Computer Vision Training in Tours

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

Upon completing this training, participants will be able to:

• Grasp the fundamentals of Edge AI and its 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 performance, power efficiency, and low-latency inference.

This instructor-led, live training in Tours (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 Tours (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 session (available online or onsite) is designed for entry-level law enforcement professionals aiming to shift from manual facial sketching to utilizing AI tools for developing facial recognition systems.

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

• Understand the core concepts of Artificial Intelligence and Machine Learning.
• Learn the fundamentals of digital image processing and how it applies to facial recognition.
• Develop competencies in using AI tools and frameworks to build facial recognition models.
• Gain practical experience in creating, training, and testing facial recognition systems.
• Comprehend the ethical considerations and best practices associated with facial recognition technology.

This practical course on Tours provides a comprehensive overview of Fiji and ImageJ fundamentals for biological research. Participants will develop practical 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 Tours (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 instructor-led live training in Tours (delivered online or on-site) targets intermediate to advanced developers, researchers, and data scientists aiming to implement real-time object detection with YOLOv7.

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

• Comprehend the essential concepts of object detection.
• Install and set up YOLOv7 for detection tasks.
• Train and assess custom object detection models using YOLOv7.
• Integrate YOLOv7 with other computer vision frameworks and tools.
• Address common implementation issues related to YOLOv7.