QKFormer: Hierarchical Spiking Transformer using Q-K Attention This link
QKFormer achieves a groundbreaking top-1 accuracy of 85.65% on ImageNet-1k, which is the first time that directly training SNNs have exceeded 85% accuracy on ImageNet-1K in 2024/03.
| Model | Resolution | T | Type | Param. | Top-1 Acc (%) | Download |
|---|---|---|---|---|---|---|
| ViT | 384x384 | - | ANN | 85.9M | 77.9 | - |
| Deit-B | 384x384 | - | ANN | 86.0M | 83.1 | - |
| Swin transformer | 384x384 | - | ANN | 88.0M | 84.5 | - |
| Spikformer-8-768 | 224x224 | 4 | SNN | 66.34M | 74.81 | - |
| Spikingformer-8-768 | 224x224 | 4 | SNN | 66.34M | 75.85 | - |
| QKFormer-10-384 | 224x224 | 4 | SNN | 16.47M | 78.80 | - |
| QKFormer-10-512 | 224x224 | 4 | SNN | 29.08M | 82.04 | - |
| QKFormer-10-768 | 224x224 | 4 | SNN | 64.96M | 84.22 | - |
| QKFormer-10-768 | 288x288 | 4 | SNN | 64.96M | 85.25 | - |
| QKFormer-10-768 | 384x384 | 4 | SNN | 64.96M | 85.65 | - |