TinySAM

TinySAM: Pushing the Envelope for Efficient Segment Anything Model

Han Shu, Wenshuo Li, Yehui Tang, Yiman Zhang, Yihao Chen, Houqiang Li, Yunhe Wang, Xinghao Chen

[Paper] [BibTeX]

Updates

2023/12/22: Pre-trained models and codes of TinySAM are released both in Pytorch and Mindspore.

Overview

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 online hard prompt sampling strategy to distill a lightweight student model. We also adapt the post-training quantization to the promptable segmentation task and further reducing the computational cost. Moreover, a hierarchical segmenting everything strategy is proposed to accelerate the everything inference by 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.

^{Figure 1: Overall framework and zero-shot results of TinySAM.}

^{Figure 2: Our hierarchical strategy for everything mode.}

^{Figure 3: Visualization results of TinySAM.}

Requirements

The code requires python>=3.7 and we use torch==1.10.2 and torchvision==0.11.3. To visualize the results, matplotlib>=3.5.1 is also required.

python 3.7
pytorch == 1.10.2
torchvision == 0.11.3
matplotlib==3.5.1

Usage

Download checkpoints into the directory of weights.
Run the demo code for single prompt of point or box.

python demo.py

Run the demo code for hierarchical segment everything strategy.

python demo_hierachical_everything.py

Evaluation

We follow the settting of original SAM paper and evaluate the zero-shot instance segmentaion on COCO and LVIS dataset. The experiment results are described as followed.

Model	FLOPs (G)	COCO AP (%)	LVIS AP (%)
SAM-H	3166	46.5	44.7
SAM-L	1681	45.5	43.5
SAM-B	677	41.0	40.8
FastSAM	344	37.9	34.5
MobileSAM	232	41.0	37.0
TinySAM [ckpt]	232	41.9	38.6
Q-TinySAM	61	41.3	37.7

Acknowledgements

We thank the following projects: SAM, MobileSAM, TinyViT.

Citation

@article{tinysam,
  title={TinySAM: Pushing the Envelope for Efficient Segment Anything Model},
  author={Shu, Han and Li, Wenshuo and Tang, Yehui and Zhang, Yiman and Chen, Yihao and Wang, Yunhe and Chen, Xinghao},
  journal={arXiv preprint arXiv:2312.13789},
  year={2023}
}

License

This project is licensed under Apache License 2.0. Redistribution and use should follow this license.

justld / TinySAM