Ultra-lightweight human body posture key point CNN model. ModelSize:2.3MB HUAWEI P40 NCNN benchmark: 6ms/img,

Related tags

Deep Learningmxnetopenposehumanposemxnet-gluongluoncvsimplepose
Overview

Ultralight-SimplePose

image

  • 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

image

Test camera stream

python cam_demo

How To Train

Download the coco2017 dataset

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

image

Android sample


Thanks

Owner
qiuqiuqiuqiu ...球
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