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Data Labeling and Annotation Strategies for AI

Build high-quality training datasets. Labeling tools, crowdsourcing, and quality assurance.

Rottawhite Team10 min readNovember 24, 2024
Data LabelingAnnotationTraining Data

The Labeling Challenge

Quality labeled data is essential for supervised learning, but creating it is often expensive and time-consuming.

Labeling Types

Classification

  • Single-label
  • Multi-label
  • Hierarchical

    • Object Detection

  • Bounding boxes
  • Polygons
  • Keypoints

    • Segmentation

  • Semantic
  • Instance
  • Panoptic

    • Text Annotation

  • Entity recognition
  • Sentiment
  • Relations

    • Labeling Approaches

    In-House

  • Domain expertise
  • Quality control
  • Higher cost

    • Crowdsourcing

  • Scale
  • Speed
  • Quality challenges

    • Specialized Services

  • Expert annotators
  • Quality guarantees
  • Project management

    • Automated + Human

  • Model-assisted labeling
  • Active learning
  • Human verification

    • Quality Assurance

    Guidelines

  • Clear instructions
  • Examples
  • Edge cases

    • Metrics

  • Inter-annotator agreement
  • Accuracy checks
  • Consistency

    • Processes

  • Multiple annotators
  • Review workflows
  • Calibration

    • Tools

    Open Source

  • Label Studio
  • CVAT
  • Doccano

    • Commercial

  • Labelbox
  • Scale AI
  • Amazon SageMaker Ground Truth

    • Best Practices

  • Invest in clear guidelines
  • Start with pilot labeling
  • Measure quality continuously
  • Iterate on edge cases
  • Document decisions

    • Conclusion

    High-quality labeled data is foundational to ML success.

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