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Models/HRNetV2 + OCR + RMI (PaddleClas pretrained)

HRNetV2 + OCR + RMI (PaddleClas pretrained)

Reported on 8 benchmarks across 2 tasks · 1 paper · 8 SOTA

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

Medical4 results

Semantic SegmentationonCityscapes val
mIoU· 2019-09-24
83.6
best: 90.3 (EfficientPS (Cityscapes-fine))
SOTA
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation arXiv:1909.11065
Semantic SegmentationonADE20K val
mIoU· 2019-09-24
47.98
best: 62.8 (BEiT-3)
SOTA
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation arXiv:1909.11065
Semantic SegmentationonPASCAL Context
mIoU· 2019-09-24
59.6
best: 71.1 (VPNeXt)
SOTA
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation arXiv:1909.11065
Semantic SegmentationonADE20K
Validation mIoU· 2019-09-24
47.98
best: 63.6 (ViT-P (InternImage-H))
SOTA
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation arXiv:1909.11065

Audio4 results

10-shot image generationonCityscapes val
mIoU· 2019-09-24
83.6
best: 90.3 (EfficientPS (Cityscapes-fine))
SOTA
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation arXiv:1909.11065
10-shot image generationonADE20K val
mIoU· 2019-09-24
47.98
best: 62.8 (BEiT-3)
SOTA
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation arXiv:1909.11065
10-shot image generationonPASCAL Context
mIoU· 2019-09-24
59.6
best: 71.1 (VPNeXt)
SOTA
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation arXiv:1909.11065
10-shot image generationonADE20K
Validation mIoU· 2019-09-24
47.98
best: 63.6 (ViT-P (InternImage-H))
SOTA
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation arXiv:1909.11065