# facebookresearch/Mask2Former

Code release for "Masked-attention Mask Transformer for Universal Image Segmentation"

Repository: https://github.com/facebookresearch/Mask2Former
Canonical: https://ross.abutalabs.com/products/mask2former
Language: Python
License: MIT
License Family: permissive
Archived: true
Last push: 2024-07-29T11:47:14+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1743, "days_push": 765, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3416, forks 528 (observed 2026-08-28T04:08:03.726475+00:00)

## What it is
Mask2Former is the official PyTorch implementation of the CVPR 2022 paper 'Masked-attention Mask Transformer for Universal Image Segmentation' from Meta AI Research. It provides a single transformer architecture that handles panoptic, instance, semantic, and video instance segmentation, with pretrained models for major datasets like COCO, ADE20K, Cityscapes, and Mapillary Vistas.

## Use cases
- perform panoptic segmentation on images
- run instance segmentation with a transformer model
- semantic segmentation on Cityscapes or ADE20K
- video instance segmentation
- fine-tune a state-of-the-art segmentation model on my own dataset
- download pretrained segmentation checkpoints
- compare segmentation baselines across datasets

## When to choose
- you need one architecture covering panoptic, instance, and semantic segmentation
- you want strong pretrained baselines on COCO, ADE20K, Cityscapes, or Mapillary Vistas
- you are doing research on segmentation transformers and want a reproducible reference implementation
- you need video instance segmentation support

## When to avoid
- you need a lightweight model for real-time or edge deployment
- you want a maintained production library with frequent updates - the repo is in maintenance mode
- you need segmentation for domains without supported pretrained weights and lack training resources
- you prefer a simple API over research-style codebases built on Detectron2

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, image-processing, machine-learning, deep-learning
- domain: computer-vision, image-processing, deep-learning, machine-learning
- platform: python, cross-platform
- tags: image-segmentation, panoptic-segmentation, instance-segmentation, semantic-segmentation, transformer, cvpr-2022, research-code, pytorch, video-instance-segmentation, model-zoo, linux, gpu

## Member repositories
- facebookresearch/Mask2Former (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:03.726475+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:38:15.252205+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/Mask2Former (fetched 2026-08-28T04:08:03.726475+00:00, sha 7cfb9ade1ccc)
- Data as of 2026-08-30T08:39:29.467469+00:00.