Data augmentation method and device and electronic equipment

Abstract

The invention discloses a data augmentation method and device and electronic equipment, and relates to the technical field of deep learning, cloud computing and computer vision. According to the specific implementation scheme, the method comprises steps: acquiring a data augmentation request, wherein the augmentation request comprises an original image set and the type of a to-be-generated image;determining a current data augmentation mode according to the type of the to-be-generated image; and performing augmentation processing on the original image set according to the current data augmentation mode to obtain an augmented image set. Therefore, through the data augmentation method, for different types of data augmentation tasks, different data augmentation modes are selected to realize data augmentation, so that the problem of lack of training data sets is effectively relieved, and a relatively good directional augmentation effect can be realized.

Description

technical field [0001] This application relates to the field of computer technology, in particular to the field of deep learning, cloud computing and computer vision technology, and proposes a data augmentation method, device and electronic equipment. Background technique [0002] With the development and promotion of artificial intelligence technology, computer vision technology based on deep learning is more and more widely used in face recognition, automatic driving, quality inspection and other aspects. For example, in the industrial quality inspection scenario, the detection model is usually trained by collecting defect sample images of products to form a training data set, so as to complete the quality inspection task more reliably and efficiently through computer vision technology, so as to save manpower and time costs. [0003] In related technologies, since the effect of the deep learning model largely depends on the size and quality of the training data set, so tha...

AI Technical Summary

Problems solved by technology

However, in practical applications, it is often impossible to obtain enough and high-quality real images as training samples, which cannot meet the training needs of deep learning models.

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