What does data annotation do?

Data annotation refers to organizing and annotating the collected data, including text, pictures, voices, etc., to teach the artificial intelligence device or system to recognize people and objects, continue to learn and grow, and ultimately achieve artificial intelligence .

The process of the staff converting the photos on the mobile phone into machine recognition is data annotation. The machine cannot distinguish human speech, but it can tell it by using the language recognized by the machine that this is an object, and the machine learns Features in the photo until it can identify the object on its own, and then give the machine any photo of an object, and it will recognize that it is a certain object.

Data annotators are the cornerstone of artificial intelligence. Compared with the "high-tech" and "high value-added" labels of the artificial intelligence industry, the work performed by data annotators is still labor-intensive. The only Something related to technology may be the need to hold a computer and operate it every day.

Work content

Data annotation is to label the pictures that need to be recognized and distinguished by the computer in advance, so that the computer can continuously identify the characteristics of these pictures, and finally realize the computer can identify them independently. Data annotation provides artificial intelligence companies with a large amount of labeled data for machine training and learning, ensuring the effectiveness of algorithm models.

Several common data labeling tasks include classification labeling, which generally selects labels corresponding to data from established labels, which is a closed set. The second is frame annotation. Frame annotation in machine vision is to frame the object to be detected. The third one is regional labeling. Compared with box labeling, regional labeling requires more precision. The fourth is point annotation, which is often required in some applications that require detailed features.