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Models/DiNAT-L (Mask2Former, 640x640)

DiNAT-L (Mask2Former, 640x640)

Reported on 9 benchmarks across 3 tasks · 1 paper · 4 SOTA

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

Medical3 results

Semantic SegmentationonADE20K val
AP· 2022-09-29
35
best: 40.2 (OneFormer (InternImage-H, emb_dim=256, single-scale, 896x896))
SOTA
Dilated Neighborhood Attention Transformer arXiv:2209.15001
Semantic SegmentationonADE20K val
PQ· 2022-09-29
49.4
best: 54.5 (OneFormer (InternImage-H, emb_dim=256, single-scale, 896x896))
Dilated Neighborhood Attention Transformer arXiv:2209.15001
Semantic SegmentationonADE20K val
mIoU· 2022-09-29
56.3
best: 62.8 (BEiT-3)
Dilated Neighborhood Attention Transformer arXiv:2209.15001

Audio3 results

10-shot image generationonADE20K val
AP· 2022-09-29
35
best: 40.2 (OneFormer (InternImage-H, emb_dim=256, single-scale, 896x896))
SOTA
Dilated Neighborhood Attention Transformer arXiv:2209.15001
10-shot image generationonADE20K val
PQ· 2022-09-29
49.4
best: 54.5 (OneFormer (InternImage-H, emb_dim=256, single-scale, 896x896))
Dilated Neighborhood Attention Transformer arXiv:2209.15001
10-shot image generationonADE20K val
mIoU· 2022-09-29
56.3
best: 62.8 (BEiT-3)
Dilated Neighborhood Attention Transformer arXiv:2209.15001

Computer Vision3 results

Panoptic SegmentationonADE20K val
AP· 2022-09-29
35
best: 40.2 (OneFormer (InternImage-H, emb_dim=256, single-scale, 896x896))
SOTA
Dilated Neighborhood Attention Transformer arXiv:2209.15001
Panoptic SegmentationonADE20K val
mIoU· 2022-09-29
56.3
best: 60.4 (OneFormer (InternImage-H, emb_dim=256, single-scale, 896x896))
SOTA
Dilated Neighborhood Attention Transformer arXiv:2209.15001
Panoptic SegmentationonADE20K val
PQ· 2022-09-29
49.4
best: 54.5 (OneFormer (InternImage-H, emb_dim=256, single-scale, 896x896))
Dilated Neighborhood Attention Transformer arXiv:2209.15001