What is Annotation in Machine Learning?

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Annotation in machine learning is simply a process of making the training data recognizable for computer vision in machines. Annotation means label or annotate the object of interest in an image using the certain tools and techniques to draw the outlines or shading precisely.

And these annotated images used as training data in machine learning that is fed with a suitable algorithm to recognize the similar patterns in a class and predict the answer when used through a model in real-life. Annotation can be done either manually or using the automated software or applications but the accuracy and productivity could be different.

Annotation can be done for texts, videos and images as per the types of training data required for machine learning model developments. And there are multiple types of annotation techniques used for annotation in machine learning.

Bounding Box, Semantic Segmentation, Landmark Annotation, Polyline Annotation, 3D Cuboid Annotation and 3D Point Cloud Annotation are the popular annotation types of used for image annotation to provide the training data for various machine learning. These annotation training data sets are also used for AI based model developments.

Though, automatic tools and software are also used for annotation but manual annotation by humans using the right tools and technique are more accurate and reliable. And for different types of images or data its better to opt the human-powered annotation services that also works with scalable solution for supplying the data as per the client’s flexible needs.

Ref.link : https://anolytics.home.blog/2019/07/09/what-is-data-annotation-in-machine-learning-and-ai/

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