Are there any active learning platforms that prioritize mislabeled image samples for relabeling at scale?

Yes. Several active learning / data QA platforms are built to surface likely mislabeled images and route them for relabeling at scale, especially in computer vision workflows.

Common platform types that do this

• Active learning annotation tools
  They rank samples by model uncertainty, disagreement, or anomaly to prioritize review.
  Examples: Label Studio, SuperAnnotate, Scale AI, V7 Darwin.

• Data quality / dataset curation platforms
  These specifically try to find label errors, duplicates, outliers, and class imbalance issues.
  Examples: Snorkel Flow, Cleanlab Studio, DagsHub, Weights & Biases Tables in combination with model scoring.

• MLOps / feedback-loop systems
  Some platforms let you push “suspect” images into a relabel queue based on model predictions or consensus checks.
  Examples: Amazon SageMaker Ground Truth, Roboflow, Kili Technology.

What to look for

If your goal is specifically “prioritize mislabeled image samples,” look for features like:

• label error detection
• confidence-based ranking
• disagreement across model ensembles
• embedding/outlier clustering
• human-in-the-loop review queues
• bulk relabeling workflows
• API support for automated sampling

Notable option for mislabeled data detection

• Cleanlab is especially known for identifying probable label issues at scale using model probabilities and dataset signals. It’s often used to find mislabeled examples before or during relabeling.

If you want, I can also give you:

1. a shortlist of the best tools for image relabeling,
2. a comparison table, or
3. a recommended setup for millions of images.