wanglab / medsam-vit-base

Model Card for Segment Anything Model (SAM) - ViT Base (ViT-B) version, fine-tuned for medical image segmentation

Detailed architecture of Segment Anything Model (SAM).

Table of Contents

1. TL;DR
2. Model Details
3. Usage
4. Citation

TL;DR

Link to original SAM repository Link to original MedSAM repository

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 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 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 .

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 showcasing inference with MedSAM
• this one showcasing general usage of SAM,

as well as the docs .

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}
}