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VGG16 is a convolution neural net (CNN ) architecture which shows great accuracy and won the ILSVR competition in 2014. It is considered to be one of the excellent vision model architecture and to be one of the best computer vision models available till date. Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 convolution filter architecture with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2, which substantially exceeded previous-art settings. It follows this arrangement of convolution and max pool layers consistently throughout the whole architecture. In the end it has 2 FC(fully connected layers) followed by a softmax for output. The 16 in VGG16 refers to it has 16 layers that have weights. This network is a pretty large network and it has about 138 million (approx) parameters.

Architecture of VGG-16 is a type of VGG Net. The input to VGG-16 is a fixed size 244 X 244 RGB image as shown in Figure 4213a. Each pixel in a picture is deducted from its mean RGB value in a pre-processing phase. The pictures are then fed through a stack of convolutional layers with tiny receptive-field filters of size (33) once the pre-processing is completed. The filter size is set to (1 1) in a few setups, indicating that the input channels have been transformed linearly (followed by non-linearity). The convolution operation's stride is set at 1 by default. Five max-pooling layers, which come after numerous convolutional layers, are used to do spatial pooling.

Figure 4213a. Architecture of VGG-16.

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