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e933ed8727
* update scoring/matching * update scoring/matching * update scoring/matching * update scoring/matching * update scoring/matching * update scoring/matching * update scoring/matching * update scoring/matching * update scoring/matching * update scene_cut * update scene_cut * update scene_cut[A * update scene_cut * update scene_cut * update scene_cut * update scene_cut * update scene_cut * update scene_cut * m * m * m * m * m * m * m * m * m * m * m * m * m * m * update readme * update readme * extract frames using opencv everywhere * extract frames using opencv everywhere * extract frames using opencv everywhere * filter panda10m * filter panda10m * m * m * m * m * m * m * m * m * m * m * m * m * m * m * m * m * m * ocr * add ocr * add main.sh * add ocr * add ocr * add ocr * add ocr * add ocr * add ocr * update scene_cut * update remove main.sh
97 lines
2.9 KiB
Python
97 lines
2.9 KiB
Python
model = dict(
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type='DBNet',
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backbone=dict(
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type='CLIPResNet',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=-1,
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norm_cfg=dict(type='BN', requires_grad=True),
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norm_eval=False,
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style='pytorch',
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dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
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# init_cfg=dict(
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# type='Pretrained',
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# checkpoint='https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth'),
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stage_with_dcn=(False, True, True, True),
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),
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neck=dict(
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type='FPNC',
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in_channels=[256, 512, 1024, 2048],
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lateral_channels=256,
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asf_cfg=dict(attention_type='ScaleChannelSpatial'),
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),
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det_head=dict(
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type='DBHead',
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in_channels=256,
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module_loss=dict(type='DBModuleLoss'),
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postprocessor=dict(
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type='DBPostprocessor', text_repr_type='quad',
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epsilon_ratio=0.002,
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),
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),
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data_preprocessor=dict(
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type='TextDetDataPreprocessor',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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bgr_to_rgb=True,
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pad_size_divisor=32,
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),
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init_cfg=dict(
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type='Pretrained',
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checkpoint='https://download.openmmlab.com/mmocr/textdet/dbnetpp/'
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'dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015/'
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'dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015_20221101_124139-4ecb39ac.pth',
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)
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)
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train_pipeline = [
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dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
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dict(
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type='LoadOCRAnnotations',
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with_bbox=True,
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with_polygon=True,
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with_label=True,
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),
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dict(
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type='TorchVisionWrapper',
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op='ColorJitter',
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brightness=32.0 / 255,
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saturation=0.5),
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dict(
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type='ImgAugWrapper',
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args=[['Fliplr', 0.5],
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dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]]),
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dict(type='RandomCrop', min_side_ratio=0.1),
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dict(type='Resize', scale=(640, 640), keep_ratio=True),
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dict(type='Pad', size=(640, 640)),
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dict(
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type='PackTextDetInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape'))
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]
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test_pipeline = [
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dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
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dict(type='Resize', scale=(4068, 1024), keep_ratio=True),
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# dict(
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# type='LoadOCRAnnotations',
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# with_polygon=True,
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# with_bbox=True,
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# with_label=True,
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# ),
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dict(
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type='PackTextDetInputs',
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# meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor',
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# 'instances'),
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meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'),
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)
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]
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# Visualization
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vis_backends = [dict(type='LocalVisBackend')]
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visualizer = dict(
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type='TextDetLocalVisualizer',
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name='visualizer',
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vis_backends=vis_backends,
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)
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