  We present Segment Anything Model 2 (SAM 2), a foundation model towards
solving promptable visual segmentation in images and videos. We build a data
engine, which improves model and data via user interaction, to collect the
largest video segmentation dataset to date. Our model is a simple transformer
architecture with streaming memory for real-time video processing. SAM 2
trained on our data provides strong performance across a wide range of tasks.
In video segmentation, we observe better accuracy, using 3x fewer interactions
than prior approaches. In image segmentation, our model is more accurate and 6x
faster than the Segment Anything Model (SAM). We believe that our data, model,
and insights will serve as a significant milestone for video segmentation and
related perception tasks. We are releasing a version of our model, the dataset
and an interactive demo.
