Method for training image recognition model, image recognition method and image recognition equipment

Abstract

The invention discloses a method for training an image recognition model, an image recognition method and image recognition equipment, and the method comprises the steps: obtaining an initial training image, augmenting image signal processing of the initial training image, and obtaining a processed training image; and training an image recognition model based on the processed training image. According to the scheme of the invention, the augmentation processing of the image signal processing is carried out on the initial training image, so that the training images shot by various different cameras can be simulated based on the training images shot by one or more cameras. Therefore, generalization of the image recognition model obtained based on image training after augmentation processing on different modules is enhanced, so that the image recognition model has good performance when performing image recognition on to-be-recognized images shot by different imaging modules.

Description

technical field [0001] The present application relates to the technical field of image recognition, and more specifically relates to a method for training an image recognition model, an image recognition method, and an image recognition device. Background technique [0002] Image recognition refers to the technology of using computers to process, analyze and understand images to identify various targets and objects. It is a practical application of deep learning algorithms. Image recognition is generally performed based on a trained neural network model, and the quality of the image to be recognized will significantly affect the performance of the image recognition algorithm executed by the neural network model. [0003] In model training, the image training set used is often images taken by certain cameras, but in actual use of image recognition products (such as face recognition products) or SDK, the images to be recognized input into the model may vary widely. , its qual...

AI Technical Summary

Problems solved by technology

[0003] In model training, the image training set used is often images taken by certain cameras, but in actual use of image recognition products (such as face recognition products) or SDKs, the images to be recognized input into the model may vary widely. , its quality cannot be guaranteed, therefore, it may cause the model to perform poorly in recognition of images captured by certain cameras

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