| 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))) | |