Comparative Analysis of CNN and Pre-Trained VGG16 Model for Gender Classification

This study compares two deep learning models: a customised Convolutional Neural Network (CNN) and a pre-trained VGG16 model, with an eye towards the gender classification issue using image data. Many computer vision applications rely on gender classification; hence the model selection significantly influences the performance and efficacy of these systems. The study evaluates several models using several important criteria, including confusion matrices, accuracy, and loss, thereby determining which one works better in classifying images into male and female categories. Pre-trained on the ImageNet dataset, the VGG16 deep architecture is ideal for this particular binary classification challenge. With its several convolutional and dense layers, the custom CNN serves as a light-weight model. Experimental data shows that the pre-trained VGG16 model achieves substantially higher accuracy and generalisation than the tailored CNN. For jobs including gender classification, the VGG16 model proved better during training and obtained a higher validation accuracy. The trade-off between accuracy and processing efficiency in model selection is underlined by the fact that improved performance results from more computational demand.