<![CDATA[This study employs the ResNet50V2 Deep Learning model for the purpose of classifying five distinct animal species. To gain insights into the model's proficiency in visual recognition, we conducted training and testing procedures on a dataset comprising diverse images of animal species. The utilization of ResNet50V2 in this classification task is intended to discern visual distinctions among these species by leveraging the distinctive characteristics present in the input images. A meticulous and comprehensive training procedure was undertaken on the model, employing fine-tuning techniques to adjust its internal representation in order to accommodate diverse animal characteristics. The experimental findings illustrate the model's capacity to effectively discern and categorize various animal species with a notable degree of precision, thereby presenting encouraging outcomes for the potential utilization of this model in broader animal classification contexts. This study emphasizes the significant potential of employing Deep Learning models, specifically ResNet50V2, for the purpose of comprehending and identifying diverse fauna through visual cues.]]>
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