Computer Vision Training in Sud-Vest

This instructor-led, live training in Sud-Vest (online or onsite) is designed for intermediate to advanced computer vision engineers, AI developers, and IoT professionals who aim 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 Sud-Vest (online or onsite) is designed for advanced-level professionals who aim to deepen their knowledge of computer vision and investigate TensorFlow's capabilities for developing complex 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 to facilitate scalable and efficient cloud-based model development.
• Apply image preprocessing techniques tailored for computer vision tasks.
• Deploy computer vision models for practical, real-world applications.
• Employ transfer learning to optimize the performance of CNN models.
• Visualize and analyze the outcomes of image classification models.

This instructor-led, live training in Sud-Vest (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 Sud-Vest (online or onsite) is designed for beginner-level law enforcement personnel looking to shift from manual facial sketching techniques to utilizing AI tools for building facial recognition systems.

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

• Grasping the core principles of Artificial Intelligence and Machine Learning.
• Learning the fundamentals of digital image processing and its role in facial recognition.
• Developing proficiency in employing AI tools and frameworks to construct facial recognition models.
• Acquiring practical experience in building, training, and testing facial recognition systems.
• Understanding the ethical implications and best practices associated with facial recognition technology.

This practical training on Sud-Vest delves into the fundamentals of Fiji and ImageJ for biological research. Participants will acquire hands-on skills in image preprocessing, quantitative analysis, and macro automation, enabling them to streamline workflows for histological tissues, cells, and other samples while ensuring reproducible results.

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

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

• Comprehend the foundational concepts of object detection.
• Install and set up YOLOv7 for detection tasks.
• Train and test custom object detection models using YOLOv7.
• Integrate YOLOv7 with other computer vision frameworks and tools.
• Address typical issues encountered during YOLOv7 implementation.