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SOTA
Classification d'images fine-grainée
Fine Grained Image Classification On Stanford
Fine Grained Image Classification On Stanford
Métriques
Accuracy
PARAMS
Résultats
Résultats de performance de divers modèles sur ce benchmark
Columns
Nom du modèle
Accuracy
PARAMS
Paper Title
Repository
DeiT-B
93.3%
86M
Training data-efficient image transformers & distillation through attention
-
SEB+EfficientNet-B5
94.6%
-
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition
S3N
94.7%
-
Selective Sparse Sampling for Fine-Grained Image Recognition
PMG
95.1%
-
Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches
PCA
94.6%
-
Progressive Co-Attention Network for Fine-grained Visual Classification
SaSPA + CAL
95.72
-
Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation
ViT-L (attn finetune)
93.8%
-
Three things everyone should know about Vision Transformers
AENet
94.0%
-
Alignment Enhancement Network for Fine-grained Visual Categorization
-
ResMLP-12
84.6%
-
ResMLP: Feedforward networks for image classification with data-efficient training
SR-GNN
96.1
30.9
SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization
RDNet-T (224 res, IN-1K pretrained)
93.9%
24M
DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs
ResNet50 (A1)
92.7%
24M
ResNet strikes back: An improved training procedure in timm
Inceptionv4
95.35%
-
Non-binary deep transfer learning for image classification
WS-DAN
94.5%
-
See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification
CCFR
95.5%
-
Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition
-
MPSA
95.4%
-
Multi-Granularity Part Sampling Attention for Fine-Grained Visual Classification
CIN
94.5%
-
Channel Interaction Networks for Fine-Grained Image Categorization
-
DF-GMM
94.8%
-
Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning
-
MPFG + CLIP
86.79
-
Multiscale patch-based feature graphs for image classification
TransFG
94.8%
-
TransFG: A Transformer Architecture for Fine-grained Recognition
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