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Models/RegSeg(Cityscapes-Pretrained)

RegSeg(Cityscapes-Pretrained)

Reported on 6 benchmarks across 2 tasks · 1 paper · 2 SOTA

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

Medical3 results

Semantic SegmentationonCamVid
mIoU· uses extra data· 2021-11-18
80.9
best: 82 (PIDNet-M (Cityscapes-Pretrained))
SOTA
Rethinking Dilated Convolution for Real-time Semantic Segmentation arXiv:2111.09957
Semantic SegmentationonCamVid
Frame (fps)· uses extra data· 2021-11-18
70
best: 222.3 (PP-LiteSeg-T)
Rethinking Dilated Convolution for Real-time Semantic Segmentation arXiv:2111.09957
Semantic SegmentationonCamVid
Time (ms)· uses extra data· 2021-11-18
14
best: 227 (Dilation10)
Rethinking Dilated Convolution for Real-time Semantic Segmentation arXiv:2111.09957

Audio3 results

10-shot image generationonCamVid
mIoU· uses extra data· 2021-11-18
80.9
best: 82 (PIDNet-M (Cityscapes-Pretrained))
SOTA
Rethinking Dilated Convolution for Real-time Semantic Segmentation arXiv:2111.09957
10-shot image generationonCamVid
Frame (fps)· uses extra data· 2021-11-18
70
best: 222.3 (PP-LiteSeg-T)
Rethinking Dilated Convolution for Real-time Semantic Segmentation arXiv:2111.09957
10-shot image generationonCamVid
Time (ms)· uses extra data· 2021-11-18
14
best: 227 (Dilation10)
Rethinking Dilated Convolution for Real-time Semantic Segmentation arXiv:2111.09957