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2023/02/24 05:13:32 - mmengine - INFO -
------------------------------------------------------------
System environment:
sys.platform: linux
Python: 3.8.10 (default, Nov 14 2022, 12:59:47) [GCC 9.4.0]
CUDA available: True
numpy_random_seed: 1569491978
GPU 0: Tesla T4
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.6, V11.6.124
GCC: x86_64-linux-gnu-gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
PyTorch: 1.13.1+cu116
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- 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 v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.6
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_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 8.3.2 (built against CUDA 11.5)
- Magma 2.6.1
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.14.1+cu116
OpenCV: 4.6.0
MMEngine: 0.5.0
Runtime environment:
cudnn_benchmark: True
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: None
Distributed launcher: none
Distributed training: False
GPU number: 1
------------------------------------------------------------
2023/02/24 05:13:33 - mmengine - INFO - Config:
file_client_args = dict(backend='disk')
model = dict(
type='DBNet',
backbone=dict(
type='mmdet.ResNet',
depth=18,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='BN', requires_grad=True),
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18'),
norm_eval=False,
style='caffe'),
neck=dict(
type='FPNC', in_channels=[64, 128, 256, 512], lateral_channels=256),
det_head=dict(
type='DBHead',
in_channels=256,
module_loss=dict(type='DBModuleLoss'),
postprocessor=dict(type='DBPostprocessor', text_repr_type='quad')),
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',
file_client_args=dict(backend='disk'),
color_type='color_ignore_orientation'),
dict(
type='LoadOCRAnnotations',
with_polygon=True,
with_bbox=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',
file_client_args=dict(backend='disk'),
color_type='color_ignore_orientation'),
dict(type='Resize', scale=(1333, 736), 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'))
]
icdar2015_textdet_data_root = 'data/det/textdet-thvote'
icdar2015_textdet_train = dict(
type='OCRDataset',
data_root='data/det/textdet-thvote',
ann_file='textdet_train.json',
data_prefix=dict(img_path='imgs/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk'),
color_type='color_ignore_orientation'),
dict(
type='LoadOCRAnnotations',
with_polygon=True,
with_bbox=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'))
])
icdar2015_textdet_test = dict(
type='OCRDataset',
data_root='data/det/textdet-thvote',
ann_file='textdet_test.json',
data_prefix=dict(img_path='imgs/'),
test_mode=True,
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk'),
color_type='color_ignore_orientation'),
dict(type='Resize', scale=(1333, 736), 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'))
])
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),
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 = None
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')])
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_dataloader = dict(
batch_size=16,
num_workers=8,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='OCRDataset',
data_root='data/det/textdet-thvote',
ann_file='textdet_train.json',
data_prefix=dict(img_path='imgs/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk'),
color_type='color_ignore_orientation'),
dict(
type='LoadOCRAnnotations',
with_polygon=True,
with_bbox=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=1,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='OCRDataset',
data_root='data/det/textdet-thvote',
ann_file='textdet_test.json',
data_prefix=dict(img_path='imgs/'),
test_mode=True,
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk'),
color_type='color_ignore_orientation'),
dict(type='Resize', scale=(1333, 736), 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'))
]))
test_dataloader = dict(
batch_size=1,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='OCRDataset',
data_root='data/det/textdet-thvote',
ann_file='textdet_test.json',
data_prefix=dict(img_path='imgs/'),
test_mode=True,
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk'),
color_type='color_ignore_orientation'),
dict(type='Resize', scale=(1333, 736), 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'))
]))
auto_scale_lr = dict(base_batch_size=16)
launcher = 'none'
work_dir = './work_dirs/dbnet_resnet18_fpnc_1200e_icdar2015'
2023/02/24 05:13:33 - mmengine - WARNING - The "visualizer" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:33 - mmengine - WARNING - The "vis_backend" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:34 - mmengine - WARNING - The "model" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:34 - mmengine - WARNING - The "model" registry in mmdet did not set import location. Fallback to call `mmdet.utils.register_all_modules` instead.
