Segment Anything | RDL Research Database

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 segment-anything.com to foster research into foundation models for computer vision. We recommend reading the full paper at: arxiv.org/abs/2304.02643.

Keywords

Computer scienceSegmentationTask (project management)Artificial intelligenceShot (pellet)Image (mathematics)Image segmentationComputer visionReading (process)Zero (linguistics)Machine learning

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Publication Info

Year: 2023
Type: article
Pages: 3992-4003
Citations: 6703
Access: Closed

External Links

Download PDF (Free) View on DOI.org arXiv Semantic Scholar

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APA Style

                            
                                    Alexander M. Kirillov, 
                                
                                    Eric Mintun, 
                                
                                    Nikhila Ravi
                                
                                et al.
                            
                            (2023). 
                            Segment Anything. 
                            2023 IEEE/CVF International Conference on Computer Vision (ICCV)
                            
                            , 3992-4003.
                            https://doi.org/10.1109/iccv51070.2023.00371

Identifiers

DOI: 10.1109/iccv51070.2023.00371
arXiv: 2304.02643

Data Quality

Data completeness: 79%