Awesome
</head> <body> <h1>Fine-Tuning SAM (Segment anything) for River Water Segmentation</h1> <h2>Overview</h2> <p> This repository presents the Python code for fine-tuning the Segment Anything Model (SAM) to perform river water segmentation from close-range remote sensing imagery. This work is based on our paper published in IEEE Access: </p> <p> <strong>A. Moghimi, M. Welzel, T. Celik, and T. Schlurmann</strong>, "A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery," IEEE Access, 2024. <a href="https://ieeexplore.ieee.org/document/10493013">IEEE Access</a> </p>The easy-to-use and adaptable code for river water and other segmentation tasks and use for other remote sensing datasets:
Try it in Colab: ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)
The LuFI-RiverSNAP.v1 (river water segmentation) Dataset in Google Drive: ![Open In Google Drive](https://upload.wikimedia.org/wikipedia/commons/archive/1/12/20201006132532%21Google_Drive_icon_%282020%29.svg)
Try it in Colab:</br></br>
Please also follow and read the reference codes we created for our fine-tuning SAM based on.
<li>Original SAM Code: <a href="https://github.com/facebookresearch/segment-anything">GitHub - Segment Anything</a></li> <li>Fine-Tuning Tutorial: <a href="https://encord.com/blog/learn-how-to-fine-tune-the-segment-anything-model-sam/">Encord Blog - Fine-Tune SAM</a></li> </ul>
{Some examples of river water segmentation results on the LuFI-RiverSnap.\textit{v}1. (a) Images and segmentation results generated by (b) MobileSAM (TinyViT), (c) SAM (ViT-B), (d) and SAM (ViT-L)}
Try it in Colab:</br></br>
Cite
Please cite the following papers if they help your research. You can use the following BibTeX entry:
@article{moghimi2024comparative,
title={A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery},
author={Moghimi, Armin and Welzel, Mario and Celik, Turgay and Schlurmann, Torsten},
journal={IEEE Access},
year={2024},
doi={https://doi.org/10.48550/arXiv.2304.02643},
publisher={IEEE}
}
A. Moghimi, M. Welzel, T. Celik, and T. Schlurmann, "A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery," in IEEE Access, doi: 10.1109/ACCESS.2024.3385425. https://ieeexplore.ieee.org/document/10493013
@inproceedings{kirillov2023segment,
title={Segment anything},
author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C and Lo, Wan-Yen and others},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={4015--4026},
doi={https://doi.org/10.48550/arXiv.2304.02643},
year={2023}
}
</code> </pre> <h2>Contact</h2> <p> For any queries or contributions, feel free to contact us. </p> </body>