2023/02/24 05:13:38 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
2023/02/24 05:13:38 - mmengine - WARNING - The "hook" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:38 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) RuntimeInfoHook
(BELOW_NORMAL) LoggerHook
--------------------
before_train:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(VERY_LOW ) CheckpointHook
--------------------
before_train_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) DistSamplerSeedHook
--------------------
before_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
--------------------
after_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_train_epoch:
(NORMAL ) IterTimerHook
(NORMAL ) SyncBuffersHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
before_val_epoch:
(NORMAL ) IterTimerHook
--------------------
before_val_iter:
(NORMAL ) IterTimerHook
--------------------
after_val_iter:
(NORMAL ) IterTimerHook
(NORMAL ) VisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_val_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
before_test_epoch:
(NORMAL ) IterTimerHook
--------------------
before_test_iter:
(NORMAL ) IterTimerHook
--------------------
after_test_iter:
(NORMAL ) IterTimerHook
(NORMAL ) VisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
--------------------
after_run:
(BELOW_NORMAL) LoggerHook
--------------------
2023/02/24 05:13:39 - mmengine - WARNING - The "loop" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:39 - mmengine - WARNING - The "dataset" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:39 - mmengine - WARNING - The "transform" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:39 - mmengine - WARNING - The "data sampler" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:39 - mmengine - WARNING - The "optimizer constructor" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:39 - mmengine - WARNING - The "optimizer" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:39 - mmengine - WARNING - The "optim wrapper" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:39 - mmengine - WARNING - The "parameter scheduler" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:40 - mmengine - WARNING - The "metric" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:40 - mmengine - WARNING - The "weight initializer" registry in mmocr did not set import location. Fallback to call `mmocr.utils.register_all_modules` instead.
2023/02/24 05:13:40 - mmengine - INFO - load model from: torchvision://resnet18
2023/02/24 05:13:40 - mmengine - INFO - Loads checkpoint by torchvision backend from path: torchvision://resnet18
2023/02/24 05:13:40 - mmengine - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: fc.weight, fc.bias
Name of parameter - Initialization information
backbone.conv1.weight - torch.Size([64, 3, 7, 7]):
PretrainedInit: load from torchvision://resnet18
backbone.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.0.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.0.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.0.bn2.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.0.bn2.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.1.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.1.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.1.bn2.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.1.bn2.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.bn1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.bn1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.bn2.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.bn2.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.downsample.1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.downsample.1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.1.bn1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.1.bn1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.1.bn2.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.1.bn2.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.downsample.1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.downsample.1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.1.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.1.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.1.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.1.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.bn1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.bn1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.bn2.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.bn2.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.downsample.1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.downsample.1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.1.bn1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.1.bn1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.1.bn2.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.1.bn2.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
neck.lateral_convs.0.conv.weight - torch.Size([256, 64, 1, 1]):
Initialized by user-defined `init_weights` in ConvModule
neck.lateral_convs.1.conv.weight - torch.Size([256, 128, 1, 1]):
Initialized by user-defined `init_weights` in ConvModule
neck.lateral_convs.2.conv.weight - torch.Size([256, 256, 1, 1]):
Initialized by user-defined `init_weights` in ConvModule
neck.lateral_convs.3.conv.weight - torch.Size([256, 512, 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
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
2023/02/24 05:13:40 - mmengine - INFO - Checkpoints will be saved to /content/mmocr/work_dirs/dbnet_resnet18_fpnc_1200e_icdar2015.
