COMPUTER VISION

Segment Anything

April 05, 2023

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.

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AUTHORS

Written by

Alexander Kirillov

Alex Berg

Chloe Rolland

Eric Mintun

Hanzi Mao

Laura Gustafson

Nikhila Ravi

Piotr Dollar

Ross Girshick

Spencer Whitehead

Wan-Yen Lo

Tete Xiao

Publisher

ArXiv

Research Topics

Computer Vision

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