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Which network has the highest accuracy on the ImageNet dataset?

Published in Image Classification 1 min read

The network currently demonstrating the highest reported accuracy on the ImageNet ReaL dataset is Baseline (ViT-G/14).

Top Performers on ImageNet ReaL

ImageNet ReaL is a benchmark used to evaluate the performance of image classification models, providing a challenging and realistic assessment of their capabilities. The models are ranked based on their accuracy percentage, indicating how well they correctly classify images.

Here are the top-performing models on the ImageNet ReaL benchmark:

Rank Model Accuracy
1 Baseline (ViT-G/14) 91.78%
2 ViTAE-H (MAE, 512) 91.20%
3 Model soups (ViT-G/14) 91.20%
4 Meta Pseudo Labels (EfficientNet-B6-Wide) 91.12%

The Baseline (ViT-G/14) model leads the pack with an impressive accuracy of 91.78%. This signifies its strong capability in correctly identifying objects across a wide range of categories within the complex ImageNet ReaL dataset.