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