May 5, 2023
At LinkedAI, we are on a mission to solve the primary bottleneck of AI, by providing tools which enable the generation of highly-accurate data in less time and provide actionable insights across training datasets. Today, we're bringing our solution one step further, announcing the launch of the Magic Tool 2.0 integrated with Meta's Segment Anything Model (SAM), into the LinkedAI annotation platform.
Image segmentation is a crucial but challenging task in computer vision, where an image is divided into segments based on characteristics such as color, texture, and shape. Its importance lies in its various applications, such as object detection, medical image analysis, and autonomous driving. However, the traditional method of manually annotating images with a pen tool is time-consuming and labor-intensive.
Moreover, inconsistencies in labeling can arise due to varying levels of annotator expertise, negatively impacting downstream model performance. This cumbersome and error-prone workflow is further compounded as labeling teams must repeat the process for hundreds of objects within a single image.
The Segment Anything Model
SAM, or the Segment Anything Model, is Meta’s new zero-shot foundation model in computer vision. As a zero-shot foundation model, and as its name suggests, SAM is capable of “segmenting anything” including image data it hasn’t seen before, from a simple combination of keypoints and, if you prefer, a delimiting bounding box.
What’s the secret to SAM? Well, a big factor is the data that powers it. Along with the Segment Anything Model, Meta AI is also making available for research purposes its Segment Anything 1 Billion (SA-1B). It is a massive dataset of 11 million licensed and privacy-respecting images and 1.1 billion masks — the largest and most comprehensive segmentation dataset to date.
Magic Tool 2.0
As the fastest and most accurate semantic segmentation in the world, the Magic Tool 2.0 will use the power of Meta AI’s Segment Anything Foundation Model (SAM) to rapidly generate high performance training data for the most complex computer vision use cases. LinkedAI’s improved auto-segmentation tool will significantly speed up labeling projects through an intuitive high-performance mask editor interface.
With the Magic Tool 2.0, machine learning teams can now easily generate automated mask predictions for multiple objects in their images, across various real-world computer vision applications like the detection of different plant patterns and classification for smart agriculture, fast and precise medical diagnosis of various pathologies and diseases, detection and classification of different products in retail solutions and much more.
We’re very excited to bring SAM to LinkedAI to keep supporting your AI initiatives — Get early access here.