Merge pull request #27 from dhkim2810/master

Add Gradio Demo

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app/README.md

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+---
+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. 
+
+## To run on local PC
+First, mobile_sam must be installed to run on pc. [Instructions](https://github.com/dhkim2810/MobileSAM/tree/master#installation)
+Then run the following
+
+```
+python app.py
+```
+
+## 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}
+}
+```