fudan-zvg/SETR

Health v2 · maintenance only

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1108 stars · 146 forks observed · 2026-08-28

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

SETR (SEgmentation TRansformers) is the official PyTorch implementation of the CVPR 2021 / IJCV 2024 paper 'Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers'. It provides model configs, pretrained checkpoints, and training/evaluation tooling built on the MMSegmentation framework for pure-transformer semantic segmentation.

Use cases

• run transformer-based semantic segmentation on cityscapes
• train SETR models on ADE20K and PASCAL Context
• reproduce CVPR 2021 segmentation benchmark results
• use pretrained SETR checkpoints for dense prediction
• compare pure transformer segmentation against CNN baselines
• fine-tune vision transformer segmentation models on custom datasets

When to choose

• you need a pure-transformer semantic segmentation model with published checkpoints
• you want to reproduce or extend SETR research results
• your stack already uses MMSegmentation/OpenMMLab tooling

When to avoid

• you need a lightweight segmentation model for production inference on limited hardware
• you want actively developed features or new architectures
• you need a framework-agnostic implementation outside PyTorch/MMSegmentation

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning image-processing python semantic-segmentation transformers vision-transformer cvpr-2021 mmsegmentation cityscapes ade20k research-code linux gpu

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