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

By Victor Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa Ricci. 2021

172 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Self-Supervised Models are Continual Learners (2021, methodology): “For some SSL methods, it was necessary to slightly increase the learning rate over the values provided by [16] in order for the methods to fully convergence in the CSSL setting.”
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo (2022, background): “As shown in the solo-learn [11] frameworks, the performance gap between online and offline linear evaluation is not significant.”