Labeling method and device, server and storage medium

Abstract

The embodiment of the invention discloses a labeling method and device, a server and a storage medium. The method comprises the following steps of acquiring a to-be-labeled object, a labeled object atthe previous moment and a prediction model at the previous moment; updating the prediction model at the previous moment based on the labeled object at the previous moment to obtain a prediction modelat the current moment; performing association type prediction processing on the to-be-labeled object by adopting the prediction model at the current moment to obtain a relationship among a pluralityof types associated with the to-be-labeled object; and labeling the to-be-labeled object according to the relationship among the multiple types associated with the to-be-labeled object to obtain the labeled object at the current moment, thereby realizing labeling of the to-be-labeled object. Before each classification, the prediction model can be updated according to the last classification result, so that the prediction model can be self-iterated and optimized, the prediction effect of the prediction model on the current to-be-labeled object is accurate, and the labeling accuracy can be improved.

Description

technical field [0001] The invention relates to the field of computers, in particular to a labeling method, device, server and storage medium. Background technique [0002] For a machine to recognize a thing, it needs to be recognized through the data labeling of the thing, where the data labeling is the marking note of the thing. For example, labeling can be divided into classification labeling, point labeling, area labeling, and so on. For example, common data annotations may include labels. For example, a certain user has multiple data annotations, that is, multiple labels, and these labels can be used as materials to recognize the user. [0003] However, with the expansion of the data volume of things and the increase in the number of things, the current method of manually labeling things often consumes a lot of time and labor costs. Although the current automatic machine labeling method can save labor costs, it cannot As accurate as human labeling, therefore, the curr...

AI Technical Summary

Problems solved by technology

[0003] However, with the expansion of the data volume of things and the increase in the number of things, the current method of manually labeling things often consumes a lot of time and labor costs. Although the current automatic machine labeling method can save labor costs, it cannot As accurate as human labeling, therefore, current labeling methods are inaccurate

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