Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Deep computer vision for manufacturing

Aurélien Geron (Kiwisoft)
14:55–15:35 Wednesday, 23 May 2018
Data science and machine learning, Emerging technologies and case studies
 Location: Capital Suite 13 Level: Beginner
Average rating: ***..
(3.67, 3 ratings)
Download slides (PDF)

Who is this presentation for?

  • Anyone interested in computer vision

Prerequisite knowledge

  • A basic understanding of machine learning concepts (e.g., what machine learning is, what a training set is, and what it means to train a model)

What you'll learn

  • Understand the state-of-the-art deep computer vision architectures (CNNs) and their applications, particularly in manufacturing

Description

Computer vision in manufacturing has actually been around for decades: it’s present in thousands of production lines, performing product classification, detecting defective items, gathering data for analytics, and more. Very recently, companies have started to shift from classical computer vision techniques to modern techniques based on deep learning, namely convolutional neural networks (CNNs), which can achieve amazing precision, often reaching or even exceeding human abilities.

Aurélien Géron details common CNN architectures for classification (e.g., ResNet), image segmentation (e.g., DeepLab), object detection (e.g., YOLO), and anomaly detection (e.g., ResNet+SVM), explains how they can be applied to manufacturing, and covers potential challenges along the way, including:

  • Having to gather and possibly label a training set for each new product type;
  • Difficulties in interpreting the system’s decisions;
  • Avoiding model rot;
  • Handling high volume and low latency.
Photo of Aurélien Geron

Aurélien Geron

Kiwisoft

Aurélien Géron is a machine learning consultant at Kiwisoft and author of the best-selling O’Reilly book Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. Previously, he led YouTube’s video classification team, was a founder and CTO of Wifirst, and was a consultant in a variety of domains: finance (JPMorgan and Société Générale), defense (Canada’s DOD), and healthcare (blood transfusion). He also published a few technical books (on C++, WiFi, and internet architectures), and he’s a lecturer at the Dauphine University in Paris. He lives in Singapore with his wife and three children.