The runner-up in the 2014 ImageNet challenge was VGGNet from the visual geometric group at Oxford University. This convolutional neural network is a simple and elegant architecture with a 7.3% error rate. It has two versions: VGG16 and VGG19.
VGG16 is a 16-layer neural network, not counting the max pooling layer and the softmax layer. Hence, it is known as VGG16. VGG19 consists of 19 layers. A pre-trained model is available in Keras for both Theano and TensorFlow backends.
The key design consideration here is depth. Increases in the depth of the network were achieved by adding more convolution layers, and it was done due to the small 3 x 3 convolution filters in all the layers. The default input size of an image for this model is 224 x 224 x 3. The image is passed through a stack of convolution layers with a stride of 1 pixel and padding of 1. It uses 3 x 3...
