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Torchvision the machine-vision package of torch

Authors:

Sébastien Marcel,

Yann RodriguezAuthors Info & Claims

MM '10: Proceedings of the 18th ACM international conference on Multimedia

Pages 1485 - 1488

https://doi.org/10.1145/1873951.1874254

Published: 25 October 2010 Publication History

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Abstract

This paper presents Torchvision an open source machine vision package for Torch. Torch is a machine learning library providing a series of the state-of-the-art algorithms such as Neural Networks, Support Vector Machines, Gaussian Mixture Models, Hidden Markov Models and many others. Torchvision provides additional functionalities to manipulate and process images with standard image processing algorithms. Hence, the resulting images can be used directly with the Torch machine learning algorithms as Torchvision is fully integrated with Torch. Both Torch and Torchvision are written in C++ language and are publicly available under the Free-BSD License.

References

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OpenCV. http://sourceforge.net/projects/opencvlibrary.

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RAVL. http://ravl.sourceforge.net.

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Torch. http://www.torch.ch.

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Torch 5. http://torch5.sourceforge.net.

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Torchvision. http://torch3vision.idiap.ch.

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VXL. http://vxl.sourceforge.net.

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R. Collobert, S. Bengio, and J. Mariéthoz. Torch: a modular machine learning software library. Idiap-RR Idiap-RR-46-2002, IDIAP, 2002.

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Zhou CWang YWang ZKang X(2025) -norm distortion-efficient adversarial attack Signal Processing: Image Communication10.1016/j.image.2024.117241131(117241)Online publication date: Feb-2025
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Index Terms

Torchvision the machine-vision package of torch
1. Software and its engineering
  1. Software notations and tools
    1. Software libraries and repositories

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Information

Published In

MM '10: Proceedings of the 18th ACM international conference on Multimedia

October 2010

1836 pages

ISBN:9781605589336

DOI:10.1145/1873951

General Chairs:
Alberto del Bimbo
University of Florence, Italy
,
Shih-Fu Chang
Columbia University, USA
,
Program Chair:
Arnold Smeulders
University of Amsterdam, NL

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2010

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MM '10

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SIGMM

MM '10: ACM Multimedia Conference

October 25 - 29, 2010

Firenze, Italy

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Zhang HBosch JOlsson H(2025)Enabling efficient and low-effort decentralized federated learning with the EdgeFL frameworkInformation and Software Technology10.1016/j.infsof.2024.107600178(107600)Online publication date: Feb-2025
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https://doi.org/10.1016/j.image.2024.117241
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