Image set expansion method, device and apparatus and readable storage medium

Abstract

The invention discloses an image set expansion method. The method comprises the steps of determining a current to-be-called enhancement strategy from a preset enhancement strategy library; calling a Bayesian optimization algorithm to determine to-be-tested parameters, calling the Bayesian optimization algorithm to quickly determine a plurality of parameters with excellent performance enhancement performance from calling parameters of countless enhancement strategies, and determining optimal parameters in the to-be-tested parameters, with the optimal parameters being the parameters with the best enhancement effect in the to-be-tested parameters. due to the fact that the called enhancement strategy parameters are excellent in enhancement performance, an enhancement strategy and an optimal parameter are called to carry out image enhancement on the original image set. High availability of the generated image can be ensured, so that a currently obtained image set can be directly used as theto-be-output data set, excessive resources do not need to be occupied for image validity verification, the problem that the number of the image sets is insufficient can be quickly solved, and the implementation cost can be reduced. The invention further discloses an image set expansion device and apparatus and a readable storage medium, which have the above beneficial effects.

Description

technical field [0001] The invention relates to the field of data management, in particular to an image set expansion method, device, equipment and readable storage medium. Background technique [0002] In the field of image classification (such as the current model classification, face skin color classification, etc.), the image data set is the most critical part. Insufficient labeled image data often leads to overfitting and sampling noise, which means Then the model will fail to generalize to undiscovered examples. This problem can be alleviated by image augmentation. Image augmentation is to artificially generate new images by performing image transformations (such as rotation, cropping, occlusion, scaling, etc.) on the original image set. Image augmentation can effectively increase the number and diversity of images seen by the network. [0003] For image augmentation, it is very important how to call the appropriate enhancement algorithm to expand the existing image ...

AI Technical Summary

Problems solved by technology

At present, the traditional method uses reinforcement learning to optimize the image set. Due to the wide search range, this method will occupy a lot of resources, and the requirements for computing power are too high. The implementation cost is extremely high, and it is not suitable for low-cost usage scenarios.

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