Automatic image labeling method based on unsupervised domain adaptation

Abstract

The invention provides an automatic image annotation method based on unsupervised domain adaptation. The method comprises the following steps: acquiring a source domain image and annotation, and acquiring a target domain image; building a detection framework, and constructing a domain classifier to extract global features and local features; training the existing data by using a PyTorch deep learning framework application algorithm to obtain a trained domain adaptation detection model; detecting the test data set (unlabeled pictures in the target domain) by using the existing latest model to obtain a preliminary detection result; and carrying out secondary processing and extraction by utilizing the preliminary detection result file to generate an xml annotation file in a PASCAL VOC format.On the basis of the domain adaptation method, under the condition that a large amount of target domain data are not labeled, only source domain pictures and labeling data similar to the target domaindata need to be owned, and training can be put into use for automatic labeling of the data. Compared with the prior art, the method is good in flexibility, high in classification precision, simple inmodel and high in practicability.

Description

technical field [0001] The invention relates to the fields of machine learning and computer vision, in particular to an image automatic labeling method based on unsupervised domain adaptation. Background technique [0002] Today's deep learning models need to be trained on large-scale supervised data sets-for each data, there will be a corresponding label. For a data set like ImageNet that contains up to one million pictures, it takes many people to spend several months to complete the manual labeling. Assuming that a data set with one million categories is to be created now, a total of 100 million frames must be given. Each frame in the video data set is labeled, which is basically impossible to achieve. The main goal of unsupervised learning research is to train a model that can be used for other tasks, the characteristics of this model should be as general as possible, and the characteristics of this model should be as good as possible, and the results of the supervised ...

AI Technical Summary

Problems solved by technology

[0004] Purpose of the invention: the technical problem to be solved by this invention is to provide a kind of image automatic tagging method based on unsupervised domain adaptation for the deficiencies in the prior art, and the image (video) tagging method constructed by the method of the present invention is intended to To solve the problem of huge time cost and labor cost of traditional manual labeling, the method mainly involves the distribution and alignment of domains, etc.

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