Evaluating the Effect of Common Annotation Faults on Object Detection Techniques
Bibliographic record
Abstract
Machine learning (ML) is applied in many safety-critical domains such as autonomous driving and medical diagnosis. Many ML applications in such domains require object detection, which includes both classification and localization, to provide additional context. To ensure high accuracy, state-of-the-art object detection (OD) systems require large quantities of correctly annotated images for training. However, creating such datasets is non-trivial, may involve significant human effort, and is hence inevitably prone to annotation faults. We evaluate the effect of such faults on OD applications. We present ODFI, which can inject five different types of common annotation faults into any COCO-formatted dataset. We then use ODFI to inject these faults into two road traffic and one medical X-ray imaging datasets. Finally, using these faulty datasets, we systematically evaluate and compare the efficacy of existing OD techniques that are designed to be robust against such faults. To do so, we introduce a new metric that evaluates the robustness of OD models in the presence of faults. We find that (1) single-stage detectors trained with faulty annotations perform better in scenes with more objects, (2) redundant bounding boxes have the least impact on robustness, and (3) ensembles have the highest overall robustness among the robust OD techniques considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".