lightly-ai/lightly
A python library for self-supervised learning on images.
What it solves
Self-supervised learning (SSL) allows models to learn from unlabeled data, reducing the heavy reliance on manual data annotation. This framework provides the necessary building blocks to implement complex SSL algorithms without starting from scratch.
How it works
Lightly provides a modular framework that exposes low-level components like loss functions and model heads. It is designed to be used with PyTorch and supports distributed training via PyTorch Lightning. It allows users to experiment with various backbones and implement state-of-the-art SSL methods through a PyTorch-like interface.
Who it’s for
Computer vision researchers and machine learning engineers who need to pre-train models on unlabeled datasets using advanced self-supervised learning techniques.
Highlights
- Supports numerous SSL algorithms including MoCo, SimCLR, BYOL, DINO, and MAE
- Modular design with low-level building blocks for custom research
- Compatible with PyTorch and PyTorch Lightning
- Supports custom backbone models for self-supervised pre-training