mmpose VS openvino

RTMPose-m achieves 75.8% AP on COCO with 90+ FPS on an Intel i7-11700 CPU and 430+ FPS on an NVIDIA GTX 1660 Ti GPU, and RTMPose-l achieves 67.0% AP on COCO-WholeBody with 130+ FPS.

MMPose: OpenMMLab pose estimation toolbox and benchmark.

I downloaded pytorch2onnx.py from mmPose tools. It's big, but the top half is imports and input arguments. Line 125, I hard-coded my (image) input size. I ran it on my .pth model file, and out pop'd an onnx file.

They have a specific repo called mmpose: https://github.com/open-mmlab/mmpose

I would start with mmpose. It's basically a curated list of the best models ready to go.

The [MMLab key point detection](https://github.com/open-mmlab/mmpose) is in a separate repo from detection.

One option is mmPose. They have a bunch of 2D/3D models implemented and support different skeleton structures.

This library is pretty good. It has implementations for a number of pose estimators. I think Darkpose is the best one from memory

Check out https://github.com/open-mmlab/mmpose I think they have guides for new datasets

Anomalib is an open-source deep learning library developed by Intel that makes it easy to benchmark different anomaly detection algorithms on both public and custom datasets, all by simply modifying a config file. As the largest public collection of anomaly detection algorithms and datasets, it has a strong focus on image-based anomaly detection. It’s a comprehensive, end-to-end solution that includes cutting-edge algorithms, relevant evaluation methods, prediction visualizations, hyperparameter optimization, and inference deployment code with Intel’s OpenVINO Toolkit.