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title: MobileSAM | |
emoji: ๐ | |
colorFrom: indigo | |
colorTo: yellow | |
sdk: gradio | |
python_version: 3.8.10 | |
sdk_version: 3.35.2 | |
app_file: app.py | |
pinned: false | |
license: apache-2.0 | |
# Faster Segment Anything(MobileSAM) | |
Official PyTorch Implementation of the <a href="https://github.com/ChaoningZhang/MobileSAM">. | |
**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. | |
## License | |
The model is licensed under the [Apache 2.0 license](LICENSE). | |
## Acknowledgement | |
- [Segment Anything](https://segment-anything.com/) provides the SA-1B dataset and the base codes. | |
- [TinyViT](https://github.com/microsoft/Cream/tree/main/TinyViT) provides codes and pre-trained models. | |
## Citing MobileSAM | |
If you find this project useful for your research, please consider citing the following BibTeX entry. | |
```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} | |
} | |
``` | |