Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset

• URL: http://arxiv.org/abs/2408.00002v2
• Date: Tue, 12 Nov 2024 14:55:50 GMT
• Title: Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset
• Authors: Subek Sharma, Sisir Dhakal, Mansi Bhavsar,
• Abstract summary: The study utilizes transfer learning to fine-tune pre-trained models on the dataset. YOLOv8 outperforms other models, achieving a training accuracy of 97.39% and a validation F1-score of 96.50%.
• Score: 0.0
• License:
• Abstract: 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.

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