Spikformer: When Spiking Neural Network Meets Transformer, ICLR 2023

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