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Fine Grained Image Classification On Food 101

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
Grafit (RegNet-8GF)93.7Grafit: Learning fine-grained image representations with coarse labels-
CSWin-L93.81Learning Multi-Subset of Classes for Fine-Grained Food Recognition
NAT-M489.4Neural Architecture Transfer-
NAT-M187.4Neural Architecture Transfer-
Assemble-ResNet-FGVC-5092.5Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network-
CAP98.6Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification-
DoD (SwinV2-B)94.9Dining on Details: LLM-Guided Expert Networks for Fine-Grained Food Recognition-
µ2Net+ (ViT-L/16)91.47A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems-
VOLO-D593.66Learning Multi-Subset of Classes for Fine-Grained Food Recognition
EffNet-L2 (SAM)96.18Sharpness-Aware Minimization for Efficiently Improving Generalization-
NAT-M288.5Neural Architecture Transfer-
ALIGN95.88Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision-
EfficientNet-B793.0EfficientNet: 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-M389.0Neural Architecture Transfer-
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Fine Grained Image Classification On Food 101 | SOTA | HyperAI