MAERec-Gradio / configs /textdet /dbnet /_base_dbnet_resnet50-dcnv2_fpnc.py
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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),
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', color_type='color_ignore_orientation'),
dict(
type='LoadOCRAnnotations',
with_bbox=True,
with_polygon=True,
with_label=True,
),
dict(
type='TorchVisionWrapper',
op='ColorJitter',
brightness=32.0 / 255,
saturation=0.5),
dict(
type='ImgAugWrapper',
args=[['Fliplr', 0.5],
dict(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'))
]