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Models/MaX-DeepLab-L (single-scale)

MaX-DeepLab-L (single-scale)

Reported on 18 benchmarks across 3 tasks · 1 paper · 9 SOTA

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

Medical6 results

Semantic SegmentationonCOCO test-dev
PQst· 2020-12-01
42.4
best: 58.9 (MaskConver (ResNet50, single-scale))
SOTA
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
Semantic SegmentationonCOCO minival
PQ· 2020-12-01
51.1
best: 61.2 (HyperSeg (Swin-B))
SOTA
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
Semantic SegmentationonCOCO minival
PQst· 2020-12-01
42.2
best: 49.2 (OneFormer (InternImage-H,single-scale))
SOTA
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
Semantic SegmentationonCOCO test-dev
PQ· 2020-12-01
51.3
best: 59.5 (Mask DINO (single scale))
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
Semantic SegmentationonCOCO test-dev
PQth· 2020-12-01
57.2
best: 65.1 (Mask2Former (Swin-L))
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
Semantic SegmentationonCOCO minival
PQth· 2020-12-01
57
best: 67.1 (OneFormer (InternImage-H,single-scale))
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759

Audio6 results

10-shot image generationonCOCO test-dev
PQst· 2020-12-01
42.4
best: 58.9 (MaskConver (ResNet50, single-scale))
SOTA
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
10-shot image generationonCOCO minival
PQ· 2020-12-01
51.1
best: 61.2 (HyperSeg (Swin-B))
SOTA
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
10-shot image generationonCOCO minival
PQst· 2020-12-01
42.2
best: 49.2 (OneFormer (InternImage-H,single-scale))
SOTA
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
10-shot image generationonCOCO test-dev
PQ· 2020-12-01
51.3
best: 59.5 (Mask DINO (single scale))
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
10-shot image generationonCOCO test-dev
PQth· 2020-12-01
57.2
best: 65.1 (Mask2Former (Swin-L))
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
10-shot image generationonCOCO minival
PQth· 2020-12-01
57
best: 67.1 (OneFormer (InternImage-H,single-scale))
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759

Computer Vision6 results

Panoptic SegmentationonCOCO test-dev
PQst· 2020-12-01
42.4
best: 58.9 (MaskConver (ResNet50, single-scale))
SOTA
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
Panoptic SegmentationonCOCO minival
PQ· 2020-12-01
51.1
best: 61.2 (HyperSeg (Swin-B))
SOTA
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
Panoptic SegmentationonCOCO minival
PQst· 2020-12-01
42.2
best: 49.2 (OneFormer (InternImage-H,single-scale))
SOTA
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
Panoptic SegmentationonCOCO test-dev
PQ· 2020-12-01
51.3
best: 59.5 (Mask DINO (single scale))
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
Panoptic SegmentationonCOCO test-dev
PQth· 2020-12-01
57.2
best: 65.1 (Mask2Former (Swin-L))
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759
Panoptic SegmentationonCOCO minival
PQth· 2020-12-01
57
best: 67.1 (OneFormer (InternImage-H,single-scale))
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers arXiv:2012.00759