Tramac/awesome-semantic-segmentation-pytorch

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3069 stars · 581 forks observed · 2026-08-28

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

A PyTorch library providing concise, modifiable reference implementations of many semantic segmentation models such as FCN, PSPNet, DeepLabv3+, BiSeNet, and DANet. It includes training, evaluation, and demo scripts with single- and multi-GPU support.

Use cases

• train semantic segmentation models on pascal voc
• implement deeplabv3+ in pytorch
• compare segmentation model architectures
• run a segmentation demo on an image
• evaluate segmentation models on multiple gpus
• learn how semantic segmentation networks work

When to choose

• you want readable, modifiable reference code for classic segmentation models
• you need to train or benchmark many segmentation architectures in one codebase
• you are studying or reproducing segmentation papers

When to avoid

• you need a production-ready, actively maintained segmentation toolkit
• you want the latest transformer-based segmentation models
• you need pretrained models for many datasets out of the box

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision computer-vision deep-learning machine-learning python cross-platform semantic-segmentation pytorch fcn pspnet deeplabv3 bisenet reference-implementation gpu linux

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markdown · JSON · MCP: product_card(name="Tramac/awesome-semantic-segmentation-pytorch")