AN EFFECTIVE DEEP LEARNING MODEL FOR RECOGNITION OF ANIMALS AND PLANTS

Abstract

This paper presents a deep learning model to address the problem of recognition of animals and plants. The context of this work is to make an effort in protection of rare species that are seriously faced to the risk of extinction in Vietnam such as Panthera pardus, Dalbergia cochinchinensis, Macaca mulatta. The proposed approach exploits the advanced learning ability of convolutional neural networks and Inception residual structures to design a lightweight model for classification task. We also apply the transfer learning technique to fine-tune the two state-of-the-art methods, MobileNetV2 and InceptionV3, specific to our own dataset. Experimental results demonstrate the superiority of our object predictor (e.g., 95.8% accuracy) in comparison with other methods. In addition, the proposed model works very efficiently with the inference speed of around 113 FPS on a CPU machine, enabling it for deployment on mobile environment.