VGG-16 and VGG-19 CNN Architectures .

VGG-16 and VGG-19 CNN architectures explained in details using illustrations and their implementation in Keras and PyTorch .

VGG16 Architecture took second place in the ImageNet Large Scale Visual Recognition Challenge in 2014 (ILSVRC 2014), after GoogleNet (Inception-V1), taking first place in the Localisation task, and VGG19 is an improved version of VGG16 .
In this article you will see vgg16 and vgg19 cnn architectures explained in detail, and you will see how to implement them using Keras and PyTorch.

VGG-16 :

Paper : Very Deep Convolutional Networks for Large-Scale Image Recognition

Authors : Karen Simonyan, Andrew Zisserman. University of Oxford, UK.

Published in : ILSVRC 2014 .

Architecture :

Vgg16 Architecture

Implementation :

The above illustration has everything you need to implement this Architecture , try to implement it and compare what you got with my result .

1. Keras :

2. PyTorch :

VGG-19 :

vgg19 cnn Architecture was published in the same paper with vgg16.

Architecture :

vgg19 Architecture

Implementation :

The above illustration has everything you need to implement this Architecture , try to implement it and compare what you got with my result .

1. Keras :

2. PyTorch :

References :

• vgg16 and vgg19 Original paper .
• if you want to see this architectures in a better quality (svg format), check this repository .
• PyTorch documentation .
• Keras documentation .