 loss_thr: 0.4673 loss_db: 0.1254 loss: 1.3367 2022/08/30 05:24:04 - mmengine - INFO - Epoch(train) [229][50/63] lr: 5.7897e-03 eta: 1 day, 1:15:00 time: 1.3541 data_time: 0.0473 memory: 16201 loss_prob: 0.8219 loss_thr: 0.5021 loss_db: 0.1375 loss: 1.4616 2022/08/30 05:24:10 - mmengine - INFO - Epoch(train) [229][55/63] lr: 5.7897e-03 eta: 1 day, 1:15:00 time: 1.3071 data_time: 0.0308 memory: 16201 loss_prob: 0.7558 loss_thr: 0.4828 loss_db: 0.1284 loss: 1.3671 2022/08/30 05:24:17 - mmengine - INFO - Epoch(train) [229][60/63] lr: 5.7897e-03 eta: 1 day, 1:14:39 time: 1.3383 data_time: 0.0325 memory: 16201 loss_prob: 0.7008 loss_thr: 0.4400 loss_db: 0.1189 loss: 1.2597 2022/08/30 05:24:21 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:24:30 - mmengine - INFO - Epoch(train) [230][5/63] lr: 5.7844e-03 eta: 1 day, 1:14:39 time: 1.6229 data_time: 0.2419 memory: 16201 loss_prob: 0.7821 loss_thr: 0.4528 loss_db: 0.1293 loss: 1.3643 2022/08/30 05:24:38 - mmengine - INFO - Epoch(train) [230][10/63] lr: 5.7844e-03 eta: 1 day, 1:14:07 time: 1.6405 data_time: 0.2513 memory: 16201 loss_prob: 0.7605 loss_thr: 0.4526 loss_db: 0.1297 loss: 1.3427 2022/08/30 05:24:44 - mmengine - INFO - Epoch(train) [230][15/63] lr: 5.7844e-03 eta: 1 day, 1:14:07 time: 1.3397 data_time: 0.0347 memory: 16201 loss_prob: 0.7178 loss_thr: 0.4491 loss_db: 0.1216 loss: 1.2886 2022/08/30 05:24:51 - mmengine - INFO - Epoch(train) [230][20/63] lr: 5.7844e-03 eta: 1 day, 1:13:46 time: 1.3397 data_time: 0.0333 memory: 16201 loss_prob: 0.7974 loss_thr: 0.4740 loss_db: 0.1279 loss: 1.3993 2022/08/30 05:24:58 - mmengine - INFO - Epoch(train) [230][25/63] lr: 5.7844e-03 eta: 1 day, 1:13:46 time: 1.3782 data_time: 0.0356 memory: 16201 loss_prob: 0.7978 loss_thr: 0.4770 loss_db: 0.1288 loss: 1.4036 2022/08/30 05:25:04 - mmengine - INFO - Epoch(train) [230][30/63] lr: 5.7844e-03 eta: 1 day, 1:13:24 time: 1.3286 data_time: 0.0350 memory: 16201 loss_prob: 0.7363 loss_thr: 0.4670 loss_db: 0.1243 loss: 1.3276 2022/08/30 05:25:11 - mmengine - INFO - Epoch(train) [230][35/63] lr: 5.7844e-03 eta: 1 day, 1:13:24 time: 1.3042 data_time: 0.0387 memory: 16201 loss_prob: 0.7167 loss_thr: 0.4647 loss_db: 0.1232 loss: 1.3047 2022/08/30 05:25:18 - mmengine - INFO - Epoch(train) [230][40/63] lr: 5.7844e-03 eta: 1 day, 1:13:04 time: 1.3408 data_time: 0.0310 memory: 16201 loss_prob: 0.6729 loss_thr: 0.4415 loss_db: 0.1155 loss: 1.2299 2022/08/30 05:25:24 - mmengine - INFO - Epoch(train) [230][45/63] lr: 5.7844e-03 eta: 1 day, 1:13:04 time: 1.3622 data_time: 0.0368 memory: 16201 loss_prob: 0.7045 loss_thr: 0.4606 loss_db: 0.1202 loss: 1.2853 2022/08/30 05:25:31 - mmengine - INFO - Epoch(train) [230][50/63] lr: 5.7844e-03 eta: 1 day, 1:12:43 time: 1.3534 data_time: 0.0420 memory: 16201 loss_prob: 0.7346 loss_thr: 0.4747 loss_db: 0.1263 loss: 1.3356 2022/08/30 05:25:38 - mmengine - INFO - Epoch(train) [230][55/63] lr: 5.7844e-03 eta: 1 day, 1:12:43 time: 1.4005 data_time: 0.0343 memory: 16201 loss_prob: 0.7141 loss_thr: 0.4479 loss_db: 0.1235 loss: 1.2856 2022/08/30 05:25:44 - mmengine - INFO - Epoch(train) [230][60/63] lr: 5.7844e-03 eta: 1 day, 1:12:22 time: 1.3317 data_time: 0.0389 memory: 16201 loss_prob: 0.6773 loss_thr: 0.4322 loss_db: 0.1149 loss: 1.2244 2022/08/30 05:25:48 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:25:56 - mmengine - INFO - Epoch(train) [231][5/63] lr: 5.7790e-03 eta: 1 day, 1:12:22 time: 1.4538 data_time: 0.2320 memory: 16201 loss_prob: 0.7089 loss_thr: 0.4535 loss_db: 0.1216 loss: 1.2839 2022/08/30 05:26:04 - mmengine - INFO - Epoch(train) [231][10/63] lr: 5.7790e-03 eta: 1 day, 1:11:51 time: 1.6493 data_time: 0.2527 memory: 16201 loss_prob: 0.7611 loss_thr: 0.4592 loss_db: 0.1316 loss: 1.3518 2022/08/30 05:26:10 - mmengine - INFO - Epoch(train) [231][15/63] lr: 5.7790e-03 eta: 1 day, 1:11:51 time: 1.3888 data_time: 0.0378 memory: 16201 loss_prob: 0.8582 loss_thr: 0.5146 loss_db: 0.1407 loss: 1.5136 2022/08/30 05:26:16 - mmengine - INFO - Epoch(train) [231][20/63] lr: 5.7790e-03 eta: 1 day, 1:11:25 time: 1.2332 data_time: 0.0336 memory: 16201 loss_prob: 0.8172 loss_thr: 0.4879 loss_db: 0.1359 loss: 1.4410 2022/08/30 05:26:23 - mmengine - INFO - Epoch(train) [231][25/63] lr: 5.7790e-03 eta: 1 day, 1:11:25 time: 1.2840 data_time: 0.0510 memory: 16201 loss_prob: 0.7891 loss_thr: 0.4810 loss_db: 0.1370 loss: 1.4071 2022/08/30 05:26:30 - mmengine - INFO - Epoch(train) [231][30/63] lr: 5.7790e-03 eta: 1 day, 1:11:07 time: 1.4009 data_time: 0.0403 memory: 16201 loss_prob: 0.7250 loss_thr: 0.4674 loss_db: 0.1241 loss: 1.3166 2022/08/30 05:26:38 - mmengine - INFO - Epoch(train) [231][35/63] lr: 5.7790e-03 eta: 1 day, 1:11:07 time: 1.4426 data_time: 0.0288 memory: 16201 loss_prob: 0.7102 loss_thr: 0.4613 loss_db: 0.1182 loss: 1.2896 2022/08/30 05:26:44 - mmengine - INFO - Epoch(train) [231][40/63] lr: 5.7790e-03 eta: 1 day, 1:10:48 time: 1.3953 data_time: 0.0329 memory: 16201 loss_prob: 0.7223 loss_thr: 0.4623 loss_db: 0.1200 loss: 1.3046 2022/08/30 05:26:51 - mmengine - INFO - Epoch(train) [231][45/63] lr: 5.7790e-03 eta: 1 day, 1:10:48 time: 1.3433 data_time: 0.0348 memory: 16201 loss_prob: 0.6914 loss_thr: 0.4389 loss_db: 0.1162 loss: 1.2464 2022/08/30 05:26:58 - mmengine - INFO - Epoch(train) [231][50/63] lr: 5.7790e-03 eta: 1 day, 1:10:28 time: 1.3532 data_time: 0.0401 memory: 16201 loss_prob: 0.7117 loss_thr: 0.4545 loss_db: 0.1219 loss: 1.2881 2022/08/30 05:27:05 - mmengine - INFO - Epoch(train) [231][55/63] lr: 5.7790e-03 eta: 1 day, 1:10:28 time: 1.3586 data_time: 0.0379 memory: 16201 loss_prob: 0.6949 loss_thr: 0.4394 loss_db: 0.1196 loss: 1.2539 2022/08/30 05:27:11 - mmengine - INFO - Epoch(train) [231][60/63] lr: 5.7790e-03 eta: 1 day, 1:10:06 time: 1.3206 data_time: 0.0327 memory: 16201 loss_prob: 0.7613 loss_thr: 0.4464 loss_db: 0.1236 loss: 1.3313 2022/08/30 05:27:16 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:27:24 - mmengine - INFO - Epoch(train) [232][5/63] lr: 5.7736e-03 eta: 1 day, 1:10:06 time: 1.5660 data_time: 0.2455 memory: 16201 loss_prob: 0.7874 loss_thr: 0.4763 loss_db: 0.1344 loss: 1.3981 2022/08/30 05:27:31 - mmengine - INFO - Epoch(train) [232][10/63] lr: 5.7736e-03 eta: 1 day, 1:09:30 time: 1.5314 data_time: 0.2446 memory: 16201 loss_prob: 0.7500 loss_thr: 0.4510 loss_db: 0.1313 loss: 1.3323 2022/08/30 05:27:38 - mmengine - INFO - Epoch(train) [232][15/63] lr: 5.7736e-03 eta: 1 day, 1:09:30 time: 1.3944 data_time: 0.0338 memory: 16201 loss_prob: 0.7177 loss_thr: 0.4221 loss_db: 0.1231 loss: 1.2629 2022/08/30 05:27:45 - mmengine - INFO - Epoch(train) [232][20/63] lr: 5.7736e-03 eta: 1 day, 1:09:13 time: 1.4245 data_time: 0.0304 memory: 16201 loss_prob: 0.7594 loss_thr: 0.4596 loss_db: 0.1200 loss: 1.3390 2022/08/30 05:27:51 - mmengine - INFO - Epoch(train) [232][25/63] lr: 5.7736e-03 eta: 1 day, 1:09:13 time: 1.2836 data_time: 0.0401 memory: 16201 loss_prob: 0.8564 loss_thr: 0.4950 loss_db: 0.1313 loss: 1.4826 2022/08/30 05:27:57 - mmengine - INFO - Epoch(train) [232][30/63] lr: 5.7736e-03 eta: 1 day, 1:08:47 time: 1.2198 data_time: 0.0299 memory: 16201 loss_prob: 0.8484 loss_thr: 0.4954 loss_db: 0.1366 loss: 1.4804 2022/08/30 05:28:05 - mmengine - INFO - Epoch(train) [232][35/63] lr: 5.7736e-03 eta: 1 day, 1:08:47 time: 1.3465 data_time: 0.0327 memory: 16201 loss_prob: 0.8344 loss_thr: 0.5011 loss_db: 0.1406 loss: 1.4761 2022/08/30 05:28:12 - mmengine - INFO - Epoch(train) [232][40/63] lr: 5.7736e-03 eta: 1 day, 1:08:30 time: 1.4431 data_time: 0.0312 memory: 16201 loss_prob: 0.8238 loss_thr: 0.4809 loss_db: 0.1364 loss: 1.4411 2022/08/30 05:28:19 - mmengine - INFO - Epoch(train) [232][45/63] lr: 5.7736e-03 eta: 1 day, 1:08:30 time: 1.4083 data_time: 0.0261 memory: 16201 loss_prob: 0.8021 loss_thr: 0.4706 loss_db: 0.1339 loss: 1.4066 2022/08/30 05:28:26 - mmengine - INFO - Epoch(train) [232][50/63] lr: 5.7736e-03 eta: 1 day, 1:08:12 time: 1.4040 data_time: 0.0635 memory: 16201 loss_prob: 0.7842 loss_thr: 0.4704 loss_db: 0.1342 loss: 1.3889 2022/08/30 05:28:33 - mmengine - INFO - Epoch(train) [232][55/63] lr: 5.7736e-03 eta: 1 day, 1:08:12 time: 1.4175 data_time: 0.0568 memory: 16201 loss_prob: 0.7223 loss_thr: 0.4479 loss_db: 0.1240 loss: 1.2942 2022/08/30 05:28:40 - mmengine - INFO - Epoch(train) [232][60/63] lr: 5.7736e-03 eta: 1 day, 1:07:54 time: 1.4015 data_time: 0.0371 memory: 16201 loss_prob: 0.7281 loss_thr: 0.4455 loss_db: 0.1203 loss: 1.2939 2022/08/30 05:28:43 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:28:52 - mmengine - INFO - Epoch(train) [233][5/63] lr: 5.7683e-03 eta: 1 day, 1:07:54 time: 1.4500 data_time: 0.2294 memory: 16201 loss_prob: 0.7314 loss_thr: 0.4515 loss_db: 0.1231 loss: 1.3061 2022/08/30 05:28:59 - mmengine - INFO - Epoch(train) [233][10/63] lr: 5.7683e-03 eta: 1 day, 1:07:22 time: 1.6293 data_time: 0.2448 memory: 16201 loss_prob: 0.9019 loss_thr: 0.4870 loss_db: 0.1511 loss: 1.5400 2022/08/30 05:29:06 - mmengine - INFO - Epoch(train) [233][15/63] lr: 5.7683e-03 eta: 1 day, 1:07:22 time: 1.4040 data_time: 0.0355 memory: 16201 loss_prob: 0.9865 loss_thr: 0.4877 loss_db: 0.1623 loss: 1.6365 2022/08/30 05:29:13 - mmengine - INFO - Epoch(train) [233][20/63] lr: 5.7683e-03 eta: 1 day, 1:07:05 time: 1.4122 data_time: 0.0348 memory: 16201 loss_prob: 1.1370 loss_thr: 0.5823 loss_db: 0.1811 loss: 1.9003 2022/08/30 05:29:20 - mmengine - INFO - Epoch(train) [233][25/63] lr: 5.7683e-03 eta: 1 day, 1:07:05 time: 1.4354 data_time: 0.0393 memory: 16201 loss_prob: 1.3305 loss_thr: 0.6208 loss_db: 0.2033 loss: 2.1546 2022/08/30 05:29:27 - mmengine - INFO - Epoch(train) [233][30/63] lr: 5.7683e-03 eta: 1 day, 1:06:45 time: 1.3691 data_time: 0.0373 memory: 16201 loss_prob: 1.3955 loss_thr: 0.5656 loss_db: 0.2159 loss: 2.1770 2022/08/30 05:29:35 - mmengine - INFO - Epoch(train) [233][35/63] lr: 5.7683e-03 eta: 1 day, 1:06:45 time: 1.4189 data_time: 0.0444 memory: 16201 loss_prob: 1.3468 loss_thr: 0.5778 loss_db: 0.2187 loss: 2.1432 2022/08/30 05:29:41 - mmengine - INFO - Epoch(train) [233][40/63] lr: 5.7683e-03 eta: 1 day, 1:06:27 time: 1.4026 data_time: 0.0348 memory: 16201 loss_prob: 1.2531 loss_thr: 0.5736 loss_db: 0.2002 loss: 2.0269 2022/08/30 05:29:48 - mmengine - INFO - Epoch(train) [233][45/63] lr: 5.7683e-03 eta: 1 day, 1:06:27 time: 1.3065 data_time: 0.0296 memory: 16201 loss_prob: 1.1822 loss_thr: 0.5949 loss_db: 0.1901 loss: 1.9672 2022/08/30 05:29:54 - mmengine - INFO - Epoch(train) [233][50/63] lr: 5.7683e-03 eta: 1 day, 1:06:04 time: 1.2970 data_time: 0.0430 memory: 16201 loss_prob: 1.1101 loss_thr: 0.5979 loss_db: 0.1836 loss: 1.8916 2022/08/30 05:30:01 - mmengine - INFO - Epoch(train) [233][55/63] lr: 5.7683e-03 eta: 1 day, 1:06:04 time: 1.3038 data_time: 0.0337 memory: 16201 loss_prob: 1.0929 loss_thr: 0.5793 loss_db: 0.1835 loss: 1.8556 2022/08/30 05:30:07 - mmengine - INFO - Epoch(train) [233][60/63] lr: 5.7683e-03 eta: 1 day, 1:05:43 time: 1.3220 data_time: 0.0265 memory: 16201 loss_prob: 1.0474 loss_thr: 0.5569 loss_db: 0.1717 loss: 1.7760 2022/08/30 05:30:11 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:30:20 - mmengine - INFO - Epoch(train) [234][5/63] lr: 5.7629e-03 eta: 1 day, 1:05:43 time: 1.4909 data_time: 0.2165 memory: 16201 loss_prob: 0.9087 loss_thr: 0.4983 loss_db: 0.1494 loss: 1.5564 2022/08/30 05:30:26 - mmengine - INFO - Epoch(train) [234][10/63] lr: 5.7629e-03 eta: 1 day, 1:05:08 time: 1.5518 data_time: 0.2362 memory: 16201 loss_prob: 0.8789 loss_thr: 0.4859 loss_db: 0.1497 loss: 1.5145 2022/08/30 05:30:33 - mmengine - INFO - Epoch(train) [234][15/63] lr: 5.7629e-03 eta: 1 day, 1:05:08 time: 1.3673 data_time: 0.0381 memory: 16201 loss_prob: 0.8600 loss_thr: 0.4745 loss_db: 0.1434 loss: 1.4779 2022/08/30 05:30:40 - mmengine - INFO - Epoch(train) [234][20/63] lr: 5.7629e-03 eta: 1 day, 1:04:50 time: 1.3997 data_time: 0.0395 memory: 16201 loss_prob: 0.9360 loss_thr: 0.5120 loss_db: 0.1535 loss: 1.6015 2022/08/30 05:30:47 - mmengine - INFO - Epoch(train) [234][25/63] lr: 5.7629e-03 eta: 1 day, 1:04:50 time: 1.3537 data_time: 0.0314 memory: 16201 loss_prob: 0.9539 loss_thr: 0.5252 loss_db: 0.1555 loss: 1.6346 2022/08/30 05:30:54 - mmengine - INFO - Epoch(train) [234][30/63] lr: 5.7629e-03 eta: 1 day, 1:04:29 time: 1.3368 data_time: 0.0278 memory: 16201 loss_prob: 0.9659 loss_thr: 0.5366 loss_db: 0.1581 loss: 1.6605 2022/08/30 05:31:00 - mmengine - INFO - Epoch(train) [234][35/63] lr: 5.7629e-03 eta: 1 day, 1:04:29 time: 1.3637 data_time: 0.0454 memory: 16201 loss_prob: 0.8751 loss_thr: 0.5183 loss_db: 0.1463 loss: 1.5397 2022/08/30 05:31:08 - mmengine - INFO - Epoch(train) [234][40/63] lr: 5.7629e-03 eta: 1 day, 1:04:10 time: 1.3982 data_time: 0.0321 memory: 16201 loss_prob: 0.8708 loss_thr: 0.5020 loss_db: 0.1434 loss: 1.5162 2022/08/30 05:31:15 - mmengine - INFO - Epoch(train) [234][45/63] lr: 5.7629e-03 eta: 1 day, 1:04:10 time: 1.4111 data_time: 0.0287 memory: 16201 loss_prob: 0.8950 loss_thr: 0.5055 loss_db: 0.1473 loss: 1.5478 2022/08/30 05:31:21 - mmengine - INFO - Epoch(train) [234][50/63] lr: 5.7629e-03 eta: 1 day, 1:03:51 time: 1.3668 data_time: 0.0368 memory: 16201 loss_prob: 0.8358 loss_thr: 0.4902 loss_db: 0.1391 loss: 1.4650 2022/08/30 05:31:28 - mmengine - INFO - Epoch(train) [234][55/63] lr: 5.7629e-03 eta: 1 day, 1:03:51 time: 1.3235 data_time: 0.0289 memory: 16201 loss_prob: 0.8469 loss_thr: 0.4917 loss_db: 0.1398 loss: 1.4784 2022/08/30 05:31:35 - mmengine - INFO - Epoch(train) [234][60/63] lr: 5.7629e-03 eta: 1 day, 1:03:31 time: 1.3719 data_time: 0.0359 memory: 16201 loss_prob: 0.8789 loss_thr: 0.5018 loss_db: 0.1500 loss: 1.5307 2022/08/30 05:31:38 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:31:48 - mmengine - INFO - Epoch(train) [235][5/63] lr: 5.7575e-03 eta: 1 day, 1:03:31 time: 1.6281 data_time: 0.2318 memory: 16201 loss_prob: 0.8232 loss_thr: 0.4753 loss_db: 0.1342 loss: 1.4327 2022/08/30 05:31:55 - mmengine - INFO - Epoch(train) [235][10/63] lr: 5.7575e-03 eta: 1 day, 1:03:02 time: 1.6928 data_time: 0.2438 memory: 16201 loss_prob: 0.8780 loss_thr: 0.4862 loss_db: 0.1460 loss: 1.5102 2022/08/30 05:32:02 - mmengine - INFO - Epoch(train) [235][15/63] lr: 5.7575e-03 eta: 1 day, 1:03:02 time: 1.4143 data_time: 0.0321 memory: 16201 loss_prob: 0.8237 loss_thr: 0.4780 loss_db: 0.1379 loss: 1.4396 2022/08/30 05:32:09 - mmengine - INFO - Epoch(train) [235][20/63] lr: 5.7575e-03 eta: 1 day, 1:02:42 time: 1.3367 data_time: 0.0295 memory: 16201 loss_prob: 0.7887 loss_thr: 0.4690 loss_db: 0.1283 loss: 1.3860 2022/08/30 05:32:16 - mmengine - INFO - Epoch(train) [235][25/63] lr: 5.7575e-03 eta: 1 day, 1:02:42 time: 1.3906 data_time: 0.0358 memory: 16201 loss_prob: 0.8296 loss_thr: 0.4880 loss_db: 0.1370 loss: 1.4546 2022/08/30 05:32:23 - mmengine - INFO - Epoch(train) [235][30/63] lr: 5.7575e-03 eta: 1 day, 1:02:27 time: 1.4811 data_time: 0.0328 memory: 16201 loss_prob: 0.9062 loss_thr: 0.4851 loss_db: 0.1536 loss: 1.5450 2022/08/30 05:32:30 - mmengine - INFO - Epoch(train) [235][35/63] lr: 5.7575e-03 eta: 1 day, 1:02:27 time: 1.4102 data_time: 0.0387 memory: 16201 loss_prob: 0.9013 loss_thr: 0.4844 loss_db: 0.1476 loss: 1.5333 2022/08/30 05:32:37 - mmengine - INFO - Epoch(train) [235][40/63] lr: 5.7575e-03 eta: 1 day, 1:02:08 time: 1.3971 data_time: 0.0292 memory: 16201 loss_prob: 0.8520 loss_thr: 0.5015 loss_db: 0.1376 loss: 1.4912 2022/08/30 05:32:44 - mmengine - INFO - Epoch(train) [235][45/63] lr: 5.7575e-03 eta: 1 day, 1:02:08 time: 1.3309 data_time: 0.0319 memory: 16201 loss_prob: 0.9100 loss_thr: 0.5144 loss_db: 0.1507 loss: 1.5751 2022/08/30 05:32:50 - mmengine - INFO - Epoch(train) [235][50/63] lr: 5.7575e-03 eta: 1 day, 1:01:46 time: 1.2962 data_time: 0.0435 memory: 16201 loss_prob: 1.1264 loss_thr: 0.5514 loss_db: 0.1720 loss: 1.8498 2022/08/30 05:32:58 - mmengine - INFO - Epoch(train) [235][55/63] lr: 5.7575e-03 eta: 1 day, 1:01:46 time: 1.4176 data_time: 0.0377 memory: 16201 loss_prob: 1.0995 loss_thr: 0.5318 loss_db: 0.1626 loss: 1.7939 2022/08/30 05:33:04 - mmengine - INFO - Epoch(train) [235][60/63] lr: 5.7575e-03 eta: 1 day, 1:01:26 time: 1.3601 data_time: 0.0434 memory: 16201 loss_prob: 0.9314 loss_thr: 0.5065 loss_db: 0.1527 loss: 1.5906 2022/08/30 05:33:07 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:33:17 - mmengine - INFO - Epoch(train) [236][5/63] lr: 5.7522e-03 eta: 1 day, 1:01:26 time: 1.4954 data_time: 0.2442 memory: 16201 loss_prob: 0.8146 loss_thr: 0.5085 loss_db: 0.1395 loss: 1.4627 2022/08/30 05:33:24 - mmengine - INFO - Epoch(train) [236][10/63] lr: 5.7522e-03 eta: 1 day, 1:00:57 time: 1.6924 data_time: 0.2680 memory: 16201 loss_prob: 0.7916 loss_thr: 0.4855 loss_db: 0.1320 loss: 1.4091 2022/08/30 05:33:30 - mmengine - INFO - Epoch(train) [236][15/63] lr: 5.7522e-03 eta: 1 day, 1:00:57 time: 1.3712 data_time: 0.0389 memory: 16201 loss_prob: 0.8266 loss_thr: 0.4656 loss_db: 0.1389 loss: 1.4311 2022/08/30 05:33:38 - mmengine - INFO - Epoch(train) [236][20/63] lr: 5.7522e-03 eta: 1 day, 1:00:39 time: 1.3954 data_time: 0.0283 memory: 16201 loss_prob: 0.8474 loss_thr: 0.4808 loss_db: 0.1469 loss: 1.4751 2022/08/30 05:33:45 - mmengine - INFO - Epoch(train) [236][25/63] lr: 5.7522e-03 eta: 1 day, 1:00:39 time: 1.4694 data_time: 0.0421 memory: 16201 loss_prob: 0.8694 loss_thr: 0.5059 loss_db: 0.1455 loss: 1.5209 2022/08/30 05:33:52 - mmengine - INFO - Epoch(train) [236][30/63] lr: 5.7522e-03 eta: 1 day, 1:00:22 time: 1.4235 data_time: 0.0340 memory: 16201 loss_prob: 0.8388 loss_thr: 0.5069 loss_db: 0.1368 loss: 1.4824 2022/08/30 05:33:59 - mmengine - INFO - Epoch(train) [236][35/63] lr: 5.7522e-03 eta: 1 day, 1:00:22 time: 1.3541 data_time: 0.0314 memory: 16201 loss_prob: 0.8746 loss_thr: 0.5232 loss_db: 0.1457 loss: 1.5436 2022/08/30 05:34:06 - mmengine - INFO - Epoch(train) [236][40/63] lr: 5.7522e-03 eta: 1 day, 1:00:03 time: 1.3911 data_time: 0.0321 memory: 16201 loss_prob: 0.7854 loss_thr: 0.4838 loss_db: 0.1328 loss: 1.4020 2022/08/30 05:34:13 - mmengine - INFO - Epoch(train) [236][45/63] lr: 5.7522e-03 eta: 1 day, 1:00:03 time: 1.3941 data_time: 0.0301 memory: 16201 loss_prob: 0.7166 loss_thr: 0.4428 loss_db: 0.1207 loss: 1.2802 2022/08/30 05:34:19 - mmengine - INFO - Epoch(train) [236][50/63] lr: 5.7522e-03 eta: 1 day, 0:59:41 time: 1.2946 data_time: 0.0415 memory: 16201 loss_prob: 0.8072 loss_thr: 0.4858 loss_db: 0.1372 loss: 1.4302 2022/08/30 05:34:26 - mmengine - INFO - Epoch(train) [236][55/63] lr: 5.7522e-03 eta: 1 day, 0:59:41 time: 1.3319 data_time: 0.0293 memory: 16201 loss_prob: 0.8972 loss_thr: 0.5181 loss_db: 0.1501 loss: 1.5654 2022/08/30 05:34:32 - mmengine - INFO - Epoch(train) [236][60/63] lr: 5.7522e-03 eta: 1 day, 0:59:21 time: 1.3493 data_time: 0.0384 memory: 16201 loss_prob: 0.8828 loss_thr: 0.4885 loss_db: 0.1437 loss: 1.5150 2022/08/30 05:34:36 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:34:44 - mmengine - INFO - Epoch(train) [237][5/63] lr: 5.7468e-03 eta: 1 day, 0:59:21 time: 1.4425 data_time: 0.2282 memory: 16201 loss_prob: 0.7927 loss_thr: 0.4655 loss_db: 0.1297 loss: 1.3880 2022/08/30 05:34:51 - mmengine - INFO - Epoch(train) [237][10/63] lr: 5.7468e-03 eta: 1 day, 0:58:43 time: 1.4810 data_time: 0.2394 memory: 16201 loss_prob: 0.7827 loss_thr: 0.4709 loss_db: 0.1325 loss: 1.3861 2022/08/30 05:34:57 - mmengine - INFO - Epoch(train) [237][15/63] lr: 5.7468e-03 eta: 1 day, 0:58:43 time: 1.3385 data_time: 0.0373 memory: 16201 loss_prob: 0.8659 loss_thr: 0.4828 loss_db: 0.1420 loss: 1.4908 2022/08/30 05:35:04 - mmengine - INFO - Epoch(train) [237][20/63] lr: 5.7468e-03 eta: 1 day, 0:58:23 time: 1.3486 data_time: 0.0443 memory: 16201 loss_prob: 0.8284 loss_thr: 0.4702 loss_db: 0.1335 loss: 1.4321 2022/08/30 05:35:11 - mmengine - INFO - Epoch(train) [237][25/63] lr: 5.7468e-03 eta: 1 day, 0:58:23 time: 1.3278 data_time: 0.0427 memory: 16201 loss_prob: 0.8356 loss_thr: 0.4806 loss_db: 0.1413 loss: 1.4574 2022/08/30 05:35:17 - mmengine - INFO - Epoch(train) [237][30/63] lr: 5.7468e-03 eta: 1 day, 0:58:03 time: 1.3512 data_time: 0.0321 memory: 16201 loss_prob: 0.8904 loss_thr: 0.4991 loss_db: 0.1498 loss: 1.5393 2022/08/30 05:35:25 - mmengine - INFO - Epoch(train) [237][35/63] lr: 5.7468e-03 eta: 1 day, 0:58:03 time: 1.4179 data_time: 0.0369 memory: 16201 loss_prob: 0.8506 loss_thr: 0.4850 loss_db: 0.1420 loss: 1.4776 2022/08/30 05:35:32 - mmengine - INFO - Epoch(train) [237][40/63] lr: 5.7468e-03 eta: 1 day, 0:57:48 time: 1.4799 data_time: 0.0383 memory: 16201 loss_prob: 0.8425 loss_thr: 0.4954 loss_db: 0.1447 loss: 1.4826 2022/08/30 05:35:39 - mmengine - INFO - Epoch(train) [237][45/63] lr: 5.7468e-03 eta: 1 day, 0:57:48 time: 1.3751 data_time: 0.0325 memory: 16201 loss_prob: 0.8443 loss_thr: 0.5075 loss_db: 0.1447 loss: 1.4965 2022/08/30 05:35:45 - mmengine - INFO - Epoch(train) [237][50/63] lr: 5.7468e-03 eta: 1 day, 0:57:25 time: 1.2803 data_time: 0.0384 memory: 16201 loss_prob: 0.8149 loss_thr: 0.4903 loss_db: 0.1377 loss: 1.4429 2022/08/30 05:35:52 - mmengine - INFO - Epoch(train) [237][55/63] lr: 5.7468e-03 eta: 1 day, 0:57:25 time: 1.3205 data_time: 0.0407 memory: 16201 loss_prob: 0.8469 loss_thr: 0.4922 loss_db: 0.1413 loss: 1.4803 2022/08/30 05:35:58 - mmengine - INFO - Epoch(train) [237][60/63] lr: 5.7468e-03 eta: 1 day, 0:57:04 time: 1.3276 data_time: 0.0387 memory: 16201 loss_prob: 0.8184 loss_thr: 0.4731 loss_db: 0.1355 loss: 1.4270 2022/08/30 05:36:02 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:36:11 - mmengine - INFO - Epoch(train) [238][5/63] lr: 5.7414e-03 eta: 1 day, 0:57:04 time: 1.5670 data_time: 0.2496 memory: 16201 loss_prob: 0.9113 loss_thr: 0.4995 loss_db: 0.1528 loss: 1.5636 2022/08/30 05:36:18 - mmengine - INFO - Epoch(train) [238][10/63] lr: 5.7414e-03 eta: 1 day, 0:56:33 time: 1.6404 data_time: 0.2727 memory: 16201 loss_prob: 0.8462 loss_thr: 0.4937 loss_db: 0.1424 loss: 1.4822 2022/08/30 05:36:25 - mmengine - INFO - Epoch(train) [238][15/63] lr: 5.7414e-03 eta: 1 day, 0:56:33 time: 1.3566 data_time: 0.0339 memory: 16201 loss_prob: 0.8700 loss_thr: 0.4935 loss_db: 0.1430 loss: 1.5065 2022/08/30 05:36:33 - mmengine - INFO - Epoch(train) [238][20/63] lr: 5.7414e-03 eta: 1 day, 0:56:16 time: 1.4147 data_time: 0.0277 memory: 16201 loss_prob: 0.8245 loss_thr: 0.4916 loss_db: 0.1376 loss: 1.4538 2022/08/30 05:36:39 - mmengine - INFO - Epoch(train) [238][25/63] lr: 5.7414e-03 eta: 1 day, 0:56:16 time: 1.4240 data_time: 0.0405 memory: 16201 loss_prob: 0.7773 loss_thr: 0.4832 loss_db: 0.1326 loss: 1.3931 2022/08/30 05:36:46 - mmengine - INFO - Epoch(train) [238][30/63] lr: 5.7414e-03 eta: 1 day, 0:55:55 time: 1.3272 data_time: 0.0285 memory: 16201 loss_prob: 0.7643 loss_thr: 0.4801 loss_db: 0.1307 loss: 1.3750 2022/08/30 05:36:52 - mmengine - INFO - Epoch(train) [238][35/63] lr: 5.7414e-03 eta: 1 day, 0:55:55 time: 1.3300 data_time: 0.0293 memory: 16201 loss_prob: 0.7750 loss_thr: 0.4730 loss_db: 0.1297 loss: 1.3777 2022/08/30 05:37:00 - mmengine - INFO - Epoch(train) [238][40/63] lr: 5.7414e-03 eta: 1 day, 0:55:36 time: 1.3689 data_time: 0.0315 memory: 16201 loss_prob: 1.0120 loss_thr: 0.4718 loss_db: 0.1478 loss: 1.6317 2022/08/30 05:37:07 - mmengine - INFO - Epoch(train) [238][45/63] lr: 5.7414e-03 eta: 1 day, 0:55:36 time: 1.4492 data_time: 0.0339 memory: 16201 loss_prob: 1.0292 loss_thr: 0.4952 loss_db: 0.1521 loss: 1.6765 2022/08/30 05:37:14 - mmengine - INFO - Epoch(train) [238][50/63] lr: 5.7414e-03 eta: 1 day, 0:55:20 time: 1.4509 data_time: 0.0431 memory: 16201 loss_prob: 0.7767 loss_thr: 0.4970 loss_db: 0.1309 loss: 1.4046 2022/08/30 05:37:21 - mmengine - INFO - Epoch(train) [238][55/63] lr: 5.7414e-03 eta: 1 day, 0:55:20 time: 1.3942 data_time: 0.0293 memory: 16201 loss_prob: 0.7905 loss_thr: 0.4886 loss_db: 0.1327 loss: 1.4118 2022/08/30 05:37:29 - mmengine - INFO - Epoch(train) [238][60/63] lr: 5.7414e-03 eta: 1 day, 0:55:04 time: 1.4600 data_time: 0.0314 memory: 16201 loss_prob: 0.8733 loss_thr: 0.4868 loss_db: 0.1476 loss: 1.5078 2022/08/30 05:37:32 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:37:41 - mmengine - INFO - Epoch(train) [239][5/63] lr: 5.7361e-03 eta: 1 day, 0:55:04 time: 1.4720 data_time: 0.2269 memory: 16201 loss_prob: 0.9360 loss_thr: 0.4957 loss_db: 0.1529 loss: 1.5846 2022/08/30 05:37:42 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:37:47 - mmengine - INFO - Epoch(train) [239][10/63] lr: 5.7361e-03 eta: 1 day, 0:54:29 time: 1.5432 data_time: 0.2400 memory: 16201 loss_prob: 0.9048 loss_thr: 0.4923 loss_db: 0.1448 loss: 1.5418 2022/08/30 05:37:54 - mmengine - INFO - Epoch(train) [239][15/63] lr: 5.7361e-03 eta: 1 day, 0:54:29 time: 1.3210 data_time: 0.0265 memory: 16201 loss_prob: 0.9194 loss_thr: 0.4981 loss_db: 0.1483 loss: 1.5658 2022/08/30 05:38:00 - mmengine - INFO - Epoch(train) [239][20/63] lr: 5.7361e-03 eta: 1 day, 0:54:08 time: 1.3303 data_time: 0.0273 memory: 16201 loss_prob: 0.8191 loss_thr: 0.4932 loss_db: 0.1389 loss: 1.4512 2022/08/30 05:38:08 - mmengine - INFO - Epoch(train) [239][25/63] lr: 5.7361e-03 eta: 1 day, 0:54:08 time: 1.3712 data_time: 0.0355 memory: 16201 loss_prob: 0.7956 loss_thr: 0.4783 loss_db: 0.1359 loss: 1.4098 2022/08/30 05:38:14 - mmengine - INFO - Epoch(train) [239][30/63] lr: 5.7361e-03 eta: 1 day, 0:53:49 time: 1.3642 data_time: 0.0282 memory: 16201 loss_prob: 0.7418 loss_thr: 0.4514 loss_db: 0.1244 loss: 1.3176 2022/08/30 05:38:21 - mmengine - INFO - Epoch(train) [239][35/63] lr: 5.7361e-03 eta: 1 day, 0:53:49 time: 1.3575 data_time: 0.0337 memory: 16201 loss_prob: 0.7074 loss_thr: 0.4322 loss_db: 0.1185 loss: 1.2581 2022/08/30 05:38:28 - mmengine - INFO - Epoch(train) [239][40/63] lr: 5.7361e-03 eta: 1 day, 0:53:31 time: 1.4074 data_time: 0.0282 memory: 16201 loss_prob: 0.7338 loss_thr: 0.4519 loss_db: 0.1246 loss: 1.3103 2022/08/30 05:38:35 - mmengine - INFO - Epoch(train) [239][45/63] lr: 5.7361e-03 eta: 1 day, 0:53:31 time: 1.4134 data_time: 0.0306 memory: 16201 loss_prob: 0.8495 loss_thr: 0.5107 loss_db: 0.1413 loss: 1.5016 2022/08/30 05:38:43 - mmengine - INFO - Epoch(train) [239][50/63] lr: 5.7361e-03 eta: 1 day, 0:53:17 time: 1.4870 data_time: 0.0422 memory: 16201 loss_prob: 0.9201 loss_thr: 0.5496 loss_db: 0.1535 loss: 1.6231 2022/08/30 05:38:50 - mmengine - INFO - Epoch(train) [239][55/63] lr: 5.7361e-03 eta: 1 day, 0:53:17 time: 1.4857 data_time: 0.0292 memory: 16201 loss_prob: 0.8804 loss_thr: 0.5140 loss_db: 0.1474 loss: 1.5418 2022/08/30 05:38:56 - mmengine - INFO - Epoch(train) [239][60/63] lr: 5.7361e-03 eta: 1 day, 0:52:56 time: 1.3343 data_time: 0.0325 memory: 16201 loss_prob: 0.8386 loss_thr: 0.4779 loss_db: 0.1381 loss: 1.4546 2022/08/30 05:39:00 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:39:09 - mmengine - INFO - Epoch(train) [240][5/63] lr: 5.7307e-03 eta: 1 day, 0:52:56 time: 1.5174 data_time: 0.2261 memory: 16201 loss_prob: 0.7940 loss_thr: 0.4876 loss_db: 0.1362 loss: 1.4179 2022/08/30 05:39:16 - mmengine - INFO - Epoch(train) [240][10/63] lr: 5.7307e-03 eta: 1 day, 0:52:27 time: 1.6741 data_time: 0.2525 memory: 16201 loss_prob: 0.6929 loss_thr: 0.4518 loss_db: 0.1177 loss: 1.2623 2022/08/30 05:39:23 - mmengine - INFO - Epoch(train) [240][15/63] lr: 5.7307e-03 eta: 1 day, 0:52:27 time: 1.3446 data_time: 0.0349 memory: 16201 loss_prob: 0.7122 loss_thr: 0.4689 loss_db: 0.1195 loss: 1.3006 2022/08/30 05:39:30 - mmengine - INFO - Epoch(train) [240][20/63] lr: 5.7307e-03 eta: 1 day, 0:52:06 time: 1.3288 data_time: 0.0280 memory: 16201 loss_prob: 0.7704 loss_thr: 0.4757 loss_db: 0.1273 loss: 1.3734 2022/08/30 05:39:37 - mmengine - INFO - Epoch(train) [240][25/63] lr: 5.7307e-03 eta: 1 day, 0:52:06 time: 1.3810 data_time: 0.0420 memory: 16201 loss_prob: 0.7372 loss_thr: 0.4639 loss_db: 0.1250 loss: 1.3261 2022/08/30 05:39:43 - mmengine - INFO - Epoch(train) [240][30/63] lr: 5.7307e-03 eta: 1 day, 0:51:46 time: 1.3574 data_time: 0.0279 memory: 16201 loss_prob: 0.7620 loss_thr: 0.4763 loss_db: 0.1294 loss: 1.3677 2022/08/30 05:39:49 - mmengine - INFO - Epoch(train) [240][35/63] lr: 5.7307e-03 eta: 1 day, 0:51:46 time: 1.2583 data_time: 0.0288 memory: 16201 loss_prob: 0.8109 loss_thr: 0.4997 loss_db: 0.1372 loss: 1.4478 2022/08/30 05:39:55 - mmengine - INFO - Epoch(train) [240][40/63] lr: 5.7307e-03 eta: 1 day, 0:51:20 time: 1.1829 data_time: 0.0333 memory: 16201 loss_prob: 0.7590 loss_thr: 0.4834 loss_db: 0.1317 loss: 1.3741 2022/08/30 05:40:02 - mmengine - INFO - Epoch(train) [240][45/63] lr: 5.7307e-03 eta: 1 day, 0:51:20 time: 1.2873 data_time: 0.0283 memory: 16201 loss_prob: 0.7749 loss_thr: 0.4706 loss_db: 0.1335 loss: 1.3791 2022/08/30 05:40:09 - mmengine - INFO - Epoch(train) [240][50/63] lr: 5.7307e-03 eta: 1 day, 0:51:00 time: 1.3641 data_time: 0.0411 memory: 16201 loss_prob: 0.8294 loss_thr: 0.4834 loss_db: 0.1397 loss: 1.4525 2022/08/30 05:40:15 - mmengine - INFO - Epoch(train) [240][55/63] lr: 5.7307e-03 eta: 1 day, 0:51:00 time: 1.3301 data_time: 0.0314 memory: 16201 loss_prob: 0.8006 loss_thr: 0.4724 loss_db: 0.1321 loss: 1.4050 2022/08/30 05:40:22 - mmengine - INFO - Epoch(train) [240][60/63] lr: 5.7307e-03 eta: 1 day, 0:50:41 time: 1.3543 data_time: 0.0296 memory: 16201 loss_prob: 0.7807 loss_thr: 0.4757 loss_db: 0.1307 loss: 1.3872 2022/08/30 05:40:26 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:40:26 - mmengine - INFO - Saving checkpoint at 240 epochs 2022/08/30 05:40:35 - mmengine - INFO - Epoch(val) [240][5/32] eta: 1 day, 0:50:41 time: 0.6910 data_time: 0.1406 memory: 16201 2022/08/30 05:40:38 - mmengine - INFO - Epoch(val) [240][10/32] eta: 0:00:17 time: 0.7829 data_time: 0.1828 memory: 15734 2022/08/30 05:40:41 - mmengine - INFO - Epoch(val) [240][15/32] eta: 0:00:17 time: 0.6486 data_time: 0.0620 memory: 15734 2022/08/30 05:40:45 - mmengine - INFO - Epoch(val) [240][20/32] eta: 0:00:08 time: 0.7168 data_time: 0.0622 memory: 15734 2022/08/30 05:40:49 - mmengine - INFO - Epoch(val) [240][25/32] eta: 0:00:08 time: 0.7311 data_time: 0.0691 memory: 15734 2022/08/30 05:40:51 - mmengine - INFO - Epoch(val) [240][30/32] eta: 0:00:01 time: 0.6084 data_time: 0.0299 memory: 15734 2022/08/30 05:40:52 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 05:40:52 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8416, precision: 0.7738, hmean: 0.8063 2022/08/30 05:40:52 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8416, precision: 0.8238, hmean: 0.8326 2022/08/30 05:40:52 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8334, precision: 0.8565, hmean: 0.8448 2022/08/30 05:40:52 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8195, precision: 0.8764, hmean: 0.8470 2022/08/30 05:40:52 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7867, precision: 0.9103, hmean: 0.8440 2022/08/30 05:40:52 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.5725, precision: 0.9589, hmean: 0.7169 2022/08/30 05:40:52 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0217, precision: 0.9783, hmean: 0.0424 2022/08/30 05:40:52 - mmengine - INFO - Epoch(val) [240][32/32] icdar/precision: 0.8764 icdar/recall: 0.8195 icdar/hmean: 0.8470 2022/08/30 05:41:02 - mmengine - INFO - Epoch(train) [241][5/63] lr: 5.7253e-03 eta: 0:00:01 time: 1.5444 data_time: 0.2225 memory: 16201 loss_prob: 0.8895 loss_thr: 0.4952 loss_db: 0.1436 loss: 1.5283 2022/08/30 05:41:08 - mmengine - INFO - Epoch(train) [241][10/63] lr: 5.7253e-03 eta: 1 day, 0:50:07 time: 1.5714 data_time: 0.2331 memory: 16201 loss_prob: 0.8574 loss_thr: 0.4937 loss_db: 0.1434 loss: 1.4946 2022/08/30 05:41:15 - mmengine - INFO - Epoch(train) [241][15/63] lr: 5.7253e-03 eta: 1 day, 0:50:07 time: 1.3087 data_time: 0.0321 memory: 16201 loss_prob: 0.7789 loss_thr: 0.4672 loss_db: 0.1322 loss: 1.3783 2022/08/30 05:41:22 - mmengine - INFO - Epoch(train) [241][20/63] lr: 5.7253e-03 eta: 1 day, 0:49:47 time: 1.3496 data_time: 0.0353 memory: 16201 loss_prob: 0.6944 loss_thr: 0.4347 loss_db: 0.1175 loss: 1.2466 2022/08/30 05:41:28 - mmengine - INFO - Epoch(train) [241][25/63] lr: 5.7253e-03 eta: 1 day, 0:49:47 time: 1.3645 data_time: 0.0365 memory: 16201 loss_prob: 0.7216 loss_thr: 0.4689 loss_db: 0.1244 loss: 1.3149 2022/08/30 05:41:35 - mmengine - INFO - Epoch(train) [241][30/63] lr: 5.7253e-03 eta: 1 day, 0:49:26 time: 1.3082 data_time: 0.0328 memory: 16201 loss_prob: 0.7738 loss_thr: 0.5009 loss_db: 0.1332 loss: 1.4078 2022/08/30 05:41:41 - mmengine - INFO - Epoch(train) [241][35/63] lr: 5.7253e-03 eta: 1 day, 0:49:26 time: 1.2390 data_time: 0.0423 memory: 16201 loss_prob: 0.7868 loss_thr: 0.4939 loss_db: 0.1336 loss: 1.4142 2022/08/30 05:41:47 - mmengine - INFO - Epoch(train) [241][40/63] lr: 5.7253e-03 eta: 1 day, 0:49:03 time: 1.2733 data_time: 0.0381 memory: 16201 loss_prob: 0.7616 loss_thr: 0.4809 loss_db: 0.1290 loss: 1.3715 2022/08/30 05:41:54 - mmengine - INFO - Epoch(train) [241][45/63] lr: 5.7253e-03 eta: 1 day, 0:49:03 time: 1.2833 data_time: 0.0344 memory: 16201 loss_prob: 0.7596 loss_thr: 0.4782 loss_db: 0.1281 loss: 1.3659 2022/08/30 05:42:01 - mmengine - INFO - Epoch(train) [241][50/63] lr: 5.7253e-03 eta: 1 day, 0:48:42 time: 1.3272 data_time: 0.0474 memory: 16201 loss_prob: 0.8358 loss_thr: 0.5052 loss_db: 0.1398 loss: 1.4807 2022/08/30 05:42:07 - mmengine - INFO - Epoch(train) [241][55/63] lr: 5.7253e-03 eta: 1 day, 0:48:42 time: 1.2934 data_time: 0.0411 memory: 16201 loss_prob: 0.8973 loss_thr: 0.5109 loss_db: 0.1481 loss: 1.5563 2022/08/30 05:42:14 - mmengine - INFO - Epoch(train) [241][60/63] lr: 5.7253e-03 eta: 1 day, 0:48:23 time: 1.3597 data_time: 0.0359 memory: 16201 loss_prob: 0.9644 loss_thr: 0.5088 loss_db: 0.1649 loss: 1.6380 2022/08/30 05:42:18 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:42:27 - mmengine - INFO - Epoch(train) [242][5/63] lr: 5.7199e-03 eta: 1 day, 0:48:23 time: 1.5895 data_time: 0.2714 memory: 16201 loss_prob: 0.9381 loss_thr: 0.4953 loss_db: 0.1561 loss: 1.5895 2022/08/30 05:42:35 - mmengine - INFO - Epoch(train) [242][10/63] lr: 5.7199e-03 eta: 1 day, 0:47:54 time: 1.6900 data_time: 0.2629 memory: 16201 loss_prob: 1.0129 loss_thr: 0.5623 loss_db: 0.1612 loss: 1.7364 2022/08/30 05:42:42 - mmengine - INFO - Epoch(train) [242][15/63] lr: 5.7199e-03 eta: 1 day, 0:47:54 time: 1.4371 data_time: 0.0334 memory: 16201 loss_prob: 0.9038 loss_thr: 0.5192 loss_db: 0.1491 loss: 1.5720 2022/08/30 05:42:48 - mmengine - INFO - Epoch(train) [242][20/63] lr: 5.7199e-03 eta: 1 day, 0:47:34 time: 1.3449 data_time: 0.0353 memory: 16201 loss_prob: 0.8375 loss_thr: 0.4818 loss_db: 0.1375 loss: 1.4568 2022/08/30 05:42:55 - mmengine - INFO - Epoch(train) [242][25/63] lr: 5.7199e-03 eta: 1 day, 0:47:34 time: 1.3224 data_time: 0.0364 memory: 16201 loss_prob: 0.8887 loss_thr: 0.5010 loss_db: 0.1427 loss: 1.5324 2022/08/30 05:43:02 - mmengine - INFO - Epoch(train) [242][30/63] lr: 5.7199e-03 eta: 1 day, 0:47:17 time: 1.4261 data_time: 0.0315 memory: 16201 loss_prob: 0.8845 loss_thr: 0.4980 loss_db: 0.1475 loss: 1.5300 2022/08/30 05:43:09 - mmengine - INFO - Epoch(train) [242][35/63] lr: 5.7199e-03 eta: 1 day, 0:47:17 time: 1.4490 data_time: 0.0352 memory: 16201 loss_prob: 0.8475 loss_thr: 0.4933 loss_db: 0.1452 loss: 1.4861 2022/08/30 05:43:16 - mmengine - INFO - Epoch(train) [242][40/63] lr: 5.7199e-03 eta: 1 day, 0:46:59 time: 1.3881 data_time: 0.0311 memory: 16201 loss_prob: 0.8162 loss_thr: 0.4975 loss_db: 0.1389 loss: 1.4526 2022/08/30 05:43:23 - mmengine - INFO - Epoch(train) [242][45/63] lr: 5.7199e-03 eta: 1 day, 0:46:59 time: 1.3379 data_time: 0.0289 memory: 16201 loss_prob: 0.8560 loss_thr: 0.5050 loss_db: 0.1418 loss: 1.5028 2022/08/30 05:43:30 - mmengine - INFO - Epoch(train) [242][50/63] lr: 5.7199e-03 eta: 1 day, 0:46:40 time: 1.3747 data_time: 0.0410 memory: 16201 loss_prob: 0.8162 loss_thr: 0.4998 loss_db: 0.1384 loss: 1.4544 2022/08/30 05:43:36 - mmengine - INFO - Epoch(train) [242][55/63] lr: 5.7199e-03 eta: 1 day, 0:46:40 time: 1.3396 data_time: 0.0343 memory: 16201 loss_prob: 0.7906 loss_thr: 0.4909 loss_db: 0.1362 loss: 1.4177 2022/08/30 05:43:43 - mmengine - INFO - Epoch(train) [242][60/63] lr: 5.7199e-03 eta: 1 day, 0:46:19 time: 1.3160 data_time: 0.0307 memory: 16201 loss_prob: 0.8684 loss_thr: 0.4707 loss_db: 0.1391 loss: 1.4782 2022/08/30 05:43:46 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:43:55 - mmengine - INFO - Epoch(train) [243][5/63] lr: 5.7146e-03 eta: 1 day, 0:46:19 time: 1.4436 data_time: 0.2529 memory: 16201 loss_prob: 0.9468 loss_thr: 0.4703 loss_db: 0.1532 loss: 1.5703 2022/08/30 05:44:02 - mmengine - INFO - Epoch(train) [243][10/63] lr: 5.7146e-03 eta: 1 day, 0:45:47 time: 1.6095 data_time: 0.2640 memory: 16201 loss_prob: 0.8670 loss_thr: 0.4748 loss_db: 0.1501 loss: 1.4919 2022/08/30 05:44:08 - mmengine - INFO - Epoch(train) [243][15/63] lr: 5.7146e-03 eta: 1 day, 0:45:47 time: 1.3465 data_time: 0.0326 memory: 16201 loss_prob: 0.9131 loss_thr: 0.4845 loss_db: 0.1550 loss: 1.5526 2022/08/30 05:44:15 - mmengine - INFO - Epoch(train) [243][20/63] lr: 5.7146e-03 eta: 1 day, 0:45:24 time: 1.2610 data_time: 0.0317 memory: 16201 loss_prob: 0.8326 loss_thr: 0.4731 loss_db: 0.1369 loss: 1.4426 2022/08/30 05:44:22 - mmengine - INFO - Epoch(train) [243][25/63] lr: 5.7146e-03 eta: 1 day, 0:45:24 time: 1.3181 data_time: 0.0483 memory: 16201 loss_prob: 0.9113 loss_thr: 0.4974 loss_db: 0.1464 loss: 1.5551 2022/08/30 05:44:28 - mmengine - INFO - Epoch(train) [243][30/63] lr: 5.7146e-03 eta: 1 day, 0:45:04 time: 1.3369 data_time: 0.0377 memory: 16201 loss_prob: 1.0334 loss_thr: 0.5243 loss_db: 0.1662 loss: 1.7239 2022/08/30 05:44:35 - mmengine - INFO - Epoch(train) [243][35/63] lr: 5.7146e-03 eta: 1 day, 0:45:04 time: 1.3571 data_time: 0.0272 memory: 16201 loss_prob: 0.9694 loss_thr: 0.5280 loss_db: 0.1591 loss: 1.6566 2022/08/30 05:44:42 - mmengine - INFO - Epoch(train) [243][40/63] lr: 5.7146e-03 eta: 1 day, 0:44:45 time: 1.3680 data_time: 0.0299 memory: 16201 loss_prob: 0.8256 loss_thr: 0.5041 loss_db: 0.1377 loss: 1.4674 2022/08/30 05:44:48 - mmengine - INFO - Epoch(train) [243][45/63] lr: 5.7146e-03 eta: 1 day, 0:44:45 time: 1.2772 data_time: 0.0290 memory: 16201 loss_prob: 1.0050 loss_thr: 0.5443 loss_db: 0.1580 loss: 1.7074 2022/08/30 05:44:55 - mmengine - INFO - Epoch(train) [243][50/63] lr: 5.7146e-03 eta: 1 day, 0:44:24 time: 1.3149 data_time: 0.0381 memory: 16201 loss_prob: 1.0709 loss_thr: 0.5646 loss_db: 0.1716 loss: 1.8071 2022/08/30 05:45:01 - mmengine - INFO - Epoch(train) [243][55/63] lr: 5.7146e-03 eta: 1 day, 0:44:24 time: 1.3430 data_time: 0.0388 memory: 16201 loss_prob: 0.9177 loss_thr: 0.5100 loss_db: 0.1533 loss: 1.5809 2022/08/30 05:45:08 - mmengine - INFO - Epoch(train) [243][60/63] lr: 5.7146e-03 eta: 1 day, 0:44:03 time: 1.3227 data_time: 0.0410 memory: 16201 loss_prob: 0.8370 loss_thr: 0.4865 loss_db: 0.1358 loss: 1.4593 2022/08/30 05:45:11 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:45:21 - mmengine - INFO - Epoch(train) [244][5/63] lr: 5.7092e-03 eta: 1 day, 0:44:03 time: 1.4881 data_time: 0.2475 memory: 16201 loss_prob: 0.8534 loss_thr: 0.4746 loss_db: 0.1365 loss: 1.4645 2022/08/30 05:45:27 - mmengine - INFO - Epoch(train) [244][10/63] lr: 5.7092e-03 eta: 1 day, 0:43:27 time: 1.5022 data_time: 0.2642 memory: 16201 loss_prob: 0.8942 loss_thr: 0.5055 loss_db: 0.1455 loss: 1.5452 2022/08/30 05:45:32 - mmengine - INFO - Epoch(train) [244][15/63] lr: 5.7092e-03 eta: 1 day, 0:43:27 time: 1.1753 data_time: 0.0327 memory: 16201 loss_prob: 0.8407 loss_thr: 0.5055 loss_db: 0.1386 loss: 1.4848 2022/08/30 05:45:38 - mmengine - INFO - Epoch(train) [244][20/63] lr: 5.7092e-03 eta: 1 day, 0:43:01 time: 1.1875 data_time: 0.0326 memory: 16201 loss_prob: 0.8134 loss_thr: 0.4825 loss_db: 0.1325 loss: 1.4284 2022/08/30 05:45:45 - mmengine - INFO - Epoch(train) [244][25/63] lr: 5.7092e-03 eta: 1 day, 0:43:01 time: 1.2581 data_time: 0.0341 memory: 16201 loss_prob: 0.7943 loss_thr: 0.4974 loss_db: 0.1334 loss: 1.4251 2022/08/30 05:45:51 - mmengine - INFO - Epoch(train) [244][30/63] lr: 5.7092e-03 eta: 1 day, 0:42:38 time: 1.2679 data_time: 0.0352 memory: 16201 loss_prob: 0.7768 loss_thr: 0.4974 loss_db: 0.1315 loss: 1.4057 2022/08/30 05:45:57 - mmengine - INFO - Epoch(train) [244][35/63] lr: 5.7092e-03 eta: 1 day, 0:42:38 time: 1.2503 data_time: 0.0411 memory: 16201 loss_prob: 0.8324 loss_thr: 0.4888 loss_db: 0.1322 loss: 1.4533 2022/08/30 05:46:04 - mmengine - INFO - Epoch(train) [244][40/63] lr: 5.7092e-03 eta: 1 day, 0:42:15 time: 1.2650 data_time: 0.0326 memory: 16201 loss_prob: 0.8121 loss_thr: 0.4696 loss_db: 0.1286 loss: 1.4103 2022/08/30 05:46:10 - mmengine - INFO - Epoch(train) [244][45/63] lr: 5.7092e-03 eta: 1 day, 0:42:15 time: 1.2956 data_time: 0.0344 memory: 16201 loss_prob: 0.7673 loss_thr: 0.4567 loss_db: 0.1288 loss: 1.3529 2022/08/30 05:46:17 - mmengine - INFO - Epoch(train) [244][50/63] lr: 5.7092e-03 eta: 1 day, 0:41:56 time: 1.3609 data_time: 0.0410 memory: 16201 loss_prob: 0.8169 loss_thr: 0.4877 loss_db: 0.1386 loss: 1.4432 2022/08/30 05:46:24 - mmengine - INFO - Epoch(train) [244][55/63] lr: 5.7092e-03 eta: 1 day, 0:41:56 time: 1.3649 data_time: 0.0323 memory: 16201 loss_prob: 0.8402 loss_thr: 0.4924 loss_db: 0.1398 loss: 1.4724 2022/08/30 05:46:31 - mmengine - INFO - Epoch(train) [244][60/63] lr: 5.7092e-03 eta: 1 day, 0:41:35 time: 1.3243 data_time: 0.0347 memory: 16201 loss_prob: 1.0821 loss_thr: 0.5038 loss_db: 0.1647 loss: 1.7505 2022/08/30 05:46:35 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:46:44 - mmengine - INFO - Epoch(train) [245][5/63] lr: 5.7038e-03 eta: 1 day, 0:41:35 time: 1.5661 data_time: 0.2274 memory: 16201 loss_prob: 0.8252 loss_thr: 0.4912 loss_db: 0.1364 loss: 1.4528 2022/08/30 05:46:51 - mmengine - INFO - Epoch(train) [245][10/63] lr: 5.7038e-03 eta: 1 day, 0:41:04 time: 1.6177 data_time: 0.2426 memory: 16201 loss_prob: 0.8037 loss_thr: 0.4830 loss_db: 0.1339 loss: 1.4205 2022/08/30 05:46:58 - mmengine - INFO - Epoch(train) [245][15/63] lr: 5.7038e-03 eta: 1 day, 0:41:04 time: 1.3990 data_time: 0.0334 memory: 16201 loss_prob: 0.8605 loss_thr: 0.4900 loss_db: 0.1410 loss: 1.4916 2022/08/30 05:47:04 - mmengine - INFO - Epoch(train) [245][20/63] lr: 5.7038e-03 eta: 1 day, 0:40:45 time: 1.3671 data_time: 0.0344 memory: 16201 loss_prob: 0.8526 loss_thr: 0.4902 loss_db: 0.1370 loss: 1.4798 2022/08/30 05:47:11 - mmengine - INFO - Epoch(train) [245][25/63] lr: 5.7038e-03 eta: 1 day, 0:40:45 time: 1.2949 data_time: 0.0447 memory: 16201 loss_prob: 0.8290 loss_thr: 0.4983 loss_db: 0.1340 loss: 1.4613 2022/08/30 05:47:17 - mmengine - INFO - Epoch(train) [245][30/63] lr: 5.7038e-03 eta: 1 day, 0:40:23 time: 1.2849 data_time: 0.0330 memory: 16201 loss_prob: 0.8037 loss_thr: 0.5037 loss_db: 0.1319 loss: 1.4392 2022/08/30 05:47:24 - mmengine - INFO - Epoch(train) [245][35/63] lr: 5.7038e-03 eta: 1 day, 0:40:23 time: 1.3163 data_time: 0.0299 memory: 16201 loss_prob: 0.7674 loss_thr: 0.4910 loss_db: 0.1301 loss: 1.3886 2022/08/30 05:47:30 - mmengine - INFO - Epoch(train) [245][40/63] lr: 5.7038e-03 eta: 1 day, 0:40:01 time: 1.2996 data_time: 0.0325 memory: 16201 loss_prob: 0.7707 loss_thr: 0.4564 loss_db: 0.1288 loss: 1.3559 2022/08/30 05:47:37 - mmengine - INFO - Epoch(train) [245][45/63] lr: 5.7038e-03 eta: 1 day, 0:40:01 time: 1.3328 data_time: 0.0335 memory: 