In machine learning, data labeling is the process of identifying raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it.
Draw and annotate bounding boxes around objects of interest. Important for object detection models.Polygon Plot points on each vertex of a target object. This annotation method allows all object shapes to be annotated.
Annotate objects across video frames. Important to build ground truth video datasets for object detection and tracking in a sequence of frames.
This is what puts the "deep" in deep learning. Each layer categorizes some kind of information, refines it, and passes it along to the next. The parallel computing nature of GPUs accelerates this process to enable breakthroughs like facial recognition, real-time voice translation, and self-driving cars.
We are Building labeling images/ Annotation of Images and making them readable for machines. Image annotation services involve tagging an image entirely using a single label or tag every object in an image with numerous labels. Image labeling acts as a marking tool that highlights objects in an image by sketching around them.
Video annotation services are leveraged for training self-driving cars, CCTV, Drone base object tracking . It helps the autonomous vehicular models to recognize sign boards, other vehicles, street lights, pedestrians, cyclists, and traffic signals. Live video annotations help the computer vision models track human poses easily and understand the facial expressions of humans while performing various tasks.
Text annotation services can be used to develop and train virtual assistants and extraction of text from images with text moderation models. It helps the models to perform certain NLP tasks like document classification, intent recognition, entity recognition, and eCommerce tagging, Pos.
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