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 | Resnet50 + PMAL | Accuracy | 99.1 | — | Unverified |
| 2 | ResNet101-swp | Accuracy | 97.6 | — | Unverified |
| 3 | Fine-Tuning DARTS | Accuracy | 95.9 | — | Unverified |
| 4 | Resnet50 + COOC | Accuracy | 95.6 | — | Unverified |
| 5 | A3M | Accuracy | 95.4 | — | Unverified |
| 6 | GoogLeNet | Accuracy | 91.2 | — | Unverified |
| 7 | AlexNet | Accuracy | 81.9 | — | Unverified |