Datasets:

UESTC-BICS
/

I2E

+---
+license: mit
+---
+<div align="center">
+<h1>I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks</h1>
+[![Paper](https://img.shields.io/badge/Arxiv-2511.08065-B31B1B.svg)](https://arxiv.org/abs/2511.08065)
+[![AAAI 2026](https://img.shields.io/badge/AAAI%202026-Oral-4b44ce.svg)](https://aaai.org/)
+[![Google Scholar](https://img.shields.io/badge/Google%20Scholar-Paper-4285F4?style=flat-square&logo=google-scholar&logoColor=white)](https://scholar.google.com/scholar?cluster=1814482600796011970)
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-black?logo=github)](https://github.com/Ruichen0424/I2E)
+[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Paper-FFD21E?style=flat-square&logo=huggingface&logoColor=black)](https://huggingface.co/papers/2511.08065)
+[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Models-FFD21E?style=flat-square&logo=huggingface&logoColor=black)](https://huggingface.co/Ruichen0424/I2E)
+</div>
+## 🚀 Introduction
+This repository contains the **I2E-Datasets** 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 model repository.
+[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Models-FFD21E?style=flat-square&logo=huggingface&logoColor=black)](https://huggingface.co/Ruichen0424/I2E)
+<table border="1">
+    <tr>
+        <th>Target Dataset</th>
+        <th align="center">Architecture</th>
+        <th align="center">Method</th>
+        <th align="center">Top-1 Acc</th>
+    </tr>
+    <!-- CIFAR10-DVS -->
+    <tr>
+        <td rowspan="3" align="center" style="vertical-align: middle;"><strong>CIFAR10-DVS</strong><br>(Real)</td>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Baseline</td>
+        <td align="center" style="vertical-align: middle;">65.6%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Transfer-I</td>
+        <td align="center" style="vertical-align: middle;">83.1%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Transfer-II (Sim-to-Real)</td>
+        <td align="center" style="vertical-align: middle;"><strong>92.5%</strong></td>
+    </tr>
+    <!-- I2E-CIFAR10 -->
+    <tr>
+        <td rowspan="3" align="center" style="vertical-align: middle;"><strong>I2E-CIFAR10</strong></td>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Baseline-I</td>
+        <td align="center" style="vertical-align: middle;">85.07%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Baseline-II</td>
+        <td align="center" style="vertical-align: middle;">89.23%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Transfer-I</td>
+        <td align="center" style="vertical-align: middle;"><strong>90.86%</strong></td>
+    </tr>
+    <!-- I2E-CIFAR100 -->
+    <tr>
+        <td rowspan="3" align="center" style="vertical-align: middle;"><strong>I2E-CIFAR100</strong></td>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Baseline-I</td>
+        <td align="center" style="vertical-align: middle;">51.32%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Baseline-II</td>
+        <td align="center" style="vertical-align: middle;">60.68%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Transfer-I</td>
+        <td align="center" style="vertical-align: middle;"><strong>64.53%</strong></td>
+    </tr>
+    <!-- I2E-ImageNet -->
+    <tr>
+        <td rowspan="4" align="center" style="vertical-align: middle;"><strong>I2E-ImageNet</strong></td>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Baseline-I</td>
+        <td align="center" style="vertical-align: middle;">48.30%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Baseline-II</td>
+        <td align="center" style="vertical-align: middle;">57.97%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet18</td>
+        <td align="center" style="vertical-align: middle;">Transfer-I</td>
+        <td align="center" style="vertical-align: middle;">59.28%</td>
+    </tr>
+    <tr>
+        <td align="center" style="vertical-align: middle;">MS-ResNet34</td>
+        <td align="center" style="vertical-align: middle;">Baseline-II</td>
+        <td align="center" style="vertical-align: middle;"><strong>60.50%</strong></td>
+    </tr>
+</table>
+> **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).
+<table border="0" style="width: 100%">
+  <tr>
+    <td width="25%" align="center"><img src="./assets/original_1.jpg" alt="Original 1" style="width:100%"></td>
+    <td width="25%" align="center"><img src="./assets/converted_1.gif" alt="Converted 1" style="width:100%"></td>
+    <td width="25%" align="center"><img src="./assets/original_2.jpg" alt="Original 2" style="width:100%"></td>
+    <td width="25%" align="center"><img src="./assets/converted_2.gif" alt="Converted 2" style="width:100%"></td>
+  </tr>
+  <tr>
+    <td width="25%" align="center"><img src="./assets/original_3.jpg" alt="Original 3" style="width:100%"></td>
+    <td width="25%" align="center"><img src="./assets/converted_3.gif" alt="Converted 3" style="width:100%"></td>
+    <td width="25%" align="center"><img src="./assets/original_4.jpg" alt="Original 4" style="width:100%"></td>
+    <td width="25%" align="center"><img src="./assets/converted_4.gif" alt="Converted 4" style="width:100%"></td>
+  </tr>
+</table>
+## 💻 Usage
+The generated I2E-Datasets are provided in the [**Files and versions**](https://huggingface.co/datasets/UESTC-BICS/I2E/tree/main) section, including I2E-CIFAR10, I2E-CIFAR100, and I2E-ImageNet.
+For I2E-ImageNet, the following command can be used to concatenate the zip file.
+``` bash
+cat ./I2E-ImageNet_split.part_* > ./I2E-ImageNet.zip
+```
+We also provide MD5 checksums to facilitate verification. Use the following command to verify the compressed files:
+``` bash
+md5sum -c md5.txt
+```
+This repository hosts the **datasets 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.
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-black?logo=github)](https://github.com/Ruichen0424/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}
+}
+```