We introduce the Segment Anything (SA) project: a new task, model, anddataset for image segmentation. Using our efficient model in a data collectionloop, we built the largest segmentation dataset to date (by far), with over 1billion masks on 11M licensed and privacy respecting images. The model isdesigned and trained to be promptable, so it can transfer zero-shot to newimage distributions and tasks. We evaluate its capabilities on numerous tasksand find that its zero-shot performance is impressive -- often competitive withor even superior to prior fully supervised results. We are releasing theSegment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and11M images at https://segment-anything.com to foster research into foundationmodels for computer vision.