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Error rates of large-scale visual recognition challenge 2010-2017

Published by Shanhong Liu, Sep 6, 2019
The statistic shows the best classification error rate achieved by computer vision algorithms tested on a large-scale visual recognition challenge, from 2010 to 2017. In 2015, the winning algorithm became the first to surpass the average human classification error rate of five percent, and by 2017 machine learning algorithms were able to achieve a classification error rate of 2.3 percent, making fewer than half the number of classification errors as a human.

Computer vision large-scale visual recognition challenge (ILSVRC) winning error rates, from 2020 to 2017

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Sources

Release date

November 2017

Region

Worldwide

Survey time period

2010 to 2017

Special properties

Other factors beyond classification were tested: localization, object detection (still & video)

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