Abstract: The present invention refers to a method of training a neural network to detect anomalies in a manufacturing product comprising: obtaining a dataset including multiple manufacturing product images and annotation files with coordinates of predetermined anomalous regions corresponding to the manufacturing product images, respectively; selecting a test query set of images and a support set of images from the dataset multiple to train a deep learning model, the support set of images comprises pairs of reference images and each pair comprises at least one anomalous manufacturing product image and at least one non-anomalous manufacturing product image; inputting the test query set into a deep learning model to rate a similarity score based on a similarity distance between the pairs of reference images of the support set based on the annotation files; and adjusting parameters characterizing the deep learning model through a model based on the similarity score.