Deep Learning for Coders with fastai and PyTorch

Chapter 6. Other Computer Vision Problems

In the previous chapter, you learned some important practical techniques for training models in practice. Considerations like selecting learning rates and the number of epochs are very important to getting good results.

In this chapter, we are going to look at two other types of computer vision problems: multi-label classification and regression. The first one occurs when you want to predict more than one label per image (or sometimes none at all), and the second occurs when your labels are one or several numbers—a quantity instead of a category.

In the process, we will study more deeply the output activations, targets, and loss functions in deep learning models.

Multi-Label Classification

Multi-label classification refers to the problem of identifying the categories of objects in images that may not contain exactly one type of object. There may be more than one kind of object, or there may be no objects at all in the classes you are looking for.

For instance, this would have been a great approach for our bear classifier. One problem with the bear classifier that we rolled out in Chapter 2 was that if a user uploaded something that wasn’t any kind of bear, the model would still say it was either a grizzly, black, or teddy bear—it had no ability to predict “not a bear at all.” In fact, after we have completed this chapter, it would be a great exercise for you to go back to your image classifier application and try to retrain it using the ...