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  ## Dataset Summary
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- The **ImageNet-Hard** is a new benchmark that comprises 11,350 images, collected from various existing ImageNet-scale benchmarks (ImageNet, ImageNet-V2, ImageNet-Sketch, ImageNet-C, ImageNet-R, ImageNet-ReaL, ImageNet-A, and ObjectNet).
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- This dataset is challenging to state-of-the-art vision models, as merely zooming in often fails to enhance their ability to classify images correctly. Consequently, even the most advanced models, such as `CLIP-ViT-L/14@336px`, struggle to perform well on this dataset, achieving only `2.02%` accuracy.
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  ### Dataset Distribution
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  | Model | Accuracy |
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  | ------------------- | -------- |
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- | ResNet-18 | 11.11 |
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- | ResNet-50 | 14.91 |
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- | ViT-B/32 | 18.78 |
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- | VGG19 | 12.15 |
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- | AlexNet | 7.30 |
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- | EfficientNet-B7 | 18.02 |
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- | EfficientNet-L2-Ns | 38.79 |
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- | CLIP-ViT-L/14@224px | 2.11 |
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- | CLIP-ViT-L/14@336px | 2.30 |
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  **Evaluation Code**
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  ## Dataset Summary
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+ The **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).
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+ This dataset is challenging for state-of-the-art vision models, as merely zooming in often fails to enhance their ability to classify images correctly. Consequently, even the most advanced models, such as `CLIP-ViT-L/14@336px`, struggle to perform well on this dataset, achieving only `2.02%` accuracy.
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  ### Dataset Distribution
 
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  | Model | Accuracy |
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  | ------------------- | -------- |
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+ | ResNet-18 | 10.86 |
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+ | ResNet-50 | 14.74 |
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+ | ViT-B/32 | 18.52 |
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+ | VGG19 | 11.99 |
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+ | AlexNet | 7.34 |
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+ | EfficientNet-B7 | 17.81 |
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+ | EfficientNet-L2-Ns | 39.00 |
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+ | CLIP-ViT-L/14@224px | 1.86 |
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+ | CLIP-ViT-L/14@336px | 2.02 |
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  **Evaluation Code**
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