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The 2015 ImageNet classification contest was won by ResNet, a network up to 152 layers deep whose layers learn residual functions relative to their inputs; it reached 3.57% error on the test set and, by one account, exceeded human performance.
At ILSVRC 2014, GoogLeNet won the classification track with 6.67% top-5 error, while VGG's network won localisation and took second in classification; more than 50 institutions took part.
In 2013 the ILSVRC classification task was won by Clarifai with an ensemble of convolutional networks at 11.7% top-5 error, while OverFeat won localisation. Zeiler and Fergus's visualization paper, also reporting ImageNet results, appeared the same month.
In October 2012 AlexNet, a deep neural network built by a University of Toronto team, won the ImageNet image-classification challenge by a wide margin. Its result showed that large neural networks trained on GPUs could outperform hand-designed vision methods.
On 13 April 2010, Fei-Fei Li announced the first ImageNet Large Scale Visual Recognition Challenge, a 1,000-category image categorization benchmark. It ran for eight editions, from 2010 to 2017.
At ICDAR 2009, Alex Graves won three connected handwriting competitions — French, Farsi and Arabic — with LSTM networks trained using Connectionist Temporal Classification, the first time a recurrent neural network won an international contest.
