Convolutional Neural Network for Image Classification Research - Based on VGG16

In recent years, convolutional neural networks (CNNs) have increasingly become the predominant approach for image classification, demonstrating remarkable performance in this domain. Notably, VGG16 has gained widespread adoption due to its deep architecture and robust feature extraction capabilities. Consequently, this paper investigates the efficacy of employing the VGG16 convolutional neural network model for image classification on the CIFAR-10 dataset. The architecture of the VGG16 model and its distinctive application characteristics are first introduced, followed by an exploration of key steps including data preprocessing, model training, and hyperparameter tuning. 99.4% accuracy on the training set and 90.6% accuracy on the test set were attained after the training procedure was optimized. To improve the model's classification performance even more, this paper identifies limitations inherent in the current framework and proposes optimization strategies such as incorporating attention mechanisms and transfer learning. These strategies provide valuable insights for future applications of deep learning techniques on small-scale image datasets.