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_base_ = [ |
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'../_base_/datasets/coco_detection.py', |
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'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' |
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] |
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model = dict( |
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type='RepPointsDetector', |
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pretrained='torchvision://resnet50', |
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backbone=dict( |
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type='ResNet', |
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depth=50, |
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num_stages=4, |
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out_indices=(0, 1, 2, 3), |
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frozen_stages=1, |
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norm_cfg=dict(type='BN', requires_grad=True), |
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norm_eval=True, |
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style='pytorch'), |
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neck=dict( |
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type='FPN', |
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in_channels=[256, 512, 1024, 2048], |
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out_channels=256, |
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start_level=1, |
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add_extra_convs='on_input', |
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num_outs=5), |
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bbox_head=dict( |
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type='RepPointsHead', |
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num_classes=80, |
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in_channels=256, |
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feat_channels=256, |
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point_feat_channels=256, |
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stacked_convs=3, |
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num_points=9, |
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gradient_mul=0.1, |
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point_strides=[8, 16, 32, 64, 128], |
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point_base_scale=4, |
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loss_cls=dict( |
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type='FocalLoss', |
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use_sigmoid=True, |
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gamma=2.0, |
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alpha=0.25, |
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loss_weight=1.0), |
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loss_bbox_init=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.5), |
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loss_bbox_refine=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0), |
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transform_method='moment'), |
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|
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train_cfg=dict( |
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init=dict( |
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assigner=dict(type='PointAssigner', scale=4, pos_num=1), |
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allowed_border=-1, |
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pos_weight=-1, |
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debug=False), |
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refine=dict( |
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assigner=dict( |
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type='MaxIoUAssigner', |
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pos_iou_thr=0.5, |
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neg_iou_thr=0.4, |
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min_pos_iou=0, |
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ignore_iof_thr=-1), |
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allowed_border=-1, |
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pos_weight=-1, |
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debug=False)), |
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test_cfg=dict( |
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nms_pre=1000, |
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min_bbox_size=0, |
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score_thr=0.05, |
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nms=dict(type='nms', iou_threshold=0.5), |
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max_per_img=100)) |
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optimizer = dict(lr=0.01) |
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|
