Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithms

• URL: http://arxiv.org/abs/2306.17551v1
• Date: Fri, 30 Jun 2023 11:09:20 GMT
• Title: Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithms
• Authors: Konrad Lis, Tomasz Kryjak
• Abstract summary: prototyping object detection algorithms is time-consuming and costly in terms of energy and environmental impact. We present a comparison of three algorithms for selecting such a subset - random sampling, random per class sampling, and our proposed MONSPeC. We provide empirical evidence for the superior effectiveness of random per class sampling and MONSPeC over basic random sampling.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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