Digital MVTec Technology Days – Deep Learning 1 & 2 in November

Due to the great feedback from our first online trainings and the continuously high demand, we will again provide our MVTec Technology Days on a digital basis in November. In the online seminar you will learn how to use deep learning in your image processing applications using the deep learning technology classification, object detection and semantic segmentation.

About MVTec Technology Days

In small groups and different virtual rooms, our experienced trainers will show HALCON users what deep learning can accomplish within your application. In order to provide a hands-on approach on deep learning we ask you to organize a setup of a laptop and if possible a camera.

Deep Learning 1

With the help of the deep learning technology classification, you will learn how to use deep learning in your image processing applications:

• Introduction to HALCON-specific basics regarding deep learning
• Background knowledge about deep learning in industrial machine vision applications
• Best practices for setting up one's own deep learning applications
• "Hands-on" approach: programming a deep learning image classification application
• Analysis of a deep learning image classification

Deep Learning 2

With the deep learning technology object detection and semantic segmentation you will learn how to use deep learning in your image processing applications.
Based on MVTec Technology Day - Deep Learning 1, learn more about how to use the deep learning technologies object detection and semantic segmentation:

• Introduction to the MVTec Deep Learning Tool
• Introduction and best practices for the deep learning technologies object detection and semantic segmentation
• "Hands-on" approach to a deep learning problem for object detection – from labeling to inference
• Choosing the right deep learning method for your own application

Please note that seats are limited ("first come - first serve").

Each seminar is a stand-alone seminar and can be booked separately. However, to gain most of this seminar, we recommend to visit the introductory seminar Deep Learning 1 first.