A Comparison of Adaptive Moment Estimation (Adam) and RMSProp Optimisation Techniques for Wildlife Animal Classification Using Convolutional Neural Networks

Abstract

The rapid decline in wildlife animal diversity necessitates expedited evaluations of biodiversity and population dynamics. Accurate image recognition from camera traps is central to such assessments. This study investigates the impact of different optimisation techniques and hyperparameter configurations on the accuracy of wildlife animal classification. Specifically, the comparative effectiveness of the Adaptive Moment Estimation (Adam) and Root Mean Square Propagation (RMSProp) optimisation algorithms is examined. The influence of learning rates on these optimisation techniques is evaluated, while other hyperparameters are held constant. Convolutional Neural Networks (CNN) models, namely DenseNet-121, ResNet-50, and AlexNet, are utilised for this study. The investigation employs a dataset composed of 47,841 images sourced from the Serengeti Project Season 1 Snapshot in Tanzania. The images depict wild animals in diverse perspectives within their natural habitats, with some providing a complete view of the animal's body, while others do not. The dataset, characterised by an imbalanced distribution, is segregated into training, validation, and testing sets at proportions of 80%, 10%, and 10%, respectively. The results reveal that the application of the Adam optimisation technique yields the highest average accuracy of 80.66% with the ResNet-50 model. However, the DenseNet-121 model achieved an overall accuracy exceeding 95%. Notably, the ResNet-50 architecture, with learning rates of 0.1 and 0.01, encountered challenges during the training and validation of all images due to the complexity of the dataset. Irrespective of the optimisation technique employed, the most effective performance was observed with the ResNet-50 model, utilising the Adam optimiser and a learning rate of 0.001. The study proposes suitable learning rate values for training scenarios similar to the present investigation.