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Flaws of ImageNet, Computer Vision's Favorite Dataset
Since its release, ImageNet-1k has been a gold standard for evaluating model performance. It has served as the foundation of numerous other datasets and it has been widely used for pretraining.
As models have improved, issues related to label correctness have become increasingly apparent. In this blog post, we analyze the issues, including incorrect labels, overlapping or ambiguous class definitions, training-evaluation domain shifts, and image duplicates. The solutions for some problems are straightforward. For others, we hope to start a broader conversation about how to improve this influential dataset to better serve future research. -
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