Question about (to me) weird line used when exporting segmentation w LabelManager

[vcasellesb]

Hi! I have a doubt regarding the following line in the revert_cropping_on_probabilities method in the LabelManager class:

if not self.has_regions:
    probs_reverted_cropping[0] = 1

Can someone explain to me what the rationale behind this line is? What this does is, if we are dealing with a non-region-based task, set all the "background" probabilities to 1 -- i.e., probs_reverted_cropping is a 4d array, first dimension is the classes dimension, with N:=number of classes indices, the first one being the background always.

Why is this needed? Wouldn't it make more sense to keep the background probabilities to what the net outputs? And why is it not necessary in region-based tasks? I feel like I'm missing something very obvious.

Thanks for your time! Best,
Vicent

[Rainbow96r]

Probable Reasons:

1. Different Task Semantics
   Non-region tasks (classification, detection): The "background" class often represents the dominant case (e.g., "not the object"). Setting it to 1 acts as a strong prior.

Region tasks (segmentation): Background is naturally encoded through regional assignments, so explicit probability manipulation isn't needed.

2. Probability Normalization after Cropping
   When reverting cropped probabilities back to the original image space:

python

The network's output might be "incomplete" due to the cropping operation

Setting background=1 ensures proper probability mass distribution

This prevents unnaturally low background probabilities in non-region contexts

3. Implementation Simplification
   For non-region tasks, this provides a clean way to handle areas outside the network's "field of view"

It avoids complex probability redistribution logic

Ensures numerical stability in subsequent processing steps

4. Behavioral Consistency
   This might enforce that in non-region contexts, the default state should be "background" unless evidence strongly suggests otherwise.

Why It Might Make Sense:
In many detection/classification scenarios, the background class should indeed have high probability in most locations. The cropping operation might artificially suppress this, and this line could be correcting for that bias.

The key insight is that region-based tasks have inherently different spatial priors than global classification tasks.