# facebookresearch/vissl

VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

Repository: https://github.com/facebookresearch/vissl
Canonical: https://ross.abutalabs.com/products/vissl
Homepage: https://vissl.ai
Language: Jupyter Notebook
License: MIT
License Family: permissive
Archived: true
Last push: 2024-03-03T01:41:37+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2337, "days_push": 914, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3293, forks 324 (observed 2026-08-28T04:07:54.021939+00:00)

## What it is
VISSL is Facebook AI Research's extensible, modular and scalable PyTorch library for state-of-the-art self-supervised learning with images. It provides reproducible reference implementations of methods like SimCLR, MoCo, SwAV, PIRL and DINO, plus benchmark tasks for evaluating learned representations.

## Use cases
- train self-supervised image models like SimCLR or MoCo
- reproduce SOTA self-supervised learning research results
- benchmark pretrained vision representations with linear classification or nearest neighbor tasks
- pretrain vision transformers on large image datasets
- evaluate embeddings for low-shot classification and object detection
- scale self-supervised training across multiple GPUs and nodes

## When to choose
- you need reference implementations of many SSL methods in one framework
- you want reproducible benchmarks for evaluating visual representations
- you need multi-GPU/multi-node scaling with FP16 and FSDP support
- you want to build on or extend modular SSL components in PyTorch

## When to avoid
- you need supervised-only training without self-supervision features
- you work outside image/vision domains
- you need a lightweight minimal solution rather than a full research framework
- you require frequent updates - the project's latest release was March 2024

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, benchmarking
- domain: computer-vision, deep-learning, machine-learning
- platform: python
- tags: self-supervised-learning, pytorch, computer-vision, representation-learning, simclr, moco, swav, dino, facebook-research, vision-transformers, gpu, linux, docker

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

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:54.021939+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 2026-08-30T07:22:44.476826+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/vissl (fetched 2026-08-28T04:07:54.021939+00:00, sha caedebc18958)
  - homepage: https://vissl.ai (fetched 2026-08-29T09:35:45.596449+00:00, sha 21412d1d8283)
  - registry_pypi: https://pypi.org/pypi/vissl/json (fetched 2026-08-29T09:35:45.598992+00:00, sha efc8e5c56c12)
- Data as of 2026-08-30T08:39:29.467469+00:00.