Image recognition method, device, computer equipment and storage medium

Abstract

The embodiment of the present application discloses an image recognition method, device, computer equipment and storage medium. The first image and the second image containing the target object are obtained, and the category and position of the target object in the first image are predicted through the initial recognition model. Obtaining the first predicted category and the first predicted location; converging the first predicted category with the target category, converging the first predicted location with the target location, and performing adversarial learning on the first image and the second image through the initial recognition model, Obtaining a candidate recognition model; obtaining a target category and a false target position corresponding to the target object in the second image through the candidate recognition model; inputting the second image into the candidate recognition model for category and position prediction to obtain a second predicted category and a second predicted position; The second predicted category is converged with the pseudo-target category, and the second predicted position is converged with the pseudo-target position to obtain a post-training recognition model, which improves the accuracy and reliability of model training.

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 the recognition model training, thereby reducing the accuracy of the trained recognition model in identifying the nucleus in the image

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