Add metadata and improve model card

README.md

@@ -1,15 +1,75 @@
 ---
 license: apache-2.0
+pipeline_tag: image-classification
+tags:
+- remote-sensing
+- mamba
+- mixture-of-experts
+- cnn
 ---
-# Models
-This repository contains the pre-trained model weights for remote sensing image scene classification, trained on a combination of the UC Merced, AID, and NWPU-RESISC45 datasets.
-# Installation
+
+# AFM-Net: Hierarchical Fusion of Local and Global Visual Features with Mixture-of-Experts for Remote Sensing Image Scene Classification
+
+This repository contains the pre-trained model weights for **AFM-Net**, presented in the paper [Hierarchical Fusion of Local and Global Visual Features with Mixture-of-Experts for Remote Sensing Image Scene Classification](https://huggingface.co/papers/2510.27155).
+
+AFM-Net is a parallel heterogeneous framework that synergizes CNN-based local texture extraction with Mamba-based global sequence modeling. It utilizes a hierarchical fusion module to integrate features across different semantic levels and an adaptive Mixture-of-Experts (MoE) classifier head for fine-grained scene recognition.
+
+## 🔍 Introduction 
+
+Remote sensing scene classification of high-resolution images remains a challenging task due to complex spatial structures and high intra-class variance. We propose a parallel heterogeneous framework that:
+- **Synergizes Local and Global Visual Encoder**: Coupling CNN-based local texture extraction with Mamba-based global sequence modeling to ensure robust co-representation.
+- **Hierarchical Fusion Strategy**: Employs a hierarchical fusion module to densely integrate heterogeneous features across different semantic levels.
+- **Adaptive MoE Classifier**: Uses a Mixture-of-Experts (MoE) head to dynamically select optimal features, balancing performance with efficiency.
+
+Extensive experiments show our model achieves state-of-the-art performance on AID, NWPU-RESISC45, and UC Merced datasets.
+
+## ⚙️ Installation 
+
+### 1. Clone this repository:
 ```bash
 git clone https://huggingface.co/Jamtang/classification
 cd classification
 ```
-# Usage
+
+### 2. Create a Python virtual environment and install dependencies:
+```bash
+conda create -n class python=3.8 -y
+conda activate class
+pip install -r requirements.txt
+```
+
+## 🚀 Usage 
+
+### Inference / Application
+To run the provided application:
 ```bash
 python app.py
 ```
 
+### Training 
+To train the model on a specific dataset (e.g., AID):
+ ```bash
+python train.py --dataset AID --batch_size 32 --epochs 500
+ ```
+
+## 🔥 Performance 
+| Dataset | F1 Score |
+| :--- | :---: |
+| UC Merced | 96.81 |
+| AID | 93.71 |
+| NWPU-RESISC45 | 95.52 |
+
+## Links
+- **Paper**: [https://huggingface.co/papers/2510.27155](https://huggingface.co/papers/2510.27155)
+- **Code**: [https://github.com/tangyuanhao-qhu/AFM-Net](https://github.com/tangyuanhao-qhu/AFM-Net)
+
+## Citation
+If you find this work helpful or inspiring, please consider citing it:
+```bibtex
+@article{tang2025hierarchical,
+  title={Hierarchical Fusion of Local and Global Visual Features with Mixture-of-Experts for Remote Sensing Image Scene Classification},
+  author={Tang, Yuanhao and Zou, Xuechao and Hu, Zhengpei and Xing, Junliang and Zhang, Chengkun and Huang, Jianqiang},
+  journal={arXiv preprint arXiv:2510.27155},
+  year={2025}
+}
+```