LightlySSL is a computer vision framework for self-supervised learning and latent world models.
For a commercial version with more features, including Docker support and pretraining
models for embedding, classification, detection, and segmentation tasks with
a single command, please contact [email protected].
We've also built a whole platform on top, with additional features for active learning
and data curation. If you're interested in the
LightlyStudio, our all-in-one product for data curation and labeling
on your data, check out lightly.ai. It's free to get started!
News 🚀
- October 26, 2026 - LightlySSL now trains world models! Learn how a scene responds to actions with LeWM and plan with it in our new tutorial. 🌍
- May 28, 2026 - We now have LeJEPA support for self-supervised learning! 🧬
- March 23, 2026 - Check out our latest open-source project LightlyStudio to visualize, annotate, and manage your data with ease! 🔍
- April 15, 2025 - We are excited to announce that you can now leverage SSL and distillation pretraining in just a few lines of code! We've worked hard to make self-supervised learning even more accessible with our new project LightlyTrain. Head over there to get started and supercharge your models! ⚡️
World Models 🌍
A world model learns how a scene responds to actions. LeWM
learns it from raw frames and actions, then plans by imagining the outcome of
each move in its own embedding space.
pip install "lightly[timm]" # LeWM needs PyTorch 2.0+
Features
This self-supervised learning framework offers the following features:
- Modular framework, which exposes low-level building blocks such as loss functions and
model heads.
- Easy to use and written in a PyTorch-like style.
- Supports custom backbone models for self-supervised pre-training.
- Support for distributed training using PyTorch Lightning.
Supported Models
You can find sample code for all the supported models here. We provide PyTorch, PyTorch Lightning,
and PyTorch Lightning distributed examples for all models to kickstart your project.
Models:
This is the beginning of the README. Read the rest at the original. Read the full README ↗
