Author

Jirí Matas

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Preprint Sep 2026

Fi-ImageNet-1k: An OOD Benchmark From the Inside of the ImageNet-1k Validation Set

Out-of-distribution (OOD) detection predicts whether a test image belongs to none of the predefined classes. To evaluate this task, benchmarks need images from outside the in-distribution (ID) data; typically, these are defined or collected in an ad hoc fashion. Since no ground truth is perfect, ID-labeled datasets the...

Ruslan Rozumnyi, Matej Suchanek, Tomás Vojír et al. · 0 citations

Preprint Aug 2026

Doomed to Re-Annotate, Forever: The ImageNet Story

It is argued that annotation at ImageNet scale cannot realistically be completed in one pass, as errors and definitional issues are discovered only through annotating, and the pipeline is built around repeated refinement and error checking.

Illia Volkov, Nikita Kisel, Tetiana Mishkina et al. · 1 citation · ⚡1