Image Classification On Emnist Balanced
Metriken
Accuracy
Trainable Parameters
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Accuracy | Trainable Parameters |
|---|---|---|
| spinalnet-deep-neural-network-with-gradual-1 | 83.21 | 16050 |
| efficient-global-neural-architecture-search | 91.20 | 400000 |
| ondev-lct-on-device-lightweight-convolutional | 89.39 | 514960 |
| parametric-matrix-models | 85.95 | 349172 |
| hybrid-macromicro-level-backpropagation-for | 85.57 | 665647 |
| improving-k-means-clustering-performance-with | 78.5 | - |
| spinalnet-deep-neural-network-with-gradual-1 | 79.61 | 21840 |
| dynamic-routing-between-capsules | 90.46 | - |
| wavemix-lite-a-resource-efficient-neural | 91.06 | - |
| emnist-an-extension-of-mnist-to-handwritten | 50.93 | - |
| spinalnet-deep-neural-network-with-gradual-1 | 91.04 | 3646000 |
| efficient-global-neural-architecture-search | 91.48 | 2250000 |
| ondev-lct-on-device-lightweight-convolutional | 89.52 | 216208 |
| spinalnet-deep-neural-network-with-gradual-1 | 82.77 | 13820 |
| ondev-lct-on-device-lightweight-convolutional | 89.18 | 315792 |
| efficient-neural-vision-systems-based-on | 87.69 | - |
| spinalnet-deep-neural-network-with-gradual-1 | 91.05 | 3630000 |
| ondev-lct-on-device-lightweight-convolutional | 89.55 | 913296 |
| parametric-matrix-models | 81.57 | 13792 |
| emnist-an-extension-of-mnist-to-handwritten | 78.94 | - |