Update README.md

README.md

@@ -21,6 +21,7 @@ base_model:
 [![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-Datasets-FFD21E?style=flat-square&logo=huggingface&logoColor=black)](https://huggingface.co/datasets/UESTC-BICS/I2E)
 </div>
 
 ## 🚀 Introduction
@@ -130,6 +131,8 @@ We provide pre-trained models for **I2E-CIFAR** and **I2E-ImageNet**. You can do
 
 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>
@@ -155,6 +158,10 @@ To generate the datasets (I2E-CIFAR10, I2E-CIFAR100, I2E-ImageNet) yourself usin
 
 [![GitHub](https://img.shields.io/badge/GitHub-Repository-black?logo=github)](https://github.com/Ruichen0424/I2E)
 
+The download address for the datasets generated by the I2E algorithm is as follows.
+
+[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Datasets-FFD21E?style=flat-square&logo=huggingface&logoColor=black)](https://huggingface.co/datasets/UESTC-BICS/I2E)
+
 ## 📜 Citation
 
 If you find this work or the models useful, please cite our AAAI 2026 paper: