Create README.md

README.md

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+---
+license: apache-2.0
+---
+
+# Model Card for Segment Anything Model (SAM) - ViT Base (ViT-B) version, fine-tuned for medical image segmentation
+
+<p>
+	<img src="https://s3.amazonaws.com/moonup/production/uploads/62441d1d9fdefb55a0b7d12c/F1LWM9MXjHJsiAtgBFpDP.png" alt="Model architecture">
+	<em> Detailed architecture of Segment Anything Model (SAM).</em>
+</p>
+
+
+#  Table of Contents
+
+0. [TL;DR](#TL;DR)
+1. [Model Details](#model-details)
+2. [Usage](#usage)
+3. [Citation](#citation)
+
+# TL;DR
+
+
+[Link to original SAM repository](https://github.com/facebookresearch/segment-anything)
+[Link to original MedSAM repository](https://github.com/bowang-lab/medsam)
+
+| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-beancans.png" alt="Snow" width="600" height="600"> | <img src="https://s3.amazonaws.com/moonup/production/uploads/62441d1d9fdefb55a0b7d12c/wHXbJx1oXqHCYNeUNKHs8.png" alt="Forest" width="600" height="600"> | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car-seg.png" alt="Mountains" width="600" height="600"> |
+|---------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------|
+
+
+The **Segment Anything Model (SAM)** produces high-quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a [dataset](https://segment-anything.com/dataset/index.html) of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks.
+The abstract of the paper states:
+
+>  We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at [https://segment-anything.com](https://segment-anything.com) to foster research into foundation models for computer vision.
+
+**Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the original [SAM model card](https://github.com/facebookresearch/segment-anything).
+
+# Model Details
+
+The SAM model is made up of 3 modules:
+  - The `VisionEncoder`: a VIT based image encoder. It computes the image embeddings using attention on patches of the image. Relative Positional Embedding is used.
+  - The `PromptEncoder`: generates embeddings for points and bounding boxes
+  - The `MaskDecoder`: a two-ways transformer which performs cross attention between the image embedding and the point embeddings (->) and between the point embeddings and the image embeddings. The outputs are fed
+  - The `Neck`: predicts the output masks based on the contextualized masks produced by the `MaskDecoder`.
+
+# Usage
+
+Refer to the demo notebooks:
+
+- [this one](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Run_inference_with_MedSAM_using_HuggingFace_Transformers.ipynb) showcasing inference with MedSAM
+- [this one](https://github.com/huggingface/notebooks/blob/main/examples/segment_anything.ipynb) showcasing general usage of SAM,
+
+as well as the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/sam).
+
+
+# Citation
+
+If you use this model, please use the following BibTeX entry.
+
+```
+@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}
+}
+```