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- _base_ = '../common/ms-90k_coco.py'
- # model settings
- model = dict(
- type='Detectron2Wrapper',
- bgr_to_rgb=False,
- detector=dict(
- # The settings in `d2_detector` will merged into default settings
- # in detectron2. More details please refer to
- # https://github.com/facebookresearch/detectron2/blob/main/detectron2/config/defaults.py # noqa
- meta_architecture='RetinaNet',
- # If you want to finetune the detector, you can use the
- # checkpoint released by detectron2, for example:
- # weights='detectron2://COCO-Detection/retinanet_R_50_FPN_1x/190397773/model_final_bfca0b.pkl' # noqa
- weights='detectron2://ImageNetPretrained/MSRA/R-50.pkl',
- mask_on=False,
- pixel_mean=[103.530, 116.280, 123.675],
- pixel_std=[1.0, 1.0, 1.0],
- backbone=dict(name='build_retinanet_resnet_fpn_backbone', freeze_at=2),
- resnets=dict(
- depth=50,
- out_features=['res3', 'res4', 'res5'],
- num_groups=1,
- norm='FrozenBN'),
- fpn=dict(in_features=['res3', 'res4', 'res5'], out_channels=256),
- anchor_generator=dict(
- name='DefaultAnchorGenerator',
- sizes=[[x, x * 2**(1.0 / 3), x * 2**(2.0 / 3)]
- for x in [32, 64, 128, 256, 512]],
- aspect_ratios=[[0.5, 1.0, 2.0]],
- angles=[[-90, 0, 90]]),
- retinanet=dict(
- num_classes=80,
- in_features=['p3', 'p4', 'p5', 'p6', 'p7'],
- num_convs=4,
- iou_thresholds=[0.4, 0.5],
- iou_labels=[0, -1, 1],
- bbox_reg_weights=(1.0, 1.0, 1.0, 1.0),
- bbox_reg_loss_type='smooth_l1',
- smooth_l1_loss_beta=0.0,
- focal_loss_gamma=2.0,
- focal_loss_alpha=0.25,
- prior_prob=0.01,
- score_thresh_test=0.05,
- topk_candidates_test=1000,
- nms_thresh_test=0.5)))
- optim_wrapper = dict(optimizer=dict(lr=0.01))
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