Abstract: A teacher data preparation method for preparing non-defective product teacher data and defective product teacher data, in a defect classification model that performs learning using a non-defective product image and a defective product image, the teacher data preparation method comprising: acquiring, as the non-defective product teacher data, many pieces of non-defective product feature quantity data obtained by extracting a feature quantity in a predetermined first number of dimensions from a large number of the non-defective product images; and acquiring, as the defective product teacher data, many pieces of generated defective product feature quantity data obtained by generating the feature quantity in the predetermined first number of dimensions, by using a generation model that has performed learning using defective product feature quantity data obtained by extracting the feature quantity in the predetermined first number of dimensions from the defective product images smaller in number than the non-de

Abstract: A teacher data generating method includes: causing a generation model to perform learning by using a learning image in which boundary information indicating in a chromatic color a range of a defect is superimposed on a defective product image in a gray scale so as to generate a generated defect image including a new image of a defect in the gray scale and an image of the boundary information in the chromatic color; generating the generated defect image by using the generation model; extracting a pixel having a pixel value corresponding to the chromatic color from the generated defect image, extracting the boundary information corresponding to the generated defect image, and acquiring a gray scale defect image without including an image of the boundary information; calculating a coordinate of the boundary information; and associating the gray scale defect image with the coordinate to obtain defective product teacher data.