In this chapter, we illustrated the use of pretrained deep neural networks for developing image classification models. Such pretrained networks, which are trained using over 1 million images, capture reusable features that can be applied to similar but new data. This aspect becomes valuable when developing image classification models with relatively smaller datasets. In addition, they provide savings in terms of the use of computational resources and time. We started by making use of the RESNET50 pretrained network to identify an image of a Norwich terrier dog. Subsequently, we made use of 2,000 images from the CIFAR10 dataset to illustrate the usefulness of applying pretrained networks to a relatively smaller dataset. The initial convolutional neural networks model that we built from scratch suffered from overfitting and did not yield useful results.
Next, we used the...
