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 | IELT | Accuracy | 99.64 | — | Unverified |
| 2 | BiT-L (ResNet) | Accuracy | 99.63 | — | Unverified |
| 3 | µ2Net (ViT-L/16) | Accuracy | 99.61 | — | Unverified |
| 4 | Wide-ResNet-101 (Spinal FC) | Accuracy | 99.3 | — | Unverified |
| 5 | BiT-M (ResNet) | Accuracy | 99.3 | — | Unverified |
| 6 | TResNet-L | Accuracy | 99.1 | — | Unverified |
| 7 | Grafit (RegNet-8GF) | Accuracy | 99.1 | — | Unverified |
| 8 | TNT-B | Accuracy | 99 | — | Unverified |
| 9 | Assemble-ResNet | Accuracy | 98.9 | — | Unverified |
| 10 | AutoFormer-S | 384 | Top 1 Accuracy | 98.8 | — | Unverified |