Bird Species Detection Using CNN and EfficientNet-B0

This research paper presents an innovative approach for bird species detection using Convolutional Neural Networks (CNN) and the EfficientNet-B0 architecture. The study focuses on the application of deep learning techniques to address the challenging task of automatically identifying bird species from images. Leveraging a diverse dataset of bird images, the proposed methodology demonstrates the effectiveness of transfer learning with the state-of-the-art EfficientNet-B0 model. We explore various aspects of the model's architecture, such as fine-tuning and feature extraction, to optimize performance. The results showcase the potential of this approach for accurate and efficient bird species classification, offering promising implications for biodiversity monitoring, ornithology, and wildlife conservation efforts.