16201 loss_prob: 0.8179 loss_thr: 0.4667 loss_db: 0.1338 loss: 1.4184 2022/08/30 05:47:44 - mmengine - INFO - Epoch(train) [245][50/63] lr: 5.7038e-03 eta: 1 day, 0:39:42 time: 1.3590 data_time: 0.0405 memory: 16201 loss_prob: 0.7664 loss_thr: 0.4572 loss_db: 0.1253 loss: 1.3489 2022/08/30 05:47:51 - mmengine - INFO - Epoch(train) [245][55/63] lr: 5.7038e-03 eta: 1 day, 0:39:42 time: 1.3957 data_time: 0.0408 memory: 16201 loss_prob: 0.7943 loss_thr: 0.4564 loss_db: 0.1280 loss: 1.3787 2022/08/30 05:47:58 - mmengine - INFO - Epoch(train) [245][60/63] lr: 5.7038e-03 eta: 1 day, 0:39:24 time: 1.4000 data_time: 0.0373 memory: 16201 loss_prob: 0.8638 loss_thr: 0.4898 loss_db: 0.1412 loss: 1.4948 2022/08/30 05:48:02 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:48:11 - mmengine - INFO - Epoch(train) [246][5/63] lr: 5.6984e-03 eta: 1 day, 0:39:24 time: 1.5275 data_time: 0.2261 memory: 16201 loss_prob: 0.8426 loss_thr: 0.5045 loss_db: 0.1401 loss: 1.4872 2022/08/30 05:48:18 - mmengine - INFO - Epoch(train) [246][10/63] lr: 5.6984e-03 eta: 1 day, 0:38:55 time: 1.6648 data_time: 0.2355 memory: 16201 loss_prob: 0.8180 loss_thr: 0.4843 loss_db: 0.1385 loss: 1.4407 2022/08/30 05:48:26 - mmengine - INFO - Epoch(train) [246][15/63] lr: 5.6984e-03 eta: 1 day, 0:38:55 time: 1.4997 data_time: 0.0319 memory: 16201 loss_prob: 0.8055 loss_thr: 0.4922 loss_db: 0.1343 loss: 1.4321 2022/08/30 05:48:33 - mmengine - INFO - Epoch(train) [246][20/63] lr: 5.6984e-03 eta: 1 day, 0:38:40 time: 1.4575 data_time: 0.0546 memory: 16201 loss_prob: 0.8465 loss_thr: 0.4983 loss_db: 0.1404 loss: 1.4852 2022/08/30 05:48:40 - mmengine - INFO - Epoch(train) [246][25/63] lr: 5.6984e-03 eta: 1 day, 0:38:40 time: 1.4553 data_time: 0.0458 memory: 16201 loss_prob: 0.8804 loss_thr: 0.4905 loss_db: 0.1446 loss: 1.5155 2022/08/30 05:48:47 - mmengine - INFO - Epoch(train) [246][30/63] lr: 5.6984e-03 eta: 1 day, 0:38:22 time: 1.4093 data_time: 0.0291 memory: 16201 loss_prob: 0.7963 loss_thr: 0.4653 loss_db: 0.1323 loss: 1.3939 2022/08/30 05:48:53 - mmengine - INFO - Epoch(train) [246][35/63] lr: 5.6984e-03 eta: 1 day, 0:38:22 time: 1.3059 data_time: 0.0326 memory: 16201 loss_prob: 0.8443 loss_thr: 0.4883 loss_db: 0.1388 loss: 1.4714 2022/08/30 05:49:00 - mmengine - INFO - Epoch(train) [246][40/63] lr: 5.6984e-03 eta: 1 day, 0:38:02 time: 1.3198 data_time: 0.0298 memory: 16201 loss_prob: 0.9071 loss_thr: 0.5082 loss_db: 0.1478 loss: 1.5631 2022/08/30 05:49:06 - mmengine - INFO - Epoch(train) [246][45/63] lr: 5.6984e-03 eta: 1 day, 0:38:02 time: 1.2961 data_time: 0.0377 memory: 16201 loss_prob: 0.8042 loss_thr: 0.4797 loss_db: 0.1364 loss: 1.4203 2022/08/30 05:49:13 - mmengine - INFO - Epoch(train) [246][50/63] lr: 5.6984e-03 eta: 1 day, 0:37:37 time: 1.2287 data_time: 0.0342 memory: 16201 loss_prob: 0.7062 loss_thr: 0.4482 loss_db: 0.1197 loss: 1.2742 2022/08/30 05:49:19 - mmengine - INFO - Epoch(train) [246][55/63] lr: 5.6984e-03 eta: 1 day, 0:37:37 time: 1.2946 data_time: 0.0304 memory: 16201 loss_prob: 0.7553 loss_thr: 0.4564 loss_db: 0.1281 loss: 1.3398 2022/08/30 05:49:26 - mmengine - INFO - Epoch(train) [246][60/63] lr: 5.6984e-03 eta: 1 day, 0:37:16 time: 1.2987 data_time: 0.0398 memory: 16201 loss_prob: 0.8351 loss_thr: 0.4860 loss_db: 0.1410 loss: 1.4621 2022/08/30 05:49:29 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:49:38 - mmengine - INFO - Epoch(train) [247][5/63] lr: 5.6931e-03 eta: 1 day, 0:37:16 time: 1.4464 data_time: 0.2281 memory: 16201 loss_prob: 0.7582 loss_thr: 0.4635 loss_db: 0.1259 loss: 1.3475 2022/08/30 05:49:44 - mmengine - INFO - Epoch(train) [247][10/63] lr: 5.6931e-03 eta: 1 day, 0:36:40 time: 1.5012 data_time: 0.2354 memory: 16201 loss_prob: 0.7786 loss_thr: 0.4638 loss_db: 0.1311 loss: 1.3735 2022/08/30 05:49:50 - mmengine - INFO - Epoch(train) [247][15/63] lr: 5.6931e-03 eta: 1 day, 0:36:40 time: 1.2847 data_time: 0.0346 memory: 16201 loss_prob: 0.8222 loss_thr: 0.4911 loss_db: 0.1385 loss: 1.4519 2022/08/30 05:49:57 - mmengine - INFO - Epoch(train) [247][20/63] lr: 5.6931e-03 eta: 1 day, 0:36:17 time: 1.2541 data_time: 0.0359 memory: 16201 loss_prob: 0.8305 loss_thr: 0.4956 loss_db: 0.1378 loss: 1.4640 2022/08/30 05:50:03 - mmengine - INFO - Epoch(train) [247][25/63] lr: 5.6931e-03 eta: 1 day, 0:36:17 time: 1.2430 data_time: 0.0373 memory: 16201 loss_prob: 0.7650 loss_thr: 0.4515 loss_db: 0.1266 loss: 1.3431 2022/08/30 05:50:09 - mmengine - INFO - Epoch(train) [247][30/63] lr: 5.6931e-03 eta: 1 day, 0:35:54 time: 1.2536 data_time: 0.0394 memory: 16201 loss_prob: 0.8103 loss_thr: 0.5046 loss_db: 0.1334 loss: 1.4483 2022/08/30 05:50:16 - mmengine - INFO - Epoch(train) [247][35/63] lr: 5.6931e-03 eta: 1 day, 0:35:54 time: 1.3312 data_time: 0.0352 memory: 16201 loss_prob: 0.9486 loss_thr: 0.5511 loss_db: 0.1559 loss: 1.6556 2022/08/30 05:50:23 - mmengine - INFO - Epoch(train) [247][40/63] lr: 5.6931e-03 eta: 1 day, 0:35:34 time: 1.3331 data_time: 0.0334 memory: 16201 loss_prob: 0.9236 loss_thr: 0.5255 loss_db: 0.1486 loss: 1.5977 2022/08/30 05:50:29 - mmengine - INFO - Epoch(train) [247][45/63] lr: 5.6931e-03 eta: 1 day, 0:35:34 time: 1.2834 data_time: 0.0412 memory: 16201 loss_prob: 0.7929 loss_thr: 0.4927 loss_db: 0.1302 loss: 1.4158 2022/08/30 05:50:36 - mmengine - INFO - Epoch(train) [247][50/63] lr: 5.6931e-03 eta: 1 day, 0:35:13 time: 1.3306 data_time: 0.0330 memory: 16201 loss_prob: 0.7441 loss_thr: 0.4587 loss_db: 0.1266 loss: 1.3294 2022/08/30 05:50:42 - mmengine - INFO - Epoch(train) [247][55/63] lr: 5.6931e-03 eta: 1 day, 0:35:13 time: 1.3480 data_time: 0.0296 memory: 16201 loss_prob: 0.7967 loss_thr: 0.4660 loss_db: 0.1302 loss: 1.3929 2022/08/30 05:50:50 - mmengine - INFO - Epoch(train) [247][60/63] lr: 5.6931e-03 eta: 1 day, 0:34:57 time: 1.4355 data_time: 0.0416 memory: 16201 loss_prob: 0.8091 loss_thr: 0.4815 loss_db: 0.1301 loss: 1.4206 2022/08/30 05:50:54 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:51:02 - mmengine - INFO - Epoch(train) [248][5/63] lr: 5.6877e-03 eta: 1 day, 0:34:57 time: 1.4706 data_time: 0.2373 memory: 16201 loss_prob: 0.8967 loss_thr: 0.5109 loss_db: 0.1509 loss: 1.5584 2022/08/30 05:51:09 - mmengine - INFO - Epoch(train) [248][10/63] lr: 5.6877e-03 eta: 1 day, 0:34:21 time: 1.4841 data_time: 0.2454 memory: 16201 loss_prob: 0.7924 loss_thr: 0.4730 loss_db: 0.1375 loss: 1.4029 2022/08/30 05:51:15 - mmengine - INFO - Epoch(train) [248][15/63] lr: 5.6877e-03 eta: 1 day, 0:34:21 time: 1.2955 data_time: 0.0415 memory: 16201 loss_prob: 0.7985 loss_thr: 0.4764 loss_db: 0.1346 loss: 1.4095 2022/08/30 05:51:22 - mmengine - INFO - Epoch(train) [248][20/63] lr: 5.6877e-03 eta: 1 day, 0:34:02 time: 1.3459 data_time: 0.0441 memory: 16201 loss_prob: 0.8031 loss_thr: 0.4993 loss_db: 0.1350 loss: 1.4374 2022/08/30 05:51:28 - mmengine - INFO - Epoch(train) [248][25/63] lr: 5.6877e-03 eta: 1 day, 0:34:02 time: 1.2646 data_time: 0.0352 memory: 16201 loss_prob: 0.7331 loss_thr: 0.4798 loss_db: 0.1233 loss: 1.3362 2022/08/30 05:51:35 - mmengine - INFO - Epoch(train) [248][30/63] lr: 5.6877e-03 eta: 1 day, 0:33:42 time: 1.3389 data_time: 0.1029 memory: 16201 loss_prob: 0.6953 loss_thr: 0.4512 loss_db: 0.1178 loss: 1.2643 2022/08/30 05:51:42 - mmengine - INFO - Epoch(train) [248][35/63] lr: 5.6877e-03 eta: 1 day, 0:33:42 time: 1.3724 data_time: 0.1032 memory: 16201 loss_prob: 0.7079 loss_thr: 0.4655 loss_db: 0.1208 loss: 1.2942 2022/08/30 05:51:49 - mmengine - INFO - Epoch(train) [248][40/63] lr: 5.6877e-03 eta: 1 day, 0:33:23 time: 1.3766 data_time: 0.0309 memory: 16201 loss_prob: 0.7142 loss_thr: 0.4678 loss_db: 0.1204 loss: 1.3024 2022/08/30 05:51:56 - mmengine - INFO - Epoch(train) [248][45/63] lr: 5.6877e-03 eta: 1 day, 0:33:23 time: 1.4170 data_time: 0.0341 memory: 16201 loss_prob: 0.7051 loss_thr: 0.4622 loss_db: 0.1198 loss: 1.2872 2022/08/30 05:52:03 - mmengine - INFO - Epoch(train) [248][50/63] lr: 5.6877e-03 eta: 1 day, 0:33:05 time: 1.3767 data_time: 0.0378 memory: 16201 loss_prob: 0.7604 loss_thr: 0.4628 loss_db: 0.1352 loss: 1.3583 2022/08/30 05:52:10 - mmengine - INFO - Epoch(train) [248][55/63] lr: 5.6877e-03 eta: 1 day, 0:33:05 time: 1.3644 data_time: 0.0418 memory: 16201 loss_prob: 0.8770 loss_thr: 0.4759 loss_db: 0.1542 loss: 1.5071 2022/08/30 05:52:16 - mmengine - INFO - Epoch(train) [248][60/63] lr: 5.6877e-03 eta: 1 day, 0:32:43 time: 1.3009 data_time: 0.0420 memory: 16201 loss_prob: 1.0114 loss_thr: 0.5156 loss_db: 0.1661 loss: 1.6931 2022/08/30 05:52:19 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:52:28 - mmengine - INFO - Epoch(train) [249][5/63] lr: 5.6823e-03 eta: 1 day, 0:32:43 time: 1.4793 data_time: 0.2442 memory: 16201 loss_prob: 0.9881 loss_thr: 0.5416 loss_db: 0.1620 loss: 1.6916 2022/08/30 05:52:36 - mmengine - INFO - Epoch(train) [249][10/63] lr: 5.6823e-03 eta: 1 day, 0:32:14 time: 1.6525 data_time: 0.2460 memory: 16201 loss_prob: 0.9208 loss_thr: 0.4878 loss_db: 0.1507 loss: 1.5593 2022/08/30 05:52:42 - mmengine - INFO - Epoch(train) [249][15/63] lr: 5.6823e-03 eta: 1 day, 0:32:14 time: 1.3852 data_time: 0.0332 memory: 16201 loss_prob: 0.8997 loss_thr: 0.5030 loss_db: 0.1475 loss: 1.5503 2022/08/30 05:52:48 - mmengine - INFO - Epoch(train) [249][20/63] lr: 5.6823e-03 eta: 1 day, 0:31:50 time: 1.2221 data_time: 0.0400 memory: 16201 loss_prob: 0.8990 loss_thr: 0.5102 loss_db: 0.1483 loss: 1.5575 2022/08/30 05:52:54 - mmengine - INFO - Epoch(train) [249][25/63] lr: 5.6823e-03 eta: 1 day, 0:31:50 time: 1.2525 data_time: 0.0404 memory: 16201 loss_prob: 0.9505 loss_thr: 0.5178 loss_db: 0.1586 loss: 1.6270 2022/08/30 05:53:01 - mmengine - INFO - Epoch(train) [249][30/63] lr: 5.6823e-03 eta: 1 day, 0:31:29 time: 1.3005 data_time: 0.0291 memory: 16201 loss_prob: 0.9751 loss_thr: 0.5278 loss_db: 0.1575 loss: 1.6604 2022/08/30 05:53:08 - mmengine - INFO - Epoch(train) [249][35/63] lr: 5.6823e-03 eta: 1 day, 0:31:29 time: 1.3012 data_time: 0.0339 memory: 16201 loss_prob: 1.0368 loss_thr: 0.5110 loss_db: 0.1695 loss: 1.7173 2022/08/30 05:53:14 - mmengine - INFO - Epoch(train) [249][40/63] lr: 5.6823e-03 eta: 1 day, 0:31:07 time: 1.3057 data_time: 0.0319 memory: 16201 loss_prob: 1.0362 loss_thr: 0.5096 loss_db: 0.1705 loss: 1.7163 2022/08/30 05:53:21 - mmengine - INFO - Epoch(train) [249][45/63] lr: 5.6823e-03 eta: 1 day, 0:31:07 time: 1.3642 data_time: 0.0333 memory: 16201 loss_prob: 1.0148 loss_thr: 0.5231 loss_db: 0.1593 loss: 1.6972 2022/08/30 05:53:28 - mmengine - INFO - Epoch(train) [249][50/63] lr: 5.6823e-03 eta: 1 day, 0:30:51 time: 1.4153 data_time: 0.0364 memory: 16201 loss_prob: 1.0183 loss_thr: 0.5093 loss_db: 0.1637 loss: 1.6913 2022/08/30 05:53:35 - mmengine - INFO - Epoch(train) [249][55/63] lr: 5.6823e-03 eta: 1 day, 0:30:51 time: 1.3563 data_time: 0.0277 memory: 16201 loss_prob: 0.9901 loss_thr: 0.5244 loss_db: 0.1667 loss: 1.6812 2022/08/30 05:53:42 - mmengine - INFO - Epoch(train) [249][60/63] lr: 5.6823e-03 eta: 1 day, 0:30:30 time: 1.3217 data_time: 0.0292 memory: 16201 loss_prob: 0.9798 loss_thr: 0.5315 loss_db: 0.1658 loss: 1.6771 2022/08/30 05:53:46 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:53:54 - mmengine - INFO - Epoch(train) [250][5/63] lr: 5.6769e-03 eta: 1 day, 0:30:30 time: 1.5690 data_time: 0.2128 memory: 16201 loss_prob: 0.8939 loss_thr: 0.4776 loss_db: 0.1501 loss: 1.5216 2022/08/30 05:54:01 - mmengine - INFO - Epoch(train) [250][10/63] lr: 5.6769e-03 eta: 1 day, 0:29:58 time: 1.5652 data_time: 0.2248 memory: 16201 loss_prob: 0.9448 loss_thr: 0.5281 loss_db: 0.1572 loss: 1.6301 2022/08/30 05:54:08 - mmengine - INFO - Epoch(train) [250][15/63] lr: 5.6769e-03 eta: 1 day, 0:29:58 time: 1.3196 data_time: 0.0396 memory: 16201 loss_prob: 0.9938 loss_thr: 0.5173 loss_db: 0.1635 loss: 1.6746 2022/08/30 05:54:15 - mmengine - INFO - Epoch(train) [250][20/63] lr: 5.6769e-03 eta: 1 day, 0:29:38 time: 1.3365 data_time: 0.0392 memory: 16201 loss_prob: 0.9734 loss_thr: 0.4955 loss_db: 0.1634 loss: 1.6323 2022/08/30 05:54:21 - mmengine - INFO - Epoch(train) [250][25/63] lr: 5.6769e-03 eta: 1 day, 0:29:38 time: 1.3381 data_time: 0.0350 memory: 16201 loss_prob: 0.9710 loss_thr: 0.5074 loss_db: 0.1585 loss: 1.6369 2022/08/30 05:54:28 - mmengine - INFO - Epoch(train) [250][30/63] lr: 5.6769e-03 eta: 1 day, 0:29:19 time: 1.3604 data_time: 0.0337 memory: 16201 loss_prob: 0.9045 loss_thr: 0.4951 loss_db: 0.1466 loss: 1.5463 2022/08/30 05:54:35 - mmengine - INFO - Epoch(train) [250][35/63] lr: 5.6769e-03 eta: 1 day, 0:29:19 time: 1.4397 data_time: 0.0359 memory: 16201 loss_prob: 0.7932 loss_thr: 0.4808 loss_db: 0.1334 loss: 1.4075 2022/08/30 05:54:41 - mmengine - INFO - Epoch(train) [250][40/63] lr: 5.6769e-03 eta: 1 day, 0:28:58 time: 1.3251 data_time: 0.0314 memory: 16201 loss_prob: 0.7906 loss_thr: 0.4733 loss_db: 0.1340 loss: 1.3980 2022/08/30 05:54:48 - mmengine - INFO - Epoch(train) [250][45/63] lr: 5.6769e-03 eta: 1 day, 0:28:58 time: 1.2253 data_time: 0.0286 memory: 16201 loss_prob: 0.8763 loss_thr: 0.5082 loss_db: 0.1485 loss: 1.5330 2022/08/30 05:54:54 - mmengine - INFO - Epoch(train) [250][50/63] lr: 5.6769e-03 eta: 1 day, 0:28:37 time: 1.2942 data_time: 0.0329 memory: 16201 loss_prob: 0.9120 loss_thr: 0.5259 loss_db: 0.1519 loss: 1.5898 2022/08/30 05:55:02 - mmengine - INFO - Epoch(train) [250][55/63] lr: 5.6769e-03 eta: 1 day, 0:28:37 time: 1.3863 data_time: 0.0331 memory: 16201 loss_prob: 0.8365 loss_thr: 0.4923 loss_db: 0.1392 loss: 1.4680 2022/08/30 05:55:09 - mmengine - INFO - Epoch(train) [250][60/63] lr: 5.6769e-03 eta: 1 day, 0:28:22 time: 1.4749 data_time: 0.0474 memory: 16201 loss_prob: 0.8396 loss_thr: 0.4755 loss_db: 0.1370 loss: 1.4522 2022/08/30 05:55:12 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:55:21 - mmengine - INFO - Epoch(train) [251][5/63] lr: 5.6716e-03 eta: 1 day, 0:28:22 time: 1.4427 data_time: 0.2286 memory: 16201 loss_prob: 0.8688 loss_thr: 0.4915 loss_db: 0.1432 loss: 1.5034 2022/08/30 05:55:28 - mmengine - INFO - Epoch(train) [251][10/63] lr: 5.6716e-03 eta: 1 day, 0:27:51 time: 1.5914 data_time: 0.2422 memory: 16201 loss_prob: 0.7801 loss_thr: 0.4652 loss_db: 0.1302 loss: 1.3755 2022/08/30 05:55:35 - mmengine - INFO - Epoch(train) [251][15/63] lr: 5.6716e-03 eta: 1 day, 0:27:51 time: 1.4489 data_time: 0.0444 memory: 16201 loss_prob: 0.7293 loss_thr: 0.4561 loss_db: 0.1218 loss: 1.3072 2022/08/30 05:55:43 - mmengine - INFO - Epoch(train) [251][20/63] lr: 5.6716e-03 eta: 1 day, 0:27:35 time: 1.4455 data_time: 0.0326 memory: 16201 loss_prob: 0.7667 loss_thr: 0.4661 loss_db: 0.1289 loss: 1.3617 2022/08/30 05:55:50 - mmengine - INFO - Epoch(train) [251][25/63] lr: 5.6716e-03 eta: 1 day, 0:27:35 time: 1.4982 data_time: 0.0348 memory: 16201 loss_prob: 0.7343 loss_thr: 0.4496 loss_db: 0.1265 loss: 1.3103 2022/08/30 05:55:57 - mmengine - INFO - Epoch(train) [251][30/63] lr: 5.6716e-03 eta: 1 day, 0:27:17 time: 1.3873 data_time: 0.0370 memory: 16201 loss_prob: 0.7137 loss_thr: 0.4484 loss_db: 0.1227 loss: 1.2847 2022/08/30 05:56:04 - mmengine - INFO - Epoch(train) [251][35/63] lr: 5.6716e-03 eta: 1 day, 0:27:17 time: 1.3904 data_time: 0.0385 memory: 16201 loss_prob: 0.7416 loss_thr: 0.4537 loss_db: 0.1247 loss: 1.3200 2022/08/30 05:56:11 - mmengine - INFO - Epoch(train) [251][40/63] lr: 5.6716e-03 eta: 1 day, 0:27:01 time: 1.4402 data_time: 0.0303 memory: 16201 loss_prob: 0.7849 loss_thr: 0.4726 loss_db: 0.1310 loss: 1.3885 2022/08/30 05:56:17 - mmengine - INFO - Epoch(train) [251][45/63] lr: 5.6716e-03 eta: 1 day, 0:27:01 time: 1.3313 data_time: 0.0305 memory: 16201 loss_prob: 0.7930 loss_thr: 0.4730 loss_db: 0.1298 loss: 1.3958 2022/08/30 05:56:24 - mmengine - INFO - Epoch(train) [251][50/63] lr: 5.6716e-03 eta: 1 day, 0:26:40 time: 1.3088 data_time: 0.0362 memory: 16201 loss_prob: 0.7746 loss_thr: 0.4565 loss_db: 0.1290 loss: 1.3601 2022/08/30 05:56:31 - mmengine - INFO - Epoch(train) [251][55/63] lr: 5.6716e-03 eta: 1 day, 0:26:40 time: 1.3758 data_time: 0.0350 memory: 16201 loss_prob: 0.8296 loss_thr: 0.4909 loss_db: 0.1400 loss: 1.4605 2022/08/30 05:56:38 - mmengine - INFO - Epoch(train) [251][60/63] lr: 5.6716e-03 eta: 1 day, 0:26:22 time: 1.3795 data_time: 0.0315 memory: 16201 loss_prob: 0.8479 loss_thr: 0.5085 loss_db: 0.1443 loss: 1.5007 2022/08/30 05:56:41 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:56:50 - mmengine - INFO - Epoch(train) [252][5/63] lr: 5.6662e-03 eta: 1 day, 0:26:22 time: 1.4456 data_time: 0.2271 memory: 16201 loss_prob: 0.7805 loss_thr: 0.4670 loss_db: 0.1262 loss: 1.3737 2022/08/30 05:56:57 - mmengine - INFO - Epoch(train) [252][10/63] lr: 5.6662e-03 eta: 1 day, 0:25:50 time: 1.5655 data_time: 0.2482 memory: 16201 loss_prob: 0.7734 loss_thr: 0.4517 loss_db: 0.1318 loss: 1.3569 2022/08/30 05:57:04 - mmengine - INFO - Epoch(train) [252][15/63] lr: 5.6662e-03 eta: 1 day, 0:25:50 time: 1.4215 data_time: 0.0330 memory: 16201 loss_prob: 0.7852 loss_thr: 0.4730 loss_db: 0.1357 loss: 1.3938 2022/08/30 05:57:11 - mmengine - INFO - Epoch(train) [252][20/63] lr: 5.6662e-03 eta: 1 day, 0:25:32 time: 1.3798 data_time: 0.0264 memory: 16201 loss_prob: 0.7396 loss_thr: 0.4560 loss_db: 0.1237 loss: 1.3193 2022/08/30 05:57:17 - mmengine - INFO - Epoch(train) [252][25/63] lr: 5.6662e-03 eta: 1 day, 0:25:32 time: 1.3184 data_time: 0.0323 memory: 16201 loss_prob: 0.7218 loss_thr: 0.4555 loss_db: 0.1220 loss: 1.2992 2022/08/30 05:57:25 - mmengine - INFO - Epoch(train) [252][30/63] lr: 5.6662e-03 eta: 1 day, 0:25:15 time: 1.4128 data_time: 0.0277 memory: 16201 loss_prob: 0.8101 loss_thr: 0.5021 loss_db: 0.1340 loss: 1.4461 2022/08/30 05:57:32 - mmengine - INFO - Epoch(train) [252][35/63] lr: 5.6662e-03 eta: 1 day, 0:25:15 time: 1.5126 data_time: 0.0353 memory: 16201 loss_prob: 0.9277 loss_thr: 0.5297 loss_db: 0.1503 loss: 1.6077 2022/08/30 05:57:39 - mmengine - INFO - Epoch(train) [252][40/63] lr: 5.6662e-03 eta: 1 day, 0:25:00 time: 1.4541 data_time: 0.0281 memory: 16201 loss_prob: 0.8494 loss_thr: 0.4971 loss_db: 0.1408 loss: 1.4873 2022/08/30 05:57:46 - mmengine - INFO - Epoch(train) [252][45/63] lr: 5.6662e-03 eta: 1 day, 0:25:00 time: 1.3760 data_time: 0.0278 memory: 16201 loss_prob: 0.7994 loss_thr: 0.4825 loss_db: 0.1348 loss: 1.4167 2022/08/30 05:57:53 - mmengine - INFO - Epoch(train) [252][50/63] lr: 5.6662e-03 eta: 1 day, 0:24:40 time: 1.3417 data_time: 0.0435 memory: 16201 loss_prob: 0.8187 loss_thr: 0.4889 loss_db: 0.1377 loss: 1.4453 2022/08/30 05:57:59 - mmengine - INFO - Epoch(train) [252][55/63] lr: 5.6662e-03 eta: 1 day, 0:24:40 time: 1.2604 data_time: 0.0313 memory: 16201 loss_prob: 0.8941 loss_thr: 0.5082 loss_db: 0.1465 loss: 1.5488 2022/08/30 05:58:05 - mmengine - INFO - Epoch(train) [252][60/63] lr: 5.6662e-03 eta: 1 day, 0:24:18 time: 1.2697 data_time: 0.0274 memory: 16201 loss_prob: 0.9066 loss_thr: 0.5153 loss_db: 0.1497 loss: 1.5715 2022/08/30 05:58:08 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:58:17 - mmengine - INFO - Epoch(train) [253][5/63] lr: 5.6608e-03 eta: 1 day, 0:24:18 time: 1.4453 data_time: 0.2203 memory: 16201 loss_prob: 0.8188 loss_thr: 0.4778 loss_db: 0.1393 loss: 1.4359 2022/08/30 05:58:25 - mmengine - INFO - Epoch(train) [253][10/63] lr: 5.6608e-03 eta: 1 day, 0:23:49 time: 1.6529 data_time: 0.2287 memory: 16201 loss_prob: 0.7603 loss_thr: 0.4673 loss_db: 0.1306 loss: 1.3582 2022/08/30 05:58:32 - mmengine - INFO - Epoch(train) [253][15/63] lr: 5.6608e-03 eta: 1 day, 0:23:49 time: 1.4262 data_time: 0.0331 memory: 16201 loss_prob: 0.8119 loss_thr: 0.4832 loss_db: 0.1397 loss: 1.4348 2022/08/30 05:58:38 - mmengine - INFO - Epoch(train) [253][20/63] lr: 5.6608e-03 eta: 1 day, 0:23:27 time: 1.2866 data_time: 0.0361 memory: 16201 loss_prob: 0.7762 loss_thr: 0.4686 loss_db: 0.1306 loss: 1.3753 2022/08/30 05:58:45 - mmengine - INFO - Epoch(train) [253][25/63] lr: 5.6608e-03 eta: 1 day, 0:23:27 time: 1.3313 data_time: 0.0367 memory: 16201 loss_prob: 0.6691 loss_thr: 0.4303 loss_db: 0.1120 loss: 1.2115 2022/08/30 05:58:51 - mmengine - INFO - Epoch(train) [253][30/63] lr: 5.6608e-03 eta: 1 day, 0:23:07 time: 1.3312 data_time: 0.0315 memory: 16201 loss_prob: 0.6921 loss_thr: 0.4403 loss_db: 0.1187 loss: 1.2511 2022/08/30 05:58:58 - mmengine - INFO - Epoch(train) [253][35/63] lr: 5.6608e-03 eta: 1 day, 0:23:07 time: 1.2885 data_time: 0.0296 memory: 16201 loss_prob: 0.8330 loss_thr: 0.4814 loss_db: 0.1427 loss: 1.4571 2022/08/30 05:59:04 - mmengine - INFO - Epoch(train) [253][40/63] lr: 5.6608e-03 eta: 1 day, 0:22:47 time: 1.3173 data_time: 0.0286 memory: 16201 loss_prob: 0.9118 loss_thr: 0.5129 loss_db: 0.1540 loss: 1.5787 2022/08/30 05:59:12 - mmengine - INFO - Epoch(train) [253][45/63] lr: 5.6608e-03 eta: 1 day, 0:22:47 time: 1.4107 data_time: 0.0366 memory: 16201 loss_prob: 0.7988 loss_thr: 0.5008 loss_db: 0.1382 loss: 1.4378 2022/08/30 05:59:19 - mmengine - INFO - Epoch(train) [253][50/63] lr: 5.6608e-03 eta: 1 day, 0:22:32 time: 1.4619 data_time: 0.0434 memory: 16201 loss_prob: 0.8251 loss_thr: 0.5120 loss_db: 0.1419 loss: 1.4791 2022/08/30 05:59:26 - mmengine - INFO - Epoch(train) [253][55/63] lr: 5.6608e-03 eta: 1 day, 0:22:32 time: 1.4331 data_time: 0.0333 memory: 16201 loss_prob: 0.9538 loss_thr: 0.5304 loss_db: 0.1556 loss: 1.6398 2022/08/30 05:59:33 - mmengine - INFO - Epoch(train) [253][60/63] lr: 5.6608e-03 eta: 1 day, 0:22:15 time: 1.4049 data_time: 0.0300 memory: 16201 loss_prob: 0.9318 loss_thr: 0.5140 loss_db: 0.1513 loss: 1.5971 2022/08/30 05:59:36 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 05:59:46 - mmengine - INFO - Epoch(train) [254][5/63] lr: 5.6554e-03 eta: 1 day, 0:22:15 time: 1.5597 data_time: 0.2387 memory: 16201 loss_prob: 0.8608 loss_thr: 0.4778 loss_db: 0.1455 loss: 1.4841 2022/08/30 05:59:53 - mmengine - INFO - Epoch(train) [254][10/63] lr: 5.6554e-03 eta: 1 day, 0:21:45 time: 1.6403 data_time: 0.2487 memory: 16201 loss_prob: 0.8207 loss_thr: 0.4738 loss_db: 0.1334 loss: 1.4279 2022/08/30 06:00:00 - mmengine - INFO - Epoch(train) [254][15/63] lr: 5.6554e-03 eta: 1 day, 0:21:45 time: 1.3403 data_time: 0.0351 memory: 16201 loss_prob: 0.7988 loss_thr: 0.4707 loss_db: 0.1313 loss: 1.4008 2022/08/30 06:00:11 - mmengine - INFO - Epoch(train) [254][20/63] lr: 5.6554e-03 eta: 1 day, 0:21:42 time: 1.7797 data_time: 0.0393 memory: 16201 loss_prob: 0.7749 loss_thr: 0.4669 loss_db: 0.1306 loss: 1.3723 2022/08/30 06:00:17 - mmengine - INFO - Epoch(train) [254][25/63] lr: 5.6554e-03 eta: 1 day, 0:21:42 time: 1.7620 data_time: 0.0512 memory: 16201 loss_prob: 0.8434 loss_thr: 0.4707 loss_db: 0.1453 loss: 1.4594 2022/08/30 06:00:24 - mmengine - INFO - Epoch(train) [254][30/63] lr: 5.6554e-03 eta: 1 day, 0:21:23 time: 1.3579 data_time: 0.0374 memory: 16201 loss_prob: 0.8989 loss_thr: 0.4881 loss_db: 0.1553 loss: 1.5424 2022/08/30 06:00:31 - mmengine - INFO - Epoch(train) [254][35/63] lr: 5.6554e-03 eta: 1 day, 0:21:23 time: 1.3960 data_time: 0.0290 memory: 16201 loss_prob: 0.8506 loss_thr: 0.4936 loss_db: 0.1437 loss: 1.4879 2022/08/30 06:00:38 - mmengine - INFO - Epoch(train) [254][40/63] lr: 5.6554e-03 eta: 1 day, 0:21:04 time: 1.3571 data_time: 0.0311 memory: 16201 loss_prob: 0.8002 loss_thr: 0.4932 loss_db: 0.1338 loss: 1.4272 2022/08/30 06:00:45 - mmengine - INFO - Epoch(train) [254][45/63] lr: 5.6554e-03 eta: 1 day, 0:21:04 time: 1.3317 data_time: 0.0350 memory: 16201 loss_prob: 0.8315 loss_thr: 0.4949 loss_db: 0.1467 loss: 1.4731 2022/08/30 06:00:52 - mmengine - INFO - Epoch(train) [254][50/63] lr: 5.6554e-03 eta: 1 day, 0:20:48 time: 1.4239 data_time: 0.0453 memory: 16201 loss_prob: 0.8208 loss_thr: 0.4825 loss_db: 0.1426 loss: 1.4459 2022/08/30 06:00:58 - mmengine - INFO - Epoch(train) [254][55/63] lr: 5.6554e-03 eta: 1 day, 0:20:48 time: 1.3785 data_time: 0.0402 memory: 16201 loss_prob: 0.8829 loss_thr: 0.4963 loss_db: 0.1481 loss: 1.5273 2022/08/30 06:01:04 - mmengine - INFO - Epoch(train) [254][60/63] lr: 5.6554e-03 eta: 1 day, 0:20:24 time: 1.2274 data_time: 0.0270 memory: 16201 loss_prob: 0.9646 loss_thr: 0.5097 loss_db: 0.1630 loss: 1.6373 2022/08/30 06:01:06 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:01:08 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:01:16 - mmengine - INFO - Epoch(train) [255][5/63] lr: 5.6500e-03 eta: 1 day, 0:20:24 time: 1.4305 data_time: 0.2265 memory: 16201 loss_prob: 0.8603 loss_thr: 0.5013 loss_db: 0.1420 loss: 1.5036 2022/08/30 06:01:24 - mmengine - INFO - Epoch(train) [255][10/63] lr: 5.6500e-03 eta: 1 day, 0:19:53 time: 1.5968 data_time: 0.2278 memory: 16201 loss_prob: 0.8053 loss_thr: 0.4710 loss_db: 0.1401 loss: 1.4165 2022/08/30 06:01:31 - mmengine - INFO - Epoch(train) [255][15/63] lr: 5.6500e-03 eta: 1 day, 0:19:53 time: 1.4626 data_time: 0.0374 memory: 16201 loss_prob: 0.8131 loss_thr: 0.4832 loss_db: 0.1388 loss: 1.4350 2022/08/30 06:01:38 - mmengine - INFO - Epoch(train) [255][20/63] lr: 5.6500e-03 eta: 1 day, 0:19:36 time: 1.4027 data_time: 0.0454 memory: 16201 loss_prob: 0.8669 loss_thr: 0.5029 loss_db: 0.1399 loss: 1.5098 2022/08/30 06:01:44 - mmengine - INFO - Epoch(train) [255][25/63] lr: 5.6500e-03 eta: 1 day, 0:19:36 time: 1.3207 data_time: 0.0363 memory: 16201 loss_prob: 0.9050 loss_thr: 0.5110 loss_db: 0.1553 loss: 1.5713 2022/08/30 06:01:51 - mmengine - INFO - Epoch(train) [255][30/63] lr: 5.6500e-03 eta: 1 day, 0:19:17 time: 1.3550 data_time: 0.0325 memory: 16201 loss_prob: 0.9259 loss_thr: 0.5158 loss_db: 0.1614 loss: 1.6031 2022/08/30 06:01:58 - mmengine - INFO - Epoch(train) [255][35/63] lr: 5.6500e-03 eta: 1 day, 0:19:17 time: 1.3641 data_time: 0.0413 memory: 16201 loss_prob: 0.8356 loss_thr: 0.4870 loss_db: 0.1385 loss: 1.4611 2022/08/30 06:02:05 - mmengine - INFO - Epoch(train) [255][40/63] lr: 5.6500e-03 eta: 1 day, 0:18:58 time: 1.3541 data_time: 0.0340 memory: 16201 loss_prob: 0.7840 loss_thr: 0.4689 loss_db: 0.1326 loss: 1.3856 2022/08/30 06:02:12 - mmengine - INFO - Epoch(train) [255][45/63] lr: 5.6500e-03 eta: 1 day, 0:18:58 time: 1.3715 data_time: 0.0404 memory: 16201 loss_prob: 0.8826 loss_thr: 0.4866 loss_db: 0.1462 loss: 1.5154 2022/08/30 06:02:18 - mmengine - INFO - Epoch(train) [255][50/63] lr: 5.6500e-03 eta: 1 day, 0:18:38 time: 1.3263 data_time: 0.0424 memory: 16201 loss_prob: 1.0503 loss_thr: 0.5275 loss_db: 0.1782 loss: 1.7560 2022/08/30 06:02:24 - mmengine - INFO - Epoch(train) [255][55/63] lr: 5.6500e-03 eta: 1 day, 0:18:38 time: 1.2324 data_time: 0.0299 memory: 16201 loss_prob: 1.0758 loss_thr: 0.5461 loss_db: 0.1844 loss: 1.8063 2022/08/30 06:02:31 - mmengine - INFO - Epoch(train) [255][60/63] lr: 5.6500e-03 eta: 1 day, 0:18:18 time: 1.3118 data_time: 0.0411 memory: 16201 loss_prob: 1.0404 loss_thr: 0.5427 loss_db: 0.1684 loss: 1.7515 2022/08/30 06:02:34 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:02:43 - mmengine - INFO - Epoch(train) [256][5/63] lr: 5.6447e-03 eta: 1 day, 0:18:18 time: 1.4972 data_time: 0.2289 memory: 16201 loss_prob: 0.9001 loss_thr: 0.4968 loss_db: 0.1494 loss: 1.5463 2022/08/30 06:02:49 - mmengine - INFO - Epoch(train) [256][10/63] lr: 5.6447e-03 eta: 1 day, 0:17:44 time: 1.5305 data_time: 0.2428 memory: 16201 loss_prob: 0.9561 loss_thr: 0.4967 loss_db: 0.1560 loss: 1.6089 2022/08/30 06:02:57 - mmengine - INFO - Epoch(train) [256][15/63] lr: 5.6447e-03 eta: 1 day, 0:17:44 time: 1.3414 data_time: 0.0398 memory: 16201 loss_prob: 0.9535 loss_thr: 0.5068 loss_db: 0.1564 loss: 1.6167 2022/08/30 06:03:03 - mmengine - INFO - Epoch(train) [256][20/63] lr: 5.6447e-03 eta: 1 day, 0:17:27 time: 1.3846 data_time: 0.0311 memory: 16201 loss_prob: 0.8754 loss_thr: 0.5050 loss_db: 0.1454 loss: 1.5258 2022/08/30 06:03:10 - mmengine - INFO - Epoch(train) [256][25/63] lr: 5.6447e-03 eta: 1 day, 0:17:27 time: 1.3792 data_time: 0.0389 memory: 16201 loss_prob: 0.8265 loss_thr: 0.4803 loss_db: 0.1364 loss: 1.4432 2022/08/30 06:03:17 - mmengine - INFO - Epoch(train) [256][30/63] lr: 5.6447e-03 eta: 1 day, 0:17:08 time: 1.3627 data_time: 0.0390 memory: 16201 loss_prob: 0.7739 loss_thr: 0.4654 loss_db: 0.1311 loss: 1.3705 2022/08/30 06:03:24 - mmengine - INFO - Epoch(train) [256][35/63] lr: 5.6447e-03 eta: 1 day, 0:17:08 time: 1.3622 data_time: 0.0338 memory: 16201 loss_prob: 0.9691 loss_thr: 0.4863 loss_db: 0.1476 loss: 1.6030 2022/08/30 06:03:31 - mmengine - INFO - Epoch(train) [256][40/63] lr: 5.6447e-03 eta: 1 day, 0:16:51 time: 1.4133 data_time: 0.0349 memory: 16201 loss_prob: 1.0011 loss_thr: 0.5058 loss_db: 0.1493 loss: 1.6562 2022/08/30 06:03:38 - mmengine - INFO - Epoch(train) [256][45/63] lr: 5.6447e-03 eta: 1 day, 0:16:51 time: 1.3854 data_time: 0.0428 memory: 16201 loss_prob: 0.8046 loss_thr: 0.4729 loss_db: 0.1324 loss: 1.4100 2022/08/30 06:03:44 - mmengine - INFO - Epoch(train) [256][50/63] lr: 5.6447e-03 eta: 1 day, 0:16:30 time: 1.3031 data_time: 0.0376 memory: 16201 loss_prob: 0.8196 loss_thr: 0.4695 loss_db: 0.1396 loss: 1.4288 2022/08/30 06:03:51 - mmengine - INFO - Epoch(train) [256][55/63] lr: 5.6447e-03 eta: 1 day, 0:16:30 time: 1.3257 data_time: 0.0280 memory: 16201 loss_prob: 0.8206 loss_thr: 0.4771 loss_db: 0.1386 loss: 1.4362 2022/08/30 06:03:58 - mmengine - INFO - Epoch(train) [256][60/63] lr: 5.6447e-03 eta: 1 day, 0:16:11 time: 1.3479 data_time: 0.0310 memory: 16201 loss_prob: 0.7921 loss_thr: 0.4672 loss_db: 0.1312 loss: 1.3905 2022/08/30 06:04:01 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:04:10 - mmengine - INFO - Epoch(train) [257][5/63] lr: 5.6393e-03 eta: 1 day, 0:16:11 time: 1.4881 data_time: 0.2321 memory: 16201 loss_prob: 0.8301 loss_thr: 0.4974 loss_db: 0.1387 loss: 1.4662 2022/08/30 06:04:17 - mmengine - INFO - Epoch(train) [257][10/63] lr: 5.6393e-03 eta: 1 day, 0:15:41 time: 1.6104 data_time: 0.2468 memory: 16201 loss_prob: 0.8039 loss_thr: 0.4873 loss_db: 0.1373 loss: 1.4285 2022/08/30 06:04:23 - mmengine - INFO - Epoch(train) [257][15/63] lr: 5.6393e-03 eta: 1 day, 0:15:41 time: 1.2921 data_time: 0.0340 memory: 16201 loss_prob: 0.8149 loss_thr: 0.4978 loss_db: 0.1393 loss: 1.4521 2022/08/30 06:04:30 - mmengine - INFO - Epoch(train) [257][20/63] lr: 5.6393e-03 eta: 1 day, 0:15:17 time: 1.2237 data_time: 0.0317 memory: 16201 loss_prob: 0.9039 loss_thr: 0.5212 loss_db: 0.1519 loss: 1.5770 2022/08/30 06:04:37 - mmengine - INFO - Epoch(train) [257][25/63] lr: 5.6393e-03 eta: 1 day, 0:15:17 time: 1.3859 data_time: 0.0419 memory: 16201 loss_prob: 0.8979 loss_thr: 0.5069 loss_db: 0.1523 loss: 1.5572 2022/08/30 06:04:44 - mmengine - INFO - Epoch(train) [257][30/63] lr: 5.6393e-03 eta: 1 day, 0:15:01 time: 1.4152 data_time: 0.0372 memory: 16201 loss_prob: 0.8463 loss_thr: 0.4890 loss_db: 0.1440 loss: 1.4793 2022/08/30 06:04:50 - mmengine - INFO - Epoch(train) [257][35/63] lr: 5.6393e-03 eta: 1 day, 0:15:01 time: 1.3213 data_time: 0.0392 memory: 16201 loss_prob: 0.8769 loss_thr: 0.4935 loss_db: 0.1450 loss: 1.5154 2022/08/30 06:04:58 - mmengine - INFO - Epoch(train) [257][40/63] lr: 5.6393e-03 eta: 1 day, 0:14:44 time: 1.4221 data_time: 0.0367 memory: 16201 loss_prob: 0.9002 loss_thr: 0.5000 loss_db: 0.1478 loss: 1.5480 2022/08/30 06:05:05 - mmengine - INFO - Epoch(train) [257][45/63] lr: 5.6393e-03 eta: 1 day, 0:14:44 time: 1.5147 data_time: 0.0319 memory: 16201 loss_prob: 0.8550 loss_thr: 0.4923 loss_db: 0.1430 loss: 1.4904 2022/08/30 06:05:12 - mmengine - INFO - Epoch(train) [257][50/63] lr: 5.6393e-03 eta: 1 day, 0:14:27 time: 1.4041 data_time: 0.0347 memory: 16201 loss_prob: 0.7412 loss_thr: 0.4511 loss_db: 0.1247 loss: 1.3170 2022/08/30 06:05:19 - mmengine - INFO - Epoch(train) [257][55/63] lr: 5.6393e-03 eta: 1 day, 0:14:27 time: 1.3414 data_time: 0.0270 memory: 16201 loss_prob: 0.7491 loss_thr: 0.4594 loss_db: 0.1262 loss: 1.3347 2022/08/30 06:05:25 - mmengine - INFO - Epoch(train) [257][60/63] lr: 5.6393e-03 eta: 1 day, 0:14:06 time: 1.2940 data_time: 0.0336 memory: 16201 loss_prob: 0.7859 loss_thr: 0.4720 loss_db: 0.1306 loss: 1.3884 2022/08/30 06:05:28 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:05:37 - mmengine - INFO - Epoch(train) [258][5/63] lr: 5.6339e-03 eta: 1 day, 0:14:06 time: 1.4574 data_time: 0.2182 memory: 16201 loss_prob: 0.7642 loss_thr: 0.4491 loss_db: 0.1287 loss: 1.3420 2022/08/30 06:05:44 - mmengine - INFO - Epoch(train) [258][10/63] lr: 5.6339e-03 eta: 1 day, 0:13:35 time: 1.5885 data_time: 0.2321 memory: 16201 loss_prob: 0.7226 loss_thr: 0.4397 loss_db: 0.1204 loss: 1.2827 2022/08/30 06:05:52 - mmengine - INFO - Epoch(train) [258][15/63] lr: 5.6339e-03 eta: 1 day, 0:13:35 time: 1.5118 data_time: 0.0293 memory: 16201 loss_prob: 0.7394 loss_thr: 0.4647 loss_db: 0.1246 loss: 1.3287 2022/08/30 06:05:59 - mmengine - INFO - Epoch(train) [258][20/63] lr: 5.6339e-03 eta: 1 day, 0:13:23 time: 1.5382 data_time: 0.0312 memory: 16201 loss_prob: 0.7963 loss_thr: 0.4823 loss_db: 0.1363 loss: 1.4149 2022/08/30 06:06:07 - mmengine - INFO - Epoch(train) [258][25/63] lr: 5.6339e-03 eta: 1 day, 0:13:23 time: 1.4388 data_time: 0.0415 memory: 16201 loss_prob: 0.7842 loss_thr: 0.4522 loss_db: 0.1354 loss: 1.3718 2022/08/30 06:06:13 - mmengine - INFO - Epoch(train) [258][30/63] lr: 5.6339e-03 eta: 1 day, 0:13:06 time: 1.4005 data_time: 0.0300 memory: 16201 loss_prob: 0.7954 loss_thr: 0.4534 loss_db: 0.1332 loss: 1.3820 2022/08/30 06:06:21 - mmengine - INFO - Epoch(train) [258][35/63] lr: 5.6339e-03 eta: 1 day, 0:13:06 time: 1.4069 data_time: 0.0362 memory: 16201 loss_prob: 0.8039 loss_thr: 0.4746 loss_db: 0.1307 loss: 1.4092 2022/08/30 06:06:27 - mmengine - INFO - Epoch(train) [258][40/63] lr: 5.6339e-03 eta: 1 day, 0:12:47 time: 1.3625 data_time: 0.0383 memory: 16201 loss_prob: 0.8859 loss_thr: 0.5093 loss_db: 0.1426 loss: 1.5377 2022/08/30 06:06:33 - mmengine - INFO - Epoch(train) [258][45/63] lr: 5.6339e-03 eta: 1 day, 0:12:47 time: 1.2677 data_time: 0.0316 memory: 16201 loss_prob: 0.8275 loss_thr: 0.4941 loss_db: 0.1357 loss: 1.4574 2022/08/30 06:06:41 - mmengine - INFO - Epoch(train) [258][50/63] lr: 5.6339e-03 eta: 1 day, 0:12:29 time: 1.3663 data_time: 0.0398 memory: 16201 loss_prob: 0.7148 loss_thr: 0.4594 loss_db: 0.1198 loss: 1.2940 2022/08/30 06:06:48 - mmengine - INFO - Epoch(train) [258][55/63] lr: 5.6339e-03 eta: 1 day, 0:12:29 time: 1.4123 data_time: 0.0330 memory: 16201 loss_prob: 0.7135 loss_thr: 0.4502 loss_db: 0.1203 loss: 1.2840 2022/08/30 06:06:55 - mmengine - INFO - Epoch(train) [258][60/63] lr: 5.6339e-03 eta: 1 day, 0:12:12 time: 1.4005 data_time: 0.0345 memory: 16201 loss_prob: 0.7189 loss_thr: 0.4495 loss_db: 0.1212 loss: 1.2896 2022/08/30 06:06:58 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:07:07 - mmengine - INFO - Epoch(train) [259][5/63] lr: 5.6285e-03 eta: 1 day, 0:12:12 time: 1.5625 data_time: 0.2243 memory: 16201 loss_prob: 0.7939 loss_thr: 0.5025 loss_db: 0.1331 loss: 1.4295 2022/08/30 06:07:14 - mmengine - INFO - Epoch(train) [259][10/63] lr: 5.6285e-03 eta: 1 day, 0:11:38 time: 1.5085 data_time: 0.2419 memory: 16201 loss_prob: 0.7945 loss_thr: 0.5161 loss_db: 0.1331 loss: 1.4436 2022/08/30 06:07:21 - mmengine - INFO - Epoch(train) [259][15/63] lr: 5.6285e-03 eta: 1 day, 0:11:38 time: 1.3512 data_time: 0.0359 memory: 16201 loss_prob: 0.7064 loss_thr: 0.4636 loss_db: 0.1168 loss: 1.2868 2022/08/30 06:07:27 - mmengine - INFO - Epoch(train) [259][20/63] lr: 5.6285e-03 eta: 1 day, 0:11:20 time: 1.3842 data_time: 0.0334 memory: 16201 loss_prob: 0.6874 loss_thr: 0.4541 loss_db: 0.1151 loss: 1.2566 2022/08/30 06:07:35 - mmengine - INFO - Epoch(train) [259][25/63] lr: 5.6285e-03 eta: 1 day, 0:11:20 time: 1.4230 data_time: 0.0327 memory: 16201 loss_prob: 0.7475 loss_thr: 0.4804 loss_db: 0.1273 loss: 1.3552 2022/08/30 06:07:42 - mmengine - INFO - Epoch(train) [259][30/63] lr: 5.6285e-03 eta: 1 day, 0:11:04 time: 1.4205 data_time: 0.0284 memory: 16201 loss_prob: 0.6955 loss_thr: 0.4650 loss_db: 0.1183 loss: 1.2788 2022/08/30 06:07:48 - mmengine - INFO - Epoch(train) [259][35/63] lr: 5.6285e-03 eta: 1 day, 0:11:04 time: 1.3353 data_time: 0.0347 memory: 16201 loss_prob: 0.7170 loss_thr: 0.4558 loss_db: 0.1189 loss: 1.2917 2022/08/30 06:07:55 - mmengine - INFO - Epoch(train) [259][40/63] lr: 5.6285e-03 eta: 1 day, 0:10:45 time: 1.3601 data_time: 0.0293 memory: 16201 loss_prob: 0.7970 loss_thr: 0.4711 loss_db: 0.1322 loss: 1.4003 2022/08/30 06:08:02 - mmengine - INFO - Epoch(train) [259][45/63] lr: 5.6285e-03 eta: 1 day, 0:10:45 time: 1.3994 data_time: 0.0309 memory: 16201 loss_prob: 0.7250 loss_thr: 0.4601 loss_db: 0.1223 loss: 1.3074 2022/08/30 06:08:09 - mmengine - INFO - Epoch(train) [259][50/63] lr: 5.6285e-03 eta: 1 day, 0:10:28 time: 1.3890 data_time: 0.0440 memory: 16201 loss_prob: 0.7439 loss_thr: 0.4704 loss_db: 0.1255 loss: 1.3398 2022/08/30 06:08:16 - mmengine - INFO - Epoch(train) [259][55/63] lr: 5.6285e-03 eta: 1 day, 0:10:28 time: 1.4233 data_time: 0.0338 memory: 16201 loss_prob: 0.8252 loss_thr: 0.4813 loss_db: 0.1386 loss: 1.4452 2022/08/30 06:08:23 - mmengine - INFO - Epoch(train) [259][60/63] lr: 5.6285e-03 eta: 1 day, 0:10:09 time: 1.3618 data_time: 0.0319 memory: 16201 loss_prob: 0.7988 loss_thr: 0.4808 loss_db: 0.1351 loss: 1.4148 2022/08/30 06:08:26 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:08:35 - mmengine - INFO - Epoch(train) [260][5/63] lr: 5.6231e-03 eta: 1 day, 0:10:09 time: 1.5049 data_time: 0.2348 memory: 16201 loss_prob: 0.7433 loss_thr: 0.4682 loss_db: 0.1243 loss: 1.3358 2022/08/30 06:08:43 - mmengine - INFO - Epoch(train) [260][10/63] lr: 5.6231e-03 eta: 1 day, 0:09:43 time: 1.6926 data_time: 0.2580 memory: 16201 loss_prob: 0.7062 loss_thr: 0.4383 loss_db: 0.1220 loss: 1.2665 2022/08/30 06:08:50 - mmengine - INFO - Epoch(train) [260][15/63] lr: 5.6231e-03 eta: 1 day, 0:09:43 time: 1.5098 data_time: 0.0400 memory: 16201 loss_prob: 0.7680 loss_thr: 0.4757 loss_db: 0.1299 loss: 1.3736 2022/08/30 06:08:57 - mmengine - INFO - Epoch(train) [260][20/63] lr: 5.6231e-03 eta: 1 day, 0:09:28 time: 1.4653 data_time: 0.0313 memory: 16201 loss_prob: 0.7686 loss_thr: 0.4808 loss_db: 0.1289 loss: 1.3783 2022/08/30 06:09:04 - mmengine - INFO - Epoch(train) [260][25/63] lr: 5.6231e-03 eta: 1 day, 0:09:28 time: 1.3963 data_time: 0.0393 memory: 16201 loss_prob: 0.7924 loss_thr: 0.4917 loss_db: 0.1358 loss: 1.4199 2022/08/30 06:09:11 - mmengine - INFO - Epoch(train) [260][30/63] lr: 5.6231e-03 eta: 1 day, 0:09:08 time: 1.3353 data_time: 0.0271 memory: 16201 loss_prob: 0.7611 loss_thr: 0.4655 loss_db: 0.1313 loss: 1.3579 2022/08/30 06:09:18 - mmengine - INFO - Epoch(train) [260][35/63] lr: 5.6231e-03 eta: 1 day, 0:09:08 time: 1.3899 data_time: 0.0313 memory: 16201 loss_prob: 0.6789 loss_thr: 0.4385 loss_db: 0.1149 loss: 1.2322 2022/08/30 06:09:25 - mmengine - INFO - Epoch(train) [260][40/63] lr: 5.6231e-03 eta: 1 day, 0:08:53 time: 1.4460 data_time: 0.0341 memory: 16201 loss_prob: 0.7487 loss_thr: 0.4751 loss_db: 0.1245 loss: 1.3483 2022/08/30 06:09:32 - mmengine - INFO - Epoch(train) [260][45/63] lr: 5.6231e-03 eta: 1 day, 0:08:53 time: 1.3690 data_time: 0.0297 memory: 16201 loss_prob: 0.7931 loss_thr: 0.4781 loss_db: 0.1318 loss: 1.4030 2022/08/30 06:09:39 - mmengine - INFO - Epoch(train) [260][50/63] lr: 5.6231e-03 eta: 1 day, 0:08:36 time: 1.3959 data_time: 0.0397 memory: 16201 loss_prob: 0.7361 loss_thr: 0.4592 loss_db: 0.1243 loss: 1.3197 2022/08/30 06:09:47 - mmengine - INFO - Epoch(train) [260][55/63] lr: 5.6231e-03 eta: 1 day, 0:08:36 time: 1.5172 data_time: 0.0334 memory: 16201 loss_prob: 0.7511 loss_thr: 0.4704 loss_db: 0.1264 loss: 1.3478 2022/08/30 06:09:54 - mmengine - INFO - Epoch(train) [260][60/63] lr: 5.6231e-03 eta: 1 day, 0:08:22 time: 1.4837 data_time: 0.0309 memory: 16201 loss_prob: 0.7776 loss_thr: 0.4574 loss_db: 0.1290 loss: 1.3640 2022/08/30 06:09:57 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:09:57 - mmengine - INFO - Saving checkpoint at 260 epochs 2022/08/30 06:10:07 - mmengine - INFO - Epoch(val) [260][5/32] eta: 1 day, 0:08:22 time: 0.6967 data_time: 0.1300 memory: 16201 2022/08/30 06:10:11 - mmengine - INFO - Epoch(val) [260][10/32] eta: 0:00:17 time: 0.7894 data_time: 0.1792 memory: 15734 2022/08/30 06:10:14 - mmengine - INFO - Epoch(val) [260][15/32] eta: 0:00:17 time: 0.6411 data_time: 0.0653 memory: 15734 2022/08/30 06:10:17 - mmengine - INFO - Epoch(val) [260][20/32] eta: 0:00:08 time: 0.6720 data_time: 0.0606 memory: 15734 2022/08/30 06:10:21 - mmengine - INFO - Epoch(val) [260][25/32] eta: 0:00:08 time: 0.7067 data_time: 0.0682 memory: 15734 2022/08/30 06:10:24 - mmengine - INFO - Epoch(val) [260][30/32] eta: 0:00:01 time: 0.6229 data_time: 0.0264 memory: 15734 2022/08/30 06:10:24 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 06:10:24 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8618, precision: 0.7521, hmean: 0.8032 2022/08/30 06:10:24 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8618, precision: 0.8067, hmean: 0.8333 2022/08/30 06:10:24 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8584, precision: 0.8395, hmean: 0.8488 2022/08/30 06:10:24 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8483, precision: 0.8766, hmean: 0.8622 2022/08/30 06:10:24 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.8146, precision: 0.9043, hmean: 0.8571 2022/08/30 06:10:24 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.6009, precision: 0.9600, hmean: 0.7391 2022/08/30 06:10:24 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0188, precision: 1.0000, hmean: 0.0369 2022/08/30 06:10:24 - mmengine - INFO - Epoch(val) [260][32/32] icdar/precision: 0.8766 icdar/recall: 0.8483 icdar/hmean: 0.8622 2022/08/30 06:10:34 - mmengine - INFO - Epoch(train) [261][5/63] lr: 5.6177e-03 eta: 0:00:01 time: 1.5594 data_time: 0.2327 memory: 16201 loss_prob: 0.7373 loss_thr: 0.4376 loss_db: 0.1227 loss: 1.2977 2022/08/30 06:10:41 - mmengine - INFO - Epoch(train) [261][10/63] lr: 5.6177e-03 eta: 1 day, 0:07:52 time: 1.6263 data_time: 0.2442 memory: 16201 loss_prob: 0.7014 loss_thr: 0.4322 loss_db: 0.1156 loss: 1.2492 2022/08/30 06:10:49 - mmengine - INFO - Epoch(train) [261][15/63] lr: 5.6177e-03 eta: 1 day, 0:07:52 time: 1.4585 data_time: 0.0298 memory: 16201 loss_prob: 0.7365 loss_thr: 0.4528 loss_db: 0.1218 loss: 1.3111 2022/08/30 06:10:56 - mmengine - INFO - Epoch(train) [261][20/63] lr: 5.6177e-03 eta: 1 day, 0:07:41 time: 1.5646 data_time: 0.0330 memory: 16201 loss_prob: 0.7031 loss_thr: 0.4584 loss_db: 0.1213 loss: 1.2828 2022/08/30 06:11:04 - mmengine - INFO - Epoch(train) [261][25/63] lr: 5.6177e-03 eta: 1 day, 0:07:41 time: 1.5784 data_time: 0.0454 memory: 16201 loss_prob: 0.7142 loss_thr: 0.4487 loss_db: 0.1234 loss: 1.2863 2022/08/30 06:11:12 - mmengine - INFO - Epoch(train) [261][30/63] lr: 5.6177e-03 eta: 1 day, 0:07:28 time: 1.5184 data_time: 0.0319 memory: 16201 loss_prob: 0.6860 loss_thr: 0.4257 loss_db: 0.1161 loss: 1.2278 2022/08/30 06:11:19 - mmengine - INFO - Epoch(train) [261][35/63] lr: 5.6177e-03 eta: 1 day, 0:07:28 time: 1.4817 data_time: 0.0361 memory: 16201 loss_prob: 0.7080 loss_thr: 0.4453 loss_db: 0.1179 loss: 1.2713 2022/08/30 06:11:26 - mmengine - INFO - Epoch(train) [261][40/63] lr: 5.6177e-03 eta: 1 day, 0:07:11 time: 1.3969 data_time: 0.0337 memory: 16201 loss_prob: 0.7338 loss_thr: 0.4655 loss_db: 0.1238 loss: 1.3232 2022/08/30 06:11:32 - mmengine - INFO - Epoch(train) [261][45/63] lr: 5.6177e-03 eta: 1 day, 0:07:11 time: 