Object Detection On Tbbr
Métriques
Average Recall@IoU:0.5-0.95
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Average Recall@IoU:0.5-0.95 | Paper Title | Repository |
|---|---|---|---|
| Mask R-CNN (ResNet-50-FPN) | 30.8 | Deep learning approaches to building rooftop thermal bridge detection from aerial images | |
| Mask R-CNN (ResNet-50-FPN, ImageNet-1k pretrain) | 37.0 | Deep learning approaches to building rooftop thermal bridge detection from aerial images | |
| FSAF (ResNeXt-101, ImageNet-1k pretrain) | 38.0 | Deep learning approaches to building rooftop thermal bridge detection from aerial images | |
| TridentNet (ResNet-50, ImageNet-1k pretrain) | 30.0 | Deep learning approaches to building rooftop thermal bridge detection from aerial images | |
| TridentNet (ResNet-50) | 21.5 | Deep learning approaches to building rooftop thermal bridge detection from aerial images | |
| FSAF (ResNeXt-101) | 24.8 | Deep learning approaches to building rooftop thermal bridge detection from aerial images | |
| Swin-T (ImageNet-1k pretrain) | 45.4 | Deep learning approaches to building rooftop thermal bridge detection from aerial images |
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