Method for training image classification model, image processing method, and apparatuses

A model training apparatus, applicable to a computer device, and comprising:

• at least one memory configured to store program code;
• and
• at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising:
• first obtaining code configured to cause at least one of the at least one processor to obtain a first image having class annotation information, the class annotation information representing image content class information of an image content that is included in the first image;
• second obtaining code configured to cause at least one of the at least one processor to obtain first prediction class annotation information of the first image by using an image classification network based on a first model parameter of an offset network being fixed, the offset network being configured to classify the first image based on an offset variable, the image classification network being configured to classify the image content in the first image;
• first determining code configured to cause at least one of the at least one processor to determine a second model parameter corresponding to the image classification network by using a classification loss function based on the image content class information and the first prediction class annotation information;
• third obtaining code configured to cause at least one of the at least one processor to obtain second prediction class annotation information of the first image by using the offset network based on the second model parameter of the image classification network being fixed;
• second determining code configured to cause at least one of the at least one processor to determine a third model parameter corresponding to the offset network by using the classification loss function based on the image content class information and the second prediction class annotation information;
• and
• training code configured to cause at least one of the at least one processor to train a semantic image segmentation network model based on the second model parameter and the third model parameter, to obtain a final semantic image segmentation network model that is used for determining a semantic segmentation result of a second image.