Picture labeling method, device and equipment

Abstract

The embodiment of the invention provides a picture labeling method, device and equipment. The method comprises the steps of obtaining a label relationship of a preset number of picture pairs in a picture data set, wherein the label relationship is used for representing whether the picture pairs have the same label; determining a label relationship set of the picture data set according to the labelrelationship of the preset number of picture pairs and the transmissibility of the label relationship; and determining the label of each picture in the picture data set according to the label relationship set. According to the method, the picture labeling speed and accuracy are improved, and the problems that an existing picture labeling method consumes time and labor and is low in accuracy are solved.

Description

technical field

[0001] Embodiments of the present invention relate to the technical field of data processing, and in particular, to a method, device, and equipment for labeling pictures. Background technique

[0002] Artificial intelligence is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. In recent years, with the rapid development of artificial intelligence, machine learning, as an important way to realize artificial intelligence, has also achieved rapid development. Machine learning selects an appropriate mathematical model, formulates hyperparameters, and inputs sample data. According to a certain strategy, it uses an appropriate learning algorithm to train the model, and finally uses the trained model to analyze and predict the data. In order to obtain a high-quality model, a large amount of labeled data is required as sample data for training the mode...

Examples

Embodiment Construction

[0025] The present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. Wherein, similar elements in different implementations adopt associated similar element numbers. In the following implementation manners, many details are described for better understanding of the present application. However, those skilled in the art can readily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, and methods. In some cases, some operations related to the application are not shown or described in the description, this is to avoid the core part of the application being overwhelmed by too many descriptions, and for those skilled in the art, it is necessary to describe these operations in detail Relevant operations are not necessary, and they can fully understand the relevant operations according to the description in the specification and genera...

technical field

[0001] Embodiments of the present invention relate to the technical field of data processing, and in particular, to a method, device, and equipment for labeling pictures. Background technique

[0002] Artificial intelligence is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. In recent years, with the rapid development of artificial intelligence, machine learning, as an important way to realize artificial intelligence, has also achieved rapid development. Machine learning selects an appropriate mathematical model, formulates hyperparameters, and inputs sample data. According to a certain strategy, it uses an appropriate learning algorithm to train the model, and finally uses the trained model to analyze and predict the data. In order to obtain a high-quality model, a large amount of labeled data is required as sample data for training the mode...

Examples

Embodiment Construction

[0025] The present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. Wherein, similar elements in different implementations adopt associated similar element numbers. In the following implementation manners, many details are described for better understanding of the present application. However, those skilled in the art can readily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, and methods. In some cases, some operations related to the application are not shown or described in the description, this is to avoid the core part of the application being overwhelmed by too many descriptions, and for those skilled in the art, it is necessary to describe these operations in detail Relevant operations are not necessary, and they can fully understand the relevant operations according to the description in the specification and genera...

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