Image Classification On Caltech 256
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy | Paper Title | Repository |
|---|---|---|---|
| swin-transformer | 77 | - | - |
| Inceptionv4 (random initialization) | 67.2 | Non-binary deep transfer learning for image classification | |
| Inceptionv4 | 85.94 | Non-binary deep transfer learning for image classification | |
| WaveMixLite-256/7 | 54.62 | WaveMix: A Resource-efficient Neural Network for Image Analysis | |
| AG-Net | 96.89% | Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition |
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