-Understanding labeling requirements: Data annotators need to understand project requirements and labeling specifications, and define labeling contents and methods.
-Data preprocessing: Data annotators need to preprocess the data, including data cleaning, format conversion, data integration, etc., so as to facilitate the subsequent annotation work.
-Data annotation: Classify and annotate the original data according to the project requirements and annotation specifications, thus providing a basis for subsequent data mining and analysis.
-Quality control: For the completed labeling tasks, quality control is needed to ensure that the labeling results meet the project requirements and labeling specifications.
In the development of artificial intelligence, data has always been regarded as its "blood". Data annotation is the key to the effective operation of artificial intelligence algorithm. To realize artificial intelligence, computers must first learn to understand and have the ability to judge things. The process of data annotation is to provide a large number of learning samples for the machine system through manual annotation. Data labeling is to label the data that need to be recognized and distinguished by the machine, and then let the computer learn the characteristics of these data continuously, and finally realize the independent recognition of the computer.
Jinglianwen Technology | Data Collection | Data Labeling
Help artificial intelligence technology and empower the transformation and upgrading of traditional industries.