A method of building an image model for classification or recognition of image data, the image model including a neural network with a plurality of layers, from a reference image model including a neural network with a plurality of layers, wherein the reference image model was trained based on images with a first resolution, the method comprising: generating a target image model by connecting an additional layer (L0) to a layer (L 1) located on an input side of remaining layers, which are the same as the layers of the reference image model ; and training the additional layer (L0) of the target image model based on a reference image model output and a target model output that are based on applying the reference image model and the target image model , respectively, comprising: computing , from a training input image with a second resolution greater than the first resolution, the target model output, based on the target image model ; generating a conversion image by converting the training input image to a resolution, smaller than the second resolution, that may be input to an input layer of the reference image model ; computing , from the conversion image, the reference model output based on the reference image model ; computing an error between the reference model output and the target model output; and updating at least one parameter of the additional layer (L0) based on the computed error while maintaining parameters of the remaining layers in the target image model and repeating updating the at least one parameter of the additional layer (L0) until the error between the reference model output and the target model output converges. generating a target image model by connecting an additional layer (L0) to a layer (L 1) located on an input side of remaining layers, which are the same as the layers of the reference image model ; and training the additional layer (L0) of the target image model based on a reference image model output and a target model output that are based on applying the reference image model and the target image model , respectively, comprising: computing , from a training input image with a second resolution greater than the first resolution, the target model output, based on the target image model ; generating a conversion image by converting the training input image to a resolution, smaller than the second resolution, that may be input to an input layer of the reference image model ; computing , from the conversion image, the reference model output based on the reference image model ; computing an error between the reference model output and the target model output; and updating at least one parameter of the additional layer (L0) based on the computed error while maintaining parameters of the remaining layers in the target image model and repeating updating the at least one parameter of the additional layer (L0) until the error between the reference model output and the target model output converges. computing , from a training input image with a second resolution greater than the first resolution, the target model output, based on the target image model ; generating a conversion image by converting the training input image to a resolution, smaller than the second resolution, that may be input to an input layer of the reference image model ; computing , from the conversion image, the reference model output based on the reference image model ; computing an error between the reference model output and the target model output; and updating at least one parameter of the additional layer (L0) based on the computed error while maintaining parameters of the remaining layers in the target image model and repeating updating the at least one parameter of the additional layer (L0) until the error between the reference model output and the target model output converges. <EOS>