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license: apache-2.0 |
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# Cloud Adapter Models |
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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. |
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## Features |
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- Pre-trained model weights for cloud segmentation tasks. |
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- Code for fine-tuning and evaluation of the models on remote sensing datasets. |
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- A user-friendly **Gradio Demo** to test the models interactively. |
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## Installation |
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To use the code in this repository, clone it locally and install the required dependencies: |
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```bash |
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git clone https://huggingface.co/XavierJiezou/cloud-adapter-models |
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cd cloud-adapter-models |
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pip install -r requirements.txt |
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``` |
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## Usage |
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### 1. Download Pre-trained Models |
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The pre-trained model weights are available in the repository. Download the weights and place them in the appropriate directory. |
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### 2. Run the Gradio Demo |
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To interactively test the models using Gradio: |
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```bash |
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python app.py |
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``` |
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#### Notes: |
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- **GPU Requirement**: If using a GPU, ensure it has at least **16GB of VRAM** to run the model efficiently. |
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- **CPU-Only Mode**: If you wish to run the demo on CPU, set the environment variable `CUDA_VISIBLE_DEVICES` to `-1`: |
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```bash |
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CUDA_VISIBLE_DEVICES=-1 python app.py |
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``` |
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This will launch a web interface where you can upload remote sensing images and view the segmentation results. |
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## Gradio Demo |
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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. |
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## Citation |
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If you find this repository helpful, please consider citing the paper: |
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```latex |
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@{cloud-adapter, |
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title={Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images}, |
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author={Xuechao Zou and Shun Zhang and Kai Li and Shiying Wang and Junliang Xing and Lei Jin and Congyan Lang and Pin Tao}, |
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year={2024}, |
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eprint={2411.13127}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2411.13127} |
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} |
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``` |
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