  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/
