Classification on SVHN VTAB 1k (test)
93.5AccuracyLoRA_mul+VPT_add + PACE
Evaluation Results
| Method | Links | |
|---|---|---|
| LoRA_mul+VPT_add + PACEPre-training Method=MAE, Backbone=ViT-B/16, Pre-trained on=ImageNet-1K2024.09 | 93.5 | |
| LoRA_mul+VPT_add + PACEPre-training Method=DINO, Backbone=ViT-B/16, Pre-trained on=ImageNet-1K2024.09 | 91.7 | |
| FullPre-training Method=MAE, Backbone=ViT-B/16, Pre-trained on=ImageNet-1K2024.09 | 90.1 | |
| LoRA_mul+VPT_addPre-training Method=DINO, Backbone=ViT-B/16, Pre-trained on=ImageNet-1K2024.09 | 90 | |
| FullPre-training Method=DINO, Backbone=ViT-B/16, Pre-trained on=ImageNet-1K2024.09 | 89.7 | |
| LoRA_mul+VPT_addPre-training Method=MAE, Backbone=ViT-B/16, Pre-trained on=ImageNet-1K2024.09 | 89.3 | |
| LinearPre-training Method=DINO, Backbone=ViT-B/16, Pre-trained on=ImageNet-1K2024.09 | 50.7 | |
| LinearPre-training Method=MAE, Backbone=ViT-B/16, Pre-trained on=ImageNet-1K2024.09 | 44.5 |