Fine-grained Image Classification on Oxford 102 Flowers (test)
99.5AccuracyTPSKG
Evaluation Results
| Method | Links | |
|---|---|---|
| TPSKGBackbone=ViT-B-16, Resolution=448 x 4482021.07 | 99.5 | |
| ViTBackbone=ViT-B-16, Resolution=448 x 4482021.07 | 99.2 | |
| ViT-ResNet-50Backbone=ViT&ResNet-50, Resolution=448 x 4482021.07 | 98.5 | |
| SR-GNNBackbone=Xception, Attentional Refinement=true2022.09 | 97.9 | |
| MCLBackbone=Bilinear CNN, Joint Learning=true2022.09 | 97.7 | |
| CAPBackbone=Xception2022.09 | 97.7 | |
| DSTLBackbone=Inception-V3, Joint Learning=true2022.09 | 97.6 | |
| BiM-PMABackbone=VGG-16, Resolution=448 x 4482021.07 | 97.4 | |
| PMABackbone=VGG-162022.09 | 97.4 | |
| Cos.LsBackbone=ResNet-50, Joint Learning=true2022.09 | 97.2 | |
| OPAMBackbone=VGG, Joint Learning=true2022.09 | 97.1 | |
| SR-GNNBackbone=Xception, Attentional Refinement=false2022.09 | 97.1 | |
| SJFTBackbone=ResNet-152, Joint Learning=true2022.09 | 97 | |
| IntActBackbone=VGG-192022.09 | 96.4 | |
| PBCBackbone=GoogleNet, Resolution=224 x 2242021.07 | 96.1 | |
| PBCBackbone=GoogLeNet2022.09 | 96.1 | |
| MGEBackbone=ResNet-502022.09 | 95.9 | |
| PC-CNNBackbone=DenseNet-161, Resolution=224 x 2242021.07 | 93.6 | |
| Base CNNBackbone=Xception2022.09 | 91.9 | |
| Oracle ADAPTBP-free=true, Evaluation Protocol=Transductive2025.08 | 81.93 | |
| ADAPTBP-free=true, Evaluation Protocol=Transductive2025.08 | 80.11 | |
| ADAPTBP-free=true, Evaluation Protocol=Online2025.08 | 75.56 | |
| CLIPBP-free=N/A, Evaluation Protocol=Online2025.08 | 67.28 |