  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.
