  This paper introduces Lite-SAM, an efficient end-to-end solution for the
SegEvery task designed to reduce computational costs and redundancy. Lite-SAM
is composed of four main components: a streamlined CNN-Transformer hybrid
encoder (LiteViT), an automated prompt proposal network (AutoPPN), a
traditional prompt encoder, and a mask decoder. All these components are
integrated within the SAM framework. Our LiteViT, a high-performance
lightweight backbone network, has only 1.16M parameters, which is a 23%
reduction compared to the lightest existing backbone network Shufflenet. We
also introduce AutoPPN, an innovative end-to-end method for prompt boxes and
points generation. This is an improvement over traditional grid search sampling
methods, and its unique design allows for easy integration into any SAM series
algorithm, extending its usability. we have thoroughly benchmarked Lite-SAM
across a plethora of both public and private datasets. The evaluation
encompassed a broad spectrum of universal metrics, including the number of
parameters, SegEvery execution time, and accuracy. The findings reveal that
Lite-SAM, operating with a lean 4.2M parameters, significantly outpaces its
counterparts, demonstrating performance improvements of 43x, 31x, 20x, 21x, and
1.6x over SAM, MobileSAM, Edge-SAM, EfficientViT-SAM, and MobileSAM-v2
respectively, all the while maintaining competitive accuracy. This underscores
Lite-SAM's prowess in achieving an optimal equilibrium between performance and
precision, thereby setting a new state-of-the-art(SOTA) benchmark in the
domain.