2023/02/24 05:16:48 - mmengine - INFO - Epoch(train) [1][ 5/22] lr: 7.0000e-03 eta: 11 days, 10:56:37 time: 37.4994 data_time: 13.3666 memory: 12058 loss: 10.5798 loss_prob: 7.3334 loss_thr: 2.3504 loss_db: 0.8960
2023/02/24 05:17:25 - mmengine - INFO - Epoch(train) [1][10/22] lr: 7.0000e-03 eta: 6 days, 20:37:40 time: 22.4578 data_time: 6.7581 memory: 6713 loss: 8.0422 loss_prob: 5.2998 loss_thr: 1.8354 loss_db: 0.9071
2023/02/24 05:17:49 - mmengine - INFO - Epoch(train) [1][15/22] lr: 7.0000e-03 eta: 5 days, 1:36:06 time: 6.1375 data_time: 0.0814 memory: 6713 loss: 5.2709 loss_prob: 3.0675 loss_thr: 1.2472 loss_db: 0.9562
2023/02/24 05:18:13 - mmengine - INFO - Epoch(train) [1][20/22] lr: 7.0000e-03 eta: 4 days, 3:52:43 time: 4.8026 data_time: 0.0312 memory: 6713 loss: 4.9844 loss_prob: 2.8490 loss_thr: 1.1389 loss_db: 0.9965
2023/02/24 05:18:25 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 05:21:34 - mmengine - INFO - Epoch(train) [2][ 5/22] lr: 6.9947e-03 eta: 5 days, 8:31:25 time: 21.5618 data_time: 7.1003 memory: 11447 loss: 4.8425 loss_prob: 2.8106 loss_thr: 1.0607 loss_db: 0.9712
2023/02/24 05:22:09 - mmengine - INFO - Epoch(train) [2][10/22] lr: 6.9947e-03 eta: 4 days, 20:24:29 time: 22.4338 data_time: 7.1646 memory: 6712 loss: 4.7001 loss_prob: 2.7874 loss_thr: 1.1015 loss_db: 0.8112
2023/02/24 05:22:33 - mmengine - INFO - Epoch(train) [2][15/22] lr: 6.9947e-03 eta: 4 days, 9:30:51 time: 5.9429 data_time: 0.0877 memory: 6712 loss: 4.4307 loss_prob: 2.7478 loss_thr: 1.1405 loss_db: 0.5424
2023/02/24 05:22:56 - mmengine - INFO - Epoch(train) [2][20/22] lr: 6.9947e-03 eta: 4 days, 0:51:26 time: 4.7033 data_time: 0.0489 memory: 6712 loss: 4.1205 loss_prob: 2.6747 loss_thr: 1.0579 loss_db: 0.3879
2023/02/24 05:23:05 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 05:25:58 - mmengine - INFO - Epoch(train) [3][ 5/22] lr: 6.9895e-03 eta: 4 days, 14:13:27 time: 19.7292 data_time: 6.3200 memory: 6712 loss: 3.7028 loss_prob: 2.4246 loss_thr: 0.9721 loss_db: 0.3061
2023/02/24 05:26:33 - mmengine - INFO - Epoch(train) [3][10/22] lr: 6.9895e-03 eta: 4 days, 8:44:41 time: 20.8299 data_time: 6.3501 memory: 6712 loss: 3.4052 loss_prob: 2.1909 loss_thr: 0.9435 loss_db: 0.2709
2023/02/24 05:26:53 - mmengine - INFO - Epoch(train) [3][15/22] lr: 6.9895e-03 eta: 4 days, 2:14:03 time: 5.4242 data_time: 0.0758 memory: 6712 loss: 3.1914 loss_prob: 2.0126 loss_thr: 0.9125 loss_db: 0.2664
2023/02/24 05:27:15 - mmengine - INFO - Epoch(train) [3][20/22] lr: 6.9895e-03 eta: 3 days, 21:04:03 time: 4.1317 data_time: 0.0486 memory: 6712 loss: 2.9899 loss_prob: 1.8336 loss_thr: 0.8950 loss_db: 0.2613
2023/02/24 05:27:23 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 05:30:21 - mmengine - INFO - Epoch(train) [4][ 5/22] lr: 6.9842e-03 eta: 4 days, 7:06:23 time: 19.9728 data_time: 6.5625 memory: 6712 loss: 2.7135 loss_prob: 1.6040 loss_thr: 0.8757 loss_db: 0.2338
2023/02/24 05:30:55 - mmengine - INFO - Epoch(train) [4][10/22] lr: 6.9842e-03 eta: 4 days, 3:31:24 time: 21.1335 data_time: 6.5916 memory: 6712 loss: 2.5669 loss_prob: 1.4807 loss_thr: 0.8647 loss_db: 0.2215
