Update README.md

README.md

@@ -1,3 +1,90 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+
+# Cloud Adapter Models
+
+This repository contains the code and pre-trained model weights for the paper **"Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images"**. The models are specifically designed to perform robust cloud segmentation in remote sensing imagery by leveraging and fine-tuning vision foundation models.
+
+## Features
+
+- Pre-trained model weights for cloud segmentation tasks.
+- Code for fine-tuning and evaluation of the models on remote sensing datasets.
+- A user-friendly **Gradio Demo** to test the models interactively.
+
+## Installation
+
+To use the code in this repository, clone it locally and install the required dependencies:
+
+```bash
+git clone https://huggingface.co/XavierJiezou/cloud-adapter-models
+cd cloud-adapter-models
+pip install -r requirements.txt
+```
+
+## Usage
+
+### 1. Download Pre-trained Models
+
+The pre-trained model weights are available in the repository. Download the weights and place them in the appropriate directory.
+
+```bash
+# Example command to download weights
+wget <link_to_model_weights>
+```
+
+### 2. Run the Gradio Demo
+
+To interactively test the models using Gradio:
+
+```bash
+python demo.py
+```
+
+This will launch a web interface where you can upload remote sensing images and view the segmentation results.
+
+### 3. Fine-tune the Model
+
+You can fine-tune the models on your own datasets. Refer to the `train.py` script for instructions and configuration options.
+
+```bash
+python train.py --config configs/config.yaml
+```
+
+### 4. Evaluate the Model
+
+Evaluate the model on your test set using the `evaluate.py` script:
+
+```bash
+python evaluate.py --weights <path_to_weights> --data <path_to_test_data>
+```
+
+## Gradio Demo
+
+The Gradio demo allows users to upload remote sensing images, run cloud segmentation, and visualize the results. It can be easily modified to suit custom datasets or tasks.
+
+### Example Screenshot:
+*Add a screenshot of the demo interface here if available.*
+
+## Citation
+
+If you find this repository helpful, please consider citing the paper:
+
+```latex
+@{cloud-adapter,
+title={Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images}, 
+author={Xuechao Zou and Shun Zhang and Kai Li and Shiying Wang and Junliang Xing and Lei Jin and Congyan Lang and Pin Tao},
+year={2024},
+eprint={2411.13127},
+archivePrefix={arXiv},
+primaryClass={cs.CV},
+url={https://arxiv.org/abs/2411.13127}
+}
+```
+
+## Acknowledgements
+
+This project builds upon vision foundation models and uses open-source libraries for training and evaluation. Special thanks to the research community for their contributions to remote sensing and computer vision.
+
+