Advanced trainings
Deepen your knowledge of MVTec software and technologies in our advanced trainings.
Deep learning classification
This advanced training course dives deep into image classification based on deep learning. Using the MVTec Deep Learning Tool, you will learn how to solve a classification problem on your own.
It takes about 50 minutes to complete the course.
Target group
This advanced training course is designed for learners with a basic understanding of deep learning who are interested in developing expertise in image classification tasks.
Objectives
After finishing this course, you will:
- know what image classification is.
- understand that data is a critical part of deep learning applications.
- have trained and evaluated a deep learning classification model.
- be ready to start your own deep learning project.
Process integration in MERLIC
This advanced training teaches you how to use MERLIC Communicator plug-ins to communicate with external devices in your automation process.
We are focusing on the following MERLIC Communicator plug-ins:
- REST plug-in
- OPC UA server plug-in
- MQTT plug-in
- TCP socket plug-in
It will take you 100 minutes to complete the entire course.
Target group
This advanced training is aimed at
- Process engineers wanting to learn how to integrate MERLIC, with a basic understanding of system integration but limited knowledge of communication protocols.
- Automation engineers with prior knowledge of communication protocols who wish to learn how to integrate MERLIC into their processes.
Objectives
After finishing this course, you will:
- be able to evaluate which of the communication protocols supported by MERLIC suits your automation process,
- know how to integrate your MERLIC vision application in the automation process, and
- be able to control your MERLIC vision application using one of the communication protocols covered in this course.
Generic Shape Matching
This advanced training covers HALCON's shape-based matching along with its generic shape matching operator set. You will learn the benefits of shape matching, how to apply shape matching to your use case, and how to parameterize the most common parameters for optimal matching results.
It will take you 115 minutes to complete the course.
Target Group
This training is aimed at advanced HALCON users who want to use the shape matching technology for their applications.
Objectives
Upon completion of this course, you will:
- gain an understanding of what 'matching' is in the context of machine vision and where shape matching can benefit your application.
- get to know the workflow of generic shape matching.
- understand the most important parameters, that affect robustness and runtime.
- learn how to further process the shape matching results.
- be able to solve your first basic shape matching applications.
Surface-Based Matching
This advanced training introduces you how to work with 3D Surface Matching. You learn the basics of 3D Surface Matching, the whole workflow of it, and details that improve the performance.
It takes about 85 minutes to complete the course.
Target Group
This training is aimed at advanced HALCON users who has already attended course "Introduction to 3D", and who wants to do 3D object localization.
Objectives
After finishing this course, you will …
- know what Surface-Based Matching is,
- know the workflow of Surface-Based Matching in HALCON and understand what each step does,
- be able to debug the Surface-Based Matching results,
- be able to improve the Surface-Based Matching regarding speed, robustness and accuracy.
Datasets for Deep Learning
Data is crucial for any deep learning application. In fact, even the most sophisticated Deep Learning model does not help you at all without suitable data.
This advanced training will teach you what to look out for and what to avoid when curating your Deep Learning dataset.
It takes about 50-60 minutes to complete the course.
Target Group
This training is aimed at new and intermediate/advanced Deep Learning users who want to find out more about the importance of data in Deep Learning applications. Completing the “Introduction to Deep Learning” course is a prerequisite.
Objectives
Upon completion of this course, you will …
- understand why data is so important for any deep learning application.
- be able to properly and accurately define the problem you want to solve with deep learning.
- be able to assess the quality of your dataset.
- know the requirements on datasets for the different deep learning methods.
- be able to curate your own dataset and expertly evaluate the quality.
LOCATE AND READ TEXT WITH DEEP OCR
This advanced training introduces you to MVTec's Deep OCR (Optical Character Recognition). You will learn how to use Deep OCR in different scenarios.
It takes about 60 minutes to complete the course.
Target group
This training has a basic part that is aimed at new HALCON users who want to start using Deep OCR and an advanced part for users (beginners or advanced) who are interested in knowing more in depth on how to use this technology.
Objectives
After finishing this course, you will …
- know what OCR and Deep OCR is.
- be able to use Deep OCR in HDevelop in standard and non-standard scenarios.
- understand in detail how Deep OCR detection and recognition work.
- be able to optimize the results of Deep OCR to best match your task.
Deep learning semantic segmentation
This advanced training shows how to use MVTec's deep learning semantic segmentation to divide an image into regions based on predicted classes. You'll learn to label data, train, and evaluate a model for inference.
It takes about 60 minutes to complete the course.
Target group
This advanced training is aimed at HALCON and MERLIC users who want to use deep learning to segment images pixel-wise according to their class labels.
Objectives
After finishing this course, you will:
- understand what you can do with semantic segmentation,
- apply the workflow of semantic segmentation in the compatible MVTec products,
- know how to label, train and evaluate a semantic segmentation model, and
- be able to integrate the trained model in HALCON and/or MERLIC.
Global Context Anomaly Detection
This advanced training shows you how to identify unknown defects using MVTec's proprietary deep-learning-based Global Context Anomaly Detection (GC-AD). You learn how to prepare your data, train, and apply a GC-AD model to your application.
It takes about 140 minutes to complete the course.
Target group
This advanced training is aimed at HALCON and MERLIC users who want to use deep learning to find defects and detect anomalies in their application.
Objectives
After finishing this course, you will know ...
- ... what Global Context Anomaly Detection can be used for,
- ... the workflow of GC-AD in the MVTec products,
- ... how to train and evaluate a GC-AD model, and...
- ...how to integrate the trained model in HALCON and/or MERLIC