Image classification method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses an image classification method and device, electronic equipment and a storage medium. The method comprises steps of acquiring a to-be-processed image, inputting the image to a pre-trained target image classification model, and the target image classification model is acquired through training in an unsupervised learning mode according to a first sample with a standard type label and a second sample without the standard type label; and determining an image type to which the to-be-processed image belongs according to an output result of the target imageclassification model. Through carrying out unsupervised iterative training on the image classification model through a large number of second samples without standard type labels and a small number offirst samples with standard type labels, manual classification and label setting do not need to be performed on each sample image so that the processing difficulty and the processing duration of thesample data can be reduced and the total training duration of the target image classification model can be further reduced.

Description

technical field [0001] Embodiments of the present invention relate to image processing technologies, and in particular, to an image classification method, device, electronic equipment, and storage medium. Background technique [0002] With the rapid development of electronic technology and the Internet, especially the mobile Internet, electronic devices, especially smart mobile terminals, are becoming more and more powerful. Users can install various applications on smart mobile terminals according to their own needs to complete various transactions. For example, image recognition is implemented through applications installed on electronic devices. [0003] At present, in order to identify the category of the image to identify the image, the related technology is generally implemented by means of a machine learning model, and the machine learning model is generally obtained through supervised learning and training with a large amount of sample data. In the process of superv...

AI Technical Summary

Problems solved by technology

In the process of supervised learning training of machine learning models, not only a large number of data samples are required, but also each sample data needs to be manually classified, that is, the workload of the sampling process is huge, which increases the training difficulty of machine learning models. The small amount of sample data will lead to poor image classification accuracy of the machine learning model

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