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- _base_ = ['../detr/detr_r50_8xb2-150e_coco.py']
- model = dict(
- type='ConditionalDETR',
- num_queries=300,
- decoder=dict(
- num_layers=6,
- layer_cfg=dict(
- self_attn_cfg=dict(
- _delete_=True,
- embed_dims=256,
- num_heads=8,
- attn_drop=0.1,
- cross_attn=False),
- cross_attn_cfg=dict(
- _delete_=True,
- embed_dims=256,
- num_heads=8,
- attn_drop=0.1,
- cross_attn=True))),
- bbox_head=dict(
- type='ConditionalDETRHead',
- loss_cls=dict(
- _delete_=True,
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=2.0)),
- # training and testing settings
- train_cfg=dict(
- assigner=dict(
- type='HungarianAssigner',
- match_costs=[
- dict(type='FocalLossCost', weight=2.0),
- dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
- dict(type='IoUCost', iou_mode='giou', weight=2.0)
- ])))
- # learning policy
- train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=50, val_interval=1)
- param_scheduler = [dict(type='MultiStepLR', end=50, milestones=[40])]
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