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On Efficient Variants of Segment Anything Model: A Survey

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Abstract

The Segment Anything Model (SAM) is a foundational model for image segmentation tasks, known for its strong generalization across diverse applications. However, its impressive performance comes with significant computational and resource demands, making it challenging to deploy in resource-limited environments such as edge devices. To address this, a variety of SAM variants have been proposed to enhance efficiency while keeping accuracy. This survey provides the first comprehensive review of these efficient SAM variants. We begin by exploring the motivations driving this research. We then present core techniques used in SAM and model acceleration. This is followed by a detailed exploration of SAM acceleration strategies, categorized by approach, and a discussion of several future research directions. Finally, we offer a unified and extensive evaluation of these methods across various hardware, assessing their efficiency and accuracy on representative benchmarks, and providing a clear comparison of their overall performance. To complement this survey, we summarize the papers and codes related to efficient SAM variants at https://github.com/Image-and-Video-Computing-Group/On-Efficient-Variants-of-Segment-Anything-Model.

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Data Availability

The data supporting the experiments of this study are openly available, as stated in Section 4.1.

Notes

  1. https://eval.ai/web/challenges/challenge-page/1931/overview

  2. https://github.com/facebookresearch/segment-anything/blob/main/notebooks/images/groceries.jpg

  3. https://github.com/MrYxJ/calculate-flops.pytorch

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Acknowledgements

This work was supported by National Key Research and Development Program of China under Grant 2022YFA1004100 and National Natural Science Foundation of China under Grant 62476048.

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  1. School of Computer Science and Engineering, UESTC, Chengdu, China

    Xiaorui Sun, Hengtao Shen, Xiaofeng Zhu & Ping Hu

  2. School of Computing and Communications, Lancaster University, Lancaster, England

    Jun Liu

  3. School of Computer Science and Technology, Tongji University, Shanghai, China

    Hengtao Shen

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Sun, X., Liu, J., Shen, H. et al. On Efficient Variants of Segment Anything Model: A Survey. Int J Comput Vis 133, 7406–7436 (2025). https://doi.org/10.1007/s11263-025-02539-8

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