A Study of Convolutional Neural Network for Natural Image Classification | Original Article
The accuracy of deep convolutional neural networks, which can also fulfill the function of implicit model ensemble without incurring extra model training costs. Simultaneous data augmentation throughout training and testing phases helps assure network optimization and boost its generalization ability. Augmentation in two phases needs to be consistent to guarantee the appropriate transmission of particular domain knowledge. Picture classification finds its use in practically every sector including quality inspection, illness diagnosis, face identification, image, video recognition, etc. The development of convolutional neural network is a significant advance in the area of machine learning. On the other hand, a convolutional neural network receives the picture itself as the input and classifies the image based on the likelihood scores generated. A convolutional neural network may be constructed according to the demand. One of the remarkable advantages of a convolutional neural network system is its ability to handle massive volume of data. A convolutional neural network is a unique sort of neural network that is constructed by stacking many nonlinear layers one after the other. The input picture is translated into semantic features and supplied to subsequent layers and eventually gets converted to class score.