Build image systems with explicit labels, reversible geometry, grouped evaluation and threshold-specific failure review.
Learning path
- Vision dataset contract: image unit, label definition and consent
- Pixel geometry and preprocessing: preserve the mapping back to the original image
- Vision annotations: validate boxes, masks and reviewer agreement
- Vision evaluation splits: group captures and test acquisition shift
- Vision augmentation: preserve labels and match serving transforms
- Vision decision metrics: separate localization, class errors and abstention
- Project: audit a receipt-image recapture gate
Connected foundations
Use the earlier data and model boundaries as prerequisites.
Further paths
Practice
Continue into another subject: Multimodal AI Tutorial.
Continue into another subject: Data Annotation & Label Quality Tutorial.