Image classification method, electronic equipment, storage medium and program product

Abstract

The invention provides an image classification method, electronic equipment, a storage medium and a program product, relates to the technical field of image processing, and aims to realize accurate classification of to-be-classified images by using an image classification model. The method comprises the following steps: acquiring a to-be-classified image; inputting the to-be-classified image into an image classification model to obtain a classification prediction result of the to-be-classified image, the image classification model being obtained by training a preset model by using basic loss and inter-class loss; the basic loss is determined according to a classification prediction result of each sample image predicted by the preset model and a real category label of each sample image; the inter-class loss is determined according to the classification prediction result of each sample image predicted by the preset model and the soft class label of each sample image, and the soft class label of one sample image is determined according to the confidence coefficient that each sample image is predicted as the real class of the sample image.

Description

technical field [0001] The present application relates to the technical field of image processing, in particular to an image classification method, electronic equipment, storage media and program products. Background technique [0002] With the development of computer technology, the image classification model can realize the classification of various images to be classified. In the training stage, the image classification model learns and distinguishes the characteristics of each sample image. If the number of sample images of each category is unbalanced in the sample set used for model training, the image classification model may not be able to learn and distinguish the characteristics of each category of sample images well, thereby affecting the accuracy of the image classification model. [0003] However, most of the sample images in the sample set have the problem of uneven categories. Therefore, the trained image classification model often has a large room for improvem...

AI Technical Summary

Problems solved by technology

If the number of sample images in each category is unbalanced in the sample set used for model training, it may cause the image classification model to fail to learn and distinguish the characteristics of each category of sample images, thereby affecting the accuracy of the image classification model

[0003] However, most of the sample images in the sample set have the problem of uneven categories. Therefore, the trained image classification model often has a large room for improvement in terms of accuracy.

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