论文标题
潮汐：用于识别对象检测错误的一般工具箱
TIDE: A General Toolbox for Identifying Object Detection Errors
论文作者
论文摘要
我们介绍了潮汐，一个框架和关联的工具箱，用于分析对象检测和实例分割算法中错误源。重要的是，我们的框架适用于跨数据集，并且可以直接应用于输出预测文件，而无需了解基础预测系统。因此，我们的框架可以用作标准地图计算的倒入替代品，同时对每个模型的优势和劣势进行全面分析。我们将错误分为六种类型，至关重要的是，第一个以隔离其对整体性能的影响的方式引入了一种用于测量每个误差的技术。我们表明，这种表示对于通过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/上找到
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/
