Faster Segement Anything (MobileSAM)
- 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:
| Image Encoder | Original SAM | MobileSAM |
|---|---|---|
| Paramters | 611M | 5M |
| Speed | 452ms | 8ms |
Original SAM and MobileSAM have exactly the same prompt-guided mask decoder:
| Mask Decoder | Original SAM | MobileSAM |
|---|---|---|
| Paramters | 3.876M | 3.876M |
| Speed | 4ms | 4ms |
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
@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}
}
@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}
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}
}