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Models/Axial-DeepLab-L

Axial-DeepLab-L

Reported on 9 benchmarks across 3 tasks · 1 paper

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

Medical3 results

Semantic SegmentationonCOCO test-dev
PQ· 2020-03-17
43.6
best: 59.5 (Mask DINO (single scale))
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation arXiv:2003.07853
Semantic SegmentationonCOCO test-dev
PQst· 2020-03-17
35.6
best: 58.9 (MaskConver (ResNet50, single-scale))
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation arXiv:2003.07853
Semantic SegmentationonCOCO test-dev
PQth· 2020-03-17
48.9
best: 65.1 (Mask2Former (Swin-L))
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation arXiv:2003.07853

Audio3 results

10-shot image generationonCOCO test-dev
PQ· 2020-03-17
43.6
best: 59.5 (Mask DINO (single scale))
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation arXiv:2003.07853
10-shot image generationonCOCO test-dev
PQst· 2020-03-17
35.6
best: 58.9 (MaskConver (ResNet50, single-scale))
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation arXiv:2003.07853
10-shot image generationonCOCO test-dev
PQth· 2020-03-17
48.9
best: 65.1 (Mask2Former (Swin-L))
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation arXiv:2003.07853

Computer Vision3 results

Panoptic SegmentationonCOCO test-dev
PQ· 2020-03-17
43.6
best: 59.5 (Mask DINO (single scale))
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation arXiv:2003.07853
Panoptic SegmentationonCOCO test-dev
PQst· 2020-03-17
35.6
best: 58.9 (MaskConver (ResNet50, single-scale))
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation arXiv:2003.07853
Panoptic SegmentationonCOCO test-dev
PQth· 2020-03-17
48.9
best: 65.1 (Mask2Former (Swin-L))
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation arXiv:2003.07853