deep-high-resolution-net.pytorch

Why we included this project

Fitness tracking, motion analysis, AR avatars, and gesture interfaces all need to locate body joints in images and video, and this repo is the PyTorch implementation of HRNet, the pose estimation architecture introduced in a 2019 CVPR paper that became a standard baseline in the field. The key idea is to keep a high-resolution feature stream alive through the whole network instead of recovering detail only at the end, which is what gives it precise keypoint localization. The repo ships training and evaluation code for the COCO and MPII keypoint benchmarks, pretrained models, and a demo that runs inference on your own images, so you can go from reading the paper to running predictions without much setup. It is also a useful way to learn how modern top-down pose estimation pipelines are put together, because the code keeps dataset loading, model definition, and training logic in separate, readable pieces.