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Recently, Meta AI Research approaches a general, promptable Segment AnythingModel (SAM) pre-trained on an unprecedentedly large segmentation dataset(SA-1B). Without a doubt, the emergence of SAM will yield significant benefitsfor a wide array of practical image segmentation applications. In this study,we conduct a series of intriguing investigations into the performance of SAMacross various applications, particularly in the fields of natural images,agriculture, manufacturing, remote sensing, and healthcare. We analyze anddiscuss the benefits and limitations of SAM, while also presenting an outlookon its future development in segmentation tasks. By doing so, we aim to give acomprehensive understanding of SAM's practical applications. This work isexpected to provide insights that facilitate future research activities towardgeneric segmentation. Source code is publicly available.

Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications