yu-changqian/TorchSeg

Health v2 · maintenance only

Adoption not part of the score

1410 stars · 249 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A fast, modular PyTorch reference implementation for training and evaluating semantic segmentation models such as FCN, DFN, BiSeNet, PSPNet, and PSANet. It supports distributed multi-GPU training and ships pre-trained models with benchmark results on PASCAL VOC, Cityscapes, ADE20K, and CamVid.

Use cases

• train a semantic segmentation model in pytorch
• benchmark FCN PSPNet BiSeNet on cityscapes
• get pretrained segmentation models with mean IoU results
• distributed multi-gpu training for image segmentation
• reproduce DFN segmentation paper results
• segment images into per-pixel classes

When to choose

• you need a modular PyTorch codebase for semantic segmentation research
• you want fast distributed multi-GPU training of segmentation models
• you need reference benchmarks and pre-trained weights for FCN, DFN, BiSeNet, PSPNet, or PSANet

When to avoid

• you need actively maintained code with recent PyTorch version support
• you want general-purpose image classification or object detection
• you need a production inference service rather than a training framework

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision computer-vision image-processing deep-learning machine-learning python semantic-segmentation pytorch cityscapes pascal-voc distributed-training model-zoo linux gpu

Member repositories

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markdown · JSON · MCP: product_card(name="yu-changqian/TorchSeg")