Segment Anything 1-Billion mask dataset (SA-1B)

Contributors: 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
Datarows: 11,000,000 Datarows
Image segmentation
Foundation models

The Segment Anything project aims to democratize image segmentation in computer vision by introducing a new task, dataset, and model. The project includes the Segment Anything Model (SAM) and the Segment Anything 1-Billion mask dataset (SA-1B), which is the largest ever segmentation dataset. The SA-1B dataset is available for research purposes, while the SAM is accessible under the Apache 2.0 open license.

The project reduces the need for task-specific modeling expertise, training compute, and custom data annotation in image segmentation. The goal is to build a foundation model for image segmentation that can adapt to specific tasks, similar to prompting in natural language processing models. SAM can generate masks for any object in any image or video, even for objects and image types it has not encountered during training. Potential applications of SAM include multimodal understanding, AR/VR, content creation, and scientific study.

This is a subset of the original data.

Apache 2.0