Visual Dataset Size for Quality Control

SUMMARY

The power of machine learning in quality control lies in the data it learns from. But how do we know how much data is enough, and how do we ensure it’s the right kind of data?

In this lesson, you’ll explore the key factors that determine the ideal size of visual datasets for quality control applications, including the complexity of defects, variability in manufacturing processes, and the specific goals of machine learning models. We’ll also cover strategies for curating and optimizing visual datasets—ensuring they are representative, diverse, and balanced to train AI models effectively in detecting anomalies.

You’ll learn how varying dataset sizes can impact the accuracy and reliability of machine learning models, and how to assess the optimal dataset size using metrics to fine-tune your approach. By the end of this lesson, you’ll understand how to create high-quality visual datasets that empower machine learning models to deliver accurate and reliable anomaly detection in your quality control processes.

About The Author

Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using Internet of Things (IoT) and Machine Learning (ML) techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.