An open source Python library for drift detection in machine learning systems.
Open source
24 alternativeslightly
The lightly Python library supports self supervised learning on images.
About lightly
Lightly (LightlySSL) is an open-source Python framework for self-supervised learning on images, built on PyTorch. It provides modular low-level building blocks such as loss functions and model heads, a PyTorch-like API, and support for custom backbone models, so practitioners can pre-train vision models without labeled data. Distributed training is available through PyTorch Lightning, and the repository includes example code for the models it implements, including MoCo, SimCLR, BYOL, SwaV, SimSiam, Barlow Twins, DINO, MAE, SimMIM, DINOv2 and LeJEPA.
Lightly requires Python 3.8 or newer and recommends a Linux or OSX environment; Python 3.13 is not yet supported because PyTorch itself lacks Python 3.13 compatibility. Full feature use depends on PyTorch, Torchvision and PyTorch Lightning at the versions listed in the repository. A commercial version with additional features, including Docker support and pretraining models for embedding, classification, detection and segmentation, is offered separately by the vendor.
Key features
- Modular framework exposing loss functions and model heads
- PyTorch-style API for self-supervised pre-training on images
- Support for custom backbone models
- Distributed training via PyTorch Lightning
- Example code and Colab notebooks for each supported model
- Implemented methods include SimCLR, BYOL, DINO, MAE and DINOv2
Best for: Machine learning engineers and computer vision researchers using PyTorch who want to pre-train image models on unlabeled data.
Good for
- Open-source and installable from PyPI with pip3 install lightly
- Covers a broad set of published self-supervised learning methods
- Distributed training support through PyTorch Lightning
Watch out for
- Python 3.13 is not yet supported due to a PyTorch compatibility gap
- A dedicated Linux or OSX environment is recommended for installation
- Docker support and single-command pretraining models for several tasks are part of a commercial version arranged through sales
lightly at a glance
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
lightly alternatives
Tools and sites you could use instead, ranked by how closely they match.
nilearn provides Python tools for machine learning and neuroimaging analysis.
Open source
24 alternatives
A Python package to assess and improve fairness of machine learning models.
Open source
24 alternatives
Feature Engine provides Python tools for feature engineering and selection that are compatible with scikit-learn.
Open source
24 alternatives
Python package which helps to debug machine learning classifiers and explain their predictions.
Open source
24 alternatives
modAL provides a modular active learning framework for Python built on top of scikit-learn.
Open source
24 alternativesPopular in AI Data & Analytics
Well-known listings from the same topic.
Dotgetdot.aiForvioforvio.comFluidAI Medicalfluidai.mdApache Zeppelinzeppelin.apache.orgSola Securitysola.securityQuadraticquadratichq.com
lightly FAQ
How is Lightly installed and what does it require?
It can be installed from PyPI with pip3 install lightly. The project recommends a dedicated virtualenv to avoid conflicts with system packages, and a Linux or OSX environment.
Which dependencies and Python versions does it support?
Python 3.8 or newer is required, with PyTorch at 1.11.0 or later, Torchvision at 0.12.0 or later, and PyTorch Lightning at 1.7.1 or later. Python 3.13 is not yet supported; PyTorch and PyTorch Lightning v2.0+ are compatible.
Is there a commercial edition with extra features?
Yes. The repository states that a commercial version with more features, including Docker support and pretraining models for embedding, classification, detection and segmentation tasks via a single command, is available by contacting [email protected].
Is something wrong or out of date?
Tell us and an editor will check it.
Own this website?
Claim this listing
Claim the listing and suggest updates yourself. An editor checks every claim first.