README.md

---
language:
- en
license: apache-2.0
pipeline_tag: image-classification
tags:
- spiking-neural-networks
- spiking-transformer
- brain-inspired-computing
---

# STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking

<p align="center">
  <img src="imgs/STEP.jpg" alt="mp" style="width: 30%; max-width: 600px; min-width: 200px;" />
</p>

<p align="center">
  <img src="https://img.shields.io/badge/python-3.8%20%7C%203.9%20|%203.10-blue" alt="python"/>
  <img src="https://img.shields.io/badge/framework-BrainCog-blue" alt="Braincog"/>
  <img src="https://img.shields.io/badge/version-1.0.0-green" alt="Version"/>
  <img src="https://img.shields.io/badge/-continuous_integration-red" alt="Contiguous"/>
</p>

This repository contains the official model checkpoints for the paper [STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking](https://huggingface.co/papers/2505.11151).

STEP is a unified benchmark framework for Spiking Transformers that supports a wide range of tasks, including classification, segmentation, and detection across static, event-based, and sequential datasets.

- **Paper:** [Hugging Face Papers](https://huggingface.co/papers/2505.11151)
- **Code:** [GitHub - Fancyssc/STEP](https://github.com/Fancyssc/STEP)

## ⚡ Introduction
This repository is the official **checkpoint repository** for STEP. It is intended solely for storing the model checkpoints, training logs, and corresponding configuration files (.yaml) used in STEP, to facilitate usage, reproduction, and comparison of Spiking Transformer models by researchers and interested users.

For the complete STEP framework, including source code and tutorials, please refer to the [official GitHub repository](https://github.com/Fancyssc/STEP).

## 🚀 Quick Start

To use these checkpoints, clone the STEP repository and install the required dependencies (including [BrainCog](https://github.com/BrainCog-X/Brain-Cog)):

```bash
git clone https://github.com/Fancyssc/STEP.git
cd STEP
pip install git+https://github.com/braincog-X/Brain-Cog.git
```

### Sample Usage
You can start Spikformer Training on CIFAR10 as a "Hello-world" demo using the provided configurations:

```bash
python train.py --config configs/spikformer/cifar10.yml
```

## Contact & Collaboration

- **Questions or Feedback**  
  If you run into any issues, have questions about STEP, or simply want to share suggestions, please open a GitHub Issue or start a discussion thread on the main repository.

- **Integrate Your Model**  
  Have an exciting Spiking Transformer variant or related module you’d like to see supported? We welcome external contributions! Open an Issue describing your model, its licensing, and any specific requirements.

## 📝 Citation
```bibtex
@misc{shen2025stepunifiedspikingtransformer,
      title={STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking}, 
      author={Sicheng Shen and Dongcheng Zhao and Linghao Feng and Zeyang Yue and Jindong Li and Tenglong Li and Guobin Shen and Yi Zeng},
      year={2025},
      eprint={2505.11151},
      archivePrefix={arXiv},
      primaryClass={cs.NE},
      url={https://arxiv.org/abs/2505.11151}, 
}
```