Active Learning for Inspection is a strategy where the ML model selectively requests labels for the most informative samples — minimizing the total labeling effort by intelligently choosing which defect images to send to human experts for annotation.

How Active Learning Works

• Initial Model: Train a model on a small initial labeled set.
• Query Strategy: Select the most uncertain or informative unlabeled samples for labeling.
• Human Label: Expert annotates only the selected samples.
• Retrain: Update the model with newly labeled data, repeat.
• Strategies: Uncertainty sampling, query-by-committee, diversity sampling.

Why It Matters

• Label Efficiency: Achieves target accuracy with 50-80% fewer labeled samples compared to random labeling.
• Expert Time: Fab defect labeling requires expensive domain experts — active learning minimizes their workload.
• Evolving Distribution: Continuously adapts to new defect types by requesting labels for unknown patterns.

Active Learning is smart labeling for defect inspection — letting the AI ask the expert about the most confusing samples to learn faster with less labeling.