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Annotation

Definition

The process of labeling data (like images or text) to train machine learning models.

Deep Dive

Annotation, in the context of machine learning and artificial intelligence, is the process of labeling or tagging raw data to provide context and meaning that a machine learning model can understand and learn from. This labeled data, often referred to as "ground truth," is essential for training supervised machine learning models. Without carefully annotated datasets, models would be unable to identify patterns, make accurate predictions, or categorize new, unseen data effectively.

Examples & Use Cases

  • 1Drawing bounding boxes or polygons around cars, pedestrians, and traffic signs in images to train an autonomous vehicle's object detection system
  • 2Transcribing spoken words from audio recordings to create a dataset for training a speech recognition model
  • 3Categorizing email messages as "spam" or "not spam" for a machine learning model designed to filter junk mail

Related Terms

Data LabelingSupervised LearningGround Truth

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