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Models/ViT-Adapter (BEiTv2-L)

ViT-Adapter (BEiTv2-L)

Reported on 10 benchmarks across 5 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology8 results

3DonCOCO-O
Average mAP· 2022-05-17
34.25
best: 57.8 (EVA)
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
3DonCOCO-O
Effective Robustness· 2022-05-17
7.79
best: 28.86 (EVA)
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
2D ClassificationonCOCO-O
Average mAP· 2022-05-17
34.25
best: 57.8 (EVA)
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
2D ClassificationonCOCO-O
Effective Robustness· 2022-05-17
7.79
best: 28.86 (EVA)
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
2D Object DetectiononCOCO-O
Average mAP· 2022-05-17
34.25
best: 57.8 (EVA)
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
2D Object DetectiononCOCO-O
Effective Robustness· 2022-05-17
7.79
best: 28.86 (EVA)
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
16konCOCO-O
Average mAP· 2022-05-17
34.25
best: 57.8 (EVA)
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
16konCOCO-O
Effective Robustness· 2022-05-17
7.79
best: 28.86 (EVA)
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534

Computer Vision2 results

Object DetectiononCOCO-O
Average mAP· 2022-05-17
34.25
best: 57.8 (EVA)
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
Object DetectiononCOCO-O
Effective Robustness· 2022-05-17
7.79
best: 28.86 (EVA)
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534