leoxiaobin/deep-high-resolution-net.pytorch

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4480 stars · 922 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Official PyTorch implementation of HRNet (Deep High-Resolution Representation Learning for Human Pose Estimation, CVPR 2019), which maintains high-resolution representations throughout the network for accurate keypoint prediction. It includes training, testing, demo, and visualization code with pretrained models for COCO and MPII pose estimation benchmarks.

Use cases

• detect human body keypoints in images
• train a pose estimation model on COCO or MPII
• run pose estimation inference with pretrained HRNet weights
• benchmark keypoint detection models
• use HRNet as a backbone for other vision tasks
• estimate human poses from photos or video frames

When to choose

• you need accurate top-down human pose estimation with a proven architecture
• you want official pretrained models and reproducible benchmark results on COCO/MPII
• you're doing research on keypoint detection or pose tracking

When to avoid

• you need real-time pose estimation on edge devices or mobile
• you want a maintained library with frequent updates and modern PyTorch practices
• you need bottom-up multi-person pose estimation (see HigherHRNet instead)

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning python human-pose-estimation hrnet keypoint-detection pytorch cvpr-2019 coco mpii research-code gpu linux cuda

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