Abstract
Abstract: The detection of bird species from photos is a challenging problem due to the large number of species, subtle visual differences between them, and variations in lighting and camera angles. In recent years, deep learning techniques have shown promising results in solving this problem. In this study, we propose a method for detecting bird species from photos using the ResNet50 deep learning architecture. We’ve collected a large dataset of bird photos, including images of over 200 species from different geographic locations. We pre processed the images by resizing them and augmenting the data to increase the size of the dataset. We then fine-tuned the ResNet50 model on the dataset and achieved an accuracy of 98% on a test set of bird photos. Our results demonstrate the effectiveness of deep learning techniques for bird species detection and provide potential applications in biodiversity conservation and ecological research
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