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TIDE: A General Toolbox for Identifying Object Detection Errors

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12348)

Abstract

We introduce TIDE, a framework and associated toolbox (https://dbolya.github.io/tide/) 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.

Keywords

Error diagnosis Object detection Instance segmentation 

Supplementary material

504435_1_En_33_MOESM1_ESM.zip (80.5 mb)
Supplementary material 1 (zip 82450 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Georgia Institute of TechnologyAtlantaUSA

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