---
license: mit
datasets:
- ILSVRC/imagenet-1k
- uoft-cs/cifar10
- uoft-cs/cifar100
language:
- en
metrics:
- accuracy
base_model:
- MS-ResNet
---
I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks
[](https://arxiv.org/abs/2511.08065)
[](https://aaai.org/)
[](https://scholar.google.com/scholar?cluster=1814482600796011970)
[](https://github.com/Ruichen0424/I2E)
[](https://huggingface.co/papers/2511.08065)
[](https://huggingface.co/datasets/UESTC-BICS/I2E)
## 🚀 Introduction
This repository contains the **pre-trained weights** for the paper **"I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks"**, which has been accepted for **Oral Presentation at AAAI 2026**.
**I2E** is a pioneering framework that bridges the data scarcity gap in neuromorphic computing. By simulating microsaccadic eye movements via highly parallelized convolution, I2E converts static images into high-fidelity event streams in real-time (>300x faster than prior methods).
### ✨ Key Highlights
* **SOTA Performance**: Achieves **60.50%** top-1 accuracy on Event-based ImageNet.
* **Sim-to-Real Transfer**: Pre-training on I2E data enables **92.5%** accuracy on real-world CIFAR10-DVS, setting a new benchmark.
* **Real-Time Conversion**: Enables on-the-fly data augmentation for deep SNN training.
## 🏆 Model Zoo & Results
We provide pre-trained models for **I2E-CIFAR** and **I2E-ImageNet**. You can download the `.pth` files directly from the [**Files and versions**](https://huggingface.co/Ruichen0424/I2E/tree/main) tab in this repository.
| Target Dataset |
Architecture |
Method |
Top-1 Acc |
CIFAR10-DVS
(Real) |
MS-ResNet18 |
Baseline |
65.6% |
| MS-ResNet18 |
Transfer-I |
83.1% |
| MS-ResNet18 |
Transfer-II (Sim-to-Real) |
92.5% |
| I2E-CIFAR10 |
MS-ResNet18 |
Baseline-I |
85.07% |
| MS-ResNet18 |
Baseline-II |
89.23% |
| MS-ResNet18 |
Transfer-I |
90.86% |
| I2E-CIFAR100 |
MS-ResNet18 |
Baseline-I |
51.32% |
| MS-ResNet18 |
Baseline-II |
60.68% |
| MS-ResNet18 |
Transfer-I |
64.53% |
| I2E-ImageNet |
MS-ResNet18 |
Baseline-I |
48.30% |
| MS-ResNet18 |
Baseline-II |
57.97% |
| MS-ResNet18 |
Transfer-I |
59.28% |
| MS-ResNet34 |
Baseline-II |
60.50% |
> **Method Legend:**
> * **Baseline-I**: Training from scratch with minimal augmentation.
> * **Baseline-II**: Training from scratch with full augmentation.
> * **Transfer-I**: Fine-tuning from Static ImageNet (or I2E-ImageNet for CIFAR targets).
> * **Transfer-II**: Fine-tuning from I2E-CIFAR10.
## 👁️ Visualization
Below is the visualization of the I2E conversion process. We illustrate the high-fidelity conversion from static RGB images to dynamic event streams.
More than 200 additional visualization comparisons can be found in [Visualization.md](./Visualization.md).
## 💻 Usage
This repository hosts the **model weights only**.
For the **I2E dataset generation code**, **training scripts**, and detailed usage instructions, please refer to our official GitHub repository.
To generate the datasets (I2E-CIFAR10, I2E-CIFAR100, I2E-ImageNet) yourself using the I2E algorithm, please follow the instructions in the GitHub README.
[](https://github.com/Ruichen0424/I2E)
The download address for the datasets generated by the I2E algorithm is as follows.
[](https://huggingface.co/datasets/UESTC-BICS/I2E)
## 📜 Citation
If you find this work or the models useful, please cite our AAAI 2026 paper:
```bibtex
@article{ma2025i2e,
title={I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks},
author={Ma, Ruichen and Meng, Liwei and Qiao, Guanchao and Ning, Ning and Liu, Yang and Hu, Shaogang},
journal={arXiv preprint arXiv:2511.08065},
year={2025}
}
``` 
