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model_poly = dict(
type='PSENet',
backbone=dict(
type='mmdet.ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='SyncBN', requires_grad=True),
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
norm_eval=True,
style='caffe'),
neck=dict(
type='FPNF',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
fusion_type='concat'),
bbox_head=dict(
type='PSEHead',
in_channels=[256],
out_channels=7,
loss=dict(type='PSELoss'),
postprocessor=dict(type='PSEPostprocessor', text_repr_type='poly')),
train_cfg=None,
test_cfg=None)
model_quad = dict(
type='PSENet',
backbone=dict(
type='mmdet.ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='SyncBN', requires_grad=True),
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
norm_eval=True,
style='caffe'),
neck=dict(
type='FPNF',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
fusion_type='concat'),
bbox_head=dict(
type='PSEHead',
in_channels=[256],
out_channels=7,
loss=dict(type='PSELoss'),
postprocessor=dict(type='PSEPostprocessor', text_repr_type='quad')),
train_cfg=None,
test_cfg=None)
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