Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning Article Swipe
Victor G. Turrisi da Costa
,
Enrico Fini
,
Moin Nabi
,
Nicu Sebe
,
Elisa Ricci
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2108.01775
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2108.01775
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2108.01775
- https://arxiv.org/pdf/2108.01775
- OA Status
- green
- Cited By
- 42
- References
- 13
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3188717957
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3188717957Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2108.01775Digital Object Identifier
- Title
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Solo-learn: A Library of Self-supervised Methods for Visual Representation LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-08-03Full publication date if available
- Authors
-
Victor G. Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa RicciList of authors in order
- Landing page
-
https://arxiv.org/abs/2108.01775Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2108.01775Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2108.01775Direct OA link when available
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Python (programming language), Computer science, Representation (politics), Code (set theory), Source code, Artificial intelligence, Feature learning, Deep learning, Machine learning, Set (abstract data type), Programming language, Politics, Political science, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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42Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 13, 2023: 16, 2022: 8Per-year citation counts (last 5 years)
- References (count)
-
13Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| primary_location.pdf_url | https://arxiv.org/pdf/2108.01775 |
| primary_location.version | submittedVersion |
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| primary_location.landing_page_url | http://arxiv.org/abs/2108.01775 |
| publication_date | 2021-08-03 |
| publication_year | 2021 |
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