What is VGG16 and explain the architecture of VGG16?

VGG-16 is a simpler architecture model since it’s not using many hyperparameters.

• It always uses 3 x 3 filters with the stride of 1 in the convolution layer and uses SAME padding in pooling layers 2 x 2 with a stride of 2.

This architecture is from the VGG group, Oxford. It improves AlexNet by replacing the large kernel-sized filter with multiple 3X3 kernel-sized filters one after another. With a given receptive field(the effective area size of the input image on which output depends), multiple stacked smaller size kernel is better than the one with a larger size kernel because multiple non-linear layers increase the depth of the network which enables it to learn more complex features, and that too at a lower cost.

• Three fully connected layers follow the VGG convolutional layers.

• The width of the networks starts at the small value of 64 and increases by a factor of 2 after every sub-sampling/pooling layer. It achieves the top-5 accuracy of 92.3 % on ImageNet.