In a significant update for computer vision enthusiasts and professionals, the powerful Segment Anything 2 model has been integrated into the Computer Vision Annotation Tool (CVAT.ai). This cutting-edge technology, developed by Meta, improves the image segmentation speed and accuracy and streamlines the annotation process.

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So, what's new in the SAM 2?

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• SAM 2 dramatically improves over earlier methods in image annotation without prior training on 17 different datasets. It also reduces the need for human involvement by about three times, making the process much more efficient.
• SAM 2 performs better than its predecessor, SAM, on a suite of 23 datasets without prior training and operates six times faster.
• Using SAM 2 feels like real-time processing, as it can handle about 44 frames per second.
• Using SAM 2 for video segmentation annotation in the loop is 8.4 times quicker than manual per-frame annotation with the original SAM.

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CVAT.ai Cloud: Segment Anything Model v2 Now Available for Image Segmentation

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CVAT has integrated "Segment Anything 2" into its SaaS version, improving the platform's capabilities for image segmentation.

Integrating Meta AI's advanced machine learning models transforms CVAT into a more powerful tool for various users, ranging from academic researchers to industry professionals. This integration highlights a mutual commitment to advancing the field of computer vision.

For now, in CVAT.ai, SAM 2 works only for images today, but video support will be added soon!

We've Added Bounding Box Input

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The public version of CVAT.ai now supports optional bounding box input for Segment Anything 2. This feature allows users to define areas to annotate more quickly and accurately, enhancing the efficiency of model training processes for various applications.
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CVAT.ai Enterprise Edition: Added Segment Anything Model v2 

CVAT has stepped up its game for Enterprise users by integrating Segment Anything 2 interactor support. This edition is tailored to meet the high demands of corporate environments where precision and scalability are critical. Enterprises can leverage this feature to handle complex segmentation tasks more effectively, ensuring higher accuracy and productivity in machine learning projects.

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