Image recognition method and device, computer equipment and storage medium

Abstract

The embodiment of the invention discloses an image recognition method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a first image and a second image containing a target object, carrying out the category and position prediction of the target object in the first image through an initial recognition model, and obtaining a first prediction categoryand a first prediction position; carrying out convergence on the first prediction category and the target category, carrying out convergence on the first prediction position and the target position, carrying out adversarial learning on the first image and the second image through the initial recognition model, and obtaining a candidate recognition model; obtaining a target category and a pseudo target position corresponding to the target object in the second image through the candidate recognition model; inputting the second image into a candidate recognition model for category and position prediction to obtain a second prediction category and a second prediction position; and converging the second prediction category and the pseudo target category, and converging the second prediction position and the pseudo target position to obtain the trained recognition model, so that the accuracy and reliability of model training are improved.

Description

technical field [0001] The present application relates to the technical field of image processing, in particular to an image recognition method, device, computer equipment and storage medium. Background technique [0002] With the rapid development of artificial intelligence technology, the fields of artificial intelligence applications are becoming more and more extensive. For example, artificial intelligence can be used to identify images. Take the identification of cell nuclei in images as an example. At present, the detection of cancer cell nuclei in images In the process of recognition, the recognition model is firstly trained through the sample image. When the recognition model is trained, the feature information of the sample image is generally extracted through the recognition model, and the category of the nucleus in the sample image is predicted based on the feature information. The predicted category is converged with the real category, and the recognition model i...

AI Technical Summary

Problems solved by technology

Because when the recognition model is trained, it is simply predicted and converged, which reduces the accuracy and reliability of t

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