Improve model card metadata and documentation

README.md

@@ -1,9 +1,54 @@
 ---
 license: mit
+pipeline_tag: image-classification
+tags:
+- spiking-neural-networks
+- snn
+- normalization-free
 ---
 
-# DeepEISNN
+# Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition
 
-Pretrained checkpoints for paper Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition.
+This repository contains the pretrained checkpoints for the paper [Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition](https://huggingface.co/papers/2509.23253).
 
-Please refer to https://github.com/vwOvOwv/DeepEISNN for the usage of the checkpoints.
+[GitHub Repository](https://github.com/vwOvOwv/DeepEISNN) | [Paper (OpenReview)](https://openreview.net/forum?id=U8preGvn5G)
+
+## Introduction
+Spiking Neural Networks (SNNs) are energy-efficient and biologically plausible models. This work proposes a normalization-free learning framework that incorporates lateral inhibition inspired by cortical circuits. By replacing traditional feedforward SNN layers with distinct excitatory (E) and inhibitory (I) neuronal populations, the framework achieves stable training of deep SNNs without relying on explicit normalization schemes.
+
+## Model Performance
+
+| Dataset | Arch | T | Top-1 Acc(%) | Model Name|
+|:---:|:---:|:---:|:---:|:---:|
+| CIFAR-10 | ResNet-18 | 4 | 92.06 | CIFAR10-ResNet18 |
+| CIFAR-10 | VGG-8 | 4 | 87.03 | CIFAR10-VGG8 |
+| CIFAR-10 | VGG-11 | 4 | 88.43 | CIFAR10-VGG11 |
+| CIFAR-10 | VGG-16 | 4 | 91.01 | CIFAR10-VGG16 |
+| CIFAR-10 | VGG-19 | 4 | 91.36 | CIFAR10-VGG19 |
+| CIFAR-100 | VGG-16 | 4 | 65.90 | CIFAR100-VGG16 |
+| CIFAR-100 | VGG-19 | 4 | 64.06 | CIFAR100-VGG19 |
+| CIFAR10-DVS | VGG-8 | 10 | 78.40 | CIFAR10DVS-VGG8 |
+| CIFAR10-DVS | VGG-11 | 10 | 78.40 | CIFAR10DVS-VGG11 |
+| DVS-Gesture | VGG-8 | 16 | 95.83 | DVSGesture-VGG8 |
+| TinyImageNet200 | ResNet-18 | 4 | 50.29 | TinyImageNet200-ResNet18 |
+
+## Usage
+
+### Evaluation
+Use the following command from the [official repository](https://github.com/vwOvOwv/DeepEISNN) to quickly evaluate the pretrained checkpoints on the evaluation set. The script will automatically download the corresponding weights from Hugging Face.
+
+```bash
+python eval_pretrained.py --model [Dataset]-[Arch] --data_path [Dataset-Path]
+```
+
+## Citation
+```bibtex
+@inproceedings{
+  liu2026training,
+  title={Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition},
+  author={Peiyu Liu and Jianhao Ding and Zhaofei Yu},
+  booktitle={The Fourteenth International Conference on Learning Representations},
+  year={2026},
+  url={https://openreview.net/forum?id=U8preGvn5G}
+}
+```