In this post, I will show you how to use BERT as a feature extractor (embedding extractor) and perform text classification on the outputs of BERT.

(source: BERT: Pre-training of Deep Bidirectional Transformers forLanguage Understanding https://arxiv.org/pdf/1810.04805v2.pdf)

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.