Fine Grained Image Classification On Food 101
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Accuracy |
|---|---|
| grafit-learning-fine-grained-image | 93.7 |
| learning-multi-subset-of-classes-for-fine | 93.81 |
| neural-architecture-transfer | 89.4 |
| neural-architecture-transfer | 87.4 |
| compounding-the-performance-improvements-of | 92.5 |
| context-aware-attentional-pooling-cap-for | 98.6 |
| dining-on-details-llm-guided-expert-networks | 94.9 |
| a-continual-development-methodology-for-large | 91.47 |
| learning-multi-subset-of-classes-for-fine | 93.66 |
| sharpness-aware-minimization-for-efficiently-1 | 96.18 |
| neural-architecture-transfer | 88.5 |
| scaling-up-visual-and-vision-language | 95.88 |
| efficientnet-rethinking-model-scaling-for | 93.0 |
| domain-adaptive-transfer-learning-on-visual | - |
| neural-architecture-transfer | 89.0 |