ImageNet Large Scale Visual Recognition Challenge

被引:31855
作者
Russakovsky, Olga [1 ]
Deng, Jia [2 ]
Su, Hao [1 ]
Krause, Jonathan [1 ]
Satheesh, Sanjeev [1 ]
Ma, Sean [1 ]
Huang, Zhiheng [1 ]
Karpathy, Andrej [1 ]
Khosla, Aditya [3 ]
Bernstein, Michael [1 ]
Berg, Alexander C. [4 ]
Fei-Fei, Li [1 ]
机构
[1] Stanford Univ, Stanford, CA 94305 USA
[2] Univ Michigan, Ann Arbor, MI 48109 USA
[3] MIT, Cambridge, MA 02139 USA
[4] Univ N Carolina, Chapel Hill, NC USA
基金
美国国家科学基金会;
关键词
Dataset; Large-scale; Benchmark; Object recognition; Object detection;
D O I
10.1007/s11263-015-0816-y
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
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.
引用
收藏
页码:211 / 252
页数:42
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