Deep Convolutional Neural Network (CNN) for Large-Scale Images Classification

Abstract

In the field of machine vision and image classification, many models and methods have been introduced, but explicitly, different algorithms and models of neural network based researches have acquired a great importance among image classification models. beside the applications of this science in identifying patterns, image processing, artificial intelligence, and robot control, on the other hand the different influence aspects in daily and real life is indispensable to every point of view is such as agricultural domains, weather forecasts, medical sciences, engineering and so on. The accuracy and the executive algorithm path are very important in recognition and classification result. The main objective of these architectures is to provide a model similar to the internal system of the human brain to analyze various systems based on experiences, here-at the final goal of these algorithms is the possibility to create the training flow in artificial networks, in order to provide deep learning so that the network can diagnosis like human brain. This is another aspect of the architectures and the subject of the algorithm implementation accuracy as the model ability for recognize images and act like human brain?