VGG-16 is a convolutional neural network architecture known for its depth and simplicity, consisting of 16 layers that include convolutional layers, max-pooling layers, and fully connected layers. It was developed by the Visual Geometry Group at the University of Oxford and became popular due to its performance in image classification tasks and competitions, showcasing the importance of deep architectures in deep learning.
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