2023/02/24 05:31:16 - mmengine - INFO - Epoch(train) [4][15/22] lr: 6.9842e-03 eta: 3 days, 23:16:49 time: 5.4703 data_time: 0.0655 memory: 6712 loss: 2.5318 loss_prob: 1.4490 loss_thr: 0.8641 loss_db: 0.2187
2023/02/24 05:31:37 - mmengine - INFO - Epoch(train) [4][20/22] lr: 6.9842e-03 eta: 3 days, 19:28:30 time: 4.1855 data_time: 0.0463 memory: 6712 loss: 2.4536 loss_prob: 1.3779 loss_thr: 0.8595 loss_db: 0.2161
2023/02/24 05:31:43 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 05:34:41 - mmengine - INFO - Epoch(train) [5][ 5/22] lr: 6.9790e-03 eta: 4 days, 3:03:02 time: 19.6819 data_time: 6.5648 memory: 6712 loss: 2.2837 loss_prob: 1.2531 loss_thr: 0.8280 loss_db: 0.2027
2023/02/24 05:35:13 - mmengine - INFO - Epoch(train) [5][10/22] lr: 6.9790e-03 eta: 4 days, 0:23:14 time: 20.9855 data_time: 6.6279 memory: 6712 loss: 2.2122 loss_prob: 1.1990 loss_thr: 0.8168 loss_db: 0.1964
2023/02/24 05:35:36 - mmengine - INFO - Epoch(train) [5][15/22] lr: 6.9790e-03 eta: 3 days, 21:16:29 time: 5.4636 data_time: 0.0946 memory: 6712 loss: 2.1482 loss_prob: 1.1455 loss_thr: 0.8120 loss_db: 0.1906
2023/02/24 05:35:57 - mmengine - INFO - Epoch(train) [5][20/22] lr: 6.9790e-03 eta: 3 days, 18:24:00 time: 4.3929 data_time: 0.0363 memory: 6712 loss: 2.2215 loss_prob: 1.2052 loss_thr: 0.8195 loss_db: 0.1968
2023/02/24 05:36:05 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 05:39:01 - mmengine - INFO - Epoch(train) [6][ 5/22] lr: 6.9737e-03 eta: 4 days, 0:33:26 time: 19.8343 data_time: 6.6865 memory: 6712 loss: 2.2092 loss_prob: 1.1873 loss_thr: 0.8270 loss_db: 0.1949
2023/02/24 05:39:35 - mmengine - INFO - Epoch(train) [6][10/22] lr: 6.9737e-03 eta: 3 days, 22:34:41 time: 21.0220 data_time: 6.7316 memory: 6712 loss: 2.0882 loss_prob: 1.0934 loss_thr: 0.8093 loss_db: 0.1856
2023/02/24 05:39:55 - mmengine - INFO - Epoch(train) [6][15/22] lr: 6.9737e-03 eta: 3 days, 19:56:56 time: 5.3949 data_time: 0.0639 memory: 6712 loss: 2.0953 loss_prob: 1.1014 loss_thr: 0.8072 loss_db: 0.1867
2023/02/24 05:40:15 - mmengine - INFO - Epoch(train) [6][20/22] lr: 6.9737e-03 eta: 3 days, 17:30:13 time: 3.9802 data_time: 0.0307 memory: 6712 loss: 2.1803 loss_prob: 1.1807 loss_thr: 0.8064 loss_db: 0.1932
2023/02/24 05:40:24 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 05:43:18 - mmengine - INFO - Epoch(train) [7][ 5/22] lr: 6.9685e-03 eta: 3 days, 22:38:09 time: 19.3378 data_time: 6.0656 memory: 6712 loss: 2.1125 loss_prob: 1.1454 loss_thr: 0.7801 loss_db: 0.1870
2023/02/24 05:43:52 - mmengine - INFO - Epoch(train) [7][10/22] lr: 6.9685e-03 eta: 3 days, 21:03:26 time: 20.8409 data_time: 6.1127 memory: 6712 loss: 2.1082 loss_prob: 1.1444 loss_thr: 0.7752 loss_db: 0.1886
2023/02/24 05:44:14 - mmengine - INFO - Epoch(train) [7][15/22] lr: 6.9685e-03 eta: 3 days, 18:57:55 time: 5.6460 data_time: 0.0896 memory: 6712 loss: 2.0828 loss_prob: 1.1309 loss_thr: 0.7652 loss_db: 0.1867
