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The labor-intensive annotation process of semantic segmentation datasets isoften prone to errors, since humans struggle to label every pixel correctly. Westudy algorithms to automatically detect such annotation errors, in particularmethods to score label quality, such that the images with the lowest scores areleast likely to be correctly labeled. This helps prioritize what data to reviewin order to ensure a high-quality training/evaluation dataset, which iscritical in sensitive applications such as medical imaging and autonomousvehicles. Widely applicable, our label quality scores rely on probabilisticpredictions from a trained segmentation model -- any model architecture andtraining procedure can be utilized. Here we study 7 different label qualityscoring methods used in conjunction with a DeepLabV3+ or a FPN segmentationmodel to detect annotation errors in a version of the SYNTHIA dataset.Precision-recall evaluations reveal a score -- the soft-minimum of themodel-estimated likelihoods of each pixel's annotated class -- that isparticularly effective to identify images that are mislabeled, across multipletypes of annotation error.

Estimating label quality and errors in semantic segmentation data via any model