1. A computer-implemented method executed by one or more processors, the method comprising: receiving, by the one or more processors, an initial data set comprising a plurality of images, each image from the plurality of images being associated with a set of labels, wherein each label in the set of labels is assigned to the image of the plurality of images by an initial model, the initial model being trained for a particular ground-truth label; for each image in the plurality of images in the initial data set: providing, by the one or more processors, a list of categories associated with the image based on the set of labels assigned to the image by the initial model, and determining, by the one or more processors, a primary category of the image based on the list of categories; determining, by the one or more processors, a category of the ground-truth label, the category having been specified for the ground-truth label of the initial model; comparing, by the one or more processors, the category of the ground-truth label to primary categories of respective images in the plurality of images in the initial data set; selecting, by the one or more processors, a revised data set, wherein the revised data set includes only images of the initial data set that are associated with a respective primary category that is the same as the category of the ground-truth label; and providing, by the one or more processors, the revised data set to retrain the initial model to provide a revised model.