Spikformer: When Spiking Neural Network Meets Transformer, ICLR 2023.

Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket, Arxiv.

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.

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.

│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