HyperAI
HyperAI
Startseite
Plattform
Dokumentation
Neuigkeiten
Forschungsarbeiten
Tutorials
Datensätze
Wiki
SOTA
LLM-Modelle
GPU-Rangliste
Veranstaltungen
Suche
Über
Nutzungsbedingungen
Datenschutzrichtlinie
Deutsch
HyperAI
HyperAI
Toggle Sidebar
Seite durchsuchen…
⌘
K
Command Palette
Search for a command to run...
Plattform
Startseite
SOTA
Feinabstufungsbildklassifizierung
Fine Grained Image Classification On Stanford
Fine Grained Image Classification On Stanford
Metriken
Accuracy
PARAMS
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Accuracy
PARAMS
Paper Title
TResnet-L + PMD
97.3%
-
Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition
CMAL-Net
97.1%
-
Learn from Each Other to Classify Better: Cross-layer Mutual Attention Learning for Fine-grained Visual Classification
I2-HOFI
96.92%
-
Interweaving Insights: High-Order Feature Interaction for Fine-Grained Visual Recognition
TResNet-L + ML-Decoder
96.41%
-
ML-Decoder: Scalable and Versatile Classification Head
DAT
96.2%
-
Domain Adaptive Transfer Learning with Specialist Models
ALIGN
96.13%
-
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
SR-GNN
96.1
30.9
SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization
EffNet-L2 (SAM)
95.96%
-
Sharpness-Aware Minimization for Efficiently Improving Generalization
SaSPA + CAL
95.72
-
Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation
CAP
95.7%
-
Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification
CSQA-Net
95.6%
-
Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization
AttNet & AffNet
95.6%
-
Fine-Grained Visual Classification with Efficient End-to-end Localization
CCFR
95.5%
-
Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition
CAL
95.5%
-
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
MPSA
95.4%
-
Multi-Granularity Part Sampling Attention for Fine-Grained Visual Classification
Inceptionv4
95.35%
-
Non-binary deep transfer learning for image classification
DCAL
95.3%
-
Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification
API-Net
95.3%
-
Learning Attentive Pairwise Interaction for Fine-Grained Classification
PART
95.3%
-
Part-guided Relational Transformers for Fine-grained Visual Recognition
DenseNet161+MM+FRL
95.2%
-
Learning Class Unique Features in Fine-Grained Visual Classification
0 of 82 row(s) selected.
Previous
Next