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Models/Panoptic FCN* (ResNet-50-FPN)

Panoptic FCN* (ResNet-50-FPN)

Reported on 33 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.

Medical11 results

Semantic SegmentationonCityscapes val
PQst· 2020-12-01
66.6
best: 74.1 (OneFormer (ConvNeXt-L, single-scale, 512x1024, Mapillary Vistas-pretrained))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Semantic SegmentationonMapillary val
PQst· 2020-12-01
42.3
best: 54.9 (OneFormer (DiNAT-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Semantic SegmentationonCOCO minival
PQ· 2020-12-01
44.3
best: 61.2 (HyperSeg (Swin-B))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Semantic SegmentationonCOCO minival
PQst· 2020-12-01
35.6
best: 49.2 (OneFormer (InternImage-H,single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Semantic SegmentationonCOCO minival
PQth· 2020-12-01
50
best: 67.1 (OneFormer (InternImage-H,single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Semantic SegmentationonCOCO minival
RQ· 2020-12-01
53
best: 63.5 (MaskFormer (single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Semantic SegmentationonCOCO minival
RQst· 2020-12-01
43.5
best: 51.1 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Semantic SegmentationonCOCO minival
RQth· 2020-12-01
59.3
best: 68.6 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Semantic SegmentationonCOCO minival
SQ· 2020-12-01
80.7
best: 83.2 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Semantic SegmentationonCOCO minival
SQst· 2020-12-01
76.7
best: 81.1 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Semantic SegmentationonCOCO minival
SQth· 2020-12-01
83.4
best: 84.6 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720

Audio11 results

10-shot image generationonCityscapes val
PQst· 2020-12-01
66.6
best: 74.1 (OneFormer (ConvNeXt-L, single-scale, 512x1024, Mapillary Vistas-pretrained))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
10-shot image generationonMapillary val
PQst· 2020-12-01
42.3
best: 54.9 (OneFormer (DiNAT-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
10-shot image generationonCOCO minival
PQ· 2020-12-01
44.3
best: 61.2 (HyperSeg (Swin-B))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
10-shot image generationonCOCO minival
PQst· 2020-12-01
35.6
best: 49.2 (OneFormer (InternImage-H,single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
10-shot image generationonCOCO minival
PQth· 2020-12-01
50
best: 67.1 (OneFormer (InternImage-H,single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
10-shot image generationonCOCO minival
RQ· 2020-12-01
53
best: 63.5 (MaskFormer (single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
10-shot image generationonCOCO minival
RQst· 2020-12-01
43.5
best: 51.1 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
10-shot image generationonCOCO minival
RQth· 2020-12-01
59.3
best: 68.6 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
10-shot image generationonCOCO minival
SQ· 2020-12-01
80.7
best: 83.2 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
10-shot image generationonCOCO minival
SQst· 2020-12-01
76.7
best: 81.1 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
10-shot image generationonCOCO minival
SQth· 2020-12-01
83.4
best: 84.6 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720

Computer Vision11 results

Panoptic SegmentationonCityscapes val
PQst· 2020-12-01
66.6
best: 74.1 (OneFormer (ConvNeXt-L, single-scale, 512x1024, Mapillary Vistas-pretrained))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Panoptic SegmentationonMapillary val
PQst· 2020-12-01
42.3
best: 54.9 (OneFormer (DiNAT-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Panoptic SegmentationonCOCO minival
PQ· 2020-12-01
44.3
best: 61.2 (HyperSeg (Swin-B))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Panoptic SegmentationonCOCO minival
PQst· 2020-12-01
35.6
best: 49.2 (OneFormer (InternImage-H,single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Panoptic SegmentationonCOCO minival
PQth· 2020-12-01
50
best: 67.1 (OneFormer (InternImage-H,single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Panoptic SegmentationonCOCO minival
RQ· 2020-12-01
53
best: 63.5 (MaskFormer (single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Panoptic SegmentationonCOCO minival
RQst· 2020-12-01
43.5
best: 51.1 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Panoptic SegmentationonCOCO minival
RQth· 2020-12-01
59.3
best: 68.6 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Panoptic SegmentationonCOCO minival
SQ· 2020-12-01
80.7
best: 83.2 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Panoptic SegmentationonCOCO minival
SQst· 2020-12-01
76.7
best: 81.1 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720
Panoptic SegmentationonCOCO minival
SQth· 2020-12-01
83.4
best: 84.6 (Panoptic FCN* (Swin-L, single-scale))
Fully Convolutional Networks for Panoptic Segmentation arXiv:2012.00720