Computer Vision Training in Johannesburg

This instructor-led, live training in Johannesburg (online or onsite) targets intermediate to advanced computer vision engineers, AI developers, and IoT professionals who wish to implement and optimise 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 applications in computer vision.
• Deploy optimized deep learning models on edge devices for real-time image and video analysis.
• Utilise frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimise AI models for performance, power efficiency, and low-latency inference.

This instructor-led, live training in Johannesburg (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:

• Construct and train convolutional neural networks (CNNs) using TensorFlow.
• Utilise 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.
• Apply transfer learning to enhance the performance of CNN models.
• Visualise and interpret the results of image classification models.

This instructor-led, live training in Johannesburg (online or on-site) 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 Johannesburg (online or onsite) targets law enforcement personnel at the beginner level who wish to transition from traditional manual facial sketching to employing AI tools for constructing facial recognition systems.

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

• Grasp the core principles of Artificial Intelligence and Machine Learning.
• Learn the fundamentals of digital image processing and its application in facial recognition.
• Acquire the skills necessary to utilise AI tools and frameworks for building facial recognition models.
• Obtain practical experience in creating, training, and evaluating facial recognition systems.
• Comprehend the ethical considerations and best practices associated with facial recognition technology.

This practical training on Johannesburg covers the fundamentals of Fiji and ImageJ for biological research. Participants will develop hands-on skills in image pre-processing, 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 Johannesburg (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 instructor-led, live training in Johannesburg (available online or onsite) is designed for intermediate to advanced developers, researchers, and data scientists who aim to learn the implementation of real-time object detection using YOLOv7.

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

• Comprehend the fundamental concepts of object detection.
• Set up and configure YOLOv7 for object detection tasks.
• Develop and test custom object detection models using YOLOv7.
• Integrate YOLOv7 with other computer vision frameworks and tools.
• Troubleshoot typical issues arising from YOLOv7 implementation.