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 | WideResNet-101 (Spinal FC) | Accuracy | 99.56 | — | Unverified |
| 2 | Pre trained wide-resnet-101 | Accuracy | 99.55 | — | Unverified |
| 3 | WideResNet-101 | Accuracy | 99.38 | — | Unverified |
| 4 | VGG-19bn (Spinal FC) | Accuracy | 99.02 | — | Unverified |
| 5 | VGG-19bn | Accuracy | 98.67 | — | Unverified |