Mislabels
Understand how mislabels occur, why they matter, and how to detect and fix them using Visual Layer.
What Are Mislabeled Images and Objects?
Mislabeled images are those that have been assigned incorrect or inaccurate labels or annotations. This can happen during manual annotation or when using automated labeling systems. Errors like these negatively impact model performance by introducing noise and bias into the dataset.
Visual Layer’s state-of-the-art detection system helps accurately identify and surface likely mislabels—so you can build more reliable, high-quality datasets.
Common Causes of Mislabeling
- Human error: Annotators might misinterpret the image, confuse similar classes, or assign incorrect labels.
- Ambiguous images: Some visuals are inherently difficult to classify, leading to inconsistent labeling.
- Algorithmic error: Auto-labeling models may produce poor results when trained on insufficient or biased data.
- Evolving knowledge: As domain definitions evolve, previously accurate labels may become outdated.
Why Is This a Problem?
| Issue | Impact |
|---|---|
| Poor training data quality | Models trained on mislabeled data tend to learn the wrong patterns, leading to lower accuracy and reliability. |
| Bias and skewed outcomes | Mislabels can reinforce social, demographic, or class-based bias in prediction systems. |
| Wasted resources | Training on flawed data wastes compute, time, and effort—often requiring rework. |
| Downstream risk | In high-stakes environments (e.g. healthcare, AV), label errors can compromise safety or correctness. |
| Loss of user trust | Consistently mislabeled content erodes user confidence in model outputs. |
Mislabeled Images vs. Mislabeled Objects
Mislabeled Images
These occur when the entire image is labeled incorrectly.
Example: An image of a wolf might be labeled as a husky.
Another form of this is when multiple objects are collapsed into one label—for example, an image showing both a cat and a table may be labeled only as “cat,” even if both are valid classes.
Mislabeled Objects
These are errors that occur at the object level, including:
- Incorrect class assigned to a specific bounding box
- Bounding boxes that are misaligned, too large, too small, or off-center
- Occluded or overlapping objects labeled as a single object
How to Reduce Mislabels
High-quality labeling workflows are essential for reliable ML models. Consider implementing:
- Reviewer redundancy or consensus checks among annotators
- Expert reviews for edge cases or complex classes
- Regular dataset audits using prediction-label comparisons
- Continuous updates to reflect evolving class definitions
Visual Layer provides automated detection tools that make this process easier and more scalable.
Tools to Detect and Fix Mislabels in Visual Layer
Visual Layer offers multiple built-in workflows to detect, review, and correct mislabeled content:
- Find mismatches using label filters
- Run native auto-detection to automatically surface potential issues
- Use data selection tools to isolate clusters with questionable labeling
- Export for re-labeling and route the data to your QA or annotation provider