Is focal loss important for yolo?

So focal loss to my understanding was proposed to solve class imbalance for important vs Bg objects.

In my understanding of yolo the maximum number of potential objects you can detect in an image is

Size of downsampled grid say n * n * num_anchors.

But in the original focal loss paper they claim that networks can have up to 100k easy background examples. So while I agree with the authors that the majority of predictions for yolo will be …

claim computervision grid image loss objects paper solve understanding yolo