A method for optimizing one or more hyperparameters of an auto-labeling device performing auto-labeling of one or more training images to be used for training a neural network, comprising steps of: (a) an optimizing device, if one or more original images to be labeled and one or more validation images with their own true labels having shooting environments similar to those of the original images are acquired, instructing the auto-labeling device having at least one preset hyperparameter set to generate original images with their own auto labels and validation images with their own true labels and auto labels by respectively auto-labeling the original images and the validation images with their own true labels, to assort the original images with their own auto labels into easy-original images with their own auto labels and difficult-original images with their own auto labels, wherein the difficult images have a probability of being automatically labeled incorrectly equal to or greater than a prescribed value, and the easy images have a probability of being automatically labeled incorrectly less than the prescribed value, and to assort the validation images with their own true labels and auto labels into easy-validation images with their own true labels and auto labels and difficult-validation images with their own true labels and auto labels; and (b) the optimizing device, if the easy-validation images with their own true labels and auto labels are acquired, calculating a current reliability, corresponding to the preset hyperparameter set, of the auto-labeling device by referring to the easy-validation images with their own true labels and auto labels, generating one or more sample hyperparameter sets adjusted according to at least one certain rule corresponding to the preset hyperparameter set, calculating each of sample reliabilities, corresponding to each of the sample hyperparameter sets, of the auto-labeling device, determining specific sample hyperparameter sets having their corresponding sample reliabilities higher than the current reliability corresponding to the preset hyperparameter set among the sample hyperparameter sets, acquiring a direction of increasing the sample reliabilities by referring to specific sample reliabilities of the specific sample hyperparameter sets in a Gaussian distribution representing locations of the sample hyperparameter sets corresponding to each of the sample reliabilities based on a location of the preset hyperparameter set corresponding to the current reliability, and optimizing the preset hyperparameter set such that the current reliability corresponding to the preset hyperparameter set is adjusted in the direction of increasing the sample reliabilities. (a) an optimizing device, if one or more original images to be labeled and one or more validation images with their own true labels having shooting environments similar to those of the original images are acquired, instructing the auto-labeling device having at least one preset hyperparameter set to generate original images with their own auto labels and validation images with their own true labels and auto labels by respectively auto-labeling the original images and the validation images with their own true labels, to assort the original images with their own auto labels into easy-original images with their own auto labels and difficult-original images with their own auto labels, wherein the difficult images have a probability of being automatically labeled incorrectly equal to or greater than a prescribed value, and the easy images have a probability of being automatically labeled incorrectly less than the prescribed value, and to assort the validation images with their own true labels and auto labels into easy-validation images with their own true labels and auto labels and difficult-validation images with their own true labels and auto labels; and (b) the optimizing device, if the easy-validation images with their own true labels and auto labels are acquired, calculating a current reliability, corresponding to the preset hyperparameter set, of the auto-labeling device by referring to the easy-validation images with their own true labels and auto labels, generating one or more sample hyperparameter sets adjusted according to at least one certain rule corresponding to the preset hyperparameter set, calculating each of sample reliabilities, corresponding to each of the sample hyperparameter sets, of the auto-labeling device, determining specific sample hyperparameter sets having their corresponding sample reliabilities higher than the current reliability corresponding to the preset hyperparameter set among the sample hyperparameter sets, acquiring a direction of increasing the sample reliabilities by referring to specific sample reliabilities of the specific sample hyperparameter sets in a Gaussian distribution representing locations of the sample hyperparameter sets corresponding to each of the sample reliabilities based on a location of the preset hyperparameter set corresponding to the current reliability, and optimizing the preset hyperparameter set such that the current reliability corresponding to the preset hyperparameter set is adjusted in the direction of increasing the sample reliabilities.