Spaces:
Runtime error
Runtime error
File size: 1,563 Bytes
9b63b92 6695af3 9b63b92 a8a11ec 9b63b92 a8a11ec 9b63b92 a8a11ec 9b63b92 4e70241 a8a11ec 9b63b92 a8a11ec 9b63b92 a8a11ec 9b63b92 a8a11ec 9b63b92 a8a11ec 9b63b92 a8a11ec 9b63b92 a8a11ec |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
---
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}
}
```
|