Hands-On Convolutional Neural Networks with TensorFlow

More about VGG

In 2014, VGG achieved the second place in the Imagenet Classification challenge and the first place in the Imagenet Localization challenge. As we saw, the VGGNet design choice of stacking many small convolution layers allows for a deeper structure that performs better while having less parameters (if we remove the unnecessary fully connected layers). This design choice is so effective in creating power and efficient networks that pretty much all modern architectures copy this idea and will rarely, if at all, use large filters.

The VGG model is proven to work well in a lot of tasks, and because of its simple architecture, it's a go-to model to start experimenting with or adapting to the needs of your problem. However, it does ...