Datasets › ImageNet-Hard
ImageNet-Hard
ImageNet-Hard is a new benchmark that comprises 10,980 images collected from various existing ImageNet-scale benchmarks (ImageNet, ImageNet-V2, ImageNet-Sketch, ImageNet-C, ImageNet-R, ImageNet-ReaL, ImageNet-A, and ObjectNet). This dataset poses a significant challenge to state-of-the-art vision models as merely zooming in often fails to improve their ability to classify images correctly. As a result, even the most advanced models, such as CLIP-ViT-L/14@336px, struggle to perform well on this dataset, achieving a mere 2.02% accuracy.
Benchmarks archive 2025-07-28
All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Image Classification | ImageNet-Hard | EfficientNet-L2-Ns Accuracy (%) 39.00 | — | — | 2 | Compare |
Papers archive 2025-07-28
No paper in the archive has a leaderboard row on this dataset; the archive counts 5 papers for it but never published that list.
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
mit
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- ImageNet-Hard
1 variant name, as the archive lists them.
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