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How did you decide how to merge the detection classes?
Thanks for the great effort you put into this dataset!
Could you elaborate the reason behind having a separate construction_vehicle class in the detection challenge and not assigning it e.g. to truck?
Similar question for ambulance and police vehicles:
Why did you ignore them instead of assigning them to truck and car respectively?
Caring about construction vehicles (as a separate class) and ignoring ambulances and police cars in the evaluation seems a bit inconsistent (and gives me the impression that you care more about construction vehicles than ambulances and police cars).
Not meant as a critique, asking more out of curiosity.
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you can check construction_vehicle and police cars’ instance count.
Hi @MartinHahner.
There are a number of factors that we took into account for keeping/merging certain classes, such as:
- The frequency of a class. Very rare classes may have too little data to train and evaluate performance on them. According to https://www.nuscenes.org/data-annotation we have 14671 construction vehicles, but only 49 ambulances. This does not mean that they are not important (ambulance and police vehicles definitely are).
- The importance of a class. E.g. bicycle racks are only a tool used to filter out parked bicycles, we don’t care about the racks themselves.
- Whether they can be merged with other classes. E.g. trucks have a typical size and velocity range. If we add construction vehicles, which are much larger and typically stationary, the various statistics may become diluted.
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