|
|
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model = dict( |
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type='FastRCNN', |
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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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num_outs=5), |
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roi_head=dict( |
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type='StandardRoIHead', |
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bbox_roi_extractor=dict( |
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type='SingleRoIExtractor', |
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roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), |
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out_channels=256, |
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featmap_strides=[4, 8, 16, 32]), |
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bbox_head=dict( |
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type='Shared2FCBBoxHead', |
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in_channels=256, |
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fc_out_channels=1024, |
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roi_feat_size=7, |
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num_classes=80, |
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bbox_coder=dict( |
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type='DeltaXYWHBBoxCoder', |
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target_means=[0., 0., 0., 0.], |
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target_stds=[0.1, 0.1, 0.2, 0.2]), |
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reg_class_agnostic=False, |
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loss_cls=dict( |
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), |
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loss_bbox=dict(type='L1Loss', loss_weight=1.0))), |
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|
|
train_cfg=dict( |
|
rcnn=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=0.5, |
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min_pos_iou=0.5, |
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match_low_quality=False, |
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ignore_iof_thr=-1), |
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sampler=dict( |
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type='RandomSampler', |
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num=512, |
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pos_fraction=0.25, |
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neg_pos_ub=-1, |
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add_gt_as_proposals=True), |
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pos_weight=-1, |
|
debug=False)), |
|
test_cfg=dict( |
|
rcnn=dict( |
|
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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|
