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NFPN-GitHub

基于mmdetection,先安装mmdetection配置好环境。

训练方法:

1 NFPN

1.将本项目中mmdet/model/neck中的nfpn.py以及__init__.py文件复制到mmdetection/mmdet/neck下,替换__init__.py文件

python tools/train.py config/faster_rcnn/nfpn.py

2 NFPN + DROI

2.将本项目中mmdet/model/detectors中的twostage.py文件替换到mmdetection/mmdet/detectors下的twostage.py

3.将本项目中mmdet/model/roi_heads中的standard_roi_head.py文件复替换mmdetection/mmdet/roi_heads中的standard_roi_head

4.将本项目中mmdet/model/roi_heads/roi_extractors文件夹替换复替换mmdetection/mmdet/roi_heads中的/roi_extractors文件夹

3,NFPN + DROI + RFPN

表格 1 在Lisa数据集上的实验

Method mAP AP50 AP75 APS APM APL backbone
Faster RCNN 65.2 76.1 74.8 66.8 71.0 70.5 Res50-FPN
BFP 63.9 75.9 74.0 63.4 71.2 77.9 ResN50-FPN
BiFPN 58.4 70.9 69.8 55.4 65.1 83.5 Res50-FPN
BiFPN*2 49.2 59.9 58.4 49.1 56.6 78.5 Res50-FPN
NFPN(ours) 66.1 77.5 75.8 65.9 72.1 75.7 Res50-FPN
MRoIE + NFPN(ours) 67.4 78.8 77.5 68.0 74.3 76.0 Res50-FPN
NFPN*2+ Double ROI(ours) 66.0 78.1 76.4 66.2 72.5 80.7 Res50-FPN
NFPN +RFP + Double ROI(ours) 67.8 79.0 77.7 68.4 74.0 81.1 Res50-FPN

表格 2 在 VOC07+12上的实验

Method mAP backbone
Faster RCNN 73.2 VGG-16
Faster RCNN 79.5 Res-50
NFPN(ours) 81.3 Res-50
Faster RCNN 76.4 Res-101
NFPN(ours) 82.4 Res-101

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