  This paper presents solo-learn, a library of self-supervised methods for
visual representation learning. Implemented in Python, using Pytorch and
Pytorch lightning, the library fits 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 fine-tuned by the community. solo-learn opens up avenues
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.
