Image Classification on Mixed-Rand
93.01Top-1 AccLLE
Evaluation results
| Method | Links | |
|---|---|---|
| LLEarch=ViT-H/14, train data=IN-1k, base_framework=MAE2022.12 | 93.01 | |
| LLEarch=ViT-H/14, train data=IN-1k, base_framework=MAE, augmentation=Edge Aug2022.12 | 92.86 | |
| LLEarch=ViT-L/16, train data=IN-1k, base_framework=MAE2022.12 | 91.58 | |
| LLEarch=ViT-L/16, train data=IN-1k, base_framework=MAE, augmentation=Edge Aug2022.12 | 91.41 | |
| LLEarch=ViT-B/16, train data=IG-3.6B, base_framework=SWAG (FT)2022.12 | 90.12 | |
| LLEarch=ViT-B/16, train data=IG-3.6B, base_framework=SWAG (FT), augmentation=Edge Aug2022.12 | 89.98 | |
| LLEarch=ViT-B/16, train data=IN-1k, base_framework=MAE2022.12 | 89.41 | |
| LLEarch=ViT-B/16, train data=IN-1k, base_framework=MAE, augmentation=Edge Aug2022.12 | 89.36 | |
| LLEarch=ResNet-50, train data=IN-1k2022.12 | 84.4 | |
| LLEarch=ResNet-50, train data=IN-1k, augmentation=Edge Aug2022.12 | 84.3 |