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Models/Panoptic FCN*++ (DCN-101-FPN)

Panoptic FCN*++ (DCN-101-FPN)

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· 2020-12-01
47.5
best: 59.5 (Mask DINO (single scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Semantic SegmentationonCOCO test-dev
PQst· 2020-12-01
38.2
best: 58.9 (MaskConver (ResNet50, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Semantic SegmentationonCOCO test-dev
PQth· 2020-12-01
53.7
best: 65.1 (Mask2Former (Swin-L))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720

Audio3 results

10-shot image generationonCOCO test-dev
PQ· 2020-12-01
47.5
best: 59.5 (Mask DINO (single scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
10-shot image generationonCOCO test-dev
PQst· 2020-12-01
38.2
best: 58.9 (MaskConver (ResNet50, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
10-shot image generationonCOCO test-dev
PQth· 2020-12-01
53.7
best: 65.1 (Mask2Former (Swin-L))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720

Computer Vision3 results

Panoptic SegmentationonCOCO test-dev
PQ· 2020-12-01
47.5
best: 59.5 (Mask DINO (single scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Panoptic SegmentationonCOCO test-dev
PQst· 2020-12-01
38.2
best: 58.9 (MaskConver (ResNet50, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Panoptic SegmentationonCOCO test-dev
PQth· 2020-12-01
53.7
best: 65.1 (Mask2Former (Swin-L))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720