Image Classification Using Deep Neural Networks: Transfer Learning and the Handling of Unknown Images

Deep learning is a subset of machine learning that is powerful at recognizing patterns and extensively used for image classification. However, it typically requires a large amount of data and it is computationally expensive for training an application from scratch. ImageNet database has millions of images pertaining to different categories that are acquired by years of hard work. Getting such a database for every application is tough and time consuming. Transfer learning is an alternative to conventional training. Transfer learning results in much faster and easier training of a network. This research set out to evaluate the effect of transfer learning on the performance of a Deep Neural Network (DNN). Pre-trained AlexNet was selected, modified and retrained for 3 image classification applications (gears, connectors and coins) with a modest database. This approach gave 99% classification accuracy using transfer learning. To test the robustness of the network, unknown images were added to one of the classes and the accuracy was reinforced using a probability threshold. This approach succeeded in compensating for the effect of unknowns in the accuracy.