VGGNet is a convolutional neural network architecture that was developed by the Visual Geometry Group at the University of Oxford. It is known for its simplicity and depth, consisting of 16 or 19 layers, which allows it to learn complex features from images. The architecture primarily uses small 3x3 convolutional filters, which helps in capturing fine details while maintaining a manageable number of parameters. VGGNet gained popularity in the ImageNet Large Scale Visual Recognition Challenge due to its high accuracy. It serves as a foundational model for various computer vision tasks, including image classification and object detection, and has influenced many subsequent neural network designs.