Vision Quality Assurance Guide | Guide to Quality Assurance for Vision Datasets & Models

Vision Quality Assurance

Guide to Quality Assurance for Vision Datasets and Models

Introduction to Vision Quality Assurance

Quality assurance ensures reliability, accuracy, performance, confidence, and successful deployment for CV systems.

Data quality checks validate dataset quality, including image quality,

and other relevant factors to ensure the integrity of the data used in vision applications.

Performance meets requirements.

Robustness testing verifies model robustness to variations, including

adversarial examples, corruptions, transformations and edge cases – critical for real-world deployment.

Frequently asked questions