Training data set generation method and device, electronic equipment and storage medium

Abstract

The invention provides a training data set generation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a classified source data set and an unclassified target data set; extracting a first feature vector set of the source data set and a second feature vector set of the target data set through a feature extractor; determining a class center feature vector corresponding to the source data set according to the first feature vector set, and determining a clustering label of the target data set and an average feature vector in a clustering cluster according to the second feature vector set; iteratively optimizing the feature extractor, so that the overall difference between the feature vectors of samples in the source data set and the feature vectors of a class center and the overall difference between the feature vectors of the elements in a clustering cluster and average feature vectors in the clustering cluster are made to be minimum; and obtaining a training data set according to the clustering label of the target data set and the elements in the clustering cluster. According to the method, the workload of manual labeling can be reduced, the manual labeling cost is reduced, and the labeling precision is improved.

Description

technical field [0001] The present application relates to the technical field of data processing, in particular to a method and device for generating a training data set, electronic equipment, and a computer-readable storage medium. Background technique [0002] In the classification and identification of commodities in retail scenarios, it is often necessary to face the packaging differentiation of different product lines, the rapid iterative update of product packaging, as well as the differentiation of image features in the image collection process, the huge number of product categories, and the redundancy of some category data Therefore, when starting a new project, it is difficult to prepare the classification model training data with a small amount of data and a fast and concise algorithm, and it is necessary to manually classify and label a large amount of data to form an initial training set; then , how to pre-divide massive unlabeled data, improve the quality of loc...

AI Technical Summary

Problems solved by technology

[0003] At present, the preparation of image classification data is mainly through manual classification and labeling of the collected full-sample images, but the amount of data that needs to be processed at one time may be huge, and full manual labeling often results in low labeling accuracy and high labeling costs. Iterative optimization that affects subsequent models

Images