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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'
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