labels/semantic-kitti.yaml

[akouri-dd]

I'm a little confused by some of the values in the labels .yamls.

1. What method is used to generate the values for content -- I'm assuming it's the number of points in the dataset for each class divided by the total number of points in the dataset?
2. How is learning_map supposed to work? I have tried creating my own mapping, but it causes the IoU scores for the remaining classes to go down significantly.
3. What is the purpose of learning_map_inv? Why not just dynamically calculate this, since there is no new information provided?
4. What does learning_ignore do?

[jbehley]

Sorry for the late reply:

1. content is the proportion of points per class uses for weighting in the weighted cross entropy.
2. learning_map maps our semanticKITTI annotation labels (like 10 for car, 30 for person) to a range that is used for learning, such as 0-19.
3. learning_map_inv is the opposite direction, which is also the way you have to submit the results to the codalab evaluation.
4. learning_ignore are the classes that are ignored in the training. (I think.)

[jbehley]

If you have further questions regarding this specific topic feel free to re-open this issue or open another issue for other questions.

[xdtzzz]

hi, bro. @akouri-dd
I think you also use the code to train your own dataset. I wonder have you succeed? How and what the content in labels/semantic-kitti.yaml should be modified? thanks!