Fine-Grained Image Classification
Fine-Grained Image Classification is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.
( Image credit: Looking for the Devil in the Details )
Papers
Showing 1–10 of 353 papers
All datasetsStanford CarsFGVC-AircraftCUB-200-2011NABirdsOxford 102 FlowersStanford DogsOxford-IIIT PetsCaltech-101Food-101Oxford-IIIT Pet DatasetCompCarsBird-225
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | MetaFormer (MetaFormer-2,384) | Accuracy | 93 | — | Unverified |
| 2 | HERBS | Accuracy | 93 | — | Unverified |
| 3 | PIM | Accuracy | 92.8 | — | Unverified |
| 4 | ViT-NeT (SwinV2-B) | Accuracy | 92.5 | — | Unverified |
| 5 | MPSA | Accuracy | 92.5 | — | Unverified |
| 6 | CSQA-Net | Accuracy | 92.3 | — | Unverified |
| 7 | I2-HOFI | Accuracy | 92.12 | — | Unverified |
| 8 | MDCM | Accuracy | 92 | — | Unverified |
| 9 | CGL | Accuracy | 91.7 | — | Unverified |
| 10 | SR-GNN | Accuracy | 91.2 | — | Unverified |