xiaoyufenfei/Efficient-Segmentation-Networks

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1008 stars · 167 forks observed · 2026-08-28

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

A PyTorch reference implementation collection of lightweight, real-time semantic segmentation models such as ENet, ERFNet, LEDNet, Fast-SCNN, and CGNet. It includes training and evaluation scripts for datasets like Cityscapes and CamVid.

Use cases

• run real-time semantic segmentation on driving scenes
• train lightweight segmentation models on cityscapes
• compare efficient encoder-decoder segmentation architectures
• deploy fast image segmentation on edge devices
• benchmark real-time segmentation networks in pytorch

When to choose

• you need ready-to-use PyTorch implementations of many real-time segmentation models in one repo
• you want to train or benchmark lightweight segmentation networks on Cityscapes or CamVid

When to avoid

• you need state-of-the-art heavy segmentation models like Mask2Former or SAM
• you need production-grade tooling like MMSegmentation with configs and model zoo

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision computer-vision machine-learning deep-learning autonomous-vehicles python cross-platform semantic-segmentation pytorch real-time-segmentation lightweight-models cityscapes camvid scene-understanding gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="xiaoyufenfei/Efficient-Segmentation-Networks")