CSAILVision/semantic-segmentation-pytorch

Health v2 · maintenance only

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Adoption not part of the score

5078 stars · 1098 forks observed · 2026-08-28

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

A PyTorch implementation of semantic segmentation (scene parsing) models for the MIT ADE20K dataset, including pretrained model zoo and training/evaluation tooling. It supports architectures like HRNet and multi-GPU training with synchronized batch normalization.

Use cases

• segment images into semantic categories with pytorch
• train a scene parsing model on ADE20K
• run pixel-wise image segmentation with pretrained models
• evaluate semantic segmentation with mIoU
• try scene segmentation on my own photos
• multi-gpu training for segmentation models

When to choose

• you need semantic segmentation on the ADE20K benchmark with pretrained weights
• you want a reference PyTorch codebase for segmentation research
• you need multi-GPU training with synchronized batch normalization

When to avoid

• you need real-time or production-ready segmentation inference at scale
• you work with datasets other than ADE20K without adaptation effort
• you need actively developed features - the repo is in maintenance mode

Facets

library · maturity maintenance

computer-vision image-processing machine-learning deep-learning computer-vision image-processing machine-learning deep-learning python windows semantic-segmentation scene-parsing ade20k pytorch pretrained-models hrnet synchronized-batchnorm gpu linux macos

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