VGG16 is a convolutional neural network architecture that was developed by the Visual Geometry Group at the University of Oxford. It gained prominence for its performance in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014. The architecture consists of 16 layers that have learnable weights, which include 13 convolutional layers and 3 fully connected layers. The model is known for its simplicity and depth, utilizing small convolutional filters stacked on top of each other, which allows it to capture complex features while keeping the number of parameters manageable.
Key features of VGG16 include:
Overall, VGG16 has become a foundational model in deep learning, often serving as a backbone for transfer learning in various computer vision tasks.
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