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Computer Science > Computer Vision and Pattern Recognition

arXiv:2304.02643 (cs)
[Submitted on 5 Apr 2023]

Title:Segment Anything

Authors:Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, Ross Girshick
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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 this https URL to foster research into foundation models for computer vision.
Comments: Project web-page: this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2304.02643 [cs.CV]
  (or arXiv:2304.02643v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2304.02643

Submission history

From: Alexander Kirillov [view email]
[v1] Wed, 5 Apr 2023 17:59:46 UTC (14,399 KB)
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