facebookresearch/MaskFormer

Health v2 · maintenance only

Flags: no_releases archived no_license

Adoption not part of the score

1460 stars · 156 forks observed · 2026-08-28

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

MaskFormer is a PyTorch/Detectron2-based implementation of the NeurIPS 2021 paper 'Per-Pixel Classification is Not All You Need for Semantic Segmentation', which reformulates semantic segmentation as mask classification. It provides trained models, training and evaluation code for major segmentation datasets like ADE20K, Cityscapes, COCO-Stuff, and Mapillary Vistas.

Use cases

• run semantic segmentation on images with a mask classification model
• train a state-of-the-art segmentation model on ADE20K or Cityscapes
• evaluate pretrained MaskFormer checkpoints on segmentation benchmarks
• compare mask classification vs per-pixel classification segmentation approaches
• fine-tune MaskFormer on a custom segmentation dataset
• use MaskFormer as a baseline before upgrading to Mask2Former

When to choose

• you need strong semantic segmentation results with an efficient mask-classification architecture
• you want reproducible baselines and pretrained checkpoints for ADE20K, Cityscapes, COCO-Stuff, or Mapillary Vistas
• your research builds on the MaskFormer meta-architecture and you already use Detectron2

When to avoid

• you need a permissively licensed model for commercial products - the code is CC BY-NC 4.0 (non-commercial)
• you want the newest state-of-the-art universal segmentation - use Mask2Former instead
• you need panoptic or instance segmentation out of the box - MaskFormer focuses on semantic segmentation
• you want a maintained production library - this is research code with limited recent activity

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision computer-vision deep-learning machine-learning image-processing python semantic-segmentation panoptic-segmentation instance-segmentation detectron2 transformers research-code neurips-2021 non-commercial-license linux gpu

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markdown · JSON · MCP: product_card(name="facebookresearch/MaskFormer")