# facebookresearch/deepcluster

Deep Clustering for Unsupervised Learning of Visual Features

Repository: https://github.com/facebookresearch/deepcluster
Canonical: https://ross.abutalabs.com/products/deepcluster
Language: Python
License: NOASSERTION
License Family: other
Archived: true
Last push: 2021-10-12T21:32:33+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2946, "days_push": 1786, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1743, forks 318 (observed 2026-08-28T04:05:30.423420+00:00)

## What it is
DeepCluster is a research codebase from Facebook AI Research implementing the 'Deep Clustering for Unsupervised Learning of Visual Features' method, which trains convolutional neural networks without labels by iteratively clustering features and using cluster assignments as pseudo-labels. It includes training code, evaluation protocols (Pascal VOC classification, linear probing, image retrieval), a feature visualization module, and pre-trained AlexNet and VGG-16 models.

## Use cases
- train convnets without labeled data using deep clustering
- reproduce DeepCluster results on ImageNet
- evaluate unsupervised visual features with linear classification
- perform instance-level image retrieval with learned features
- classify Pascal VOC images with self-supervised features
- visualize quality of learned visual representations
- download pre-trained AlexNet and VGG-16 self-supervised models

## When to choose
- you need the original DeepCluster method for unsupervised feature learning research
- you want pre-trained self-supervised AlexNet/VGG-16 backbones
- you need baseline evaluation protocols for unsupervised visual representations

## When to avoid
- you want the improved DeepCluster-v2 or SwAV methods - use the swav repository instead
- you need modern PyTorch versions - the code targets PyTorch 0.1.8 and Python 2.7
- you need production-ready or maintained software rather than research code

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing
- domain: computer-vision, machine-learning, deep-learning
- platform: python
- tags: self-supervised-learning, unsupervised-learning, clustering, pytorch, convnets, research-code, pretrained-models, imagenet, gpu, linux

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

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:30.423420+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 2026-08-30T03:29:33.152649+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/deepcluster (fetched 2026-08-28T04:05:30.423420+00:00, sha e6c9ae9d38cd)
- Data as of 2026-08-30T08:39:29.467469+00:00.