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ArminMoghimi / Fine-tune-the-Segment-Anything-Model-SAM-

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,"
https://doi.org/10.1109/ACCESS.2024.3385425
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computer-vision deep-learning image-processing river riversnap sam segmentanythingmodel segmentation segmentation-automation segmentation-based-detection water

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Fine-Tuning SAM (Segment anything) for River Water Segmentation

Overview

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:

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," IEEE Access, 2024. IEEE Access

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

The LuFI-RiverSNAP.v1 (river water segmentation) Dataset in Google Drive: Open In Google Drive

Test Image 1

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Some examples of river water segmentation results on the LuFI-RiverSnap.v1. (a) Images and segmentation results generated by (b) U-Net(ResNet50), (c) PSPNet(ResNet50), (d) DeeplabV3+(ResNet50), (e) PAN(ResNet50), (f) LinkNet(ResNet50), and (g) SAM were used as DL models for river water segmentation. Green: False Positives (FP) detection, Pink: False Negatives (FN) detection, Blue: correct detection of river water.

Try it in Colab:

Open In Colab

Please also follow and read the reference codes we created for our fine-tuning SAM based on.

  • Original SAM Code: GitHub - Segment Anything
  • Fine-Tuning Tutorial: Encord Blog - Fine-Tune SAM
    • ![Test Image 2](https://github.com/ArminMoghimi/Fine-tune-the-Segment-Anything-Model-SAM-/blob/main/Fig18.jpg) {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)}

    Dataset

    The LuFI-RiverSNAP.v1 dataset for river water segmentation is available on multiple platforms:

    ## Try it in Colab:

    [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/169TpQs74YkzF1Dffb_SHddCdOJX6fDdE?usp=drive_link) ## 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} } ```

    Contact

    For any queries or contributions, feel free to contact us.