Fine Grained Image Classification On Birdsnap
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy | Paper Title | Repository |
|---|---|---|---|
| FixSENet-154 | 84.3% | Fixing the train-test resolution discrepancy | |
| GPIPE | 83.6% | GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism | |
| EffNet-L2 (SAM) | 90.07% | Sharpness-Aware Minimization for Efficiently Improving Generalization | |
| NNCLR | 61.4% | With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations | |
| EfficientNet-B7 | 84.3% | EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks |
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