Performance Enhancement of Animal Species Classification Using Deep Learning

Automatic recognition of animal classes by their imageries is an imperative and perplexing task, especially with different animal breeds. Many image classification systems have been projected in the literature but they involve some disadvantages like accuracy deterioration or exhaustive confined calculation. This paper focuses on two methodologies: Transfer Learning and Convolutional Neural Network (CNN) for image-based species identification for distinct animal classes and categorized around twenty-eight thousand animal images from Google Images into ten diversified animal classes. For transfer learning, we have implemented VGG16 (Visual Geometry Group), Efficient NetB2, ResNet101 (Residual Network), Efficient NetB7, and Resnet50 networks that are pre-trained and equated the results of the 5 custom-built CNN networks with these networks using various evaluation metrics that can assist practitioners and research biologists in accurately recognizing various animal species. In terms of performance, VGG-16 attained a maximum accuracy of 0.99 and a Least Validation Cross Entropy Loss of 0.044 for the classification of different species of animals.