README.md

MMPose - 2D animal pose estimation

Input

(Image from https://pixabay.com/ja/photos/%e7%89%9b-%e5%ae%b6%e7%95%9c-%e4%b9%b3%e7%89%9b-%e4%b9%b3%e7%94%a8%e7%89%9b-%e5%8b%95%e7%89%a9-5717276/)

Shape : (1, 3, 256, 256)

Output

Usage

Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python3 animalpose.py

If you want to specify the input image, put the image path after the --input option.
You can use --savepath option to change the name of the output file to save.

$ python3 animalpose.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH

By adding the --video option, you can input the video.
If you pass 0 as an argument to VIDEO_PATH, you can use the webcam input instead of the video file.

$ python3 animalpose.py --video VIDEO_PATH

By default, yolov3 and hrnet32 are used. Yolox_m and hrnet48 can also be used for accuracy.

$ python3 animalpose.py -d yolox_m -m hrnet48

Reference

• MMPose

Framework

Pytorch

Model Format

ONNX opset=11

Netron

hrnet_w32_256x256.onnx.prototxt
hrnet_w48_256x256.onnx.prototxt
res50_256x256.onnx.prototxt
res101_256x256.onnx.prototxt
res152_256x256.onnx.prototxt
yolov3.opt.onnx.prototxt