TinySAM: Pushing the Envelope for Efficient Segment Anything Model

TL;DR

TinySAM is a highly efficient, lightweight segmentation model that maintains strong zero-shot performance by employing knowledge distillation, quantization, and hierarchical strategies, significantly reducing computational costs for edge devices.

Contribution

The paper introduces TinySAM, a novel framework combining knowledge distillation, quantization, and hierarchical segmentation to create a lightweight, efficient segmentation model with minimal performance loss.

Findings

Orders of magnitude reduction in computational cost.

Accelerates inference by 2x with minimal performance impact.

Outperforms existing methods on zero-shot transfer tasks.

Abstract

Recently segment anything model (SAM) has shown powerful segmentation capability and has drawn great attention in computer vision fields. Massive following works have developed various applications based on the pre-trained SAM and achieved impressive performance on downstream vision tasks. However, SAM consists of heavy architectures and requires massive computational capacity, which hinders the further application of SAM on computation constrained edge devices. To this end, in this paper we propose a framework to obtain a tiny segment anything model (TinySAM) while maintaining the strong zero-shot performance. We first propose a full-stage knowledge distillation method with hard prompt sampling and hard mask weighting strategy to distill a lightweight student model. We also adapt the post-training quantization to the prompt-based segmentation task and further reduce the computational cost. Moreover, a hierarchical segmenting everything strategy is proposed to accelerate the everything inference by $2\times$ with almost no performance degradation. With all these proposed methods, our TinySAM leads to orders of magnitude computational reduction and pushes the envelope for efficient segment anything task. Extensive experiments on various zero-shot transfer tasks demonstrate the significantly advantageous performance of our TinySAM against counterpart methods. Codes are available at https://github.com/xinghaochen/TinySAM and https://gitee.com/mindspore/models/tree/master/research/cv/TinySAM.