1.3140 data_time: 0.0309 memory: 16201 loss_prob: 0.8419 loss_thr: 0.4669 loss_db: 0.1417 loss: 1.4505 2022/08/30 06:11:39 - mmengine - INFO - Epoch(train) [261][50/63] lr: 5.6177e-03 eta: 1 day, 0:06:52 time: 1.3383 data_time: 0.0405 memory: 16201 loss_prob: 0.8485 loss_thr: 0.4589 loss_db: 0.1388 loss: 1.4462 2022/08/30 06:11:46 - mmengine - INFO - Epoch(train) [261][55/63] lr: 5.6177e-03 eta: 1 day, 0:06:52 time: 1.3436 data_time: 0.0240 memory: 16201 loss_prob: 0.9000 loss_thr: 0.4843 loss_db: 0.1447 loss: 1.5290 2022/08/30 06:11:53 - mmengine - INFO - Epoch(train) [261][60/63] lr: 5.6177e-03 eta: 1 day, 0:06:33 time: 1.3582 data_time: 0.0309 memory: 16201 loss_prob: 0.9220 loss_thr: 0.4949 loss_db: 0.1546 loss: 1.5716 2022/08/30 06:11:56 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:12:06 - mmengine - INFO - Epoch(train) [262][5/63] lr: 5.6124e-03 eta: 1 day, 0:06:33 time: 1.6154 data_time: 0.2305 memory: 16201 loss_prob: 0.7664 loss_thr: 0.4483 loss_db: 0.1299 loss: 1.3446 2022/08/30 06:12:13 - mmengine - INFO - Epoch(train) [262][10/63] lr: 5.6124e-03 eta: 1 day, 0:06:07 time: 1.7121 data_time: 0.2413 memory: 16201 loss_prob: 0.7879 loss_thr: 0.4665 loss_db: 0.1339 loss: 1.3883 2022/08/30 06:12:20 - mmengine - INFO - Epoch(train) [262][15/63] lr: 5.6124e-03 eta: 1 day, 0:06:07 time: 1.3803 data_time: 0.0342 memory: 16201 loss_prob: 0.8275 loss_thr: 0.5012 loss_db: 0.1435 loss: 1.4722 2022/08/30 06:12:27 - mmengine - INFO - Epoch(train) [262][20/63] lr: 5.6124e-03 eta: 1 day, 0:05:48 time: 1.3555 data_time: 0.0413 memory: 16201 loss_prob: 0.8286 loss_thr: 0.5088 loss_db: 0.1411 loss: 1.4785 2022/08/30 06:12:34 - mmengine - INFO - Epoch(train) [262][25/63] lr: 5.6124e-03 eta: 1 day, 0:05:48 time: 1.3782 data_time: 0.0386 memory: 16201 loss_prob: 0.7560 loss_thr: 0.4682 loss_db: 0.1247 loss: 1.3489 2022/08/30 06:12:41 - mmengine - INFO - Epoch(train) [262][30/63] lr: 5.6124e-03 eta: 1 day, 0:05:30 time: 1.3723 data_time: 0.0258 memory: 16201 loss_prob: 0.8470 loss_thr: 0.4692 loss_db: 0.1328 loss: 1.4490 2022/08/30 06:12:48 - mmengine - INFO - Epoch(train) [262][35/63] lr: 5.6124e-03 eta: 1 day, 0:05:30 time: 1.3773 data_time: 0.0286 memory: 16201 loss_prob: 0.8799 loss_thr: 0.4662 loss_db: 0.1422 loss: 1.4882 2022/08/30 06:12:55 - mmengine - INFO - Epoch(train) [262][40/63] lr: 5.6124e-03 eta: 1 day, 0:05:13 time: 1.3818 data_time: 0.0298 memory: 16201 loss_prob: 0.9309 loss_thr: 0.4627 loss_db: 0.1538 loss: 1.5474 2022/08/30 06:13:02 - mmengine - INFO - Epoch(train) [262][45/63] lr: 5.6124e-03 eta: 1 day, 0:05:13 time: 1.4021 data_time: 0.0347 memory: 16201 loss_prob: 0.9941 loss_thr: 0.5012 loss_db: 0.1592 loss: 1.6545 2022/08/30 06:13:09 - mmengine - INFO - Epoch(train) [262][50/63] lr: 5.6124e-03 eta: 1 day, 0:04:58 time: 1.4698 data_time: 0.0393 memory: 16201 loss_prob: 0.8847 loss_thr: 0.4974 loss_db: 0.1427 loss: 1.5247 2022/08/30 06:13:16 - mmengine - INFO - Epoch(train) [262][55/63] lr: 5.6124e-03 eta: 1 day, 0:04:58 time: 1.4257 data_time: 0.0356 memory: 16201 loss_prob: 0.7862 loss_thr: 0.4621 loss_db: 0.1316 loss: 1.3800 2022/08/30 06:13:23 - mmengine - INFO - Epoch(train) [262][60/63] lr: 5.6124e-03 eta: 1 day, 0:04:41 time: 1.4115 data_time: 0.0350 memory: 16201 loss_prob: 0.7828 loss_thr: 0.4692 loss_db: 0.1323 loss: 1.3842 2022/08/30 06:13:27 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:13:36 - mmengine - INFO - Epoch(train) [263][5/63] lr: 5.6070e-03 eta: 1 day, 0:04:41 time: 1.5315 data_time: 0.2334 memory: 16201 loss_prob: 1.0307 loss_thr: 0.5409 loss_db: 0.1652 loss: 1.7368 2022/08/30 06:13:43 - mmengine - INFO - Epoch(train) [263][10/63] lr: 5.6070e-03 eta: 1 day, 0:04:12 time: 1.6060 data_time: 0.2498 memory: 16201 loss_prob: 0.8576 loss_thr: 0.4952 loss_db: 0.1406 loss: 1.4933 2022/08/30 06:13:50 - mmengine - INFO - Epoch(train) [263][15/63] lr: 5.6070e-03 eta: 1 day, 0:04:12 time: 1.3876 data_time: 0.0303 memory: 16201 loss_prob: 0.9560 loss_thr: 0.5235 loss_db: 0.1532 loss: 1.6327 2022/08/30 06:13:56 - mmengine - INFO - Epoch(train) [263][20/63] lr: 5.6070e-03 eta: 1 day, 0:03:53 time: 1.3496 data_time: 0.0309 memory: 16201 loss_prob: 0.9813 loss_thr: 0.5312 loss_db: 0.1562 loss: 1.6687 2022/08/30 06:14:04 - mmengine - INFO - Epoch(train) [263][25/63] lr: 5.6070e-03 eta: 1 day, 0:03:53 time: 1.3853 data_time: 0.0306 memory: 16201 loss_prob: 0.9124 loss_thr: 0.5132 loss_db: 0.1511 loss: 1.5767 2022/08/30 06:14:11 - mmengine - INFO - Epoch(train) [263][30/63] lr: 5.6070e-03 eta: 1 day, 0:03:38 time: 1.4627 data_time: 0.0263 memory: 16201 loss_prob: 0.8584 loss_thr: 0.4942 loss_db: 0.1443 loss: 1.4969 2022/08/30 06:14:19 - mmengine - INFO - Epoch(train) [263][35/63] lr: 5.6070e-03 eta: 1 day, 0:03:38 time: 1.5032 data_time: 0.0715 memory: 16201 loss_prob: 0.8191 loss_thr: 0.4879 loss_db: 0.1383 loss: 1.4453 2022/08/30 06:14:26 - mmengine - INFO - Epoch(train) [263][40/63] lr: 5.6070e-03 eta: 1 day, 0:03:26 time: 1.5423 data_time: 0.0598 memory: 16201 loss_prob: 0.7545 loss_thr: 0.4667 loss_db: 0.1298 loss: 1.3510 2022/08/30 06:14:34 - mmengine - INFO - Epoch(train) [263][45/63] lr: 5.6070e-03 eta: 1 day, 0:03:26 time: 1.5188 data_time: 0.0293 memory: 16201 loss_prob: 0.7673 loss_thr: 0.4441 loss_db: 0.1295 loss: 1.3408 2022/08/30 06:14:41 - mmengine - INFO - Epoch(train) [263][50/63] lr: 5.6070e-03 eta: 1 day, 0:03:11 time: 1.4466 data_time: 0.0474 memory: 16201 loss_prob: 0.8627 loss_thr: 0.4687 loss_db: 0.1424 loss: 1.4738 2022/08/30 06:14:48 - mmengine - INFO - Epoch(train) [263][55/63] lr: 5.6070e-03 eta: 1 day, 0:03:11 time: 1.3684 data_time: 0.0463 memory: 16201 loss_prob: 0.8669 loss_thr: 0.4945 loss_db: 0.1450 loss: 1.5064 2022/08/30 06:14:54 - mmengine - INFO - Epoch(train) [263][60/63] lr: 5.6070e-03 eta: 1 day, 0:02:51 time: 1.3344 data_time: 0.0396 memory: 16201 loss_prob: 0.7968 loss_thr: 0.4898 loss_db: 0.1350 loss: 1.4215 2022/08/30 06:14:58 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:15:07 - mmengine - INFO - Epoch(train) [264][5/63] lr: 5.6016e-03 eta: 1 day, 0:02:51 time: 1.5081 data_time: 0.2274 memory: 16201 loss_prob: 0.8451 loss_thr: 0.5006 loss_db: 0.1415 loss: 1.4872 2022/08/30 06:15:13 - mmengine - INFO - Epoch(train) [264][10/63] lr: 5.6016e-03 eta: 1 day, 0:02:20 time: 1.5533 data_time: 0.2504 memory: 16201 loss_prob: 0.8189 loss_thr: 0.4922 loss_db: 0.1384 loss: 1.4495 2022/08/30 06:15:20 - mmengine - INFO - Epoch(train) [264][15/63] lr: 5.6016e-03 eta: 1 day, 0:02:20 time: 1.2907 data_time: 0.0334 memory: 16201 loss_prob: 0.8191 loss_thr: 0.4903 loss_db: 0.1381 loss: 1.4475 2022/08/30 06:15:28 - mmengine - INFO - Epoch(train) [264][20/63] lr: 5.6016e-03 eta: 1 day, 0:02:04 time: 1.4341 data_time: 0.0347 memory: 16201 loss_prob: 0.7868 loss_thr: 0.4831 loss_db: 0.1311 loss: 1.4010 2022/08/30 06:15:35 - mmengine - INFO - Epoch(train) [264][25/63] lr: 5.6016e-03 eta: 1 day, 0:02:04 time: 1.4888 data_time: 0.0376 memory: 16201 loss_prob: 0.8158 loss_thr: 0.5037 loss_db: 0.1368 loss: 1.4563 2022/08/30 06:15:42 - mmengine - INFO - Epoch(train) [264][30/63] lr: 5.6016e-03 eta: 1 day, 0:01:48 time: 1.4302 data_time: 0.0295 memory: 16201 loss_prob: 0.8726 loss_thr: 0.5288 loss_db: 0.1490 loss: 1.5505 2022/08/30 06:15:49 - mmengine - INFO - Epoch(train) [264][35/63] lr: 5.6016e-03 eta: 1 day, 0:01:48 time: 1.4133 data_time: 0.0372 memory: 16201 loss_prob: 0.8187 loss_thr: 0.4974 loss_db: 0.1407 loss: 1.4569 2022/08/30 06:15:56 - mmengine - INFO - Epoch(train) [264][40/63] lr: 5.6016e-03 eta: 1 day, 0:01:29 time: 1.3517 data_time: 0.0297 memory: 16201 loss_prob: 0.7613 loss_thr: 0.4722 loss_db: 0.1303 loss: 1.3639 2022/08/30 06:16:03 - mmengine - INFO - Epoch(train) [264][45/63] lr: 5.6016e-03 eta: 1 day, 0:01:29 time: 1.4219 data_time: 0.0335 memory: 16201 loss_prob: 0.7207 loss_thr: 0.4617 loss_db: 0.1210 loss: 1.3034 2022/08/30 06:16:10 - mmengine - INFO - Epoch(train) [264][50/63] lr: 5.6016e-03 eta: 1 day, 0:01:13 time: 1.4237 data_time: 0.0437 memory: 16201 loss_prob: 0.8280 loss_thr: 0.4877 loss_db: 0.1316 loss: 1.4473 2022/08/30 06:16:16 - mmengine - INFO - Epoch(train) [264][55/63] lr: 5.6016e-03 eta: 1 day, 0:01:13 time: 1.3163 data_time: 0.0296 memory: 16201 loss_prob: 0.9453 loss_thr: 0.4946 loss_db: 0.1505 loss: 1.5904 2022/08/30 06:16:24 - mmengine - INFO - Epoch(train) [264][60/63] lr: 5.6016e-03 eta: 1 day, 0:00:55 time: 1.3781 data_time: 0.0299 memory: 16201 loss_prob: 0.8458 loss_thr: 0.4727 loss_db: 0.1402 loss: 1.4587 2022/08/30 06:16:27 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:16:37 - mmengine - INFO - Epoch(train) [265][5/63] lr: 5.5962e-03 eta: 1 day, 0:00:55 time: 1.5888 data_time: 0.2283 memory: 16201 loss_prob: 0.7848 loss_thr: 0.4722 loss_db: 0.1366 loss: 1.3937 2022/08/30 06:16:43 - mmengine - INFO - Epoch(train) [265][10/63] lr: 5.5962e-03 eta: 1 day, 0:00:26 time: 1.6079 data_time: 0.2476 memory: 16201 loss_prob: 0.7872 loss_thr: 0.4736 loss_db: 0.1386 loss: 1.3994 2022/08/30 06:16:49 - mmengine - INFO - Epoch(train) [265][15/63] lr: 5.5962e-03 eta: 1 day, 0:00:26 time: 1.2817 data_time: 0.0335 memory: 16201 loss_prob: 0.8103 loss_thr: 0.4760 loss_db: 0.1397 loss: 1.4260 2022/08/30 06:16:57 - mmengine - INFO - Epoch(train) [265][20/63] lr: 5.5962e-03 eta: 1 day, 0:00:07 time: 1.3632 data_time: 0.0295 memory: 16201 loss_prob: 0.7750 loss_thr: 0.4605 loss_db: 0.1311 loss: 1.3666 2022/08/30 06:17:04 - mmengine - INFO - Epoch(train) [265][25/63] lr: 5.5962e-03 eta: 1 day, 0:00:07 time: 1.4610 data_time: 0.0400 memory: 16201 loss_prob: 0.8287 loss_thr: 0.4849 loss_db: 0.1414 loss: 1.4550 2022/08/30 06:17:11 - mmengine - INFO - Epoch(train) [265][30/63] lr: 5.5962e-03 eta: 23:59:50 time: 1.4018 data_time: 0.0301 memory: 16201 loss_prob: 0.8841 loss_thr: 0.5213 loss_db: 0.1462 loss: 1.5517 2022/08/30 06:17:17 - mmengine - INFO - Epoch(train) [265][35/63] lr: 5.5962e-03 eta: 23:59:50 time: 1.3282 data_time: 0.0333 memory: 16201 loss_prob: 0.8398 loss_thr: 0.5023 loss_db: 0.1363 loss: 1.4784 2022/08/30 06:17:24 - mmengine - INFO - Epoch(train) [265][40/63] lr: 5.5962e-03 eta: 23:59:33 time: 1.3867 data_time: 0.0339 memory: 16201 loss_prob: 0.8275 loss_thr: 0.4860 loss_db: 0.1360 loss: 1.4495 2022/08/30 06:17:31 - mmengine - INFO - Epoch(train) [265][45/63] lr: 5.5962e-03 eta: 23:59:33 time: 1.3551 data_time: 0.0279 memory: 16201 loss_prob: 0.8068 loss_thr: 0.4870 loss_db: 0.1343 loss: 1.4281 2022/08/30 06:17:39 - mmengine - INFO - Epoch(train) [265][50/63] lr: 5.5962e-03 eta: 23:59:17 time: 1.4220 data_time: 0.0387 memory: 16201 loss_prob: 0.7691 loss_thr: 0.4742 loss_db: 0.1282 loss: 1.3715 2022/08/30 06:17:46 - mmengine - INFO - Epoch(train) [265][55/63] lr: 5.5962e-03 eta: 23:59:17 time: 1.5452 data_time: 0.0335 memory: 16201 loss_prob: 0.7303 loss_thr: 0.4624 loss_db: 0.1217 loss: 1.3144 2022/08/30 06:17:53 - mmengine - INFO - Epoch(train) [265][60/63] lr: 5.5962e-03 eta: 23:59:00 time: 1.4128 data_time: 0.0303 memory: 16201 loss_prob: 0.7217 loss_thr: 0.4572 loss_db: 0.1233 loss: 1.3022 2022/08/30 06:17:57 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:18:06 - mmengine - INFO - Epoch(train) [266][5/63] lr: 5.5908e-03 eta: 23:59:00 time: 1.5336 data_time: 0.2388 memory: 16201 loss_prob: 0.7595 loss_thr: 0.4662 loss_db: 0.1280 loss: 1.3537 2022/08/30 06:18:14 - mmengine - INFO - Epoch(train) [266][10/63] lr: 5.5908e-03 eta: 23:58:34 time: 1.7075 data_time: 0.2524 memory: 16201 loss_prob: 0.8077 loss_thr: 0.4711 loss_db: 0.1304 loss: 1.4091 2022/08/30 06:18:21 - mmengine - INFO - Epoch(train) [266][15/63] lr: 5.5908e-03 eta: 23:58:34 time: 1.5701 data_time: 0.0353 memory: 16201 loss_prob: 0.8335 loss_thr: 0.4769 loss_db: 0.1362 loss: 1.4465 2022/08/30 06:18:28 - mmengine - INFO - Epoch(train) [266][20/63] lr: 5.5908e-03 eta: 23:58:19 time: 1.4441 data_time: 0.0320 memory: 16201 loss_prob: 0.7297 loss_thr: 0.4461 loss_db: 0.1237 loss: 1.2995 2022/08/30 06:18:35 - mmengine - INFO - Epoch(train) [266][25/63] lr: 5.5908e-03 eta: 23:58:19 time: 1.3756 data_time: 0.0289 memory: 16201 loss_prob: 0.7498 loss_thr: 0.4585 loss_db: 0.1254 loss: 1.3337 2022/08/30 06:18:43 - mmengine - INFO - Epoch(train) [266][30/63] lr: 5.5908e-03 eta: 23:58:04 time: 1.4672 data_time: 0.0344 memory: 16201 loss_prob: 0.8071 loss_thr: 0.4819 loss_db: 0.1310 loss: 1.4200 2022/08/30 06:18:51 - mmengine - INFO - Epoch(train) [266][35/63] lr: 5.5908e-03 eta: 23:58:04 time: 1.5435 data_time: 0.0401 memory: 16201 loss_prob: 0.7960 loss_thr: 0.4728 loss_db: 0.1319 loss: 1.4008 2022/08/30 06:18:57 - mmengine - INFO - Epoch(train) [266][40/63] lr: 5.5908e-03 eta: 23:57:50 time: 1.4759 data_time: 0.0318 memory: 16201 loss_prob: 0.8006 loss_thr: 0.4818 loss_db: 0.1335 loss: 1.4159 2022/08/30 06:19:06 - mmengine - INFO - Epoch(train) [266][45/63] lr: 5.5908e-03 eta: 23:57:50 time: 1.4915 data_time: 0.0324 memory: 16201 loss_prob: 0.8463 loss_thr: 0.4850 loss_db: 0.1404 loss: 1.4717 2022/08/30 06:19:13 - mmengine - INFO - Epoch(train) [266][50/63] lr: 5.5908e-03 eta: 23:57:37 time: 1.5170 data_time: 0.0365 memory: 16201 loss_prob: 0.8117 loss_thr: 0.4993 loss_db: 0.1402 loss: 1.4512 2022/08/30 06:19:20 - mmengine - INFO - Epoch(train) [266][55/63] lr: 5.5908e-03 eta: 23:57:37 time: 1.4757 data_time: 0.0357 memory: 16201 loss_prob: 0.7772 loss_thr: 0.5072 loss_db: 0.1310 loss: 1.4154 2022/08/30 06:19:27 - mmengine - INFO - Epoch(train) [266][60/63] lr: 5.5908e-03 eta: 23:57:22 time: 1.4603 data_time: 0.0320 memory: 16201 loss_prob: 0.8082 loss_thr: 0.4955 loss_db: 0.1347 loss: 1.4385 2022/08/30 06:19:30 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:19:40 - mmengine - INFO - Epoch(train) [267][5/63] lr: 5.5854e-03 eta: 23:57:22 time: 1.5455 data_time: 0.2258 memory: 16201 loss_prob: 0.7446 loss_thr: 0.4622 loss_db: 0.1277 loss: 1.3344 2022/08/30 06:19:47 - mmengine - INFO - Epoch(train) [267][10/63] lr: 5.5854e-03 eta: 23:56:55 time: 1.6842 data_time: 0.2332 memory: 16201 loss_prob: 0.7759 loss_thr: 0.4674 loss_db: 0.1320 loss: 1.3753 2022/08/30 06:19:54 - mmengine - INFO - Epoch(train) [267][15/63] lr: 5.5854e-03 eta: 23:56:55 time: 1.3795 data_time: 0.0331 memory: 16201 loss_prob: 0.8050 loss_thr: 0.4786 loss_db: 0.1338 loss: 1.4174 2022/08/30 06:20:01 - mmengine - INFO - Epoch(train) [267][20/63] lr: 5.5854e-03 eta: 23:56:38 time: 1.3880 data_time: 0.0314 memory: 16201 loss_prob: 0.7538 loss_thr: 0.4552 loss_db: 0.1269 loss: 1.3358 2022/08/30 06:20:08 - mmengine - INFO - Epoch(train) [267][25/63] lr: 5.5854e-03 eta: 23:56:38 time: 1.4512 data_time: 0.0338 memory: 16201 loss_prob: 0.6969 loss_thr: 0.4416 loss_db: 0.1200 loss: 1.2585 2022/08/30 06:20:15 - mmengine - INFO - Epoch(train) [267][30/63] lr: 5.5854e-03 eta: 23:56:21 time: 1.3923 data_time: 0.0278 memory: 16201 loss_prob: 0.7113 loss_thr: 0.4574 loss_db: 0.1220 loss: 1.2907 2022/08/30 06:20:22 - mmengine - INFO - Epoch(train) [267][35/63] lr: 5.5854e-03 eta: 23:56:21 time: 1.3661 data_time: 0.0298 memory: 16201 loss_prob: 0.7580 loss_thr: 0.4690 loss_db: 0.1267 loss: 1.3537 2022/08/30 06:20:29 - mmengine - INFO - Epoch(train) [267][40/63] lr: 5.5854e-03 eta: 23:56:03 time: 1.3840 data_time: 0.0356 memory: 16201 loss_prob: 0.7668 loss_thr: 0.4861 loss_db: 0.1288 loss: 1.3817 2022/08/30 06:20:36 - mmengine - INFO - Epoch(train) [267][45/63] lr: 5.5854e-03 eta: 23:56:03 time: 1.3941 data_time: 0.0366 memory: 16201 loss_prob: 0.8004 loss_thr: 0.4871 loss_db: 0.1350 loss: 1.4225 2022/08/30 06:20:42 - mmengine - INFO - Epoch(train) [267][50/63] lr: 5.5854e-03 eta: 23:55:44 time: 1.3489 data_time: 0.0343 memory: 16201 loss_prob: 0.7640 loss_thr: 0.4702 loss_db: 0.1280 loss: 1.3623 2022/08/30 06:20:49 - mmengine - INFO - Epoch(train) [267][55/63] lr: 5.5854e-03 eta: 23:55:44 time: 1.3385 data_time: 0.0287 memory: 16201 loss_prob: 0.7361 loss_thr: 0.4589 loss_db: 0.1234 loss: 1.3184 2022/08/30 06:20:57 - mmengine - INFO - Epoch(train) [267][60/63] lr: 5.5854e-03 eta: 23:55:29 time: 1.4492 data_time: 0.0406 memory: 16201 loss_prob: 0.7261 loss_thr: 0.4450 loss_db: 0.1227 loss: 1.2939 2022/08/30 06:21:01 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:21:11 - mmengine - INFO - Epoch(train) [268][5/63] lr: 5.5800e-03 eta: 23:55:29 time: 1.6570 data_time: 0.2150 memory: 16201 loss_prob: 0.6383 loss_thr: 0.4263 loss_db: 0.1099 loss: 1.1745 2022/08/30 06:21:18 - mmengine - INFO - Epoch(train) [268][10/63] lr: 5.5800e-03 eta: 23:55:05 time: 1.7538 data_time: 0.2317 memory: 16201 loss_prob: 0.7147 loss_thr: 0.4557 loss_db: 0.1229 loss: 1.2934 2022/08/30 06:21:25 - mmengine - INFO - Epoch(train) [268][15/63] lr: 5.5800e-03 eta: 23:55:05 time: 1.4209 data_time: 0.0349 memory: 16201 loss_prob: 0.7766 loss_thr: 0.4640 loss_db: 0.1312 loss: 1.3717 2022/08/30 06:21:31 - mmengine - INFO - Epoch(train) [268][20/63] lr: 5.5800e-03 eta: 23:54:46 time: 1.3316 data_time: 0.0328 memory: 16201 loss_prob: 0.7713 loss_thr: 0.4467 loss_db: 0.1274 loss: 1.3454 2022/08/30 06:21:38 - mmengine - INFO - Epoch(train) [268][25/63] lr: 5.5800e-03 eta: 23:54:46 time: 1.3001 data_time: 0.0411 memory: 16201 loss_prob: 0.7620 loss_thr: 0.4629 loss_db: 0.1265 loss: 1.3515 2022/08/30 06:21:45 - mmengine - INFO - Epoch(train) [268][30/63] lr: 5.5800e-03 eta: 23:54:27 time: 1.3499 data_time: 0.0294 memory: 16201 loss_prob: 0.7283 loss_thr: 0.4613 loss_db: 0.1225 loss: 1.3121 2022/08/30 06:21:52 - mmengine - INFO - Epoch(train) [268][35/63] lr: 5.5800e-03 eta: 23:54:27 time: 1.3880 data_time: 0.0289 memory: 16201 loss_prob: 0.7025 loss_thr: 0.4449 loss_db: 0.1190 loss: 1.2664 2022/08/30 06:21:58 - mmengine - INFO - Epoch(train) [268][40/63] lr: 5.5800e-03 eta: 23:54:08 time: 1.3322 data_time: 0.0314 memory: 16201 loss_prob: 0.6986 loss_thr: 0.4446 loss_db: 0.1187 loss: 1.2619 2022/08/30 06:22:06 - mmengine - INFO - Epoch(train) [268][45/63] lr: 5.5800e-03 eta: 23:54:08 time: 1.3898 data_time: 0.0331 memory: 16201 loss_prob: 0.7012 loss_thr: 0.4404 loss_db: 0.1181 loss: 1.2596 2022/08/30 06:22:13 - mmengine - INFO - Epoch(train) [268][50/63] lr: 5.5800e-03 eta: 23:53:52 time: 1.4282 data_time: 0.0437 memory: 16201 loss_prob: 0.7834 loss_thr: 0.4941 loss_db: 0.1322 loss: 1.4097 2022/08/30 06:22:19 - mmengine - INFO - Epoch(train) [268][55/63] lr: 5.5800e-03 eta: 23:53:52 time: 1.3453 data_time: 0.0360 memory: 16201 loss_prob: 0.7673 loss_thr: 0.4919 loss_db: 0.1304 loss: 1.3897 2022/08/30 06:22:25 - mmengine - INFO - Epoch(train) [268][60/63] lr: 5.5800e-03 eta: 23:53:31 time: 1.2888 data_time: 0.0294 memory: 16201 loss_prob: 0.7021 loss_thr: 0.4464 loss_db: 0.1197 loss: 1.2681 2022/08/30 06:22:29 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:22:38 - mmengine - INFO - Epoch(train) [269][5/63] lr: 5.5746e-03 eta: 23:53:31 time: 1.4775 data_time: 0.2313 memory: 16201 loss_prob: 0.8403 loss_thr: 0.5057 loss_db: 0.1406 loss: 1.4866 2022/08/30 06:22:45 - mmengine - INFO - Epoch(train) [269][10/63] lr: 5.5746e-03 eta: 23:53:01 time: 1.5942 data_time: 0.2423 memory: 16201 loss_prob: 0.8376 loss_thr: 0.5001 loss_db: 0.1372 loss: 1.4748 2022/08/30 06:22:51 - mmengine - INFO - Epoch(train) [269][15/63] lr: 5.5746e-03 eta: 23:53:01 time: 1.3557 data_time: 0.0377 memory: 16201 loss_prob: 0.7472 loss_thr: 0.4744 loss_db: 0.1301 loss: 1.3517 2022/08/30 06:22:58 - mmengine - INFO - Epoch(train) [269][20/63] lr: 5.5746e-03 eta: 23:52:42 time: 1.3456 data_time: 0.0437 memory: 16201 loss_prob: 0.6856 loss_thr: 0.4470 loss_db: 0.1204 loss: 1.2530 2022/08/30 06:23:05 - mmengine - INFO - Epoch(train) [269][25/63] lr: 5.5746e-03 eta: 23:52:42 time: 1.3847 data_time: 0.0425 memory: 16201 loss_prob: 0.7391 loss_thr: 0.4786 loss_db: 0.1251 loss: 1.3429 2022/08/30 06:23:12 - mmengine - INFO - Epoch(train) [269][30/63] lr: 5.5746e-03 eta: 23:52:23 time: 1.3247 data_time: 0.0403 memory: 16201 loss_prob: 0.7502 loss_thr: 0.4816 loss_db: 0.1290 loss: 1.3609 2022/08/30 06:23:19 - mmengine - INFO - Epoch(train) [269][35/63] lr: 5.5746e-03 eta: 23:52:23 time: 1.3377 data_time: 0.0418 memory: 16201 loss_prob: 0.7462 loss_thr: 0.4607 loss_db: 0.1258 loss: 1.3328 2022/08/30 06:23:25 - mmengine - INFO - Epoch(train) [269][40/63] lr: 5.5746e-03 eta: 23:52:04 time: 1.3541 data_time: 0.0316 memory: 16201 loss_prob: 0.7545 loss_thr: 0.4723 loss_db: 0.1246 loss: 1.3514 2022/08/30 06:23:32 - mmengine - INFO - Epoch(train) [269][45/63] lr: 5.5746e-03 eta: 23:52:04 time: 1.3522 data_time: 0.0370 memory: 16201 loss_prob: 0.6698 loss_thr: 0.4432 loss_db: 0.1130 loss: 1.2260 2022/08/30 06:23:39 - mmengine - INFO - Epoch(train) [269][50/63] lr: 5.5746e-03 eta: 23:51:47 time: 1.3858 data_time: 0.0445 memory: 16201 loss_prob: 0.6535 loss_thr: 0.4296 loss_db: 0.1124 loss: 1.1955 2022/08/30 06:23:46 - mmengine - INFO - Epoch(train) [269][55/63] lr: 5.5746e-03 eta: 23:51:47 time: 1.3592 data_time: 0.0309 memory: 16201 loss_prob: 0.7117 loss_thr: 0.4570 loss_db: 0.1211 loss: 1.2898 2022/08/30 06:23:52 - mmengine - INFO - Epoch(train) [269][60/63] lr: 5.5746e-03 eta: 23:51:28 time: 1.3304 data_time: 0.0289 memory: 16201 loss_prob: 0.8011 loss_thr: 0.4695 loss_db: 0.1303 loss: 1.4009 2022/08/30 06:23:55 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:24:05 - mmengine - INFO - Epoch(train) [270][5/63] lr: 5.5693e-03 eta: 23:51:28 time: 1.4846 data_time: 0.2108 memory: 16201 loss_prob: 0.7185 loss_thr: 0.4664 loss_db: 0.1219 loss: 1.3068 2022/08/30 06:24:12 - mmengine - INFO - Epoch(train) [270][10/63] lr: 5.5693e-03 eta: 23:51:00 time: 1.6387 data_time: 0.2236 memory: 16201 loss_prob: 0.7454 loss_thr: 0.4710 loss_db: 0.1312 loss: 1.3476 2022/08/30 06:24:18 - mmengine - INFO - Epoch(train) [270][15/63] lr: 5.5693e-03 eta: 23:51:00 time: 1.2910 data_time: 0.0339 memory: 16201 loss_prob: 0.8339 loss_thr: 0.5064 loss_db: 0.1411 loss: 1.4814 2022/08/30 06:24:25 - mmengine - INFO - Epoch(train) [270][20/63] lr: 5.5693e-03 eta: 23:50:40 time: 1.3274 data_time: 0.0356 memory: 16201 loss_prob: 0.8045 loss_thr: 0.4768 loss_db: 0.1328 loss: 1.4141 2022/08/30 06:24:32 - mmengine - INFO - Epoch(train) [270][25/63] lr: 5.5693e-03 eta: 23:50:40 time: 1.4237 data_time: 0.0346 memory: 16201 loss_prob: 0.7270 loss_thr: 0.4497 loss_db: 0.1225 loss: 1.2992 2022/08/30 06:24:39 - mmengine - INFO - Epoch(train) [270][30/63] lr: 5.5693e-03 eta: 23:50:23 time: 1.3841 data_time: 0.0314 memory: 16201 loss_prob: 0.7015 loss_thr: 0.4512 loss_db: 0.1164 loss: 1.2691 2022/08/30 06:24:46 - mmengine - INFO - Epoch(train) [270][35/63] lr: 5.5693e-03 eta: 23:50:23 time: 1.4186 data_time: 0.0388 memory: 16201 loss_prob: 0.7278 loss_thr: 0.4506 loss_db: 0.1203 loss: 1.2988 2022/08/30 06:24:53 - mmengine - INFO - Epoch(train) [270][40/63] lr: 5.5693e-03 eta: 23:50:06 time: 1.4118 data_time: 0.0334 memory: 16201 loss_prob: 0.6860 loss_thr: 0.4373 loss_db: 0.1167 loss: 1.2400 2022/08/30 06:24:59 - mmengine - INFO - Epoch(train) [270][45/63] lr: 5.5693e-03 eta: 23:50:06 time: 1.2893 data_time: 0.0329 memory: 16201 loss_prob: 0.6698 loss_thr: 0.4368 loss_db: 0.1144 loss: 1.2211 2022/08/30 06:25:05 - mmengine - INFO - Epoch(train) [270][50/63] lr: 5.5693e-03 eta: 23:49:44 time: 1.2402 data_time: 0.0372 memory: 16201 loss_prob: 0.7305 loss_thr: 0.4535 loss_db: 0.1214 loss: 1.3054 2022/08/30 06:25:09 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:25:12 - mmengine - INFO - Epoch(train) [270][55/63] lr: 5.5693e-03 eta: 23:49:44 time: 1.2884 data_time: 0.0321 memory: 16201 loss_prob: 0.6648 loss_thr: 0.4197 loss_db: 0.1135 loss: 1.1979 2022/08/30 06:25:19 - mmengine - INFO - Epoch(train) [270][60/63] lr: 5.5693e-03 eta: 23:49:26 time: 1.3746 data_time: 0.0404 memory: 16201 loss_prob: 0.6620 loss_thr: 0.4260 loss_db: 0.1137 loss: 1.2016 2022/08/30 06:25:22 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:25:31 - mmengine - INFO - Epoch(train) [271][5/63] lr: 5.5639e-03 eta: 23:49:26 time: 1.4675 data_time: 0.2260 memory: 16201 loss_prob: 0.7220 loss_thr: 0.4570 loss_db: 0.1215 loss: 1.3005 2022/08/30 06:25:38 - mmengine - INFO - Epoch(train) [271][10/63] lr: 5.5639e-03 eta: 23:48:55 time: 1.5597 data_time: 0.2415 memory: 16201 loss_prob: 0.6961 loss_thr: 0.4439 loss_db: 0.1168 loss: 1.2567 2022/08/30 06:25:44 - mmengine - INFO - Epoch(train) [271][15/63] lr: 5.5639e-03 eta: 23:48:55 time: 1.3570 data_time: 0.0349 memory: 16201 loss_prob: 0.6780 loss_thr: 0.4411 loss_db: 0.1152 loss: 1.2344 2022/08/30 06:25:51 - mmengine - INFO - Epoch(train) [271][20/63] lr: 5.5639e-03 eta: 23:48:37 time: 1.3610 data_time: 0.0379 memory: 16201 loss_prob: 0.6763 loss_thr: 0.4360 loss_db: 0.1131 loss: 1.2253 2022/08/30 06:25:58 - mmengine - INFO - Epoch(train) [271][25/63] lr: 5.5639e-03 eta: 23:48:37 time: 1.3783 data_time: 0.0470 memory: 16201 loss_prob: 0.7769 loss_thr: 0.4801 loss_db: 0.1261 loss: 1.3832 2022/08/30 06:26:05 - mmengine - INFO - Epoch(train) [271][30/63] lr: 5.5639e-03 eta: 23:48:19 time: 1.3620 data_time: 0.0304 memory: 16201 loss_prob: 0.8250 loss_thr: 0.4938 loss_db: 0.1394 loss: 1.4583 2022/08/30 06:26:12 - mmengine - INFO - Epoch(train) [271][35/63] lr: 5.5639e-03 eta: 23:48:19 time: 1.3336 data_time: 0.0298 memory: 16201 loss_prob: 0.6972 loss_thr: 0.4341 loss_db: 0.1214 loss: 1.2527 2022/08/30 06:26:18 - mmengine - INFO - Epoch(train) [271][40/63] lr: 5.5639e-03 eta: 23:48:00 time: 1.3230 data_time: 0.0308 memory: 16201 loss_prob: 0.7219 loss_thr: 0.4314 loss_db: 0.1174 loss: 1.2706 2022/08/30 06:26:25 - mmengine - INFO - Epoch(train) [271][45/63] lr: 5.5639e-03 eta: 23:48:00 time: 1.3347 data_time: 0.0306 memory: 16201 loss_prob: 0.7625 loss_thr: 0.4775 loss_db: 0.1251 loss: 1.3652 2022/08/30 06:26:31 - mmengine - INFO - Epoch(train) [271][50/63] lr: 5.5639e-03 eta: 23:47:40 time: 1.3249 data_time: 0.0433 memory: 16201 loss_prob: 0.7606 loss_thr: 0.4726 loss_db: 0.1294 loss: 1.3626 2022/08/30 06:26:38 - mmengine - INFO - Epoch(train) [271][55/63] lr: 5.5639e-03 eta: 23:47:40 time: 1.2933 data_time: 0.0290 memory: 16201 loss_prob: 0.7933 loss_thr: 0.4566 loss_db: 0.1357 loss: 1.3855 2022/08/30 06:26:45 - mmengine - INFO - Epoch(train) [271][60/63] lr: 5.5639e-03 eta: 23:47:20 time: 1.3076 data_time: 0.0457 memory: 16201 loss_prob: 0.7478 loss_thr: 0.4681 loss_db: 0.1280 loss: 1.3438 2022/08/30 06:26:48 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:26:56 - mmengine - INFO - Epoch(train) [272][5/63] lr: 5.5585e-03 eta: 23:47:20 time: 1.3936 data_time: 0.2293 memory: 16201 loss_prob: 0.7466 loss_thr: 0.4488 loss_db: 0.1273 loss: 1.3227 2022/08/30 06:27:03 - mmengine - INFO - Epoch(train) [272][10/63] lr: 5.5585e-03 eta: 23:46:48 time: 1.5149 data_time: 0.2442 memory: 16201 loss_prob: 0.7018 loss_thr: 0.4452 loss_db: 0.1182 loss: 1.2651 2022/08/30 06:27:10 - mmengine - INFO - Epoch(train) [272][15/63] lr: 5.5585e-03 eta: 23:46:48 time: 1.3733 data_time: 0.0350 memory: 16201 loss_prob: 0.6993 loss_thr: 0.4491 loss_db: 0.1188 loss: 1.2672 2022/08/30 06:27:17 - mmengine - INFO - Epoch(train) [272][20/63] lr: 5.5585e-03 eta: 23:46:31 time: 1.3780 data_time: 0.0359 memory: 16201 loss_prob: 0.7042 loss_thr: 0.4406 loss_db: 0.1183 loss: 1.2632 2022/08/30 06:27:23 - mmengine - INFO - Epoch(train) [272][25/63] lr: 5.5585e-03 eta: 23:46:31 time: 1.2890 data_time: 0.0337 memory: 16201 loss_prob: 0.7324 loss_thr: 0.4453 loss_db: 0.1214 loss: 1.2992 2022/08/30 06:27:30 - mmengine - INFO - Epoch(train) [272][30/63] lr: 5.5585e-03 eta: 23:46:10 time: 1.2816 data_time: 0.0373 memory: 16201 loss_prob: 0.7278 loss_thr: 0.4628 loss_db: 0.1227 loss: 1.3133 2022/08/30 06:27:36 - mmengine - INFO - Epoch(train) [272][35/63] lr: 5.5585e-03 eta: 23:46:10 time: 1.3579 data_time: 0.0421 memory: 16201 loss_prob: 0.7218 loss_thr: 0.4662 loss_db: 0.1220 loss: 1.3100 2022/08/30 06:27:44 - mmengine - INFO - Epoch(train) [272][40/63] lr: 5.5585e-03 eta: 23:45:54 time: 1.4242 data_time: 0.0345 memory: 16201 loss_prob: 0.7890 loss_thr: 0.4961 loss_db: 0.1310 loss: 1.4161 2022/08/30 06:27:50 - mmengine - INFO - Epoch(train) [272][45/63] lr: 5.5585e-03 eta: 23:45:54 time: 1.4036 data_time: 0.0330 memory: 16201 loss_prob: 0.7463 loss_thr: 0.4740 loss_db: 0.1247 loss: 1.3450 2022/08/30 06:27:57 - mmengine - INFO - Epoch(train) [272][50/63] lr: 5.5585e-03 eta: 23:45:34 time: 1.3196 data_time: 0.0439 memory: 16201 loss_prob: 0.6916 loss_thr: 0.4400 loss_db: 0.1182 loss: 1.2498 2022/08/30 06:28:03 - mmengine - INFO - Epoch(train) [272][55/63] lr: 5.5585e-03 eta: 23:45:34 time: 1.2642 data_time: 0.0375 memory: 16201 loss_prob: 0.7093 loss_thr: 0.4560 loss_db: 0.1225 loss: 1.2878 2022/08/30 06:28:10 - mmengine - INFO - Epoch(train) [272][60/63] lr: 5.5585e-03 eta: 23:45:14 time: 1.2902 data_time: 0.0291 memory: 16201 loss_prob: 0.7082 loss_thr: 0.4621 loss_db: 0.1222 loss: 1.2924 2022/08/30 06:28:13 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:28:22 - mmengine - INFO - Epoch(train) [273][5/63] lr: 5.5531e-03 eta: 23:45:14 time: 1.4638 data_time: 0.2209 memory: 16201 loss_prob: 0.7578 loss_thr: 0.4577 loss_db: 0.1291 loss: 1.3447 2022/08/30 06:28:29 - mmengine - INFO - Epoch(train) [273][10/63] lr: 5.5531e-03 eta: 23:44:45 time: 1.6148 data_time: 0.2342 memory: 16201 loss_prob: 0.7202 loss_thr: 0.4487 loss_db: 0.1244 loss: 1.2933 2022/08/30 06:28:36 - mmengine - INFO - Epoch(train) [273][15/63] lr: 5.5531e-03 eta: 23:44:45 time: 1.4091 data_time: 0.0313 memory: 16201 loss_prob: 0.6467 loss_thr: 0.4287 loss_db: 0.1120 loss: 1.1873 2022/08/30 06:28:42 - mmengine - INFO - Epoch(train) [273][20/63] lr: 5.5531e-03 eta: 23:44:26 time: 1.3336 data_time: 0.0285 memory: 16201 loss_prob: 0.6460 loss_thr: 0.4162 loss_db: 0.1104 loss: 1.1726 2022/08/30 06:28:49 - mmengine - INFO - Epoch(train) [273][25/63] lr: 5.5531e-03 eta: 23:44:26 time: 1.3232 data_time: 0.0322 memory: 16201 loss_prob: 0.6536 loss_thr: 0.4150 loss_db: 0.1119 loss: 1.1806 2022/08/30 06:28:56 - mmengine - INFO - Epoch(train) [273][30/63] lr: 5.5531e-03 eta: 23:44:07 time: 1.3325 data_time: 0.0330 memory: 16201 loss_prob: 0.6661 loss_thr: 0.4303 loss_db: 0.1138 loss: 1.2103 2022/08/30 06:29:03 - mmengine - INFO - Epoch(train) [273][35/63] lr: 5.5531e-03 eta: 23:44:07 time: 1.3232 data_time: 0.0382 memory: 16201 loss_prob: 0.7352 loss_thr: 0.4414 loss_db: 0.1187 loss: 1.2953 2022/08/30 06:29:09 - mmengine - INFO - Epoch(train) [273][40/63] lr: 5.5531e-03 eta: 23:43:47 time: 1.3197 data_time: 0.0298 memory: 16201 loss_prob: 0.7667 loss_thr: 0.4667 loss_db: 0.1255 loss: 1.3589 2022/08/30 06:29:16 - mmengine - INFO - Epoch(train) [273][45/63] lr: 5.5531e-03 eta: 23:43:47 time: 1.3556 data_time: 0.0322 memory: 16201 loss_prob: 0.6853 loss_thr: 0.4484 loss_db: 0.1190 loss: 1.2526 2022/08/30 06:29:23 - mmengine - INFO - Epoch(train) [273][50/63] lr: 5.5531e-03 eta: 23:43:31 time: 1.4072 data_time: 0.0405 memory: 16201 loss_prob: 0.6771 loss_thr: 0.4317 loss_db: 0.1169 loss: 1.2256 2022/08/30 06:29:30 - mmengine - INFO - Epoch(train) [273][55/63] lr: 5.5531e-03 eta: 23:43:31 time: 1.4091 data_time: 0.0333 memory: 16201 loss_prob: 0.6797 loss_thr: 0.4345 loss_db: 0.1149 loss: 1.2291 2022/08/30 06:29:37 - mmengine - INFO - Epoch(train) [273][60/63] lr: 5.5531e-03 eta: 23:43:14 time: 1.3940 data_time: 0.0407 memory: 16201 loss_prob: 0.6594 loss_thr: 0.4215 loss_db: 0.1102 loss: 1.1911 2022/08/30 06:29:40 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:29:49 - mmengine - INFO - Epoch(train) [274][5/63] lr: 5.5477e-03 eta: 23:43:14 time: 1.4616 data_time: 0.2088 memory: 16201 loss_prob: 0.7056 loss_thr: 0.4430 loss_db: 0.1164 loss: 1.2650 2022/08/30 06:29:56 - mmengine - INFO - Epoch(train) [274][10/63] lr: 5.5477e-03 eta: 23:42:45 time: 1.5977 data_time: 0.2291 memory: 16201 loss_prob: 0.7350 loss_thr: 0.4645 loss_db: 0.1233 loss: 1.3228 2022/08/30 06:30:04 - mmengine - INFO - Epoch(train) [274][15/63] lr: 5.5477e-03 eta: 23:42:45 time: 1.4694 data_time: 0.0406 memory: 16201 loss_prob: 0.8217 loss_thr: 0.4855 loss_db: 0.1412 loss: 1.4483 2022/08/30 06:30:10 - mmengine - INFO - Epoch(train) [274][20/63] lr: 5.5477e-03 eta: 23:42:29 time: 1.4185 data_time: 0.0342 memory: 16201 loss_prob: 0.8161 loss_thr: 0.4774 loss_db: 0.1384 loss: 1.4319 2022/08/30 06:30:17 - mmengine - INFO - Epoch(train) [274][25/63] lr: 5.5477e-03 eta: 23:42:29 time: 1.3302 data_time: 0.0362 memory: 16201 loss_prob: 0.7466 loss_thr: 0.4559 loss_db: 0.1258 loss: 1.3283 2022/08/30 06:30:23 - mmengine - INFO - Epoch(train) [274][30/63] lr: 5.5477e-03 eta: 23:42:09 time: 1.3278 data_time: 0.0359 memory: 16201 loss_prob: 0.7676 loss_thr: 0.4507 loss_db: 0.1272 loss: 1.3454 2022/08/30 06:30:30 - mmengine - INFO - Epoch(train) [274][35/63] lr: 5.5477e-03 eta: 23:42:09 time: 1.2900 data_time: 0.0369 memory: 16201 loss_prob: 0.7772 loss_thr: 0.4541 loss_db: 0.1291 loss: 1.3605 2022/08/30 06:30:36 - mmengine - INFO - Epoch(train) [274][40/63] lr: 5.5477e-03 eta: 23:41:50 time: 1.3074 data_time: 0.0353 memory: 16201 loss_prob: 0.7698 loss_thr: 0.4727 loss_db: 0.1298 loss: 1.3723 2022/08/30 06:30:44 - mmengine - INFO - Epoch(train) [274][45/63] lr: 5.5477e-03 eta: 23:41:50 time: 1.3893 data_time: 0.0317 memory: 16201 loss_prob: 0.7396 loss_thr: 0.4696 loss_db: 0.1239 loss: 1.3331 2022/08/30 06:30:51 - mmengine - INFO - Epoch(train) [274][50/63] lr: 5.5477e-03 eta: 23:41:33 time: 1.4133 data_time: 0.0416 memory: 16201 loss_prob: 0.7133 loss_thr: 0.4549 loss_db: 0.1241 loss: 1.2923 2022/08/30 06:30:58 - mmengine - INFO - Epoch(train) [274][55/63] lr: 5.5477e-03 eta: 23:41:33 time: 1.3937 data_time: 0.0389 memory: 16201 loss_prob: 0.7048 loss_thr: 0.4462 loss_db: 0.1242 loss: 1.2752 2022/08/30 06:31:05 - mmengine - INFO - Epoch(train) [274][60/63] lr: 5.5477e-03 eta: 23:41:17 time: 1.3995 data_time: 0.0298 memory: 16201 loss_prob: 0.6737 loss_thr: 0.4400 loss_db: 0.1158 loss: 1.2295 2022/08/30 06:31:07 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:31:17 - mmengine - INFO - Epoch(train) [275][5/63] lr: 5.5423e-03 eta: 23:41:17 time: 1.4962 data_time: 0.2443 memory: 16201 loss_prob: 0.7178 loss_thr: 0.4587 loss_db: 0.1254 loss: 1.3019 2022/08/30 06:31:23 - mmengine - INFO - Epoch(train) [275][10/63] lr: 5.5423e-03 eta: 23:40:46 time: 1.5560 data_time: 0.2596 memory: 16201 loss_prob: 0.6851 loss_thr: 0.4427 loss_db: 0.1176 loss: 1.2454 2022/08/30 06:31:30 - mmengine - INFO - Epoch(train) [275][15/63] lr: 5.5423e-03 eta: 23:40:46 time: 1.2795 data_time: 0.0336 memory: 16201 loss_prob: 0.6456 loss_thr: 0.4275 loss_db: 0.1085 loss: 1.1815 2022/08/30 06:31:37 - mmengine - INFO - Epoch(train) [275][20/63] lr: 5.5423e-03 eta: 23:40:28 time: 1.3698 data_time: 0.0338 memory: 16201 loss_prob: 0.6791 loss_thr: 0.4478 loss_db: 0.1143 loss: 1.2413 2022/08/30 06:31:42 - mmengine - INFO - Epoch(train) [275][25/63] lr: 5.5423e-03 eta: 23:40:28 time: 1.2923 data_time: 0.0362 memory: 16201 loss_prob: 0.6854 loss_thr: 0.4472 loss_db: 0.1176 loss: 1.2501 2022/08/30 06:31:49 - mmengine - INFO - Epoch(train) [275][30/63] lr: 5.5423e-03 eta: 23:40:05 time: 1.1909 data_time: 0.0300 memory: 16201 loss_prob: 0.7304 loss_thr: 0.4685 loss_db: 0.1234 loss: 1.3223 2022/08/30 06:31:55 - mmengine - INFO - Epoch(train) [275][35/63] lr: 5.5423e-03 eta: 23:40:05 time: 1.2700 data_time: 0.0360 memory: 16201 loss_prob: 0.7466 loss_thr: 0.4670 loss_db: 0.1222 loss: 1.3359 2022/08/30 06:32:02 - mmengine - INFO - Epoch(train) [275][40/63] lr: 5.5423e-03 eta: 23:39:45 time: 1.3264 data_time: 0.0349 memory: 16201 loss_prob: 0.6718 loss_thr: 0.4304 loss_db: 0.1131 loss: 1.2153 2022/08/30 06:32:09 - mmengine - INFO - Epoch(train) [275][45/63] lr: 5.5423e-03 eta: 23:39:45 time: 1.3327 data_time: 0.0347 memory: 16201 loss_prob: 0.6873 loss_thr: 0.4410 loss_db: 0.1195 loss: 1.2477 2022/08/30 06:32:15 - mmengine - INFO - Epoch(train) [275][50/63] lr: 5.5423e-03 eta: 23:39:27 time: 1.3484 data_time: 0.0405 memory: 16201 loss_prob: 0.7189 loss_thr: 0.4556 loss_db: 0.1213 loss: 1.2959 2022/08/30 06:32:22 - mmengine - INFO - Epoch(train) [275][55/63] lr: 5.5423e-03 eta: 23:39:27 time: 1.3257 data_time: 0.0281 memory: 16201 loss_prob: 0.6867 loss_thr: 0.4340 loss_db: 0.1160 loss: 1.2367 2022/08/30 06:32:28 - mmengine - INFO - Epoch(train) [275][60/63] lr: 5.5423e-03 eta: 23:39:07 time: 1.2929 data_time: 0.0300 memory: 16201 loss_prob: 0.6855 loss_thr: 0.4425 loss_db: 0.1173 loss: 1.2453 2022/08/30 06:32:32 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:32:41 - mmengine - INFO - Epoch(train) [276][5/63] lr: 5.5369e-03 eta: 23:39:07 time: 1.4987 data_time: 0.2337 memory: 16201 loss_prob: 0.7249 loss_thr: 0.4559 loss_db: 0.1223 loss: 1.3031 2022/08/30 06:32:47 - mmengine - INFO - Epoch(train) [276][10/63] lr: 5.5369e-03 eta: 23:38:35 time: 1.5182 data_time: 0.2491 memory: 16201 loss_prob: 0.6668 loss_thr: 0.4265 loss_db: 0.1139 loss: 1.2072 2022/08/30 06:32:54 - mmengine - INFO - Epoch(train) [276][15/63] lr: 5.5369e-03 eta: 23:38:35 time: 1.2990 data_time: 0.0310 memory: 16201 loss_prob: 0.6521 loss_thr: 0.4204 loss_db: 0.1116 loss: 1.1841 2022/08/30 06:33:00 - mmengine - INFO - Epoch(train) [276][20/63] lr: 5.5369e-03 eta: 23:38:15 time: 1.2912 data_time: 0.0281 memory: 16201 loss_prob: 0.6942 loss_thr: 0.4531 loss_db: 0.1185 loss: 1.2658 2022/08/30 06:33:07 - mmengine - INFO - Epoch(train) [276][25/63] lr: 5.5369e-03 eta: 23:38:15 time: 1.2826 data_time: 0.0425 memory: 16201 loss_prob: 0.6953 loss_thr: 0.4573 loss_db: 0.1191 loss: 1.2717 2022/08/30 06:33:14 - mmengine - INFO - Epoch(train) [276][30/63] lr: 5.5369e-03 eta: 23:37:57 time: 1.3581 data_time: 0.0298 memory: 16201 loss_prob: 0.6633 loss_thr: 0.4382 loss_db: 0.1141 loss: 1.2156 2022/08/30 06:33:20 - mmengine - INFO - Epoch(train) [276][35/63] lr: 5.5369e-03 eta: 23:37:57 time: 1.3800 data_time: 0.0306 memory: 16201 loss_prob: 0.6935 loss_thr: 0.4531 loss_db: 0.1179 loss: 1.2645 2022/08/30 06:33:27 - mmengine - INFO - Epoch(train) [276][40/63] lr: 5.5369e-03 eta: 23:37:37 time: 1.3199 data_time: 0.0337 memory: 16201 loss_prob: 0.7556 loss_thr: 0.4662 loss_db: 0.1283 loss: 1.3500 2022/08/30 06:33:34 - mmengine - INFO - Epoch(train) [276][45/63] lr: 5.5369e-03 eta: 23:37:37 time: 1.3388 data_time: 0.0351 memory: 16201 loss_prob: 0.7567 loss_thr: 0.4550 loss_db: 0.1271 loss: 1.3387 2022/08/30 06:33:41 - mmengine - INFO - Epoch(train) [276][50/63] lr: 5.5369e-03 eta: 23:37:22 time: 1.4306 data_time: 0.0485 memory: 16201 loss_prob: 0.7919 loss_thr: 0.4721 loss_db: 0.1296 loss: 1.3937 2022/08/30 06:33:48 - mmengine - INFO - Epoch(train) [276][55/63] lr: 5.5369e-03 eta: 23:37:22 time: 1.4491 data_time: 0.0478 memory: 16201 loss_prob: 0.7254 loss_thr: 0.4638 loss_db: 0.1219 loss: 1.3111 2022/08/30 06:33:55 - mmengine - INFO - Epoch(train) [276][60/63] lr: 5.5369e-03 eta: 23:37:02 time: 1.3245 data_time: 0.0466 memory: 16201 loss_prob: 0.7050 loss_thr: 0.4559 loss_db: 0.1229 loss: 1.2838 2022/08/30 06:33:58 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:34:06 - mmengine - INFO - Epoch(train) [277][5/63] lr: 5.5315e-03 eta: 23:37:02 time: 1.4251 data_time: 0.2271 memory: 16201 loss_prob: 0.7759 loss_thr: 0.4630 loss_db: 0.1297 loss: 1.3686 2022/08/30 06:34:13 - mmengine - INFO - Epoch(train) [277][10/63] lr: 5.5315e-03 eta: 23:36:31 time: 1.5132 data_time: 0.2461 memory: 16201 loss_prob: 0.7309 loss_thr: 0.4512 loss_db: 0.1217 loss: 1.3038 2022/08/30 06:34:20 - mmengine - INFO - Epoch(train) [277][15/63] lr: 5.5315e-03 eta: 23:36:31 time: 1.3987 data_time: 0.0343 memory: 16201 loss_prob: 0.7294 loss_thr: 0.4513 loss_db: 0.1252 loss: 1.3060 2022/08/30 06:34:28 - mmengine - INFO - Epoch(train) [277][20/63] lr: 5.5315e-03 eta: 23:36:16 time: 1.4619 data_time: 0.0327 memory: 16201 loss_prob: 0.7205 loss_thr: 0.4521 loss_db: 0.1248 loss: 1.2974 2022/08/30 06:34:35 - mmengine - INFO - Epoch(train) [277][25/63] lr: 5.5315e-03 eta: 23:36:16 time: 1.4397 data_time: 0.0425 memory: 16201 loss_prob: 0.7166 loss_thr: 0.4520 loss_db: 0.1204 loss: 1.2890 2022/08/30 06:34:41 - mmengine - INFO - Epoch(train) [277][30/63] lr: 5.5315e-03 eta: 23:35:57 time: 1.3371 data_time: 0.0416 memory: 16201 loss_prob: 0.7138 loss_thr: 0.4501 loss_db: 0.1195 loss: 1.2835 2022/08/30 06:34:48 - mmengine - INFO - Epoch(train) [277][35/63] lr: 5.5315e-03 eta: 23:35:57 time: 1.3141 data_time: 0.0394 memory: 16201 loss_prob: 0.7124 loss_thr: 0.4500 loss_db: 0.1237 loss: 1.2860 2022/08/30 06:34:54 - mmengine - INFO - Epoch(train) [277][40/63] lr: 5.5315e-03 eta: 23:35:38 time: 1.3051 data_time: 0.0319 memory: 16201 loss_prob: 0.7011 loss_thr: 0.4394 loss_db: 0.1251 loss: 1.2656 2022/08/30 06:35:01 - mmengine - INFO - Epoch(train) [277][45/63] lr: 5.5315e-03 eta: 23:35:38 time: 1.3086 data_time: 0.0303 memory: 16201 loss_prob: 0.7316 loss_thr: 0.4578 loss_db: 0.1260 loss: 1.3154 2022/08/30 06:35:08 - mmengine - INFO - Epoch(train) [277][50/63] lr: 5.5315e-03 eta: 23:35:20 time: 1.3740 data_time: 0.0402 memory: 16201 loss_prob: 0.7794 loss_thr: 0.4673 loss_db: 0.1333 loss: 1.3800 2022/08/30 06:35:14 - mmengine - INFO - Epoch(train) [277][55/63] lr: 5.5315e-03 eta: 23:35:20 time: 1.3354 data_time: 0.0355 memory: 16201 loss_prob: 0.7244 loss_thr: 0.4416 loss_db: 0.1251 loss: 1.2911 2022/08/30 06:35:21 - mmengine - INFO - Epoch(train) [277][60/63] lr: 5.5315e-03 eta: 23:34:59 time: 1.2768 data_time: 0.0358 memory: 16201 loss_prob: 0.6818 loss_thr: 0.4400 loss_db: 0.1142 loss: 1.2360 2022/08/30 06:35:24 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:35:33 - mmengine - INFO - Epoch(train) [278][5/63] lr: 5.5261e-03 eta: 23:34:59 time: 1.4986 data_time: 0.2394 memory: 16201 loss_prob: 0.6771 loss_thr: 0.4432 loss_db: 0.1169 loss: 1.2372 2022/08/30 06:35:40 - mmengine - INFO - Epoch(train) [278][10/63] lr: 5.5261e-03 eta: 23:34:31 time: 1.6106 data_time: 0.2543 memory: 16201 loss_prob: 0.5819 loss_thr: 0.3997 loss_db: 0.1015 loss: 1.0831 2022/08/30 06:35:47 - mmengine - INFO - Epoch(train) [278][15/63] lr: 5.5261e-03 eta: 23:34:31 time: 1.3459 data_time: 0.0308 memory: 16201 loss_prob: 0.5616 loss_thr: 0.3813 loss_db: 0.0974 loss: 1.0403 2022/08/30 06:35:53 - mmengine - INFO - Epoch(train) [278][20/63] lr: 5.5261e-03 eta: 23:34:10 time: 1.2791 data_time: 0.0362 memory: 16201 loss_prob: 0.6882 loss_thr: 0.4382 loss_db: 0.1191 loss: 1.2455 2022/08/30 06:35:59 - mmengine - INFO - Epoch(train) [278][25/63] lr: 5.5261e-03 eta: 23:34:10 time: 1.2435 data_time: 0.0376 memory: 16201 loss_prob: 0.7077 loss_thr: 0.4567 loss_db: 0.1215 loss: 1.2859 2022/08/30 06:36:06 - mmengine - INFO - Epoch(train) [278][30/63] lr: 5.5261e-03 eta: 23:33:51 time: 1.3205 data_time: 0.0323 memory: 16201 loss_prob: 0.7295 loss_thr: 0.4719 loss_db: 0.1194 loss: 1.3209 2022/08/30 06:36:13 - mmengine - INFO - Epoch(train) [278][35/63] lr: 5.5261e-03 eta: 23:33:51 time: 1.3442 data_time: 0.0439 memory: 16201 loss_prob: 0.7579 loss_thr: 0.4623 loss_db: 0.1269 loss: 1.3472 2022/08/30 06:36:19 - mmengine - INFO - Epoch(train) [278][40/63] lr: 5.5261e-03 eta: 23:33:31 time: 1.2999 data_time: 0.0305 memory: 16201 loss_prob: 0.7004 loss_thr: 0.4369 loss_db: 0.1215 loss: 1.2589 2022/08/30 06:36:26 - mmengine - INFO - Epoch(train) [278][45/63] lr: 5.5261e-03 eta: 23:33:31 time: 1.2985 data_time: 0.0321 memory: 16201 loss_prob: 0.6870 loss_thr: 0.4479 loss_db: 0.1164 loss: 1.2514 2022/08/30 06:36:33 - mmengine - INFO - Epoch(train) [278][50/63] lr: 5.5261e-03 eta: 23:33:15 time: 1.4183 data_time: 0.0452 memory: 16201 loss_prob: 0.7129 loss_thr: 0.4526 loss_db: 0.1234 loss: 1.2889 2022/08/30 06:36:39 - mmengine - INFO - Epoch(train) [278][55/63] lr: 5.5261e-03 eta: 23:33:15 time: 1.3969 data_time: 0.0326 memory: 16201 loss_prob: 0.7455 loss_thr: 0.4635 loss_db: 0.1297 loss: 1.3387 2022/08/30 06:36:46 - mmengine - INFO - Epoch(train) [278][60/63] lr: 5.5261e-03 eta: 23:32:55 time: 1.2894 data_time: 0.0291 memory: 16201 loss_prob: 0.7196 loss_thr: 0.4495 loss_db: 0.1207 loss: 1.2898 2022/08/30 06:36:50 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:36:59 - mmengine - INFO - Epoch(train) [279][5/63] lr: 5.5207e-03 eta: 23:32:55 time: 1.5399 data_time: 0.2532 memory: 16201 loss_prob: 0.6894 loss_thr: 0.4450 loss_db: 0.1186 loss: 1.2530 2022/08/30 06:37:05 - mmengine - INFO - Epoch(train) [279][10/63] lr: 5.5207e-03 eta: 23:32:25 time: 1.5614 data_time: 0.2737 memory: 16201 loss_prob: 0.7057 loss_thr: 0.4363 loss_db: 0.1214 loss: 1.2635 2022/08/30 06:37:13 - mmengine - INFO - Epoch(train) [279][15/63] lr: 5.5207e-03 eta: 23:32:25 time: 1.3347 data_time: 0.0307 memory: 16201 loss_prob: 0.7194 loss_thr: 0.4453 loss_db: 0.1238 loss: 1.2885 2022/08/30 06:37:20 - mmengine - INFO - Epoch(train) [279][20/63] lr: 5.5207e-03 eta: 23:32:10 time: 1.4494 data_time: 0.0338 memory: 16201 loss_prob: 0.8933 loss_thr: 0.4655 loss_db: 0.1390 loss: 1.4977 2022/08/30 06:37:27 - mmengine - INFO - Epoch(train) [279][25/63] lr: 5.5207e-03 eta: 23:32:10 time: 1.4297 data_time: 0.0509 memory: 16201 loss_prob: 0.9399 loss_thr: 0.4837 loss_db: 0.1437 loss: 1.5673 2022/08/30 06:37:34 - mmengine - INFO - Epoch(train) [279][30/63] lr: 5.5207e-03 eta: 23:31:53 time: 1.4044 data_time: 0.0344 memory: 16201 loss_prob: 0.8292 loss_thr: 0.4608 loss_db: 0.1384 loss: 1.4284 2022/08/30 06:37:40 - mmengine - INFO - Epoch(train) [279][35/63] lr: 5.5207e-03 eta: 23:31:53 time: 1.3157 data_time: 0.0277 memory: 16201 loss_prob: 0.8359 loss_thr: 0.4663 loss_db: 0.1427 loss: 1.4449 2022/08/30 06:37:46 - mmengine - INFO - Epoch(train) [279][40/63] lr: 5.5207e-03 eta: 23:31:30 time: 1.2041 data_time: 0.0294 memory: 16201 loss_prob: 0.7112 loss_thr: 0.4536 loss_db: 0.1207 loss: 1.2855 2022/08/30 06:37:53 - mmengine - INFO - Epoch(train) [279][45/63] lr: 5.5207e-03 eta: 23:31:30 time: 1.2955 data_time: 0.0321 memory: 16201 loss_prob: 0.7205 loss_thr: 0.4490 loss_db: 0.1191 loss: 1.2886 2022/08/30 06:38:00 - mmengine - INFO - Epoch(train) [279][50/63] lr: 5.5207e-03 eta: 23:31:13 time: 1.3696 data_time: 0.0450 memory: 16201 loss_prob: 0.8079 loss_thr: 0.4657 loss_db: 0.1346 loss: 1.4082 2022/08/30 06:38:06 - mmengine - INFO - Epoch(train) [279][55/63] lr: 5.5207e-03 eta: 23:31:13 time: 1.3180 data_time: 0.0301 memory: 16201 loss_prob: 0.7742 loss_thr: 0.4621 loss_db: 0.1324 loss: 1.3687 2022/08/30 06:38:13 - mmengine - INFO - Epoch(train) [279][60/63] lr: 5.5207e-03 eta: 23:30:55 time: 1.3638 data_time: 0.0279 memory: 16201 loss_prob: 0.7583 loss_thr: 0.4714 loss_db: 0.1286 loss: 1.3583 2022/08/30 06:38:17 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:38:26 - mmengine - INFO - Epoch(train) [280][5/63] lr: 5.5153e-03 eta: 23:30:55 time: 1.4901 data_time: 0.2276 memory: 16201 loss_prob: 0.8889 loss_thr: 0.5043 loss_db: 0.1424 loss: 1.5356 2022/08/30 06:38:32 - mmengine - INFO - Epoch(train) [280][10/63] lr: 5.5153e-03 eta: 23:30:24 time: 1.5291 data_time: 0.2427 memory: 16201 loss_prob: 0.8737 loss_thr: 0.4782 loss_db: 0.1445 loss: 1.4965 2022/08/30 06:38:38 - mmengine - INFO - Epoch(train) [280][15/63] lr: 5.5153e-03 eta: 23:30:24 time: 1.2794 data_time: 0.0359 memory: 16201 loss_prob: 0.8081 loss_thr: 0.4626 loss_db: 0.1364 loss: 1.4071 2022/08/30 06:38:46 - mmengine - INFO - Epoch(train) [280][20/63] lr: 5.5153e-03 eta: 23:30:08 time: 1.4109 data_time: 0.0388 memory: 16201 loss_prob: 0.7432 loss_thr: 0.4578 loss_db: 0.1222 loss: 1.3233 2022/08/30 06:38:52 - mmengine - INFO - Epoch(train) [280][25/63] lr: 5.5153e-03 eta: 23:30:08 time: 1.3993 data_time: 0.0373 memory: 16201 loss_prob: 0.7565 loss_thr: 0.4577 loss_db: 0.1286 loss: 1.3428 2022/08/30 06:38:59 - mmengine - INFO - Epoch(train) [280][30/63] lr: 5.5153e-03 eta: 23:29:49 time: 1.3219 data_time: 0.0355 memory: 16201 loss_prob: 0.8127 loss_thr: 0.4777 loss_db: 0.1385 loss: 1.4289 2022/08/30 06:39:06 - mmengine - INFO - Epoch(train) [280][35/63] lr: 5.5153e-03 eta: 23:29:49 time: 1.3362 data_time: 0.0356 memory: 16201 loss_prob: 0.7881 loss_thr: 0.4751 loss_db: 0.1330 loss: 1.3962 2022/08/30 06:39:12 - mmengine - INFO - Epoch(train) [280][40/63] lr: 5.5153e-03 eta: 23:29:29 time: 1.2908 data_time: 0.0315 memory: 16201 loss_prob: 0.7065 loss_thr: 0.4336 loss_db: 0.1182 loss: 1.2583 2022/08/30 06:39:19 - mmengine - INFO - Epoch(train) [280][45/63] lr: 5.5153e-03 eta: 23:29:29 time: 1.3119 data_time: 0.0367 memory: 16201 loss_prob: 0.6726 loss_thr: 0.4186 loss_db: 0.1127 loss: 1.2040 2022/08/30 06:39:26 - mmengine - INFO - Epoch(train) [280][50/63] lr: 5.5153e-03 eta: 23:29:10 time: 1.3385 data_time: 0.0354 memory: 16201 loss_prob: 0.7365 loss_thr: 0.4612 loss_db: 0.1251 loss: 1.3228 2022/08/30 06:39:33 - mmengine - INFO - Epoch(train) [280][55/63] lr: 5.5153e-03 eta: 23:29:10 time: 1.3757 data_time: 0.0405 memory: 16201 loss_prob: 0.8366 loss_thr: 0.4773 loss_db: 0.1377 loss: 1.4516 2022/08/30 06:39:40 - mmengine - INFO - Epoch(train) [280][60/63] lr: 5.5153e-03 eta: 23:28:54 time: 1.4156 data_time: 0.0443 memory: 16201 loss_prob: 0.7681 loss_thr: 0.4405 loss_db: 0.1233 loss: 1.3318 2022/08/30 06:39:43 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:39:43 - mmengine - INFO - Saving checkpoint at 280 epochs 2022/08/30 06:39:51 - mmengine - INFO - Epoch(val) [280][5/32] eta: 23:28:54 time: 0.6934 data_time: 0.1408 memory: 16201 2022/08/30 06:39:55 - mmengine - INFO - Epoch(val) [280][10/32] eta: 0:00:17 time: 0.7972 data_time: 0.1910 memory: 15734 2022/08/30 06:39:58 - mmengine - INFO - Epoch(val) [280][15/32] eta: 0:00:17 time: 0.6543 data_time: 0.0666 memory: 15734 2022/08/30 06:40:02 - mmengine - INFO - Epoch(val) [280][20/32] eta: 0:00:09 time: 0.7549 data_time: 0.1368 memory: 15734 2022/08/30 06:40:06 - mmengine - INFO - Epoch(val) [280][25/32] eta: 0:00:09 time: 0.7814 data_time: 0.1400 memory: 15734 2022/08/30 06:40:09 - mmengine - INFO - Epoch(val) [280][30/32] eta: 0:00:01 time: 0.6363 data_time: 0.0303 memory: 15734 2022/08/30 06:40:10 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 06:40:10 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8483, precision: 0.7658, hmean: 0.8049 2022/08/30 06:40:10 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8479, precision: 0.8256, hmean: 0.8366 2022/08/30 06:40:10 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8416, precision: 0.8535, hmean: 0.8475 2022/08/30 06:40:10 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8296, precision: 0.8800, hmean: 0.8540 2022/08/30 06:40:10 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7939, precision: 0.9126, hmean: 0.8491 2022/08/30 06:40:10 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.6225, precision: 0.9500, hmean: 0.7522 2022/08/30 06:40:10 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0221, precision: 1.0000, hmean: 0.0433 2022/08/30 06:40:10 - mmengine - INFO - Epoch(val) [280][32/32] icdar/precision: 0.8800 icdar/recall: 0.8296 icdar/hmean: 0.8540 2022/08/30 06:40:19 - mmengine - INFO - Epoch(train) [281][5/63] lr: 5.5099e-03 eta: 0:00:01 time: 1.5302 data_time: 0.2328 memory: 16201 loss_prob: 0.7206 loss_thr: 0.4503 loss_db: 0.1196 loss: 1.2906 2022/08/30 06:40:25 - mmengine - INFO - Epoch(train) [281][10/63] lr: 5.5099e-03 eta: 23:28:25 time: 1.5788 data_time: 0.2373 memory: 16201 loss_prob: 0.6335 loss_thr: 0.4354 loss_db: 0.1104 loss: 1.1793 2022/08/30 06:40:33 - mmengine - INFO - Epoch(train) [281][15/63] lr: 5.5099e-03 eta: 23:28:25 time: 1.3919 data_time: 0.0428 memory: 16201 loss_prob: 0.6828 loss_thr: 0.4419 loss_db: 0.1176 loss: 1.2423 2022/08/30 06:40:40 - mmengine - INFO - Epoch(train) [281][20/63] lr: 5.5099e-03 eta: 23:28:09 time: 1.4159 data_time: 0.0410 memory: 16201 loss_prob: 0.7866 loss_thr: 0.4371 loss_db: 0.1257 loss: 1.3493 2022/08/30 06:40:46 - mmengine - INFO - Epoch(train) [281][25/63] lr: 5.5099e-03 eta: 23:28:09 time: 1.3412 data_time: 0.0292 memory: 16201 loss_prob: 0.7211 loss_thr: 0.4266 loss_db: 0.1138 loss: 1.2614 2022/08/30 06:40:53 - mmengine - INFO - Epoch(train) [281][30/63] lr: 5.5099e-03 eta: 23:27:51 time: 1.3561 data_time: 0.0318 memory: 16201 loss_prob: 0.6696 loss_thr: 0.4364 loss_db: 0.1145 loss: 1.2205 2022/08/30 06:40:59 - mmengine - INFO - Epoch(train) [281][35/63] lr: 5.5099e-03 eta: 23:27:51 time: 1.3231 data_time: 0.0431 memory: 16201 loss_prob: 0.7506 loss_thr: 0.4660 loss_db: 0.1262 loss: 1.3428 2022/08/30 06:41:06 - mmengine - INFO - Epoch(train) [281][40/63] lr: 5.5099e-03 eta: 23:27:29 time: 1.2486 data_time: 0.0319 memory: 16201 loss_prob: 0.7790 loss_thr: 0.4734 loss_db: 0.1297 loss: 1.3822 2022/08/30 06:41:13 - mmengine - INFO - Epoch(train) [281][45/63] lr: 5.5099e-03 eta: 23:27:29 time: 1.3370 data_time: 0.0303 memory: 16201 loss_prob: 0.7787 loss_thr: 0.4685 loss_db: 0.1290 loss: 1.3761 2022/08/30 06:41:20 - mmengine - INFO - Epoch(train) [281][50/63] lr: 5.5099e-03 eta: 23:27:13 time: 1.4006 data_time: 0.0404 memory: 16201 loss_prob: 0.7335 loss_thr: 0.4496 loss_db: 0.1195 loss: 1.3025 2022/08/30 06:41:26 - mmengine - INFO - Epoch(train) [281][55/63] lr: 5.5099e-03 eta: 23:27:13 time: 1.3105 data_time: 0.0310 memory: 16201 loss_prob: 0.6911 loss_thr: 0.4390 loss_db: 0.1162 loss: 1.2462 2022/08/30 06:41:32 - mmengine - INFO - Epoch(train) [281][60/63] lr: 5.5099e-03 eta: 23:26:52 time: 1.2745 data_time: 0.0332 memory: 16201 loss_prob: 0.7033 loss_thr: 0.4422 loss_db: 0.1200 loss: 1.2655 2022/08/30 06:41:36 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:41:45 - mmengine - INFO - Epoch(train) [282][5/63] lr: 5.5045e-03 eta: 23:26:52 time: 1.4693 data_time: 0.2383 memory: 16201 loss_prob: 0.6999 loss_thr: 0.4555 loss_db: 0.1162 loss: 1.2716 2022/08/30 06:41:52 - mmengine - INFO - Epoch(train) [282][10/63] lr: 5.5045e-03 eta: 23:26:23 time: 1.5905 data_time: 0.2551 memory: 16201 loss_prob: 0.6501 loss_thr: 0.4170 loss_db: 0.1099 loss: 1.1770 2022/08/30 06:41:58 - mmengine - INFO - Epoch(train) [282][15/63] lr: 5.5045e-03 eta: 23:26:23 time: 1.3079 data_time: 0.0403 memory: 16201 loss_prob: 0.6905 loss_thr: 0.4368 loss_db: 0.1195 loss: 1.2467 2022/08/30 06:42:04 - mmengine - INFO - Epoch(train) [282][20/63] lr: 5.5045e-03 eta: 23:26:02 time: 1.2350 data_time: 0.0312 memory: 16201 loss_prob: 0.6985 loss_thr: 0.4532 loss_db: 0.1177 loss: 1.2694 2022/08/30 06:42:10 - mmengine - INFO - Epoch(train) [282][25/63] lr: 5.5045e-03 eta: 23:26:02 time: 1.2621 data_time: 0.0409 memory: 16201 loss_prob: 0.6875 loss_thr: 0.4611 loss_db: 0.1165 loss: 1.2651 2022/08/30 06:42:18 - mmengine - INFO - Epoch(train) [282][30/63] lr: 5.5045e-03 eta: 23:25:44 time: 1.3577 data_time: 0.0354 memory: 16201 loss_prob: 0.6425 loss_thr: 0.4423 loss_db: 0.1120 loss: 1.1967 2022/08/30 06:42:24 - mmengine - INFO - Epoch(train) [282][35/63] lr: 5.5045e-03 eta: 23:25:44 time: 1.3761 data_time: 0.0341 memory: 16201 loss_prob: 0.6301 loss_thr: 0.4096 loss_db: 0.1089 loss: 1.1486 2022/08/30 06:42:30 - mmengine - INFO - Epoch(train) [282][40/63] lr: 5.5045e-03 eta: 23:25:23 time: 1.2822 data_time: 0.0346 memory: 16201 loss_prob: 0.7181 loss_thr: 0.4456 loss_db: 0.1224 loss: 1.2861 2022/08/30 06:42:37 - mmengine - INFO - Epoch(train) [282][45/63] lr: 5.5045e-03 eta: 23:25:23 time: 1.3155 data_time: 0.0318 memory: 16201 loss_prob: 0.7624 loss_thr: 0.4651 loss_db: 0.1288 loss: 1.3564 2022/08/30 06:42:44 - mmengine - INFO - Epoch(train) [282][50/63] lr: 5.5045e-03 eta: 23:25:05 time: 1.3333 data_time: 0.0410 memory: 16201 loss_prob: 0.7344 loss_thr: 0.4476 loss_db: 0.1234 loss: 1.3054 2022/08/30 06:42:50 - mmengine - INFO - Epoch(train) [282][55/63] lr: 5.5045e-03 eta: 23:25:05 time: 1.2733 data_time: 0.0370 memory: 16201 loss_prob: 0.7322 loss_thr: 0.4563 loss_db: 0.1233 loss: 1.3119 2022/08/30 06:42:57 - mmengine - INFO - Epoch(train) [282][60/63] lr: 5.5045e-03 eta: 23:24:45 time: 1.2939 data_time: 0.0417 memory: 16201 loss_prob: 0.7322 loss_thr: 0.4541 loss_db: 0.1235 loss: 1.3098 2022/08/30 06:43:00 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:43:09 - mmengine - INFO - Epoch(train) [283][5/63] lr: 5.4991e-03 eta: 23:24:45 time: 1.4624 data_time: 0.2361 memory: 16201 loss_prob: 0.8108 loss_thr: 0.5200 loss_db: 0.1364 loss: 1.4672 2022/08/30 06:43:15 - mmengine - INFO - Epoch(train) [283][10/63] lr: 5.4991e-03 eta: 23:24:16 time: 1.5737 data_time: 0.2565 memory: 16201 loss_prob: 0.8255 loss_thr: 0.5094 loss_db: 0.1400 loss: 1.4749 2022/08/30 06:43:22 - mmengine - INFO - Epoch(train) [283][15/63] lr: 5.4991e-03 eta: 23:24:16 time: 1.2850 data_time: 0.0384 memory: 16201 loss_prob: 0.7820 loss_thr: 0.4783 loss_db: 0.1352 loss: 1.3955 2022/08/30 06:43:28 - mmengine - INFO - Epoch(train) [283][20/63] lr: 5.4991e-03 eta: 23:23:54 time: 1.2439 data_time: 0.0319 memory: 16201 loss_prob: 0.7926 loss_thr: 0.4549 loss_db: 0.1319 loss: 1.3794 2022/08/30 06:43:35 - mmengine - INFO - Epoch(train) [283][25/63] lr: 5.4991e-03 eta: 23:23:54 time: 1.2862 data_time: 0.0444 memory: 16201 loss_prob: 0.8271 loss_thr: 0.4568 loss_db: 0.1396 loss: 1.4236 2022/08/30 06:43:41 - mmengine - INFO - Epoch(train) [283][30/63] lr: 5.4991e-03 eta: 23:23:33 time: 1.2684 data_time: 0.0307 memory: 16201 loss_prob: 0.7479 loss_thr: 0.4498 loss_db: 0.1324 loss: 1.3301 2022/08/30 06:43:47 - mmengine - INFO - Epoch(train) [283][35/63] lr: 5.4991e-03 eta: 23:23:33 time: 1.2935 data_time: 0.0280 memory: 16201 loss_prob: 0.7258 loss_thr: 0.4520 loss_db: 0.1205 loss: 1.2983 2022/08/30 06:43:54 - mmengine - INFO - Epoch(train) [283][40/63] lr: 5.4991e-03 eta: 23:23:14 time: 1.2970 data_time: 0.0294 memory: 16201 loss_prob: 0.7268 loss_thr: 0.4429 loss_db: 0.1179 loss: 1.2876 2022/08/30 06:44:00 - mmengine - INFO - Epoch(train) [283][45/63] lr: 5.4991e-03 eta: 23:23:14 time: 1.2302 data_time: 0.0310 memory: 16201 loss_prob: 0.7017 loss_thr: 0.4588 loss_db: 0.1183 loss: 1.2789 2022/08/30 06:44:06 - mmengine - INFO - Epoch(train) [283][50/63] lr: 5.4991e-03 eta: 23:22:53 time: 1.2628 data_time: 0.0441 memory: 16201 loss_prob: 0.7079 loss_thr: 0.4605 loss_db: 0.1231 loss: 1.2914 2022/08/30 06:44:12 - mmengine - INFO - Epoch(train) [283][55/63] lr: 5.4991e-03 eta: 23:22:53 time: 1.2489 data_time: 0.0292 memory: 16201 loss_prob: 0.7252 loss_thr: 0.4666 loss_db: 0.1234 loss: 1.3152 2022/08/30 06:44:19 - mmengine - INFO - Epoch(train) [283][60/63] lr: 5.4991e-03 eta: 23:22:31 time: 1.2491 data_time: 0.0290 memory: 16201 loss_prob: 0.6800 loss_thr: 0.4384 loss_db: 0.1146 loss: 1.2329 2022/08/30 06:44:22 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:44:30 - mmengine - INFO - Epoch(train) [284][5/63] lr: 5.4937e-03 eta: 23:22:31 time: 1.3890 data_time: 0.2201 memory: 16201 loss_prob: 0.6735 loss_thr: 0.4359 loss_db: 0.1165 loss: 1.2259 2022/08/30 06:44:37 - mmengine - INFO - Epoch(train) [284][10/63] lr: 5.4937e-03 eta: 23:22:01 time: 1.5258 data_time: 0.2303 memory: 16201 loss_prob: 0.7154 loss_thr: 0.4414 loss_db: 0.1218 loss: 1.2786 2022/08/30 06:44:45 - mmengine - INFO - Epoch(train) [284][15/63] lr: 5.4937e-03 eta: 23:22:01 time: 1.4591 data_time: 0.0349 memory: 16201 loss_prob: 0.7051 loss_thr: 0.4442 loss_db: 0.1173 loss: 1.2665 2022/08/30 06:44:51 - mmengine - INFO - Epoch(train) [284][20/63] lr: 5.4937e-03 eta: 23:21:43 time: 1.3721 data_time: 0.0389 memory: 16201 loss_prob: 0.7017 loss_thr: 0.4562 loss_db: 0.1194 loss: 1.2773 2022/08/30 06:44:57 - mmengine - INFO - Epoch(train) [284][25/63] lr: 5.4937e-03 eta: 23:21:43 time: 1.2786 data_time: 0.0326 memory: 16201 loss_prob: 0.7514 loss_thr: 0.4727 loss_db: 0.1286 loss: 1.3526 2022/08/30 06:45:03 - mmengine - INFO - Epoch(train) [284][30/63] lr: 5.4937e-03 eta: 23:21:22 time: 1.2525 data_time: 0.0339 memory: 16201 loss_prob: 0.7177 loss_thr: 0.4562 loss_db: 0.1209 loss: 1.2948 2022/08/30 06:45:10 - mmengine - INFO - Epoch(train) [284][35/63] lr: 5.4937e-03 eta: 23:21:22 time: 1.2679 data_time: 0.0326 memory: 16201 loss_prob: 0.6712 loss_thr: 0.4544 loss_db: 0.1133 loss: 1.2390 2022/08/30 06:45:17 - mmengine - INFO - Epoch(train) [284][40/63] lr: 5.4937e-03 eta: 23:21:04 time: 1.3375 data_time: 0.0307 memory: 16201 loss_prob: 0.7134 loss_thr: 0.4685 loss_db: 0.1206 loss: 1.3026 2022/08/30 06:45:23 - mmengine - INFO - Epoch(train) [284][45/63] lr: 5.4937e-03 eta: 23:21:04 time: 1.2662 data_time: 0.0367 memory: 16201 loss_prob: 0.6953 loss_thr: 0.4527 loss_db: 0.1170 loss: 1.2651 2022/08/30 06:45:29 - mmengine - INFO - Epoch(train) [284][50/63] lr: 5.4937e-03 eta: 23:20:43 time: 1.2736 data_time: 0.0337 memory: 16201 loss_prob: 0.7035 loss_thr: 0.4471 loss_db: 0.1205 loss: 1.2710 2022/08/30 06:45:35 - mmengine - INFO - Epoch(train) [284][55/63] lr: 5.4937e-03 eta: 23:20:43 time: 1.2668 data_time: 0.0357 memory: 16201 loss_prob: 0.6584 loss_thr: 0.4218 loss_db: 0.1137 loss: 1.1940 2022/08/30 06:45:42 - mmengine - INFO - Epoch(train) [284][60/63] lr: 5.4937e-03 eta: 23:20:23 time: 1.2831 data_time: 0.0394 memory: 16201 loss_prob: 0.7087 loss_thr: 0.4367 loss_db: 0.1192 loss: 1.2645 2022/08/30 06:45:45 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:45:55 - mmengine - INFO - Epoch(train) [285][5/63] lr: 5.4883e-03 eta: 23:20:23 time: 1.5214 data_time: 0.2456 memory: 16201 loss_prob: 0.8671 loss_thr: 0.4379 loss_db: 0.1300 loss: 1.4349 2022/08/30 06:46:02 - mmengine - INFO - Epoch(train) [285][10/63] lr: 5.4883e-03 eta: 23:19:57 time: 1.6748 data_time: 0.2521 memory: 16201 loss_prob: 0.7201 loss_thr: 0.4377 loss_db: 0.1177 loss: 1.2755 2022/08/30 06:46:09 - mmengine - INFO - Epoch(train) [285][15/63] lr: 5.4883e-03 eta: 23:19:57 time: 1.3924 data_time: 0.0350 memory: 16201 loss_prob: 0.7427 loss_thr: 0.4687 loss_db: 0.1239 loss: 1.3353 2022/08/30 06:46:16 - mmengine - INFO - Epoch(train) [285][20/63] lr: 5.4883e-03 eta: 23:19:39 time: 1.3362 data_time: 0.0365 memory: 16201 loss_prob: 0.6841 loss_thr: 0.4389 loss_db: 0.1197 loss: 1.2426 2022/08/30 06:46:23 - mmengine - INFO - Epoch(train) [285][25/63] lr: 5.4883e-03 eta: 23:19:39 time: 1.3803 data_time: 0.0321 memory: 16201 loss_prob: 0.6815 loss_thr: 0.4302 loss_db: 0.1177 loss: 1.2294 2022/08/30 06:46:29 - mmengine - INFO - Epoch(train) [285][30/63] lr: 5.4883e-03 eta: 23:19:21 time: 1.3551 data_time: 0.0313 memory: 16201 loss_prob: 0.6832 loss_thr: 0.4350 loss_db: 0.1146 loss: 1.2327 2022/08/30 06:46:35 - mmengine - INFO - Epoch(train) [285][35/63] lr: 5.4883e-03 eta: 23:19:21 time: 1.2646 data_time: 0.0408 memory: 16201 loss_prob: 0.6428 loss_thr: 0.4357 loss_db: 0.1080 loss: 1.1865 2022/08/30 06:46:42 - mmengine - INFO - Epoch(train) [285][40/63] lr: 5.4883e-03 eta: 23:19:02 time: 1.3093 data_time: 0.0369 memory: 16201 loss_prob: 0.6279 loss_thr: 0.4295 loss_db: 0.1075 loss: 1.1649 2022/08/30 06:46:49 - mmengine - INFO - Epoch(train) [285][45/63] lr: 5.4883e-03 eta: 23:19:02 time: 1.3312 data_time: 0.0473 memory: 16201 loss_prob: 0.7038 loss_thr: 0.4315 loss_db: 0.1184 loss: 1.2538 2022/08/30 06:46:55 - mmengine - INFO - Epoch(train) [285][50/63] lr: 5.4883e-03 eta: 23:18:40 time: 1.2354 data_time: 0.0453 memory: 16201 loss_prob: 0.7710 loss_thr: 0.4708 loss_db: 0.1278 loss: 1.3696 2022/08/30 06:47:01 - mmengine - INFO - Epoch(train) [285][55/63] lr: 5.4883e-03 eta: 23:18:40 time: 1.2438 data_time: 0.0303 memory: 16201 loss_prob: 0.7396 loss_thr: 0.4857 loss_db: 0.1242 loss: 1.3495 2022/08/30 06:47:08 - mmengine - INFO - Epoch(train) [285][60/63] lr: 5.4883e-03 eta: 23:18:22 time: 1.3503 data_time: 0.0431 memory: 16201 loss_prob: 0.7038 loss_thr: 0.4685 loss_db: 0.1196 loss: 1.2919 2022/08/30 06:47:11 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:47:20 - mmengine - INFO - Epoch(train) [286][5/63] lr: 5.4829e-03 eta: 23:18:22 time: 1.4229 data_time: 0.2157 memory: 16201 loss_prob: 0.6916 loss_thr: 0.4445 loss_db: 0.1152 loss: 1.2513 2022/08/30 06:47:27 - mmengine - INFO - Epoch(train) [286][10/63] lr: 5.4829e-03 eta: 23:17:54 time: 1.5920 data_time: 0.2275 memory: 16201 loss_prob: 0.7064 loss_thr: 0.4438 loss_db: 0.1150 loss: 1.2653 2022/08/30 06:47:34 - mmengine - INFO - Epoch(train) [286][15/63] lr: 5.4829e-03 eta: 23:17:54 time: 1.4194 data_time: 0.0410 memory: 16201 loss_prob: 0.6559 loss_thr: 0.4273 loss_db: 0.1098 loss: 1.1930 2022/08/30 06:47:40 - mmengine - INFO - Epoch(train) [286][20/63] lr: 5.4829e-03 eta: 23:17:36 time: 1.3461 data_time: 0.0381 memory: 16201 loss_prob: 0.6676 loss_thr: 0.4315 loss_db: 0.1157 loss: 1.2148 2022/08/30 06:47:47 - mmengine - INFO - Epoch(train) [286][25/63] lr: 5.4829e-03 eta: 23:17:36 time: 1.3322 data_time: 0.0349 memory: 16201 loss_prob: 0.6404 loss_thr: 0.4228 loss_db: 0.1117 loss: 1.1749 2022/08/30 06:47:54 - mmengine - INFO - Epoch(train) [286][30/63] lr: 5.4829e-03 eta: 23:17:17 time: 1.3302 data_time: 0.0328 memory: 16201 loss_prob: 0.6576 loss_thr: 0.4234 loss_db: 0.1084 loss: 1.1894 2022/08/30 06:48:00 - mmengine - INFO - Epoch(train) [286][35/63] lr: 5.4829e-03 eta: 23:17:17 time: 1.3279 data_time: 0.0322 memory: 16201 loss_prob: 0.7361 loss_thr: 0.4383 loss_db: 0.1213 loss: 1.2957 2022/08/30 06:48:07 - mmengine - INFO - Epoch(train) [286][40/63] lr: 5.4829e-03 eta: 23:16:59 time: 1.3591 data_time: 0.0354 memory: 16201 loss_prob: 0.7881 loss_thr: 0.4608 loss_db: 0.1332 loss: 1.3821 2022/08/30 06:48:14 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:48:14 - mmengine - INFO - Epoch(train) [286][45/63] lr: 5.4829e-03 eta: 23:16:59 time: 1.3399 data_time: 0.0372 memory: 16201 loss_prob: 0.7532 loss_thr: 0.4596 loss_db: 0.1300 loss: 1.3428 2022/08/30 06:48:20 - mmengine - INFO - Epoch(train) [286][50/63] lr: 5.4829e-03 eta: 23:16:40 time: 1.3008 data_time: 0.0409 memory: 16201 loss_prob: 0.7571 loss_thr: 0.4627 loss_db: 0.1304 loss: 1.3502 2022/08/30 06:48:27 - mmengine - INFO - Epoch(train) [286][55/63] lr: 5.4829e-03 eta: 23:16:40 time: 1.3010 data_time: 0.0385 memory: 16201 loss_prob: 0.8602 loss_thr: 0.4848 loss_db: 0.1387 loss: 1.4836 2022/08/30 06:48:33 - mmengine - INFO - Epoch(train) [286][60/63] lr: 5.4829e-03 eta: 23:16:21 time: 1.3199 data_time: 0.0352 memory: 16201 loss_prob: 0.7997 loss_thr: 0.4678 loss_db: 0.1294 loss: 1.3970 2022/08/30 06:48:37 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:48:46 - mmengine - INFO - Epoch(train) [287][5/63] lr: 5.4775e-03 eta: 23:16:21 time: 1.5140 data_time: 0.2435 memory: 16201 loss_prob: 0.7086 loss_thr: 0.4452 loss_db: 0.1209 loss: 1.2747 2022/08/30 06:48:53 - mmengine - INFO - Epoch(train) [287][10/63] lr: 5.4775e-03 eta: 23:15:53 time: 1.6124 data_time: 0.2569 memory: 16201 loss_prob: 0.7212 loss_thr: 0.4302 loss_db: 0.1195 loss: 1.2709 2022/08/30 06:48:59 - mmengine - INFO - Epoch(train) [287][15/63] lr: 5.4775e-03 eta: 23:15:53 time: 1.2977 data_time: 0.0328 memory: 16201 loss_prob: 0.7434 loss_thr: 0.4343 loss_db: 0.1235 loss: 1.3012 2022/08/30 06:49:05 - mmengine - INFO - Epoch(train) [287][20/63] lr: 5.4775e-03 eta: 23:15:32 time: 1.2287 data_time: 0.0277 memory: 16201 loss_prob: 0.7465 loss_thr: 0.4592 loss_db: 0.1270 loss: 1.3327 2022/08/30 06:49:11 - mmengine - INFO - Epoch(train) [287][25/63] lr: 5.4775e-03 eta: 23:15:32 time: 1.2165 data_time: 0.0456 memory: 16201 loss_prob: 0.7399 loss_thr: 0.4435 loss_db: 0.1278 loss: 1.3111 2022/08/30 06:49:18 - mmengine - INFO - Epoch(train) [287][30/63] lr: 5.4775e-03 eta: 23:15:12 time: 1.3056 data_time: 0.0345 memory: 16201 loss_prob: 0.7022 loss_thr: 0.4319 loss_db: 0.1187 loss: 1.2529 2022/08/30 06:49:25 - mmengine - INFO - Epoch(train) [287][35/63] lr: 5.4775e-03 eta: 23:15:12 time: 1.4069 data_time: 0.0318 memory: 16201 loss_prob: 0.7786 loss_thr: 0.4807 loss_db: 0.1320 loss: 1.3913 2022/08/30 06:49:32 - mmengine - INFO - Epoch(train) [287][40/63] lr: 5.4775e-03 eta: 23:14:54 time: 1.3444 data_time: 0.0334 memory: 16201 loss_prob: 0.7760 loss_thr: 0.4713 loss_db: 0.1328 loss: 1.3800 2022/08/30 06:49:38 - mmengine - INFO - Epoch(train) [287][45/63] lr: 5.4775e-03 eta: 23:14:54 time: 1.2354 data_time: 0.0291 memory: 16201 loss_prob: 0.6992 loss_thr: 0.4375 loss_db: 0.1207 loss: 1.2575 2022/08/30 06:49:44 - mmengine - INFO - Epoch(train) [287][50/63] lr: 5.4775e-03 eta: 23:14:33 time: 1.2350 data_time: 0.0409 memory: 16201 loss_prob: 0.6236 loss_thr: 0.4042 loss_db: 0.1084 loss: 1.1362 2022/08/30 06:49:51 - mmengine - INFO - Epoch(train) [287][55/63] lr: 5.4775e-03 eta: 23:14:33 time: 1.2963 data_time: 0.0290 memory: 16201 loss_prob: 0.6656 loss_thr: 0.4235 loss_db: 0.1115 loss: 1.2006 2022/08/30 06:49:57 - mmengine - INFO - Epoch(train) [287][60/63] lr: 5.4775e-03 eta: 23:14:14 time: 1.3220 data_time: 0.0321 memory: 16201 loss_prob: 0.7307 loss_thr: 0.4494 loss_db: 0.1215 loss: 1.3017 2022/08/30 06:50:00 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:50:09 - mmengine - INFO - Epoch(train) [288][5/63] lr: 5.4721e-03 eta: 23:14:14 time: 1.4442 data_time: 0.2446 memory: 16201 loss_prob: 0.6922 loss_thr: 0.4355 loss_db: 0.1176 loss: 1.2453 2022/08/30 06:50:15 - mmengine - INFO - Epoch(train) [288][10/63] lr: 5.4721e-03 eta: 23:13:42 time: 1.4723 data_time: 0.2605 memory: 16201 loss_prob: 0.6852 loss_thr: 0.4286 loss_db: 0.1163 loss: 1.2301 2022/08/30 06:50:21 - mmengine - INFO - Epoch(train) [288][15/63] lr: 5.4721e-03 eta: 23:13:42 time: 1.2312 data_time: 0.0284 memory: 16201 loss_prob: 0.7322 loss_thr: 0.4370 loss_db: 0.1236 loss: 1.2927 2022/08/30 06:50:28 - mmengine - INFO - Epoch(train) [288][20/63] lr: 5.4721e-03 eta: 23:13:21 time: 1.2770 data_time: 0.0355 memory: 16201 loss_prob: 0.7053 loss_thr: 0.4460 loss_db: 0.1201 loss: 1.2715 2022/08/30 06:50:34 - mmengine - INFO - Epoch(train) [288][25/63] lr: 5.4721e-03 eta: 23:13:21 time: 1.3074 data_time: 0.0395 memory: 16201 loss_prob: 0.6519 loss_thr: 0.4432 loss_db: 0.1119 loss: 1.2070 2022/08/30 06:50:41 - mmengine - INFO - Epoch(train) [288][30/63] lr: 5.4721e-03 eta: 23:13:05 time: 1.3921 data_time: 0.0266 memory: 16201 loss_prob: 0.6700 loss_thr: 0.4513 loss_db: 0.1137 loss: 1.2349 2022/08/30 06:50:49 - mmengine - INFO - Epoch(train) [288][35/63] lr: 5.4721e-03 eta: 23:13:05 time: 1.4558 data_time: 0.0354 memory: 16201 loss_prob: 0.6277 loss_thr: 0.4427 loss_db: 0.1076 loss: 1.1779 2022/08/30 06:50:55 - mmengine - INFO - Epoch(train) [288][40/63] lr: 5.4721e-03 eta: 23:12:48 time: 1.3764 data_time: 0.0332 memory: 16201 loss_prob: 0.6286 loss_thr: 0.4491 loss_db: 0.1070 loss: 1.1847 2022/08/30 06:51:02 - mmengine - INFO - Epoch(train) [288][45/63] lr: 5.4721e-03 eta: 23:12:48 time: 1.2930 data_time: 0.0326 memory: 16201 loss_prob: 0.6791 loss_thr: 0.4524 loss_db: 0.1155 loss: 1.2469 2022/08/30 06:51:08 - mmengine - INFO - Epoch(train) [288][50/63] lr: 5.4721e-03 eta: 23:12:28 time: 1.2900 data_time: 0.0422 memory: 16201 loss_prob: 0.7266 loss_thr: 0.4611 loss_db: 0.1230 loss: 1.3107 2022/08/30 06:51:14 - mmengine - INFO - Epoch(train) [288][55/63] lr: 5.4721e-03 eta: 23:12:28 time: 1.1740 data_time: 0.0323 memory: 16201 loss_prob: 0.6972 loss_thr: 0.4519 loss_db: 0.1173 loss: 1.2664 2022/08/30 06:51:21 - mmengine - INFO - Epoch(train) [288][60/63] lr: 5.4721e-03 eta: 23:12:09 time: 1.3204 data_time: 0.0340 memory: 16201 loss_prob: 0.6417 loss_thr: 0.4151 loss_db: 0.1103 loss: 1.1670 2022/08/30 06:51:25 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:51:33 - mmengine - INFO - Epoch(train) [289][5/63] lr: 5.4667e-03 eta: 23:12:09 time: 1.5217 data_time: 0.2359 memory: 16201 loss_prob: 0.6567 loss_thr: 0.4369 loss_db: 0.1128 loss: 1.2064 2022/08/30 06:51:40 - mmengine - INFO - Epoch(train) [289][10/63] lr: 5.4667e-03 eta: 23:11:38 time: 1.4970 data_time: 0.2465 memory: 16201 loss_prob: 0.6558 loss_thr: 0.4279 loss_db: 0.1130 loss: 1.1968 2022/08/30 06:51:46 - mmengine - INFO - Epoch(train) [289][15/63] lr: 5.4667e-03 eta: 23:11:38 time: 1.3211 data_time: 0.0339 memory: 16201 loss_prob: 0.6188 loss_thr: 0.4127 loss_db: 0.1067 loss: 1.1382 2022/08/30 06:51:52 - mmengine - INFO - Epoch(train) [289][20/63] lr: 5.4667e-03 eta: 23:11:17 time: 1.2581 data_time: 0.0354 memory: 16201 loss_prob: 0.6430 loss_thr: 0.4443 loss_db: 0.1127 loss: 1.2000 2022/08/30 06:51:58 - mmengine - INFO - Epoch(train) [289][25/63] lr: 5.4667e-03 eta: 23:11:17 time: 1.2080 data_time: 0.0361 memory: 16201 loss_prob: 0.7079 loss_thr: 0.4538 loss_db: 0.1167 loss: 1.2784 2022/08/30 06:52:05 - mmengine - INFO - Epoch(train) [289][30/63] lr: 5.4667e-03 eta: 23:10:58 time: 1.2936 data_time: 0.0299 memory: 16201 loss_prob: 0.6572 loss_thr: 0.4267 loss_db: 0.1071 loss: 1.1910 2022/08/30 06:52:12 - mmengine - INFO - Epoch(train) [289][35/63] lr: 5.4667e-03 eta: 23:10:58 time: 1.3072 data_time: 0.0346 memory: 16201 loss_prob: 0.6345 loss_thr: 0.4313 loss_db: 0.1082 loss: 1.1739 2022/08/30 06:52:18 - mmengine - INFO - Epoch(train) [289][40/63] lr: 5.4667e-03 eta: 23:10:37 time: 1.2628 data_time: 0.0321 memory: 16201 loss_prob: 0.6774 loss_thr: 0.4353 loss_db: 0.1135 loss: 1.2261 2022/08/30 06:52:24 - mmengine - INFO - Epoch(train) [289][45/63] lr: 5.4667e-03 eta: 23:10:37 time: 1.2032 data_time: 0.0322 memory: 16201 loss_prob: 0.7357 loss_thr: 0.4628 loss_db: 0.1209 loss: 1.3194 2022/08/30 06:52:30 - mmengine - INFO - Epoch(train) [289][50/63] lr: 5.4667e-03 eta: 23:10:16 time: 1.2434 data_time: 0.0383 memory: 16201 loss_prob: 0.6776 loss_thr: 0.4416 loss_db: 0.1146 loss: 1.2338 2022/08/30 06:52:37 - mmengine - INFO - Epoch(train) [289][55/63] lr: 5.4667e-03 eta: 23:10:16 time: 1.3692 data_time: 0.0392 memory: 16201 loss_prob: 0.5949 loss_thr: 0.4060 loss_db: 0.1039 loss: 1.1048 2022/08/30 06:52:44 - mmengine - INFO - Epoch(train) [289][60/63] lr: 5.4667e-03 eta: 23:09:58 time: 1.3302 data_time: 0.0396 memory: 16201 loss_prob: 0.6036 loss_thr: 0.4018 loss_db: 0.1031 loss: 1.1085 2022/08/30 06:52:47 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:52:55 - mmengine - INFO - Epoch(train) [290][5/63] lr: 5.4613e-03 eta: 23:09:58 time: 1.4171 data_time: 0.2179 memory: 16201 loss_prob: 0.6788 loss_thr: 0.4384 loss_db: 0.1150 loss: 1.2322 2022/08/30 06:53:02 - mmengine - INFO - Epoch(train) [290][10/63] lr: 5.4613e-03 eta: 23:09:28 time: 1.5538 data_time: 0.2306 memory: 16201 loss_prob: 0.7093 loss_thr: 0.4594 loss_db: 0.1213 loss: 1.2899 2022/08/30 06:53:08 - mmengine - INFO - Epoch(train) [290][15/63] lr: 5.4613e-03 eta: 23:09:28 time: 1.3265 data_time: 0.0348 memory: 16201 loss_prob: 0.7053 loss_thr: 0.4632 loss_db: 0.1202 loss: 1.2887 2022/08/30 06:53:15 - mmengine - INFO - Epoch(train) [290][20/63] lr: 5.4613e-03 eta: 23:09:08 time: 1.2574 data_time: 0.0383 memory: 16201 loss_prob: 0.6695 loss_thr: 0.4456 loss_db: 0.1153 loss: 1.2304 2022/08/30 06:53:22 - mmengine - INFO - Epoch(train) [290][25/63] lr: 5.4613e-03 eta: 23:09:08 time: 1.3180 data_time: 0.0331 memory: 16201 loss_prob: 0.6804 loss_thr: 0.4483 loss_db: 0.1181 loss: 1.2468 2022/08/30 06:53:28 - mmengine - INFO - Epoch(train) [290][30/63] lr: 5.4613e-03 eta: 23:08:49 time: 1.3374 data_time: 0.0315 memory: 16201 loss_prob: 0.8251 loss_thr: 0.4511 loss_db: 0.1412 loss: 1.4174 2022/08/30 06:53:34 - mmengine - INFO - Epoch(train) [290][35/63] lr: 5.4613e-03 eta: 23:08:49 time: 1.2666 data_time: 0.0360 memory: 16201 loss_prob: 0.9213 loss_thr: 0.4549 loss_db: 0.1578 loss: 1.5339 2022/08/30 06:53:41 - mmengine - INFO - Epoch(train) [290][40/63] lr: 5.4613e-03 eta: 23:08:30 time: 1.2996 data_time: 0.0279 memory: 16201 loss_prob: 0.7662 loss_thr: 0.4382 loss_db: 0.1305 loss: 1.3348 2022/08/30 06:53:47 - mmengine - INFO - Epoch(train) [290][45/63] lr: 5.4613e-03 eta: 23:08:30 time: 1.3158 data_time: 0.0301 memory: 16201 loss_prob: 0.6792 loss_thr: 0.4150 loss_db: 0.1169 loss: 1.2111 2022/08/30 06:53:54 - mmengine - INFO - Epoch(train) [290][50/63] lr: 5.4613e-03 eta: 23:08:10 time: 1.2669 data_time: 0.0331 memory: 16201 loss_prob: 0.7172 loss_thr: 0.4242 loss_db: 0.1214 loss: 1.2628 2022/08/30 06:54:00 - mmengine - INFO - Epoch(train) [290][55/63] lr: 5.4613e-03 eta: 23:08:10 time: 1.2325 data_time: 0.0324 memory: 16201 loss_prob: 0.7046 loss_thr: 0.4222 loss_db: 0.1147 loss: 1.2415 2022/08/30 06:54:07 - mmengine - INFO - Epoch(train) [290][60/63] lr: 5.4613e-03 eta: 23:07:50 time: 1.2934 data_time: 0.0376 memory: 16201 loss_prob: 0.7381 loss_thr: 0.4449 loss_db: 0.1236 loss: 1.3066 2022/08/30 06:54:10 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:54:19 - mmengine - INFO - Epoch(train) [291][5/63] lr: 5.4559e-03 eta: 23:07:50 time: 1.5284 data_time: 0.2450 memory: 16201 loss_prob: 0.7170 loss_thr: 0.4574 loss_db: 0.1221 loss: 1.2965 2022/08/30 06:54:26 - mmengine - INFO - Epoch(train) [291][10/63] lr: 5.4559e-03 eta: 23:07:23 time: 1.6129 data_time: 0.2532 memory: 16201 loss_prob: 0.6868 loss_thr: 0.4479 loss_db: 0.1175 loss: 1.2523 2022/08/30 06:54:32 - mmengine - INFO - Epoch(train) [291][15/63] lr: 5.4559e-03 eta: 23:07:23 time: 1.2900 data_time: 0.0317 memory: 16201 loss_prob: 0.7120 loss_thr: 0.4431 loss_db: 0.1169 loss: 1.2720 2022/08/30 06:54:38 - mmengine - INFO - Epoch(train) [291][20/63] lr: 5.4559e-03 eta: 23:07:02 time: 1.2450 data_time: 0.0351 memory: 16201 loss_prob: 0.6575 loss_thr: 0.4072 loss_db: 0.1089 loss: 1.1736 2022/08/30 06:54:45 - mmengine - INFO - Epoch(train) [291][25/63] lr: 5.4559e-03 eta: 23:07:02 time: 1.3020 data_time: 0.0303 memory: 16201 loss_prob: 0.6630 loss_thr: 0.4238 loss_db: 0.1140 loss: 1.2009 2022/08/30 06:54:51 - mmengine - INFO - Epoch(train) [291][30/63] lr: 5.4559e-03 eta: 23:06:42 time: 1.2871 data_time: 0.0301 memory: 16201 loss_prob: 0.7150 loss_thr: 0.4560 loss_db: 0.1229 loss: 1.2938 2022/08/30 06:54:58 - mmengine - INFO - Epoch(train) [291][35/63] lr: 5.4559e-03 eta: 23:06:42 time: 1.2557 data_time: 0.0409 memory: 16201 loss_prob: 0.6851 loss_thr: 0.4428 loss_db: 0.1175 loss: 1.2454 2022/08/30 06:55:04 - mmengine - INFO - Epoch(train) [291][40/63] lr: 5.4559e-03 eta: 23:06:22 time: 1.2704 data_time: 0.0311 memory: 16201 loss_prob: 0.6820 loss_thr: 0.4361 loss_db: 0.1165 loss: 1.2346 2022/08/30 06:55:11 - mmengine - INFO - Epoch(train) [291][45/63] lr: 5.4559e-03 eta: 23:06:22 time: 1.3113 data_time: 0.0335 memory: 16201 loss_prob: 0.6589 loss_thr: 0.4199 loss_db: 0.1114 loss: 1.1903 2022/08/30 06:55:17 - mmengine - INFO - Epoch(train) [291][50/63] lr: 5.4559e-03 eta: 23:06:02 time: 1.2811 data_time: 0.0377 memory: 16201 loss_prob: 0.6194 loss_thr: 0.4152 loss_db: 0.1048 loss: 1.1395 2022/08/30 06:55:24 - mmengine - INFO - Epoch(train) [291][55/63] lr: 5.4559e-03 eta: 23:06:02 time: 1.3019 data_time: 0.0301 memory: 16201 loss_prob: 0.6527 loss_thr: 0.4426 loss_db: 0.1118 loss: 1.2071 2022/08/30 06:55:29 - mmengine - INFO - Epoch(train) [291][60/63] lr: 5.4559e-03 eta: 23:05:42 time: 1.2661 data_time: 0.0336 memory: 16201 loss_prob: 0.6563 loss_thr: 0.4358 loss_db: 0.1120 loss: 1.2041 2022/08/30 06:55:32 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:55:41 - mmengine - INFO - Epoch(train) [292][5/63] lr: 5.4505e-03 eta: 23:05:42 time: 1.3531 data_time: 0.2240 memory: 16201 loss_prob: 0.6684 loss_thr: 0.4401 loss_db: 0.1145 loss: 1.2231 2022/08/30 06:55:47 - mmengine - INFO - Epoch(train) [292][10/63] lr: 5.4505e-03 eta: 23:05:09 time: 1.4533 data_time: 0.2462 memory: 16201 loss_prob: 0.7068 loss_thr: 0.4501 loss_db: 0.1206 loss: 1.2774 2022/08/30 06:55:54 - mmengine - INFO - Epoch(train) [292][15/63] lr: 5.4505e-03 eta: 23:05:09 time: 1.3457 data_time: 0.0421 memory: 16201 loss_prob: 0.6521 loss_thr: 0.4265 loss_db: 0.1133 loss: 1.1919 2022/08/30 06:56:00 - mmengine - INFO - Epoch(train) [292][20/63] lr: 5.4505e-03 eta: 23:04:50 time: 1.3112 data_time: 0.0289 memory: 16201 loss_prob: 0.6596 loss_thr: 0.4388 loss_db: 0.1111 loss: 1.2096 2022/08/30 06:56:06 - mmengine - INFO - Epoch(train) [292][25/63] lr: 5.4505e-03 eta: 23:04:50 time: 1.2353 data_time: 0.0433 memory: 16201 loss_prob: 0.6976 loss_thr: 0.4525 loss_db: 0.1166 loss: 1.2667 2022/08/30 06:56:14 - mmengine - INFO - Epoch(train) [292][30/63] lr: 5.4505e-03 eta: 23:04:33 time: 1.3598 data_time: 0.0361 memory: 16201 loss_prob: 0.6797 loss_thr: 0.4298 loss_db: 0.1168 loss: 1.2263 2022/08/30 06:56:20 - mmengine - INFO - Epoch(train) [292][35/63] lr: 5.4505e-03 eta: 23:04:33 time: 1.3777 data_time: 0.0322 memory: 16201 loss_prob: 0.7105 loss_thr: 0.4508 loss_db: 0.1212 loss: 1.2825 2022/08/30 06:56:27 - mmengine - INFO - Epoch(train) [292][40/63] lr: 5.4505e-03 eta: 23:04:15 time: 1.3288 data_time: 0.0345 memory: 16201 loss_prob: 0.7344 loss_thr: 0.4538 loss_db: 0.1233 loss: 1.3115 2022/08/30 06:56:34 - mmengine - INFO - Epoch(train) [292][45/63] lr: 5.4505e-03 eta: 23:04:15 time: 1.3593 data_time: 0.0288 memory: 16201 loss_prob: 0.6851 loss_thr: 0.4237 loss_db: 0.1137 loss: 1.2225 2022/08/30 06:56:40 - mmengine - INFO - Epoch(train) [292][50/63] lr: 5.4505e-03 eta: 23:03:57 time: 1.3457 data_time: 0.0396 memory: 16201 loss_prob: 0.6895 loss_thr: 0.4567 loss_db: 0.1180 loss: 1.2642 2022/08/30 06:56:46 - mmengine - INFO - Epoch(train) [292][55/63] lr: 5.4505e-03 eta: 23:03:57 time: 1.2252 data_time: 0.0406 memory: 16201 loss_prob: 0.7646 loss_thr: 0.4758 loss_db: 0.1302 loss: 1.3706 2022/08/30 06:56:54 - mmengine - INFO - Epoch(train) [292][60/63] lr: 5.4505e-03 eta: 23:03:38 time: 1.3229 data_time: 0.0322 memory: 16201 loss_prob: 0.7446 loss_thr: 0.4445 loss_db: 0.1245 loss: 1.3136 2022/08/30 06:56:57 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:57:06 - mmengine - INFO - Epoch(train) [293][5/63] lr: 5.4451e-03 eta: 23:03:38 time: 1.4704 data_time: 0.2424 memory: 16201 loss_prob: 0.6278 loss_thr: 0.4167 loss_db: 0.1102 loss: 1.1547 2022/08/30 06:57:12 - mmengine - INFO - Epoch(train) [293][10/63] lr: 5.4451e-03 eta: 23:03:08 time: 1.5256 data_time: 0.2451 memory: 16201 loss_prob: 0.6555 loss_thr: 0.4256 loss_db: 0.1099 loss: 1.1910 2022/08/30 06:57:19 - mmengine - INFO - Epoch(train) [293][15/63] lr: 5.4451e-03 eta: 23:03:08 time: 1.3475 data_time: 0.0369 memory: 16201 loss_prob: 0.6909 loss_thr: 0.4533 loss_db: 0.1155 loss: 1.2596 2022/08/30 06:57:25 - mmengine - INFO - Epoch(train) [293][20/63] lr: 5.4451e-03 eta: 23:02:49 time: 1.2912 data_time: 0.0467 memory: 16201 loss_prob: 0.6933 loss_thr: 0.4660 loss_db: 0.1189 loss: 1.2782 2022/08/30 06:57:32 - mmengine - INFO - Epoch(train) [293][25/63] lr: 5.4451e-03 eta: 23:02:49 time: 1.2983 data_time: 0.0381 memory: 16201 loss_prob: 0.7459 loss_thr: 0.4628 loss_db: 0.1281 loss: 1.3368 2022/08/30 06:57:39 - mmengine - INFO - Epoch(train) [293][30/63] lr: 5.4451e-03 eta: 23:02:33 time: 1.3949 data_time: 0.0322 memory: 16201 loss_prob: 0.7390 loss_thr: 0.4550 loss_db: 0.1273 loss: 1.3214 2022/08/30 06:57:46 - mmengine - INFO - Epoch(train) [293][35/63] lr: 5.4451e-03 eta: 23:02:33 time: 1.3728 data_time: 0.0408 memory: 16201 loss_prob: 0.7091 loss_thr: 0.4406 loss_db: 0.1222 loss: 1.2718 2022/08/30 06:57:52 - mmengine - INFO - Epoch(train) [293][40/63] lr: 5.4451e-03 eta: 23:02:14 time: 1.3248 data_time: 0.0289 memory: 16201 loss_prob: 0.6999 loss_thr: 0.4323 loss_db: 0.1169 loss: 1.2491 2022/08/30 06:57:59 - mmengine - INFO - Epoch(train) [293][45/63] lr: 5.4451e-03 eta: 23:02:14 time: 1.2984 data_time: 0.0296 memory: 16201 loss_prob: 0.7147 loss_thr: 0.4414 loss_db: 0.1183 loss: 1.2744 2022/08/30 06:58:05 - mmengine - INFO - Epoch(train) [293][50/63] lr: 5.4451e-03 eta: 23:01:54 time: 1.2581 data_time: 0.0328 memory: 16201 loss_prob: 0.8337 loss_thr: 0.4807 loss_db: 0.1355 loss: 1.4499 2022/08/30 06:58:12 - mmengine - INFO - Epoch(train) [293][55/63] lr: 5.4451e-03 eta: 23:01:54 time: 1.2618 data_time: 0.0310 memory: 16201 loss_prob: 0.8391 loss_thr: 0.4830 loss_db: 0.1390 loss: 1.4612 2022/08/30 06:58:18 - mmengine - INFO - Epoch(train) [293][60/63] lr: 5.4451e-03 eta: 23:01:34 time: 1.2869 data_time: 0.0475 memory: 16201 loss_prob: 0.7067 loss_thr: 0.4438 loss_db: 0.1209 loss: 1.2714 2022/08/30 06:58:21 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:58:30 - mmengine - INFO - Epoch(train) [294][5/63] lr: 5.4397e-03 eta: 23:01:34 time: 1.4544 data_time: 0.2269 memory: 16201 loss_prob: 0.7393 loss_thr: 0.4552 loss_db: 0.1248 loss: 1.3193 2022/08/30 06:58:37 - mmengine - INFO - Epoch(train) [294][10/63] lr: 5.4397e-03 eta: 23:01:04 time: 1.5341 data_time: 0.2381 memory: 16201 loss_prob: 0.7388 loss_thr: 0.4531 loss_db: 0.1251 loss: 1.3171 2022/08/30 06:58:43 - mmengine - INFO - Epoch(train) [294][15/63] lr: 5.4397e-03 eta: 23:01:04 time: 1.3138 data_time: 0.0395 memory: 16201 loss_prob: 0.7972 loss_thr: 0.4661 loss_db: 0.1387 loss: 1.4020 2022/08/30 06:58:49 - mmengine - INFO - Epoch(train) [294][20/63] lr: 5.4397e-03 eta: 23:00:45 time: 1.2822 data_time: 0.0352 memory: 16201 loss_prob: 0.7588 loss_thr: 0.4534 loss_db: 0.1340 loss: 1.3463 2022/08/30 06:58:56 - mmengine - INFO - Epoch(train) [294][25/63] lr: 5.4397e-03 eta: 23:00:45 time: 1.3016 data_time: 0.0390 memory: 16201 loss_prob: 0.7419 loss_thr: 0.4535 loss_db: 0.1275 loss: 1.3229 2022/08/30 06:59:03 - mmengine - INFO - Epoch(train) [294][30/63] lr: 5.4397e-03 eta: 23:00:29 time: 1.4045 data_time: 0.0425 memory: 16201 loss_prob: 0.7431 loss_thr: 0.4570 loss_db: 0.1252 loss: 1.3254 2022/08/30 06:59:10 - mmengine - INFO - Epoch(train) [294][35/63] lr: 5.4397e-03 eta: 23:00:29 time: 1.3380 data_time: 0.0371 memory: 16201 loss_prob: 0.6995 loss_thr: 0.4522 loss_db: 0.1178 loss: 1.2694 2022/08/30 06:59:16 - mmengine - INFO - Epoch(train) [294][40/63] lr: 5.4397e-03 eta: 23:00:09 time: 1.2845 data_time: 0.0312 memory: 16201 loss_prob: 0.7092 loss_thr: 0.4573 loss_db: 0.1217 loss: 1.2882 2022/08/30 06:59:22 - mmengine - INFO - Epoch(train) [294][45/63] lr: 5.4397e-03 eta: 23:00:09 time: 1.2408 data_time: 0.0298 memory: 16201 loss_prob: 0.7262 loss_thr: 0.4357 loss_db: 0.1242 loss: 1.2861 2022/08/30 06:59:28 - mmengine - INFO - Epoch(train) [294][50/63] lr: 5.4397e-03 eta: 22:59:47 time: 1.2124 data_time: 0.0354 memory: 16201 loss_prob: 0.7786 loss_thr: 0.4483 loss_db: 0.1305 loss: 1.3573 2022/08/30 06:59:35 - mmengine - INFO - Epoch(train) [294][55/63] lr: 5.4397e-03 eta: 22:59:47 time: 1.2776 data_time: 0.0368 memory: 16201 loss_prob: 0.7257 loss_thr: 0.4546 loss_db: 0.1248 loss: 1.3050 2022/08/30 06:59:42 - mmengine - INFO - Epoch(train) [294][60/63] lr: 5.4397e-03 eta: 22:59:30 time: 1.3693 data_time: 0.0361 memory: 16201 loss_prob: 0.7183 loss_thr: 0.4480 loss_db: 0.1243 loss: 1.2906 2022/08/30 06:59:46 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 06:59:54 - mmengine - INFO - Epoch(train) [295][5/63] lr: 5.4343e-03 eta: 22:59:30 time: 1.5113 data_time: 0.2340 memory: 16201 loss_prob: 0.7657 loss_thr: 0.4508 loss_db: 0.1260 loss: 1.3425 2022/08/30 07:00:01 - mmengine - INFO - Epoch(train) [295][10/63] lr: 5.4343e-03 eta: 22:59:00 time: 1.5228 data_time: 0.2462 memory: 16201 loss_prob: 0.7038 loss_thr: 0.4376 loss_db: 0.1161 loss: 1.2574 2022/08/30 07:00:07 - mmengine - INFO - Epoch(train) [295][15/63] lr: 5.4343e-03 eta: 22:59:00 time: 1.2519 data_time: 0.0323 memory: 16201 loss_prob: 0.6723 loss_thr: 0.4224 loss_db: 0.1167 loss: 1.2113 2022/08/30 07:00:13 - mmengine - INFO - Epoch(train) [295][20/63] lr: 5.4343e-03 eta: 22:58:40 time: 1.2560 data_time: 0.0288 memory: 16201 loss_prob: 0.7912 loss_thr: 0.4446 loss_db: 0.1331 loss: 1.3689 2022/08/30 07:00:20 - mmengine - INFO - Epoch(train) [295][25/63] lr: 5.4343e-03 eta: 22:58:40 time: 1.3029 data_time: 0.0397 memory: 16201 loss_prob: 0.7740 loss_thr: 0.4429 loss_db: 0.1318 loss: 1.3488 2022/08/30 07:00:27 - mmengine - INFO - Epoch(train) [295][30/63] lr: 5.4343e-03 eta: 22:58:22 time: 1.3420 data_time: 0.0315 memory: 16201 loss_prob: 0.6592 loss_thr: 0.4123 loss_db: 0.1134 loss: 1.1848 2022/08/30 07:00:33 - mmengine - INFO - Epoch(train) [295][35/63] lr: 5.4343e-03 eta: 22:58:22 time: 1.3304 data_time: 0.0287 memory: 16201 loss_prob: 0.6830 loss_thr: 0.4236 loss_db: 0.1125 loss: 1.2192 2022/08/30 07:00:40 - mmengine - INFO - Epoch(train) [295][40/63] lr: 5.4343e-03 eta: 22:58:03 time: 1.3098 data_time: 0.0318 memory: 16201 loss_prob: 0.6910 loss_thr: 0.4392 loss_db: 0.1161 loss: 1.2463 2022/08/30 07:00:46 - mmengine - INFO - Epoch(train) [295][45/63] lr: 5.4343e-03 eta: 22:58:03 time: 1.2717 data_time: 0.0285 memory: 16201 loss_prob: 0.6790 loss_thr: 0.4476 loss_db: 0.1162 loss: 1.2429 2022/08/30 07:00:52 - mmengine - INFO - Epoch(train) [295][50/63] lr: 5.4343e-03 eta: 22:57:42 time: 1.2436 data_time: 0.0354 memory: 16201 loss_prob: 0.6850 loss_thr: 0.4468 loss_db: 0.1168 loss: 1.2485 2022/08/30 07:00:58 - mmengine - INFO - Epoch(train) [295][55/63] lr: 5.4343e-03 eta: 22:57:42 time: 1.2412 data_time: 0.0364 memory: 16201 loss_prob: 0.7874 loss_thr: 0.4731 loss_db: 0.1352 loss: 1.3958 2022/08/30 07:01:05 - mmengine - INFO - Epoch(train) [295][60/63] lr: 5.4343e-03 eta: 22:57:23 time: 1.2788 data_time: 0.0318 memory: 16201 loss_prob: 0.8380 loss_thr: 0.4761 loss_db: 0.1412 loss: 1.4553 2022/08/30 07:01:08 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:01:17 - mmengine - INFO - Epoch(train) [296][5/63] lr: 5.4289e-03 eta: 22:57:23 time: 1.4403 data_time: 0.2168 memory: 16201 loss_prob: 0.7328 loss_thr: 0.4440 loss_db: 0.1221 loss: 1.2988 2022/08/30 07:01:24 - mmengine - INFO - Epoch(train) [296][10/63] lr: 5.4289e-03 eta: 22:56:55 time: 1.5936 data_time: 0.2331 memory: 16201 loss_prob: 0.7543 loss_thr: 0.4498 loss_db: 0.1246 loss: 1.3287 2022/08/30 07:01:30 - mmengine - INFO - Epoch(train) [296][15/63] lr: 5.4289e-03 eta: 22:56:55 time: 1.2714 data_time: 0.0345 memory: 16201 loss_prob: 0.7208 loss_thr: 0.4519 loss_db: 0.1188 loss: 1.2916 2022/08/30 07:01:36 - mmengine - INFO - Epoch(train) [296][20/63] lr: 5.4289e-03 eta: 22:56:32 time: 1.1882 data_time: 0.0343 memory: 16201 loss_prob: 0.6949 loss_thr: 0.4385 loss_db: 0.1162 loss: 1.2496 2022/08/30 07:01:43 - mmengine - INFO - Epoch(train) [296][25/63] lr: 5.4289e-03 eta: 22:56:32 time: 1.2918 data_time: 0.0379 memory: 16201 loss_prob: 0.7214 loss_thr: 0.4364 loss_db: 0.1213 loss: 1.2791 2022/08/30 07:01:49 - mmengine - INFO - Epoch(train) [296][30/63] lr: 5.4289e-03 eta: 22:56:14 time: 1.3223 data_time: 0.0359 memory: 16201 loss_prob: 0.7274 loss_thr: 0.4574 loss_db: 0.1199 loss: 1.3047 2022/08/30 07:01:56 - mmengine - INFO - Epoch(train) [296][35/63] lr: 5.4289e-03 eta: 22:56:14 time: 1.3385 data_time: 0.0336 memory: 16201 loss_prob: 0.6952 loss_thr: 0.4559 loss_db: 0.1160 loss: 1.2672 2022/08/30 07:02:04 - mmengine - INFO - Epoch(train) [296][40/63] lr: 5.4289e-03 eta: 22:56:01 time: 1.4994 data_time: 0.0874 memory: 16201 loss_prob: 0.8139 loss_thr: 0.4981 loss_db: 0.1332 loss: 1.4452 2022/08/30 07:02:11 - mmengine - INFO - Epoch(train) [296][45/63] lr: 5.4289e-03 eta: 22:56:01 time: 1.5328 data_time: 0.0995 memory: 16201 loss_prob: 0.8314 loss_thr: 0.5082 loss_db: 0.1381 loss: 1.4777 2022/08/30 07:02:18 - mmengine - INFO - Epoch(train) [296][50/63] lr: 5.4289e-03 eta: 22:55:44 time: 1.3557 data_time: 0.0431 memory: 16201 loss_prob: 0.7155 loss_thr: 0.4293 loss_db: 0.1164 loss: 1.2612 2022/08/30 07:02:24 - mmengine - INFO - Epoch(train) [296][55/63] lr: 5.4289e-03 eta: 22:55:44 time: 1.2236 data_time: 0.0336 memory: 16201 loss_prob: 0.7131 loss_thr: 0.4201 loss_db: 0.1148 loss: 1.2480 2022/08/30 07:02:31 - mmengine - INFO - Epoch(train) [296][60/63] lr: 5.4289e-03 eta: 22:55:24 time: 1.2774 data_time: 0.0324 memory: 16201 loss_prob: 0.7159 loss_thr: 0.4602 loss_db: 0.1220 loss: 1.2982 2022/08/30 07:02:34 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:02:42 - mmengine - INFO - Epoch(train) [297][5/63] lr: 5.4235e-03 eta: 22:55:24 time: 1.4712 data_time: 0.2259 memory: 16201 loss_prob: 0.7066 loss_thr: 0.4501 loss_db: 0.1189 loss: 1.2755 2022/08/30 07:02:48 - mmengine - INFO - Epoch(train) [297][10/63] lr: 5.4235e-03 eta: 22:54:53 time: 1.4740 data_time: 0.2461 memory: 16201 loss_prob: 0.7393 loss_thr: 0.4382 loss_db: 0.1350 loss: 1.3126 2022/08/30 07:02:56 - mmengine - INFO - Epoch(train) [297][15/63] lr: 5.4235e-03 eta: 22:54:53 time: 1.3151 data_time: 0.0382 memory: 16201 loss_prob: 0.7794 loss_thr: 0.4655 loss_db: 0.1410 loss: 1.3859 2022/08/30 07:03:02 - mmengine - INFO - Epoch(train) [297][20/63] lr: 5.4235e-03 eta: 22:54:36 time: 1.3715 data_time: 0.0297 memory: 16201 loss_prob: 0.7626 loss_thr: 0.4703 loss_db: 0.1249 loss: 1.3577 2022/08/30 07:03:09 - mmengine - INFO - Epoch(train) [297][25/63] lr: 5.4235e-03 eta: 22:54:36 time: 1.3453 data_time: 0.0423 memory: 16201 loss_prob: 1.0649 loss_thr: 0.4886 loss_db: 0.1652 loss: 1.7187 2022/08/30 07:03:16 - mmengine - INFO - Epoch(train) [297][30/63] lr: 5.4235e-03 eta: 22:54:19 time: 1.3619 data_time: 0.0303 memory: 16201 loss_prob: 1.3187 loss_thr: 0.5233 loss_db: 0.1970 loss: 2.0390 2022/08/30 07:03:23 - mmengine - INFO - Epoch(train) [297][35/63] lr: 5.4235e-03 eta: 22:54:19 time: 1.4061 data_time: 0.0283 memory: 16201 loss_prob: 1.2893 loss_thr: 0.5358 loss_db: 0.2028 loss: 2.0278 2022/08/30 07:03:29 - mmengine - INFO - Epoch(train) [297][40/63] lr: 5.4235e-03 eta: 22:54:00 time: 1.2970 data_time: 0.0302 memory: 16201 loss_prob: 1.0548 loss_thr: 0.5257 loss_db: 0.1736 loss: 1.7541 2022/08/30 07:03:36 - mmengine - INFO - Epoch(train) [297][45/63] lr: 5.4235e-03 eta: 22:54:00 time: 1.3017 data_time: 0.0287 memory: 16201 loss_prob: 0.8951 loss_thr: 0.5158 loss_db: 0.1478 loss: 1.5587 2022/08/30 07:03:42 - mmengine - INFO - Epoch(train) [297][50/63] lr: 5.4235e-03 eta: 22:53:42 time: 1.3606 data_time: 0.0379 memory: 16201 loss_prob: 0.9963 loss_thr: 0.5267 loss_db: 0.1639 loss: 1.6869 2022/08/30 07:03:48 - mmengine - INFO - Epoch(train) [297][55/63] lr: 5.4235e-03 eta: 22:53:42 time: 1.2450 data_time: 0.0270 memory: 16201 loss_prob: 0.9641 loss_thr: 0.5241 loss_db: 0.1574 loss: 1.6457 2022/08/30 07:03:56 - mmengine - INFO - Epoch(train) [297][60/63] lr: 5.4235e-03 eta: 22:53:24 time: 1.3316 data_time: 0.0280 memory: 16201 loss_prob: 0.8762 loss_thr: 0.5096 loss_db: 0.1451 loss: 1.5309 2022/08/30 07:03:59 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:04:08 - mmengine - INFO - Epoch(train) [298][5/63] lr: 5.4181e-03 eta: 22:53:24 time: 1.4962 data_time: 0.2490 memory: 16201 loss_prob: 0.8578 loss_thr: 0.4865 loss_db: 0.1497 loss: 1.4940 2022/08/30 07:04:16 - mmengine - INFO - Epoch(train) [298][10/63] lr: 5.4181e-03 eta: 22:53:00 time: 1.6895 data_time: 0.2670 memory: 16201 loss_prob: 0.8525 loss_thr: 0.4703 loss_db: 0.1410 loss: 1.4638 2022/08/30 07:04:22 - mmengine - INFO - Epoch(train) [298][15/63] lr: 5.4181e-03 eta: 22:53:00 time: 1.3795 data_time: 0.0352 memory: 16201 loss_prob: 0.9796 loss_thr: 0.4926 loss_db: 0.1628 loss: 1.6350 2022/08/30 07:04:29 - mmengine - INFO - Epoch(train) [298][20/63] lr: 5.4181e-03 eta: 22:52:41 time: 1.3100 data_time: 0.0286 memory: 16201 loss_prob: 0.9119 loss_thr: 0.4946 loss_db: 0.1509 loss: 1.5573 2022/08/30 07:04:36 - mmengine - INFO - Epoch(train) [298][25/63] lr: 5.4181e-03 eta: 22:52:41 time: 1.3613 data_time: 0.0400 memory: 16201 loss_prob: 0.8991 loss_thr: 0.4911 loss_db: 0.1462 loss: 1.5364 2022/08/30 07:04:42 - mmengine - INFO - Epoch(train) [298][30/63] lr: 5.4181e-03 eta: 22:52:23 time: 1.3237 data_time: 0.0297 memory: 16201 loss_prob: 0.8665 loss_thr: 0.4960 loss_db: 0.1453 loss: 1.5078 2022/08/30 07:04:49 - mmengine - INFO - Epoch(train) [298][35/63] lr: 5.4181e-03 eta: 22:52:23 time: 1.3264 data_time: 0.0343 memory: 16201 loss_prob: 0.8432 loss_thr: 0.4829 loss_db: 0.1421 loss: 1.4682 2022/08/30 07:04:56 - mmengine - INFO - Epoch(train) [298][40/63] lr: 5.4181e-03 eta: 22:52:05 time: 1.3534 data_time: 0.0369 memory: 16201 loss_prob: 0.8815 loss_thr: 0.4931 loss_db: 0.1439 loss: 1.5185 2022/08/30 07:05:02 - mmengine - INFO - Epoch(train) [298][45/63] lr: 5.4181e-03 eta: 22:52:05 time: 1.3324 data_time: 0.0307 memory: 16201 loss_prob: 0.8128 loss_thr: 0.4898 loss_db: 0.1367 loss: 1.4394 2022/08/30 07:05:09 - mmengine - INFO - Epoch(train) [298][50/63] lr: 5.4181e-03 eta: 22:51:47 time: 1.3134 data_time: 0.0418 memory: 16201 loss_prob: 0.8060 loss_thr: 0.4820 loss_db: 0.1395 loss: 1.4275 2022/08/30 07:05:15 - mmengine - INFO - Epoch(train) [298][55/63] lr: 5.4181e-03 eta: 22:51:47 time: 1.3216 data_time: 0.0293 memory: 16201 loss_prob: 0.8681 loss_thr: 0.4953 loss_db: 0.1456 loss: 1.5091 2022/08/30 07:05:22 - mmengine - INFO - Epoch(train) [298][60/63] lr: 5.4181e-03 eta: 22:51:28 time: 1.3217 data_time: 0.0312 memory: 16201 loss_prob: 0.9073 loss_thr: 0.5079 loss_db: 0.1466 loss: 1.5617 2022/08/30 07:05:25 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:05:34 - mmengine - INFO - Epoch(train) [299][5/63] lr: 5.4127e-03 eta: 22:51:28 time: 1.4978 data_time: 0.2242 memory: 16201 loss_prob: 0.8490 loss_thr: 0.4843 loss_db: 0.1440 loss: 1.4773 2022/08/30 07:05:41 - mmengine - INFO - Epoch(train) [299][10/63] lr: 5.4127e-03 eta: 22:51:01 time: 1.6098 data_time: 0.2470 memory: 16201 loss_prob: 0.9041 loss_thr: 0.5053 loss_db: 0.1539 loss: 1.5632 2022/08/30 07:05:48 - mmengine - INFO - Epoch(train) [299][15/63] lr: 5.4127e-03 eta: 22:51:01 time: 1.3659 data_time: 0.0416 memory: 16201 loss_prob: 0.8698 loss_thr: 0.4982 loss_db: 0.1428 loss: 1.5108 2022/08/30 07:05:55 - mmengine - INFO - Epoch(train) [299][20/63] lr: 5.4127e-03 eta: 22:50:44 time: 1.3642 data_time: 0.0383 memory: 16201 loss_prob: 0.8785 loss_thr: 0.4960 loss_db: 0.1428 loss: 1.5173 2022/08/30 07:06:02 - mmengine - INFO - Epoch(train) [299][25/63] lr: 5.4127e-03 eta: 22:50:44 time: 1.3657 data_time: 0.0390 memory: 16201 loss_prob: 0.8226 loss_thr: 0.4911 loss_db: 0.1365 loss: 1.4502 2022/08/30 07:06:08 - mmengine - INFO - Epoch(train) [299][30/63] lr: 5.4127e-03 eta: 22:50:26 time: 1.3277 data_time: 0.0319 memory: 16201 loss_prob: 0.8204 loss_thr: 0.4874 loss_db: 0.1426 loss: 1.4504 2022/08/30 07:06:14 - mmengine - INFO - Epoch(train) [299][35/63] lr: 5.4127e-03 eta: 22:50:26 time: 1.2880 data_time: 0.0311 memory: 16201 loss_prob: 0.7961 loss_thr: 0.4613 loss_db: 0.1357 loss: 1.3930 2022/08/30 07:06:21 - mmengine - INFO - Epoch(train) [299][40/63] lr: 5.4127e-03 eta: 22:50:07 time: 1.2793 data_time: 0.0320 memory: 16201 loss_prob: 0.8401 loss_thr: 0.4800 loss_db: 0.1339 loss: 1.4539 2022/08/30 07:06:28 - mmengine - INFO - Epoch(train) [299][45/63] lr: 5.4127e-03 eta: 22:50:07 time: 1.3369 data_time: 0.0373 memory: 16201 loss_prob: 0.8973 loss_thr: 0.5043 loss_db: 0.1465 loss: 1.5481 2022/08/30 07:06:35 - mmengine - INFO - Epoch(train) [299][50/63] lr: 5.4127e-03 eta: 22:49:50 time: 1.3706 data_time: 0.0424 memory: 16201 loss_prob: 0.8583 loss_thr: 0.4862 loss_db: 0.1427 loss: 1.4872 2022/08/30 07:06:41 - mmengine - INFO - Epoch(train) [299][55/63] lr: 5.4127e-03 eta: 22:49:50 time: 1.3379 data_time: 0.0303 memory: 16201 loss_prob: 0.8341 loss_thr: 0.4827 loss_db: 0.1345 loss: 1.4513 2022/08/30 07:06:47 - mmengine - INFO - Epoch(train) [299][60/63] lr: 5.4127e-03 eta: 22:49:30 time: 1.2653 data_time: 0.0315 memory: 16201 loss_prob: 0.8471 loss_thr: 0.4929 loss_db: 0.1369 loss: 1.4768 2022/08/30 07:06:50 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:06:59 - mmengine - INFO - Epoch(train) [300][5/63] lr: 5.4073e-03 eta: 22:49:30 time: 1.4264 data_time: 0.2173 memory: 16201 loss_prob: 0.8727 loss_thr: 0.4696 loss_db: 0.1440 loss: 1.4863 2022/08/30 07:07:06 - mmengine - INFO - Epoch(train) [300][10/63] lr: 5.4073e-03 eta: 22:49:01 time: 1.5570 data_time: 0.2333 memory: 16201 loss_prob: 0.8411 loss_thr: 0.4646 loss_db: 0.1361 loss: 1.4418 2022/08/30 07:07:13 - mmengine - INFO - Epoch(train) [300][15/63] lr: 5.4073e-03 eta: 22:49:01 time: 1.3574 data_time: 0.0336 memory: 16201 loss_prob: 0.7516 loss_thr: 0.4640 loss_db: 0.1231 loss: 1.3387 2022/08/30 07:07:19 - mmengine - INFO - Epoch(train) [300][20/63] lr: 5.4073e-03 eta: 22:48:44 time: 1.3561 data_time: 0.0365 memory: 16201 loss_prob: 0.7958 loss_thr: 0.4749 loss_db: 0.1331 loss: 1.4039 2022/08/30 07:07:26 - mmengine - INFO - Epoch(train) [300][25/63] lr: 5.4073e-03 eta: 22:48:44 time: 1.3536 data_time: 0.0313 memory: 16201 loss_prob: 0.9865 loss_thr: 0.4942 loss_db: 0.1618 loss: 1.6425 2022/08/30 07:07:33 - mmengine - INFO - Epoch(train) [300][30/63] lr: 5.4073e-03 eta: 22:48:29 time: 1.4308 data_time: 0.0340 memory: 16201 loss_prob: 1.4145 loss_thr: 0.5414 loss_db: 0.2187 loss: 2.1746 2022/08/30 07:07:41 - mmengine - INFO - Epoch(train) [300][35/63] lr: 5.4073e-03 eta: 22:48:29 time: 1.4624 data_time: 0.0395 memory: 16201 loss_prob: 1.6070 loss_thr: 0.6083 loss_db: 0.2346 loss: 2.4499 2022/08/30 07:07:48 - mmengine - INFO - Epoch(train) [300][40/63] lr: 5.4073e-03 eta: 22:48:14 time: 1.4239 data_time: 0.0307 memory: 16201 loss_prob: 1.2876 loss_thr: 0.5755 loss_db: 0.1939 loss: 2.0570 2022/08/30 07:07:54 - mmengine - INFO - Epoch(train) [300][45/63] lr: 5.4073e-03 eta: 22:48:14 time: 1.3695 data_time: 0.0345 memory: 16201 loss_prob: 1.2745 loss_thr: 0.5705 loss_db: 0.2123 loss: 2.0573 2022/08/30 07:08:01 - mmengine - INFO - Epoch(train) [300][50/63] lr: 5.4073e-03 eta: 22:47:56 time: 1.3207 data_time: 0.0335 memory: 16201 loss_prob: 1.4056 loss_thr: 0.5853 loss_db: 0.2250 loss: 2.2159 2022/08/30 07:08:08 - mmengine - INFO - Epoch(train) [300][55/63] lr: 5.4073e-03 eta: 22:47:56 time: 1.3184 data_time: 0.0343 memory: 16201 loss_prob: 1.2380 loss_thr: 0.5572 loss_db: 0.1971 loss: 1.9922 2022/08/30 07:08:15 - mmengine - INFO - Epoch(train) [300][60/63] lr: 5.4073e-03 eta: 22:47:40 time: 1.4143 data_time: 0.0366 memory: 16201 loss_prob: 1.1887 loss_thr: 0.5672 loss_db: 0.1911 loss: 1.9470 2022/08/30 07:08:18 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:08:18 - mmengine - INFO - Saving checkpoint at 300 epochs 2022/08/30 07:08:27 - mmengine - INFO - Epoch(val) [300][5/32] eta: 22:47:40 time: 0.7063 data_time: 0.1340 memory: 16201 2022/08/30 07:08:31 - mmengine - INFO - Epoch(val) [300][10/32] eta: 0:00:17 time: 0.8078 data_time: 0.1777 memory: 15734 2022/08/30 07:08:34 - mmengine - INFO - Epoch(val) [300][15/32] eta: 0:00:17 time: 0.6713 data_time: 0.0603 memory: 15734 2022/08/30 07:08:38 - mmengine - INFO - Epoch(val) [300][20/32] eta: 0:00:08 time: 0.7394 data_time: 0.0745 memory: 15734 2022/08/30 07:08:42 - mmengine - INFO - Epoch(val) [300][25/32] eta: 0:00:08 time: 0.7657 data_time: 0.0801 memory: 15734 2022/08/30 07:08:45 - mmengine - INFO - Epoch(val) [300][30/32] eta: 0:00:01 time: 0.6505 data_time: 0.0335 memory: 15734 2022/08/30 07:08:46 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 07:08:46 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.7920, precision: 0.6342, hmean: 0.7043 2022/08/30 07:08:46 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.7920, precision: 0.7450, hmean: 0.7678 2022/08/30 07:08:46 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.7766, precision: 0.8037, hmean: 0.7899 2022/08/30 07:08:46 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.7477, precision: 0.8500, hmean: 0.7956 2022/08/30 07:08:46 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.6808, precision: 0.8989, hmean: 0.7748 2022/08/30 07:08:46 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.3385, precision: 0.9398, hmean: 0.4977 2022/08/30 07:08:46 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0039, precision: 1.0000, hmean: 0.0077 2022/08/30 07:08:46 - mmengine - INFO - Epoch(val) [300][32/32] icdar/precision: 0.8500 icdar/recall: 0.7477 icdar/hmean: 0.7956 2022/08/30 07:08:56 - mmengine - INFO - Epoch(train) [301][5/63] lr: 5.4019e-03 eta: 0:00:01 time: 1.6506 data_time: 0.2253 memory: 16201 loss_prob: 1.0302 loss_thr: 0.5345 loss_db: 0.1673 loss: 1.7319 2022/08/30 07:09:03 - mmengine - INFO - Epoch(train) [301][10/63] lr: 5.4019e-03 eta: 22:47:17 time: 1.7346 data_time: 0.2394 memory: 16201 loss_prob: 1.0175 loss_thr: 0.5392 loss_db: 0.1669 loss: 1.7236 2022/08/30 07:09:10 - mmengine - INFO - Epoch(train) [301][15/63] lr: 5.4019e-03 eta: 22:47:17 time: 1.4190 data_time: 0.0351 memory: 16201 loss_prob: 1.0761 loss_thr: 0.5532 loss_db: 0.1822 loss: 1.8114 2022/08/30 07:09:18 - mmengine - INFO - Epoch(train) [301][20/63] lr: 5.4019e-03 eta: 22:47:02 time: 1.4451 data_time: 0.0322 memory: 16201 loss_prob: 1.1132 loss_thr: 0.5602 loss_db: 0.1840 loss: 1.8574 2022/08/30 07:09:24 - mmengine - INFO - Epoch(train) [301][25/63] lr: 5.4019e-03 eta: 22:47:02 time: 1.4300 data_time: 0.0343 memory: 16201 loss_prob: 1.1438 loss_thr: 0.5642 loss_db: 0.1862 loss: 1.8943 2022/08/30 07:09:31 - mmengine - INFO - Epoch(train) [301][30/63] lr: 5.4019e-03 eta: 22:46:44 time: 1.3221 data_time: 0.0282 memory: 16201 loss_prob: 1.0383 loss_thr: 0.5400 loss_db: 0.1724 loss: 1.7506 2022/08/30 07:09:38 - mmengine - INFO - Epoch(train) [301][35/63] lr: 5.4019e-03 eta: 22:46:44 time: 1.3946 data_time: 0.0392 memory: 16201 loss_prob: 1.0690 loss_thr: 0.5189 loss_db: 0.1797 loss: 1.7676 2022/08/30 07:09:46 - mmengine - INFO - Epoch(train) [301][40/63] lr: 5.4019e-03 eta: 22:46:31 time: 1.4942 data_time: 0.0329 memory: 16201 loss_prob: 1.0681 loss_thr: 0.5039 loss_db: 0.1794 loss: 1.7513 2022/08/30 07:09:52 - mmengine - INFO - Epoch(train) [301][45/63] lr: 5.4019e-03 eta: 22:46:31 time: 1.3731 data_time: 0.0306 memory: 16201 loss_prob: 0.9845 loss_thr: 0.5034 loss_db: 0.1622 loss: 1.6501 2022/08/30 07:09:59 - mmengine - INFO - Epoch(train) [301][50/63] lr: 5.4019e-03 eta: 22:46:13 time: 1.3361 data_time: 0.0390 memory: 16201 loss_prob: 1.0092 loss_thr: 0.5183 loss_db: 0.1652 loss: 1.6927 2022/08/30 07:10:06 - mmengine - INFO - Epoch(train) [301][55/63] lr: 5.4019e-03 eta: 22:46:13 time: 1.4041 data_time: 0.0287 memory: 16201 loss_prob: 1.0063 loss_thr: 0.5170 loss_db: 0.1623 loss: 1.6856 2022/08/30 07:10:13 - mmengine - INFO - Epoch(train) [301][60/63] lr: 5.4019e-03 eta: 22:45:57 time: 1.3732 data_time: 0.0386 memory: 16201 loss_prob: 0.9600 loss_thr: 0.5009 loss_db: 0.1562 loss: 1.6171 2022/08/30 07:10:17 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:10:25 - mmengine - INFO - Epoch(train) [302][5/63] lr: 5.3965e-03 eta: 22:45:57 time: 1.5393 data_time: 0.2326 memory: 16201 loss_prob: 0.9656 loss_thr: 0.5106 loss_db: 0.1589 loss: 1.6350 2022/08/30 07:10:34 - mmengine - INFO - Epoch(train) [302][10/63] lr: 5.3965e-03 eta: 22:45:34 time: 1.7433 data_time: 0.2523 memory: 16201 loss_prob: 0.9767 loss_thr: 0.5168 loss_db: 0.1647 loss: 1.6582 2022/08/30 07:10:41 - mmengine - INFO - Epoch(train) [302][15/63] lr: 5.3965e-03 eta: 22:45:34 time: 1.5324 data_time: 0.0386 memory: 16201 loss_prob: 0.8458 loss_thr: 0.4904 loss_db: 0.1388 loss: 1.4750 2022/08/30 07:10:47 - mmengine - INFO - Epoch(train) [302][20/63] lr: 5.3965e-03 eta: 22:45:15 time: 1.3020 data_time: 0.0325 memory: 16201 loss_prob: 0.7685 loss_thr: 0.4655 loss_db: 0.1274 loss: 1.3613 2022/08/30 07:10:55 - mmengine - INFO - Epoch(train) [302][25/63] lr: 5.3965e-03 eta: 22:45:15 time: 1.4190 data_time: 0.0363 memory: 16201 loss_prob: 0.9537 loss_thr: 0.5130 loss_db: 0.1580 loss: 1.6246 2022/08/30 07:11:02 - mmengine - INFO - Epoch(train) [302][30/63] lr: 5.3965e-03 eta: 22:45:01 time: 1.4630 data_time: 0.0320 memory: 16201 loss_prob: 1.0379 loss_thr: 0.5227 loss_db: 0.1687 loss: 1.7293 2022/08/30 07:11:09 - mmengine - INFO - Epoch(train) [302][35/63] lr: 5.3965e-03 eta: 22:45:01 time: 1.3561 data_time: 0.0361 memory: 16201 loss_prob: 0.9777 loss_thr: 0.5059 loss_db: 0.1571 loss: 1.6407 2022/08/30 07:11:12 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:11:16 - mmengine - INFO - Epoch(train) [302][40/63] lr: 5.3965e-03 eta: 22:44:45 time: 1.4016 data_time: 0.0304 memory: 16201 loss_prob: 1.0336 loss_thr: 0.5329 loss_db: 0.1647 loss: 1.7312 2022/08/30 07:11:23 - mmengine - INFO - Epoch(train) [302][45/63] lr: 5.3965e-03 eta: 22:44:45 time: 1.4144 data_time: 0.0322 memory: 16201 loss_prob: 0.9190 loss_thr: 0.5046 loss_db: 0.1518 loss: 1.5754 2022/08/30 07:11:30 - mmengine - INFO - Epoch(train) [302][50/63] lr: 5.3965e-03 eta: 22:44:30 time: 1.4105 data_time: 0.0362 memory: 16201 loss_prob: 0.8050 loss_thr: 0.4675 loss_db: 0.1359 loss: 1.4084 2022/08/30 07:11:37 - mmengine - INFO - Epoch(train) [302][55/63] lr: 5.3965e-03 eta: 22:44:30 time: 1.3906 data_time: 0.0261 memory: 16201 loss_prob: 0.9491 loss_thr: 0.5043 loss_db: 0.1563 loss: 1.6097 2022/08/30 07:11:43 - mmengine - INFO - Epoch(train) [302][60/63] lr: 5.3965e-03 eta: 22:44:12 time: 1.3390 data_time: 0.0376 memory: 16201 loss_prob: 0.8909 loss_thr: 0.4824 loss_db: 0.1463 loss: 1.5197 2022/08/30 07:11:46 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:11:56 - mmengine - INFO - Epoch(train) [303][5/63] lr: 5.3911e-03 eta: 22:44:12 time: 1.4697 data_time: 0.2278 memory: 16201 loss_prob: 0.8191 loss_thr: 0.4763 loss_db: 0.1369 loss: 1.4324 2022/08/30 07:12:02 - mmengine - INFO - Epoch(train) [303][10/63] lr: 5.3911e-03 eta: 22:43:44 time: 1.5758 data_time: 0.2452 memory: 16201 loss_prob: 0.9069 loss_thr: 0.5050 loss_db: 0.1528 loss: 1.5647 2022/08/30 07:12:09 - mmengine - INFO - Epoch(train) [303][15/63] lr: 5.3911e-03 eta: 22:43:44 time: 1.2826 data_time: 0.0332 memory: 16201 loss_prob: 0.8899 loss_thr: 0.4807 loss_db: 0.1473 loss: 1.5179 2022/08/30 07:12:15 - mmengine - INFO - Epoch(train) [303][20/63] lr: 5.3911e-03 eta: 22:43:25 time: 1.2816 data_time: 0.0270 memory: 16201 loss_prob: 0.8179 loss_thr: 0.4563 loss_db: 0.1326 loss: 1.4068 2022/08/30 07:12:22 - mmengine - INFO - Epoch(train) [303][25/63] lr: 5.3911e-03 eta: 22:43:25 time: 1.3275 data_time: 0.0436 memory: 16201 loss_prob: 0.7935 loss_thr: 0.4400 loss_db: 0.1349 loss: 1.3684 2022/08/30 07:12:28 - mmengine - INFO - Epoch(train) [303][30/63] lr: 5.3911e-03 eta: 22:43:07 time: 1.3461 data_time: 0.0325 memory: 16201 loss_prob: 0.8399 loss_thr: 0.4761 loss_db: 0.1419 loss: 1.4579 2022/08/30 07:12:35 - mmengine - INFO - Epoch(train) [303][35/63] lr: 5.3911e-03 eta: 22:43:07 time: 1.3134 data_time: 0.0317 memory: 16201 loss_prob: 0.9036 loss_thr: 0.5132 loss_db: 0.1475 loss: 1.5644 2022/08/30 07:12:41 - mmengine - INFO - Epoch(train) [303][40/63] lr: 5.3911e-03 eta: 22:42:49 time: 1.3079 data_time: 0.0346 memory: 16201 loss_prob: 0.8515 loss_thr: 0.4985 loss_db: 0.1416 loss: 1.4916 2022/08/30 07:12:48 - mmengine - INFO - Epoch(train) [303][45/63] lr: 5.3911e-03 eta: 22:42:49 time: 1.2681 data_time: 0.0370 memory: 16201 loss_prob: 0.7741 loss_thr: 0.4714 loss_db: 0.1286 loss: 1.3741 2022/08/30 07:12:54 - mmengine - INFO - Epoch(train) [303][50/63] lr: 5.3911e-03 eta: 22:42:30 time: 1.2950 data_time: 0.0468 memory: 16201 loss_prob: 0.8154 loss_thr: 0.4621 loss_db: 0.1303 loss: 1.4078 2022/08/30 07:13:01 - mmengine - INFO - Epoch(train) [303][55/63] lr: 5.3911e-03 eta: 22:42:30 time: 1.3383 data_time: 0.0404 memory: 16201 loss_prob: 0.8809 loss_thr: 0.4745 loss_db: 0.1421 loss: 1.4976 2022/08/30 07:13:08 - mmengine - INFO - Epoch(train) [303][60/63] lr: 5.3911e-03 eta: 22:42:13 time: 1.3692 data_time: 0.0440 memory: 16201 loss_prob: 0.8247 loss_thr: 0.4780 loss_db: 0.1375 loss: 1.4403 2022/08/30 07:13:11 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:13:20 - mmengine - INFO - Epoch(train) [304][5/63] lr: 5.3857e-03 eta: 22:42:13 time: 1.4937 data_time: 0.2233 memory: 16201 loss_prob: 0.8325 loss_thr: 0.4865 loss_db: 0.1435 loss: 1.4624 2022/08/30 07:13:28 - mmengine - INFO - Epoch(train) [304][10/63] lr: 5.3857e-03 eta: 22:41:48 time: 1.6814 data_time: 0.2424 memory: 16201 loss_prob: 0.8941 loss_thr: 0.5064 loss_db: 0.1521 loss: 1.5526 2022/08/30 07:13:35 - mmengine - INFO - Epoch(train) [304][15/63] lr: 5.3857e-03 eta: 22:41:48 time: 1.4204 data_time: 0.0361 memory: 16201 loss_prob: 0.8294 loss_thr: 0.5043 loss_db: 0.1382 loss: 1.4719 2022/08/30 07:13:41 - mmengine - INFO - Epoch(train) [304][20/63] lr: 5.3857e-03 eta: 22:41:30 time: 1.3241 data_time: 0.0334 memory: 16201 loss_prob: 0.9950 loss_thr: 0.5299 loss_db: 0.1698 loss: 1.6946 2022/08/30 07:13:48 - mmengine - INFO - Epoch(train) [304][25/63] lr: 5.3857e-03 eta: 22:41:30 time: 1.3306 data_time: 0.0322 memory: 16201 loss_prob: 1.0668 loss_thr: 0.5036 loss_db: 0.1795 loss: 1.7498 2022/08/30 07:13:54 - mmengine - INFO - Epoch(train) [304][30/63] lr: 5.3857e-03 eta: 22:41:12 time: 1.3266 data_time: 0.0392 memory: 16201 loss_prob: 1.0742 loss_thr: 0.4838 loss_db: 0.1751 loss: 1.7331 2022/08/30 07:14:01 - mmengine - INFO - Epoch(train) [304][35/63] lr: 5.3857e-03 eta: 22:41:12 time: 1.2861 data_time: 0.0470 memory: 16201 loss_prob: 1.1851 loss_thr: 0.5204 loss_db: 0.1855 loss: 1.8910 2022/08/30 07:14:07 - mmengine - INFO - Epoch(train) [304][40/63] lr: 5.3857e-03 eta: 22:40:54 time: 1.3067 data_time: 0.0330 memory: 16201 loss_prob: 1.1903 loss_thr: 0.5606 loss_db: 0.1911 loss: 1.9420 2022/08/30 07:14:14 - mmengine - INFO - Epoch(train) [304][45/63] lr: 5.3857e-03 eta: 22:40:54 time: 1.2962 data_time: 0.0295 memory: 16201 loss_prob: 1.0758 loss_thr: 0.5431 loss_db: 0.1835 loss: 1.8024 2022/08/30 07:14:21 - mmengine - INFO - Epoch(train) [304][50/63] lr: 5.3857e-03 eta: 22:40:35 time: 1.3139 data_time: 0.0398 memory: 16201 loss_prob: 1.0277 loss_thr: 0.5159 loss_db: 0.1663 loss: 1.7099 2022/08/30 07:14:27 - mmengine - INFO - Epoch(train) [304][55/63] lr: 5.3857e-03 eta: 22:40:35 time: 1.3098 data_time: 0.0399 memory: 16201 loss_prob: 1.1372 loss_thr: 0.5672 loss_db: 0.1819 loss: 1.8863 2022/08/30 07:14:33 - mmengine - INFO - Epoch(train) [304][60/63] lr: 5.3857e-03 eta: 22:40:16 time: 1.2846 data_time: 0.0467 memory: 16201 loss_prob: 1.1140 loss_thr: 0.5660 loss_db: 0.1814 loss: 1.8614 2022/08/30 07:14:37 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:14:46 - mmengine - INFO - Epoch(train) [305][5/63] lr: 5.3803e-03 eta: 22:40:16 time: 1.4826 data_time: 0.2266 memory: 16201 loss_prob: 0.9785 loss_thr: 0.5196 loss_db: 0.1596 loss: 1.6577 2022/08/30 07:14:53 - mmengine - INFO - Epoch(train) [305][10/63] lr: 5.3803e-03 eta: 22:39:49 time: 1.6022 data_time: 0.2343 memory: 16201 loss_prob: 1.0021 loss_thr: 0.5605 loss_db: 0.1623 loss: 1.7249 2022/08/30 07:15:00 - mmengine - INFO - Epoch(train) [305][15/63] lr: 5.3803e-03 eta: 22:39:49 time: 1.3959 data_time: 0.0314 memory: 16201 loss_prob: 0.8899 loss_thr: 0.5446 loss_db: 0.1491 loss: 1.5837 2022/08/30 07:15:06 - mmengine - INFO - Epoch(train) [305][20/63] lr: 5.3803e-03 eta: 22:39:32 time: 1.3682 data_time: 0.0380 memory: 16201 loss_prob: 0.9210 loss_thr: 0.5215 loss_db: 0.1570 loss: 1.5994 2022/08/30 07:15:13 - mmengine - INFO - Epoch(train) [305][25/63] lr: 5.3803e-03 eta: 22:39:32 time: 1.3073 data_time: 0.0341 memory: 16201 loss_prob: 0.9207 loss_thr: 0.5151 loss_db: 0.1517 loss: 1.5875 2022/08/30 07:15:19 - mmengine - INFO - Epoch(train) [305][30/63] lr: 5.3803e-03 eta: 22:39:14 time: 1.2963 data_time: 0.0342 memory: 16201 loss_prob: 0.8484 loss_thr: 0.4813 loss_db: 0.1373 loss: 1.4670 2022/08/30 07:15:25 - mmengine - INFO - Epoch(train) [305][35/63] lr: 5.3803e-03 eta: 22:39:14 time: 1.2196 data_time: 0.0406 memory: 16201 loss_prob: 0.8452 loss_thr: 0.4786 loss_db: 0.1422 loss: 1.4660 2022/08/30 07:15:32 - mmengine - INFO - Epoch(train) [305][40/63] lr: 5.3803e-03 eta: 22:38:53 time: 1.2251 data_time: 0.0286 memory: 16201 loss_prob: 0.9321 loss_thr: 0.4988 loss_db: 0.1569 loss: 1.5877 2022/08/30 07:15:39 - mmengine - INFO - Epoch(train) [305][45/63] lr: 5.3803e-03 eta: 22:38:53 time: 1.3850 data_time: 0.0305 memory: 16201 loss_prob: 0.9298 loss_thr: 0.4863 loss_db: 0.1572 loss: 1.5733 2022/08/30 07:15:45 - mmengine - INFO - Epoch(train) [305][50/63] lr: 5.3803e-03 eta: 22:38:35 time: 1.3310 data_time: 0.0383 memory: 16201 loss_prob: 0.8771 loss_thr: 0.4865 loss_db: 0.1490 loss: 1.5126 2022/08/30 07:15:51 - mmengine - INFO - Epoch(train) [305][55/63] lr: 5.3803e-03 eta: 22:38:35 time: 1.2670 data_time: 0.0321 memory: 16201 loss_prob: 0.8605 loss_thr: 0.4802 loss_db: 0.1411 loss: 1.4817 2022/08/30 07:15:59 - mmengine - INFO - Epoch(train) [305][60/63] lr: 5.3803e-03 eta: 22:38:19 time: 1.3820 data_time: 0.0349 memory: 16201 loss_prob: 0.8422 loss_thr: 0.4718 loss_db: 0.1397 loss: 1.4537 2022/08/30 07:16:02 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:16:11 - mmengine - INFO - Epoch(train) [306][5/63] lr: 5.3749e-03 eta: 22:38:19 time: 1.4529 data_time: 0.2563 memory: 16201 loss_prob: 0.9019 loss_thr: 0.5018 loss_db: 0.1463 loss: 1.5500 2022/08/30 07:16:17 - mmengine - INFO - Epoch(train) [306][10/63] lr: 5.3749e-03 eta: 22:37:49 time: 1.5233 data_time: 0.2664 memory: 16201 loss_prob: 0.7838 loss_thr: 0.4754 loss_db: 0.1309 loss: 1.3901 2022/08/30 07:16:24 - mmengine - INFO - Epoch(train) [306][15/63] lr: 5.3749e-03 eta: 22:37:49 time: 1.2725 data_time: 0.0284 memory: 16201 loss_prob: 0.8167 loss_thr: 0.4824 loss_db: 0.1378 loss: 1.4369 2022/08/30 07:16:31 - mmengine - INFO - Epoch(train) [306][20/63] lr: 5.3749e-03 eta: 22:37:32 time: 1.3292 data_time: 0.0299 memory: 16201 loss_prob: 0.8932 loss_thr: 0.5105 loss_db: 0.1471 loss: 1.5507 2022/08/30 07:16:37 - mmengine - INFO - Epoch(train) [306][25/63] lr: 5.3749e-03 eta: 22:37:32 time: 1.3449 data_time: 0.0423 memory: 16201 loss_prob: 0.8881 loss_thr: 0.5030 loss_db: 0.1426 loss: 1.5337 2022/08/30 07:16:44 - mmengine - INFO - Epoch(train) [306][30/63] lr: 5.3749e-03 eta: 22:37:14 time: 1.3368 data_time: 0.0303 memory: 16201 loss_prob: 0.8350 loss_thr: 0.4777 loss_db: 0.1418 loss: 1.4545 2022/08/30 07:16:51 - mmengine - INFO - Epoch(train) [306][35/63] lr: 5.3749e-03 eta: 22:37:14 time: 1.3840 data_time: 0.0288 memory: 16201 loss_prob: 0.8423 loss_thr: 0.4789 loss_db: 0.1417 loss: 1.4629 2022/08/30 07:16:58 - mmengine - INFO - Epoch(train) [306][40/63] lr: 5.3749e-03 eta: 22:36:57 time: 1.3712 data_time: 0.0310 memory: 16201 loss_prob: 0.7649 loss_thr: 0.4582 loss_db: 0.1227 loss: 1.3457 2022/08/30 07:17:04 - mmengine - INFO - Epoch(train) [306][45/63] lr: 5.3749e-03 eta: 22:36:57 time: 1.3534 data_time: 0.0314 memory: 16201 loss_prob: 0.9436 loss_thr: 0.4969 loss_db: 0.1481 loss: 1.5886 2022/08/30 07:17:10 - mmengine - INFO - Epoch(train) [306][50/63] lr: 5.3749e-03 eta: 22:36:38 time: 1.2811 data_time: 0.0385 memory: 16201 loss_prob: 1.0521 loss_thr: 0.5239 loss_db: 0.1695 loss: 1.7455 2022/08/30 07:17:17 - mmengine - INFO - Epoch(train) [306][55/63] lr: 5.3749e-03 eta: 22:36:38 time: 1.2361 data_time: 0.0283 memory: 16201 loss_prob: 0.8322 loss_thr: 0.4720 loss_db: 0.1419 loss: 1.4461 2022/08/30 07:17:24 - mmengine - INFO - Epoch(train) [306][60/63] lr: 5.3749e-03 eta: 22:36:20 time: 1.3242 data_time: 0.0260 memory: 16201 loss_prob: 0.7881 loss_thr: 0.4734 loss_db: 0.1308 loss: 1.3923 2022/08/30 07:17:27 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:17:36 - mmengine - INFO - Epoch(train) [307][5/63] lr: 5.3694e-03 eta: 22:36:20 time: 1.4586 data_time: 0.2302 memory: 16201 loss_prob: 0.8465 loss_thr: 0.4863 loss_db: 0.1405 loss: 1.4733 2022/08/30 07:17:43 - mmengine - INFO - Epoch(train) [307][10/63] lr: 5.3694e-03 eta: 22:35:52 time: 1.5638 data_time: 0.2447 memory: 16201 loss_prob: 0.9148 loss_thr: 0.4943 loss_db: 0.1517 loss: 1.5608 2022/08/30 07:17:48 - mmengine - INFO - Epoch(train) [307][15/63] lr: 5.3694e-03 eta: 22:35:52 time: 1.2634 data_time: 0.0349 memory: 16201 loss_prob: 0.8658 loss_thr: 0.4840 loss_db: 0.1485 loss: 1.4982 2022/08/30 07:17:54 - mmengine - INFO - Epoch(train) [307][20/63] lr: 5.3694e-03 eta: 22:35:29 time: 1.1638 data_time: 0.0338 memory: 16201 loss_prob: 0.8668 loss_thr: 0.4680 loss_db: 0.1446 loss: 1.4794 2022/08/30 07:18:01 - mmengine - INFO - Epoch(train) [307][25/63] lr: 5.3694e-03 eta: 22:35:29 time: 1.2796 data_time: 0.0414 memory: 16201 loss_prob: 0.9349 loss_thr: 0.4739 loss_db: 0.1479 loss: 1.5566 2022/08/30 07:18:08 - mmengine - INFO - Epoch(train) [307][30/63] lr: 5.3694e-03 eta: 22:35:12 time: 1.3400 data_time: 0.0308 memory: 16201 loss_prob: 0.8483 loss_thr: 0.4684 loss_db: 0.1368 loss: 1.4535 2022/08/30 07:18:14 - mmengine - INFO - Epoch(train) [307][35/63] lr: 5.3694e-03 eta: 22:35:12 time: 1.2913 data_time: 0.0328 memory: 16201 loss_prob: 0.8629 loss_thr: 0.4860 loss_db: 0.1429 loss: 1.4918 2022/08/30 07:18:21 - mmengine - INFO - Epoch(train) [307][40/63] lr: 5.3694e-03 eta: 22:34:53 time: 1.2955 data_time: 0.0370 memory: 16201 loss_prob: 0.8154 loss_thr: 0.4723 loss_db: 0.1341 loss: 1.4218 2022/08/30 07:18:27 - mmengine - INFO - Epoch(train) [307][45/63] lr: 5.3694e-03 eta: 22:34:53 time: 1.2974 data_time: 0.0386 memory: 16201 loss_prob: 0.7971 loss_thr: 0.4483 loss_db: 0.1364 loss: 1.3818 2022/08/30 07:18:34 - mmengine - INFO - Epoch(train) [307][50/63] lr: 5.3694e-03 eta: 22:34:34 time: 1.2907 data_time: 0.0400 memory: 16201 loss_prob: 1.1014 loss_thr: 0.5068 loss_db: 0.1791 loss: 1.7872 2022/08/30 07:18:40 - mmengine - INFO - Epoch(train) [307][55/63] lr: 5.3694e-03 eta: 22:34:34 time: 1.2989 data_time: 0.0411 memory: 16201 loss_prob: 1.1056 loss_thr: 0.5335 loss_db: 0.1729 loss: 1.8120 2022/08/30 07:18:46 - mmengine - INFO - Epoch(train) [307][60/63] lr: 5.3694e-03 eta: 22:34:15 time: 1.2837 data_time: 0.0444 memory: 16201 loss_prob: 0.9445 loss_thr: 0.5128 loss_db: 0.1507 loss: 1.6079 2022/08/30 07:18:50 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:18:58 - mmengine - INFO - Epoch(train) [308][5/63] lr: 5.3640e-03 eta: 22:34:15 time: 1.4123 data_time: 0.2390 memory: 16201 loss_prob: 0.8583 loss_thr: 0.4992 loss_db: 0.1422 loss: 1.4996 2022/08/30 07:19:05 - mmengine - INFO - Epoch(train) [308][10/63] lr: 5.3640e-03 eta: 22:33:45 time: 1.4923 data_time: 0.2550 memory: 16201 loss_prob: 0.8920 loss_thr: 0.4893 loss_db: 0.1437 loss: 1.5250 2022/08/30 07:19:11 - mmengine - INFO - Epoch(train) [308][15/63] lr: 5.3640e-03 eta: 22:33:45 time: 1.3353 data_time: 0.0334 memory: 16201 loss_prob: 0.8679 loss_thr: 0.4897 loss_db: 0.1414 loss: 1.4990 2022/08/30 07:19:18 - mmengine - INFO - Epoch(train) [308][20/63] lr: 5.3640e-03 eta: 22:33:29 time: 1.3701 data_time: 0.0292 memory: 16201 loss_prob: 0.8247 loss_thr: 0.4980 loss_db: 0.1389 loss: 1.4615 2022/08/30 07:19:24 - mmengine - INFO - Epoch(train) [308][25/63] lr: 5.3640e-03 eta: 22:33:29 time: 1.2688 data_time: 0.0325 memory: 16201 loss_prob: 0.7752 loss_thr: 0.4742 loss_db: 0.1317 loss: 1.3811 2022/08/30 07:19:31 - mmengine - INFO - Epoch(train) [308][30/63] lr: 5.3640e-03 eta: 22:33:08 time: 1.2243 data_time: 0.0326 memory: 16201 loss_prob: 0.8219 loss_thr: 0.4750 loss_db: 0.1350 loss: 1.4320 2022/08/30 07:19:38 - mmengine - INFO - Epoch(train) [308][35/63] lr: 5.3640e-03 eta: 22:33:08 time: 1.3559 data_time: 0.0416 memory: 16201 loss_prob: 0.7995 loss_thr: 0.4682 loss_db: 0.1295 loss: 1.3972 2022/08/30 07:19:44 - mmengine - INFO - Epoch(train) [308][40/63] lr: 5.3640e-03 eta: 22:32:51 time: 1.3746 data_time: 0.0331 memory: 16201 loss_prob: 0.7790 loss_thr: 0.4614 loss_db: 0.1262 loss: 1.3665 2022/08/30 07:19:51 - mmengine - INFO - Epoch(train) [308][45/63] lr: 5.3640e-03 eta: 22:32:51 time: 1.3384 data_time: 0.0318 memory: 16201 loss_prob: 0.8205 loss_thr: 0.4688 loss_db: 0.1335 loss: 1.4228 2022/08/30 07:19:58 - mmengine - INFO - Epoch(train) [308][50/63] lr: 5.3640e-03 eta: 22:32:34 time: 1.3328 data_time: 0.0424 memory: 16201 loss_prob: 0.8263 loss_thr: 0.4807 loss_db: 0.1361 loss: 1.4431 2022/08/30 07:20:04 - mmengine - INFO - Epoch(train) [308][55/63] lr: 5.3640e-03 eta: 22:32:34 time: 1.3014 data_time: 0.0294 memory: 16201 loss_prob: 0.7995 loss_thr: 0.4854 loss_db: 0.1347 loss: 1.4195 2022/08/30 07:20:11 - mmengine - INFO - Epoch(train) [308][60/63] lr: 5.3640e-03 eta: 22:32:16 time: 1.3200 data_time: 0.0292 memory: 16201 loss_prob: 0.8412 loss_thr: 0.4991 loss_db: 0.1401 loss: 1.4804 2022/08/30 07:20:14 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:20:23 - mmengine - INFO - Epoch(train) [309][5/63] lr: 5.3586e-03 eta: 22:32:16 time: 1.4396 data_time: 0.2098 memory: 16201 loss_prob: 0.8019 loss_thr: 0.4785 loss_db: 0.1369 loss: 1.4173 2022/08/30 07:20:30 - mmengine - INFO - Epoch(train) [309][10/63] lr: 5.3586e-03 eta: 22:31:48 time: 1.5618 data_time: 0.2253 memory: 16201 loss_prob: 0.7020 loss_thr: 0.4502 loss_db: 0.1196 loss: 1.2718 2022/08/30 07:20:37 - mmengine - INFO - Epoch(train) [309][15/63] lr: 5.3586e-03 eta: 22:31:48 time: 1.4628 data_time: 0.0319 memory: 16201 loss_prob: 0.7330 loss_thr: 0.4656 loss_db: 0.1265 loss: 1.3251 2022/08/30 07:20:44 - mmengine - INFO - Epoch(train) [309][20/63] lr: 5.3586e-03 eta: 22:31:32 time: 1.3900 data_time: 0.0311 memory: 16201 loss_prob: 0.8268 loss_thr: 0.4925 loss_db: 0.1386 loss: 1.4579 2022/08/30 07:20:50 - mmengine - INFO - Epoch(train) [309][25/63] lr: 5.3586e-03 eta: 22:31:32 time: 1.2912 data_time: 0.0335 memory: 16201 loss_prob: 0.7792 loss_thr: 0.4688 loss_db: 0.1285 loss: 1.3765 2022/08/30 07:20:57 - mmengine - INFO - Epoch(train) [309][30/63] lr: 5.3586e-03 eta: 22:31:14 time: 1.3369 data_time: 0.0306 memory: 16201 loss_prob: 0.6587 loss_thr: 0.4327 loss_db: 0.1118 loss: 1.2032 2022/08/30 07:21:04 - mmengine - INFO - Epoch(train) [309][35/63] lr: 5.3586e-03 eta: 22:31:14 time: 1.4190 data_time: 0.0378 memory: 16201 loss_prob: 0.7234 loss_thr: 0.4597 loss_db: 0.1253 loss: 1.3084 2022/08/30 07:21:10 - mmengine - INFO - Epoch(train) [309][40/63] lr: 5.3586e-03 eta: 22:30:56 time: 1.3008 data_time: 0.0313 memory: 16201 loss_prob: 0.8085 loss_thr: 0.4786 loss_db: 0.1355 loss: 1.4226 2022/08/30 07:21:17 - mmengine - INFO - Epoch(train) [309][45/63] lr: 5.3586e-03 eta: 22:30:56 time: 1.2487 data_time: 0.0291 memory: 16201 loss_prob: 0.7804 loss_thr: 0.4745 loss_db: 0.1303 loss: 1.3851 2022/08/30 07:21:23 - mmengine - INFO - Epoch(train) [309][50/63] lr: 5.3586e-03 eta: 22:30:37 time: 1.2914 data_time: 0.0390 memory: 16201 loss_prob: 0.7947 loss_thr: 0.4937 loss_db: 0.1364 loss: 1.4247 2022/08/30 07:21:30 - mmengine - INFO - Epoch(train) [309][55/63] lr: 5.3586e-03 eta: 22:30:37 time: 1.2777 data_time: 0.0337 memory: 16201 loss_prob: 0.7985 loss_thr: 0.4902 loss_db: 0.1334 loss: 1.4220 2022/08/30 07:21:36 - mmengine - INFO - Epoch(train) [309][60/63] lr: 5.3586e-03 eta: 22:30:19 time: 1.3217 data_time: 0.0408 memory: 16201 loss_prob: 0.9677 loss_thr: 0.4932 loss_db: 0.1612 loss: 1.6221 2022/08/30 07:21:40 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:21:49 - mmengine - INFO - Epoch(train) [310][5/63] lr: 5.3532e-03 eta: 22:30:19 time: 1.5391 data_time: 0.2297 memory: 16201 loss_prob: 0.9445 loss_thr: 0.4980 loss_db: 0.1504 loss: 1.5928 2022/08/30 07:21:58 - mmengine - INFO - Epoch(train) [310][10/63] lr: 5.3532e-03 eta: 22:30:00 time: 1.8726 data_time: 0.2416 memory: 16201 loss_prob: 0.9068 loss_thr: 0.4739 loss_db: 0.1493 loss: 1.5300 2022/08/30 07:22:06 - mmengine - INFO - Epoch(train) [310][15/63] lr: 5.3532e-03 eta: 22:30:00 time: 1.7366 data_time: 0.0363 memory: 16201 loss_prob: 0.8463 loss_thr: 0.4900 loss_db: 0.1421 loss: 1.4784 2022/08/30 07:22:20 - mmengine - INFO - Epoch(train) [310][20/63] lr: 5.3532e-03 eta: 22:30:05 time: 2.1146 data_time: 0.0534 memory: 16201 loss_prob: 0.9039 loss_thr: 0.5251 loss_db: 0.1477 loss: 1.5767 2022/08/30 07:22:33 - mmengine - INFO - Epoch(train) [310][25/63] lr: 5.3532e-03 eta: 22:30:05 time: 2.6759 data_time: 0.0669 memory: 16201 loss_prob: 0.8257 loss_thr: 0.4702 loss_db: 0.1391 loss: 1.4349 2022/08/30 07:22:45 - mmengine - INFO - Epoch(train) [310][30/63] lr: 5.3532e-03 eta: 22:30:23 time: 2.5868 data_time: 0.0600 memory: 16201 loss_prob: 0.8433 loss_thr: 0.4526 loss_db: 0.1360 loss: 1.4319 2022/08/30 07:23:01 - mmengine - INFO - Epoch(train) [310][35/63] lr: 5.3532e-03 eta: 22:30:23 time: 2.7729 data_time: 0.0543 memory: 16201 loss_prob: 0.8006 loss_thr: 0.4528 loss_db: 0.1268 loss: 1.3802 2022/08/30 07:23:15 - mmengine - INFO - Epoch(train) [310][40/63] lr: 5.3532e-03 eta: 22:30:53 time: 2.9718 data_time: 0.0534 memory: 16201 loss_prob: 0.7563 loss_thr: 0.4652 loss_db: 0.1306 loss: 1.3521 2022/08/30 07:23:29 - mmengine - INFO - Epoch(train) [310][45/63] lr: 5.3532e-03 eta: 22:30:53 time: 2.7948 data_time: 0.0765 memory: 16201 loss_prob: 0.8127 loss_thr: 0.4869 loss_db: 0.1399 loss: 1.4394 2022/08/30 07:23:43 - mmengine - INFO - Epoch(train) [310][50/63] lr: 5.3532e-03 eta: 22:31:18 time: 2.8263 data_time: 0.0866 memory: 16201 loss_prob: 0.8193 loss_thr: 0.4735 loss_db: 0.1350 loss: 1.4278 2022/08/30 07:23:56 - mmengine - INFO - Epoch(train) [310][55/63] lr: 5.3532e-03 eta: 22:31:18 time: 2.7173 data_time: 0.0720 memory: 16201 loss_prob: 0.8476 loss_thr: 0.4840 loss_db: 0.1374 loss: 1.4689 2022/08/30 07:24:08 - mmengine - INFO - Epoch(train) [310][60/63] lr: 5.3532e-03 eta: 22:31:34 time: 2.4996 data_time: 0.0783 memory: 16201 loss_prob: 0.8603 loss_thr: 0.5007 loss_db: 0.1436 loss: 1.5046 2022/08/30 07:24:17 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:24:34 - mmengine - INFO - Epoch(train) [311][5/63] lr: 5.3478e-03 eta: 22:31:34 time: 2.9478 data_time: 0.2874 memory: 16201 loss_prob: 0.8621 loss_thr: 0.4863 loss_db: 0.1409 loss: 1.4893 2022/08/30 07:24:48 - mmengine - INFO - Epoch(train) [311][10/63] lr: 5.3478e-03 eta: 22:31:50 time: 3.0979 data_time: 0.2892 memory: 16201 loss_prob: 0.8911 loss_thr: 0.4970 loss_db: 0.1475 loss: 1.5356 2022/08/30 07:25:00 - mmengine - INFO - Epoch(train) [311][15/63] lr: 5.3478e-03 eta: 22:31:50 time: 2.6414 data_time: 0.0605 memory: 16201 loss_prob: 0.8289 loss_thr: 0.4566 loss_db: 0.1409 loss: 1.4264 2022/08/30 07:25:12 - mmengine - INFO - Epoch(train) [311][20/63] lr: 5.3478e-03 eta: 22:32:05 time: 2.4754 data_time: 0.0649 memory: 16201 loss_prob: 0.8397 loss_thr: 0.4587 loss_db: 0.1422 loss: 1.4406 2022/08/30 07:25:26 - mmengine - INFO - Epoch(train) [311][25/63] lr: 5.3478e-03 eta: 22:32:05 time: 2.6424 data_time: 0.0660 memory: 16201 loss_prob: 0.8154 loss_thr: 0.4786 loss_db: 0.1387 loss: 1.4327 2022/08/30 07:25:40 - mmengine - INFO - Epoch(train) [311][30/63] lr: 5.3478e-03 eta: 22:32:28 time: 2.7397 data_time: 0.0677 memory: 16201 loss_prob: 0.7673 loss_thr: 0.4879 loss_db: 0.1327 loss: 1.3879 2022/08/30 07:25:52 - mmengine - INFO - Epoch(train) [311][35/63] lr: 5.3478e-03 eta: 22:32:28 time: 2.6011 data_time: 0.0696 memory: 16201 loss_prob: 0.7513 loss_thr: 0.4730 loss_db: 0.1299 loss: 1.3543 2022/08/30 07:26:06 - mmengine - INFO - Epoch(train) [311][40/63] lr: 5.3478e-03 eta: 22:32:47 time: 2.6432 data_time: 0.1124 memory: 16201 loss_prob: 0.7583 loss_thr: 0.4515 loss_db: 0.1276 loss: 1.3375 2022/08/30 07:26:19 - mmengine - INFO - Epoch(train) [311][45/63] lr: 5.3478e-03 eta: 22:32:47 time: 2.6409 data_time: 0.1022 memory: 16201 loss_prob: 0.7823 loss_thr: 0.4655 loss_db: 0.1286 loss: 1.3764 2022/08/30 07:26:32 - mmengine - INFO - Epoch(train) [311][50/63] lr: 5.3478e-03 eta: 22:33:06 time: 2.6143 data_time: 0.0558 memory: 16201 loss_prob: 0.7602 loss_thr: 0.4636 loss_db: 0.1254 loss: 1.3492 2022/08/30 07:26:47 - mmengine - INFO - Epoch(train) [311][55/63] lr: 5.3478e-03 eta: 22:33:06 time: 2.8555 data_time: 0.0618 memory: 16201 loss_prob: 0.7957 loss_thr: 0.4853 loss_db: 0.1347 loss: 1.4157 2022/08/30 07:26:59 - mmengine - INFO - Epoch(train) [311][60/63] lr: 5.3478e-03 eta: 22:33:26 time: 2.6293 data_time: 0.0576 memory: 16201 loss_prob: 0.7612 loss_thr: 0.4845 loss_db: 0.1299 loss: 1.3756 2022/08/30 07:27:06 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:27:22 - mmengine - INFO - Epoch(train) [312][5/63] lr: 5.3424e-03 eta: 22:33:26 time: 2.7794 data_time: 0.2802 memory: 16201 loss_prob: 0.7527 loss_thr: 0.4801 loss_db: 0.1266 loss: 1.3594 2022/08/30 07:27:34 - mmengine - INFO - Epoch(train) [312][10/63] lr: 5.3424e-03 eta: 22:33:33 time: 2.7935 data_time: 0.2844 memory: 16201 loss_prob: 0.6903 loss_thr: 0.4673 loss_db: 0.1137 loss: 1.2713 2022/08/30 07:27:49 - mmengine - INFO - Epoch(train) [312][15/63] lr: 5.3424e-03 eta: 22:33:33 time: 2.6976 data_time: 0.0453 memory: 16201 loss_prob: 0.7497 loss_thr: 0.4672 loss_db: 0.1258 loss: 1.3427 2022/08/30 07:28:01 - mmengine - INFO - Epoch(train) [312][20/63] lr: 5.3424e-03 eta: 22:33:54 time: 2.7017 data_time: 0.0520 memory: 16201 loss_prob: 0.7967 loss_thr: 0.4627 loss_db: 0.1335 loss: 1.3929 2022/08/30 07:28:14 - mmengine - INFO - Epoch(train) [312][25/63] lr: 5.3424e-03 eta: 22:33:54 time: 2.4761 data_time: 0.0755 memory: 16201 loss_prob: 0.7137 loss_thr: 0.4433 loss_db: 0.1180 loss: 1.2749 2022/08/30 07:28:26 - mmengine - INFO - Epoch(train) [312][30/63] lr: 5.3424e-03 eta: 22:34:09 time: 2.5073 data_time: 0.0580 memory: 16201 loss_prob: 0.6553 loss_thr: 0.4207 loss_db: 0.1117 loss: 1.1877 2022/08/30 07:28:39 - mmengine - INFO - Epoch(train) [312][35/63] lr: 5.3424e-03 eta: 22:34:09 time: 2.5467 data_time: 0.0536 memory: 16201 loss_prob: 0.6476 loss_thr: 0.4071 loss_db: 0.1098 loss: 1.1645 2022/08/30 07:28:54 - mmengine - INFO - Epoch(train) [312][40/63] lr: 5.3424e-03 eta: 22:34:33 time: 2.7911 data_time: 0.0545 memory: 16201 loss_prob: 0.6879 loss_thr: 0.4251 loss_db: 0.1149 loss: 1.2279 2022/08/30 07:29:07 - mmengine - INFO - Epoch(train) [312][45/63] lr: 5.3424e-03 eta: 22:34:33 time: 2.7641 data_time: 0.0461 memory: 16201 loss_prob: 0.7317 loss_thr: 0.4496 loss_db: 0.1221 loss: 1.3035 2022/08/30 07:29:20 - mmengine - INFO - Epoch(train) [312][50/63] lr: 5.3424e-03 eta: 22:34:52 time: 2.6299 data_time: 0.0556 memory: 16201 loss_prob: 0.7137 loss_thr: 0.4359 loss_db: 0.1207 loss: 1.2703 2022/08/30 07:29:33 - mmengine - INFO - Epoch(train) [312][55/63] lr: 5.3424e-03 eta: 22:34:52 time: 2.6131 data_time: 0.0433 memory: 16201 loss_prob: 0.7160 loss_thr: 0.4364 loss_db: 0.1218 loss: 1.2742 2022/08/30 07:29:47 - mmengine - INFO - Epoch(train) [312][60/63] lr: 5.3424e-03 eta: 22:35:12 time: 2.6670 data_time: 0.0417 memory: 16201 loss_prob: 0.7068 loss_thr: 0.4476 loss_db: 0.1181 loss: 1.2726 2022/08/30 07:29:53 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:30:10 - mmengine - INFO - Epoch(train) [313][5/63] lr: 5.3370e-03 eta: 22:35:12 time: 2.8017 data_time: 0.2659 memory: 16201 loss_prob: 0.7472 loss_thr: 0.4953 loss_db: 0.1206 loss: 1.3631 2022/08/30 07:30:22 - mmengine - INFO - Epoch(train) [313][10/63] lr: 5.3370e-03 eta: 22:35:22 time: 2.9015 data_time: 0.2780 memory: 16201 loss_prob: 0.7632 loss_thr: 0.4875 loss_db: 0.1295 loss: 1.3801 2022/08/30 07:30:36 - mmengine - INFO - Epoch(train) [313][15/63] lr: 5.3370e-03 eta: 22:35:22 time: 2.5304 data_time: 0.0527 memory: 16201 loss_prob: 0.7611 loss_thr: 0.4766 loss_db: 0.1319 loss: 1.3697 2022/08/30 07:30:47 - mmengine - INFO - Epoch(train) [313][20/63] lr: 5.3370e-03 eta: 22:35:38 time: 2.5402 data_time: 0.0651 memory: 16201 loss_prob: 0.7030 loss_thr: 0.4597 loss_db: 0.1185 loss: 1.2812 2022/08/30 07:31:00 - mmengine - INFO - Epoch(train) [313][25/63] lr: 5.3370e-03 eta: 22:35:38 time: 2.4543 data_time: 0.0636 memory: 16201 loss_prob: 0.7224 loss_thr: 0.4634 loss_db: 0.1195 loss: 1.3053 2022/08/30 07:31:14 - mmengine - INFO - Epoch(train) [313][30/63] lr: 5.3370e-03 eta: 22:35:58 time: 2.6649 data_time: 0.0549 memory: 16201 loss_prob: 0.8345 loss_thr: 0.4546 loss_db: 0.1381 loss: 1.4272 2022/08/30 07:31:28 - mmengine - INFO - Epoch(train) [313][35/63] lr: 5.3370e-03 eta: 22:35:58 time: 2.7929 data_time: 0.0731 memory: 16201 loss_prob: 0.7715 loss_thr: 0.4200 loss_db: 0.1293 loss: 1.3208 2022/08/30 07:31:42 - mmengine - INFO - Epoch(train) [313][40/63] lr: 5.3370e-03 eta: 22:36:22 time: 2.7909 data_time: 0.0551 memory: 16201 loss_prob: 0.7134 loss_thr: 0.4281 loss_db: 0.1142 loss: 1.2557 2022/08/30 07:31:55 - mmengine - INFO - Epoch(train) [313][45/63] lr: 5.3370e-03 eta: 22:36:22 time: 2.6509 data_time: 0.0534 memory: 16201 loss_prob: 0.7951 loss_thr: 0.4766 loss_db: 0.1296 loss: 1.4012 2022/08/30 07:32:08 - mmengine - INFO - Epoch(train) [313][50/63] lr: 5.3370e-03 eta: 22:36:39 time: 2.5970 data_time: 0.0718 memory: 16201 loss_prob: 0.8536 loss_thr: 0.4924 loss_db: 0.1462 loss: 1.4923 2022/08/30 07:32:22 - mmengine - INFO - Epoch(train) [313][55/63] lr: 5.3370e-03 eta: 22:36:39 time: 2.7139 data_time: 0.0569 memory: 16201 loss_prob: 0.8316 loss_thr: 0.4718 loss_db: 0.1397 loss: 1.4431 2022/08/30 07:32:37 - mmengine - INFO - Epoch(train) [313][60/63] lr: 5.3370e-03 eta: 22:37:05 time: 2.8595 data_time: 0.0550 memory: 16201 loss_prob: 0.7599 loss_thr: 0.4353 loss_db: 0.1330 loss: 1.3283 2022/08/30 07:32:42 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:32:56 - mmengine - INFO - Epoch(train) [314][5/63] lr: 5.3315e-03 eta: 22:37:05 time: 2.5165 data_time: 0.2923 memory: 16201 loss_prob: 0.6749 loss_thr: 0.4306 loss_db: 0.1128 loss: 1.2183 2022/08/30 07:33:11 - mmengine - INFO - Epoch(train) [314][10/63] lr: 5.3315e-03 eta: 22:37:12 time: 2.8283 data_time: 0.2953 memory: 16201 loss_prob: 0.7366 loss_thr: 0.4585 loss_db: 0.1256 loss: 1.3207 2022/08/30 07:33:24 - mmengine - INFO - Epoch(train) [314][15/63] lr: 5.3315e-03 eta: 22:37:12 time: 2.7721 data_time: 0.0618 memory: 16201 loss_prob: 0.7960 loss_thr: 0.4820 loss_db: 0.1336 loss: 1.4116 2022/08/30 07:33:38 - mmengine - INFO - Epoch(train) [314][20/63] lr: 5.3315e-03 eta: 22:37:33 time: 2.6943 data_time: 0.0675 memory: 16201 loss_prob: 0.7456 loss_thr: 0.4512 loss_db: 0.1261 loss: 1.3229 2022/08/30 07:33:52 - mmengine - INFO - Epoch(train) [314][25/63] lr: 5.3315e-03 eta: 22:37:33 time: 2.7840 data_time: 0.0768 memory: 16201 loss_prob: 0.6711 loss_thr: 0.4212 loss_db: 0.1127 loss: 1.2050 2022/08/30 07:34:05 - mmengine - INFO - Epoch(train) [314][30/63] lr: 5.3315e-03 eta: 22:37:53 time: 2.6914 data_time: 0.0609 memory: 16201 loss_prob: 0.6832 loss_thr: 0.4376 loss_db: 0.1106 loss: 1.2314 2022/08/30 07:34:16 - mmengine - INFO - Epoch(train) [314][35/63] lr: 5.3315e-03 eta: 22:37:53 time: 2.3722 data_time: 0.0771 memory: 16201 loss_prob: 0.7385 loss_thr: 0.4651 loss_db: 0.1231 loss: 1.3266 2022/08/30 07:34:29 - mmengine - INFO - Epoch(train) [314][40/63] lr: 5.3315e-03 eta: 22:38:05 time: 2.4202 data_time: 0.0708 memory: 16201 loss_prob: 0.7369 loss_thr: 0.4653 loss_db: 0.1247 loss: 1.3269 2022/08/30 07:34:42 - mmengine - INFO - Epoch(train) [314][45/63] lr: 5.3315e-03 eta: 22:38:05 time: 2.5877 data_time: 0.0530 memory: 16201 loss_prob: 0.6858 loss_thr: 0.4396 loss_db: 0.1152 loss: 1.2406 2022/08/30 07:34:54 - mmengine - INFO - Epoch(train) [314][50/63] lr: 5.3315e-03 eta: 22:38:22 time: 2.5555 data_time: 0.0628 memory: 16201 loss_prob: 0.7261 loss_thr: 0.4614 loss_db: 0.1255 loss: 1.3131 2022/08/30 07:35:08 - mmengine - INFO - Epoch(train) [314][55/63] lr: 5.3315e-03 eta: 22:38:22 time: 2.6536 data_time: 0.0589 memory: 16201 loss_prob: 0.7173 loss_thr: 0.4566 loss_db: 0.1235 loss: 1.2974 2022/08/30 07:35:22 - mmengine - INFO - Epoch(train) [314][60/63] lr: 5.3315e-03 eta: 22:38:44 time: 2.7486 data_time: 0.0661 memory: 16201 loss_prob: 0.6653 loss_thr: 0.4345 loss_db: 0.1125 loss: 1.2123 2022/08/30 07:35:30 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:35:48 - mmengine - INFO - Epoch(train) [315][5/63] lr: 5.3261e-03 eta: 22:38:44 time: 3.2617 data_time: 0.2766 memory: 16201 loss_prob: 0.7193 loss_thr: 0.4410 loss_db: 0.1227 loss: 1.2830 2022/08/30 07:36:02 - mmengine - INFO - Epoch(train) [315][10/63] lr: 5.3261e-03 eta: 22:39:01 time: 3.2031 data_time: 0.2873 memory: 16201 loss_prob: 0.7185 loss_thr: 0.4391 loss_db: 0.1240 loss: 1.2816 2022/08/30 07:36:14 - mmengine - INFO - Epoch(train) [315][15/63] lr: 5.3261e-03 eta: 22:39:01 time: 2.5816 data_time: 0.0615 memory: 16201 loss_prob: 0.6869 loss_thr: 0.4451 loss_db: 0.1174 loss: 1.2494 2022/08/30 07:36:28 - mmengine - INFO - Epoch(train) [315][20/63] lr: 5.3261e-03 eta: 22:39:18 time: 2.5652 data_time: 0.0525 memory: 16201 loss_prob: 0.6627 loss_thr: 0.4401 loss_db: 0.1094 loss: 1.2122 2022/08/30 07:36:40 - mmengine - INFO - Epoch(train) [315][25/63] lr: 5.3261e-03 eta: 22:39:18 time: 2.5704 data_time: 0.0554 memory: 16201 loss_prob: 0.7070 loss_thr: 0.4384 loss_db: 0.1197 loss: 1.2652 2022/08/30 07:36:53 - mmengine - INFO - Epoch(train) [315][30/63] lr: 5.3261e-03 eta: 22:39:34 time: 2.5532 data_time: 0.0526 memory: 16201 loss_prob: 0.7670 loss_thr: 0.4617 loss_db: 0.1309 loss: 1.3596 2022/08/30 07:37:05 - mmengine - INFO - Epoch(train) [315][35/63] lr: 5.3261e-03 eta: 22:39:34 time: 2.5623 data_time: 0.0785 memory: 16201 loss_prob: 0.7260 loss_thr: 0.4593 loss_db: 0.1224 loss: 1.3076 2022/08/30 07:37:19 - mmengine - INFO - Epoch(train) [315][40/63] lr: 5.3261e-03 eta: 22:39:50 time: 2.5655 data_time: 0.0721 memory: 16201 loss_prob: 0.7071 loss_thr: 0.4533 loss_db: 0.1216 loss: 1.2821 2022/08/30 07:37:31 - mmengine - INFO - Epoch(train) [315][45/63] lr: 5.3261e-03 eta: 22:39:50 time: 2.6017 data_time: 0.0490 memory: 16201 loss_prob: 0.6569 loss_thr: 0.4284 loss_db: 0.1159 loss: 1.2013 2022/08/30 07:37:46 - mmengine - INFO - Epoch(train) [315][50/63] lr: 5.3261e-03 eta: 22:40:12 time: 2.7349 data_time: 0.0627 memory: 16201 loss_prob: 0.6374 loss_thr: 0.4204 loss_db: 0.1109 loss: 1.1686 2022/08/30 07:38:00 - mmengine - INFO - Epoch(train) [315][55/63] lr: 5.3261e-03 eta: 22:40:12 time: 2.8221 data_time: 0.0808 memory: 16201 loss_prob: 0.7068 loss_thr: 0.4506 loss_db: 0.1188 loss: 1.2761 2022/08/30 07:38:13 - mmengine - INFO - Epoch(train) [315][60/63] lr: 5.3261e-03 eta: 22:40:30 time: 2.6503 data_time: 0.0612 memory: 16201 loss_prob: 0.7476 loss_thr: 0.4589 loss_db: 0.1260 loss: 1.3325 2022/08/30 07:38:20 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:38:35 - mmengine - INFO - Epoch(train) [316][5/63] lr: 5.3207e-03 eta: 22:40:30 time: 2.7897 data_time: 0.2719 memory: 16201 loss_prob: 0.7620 loss_thr: 0.4722 loss_db: 0.1313 loss: 1.3656 2022/08/30 07:38:48 - mmengine - INFO - Epoch(train) [316][10/63] lr: 5.3207e-03 eta: 22:40:38 time: 2.8497 data_time: 0.2839 memory: 16201 loss_prob: 0.6842 loss_thr: 0.4330 loss_db: 0.1174 loss: 1.2346 2022/08/30 07:38:59 - mmengine - INFO - Epoch(train) [316][15/63] lr: 5.3207e-03 eta: 22:40:38 time: 2.4185 data_time: 0.0532 memory: 16201 loss_prob: 0.6651 loss_thr: 0.4380 loss_db: 0.1134 loss: 1.2165 2022/08/30 07:39:14 - mmengine - INFO - Epoch(train) [316][20/63] lr: 5.3207e-03 eta: 22:40:54 time: 2.5601 data_time: 0.0571 memory: 16201 loss_prob: 0.7068 loss_thr: 0.4747 loss_db: 0.1219 loss: 1.3034 2022/08/30 07:39:27 - mmengine - INFO - Epoch(train) [316][25/63] lr: 5.3207e-03 eta: 22:40:54 time: 2.7611 data_time: 0.0753 memory: 16201 loss_prob: 0.7327 loss_thr: 0.4816 loss_db: 0.1265 loss: 1.3408 2022/08/30 07:39:40 - mmengine - INFO - Epoch(train) [316][30/63] lr: 5.3207e-03 eta: 22:41:12 time: 2.6409 data_time: 0.0665 memory: 16201 loss_prob: 0.7191 loss_thr: 0.4482 loss_db: 0.1220 loss: 1.2893 2022/08/30 07:39:53 - mmengine - INFO - Epoch(train) [316][35/63] lr: 5.3207e-03 eta: 22:41:12 time: 2.6501 data_time: 0.0657 memory: 16201 loss_prob: 0.7009 loss_thr: 0.4340 loss_db: 0.1190 loss: 1.2539 2022/08/30 07:40:06 - mmengine - INFO - Epoch(train) [316][40/63] lr: 5.3207e-03 eta: 22:41:30 time: 2.6156 data_time: 0.0568 memory: 16201 loss_prob: 0.6730 loss_thr: 0.4223 loss_db: 0.1167 loss: 1.2120 2022/08/30 07:40:22 - mmengine - INFO - Epoch(train) [316][45/63] lr: 5.3207e-03 eta: 22:41:30 time: 2.8233 data_time: 0.0503 memory: 16201 loss_prob: 0.7044 loss_thr: 0.4539 loss_db: 0.1212 loss: 1.2795 2022/08/30 07:40:35 - mmengine - INFO - Epoch(train) [316][50/63] lr: 5.3207e-03 eta: 22:41:55 time: 2.8909 data_time: 0.0681 memory: 16201 loss_prob: 0.6825 loss_thr: 0.4535 loss_db: 0.1152 loss: 1.2512 2022/08/30 07:40:49 - mmengine - INFO - Epoch(train) [316][55/63] lr: 5.3207e-03 eta: 22:41:55 time: 2.7026 data_time: 0.0522 memory: 16201 loss_prob: 0.6253 loss_thr: 0.4328 loss_db: 0.1068 loss: 1.1649 2022/08/30 07:41:03 - mmengine - INFO - Epoch(train) [316][60/63] lr: 5.3207e-03 eta: 22:42:16 time: 2.7495 data_time: 0.0535 memory: 16201 loss_prob: 0.6891 loss_thr: 0.4447 loss_db: 0.1160 loss: 1.2498 2022/08/30 07:41:09 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:41:23 - mmengine - INFO - Epoch(train) [317][5/63] lr: 5.3153e-03 eta: 22:42:16 time: 2.6256 data_time: 0.2802 memory: 16201 loss_prob: 0.7174 loss_thr: 0.4279 loss_db: 0.1163 loss: 1.2616 2022/08/30 07:41:37 - mmengine - INFO - Epoch(train) [317][10/63] lr: 5.3153e-03 eta: 22:42:22 time: 2.8108 data_time: 0.2821 memory: 16201 loss_prob: 0.6563 loss_thr: 0.3955 loss_db: 0.1065 loss: 1.1583 2022/08/30 07:41:49 - mmengine - INFO - Epoch(train) [317][15/63] lr: 5.3153e-03 eta: 22:42:22 time: 2.5774 data_time: 0.0553 memory: 16201 loss_prob: 0.6656 loss_thr: 0.4005 loss_db: 0.1105 loss: 1.1766 2022/08/30 07:42:03 - mmengine - INFO - Epoch(train) [317][20/63] lr: 5.3153e-03 eta: 22:42:41 time: 2.6644 data_time: 0.0595 memory: 16201 loss_prob: 0.6546 loss_thr: 0.4192 loss_db: 0.1114 loss: 1.1853 2022/08/30 07:42:15 - mmengine - INFO - Epoch(train) [317][25/63] lr: 5.3153e-03 eta: 22:42:41 time: 2.6571 data_time: 0.0685 memory: 16201 loss_prob: 0.7115 loss_thr: 0.4501 loss_db: 0.1203 loss: 1.2819 2022/08/30 07:42:29 - mmengine - INFO - Epoch(train) [317][30/63] lr: 5.3153e-03 eta: 22:42:56 time: 2.5401 data_time: 0.0569 memory: 16201 loss_prob: 0.7278 loss_thr: 0.4665 loss_db: 0.1246 loss: 1.3189 2022/08/30 07:42:43 - mmengine - INFO - Epoch(train) [317][35/63] lr: 5.3153e-03 eta: 22:42:56 time: 2.7370 data_time: 0.0564 memory: 16201 loss_prob: 0.7185 loss_thr: 0.4441 loss_db: 0.1230 loss: 1.2856 2022/08/30 07:42:55 - mmengine - INFO - Epoch(train) [317][40/63] lr: 5.3153e-03 eta: 22:43:14 time: 2.6411 data_time: 0.0586 memory: 16201 loss_prob: 0.6794 loss_thr: 0.4247 loss_db: 0.1162 loss: 1.2203 2022/08/30 07:43:09 - mmengine - INFO - Epoch(train) [317][45/63] lr: 5.3153e-03 eta: 22:43:14 time: 2.5970 data_time: 0.0637 memory: 16201 loss_prob: 0.6678 loss_thr: 0.4315 loss_db: 0.1116 loss: 1.2109 2022/08/30 07:43:22 - mmengine - INFO - Epoch(train) [317][50/63] lr: 5.3153e-03 eta: 22:43:33 time: 2.6527 data_time: 0.0744 memory: 16201 loss_prob: 0.6970 loss_thr: 0.4442 loss_db: 0.1145 loss: 1.2557 2022/08/30 07:43:35 - mmengine - INFO - Epoch(train) [317][55/63] lr: 5.3153e-03 eta: 22:43:33 time: 2.6607 data_time: 0.0639 memory: 16201 loss_prob: 0.7076 loss_thr: 0.4537 loss_db: 0.1195 loss: 1.2808 2022/08/30 07:43:48 - mmengine - INFO - Epoch(train) [317][60/63] lr: 5.3153e-03 eta: 22:43:50 time: 2.6142 data_time: 0.0626 memory: 16201 loss_prob: 0.6858 loss_thr: 0.4419 loss_db: 0.1171 loss: 1.2448 2022/08/30 07:43:55 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:44:11 - mmengine - INFO - Epoch(train) [318][5/63] lr: 5.3099e-03 eta: 22:43:50 time: 2.9500 data_time: 0.2894 memory: 16201 loss_prob: 0.6929 loss_thr: 0.4684 loss_db: 0.1175 loss: 1.2788 2022/08/30 07:44:23 - mmengine - INFO - Epoch(train) [318][10/63] lr: 5.3099e-03 eta: 22:43:57 time: 2.8410 data_time: 0.3109 memory: 16201 loss_prob: 0.7027 loss_thr: 0.4620 loss_db: 0.1194 loss: 1.2841 2022/08/30 07:44:36 - mmengine - INFO - Epoch(train) [318][15/63] lr: 5.3099e-03 eta: 22:43:57 time: 2.5166 data_time: 0.0552 memory: 16201 loss_prob: 0.6997 loss_thr: 0.4298 loss_db: 0.1148 loss: 1.2443 2022/08/30 07:44:50 - mmengine - INFO - Epoch(train) [318][20/63] lr: 5.3099e-03 eta: 22:44:14 time: 2.6405 data_time: 0.0470 memory: 16201 loss_prob: 0.7108 loss_thr: 0.4240 loss_db: 0.1172 loss: 1.2520 2022/08/30 07:45:01 - mmengine - INFO - Epoch(train) [318][25/63] lr: 5.3099e-03 eta: 22:44:14 time: 2.5227 data_time: 0.0655 memory: 16201 loss_prob: 0.6711 loss_thr: 0.4210 loss_db: 0.1146 loss: 1.2067 2022/08/30 07:45:12 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:45:13 - mmengine - INFO - Epoch(train) [318][30/63] lr: 5.3099e-03 eta: 22:44:25 time: 2.3661 data_time: 0.0604 memory: 16201 loss_prob: 0.7165 loss_thr: 0.4301 loss_db: 0.1210 loss: 1.2676 2022/08/30 07:45:26 - mmengine - INFO - Epoch(train) [318][35/63] lr: 5.3099e-03 eta: 22:44:25 time: 2.4794 data_time: 0.0583 memory: 16201 loss_prob: 0.7231 loss_thr: 0.4484 loss_db: 0.1212 loss: 1.2926 2022/08/30 07:45:39 - mmengine - INFO - Epoch(train) [318][40/63] lr: 5.3099e-03 eta: 22:44:40 time: 2.5567 data_time: 0.0499 memory: 16201 loss_prob: 0.6441 loss_thr: 0.4058 loss_db: 0.1089 loss: 1.1588 2022/08/30 07:45:52 - mmengine - INFO - Epoch(train) [318][45/63] lr: 5.3099e-03 eta: 22:44:40 time: 2.5723 data_time: 0.0554 memory: 16201 loss_prob: 0.6693 loss_thr: 0.4133 loss_db: 0.1126 loss: 1.1951 2022/08/30 07:46:04 - mmengine - INFO - Epoch(train) [318][50/63] lr: 5.3099e-03 eta: 22:44:55 time: 2.5462 data_time: 0.0634 memory: 16201 loss_prob: 0.6652 loss_thr: 0.4378 loss_db: 0.1134 loss: 1.2164 2022/08/30 07:46:18 - mmengine - INFO - Epoch(train) [318][55/63] lr: 5.3099e-03 eta: 22:44:55 time: 2.6869 data_time: 0.0574 memory: 16201 loss_prob: 0.6396 loss_thr: 0.4307 loss_db: 0.1116 loss: 1.1820 2022/08/30 07:46:31 - mmengine - INFO - Epoch(train) [318][60/63] lr: 5.3099e-03 eta: 22:45:14 time: 2.7014 data_time: 0.0618 memory: 16201 loss_prob: 0.6828 loss_thr: 0.4250 loss_db: 0.1129 loss: 1.2207 2022/08/30 07:46:37 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:46:52 - mmengine - INFO - Epoch(train) [319][5/63] lr: 5.3045e-03 eta: 22:45:14 time: 2.6493 data_time: 0.2667 memory: 16201 loss_prob: 0.6888 loss_thr: 0.4377 loss_db: 0.1147 loss: 1.2412 2022/08/30 07:47:05 - mmengine - INFO - Epoch(train) [319][10/63] lr: 5.3045e-03 eta: 22:45:20 time: 2.7943 data_time: 0.2641 memory: 16201 loss_prob: 0.7357 loss_thr: 0.4599 loss_db: 0.1236 loss: 1.3191 2022/08/30 07:47:18 - mmengine - INFO - Epoch(train) [319][15/63] lr: 5.3045e-03 eta: 22:45:20 time: 2.5296 data_time: 0.0634 memory: 16201 loss_prob: 0.7457 loss_thr: 0.4555 loss_db: 0.1230 loss: 1.3241 2022/08/30 07:47:31 - mmengine - INFO - Epoch(train) [319][20/63] lr: 5.3045e-03 eta: 22:45:36 time: 2.6171 data_time: 0.0653 memory: 16201 loss_prob: 0.7119 loss_thr: 0.4513 loss_db: 0.1206 loss: 1.2838 2022/08/30 07:47:44 - mmengine - INFO - Epoch(train) [319][25/63] lr: 5.3045e-03 eta: 22:45:36 time: 2.6417 data_time: 0.0610 memory: 16201 loss_prob: 0.6930 loss_thr: 0.4405 loss_db: 0.1188 loss: 1.2523 2022/08/30 07:47:56 - mmengine - INFO - Epoch(train) [319][30/63] lr: 5.3045e-03 eta: 22:45:51 time: 2.5184 data_time: 0.0629 memory: 16201 loss_prob: 0.6452 loss_thr: 0.4208 loss_db: 0.1112 loss: 1.1772 2022/08/30 07:48:10 - mmengine - INFO - Epoch(train) [319][35/63] lr: 5.3045e-03 eta: 22:45:51 time: 2.5778 data_time: 0.0611 memory: 16201 loss_prob: 0.6504 loss_thr: 0.4315 loss_db: 0.1138 loss: 1.1956 2022/08/30 07:48:24 - mmengine - INFO - Epoch(train) [319][40/63] lr: 5.3045e-03 eta: 22:46:11 time: 2.7567 data_time: 0.0603 memory: 16201 loss_prob: 0.6572 loss_thr: 0.4355 loss_db: 0.1130 loss: 1.2057 2022/08/30 07:48:36 - mmengine - INFO - Epoch(train) [319][45/63] lr: 5.3045e-03 eta: 22:46:11 time: 2.6289 data_time: 0.0684 memory: 16201 loss_prob: 0.7402 loss_thr: 0.4524 loss_db: 0.1217 loss: 1.3143 2022/08/30 07:48:48 - mmengine - INFO - Epoch(train) [319][50/63] lr: 5.3045e-03 eta: 22:46:22 time: 2.3950 data_time: 0.0679 memory: 16201 loss_prob: 0.7411 loss_thr: 0.4608 loss_db: 0.1245 loss: 1.3264 2022/08/30 07:49:02 - mmengine - INFO - Epoch(train) [319][55/63] lr: 5.3045e-03 eta: 22:46:22 time: 2.6290 data_time: 0.0637 memory: 16201 loss_prob: 0.6094 loss_thr: 0.4235 loss_db: 0.1047 loss: 1.1376 2022/08/30 07:49:16 - mmengine - INFO - Epoch(train) [319][60/63] lr: 5.3045e-03 eta: 22:46:44 time: 2.8223 data_time: 0.0626 memory: 16201 loss_prob: 0.6871 loss_thr: 0.4568 loss_db: 0.1144 loss: 1.2583 2022/08/30 07:49:22 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:49:37 - mmengine - INFO - Epoch(train) [320][5/63] lr: 5.2990e-03 eta: 22:46:44 time: 2.7103 data_time: 0.3022 memory: 16201 loss_prob: 0.7231 loss_thr: 0.4727 loss_db: 0.1239 loss: 1.3197 2022/08/30 07:49:51 - mmengine - INFO - Epoch(train) [320][10/63] lr: 5.2990e-03 eta: 22:46:52 time: 2.8953 data_time: 0.3055 memory: 16201 loss_prob: 0.6601 loss_thr: 0.4230 loss_db: 0.1087 loss: 1.1919 2022/08/30 07:50:04 - mmengine - INFO - Epoch(train) [320][15/63] lr: 5.2990e-03 eta: 22:46:52 time: 2.6477 data_time: 0.0683 memory: 16201 loss_prob: 0.6936 loss_thr: 0.4316 loss_db: 0.1154 loss: 1.2406 2022/08/30 07:50:18 - mmengine - INFO - Epoch(train) [320][20/63] lr: 5.2990e-03 eta: 22:47:09 time: 2.6402 data_time: 0.0771 memory: 16201 loss_prob: 0.7549 loss_thr: 0.4603 loss_db: 0.1289 loss: 1.3441 2022/08/30 07:50:32 - mmengine - INFO - Epoch(train) [320][25/63] lr: 5.2990e-03 eta: 22:47:09 time: 2.8212 data_time: 0.0621 memory: 16201 loss_prob: 0.7049 loss_thr: 0.4530 loss_db: 0.1207 loss: 1.2786 2022/08/30 07:50:46 - mmengine - INFO - Epoch(train) [320][30/63] lr: 5.2990e-03 eta: 22:47:31 time: 2.8202 data_time: 0.0614 memory: 16201 loss_prob: 0.6277 loss_thr: 0.4192 loss_db: 0.1077 loss: 1.1546 2022/08/30 07:50:59 - mmengine - INFO - Epoch(train) [320][35/63] lr: 5.2990e-03 eta: 22:47:31 time: 2.7169 data_time: 0.0793 memory: 16201 loss_prob: 0.6957 loss_thr: 0.4434 loss_db: 0.1187 loss: 1.2578 2022/08/30 07:51:14 - mmengine - INFO - Epoch(train) [320][40/63] lr: 5.2990e-03 eta: 22:47:52 time: 2.7702 data_time: 0.0616 memory: 16201 loss_prob: 0.6833 loss_thr: 0.4387 loss_db: 0.1191 loss: 1.2412 2022/08/30 07:51:26 - mmengine - INFO - Epoch(train) [320][45/63] lr: 5.2990e-03 eta: 22:47:52 time: 2.6622 data_time: 0.0590 memory: 16201 loss_prob: 0.6615 loss_thr: 0.4264 loss_db: 0.1139 loss: 1.2018 2022/08/30 07:51:39 - mmengine - INFO - Epoch(train) [320][50/63] lr: 5.2990e-03 eta: 22:48:07 time: 2.5513 data_time: 0.0649 memory: 16201 loss_prob: 0.6946 loss_thr: 0.4562 loss_db: 0.1159 loss: 1.2668 2022/08/30 07:51:53 - mmengine - INFO - Epoch(train) [320][55/63] lr: 5.2990e-03 eta: 22:48:07 time: 2.7178 data_time: 0.0570 memory: 16201 loss_prob: 0.6957 loss_thr: 0.4556 loss_db: 0.1201 loss: 1.2714 2022/08/30 07:52:06 - mmengine - INFO - Epoch(train) [320][60/63] lr: 5.2990e-03 eta: 22:48:25 time: 2.6742 data_time: 0.0596 memory: 16201 loss_prob: 0.6811 loss_thr: 0.4380 loss_db: 0.1197 loss: 1.2387 2022/08/30 07:52:12 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:52:12 - mmengine - INFO - Saving checkpoint at 320 epochs 2022/08/30 07:52:21 - mmengine - INFO - Epoch(val) [320][5/32] eta: 22:48:25 time: 0.7262 data_time: 0.1572 memory: 16201 2022/08/30 07:52:25 - mmengine - INFO - Epoch(val) [320][10/32] eta: 0:00:17 time: 0.8067 data_time: 0.1958 memory: 15734 2022/08/30 07:52:28 - mmengine - INFO - Epoch(val) [320][15/32] eta: 0:00:17 time: 0.6553 data_time: 0.0630 memory: 15734 2022/08/30 07:52:31 - mmengine - INFO - Epoch(val) [320][20/32] eta: 0:00:07 time: 0.6463 data_time: 0.0593 memory: 15734 2022/08/30 07:52:35 - mmengine - INFO - Epoch(val) [320][25/32] eta: 0:00:07 time: 0.7105 data_time: 0.0637 memory: 15734 2022/08/30 07:52:38 - mmengine - INFO - Epoch(val) [320][30/32] eta: 0:00:01 time: 0.6826 data_time: 0.0451 memory: 15734 2022/08/30 07:52:39 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 07:52:39 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8185, precision: 0.7944, hmean: 0.8063 2022/08/30 07:52:39 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8180, precision: 0.8386, hmean: 0.8282 2022/08/30 07:52:39 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8137, precision: 0.8707, hmean: 0.8412 2022/08/30 07:52:39 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8002, precision: 0.8921, hmean: 0.8437 2022/08/30 07:52:39 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.7689, precision: 0.9152, hmean: 0.8357 2022/08/30 07:52:39 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.6081, precision: 0.9496, hmean: 0.7414 2022/08/30 07:52:39 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0448, precision: 0.9894, hmean: 0.0857 2022/08/30 07:52:39 - mmengine - INFO - Epoch(val) [320][32/32] icdar/precision: 0.8921 icdar/recall: 0.8002 icdar/hmean: 0.8437 2022/08/30 07:52:54 - mmengine - INFO - Epoch(train) [321][5/63] lr: 5.2936e-03 eta: 0:00:01 time: 2.6364 data_time: 0.2757 memory: 16201 loss_prob: 0.7237 loss_thr: 0.4751 loss_db: 0.1205 loss: 1.3193 2022/08/30 07:53:06 - mmengine - INFO - Epoch(train) [321][10/63] lr: 5.2936e-03 eta: 22:48:27 time: 2.7002 data_time: 0.2878 memory: 16201 loss_prob: 0.7155 loss_thr: 0.4649 loss_db: 0.1226 loss: 1.3030 2022/08/30 07:53:19 - mmengine - INFO - Epoch(train) [321][15/63] lr: 5.2936e-03 eta: 22:48:27 time: 2.4669 data_time: 0.0566 memory: 16201 loss_prob: 0.7205 loss_thr: 0.4477 loss_db: 0.1230 loss: 1.2912 2022/08/30 07:53:32 - mmengine - INFO - Epoch(train) [321][20/63] lr: 5.2936e-03 eta: 22:48:44 time: 2.6231 data_time: 0.0672 memory: 16201 loss_prob: 0.7324 loss_thr: 0.4506 loss_db: 0.1224 loss: 1.3054 2022/08/30 07:53:45 - mmengine - INFO - Epoch(train) [321][25/63] lr: 5.2936e-03 eta: 22:48:44 time: 2.5685 data_time: 0.0794 memory: 16201 loss_prob: 0.6389 loss_thr: 0.4139 loss_db: 0.1091 loss: 1.1619 2022/08/30 07:53:58 - mmengine - INFO - Epoch(train) [321][30/63] lr: 5.2936e-03 eta: 22:48:59 time: 2.5876 data_time: 0.0501 memory: 16201 loss_prob: 0.6199 loss_thr: 0.4003 loss_db: 0.1074 loss: 1.1275 2022/08/30 07:54:11 - mmengine - INFO - Epoch(train) [321][35/63] lr: 5.2936e-03 eta: 22:48:59 time: 2.6275 data_time: 0.0665 memory: 16201 loss_prob: 0.6917 loss_thr: 0.4400 loss_db: 0.1158 loss: 1.2476 2022/08/30 07:54:24 - mmengine - INFO - Epoch(train) [321][40/63] lr: 5.2936e-03 eta: 22:49:14 time: 2.5850 data_time: 0.0603 memory: 16201 loss_prob: 0.7327 loss_thr: 0.4693 loss_db: 0.1204 loss: 1.3223 2022/08/30 07:54:39 - mmengine - INFO - Epoch(train) [321][45/63] lr: 5.2936e-03 eta: 22:49:14 time: 2.7977 data_time: 0.0545 memory: 16201 loss_prob: 0.7317 loss_thr: 0.4433 loss_db: 0.1237 loss: 1.2987 2022/08/30 07:54:53 - mmengine - INFO - Epoch(train) [321][50/63] lr: 5.2936e-03 eta: 22:49:39 time: 2.9265 data_time: 0.0694 memory: 16201 loss_prob: 0.7210 loss_thr: 0.4401 loss_db: 0.1227 loss: 1.2838 2022/08/30 07:55:05 - mmengine - INFO - Epoch(train) [321][55/63] lr: 5.2936e-03 eta: 22:49:39 time: 2.6230 data_time: 0.0432 memory: 16201 loss_prob: 0.7118 loss_thr: 0.4590 loss_db: 0.1204 loss: 1.2912 2022/08/30 07:55:17 - mmengine - INFO - Epoch(train) [321][60/63] lr: 5.2936e-03 eta: 22:49:48 time: 2.3633 data_time: 0.0436 memory: 16201 loss_prob: 0.6625 loss_thr: 0.4239 loss_db: 0.1138 loss: 1.2002 2022/08/30 07:55:23 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:55:40 - mmengine - INFO - Epoch(train) [322][5/63] lr: 5.2882e-03 eta: 22:49:48 time: 2.7263 data_time: 0.2820 memory: 16201 loss_prob: 0.6690 loss_thr: 0.4488 loss_db: 0.1135 loss: 1.2312 2022/08/30 07:55:54 - mmengine - INFO - Epoch(train) [322][10/63] lr: 5.2882e-03 eta: 22:50:00 time: 3.0581 data_time: 0.2994 memory: 16201 loss_prob: 0.7339 loss_thr: 0.4602 loss_db: 0.1254 loss: 1.3195 2022/08/30 07:56:07 - mmengine - INFO - Epoch(train) [322][15/63] lr: 5.2882e-03 eta: 22:50:00 time: 2.7323 data_time: 0.0522 memory: 16201 loss_prob: 0.7521 loss_thr: 0.4601 loss_db: 0.1266 loss: 1.3388 2022/08/30 07:56:21 - mmengine - INFO - Epoch(train) [322][20/63] lr: 5.2882e-03 eta: 22:50:20 time: 2.7430 data_time: 0.0801 memory: 16201 loss_prob: 0.8102 loss_thr: 0.4825 loss_db: 0.1275 loss: 1.4202 2022/08/30 07:56:34 - mmengine - INFO - Epoch(train) [322][25/63] lr: 5.2882e-03 eta: 22:50:20 time: 2.7040 data_time: 0.0890 memory: 16201 loss_prob: 0.8619 loss_thr: 0.5172 loss_db: 0.1397 loss: 1.5188 2022/08/30 07:56:48 - mmengine - INFO - Epoch(train) [322][30/63] lr: 5.2882e-03 eta: 22:50:36 time: 2.6336 data_time: 0.0690 memory: 16201 loss_prob: 0.8139 loss_thr: 0.5042 loss_db: 0.1400 loss: 1.4581 2022/08/30 07:57:01 - mmengine - INFO - Epoch(train) [322][35/63] lr: 5.2882e-03 eta: 22:50:36 time: 2.6422 data_time: 0.0741 memory: 16201 loss_prob: 0.7853 loss_thr: 0.4666 loss_db: 0.1298 loss: 1.3818 2022/08/30 07:57:13 - mmengine - INFO - Epoch(train) [322][40/63] lr: 5.2882e-03 eta: 22:50:50 time: 2.5386 data_time: 0.0654 memory: 16201 loss_prob: 0.7675 loss_thr: 0.4623 loss_db: 0.1275 loss: 1.3572 2022/08/30 07:57:26 - mmengine - INFO - Epoch(train) [322][45/63] lr: 5.2882e-03 eta: 22:50:50 time: 2.5545 data_time: 0.0543 memory: 16201 loss_prob: 0.7758 loss_thr: 0.4637 loss_db: 0.1313 loss: 1.3708 2022/08/30 07:57:40 - mmengine - INFO - Epoch(train) [322][50/63] lr: 5.2882e-03 eta: 22:51:08 time: 2.6931 data_time: 0.0549 memory: 16201 loss_prob: 0.7807 loss_thr: 0.4440 loss_db: 0.1291 loss: 1.3538 2022/08/30 07:57:54 - mmengine - INFO - Epoch(train) [322][55/63] lr: 5.2882e-03 eta: 22:51:08 time: 2.7486 data_time: 0.0689 memory: 16201 loss_prob: 0.7512 loss_thr: 0.4391 loss_db: 0.1220 loss: 1.3123 2022/08/30 07:58:07 - mmengine - INFO - Epoch(train) [322][60/63] lr: 5.2882e-03 eta: 22:51:26 time: 2.7041 data_time: 0.0707 memory: 16201 loss_prob: 0.7922 loss_thr: 0.4592 loss_db: 0.1290 loss: 1.3804 2022/08/30 07:58:13 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 07:58:30 - mmengine - INFO - Epoch(train) [323][5/63] lr: 5.2828e-03 eta: 22:51:26 time: 2.8765 data_time: 0.2660 memory: 16201 loss_prob: 0.8089 loss_thr: 0.4894 loss_db: 0.1338 loss: 1.4321 2022/08/30 07:58:44 - mmengine - INFO - Epoch(train) [323][10/63] lr: 5.2828e-03 eta: 22:51:38 time: 3.0638 data_time: 0.2853 memory: 16201 loss_prob: 0.7752 loss_thr: 0.5002 loss_db: 0.1303 loss: 1.4057 2022/08/30 07:58:58 - mmengine - INFO - Epoch(train) [323][15/63] lr: 5.2828e-03 eta: 22:51:38 time: 2.7133 data_time: 0.0559 memory: 16201 loss_prob: 0.7339 loss_thr: 0.4815 loss_db: 0.1255 loss: 1.3410 2022/08/30 07:59:10 - mmengine - INFO - Epoch(train) [323][20/63] lr: 5.2828e-03 eta: 22:51:55 time: 2.6666 data_time: 0.0573 memory: 16201 loss_prob: 0.6635 loss_thr: 0.4475 loss_db: 0.1155 loss: 1.2265 2022/08/30 07:59:24 - mmengine - INFO - Epoch(train) [323][25/63] lr: 5.2828e-03 eta: 22:51:55 time: 2.6546 data_time: 0.0609 memory: 16201 loss_prob: 0.6604 loss_thr: 0.4403 loss_db: 0.1134 loss: 1.2142 2022/08/30 07:59:37 - mmengine - INFO - Epoch(train) [323][30/63] lr: 5.2828e-03 eta: 22:52:11 time: 2.6300 data_time: 0.0525 memory: 16201 loss_prob: 0.6629 loss_thr: 0.4455 loss_db: 0.1112 loss: 1.2196 2022/08/30 07:59:49 - mmengine - INFO - Epoch(train) [323][35/63] lr: 5.2828e-03 eta: 22:52:11 time: 2.4594 data_time: 0.0778 memory: 16201 loss_prob: 0.6704 loss_thr: 0.4436 loss_db: 0.1156 loss: 1.2296 2022/08/30 08:00:01 - mmengine - INFO - Epoch(train) [323][40/63] lr: 5.2828e-03 eta: 22:52:23 time: 2.4693 data_time: 0.0632 memory: 16201 loss_prob: 0.6448 loss_thr: 0.4321 loss_db: 0.1110 loss: 1.1879 2022/08/30 08:00:13 - mmengine - INFO - Epoch(train) [323][45/63] lr: 5.2828e-03 eta: 22:52:23 time: 2.4692 data_time: 0.0760 memory: 16201 loss_prob: 0.5958 loss_thr: 0.4195 loss_db: 0.1018 loss: 1.1171 2022/08/30 08:00:27 - mmengine - INFO - Epoch(train) [323][50/63] lr: 5.2828e-03 eta: 22:52:37 time: 2.5384 data_time: 0.0802 memory: 16201 loss_prob: 0.5924 loss_thr: 0.4371 loss_db: 0.1036 loss: 1.1332 2022/08/30 08:00:40 - mmengine - INFO - Epoch(train) [323][55/63] lr: 5.2828e-03 eta: 22:52:37 time: 2.6394 data_time: 0.0479 memory: 16201 loss_prob: 0.6827 loss_thr: 0.4730 loss_db: 0.1149 loss: 1.2705 2022/08/30 08:00:50 - mmengine - INFO - Epoch(train) [323][60/63] lr: 5.2828e-03 eta: 22:52:45 time: 2.3374 data_time: 0.0549 memory: 16201 loss_prob: 0.7617 loss_thr: 0.4641 loss_db: 0.1227 loss: 1.3485 2022/08/30 08:00:57 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:01:13 - mmengine - INFO - Epoch(train) [324][5/63] lr: 5.2774e-03 eta: 22:52:45 time: 2.6312 data_time: 0.2794 memory: 16201 loss_prob: 0.6557 loss_thr: 0.4619 loss_db: 0.1123 loss: 1.2299 2022/08/30 08:01:26 - mmengine - INFO - Epoch(train) [324][10/63] lr: 5.2774e-03 eta: 22:52:52 time: 2.9183 data_time: 0.2988 memory: 16201 loss_prob: 0.6488 loss_thr: 0.4551 loss_db: 0.1121 loss: 1.2161 2022/08/30 08:01:38 - mmengine - INFO - Epoch(train) [324][15/63] lr: 5.2774e-03 eta: 22:52:52 time: 2.5345 data_time: 0.0651 memory: 16201 loss_prob: 0.6829 loss_thr: 0.4362 loss_db: 0.1167 loss: 1.2358 2022/08/30 08:01:52 - mmengine - INFO - Epoch(train) [324][20/63] lr: 5.2774e-03 eta: 22:53:07 time: 2.5794 data_time: 0.0656 memory: 16201 loss_prob: 0.6214 loss_thr: 0.3967 loss_db: 0.1059 loss: 1.1240 2022/08/30 08:02:04 - mmengine - INFO - Epoch(train) [324][25/63] lr: 5.2774e-03 eta: 22:53:07 time: 2.6361 data_time: 0.0650 memory: 16201 loss_prob: 0.6546 loss_thr: 0.4230 loss_db: 0.1106 loss: 1.1883 2022/08/30 08:02:17 - mmengine - INFO - Epoch(train) [324][30/63] lr: 5.2774e-03 eta: 22:53:20 time: 2.5254 data_time: 0.0622 memory: 16201 loss_prob: 0.7189 loss_thr: 0.4586 loss_db: 0.1209 loss: 1.2985 2022/08/30 08:02:29 - mmengine - INFO - Epoch(train) [324][35/63] lr: 5.2774e-03 eta: 22:53:20 time: 2.4381 data_time: 0.0711 memory: 16201 loss_prob: 0.6982 loss_thr: 0.4216 loss_db: 0.1178 loss: 1.2376 2022/08/30 08:02:43 - mmengine - INFO - Epoch(train) [324][40/63] lr: 5.2774e-03 eta: 22:53:34 time: 2.5607 data_time: 0.0539 memory: 16201 loss_prob: 0.6977 loss_thr: 0.4298 loss_db: 0.1200 loss: 1.2476 2022/08/30 08:02:56 - mmengine - INFO - Epoch(train) [324][45/63] lr: 5.2774e-03 eta: 22:53:34 time: 2.7346 data_time: 0.0483 memory: 16201 loss_prob: 0.6889 loss_thr: 0.4449 loss_db: 0.1160 loss: 1.2499 2022/08/30 08:03:11 - mmengine - INFO - Epoch(train) [324][50/63] lr: 5.2774e-03 eta: 22:53:54 time: 2.7980 data_time: 0.0606 memory: 16201 loss_prob: 0.7010 loss_thr: 0.4499 loss_db: 0.1175 loss: 1.2684 2022/08/30 08:03:24 - mmengine - INFO - Epoch(train) [324][55/63] lr: 5.2774e-03 eta: 22:53:54 time: 2.7801 data_time: 0.0568 memory: 16201 loss_prob: 0.7986 loss_thr: 0.4893 loss_db: 0.1326 loss: 1.4205 2022/08/30 08:03:37 - mmengine - INFO - Epoch(train) [324][60/63] lr: 5.2774e-03 eta: 22:54:10 time: 2.6498 data_time: 0.0587 memory: 16201 loss_prob: 0.7974 loss_thr: 0.4763 loss_db: 0.1335 loss: 1.4072 2022/08/30 08:03:44 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:04:01 - mmengine - INFO - Epoch(train) [325][5/63] lr: 5.2719e-03 eta: 22:54:10 time: 2.8166 data_time: 0.2909 memory: 16201 loss_prob: 0.7226 loss_thr: 0.4433 loss_db: 0.1251 loss: 1.2909 2022/08/30 08:04:15 - mmengine - INFO - Epoch(train) [325][10/63] lr: 5.2719e-03 eta: 22:54:23 time: 3.1265 data_time: 0.3044 memory: 16201 loss_prob: 0.6764 loss_thr: 0.4398 loss_db: 0.1148 loss: 1.2310 2022/08/30 08:04:28 - mmengine - INFO - Epoch(train) [325][15/63] lr: 5.2719e-03 eta: 22:54:23 time: 2.6573 data_time: 0.0592 memory: 16201 loss_prob: 0.6499 loss_thr: 0.4397 loss_db: 0.1115 loss: 1.2012 2022/08/30 08:04:40 - mmengine - INFO - Epoch(train) [325][20/63] lr: 5.2719e-03 eta: 22:54:35 time: 2.4917 data_time: 0.0602 memory: 16201 loss_prob: 0.6326 loss_thr: 0.4307 loss_db: 0.1103 loss: 1.1736 2022/08/30 08:04:52 - mmengine - INFO - Epoch(train) [325][25/63] lr: 5.2719e-03 eta: 22:54:35 time: 2.3943 data_time: 0.0666 memory: 16201 loss_prob: 0.6943 loss_thr: 0.4403 loss_db: 0.1190 loss: 1.2535 2022/08/30 08:05:05 - mmengine - INFO - Epoch(train) [325][30/63] lr: 5.2719e-03 eta: 22:54:48 time: 2.5471 data_time: 0.0524 memory: 16201 loss_prob: 0.6506 loss_thr: 0.4007 loss_db: 0.1103 loss: 1.1616 2022/08/30 08:05:17 - mmengine - INFO - Epoch(train) [325][35/63] lr: 5.2719e-03 eta: 22:54:48 time: 2.5646 data_time: 0.0612 memory: 16201 loss_prob: 0.6398 loss_thr: 0.4244 loss_db: 0.1092 loss: 1.1735 2022/08/30 08:05:30 - mmengine - INFO - Epoch(train) [325][40/63] lr: 5.2719e-03 eta: 22:55:00 time: 2.4724 data_time: 0.0617 memory: 16201 loss_prob: 0.7243 loss_thr: 0.4566 loss_db: 0.1241 loss: 1.3050 2022/08/30 08:05:41 - mmengine - INFO - Epoch(train) [325][45/63] lr: 5.2719e-03 eta: 22:55:00 time: 2.3913 data_time: 0.0593 memory: 16201 loss_prob: 0.7089 loss_thr: 0.4386 loss_db: 0.1229 loss: 1.2705 2022/08/30 08:05:54 - mmengine - INFO - Epoch(train) [325][50/63] lr: 5.2719e-03 eta: 22:55:10 time: 2.4441 data_time: 0.0656 memory: 16201 loss_prob: 0.7119 loss_thr: 0.4465 loss_db: 0.1209 loss: 1.2792 2022/08/30 08:06:08 - mmengine - INFO - Epoch(train) [325][55/63] lr: 5.2719e-03 eta: 22:55:10 time: 2.6280 data_time: 0.0521 memory: 16201 loss_prob: 0.6818 loss_thr: 0.4226 loss_db: 0.1115 loss: 1.2159 2022/08/30 08:06:20 - mmengine - INFO - Epoch(train) [325][60/63] lr: 5.2719e-03 eta: 22:55:24 time: 2.5696 data_time: 0.0548 memory: 16201 loss_prob: 0.6898 loss_thr: 0.4287 loss_db: 0.1170 loss: 1.2355 2022/08/30 08:06:26 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:06:42 - mmengine - INFO - Epoch(train) [326][5/63] lr: 5.2665e-03 eta: 22:55:24 time: 2.6944 data_time: 0.2746 memory: 16201 loss_prob: 0.7199 loss_thr: 0.4524 loss_db: 0.1232 loss: 1.2955 2022/08/30 08:06:56 - mmengine - INFO - Epoch(train) [326][10/63] lr: 5.2665e-03 eta: 22:55:32 time: 2.9566 data_time: 0.2910 memory: 16201 loss_prob: 0.7040 loss_thr: 0.4513 loss_db: 0.1191 loss: 1.2745 2022/08/30 08:07:08 - mmengine - INFO - Epoch(train) [326][15/63] lr: 5.2665e-03 eta: 22:55:32 time: 2.5674 data_time: 0.0589 memory: 16201 loss_prob: 0.6795 loss_thr: 0.4260 loss_db: 0.1136 loss: 1.2191 2022/08/30 08:07:22 - mmengine - INFO - Epoch(train) [326][20/63] lr: 5.2665e-03 eta: 22:55:46 time: 2.5700 data_time: 0.0548 memory: 16201 loss_prob: 0.6415 loss_thr: 0.4137 loss_db: 0.1077 loss: 1.1629 2022/08/30 08:07:33 - mmengine - INFO - Epoch(train) [326][25/63] lr: 5.2665e-03 eta: 22:55:46 time: 2.4527 data_time: 0.0763 memory: 16201 loss_prob: 0.6130 loss_thr: 0.4044 loss_db: 0.1044 loss: 1.1218 2022/08/30 08:07:48 - mmengine - INFO - Epoch(train) [326][30/63] lr: 5.2665e-03 eta: 22:56:00 time: 2.5942 data_time: 0.0663 memory: 16201 loss_prob: 0.7723 loss_thr: 0.3991 loss_db: 0.1300 loss: 1.3014 2022/08/30 08:08:01 - mmengine - INFO - Epoch(train) [326][35/63] lr: 5.2665e-03 eta: 22:56:00 time: 2.8229 data_time: 0.0619 memory: 16201 loss_prob: 0.8100 loss_thr: 0.4175 loss_db: 0.1317 loss: 1.3592 2022/08/30 08:08:15 - mmengine - INFO - Epoch(train) [326][40/63] lr: 5.2665e-03 eta: 22:56:18 time: 2.7021 data_time: 0.0603 memory: 16201 loss_prob: 0.6931 loss_thr: 0.4177 loss_db: 0.1104 loss: 1.2211 2022/08/30 08:08:27 - mmengine - INFO - Epoch(train) [326][45/63] lr: 5.2665e-03 eta: 22:56:18 time: 2.6420 data_time: 0.0456 memory: 16201 loss_prob: 0.7719 loss_thr: 0.4567 loss_db: 0.1253 loss: 1.3539 2022/08/30 08:08:42 - mmengine - INFO - Epoch(train) [326][50/63] lr: 5.2665e-03 eta: 22:56:34 time: 2.6809 data_time: 0.0763 memory: 16201 loss_prob: 0.8077 loss_thr: 0.4633 loss_db: 0.1344 loss: 1.4054 2022/08/30 08:08:52 - mmengine - INFO - Epoch(train) [326][55/63] lr: 5.2665e-03 eta: 22:56:34 time: 2.4285 data_time: 0.0816 memory: 16201 loss_prob: 0.9265 loss_thr: 0.4812 loss_db: 0.1551 loss: 1.5628 2022/08/30 08:09:05 - mmengine - INFO - Epoch(train) [326][60/63] lr: 5.2665e-03 eta: 22:56:42 time: 2.3489 data_time: 0.0536 memory: 16201 loss_prob: 0.9311 loss_thr: 0.5002 loss_db: 0.1551 loss: 1.5864 2022/08/30 08:09:12 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:09:27 - mmengine - INFO - Epoch(train) [327][5/63] lr: 5.2611e-03 eta: 22:56:42 time: 2.7915 data_time: 0.2985 memory: 16201 loss_prob: 0.8163 loss_thr: 0.4732 loss_db: 0.1365 loss: 1.4260 2022/08/30 08:09:41 - mmengine - INFO - Epoch(train) [327][10/63] lr: 5.2611e-03 eta: 22:56:47 time: 2.8741 data_time: 0.3062 memory: 16201 loss_prob: 0.7966 loss_thr: 0.4786 loss_db: 0.1388 loss: 1.4139 2022/08/30 08:09:55 - mmengine - INFO - Epoch(train) [327][15/63] lr: 5.2611e-03 eta: 22:56:47 time: 2.7611 data_time: 0.0708 memory: 16201 loss_prob: 0.7801 loss_thr: 0.4709 loss_db: 0.1328 loss: 1.3838 2022/08/30 08:10:07 - mmengine - INFO - Epoch(train) [327][20/63] lr: 5.2611e-03 eta: 22:57:03 time: 2.6585 data_time: 0.0558 memory: 16201 loss_prob: 0.8323 loss_thr: 0.4741 loss_db: 0.1345 loss: 1.4409 2022/08/30 08:10:20 - mmengine - INFO - Epoch(train) [327][25/63] lr: 5.2611e-03 eta: 22:57:03 time: 2.5405 data_time: 0.0821 memory: 16201 loss_prob: 0.8245 loss_thr: 0.4753 loss_db: 0.1339 loss: 1.4338 2022/08/30 08:10:32 - mmengine - INFO - Epoch(train) [327][30/63] lr: 5.2611e-03 eta: 22:57:16 time: 2.5206 data_time: 0.0688 memory: 16201 loss_prob: 0.7656 loss_thr: 0.4846 loss_db: 0.1272 loss: 1.3773 2022/08/30 08:10:46 - mmengine - INFO - Epoch(train) [327][35/63] lr: 5.2611e-03 eta: 22:57:16 time: 2.6185 data_time: 0.0592 memory: 16201 loss_prob: 0.7386 loss_thr: 0.4710 loss_db: 0.1243 loss: 1.3339 2022/08/30 08:11:01 - mmengine - INFO - Epoch(train) [327][40/63] lr: 5.2611e-03 eta: 22:57:37 time: 2.8524 data_time: 0.0536 memory: 16201 loss_prob: 0.6859 loss_thr: 0.4297 loss_db: 0.1183 loss: 1.2339 2022/08/30 08:11:14 - mmengine - INFO - Epoch(train) [327][45/63] lr: 5.2611e-03 eta: 22:57:37 time: 2.7800 data_time: 0.0608 memory: 16201 loss_prob: 0.7378 loss_thr: 0.4500 loss_db: 0.1325 loss: 1.3203 2022/08/30 08:11:25 - mmengine - INFO - Epoch(train) [327][50/63] lr: 5.2611e-03 eta: 22:57:47 time: 2.4374 data_time: 0.0730 memory: 16201 loss_prob: 0.7700 loss_thr: 0.4772 loss_db: 0.1364 loss: 1.3836 2022/08/30 08:11:38 - mmengine - INFO - Epoch(train) [327][55/63] lr: 5.2611e-03 eta: 22:57:47 time: 2.4098 data_time: 0.0578 memory: 16201 loss_prob: 0.7832 loss_thr: 0.4756 loss_db: 0.1315 loss: 1.3903 2022/08/30 08:11:51 - mmengine - INFO - Epoch(train) [327][60/63] lr: 5.2611e-03 eta: 22:57:59 time: 2.5266 data_time: 0.0647 memory: 16201 loss_prob: 0.7895 loss_thr: 0.4551 loss_db: 0.1329 loss: 1.3776 2022/08/30 08:11:58 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:12:13 - mmengine - INFO - Epoch(train) [328][5/63] lr: 5.2557e-03 eta: 22:57:59 time: 2.5786 data_time: 0.2559 memory: 16201 loss_prob: 0.7629 loss_thr: 0.4790 loss_db: 0.1284 loss: 1.3703 2022/08/30 