![]() ![]() To use Segment Anything on a local machine, we'll follow these steps: ![]() Note how a large part of the bounding box is not really related to detection. ![]() Figure showing the difference between detection by bounding box (left) and segmentation (right). This allows for a more precise understanding of the object's shape, size, and position. Segmentation masks, on the other hand, are like drawing a detailed outline around the object, following its exact shape. They may also include parts of the background or other objects inside the rectangle, making it difficult to separate objects from their surroundings. These rectangles give a general idea of the object's location, but they don't show the exact shape of the object. In object detection, objects are often represented by bounding boxes, which are like drawing a rectangle around the object. ![]() If you're interested in using SAM to label data for computer vision, Roboflow Annotate uses SAM to power automated polygon labeling in the browser which you can try for free. In this written tutorial (and the video below), we will explore how to use SAM to generate masks automatically, create segmentation masks using bounding boxes, and convert object detection datasets into segmentation masks. For more information on how SAM works and the model architecture, read our SAM technical deep dive. With over 1 billion masks on 11M licensed and privacy-respecting images, SAM’s zero-shot performance is often competitive with or even superior to prior fully supervised results. We dive into SAM, an efficient and promptable model for image segmentation. Discover the potential of Meta AI’s Segment Anything Model (SAM) in this comprehensive tutorial. ![]()
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