2023/02/24 05:44:35 - mmengine - INFO - Epoch(train) [7][20/22] lr: 6.9685e-03 eta: 3 days, 16:56:45 time: 4.2613 data_time: 0.0588 memory: 6712 loss: 1.9454 loss_prob: 1.0347 loss_thr: 0.7355 loss_db: 0.1752
2023/02/24 05:44:42 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 05:47:42 - mmengine - INFO - Epoch(train) [8][ 5/22] lr: 6.9632e-03 eta: 3 days, 21:35:37 time: 20.0738 data_time: 7.0659 memory: 6712 loss: 1.9103 loss_prob: 1.0182 loss_thr: 0.7198 loss_db: 0.1723
2023/02/24 05:48:18 - mmengine - INFO - Epoch(train) [8][10/22] lr: 6.9632e-03 eta: 3 days, 20:19:25 time: 21.6464 data_time: 7.0947 memory: 6712 loss: 1.9593 loss_prob: 1.0665 loss_thr: 0.7176 loss_db: 0.1751
2023/02/24 05:48:41 - mmengine - INFO - Epoch(train) [8][15/22] lr: 6.9632e-03 eta: 3 days, 18:33:12 time: 5.8713 data_time: 0.0769 memory: 6712 loss: 1.9544 loss_prob: 1.0733 loss_thr: 0.7049 loss_db: 0.1762
2023/02/24 05:49:01 - mmengine - INFO - Epoch(train) [8][20/22] lr: 6.9632e-03 eta: 3 days, 16:48:01 time: 4.3373 data_time: 0.0467 memory: 6712 loss: 1.8306 loss_prob: 0.9863 loss_thr: 0.6770 loss_db: 0.1673
2023/02/24 05:49:08 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 05:52:08 - mmengine - INFO - Epoch(train) [9][ 5/22] lr: 6.9580e-03 eta: 3 days, 20:51:50 time: 20.0004 data_time: 6.3228 memory: 6712 loss: 1.9089 loss_prob: 1.0586 loss_thr: 0.6772 loss_db: 0.1731
2023/02/24 05:52:41 - mmengine - INFO - Epoch(train) [9][10/22] lr: 6.9580e-03 eta: 3 days, 19:38:00 time: 21.3337 data_time: 6.3790 memory: 6712 loss: 1.8955 loss_prob: 1.0480 loss_thr: 0.6761 loss_db: 0.1714
2023/02/24 05:53:02 - mmengine - INFO - Epoch(train) [9][15/22] lr: 6.9580e-03 eta: 3 days, 17:59:55 time: 5.3263 data_time: 0.0722 memory: 6712 loss: 1.7788 loss_prob: 0.9520 loss_thr: 0.6654 loss_db: 0.1614
2023/02/24 05:53:21 - mmengine - INFO - Epoch(train) [9][20/22] lr: 6.9580e-03 eta: 3 days, 16:25:34 time: 4.0420 data_time: 0.0361 memory: 6712 loss: 1.8003 loss_prob: 0.9682 loss_thr: 0.6678 loss_db: 0.1643
2023/02/24 05:53:31 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 05:56:27 - mmengine - INFO - Epoch(train) [10][ 5/22] lr: 6.9527e-03 eta: 3 days, 20:00:04 time: 19.6905 data_time: 6.3250 memory: 6712 loss: 1.8357 loss_prob: 0.9859 loss_thr: 0.6834 loss_db: 0.1663
2023/02/24 05:57:04 - mmengine - INFO - Epoch(train) [10][10/22] lr: 6.9527e-03 eta: 3 days, 19:04:40 time: 21.3082 data_time: 6.3498 memory: 6712 loss: 1.8376 loss_prob: 0.9889 loss_thr: 0.6809 loss_db: 0.1677
2023/02/24 05:57:27 - mmengine - INFO - Epoch(train) [10][15/22] lr: 6.9527e-03 eta: 3 days, 17:43:00 time: 6.0559 data_time: 0.0570 memory: 6712 loss: 1.7998 loss_prob: 0.9688 loss_thr: 0.6660 loss_db: 0.1651
2023/02/24 05:57:48 - mmengine - INFO - Epoch(train) [10][20/22] lr: 6.9527e-03 eta: 3 days, 16:20:24 time: 4.4162 data_time: 0.0306 memory: 6712 loss: 1.8812 loss_prob: 1.0357 loss_thr: 0.6779 loss_db: 0.1676
2023/02/24 05:57:55 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 06:01:01 - mmengine - INFO - Epoch(train) [11][ 5/22] lr: 6.9474e-03 eta: 3 days, 19:46:57 time: 20.5701 data_time: 7.2858 memory: 6712 loss: 1.8385 loss_prob: 1.0164 loss_thr: 0.6580 loss_db: 0.1641
