Computer Vision Training in Busan

This instructor-led, live training in Busan (online or onsite) targets intermediate to advanced computer vision engineers, AI developers, and IoT professionals who wish 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 for performance, power efficiency, and low-latency inference.

This instructor-led, live training in Busan (online or onsite) is designed for advanced-level professionals seeking 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.

The CANN SDK (Compute Architecture for Neural Networks) delivers robust deployment and optimization capabilities for real-time AI solutions in computer vision and natural language processing, particularly on Huawei Ascend hardware.

This instructor-led live training, available online or onsite, targets intermediate AI professionals seeking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.

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

• Deploy and optimize CV and NLP models utilizing CANN and AscendCL.
• Leverage CANN tools to convert models and integrate them into operational pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures combined with live demonstrations.
• Hands-on labs focused on model deployment and performance profiling.
• Practical pipeline design exercises using real-world CV and NLP use cases.

Customization Options

• For customized training arrangements, please reach out to us.

This instructor-led live training in Busan (online or onsite) targets intermediate-level AI developers and computer vision engineers who aim to build robust vision systems for autonomous driving applications.

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

• Grasp 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 Busan (available online or on-site) targets beginner-level law enforcement professionals seeking to shift from manual facial sketching to AI-driven tools for developing facial recognition systems.

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

• Grasp the core principles of Artificial Intelligence and Machine Learning.
• Acquire foundational knowledge of digital image processing and its relevance to facial recognition.
• Build competence in employing AI tools and frameworks to construct facial recognition models.
• Obtain practical experience in developing, training, and evaluating facial recognition systems.
• Comprehend the ethical implications and industry best practices associated with facial recognition technology.

This instructor-led, live training in Busan (online or onsite) targets beginner to intermediate-level researchers and laboratory professionals who want to process and analyze images of histological tissues, blood cells, algae, and other biological specimens.

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

• Navigate the Fiji interface and leverage ImageJ’s core functionalities.
• Preprocess and enhance scientific images to improve analysis accuracy.
• Perform quantitative image analysis, such as cell counting and area measurement.
• Automate repetitive tasks using macros and plugins.
• Customize workflows to meet specific image analysis requirements in biological research.

This instructor-led, live training in Busan (online or onsite) is designed for 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 instructor-led, live training in Busan (online or onsite) caters to intermediate to advanced developers, researchers, and data scientists who aim to learn real-time object detection implementation using YOLOv7.

By the conclusion of this training, participants will be capable of:

• Grasping the fundamental concepts of object detection.
• Installing and configuring YOLOv7 for object detection tasks.
• Training and testing custom object detection models using YOLOv7.
• Integrating YOLOv7 with other computer vision frameworks and tools.
• Troubleshooting common issues related to YOLOv7 implementation.

Computer Vision is a discipline dedicated to automatically extracting, analyzing, and interpreting meaningful data from digital media. Python stands out as a high-level programming language, renowned for its clear syntax and code readability.

In this instructor-led live training, participants will explore the fundamentals of Computer Vision by working through the development of several simple Computer Vision applications using Python.

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

• Grasp the core concepts of Computer Vision
• Apply Python to implement Computer Vision tasks
• Develop custom systems for face, object, and motion detection

Audience

• Python developers interested in Computer Vision

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice