Visual Prompt Tuning On Vtab 1K Structured 8
评估指标
Mean Accuracy
评测结果
各个模型在此基准测试上的表现结果
模型名称 | Mean Accuracy | Paper Title | Repository |
|---|---|---|---|
| SPT-Deep(ViT-B/16_MAE_pretrained_ImageNet-1K) | 59.23 | Revisiting the Power of Prompt for Visual Tuning | - |
| GateVPT(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 49.10 | Improving Visual Prompt Tuning for Self-supervised Vision Transformers | |
| VPT-Shallow(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 37.55 | Visual Prompt Tuning | |
| SPT-Shallow(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 55.16 | Revisiting the Power of Prompt for Visual Tuning | - |
| VPT-Deep(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 42.38 | Visual Prompt Tuning | |
| SPT-Deep(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 58.36 | Revisiting the Power of Prompt for Visual Tuning | - |
| SPT-Shallow(ViT-B/16_MAE_pretrained_ImageNet-1K) | 53.46 | Revisiting the Power of Prompt for Visual Tuning | - |
| VPT-Deep(ViT-B/16_MAE_pretrained_ImageNet-1K) | 26.57 | Visual Prompt Tuning | |
| GateVPT(ViT-B/16_MAE_pretrained_ImageNet-1K) | 36.80 | Improving Visual Prompt Tuning for Self-supervised Vision Transformers | |
| VPT-Shallow(ViT-B/16_MAE_pretrained_ImageNet-1K) | 27.50 | Visual Prompt Tuning |
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