1. A computer-implemented method for forming a computer system for producing label data for corresponding areas of an input image, the label data being one of a set of predetermined values and being indicative that the corresponding area of the image is an image of an object which is in a respective one of a set of object categories, the method comprising: adaptively generating a source deconvolutional network defined by a plurality of first values, by supervised learning using training data comprising (i) first image data encoding training images and (ii) for each training image a corresponding set of annotation data, the set of annotation data for each training image indicating, for a plurality of corresponding areas of the training image, that the area of the training image is an image of an object which is in a respective one of the set of object categories; inputting second image data encoding training images to the source deconvolutional network and collecting corresponding output data describing one or more outputs of the source deconvolutional network; using the second image data and the output data to generate adaptively a target deconvolutional network defined by a plurality of second values, the cardinality of the second values being lower than that of the first values; and forming a computer system which implements the target deconvolutional network.