How SAM 2 and Encord Transforms Video Annotation

The future of video annotation is here, thanks to Meta's Segment Anything Model 2 (SAM 2), a new foundation model that extends the capabilities of the original Segment Anything Model into the video domain. SAM 2 integrates advanced segmentation and tracking functionalities within a single, efficient framework, enabling real-time object tracking, memory module, improved performance, and efficiency. The SA-V dataset, created using the SAM 2 data engine, is an extensive collection of video annotations designed to support the development and evaluation of advanced video segmentation models. With SAM 2's interactive model-in-the-loop setup, annotators can refine and correct mask predictions dynamically, significantly speeding up the annotation process while maintaining accuracy. The SAM 2 data engine addresses the challenge of starting from scratch by progressively building up a high-quality dataset and improving annotation efficiency over time. SAM 2 has been shown to be faster, more efficient, and maintain quality with each phase of its development, and is now available for use in Encord's automated labeling suite.

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