Abstract: A method for classifying images, in which the images are classified according to good images and bad images, comprises the following steps: capturing image data of an image, and classifying the image as a good image (GB) or a bad image (SB, SB2), wherein the classification is made using an artificial neural network trained by supervised learning using training data from a plurality of good images (GB) and a plurality of bad images (SB), wherein each bad image (SB) of at least a subset of the plurality of bad images (SB) of the training data corresponds to a respective good image (GB) of at least a subset of the plurality of good images (GB) of the training data, into which at least one image error (11) is inserted, and wherein the artificial neural network is trained using respective pairs of a respective good image (GB) from the subset of the plurality of good images (GB) and a respective bad image (SB) from the subset of the plurality of bad images (SB), wherein a respective bad image (SB) corresponds to