2023/02/24 06:01:42 - mmengine - INFO - Epoch(train) [11][10/22] lr: 6.9474e-03 eta: 3 days, 19:04:25 time: 22.6877 data_time: 7.3177 memory: 6712 loss: 1.7372 loss_prob: 0.9383 loss_thr: 0.6403 loss_db: 0.1586
2023/02/24 06:02:04 - mmengine - INFO - Epoch(train) [11][15/22] lr: 6.9474e-03 eta: 3 days, 17:48:05 time: 6.3309 data_time: 0.0664 memory: 6712 loss: 1.8261 loss_prob: 1.0116 loss_thr: 0.6501 loss_db: 0.1644
2023/02/24 06:02:27 - mmengine - INFO - Epoch(train) [11][20/22] lr: 6.9474e-03 eta: 3 days, 16:36:23 time: 4.4944 data_time: 0.0463 memory: 6712 loss: 1.8030 loss_prob: 0.9974 loss_thr: 0.6439 loss_db: 0.1618
2023/02/24 06:02:35 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 06:05:43 - mmengine - INFO - Epoch(train) [12][ 5/22] lr: 6.9422e-03 eta: 3 days, 19:50:54 time: 21.0802 data_time: 6.7792 memory: 6712 loss: 1.7311 loss_prob: 0.9384 loss_thr: 0.6339 loss_db: 0.1588
2023/02/24 06:06:16 - mmengine - INFO - Epoch(train) [12][10/22] lr: 6.9422e-03 eta: 3 days, 18:57:51 time: 22.1269 data_time: 6.7959 memory: 6712 loss: 1.7188 loss_prob: 0.9327 loss_thr: 0.6281 loss_db: 0.1580
2023/02/24 06:06:38 - mmengine - INFO - Epoch(train) [12][15/22] lr: 6.9422e-03 eta: 3 days, 17:47:34 time: 5.4922 data_time: 0.0768 memory: 6712 loss: 1.7922 loss_prob: 0.9895 loss_thr: 0.6431 loss_db: 0.1596
2023/02/24 06:06:59 - mmengine - INFO - Epoch(train) [12][20/22] lr: 6.9422e-03 eta: 3 days, 16:38:54 time: 4.2930 data_time: 0.0734 memory: 6712 loss: 1.8091 loss_prob: 1.0073 loss_thr: 0.6390 loss_db: 0.1628
2023/02/24 06:07:07 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 06:10:14 - mmengine - INFO - Epoch(train) [13][ 5/22] lr: 6.9369e-03 eta: 3 days, 19:34:46 time: 20.8475 data_time: 6.4862 memory: 6712 loss: 1.7225 loss_prob: 0.9462 loss_thr: 0.6158 loss_db: 0.1605
2023/02/24 06:10:45 - mmengine - INFO - Epoch(train) [13][10/22] lr: 6.9369e-03 eta: 3 days, 18:42:55 time: 21.8411 data_time: 6.5244 memory: 6712 loss: 1.6861 loss_prob: 0.9208 loss_thr: 0.6085 loss_db: 0.1568
2023/02/24 06:11:08 - mmengine - INFO - Epoch(train) [13][15/22] lr: 6.9369e-03 eta: 3 days, 17:39:18 time: 5.3518 data_time: 0.0755 memory: 6712 loss: 1.6869 loss_prob: 0.9212 loss_thr: 0.6091 loss_db: 0.1566
2023/02/24 06:11:29 - mmengine - INFO - Epoch(train) [13][20/22] lr: 6.9369e-03 eta: 3 days, 16:36:44 time: 4.4030 data_time: 0.0427 memory: 6712 loss: 1.6707 loss_prob: 0.9171 loss_thr: 0.5988 loss_db: 0.1549
2023/02/24 06:11:39 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 06:14:33 - mmengine - INFO - Epoch(train) [14][ 5/22] lr: 6.9317e-03 eta: 3 days, 19:01:21 time: 19.7550 data_time: 6.6183 memory: 6712 loss: 1.7619 loss_prob: 1.0020 loss_thr: 0.6010 loss_db: 0.1589
2023/02/24 06:15:10 - mmengine - INFO - Epoch(train) [14][10/22] lr: 6.9317e-03 eta: 3 days, 18:23:36 time: 21.1018 data_time: 6.6633 memory: 6712 loss: 1.7161 loss_prob: 0.9654 loss_thr: 0.5944 loss_db: 0.1563
