Deep-Learning-with-PyTorch

Adding a second model to our project
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will accomplish this will be very similar to the code from the last chapter, especially if
we focus on the larger structure. All of the changes we’re going to make will be
smaller and targeted. As we see in figure 13.2, we need to make updates to our model
(step 2A in the figure), dataset (2B), and training loop (2C) to account for the new
model’s inputs, outputs, and other requirements. (Don’t worry if you don’t recognize
each component in each of these steps in step 2 on the right side of the diagram. We’ll
go through the details when we get to each step.) Finally, we’ll examine the results we
get when running our new model (step 3 in the figure).
1. Segmentation
UNet
2. Update:
2a. Model
T/F
2b. Dataset
3. Results
2c. Training
Figure 13.2 The new model architecture for segmentation, along with the model, dataset,
and training loop updates we will implement
Breaking down figure 13.2 into steps, our plan for this chapter is as follows:
1 Segmentation. First we will learn how segmentation works with a U-Net model,
including what the new model components are and what happens to them as
we go through the segmentation process. This is step 1 in figure 13.2.
2 Update. To implement segmentation, we need to change our existing code base
in three main places, shown in the substeps on the right side of figure 13.2.The
code will be structurally very similar to what we developed for classification, but
will differ in detail:
a Update the model (step 2A). We will integrate a preexisting U-Net into our segmentation
model. Our model in chapter 12 output a simple true/false classification;
our model in this chapter will instead output an entire image.