Computer Vision Training in Gaia

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

Upon completing this training, participants will be capable of:

• Grasping the fundamentals of Edge AI and its role in computer vision.
• Deploying optimized deep learning models on edge devices for real-time image and video analysis.
• Utilizing frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimizing AI models to achieve superior performance, power efficiency, and low-latency inference.

This instructor-led, live training in Gaia (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 Gaia (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 Gaia (online or onsite) is designed for beginner-level law enforcement personnel aiming to transition from manual facial sketching to leveraging AI tools for developing facial recognition systems.

Upon completing this training, participants will be equipped 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 employing AI tools and frameworks to construct facial recognition models.
• Obtain practical experience in developing, training, and testing facial recognition systems.
• Comprehend the ethical implications and industry best practices surrounding the deployment of facial recognition technology.

This hands-on training in Gaia provides a comprehensive overview of Fiji and ImageJ fundamentals for biological research. Participants will acquire practical 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 Gaia (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 training in Gaia—delivered online or on-site—is designed for intermediate to advanced developers, researchers, and data scientists looking to implement real-time object detection using YOLOv7.

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

• Comprehend the fundamental concepts of object detection.
• Install and configure YOLOv7 for detection workflows.
• Train and test custom object detection models with YOLOv7.
• Integrate YOLOv7 with various computer vision frameworks and tools.
• Troubleshoot typical issues arising from YOLOv7 implementation.