Computer Vision Training in Thessaloniki

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

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

• Grasp the core concepts of Edge AI and its role 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.
• Enhance AI models for performance, power efficiency, and low-latency inference.

This instructor-led live training in Thessaloniki (online or onsite) is designed for advanced professionals who wish to expand their expertise in computer vision and investigate TensorFlow's capabilities for developing advanced vision models using Google Colab.

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

• Construct and train Convolutional Neural Networks (CNNs) using TensorFlow.
• Utilize Google Colab for scalable and efficient cloud-based model development.
• Apply image preprocessing techniques specifically for computer vision applications.
• Deploy computer vision models for practical, real-world solutions.
• Employ transfer learning to optimize the performance of CNN models.
• Visualize and interpret outcomes from image classification models.

This instructor-led, live training in Thessaloniki (online or onsite) targets intermediate-level AI developers and computer vision engineers who aim to construct robust vision systems for autonomous driving applications.

Upon completing this training, participants will be able to:

• Grasp the fundamental concepts of computer vision within autonomous vehicles.
• Deploy 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 Thessaloniki (online or onsite) is designed for beginner-level law enforcement officers aiming to transition from manual facial sketching to using 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.
• Build proficiency in using AI tools and frameworks to construct facial recognition models.
• Gain practical experience in creating, training, and evaluating facial recognition systems.
• Comprehend the ethical implications and best practices associated with facial recognition technology.

This hands-on training in Thessaloniki explores the fundamentals of Fiji and ImageJ for biological research. Participants develop practical capabilities in image preprocessing, quantitative analysis, and macro automation, enabling them to streamline workflows for histological tissues, cells, and other samples with highly reproducible outcomes.

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

By the end 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 session in Thessaloniki (either online or on-site) targets intermediate to advanced developers, researchers, and data scientists eager to learn the implementation of real-time object detection using YOLOv7.

By the conclusion of this training, learners will be capable of:

• Comprehending the essential concepts of object detection.
• Installing and setting up YOLOv7 for detection workflows.
• Training and validating custom detection models via YOLOv7.
• Integrating YOLOv7 with other computer vision ecosystems.
• Diagnosing and fixing common issues in YOLOv7 implementations.