# ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data

Source: [ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data](<https://www.sota2.com/research/paper/68fa707f1e209131cea4b806>)

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

Data updated: 2025-10-26T01:43:00.780Z

Authors: Ulyana Tkachenko, Aditya Thyagarajan, Jonas Mueller

Published: 2023-09-02T05:40:05.000Z

## Abstract

Despite powering sensitive systems like autonomous vehicles, object detection remains fairly brittle in part due to annotation errors that plague most real-world training datasets. We propose ObjectLab, a straightforward algorithm to detect diverse errors in object detection labels, including: overlooked bounding boxes, badly located boxes, and incorrect class label assignments. ObjectLab utilizes any trained object detection model to score the label quality of each image, such that mislabeled images can be automatically prioritized for label review/correction. Properly handling erroneous data enables training a better version of the same object detection model, without any change in existing modeling code. Across different object detection datasets (including COCO) and different models (including Detectron-X101 and Faster-RCNN), ObjectLab consistently detects annotation errors with much better precision/recall compared to other label quality scores.

## 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/2309.00832>)

[PDF](<https://arxiv.org/pdf/2309.00832.pdf>)