Deep Learning based Cat Breed Recognition

This paper contributes to the field of computer vision and cat breed recognition, demonstrating the feasibility of applying machine learning techniques for cat breed classification. The findings of this study have practical implications in the context of animal welfare, pet breeding, and veterinary medicine. Additionally, this Project serves as a valuable foundation for future work in the development of real-world applications and automated tools for cat breed identification. It uses a ResNet50 model, which is a type of convolutional neural network (CNN). CNNs are well-suited for image classification tasks, and ResNet50 is a particularly powerful model that has been shown to achieve state-of-the-art results on many different datasets. The code first loads the ResNet50 model from PyTorch. Then, it creates a dataset of cat images, with each image labeled with its corresponding breed. The data is divided into train and validation set. The model is learning from training set, and the test set is used to check the performance on unseen record.