The application discloses a
noise label robust training method and device for an image recognition model, and belongs to the technical field of image recognition model training, and comprises the following steps: inputting image training samples into a deep neural network for
feature extraction; constructing a multi-
granularity granular ball structure in a feature space based on a
feature vector; performing hierarchical correction on the labels of the image training samples; performing
label propagation in the granular ball, and calculating the propagation confidence distribution and consistency
score of each image sample; screening a clean sample subset according to the consistency
score, and iteratively training the deep neural network to update network parameters. Through the adaptive multi-
granularity granular ball division mechanism, the application can fully depict the
local structure characteristics of data in the feature space, avoid the problems of excessive fragmentation or insufficient purity caused by traditional fixed-
granularity clustering in a high-
noise environment, and provide a stable and reliable structure prior for subsequent
label correction and
information propagation.