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

Last update: Dec 27, 2022

Related tags

Deep Learning mxnet openpose humanpose mxnet-gluon gluoncv simplepose

Overview

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

https://github.com/WZTENG/YOLOv5_NCNN

Thanks

SimplePose Paper:https://arxiv.org/abs/1804.06208
https://github.com/Tencent/ncnn
https://gluon-cv.mxnet.io/

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（1）INVALID_ARGUMENT : Non-zero status code returned while running BatchNormalization node. （2）Status Message: Invalid input scale: NumDimensions() != 3 参考：https://github.com/onnx/models/issues/156解决原因：mxnet转化为onnx时，产生错误的模型，BatchNormalization(space=0)

opened by JayL323 1
How to use Yolov3-Nano for object detection instead of yolo3_mobilenet1.0_coco

Hi,

In the current code you are using "yolo3_mobilenet1.0_coco" for object detection. Can you please tell me how to use "Yolov3-Nano"( https://github.com/dog-qiuqiu/MobileNet-Yolo/tree/master/MobileNetV2-YOLOv3-Nano ) for object detection in the code instead of "yolo3_mobilenet1.0_coco".

Thank you @dog-qiuqiu

opened by sakethkhandavalli 1

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

Related tags

Overview

Ultralight-SimplePose

Model

Mobile inference frameworks benchmark (4*ARM_CPU)

COCO2017 val keypoints metrics evaluate

Install

Demo

Test picture

Test camera stream

How To Train

Download the coco2017 dataset

Train

Ncnn Deploy

Install ncnn

Run ncnn sample

Ncnn Picture test results

Android sample

Thanks

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Example scripts for the detection of lanes using the ultra fast lane detection model in Tensorflow Lite.

Lane assist for ETS2, built with the ultra-fast-lane-detection model.

Comments

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How to use Yolov3-Nano for object detection instead of yolo3_mobilenet1.0_coco

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