Prediction of Endangered Species using Variants of EfficientNet

Biodiversity conservation is critical in addressing the growing threats to endangered species worldwide. Accurate prediction of endangered species status is vital for developing targeted conservation strategies. This study explores the application of machine learning models, specifically the EfficientNetV2 family of architectures, to predict endangered species using image data. The training process employed a two-stage approach. Initially, the pretrained EfficientNet models (variants B0, B1, B2, B3, S, M, and L) were fine-tuned by freezing their base layers and training custom classification heads using sparse categorical cross-entropy loss. Subsequently, the entire models were unfrozen for comprehensive fine-tuning at reduced learning rates, optimizing feature extraction for the specific task. The results highlight the potential of advanced machine learning architectures for biodiversity conservation, offering a scalable and accurate solution for species identification and classification. This study emphasizes the importance of integrating computational methods with conservation efforts to address global challenges in preserving biodiversity.