  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.
