lightly

Why we included this project

Image-heavy teams that can't afford to label everything will find Lightly useful: it lets you pre-train an encoder with no manual annotation and reuse it as a starting point for downstream tasks. The library implements several established self-supervised methods, including SimCLR, BYOL, SwAV, MoCo, SimSiam, and Barlow Twins, behind a modular PyTorch-style API. Because the pieces (loss functions, model heads) are reusable building blocks, experimenting with custom backbones is easier than fighting a monolithic pipeline. Ready-made PyTorch Lightning examples and distributed training support also shorten the trip from a research prototype to multi-GPU runs. If you've ever wished you could bootstrap a representation for your image data before paying for labeling, this is worth a look.