CoinCheung/BiSeNet

Health v2 · maintenance only

Adoption not part of the score

1638 stars · 336 forks observed · 2026-08-28

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

A PyTorch implementation of the BiSeNet V1 and V2 real-time semantic segmentation models, with pretrained weights for Cityscapes, COCO-Stuff, and ADE20K. It also provides export and deployment guides for TensorRT, ncnn, OpenVINO, and Triton Inference Server.

Use cases

• train a real-time semantic segmentation model on cityscapes
• run fast image segmentation on GPU with pretrained bisenet weights
• deploy a segmentation model to tensorrt or ncnn for inference
• evaluate mIoU of bisenet on ade20k or cocostuff
• segment street scenes for autonomous driving research
• convert a pytorch segmentation model to openvino

When to choose

• you need fast real-time semantic segmentation with proven mIoU benchmarks
• you want pretrained weights on multiple segmentation datasets
• you plan to deploy segmentation models to edge inference runtimes like tensorrt or ncnn

When to avoid

• you need state-of-the-art accuracy rather than speed-oriented segmentation
• you work outside PyTorch or need non-CUDA training environments
• you need official paper-exact training settings for ade20k

Facets

library · maturity active

machine-learning deep-learning image-processing computer-vision computer-vision image-processing deep-learning machine-learning python semantic-segmentation bisenet pytorch cityscapes tensorrt ncnn openvino model-deployment linux gpu

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