# wvangansbeke/Unsupervised-Classification

SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]

Repository: https://github.com/wvangansbeke/Unsupervised-Classification
Canonical: https://ross.abutalabs.com/products/unsupervised-classification
Homepage: https://arxiv.org/abs/2005.12320
Language: Python
License: NOASSERTION
License Family: other
Topics: unsupervised-learning, image-classification, self-supervised-learning, clustering, eccv-2020, eccv2020, representation-learning, contrastive-learning, simclr, moco
Last push: 2023-07-27T19:24:04+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2361, "days_push": 1133, "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 1455, forks 271 (observed 2026-08-28T04:04:46.865956+00:00)

## What it is
PyTorch implementation of SCAN (ECCV 2020), a two-step method for unsupervised image classification that combines self-supervised representation learning (e.g., SimCLR) with learnable clustering. It achieved state-of-the-art results on CIFAR-10, CIFAR-100-20, STL-10, and ImageNet image clustering benchmarks.

## Use cases
- cluster images into semantic groups without labels
- perform unsupervised image classification on CIFAR or ImageNet
- learn self-supervised visual representations with SimCLR
- reproduce ECCV 2020 SCAN paper results
- benchmark unsupervised clustering on image datasets
- explore representation learning prior to clustering

## When to choose
- you need to classify or cluster images without ground-truth labels
- you want a research-grade baseline for unsupervised image classification
- you need self-supervised feature learning combined with clustering

## When to avoid
- you need a production-ready, actively maintained library
- you require a permissive license (license is non-standard)
- you need supervised classification with labeled data

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, computer-vision, image-processing
- domain: machine-learning, computer-vision, image-processing, deep-learning
- platform: python
- tags: unsupervised-learning, self-supervised-learning, contrastive-learning, simclr, clustering, image-classification, eccv-2020, pytorch, research-code, gpu

## Member repositories
- wvangansbeke/Unsupervised-Classification (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:46.865956+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 2026-08-30T04:35:40.670919+00:00, confidence not recorded.
  - readme: https://github.com/wvangansbeke/Unsupervised-Classification (fetched 2026-08-28T04:04:46.865956+00:00, sha 2623338a3f10)
  - homepage: https://arxiv.org/abs/2005.12320 (fetched 2026-08-29T11:44:51.313875+00:00, sha 08cf1cc470d2)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T11:44:51.323338+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T11:44:51.327634+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T11:44:51.330095+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T11:44:51.325760+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.