Documentation confusing on whether SSD and RetinaNet count background as class object

The documentation for the SSD class mentions that we should not count the background as an object class when passing the number of classes as a parameter to instantiate an SSD object.

https://github.com/pytorch/vision/blob/959666891589c35e6d225943253f523cffbae4cc/torchvision/models/detection/ssd.py#L144

However, further down in the same file, an SSD object is instantiated in a function that explicitly says that the background should be counted as an object class, but this is not taken into account in the code (i.e. I did not see num_classes be decremented by one when creating the SSD object).

https://github.com/pytorch/vision/blob/959666891589c35e6d225943253f523cffbae4cc/torchvision/models/detection/ssd.py#L589

Here is the documentation for this function, which says we should include the background in the number of classes.

https://github.com/pytorch/vision/blob/959666891589c35e6d225943253f523cffbae4cc/torchvision/models/detection/ssd.py#L563

This is confusing. Should we or should we not count the background as an object class when instantiating the SSD? In either case, how should object classes be ID’d during training?

As an example, with Faster RCNN, the background is counted as an object class (with ID 0 reserved for it) and actual object classes are identified during training starting from ID 1. What should be the procedure for SSD?

I have also opened a topic in the forums, since this is both a personal question of mine as well as a possible issue in the docs (or the code).

[WZMIAOMIAO]

@datumbox hi, I think num_classes in retinanet should exclude the background. because in classification loss using binary_cross_entropy_with_logits, don’t need to consider background.

[datumbox]

@Xonxt The documentation was improved to reflect on he situation. To answer your question, the num_classes should include the background which is encoded with 0. During inference the model predicts labels starting from 1.