Image Classification based on CNN: Models and Modules

With the recent development of deep learning techniques, deep learning methods are widely used in image classification tasks, especially for those based on convolutional neural networks (CNN). In this paper, a general overview on the image classification tasks will be presented. Besides, the differences and contributions to essential progress in the image classification tasks of the deep learning models including LeNet, AlexNet, Inception, VggNet and ResNet are introduced. This paper will also explain in detail, how different units in these CNN models, other than the convolutional layer, including pooling, activation, and dropout functionalize to support better results for these models. These results offer a guideline for deeply understanding the utility of CNN units.