The Common Objects in Context (COCO) dataset has been instrumental in
benchmarking object detectors over the past decade. Like every dataset, COCO
contains subtle errors and imperfections stemming from its annotation
procedure. With the advent of high-performing models, we ask whether these
errors of COCO are hindering its utility in reliably benchmarking further
progress. In search for an answer, we inspect thousands of masks from COCO
(2017 version) and uncover different types of errors such as imprecise mask
boundaries, non-exhaustively annotated instances, and mislabeled masks. Due to
the prevalence of COCO, we choose to correct these errors to maintain
continuity with prior research. We develop COCO-ReM (Refined Masks), a cleaner
set of annotations with visibly better mask quality than COCO-2017. We evaluate
fifty object detectors and find that models that predict visually sharper masks
score higher on COCO-ReM, affirming that they were being incorrectly penalized
due to errors in COCO-2017. Moreover, our models trained using COCO-ReM
converge faster and score higher than their larger variants trained using
COCO-2017, highlighting the importance of data quality in improving object
detectors. With these findings, we advocate using COCO-ReM for future object
detection research. Our dataset is available at this https URL

通过检查 COCO (2017 版本) 中的成千上万个掩膜，我们发现了不同类型的错误，例如不精确的掩膜边界、未全面注释的实例和错误标记的掩膜。为了保持与先前研究的连续性，我们开发了 COCO-ReM (Refined Masks)，这是一组注释更清晰、掩膜质量明显更好的数据集。我们评估了五十个目标检测器，并发现预测视觉效果更好的掩膜的模型在 COCO-ReM 上得分更高，证实它们由于 COCO-2017 的错误而受到了不正确的惩罚。此外，我们使用 COCO-ReM 训练的模型收敛更快，得分更高，优于使用 COCO-2017 训练的更大的变体，凸显了数据质量在改进目标检测器中的重要性。基于这些发现，我们提倡在未来的目标检测研究中使用 COCO-ReM 数据集。我们的数据集可在此 https URL 获取。