Are we done with ImageNet?

Is ImageNet Losing Its Grip on AI?

Researchers took a fresh look at ImageNet by asking people to re-label the validation images, using a tougher process so labels are cleaner.
When they tested recent models on this new set the big improvements looked much smaller than first shown, which means reported wins might be partly about fitting to old quirks, not real progress.
The original labels now predict the new answers less well, so the old scoreboards may be less useful for judging how smart vision systems really are.
Still, the new labeling method fixed many mistakes in the dataset, and that helps keep ImageNet as a useful benchmark for future work.
In short, progress on paper wasn't always the same as true general skill, and this study shows why careful human checks matter.
It's not the end for ImageNet, but it's a sign we should trust accuracy numbers less blindly and keep testing with better data, or else we will think systems are better than they really are.

Read article comprehensive review in Paperium.net:
Are we done with ImageNet?

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