VGG is a convolutional neural network architecture known for its simplicity and deep architecture. Developed by the Visual Graphics Group at Oxford (hence VGG), it was a strong contender in the ImageNet Challenge.
VGG in Practice
What distinguishes VGG is its use of a series of convolutional layers with small receptive fields followed by max-pooling layers, repeated multiple times. This architecture has been influential in the development of deep learning for image recognition tasks, due to its effectiveness and simplicity.
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