Awesome
Ultralight-SimplePose
- 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
Model
Mobile inference frameworks benchmark (4*ARM_CPU)
| Network | Resolution | Inference time (NCNN/Kirin 990) | FLOPS | Weight size | HeatmapAccuracy |
|---|---|---|---|---|---|
| Ultralight-Nano-SimplePose | W:192 H:256 | ~5.4ms | 0.224BFlops | 2.3MB | 74.3% |
COCO2017 val keypoints metrics evaluate
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.518
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.816
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.558
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.498
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.549
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.563
Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.837
Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.607
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.535
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.604
Install
pip install mxnet-cu101 gluoncv
pip install opencv-python cython pycocotools
- Install mxnet according to your own cuda version
Demo
Test picture
python img_demo.py
Test camera stream
python cam_demo
How To Train
Download the coco2017 dataset
- 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
Train
python train_simple_pose.py
Ncnn Deploy
- Dependent library: Opencv Ncnn
- Read the camera video stream test by default, if you test the picture, please modify the code
Install ncnn
$ git clone https://github.com/Tencent/ncnn.git
$ cd <ncnn-root-dir>
$ mkdir -p build
$ cd build
$ make -j4
$ make install
Run ncnn sample
$ cp -rf ncnn/build/install/include ./Ultralight-SimplePose/ncnnsample/
$ cp -rf ncnn/build/install/lib ./Ultralight-SimplePose/ncnnsample/
$ g++ -o ncnnpose ncnnpose.cpp -I include/ncnn/ lib/libncnn.a `pkg-config --libs --cflags opencv` -fopenmp
$ ./ncnnpose
Ncnn Picture test results
Android sample
<img src="https://github.com/dog-qiuqiu/Ultralight-SimplePose/blob/master/data/Android_Meizu16x_simple_pose.jpg" width="330" height="660" /><br/>