Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B). Without a double, the emergence of SAM will yield significant benefits for a wide array of practical image segmentation applications. In this study, we conduct a series of intriguing investigations into the performance of SAM across various applications, particularly in the fields of natural images, agriculture, manufacturing, remote sensing, and healthcare. We analyze and discuss the benefits and limitations of SAM and provide an outlook on future development of segmentation tasks. Note that our work does not intend to propose new algorithms or theories, but rather provide a comprehensive view of SAM in practice. This work is expected to provide insights that facilitate future research activities toward generic segmentation.

翻译：近期，Meta AI研究团队提出了一个通用的、可提示的分割一切模型（Segment Anything Model，简称SAM），该模型基于前所未有的超大规模分割数据集（SA-1B）进行预训练。毋庸置疑，SAM的出现将为广泛的实际图像分割应用带来重大益处。本研究针对SAM在自然图像、农业、制造业、遥感及医疗等多个领域的性能展开了一系列饶有趣味的探讨。我们分析并讨论了SAM的优势与局限性，并对分割任务的未来发展进行了展望。需指出的是，本研究并非旨在提出新算法或新理论，而是旨在提供SAM在实际应用中的全面视角。期望本研究能为未来朝向通用分割的研究活动提供启发与洞见。