Computer Vision Training in Oslo

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

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

• Grasp the fundamentals of Edge AI and its practical applications in computer vision.
• Deploy optimized deep learning models on edge devices for real-time image and video analysis.
• Utilize frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimize AI models for enhanced performance, power efficiency, and low-latency inference.

This instructor-led, live training in Oslo (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding 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:

• Build 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.

This instructor-led, live training in Oslo (online or onsite) is aimed at intermediate-level AI developers and computer vision engineers who wish to build robust vision systems for autonomous driving applications.

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

• Understand the fundamental concepts of computer vision in autonomous vehicles.
• Implement algorithms for object detection, lane detection, and semantic segmentation.
• Integrate vision systems with other autonomous vehicle subsystems.
• Apply deep learning techniques for advanced perception tasks.
• Evaluate the performance of computer vision models in real-world scenarios.

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

By the end 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.
• Build competence in employing AI tools and frameworks to construct facial recognition models.
• Obtain practical experience in developing, training, and evaluating facial recognition systems.
• Comprehend the ethical implications and adhere to best practices when deploying facial recognition technology.

This practical training in Oslo focuses on the fundamentals of Fiji and ImageJ for biological research. Learners will develop hands-on skills in image preprocessing, quantitative analysis, and macro automation, enabling them to optimize workflows for histological tissues, cells, and other samples while ensuring reproducible results.

This instructor-led live training in Oslo (available online or onsite) is targeted at intermediate-level professionals who wish to leverage Vision Builder AI to design, implement, and optimize automated inspection systems for SMT (Surface-Mount Technology) processes.

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

• Set up and configure automated inspections using Vision Builder AI.
• Acquire and preprocess high-quality images for analysis.
• Implement logic-based decisions for defect detection and process validation.
• Generate inspection reports and optimize system performance.

This live, instructor-led training in Oslo (online or onsite) is designed for intermediate to advanced developers, researchers, and data scientists looking to implement real-time object detection with YOLOv7.

By the end of the session, participants will be able to:

• Comprehend the fundamental concepts behind object detection.
• Install and configure YOLOv7 for detection workflows.
• Train and test custom models using YOLOv7.
• Integrate YOLOv7 with other computer vision ecosystems.
• Address common implementation issues related to YOLOv7.