Cross-supervised model training method, image segmentation method and related equipment

Abstract

The invention discloses a cross-supervised model training method, an image segmentation method and related equipment. The cross-supervised model training method comprises the following steps: acquiring a sample image; the sample image is input into a basic twin network, the twin network comprises a first network and a second network which are the same in network structure, and basic parameters of the first network are different from basic parameters of the second network; calculating the loss of the second network by taking the prediction result of the first network as the label of the second network, and calculating the loss of the first network by taking the prediction result of the second network as the label of the first network so as to obtain the loss of the twin network; iteratively updating the parameters of the first network and the second network based on the loss of the twin network until a training cut-off condition is met; and taking the trained and updated first network or second network as a target model. By means of the mode, annotation of the sample images can be reduced, and the model operation accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a cross-supervised model training method, an image segmentation method and related equipment. Background technique [0002] At present, when training network models such as segmentation models and classification models, a large amount of sample data needs to be manually labeled. When pixel-level labels need to be labeled, a lot of labeling resources are required, and labeling is difficult, and when the number of labeled samples is small. , the operational accuracy of the model obtained by training cannot be guaranteed. SUMMARY OF THE INVENTION [0003] The main technical problem solved by the present invention is to provide a cross-supervised model training method, an image segmentation method and related equipment, which can reduce the labeling of sample images and improve the accuracy of model operation. [0004] In order to solve the above technical problem, a tech...

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Problems solved by technology

[0002] At present, when training network models such as segmentation models and classification models, it is necessary to manually label a large number of sample data. When labeling pixel-level labels is required, a lot of labeling resources are required, and labeling is difficult. When the number of labeled samples is small , the calculation accuracy of the trained model cannot be guaranteed

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