Abstract: An image abnormality detection model training method includes acquiring a noise-containing training label as a current mapping label of a training image input into an initial image abnormality detection model to obtain a prediction label. The method further includes generating model feedback data based on the current mapping label and the prediction label, generating a label loss based on a data change of the model feedback data, and adjusting the current mapping label based on the label loss. The method also includes adjusting model parameters of the initial image abnormality detection model based on the model feedback data, and iteratively performing the inputting the training image, the generating the model feedback data, the generating the label loss, and the adjusting the model parameters until a training end condition is satisfied to obtain a trained image abnormality detection model.

Abstract: Disclosed herein are an image detection method and apparatus, a computer-readable storage medium, and a computer device. The method includes iteratively training a plurality of neural network models to obtain a plurality of trained neural network model; and performing detection on an image to be detected using the trained plurality of neural network models to obtain a detection result. Each iteration of training includes: for each of a plurality of sample images, separately inputting the sample image into the neural network models to obtain a fuzzy probability value set, and calculating, based on the fuzzy probability value set and preset label information of the sample image, a loss parameter of the sample image; selecting target sample images based on a distribution of loss parameters of the plurality of sample images; and updating the plurality of neural network models based on the target sample images.

Abstract: An image processing method and apparatus that includes: performing feature extraction processing on N image regions of a to-be-processed image respectively to obtain feature data corresponding to the N image regions respectively, N being an integer greater than or equal to 1, performing defect detection on the N image regions respectively according to the feature data corresponding to the N image regions respectively to obtain a prediction probability of a defect in each of the N image regions, obtaining attention for each image region, adjusting the prediction probability of the defect in each image region according to the attention for each image region, and generating a prediction result of the to-be-processed image according to the adjusted prediction probability of each image region.