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‫Next is VGG16

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‫This was The Runner-Up in 2014, a ILSVRC challenge.

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‫This was a very big architecture and it had 138 million parameters to be trained.

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‫So it took a lot of time to train.

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‫If you look at the architecture instead of convolutional layers, it has convolutional blocks.

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‫And one block consists of several convolutional layers and a max pooling layer at the end so this convolutional

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‫block has to convolutional layers and one max pooling layer.

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‫Next convolutional block has two corners, layers and a max pooling layer .

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‫And then we again have a convolutional block.

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‫The last two convolutional blocks have three convolutional lives instead of two convolutional layers.

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‫After processing a 224 by 224 pixel image at the end of all of these convolutional layers, we

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‫had 512 feature maps of seven by seven size.

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‫These feature maps were fed into a fully connected neural network with so many neurons.

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‫At the end of it, there were 1000 output neurons with softmax activation.

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‫This is because VGG sixteen was run on a problem in which there were 1000 classes into which images

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‫were to be classified.

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‫So it was a huge database.

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‫It had over a million images and those images were to be classified into 1000 classes.

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‫Classes were of dog breeds.

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‫Cat breed etc.