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README.md

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+# Faster Segement Anything (MobileSAM)
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+- **Repository:** [Github - MobileSAM](https://github.com/ChaoningZhang/MobileSAM)
+- **Paper:** [Faster Segment Anything: Towards Lightweight SAM for Mobile Applications](https://arxiv.org/pdf/2306.14289.pdf)
+- **Demo:** [HuggingFace Demo](https://huggingface.co/dhkim2810/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. 
+
+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
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+<details>
+<summary>
+<a href="https://github.com/facebookresearch/segment-anything">SAM</a> (Segment Anything) [<b>bib</b>]
+</summary>
+
+```bibtex
+@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}
+}
+```
+</details>
+
+
+
+<details>
+<summary>
+<a href="https://github.com/microsoft/Cream/tree/main/TinyViT">TinyViT</a> (TinyViT: Fast Pretraining Distillation for Small Vision Transformers) [<b>bib</b>]
+</summary>
+
+```bibtex
+@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}
+```
+</details>
+
+
+**BibTeX:**
+```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}
+}
+```