solo-learn: A Library of Self-supervised Methods for Visual Representation Learning

This paper presents solo-learn, a library of self-supervised methods for visual rep-
resentation learning. Implemented in Python, using Pytorch and Pytorch lightning, the
library ts both research and industry needs by featuring distributed training pipelines
with mixed-precision, faster data loading via Nvidia DALI, online linear evaluation for
better prototyping, and many additional training tricks. Our goal is to provide an easy-to-
use library comprising a large amount of Self-supervised Learning (SSL) methods, that
can be easily extended and ne-tuned by the community. solo-learn opens up av-
enues for exploiting large-budget SSL solutions on inexpensive smaller infrastructures and
seeks to democratize SSL by making it accessible to all. The source code is available at
https://github.com/vturrisi/solo-learn.