Closed stefan-balke closed 6 months ago
Saw that you refactored that in the new DCASE24 baseline: https://github.com/CPJKU/dcase2024_task1_baseline/blob/main/dataset/dcase24.py
Hi! Thanks for pointing this out. Indeed, I messed up the MixupDataset for DCASE when I simplified the repo for public access.
It should be fixed now.
Hi there,
thanks for sharing the code. I was in particular interested in the dcase20 dataset loader. However, something in the
MixupDataset
classed confused me.See this link: https://github.com/fschmid56/EfficientAT/blob/main/datasets/dcase20.py#L101
SimpleSelectionDataset
is returningx, label, device, city, self.available_indices[index]
. It is then interpreted asx1, f1, y1, d1, c1
. It turns out that during training,x1
andf1
are used. However, I think the goal is to return a one-hot encoded and weighted version of the label(y1 * l + y2 * (1. - l))
.Maybe not relevant for this training but in case someone stumbles on this or wants to reuse it might find it useful!