1. A method comprising: obtaining a plurality of training images, wherein the training images have been classified as images of objects of a particular object type; generating a plurality of triplets of training images, wherein each of the triplets comprises a respective anchor image, a respective positive image, and a respective negative image, and wherein, for each triplet, the anchor image and the positive image have both been classified as images of the same object of the particular object type and the negative image has been classified as an image of a different object of the particular object type; and training a neural network on each of the triplets to determine trained values of a plurality of parameters of the neural network, wherein the neural network is configured to receive an input image of an object of the particular object type and to process the input image to generate a numeric embedding of the input image, wherein training the neural network comprises, for each of the triplets: processing the anchor image in the triplet using the neural network in accordance with current values of the parameters of the neural network to generate a numeric embedding of the anchor image; processing the positive image in the triplet using the neural network in accordance with the current values of the parameters of the neural network to generate a numeric embedding of the positive image; processing the negative image in the triplet using the neural network in accordance with the current values of the parameters of the neural network to generate a numeric embedding of the negative image; computing a triplet loss from the numeric embedding of the anchor image, the positive image, and the negative image; and adjusting the current values of the parameters of the neural network using the triplet loss.