  As a promptable generic object segmentation model, segment anything model
(SAM) has recently attracted significant attention, and also demonstrates its
powerful performance. Nevertheless, it still meets its Waterloo when
encountering several tasks, e.g., medical image segmentation, camouflaged
object detection, etc. In this report, we try SAM on an unexplored popular
task: shadow detection. Specifically, four benchmarks were chosen and evaluated
with widely used metrics. The experimental results show that the performance
for shadow detection using SAM is not satisfactory, especially when comparing
with the elaborate models. Code is available at
https://github.com/LeipingJie/SAMSh.
