🚀 Feature

When running object detector on similar objects (i.e. for custom datasets) some objects might be correctly detected but wrongly classified. It would be good to add a flag in the testing code to be able to ignore those cases (my understanding is that these are now considered false positives)

Motivation

In many robotics application, wrongly classified objects are not necessarily an issue. Therefore the community would need to be able to evaluate the network and ignore those cases.

Pitch

I would love to have a flag that can ignore wrongly classified detection during evaluation (i.e. consider them neither true positive nor false positives)

Alternatives

This is a straightforward feature request. An alternative (or another feature) would be to consider wrongly classified objects (good detection bounding box but bad label) as true positives. That would be extremely useful to evaluate the best possible recall if the classification was perfect - and quantify how much classification can be improved.

An idea would be : flag = 0 --> current implementation, flag = 1 --> ignore wrongly classified, flag = 2 --> consider wrongly classified as true positives

Additional context

this is an important feature for robotics and other applications, which would benefit many