Real Time Semantic Segmentation On Cityscapes 1
Métriques
mIoU
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | mIoU |
|---|---|
| pp-liteseg-a-superior-real-time-semantic | 73.1 |
| segnext-rethinking-convolutional-attention | 79.8% |
| dsnet-a-novel-way-to-use-atrous-convolutions | 80.4% |
| rtformer-efficient-design-for-real-time | 76.3% |
| pidnet-a-real-time-semantic-segmentation | 79.9% |
| deep-dual-resolution-networks-for-real-time | 77.4 |
| deep-dual-resolution-networks-for-real-time | 79.4 |
| pidnet-a-real-time-semantic-segmentation | 78.8% |
| csfnet-a-cosine-similarity-fusion-network-for | 74.73 |
| rtformer-efficient-design-for-real-time | 79.3% |
| liteseg-a-novel-lightweight-convnet-for | 67.8% |
| fasterseg-searching-for-faster-real-time-1 | 73.1 |
| pidnet-a-real-time-semantic-segmentation | 80.9% |
| bisenet-v2-bilateral-network-with-guided | 75.8% |
| csfnet-a-cosine-similarity-fusion-network-for | 76.36 |
| pp-liteseg-a-superior-real-time-semantic | 78.2 |
| in-defence-of-metric-learning-for-speaker | 75.5% |
| bisenet-v2-bilateral-network-with-guided | 73.5% |
| pp-liteseg-a-superior-real-time-semantic | 76 |
| mobile-seed-joint-semantic-segmentation-and | 78.4% |
| rethinking-bisenet-for-real-time-semantic | 74.5% |
| pp-liteseg-a-superior-real-time-semantic | 75.3 |
| rethinking-bisenet-for-real-time-semantic | 77% |
| incorporating-luminance-depth-and-color | 68.48% |