Semantic segmentation doesn't just put bounding boxes and labels around more objects, it actually classifies every single pixel within an image and displays displays all entities of one class with one exact color. The output is typically a high-resolution image that usually maintains the same size as the original image. The “semantic” part of the term semantic segmentation describes how regions of the image are classified based off of their semantic meaning.
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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.