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Data annotation (commonly referred to as data labeling) plays a crucial role in ensuring your AI and machine learning projects are trained with the right information to learn from.Humans are needed to identify and annotate specific data so machines can learn to identify and classify information.

To summarize how you will annotate text:Identify the BIG IDEA.Underline topic sentences or main ideas.Connect ideas with arrows.Ask questions.Add personal notes.Define technical words.

HIGHLIGHTING/UNDERLINING. Highlighting or underlining key words and phrases or major ideas is the most common form of annotating texts.PARAPHRASE/SUMMARY OF MAIN IDEAS.DESCRIPTIVE OUTLINE.COMMENTS/RESPONSES.

An annotation is a note or comment added to a text to provide explanation or criticism about a particular part of it.Something that has had such notes added to it can be described as annotated. The word annotation is sometimes abbreviated as annot. (which can also mean annotated or annotator).

A reader might annotate a book, paper, pamphlet. or other texts for the following reasons: a student noting important ideas from the content by highlighting or underlining passages in their textbook. a student noting examples or quotes in the margins of a textbook. a reader noting content to be revisited at a later

It involves creating bounding boxes (for object detection) and segmentation masks (for semantic and instance segmentation) to differentiate the objects of different classes. In V7, you can also annotate the image using tools such as keypoint, 3D cuboids, polyline, keypoint skeleton, and a brush.

Types of AnnotationsDescriptive.Evaluative.Informative.Combination.Feb 16, 2021