Fusion of pretrained CNN models for cat breed classification: A comparative study

Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480

^* Corresponding author: emmanuel.hamdi@binus.ac.id

Abstract

Cat is one of the most popular domestic animals that human has domesticated for a long time, since then, there have been many breeds that can be difficult to identify with each breed having different health issues and care requirement, to resolve this problem we used Convolutional Neural Network (CNN) a widely used artificial intelligence deep learning model that has been used in many image classification problem, in this study we explored 11 different types of CNN-Based model architecture to be used in a fusion-based technique and fine-tune the model to further increase its performance, our results show that fusion model is a promising technique in classifying cat breeds that outperforms all of the individual CNN- Based model architecture with the 3 fusion model having an accuracy of 0.9053, precision of 0.9075, recall of 0.9053, and F1 score of 0.9016, additionally, fine-tuning only shows a small effect in increasing the fusion model performance.