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Visual Dataset Size for Quality Control

By University of Groningen

Type of course:

Digital learning, Lesson

Language:

EN

Duration:

15 minutes

Proficiency:

Intermediate

Target:

Manager, Professionals, Workers

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.


Learning outcomes

  1. By the end of this lesson, learners will be able to discuss the factors that influence 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.
  2. By the end of this lesson, learners will be able to describe strategies for curating and optimizing visual datasets, ensuring that they are representative, diverse, and balanced, to enhance the performance and reliability of AI models in detecting anomalies.
  3. By the end of this lesson, learners will be able to assess the impact of varying dataset sizes on the accuracy and reliability of machine learning models in quality control, using metrics to determine the optimal dataset size for effective anomaly detection.

Topics

Automation and Sensoring, Automation and Robotics, Digital Transformation, Machine Learning

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Content created in 2024
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Course Includes

  • 1 Quiz

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