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- _base_ = '../fast_rcnn/fast-rcnn_r50-caffe_fpn_1x_coco.py'
- model = dict(
- roi_head=dict(
- bbox_head=dict(
- bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
- # model training and testing settings
- train_cfg=dict(
- rcnn=dict(
- assigner=dict(
- pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65),
- sampler=dict(num=256))),
- test_cfg=dict(rcnn=dict(score_thr=1e-3)))
- # MMEngine support the following two ways, users can choose
- # according to convenience
- # train_dataloader = dict(dataset=dict(proposal_file='proposals/crpn_r50_caffe_fpn_1x_train2017.pkl')) # noqa
- _base_.train_dataloader.dataset.proposal_file = 'proposals/crpn_r50_caffe_fpn_1x_train2017.pkl' # noqa
- # val_dataloader = dict(dataset=dict(proposal_file='proposals/crpn_r50_caffe_fpn_1x_val2017.pkl')) # noqa
- # test_dataloader = val_dataloader
- _base_.val_dataloader.dataset.proposal_file = 'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl' # noqa
- test_dataloader = _base_.val_dataloader
- optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
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