Computer Vision Training in Delft

This instructor-led, live training in Delft (online or onsite) is designed for intermediate to advanced computer vision engineers, AI developers, and IoT professionals aiming to implement and optimize computer vision models for real-time processing on edge devices.

Upon completion of this training, participants will be capable of:

• Grasping the fundamentals of Edge AI and its applications within computer vision.
• Deploying optimized deep learning models on edge devices for real-time image and video analysis.
• Utilizing frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimizing AI models to enhance performance, power efficiency, and achieve low-latency inference.

This instructor-led, live training in Delft (online or on-site) is aimed at experienced professionals who wish to deepen their knowledge of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the end of this training, participants will be able to:

• Construct and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

The CANN SDK (Compute Architecture for Neural Networks) delivers robust deployment and optimization capabilities for real-time AI applications in computer vision and natural language processing (NLP), particularly on Huawei Ascend hardware.

This instructor-led live training, available online or on-site, is designed for intermediate-level AI practitioners looking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.

Upon completion of this training, participants will be able to:

• Deploy and optimize computer vision (CV) and NLP models using CANN and AscendCL.
• Utilize CANN utilities to convert models and integrate them into live processing pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment.

Course Format

• Interactive lectures and practical demonstrations.
• Hands-on labs focusing on model deployment and performance profiling.
• Live design of pipelines using real-world CV and NLP use cases.

Customization Options

• To request a customized training session for this course, please contact us to make arrangements.

This instructor-led, live training in Delft (online or onsite) targets intermediate-level AI developers and computer vision engineers who aim to develop robust vision systems for autonomous driving applications. <\/p>

Upon completing this training, participants will be capable of: <\/p>

• Grasping the fundamental concepts of computer vision in autonomous vehicles. <\/li>
• Implementing algorithms for object detection, lane detection, and semantic segmentation. <\/li>
• Integrating vision systems with other autonomous vehicle subsystems. <\/li>
• Applying deep learning techniques for advanced perception tasks. <\/li>
• Evaluating the performance of computer vision models in real-world scenarios. <\/li> <\/ul>

This instructor-led, live training in Delft (online or onsite) is designed for law enforcement personnel at the beginner level who wish to transition from manual facial sketching to utilizing AI tools for developing facial recognition systems.

Upon completion of this training, participants will be able to:

• Grasp the core principles of Artificial Intelligence and Machine Learning.
• Acquire foundational knowledge of digital image processing and its role in facial recognition.
• Develop proficiency in utilizing AI tools and frameworks to construct facial recognition models.
• Obtain practical experience in building, training, and evaluating facial recognition systems.
• Comprehend ethical considerations and adhere to best practices regarding the deployment of facial recognition technology.

This instructor-led, live training in the Netherlands (online or onsite) is aimed at beginner-level to intermediate-level researchers and laboratory professionals who wish to process and analyze images related to histological tissues, blood cells, algae, and other biological samples.

By the end of this training, participants will be able to:

• Navigate the Fiji interface and utilize ImageJ’s core functions.
• Preprocess and enhance scientific images for better analysis.
• Analyze images quantitatively, including cell counting and area measurement.
• Automate repetitive tasks using macros and plugins.
• Customize workflows for specific image analysis needs in biological research.

This instructor-led, live training session (available online or on-site) is tailored for intermediate-level professionals who wish to utilize Vision Builder AI to design, implement, and optimize automated inspection systems for SMT (Surface-Mount Technology) processes.

Upon completing this training, participants will be capable of:

• Setting up and configuring automated inspections using Vision Builder AI.
• Acquiring and preprocessing high-quality images ready for analysis.
• Implementing logic-based decisions to detect defects and validate processes.
• Generating detailed inspection reports and optimizing overall system performance.

This instructor-led, live training in Delft (online or onsite) is tailored for intermediate to advanced developers, researchers, and data scientists who wish to learn how to implement real-time object detection using YOLOv7.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of object detection.
• Install and configure YOLOv7 for object detection tasks.
• Train and test custom object detection models using YOLOv7.
• Integrate YOLOv7 with other computer vision frameworks and tools.
• Troubleshoot common issues related to YOLOv7 implementation.

Computer Vision enables the automatic extraction, analysis, and interpretation of meaningful data from digital media. Python is a high-level programming language renowned for its clear syntax and code readability.

In this instructor-led live training, participants will grasp the fundamentals of Computer Vision by building a series of simple Computer Vision applications using Python.

By the end of this training, participants will be able to:

• Understand the basics of Computer Vision
• Use Python to implement Computer Vision tasks
• Build their own face, object, and motion detection systems

Audience

• Python programmers interested in Computer Vision

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice