Segment Anything

PubDate: Apr 2023

Teams: Meta

Writers: Alexander Kirillov；Eric Mintun；Nikhila Ravi；Hanzi Mao；Chloe Rolland；Laura Gustafson；Alex Berg；Wan-Yen Lo；Piotr Dollar；Ross Girshick

PDF: Segment Anything

Abstract

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 \href{https://segment-anything.com}{https://segment-anything.com} to foster research into foundation models for computer vision.