Image Classification on N-Caltech101 (test)
81.73AccuracyMD-SNN
Evaluation results
| Method | Links | |
|---|---|---|
| MD-SNNModel=ResNet18, Timestep=10, Quantization=8-bit2025.12 | 81.73 | |
| MD-SNNModel=ResNet18, Timestep=10, Quantization=4-bit2025.12 | 81.1 | |
| FP32Model=ResNet18, Timestep=10, Quantization=32-bit2025.12 | 80.31 | |
| EventMixNetwork=ResNet18, Average timestep=10, Data augmentation=true2024.01 | 79.47 | |
| SSNNNetwork=VGG-9, Average timestep=82024.01 | 79.25 | |
| tdBN+NDANetwork=VGG-11, Average timestep=10, Data augmentation=true2024.01 | 78.2 | |
| SSNNNetwork=VGG-9, Average timestep=52024.01 | 77.97 | |
| SpikformerNetwork=Spikformer, Average timestep=5, Self-implementation=true2024.01 | 72.83 | |
| MLFNetwork=VGG-9, Average timestep=5, Self-implementation=true2024.01 | 70.42 | |
| STBP-tdBNNetwork=VGG-9, Average timestep=5, Self-implementation=true2024.01 | 66.01 | |
| BackEISNNNetwork=VGG-9, Average timestep=5, Self-implementation=true2024.01 | 65.53 | |
| PLIFNetwork=VGG-9, Average timestep=5, Self-implementation=true2024.01 | 64.73 | |
| TEBNNetwork=VGG-9, Average timestep=5, Self-implementation=true2024.01 | 63.13 |