To be clear - this is a human viewing the images, and only narrowly beating the neural net. Furthermore, the human spent some time 'training' himself on the im

To be clear - this is a human viewing the images, and only narrowly beating the neural net. Furthermore, the human spent some time 'training' himself on the images, plus he used a neural net to reduce the possible answer space - he didn't come to it cold. "Then I organized a labeling party of intense labeling effort only among the (expert labelers) in our lab. Then I developed a modified interface that used GoogLeNet predictions to prune the number of categories from 1000 to only about 100. It was still too hard - people kept missing categories and getting up to ranges of 13-15% error rates. In the end I realized that to get anywhere competitively close to GoogLeNet, it was most efficient if I sat down and went through the painfully long training process and the subsequent careful annotation process myself."