  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 pretrained 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 promptable 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. Pre-trained models and codes are available
at https://github.com/xinghaochen/TinySAM and
https://gitee.com/mindspore/models/tree/master/research/cv/TinySAM.
