Computer Vision-Driven AI Techniques for Classification of Animal Species

This study explores the application of deep learning techniques, specifically Convolutional Neural Networks, for the classification of dog breeds from images. By employing varying input image resolutions, the research evaluates the impact of resolution on the accuracy and efficiency of the model. Five experiments were conducted using resolutions of 64, 128, 224, 256, and 512 pixels to assess model performance. The results indicate that an input resolution of 256x256 pixels yields the highest accuracy, achieving 94.74% with an optimal balance between detail and processing complexity. However, certain breeds, such as the American Foxhound and Anatolian Shepherd Dog, exhibited lower classification performance, highlighting the importance of considering breed-specific characteristics in model development. The findings emphasize the critical role of image resolution in training deep learning models and suggest that a 256x256 resolution offers the best trade-off between accuracy and computational efficiency for dog breed classification.