08:12:27 - mmengine - INFO - Epoch(train) [328][10/63] lr: 5.2557e-03 eta: 22:58:07 time: 2.9768 data_time: 0.3589 memory: 16201 loss_prob: 0.8327 loss_thr: 0.4939 loss_db: 0.1427 loss: 1.4693 2022/08/30 08:12:41 - mmengine - INFO - Epoch(train) [328][15/63] lr: 5.2557e-03 eta: 22:58:07 time: 2.8561 data_time: 0.1382 memory: 16201 loss_prob: 0.7246 loss_thr: 0.4538 loss_db: 0.1236 loss: 1.3020 2022/08/30 08:12:55 - mmengine - INFO - Epoch(train) [328][20/63] lr: 5.2557e-03 eta: 22:58:25 time: 2.7675 data_time: 0.0529 memory: 16201 loss_prob: 0.6209 loss_thr: 0.4155 loss_db: 0.1048 loss: 1.1412 2022/08/30 08:13:08 - mmengine - INFO - Epoch(train) [328][25/63] lr: 5.2557e-03 eta: 22:58:25 time: 2.7279 data_time: 0.0527 memory: 16201 loss_prob: 0.6519 loss_thr: 0.4299 loss_db: 0.1114 loss: 1.1932 2022/08/30 08:13:21 - mmengine - INFO - Epoch(train) [328][30/63] lr: 5.2557e-03 eta: 22:58:39 time: 2.5836 data_time: 0.0551 memory: 16201 loss_prob: 0.6705 loss_thr: 0.4389 loss_db: 0.1169 loss: 1.2264 2022/08/30 08:13:36 - mmengine - INFO - Epoch(train) [328][35/63] lr: 5.2557e-03 eta: 22:58:39 time: 2.7194 data_time: 0.0691 memory: 16201 loss_prob: 0.6433 loss_thr: 0.4260 loss_db: 0.1100 loss: 1.1794 2022/08/30 08:13:48 - mmengine - INFO - Epoch(train) [328][40/63] lr: 5.2557e-03 eta: 22:58:56 time: 2.7100 data_time: 0.0488 memory: 16201 loss_prob: 0.6868 loss_thr: 0.4474 loss_db: 0.1151 loss: 1.2492 2022/08/30 08:13:59 - mmengine - INFO - Epoch(train) [328][45/63] lr: 5.2557e-03 eta: 22:58:56 time: 2.3683 data_time: 0.0529 memory: 16201 loss_prob: 0.7465 loss_thr: 0.4961 loss_db: 0.1270 loss: 1.3695 2022/08/30 08:14:13 - mmengine - INFO - Epoch(train) [328][50/63] lr: 5.2557e-03 eta: 22:59:07 time: 2.4786 data_time: 0.0774 memory: 16201 loss_prob: 0.7530 loss_thr: 0.4738 loss_db: 0.1266 loss: 1.3533 2022/08/30 08:14:26 - mmengine - INFO - Epoch(train) [328][55/63] lr: 5.2557e-03 eta: 22:59:07 time: 2.6834 data_time: 0.0589 memory: 16201 loss_prob: 0.7004 loss_thr: 0.4200 loss_db: 0.1162 loss: 1.2366 2022/08/30 08:14:38 - mmengine - INFO - Epoch(train) [328][60/63] lr: 5.2557e-03 eta: 22:59:20 time: 2.5531 data_time: 0.0519 memory: 16201 loss_prob: 0.6844 loss_thr: 0.4354 loss_db: 0.1158 loss: 1.2356 2022/08/30 08:14:46 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:15:02 - mmengine - INFO - Epoch(train) [329][5/63] lr: 5.2502e-03 eta: 22:59:20 time: 2.8036 data_time: 0.2924 memory: 16201 loss_prob: 0.7614 loss_thr: 0.4675 loss_db: 0.1268 loss: 1.3556 2022/08/30 08:15:14 - mmengine - INFO - Epoch(train) [329][10/63] lr: 5.2502e-03 eta: 22:59:24 time: 2.8582 data_time: 0.2897 memory: 16201 loss_prob: 0.7070 loss_thr: 0.4459 loss_db: 0.1178 loss: 1.2707 2022/08/30 08:15:28 - mmengine - INFO - Epoch(train) [329][15/63] lr: 5.2502e-03 eta: 22:59:24 time: 2.6240 data_time: 0.0652 memory: 16201 loss_prob: 0.6724 loss_thr: 0.4469 loss_db: 0.1137 loss: 1.2329 2022/08/30 08:15:42 - mmengine - INFO - Epoch(train) [329][20/63] lr: 5.2502e-03 eta: 22:59:42 time: 2.7467 data_time: 0.0795 memory: 16201 loss_prob: 0.7049 loss_thr: 0.4582 loss_db: 0.1198 loss: 1.2829 2022/08/30 08:15:54 - mmengine - INFO - Epoch(train) [329][25/63] lr: 5.2502e-03 eta: 22:59:42 time: 2.5591 data_time: 0.0858 memory: 16201 loss_prob: 0.7001 loss_thr: 0.4495 loss_db: 0.1178 loss: 1.2673 2022/08/30 08:16:06 - mmengine - INFO - Epoch(train) [329][30/63] lr: 5.2502e-03 eta: 22:59:50 time: 2.3743 data_time: 0.0747 memory: 16201 loss_prob: 0.6504 loss_thr: 0.4245 loss_db: 0.1111 loss: 1.1860 2022/08/30 08:16:18 - mmengine - INFO - Epoch(train) [329][35/63] lr: 5.2502e-03 eta: 22:59:50 time: 2.4361 data_time: 0.0506 memory: 16201 loss_prob: 0.6943 loss_thr: 0.4282 loss_db: 0.1186 loss: 1.2411 2022/08/30 08:16:31 - mmengine - INFO - Epoch(train) [329][40/63] lr: 5.2502e-03 eta: 23:00:03 time: 2.5803 data_time: 0.0595 memory: 16201 loss_prob: 0.7040 loss_thr: 0.4410 loss_db: 0.1194 loss: 1.2644 2022/08/30 08:16:45 - mmengine - INFO - Epoch(train) [329][45/63] lr: 5.2502e-03 eta: 23:00:03 time: 2.6659 data_time: 0.0726 memory: 16201 loss_prob: 0.6562 loss_thr: 0.4146 loss_db: 0.1122 loss: 1.1829 2022/08/30 08:16:58 - mmengine - INFO - Epoch(train) [329][50/63] lr: 5.2502e-03 eta: 23:00:19 time: 2.6544 data_time: 0.0699 memory: 16201 loss_prob: 0.6730 loss_thr: 0.4189 loss_db: 0.1146 loss: 1.2065 2022/08/30 08:17:11 - mmengine - INFO - Epoch(train) [329][55/63] lr: 5.2502e-03 eta: 23:00:19 time: 2.6683 data_time: 0.0670 memory: 16201 loss_prob: 0.6593 loss_thr: 0.4389 loss_db: 0.1139 loss: 1.2121 2022/08/30 08:17:25 - mmengine - INFO - Epoch(train) [329][60/63] lr: 5.2502e-03 eta: 23:00:36 time: 2.7185 data_time: 0.0758 memory: 16201 loss_prob: 0.6341 loss_thr: 0.4217 loss_db: 0.1084 loss: 1.1642 2022/08/30 08:17:33 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:17:50 - mmengine - INFO - Epoch(train) [330][5/63] lr: 5.2448e-03 eta: 23:00:36 time: 3.0911 data_time: 0.2990 memory: 16201 loss_prob: 0.6958 loss_thr: 0.4604 loss_db: 0.1200 loss: 1.2762 2022/08/30 08:18:03 - mmengine - INFO - Epoch(train) [330][10/63] lr: 5.2448e-03 eta: 23:00:46 time: 3.0896 data_time: 0.3042 memory: 16201 loss_prob: 0.7603 loss_thr: 0.4603 loss_db: 0.1273 loss: 1.3478 2022/08/30 08:18:17 - mmengine - INFO - Epoch(train) [330][15/63] lr: 5.2448e-03 eta: 23:00:46 time: 2.7413 data_time: 0.0484 memory: 16201 loss_prob: 0.8121 loss_thr: 0.4696 loss_db: 0.1336 loss: 1.4153 2022/08/30 08:18:31 - mmengine - INFO - Epoch(train) [330][20/63] lr: 5.2448e-03 eta: 23:01:04 time: 2.7636 data_time: 0.0474 memory: 16201 loss_prob: 0.8605 loss_thr: 0.4680 loss_db: 0.1382 loss: 1.4667 2022/08/30 08:18:45 - mmengine - INFO - Epoch(train) [330][25/63] lr: 5.2448e-03 eta: 23:01:04 time: 2.7436 data_time: 0.0619 memory: 16201 loss_prob: 0.8361 loss_thr: 0.4614 loss_db: 0.1341 loss: 1.4316 2022/08/30 08:18:58 - mmengine - INFO - Epoch(train) [330][30/63] lr: 5.2448e-03 eta: 23:01:21 time: 2.7240 data_time: 0.0523 memory: 16201 loss_prob: 0.8155 loss_thr: 0.4794 loss_db: 0.1364 loss: 1.4314 2022/08/30 08:19:11 - mmengine - INFO - Epoch(train) [330][35/63] lr: 5.2448e-03 eta: 23:01:21 time: 2.6787 data_time: 0.0578 memory: 16201 loss_prob: 0.8294 loss_thr: 0.4919 loss_db: 0.1411 loss: 1.4624 2022/08/30 08:19:24 - mmengine - INFO - Epoch(train) [330][40/63] lr: 5.2448e-03 eta: 23:01:33 time: 2.5489 data_time: 0.0483 memory: 16201 loss_prob: 0.7203 loss_thr: 0.4701 loss_db: 0.1248 loss: 1.3152 2022/08/30 08:19:37 - mmengine - INFO - Epoch(train) [330][45/63] lr: 5.2448e-03 eta: 23:01:33 time: 2.5341 data_time: 0.0476 memory: 16201 loss_prob: 0.6820 loss_thr: 0.4380 loss_db: 0.1186 loss: 1.2386 2022/08/30 08:19:49 - mmengine - INFO - Epoch(train) [330][50/63] lr: 5.2448e-03 eta: 23:01:45 time: 2.5313 data_time: 0.0711 memory: 16201 loss_prob: 0.6614 loss_thr: 0.4108 loss_db: 0.1128 loss: 1.1850 2022/08/30 08:20:03 - mmengine - INFO - Epoch(train) [330][55/63] lr: 5.2448e-03 eta: 23:01:45 time: 2.6049 data_time: 0.0541 memory: 16201 loss_prob: 0.6652 loss_thr: 0.4129 loss_db: 0.1095 loss: 1.1875 2022/08/30 08:20:17 - mmengine - INFO - Epoch(train) [330][60/63] lr: 5.2448e-03 eta: 23:02:03 time: 2.7935 data_time: 0.0554 memory: 16201 loss_prob: 0.6773 loss_thr: 0.4135 loss_db: 0.1143 loss: 1.2051 2022/08/30 08:20:22 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:20:36 - mmengine - INFO - Epoch(train) [331][5/63] lr: 5.2394e-03 eta: 23:02:03 time: 2.4740 data_time: 0.2971 memory: 16201 loss_prob: 0.7000 loss_thr: 0.4143 loss_db: 0.1169 loss: 1.2311 2022/08/30 08:20:50 - mmengine - INFO - Epoch(train) [331][10/63] lr: 5.2394e-03 eta: 23:02:07 time: 2.8603 data_time: 0.3260 memory: 16201 loss_prob: 0.7369 loss_thr: 0.4721 loss_db: 0.1242 loss: 1.3332 2022/08/30 08:21:04 - mmengine - INFO - Epoch(train) [331][15/63] lr: 5.2394e-03 eta: 23:02:07 time: 2.8183 data_time: 0.0559 memory: 16201 loss_prob: 0.7123 loss_thr: 0.4755 loss_db: 0.1246 loss: 1.3124 2022/08/30 08:21:18 - mmengine - INFO - Epoch(train) [331][20/63] lr: 5.2394e-03 eta: 23:02:25 time: 2.7764 data_time: 0.0513 memory: 16201 loss_prob: 0.6862 loss_thr: 0.4486 loss_db: 0.1194 loss: 1.2542 2022/08/30 08:21:32 - mmengine - INFO - Epoch(train) [331][25/63] lr: 5.2394e-03 eta: 23:02:25 time: 2.7724 data_time: 0.0794 memory: 16201 loss_prob: 0.6935 loss_thr: 0.4343 loss_db: 0.1154 loss: 1.2432 2022/08/30 08:21:45 - mmengine - INFO - Epoch(train) [331][30/63] lr: 5.2394e-03 eta: 23:02:41 time: 2.6672 data_time: 0.0569 memory: 16201 loss_prob: 0.6818 loss_thr: 0.4220 loss_db: 0.1150 loss: 1.2188 2022/08/30 08:21:57 - mmengine - INFO - Epoch(train) [331][35/63] lr: 5.2394e-03 eta: 23:02:41 time: 2.5287 data_time: 0.0419 memory: 16201 loss_prob: 0.6862 loss_thr: 0.4213 loss_db: 0.1180 loss: 1.2256 2022/08/30 08:22:10 - mmengine - INFO - Epoch(train) [331][40/63] lr: 5.2394e-03 eta: 23:02:52 time: 2.5321 data_time: 0.0426 memory: 16201 loss_prob: 0.6968 loss_thr: 0.4400 loss_db: 0.1188 loss: 1.2556 2022/08/30 08:22:23 - mmengine - INFO - Epoch(train) [331][45/63] lr: 5.2394e-03 eta: 23:02:52 time: 2.5463 data_time: 0.0625 memory: 16201 loss_prob: 0.7168 loss_thr: 0.4583 loss_db: 0.1223 loss: 1.2974 2022/08/30 08:22:36 - mmengine - INFO - Epoch(train) [331][50/63] lr: 5.2394e-03 eta: 23:03:06 time: 2.6103 data_time: 0.0801 memory: 16201 loss_prob: 0.7110 loss_thr: 0.4499 loss_db: 0.1179 loss: 1.2789 2022/08/30 08:22:50 - mmengine - INFO - Epoch(train) [331][55/63] lr: 5.2394e-03 eta: 23:03:06 time: 2.7428 data_time: 0.0536 memory: 16201 loss_prob: 0.6929 loss_thr: 0.4435 loss_db: 0.1132 loss: 1.2496 2022/08/30 08:23:03 - mmengine - INFO - Epoch(train) [331][60/63] lr: 5.2394e-03 eta: 23:03:21 time: 2.6597 data_time: 0.0668 memory: 16201 loss_prob: 0.7155 loss_thr: 0.4689 loss_db: 0.1188 loss: 1.3032 2022/08/30 08:23:09 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:23:23 - mmengine - INFO - Epoch(train) [332][5/63] lr: 5.2340e-03 eta: 23:03:21 time: 2.6864 data_time: 0.2631 memory: 16201 loss_prob: 0.7691 loss_thr: 0.4679 loss_db: 0.1255 loss: 1.3625 2022/08/30 08:23:36 - mmengine - INFO - Epoch(train) [332][10/63] lr: 5.2340e-03 eta: 23:03:20 time: 2.6941 data_time: 0.2910 memory: 16201 loss_prob: 0.6900 loss_thr: 0.4420 loss_db: 0.1186 loss: 1.2506 2022/08/30 08:23:50 - mmengine - INFO - Epoch(train) [332][15/63] lr: 5.2340e-03 eta: 23:03:20 time: 2.6540 data_time: 0.0606 memory: 16201 loss_prob: 0.6870 loss_thr: 0.4419 loss_db: 0.1216 loss: 1.2504 2022/08/30 08:24:02 - mmengine - INFO - Epoch(train) [332][20/63] lr: 5.2340e-03 eta: 23:03:35 time: 2.6637 data_time: 0.0628 memory: 16201 loss_prob: 0.6688 loss_thr: 0.4244 loss_db: 0.1165 loss: 1.2098 2022/08/30 08:24:16 - mmengine - INFO - Epoch(train) [332][25/63] lr: 5.2340e-03 eta: 23:03:35 time: 2.6620 data_time: 0.0571 memory: 16201 loss_prob: 0.7211 loss_thr: 0.4315 loss_db: 0.1192 loss: 1.2718 2022/08/30 08:24:30 - mmengine - INFO - Epoch(train) [332][30/63] lr: 5.2340e-03 eta: 23:03:52 time: 2.7542 data_time: 0.0522 memory: 16201 loss_prob: 0.7105 loss_thr: 0.4484 loss_db: 0.1159 loss: 1.2748 2022/08/30 08:24:43 - mmengine - INFO - Epoch(train) [332][35/63] lr: 5.2340e-03 eta: 23:03:52 time: 2.6583 data_time: 0.0732 memory: 16201 loss_prob: 0.6114 loss_thr: 0.4277 loss_db: 0.1035 loss: 1.1426 2022/08/30 08:24:57 - mmengine - INFO - Epoch(train) [332][40/63] lr: 5.2340e-03 eta: 23:04:09 time: 2.7332 data_time: 0.0606 memory: 16201 loss_prob: 0.6269 loss_thr: 0.4297 loss_db: 0.1091 loss: 1.1657 2022/08/30 08:25:13 - mmengine - INFO - Epoch(train) [332][45/63] lr: 5.2340e-03 eta: 23:04:09 time: 2.9429 data_time: 0.0620 memory: 16201 loss_prob: 0.7357 loss_thr: 0.4633 loss_db: 0.1236 loss: 1.3226 2022/08/30 08:25:25 - mmengine - INFO - Epoch(train) [332][50/63] lr: 5.2340e-03 eta: 23:04:27 time: 2.7866 data_time: 0.0734 memory: 16201 loss_prob: 0.7419 loss_thr: 0.4572 loss_db: 0.1205 loss: 1.3196 2022/08/30 08:25:39 - mmengine - INFO - Epoch(train) [332][55/63] lr: 5.2340e-03 eta: 23:04:27 time: 2.6162 data_time: 0.0567 memory: 16201 loss_prob: 0.7032 loss_thr: 0.4451 loss_db: 0.1191 loss: 1.2673 2022/08/30 08:25:51 - mmengine - INFO - Epoch(train) [332][60/63] lr: 5.2340e-03 eta: 23:04:39 time: 2.5712 data_time: 0.0628 memory: 16201 loss_prob: 0.7415 loss_thr: 0.4445 loss_db: 0.1318 loss: 1.3179 2022/08/30 08:25:58 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:26:13 - mmengine - INFO - Epoch(train) [333][5/63] lr: 5.2285e-03 eta: 23:04:39 time: 2.6538 data_time: 0.2930 memory: 16201 loss_prob: 0.7070 loss_thr: 0.4487 loss_db: 0.1193 loss: 1.2751 2022/08/30 08:26:26 - mmengine - INFO - Epoch(train) [333][10/63] lr: 5.2285e-03 eta: 23:04:41 time: 2.7920 data_time: 0.3147 memory: 16201 loss_prob: 0.6972 loss_thr: 0.4275 loss_db: 0.1173 loss: 1.2420 2022/08/30 08:26:39 - mmengine - INFO - Epoch(train) [333][15/63] lr: 5.2285e-03 eta: 23:04:41 time: 2.6522 data_time: 0.0549 memory: 16201 loss_prob: 0.7297 loss_thr: 0.4265 loss_db: 0.1249 loss: 1.2811 2022/08/30 08:26:51 - mmengine - INFO - Epoch(train) [333][20/63] lr: 5.2285e-03 eta: 23:04:50 time: 2.4451 data_time: 0.0700 memory: 16201 loss_prob: 0.6709 loss_thr: 0.4441 loss_db: 0.1148 loss: 1.2298 2022/08/30 08:27:04 - mmengine - INFO - Epoch(train) [333][25/63] lr: 5.2285e-03 eta: 23:04:50 time: 2.5114 data_time: 0.0894 memory: 16201 loss_prob: 0.6415 loss_thr: 0.4245 loss_db: 0.1097 loss: 1.1758 2022/08/30 08:27:17 - mmengine - INFO - Epoch(train) [333][30/63] lr: 5.2285e-03 eta: 23:05:03 time: 2.6138 data_time: 0.0535 memory: 16201 loss_prob: 0.6716 loss_thr: 0.4114 loss_db: 0.1155 loss: 1.1984 2022/08/30 08:27:30 - mmengine - INFO - Epoch(train) [333][35/63] lr: 5.2285e-03 eta: 23:05:03 time: 2.6047 data_time: 0.0471 memory: 16201 loss_prob: 0.7743 loss_thr: 0.4342 loss_db: 0.1304 loss: 1.3389 2022/08/30 08:27:45 - mmengine - INFO - Epoch(train) [333][40/63] lr: 5.2285e-03 eta: 23:05:22 time: 2.8305 data_time: 0.0589 memory: 16201 loss_prob: 0.8263 loss_thr: 0.4408 loss_db: 0.1371 loss: 1.4043 2022/08/30 08:27:59 - mmengine - INFO - Epoch(train) [333][45/63] lr: 5.2285e-03 eta: 23:05:22 time: 2.8468 data_time: 0.0609 memory: 16201 loss_prob: 0.7692 loss_thr: 0.4357 loss_db: 0.1266 loss: 1.3314 2022/08/30 08:28:11 - mmengine - INFO - Epoch(train) [333][50/63] lr: 5.2285e-03 eta: 23:05:34 time: 2.5736 data_time: 0.0742 memory: 16201 loss_prob: 0.7628 loss_thr: 0.4529 loss_db: 0.1272 loss: 1.3429 2022/08/30 08:28:25 - mmengine - INFO - Epoch(train) [333][55/63] lr: 5.2285e-03 eta: 23:05:34 time: 2.6326 data_time: 0.0698 memory: 16201 loss_prob: 0.8363 loss_thr: 0.4881 loss_db: 0.1372 loss: 1.4617 2022/08/30 08:28:37 - mmengine - INFO - Epoch(train) [333][60/63] lr: 5.2285e-03 eta: 23:05:48 time: 2.6359 data_time: 0.0594 memory: 16201 loss_prob: 0.7919 loss_thr: 0.4730 loss_db: 0.1273 loss: 1.3921 2022/08/30 08:28:44 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:29:00 - mmengine - INFO - Epoch(train) [334][5/63] lr: 5.2231e-03 eta: 23:05:48 time: 2.7454 data_time: 0.2910 memory: 16201 loss_prob: 0.6801 loss_thr: 0.4489 loss_db: 0.1173 loss: 1.2462 2022/08/30 08:29:13 - mmengine - INFO - Epoch(train) [334][10/63] lr: 5.2231e-03 eta: 23:05:51 time: 2.8406 data_time: 0.3171 memory: 16201 loss_prob: 0.6545 loss_thr: 0.4369 loss_db: 0.1140 loss: 1.2053 2022/08/30 08:29:27 - mmengine - INFO - Epoch(train) [334][15/63] lr: 5.2231e-03 eta: 23:05:51 time: 2.7860 data_time: 0.0547 memory: 16201 loss_prob: 0.7163 loss_thr: 0.4488 loss_db: 0.1192 loss: 1.2843 2022/08/30 08:29:40 - mmengine - INFO - Epoch(train) [334][20/63] lr: 5.2231e-03 eta: 23:06:06 time: 2.6966 data_time: 0.0481 memory: 16201 loss_prob: 0.7992 loss_thr: 0.4911 loss_db: 0.1275 loss: 1.4177 2022/08/30 08:29:43 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:29:53 - mmengine - INFO - Epoch(train) [334][25/63] lr: 5.2231e-03 eta: 23:06:06 time: 2.5398 data_time: 0.0615 memory: 16201 loss_prob: 0.7500 loss_thr: 0.4776 loss_db: 0.1235 loss: 1.3511 2022/08/30 08:30:04 - mmengine - INFO - Epoch(train) [334][30/63] lr: 5.2231e-03 eta: 23:06:15 time: 2.4461 data_time: 0.0520 memory: 16201 loss_prob: 0.7496 loss_thr: 0.4651 loss_db: 0.1267 loss: 1.3414 2022/08/30 08:30:17 - mmengine - INFO - Epoch(train) [334][35/63] lr: 5.2231e-03 eta: 23:06:15 time: 2.4421 data_time: 0.0658 memory: 16201 loss_prob: 0.7666 loss_thr: 0.4642 loss_db: 0.1274 loss: 1.3582 2022/08/30 08:30:29 - mmengine - INFO - Epoch(train) [334][40/63] lr: 5.2231e-03 eta: 23:06:25 time: 2.4949 data_time: 0.0576 memory: 16201 loss_prob: 0.7030 loss_thr: 0.4378 loss_db: 0.1211 loss: 1.2619 2022/08/30 08:30:43 - mmengine - INFO - Epoch(train) [334][45/63] lr: 5.2231e-03 eta: 23:06:25 time: 2.5431 data_time: 0.0610 memory: 16201 loss_prob: 0.7282 loss_thr: 0.4452 loss_db: 0.1260 loss: 1.2994 2022/08/30 08:30:56 - mmengine - INFO - Epoch(train) [334][50/63] lr: 5.2231e-03 eta: 23:06:41 time: 2.7542 data_time: 0.0831 memory: 16201 loss_prob: 0.6949 loss_thr: 0.4166 loss_db: 0.1191 loss: 1.2306 2022/08/30 08:31:10 - mmengine - INFO - Epoch(train) [334][55/63] lr: 5.2231e-03 eta: 23:06:41 time: 2.7433 data_time: 0.0525 memory: 16201 loss_prob: 0.6754 loss_thr: 0.4077 loss_db: 0.1165 loss: 1.1996 2022/08/30 08:31:24 - mmengine - INFO - Epoch(train) [334][60/63] lr: 5.2231e-03 eta: 23:06:57 time: 2.7252 data_time: 0.0502 memory: 16201 loss_prob: 0.6846 loss_thr: 0.4310 loss_db: 0.1167 loss: 1.2323 2022/08/30 08:31:29 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:31:45 - mmengine - INFO - Epoch(train) [335][5/63] lr: 5.2177e-03 eta: 23:06:57 time: 2.6046 data_time: 0.3087 memory: 16201 loss_prob: 0.6615 loss_thr: 0.4201 loss_db: 0.1117 loss: 1.1933 2022/08/30 08:31:57 - mmengine - INFO - Epoch(train) [335][10/63] lr: 5.2177e-03 eta: 23:06:58 time: 2.7880 data_time: 0.3292 memory: 16201 loss_prob: 0.7135 loss_thr: 0.4532 loss_db: 0.1229 loss: 1.2896 2022/08/30 08:32:10 - mmengine - INFO - Epoch(train) [335][15/63] lr: 5.2177e-03 eta: 23:06:58 time: 2.5391 data_time: 0.0479 memory: 16201 loss_prob: 0.6944 loss_thr: 0.4260 loss_db: 0.1197 loss: 1.2402 2022/08/30 08:32:24 - mmengine - INFO - Epoch(train) [335][20/63] lr: 5.2177e-03 eta: 23:07:12 time: 2.6556 data_time: 0.0552 memory: 16201 loss_prob: 0.6748 loss_thr: 0.4062 loss_db: 0.1140 loss: 1.1950 2022/08/30 08:32:39 - mmengine - INFO - Epoch(train) [335][25/63] lr: 5.2177e-03 eta: 23:07:12 time: 2.8288 data_time: 0.0751 memory: 16201 loss_prob: 0.6568 loss_thr: 0.4198 loss_db: 0.1142 loss: 1.1908 2022/08/30 08:32:53 - mmengine - INFO - Epoch(train) [335][30/63] lr: 5.2177e-03 eta: 23:07:33 time: 2.9082 data_time: 0.0565 memory: 16201 loss_prob: 0.6218 loss_thr: 0.4129 loss_db: 0.1090 loss: 1.1436 2022/08/30 08:33:06 - mmengine - INFO - Epoch(train) [335][35/63] lr: 5.2177e-03 eta: 23:07:33 time: 2.7451 data_time: 0.0619 memory: 16201 loss_prob: 0.6800 loss_thr: 0.4211 loss_db: 0.1160 loss: 1.2171 2022/08/30 08:33:20 - mmengine - INFO - Epoch(train) [335][40/63] lr: 5.2177e-03 eta: 23:07:48 time: 2.6910 data_time: 0.0629 memory: 16201 loss_prob: 0.6891 loss_thr: 0.4257 loss_db: 0.1199 loss: 1.2347 2022/08/30 08:33:34 - mmengine - INFO - Epoch(train) [335][45/63] lr: 5.2177e-03 eta: 23:07:48 time: 2.7456 data_time: 0.0661 memory: 16201 loss_prob: 0.6183 loss_thr: 0.4108 loss_db: 0.1083 loss: 1.1375 2022/08/30 08:33:47 - mmengine - INFO - Epoch(train) [335][50/63] lr: 5.2177e-03 eta: 23:08:02 time: 2.6726 data_time: 0.0784 memory: 16201 loss_prob: 0.6474 loss_thr: 0.4205 loss_db: 0.1086 loss: 1.1764 2022/08/30 08:33:59 - mmengine - INFO - Epoch(train) [335][55/63] lr: 5.2177e-03 eta: 23:08:02 time: 2.4850 data_time: 0.0628 memory: 16201 loss_prob: 0.7131 loss_thr: 0.4484 loss_db: 0.1199 loss: 1.2814 2022/08/30 08:34:14 - mmengine - INFO - Epoch(train) [335][60/63] lr: 5.2177e-03 eta: 23:08:18 time: 2.7208 data_time: 0.0621 memory: 16201 loss_prob: 0.7120 loss_thr: 0.4576 loss_db: 0.1243 loss: 1.2939 2022/08/30 08:34:20 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:34:34 - mmengine - INFO - Epoch(train) [336][5/63] lr: 5.2123e-03 eta: 23:08:18 time: 2.6974 data_time: 0.2827 memory: 16201 loss_prob: 0.6772 loss_thr: 0.4313 loss_db: 0.1171 loss: 1.2256 2022/08/30 08:34:48 - mmengine - INFO - Epoch(train) [336][10/63] lr: 5.2123e-03 eta: 23:08:19 time: 2.7927 data_time: 0.2797 memory: 16201 loss_prob: 0.7462 loss_thr: 0.4430 loss_db: 0.1246 loss: 1.3138 2022/08/30 08:35:03 - mmengine - INFO - Epoch(train) [336][15/63] lr: 5.2123e-03 eta: 23:08:19 time: 2.8277 data_time: 0.0569 memory: 16201 loss_prob: 0.7533 loss_thr: 0.4462 loss_db: 0.1260 loss: 1.3254 2022/08/30 08:35:16 - mmengine - INFO - Epoch(train) [336][20/63] lr: 5.2123e-03 eta: 23:08:37 time: 2.8387 data_time: 0.0596 memory: 16201 loss_prob: 0.6877 loss_thr: 0.4197 loss_db: 0.1159 loss: 1.2233 2022/08/30 08:35:29 - mmengine - INFO - Epoch(train) [336][25/63] lr: 5.2123e-03 eta: 23:08:37 time: 2.5979 data_time: 0.0635 memory: 16201 loss_prob: 0.6726 loss_thr: 0.4358 loss_db: 0.1137 loss: 1.2221 2022/08/30 08:35:41 - mmengine - INFO - Epoch(train) [336][30/63] lr: 5.2123e-03 eta: 23:08:46 time: 2.4700 data_time: 0.0583 memory: 16201 loss_prob: 0.6494 loss_thr: 0.4355 loss_db: 0.1121 loss: 1.1971 2022/08/30 08:35:55 - mmengine - INFO - Epoch(train) [336][35/63] lr: 5.2123e-03 eta: 23:08:46 time: 2.6281 data_time: 0.0534 memory: 16201 loss_prob: 0.6656 loss_thr: 0.4203 loss_db: 0.1144 loss: 1.2002 2022/08/30 08:36:07 - mmengine - INFO - Epoch(train) [336][40/63] lr: 5.2123e-03 eta: 23:09:00 time: 2.6526 data_time: 0.0519 memory: 16201 loss_prob: 0.6755 loss_thr: 0.4222 loss_db: 0.1130 loss: 1.2107 2022/08/30 08:36:21 - mmengine - INFO - Epoch(train) [336][45/63] lr: 5.2123e-03 eta: 23:09:00 time: 2.5606 data_time: 0.0637 memory: 16201 loss_prob: 0.6470 loss_thr: 0.4243 loss_db: 0.1102 loss: 1.1815 2022/08/30 08:36:34 - mmengine - INFO - Epoch(train) [336][50/63] lr: 5.2123e-03 eta: 23:09:13 time: 2.6555 data_time: 0.0703 memory: 16201 loss_prob: 0.6077 loss_thr: 0.4155 loss_db: 0.1064 loss: 1.1296 2022/08/30 08:36:50 - mmengine - INFO - Epoch(train) [336][55/63] lr: 5.2123e-03 eta: 23:09:13 time: 2.9514 data_time: 0.0513 memory: 16201 loss_prob: 0.5982 loss_thr: 0.4062 loss_db: 0.1022 loss: 1.1066 2022/08/30 08:37:03 - mmengine - INFO - Epoch(train) [336][60/63] lr: 5.2123e-03 eta: 23:09:35 time: 2.9541 data_time: 0.0525 memory: 16201 loss_prob: 0.6457 loss_thr: 0.4272 loss_db: 0.1095 loss: 1.1825 2022/08/30 08:37:09 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:37:25 - mmengine - INFO - Epoch(train) [337][5/63] lr: 5.2068e-03 eta: 23:09:35 time: 2.7871 data_time: 0.2836 memory: 16201 loss_prob: 0.6483 loss_thr: 0.4267 loss_db: 0.1125 loss: 1.1875 2022/08/30 08:37:38 - mmengine - INFO - Epoch(train) [337][10/63] lr: 5.2068e-03 eta: 23:09:36 time: 2.8206 data_time: 0.2902 memory: 16201 loss_prob: 0.6586 loss_thr: 0.4392 loss_db: 0.1120 loss: 1.2098 2022/08/30 08:37:53 - mmengine - INFO - Epoch(train) [337][15/63] lr: 5.2068e-03 eta: 23:09:36 time: 2.8011 data_time: 0.0494 memory: 16201 loss_prob: 0.7033 loss_thr: 0.4419 loss_db: 0.1205 loss: 1.2658 2022/08/30 08:38:06 - mmengine - INFO - Epoch(train) [337][20/63] lr: 5.2068e-03 eta: 23:09:53 time: 2.8020 data_time: 0.0612 memory: 16201 loss_prob: 0.7120 loss_thr: 0.4540 loss_db: 0.1249 loss: 1.2909 2022/08/30 08:38:18 - mmengine - INFO - Epoch(train) [337][25/63] lr: 5.2068e-03 eta: 23:09:53 time: 2.5033 data_time: 0.0678 memory: 16201 loss_prob: 0.6592 loss_thr: 0.4451 loss_db: 0.1120 loss: 1.2163 2022/08/30 08:38:31 - mmengine - INFO - Epoch(train) [337][30/63] lr: 5.2068e-03 eta: 23:10:03 time: 2.5276 data_time: 0.0600 memory: 16201 loss_prob: 0.6562 loss_thr: 0.4260 loss_db: 0.1082 loss: 1.1905 2022/08/30 08:38:44 - mmengine - INFO - Epoch(train) [337][35/63] lr: 5.2068e-03 eta: 23:10:03 time: 2.6412 data_time: 0.0589 memory: 16201 loss_prob: 0.9675 loss_thr: 0.4636 loss_db: 0.1519 loss: 1.5830 2022/08/30 08:38:55 - mmengine - INFO - Epoch(train) [337][40/63] lr: 5.2068e-03 eta: 23:10:10 time: 2.4017 data_time: 0.0545 memory: 16201 loss_prob: 0.9530 loss_thr: 0.4625 loss_db: 0.1508 loss: 1.5663 2022/08/30 08:39:09 - mmengine - INFO - Epoch(train) [337][45/63] lr: 5.2068e-03 eta: 23:10:10 time: 2.4539 data_time: 0.0607 memory: 16201 loss_prob: 0.6518 loss_thr: 0.4116 loss_db: 0.1089 loss: 1.1722 2022/08/30 08:39:22 - mmengine - INFO - Epoch(train) [337][50/63] lr: 5.2068e-03 eta: 23:10:26 time: 2.7271 data_time: 0.0676 memory: 16201 loss_prob: 0.8720 loss_thr: 0.4081 loss_db: 0.1440 loss: 1.4241 2022/08/30 08:39:36 - mmengine - INFO - Epoch(train) [337][55/63] lr: 5.2068e-03 eta: 23:10:26 time: 2.6981 data_time: 0.0522 memory: 16201 loss_prob: 0.9714 loss_thr: 0.4403 loss_db: 0.1577 loss: 1.5694 2022/08/30 08:39:50 - mmengine - INFO - Epoch(train) [337][60/63] lr: 5.2068e-03 eta: 23:10:42 time: 2.7793 data_time: 0.0527 memory: 16201 loss_prob: 0.8741 loss_thr: 0.4804 loss_db: 0.1398 loss: 1.4943 2022/08/30 08:39:57 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:40:12 - mmengine - INFO - Epoch(train) [338][5/63] lr: 5.2014e-03 eta: 23:10:42 time: 2.8488 data_time: 0.2983 memory: 16201 loss_prob: 0.8148 loss_thr: 0.4732 loss_db: 0.1396 loss: 1.4275 2022/08/30 08:40:27 - mmengine - INFO - Epoch(train) [338][10/63] lr: 5.2014e-03 eta: 23:10:46 time: 2.9318 data_time: 0.3106 memory: 16201 loss_prob: 0.7370 loss_thr: 0.4347 loss_db: 0.1269 loss: 1.2986 2022/08/30 08:40:41 - mmengine - INFO - Epoch(train) [338][15/63] lr: 5.2014e-03 eta: 23:10:46 time: 2.8544 data_time: 0.0741 memory: 16201 loss_prob: 0.7105 loss_thr: 0.4465 loss_db: 0.1172 loss: 1.2742 2022/08/30 08:40:55 - mmengine - INFO - Epoch(train) [338][20/63] lr: 5.2014e-03 eta: 23:11:03 time: 2.7869 data_time: 0.0698 memory: 16201 loss_prob: 0.7188 loss_thr: 0.4535 loss_db: 0.1200 loss: 1.2923 2022/08/30 08:41:07 - mmengine - INFO - Epoch(train) [338][25/63] lr: 5.2014e-03 eta: 23:11:03 time: 2.6357 data_time: 0.0683 memory: 16201 loss_prob: 0.7965 loss_thr: 0.4374 loss_db: 0.1259 loss: 1.3598 2022/08/30 08:41:21 - mmengine - INFO - Epoch(train) [338][30/63] lr: 5.2014e-03 eta: 23:11:14 time: 2.5875 data_time: 0.0654 memory: 16201 loss_prob: 0.7888 loss_thr: 0.4403 loss_db: 0.1240 loss: 1.3531 2022/08/30 08:41:35 - mmengine - INFO - Epoch(train) [338][35/63] lr: 5.2014e-03 eta: 23:11:14 time: 2.8380 data_time: 0.0577 memory: 16201 loss_prob: 0.7237 loss_thr: 0.4483 loss_db: 0.1193 loss: 1.2912 2022/08/30 08:41:48 - mmengine - INFO - Epoch(train) [338][40/63] lr: 5.2014e-03 eta: 23:11:29 time: 2.7268 data_time: 0.0561 memory: 16201 loss_prob: 0.7321 loss_thr: 0.4514 loss_db: 0.1218 loss: 1.3054 2022/08/30 08:42:01 - mmengine - INFO - Epoch(train) [338][45/63] lr: 5.2014e-03 eta: 23:11:29 time: 2.5305 data_time: 0.0703 memory: 16201 loss_prob: 0.6997 loss_thr: 0.4288 loss_db: 0.1176 loss: 1.2461 2022/08/30 08:42:14 - mmengine - INFO - Epoch(train) [338][50/63] lr: 5.2014e-03 eta: 23:11:42 time: 2.6181 data_time: 0.0585 memory: 16201 loss_prob: 0.6805 loss_thr: 0.4128 loss_db: 0.1134 loss: 1.2067 2022/08/30 08:42:28 - mmengine - INFO - Epoch(train) [338][55/63] lr: 5.2014e-03 eta: 23:11:42 time: 2.7623 data_time: 0.0521 memory: 16201 loss_prob: 0.7032 loss_thr: 0.4306 loss_db: 0.1204 loss: 1.2542 2022/08/30 08:42:40 - mmengine - INFO - Epoch(train) [338][60/63] lr: 5.2014e-03 eta: 23:11:54 time: 2.6215 data_time: 0.0665 memory: 16201 loss_prob: 0.6742 loss_thr: 0.4362 loss_db: 0.1156 loss: 1.2260 2022/08/30 08:42:46 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:43:00 - mmengine - INFO - Epoch(train) [339][5/63] lr: 5.1960e-03 eta: 23:11:54 time: 2.4328 data_time: 0.2963 memory: 16201 loss_prob: 0.6143 loss_thr: 0.4253 loss_db: 0.1047 loss: 1.1443 2022/08/30 08:43:13 - mmengine - INFO - Epoch(train) [339][10/63] lr: 5.1960e-03 eta: 23:11:52 time: 2.6902 data_time: 0.3034 memory: 16201 loss_prob: 0.6468 loss_thr: 0.4333 loss_db: 0.1120 loss: 1.1921 2022/08/30 08:43:26 - mmengine - INFO - Epoch(train) [339][15/63] lr: 5.1960e-03 eta: 23:11:52 time: 2.5948 data_time: 0.0487 memory: 16201 loss_prob: 0.6121 loss_thr: 0.4247 loss_db: 0.1053 loss: 1.1421 2022/08/30 08:43:40 - mmengine - INFO - Epoch(train) [339][20/63] lr: 5.1960e-03 eta: 23:12:05 time: 2.6517 data_time: 0.0591 memory: 16201 loss_prob: 0.6208 loss_thr: 0.4285 loss_db: 0.1061 loss: 1.1554 2022/08/30 08:43:52 - mmengine - INFO - Epoch(train) [339][25/63] lr: 5.1960e-03 eta: 23:12:05 time: 2.5559 data_time: 0.0801 memory: 16201 loss_prob: 0.7124 loss_thr: 0.4589 loss_db: 0.1216 loss: 1.2930 2022/08/30 08:44:06 - mmengine - INFO - Epoch(train) [339][30/63] lr: 5.1960e-03 eta: 23:12:17 time: 2.6194 data_time: 0.0520 memory: 16201 loss_prob: 0.7529 loss_thr: 0.4705 loss_db: 0.1291 loss: 1.3525 2022/08/30 08:44:20 - mmengine - INFO - Epoch(train) [339][35/63] lr: 5.1960e-03 eta: 23:12:17 time: 2.8027 data_time: 0.0514 memory: 16201 loss_prob: 0.7026 loss_thr: 0.4408 loss_db: 0.1207 loss: 1.2641 2022/08/30 08:44:32 - mmengine - INFO - Epoch(train) [339][40/63] lr: 5.1960e-03 eta: 23:12:30 time: 2.6688 data_time: 0.0618 memory: 16201 loss_prob: 0.7353 loss_thr: 0.4416 loss_db: 0.1246 loss: 1.3014 2022/08/30 08:44:46 - mmengine - INFO - Epoch(train) [339][45/63] lr: 5.1960e-03 eta: 23:12:30 time: 2.6261 data_time: 0.0599 memory: 16201 loss_prob: 0.6921 loss_thr: 0.4244 loss_db: 0.1165 loss: 1.2330 2022/08/30 08:44:59 - mmengine - INFO - Epoch(train) [339][50/63] lr: 5.1960e-03 eta: 23:12:43 time: 2.6628 data_time: 0.0791 memory: 16201 loss_prob: 0.6507 loss_thr: 0.4169 loss_db: 0.1083 loss: 1.1758 2022/08/30 08:45:11 - mmengine - INFO - Epoch(train) [339][55/63] lr: 5.1960e-03 eta: 23:12:43 time: 2.4701 data_time: 0.0610 memory: 16201 loss_prob: 0.6591 loss_thr: 0.4347 loss_db: 0.1122 loss: 1.2060 2022/08/30 08:45:24 - mmengine - INFO - Epoch(train) [339][60/63] lr: 5.1960e-03 eta: 23:12:51 time: 2.4463 data_time: 0.0512 memory: 16201 loss_prob: 0.6306 loss_thr: 0.4270 loss_db: 0.1093 loss: 1.1669 2022/08/30 08:45:31 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:45:46 - mmengine - INFO - Epoch(train) [340][5/63] lr: 5.1905e-03 eta: 23:12:51 time: 2.6642 data_time: 0.2742 memory: 16201 loss_prob: 0.7567 loss_thr: 0.4628 loss_db: 0.1279 loss: 1.3474 2022/08/30 08:45:58 - mmengine - INFO - Epoch(train) [340][10/63] lr: 5.1905e-03 eta: 23:12:50 time: 2.7443 data_time: 0.2946 memory: 16201 loss_prob: 0.6605 loss_thr: 0.4234 loss_db: 0.1153 loss: 1.1992 2022/08/30 08:46:11 - mmengine - INFO - Epoch(train) [340][15/63] lr: 5.1905e-03 eta: 23:12:50 time: 2.5249 data_time: 0.0609 memory: 16201 loss_prob: 0.5990 loss_thr: 0.4174 loss_db: 0.1045 loss: 1.1210 2022/08/30 08:46:24 - mmengine - INFO - Epoch(train) [340][20/63] lr: 5.1905e-03 eta: 23:13:00 time: 2.5761 data_time: 0.0671 memory: 16201 loss_prob: 0.6071 loss_thr: 0.4220 loss_db: 0.1034 loss: 1.1325 2022/08/30 08:46:38 - mmengine - INFO - Epoch(train) [340][25/63] lr: 5.1905e-03 eta: 23:13:00 time: 2.6455 data_time: 0.0752 memory: 16201 loss_prob: 0.6544 loss_thr: 0.4388 loss_db: 0.1113 loss: 1.2045 2022/08/30 08:46:50 - mmengine - INFO - Epoch(train) [340][30/63] lr: 5.1905e-03 eta: 23:13:12 time: 2.6208 data_time: 0.0519 memory: 16201 loss_prob: 0.7072 loss_thr: 0.4503 loss_db: 0.1210 loss: 1.2786 2022/08/30 08:47:05 - mmengine - INFO - Epoch(train) [340][35/63] lr: 5.1905e-03 eta: 23:13:12 time: 2.7434 data_time: 0.0733 memory: 16201 loss_prob: 0.6649 loss_thr: 0.4277 loss_db: 0.1120 loss: 1.2046 2022/08/30 08:47:20 - mmengine - INFO - Epoch(train) [340][40/63] lr: 5.1905e-03 eta: 23:13:33 time: 2.9773 data_time: 0.0698 memory: 16201 loss_prob: 0.6463 loss_thr: 0.4160 loss_db: 0.1075 loss: 1.1698 2022/08/30 08:47:34 - mmengine - INFO - Epoch(train) [340][45/63] lr: 5.1905e-03 eta: 23:13:33 time: 2.8750 data_time: 0.0610 memory: 16201 loss_prob: 0.6589 loss_thr: 0.4233 loss_db: 0.1090 loss: 1.1912 2022/08/30 08:47:49 - mmengine - INFO - Epoch(train) [340][50/63] lr: 5.1905e-03 eta: 23:13:53 time: 2.9135 data_time: 0.0833 memory: 16201 loss_prob: 0.6211 loss_thr: 0.4069 loss_db: 0.1044 loss: 1.1324 2022/08/30 08:48:03 - mmengine - INFO - Epoch(train) [340][55/63] lr: 5.1905e-03 eta: 23:13:53 time: 2.9512 data_time: 0.0617 memory: 16201 loss_prob: 0.6875 loss_thr: 0.4455 loss_db: 0.1158 loss: 1.2488 2022/08/30 08:48:17 - mmengine - INFO - Epoch(train) [340][60/63] lr: 5.1905e-03 eta: 23:14:08 time: 2.7592 data_time: 0.0581 memory: 16201 loss_prob: 0.7017 loss_thr: 0.4676 loss_db: 0.1186 loss: 1.2879 2022/08/30 08:48:24 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:48:24 - mmengine - INFO - Saving checkpoint at 340 epochs 2022/08/30 08:48:33 - mmengine - INFO - Epoch(val) [340][5/32] eta: 23:14:08 time: 0.7034 data_time: 0.1583 memory: 16201 2022/08/30 08:48:36 - mmengine - INFO - Epoch(val) [340][10/32] eta: 0:00:16 time: 0.7711 data_time: 0.1706 memory: 15734 2022/08/30 08:48:39 - mmengine - INFO - Epoch(val) [340][15/32] eta: 0:00:16 time: 0.6675 data_time: 0.0696 memory: 15734 2022/08/30 08:48:44 - mmengine - INFO - Epoch(val) [340][20/32] eta: 0:00:09 time: 0.7577 data_time: 0.1041 memory: 15734 2022/08/30 08:48:47 - mmengine - INFO - Epoch(val) [340][25/32] eta: 0:00:09 time: 0.7693 data_time: 0.0820 memory: 15734 2022/08/30 08:48:50 - mmengine - INFO - Epoch(val) [340][30/32] eta: 0:00:01 time: 0.6533 data_time: 0.0341 memory: 15734 2022/08/30 08:48:51 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 08:48:51 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8377, precision: 0.7775, hmean: 0.8065 2022/08/30 08:48:51 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8373, precision: 0.8157, hmean: 0.8263 2022/08/30 08:48:51 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8353, precision: 0.8410, hmean: 0.8382 2022/08/30 08:48:51 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8305, precision: 0.8625, hmean: 0.8462 2022/08/30 08:48:51 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.8098, precision: 0.8843, hmean: 0.8454 2022/08/30 08:48:51 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.6822, precision: 0.9329, hmean: 0.7881 2022/08/30 08:48:51 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0462, precision: 0.9697, hmean: 0.0882 2022/08/30 08:48:51 - mmengine - INFO - Epoch(val) [340][32/32] icdar/precision: 0.8625 icdar/recall: 0.8305 icdar/hmean: 0.8462 2022/08/30 08:49:07 - mmengine - INFO - Epoch(train) [341][5/63] lr: 5.1851e-03 eta: 0:00:01 time: 2.8979 data_time: 0.3049 memory: 16201 loss_prob: 0.6478 loss_thr: 0.4311 loss_db: 0.1128 loss: 1.1917 2022/08/30 08:49:19 - mmengine - INFO - Epoch(train) [341][10/63] lr: 5.1851e-03 eta: 23:14:09 time: 2.8320 data_time: 0.3100 memory: 16201 loss_prob: 0.6589 loss_thr: 0.4263 loss_db: 0.1121 loss: 1.1973 2022/08/30 08:49:30 - mmengine - INFO - Epoch(train) [341][15/63] lr: 5.1851e-03 eta: 23:14:09 time: 2.2863 data_time: 0.0550 memory: 16201 loss_prob: 0.6912 loss_thr: 0.4537 loss_db: 0.1148 loss: 1.2597 2022/08/30 08:49:43 - mmengine - INFO - Epoch(train) [341][20/63] lr: 5.1851e-03 eta: 23:14:14 time: 2.3712 data_time: 0.0563 memory: 16201 loss_prob: 0.6727 loss_thr: 0.4494 loss_db: 0.1134 loss: 1.2355 2022/08/30 08:49:57 - mmengine - INFO - Epoch(train) [341][25/63] lr: 5.1851e-03 eta: 23:14:14 time: 2.6856 data_time: 0.0856 memory: 16201 loss_prob: 0.6500 loss_thr: 0.4329 loss_db: 0.1125 loss: 1.1953 2022/08/30 08:50:09 - mmengine - INFO - Epoch(train) [341][30/63] lr: 5.1851e-03 eta: 23:14:25 time: 2.6008 data_time: 0.0693 memory: 16201 loss_prob: 0.6672 loss_thr: 0.4210 loss_db: 0.1141 loss: 1.2023 2022/08/30 08:50:23 - mmengine - INFO - Epoch(train) [341][35/63] lr: 5.1851e-03 eta: 23:14:25 time: 2.6156 data_time: 0.0497 memory: 16201 loss_prob: 0.6578 loss_thr: 0.4176 loss_db: 0.1112 loss: 1.1866 2022/08/30 08:50:36 - mmengine - INFO - Epoch(train) [341][40/63] lr: 5.1851e-03 eta: 23:14:38 time: 2.6462 data_time: 0.0507 memory: 16201 loss_prob: 0.7047 loss_thr: 0.4547 loss_db: 0.1209 loss: 1.2802 2022/08/30 08:50:49 - mmengine - INFO - Epoch(train) [341][45/63] lr: 5.1851e-03 eta: 23:14:38 time: 2.6121 data_time: 0.0538 memory: 16201 loss_prob: 0.6780 loss_thr: 0.4373 loss_db: 0.1161 loss: 1.2314 2022/08/30 08:51:03 - mmengine - INFO - Epoch(train) [341][50/63] lr: 5.1851e-03 eta: 23:14:53 time: 2.7541 data_time: 0.1072 memory: 16201 loss_prob: 0.6136 loss_thr: 0.3990 loss_db: 0.1065 loss: 1.1191 2022/08/30 08:51:17 - mmengine - INFO - Epoch(train) [341][55/63] lr: 5.1851e-03 eta: 23:14:53 time: 2.7774 data_time: 0.1218 memory: 16201 loss_prob: 0.6523 loss_thr: 0.4292 loss_db: 0.1157 loss: 1.1971 2022/08/30 08:51:28 - mmengine - INFO - Epoch(train) [341][60/63] lr: 5.1851e-03 eta: 23:15:01 time: 2.4856 data_time: 0.0606 memory: 16201 loss_prob: 0.7029 loss_thr: 0.4620 loss_db: 0.1200 loss: 1.2849 2022/08/30 08:51:35 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:51:49 - mmengine - INFO - Epoch(train) [342][5/63] lr: 5.1797e-03 eta: 23:15:01 time: 2.5892 data_time: 0.3006 memory: 16201 loss_prob: 0.5915 loss_thr: 0.4038 loss_db: 0.0999 loss: 1.0952 2022/08/30 08:52:02 - mmengine - INFO - Epoch(train) [342][10/63] lr: 5.1797e-03 eta: 23:14:58 time: 2.7121 data_time: 0.2958 memory: 16201 loss_prob: 0.5547 loss_thr: 0.3850 loss_db: 0.0957 loss: 1.0354 2022/08/30 08:52:17 - mmengine - INFO - Epoch(train) [342][15/63] lr: 5.1797e-03 eta: 23:14:58 time: 2.7357 data_time: 0.0586 memory: 16201 loss_prob: 0.6013 loss_thr: 0.4002 loss_db: 0.1034 loss: 1.1049 2022/08/30 08:52:29 - mmengine - INFO - Epoch(train) [342][20/63] lr: 5.1797e-03 eta: 23:15:10 time: 2.6391 data_time: 0.0657 memory: 16201 loss_prob: 0.6711 loss_thr: 0.4155 loss_db: 0.1130 loss: 1.1995 2022/08/30 08:52:41 - mmengine - INFO - Epoch(train) [342][25/63] lr: 5.1797e-03 eta: 23:15:10 time: 2.4458 data_time: 0.0600 memory: 16201 loss_prob: 0.6852 loss_thr: 0.4324 loss_db: 0.1166 loss: 1.2342 2022/08/30 08:52:53 - mmengine - INFO - Epoch(train) [342][30/63] lr: 5.1797e-03 eta: 23:15:17 time: 2.4301 data_time: 0.0572 memory: 16201 loss_prob: 0.6717 loss_thr: 0.4403 loss_db: 0.1151 loss: 1.2272 2022/08/30 08:53:06 - mmengine - INFO - Epoch(train) [342][35/63] lr: 5.1797e-03 eta: 23:15:17 time: 2.5233 data_time: 0.0578 memory: 16201 loss_prob: 0.6384 loss_thr: 0.4149 loss_db: 0.1091 loss: 1.1623 2022/08/30 08:53:21 - mmengine - INFO - Epoch(train) [342][40/63] lr: 5.1797e-03 eta: 23:15:32 time: 2.7657 data_time: 0.0478 memory: 16201 loss_prob: 0.6760 loss_thr: 0.4349 loss_db: 0.1144 loss: 1.2253 2022/08/30 08:53:33 - mmengine - INFO - Epoch(train) [342][45/63] lr: 5.1797e-03 eta: 23:15:32 time: 2.6962 data_time: 0.0601 memory: 16201 loss_prob: 0.7555 loss_thr: 0.4442 loss_db: 0.1242 loss: 1.3240 2022/08/30 08:53:47 - mmengine - INFO - Epoch(train) [342][50/63] lr: 5.1797e-03 eta: 23:15:43 time: 2.5898 data_time: 0.0635 memory: 16201 loss_prob: 0.6627 loss_thr: 0.4069 loss_db: 0.1116 loss: 1.1812 2022/08/30 08:54:01 - mmengine - INFO - Epoch(train) [342][55/63] lr: 5.1797e-03 eta: 23:15:43 time: 2.8242 data_time: 0.0473 memory: 16201 loss_prob: 0.6232 loss_thr: 0.4165 loss_db: 0.1079 loss: 1.1475 2022/08/30 08:54:15 - mmengine - INFO - Epoch(train) [342][60/63] lr: 5.1797e-03 eta: 23:16:00 time: 2.8555 data_time: 0.0564 memory: 16201 loss_prob: 0.6835 loss_thr: 0.4321 loss_db: 0.1182 loss: 1.2339 2022/08/30 08:54:22 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:54:35 - mmengine - INFO - Epoch(train) [343][5/63] lr: 5.1742e-03 eta: 23:16:00 time: 2.6261 data_time: 0.3164 memory: 16201 loss_prob: 0.5680 loss_thr: 0.3847 loss_db: 0.0986 loss: 1.0514 2022/08/30 08:54:50 - mmengine - INFO - Epoch(train) [343][10/63] lr: 5.1742e-03 eta: 23:15:59 time: 2.7594 data_time: 0.3218 memory: 16201 loss_prob: 0.6146 loss_thr: 0.3990 loss_db: 0.1064 loss: 1.1200 2022/08/30 08:55:03 - mmengine - INFO - Epoch(train) [343][15/63] lr: 5.1742e-03 eta: 23:15:59 time: 2.7760 data_time: 0.0581 memory: 16201 loss_prob: 0.6269 loss_thr: 0.4074 loss_db: 0.1090 loss: 1.1433 2022/08/30 08:55:16 - mmengine - INFO - Epoch(train) [343][20/63] lr: 5.1742e-03 eta: 23:16:12 time: 2.6772 data_time: 0.0654 memory: 16201 loss_prob: 0.6057 loss_thr: 0.4159 loss_db: 0.1058 loss: 1.1274 2022/08/30 08:55:29 - mmengine - INFO - Epoch(train) [343][25/63] lr: 5.1742e-03 eta: 23:16:12 time: 2.6387 data_time: 0.0756 memory: 16201 loss_prob: 0.6568 loss_thr: 0.4297 loss_db: 0.1138 loss: 1.2003 2022/08/30 08:55:41 - mmengine - INFO - Epoch(train) [343][30/63] lr: 5.1742e-03 eta: 23:16:18 time: 2.4344 data_time: 0.0497 memory: 16201 loss_prob: 0.7320 loss_thr: 0.4526 loss_db: 0.1221 loss: 1.3066 2022/08/30 08:55:52 - mmengine - INFO - Epoch(train) [343][35/63] lr: 5.1742e-03 eta: 23:16:18 time: 2.3158 data_time: 0.0651 memory: 16201 loss_prob: 0.6826 loss_thr: 0.4373 loss_db: 0.1153 loss: 1.2353 2022/08/30 08:56:07 - mmengine - INFO - Epoch(train) [343][40/63] lr: 5.1742e-03 eta: 23:16:29 time: 2.5991 data_time: 0.0686 memory: 16201 loss_prob: 0.6284 loss_thr: 0.4238 loss_db: 0.1089 loss: 1.1611 2022/08/30 08:56:20 - mmengine - INFO - Epoch(train) [343][45/63] lr: 5.1742e-03 eta: 23:16:29 time: 2.7948 data_time: 0.0698 memory: 16201 loss_prob: 0.6471 loss_thr: 0.4350 loss_db: 0.1092 loss: 1.1913 2022/08/30 08:56:33 - mmengine - INFO - Epoch(train) [343][50/63] lr: 5.1742e-03 eta: 23:16:42 time: 2.6751 data_time: 0.0769 memory: 16201 loss_prob: 0.6477 loss_thr: 0.4260 loss_db: 0.1091 loss: 1.1828 2022/08/30 08:56:48 - mmengine - INFO - Epoch(train) [343][55/63] lr: 5.1742e-03 eta: 23:16:42 time: 2.7666 data_time: 0.0498 memory: 16201 loss_prob: 0.6738 loss_thr: 0.4293 loss_db: 0.1129 loss: 1.2160 2022/08/30 08:57:03 - mmengine - INFO - Epoch(train) [343][60/63] lr: 5.1742e-03 eta: 23:17:01 time: 2.9349 data_time: 0.0638 memory: 16201 loss_prob: 0.6425 loss_thr: 0.4175 loss_db: 0.1068 loss: 1.1669 2022/08/30 08:57:10 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 08:57:27 - mmengine - INFO - Epoch(train) [344][5/63] lr: 5.1688e-03 eta: 23:17:01 time: 3.0988 data_time: 0.2817 memory: 16201 loss_prob: 0.6347 loss_thr: 0.3996 loss_db: 0.1056 loss: 1.1399 2022/08/30 08:57:41 - mmengine - INFO - Epoch(train) [344][10/63] lr: 5.1688e-03 eta: 23:17:06 time: 3.0578 data_time: 0.2900 memory: 16201 loss_prob: 0.6080 loss_thr: 0.3936 loss_db: 0.1023 loss: 1.1040 2022/08/30 08:57:55 - mmengine - INFO - Epoch(train) [344][15/63] lr: 5.1688e-03 eta: 23:17:06 time: 2.7760 data_time: 0.0575 memory: 16201 loss_prob: 0.6844 loss_thr: 0.4366 loss_db: 0.1189 loss: 1.2399 2022/08/30 08:58:08 - mmengine - INFO - Epoch(train) [344][20/63] lr: 5.1688e-03 eta: 23:17:21 time: 2.7618 data_time: 0.0666 memory: 16201 loss_prob: 0.7104 loss_thr: 0.4568 loss_db: 0.1248 loss: 1.2920 2022/08/30 08:58:21 - mmengine - INFO - Epoch(train) [344][25/63] lr: 5.1688e-03 eta: 23:17:21 time: 2.6196 data_time: 0.0581 memory: 16201 loss_prob: 0.7563 loss_thr: 0.4817 loss_db: 0.1260 loss: 1.3640 2022/08/30 08:58:33 - mmengine - INFO - Epoch(train) [344][30/63] lr: 5.1688e-03 eta: 23:17:28 time: 2.4519 data_time: 0.0618 memory: 16201 loss_prob: 0.7094 loss_thr: 0.4434 loss_db: 0.1155 loss: 1.2683 2022/08/30 08:58:46 - mmengine - INFO - Epoch(train) [344][35/63] lr: 5.1688e-03 eta: 23:17:28 time: 2.5015 data_time: 