TIDE：一种识别目标检测错误的通用方法
TIDE: A General Toolbox for Identifying Object Detection Errors
摘要：
我们介绍TIDE，一个框架和相关的工具箱，用于分析目标检测和实例分割算法中的错误来源。重要的是，我们的框架适用于所有数据集，并且可以直接应用于输出预测文件，而无需了解底层预测系统。因此，我们的框架可以用作标准mAP计算的替代品，同时提供对每个模型的优点和缺点的全面分析。我们将错误分为六种类型，至关重要的是，我们是第一个引入一种技术来测量每个错误的贡献，以隔离其对整体性能的影响。我们表明，这种表示对于通过对4个数据集和7个识别模型的深入分析得出准确，全面的结论至关重要。可在https://dbolya.github.io/tide/上获得
英文摘要：
We introduce TIDE, a framework and associated toolbox for analyzing the sources of error in object detection and instance segmentation algorithms. Importantly, our framework is applicable across datasets and can be applied directly to output prediction files without required knowledge of the underlying prediction system. Thus, our framework can be used as a drop-in replacement for the standard mAP computation while providing a comprehensive analysis of each model's strengths and weaknesses. We segment errors into six types and, crucially, are the first to introduce a technique for measuring the contribution of each error in a way that isolates its effect on overall performance. We show that such a representation is critical for drawing accurate, comprehensive conclusions through in-depth analysis across 4 datasets and 7 recognition models. Available at https://dbolya.github.io/tide/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
