Image Classification Based On Convolutional Neural Networks

Deep learning is a new area of Machine learning research, which has been introduced with the objective of moving machine learning closer to one of its original goals:Artificial Intelligence. Convolutional neural networks (CNNs), which is one of deep learning algorithms that is widely used in image processing and pattern recognition with the characteristics of simplicity, strong adaptability and few parameters. Dropout is a novel neural network training method, which randomly omits half of the feature detectors on each training case to reduce test error and improve the generality of the networks. Support vector machine is one classification algorithm, which improves the generalization ability of model through structural risk minimization. Caffe is a deep learning frame-work with expression, speed, and modularity in mind. In this article, we introduced a new CNN model using hinge loss as the last layer instead of traditional soft-Max regression and improved top accuracy from 99.05% to 99.36% and average top accuracy from 98.964% to 99.278% without dropout and top accuracy from 99.14% to 99.39% and top accuracy from 99.024% to 99.321% with dropout.