Abstract: The invention provides a model training method and system that uses pretrained features of a teacher neural network trained on a billion-size dataset to train a student neural network. The model training method leverages the teacher neural network to design a more stable loss function that works well with more sophisticated learning rate schedules to reduce training time and make the augmentation designing process more natural.

Abstract: Provided is a label inference system including a data generator configured to generate a training set and a test set, each including a plurality of images labeled with experts' annotations, a data trainer configured to perform training for a base model based on the generated training set and test set, a determiner configured to identify whether an evaluation metric f1 of the training model satisfies a base evaluation metric f1base, and a data inference unit configured to perform inference using the training set, the test set, and an unlabeled data set with the training model satisfying the base evaluation metric f1base.