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Semantic Segmentation On Foodseg103
Métriques
mIoU
Résultats
Résultats de performance de divers modèles sur ce benchmark
| Paper Title | ||
|---|---|---|
| FoodSAM | 46.4 | FoodSAM: Any Food Segmentation |
| SeTR-MLA (ViT-16/B) | 45.1 | Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers |
| SeTR-Naive (ReLeM-ViT-16/B) | 43.9 | A Large-Scale Benchmark for Food Image Segmentation |
| Swin-Transformer (Swin-Small) | 41.6 | Swin Transformer: Hierarchical Vision Transformer using Shifted Windows |
| SeTR-Naive (ViT-16/B) | 41.3 | Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers |
| CCNet (ReLeM-ResNet-50) | 36.8 | A Large-Scale Benchmark for Food Image Segmentation |
| CCNet (ResNet-50) | 35.5 | CCNet: Criss-Cross Attention for Semantic Segmentation |
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