NVlabs/SegFormer

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

3629 stars · 455 forks observed · 2026-08-28

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

Official PyTorch implementation of SegFormer, a transformer-based semantic segmentation framework with a hierarchical encoder and lightweight MLP decoder, built on MMSegmentation v0.13.0. It includes training and evaluation code plus pretrained models (B0-B5) for ADE20K and Cityscapes.

Use cases

• segment images into semantic classes with transformers
• train a semantic segmentation model on ADE20K
• evaluate pretrained SegFormer checkpoints on Cityscapes
• run efficient real-time scene segmentation on autonomous driving imagery
• fine-tune SegFormer on a custom segmentation dataset
• compare transformer vs CNN segmentation baselines

When to choose

• you need state-of-the-art transformer-based semantic segmentation with pretrained weights
• you want efficient segmentation models ranging from lightweight (B0) to high-accuracy (B5)
• you are already using the MMSegmentation ecosystem
• you need robustness to resolution differences between training and testing

When to avoid

• you need a non-PyTorch framework like TensorFlow or JAX
• you want actively maintained code - the repo pins old dependencies (mmcv-full 1.2.7, timm 0.3.2) and recommends using MMSegmentation instead
• you need instance or panoptic segmentation rather than semantic segmentation
• you lack GPU resources for training or inference

Facets

library · maturity maintenance

machine-learning computer-vision image-processing computer-vision deep-learning machine-learning python semantic-segmentation transformer pytorch ade20k cityscapes mmsegmentation pretrained-models linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="NVlabs/SegFormer")