VGGNet is a convolutional neural network architecture known for its simplicity and effectiveness in image classification tasks. Developed by the Visual Geometry Group at the University of Oxford, VGGNet uses a deep architecture composed of 16 to 19 layers, primarily relying on small convolutional filters (3x3) stacked on top of each other, which helps to learn hierarchical features from images. Its straightforward design has made it a popular choice for various computer vision applications, influencing many subsequent models in the field.
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