# lucidrains/byol-pytorch

Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch

Repository: https://github.com/lucidrains/byol-pytorch
Canonical: https://ross.abutalabs.com/products/byol-pytorch
Language: Python
License: MIT
License Family: permissive
Topics: artificial-intelligence, deep-learning, self-supervised-learning
Last push: 2026-04-27T20:33:48+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 79, release rhythm 8, longevity 100
- inputs: {"age_days": 2269, "days_push": 128, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1903, forks 248 (observed 2026-08-28T04:05:51.723931+00:00)

## What it is
A PyTorch library implementing the Bootstrap Your Own Latent (BYOL) self-supervised learning method from DeepMind. It wraps any image-based neural network so it can be trained on unlabeled images without contrastive learning or negative pairs.

## Use cases
- train image encoders without labeled data
- pretrain a resnet on unlabeled images
- self-supervised representation learning in pytorch
- improve downstream supervised performance with unlabeled images
- avoid paying for image labels
- implement BYOL without contrastive negative pairs

## When to choose
- you have lots of unlabeled images and few labels
- you want a simple, pip-installable BYOL implementation to wrap an existing PyTorch network
- you want to avoid contrastive learning setups like SimCLR

## When to avoid
- your data is not images
- you need a full training pipeline with data loading and experiment tracking
- you need contrastive methods like SimCLR or MoCo specifically

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning
- domain: deep-learning, machine-learning, artificial-intelligence, image-processing
- platform: python
- tags: self-supervised-learning, byol, pytorch, representation-learning, unsupervised-learning, computer-vision

## Member repositories
- lucidrains/byol-pytorch (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:51.723931+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 2026-08-30T03:11:53.134450+00:00, confidence not recorded.
  - readme: https://github.com/lucidrains/byol-pytorch (fetched 2026-08-28T04:05:51.723931+00:00, sha c868840b4c2b)
- Data as of 2026-08-30T08:39:29.467469+00:00.