Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset
This work addresses wildlife monitoring and conservation by automating species classification, but it is incremental as it applies existing methods to a new dataset.
This study tackled wildlife species classification by evaluating deep learning models on a custom dataset of 575 images of 23 endangered species, finding that YOLOv8 achieved 97.39% training accuracy and 96.50% validation F1-score, outperforming DenseNet, ResNet, and VGGNet.
This study evaluates the performance of various deep learning models, specifically DenseNet, ResNet, VGGNet, and YOLOv8, for wildlife species classification on a custom dataset. The dataset comprises 575 images of 23 endangered species sourced from reputable online repositories. The study utilizes transfer learning to fine-tune pre-trained models on the dataset, focusing on reducing training time and enhancing classification accuracy. The results demonstrate that YOLOv8 outperforms other models, achieving a training accuracy of 97.39% and a validation F1-score of 96.50%. These findings suggest that YOLOv8, with its advanced architecture and efficient feature extraction capabilities, holds great promise for automating wildlife monitoring and conservation efforts.