Thanks so much for your detailed response!
I have a few questions to gain more clarity and understanding from your response:

" If you define your  Dataset , you would have to make sure that each sample is mapped to a valid class index."

When you are talking about mapping, is this done simply by using the masked image? Like in my masked images, all the background pixels are black, and the ones that belong to the car class are blue. Is using that enough to map the classes correctly? Or do you need to do something else besides that to map it?  You mentioned output[0], but I didn’t see anything in the tutorial where you index an output and map it to a class.

“if you only care about the background and car class, you would have to make sure the you are only using samples containing these two classes.”

In my image data set, I only have two labels/classes ‘background’ or ‘car’. Basically everything that is not a car is marked as the background. In some of those unlabeled images, I have objects in the background like a ‘chair’ or ‘bird’ that are classes in the original pre-trained model, but in my masked images I mark them as background because I don’t care about those classes. Would doing this (masking objects that belonged to a different class in the original pre-trained model as background) confuse the model and cause a loss in accuracy?
Basically I want to clarify if what you mean by I should “only have samples containing those two classes”  means that the original unlabeled images should not have objects that belong to other classes other than ‘background’ and ‘car’. Or if it’s ok to have images like these, as long as the masked/labeled images only have ‘background’ and ‘car’ class?