Was the corrected datasets larger or smaller than the originals?
Would also be interesting to see these improved datasets run thru simulation of crashes with existing datasets and see how they handle? Though not sure how you would go about that beyond approaching current providers of such cars for data to work thru and suspect they may be less open to admitting flaws and with that, may be a stumbling block.
Certainly makes you wonder how far we can optimise such datasets to get better results. I know some ML datasets are a case of humans fine tuning and going thru examples and classifying them, and wonder how much that skews or effects error rates as we all know humans error.
To answer your first question, we had both bounding boxes added and removed, and depending on the dataset, the main type of error was different (I'd say it was overall more objectifs that were forgotten, especially small objects).
It would indeed be very interesting to see the impact of those improved datasets on driving, which is ultimately the task that is automated for cars.
We've been working on many projects at Deepomatic not only related to autonomous cars, and we did see some concrete impact of cleaning the datasets beyond performance metrics.
Would also be interesting to see these improved datasets run thru simulation of crashes with existing datasets and see how they handle? Though not sure how you would go about that beyond approaching current providers of such cars for data to work thru and suspect they may be less open to admitting flaws and with that, may be a stumbling block.
Certainly makes you wonder how far we can optimise such datasets to get better results. I know some ML datasets are a case of humans fine tuning and going thru examples and classifying them, and wonder how much that skews or effects error rates as we all know humans error.