Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B). Without a doubt, 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, while also presenting an outlook on its future development in segmentation tasks. By doing so, we aim to give a comprehensive understanding of SAM's practical applications. This work is expected to provide insights that facilitate future research activities toward generic segmentation. Source code is publicly available.

翻译：近期，Meta AI研究团队提出了一种通用的、可提示的分割一切模型（SAM），该模型在规模空前的分割数据集（SA-1B）上进行了预训练。毫无疑问，SAM的出现将为大量实际的图像分割应用带来显著益处。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开展了一系列有趣探究。我们分析并讨论了SAM的优势与局限性，同时展望了其在分割任务中的未来发展前景。通过上述工作，我们旨在全面理解SAM的实际应用价值，期望能为面向通用分割的未来研究活动提供有益启示。源代码已公开发布。