Based on the sample of images I worked on, the GoogLeNet classification error turned out to be 6.8% (the error on the full test set of 100,000 images is 6.7%).

Based on the sample of images I worked on, the GoogLeNet classification error turned out to be 6.8% (the error on the full test set of 100,000 images is 6.7%). My own error in the end turned out to be 5.1%, approximately 1.7% better. To put this in perspective, the task is basically "given a picture, identify the correct class out of 1000 categories" (it gets into specific breeds of dog, for instance). Humans turn out to miss some of these, and neural networks are now nearing human level performance. It's fun to note that about 4 years ago I performed a similar (but much quicker and less detailed) human classification accuracy analysis on CIFAR-10. This was back when the state of the art was at 77% by Adam Coates, and my own accuracy turned out to be 94%. I think the best ConvNets now get about 92%. Things are moving quickly in this field. (My personal) ILSVRC 2014 TLDR: 50% more teams. 50% improved classification and detection. ConvNet ensembles all over the place. Google team wins.