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- _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
- # model settings
- model = dict(
- type='FSAF',
- bbox_head=dict(
- type='FSAFHead',
- num_classes=80,
- in_channels=256,
- stacked_convs=4,
- feat_channels=256,
- reg_decoded_bbox=True,
- # Only anchor-free branch is implemented. The anchor generator only
- # generates 1 anchor at each feature point, as a substitute of the
- # grid of features.
- anchor_generator=dict(
- type='AnchorGenerator',
- octave_base_scale=1,
- scales_per_octave=1,
- ratios=[1.0],
- strides=[8, 16, 32, 64, 128]),
- bbox_coder=dict(_delete_=True, type='TBLRBBoxCoder', normalizer=4.0),
- loss_cls=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0,
- reduction='none'),
- loss_bbox=dict(
- _delete_=True,
- type='IoULoss',
- eps=1e-6,
- loss_weight=1.0,
- reduction='none')),
- # training and testing settings
- train_cfg=dict(
- assigner=dict(
- _delete_=True,
- type='CenterRegionAssigner',
- pos_scale=0.2,
- neg_scale=0.2,
- min_pos_iof=0.01),
- allowed_border=-1,
- pos_weight=-1,
- debug=False))
- optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
- optimizer_config = dict(
- _delete_=True, grad_clip=dict(max_norm=10, norm_type=2))
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