The Segment Anything Model (SAM), introduced by Meta, stands as the pioneering foundation model for image segmentation, boasting remarkable zero - shot inference capabilities. The Segment Project endeavors to make image segmentation more accessible and inclusive by presenting a novel task, a new dataset, and an innovative model. The Segment Anything Model is trained on the Segment Anything 1-Billion mask dataset (SA-1B), the largest segmentation dataset to date.
Previously, two primary methods were employed to tackle segmentation problems. The first was interactive segmentation, which enabled the segmentation of diverse object types. Nevertheless, it demanded human intervention in an iterative mask-refinement process. The second method was automatic segmentation, capable of segmenting only pre-defined object categories. This approach required a substantial number of manually labeled objects for training. Unfortunately, neither method provided a comprehensive, fully-annotated solution for segmentation. By integrating these two techniques, the SAM model can adapt to novel tasks and domains, thereby becoming the first-of-its-kind segmentation model to offer such versatility.