Enhancing Image Classification Accuracy Based on AlexNet

Abstract

This study explores the potential of deep learning for small-scale image classification tasks through the utilization of the classical AlexNet model on the CIFAR-10 dataset. The methodology involves meticulous architectural adjustments, comprehensive data preprocessing, and strategic optimization of the learning rate decay, resulting in substantial improvements in model performance. In addition, innovative data augmentation techniques are introduced to enhance the model's robustness and generalization capabilities. The experimental outcomes unequivocally underscore the efficacy of deep learning in addressing small-scale image classification challenges, offering versatile applications across diverse domains such as image recognition, autonomous driving, and medical diagnostics. Acknowledging the dynamic nature of deep learning, ongoing research endeavors are warranted. Future directions may encompass the exploration of more intricate neural network architectures, advanced data augmentation methodologies, and the implementation of interpretability tools to augment both performance and comprehensibility. This study provides compelling evidence of deep learning's aptitude for small-scale image classification tasks, offering valuable insights and guidance for future research and practical implementations. As the field of deep learning continues to evolve, this work aims to serve as a valuable reference and catalyst for further advancements in the domain.