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
View a PDF of the paper titled Segment Anything, by Alexander Kirillov and 11 other authors
View PDFAbstract: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 atthis 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] |
| (orarXiv:2304.02643v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2304.02643 arXiv-issued DOI via DataCite |
Submission history
From: Alexander Kirillov [view email][v1] Wed, 5 Apr 2023 17:59:46 UTC (14,399 KB)
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