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

arXiv: https://arxiv.org/abs/2309.00832 · HTML: https://mlsanity.com/paper/2309.00832/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 2 Sep 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation](https://mlsanity.com/paper/2104.14812/) (2021)
- [TIDE: A General Toolbox for Identifying Object Detection Errors](https://mlsanity.com/paper/2008.08115/) (2020)
- [A Survey of Modern Deep Learning based Object Detection Models](https://mlsanity.com/paper/2104.11892/) (2021)
- [Augmentation Strategies for Learning with Noisy Labels](https://mlsanity.com/paper/2103.02130/) (2021)
- [Training Convolutional Networks with Noisy Labels](https://mlsanity.com/paper/1406.2080/) (2014)
- [Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future](https://mlsanity.com/paper/2206.02280/) (2022)
- [Identifying Mislabeled Instances in Classification Datasets](https://mlsanity.com/paper/1912.05283/) (2019)
- [Temporal Coherence for Active Learning in Videos](https://mlsanity.com/paper/1908.11757/) (2019)
- [Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification](https://mlsanity.com/paper/2207.06104/) (2022)
- [Detecting Label Errors in Token Classification Data](https://mlsanity.com/paper/2210.03920/) (2022)
- [Identifying Incorrect Annotations in Multi-Label Classification Data](https://mlsanity.com/paper/2211.13895/) (2022)
- [Combating noisy labels in object detection datasets](https://mlsanity.com/paper/2211.13993/) (2022)

## Built on by (most cited first, 11 of 11)

- [LED Benchmark: Diagnosing Structural Layout Errors for Document Layout Analysis](https://mlsanity.com/paper/2507.23295/) (2025)
- [Data-Centric Long-Tailed Image Recognition](https://mlsanity.com/paper/2311.01744/) (2023)
- [Pseudo-Labeling Driven Refinement of Benchmark Object Detection Datasets via Analysis of Learning Patterns](https://mlsanity.com/paper/2506.00997/) (2025)
- [Seeing to Ground: Visual Attention for Hallucination-Resilient MDLLMs](https://mlsanity.com/paper/2603.25711/) (2026)
- [Data-Centric Label Smoothing for Explainable Glaucoma Screening from Eye Fundus Images](https://mlsanity.com/paper/2406.03903/) (2024)
- [From Label Error Detection to Correction: A Modular Framework and Benchmark for Object Detection Datasets](https://mlsanity.com/paper/2508.06556/) (2025)
- [Learning to Detect Label Errors by Making Them: A Method for Segmentation and Object Detection Datasets](https://mlsanity.com/paper/2508.17930/) (2025)
- [Ordinal Adaptive Correction: A Data-Centric Approach to Ordinal Image Classification with Noisy Labels](https://mlsanity.com/paper/2509.02351/) (2025)
- [Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance](https://mlsanity.com/paper/2511.08439/) (2025)
- [Semantically Orthogonal Framework for Citation Classification: Disentangling Intent and Content](https://mlsanity.com/paper/2601.05103/) (2026)
- [Analyzing Training-Free Corruption Detection for Object Detection Datasets](https://mlsanity.com/paper/2606.10666/) (2026)

## Abstract (arXiv)

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.