A processing device comprising: an input unit configured to receive an input image (120, 140) to be subjected to an image recognition process; a processing unit that includes a down-sampling processing unit configured to subject the input image (120, 140) to the image recognition process using a fully convolutional neural network that includes convolutional layers and pooling layers and which does not include a fully connected layer, wherein the image recognition process performed by the down-sampling processing unit subjects the input image (120, 140) to a down-sampling process and does not include an up-sampling process, wherein the image recognition process performed by the down-sampling processing unit includes spatial filtering of the input image (120, 140) using spatial filters of the convolutional layers; and an output unit configured to output an output image as a result of the image recognition process in the processing unit performed on the input image (120, 140), wherein the output image has a spatial dimension smaller than the size of the input image (120, 140) and larger than 1×1 and represents a feature map indicating presence or absence of an object of a respective class in the input image (120, 140); wherein the processing unit is further configured to perform a learning process in which the spatial filters of the convolutional layers in the fully convolutional neural network are trained using an image for learning and training data having a 1×1 spatial dimension and indicating the presence of the object of the respective class, wherein image recognition performed on the image for learning results in the training data as the result of the image recognition process in the processing unit, and wherein a size of the input image (120, 140) is larger than a size of the image for learning. an input unit configured to receive an input image (120, 140) to be subjected to an image recognition process; a processing unit that includes a down-sampling processing unit configured to subject the input image (120, 140) to the image recognition process using a fully convolutional neural network that includes convolutional layers and pooling layers and which does not include a fully connected layer, wherein the image recognition process performed by the down-sampling processing unit subjects the input image (120, 140) to a down-sampling process and does not include an up-sampling process, wherein the image recognition process performed by the down-sampling processing unit includes spatial filtering of the input image (120, 140) using spatial filters of the convolutional layers; and an output unit configured to output an output image as a result of the image recognition process in the processing unit performed on the input image (120, 140), wherein the output image has a spatial dimension smaller than the size of the input image (120, 140) and larger than 1×1 and represents a feature map indicating presence or absence of an object of a respective class in the input image (120, 140); wherein the processing unit is further configured to perform a learning process in which the spatial filters of the convolutional layers in the fully convolutional neural network are trained using an image for learning and training data having a 1×1 spatial dimension and indicating the presence of the object of the respective class, wherein image recognition performed on the image for learning results in the training data as the result of the image recognition process in the processing unit, and wherein a size of the input image (120, 140) is larger than a size of the image for learning.