A method of training a machine learning model to classify images as including objects from one or more object categories, comprising: obtaining training data for training the machine learning model, wherein the machine learning model has a plurality of parameters and is configured to process input images to generate, for each input image, a predicted point in an embedding space, and wherein the training data comprises a plurality of training images and, for each training image, label data that identifies one or more object categories from a set of object categories to which one or more objects depicted in the training image belong; determining, from the label data for the training images in the training data, a respective numeric embedding in the embedding space of each of the object categories in the set of object categories, wherein a distance in the embedding space between the numeric embeddings of any two object categories reflects a degree of visual co-occurrence of the two object categories in the training images, wherein the degree of visual co-occurrence is based on a relative frequency with which the label data for a training image associates both of the object categories with the training image; and training the machine learning model on the training data, comprising, for each of the training images: processing the training image using the machine learning model in accordance with current values of the parameters to generate a predicted point in the embedding space for the training image; and adjusting the current values of the parameters to reduce a distance between the predicted point in the embedding space and the numeric embeddings of the object categories identified in the label data for the training image. obtaining training data for training the machine learning model, wherein the machine learning model has a plurality of parameters and is configured to process input images to generate, for each input image, a predicted point in an embedding space, and wherein the training data comprises a plurality of training images and, for each training image, label data that identifies one or more object categories from a set of object categories to which one or more objects depicted in the training image belong; wherein the machine learning model has a plurality of parameters and is configured to process input images to generate, for each input image, a predicted point in an embedding space, and wherein the training data comprises a plurality of training images and, for each training image, label data that identifies one or more object categories from a set of object categories to which one or more objects depicted in the training image belong; determining, from the label data for the training images in the training data, a respective numeric embedding in the embedding space of each of the object categories in the set of object categories, wherein a distance in the embedding space between the numeric embeddings of any two object categories reflects a degree of visual co-occurrence of the two object categories in the training images, wherein the degree of visual co-occurrence is based on a relative frequency with which the label data for a training image associates both of the object categories with the training image; and training the machine learning model on the training data, comprising, for each of the training images: processing the training image using the machine learning model in accordance with current values of the parameters to generate a predicted point in the embedding space for the training image; and adjusting the current values of the parameters to reduce a distance between the predicted point in the embedding space and the numeric embeddings of the object categories identified in the label data for the training image. processing the training image using the machine learning model in accordance with current values of the parameters to generate a predicted point in the embedding space for the training image; and adjusting the current values of the parameters to reduce a distance between the predicted point in the embedding space and the numeric embeddings of the object categories identified in the label data for the training image.