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SAM.gov is the official system that consolidated the capabilities of CCR/FedReg, ORCA, and EPLS. It is for official use only and contains Controlled Unclassified Information (CUI).
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SAM is an AI model that can segment any object in any image with a single click. It uses prompts, a data engine, and a lightweight design to achieve zero-shot generalization and flexible integration with other systems.
- Overview
- Installation
- Getting Started
- ONNX Export
- Model Checkpoints
- Dataset
- License
- Contributing
- Contributors
- Citing Segment Anything
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Meta AI Research, FAIR
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick
[Paper] [Project] [Demo] [Dataset] [Blog] [BibTeX]
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 code requires python>=3.8, as well as pytorch>=1.7 and torchvision>=0.8. Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.
Install Segment Anything:
or clone the repository locally and install with
The following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. jupyter is also required to run the example notebooks.
First download a model checkpoint. Then the model can be used in just a few lines to get masks from a given prompt:
or generate masks for an entire image:
Additionally, masks can be generated for images from the command line:
See the examples notebooks on using SAM with prompts and automatically generating masks for more details.
SAM's lightweight mask decoder can be exported to ONNX format so that it can be run in any environment that supports ONNX runtime, such as in-browser as showcased in the demo. Export the model with
See the example notebook for details on how to combine image preprocessing via SAM's backbone with mask prediction using the ONNX model. It is recommended to use the latest stable version of PyTorch for ONNX export.
Three model versions of the model are available with different backbone sizes. These models can be instantiated by running
Click the links below to download the checkpoint for the corresponding model type.
•default or vit_h: ViT-H SAM model.
•vit_l: ViT-L SAM model.
See here for an overview of the datastet. The dataset can be downloaded here. By downloading the datasets you agree that you have read and accepted the terms of the SA-1B Dataset Research License.
We save masks per image as a json file. It can be loaded as a dictionary in python in the below format.
Image ids can be found in sa_images_ids.txt which can be downloaded using the above link as well.
To decode a mask in COCO RLE format into binary:
The model is licensed under the Apache 2.0 license.
See contributing and the code of conduct.
The Segment Anything project was made possible with the help of many contributors (alphabetical):
Aaron Adcock, Vaibhav Aggarwal, Morteza Behrooz, Cheng-Yang Fu, Ashley Gabriel, Ahuva Goldstand, Allen Goodman, Sumanth Gurram, Jiabo Hu, Somya Jain, Devansh Kukreja, Robert Kuo, Joshua Lane, Yanghao Li, Lilian Luong, Jitendra Malik, Mallika Malhotra, William Ngan, Omkar Parkhi, Nikhil Raina, Dirk Rowe, Neil Sejoor, Vanessa Stark, Bala Varadarajan, Bram Wasti, Zachary Winstrom
If you use SAM or SA-1B in your research, please use the following BibTeX entry.
Segment Anything (SAM) is a model that produces high quality object masks from input prompts such as points or boxes. It can be used for various segmentation tasks and has been trained on a large dataset of 11 million images and 1.1 billion masks.
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On July 30, 2012, the CCR transitioned to the System for Award Management (SAM), which combined legacy users ' records in the CCR and eight other separate websites and databases that aided in the management of Federal procurement from start to finish. [2]
Entity registration, searching, and data entry in SAM.gov now require use of the new Unique Entity ID. Existing registered entities can find their Unique Entity ID by following the steps here . New entities can get their Unique Entity ID at SAM.gov and, if required, complete an entity registration.
Jun 28, 2023 · SAM.gov is the official website for registering and doing business with the U.S. government. It integrates multiple online systems, such as FBO.gov, CFDA.gov, and FPDS.gov, into a single platform.
Apr 25, 2019 · Learn how to register your organization or business on SAM.gov, the official U.S. government website for federal grants and contracts. Find out the eligibility, benefits, and steps of the registration process.
