Imagenet Image Classification Contest

Match settings: 1000 categories of image classification problems, training data set 1.26 million images, validation set 50,000, test set 100,000 (callout not advertised). The data set is used by the 2012,2013,2014. The evaluation standard uses the TOP-5 error rate, that is, to predict an image 5 categories, as long as there is one and the same as the manual label category even if the right, otherwise wrong.

Score Leaderboard

Results announcement Time	Institutions	Top-5 error Rate (%)	Number of models	Method
2015.2.6	MSRA	4.94		http://arxiv.org/abs/1502.01852
2015.2.6	Baidu	5.33		http://arxiv.org/abs/1501.02876
2015.1.13	Baidu	5.98		——
2014.8.18	Google	6.66		http://arxiv.org/abs/1409.4842
2014.8.18	Oxford	7.33		http://arxiv.org/abs/1409.1556
2013.11.14	NYU	11.7		http://arxiv.org/abs/1311.2901
2012.10.13	U.toronto	16.4		Http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf

Continuous update, welcome comments to inform the latest results

Imagenet Image Classification Contest