# TIDE: A General Toolbox for Identifying Object Detection Errors

Source: [TIDE: A General Toolbox for Identifying Object Detection Errors](<https://www.sota2.com/research/paper/68fa684b1e209131cea1b2e5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:33:00.328Z

Authors: Daniel Bolya, Sean Foley, James Hays, Judy Hoffman

Published: 2020-08-18T18:30:53.000Z

## Abstract

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/

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2008.08115>)

[PDF](<https://arxiv.org/pdf/2008.08115.pdf>)