HRNet/HRNet-Semantic-Segmentation

Health v2 · maintenance only

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3331 stars · 695 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 (High-Resolution Network) and the Segmentation Transformer (OCR) approach for semantic segmentation. It provides pretrained models and training/evaluation code achieving state-of-the-art results on benchmarks like Cityscapes, PASCAL-Context, LIP, ADE20K, and COCO-Stuff.

Use cases

• train a semantic segmentation model on Cityscapes
• segment images into per-pixel class labels with HRNet
• use pretrained HRNet weights for scene parsing
• reproduce state-of-the-art OCR segmentation results
• benchmark segmentation models on ADE20K or PASCAL-Context
• human part segmentation with LIP dataset

When to choose

• you need high-accuracy semantic segmentation with strong multi-scale representations
• you want a research-grade PyTorch codebase with pretrained HRNet checkpoints
• you are benchmarking on standard segmentation datasets like Cityscapes or ADE20K

When to avoid

• you need a lightweight model for real-time or mobile inference
• you want a maintained production framework rather than research code
• you need segmentation support outside PyTorch

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision image-processing deep-learning machine-learning python semantic-segmentation hrnet pytorch transformer cityscapes computer-vision-models research-code linux gpu

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markdown · JSON · MCP: product_card(name="HRNet/HRNet-Semantic-Segmentation")