Implementation of deep-learning based image classification on single board computer

In this paper, a deep-learning algorithm based on convolutional neural-network is implemented using python and tflearn for image classification. A large number of different images which contains two types of animals, namely cat and dog are used for classification. Two different structures of CNN are used, namely with two and five layers. It is shown that the CNN with higher layer performs classification process with much higher accuracy. The best CNN model with high accuracy and small loss function deployed in single board computer.