- Support NCNN mobile terminal deployment
- Based on MXNET(>=1.5.1) GLUON(>=0.7.0) framework
- Top-down strategy: The input image is the person ROI detected by the object detector
- Lightweight mobile terminal human body posture key point model(COCO 17 person_keypoints)
- Detector:https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3
Network | COCO AP(0.5) | Resolution | Inference time (NCNN/Kirin 990) | FLOPS | Weight size |
---|---|---|---|---|---|
Ultralight-Nano-SimplePose | 71.0% | W:192 H:256 | ~5.4ms | &BFlops | 2.3MB |
python img_demo.py
python cam_demo
- http://images.cocodataset.org/zips/train2017.zip
- http://images.cocodataset.org/annotations/annotations_trainval2017.zip
- http://images.cocodataset.org/zips/val2017.zip
- Unzip the downloaded dataset zip file to the coco directory
- 交流qq群:1062122604
python train_simple_pose.py
- zzzz