Awesome
<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.
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.
<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.
│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