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ImageNet Large Scale Visual Recognition Challenge

Author(s)
Russakovsky, Olga; Deng, Jia; Su, Hao; Krause, Jonathan; Satheesh, Sanjeev; Ma, Sean; Huang, Zhiheng; Karpathy, Andrej; Khosla, Aditya; Bernstein, Michael; Berg, Alexander C.; Fei-Fei, Li; ... Show more Show less
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.

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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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Abstract
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.
Date issued
2015-04
URI
http://hdl.handle.net/1721.1/104944
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
International Journal of Computer Vision
Publisher
Springer US
Citation
Russakovsky, Olga et al. “ImageNet Large Scale Visual Recognition Challenge.” International Journal of Computer Vision 115.3 (2015): 211–252.
Version: Author's final manuscript
ISSN
0920-5691
1573-1405

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