ImageNet V2 (ImageNetV2) is a set of new test splits for the ImageNet (ILSVRC2012) classification benchmark created to measure how well ImageNet classifiers generalize to new data sampled from the same source. The authors followed the original ImageNet collection and labeling protocol and released a pool of candidate Flickr images, the final test splits, and rich metadata (Flickr queries, MTurk annotations). ImageNetV2 contains three curated 10,000-image test sets (each ~10k images): "matched-frequency" (matched-frequency), "top-images" (top-images), and "threshold-0.7" (threshold-0.7) — corresponding to different image-selection strategies described in the paper. The label space matches ImageNet2012 (1000 classes). ImageNetV2 was introduced and evaluated in Recht et al., "Do ImageNet Classifiers Generalize to ImageNet?", and is intended as an independent, distribution-matched testbed to detect adaptive overfitting and measure true generalization of ImageNet models.
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