In this post, we'll explore how to perform transfer learning using Pytorch.
We will use a subset of Food-11k that contains 11 different kinds of food categories. We will go over the dataset preparation, data augmentation and then steps to build the classifier. We use transfer learning to use the low level image features like edges, textures etc. learnt by a pretrained model, ResNet50, and then train our classifier to learn the higher level details in our dataset images. ResNet50 has already been trained on ImageNet with millions of images.

In this post, we will try to use pre-trained models to do image classification. We will use two popular deep learning frameworks, PyTorch and Keras. Let's find out the workflow of using pre-trained models in these two frameworks.