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

This instructor-led training in Uzbekistan focuses on deploying and optimizing CV and NLP models using the CANN SDK for Ascend hardware. Learners will master model conversion, live pipeline integration, and strategies to boost inference performance for real-time detection and analysis.

This guided, live training in Uzbekistan (online or in-person) targets intermediate-level AI developers and computer vision engineers who aim to build robust vision systems for autonomous driving applications.

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

• Grasping the core principles of computer vision within autonomous vehicles.
• Developing algorithms for object detection, lane recognition, and semantic segmentation.
• Combining vision systems with other autonomous vehicle components.
• Utilizing deep learning methods for sophisticated perception tasks.
• Assessing the performance of computer vision models in practical situations.

This instructor-led, live training in Uzbekistan (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 Uzbekistan (online or on-site) is designed for intermediate to advanced-level computer vision engineers, AI developers, and IoT professionals who aim to implement and optimise 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 optimised 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 Uzbekistan (online or onsite) targets beginner-level law enforcement personnel who aim to shift from manual facial sketching to leveraging AI tools for facial recognition system development.

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

• Grasping the core concepts of Artificial Intelligence and Machine Learning.
• Mastering the fundamentals of digital image processing and its role in facial recognition.
• Acquiring practical skills in employing AI tools and frameworks to construct facial recognition models.
• Gaining hands-on experience in the creation, training, and testing of facial recognition systems.
• Understanding ethical implications and industry best practices regarding facial recognition technology.

This practical training program covering Uzbekistan focuses on the fundamentals of Fiji and ImageJ for biological research. Participants will develop 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.

Explore the fundamentals of Computer Vision through this practical, instructor-led training in Uzbekistan. Participants will utilize Python to construct face, object, and motion detection systems. This course provides a robust, hands-on foundation in image processing and key Computer Vision tasks.

This instructor-led, live training in Uzbekistan (available online or on-site) is designed for intermediate-level professionals who aim to use Vision Builder AI to design, implement, and optimise automated inspection systems for SMT (Surface-Mount Technology) processes.

Upon completion 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 detailed analysis.
• Implement logic-based decision-making for defect detection and process validation.
• Generate comprehensive inspection reports and optimise system performance.