| | --- |
| | title: MobileSAM |
| | emoji: 🐠 |
| | 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. |
| |
|
| | - Github [link](https://github.com/ChaoningZhang/MobileSAM) |
| | - Model Card [link](https://huggingface.co/dhkim2810/MobileSAM) |
| |
|
| |
|
| | ## 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} |
| | } |
| | ``` |
| |
|
