Image Classification on Chicken Dataset
98.32AccuracyHybrid SNN
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| Hybrid SNNBackbone=mnasnet1_0, Model Type=Hybrid SNN, Integration Time (Tc)=300ms2025.12 | 98.32 | 0 | 225.75 | |
| Hybrid SNNBackbone=resnet34, Model Type=Hybrid SNN, Integration Time (Tc)=300ms2025.12 | 97.42 | 0 | 6,878.45 | |
| ResTFGBackbone=ResTFG2025.12 | 96.9 | — | — | |
| XceptionBackbone=Xception2025.12 | 96.4 | — | — | |
| Hybrid SNNBackbone=resnet18, Model Type=Hybrid SNN, Integration Time (Tc)=300ms2025.12 | 95.63 | 0 | 4,555.8 | |
| Prunned EfficientNet-B0Backbone=Prunned EfficientNet-B02025.12 | 95.1 | — | — | |
| Hybrid SNNBackbone=mobilenet_v2, Model Type=Hybrid SNN, Integration Time (Tc)=300ms2025.12 | 94.51 | 0 | 253.43 | |
| densenet169 (ANN)Backbone=densenet169, Model Type=ANN2025.12 | 90.67 | 0.0429 | — | |
| Hybrid SNNBackbone=resnet50, Model Type=Hybrid SNN, Integration Time (Tc)=300ms2025.12 | 90.59 | 0 | 3,895.43 | |
| densenet121 (ANN)Backbone=densenet121, Model Type=ANN2025.12 | 90.54 | 0.0362 | — | |
| ViT Hybrid SNNBackbone=ViT Hybrid SNN2025.12 | 87.6 | — | — | |
| resnet34 (ANN)Backbone=resnet34, Model Type=ANN2025.12 | 87.01 | 0.046 | — | |
| mobilenet_v2 (ANN)Backbone=mobilenet_v2, Model Type=ANN2025.12 | 86.96 | 0.0041 | — | |
| mnasnet1_0 (ANN)Backbone=mnasnet1_0, Model Type=ANN2025.12 | 86.58 | 0.0042 | — | |
| resnet18 (ANN)Backbone=resnet18, Model Type=ANN2025.12 | 84.21 | 0.0228 | — | |
| resnet50 (ANN)Backbone=resnet50, Model Type=ANN2025.12 | 83.36 | 0.0517 | — | |
| Hybrid SNNBackbone=densenet121, Model Type=Hybrid SNN, Integration Time (Tc)=300ms2025.12 | 69.43 | 0 | 3,254.57 | |
| Hybrid SNNBackbone=densenet169, Model Type=Hybrid SNN, Integration Time (Tc)=300ms2025.12 | 60.92 | 0 | 4,127.91 |