Accueil Actualités Articles de recherche récents Tutoriels Ensembles de données Wiki SOTA Modèles LLM Classement GPU Événements

Français

Fine Grained Image Classification On Food 101

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
Grafit (RegNet-8GF)	93.7	Grafit: Learning fine-grained image representations with coarse labels	-
CSWin-L	93.81	Learning Multi-Subset of Classes for Fine-Grained Food Recognition
NAT-M4	89.4	Neural Architecture Transfer
NAT-M1	87.4	Neural Architecture Transfer
Assemble-ResNet-FGVC-50	92.5	Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network
CAP	98.6	Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification
DoD (SwinV2-B)	94.9	Dining on Details: LLM-Guided Expert Networks for Fine-Grained Food Recognition	-
µ2Net+ (ViT-L/16)	91.47	A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems
VOLO-D5	93.66	Learning Multi-Subset of Classes for Fine-Grained Food Recognition
EffNet-L2 (SAM)	96.18	Sharpness-Aware Minimization for Efficiently Improving Generalization
NAT-M2	88.5	Neural Architecture Transfer
ALIGN	95.88	Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
EfficientNet-B7	93.0	EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
ImageNet + iNat on WS-DAN	-	Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization	-
NAT-M3	89.0	Neural Architecture Transfer

0 of 15 row(s) selected.