Lite-SAM Is Actually What You Need for Segment Everything

TL;DR

Lite-SAM is an efficient, lightweight segmentation model that significantly reduces computational costs while maintaining high accuracy, setting a new state-of-the-art in the SegEvery task.

Contribution

The paper introduces Lite-SAM, a novel end-to-end segmentation framework with a lightweight backbone and automated prompt generation, improving efficiency and usability over existing methods.

Findings

Lite-SAM has only 1.16M parameters, 23% fewer than Shufflenet.

Lite-SAM outperforms existing models by 20-43x in speed metrics.

Lite-SAM maintains competitive accuracy despite reduced complexity.

Abstract

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.