# switchablenorms/CelebAMask-HQ

A large-scale face dataset for face parsing, recognition, generation and editing.

Repository: https://github.com/switchablenorms/CelebAMask-HQ
Canonical: https://ross.abutalabs.com/products/celebamask-hq
Language: Python
License Family: other
Topics: celeba, generative-adversarial-network, face-recognition, face-generation, face-segmentation
Last push: 2024-06-20T04:33:41+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2711, "days_push": 804, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2320, forks 357 (observed 2026-08-28T04:06:37.087542+00:00)

## What it is
CelebAMask-HQ is a large-scale face image dataset containing 30,000 high-resolution face images with manually annotated 512x512 segmentation masks across 19 facial attribute classes. It is designed for training and evaluating algorithms in face parsing, face recognition, and GAN-based face generation and editing.

## Use cases
- train face parsing models on high-resolution annotated faces
- train GANs for face generation and editing
- evaluate face recognition algorithms
- segment facial components and accessories in images
- interactive facial image manipulation research
- face hallucination model training

## When to choose
- you need manually annotated facial segmentation masks at 512x512 resolution
- you are researching face parsing, recognition, or GAN-based face editing
- you need a large-scale face dataset with 19 attribute classes for non-commercial research

## When to avoid
- you need a dataset for commercial purposes (license is non-commercial only)
- you need identity or attribute labels (must be requested separately from the CelebA team)
- you need images at resolutions other than 512x512 masks or need video data

## Facets
- artifact type: dataset
- maturity: stable
- function: computer-vision, image-processing, machine-learning, deep-learning
- domain: computer-vision, image-processing, machine-learning, deep-learning
- platform: python, cross-platform
- tags: face-parsing, face-recognition, face-generation, face-editing, face-segmentation, generative-adversarial-network, celeba, facial-attributes, image-dataset, segmentation-masks, high-resolution-faces, gan-training, research-dataset, non-commercial

## Member repositories
- switchablenorms/CelebAMask-HQ (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:37.087542+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 2026-08-30T02:38:50.019043+00:00, confidence not recorded.
  - readme: https://github.com/switchablenorms/CelebAMask-HQ (fetched 2026-08-28T04:06:37.087542+00:00, sha d3bf9d2bf49d)
- Data as of 2026-08-30T08:39:29.467469+00:00.