Data annotation refers to the process of labeling original data (such as images, videos, texts, audio and 3D point clouds). The marking data is called training data. These labels form a representation of what kind of data the data belongs to, which is helpful for the machine learning model to accurately identify the content of the data when it encounters unprecedented data in the future. According to the machine learning model used and the solution at hand, the training data can take many forms, including images, voices, texts or features.
Why do you need data annotation?
The data annotation we have learned is actually one of the important components of artificial intelligence. Let's look at artificial intelligence first. There are three algorithms in artificial intelligence, computing power and labeling.
Computing power is equivalent to reading books with eyes;
Algorithm is equivalent to thinking needs brain;
Labeling is equivalent to knowledge in books.
The basic logic of artificial intelligence operation is: AI needs to check with eyes, record knowledge in the data book, then use brain algorithm to convert it into its own knowledge, and finally apply the learned knowledge to work, then data labeling is equivalent to machine's? "Fuel", with data AI, you can use algorithm+computing power to identify the scene to work.