A method for identifying an object, comprising: inputting an image of the object to a first neural network for determining candidate categories of the object; and in response to the candidate categories of the object being output from the first neural network, inputting the candidate categories of the object to second neural networks respectively, in order to determine category of the object, wherein the determined category of the object is one of the candidate categories, wherein the first neural network is a convolutional neural network from which a fully connected layer for final classification is removed, and each of the second neural networks is a single layer fully connected neural network trained for a corresponding one of the candidate categories; and each of candidate categories is input to a corresponding one of the second neural networks; and wherein each of the second neural networks is trained by: using two samples belonging to a category corresponding to the second neural network as a positive sample pair, an expected output value of which is a positive reference value; using a sample belonging to the category corresponding to the second neural network and a sample not belonging to the category corresponding to the second neural network as a negative sample pair, and an expected output value of which is a negative reference value; and using a squared error between a calculated value of a corresponding second feature similarity and an expected output value as a loss function. inputting an image of the object to a first neural network for determining candidate categories of the object; and in response to the candidate categories of the object being output from the first neural network, inputting the candidate categories of the object to second neural networks respectively, in order to determine category of the object, wherein the determined category of the object is one of the candidate categories, wherein the first neural network is a convolutional neural network from which a fully connected layer for final classification is removed, and each of the second neural networks is a single layer fully connected neural network trained for a corresponding one of the candidate categories; and each of candidate categories is input to a corresponding one of the second neural networks; and wherein each of the second neural networks is trained by: using two samples belonging to a category corresponding to the second neural network as a positive sample pair, an expected output value of which is a positive reference value; using a sample belonging to the category corresponding to the second neural network and a sample not belonging to the category corresponding to the second neural network as a negative sample pair, and an expected output value of which is a negative reference value; and using a squared error between a calculated value of a corresponding second feature similarity and an expected output value as a loss function. wherein the first neural network is a convolutional neural network from which a fully connected layer for final classification is removed, and each of the second neural networks is a single layer fully connected neural network trained for a corresponding one of the candidate categories; and each of candidate categories is input to a corresponding one of the second neural networks; and wherein each of the second neural networks is trained by: using two samples belonging to a category corresponding to the second neural network as a positive sample pair, an expected output value of which is a positive reference value; using a sample belonging to the category corresponding to the second neural network and a sample not belonging to the category corresponding to the second neural network as a negative sample pair, and an expected output value of which is a negative reference value; and using a squared error between a calculated value of a corresponding second feature similarity and an expected output value as a loss function. using two samples belonging to a category corresponding to the second neural network as a positive sample pair, an expected output value of which is a positive reference value; using a sample belonging to the category corresponding to the second neural network and a sample not belonging to the category corresponding to the second neural network as a negative sample pair, and an expected output value of which is a negative reference value; and using a squared error between a calculated value of a corresponding second feature similarity and an expected output value as a loss function.