Transfer Learning for Image Classification

In the previous chapter, we learned that as the number of images available in the training dataset increased, the classification accuracy of the model kept on increasing, to the extent that a training dataset comprising 8,000 images had a higher accuracy on the validation dataset than a training dataset comprising 1,000 images. However, we do not always have the option of hundreds or thousands of images, along with the ground truths of their corresponding classes, in order to train a model. This is where transfer learning comes to the rescue.

Transfer learning is a technique where we transfer the learning of the model on a generic dataset to the specific dataset of interest. Typically, the pretrained models used to perform transfer learning are trained on millions of images (which are generic and not the dataset of interest to us) and those pretrained models are now fine-tuned to our dataset of interest.

In this chapter, we will learn...