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model = dict(
type='FCENet',
backbone=dict(
type='mmdet.ResNet',
depth=50,
num_stages=4,
out_indices=(1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='BN', requires_grad=True),
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
norm_eval=False,
style='pytorch'),
neck=dict(
type='mmdet.FPN',
in_channels=[512, 1024, 2048],
out_channels=256,
add_extra_convs='on_output',
num_outs=3,
relu_before_extra_convs=True,
act_cfg=None),
bbox_head=dict(
type='FCEHead',
in_channels=256,
scales=(8, 16, 32),
fourier_degree=5,
loss=dict(type='FCELoss', num_sample=50),
postprocessor=dict(
type='FCEPostprocessor',
text_repr_type='quad',
num_reconstr_points=50,
alpha=1.2,
beta=1.0,
score_thr=0.3)))
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