YOLOv7: Real-time Object Detection with Computer Vision Training Course

This instructor-led training in Sweden focuses on deploying and optimizing CV and NLP models using the CANN SDK for Ascend hardware. Participants will learn how to convert models, integrate them into live pipelines, and improve inference performance for real-time detection and analysis.

This instructor-led, live training in Sweden (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 Sweden (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 session in Sweden (online or onsite) targets computer vision engineers, AI developers, and IoT professionals at an 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 able to:

• Grasp the core principles of Edge AI and its practical applications in computer vision.
• Deploy optimized deep learning models on edge devices to facilitate real-time image and video analysis.
• Utilize frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for deploying models.
• Optimize AI models to enhance performance, power efficiency, and achieve low-latency inference.

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

Upon completing this training, participants will be able to:

• Grasp the fundamentals of Artificial Intelligence and Machine Learning.
• Master the basics of digital image processing and its role in facial recognition.
• Develop proficiency in using AI tools and frameworks to build facial recognition models.
• Gain practical experience in creating, training, and testing facial recognition systems.
• Comprehend ethical considerations and best practices regarding the use of facial recognition technology.

This practical training module on Sweden provides a comprehensive overview of Fiji and ImageJ fundamentals for biological research. Participants will develop hands-on 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.

Explore the fundamentals of Computer Vision through this hands-on, instructor-led training in Sweden. Participants will utilize Python to construct face, object, and motion detection systems, gaining a practical foundation in image processing and vision tasks.

This instructor-led live training, available Sweden (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.