In this tutorial, you will learn how to use PyTorch's torchvision library to build an image classification model and expose the functionality as a REST API using Flask.
Sometimes there are scenarios where you have way lesser number of samples for some of the classes where as other classes have lots of samples. In such a scenario, you don’t want a training batch to be contain samples just from a few of the classes with lots of samples.
Recently, while working on a video dataset, I noticed that some of the videos contained a few corrupted frames. While running the training, my dataloader used to return an incorrect shaped tensor since it was not able to read the corrupted frame.