# Utilizing dataset affinity prediction in object detection to assess training data

Source: [Utilizing dataset affinity prediction in object detection to assess training data](<https://www.sota2.com/research/paper/68fa732a1e209131cea5adcb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:43:27.234Z

Authors: Stefan Becker, Jens Bayer, Ronny Hug, Wolfgang H\\"ubner, Michael Arens

Published: 2023-11-16T10:45:32.000Z

## Abstract

Data pooling offers various advantages, such as increasing the sample size, improving generalization, reducing sampling bias, and addressing data sparsity and quality, but it is not straightforward and may even be counterproductive. Assessing the effectiveness of pooling datasets in a principled manner is challenging due to the difficulty in estimating the overall information content of individual datasets. Towards this end, we propose incorporating a data source prediction module into standard object detection pipelines. The module runs with minimal overhead during inference time, providing additional information about the data source assigned to individual detections. We show the benefits of the so-called dataset affinity score by automatically selecting samples from a heterogeneous pool of vehicle datasets. The results show that object detectors can be trained on a significantly sparser set of training samples without losing detection accuracy.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2311.09768>)

[PDF](<https://arxiv.org/pdf/2311.09768.pdf>)