Computer Vision Training in Nigeria

This instructor-led, live training in Nigeria (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 to enhance performance, power efficiency, and achieve low-latency inference.

This instructor-led live training in Nigeria (online or onsite) is designed for advanced professionals seeking to expand their computer vision knowledge and explore TensorFlow’s capabilities for developing advanced 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, 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 Nigeria (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 Nigeria (online or onsite) is targeted at beginner-level law enforcement personnel who wish to move from manual facial sketching to using AI tools for developing facial recognition systems.

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

• Understand the fundamentals of Artificial Intelligence and Machine Learning.
• Learn the basics of digital image processing and its application in facial recognition.
• Develop skills in using AI tools and frameworks to create facial recognition models.
• Gain hands-on experience in creating, training, and testing facial recognition systems.
• Understand ethical considerations and best practices in the use of facial recognition technology.

This practical training on Nigeria explores the fundamentals of Fiji and ImageJ for biological research. Participants will acquire essential 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, available in Nigeria (online or onsite), is aimed at intermediate-level professionals who wish to use Vision Builder AI to design, implement, and optimise 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 optimise system performance.

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

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

• Comprehend the essential concepts of object detection.
• Install and set up YOLOv7 for detection workflows.
• Build, train, and test custom object detection models using YOLOv7.
• Combine YOLOv7 with other computer vision frameworks and tools.
• Diagnose and fix frequent issues in YOLOv7 deployments.