Enhanced data labeling for machine learning training

Abstract

Embodiments relate to enhancing data labeling for machine learning model training using context data. The context data is provided to data labelers to improve accuracy of labels assigned by the data labelers, such as those assigned for ambiguous or unclear target objects. In an example, a system determines and provides context sets for target objects at user devices. The system generates a first training dataset and a second training dataset with labels obtained in connection with a first subset and a second subset of the context sets, respectively. The system trains a first and second instance of a machine learning model on the first training dataset and the second training dataset, respectively, and determines a respective accuracy score for the model instances. If the first instance is more accurate than the second instance, the system generates subsequent context sets based on characteristics of the first subset of context sets.