mlsanityData to 7 Oct 2026

TIDE: A General Toolbox for Identifying Object Detection Errors

arXiv 2008.08115 ↗First posted 18 Aug 2020cs.CVcited by 113

Not on the map. Why?

Abstract (arXiv)

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/

Key facts. Cited by 113 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Aug 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 21; 2022: 26; 2023: 24; 2024: 13; 2025: 10; 2026: 19.

113 papers.

Sort:

By area:

  1. 2022Vision Transformer Adapter for Dense Predictionscited by 564
  2. 2021Boundary IoU: Improving Object-Centric Image Segmentation Evaluationcited by 186
  3. 2021Occluded Video Instance Segmentation: A Benchmarkcited by 154
  4. 2021FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagerycited by 143
  5. 2021Delving into Localization Errors for Monocular 3D Object Detectioncited by 138
  6. 2021Benchmarking Detection Transfer Learning with Vision Transformerscited by 130
  7. 2021You Only Look One-level Featurecited by 116
  8. 2022VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relationscited by 113
  9. 2023V3Det: Vast Vocabulary Visual Detection Datasetcited by 81
  10. 2021FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentationcited by 76
  11. 2023The effectiveness of MAE pre-pretraining for billion-scale pretrainingcited by 68
  12. 2022Sparse Instance Activation for Real-Time Instance Segmentationcited by 63
38 more
  1. 2021Points as Queries: Weakly Semi-supervised Object Detection by Pointscited by 60
  2. 2021Robust and Accurate Object Detection via Adversarial Learningcited by 42
  3. 2021Decoupled Adaptation for Cross-Domain Object Detectioncited by 37
  4. 2022BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-trainingcited by 33
  5. 2022Progressive End-to-End Object Detection in Crowded Scenescited by 31
  6. 2023Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibrationcited by 19
  7. 2021Improving Object Detection by Label Assignment Distillationcited by 17
  8. 2023Cascaded Zoom-in Detector for High Resolution Aerial Imagescited by 17
  9. 2023MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detectioncited by 17
  10. 2022Prediction-Guided Distillation for Dense Object Detectioncited by 14
  11. 2022Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake Systemcited by 14
  12. 20221st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Resultscited by 14
  13. 2024Open-Set Recognition in the Age of Vision-Language Modelscited by 13
  14. 2023Diagnosing Human-object Interaction Detectorscited by 11
  15. 2023ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Datacited by 11
  16. 2024On Calibration of Object Detectors: Pitfalls, Evaluation and Baselinescited by 11
  17. 2022Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotationscited by 10
  18. 2022Beyond RGB: Scene-Property Synthesis with Neural Radiance Fieldscited by 10
  19. 2022ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled featurescited by 10
  20. 2021USB: Universal-Scale Object Detection Benchmarkcited by 9
  21. 2023Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweightingcited by 9
  22. 2023Is Underwater Image Enhancement All Object Detectors Need?cited by 9
  23. 2022Logit Normalization for Long-tail Object Detectioncited by 8
  24. 2023A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Visioncited by 8
  25. 2024Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Futurecited by 8
  26. 2021Few-Shot Video Object Detectioncited by 7
  27. 2022What's in the Black Box? The False Negative Mechanisms Inside Object Detectorscited by 7
  28. 2023PrObeD: Proactive Object Detection Wrappercited by 7
  29. 2022On Hyperbolic Embeddings in 2D Object Detectioncited by 6
  30. 2022Beyond mAP: Towards better evaluation of instance segmentationcited by 6
  31. 2022Inverse Image Frequency for Long-tailed Image Recognitioncited by 6
  32. 2023Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detectioncited by 6
  33. 2025Fine-Tune an SLM or Prompt an LLM? The Case of Generating Low-Code Workflowscited by 6
  34. 2021Tackling the Background Bias in Sparse Object Detection via Cropped Windowscited by 5
  35. 2021Chanakya: Learning Runtime Decisions for Adaptive Real-Time Perceptioncited by 5
  36. 2023DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detectioncited by 5
  37. 2024Fractal Calibration for long-tailed object detectioncited by 5
  38. 2021Diagnosing Errors in Video Relation Detectorscited by 4

50 most cited of 113 All 113 (JSON)