Image Classification On Plantvillage
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Paper Title | Repository |
|---|---|---|---|
| µ2Net+ (ViT-L/16) | 99.89 | A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems | |
| Light-Chroma Inception V3 | 99.48% | Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated Branches | |
| Inception V3 20%L + 80%AB | 99.48% | Color-aware two-branch DCNN for efficient plant disease classification | |
| SAG-ViT | - | SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers | - |
| adaptive minimal ensembling | 100 | Improving plant disease classification by adaptive minimal ensembling | - |
| DenseNet | 99.88 | Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision |
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