Image labeling method, classification model training method and computer equipment

Abstract

The invention relates to an image labeling method, a classification model training method and computer equipment. The image labeling method comprises the steps of obtaining a to-be-labelled image and an labeled image, wherein the labelled image comprises a target label; determining to-be-labelled image feature parameters corresponding to the to-be-labelled image and labelled image feature parameters corresponding to the labelled image, where the labelled image feature parameter comprises a target label; and determining a label corresponding to the to-be-labeled image according to the feature parameters of the to-be-labeled image and the feature parameters of the labeled image. According to the invention, the to-be-labeled image is labeled through the labeled image, and the labeling quality of the to-be-labeled image is unified with the labeling quality of the labeled image, so that the labeling quality of the to-be-labeled image is relatively high, repeated labeling adjustment is not needed, and the labeling efficiency is greatly improved.

Description

technical field [0001] The present application relates to the technical field of image processing, in particular to an image labeling method, a classification model training method, and computer equipment. Background technique [0002] With the rapid development and gradual maturity of artificial intelligence technology, the application of artificial intelligence technology such as deep learning in the field of image processing technology is becoming more and more extensive. For example, in the application of deep learning technology in image classification tasks, through convolutional neural network ( CNN) model realizes the image classification task. Although the CNN model solves the image classification task by extracting image features, the training of the CNN model depends on a large amount of training data, the quantity of the training data and the labeling quality of the training data. effect has a greater impact. [0003] In the existing technology, in the process o...

AI Technical Summary

Problems solved by technology

During retraining, all these new data need to be labeled. The number of new data is large, and the quality of labeling of new data is good or bad. It is necessary to adjust the labeling repeatedly to improve the quality of labeling. Therefore, the labeling efficiency is low. As a result, the retraining efficiency of the CNN model is greatly reduced.

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