⚡Spike-Zoo is the go-to library for state-of-the-art pretrained spike-to-image models designed to reconstruct images from spike streams. Whether you're looking for a simple inference solution or aiming to train your own spike-to-image models, ⚡Spike-Zoo is a modular toolbox that supports both, with key features including:
- Fast inference with pre-trained models.
- Training support for custom-designed spike-to-image models.
- Specialized functions for processing spike data.
We are highly looking forward to your advice on our project. We welcome any issues or code contributions and will respond within one day.
- 25-02-02: Release the
Spike-Zoo v0.2code, which supports more methods, provide more usages like training your method from scratch. - 24-07-19: Release the
Spike-Zoo v0.1code for base evaluation of SOTA methods.
For users focused on utilizing pretrained models for spike-to-image conversion, we recommend installing SpikeZoo using one of the following methods:
- Install the last stable version from PyPI:
pip install spikezoo
- Install the latest developing version from the source code (recommended):
git clone https://github.com/chenkang455/Spike-Zoo
cd Spike-Zoo
python setup.py install
For users interested in training their own spike-to-image model based on our framework, we recommend cloning the repository and modifying the related code directly.
git clone https://github.com/chenkang455/Spike-Zoo
cd Spike-Zoo
python setup.py develop
Reconstructing images from the spike is super easy with Spike-Zoo. Try the following code of the single model:
from spikezoo.pipeline import Pipeline, PipelineConfig
import spikezoo as sz
pipeline = Pipeline(
cfg=PipelineConfig(save_folder="results",version="v023"),
model_cfg=sz.METHOD.BASE,
dataset_cfg=sz.DATASET.BASE
)
pipeline.infer_from_dataset(idx = 0)We provide a user-friendly code for training our provided BASE model (modified from the SpikeCLIP) for our provided BASE dataset:
from spikezoo.pipeline import TrainPipelineConfig, TrainPipeline
from spikezoo.datasets.base_dataset import BaseDatasetConfig
from spikezoo.models.base_model import BaseModelConfig
pipeline = TrainPipeline(
cfg=TrainPipelineConfig(save_folder="results", epochs = 10),
dataset_cfg=BaseDatasetConfig(),
model_cfg=BaseModelConfig(),
)
pipeline.train()🌟 We encourage users to develop their models with simple modifications to our framework. Follow the below documentation. 👇👇👇
| Link | Description |
|---|---|
| Quick Start | Learn how to quickly get started with the Spike-Zoo repository for inference and training. |
| Dataset | Learn the parameter configuration of datasets and how to construct them. |
| Model | Learn the parameter configuration of models and how to construct them. |
| Pipeline | Learn how to configure and construct the processing pipeline for models. |
| Released Version | Introduces the differences between different release versions of pre-trained weights. |
| Examples | Complete code examples for using Spike-Zoo. |
| Supports | Learn about the datasets and models supported by Spike-Zoo. |
- Support the overall pipeline for spike simulation.
- Provide the tutorials.
- Support more training settings.
- Support more spike-based image reconstruction methods and datasets.
Our code is built on the open-source projects of SpikeCV, IQA-Pytorch, BasicSR and NeRFStudio.We appreciate the effort of the contributors to these repositories. Thanks for @zhiwen_huang, @ruizhao26, @shiyan_chen and @Leozhangjiyuan for their help in building this project.
If you find our codes helpful to your research, please consider to use the following citation:
@misc{spikezoo,
title={{Spike-Zoo}: A Toolbox for Spike-to-Image Reconstruction},
author={Kang Chen and Zhiyuan Ye and Tiejun Huang and Zhaofei Yu},
year={2025},
howpublished = {\url{https://github.com/chenkang455/Spike-Zoo}},
}