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ICLR 2023, Spikformer: When Spiking Neural Network Meets Transformer
| Date | Stars |
|---|---|
| 2026-07-31 | 407 |
| 2026-08-01 | 407 |
| 2026-08-02 | 408 |
| 2026-08-04 | 409 |
| 2026-08-05 | 410 |
| 2026-08-06 | 410 |
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<p align="center">
<img src="https://github.com/ZK-Zhou/spikformer/blob/main/images/spikformer-logo.png" width="20%">
</p>
# Spikformer: When Spiking Neural Network Meets Transformer, [ICLR 2023](https://openreview.net/forum?id=frE4fUwz_h).
# Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket, [Arxiv](https://arxiv.org/abs/2401.02020).
The Spikformer V2 code will be released after it organized.
## Reference
If you find this repo useful, please consider citing:
```
@inproceedings{
zhou2023spikformer,
title={Spikformer: When Spiking Neural Network Meets Transformer },
author={Zhaokun Zhou and Yuesheng Zhu and Chao He and Yaowei Wang and Shuicheng YAN and Yonghong Tian and Li Yuan},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=frE4fUwz_h}
}
```
Our codes are based on the official imagenet example by PyTorch, pytorch-image-models by Ross Wightman and SpikingJelly by Wei Fang.
<p align="center">
<img src="https://github.com/ZK-Zhou/spikformer/blob/main/images/overview01.png">
</p>
### Requirements
timm==0.5.4
cupy==10.3.1
pytorch==1.10.0+cu111
spikingjelly==0.0.0.0.12
pyyaml
data prepare: ImageNet with the following folder structure, you can extract imagenet by this [script](https://gist.github.com/BIGBALLON/8a71d225eff18d88e469e6ea9b39cef4).
```
│imagenet/
├──train/
│ ├── n01440764
│ │ ├── n01440764_10026.JPEG
│ │ ├── n01440764_10027.JPEG
│ │ ├── ......
│ ├── ......
├──val/
│ ├── n01440764
│ │ ├── ILSVRC2012_val_00000293.JPEG
│ │ ├── ILSVRC2012_val_00002138.JPEG
│ │ ├── ......
│ ├── ......
```
### Training on ImageNet
Setting hyper-parameters in imagenet.yml
```
cd imagenet
python -m torch.distributed.launch --nproc_per_node=8 train.py
```
### Testing ImageNet Val data
```
cd imagenet
python test.py
```
### Training on cifar10
Setting hyper-parameters in cifar10.yml
```
cd cifar10
python train.py
```
### Training on cifar10DVS
```
cd cifar10dvs
python train.py
```
Excerpt of 2,042 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6fe5da2536db3762, llm:Repository description: 'ICLR 2023, Spikformer: When Spiking Neural Network Meets Transformer' — indicates a research implementation combining spiking neural networks and Transformers (neuromorphic SNN + transformer architecture).
matched fp:6fe5da2536db3762, llm:Repository description: 'ICLR 2023, Spikformer: When Spiking Neural Network Meets Transformer' — indicates a research implementation combining spiking neural networks and Transformers (neuromorphic SNN + transformer architecture).
matched fp:6fe5da2536db3762, llm:Repository description: 'ICLR 2023, Spikformer: When Spiking Neural Network Meets Transformer' — indicates a research implementation combining spiking neural networks and Transformers (neuromorphic SNN + transformer architecture).