  Since the advent of the Segment Anything Model(SAM) approximately one year
ago, it has engendered significant academic interest and has spawned a large
number of investigations and publications from various perspectives. However,
the deployment of SAM in practical assembly line scenarios has yet to
materialize due to its large image encoder, which weighs in at an imposing
632M. In this study, we have replaced the heavyweight image encoder with a
lightweight one, thereby enabling the deployment of SAM in practical assembly
line scenarios. Specifically, we have employed decoupled distillation to train
the encoder of MobileSAM in a resource-limited setting. The entire knowledge
distillation experiment can be completed in a single day on a single RTX 4090.
The resulting lightweight SAM, referred to as Group-Mix SAM, had 37.63% (2.16M)
fewer parameters and 42.5% (15614.7M) fewer floating-point operations compared
to MobileSAM. However, on our constructed industrial dataset, MALSD, its mIoU
was only marginally lower than that of MobileSAM, at 0.615. Finally, we
conducted a comprehensive comparative experiment to demonstrate the superiority
of Group-Mix SAM in the industrial domain. With its exceptional performance,
our Group-Mix SAM is more suitable for practical assembly line applications.
