Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation
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High-quality data is necessary for modern machine learning. However, the acquisition of such data is difficult due to noisy and ambiguous annotations of humans. The aggregation of such annotations to determine the label of an image leads to a lower data quality. We propose a data-centric image classification benchmark with nine real-world datasets and multiple annotations per image to investigate and quantify the impact of such data quality issues. We focus on a data-centric perspective by asking how we could improve the data quality. Across thousands of experiments, we show that multiple annotations allow a better approximation of the real underlying class distribution. We identify that hard labels can not capture the ambiguity of the data and this might lead to the common issue of overconfident models. Based on the presented datasets, benchmark baselines, and analysis, we create multiple research opportunities for the future.
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