Bird Image Classification using Convolutional Neural Network Transfer Learning Architectures

Abstract

With the technological progress of human beings, more and more animal and bird species are being endangered and sometimes even going to the verge of extinction. However, the existence of birds is highly beneficial for human civilization as birds help in pollination, destroying harmful insects for crops, etc. To ensure the healthy co-existence of all species along with human beings, almost all advanced countries have taken up some conservation measures for endangered species. To ensure conservation, the first step is to identify the species of birds found in different locations. Deep learning-based techniques are best suited for the automated identification of bird species from the captured images. In this paper, a Convolutional Neural Network based bird image identification methodology has been proposed. Four different transfer learning-based architectures, namely Resnet152V2, Inception V3, Densenet201, and MobileNetV2 have been used for bird image classification and identification. The models have been trained using 58388 images belonging to 400 species of birds, and the models have been tested using 2000 images belonging to 400 species of birds. Out of these four models, Resnet152V2 and DenseNet201 performed comparatively well. The accuracy of Resnet152V2 was highest at 95.45%, but it faced a large loss of 0.8835. But based on the results, even though DenseNet201 had an accuracy of 95.05%, it faced less loss i.e., of 0.6854. The results show that the DenseNet201 model can further be used for real-life bird image classification.