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Models/FRRN

FRRN

Reported on 8 benchmarks across 4 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 SegmentationonMFN Dataset
mIOU· 2016-11-24
44.2
best: 62.7 (RoadFormer+ (ConvNeXt-L))
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes arXiv:1611.08323
Semantic SegmentationonCityscapes test
Frame (fps)· 2016-11-24
2.1
best: 188.6 (STDC2-50)
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes arXiv:1611.08323
Semantic SegmentationonCityscapes test
Time (ms)· 2016-11-24
469
best: 4000 (DeepLab)
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes arXiv:1611.08323

Audio3 results

10-shot image generationonMFN Dataset
mIOU· 2016-11-24
44.2
best: 62.7 (RoadFormer+ (ConvNeXt-L))
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes arXiv:1611.08323
10-shot image generationonCityscapes test
Frame (fps)· 2016-11-24
2.1
best: 188.6 (STDC2-50)
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes arXiv:1611.08323
10-shot image generationonCityscapes test
Time (ms)· 2016-11-24
469
best: 4000 (DeepLab)
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes arXiv:1611.08323

Computer Vision1 result

Scene SegmentationonMFN Dataset
mIOU· 2016-11-24
44.2
best: 62.7 (RoadFormer+ (ConvNeXt-L))
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes arXiv:1611.08323

Methodology1 result

2D Object DetectiononMFN Dataset
mIOU· 2016-11-24
44.2
best: 62.7 (RoadFormer+ (ConvNeXt-L))
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes arXiv:1611.08323