2023/02/24 06:15:32 - mmengine - INFO - Epoch(train) [14][15/22] lr: 6.9317e-03 eta: 3 days, 17:22:58 time: 5.8873 data_time: 0.0648 memory: 6712 loss: 1.7192 loss_prob: 0.9679 loss_thr: 0.5954 loss_db: 0.1559
2023/02/24 06:15:54 - mmengine - INFO - Epoch(train) [14][20/22] lr: 6.9317e-03 eta: 3 days, 16:25:59 time: 4.3364 data_time: 0.0274 memory: 6712 loss: 1.6298 loss_prob: 0.8869 loss_thr: 0.5926 loss_db: 0.1503
2023/02/24 06:16:02 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 06:19:00 - mmengine - INFO - Epoch(train) [15][ 5/22] lr: 6.9264e-03 eta: 3 days, 18:44:04 time: 19.8166 data_time: 6.2719 memory: 6712 loss: 1.6233 loss_prob: 0.8843 loss_thr: 0.5895 loss_db: 0.1495
2023/02/24 06:19:35 - mmengine - INFO - Epoch(train) [15][10/22] lr: 6.9264e-03 eta: 3 days, 18:05:44 time: 21.3018 data_time: 6.3262 memory: 6712 loss: 1.6084 loss_prob: 0.8760 loss_thr: 0.5845 loss_db: 0.1478
2023/02/24 06:19:56 - mmengine - INFO - Epoch(train) [15][15/22] lr: 6.9264e-03 eta: 3 days, 17:09:18 time: 5.6332 data_time: 0.0798 memory: 6712 loss: 1.5740 loss_prob: 0.8612 loss_thr: 0.5668 loss_db: 0.1460
2023/02/24 06:20:17 - mmengine - INFO - Epoch(train) [15][20/22] lr: 6.9264e-03 eta: 3 days, 16:14:55 time: 4.2267 data_time: 0.0394 memory: 6712 loss: 1.6627 loss_prob: 0.9368 loss_thr: 0.5743 loss_db: 0.1516
2023/02/24 06:20:27 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 06:23:29 - mmengine - INFO - Epoch(train) [16][ 5/22] lr: 6.9211e-03 eta: 3 days, 18:31:35 time: 20.4682 data_time: 6.4376 memory: 6712 loss: 1.6751 loss_prob: 0.9439 loss_thr: 0.5791 loss_db: 0.1521
2023/02/24 06:24:03 - mmengine - INFO - Epoch(train) [16][10/22] lr: 6.9211e-03 eta: 3 days, 17:53:41 time: 21.5603 data_time: 6.4834 memory: 6712 loss: 1.5881 loss_prob: 0.8699 loss_thr: 0.5714 loss_db: 0.1468
2023/02/24 06:24:24 - mmengine - INFO - Epoch(train) [16][15/22] lr: 6.9211e-03 eta: 3 days, 17:01:54 time: 5.5439 data_time: 0.0674 memory: 6712 loss: 1.5751 loss_prob: 0.8581 loss_thr: 0.5713 loss_db: 0.1457
2023/02/24 06:24:46 - mmengine - INFO - Epoch(train) [16][20/22] lr: 6.9211e-03 eta: 3 days, 16:11:19 time: 4.3338 data_time: 0.0365 memory: 6712 loss: 1.6895 loss_prob: 0.9474 loss_thr: 0.5892 loss_db: 0.1528
2023/02/24 06:24:54 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 06:27:53 - mmengine - INFO - Epoch(train) [17][ 5/22] lr: 6.9159e-03 eta: 3 days, 18:13:54 time: 20.1179 data_time: 7.1602 memory: 6712 loss: 1.5890 loss_prob: 0.8658 loss_thr: 0.5758 loss_db: 0.1473
2023/02/24 06:28:29 - mmengine - INFO - Epoch(train) [17][10/22] lr: 6.9159e-03 eta: 3 days, 17:40:34 time: 21.5151 data_time: 7.1956 memory: 6712 loss: 1.5827 loss_prob: 0.8728 loss_thr: 0.5623 loss_db: 0.1476
2023/02/24 06:28:52 - mmengine - INFO - Epoch(train) [17][15/22] lr: 6.9159e-03 eta: 3 days, 16:53:08 time: 5.8176 data_time: 0.0500 memory: 6712 loss: 1.5498 loss_prob: 0.8583 loss_thr: 0.5468 loss_db: 0.1447
2023/02/24 06:29:14 - mmengine - INFO - Epoch(train) [17][20/22] lr: 6.9159e-03 eta: 3 days, 16:06:35 time: 4.5159 data_time: 0.0371 memory: 6712 loss: 1.5092 loss_prob: 0.8323 loss_thr: 0.5363 loss_db: 0.1406
