  Object detection in 3D is a crucial aspect in the context of autonomous
vehicles and drones. However, prototyping detection algorithms is
time-consuming and costly in terms of energy and environmental impact. To
address these challenges, one can check the effectiveness of different models
by training on a subset of the original training set. In this paper, we present
a comparison of three algorithms for selecting such a subset - random sampling,
random per class sampling, and our proposed MONSPeC (Maximum Object Number
Sampling per Class). We provide empirical evidence for the superior
effectiveness of random per class sampling and MONSPeC over basic random
sampling. By replacing random sampling with one of the more efficient
algorithms, the results obtained on the subset are more likely to transfer to
the results on the entire dataset. The code is available at:
https://github.com/vision-agh/monspec.
