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Models/EfficientUNets and Transformers

EfficientUNets and Transformers

Reported on 8 benchmarks across 2 tasks · 1 paper · 6 SOTA

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

Medical4 results

Semantic SegmentationonISPRS Vaihingen
Average F1· uses extra data· 2022-06-20
93.7
SOTA
Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers arXiv:2206.09731
Semantic SegmentationonISPRS Vaihingen
Overall Accuracy· uses extra data· 2022-06-20
91.8
best: 93.6 (LSKNet-S)
SOTA
Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers arXiv:2206.09731
Semantic SegmentationonISPRS Potsdam
Mean F1· uses extra data· 2022-06-20
93.7
best: 94.7 (D2LS)
SOTA
Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers arXiv:2206.09731
Semantic SegmentationonISPRS Potsdam
Overall Accuracy· uses extra data· 2022-06-20
91.8
best: 93.9 (AerialFormer-B)
Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers arXiv:2206.09731

Audio4 results

10-shot image generationonISPRS Vaihingen
Average F1· uses extra data· 2022-06-20
93.7
SOTA
Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers arXiv:2206.09731
10-shot image generationonISPRS Vaihingen
Overall Accuracy· uses extra data· 2022-06-20
91.8
best: 93.6 (LSKNet-S)
SOTA
Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers arXiv:2206.09731
10-shot image generationonISPRS Potsdam
Mean F1· uses extra data· 2022-06-20
93.7
best: 94.7 (D2LS)
SOTA
Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers arXiv:2206.09731
10-shot image generationonISPRS Potsdam
Overall Accuracy· uses extra data· 2022-06-20
91.8
best: 93.9 (AerialFormer-B)
Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers arXiv:2206.09731