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Fine Grained Image Classification On Fgvc

Metrics

Accuracy
FLOPS
PARAMS

Results

Performance results of various models on this benchmark

Paper Title
I2-HOFI96.42%--Interweaving Insights: High-Order Feature Interaction for Fine-Grained Visual Recognition
SR-GNN95.49.830.9SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization
Inceptionv495.11--Non-binary deep transfer learning for image classification
CAP94.9%-34.2Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification
CSQA-Net94.7%--Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization
CMAL-Net94.7%--Learn from Each Other to Classify Better: Cross-layer Mutual Attention Learning for Fine-grained Visual Classification
TBMSL-Net94.7%--Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
PART94.6%--Part-guided Relational Transformers for Fine-grained Visual Recognition
AENet94.5%--Alignment Enhancement Network for Fine-grained Visual Categorization
SaSPA + CAL94.5--Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation
CAL94.2--Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
CGL94.2%--Universal Fine-grained Visual Categorization by Concept Guided Learning
AttNet & AffNet94.1%--Fine-Grained Visual Classification with Efficient End-to-end Localization
CCFR94.1%--Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition
DenseNet161+MM+FRL94.0 %--Learning Class Unique Features in Fine-Grained Visual Classification
API-Net93.9%--Learning Attentive Pairwise Interaction for Fine-Grained Classification
CMN93.8%---
DF-GMM93.8%--Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning
Multi Granularity93.6%--Your "Flamingo" is My "Bird": Fine-Grained, or Not
BCN93.5%--Fine-Grained Visual Classification with Batch Confusion Norm
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Fine Grained Image Classification On Fgvc | SOTA | HyperAI