Semantic Segmentation On Isprs Potsdam
Métriques
Mean F1
Mean IoU
Overall Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Mean F1 | Mean IoU | Overall Accuracy |
|---|---|---|---|
| aerialformer-multi-resolution-transformer-for | 94.1 | 89.1 | 93.9 |
| lsknet-a-foundation-lightweight-backbone-for | 93.1 | 87.2 | 92.0 |
| stochastic-subsampling-with-average-pooling | - | 74.3 | 88.56 |
| advancing-plain-vision-transformer-towards | - | - | 90.77 |
| semantic-labeling-of-high-resolution-images | 93.7 | - | 91.8 |
| transformer-meets-convolution-a-bilateral | - | - | 91.06 |
| efficient-hybrid-transformer-learning-global | 93.3 | 87.5 | 92.0 |
| an-empirical-study-of-remote-sensing | - | - | 90.78 |
| multiattention-network-for-semantic | - | - | 91.318 |
| u-net-ensemble-for-enhanced-semantic | - | 89.45 | - |
| advancing-plain-vision-transformer-towards | - | - | 91.22 |
| a-billion-scale-foundation-model-for-remote | 92.12 | - | 92.58 |
| an-empirical-study-of-remote-sensing | - | - | 90.61 |
| an-empirical-study-of-remote-sensing | - | - | 91.21 |
| abcnet-attentive-bilateral-contextual-network | - | - | 91.3 |
| an-empirical-study-of-remote-sensing | - | - | 91.6 |
| efficient-hybrid-transformer-learning-global | 92.8 | 86.8 | 91.3 |
| transformer-meets-dcfam-a-novel-semantic | 93.25 | 87.56 | 92.0 |
| sfa-net-semantic-feature-adjustment-network | 93.5 | - | - |