What is Data annotation?
'''Data annotation''' is all about labeling or tagging relevant information/metadata in a dataset to let machines understand what they are.<ref>{{Cite news|last=Naminas|first=Karyna|date=August 5, 2024|title=Data Annotation: Your Guide to Efficient In-House Workflows|work=Label Your Data|url=https://labelyourdata.com/articles/data-annotation|access-date=2024-09-11}}</ref> The dataset could be in any form i.e., an image, an audio file, video footage, or even text. Data is one of the three key elements in the development of artificial intelligence. The development of a computer recognition engine requires massive training data, which need to be annotated. These data include images, sounds, text, etc. Annotation of these types of data includes classification, frame, annotation, marking, etc. '''Types of data annotations in computer vision''' Computer vision is one of the most debated topics in the current AI landscape. On your phone (something like facial recognition or automated classification of images), in manufacturing settings (emotion recognition for remote education), and in public settings (emotion recognition for remote education).<ref>{{Cite web|last=Anolytics|date=2023-09-12|title=The Complete Guide to Data Annotation|url=https://www.anolytics.ai/blog/data-annotation-guide|access-date=2025-02-24|website=Anolytics|language=en}}</ref> In order to understand why computer vision algorithms are so popular, let’s look at the types of annotation tasks that enable them to do so. '''Image Categorization''' In machine learning, this type of task is also known as image classification. By categorizing images, ML algorithms can be trained to group them into predefined categories. These classes will make it possible for a machine-learning model to identify what is detected in a photo after training. It can, for instance, recognize the difference between a Georgian and a rococo armchair if it is trained to recognize different furniture styles. '''Semantic Segmentation''' An image can be viewed as a separate en