Flaws of ImageNet, Computer Vision's Favourite Dataset
This work addresses dataset quality problems for computer vision researchers, but it is incremental as it builds on known critiques.
The authors analyzed issues in the ImageNet-1k dataset, such as incorrect labels and domain shifts, and proposed solutions to refine it for future research.
Since its release, ImageNet-1k dataset has become a gold standard for evaluating model performance. It has served as the foundation for numerous other datasets and training tasks in computer vision. As models have improved in accuracy, issues related to label correctness have become increasingly apparent. In this blog post, we analyze the issues in the ImageNet-1k dataset, 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 refining this influential dataset to better serve future research.