Optimizing Transfer Learning and Fine-Tuning Hyperparameters in Image Classification Problems with Firefly Algorithm - Silva, Vinícius T. M. G. da; Oliveira, Gustavo F. V. de; Silva, Fabrício A.; Mendes, Marcus H. S. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e Computacional, 2024)
Image classification is crucial in computer vision, mainly with Convolutional Neural Networks (CNNs). This paper optimizes transfer learning and fine-tuning hyperparameters of CNNs pre-trained on ImageNet for still image classification. Hyperparameter tuning is a complex task that impacts the classification results. The Firefly Algorithm (FA) was used to optimize these hyperparameters across four datasets with Xception and ResNet-152 architectures. Experiments show that FA enhances model performance, achieving state-of-the-art accuracy on three datasets: Multi-Class Weather (99.11%), Pistachio (100%), and D0 (99.89%). Despite being time-consuming, this approach offers a viable method for improving image classification, mainly with smaller datasets.