Update README.md

README.md

@@ -36,8 +36,8 @@ size_categories:
 ## Dataset Summary
 
 
-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). 
-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.
+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). 
+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.
 
 
 ### Dataset Distribution
@@ -50,15 +50,15 @@ This dataset is challenging to state-of-the-art vision models, as merely zooming
 
 | Model               | Accuracy |
 | ------------------- | -------- |
-| ResNet-18           | 11.11    |
-| ResNet-50           | 14.91    |
-| ViT-B/32            | 18.78    |
-| VGG19               | 12.15    |
-| AlexNet             | 7.30     |
-| EfficientNet-B7     | 18.02    |
-| EfficientNet-L2-Ns  | 38.79    |
-| CLIP-ViT-L/14@224px | 2.11     |
-| CLIP-ViT-L/14@336px | 2.30     |
+| ResNet-18           | 10.86    |
+| ResNet-50           | 14.74    |
+| ViT-B/32            | 18.52    |
+| VGG19               | 11.99    |
+| AlexNet             | 7.34     |
+| EfficientNet-B7     | 17.81    |
+| EfficientNet-L2-Ns  | 39.00    |
+| CLIP-ViT-L/14@224px | 1.86     |
+| CLIP-ViT-L/14@336px | 2.02     |
 
 **Evaluation Code**