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Models/MOAT-3 (IN-22K pretraining, single-scale)

MOAT-3 (IN-22K pretraining, single-scale)

Reported on 10 benchmarks across 8 tasks · 1 paper

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

Methodology4 results

3DonCOCO minival
box AP· 2022-10-04
59.2
best: 66 (PE_spatial (DETA))
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models arXiv:2210.01820
2D ClassificationonCOCO minival
box AP· 2022-10-04
59.2
best: 66 (PE_spatial (DETA))
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models arXiv:2210.01820
2D Object DetectiononCOCO minival
box AP· 2022-10-04
59.2
best: 66 (PE_spatial (DETA))
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models arXiv:2210.01820
16konCOCO minival
box AP· 2022-10-04
59.2
best: 66 (PE_spatial (DETA))
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models arXiv:2210.01820

Medical2 results

Semantic SegmentationonADE20K
Params (M)· 2022-10-04
198
best: 3000 (FD-SwinV2-G)
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models arXiv:2210.01820
Semantic SegmentationonADE20K
Validation mIoU· 2022-10-04
56.5
best: 63.6 (ViT-P (InternImage-H))
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models arXiv:2210.01820

Computer Vision2 results

Object DetectiononCOCO minival
box AP· 2022-10-04
59.2
best: 66 (PE_spatial (DETA))
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models arXiv:2210.01820
Instance SegmentationonCOCO minival
mask AP· 2022-10-04
50.3
best: 56.6 (Co-DETR)
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models arXiv:2210.01820

Audio2 results

10-shot image generationonADE20K
Params (M)· 2022-10-04
198
best: 3000 (FD-SwinV2-G)
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models arXiv:2210.01820
10-shot image generationonADE20K
Validation mIoU· 2022-10-04
56.5
best: 63.6 (ViT-P (InternImage-H))
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models arXiv:2210.01820