  Yes, and no. We ask whether recent progress on the ImageNet classification
benchmark continues to represent meaningful generalization, or whether the
community has started to overfit to the idiosyncrasies of its labeling
procedure. We therefore develop a significantly more robust procedure for
collecting human annotations of the ImageNet validation set. Using these new
labels, we reassess the accuracy of recently proposed ImageNet classifiers, and
find their gains to be substantially smaller than those reported on the
original labels. Furthermore, we find the original ImageNet labels to no longer
be the best predictors of this independently-collected set, indicating that
their usefulness in evaluating vision models may be nearing an end.
Nevertheless, we find our annotation procedure to have largely remedied the
errors in the original labels, reinforcing ImageNet as a powerful benchmark for
future research in visual recognition.
