A method in which a server recognizes and determines an object in an image, the method comprising: obtaining the image of the object; calculating a feature map for the image by using a convolutional neutral network, CNN, formed in a structure in which a convolution layer generating the feature map by applying a plurality of filters to each area of the image, and a pooling layer extracting features that are invariant to a change in a location or rotation by spatially integrating the feature map are repeated alternately; calculating compression feature information by compressing the feature map using a first filter in the prediction network, wherein the first filter is global average pooling, GAP; inputting the compression feature information in each of the k layers of a second model and calculating prediction information at each layer, wherein the second model is a multi-layer perceptron, MLP, and k is at least 1; and calculating prediction result information by determining whether each prediction information sequentially exceeds a predetermined threshold value in order from a top layer, wherein the k layers are implemented sequentially such that an object category of an upper layer of the at least one layer includes an object category of a lower layer of the at least one layer, and includes meta information about an immediately-upper layer and an immediately-lower layer for the each layer; the calculating of the prediction information further including: a) calculating first prediction information by applying the second model to the compression feature information input to the first layer; b) calculating second prediction information by applying the second model to the compression feature information and the first prediction information; and c) calculating kth prediction information by applying the second model to the compression feature information, the first prediction information the second prediction information until the (k-1)th obtaining the image of the object; calculating a feature map for the image by using a convolutional neutral network, CNN, formed in a structure in which a convolution layer generating the feature map by applying a plurality of filters to each area of the image, and a pooling layer extracting features that are invariant to a change in a location or rotation by spatially integrating the feature map are repeated alternately; calculating compression feature information by compressing the feature map using a first filter in the prediction network, wherein the first filter is global average pooling, GAP; inputting the compression feature information in each of the k layers of a second model and calculating prediction information at each layer, wherein the second model is a multi-layer perceptron, MLP, and k is at least 1; and calculating prediction result information by determining whether each prediction information sequentially exceeds a predetermined threshold value in order from a top layer, wherein the k layers are implemented sequentially such that an object category of an upper layer of the at least one layer includes an object category of a lower layer of the at least one layer, and includes meta information about an immediately-upper layer and an immediately-lower layer for the each layer; the calculating of the prediction information further including: a) calculating first prediction information by applying the second model to the compression feature information input to the first layer; b) calculating second prediction information by applying the second model to the compression feature information and the first prediction information; and c) calculating kth prediction information by applying the second model to the compression feature information, the first prediction information the second prediction information until the (k-1)th a) calculating first prediction information by applying the second model to the compression feature information input to the first layer; b) calculating second prediction information by applying the second model to the compression feature information and the first prediction information; and c) calculating kth prediction information by applying the second model to the compression feature information, the first prediction information the second prediction information until the (k-1)th