Image data augmentation method

Abstract

The invention provides an image data augmentation method. The method comprises: S1000, acquiring image data corresponding to category information of a to-be-screened image set; S2000, according to a preset screening strategy and a sample image corresponding to the category information, identifying the image data; S3000, classifying the image data based on the identification result to obtain an image data set; and S4000, carrying out adversarial training on the image data set to obtain a sample data set. The preset screening strategy includes one or more of similarity screening, hash value filtering, and keyword filtering. Therefore, the manual sample data set screening is reduced; the screening efficiency and screening reliability are improved; and the accuracy of the neural network is enhanced.

Description

technical field [0001] The invention relates to the field of data processing, in particular to an image data augmentation method. Background technique [0002] In recent years, with the continuous development of computer vision technology, especially the rapid development of neural network models, people's demand for image data required for computer vision training, especially for image data with accurate label information, is increasing. [0003] Convolutional Neural Networks (CNN) is a kind of deep learning algorithm and an important processing and analysis tool in the field of image recognition. In recent years, it has become one of the research hotspots in many scientific fields. The advantage of the neural network model algorithm is that it does not need to use any manually labeled features when training the model, and can automatically explore the features hidden in the input variables. At the same time, the weight sharing feature of the network greatly reduces the com...

AI Technical Summary

Problems solved by technology

The problem brought about is that the workload is extremely huge, the augmentation results are highly subjective, and the augmentation results are prone to errors. How to improve the accuracy of augmentation is an urgent problem to be solved.

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