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allow it to work on both images and point clouds (other inputs TBD)
do not consider never seen classes in metric computation
extend testing
[let's see if something else comes up in the discussion here]
Motivation
The just landed PanopticQuality is a welcome addition to the toolbox, but there are a number of minor issues associated with it, from deal-breaking (does not take batches as inputs) to unnecessarily restrictive (PQ is a metric for panoptic segmentation both in point cloud and image domains), to suboptimal in terms of interpretability (if a class has TP + FP + FN = 0, why should it contribute to the final metric value?)
Pitch
Let's bring in these goodies, happy to contribute 😉
Alternatives
Additional context
The text was updated successfully, but these errors were encountered:
🚀 Feature
Expand the PanopticQuality code to
Motivation
The just landed PanopticQuality is a welcome addition to the toolbox, but there are a number of minor issues associated with it, from deal-breaking (does not take batches as inputs) to unnecessarily restrictive (PQ is a metric for panoptic segmentation both in point cloud and image domains), to suboptimal in terms of interpretability (if a class has TP + FP + FN = 0, why should it contribute to the final metric value?)
Pitch
Let's bring in these goodies, happy to contribute 😉
Alternatives
Additional context
The text was updated successfully, but these errors were encountered: