Related papers: Evaluating Transfer Learning in Deep Learning Models for Classification on a Custom Wildlife Dataset: Can YOLOv8 Surpass Other Architectures?

Evaluating Transfer Learning in Deep Learning Models for Classification on a Custom Wildlife Dataset: Can YOLOv8 Surpass Other Architectures?

URL: http://arxiv.org/abs/2408.00002v1
Date: Wed, 10 Jul 2024 15:03:00 GMT
Title: Evaluating Transfer Learning in Deep Learning Models for Classification on a Custom Wildlife Dataset: Can YOLOv8 Surpass Other Architectures?
Authors: Subek Sharma, Sisir Dhakal, Mansi Bhavsar,
Abstract summary: Current human-led monitoring techniques are prone to errors and are labor-intensive. We study the application of deep learning methods like Convolutional Neural Networks (CNNs) and transfer learning. Our results indicate that YOLOv8 performs better, achieving a training accuracy of 97.39 % and an F1 score of 96.50 %.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Biodiversity plays a crucial role in maintaining the balance of the ecosystem. However, poaching and unintentional human activities contribute to the decline in the population of many species. Hence, active monitoring is required to preserve these endangered species. Current human-led monitoring techniques are prone to errors and are labor-intensive. Therefore, we study the application of deep learning methods like Convolutional Neural Networks (CNNs) and transfer learning, which can aid in automating the process of monitoring endangered species. For this, we create our custom dataset utilizing trustworthy online databases like iNaturalist and ZooChat. To choose the best model for our use case, we compare the performance of different architectures like DenseNet, ResNet, VGGNet, and YOLOv8 on the custom wildlife dataset. Transfer learning reduces training time by freezing the pre-trained weights and replacing only the output layer with custom, fully connected layers designed for our dataset. Our results indicate that YOLOv8 performs better, achieving a training accuracy of 97.39 % and an F1 score of 96.50 %, surpassing other models. Our findings suggest that integrating YOLOv8 into conservation efforts could revolutionize wildlife monitoring with its high accuracy and efficiency, potentially transforming how endangered species are monitored and protected worldwide.

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