0.0800 memory: 16201 loss_prob: 0.6010 loss_thr: 0.3930 loss_db: 0.1017 loss: 1.0956 2022/08/30 08:58:56 - mmengine - INFO - Epoch(train) [344][40/63] lr: 5.1688e-03 eta: 23:17:30 time: 2.2615 data_time: 0.0619 memory: 16201 loss_prob: 0.6027 loss_thr: 0.3990 loss_db: 0.1043 loss: 1.1059 2022/08/30 08:59:11 - mmengine - INFO - Epoch(train) [344][45/63] lr: 5.1688e-03 eta: 23:17:30 time: 2.4625 data_time: 0.0565 memory: 16201 loss_prob: 0.6703 loss_thr: 0.4126 loss_db: 0.1115 loss: 1.1944 2022/08/30 08:59:24 - mmengine - INFO - Epoch(train) [344][50/63] lr: 5.1688e-03 eta: 23:17:46 time: 2.8024 data_time: 0.0571 memory: 16201 loss_prob: 0.7189 loss_thr: 0.4380 loss_db: 0.1160 loss: 1.2729 2022/08/30 08:59:36 - mmengine - INFO - Epoch(train) [344][55/63] lr: 5.1688e-03 eta: 23:17:46 time: 2.5413 data_time: 0.0489 memory: 16201 loss_prob: 0.7103 loss_thr: 0.4660 loss_db: 0.1177 loss: 1.2941 2022/08/30 08:59:49 - mmengine - INFO - Epoch(train) [344][60/63] lr: 5.1688e-03 eta: 23:17:55 time: 2.5431 data_time: 0.0602 memory: 16201 loss_prob: 0.6607 loss_thr: 0.4405 loss_db: 0.1144 loss: 1.2155 2022/08/30 08:59:57 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:00:13 - mmengine - INFO - Epoch(train) [345][5/63] lr: 5.1634e-03 eta: 23:17:55 time: 2.9365 data_time: 0.2795 memory: 16201 loss_prob: 0.5934 loss_thr: 0.3999 loss_db: 0.1025 loss: 1.0958 2022/08/30 09:00:26 - mmengine - INFO - Epoch(train) [345][10/63] lr: 5.1634e-03 eta: 23:17:57 time: 2.9490 data_time: 0.2899 memory: 16201 loss_prob: 0.5881 loss_thr: 0.3994 loss_db: 0.0963 loss: 1.0838 2022/08/30 09:00:39 - mmengine - INFO - Epoch(train) [345][15/63] lr: 5.1634e-03 eta: 23:17:57 time: 2.6696 data_time: 0.0562 memory: 16201 loss_prob: 0.5677 loss_thr: 0.3884 loss_db: 0.0972 loss: 1.0532 2022/08/30 09:00:54 - mmengine - INFO - Epoch(train) [345][20/63] lr: 5.1634e-03 eta: 23:18:13 time: 2.8256 data_time: 0.0572 memory: 16201 loss_prob: 0.6018 loss_thr: 0.4071 loss_db: 0.1063 loss: 1.1153 2022/08/30 09:01:08 - mmengine - INFO - Epoch(train) [345][25/63] lr: 5.1634e-03 eta: 23:18:13 time: 2.8633 data_time: 0.0606 memory: 16201 loss_prob: 0.6205 loss_thr: 0.4228 loss_db: 0.1074 loss: 1.1507 2022/08/30 09:01:21 - mmengine - INFO - Epoch(train) [345][30/63] lr: 5.1634e-03 eta: 23:18:27 time: 2.7151 data_time: 0.0526 memory: 16201 loss_prob: 0.6192 loss_thr: 0.4122 loss_db: 0.1066 loss: 1.1380 2022/08/30 09:01:34 - mmengine - INFO - Epoch(train) [345][35/63] lr: 5.1634e-03 eta: 23:18:27 time: 2.6177 data_time: 0.0713 memory: 16201 loss_prob: 0.7289 loss_thr: 0.4420 loss_db: 0.1183 loss: 1.2892 2022/08/30 09:01:47 - mmengine - INFO - Epoch(train) [345][40/63] lr: 5.1634e-03 eta: 23:18:35 time: 2.5232 data_time: 0.0539 memory: 16201 loss_prob: 0.7761 loss_thr: 0.4732 loss_db: 0.1255 loss: 1.3749 2022/08/30 09:02:01 - mmengine - INFO - Epoch(train) [345][45/63] lr: 5.1634e-03 eta: 23:18:35 time: 2.7150 data_time: 0.0547 memory: 16201 loss_prob: 0.7188 loss_thr: 0.4706 loss_db: 0.1240 loss: 1.3134 2022/08/30 09:02:14 - mmengine - INFO - Epoch(train) [345][50/63] lr: 5.1634e-03 eta: 23:18:49 time: 2.7402 data_time: 0.0799 memory: 16201 loss_prob: 0.6523 loss_thr: 0.4330 loss_db: 0.1133 loss: 1.1986 2022/08/30 09:02:26 - mmengine - INFO - Epoch(train) [345][55/63] lr: 5.1634e-03 eta: 23:18:49 time: 2.4908 data_time: 0.0621 memory: 16201 loss_prob: 0.6284 loss_thr: 0.4143 loss_db: 0.1106 loss: 1.1533 2022/08/30 09:02:38 - mmengine - INFO - Epoch(train) [345][60/63] lr: 5.1634e-03 eta: 23:18:53 time: 2.3669 data_time: 0.0588 memory: 16201 loss_prob: 0.7126 loss_thr: 0.4354 loss_db: 0.1199 loss: 1.2680 2022/08/30 09:02:45 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:03:01 - mmengine - INFO - Epoch(train) [346][5/63] lr: 5.1579e-03 eta: 23:18:53 time: 2.7411 data_time: 0.2774 memory: 16201 loss_prob: 0.6889 loss_thr: 0.4465 loss_db: 0.1185 loss: 1.2539 2022/08/30 09:03:14 - mmengine - INFO - Epoch(train) [346][10/63] lr: 5.1579e-03 eta: 23:18:55 time: 2.9248 data_time: 0.3033 memory: 16201 loss_prob: 0.6683 loss_thr: 0.4523 loss_db: 0.1159 loss: 1.2365 2022/08/30 09:03:27 - mmengine - INFO - Epoch(train) [346][15/63] lr: 5.1579e-03 eta: 23:18:55 time: 2.5781 data_time: 0.0644 memory: 16201 loss_prob: 0.6983 loss_thr: 0.4663 loss_db: 0.1178 loss: 1.2825 2022/08/30 09:03:41 - mmengine - INFO - Epoch(train) [346][20/63] lr: 5.1579e-03 eta: 23:19:07 time: 2.6412 data_time: 0.0609 memory: 16201 loss_prob: 0.6321 loss_thr: 0.4209 loss_db: 0.1053 loss: 1.1584 2022/08/30 09:03:56 - mmengine - INFO - Epoch(train) [346][25/63] lr: 5.1579e-03 eta: 23:19:07 time: 2.9014 data_time: 0.0661 memory: 16201 loss_prob: 0.6018 loss_thr: 0.3882 loss_db: 0.1035 loss: 1.0935 2022/08/30 09:04:12 - mmengine - INFO - Epoch(train) [346][30/63] lr: 5.1579e-03 eta: 23:19:29 time: 3.1065 data_time: 0.0743 memory: 16201 loss_prob: 0.6374 loss_thr: 0.4121 loss_db: 0.1109 loss: 1.1604 2022/08/30 09:04:27 - mmengine - INFO - Epoch(train) [346][35/63] lr: 5.1579e-03 eta: 23:19:29 time: 3.0631 data_time: 0.0825 memory: 16201 loss_prob: 0.6527 loss_thr: 0.4265 loss_db: 0.1132 loss: 1.1924 2022/08/30 09:04:41 - mmengine - INFO - Epoch(train) [346][40/63] lr: 5.1579e-03 eta: 23:19:48 time: 2.9421 data_time: 0.0663 memory: 16201 loss_prob: 0.7225 loss_thr: 0.4384 loss_db: 0.1266 loss: 1.2874 2022/08/30 09:04:55 - mmengine - INFO - Epoch(train) [346][45/63] lr: 5.1579e-03 eta: 23:19:48 time: 2.7912 data_time: 0.0688 memory: 16201 loss_prob: 0.7592 loss_thr: 0.4475 loss_db: 0.1292 loss: 1.3359 2022/08/30 09:05:10 - mmengine - INFO - Epoch(train) [346][50/63] lr: 5.1579e-03 eta: 23:20:04 time: 2.8481 data_time: 0.0851 memory: 16201 loss_prob: 0.7416 loss_thr: 0.4387 loss_db: 0.1236 loss: 1.3039 2022/08/30 09:05:23 - mmengine - INFO - Epoch(train) [346][55/63] lr: 5.1579e-03 eta: 23:20:04 time: 2.8557 data_time: 0.0768 memory: 16201 loss_prob: 0.7235 loss_thr: 0.4409 loss_db: 0.1227 loss: 1.2872 2022/08/30 09:05:36 - mmengine - INFO - Epoch(train) [346][60/63] lr: 5.1579e-03 eta: 23:20:14 time: 2.5930 data_time: 0.0667 memory: 16201 loss_prob: 0.6823 loss_thr: 0.4436 loss_db: 0.1158 loss: 1.2417 2022/08/30 09:05:42 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:05:58 - mmengine - INFO - Epoch(train) [347][5/63] lr: 5.1525e-03 eta: 23:20:14 time: 2.7285 data_time: 0.2913 memory: 16201 loss_prob: 0.6715 loss_thr: 0.4504 loss_db: 0.1172 loss: 1.2391 2022/08/30 09:06:10 - mmengine - INFO - Epoch(train) [347][10/63] lr: 5.1525e-03 eta: 23:20:13 time: 2.8315 data_time: 0.2991 memory: 16201 loss_prob: 0.6590 loss_thr: 0.4327 loss_db: 0.1111 loss: 1.2028 2022/08/30 09:06:25 - mmengine - INFO - Epoch(train) [347][15/63] lr: 5.1525e-03 eta: 23:20:13 time: 2.6930 data_time: 0.0590 memory: 16201 loss_prob: 0.6462 loss_thr: 0.4309 loss_db: 0.1071 loss: 1.1842 2022/08/30 09:06:36 - mmengine - INFO - Epoch(train) [347][20/63] lr: 5.1525e-03 eta: 23:20:23 time: 2.5801 data_time: 0.0637 memory: 16201 loss_prob: 0.6184 loss_thr: 0.4186 loss_db: 0.1034 loss: 1.1404 2022/08/30 09:06:51 - mmengine - INFO - Epoch(train) [347][25/63] lr: 5.1525e-03 eta: 23:20:23 time: 2.6367 data_time: 0.0738 memory: 16201 loss_prob: 0.6238 loss_thr: 0.4077 loss_db: 0.1060 loss: 1.1375 2022/08/30 09:07:05 - mmengine - INFO - Epoch(train) [347][30/63] lr: 5.1525e-03 eta: 23:20:40 time: 2.9018 data_time: 0.0655 memory: 16201 loss_prob: 0.6674 loss_thr: 0.4229 loss_db: 0.1136 loss: 1.2039 2022/08/30 09:07:18 - mmengine - INFO - Epoch(train) [347][35/63] lr: 5.1525e-03 eta: 23:20:40 time: 2.6574 data_time: 0.0641 memory: 16201 loss_prob: 0.6890 loss_thr: 0.4461 loss_db: 0.1188 loss: 1.2538 2022/08/30 09:07:32 - mmengine - INFO - Epoch(train) [347][40/63] lr: 5.1525e-03 eta: 23:20:52 time: 2.6853 data_time: 0.0490 memory: 16201 loss_prob: 0.6466 loss_thr: 0.4417 loss_db: 0.1131 loss: 1.2014 2022/08/30 09:07:46 - mmengine - INFO - Epoch(train) [347][45/63] lr: 5.1525e-03 eta: 23:20:52 time: 2.8284 data_time: 0.0552 memory: 16201 loss_prob: 0.6155 loss_thr: 0.4327 loss_db: 0.1064 loss: 1.1546 2022/08/30 09:08:01 - mmengine - INFO - Epoch(train) [347][50/63] lr: 5.1525e-03 eta: 23:21:08 time: 2.8585 data_time: 0.0711 memory: 16201 loss_prob: 0.6008 loss_thr: 0.4160 loss_db: 0.1024 loss: 1.1192 2022/08/30 09:08:14 - mmengine - INFO - Epoch(train) [347][55/63] lr: 5.1525e-03 eta: 23:21:08 time: 2.7710 data_time: 0.0553 memory: 16201 loss_prob: 0.7022 loss_thr: 0.4373 loss_db: 0.1209 loss: 1.2605 2022/08/30 09:08:27 - mmengine - INFO - Epoch(train) [347][60/63] lr: 5.1525e-03 eta: 23:21:19 time: 2.6403 data_time: 0.0647 memory: 16201 loss_prob: 0.7421 loss_thr: 0.4562 loss_db: 0.1259 loss: 1.3242 2022/08/30 09:08:35 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:08:49 - mmengine - INFO - Epoch(train) [348][5/63] lr: 5.1471e-03 eta: 23:21:19 time: 2.6507 data_time: 0.2873 memory: 16201 loss_prob: 0.6333 loss_thr: 0.4115 loss_db: 0.1076 loss: 1.1523 2022/08/30 09:09:03 - mmengine - INFO - Epoch(train) [348][10/63] lr: 5.1471e-03 eta: 23:21:19 time: 2.8527 data_time: 0.3003 memory: 16201 loss_prob: 0.6298 loss_thr: 0.4050 loss_db: 0.1070 loss: 1.1418 2022/08/30 09:09:17 - mmengine - INFO - Epoch(train) [348][15/63] lr: 5.1471e-03 eta: 23:21:19 time: 2.7691 data_time: 0.0535 memory: 16201 loss_prob: 0.6434 loss_thr: 0.4304 loss_db: 0.1092 loss: 1.1831 2022/08/30 09:09:30 - mmengine - INFO - Epoch(train) [348][20/63] lr: 5.1471e-03 eta: 23:21:31 time: 2.6977 data_time: 0.0640 memory: 16201 loss_prob: 0.6686 loss_thr: 0.4471 loss_db: 0.1149 loss: 1.2306 2022/08/30 09:09:42 - mmengine - INFO - Epoch(train) [348][25/63] lr: 5.1471e-03 eta: 23:21:31 time: 2.4862 data_time: 0.0539 memory: 16201 loss_prob: 0.6645 loss_thr: 0.4184 loss_db: 0.1153 loss: 1.1982 2022/08/30 09:09:56 - mmengine - INFO - Epoch(train) [348][30/63] lr: 5.1471e-03 eta: 23:21:40 time: 2.5675 data_time: 0.0499 memory: 16201 loss_prob: 0.6291 loss_thr: 0.3973 loss_db: 0.1082 loss: 1.1346 2022/08/30 09:10:10 - mmengine - INFO - Epoch(train) [348][35/63] lr: 5.1471e-03 eta: 23:21:40 time: 2.8336 data_time: 0.0670 memory: 16201 loss_prob: 0.6649 loss_thr: 0.4234 loss_db: 0.1126 loss: 1.2009 2022/08/30 09:10:23 - mmengine - INFO - Epoch(train) [348][40/63] lr: 5.1471e-03 eta: 23:21:52 time: 2.6806 data_time: 0.0486 memory: 16201 loss_prob: 0.7785 loss_thr: 0.4458 loss_db: 0.1283 loss: 1.3527 2022/08/30 09:10:37 - mmengine - INFO - Epoch(train) [348][45/63] lr: 5.1471e-03 eta: 23:21:52 time: 2.6958 data_time: 0.0565 memory: 16201 loss_prob: 0.7900 loss_thr: 0.4406 loss_db: 0.1278 loss: 1.3584 2022/08/30 09:10:49 - mmengine - INFO - Epoch(train) [348][50/63] lr: 5.1471e-03 eta: 23:22:03 time: 2.6514 data_time: 0.0745 memory: 16201 loss_prob: 0.7846 loss_thr: 0.4461 loss_db: 0.1285 loss: 1.3592 2022/08/30 09:11:04 - mmengine - INFO - Epoch(train) [348][55/63] lr: 5.1471e-03 eta: 23:22:03 time: 2.6493 data_time: 0.0580 memory: 16201 loss_prob: 0.7960 loss_thr: 0.4464 loss_db: 0.1354 loss: 1.3777 2022/08/30 09:11:18 - mmengine - INFO - Epoch(train) [348][60/63] lr: 5.1471e-03 eta: 23:22:18 time: 2.8208 data_time: 0.0560 memory: 16201 loss_prob: 0.9763 loss_thr: 0.4698 loss_db: 0.1516 loss: 1.5976 2022/08/30 09:11:24 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:11:40 - mmengine - INFO - Epoch(train) [349][5/63] lr: 5.1416e-03 eta: 23:22:18 time: 2.8876 data_time: 0.2998 memory: 16201 loss_prob: 0.8855 loss_thr: 0.4894 loss_db: 0.1505 loss: 1.5253 2022/08/30 09:11:55 - mmengine - INFO - Epoch(train) [349][10/63] lr: 5.1416e-03 eta: 23:22:22 time: 3.0492 data_time: 0.3017 memory: 16201 loss_prob: 0.7189 loss_thr: 0.4467 loss_db: 0.1211 loss: 1.2867 2022/08/30 09:12:08 - mmengine - INFO - Epoch(train) [349][15/63] lr: 5.1416e-03 eta: 23:22:22 time: 2.7981 data_time: 0.0548 memory: 16201 loss_prob: 0.6857 loss_thr: 0.4385 loss_db: 0.1174 loss: 1.2417 2022/08/30 09:12:21 - mmengine - INFO - Epoch(train) [349][20/63] lr: 5.1416e-03 eta: 23:22:32 time: 2.6244 data_time: 0.0602 memory: 16201 loss_prob: 0.7170 loss_thr: 0.4477 loss_db: 0.1211 loss: 1.2858 2022/08/30 09:12:36 - mmengine - INFO - Epoch(train) [349][25/63] lr: 5.1416e-03 eta: 23:22:32 time: 2.8008 data_time: 0.0745 memory: 16201 loss_prob: 0.7355 loss_thr: 0.4534 loss_db: 0.1244 loss: 1.3133 2022/08/30 09:12:50 - mmengine - INFO - Epoch(train) [349][30/63] lr: 5.1416e-03 eta: 23:22:49 time: 2.8993 data_time: 0.0620 memory: 16201 loss_prob: 0.7729 loss_thr: 0.4639 loss_db: 0.1278 loss: 1.3647 2022/08/30 09:13:04 - mmengine - INFO - Epoch(train) [349][35/63] lr: 5.1416e-03 eta: 23:22:49 time: 2.7809 data_time: 0.0618 memory: 16201 loss_prob: 0.8059 loss_thr: 0.4753 loss_db: 0.1299 loss: 1.4111 2022/08/30 09:13:17 - mmengine - INFO - Epoch(train) [349][40/63] lr: 5.1416e-03 eta: 23:23:02 time: 2.7381 data_time: 0.0494 memory: 16201 loss_prob: 0.7883 loss_thr: 0.4617 loss_db: 0.1324 loss: 1.3824 2022/08/30 09:13:31 - mmengine - INFO - Epoch(train) [349][45/63] lr: 5.1416e-03 eta: 23:23:02 time: 2.6595 data_time: 0.0587 memory: 16201 loss_prob: 0.7395 loss_thr: 0.4630 loss_db: 0.1259 loss: 1.3284 2022/08/30 09:13:42 - mmengine - INFO - Epoch(train) [349][50/63] lr: 5.1416e-03 eta: 23:23:09 time: 2.5207 data_time: 0.0883 memory: 16201 loss_prob: 0.7261 loss_thr: 0.4639 loss_db: 0.1217 loss: 1.3117 2022/08/30 09:13:57 - mmengine - INFO - Epoch(train) [349][55/63] lr: 5.1416e-03 eta: 23:23:09 time: 2.5957 data_time: 0.0719 memory: 16201 loss_prob: 0.7081 loss_thr: 0.4397 loss_db: 0.1202 loss: 1.2680 2022/08/30 09:14:10 - mmengine - INFO - Epoch(train) [349][60/63] lr: 5.1416e-03 eta: 23:23:23 time: 2.7805 data_time: 0.0710 memory: 16201 loss_prob: 0.6978 loss_thr: 0.4380 loss_db: 0.1216 loss: 1.2573 2022/08/30 09:14:17 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:14:34 - mmengine - INFO - Epoch(train) [350][5/63] lr: 5.1362e-03 eta: 23:23:23 time: 2.7898 data_time: 0.2824 memory: 16201 loss_prob: 0.6440 loss_thr: 0.4148 loss_db: 0.1096 loss: 1.1684 2022/08/30 09:14:48 - mmengine - INFO - Epoch(train) [350][10/63] lr: 5.1362e-03 eta: 23:23:28 time: 3.0552 data_time: 0.3067 memory: 16201 loss_prob: 0.6354 loss_thr: 0.4136 loss_db: 0.1105 loss: 1.1595 2022/08/30 09:14:57 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:15:03 - mmengine - INFO - Epoch(train) [350][15/63] lr: 5.1362e-03 eta: 23:23:28 time: 2.8840 data_time: 0.0606 memory: 16201 loss_prob: 0.6661 loss_thr: 0.4376 loss_db: 0.1157 loss: 1.2194 2022/08/30 09:15:18 - mmengine - INFO - Epoch(train) [350][20/63] lr: 5.1362e-03 eta: 23:23:46 time: 2.9656 data_time: 0.0599 memory: 16201 loss_prob: 0.7345 loss_thr: 0.4398 loss_db: 0.1199 loss: 1.2941 2022/08/30 09:15:33 - mmengine - INFO - Epoch(train) [350][25/63] lr: 5.1362e-03 eta: 23:23:46 time: 3.0441 data_time: 0.0651 memory: 16201 loss_prob: 0.6941 loss_thr: 0.4184 loss_db: 0.1121 loss: 1.2247 2022/08/30 09:15:48 - mmengine - INFO - Epoch(train) [350][30/63] lr: 5.1362e-03 eta: 23:24:04 time: 2.9835 data_time: 0.0499 memory: 16201 loss_prob: 0.6463 loss_thr: 0.4178 loss_db: 0.1094 loss: 1.1735 2022/08/30 09:16:01 - mmengine - INFO - Epoch(train) [350][35/63] lr: 5.1362e-03 eta: 23:24:04 time: 2.7725 data_time: 0.0685 memory: 16201 loss_prob: 0.7371 loss_thr: 0.4698 loss_db: 0.1288 loss: 1.3357 2022/08/30 09:16:14 - mmengine - INFO - Epoch(train) [350][40/63] lr: 5.1362e-03 eta: 23:24:14 time: 2.6272 data_time: 0.0519 memory: 16201 loss_prob: 0.7022 loss_thr: 0.4426 loss_db: 0.1202 loss: 1.2650 2022/08/30 09:16:28 - mmengine - INFO - Epoch(train) [350][45/63] lr: 5.1362e-03 eta: 23:24:14 time: 2.7069 data_time: 0.0606 memory: 16201 loss_prob: 0.6292 loss_thr: 0.3976 loss_db: 0.1060 loss: 1.1328 2022/08/30 09:16:42 - mmengine - INFO - Epoch(train) [350][50/63] lr: 5.1362e-03 eta: 23:24:29 time: 2.8050 data_time: 0.0866 memory: 16201 loss_prob: 0.6707 loss_thr: 0.4361 loss_db: 0.1149 loss: 1.2217 2022/08/30 09:16:54 - mmengine - INFO - Epoch(train) [350][55/63] lr: 5.1362e-03 eta: 23:24:29 time: 2.6135 data_time: 0.0564 memory: 16201 loss_prob: 0.6871 loss_thr: 0.4548 loss_db: 0.1182 loss: 1.2601 2022/08/30 09:17:08 - mmengine - INFO - Epoch(train) [350][60/63] lr: 5.1362e-03 eta: 23:24:37 time: 2.5762 data_time: 0.0620 memory: 16201 loss_prob: 0.6833 loss_thr: 0.4504 loss_db: 0.1179 loss: 1.2516 2022/08/30 09:17:14 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:17:31 - mmengine - INFO - Epoch(train) [351][5/63] lr: 5.1307e-03 eta: 23:24:37 time: 2.8693 data_time: 0.3084 memory: 16201 loss_prob: 0.6726 loss_thr: 0.4689 loss_db: 0.1147 loss: 1.2562 2022/08/30 09:17:46 - mmengine - INFO - Epoch(train) [351][10/63] lr: 5.1307e-03 eta: 23:24:43 time: 3.1255 data_time: 0.3213 memory: 16201 loss_prob: 0.7118 loss_thr: 0.4531 loss_db: 0.1175 loss: 1.2824 2022/08/30 09:17:57 - mmengine - INFO - Epoch(train) [351][15/63] lr: 5.1307e-03 eta: 23:24:43 time: 2.6237 data_time: 0.0477 memory: 16201 loss_prob: 0.6917 loss_thr: 0.4184 loss_db: 0.1154 loss: 1.2255 2022/08/30 09:18:12 - mmengine - INFO - Epoch(train) [351][20/63] lr: 5.1307e-03 eta: 23:24:53 time: 2.6423 data_time: 0.0510 memory: 16201 loss_prob: 0.6831 loss_thr: 0.4268 loss_db: 0.1182 loss: 1.2281 2022/08/30 09:18:24 - mmengine - INFO - Epoch(train) [351][25/63] lr: 5.1307e-03 eta: 23:24:53 time: 2.7074 data_time: 0.0509 memory: 16201 loss_prob: 0.6781 loss_thr: 0.4372 loss_db: 0.1164 loss: 1.2318 2022/08/30 09:18:38 - mmengine - INFO - Epoch(train) [351][30/63] lr: 5.1307e-03 eta: 23:25:01 time: 2.5555 data_time: 0.0475 memory: 16201 loss_prob: 0.6313 loss_thr: 0.4265 loss_db: 0.1094 loss: 1.1673 2022/08/30 09:18:49 - mmengine - INFO - Epoch(train) [351][35/63] lr: 5.1307e-03 eta: 23:25:01 time: 2.4430 data_time: 0.0684 memory: 16201 loss_prob: 0.6099 loss_thr: 0.4079 loss_db: 0.1074 loss: 1.1251 2022/08/30 09:19:03 - mmengine - INFO - Epoch(train) [351][40/63] lr: 5.1307e-03 eta: 23:25:08 time: 2.4935 data_time: 0.0477 memory: 16201 loss_prob: 0.5999 loss_thr: 0.4104 loss_db: 0.1034 loss: 1.1137 2022/08/30 09:19:15 - mmengine - INFO - Epoch(train) [351][45/63] lr: 5.1307e-03 eta: 23:25:08 time: 2.6392 data_time: 0.0487 memory: 16201 loss_prob: 0.6207 loss_thr: 0.4289 loss_db: 0.1059 loss: 1.1555 2022/08/30 09:19:28 - mmengine - INFO - Epoch(train) [351][50/63] lr: 5.1307e-03 eta: 23:25:15 time: 2.5375 data_time: 0.0752 memory: 16201 loss_prob: 0.7071 loss_thr: 0.4519 loss_db: 0.1202 loss: 1.2792 2022/08/30 09:19:42 - mmengine - INFO - Epoch(train) [351][55/63] lr: 5.1307e-03 eta: 23:25:15 time: 2.7005 data_time: 0.0534 memory: 16201 loss_prob: 0.7117 loss_thr: 0.4472 loss_db: 0.1213 loss: 1.2803 2022/08/30 09:19:58 - mmengine - INFO - Epoch(train) [351][60/63] lr: 5.1307e-03 eta: 23:25:33 time: 2.9739 data_time: 0.0523 memory: 16201 loss_prob: 0.6076 loss_thr: 0.4018 loss_db: 0.1040 loss: 1.1134 2022/08/30 09:20:04 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:20:19 - mmengine - INFO - Epoch(train) [352][5/63] lr: 5.1253e-03 eta: 23:25:33 time: 2.7376 data_time: 0.2657 memory: 16201 loss_prob: 0.6544 loss_thr: 0.4162 loss_db: 0.1142 loss: 1.1848 2022/08/30 09:20:31 - mmengine - INFO - Epoch(train) [352][10/63] lr: 5.1253e-03 eta: 23:25:29 time: 2.7302 data_time: 0.2858 memory: 16201 loss_prob: 0.7184 loss_thr: 0.4575 loss_db: 0.1244 loss: 1.3003 2022/08/30 09:20:45 - mmengine - INFO - Epoch(train) [352][15/63] lr: 5.1253e-03 eta: 23:25:29 time: 2.6556 data_time: 0.0469 memory: 16201 loss_prob: 0.6894 loss_thr: 0.4365 loss_db: 0.1184 loss: 1.2443 2022/08/30 09:21:00 - mmengine - INFO - Epoch(train) [352][20/63] lr: 5.1253e-03 eta: 23:25:45 time: 2.8956 data_time: 0.0416 memory: 16201 loss_prob: 0.6057 loss_thr: 0.4060 loss_db: 0.1049 loss: 1.1166 2022/08/30 09:21:14 - mmengine - INFO - Epoch(train) [352][25/63] lr: 5.1253e-03 eta: 23:25:45 time: 2.8150 data_time: 0.0565 memory: 16201 loss_prob: 0.6443 loss_thr: 0.4352 loss_db: 0.1091 loss: 1.1886 2022/08/30 09:21:25 - mmengine - INFO - Epoch(train) [352][30/63] lr: 5.1253e-03 eta: 23:25:51 time: 2.4630 data_time: 0.0508 memory: 16201 loss_prob: 0.6872 loss_thr: 0.4516 loss_db: 0.1173 loss: 1.2560 2022/08/30 09:21:39 - mmengine - INFO - Epoch(train) [352][35/63] lr: 5.1253e-03 eta: 23:25:51 time: 2.5027 data_time: 0.0635 memory: 16201 loss_prob: 0.6725 loss_thr: 0.4408 loss_db: 0.1168 loss: 1.2301 2022/08/30 09:21:50 - mmengine - INFO - Epoch(train) [352][40/63] lr: 5.1253e-03 eta: 23:25:57 time: 2.4963 data_time: 0.0547 memory: 16201 loss_prob: 0.6374 loss_thr: 0.4136 loss_db: 0.1093 loss: 1.1603 2022/08/30 09:22:02 - mmengine - INFO - Epoch(train) [352][45/63] lr: 5.1253e-03 eta: 23:25:57 time: 2.3529 data_time: 0.0492 memory: 16201 loss_prob: 0.5959 loss_thr: 0.3847 loss_db: 0.1039 loss: 1.0846 2022/08/30 09:22:16 - mmengine - INFO - Epoch(train) [352][50/63] lr: 5.1253e-03 eta: 23:26:07 time: 2.6400 data_time: 0.0779 memory: 16201 loss_prob: 0.6579 loss_thr: 0.4236 loss_db: 0.1143 loss: 1.1958 2022/08/30 09:22:31 - mmengine - INFO - Epoch(train) [352][55/63] lr: 5.1253e-03 eta: 23:26:07 time: 2.8411 data_time: 0.0566 memory: 16201 loss_prob: 0.6718 loss_thr: 0.4367 loss_db: 0.1125 loss: 1.2211 2022/08/30 09:22:44 - mmengine - INFO - Epoch(train) [352][60/63] lr: 5.1253e-03 eta: 23:26:20 time: 2.7580 data_time: 0.0481 memory: 16201 loss_prob: 0.6431 loss_thr: 0.4350 loss_db: 0.1079 loss: 1.1859 2022/08/30 09:22:51 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:23:06 - mmengine - INFO - Epoch(train) [353][5/63] lr: 5.1199e-03 eta: 23:26:20 time: 2.8376 data_time: 0.2751 memory: 16201 loss_prob: 0.6384 loss_thr: 0.4161 loss_db: 0.1121 loss: 1.1667 2022/08/30 09:23:19 - mmengine - INFO - Epoch(train) [353][10/63] lr: 5.1199e-03 eta: 23:26:19 time: 2.8586 data_time: 0.2961 memory: 16201 loss_prob: 0.6053 loss_thr: 0.3889 loss_db: 0.1050 loss: 1.0993 2022/08/30 09:23:34 - mmengine - INFO - Epoch(train) [353][15/63] lr: 5.1199e-03 eta: 23:26:19 time: 2.7503 data_time: 0.0638 memory: 16201 loss_prob: 0.6247 loss_thr: 0.3911 loss_db: 0.1053 loss: 1.1211 2022/08/30 09:23:49 - mmengine - INFO - Epoch(train) [353][20/63] lr: 5.1199e-03 eta: 23:26:37 time: 2.9819 data_time: 0.0608 memory: 16201 loss_prob: 0.6459 loss_thr: 0.4229 loss_db: 0.1121 loss: 1.1809 2022/08/30 09:24:02 - mmengine - INFO - Epoch(train) [353][25/63] lr: 5.1199e-03 eta: 23:26:37 time: 2.7972 data_time: 0.0748 memory: 16201 loss_prob: 0.6484 loss_thr: 0.4344 loss_db: 0.1125 loss: 1.1953 2022/08/30 09:24:15 - mmengine - INFO - Epoch(train) [353][30/63] lr: 5.1199e-03 eta: 23:26:44 time: 2.5668 data_time: 0.0565 memory: 16201 loss_prob: 0.6352 loss_thr: 0.4177 loss_db: 0.1098 loss: 1.1627 2022/08/30 09:24:28 - mmengine - INFO - Epoch(train) [353][35/63] lr: 5.1199e-03 eta: 23:26:44 time: 2.5858 data_time: 0.0500 memory: 16201 loss_prob: 0.6072 loss_thr: 0.4080 loss_db: 0.1063 loss: 1.1215 2022/08/30 09:24:41 - mmengine - INFO - Epoch(train) [353][40/63] lr: 5.1199e-03 eta: 23:26:55 time: 2.6613 data_time: 0.0529 memory: 16201 loss_prob: 0.6382 loss_thr: 0.4320 loss_db: 0.1099 loss: 1.1802 2022/08/30 09:24:55 - mmengine - INFO - Epoch(train) [353][45/63] lr: 5.1199e-03 eta: 23:26:55 time: 2.7383 data_time: 0.0551 memory: 16201 loss_prob: 0.6638 loss_thr: 0.4522 loss_db: 0.1135 loss: 1.2296 2022/08/30 09:25:08 - mmengine - INFO - Epoch(train) [353][50/63] lr: 5.1199e-03 eta: 23:27:06 time: 2.6998 data_time: 0.0692 memory: 16201 loss_prob: 0.6465 loss_thr: 0.4389 loss_db: 0.1108 loss: 1.1962 2022/08/30 09:25:20 - mmengine - INFO - Epoch(train) [353][55/63] lr: 5.1199e-03 eta: 23:27:06 time: 2.5129 data_time: 0.0644 memory: 16201 loss_prob: 0.6783 loss_thr: 0.4472 loss_db: 0.1163 loss: 1.2418 2022/08/30 09:25:33 - mmengine - INFO - Epoch(train) [353][60/63] lr: 5.1199e-03 eta: 23:27:11 time: 2.4526 data_time: 0.0584 memory: 16201 loss_prob: 0.6905 loss_thr: 0.4599 loss_db: 0.1192 loss: 1.2696 2022/08/30 09:25:40 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:25:56 - mmengine - INFO - Epoch(train) [354][5/63] lr: 5.1144e-03 eta: 23:27:11 time: 2.7224 data_time: 0.2847 memory: 16201 loss_prob: 0.6030 loss_thr: 0.3952 loss_db: 0.1064 loss: 1.1046 2022/08/30 09:26:08 - mmengine - INFO - Epoch(train) [354][10/63] lr: 5.1144e-03 eta: 23:27:06 time: 2.7192 data_time: 0.2872 memory: 16201 loss_prob: 0.5513 loss_thr: 0.3617 loss_db: 0.0948 loss: 1.0077 2022/08/30 09:26:21 - mmengine - INFO - Epoch(train) [354][15/63] lr: 5.1144e-03 eta: 23:27:06 time: 2.5321 data_time: 0.0667 memory: 16201 loss_prob: 0.6582 loss_thr: 0.4206 loss_db: 0.1097 loss: 1.1885 2022/08/30 09:26:35 - mmengine - INFO - Epoch(train) [354][20/63] lr: 5.1144e-03 eta: 23:27:17 time: 2.6992 data_time: 0.0544 memory: 16201 loss_prob: 0.7174 loss_thr: 0.4519 loss_db: 0.1210 loss: 1.2902 2022/08/30 09:26:47 - mmengine - INFO - Epoch(train) [354][25/63] lr: 5.1144e-03 eta: 23:27:17 time: 2.6376 data_time: 0.0714 memory: 16201 loss_prob: 0.6779 loss_thr: 0.4476 loss_db: 0.1161 loss: 1.2416 2022/08/30 09:27:02 - mmengine - INFO - Epoch(train) [354][30/63] lr: 5.1144e-03 eta: 23:27:28 time: 2.7058 data_time: 0.0782 memory: 16201 loss_prob: 0.6415 loss_thr: 0.4492 loss_db: 0.1099 loss: 1.2006 2022/08/30 09:27:13 - mmengine - INFO - Epoch(train) [354][35/63] lr: 5.1144e-03 eta: 23:27:28 time: 2.5174 data_time: 0.0573 memory: 16201 loss_prob: 0.6155 loss_thr: 0.4167 loss_db: 0.1066 loss: 1.1388 2022/08/30 09:27:26 - mmengine - INFO - Epoch(train) [354][40/63] lr: 5.1144e-03 eta: 23:27:34 time: 2.4882 data_time: 0.0555 memory: 16201 loss_prob: 0.6380 loss_thr: 0.4087 loss_db: 0.1099 loss: 1.1566 2022/08/30 09:27:39 - mmengine - INFO - Epoch(train) [354][45/63] lr: 5.1144e-03 eta: 23:27:34 time: 2.6609 data_time: 0.0614 memory: 16201 loss_prob: 0.6333 loss_thr: 0.4218 loss_db: 0.1066 loss: 1.1617 2022/08/30 09:27:53 - mmengine - INFO - Epoch(train) [354][50/63] lr: 5.1144e-03 eta: 23:27:44 time: 2.6633 data_time: 0.0699 memory: 16201 loss_prob: 0.5989 loss_thr: 0.4027 loss_db: 0.1029 loss: 1.1045 2022/08/30 09:28:07 - mmengine - INFO - Epoch(train) [354][55/63] lr: 5.1144e-03 eta: 23:27:44 time: 2.7562 data_time: 0.0668 memory: 16201 loss_prob: 0.6096 loss_thr: 0.3904 loss_db: 0.1045 loss: 1.1044 2022/08/30 09:28:18 - mmengine - INFO - Epoch(train) [354][60/63] lr: 5.1144e-03 eta: 23:27:50 time: 2.5246 data_time: 0.0621 memory: 16201 loss_prob: 0.6362 loss_thr: 0.4016 loss_db: 0.1046 loss: 1.1423 2022/08/30 09:28:25 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:28:41 - mmengine - INFO - Epoch(train) [355][5/63] lr: 5.1090e-03 eta: 23:27:50 time: 2.6626 data_time: 0.3068 memory: 16201 loss_prob: 0.7039 loss_thr: 0.4284 loss_db: 0.1172 loss: 1.2496 2022/08/30 09:28:51 - mmengine - INFO - Epoch(train) [355][10/63] lr: 5.1090e-03 eta: 23:27:42 time: 2.5675 data_time: 0.3208 memory: 16201 loss_prob: 0.6366 loss_thr: 0.4098 loss_db: 0.1100 loss: 1.1564 2022/08/30 09:29:05 - mmengine - INFO - Epoch(train) [355][15/63] lr: 5.1090e-03 eta: 23:27:42 time: 2.4680 data_time: 0.0682 memory: 16201 loss_prob: 0.6023 loss_thr: 0.4046 loss_db: 0.1046 loss: 1.1115 2022/08/30 09:29:19 - mmengine - INFO - Epoch(train) [355][20/63] lr: 5.1090e-03 eta: 23:27:55 time: 2.7858 data_time: 0.0662 memory: 16201 loss_prob: 0.6563 loss_thr: 0.4190 loss_db: 0.1111 loss: 1.1863 2022/08/30 09:29:31 - mmengine - INFO - Epoch(train) [355][25/63] lr: 5.1090e-03 eta: 23:27:55 time: 2.5389 data_time: 0.0610 memory: 16201 loss_prob: 0.6642 loss_thr: 0.4150 loss_db: 0.1129 loss: 1.1921 2022/08/30 09:29:43 - mmengine - INFO - Epoch(train) [355][30/63] lr: 5.1090e-03 eta: 23:27:57 time: 2.3529 data_time: 0.0595 memory: 16201 loss_prob: 0.6487 loss_thr: 0.4129 loss_db: 0.1106 loss: 1.1722 2022/08/30 09:29:57 - mmengine - INFO - Epoch(train) [355][35/63] lr: 5.1090e-03 eta: 23:27:57 time: 2.6197 data_time: 0.0737 memory: 16201 loss_prob: 0.6469 loss_thr: 0.4146 loss_db: 0.1085 loss: 1.1700 2022/08/30 09:30:10 - mmengine - INFO - Epoch(train) [355][40/63] lr: 5.1090e-03 eta: 23:28:09 time: 2.7532 data_time: 0.0545 memory: 16201 loss_prob: 0.6051 loss_thr: 0.4032 loss_db: 0.1032 loss: 1.1115 2022/08/30 09:30:24 - mmengine - INFO - Epoch(train) [355][45/63] lr: 5.1090e-03 eta: 23:28:09 time: 2.6803 data_time: 0.0507 memory: 16201 loss_prob: 0.6632 loss_thr: 0.4168 loss_db: 0.1102 loss: 1.1903 2022/08/30 09:30:38 - mmengine - INFO - Epoch(train) [355][50/63] lr: 5.1090e-03 eta: 23:28:23 time: 2.8180 data_time: 0.0591 memory: 16201 loss_prob: 0.7336 loss_thr: 0.4416 loss_db: 0.1203 loss: 1.2954 2022/08/30 09:30:50 - mmengine - INFO - Epoch(train) [355][55/63] lr: 5.1090e-03 eta: 23:28:23 time: 2.6139 data_time: 0.0515 memory: 16201 loss_prob: 0.6747 loss_thr: 0.4268 loss_db: 0.1158 loss: 1.2173 2022/08/30 09:31:04 - mmengine - INFO - Epoch(train) [355][60/63] lr: 5.1090e-03 eta: 23:28:30 time: 2.5583 data_time: 0.0718 memory: 16201 loss_prob: 0.6089 loss_thr: 0.3937 loss_db: 0.1066 loss: 1.1092 2022/08/30 09:31:10 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:31:26 - mmengine - INFO - Epoch(train) [356][5/63] lr: 5.1035e-03 eta: 23:28:30 time: 2.7196 data_time: 0.3141 memory: 16201 loss_prob: 0.6010 loss_thr: 0.4079 loss_db: 0.1030 loss: 1.1118 2022/08/30 09:31:37 - mmengine - INFO - Epoch(train) [356][10/63] lr: 5.1035e-03 eta: 23:28:26 time: 2.7639 data_time: 0.3278 memory: 16201 loss_prob: 0.6245 loss_thr: 0.4246 loss_db: 0.1079 loss: 1.1570 2022/08/30 09:31:52 - mmengine - INFO - Epoch(train) [356][15/63] lr: 5.1035e-03 eta: 23:28:26 time: 2.5905 data_time: 0.0637 memory: 16201 loss_prob: 0.6449 loss_thr: 0.4188 loss_db: 0.1102 loss: 1.1740 2022/08/30 09:32:06 - mmengine - INFO - Epoch(train) [356][20/63] lr: 5.1035e-03 eta: 23:28:40 time: 2.8519 data_time: 0.0512 memory: 16201 loss_prob: 0.6348 loss_thr: 0.3917 loss_db: 0.1072 loss: 1.1337 2022/08/30 09:32:20 - mmengine - INFO - Epoch(train) [356][25/63] lr: 5.1035e-03 eta: 23:28:40 time: 2.7857 data_time: 0.0731 memory: 16201 loss_prob: 0.6272 loss_thr: 0.4021 loss_db: 0.1046 loss: 1.1340 2022/08/30 09:32:33 - mmengine - INFO - Epoch(train) [356][30/63] lr: 5.1035e-03 eta: 23:28:52 time: 2.7393 data_time: 0.0598 memory: 16201 loss_prob: 0.5903 loss_thr: 0.4126 loss_db: 0.1013 loss: 1.1041 2022/08/30 09:32:47 - mmengine - INFO - Epoch(train) [356][35/63] lr: 5.1035e-03 eta: 23:28:52 time: 2.6966 data_time: 0.0542 memory: 16201 loss_prob: 0.5829 loss_thr: 0.3999 loss_db: 0.1026 loss: 1.0854 2022/08/30 09:32:59 - mmengine - INFO - Epoch(train) [356][40/63] lr: 5.1035e-03 eta: 23:28:59 time: 2.5790 data_time: 0.0530 memory: 16201 loss_prob: 0.6154 loss_thr: 0.4123 loss_db: 0.1041 loss: 1.1318 2022/08/30 09:33:11 - mmengine - INFO - Epoch(train) [356][45/63] lr: 5.1035e-03 eta: 23:28:59 time: 2.4025 data_time: 0.0520 memory: 16201 loss_prob: 0.6248 loss_thr: 0.4252 loss_db: 0.1058 loss: 1.1558 2022/08/30 09:33:24 - mmengine - INFO - Epoch(train) [356][50/63] lr: 5.1035e-03 eta: 23:29:05 time: 2.5099 data_time: 0.0833 memory: 16201 loss_prob: 0.6482 loss_thr: 0.4355 loss_db: 0.1137 loss: 1.1974 2022/08/30 09:33:36 - mmengine - INFO - Epoch(train) [356][55/63] lr: 5.1035e-03 eta: 23:29:05 time: 2.5467 data_time: 0.0619 memory: 16201 loss_prob: 0.6557 loss_thr: 0.4278 loss_db: 0.1116 loss: 1.1952 2022/08/30 09:33:48 - mmengine - INFO - Epoch(train) [356][60/63] lr: 5.1035e-03 eta: 23:29:09 time: 2.4297 data_time: 0.0501 memory: 16201 loss_prob: 0.6408 loss_thr: 0.4191 loss_db: 0.1067 loss: 1.1666 2022/08/30 09:33:55 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:34:12 - mmengine - INFO - Epoch(train) [357][5/63] lr: 5.0981e-03 eta: 23:29:09 time: 2.8240 data_time: 0.2660 memory: 16201 loss_prob: 0.7364 loss_thr: 0.4613 loss_db: 0.1285 loss: 1.3261 2022/08/30 09:34:24 - mmengine - INFO - Epoch(train) [357][10/63] lr: 5.0981e-03 eta: 23:29:08 time: 2.8721 data_time: 0.2958 memory: 16201 loss_prob: 0.7311 loss_thr: 0.4395 loss_db: 0.1231 loss: 1.2938 2022/08/30 09:34:36 - mmengine - INFO - Epoch(train) [357][15/63] lr: 5.0981e-03 eta: 23:29:08 time: 2.4780 data_time: 0.0695 memory: 16201 loss_prob: 0.7227 loss_thr: 0.4515 loss_db: 0.1221 loss: 1.2963 2022/08/30 09:34:49 - mmengine - INFO - Epoch(train) [357][20/63] lr: 5.0981e-03 eta: 23:29:15 time: 2.5751 data_time: 0.0659 memory: 16201 loss_prob: 0.6944 loss_thr: 0.4253 loss_db: 0.1178 loss: 1.2375 2022/08/30 09:35:03 - mmengine - INFO - Epoch(train) [357][25/63] lr: 5.0981e-03 eta: 23:29:15 time: 2.6679 data_time: 0.0696 memory: 16201 loss_prob: 0.6538 loss_thr: 0.3933 loss_db: 0.1108 loss: 1.1579 2022/08/30 09:35:16 - mmengine - INFO - Epoch(train) [357][30/63] lr: 5.0981e-03 eta: 23:29:24 time: 2.6356 data_time: 0.0651 memory: 16201 loss_prob: 0.6634 loss_thr: 0.4300 loss_db: 0.1139 loss: 1.2073 2022/08/30 09:35:29 - mmengine - INFO - Epoch(train) [357][35/63] lr: 5.0981e-03 eta: 23:29:24 time: 2.5483 data_time: 0.0588 memory: 16201 loss_prob: 0.6677 loss_thr: 0.4437 loss_db: 0.1148 loss: 1.2262 2022/08/30 09:35:40 - mmengine - INFO - Epoch(train) [357][40/63] lr: 5.0981e-03 eta: 23:29:27 time: 2.3992 data_time: 0.0468 memory: 16201 loss_prob: 0.6077 loss_thr: 0.4130 loss_db: 0.1057 loss: 1.1264 2022/08/30 09:35:54 - mmengine - INFO - Epoch(train) [357][45/63] lr: 5.0981e-03 eta: 23:29:27 time: 2.5205 data_time: 0.0508 memory: 16201 loss_prob: 0.6370 loss_thr: 0.4289 loss_db: 0.1104 loss: 1.1763 2022/08/30 09:36:09 - mmengine - INFO - Epoch(train) [357][50/63] lr: 5.0981e-03 eta: 23:29:44 time: 2.9709 data_time: 0.0678 memory: 16201 loss_prob: 0.6556 loss_thr: 0.4312 loss_db: 0.1122 loss: 1.1991 2022/08/30 09:36:21 - mmengine - INFO - Epoch(train) [357][55/63] lr: 5.0981e-03 eta: 23:29:44 time: 2.6833 data_time: 0.0671 memory: 16201 loss_prob: 0.6606 loss_thr: 0.4290 loss_db: 0.1111 loss: 1.2007 2022/08/30 09:36:36 - mmengine - INFO - Epoch(train) [357][60/63] lr: 5.0981e-03 eta: 23:29:52 time: 2.6238 data_time: 0.0568 memory: 16201 loss_prob: 0.6631 loss_thr: 0.4296 loss_db: 0.1135 loss: 1.2063 2022/08/30 09:36:41 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:36:57 - mmengine - INFO - Epoch(train) [358][5/63] lr: 5.0927e-03 eta: 23:29:52 time: 2.7183 data_time: 0.2723 memory: 16201 loss_prob: 0.5951 loss_thr: 0.4190 loss_db: 0.1040 loss: 1.1181 2022/08/30 09:37:11 - mmengine - INFO - Epoch(train) [358][10/63] lr: 5.0927e-03 eta: 23:29:52 time: 2.9430 data_time: 0.2923 memory: 16201 loss_prob: 0.6134 loss_thr: 0.3998 loss_db: 0.1058 loss: 1.1190 2022/08/30 09:37:24 - mmengine - INFO - Epoch(train) [358][15/63] lr: 5.0927e-03 eta: 23:29:52 time: 2.6925 data_time: 0.0760 memory: 16201 loss_prob: 0.6768 loss_thr: 0.4243 loss_db: 0.1159 loss: 1.2169 2022/08/30 09:37:36 - mmengine - INFO - Epoch(train) [358][20/63] lr: 5.0927e-03 eta: 23:29:58 time: 2.5242 data_time: 0.0684 memory: 16201 loss_prob: 0.7342 loss_thr: 0.4583 loss_db: 0.1255 loss: 1.3181 2022/08/30 09:37:50 - mmengine - INFO - Epoch(train) [358][25/63] lr: 5.0927e-03 eta: 23:29:58 time: 2.5661 data_time: 0.0676 memory: 16201 loss_prob: 0.6656 loss_thr: 0.4363 loss_db: 0.1128 loss: 1.2147 2022/08/30 09:38:01 - mmengine - INFO - Epoch(train) [358][30/63] lr: 5.0927e-03 eta: 23:30:04 time: 2.5146 data_time: 0.0614 memory: 16201 loss_prob: 0.6713 loss_thr: 0.4239 loss_db: 0.1126 loss: 1.2078 2022/08/30 09:38:16 - mmengine - INFO - Epoch(train) [358][35/63] lr: 5.0927e-03 eta: 23:30:04 time: 2.6482 data_time: 0.0585 memory: 16201 loss_prob: 0.6688 loss_thr: 0.4230 loss_db: 0.1122 loss: 1.2040 2022/08/30 09:38:29 - mmengine - INFO - Epoch(train) [358][40/63] lr: 5.0927e-03 eta: 23:30:16 time: 2.7870 data_time: 0.0616 memory: 16201 loss_prob: 0.6416 loss_thr: 0.4225 loss_db: 0.1100 loss: 1.1740 2022/08/30 09:38:43 - mmengine - INFO - Epoch(train) [358][45/63] lr: 5.0927e-03 eta: 23:30:16 time: 2.6694 data_time: 0.0652 memory: 16201 loss_prob: 0.6408 loss_thr: 0.4097 loss_db: 0.1108 loss: 1.1613 2022/08/30 09:38:55 - mmengine - INFO - Epoch(train) [358][50/63] lr: 5.0927e-03 eta: 23:30:24 time: 2.6051 data_time: 0.0714 memory: 16201 loss_prob: 0.6664 loss_thr: 0.4295 loss_db: 0.1135 loss: 1.2094 2022/08/30 09:39:08 - mmengine - INFO - Epoch(train) [358][55/63] lr: 5.0927e-03 eta: 23:30:24 time: 2.5278 data_time: 0.0549 memory: 16201 loss_prob: 0.7019 loss_thr: 0.4448 loss_db: 0.1190 loss: 1.2657 2022/08/30 09:39:23 - mmengine - INFO - Epoch(train) [358][60/63] lr: 5.0927e-03 eta: 23:30:35 time: 2.7658 data_time: 0.0470 memory: 16201 loss_prob: 0.6286 loss_thr: 0.4245 loss_db: 0.1066 loss: 1.1597 2022/08/30 09:39:30 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:39:45 - mmengine - INFO - Epoch(train) [359][5/63] lr: 5.0872e-03 eta: 23:30:35 time: 2.7345 data_time: 0.2794 memory: 16201 loss_prob: 0.8899 loss_thr: 0.4783 loss_db: 0.1375 loss: 1.5057 2022/08/30 09:39:59 - mmengine - INFO - Epoch(train) [359][10/63] lr: 5.0872e-03 eta: 23:30:36 time: 2.9669 data_time: 0.2979 memory: 16201 loss_prob: 0.8592 loss_thr: 0.4785 loss_db: 0.1349 loss: 1.4726 2022/08/30 09:40:11 - mmengine - INFO - Epoch(train) [359][15/63] lr: 5.0872e-03 eta: 23:30:36 time: 2.5918 data_time: 0.0583 memory: 16201 loss_prob: 0.6482 loss_thr: 0.4563 loss_db: 0.1127 loss: 1.2171 2022/08/30 09:40:26 - mmengine - INFO - Epoch(train) [359][20/63] lr: 5.0872e-03 eta: 23:30:44 time: 2.6213 data_time: 0.0610 memory: 16201 loss_prob: 0.6098 loss_thr: 0.4308 loss_db: 0.1054 loss: 1.1460 2022/08/30 09:40:40 - mmengine - INFO - Epoch(train) [359][25/63] lr: 5.0872e-03 eta: 23:30:44 time: 2.9044 data_time: 0.0729 memory: 16201 loss_prob: 0.6257 loss_thr: 0.4106 loss_db: 0.1085 loss: 1.1448 2022/08/30 09:40:53 - mmengine - INFO - Epoch(train) [359][30/63] lr: 5.0872e-03 eta: 23:30:54 time: 2.7176 data_time: 0.0629 memory: 16201 loss_prob: 0.6799 loss_thr: 0.4255 loss_db: 0.1154 loss: 1.2208 2022/08/30 09:41:07 - mmengine - INFO - Epoch(train) [359][35/63] lr: 5.0872e-03 eta: 23:30:54 time: 2.7264 data_time: 0.0722 memory: 16201 loss_prob: 0.7630 loss_thr: 0.4232 loss_db: 0.1311 loss: 1.3172 2022/08/30 09:41:20 - mmengine - INFO - Epoch(train) [359][40/63] lr: 5.0872e-03 eta: 23:31:04 time: 2.7131 data_time: 0.0664 memory: 16201 loss_prob: 0.7860 loss_thr: 0.4262 loss_db: 0.1395 loss: 1.3517 2022/08/30 09:41:36 - mmengine - INFO - Epoch(train) [359][45/63] lr: 5.0872e-03 eta: 23:31:04 time: 2.8900 data_time: 0.0696 memory: 16201 loss_prob: 0.7309 loss_thr: 0.4600 loss_db: 0.1226 loss: 1.3136 2022/08/30 09:41:50 - mmengine - INFO - Epoch(train) [359][50/63] lr: 5.0872e-03 eta: 23:31:21 time: 2.9816 data_time: 0.0669 memory: 16201 loss_prob: 0.6753 loss_thr: 0.4420 loss_db: 0.1110 loss: 1.2283 2022/08/30 09:42:02 - mmengine - INFO - Epoch(train) [359][55/63] lr: 5.0872e-03 eta: 23:31:21 time: 2.5852 data_time: 0.0615 memory: 16201 loss_prob: 0.6407 loss_thr: 0.3941 loss_db: 0.1101 loss: 1.1449 2022/08/30 09:42:15 - mmengine - INFO - Epoch(train) [359][60/63] lr: 5.0872e-03 eta: 23:31:26 time: 2.4788 data_time: 0.0869 memory: 16201 loss_prob: 0.6495 loss_thr: 0.3979 loss_db: 0.1107 loss: 1.1581 2022/08/30 09:42:22 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:42:37 - mmengine - INFO - Epoch(train) [360][5/63] lr: 5.0818e-03 eta: 23:31:26 time: 2.7600 data_time: 0.2833 memory: 16201 loss_prob: 0.7737 loss_thr: 0.4285 loss_db: 0.1309 loss: 1.3331 2022/08/30 09:42:52 - mmengine - INFO - Epoch(train) [360][10/63] lr: 5.0818e-03 eta: 23:31:27 time: 3.0249 data_time: 0.2904 memory: 16201 loss_prob: 0.7365 loss_thr: 0.4304 loss_db: 0.1219 loss: 1.2889 2022/08/30 09:43:06 - mmengine - INFO - Epoch(train) [360][15/63] lr: 5.0818e-03 eta: 23:31:27 time: 2.9067 data_time: 0.0547 memory: 16201 loss_prob: 0.6851 loss_thr: 0.4345 loss_db: 0.1150 loss: 1.2346 2022/08/30 09:43:19 - mmengine - INFO - Epoch(train) [360][20/63] lr: 5.0818e-03 eta: 23:31:36 time: 2.6893 data_time: 0.0531 memory: 16201 loss_prob: 0.6406 loss_thr: 0.4491 loss_db: 0.1094 loss: 1.1990 2022/08/30 09:43:32 - mmengine - INFO - Epoch(train) [360][25/63] lr: 5.0818e-03 eta: 23:31:36 time: 2.5659 data_time: 0.0718 memory: 16201 loss_prob: 0.6636 loss_thr: 0.4476 loss_db: 0.1137 loss: 1.2249 2022/08/30 09:43:45 - mmengine - INFO - Epoch(train) [360][30/63] lr: 5.0818e-03 eta: 23:31:45 time: 2.6338 data_time: 0.0607 memory: 16201 loss_prob: 0.7123 loss_thr: 0.4515 loss_db: 0.1217 loss: 1.2856 2022/08/30 09:43:57 - mmengine - INFO - Epoch(train) [360][35/63] lr: 5.0818e-03 eta: 23:31:45 time: 2.5044 data_time: 0.0648 memory: 16201 loss_prob: 0.6947 loss_thr: 0.4496 loss_db: 0.1193 loss: 1.2636 2022/08/30 09:44:11 - mmengine - INFO - Epoch(train) [360][40/63] lr: 5.0818e-03 eta: 23:31:51 time: 2.5610 data_time: 0.0588 memory: 16201 loss_prob: 0.6945 loss_thr: 0.4526 loss_db: 0.1211 loss: 1.2682 2022/08/30 09:44:25 - mmengine - INFO - Epoch(train) [360][45/63] lr: 5.0818e-03 eta: 23:31:51 time: 2.7564 data_time: 0.0485 memory: 16201 loss_prob: 0.7258 loss_thr: 0.4501 loss_db: 0.1247 loss: 1.3005 2022/08/30 09:44:38 - mmengine - INFO - Epoch(train) [360][50/63] lr: 5.0818e-03 eta: 23:32:00 time: 2.6826 data_time: 0.0622 memory: 16201 loss_prob: 0.6904 loss_thr: 0.4264 loss_db: 0.1174 loss: 1.2342 2022/08/30 09:44:52 - mmengine - INFO - Epoch(train) [360][55/63] lr: 5.0818e-03 eta: 23:32:00 time: 2.7289 data_time: 0.0526 memory: 16201 loss_prob: 0.6960 loss_thr: 0.4294 loss_db: 0.1183 loss: 1.2437 2022/08/30 09:45:06 - mmengine - INFO - Epoch(train) [360][60/63] lr: 5.0818e-03 eta: 23:32:12 time: 2.7621 data_time: 0.0615 memory: 16201 loss_prob: 0.6624 loss_thr: 0.4294 loss_db: 0.1119 loss: 1.2037 2022/08/30 09:45:12 - mmengine - INFO - Exp name: dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108 2022/08/30 09:45:12 - mmengine - INFO - Saving checkpoint at 360 epochs 2022/08/30 09:45:21 - mmengine - INFO - Epoch(val) [360][5/32] eta: 23:32:12 time: 0.6997 data_time: 0.1441 memory: 16201 2022/08/30 09:45:25 - mmengine - INFO - Epoch(val) [360][10/32] eta: 0:00:17 time: 0.8038 data_time: 0.1944 memory: 15734 2022/08/30 09:45:28 - mmengine - INFO - Epoch(val) [360][15/32] eta: 0:00:17 time: 0.6772 data_time: 0.0706 memory: 15734 2022/08/30 09:45:32 - mmengine - INFO - Epoch(val) [360][20/32] eta: 0:00:08 time: 0.7196 data_time: 0.0727 memory: 15734 2022/08/30 09:45:36 - mmengine - INFO - Epoch(val) [360][25/32] eta: 0:00:08 time: 0.7462 data_time: 0.0878 memory: 15734 2022/08/30 09:45:39 - mmengine - INFO - Epoch(val) [360][30/32] eta: 0:00:01 time: 0.6807 data_time: 0.0385 memory: 15734 2022/08/30 09:45:40 - mmengine - INFO - Evaluating hmean-iou... 2022/08/30 09:45:40 - mmengine - INFO - prediction score threshold: 0.30, recall: 0.8546, precision: 0.7816, hmean: 0.8165 2022/08/30 09:45:40 - mmengine - INFO - prediction score threshold: 0.40, recall: 0.8541, precision: 0.8156, hmean: 0.8344 2022/08/30 09:45:40 - mmengine - INFO - prediction score threshold: 0.50, recall: 0.8527, precision: 0.8449, hmean: 0.8488 2022/08/30 09:45:40 - mmengine - INFO - prediction score threshold: 0.60, recall: 0.8469, precision: 0.8691, hmean: 0.8578 2022/08/30 09:45:40 - mmengine - INFO - prediction score threshold: 0.70, recall: 0.8238, precision: 0.8977, hmean: 0.8592 2022/08/30 09:45:40 - mmengine - INFO - prediction score threshold: 0.80, recall: 0.6875, precision: 0.9352, hmean: 0.7925 2022/08/30 09:45:40 - mmengine - INFO - prediction score threshold: 0.90, recall: 0.0