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model = dict( |
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type='TextSnake', |
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backbone=dict( |
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type='mmdet.ResNet', |
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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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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), |
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norm_eval=True, |
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style='caffe'), |
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neck=dict( |
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type='FPN_UNet', in_channels=[256, 512, 1024, 2048], out_channels=32), |
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bbox_head=dict( |
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type='TextSnakeHead', |
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in_channels=32, |
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loss=dict(type='TextSnakeLoss'), |
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postprocessor=dict( |
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type='TextSnakePostprocessor', text_repr_type='poly')), |
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train_cfg=None, |
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test_cfg=None) |
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|