Instance segmentation essentially takes the concept of semantic segmentation and applies it to every object of interest/every instance within an image. This means that instead of labeling people as one group comprising a “person” class, every individual in the image is assigned a different class, classifying every instance of that object.
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Our in-house professionals, using our image annotation tools with built-in machine learning and multiple layers of quality control, create the tightest and most accurately bounding boxes for computer vision and deep learning applications.
Each pixel in an image is mapped into specific classes; for example 'cars' and 'pedestrians' in autonomous driving data.
Every pixel in an image is mapped to specific classes. Regularly used in drone and autonomous driving technology.
Each frame or key-frames annotated with or without object tracking.