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Fine Grained Image Classification On Food 101

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	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

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