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:
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.
2. Run the Gradio Demo
To interactively test the models using Gradio:
python app.py
Notes:
- GPU Requirement: If using a GPU, ensure it has at least 16GB of VRAM to run the model efficiently.
- CPU-Only Mode: If you wish to run the demo on CPU, set the environment variable
CUDA_VISIBLE_DEVICESto-1:
CUDA_VISIBLE_DEVICES=-1 python app.py
This will launch a web interface where you can upload remote sensing images and view the segmentation results.
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.
Citation
If you find this repository helpful, please consider citing the paper:
@{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}
}