REVIEW 1 cited by
Flaws of ImageNet, Computer Vision's Favourite Dataset
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 1 Pith paper
-
Why Domain Matters: Domain-Aware Benchmarking of Underwater Object Detection and Annotation Quality
Physically grounded domain labels for underwater images expose large, consistent gaps in both human annotation quality and detector mAP that aggregate metrics conceal.
Discussion (0). Continue with ORCID to comment.