‫Next is VGG16
‫This was The Runner-Up in 2014, a ILSVRC challenge.
‫This was a very big architecture and it had 138 million parameters to be trained.
‫So it took a lot of time to train.
‫If you look at the architecture instead of convolutional layers, it has convolutional blocks.
‫And one block consists of several convolutional layers and a max pooling layer at the end so this convolutional
‫block has to convolutional layers and one max pooling layer.
‫Next convolutional block has two corners, layers and a max pooling layer .
‫And then we again have a convolutional block.
‫The last two convolutional blocks have three convolutional lives instead of two convolutional layers.
‫After processing a 224 by 224 pixel image at the end of all of these convolutional layers, we
‫had 512 feature maps of seven by seven size.
‫These feature maps were fed into a fully connected neural network with so many neurons.
‫At the end of it, there were 1000 output neurons with softmax activation.
‫This is because VGG sixteen was run on a problem in which there were 1000 classes into which images
‫were to be classified.
‫So it was a huge database.
‫It had over a million images and those images were to be classified into 1000 classes.
‫Classes were of dog breeds.
‫Cat breed etc.