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Models/Panoptic FCN* (Swin-L)

Panoptic FCN* (Swin-L)

Reported on 6 benchmarks across 3 tasks · 1 paper · 6 SOTA

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

Medical2 results

Semantic SegmentationonCOCO test-dev
PQ· 2020-12-01
52.7
best: 59.5 (Mask DINO (single scale))
SOTA
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Semantic SegmentationonCOCO test-dev
PQth· 2020-12-01
59.4
best: 65.1 (Mask2Former (Swin-L))
SOTA
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720

Audio2 results

10-shot image generationonCOCO test-dev
PQ· 2020-12-01
52.7
best: 59.5 (Mask DINO (single scale))
SOTA
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
10-shot image generationonCOCO test-dev
PQth· 2020-12-01
59.4
best: 65.1 (Mask2Former (Swin-L))
SOTA
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720

Computer Vision2 results

Panoptic SegmentationonCOCO test-dev
PQ· 2020-12-01
52.7
best: 59.5 (Mask DINO (single scale))
SOTA
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Panoptic SegmentationonCOCO test-dev
PQth· 2020-12-01
59.4
best: 65.1 (Mask2Former (Swin-L))
SOTA
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720