dhkim2810/MobileSAM

Faster Segement Anything (MobileSAM)

<!-- Provide a quick summary of what the model is/does. -->

• —Repository: Github - MobileSAM
• —Paper: Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
• —Demo: HuggingFace Demo

MobileSAM performs on par with the original SAM (at least visually) and keeps exactly the same pipeline as the original SAM except for a change on the image encoder. Specifically, we replace the original heavyweight ViT-H encoder (632M) with a much smaller Tiny-ViT (5M). On a single GPU, MobileSAM runs around 12ms per image: 8ms on the image encoder and 4ms on the mask decoder.

The comparison of ViT-based image encoder is summarzed as follows:

Original SAM and MobileSAM have exactly the same prompt-guided mask decoder:

The comparison of the whole pipeline is summarzed as follows: Whole Pipeline (Enc+Dec) | Original SAM | MobileSAM :-----------------------------------------:|:---------:|:-----: Paramters | 615M | 9.66M Speed | 456ms | 12ms

Acknowledgement

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

<details> <summary> <a href="https://github.com/facebookresearch/segment-anything">SAM</a> (Segment Anything) [<b>bib</b>] </summary>

@article{kirillov2023segany,
  title={Segment Anything}, 
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
  journal={arXiv:2304.02643},
  year={2023}
}

</details>

<details> <summary> <a href="https://github.com/microsoft/Cream/tree/main/TinyViT">TinyViT</a> (TinyViT: Fast Pretraining Distillation for Small Vision Transformers) [<b>bib</b>] </summary>

@InProceedings{tiny_vit,
  title={TinyViT: Fast Pretraining Distillation for Small Vision Transformers},
  author={Wu, Kan and Zhang, Jinnian and Peng, Houwen and Liu, Mengchen and Xiao, Bin and Fu, Jianlong and Yuan, Lu},
  booktitle={European conference on computer vision (ECCV)},
  year={2022}

</details>

BibTeX:

@article{mobile_sam,
  title={Faster Segment Anything: Towards Lightweight SAM for Mobile Applications},
  author={Zhang, Chaoning and Han, Dongshen and Qiao, Yu and Kim, Jung Uk and Bae, Sung Ho and Lee, Seungkyu and Hong, Choong Seon},
  journal={arXiv preprint arXiv:2306.14289},
  year={2023}
}