Fine Grained Image Classification On Oxford 2
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | Accuracy |
|---|---|
| when-vision-transformers-outperform-resnets | 91.6 |
| an-evolutionary-approach-to-dynamic | 95.3 |
| compounding-the-performance-improvements-of | 94.3% |
| neural-architecture-transfer | 94.1 |
| when-vision-transformers-outperform-resnets | 92.5 |
| on-the-ideal-number-of-groups-for-isometric | 77.076 |
| neural-architecture-transfer | 93.5 |
| how-to-use-dropout-correctly-on-residual | 85.5897 |
| when-vision-transformers-outperform-resnets | 88.7 |
| stochastic-subsampling-with-average-pooling | 86.011 |
| a-continual-development-methodology-for-large | 95.5 |
| sharpness-aware-minimization-for-efficiently-1 | 97.10 |
| neural-architecture-transfer | 94.3 |
| when-vision-transformers-outperform-resnets | 93.1 |
| large-scale-learning-of-general-visual | 96.62 |
| when-vision-transformers-outperform-resnets | 92.9 |
| when-vision-transformers-outperform-resnets | 93.3 |
| large-scale-learning-of-general-visual | 94.47 |
| an-image-is-worth-16x16-words-transformers-1 | - |