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Implementation of the wrapping of big convolutional layers suggested by min-xu-ai

CC: min-xu-ai prigoyal

Pull Request resolved: fairinternal/ssl_scaling#135

Reviewed By: prigoyal

Differential Revision: D28198862

Pulled By: QuentinDuval

fbshipit-source-id: fbeabd2b92aa6f01c5b20b268168ee0e2924ce43
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Jan 26, 2021

CircleCIPRs Welcome

What's New

Below we share, in reverse chronological order, the updates and new releases in VISSL. All VISSL releases are available here.

Introduction

VISSL is a computer VIsion library for state-of-the-art Self-Supervised Learning research with PyTorch. VISSL aims to accelerate research cycle in self-supervised learning: from designing a new self-supervised task to evaluating the learned representations. Key features include:

Installation

See INSTALL.md.

Getting Started

Install VISSL by following the installation instructions. After installation, please see Getting Started with VISSL and the Colab Notebook to learn about basic usage.

Documentation

Learn more about VISSL at our documentation. And see the projects/ for some projects built on top of VISSL.

Tutorials

Get started with VISSL by trying one of the Colab tutorial notebooks.

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the VISSL Model Zoo.

Contributors

VISSL is written and maintained by the Facebook AI Research.

Development

We welcome new contributions to VISSL and we will be actively maintaining this library! Please refer to CONTRIBUTING.md for full instructions on how to run the code, tests and linter, and submit your pull requests.

License

VISSL is released under MIT license.

Citing VISSL

If you find VISSL useful in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.

@misc{goyal2021vissl,
  author =       {Priya Goyal and Quentin Duval and Jeremy Reizenstein and Matthew Leavitt and Min Xu and
                  Benjamin Lefaudeux and Mannat Singh and Vinicius Reis and Mathilde Caron and Piotr Bojanowski and
                  Armand Joulin and Ishan Misra},
  title =        {VISSL},
  howpublished = {\url{https://github.com/facebookresearch/vissl}},
  year =         {2021}
}

About

VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

Resources

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