9. The method of claim 1 , wherein the neural network is a deep convolutional neural network.
1. A method for training a neural network that receives an input image and outputs a predetermined number of candidate bounding boxes that each cover a respective portion of the input image at a respective position in the input image and a respective confidence score for each candidate bounding box that represents a likelihood that the candidate bounding box contains an image of an object, the method comprising: receiving a training image and object location data for the training image, wherein the object location data identifies one or more object locations in the training image; providing the training image to the neural network and obtaining bounding box data for the training image from the neural network, wherein the bounding box data comprises data defining a plurality of candidate bounding boxes in the training image and a respective confidence score for each candidate bounding box in the training image; determining an optimal set of assignments using the object location data for the training image and the bounding box data for the training image, wherein the optimal set of assignments assigns a respective candidate bounding box to each of the object locations; and training the neural network on the training image using the optimal set of assignments.