A Study on an Improved ResNet Model for Image Classification

ResNet effectively addresses the vanishing gradient problem in deep networks by introducing skip connections, making it a leading model in image classification tasks. This paper proposes an improved ResNet-based model leveraging transfer learning from ImageNet, initially incorporating additional layers such as Flatten, fully connected layers, L2 regularization, Batch Normalization, and Dropout. The research began with an “initial improved version” of ResNet50, designed to enhance stability and generalization. Subsequent experiments focused on further optimizations inspired by recent studies, particularly the strategic placement of Dropout layers. Comparative experiments on the CIFAR-10 dataset included the original ResNet50, transfer learning-enhanced ResNet, and multiple improved variants. The results demonstrate that the initial improved model significantly enhances stability and generalization compared to the original ResNet. Further optimizations, such as placing Dropout before the Global Average Pooling (GAP) layer, yielded the best performance on the training set, while placing Dropout after the Flatten layer and before the Dense (128) layer resulted in superior validation performance. This study underscores the benefits of a stepwise optimization approach, combining transfer learning, structural modifications, and strategic Dropout placement to enhance image classification performance.