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 1billionmasks 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 athttps://segment-anything.comto foster research into foundation models for computer vision.