In the future, SAM could be used to help power applications in numerous domains that require finding and segmenting any object in any image.
Segmentation — identifying which image pixels belong to an object — is a core task in computer vision and is used in a broad array of applications, from analyzing scientific imagery to editing photos. But creating an accurate segmentation model for specific tasks typically requires highly specialized work by technical experts with access to AI training infrastructure and large volumes of carefully annotated in-domain data.
With an aim to democratize segmentation Meta has introduced the Segment Anything project: a new task, dataset, and model for image segmentation. Meta is releasing both its general Segment Anything Model (SAM) and our Segment Anything 1-Billion mask dataset (SA-1B), the largest ever segmentation dataset, to enable a broad set of applications and foster further research into foundation models for computer vision.
The company is making the SA-1B dataset available for research purposes and the Segment Anything Model is available under a permissive open license (Apache 2.0).
In the future, SAM could be used to help power applications in numerous domains that require finding and segmenting any object in any image. For the AI research community and others, SAM could become a component in larger AI systems for more general multimodal understanding of the world, for example, understanding both the visual and text content of a webpage.
In the AR/VR domain, SAM could enable selecting an object based on a user’s gaze and then “lifting” it into 3D. For content creators, SAM can improve creative applications such as extracting image regions for collages or video editing. SAM could also be used to aid scientific study of natural occurrences on Earth or even in space, for example, by localizing animals or objects to study and track in video.
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