Visual Prompt Tuning On Fgvc
Metriken
Mean Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Mean Accuracy | Paper Title | Repository |
|---|---|---|---|
| VPT-Deep (ViT-B/16_MAE_pretrained_ImageNet-1K) | 72.02 | Visual Prompt Tuning | |
| GateVPT(ViT-B/16_MAE_pretrained_ImageNet-1K) | 73.39 | Improving Visual Prompt Tuning for Self-supervised Vision Transformers | |
| SPT-Deep(ViT-B/16_MAE_pretrained_ImageNet-1K) | 83.26 | Revisiting the Power of Prompt for Visual Tuning | - |
| VPT-Shallow (ViT-B/16_MAE_pretrained_ImageNet-1K) | 57.84 | Visual Prompt Tuning | |
| VPT-Deep(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 83.12 | Visual Prompt Tuning | |
| SPT-Shallow(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 84.08 | Revisiting the Power of Prompt for Visual Tuning | - |
| SPT-Shallow(ViT-B/16_MAE_pretrained_ImageNet-1K) | 73.95 | Revisiting the Power of Prompt for Visual Tuning | - |
| SPT-Deep(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 86.00 | Revisiting the Power of Prompt for Visual Tuning | - |
| GateVPT(ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 83.00 | Improving Visual Prompt Tuning for Self-supervised Vision Transformers | |
| VPT-Shallow (ViT-B/16_MoCo_v3_pretrained_ImageNet-1K) | 79.26 | Visual Prompt Tuning |
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