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- _base_ = '../common/ms-poly-90k_coco-instance.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='GeneralizedRCNN',
- # If you want to finetune the detector, you can use the
- # checkpoint released by detectron2, for example:
- # weights='detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/model_final_a54504.pkl' # noqa
- weights='detectron2://ImageNetPretrained/MSRA/R-50.pkl',
- mask_on=True,
- pixel_mean=[103.530, 116.280, 123.675],
- pixel_std=[1.0, 1.0, 1.0],
- backbone=dict(name='build_resnet_fpn_backbone', freeze_at=2),
- resnets=dict(
- depth=50,
- out_features=['res2', 'res3', 'res4', 'res5'],
- num_groups=1,
- norm='FrozenBN'),
- fpn=dict(
- in_features=['res2', 'res3', 'res4', 'res5'], out_channels=256),
- anchor_generator=dict(
- name='DefaultAnchorGenerator',
- sizes=[[32], [64], [128], [256], [512]],
- aspect_ratios=[[0.5, 1.0, 2.0]],
- angles=[[-90, 0, 90]]),
- proposal_generator=dict(name='RPN'),
- rpn=dict(
- head_name='StandardRPNHead',
- in_features=['p2', 'p3', 'p4', 'p5', 'p6'],
- iou_thresholds=[0.3, 0.7],
- iou_labels=[0, -1, 1],
- batch_size_per_image=256,
- positive_fraction=0.5,
- bbox_reg_loss_type='smooth_l1',
- bbox_reg_loss_weight=1.0,
- bbox_reg_weights=(1.0, 1.0, 1.0, 1.0),
- smooth_l1_beta=0.0,
- loss_weight=1.0,
- boundary_thresh=-1,
- pre_nms_topk_train=2000,
- post_nms_topk_train=1000,
- pre_nms_topk_test=1000,
- post_nms_topk_test=1000,
- nms_thresh=0.7,
- conv_dims=[-1]),
- roi_heads=dict(
- name='StandardROIHeads',
- num_classes=80,
- in_features=['p2', 'p3', 'p4', 'p5'],
- iou_thresholds=[0.5],
- iou_labels=[0, 1],
- batch_size_per_image=512,
- positive_fraction=0.25,
- score_thresh_test=0.05,
- nms_thresh_test=0.5,
- proposal_append_gt=True),
- roi_box_head=dict(
- name='FastRCNNConvFCHead',
- num_fc=2,
- fc_dim=1024,
- conv_dim=256,
- pooler_type='ROIAlignV2',
- pooler_resolution=7,
- pooler_sampling_ratio=0,
- bbox_reg_loss_type='smooth_l1',
- bbox_reg_loss_weight=1.0,
- bbox_reg_weights=(10.0, 10.0, 5.0, 5.0),
- smooth_l1_beta=0.0,
- cls_agnostic_bbox_reg=False),
- roi_mask_head=dict(
- name='MaskRCNNConvUpsampleHead',
- conv_dim=256,
- num_conv=4,
- pooler_type='ROIAlignV2',
- pooler_resolution=14,
- pooler_sampling_ratio=0,
- cls_agnostic_mask=False)))
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