Classification on Rabbit Dataset
97.99AccuracyHybrid SNN
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| Hybrid SNNBackbone=mnasnet1_0, Model Type=SNN, Tc=300ms2025.12 | 97.99 | 0 | 230.52 | |
| Prunned EfficientNet-B0Backbone=EfficientNet-B02025.12 | 97.4 | — | — | |
| Hybrid SNNBackbone=resnet34, Model Type=SNN, Tc=300ms2025.12 | 96.91 | 0 | 7,017.92 | |
| Hybrid SNNBackbone=resnet18, Model Type=SNN, Tc=300ms2025.12 | 96.45 | 0 | 4,795.74 | |
| Hybrid SNNBackbone=mobilenet_v2, Model Type=SNN, Tc=300ms2025.12 | 95.22 | 0 | 284.86 | |
| Hybrid SNNBackbone=resnet50, Model Type=SNN, Tc=300ms2025.12 | 91.05 | 0 | 4,522.08 | |
| densenet169 (ANN)Backbone=densenet169, Model Type=ANN, Fine-tuned=true2025.12 | 90.96 | 0.0429 | — | |
| densenet121 (ANN)Backbone=densenet121, Model Type=ANN, Fine-tuned=true2025.12 | 87.54 | 0.0362 | — | |
| mnasnet1_0 (ANN)Backbone=mnasnet1_0, Model Type=ANN, Fine-tuned=true2025.12 | 86.41 | 0.0042 | — | |
| mobilenet_v2 (ANN)Backbone=mobilenet_v2, Model Type=ANN, Fine-tuned=true2025.12 | 86.29 | 0.0041 | — | |
| resnet34 (ANN)Backbone=resnet34, Model Type=ANN, Fine-tuned=true2025.12 | 82.87 | 0.046 | — | |
| resnet50 (ANN)Backbone=resnet50, Model Type=ANN, Fine-tuned=true2025.12 | 82.67 | 0.0517 | — | |
| resnet18 (ANN)Backbone=resnet18, Model Type=ANN, Fine-tuned=true2025.12 | 82.16 | 0.0228 | — | |
| ViT Hybrid SNNBackbone=ViT2025.12 | 81.5 | — | — | |
| Hybrid SNNBackbone=densenet121, Model Type=SNN, Tc=300ms2025.12 | 65.43 | 0 | 3,449.94 | |
| Hybrid SNNBackbone=densenet169, Model Type=SNN, Tc=300ms2025.12 | 34.72 | 0 | 3,611.85 |