Image data augmentation strategy selection method and system

Abstract

The embodiment of the invention provides an image data augmentation strategy selection method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: selecting a plurality of to-be-determined strategy subsets from an augmentation strategy set, carrying out the sample augmentation of a preset sample training set, and obtaining a plurality of augmented sample training sets; training the initialized classification model by using each augmented sample training set to obtain a plurality of trained classification models; inputting a preset sample verification set into each trained classification model to obtain classification accuracy corresponding to the trained classification model; and determining an optimal strategy subset from the plurality of undetermined strategy subsets by using a Bayesian optimization algorithm based on the classification accuracy corresponding to each trained classification model. According to the technical scheme provided by the embodiment of the invention, the problem that which augmentation strategy is most effective to the current type of image sample is difficult to determine can be solved.

Description

【Technical field】 [0001] The present invention relates to the technical field of pedestal operation and maintenance, in particular to a method and system for selecting an augmentation strategy for image data. 【Background technique】 [0002] The success of deep learning in computer vision is partly due to having a large amount of labeled training data, as the performance of models usually improves with the quality, variety, and quantity of training data. However, it is often difficult and expensive to collect enough high-quality data to train a model to perform well. [0003] At present, some data augmentation strategies are commonly used to increase the amount of data for training computer vision models, such as translation, rotation, and flipping, etc., through random "expansion" to increase the number and diversity of training samples. [0004] However, there are various existing augmentation strategies, which perform differently in the face of different datasets, and it ...

AI Technical Summary

Problems solved by technology

[0005] In view of this, an embodiment of the present invention provides a method and device for selecting an augmentation strategy for image data to solve the problem in the prior art that it is difficult to determine which augmentation strategy is most effective for the current type of image data set

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