2023/02/24 06:29:21 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 06:32:11 - mmengine - INFO - Epoch(train) [18][ 5/22] lr: 6.9106e-03 eta: 3 days, 17:49:46 time: 19.0947 data_time: 6.1514 memory: 6712 loss: 1.6499 loss_prob: 0.9514 loss_thr: 0.5495 loss_db: 0.1489
2023/02/24 06:32:41 - mmengine - INFO - Epoch(train) [18][10/22] lr: 6.9106e-03 eta: 3 days, 17:13:04 time: 19.9988 data_time: 6.1911 memory: 6712 loss: 1.5199 loss_prob: 0.8403 loss_thr: 0.5375 loss_db: 0.1421
2023/02/24 06:33:02 - mmengine - INFO - Epoch(train) [18][15/22] lr: 6.9106e-03 eta: 3 days, 16:26:56 time: 5.1812 data_time: 0.0669 memory: 6712 loss: 1.6338 loss_prob: 0.9296 loss_thr: 0.5540 loss_db: 0.1502
2023/02/24 06:33:23 - mmengine - INFO - Epoch(train) [18][20/22] lr: 6.9106e-03 eta: 3 days, 15:41:20 time: 4.1944 data_time: 0.0443 memory: 6712 loss: 1.6014 loss_prob: 0.9109 loss_thr: 0.5433 loss_db: 0.1472
2023/02/24 06:33:30 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 06:36:13 - mmengine - INFO - Epoch(train) [19][ 5/22] lr: 6.9054e-03 eta: 3 days, 17:11:46 time: 18.3289 data_time: 6.0725 memory: 6712 loss: 1.5865 loss_prob: 0.9040 loss_thr: 0.5374 loss_db: 0.1451
2023/02/24 06:36:44 - mmengine - INFO - Epoch(train) [19][10/22] lr: 6.9054e-03 eta: 3 days, 16:38:02 time: 19.4111 data_time: 6.1041 memory: 6712 loss: 1.5683 loss_prob: 0.8961 loss_thr: 0.5286 loss_db: 0.1436
2023/02/24 06:37:03 - mmengine - INFO - Epoch(train) [19][15/22] lr: 6.9054e-03 eta: 3 days, 15:52:19 time: 5.0075 data_time: 0.0461 memory: 6712 loss: 1.4666 loss_prob: 0.8143 loss_thr: 0.5145 loss_db: 0.1378
2023/02/24 06:37:22 - mmengine - INFO - Epoch(train) [19][20/22] lr: 6.9054e-03 eta: 3 days, 15:07:51 time: 3.8092 data_time: 0.0239 memory: 6712 loss: 1.4776 loss_prob: 0.8202 loss_thr: 0.5185 loss_db: 0.1389
2023/02/24 06:37:30 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 06:40:10 - mmengine - INFO - Epoch(train) [20][ 5/22] lr: 6.9001e-03 eta: 3 days, 16:31:37 time: 18.0048 data_time: 5.7458 memory: 6712 loss: 1.5888 loss_prob: 0.9128 loss_thr: 0.5328 loss_db: 0.1432
2023/02/24 06:40:37 - mmengine - INFO - Epoch(train) [20][10/22] lr: 6.9001e-03 eta: 3 days, 15:56:20 time: 18.7845 data_time: 5.7862 memory: 6712 loss: 1.6013 loss_prob: 0.9205 loss_thr: 0.5363 loss_db: 0.1445
2023/02/24 06:40:58 - mmengine - INFO - Epoch(train) [20][15/22] lr: 6.9001e-03 eta: 3 days, 15:14:41 time: 4.7755 data_time: 0.0685 memory: 6712 loss: 1.4801 loss_prob: 0.8185 loss_thr: 0.5228 loss_db: 0.1389
2023/02/24 06:41:17 - mmengine - INFO - Epoch(train) [20][20/22] lr: 6.9001e-03 eta: 3 days, 14:32:56 time: 3.9535 data_time: 0.0450 memory: 6712 loss: 1.4580 loss_prob: 0.8092 loss_thr: 0.5116 loss_db: 0.1372
2023/02/24 06:41:23 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015_20230224_051330
2023/02/24 06:41:23 - mmengine - INFO - Saving checkpoint at 20 epochs
2023/02/24 06:43:59 - mmengine - INFO - Epoch(val) [20][ 5/88] eta: 0:42:55 time: 31.0259 data_time: 0.0756 memory: 8651