Abstract: The excellent performance of Convolutional Neural Networks (CNN) in image classification tasks makes CNN models widely used in various fields of computer vision.In addition to the changes in the network structure,a large part of the reason why the accuracy and efficiency of the image classification model increase year by year comes from thenormalization technology and the improvement of the classification loss function.In the face recognition task,with the increasing precision,the classification loss function change from Softmax Loss to Triplet Loss,and from L-Softmax Loss to Arcface Loss,the measurement method develops from geometric measurement to angle measurement.The change of measurement mode is actually a change of feature form,and the feature form changes from general feature to angle feature.The feature points trained on the Mnist dataset using the angle metric loss function are angularly distributed,and the accuracy is higher than the geometric metric.If the angle metric is represented by more direct angular features,the feature points of the same class are linearly distributed after training,and accuracy is also higher than the general angle metric.This makes people wonder whether angle features can be used instead of general features in the CNN classification model.In the CNN classification model,the main structure is often composed of multiple convolutional layers and one or several fully connected layers.Through unifying the normalization operation of the convolutional layer and the fully connected layer,layers in model come to the angular convolutional layers and the angular fully connected layers.On the basis of the common classification network,the convolution layer is replaced by the angle convolution layer,and the full connection layer is replaced by the angle full connection layer,and then an angle classification network composed of angular features can be obtained.The accuracy of the angle classification network constructed on ResNet-32 is 2% higher than that of the original classification network on the Cifar-100 dataset.The validity of the feature in the classification network is demonstrated.

Key words: Angular feature, Convolutional neural networks, Image classification, Loss function, Normalization