README.md

AP-10K: A Benchmark for Animal Pose Estimation in the Wild

Input

(Image from AP-10K dataset 000000030718.jpg https://github.com/AlexTheBad/AP-10K)

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 ap-10k.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 ap-10k.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 ap-10k.py --video VIDEO_PATH

By default, yolov3 and hrnet32 are used. You can use other model by specifying hrnet48, res50, res101 in -m option and yolox_m in -d option.

$ python3 ap-10k.py -d yolox_m -m hrnet48

Reference

• AP-10K

Framework

Pytorch

Model Format

ONNX opset=11

Netron

hrnet_w32_ap10k_256x256.onnx.prototxt
hrnet_w48_ap10k_256x256.onnx.prototxt
res50_ap10k_256x256.onnx.prototxt
res101_ap10k_256x256.onnx.prototxt
yolov3.opt.onnx.prototxt
yolox_m.opt.onnx.prototxt