Questiona about jaccard loss

[mrgloom]

Here is jaccard loss implementation:
https://github.com/keras-team/keras-contrib/blob/master/keras_contrib/losses/jaccard.py

Why jaccard loss is prefered to dice loss?

Why we apply K.abs? as I understand y_true, y_pred should be already in [0,1] range.
https://github.com/keras-team/keras-contrib/blob/master/keras_contrib/losses/jaccard.py#L31

Why default smooth=100? how smooth parameter affect training?

As I understand adding smooth to denominator prevents division by zero(why not smooth=eps=1e-6?), but why it's added to numerator?

Why we multiply loss by smooth at the end?
https://github.com/keras-team/keras-contrib/blob/master/keras_contrib/losses/jaccard.py#L34

[hmeine]

I wonder if there are some problems with that definition:

1. Does it work with one-hot-encoded data? In particular, I would've expected axis to be everything but the channel dimension, but it is just the channel dimension.
2. Shouldn't it skip the background label? (At least optionally)
3. I also wondered about the multiplication at the end.
4. Furthermore, smooth = 100 looks like a really arbitrary default.