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

pip install mxnet-cu101 gluoncv
pip install opencv-python cython pycocotools

python img_demo.py

python cam_demo

python train_simple_pose.py

$ git clone https://github.com/Tencent/ncnn.git
$ cd <ncnn-root-dir>
$ mkdir -p build
$ cd build
$ make -j4
$ make install

$ 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