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Models/ViT-Adapter-L (Mask2Former, BEiT pretrain)

ViT-Adapter-L (Mask2Former, BEiT pretrain)

Reported on 8 benchmarks across 2 tasks · 1 paper · 4 SOTA

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

Medical4 results

Semantic SegmentationonADE20K val
mIoU· uses extra data· 2022-05-17
60.5
best: 62.8 (BEiT-3)
SOTA
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
Semantic SegmentationonPASCAL Context
mIoU· 2022-05-17
68.2
best: 71.1 (VPNeXt)
SOTA
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
Semantic SegmentationonADE20K
Params (M)· uses extra data· 2022-05-17
571
best: 3000 (FD-SwinV2-G)
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
Semantic SegmentationonADE20K
Validation mIoU· uses extra data· 2022-05-17
60.5
best: 63.6 (ViT-P (InternImage-H))
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534

Audio4 results

10-shot image generationonADE20K val
mIoU· uses extra data· 2022-05-17
60.5
best: 62.8 (BEiT-3)
SOTA
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
10-shot image generationonPASCAL Context
mIoU· 2022-05-17
68.2
best: 71.1 (VPNeXt)
SOTA
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
10-shot image generationonADE20K
Params (M)· uses extra data· 2022-05-17
571
best: 3000 (FD-SwinV2-G)
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534
10-shot image generationonADE20K
Validation mIoU· uses extra data· 2022-05-17
60.5
best: 63.6 (ViT-P (InternImage-H))
Vision Transformer Adapter for Dense Predictions arXiv:2205.08534