VGGNet

VGGNet is a deep learning model for image recognition that was developed by researchers at the University of Oxford. It is known for its simple and effective architecture, consisting of many layers of small filters, which helps the model learn complex features in images. VGGNet processes images through multiple convolutional layers, allowing it to identify shapes and patterns. Its design emphasizes depth, using up to 19 layers, which improves accuracy in recognizing objects, making it a significant milestone in computer vision and widely used in various applications, including smartphones and autonomous vehicles.