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 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