A method for evaluating a reliability of an auto-labeling network and a reliability of an inspector operating a manual-labeling device, wherein the auto-labeling network auto-labels images with auto-labeled boxes, and wherein the inspector examines the auto-labeled images to correct the labels, comprising steps of: acquiring one or more original images, a reliability-evaluating device, instructing a similar-image selection network to (i-1) acquire manipulated images generated by resizing each of regions of sliding windows on the original images, (i-2) generate each piece of top-k class information by classifying the manipulated images via an image classification CNN, and (i-3) generate a bag-of-words histogram of the original images by referring to the top-k class information, to all of the original images, and (ii) to select from a plurality of validation image candidates with their own true labels a predetermined number of selected validation images in an order of having a class label histogram closest to the bag-of-words histogram generated by using all the original images, and instructing an auto-labeling network to auto-label with auto-labeled boxes the selected validation images and the original images by inputting the selected validation images and the original images into the auto-labeling network, wherein the original images are images that are unlabeled to thereby generate auto-labeled validation images and auto-labeled original images, the reliability-evaluating device assorts the auto-labeled validation images into auto-labeled easy-validation images and auto-labeled difficult-validation images, wherein the auto-labeled easy-validation images represent images, among the auto-labeled validation images, having a probability of being automatically labeled incorrectly less than a first prescribed value and the auto-labeled difficult-validation images represent images, among the auto-labeled validation images, having a probability of being automatically labeled incorrectly equal to or greater than a second prescribed value, the second prescribed value being equal to or greater than the first prescribed value; and (b) the reliability-evaluating device, (i) if the auto-labeled easy-validation images are acquired from the auto-labeling network, evaluating a reliability of the auto-labeling network by referring to the true labels and the auto labels of the auto-labeled easy-validation images, and (ii) if the auto-labeled difficult-validation images and manual labels are acquired from the manual-labeling device, which are acquired by applying manually labeling operation to auto-labeled difficult-validation images, evaluating a reliability of the inspector operating the manual-labeling device by referring to the true labels and the manual labels of the auto-labeled difficult-validation images. acquiring one or more original images, a reliability-evaluating device, instructing a similar-image selection network to (i-1) acquire manipulated images generated by resizing each of regions of sliding windows on the original images, (i-2) generate each piece of top-k class information by classifying the manipulated images via an image classification CNN, and (i-3) generate a bag-of-words histogram of the original images by referring to the top-k class information, to all of the original images, and (ii) to select from a plurality of validation image candidates with their own true labels a predetermined number of selected validation images in an order of having a class label histogram closest to the bag-of-words histogram generated by using all the original images, and instructing an auto-labeling network to auto-label with auto-labeled boxes the selected validation images and the original images by inputting the selected validation images and the original images into the auto-labeling network, wherein the original images are images that are unlabeled to thereby generate auto-labeled validation images and auto-labeled original images, the reliability-evaluating device assorts the auto-labeled validation images into auto-labeled easy-validation images and auto-labeled difficult-validation images, wherein the auto-labeled easy-validation images represent images, among the auto-labeled validation images, having a probability of being automatically labeled incorrectly less than a first prescribed value and the auto-labeled difficult-validation images represent images, among the auto-labeled validation images, having a probability of being automatically labeled incorrectly equal to or greater than a second prescribed value, the second prescribed value being equal to or greater than the first prescribed value; and (b) the reliability-evaluating device, (i) if the auto-labeled easy-validation images are acquired from the auto-labeling network, evaluating a reliability of the auto-labeling network by referring to the true labels and the auto labels of the auto-labeled easy-validation images, and (ii) if the auto-labeled difficult-validation images and manual labels are acquired from the manual-labeling device, which are acquired by applying manually labeling operation to auto-labeled difficult-validation images, evaluating a reliability of the inspector operating the manual-labeling device by referring to the true labels and the manual labels of the auto-labeled difficult-validation images.