ImageNet Large Scale Visual Recognition Challenge
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ImageNet Large Scale Visual Recognition Challenge
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- Title
- ImageNet Large Scale Visual Recognition Challenge
- Type
- Paper
- Created
- 2015-01-01
- Description
- 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.
- Link
- https://semanticscholar.org/paper/e74f9b7f8eec6ba4704c206b93bc8079af3da4bd
- Identifier
- DOI: 10.1007/s11263-015-0816-y
authors
created by Olga Russakovsky
created by Jia Deng
created by Hao Su
created by Jonathan Krause
created by Sanjeev Satheesh
created by Sean Ma
created by Zhiheng Huang
created by Andrej Karpathy
created by Aditya Khosla
created by Michael S. Bernstein
created by Alexander C. Berg
created by Fei-Fei Li
topics
about Computer science
gives cons of ImageNet
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