Abstract: Implementations of this specification provide methods and apparatuses for model training. One example method includes: obtaining a first dataset that comprises a plurality of initial image-text pairs, calculating a similarity score between an initial image and an initial text comprised in each of the plurality of initial image-text pair, classifying each of the plurality of initial image-text pairs as one of high-quality data, medium-quality data, or low-quality data based on the similarity score and a predetermined score threshold, and adding updated image-text pairs classified as the medium-quality data and initial image-text pairs classified as the high-quality data to a second dataset for model training.

Abstract: Implementations of the present specification disclose methods, apparatuses, and devices for training an object detection system by using a gradient fine-tuning technique. In one aspect, the method includes: providing a training image as input to the object detection system; processing the training image by the object detection system; determining a gradient norm of each neural network layer based on object annotation data and the detection result corresponding to the training image; and updating, for each neural network layer, parameter values of the neural network layer based on an average of gradient norms and the gradient norm of the neural network layer.