Computer Vision Training in Belgrade

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

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

• Understand 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.
• Use frameworks like 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 Belgrade (available online or on-site) targets advanced professionals who aim to deepen their grasp 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 applications in computer vision and natural language processing, particularly on Huawei Ascend hardware.

This instructor-led, live training, available online or onsite, is designed for intermediate-level AI practitioners looking to build, deploy, and optimize vision and language models using the CANN SDK for production scenarios.

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

• Deploy and optimize CV and NLP models leveraging CANN and AscendCL.
• Utilize CANN tools to convert models and integrate them into active 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.

Course Format

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

Customization Options

• For customized training requests, please reach out to us to arrange.

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

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

• Understand the core principles of Artificial Intelligence and Machine Learning.
• Learn the basics of digital image processing and its application in facial recognition.
• Develop proficiency in using AI tools and frameworks to build facial recognition models.
• Gain hands-on experience in creating, training, and testing facial recognition systems.
• Understand ethical considerations and best practices in the use of facial recognition technology.

This instructor-led, live training in Belgrade (online or onsite) is aimed at beginner-level to intermediate-level researchers and laboratory professionals who wish to process and analyze images related to histological tissues, blood cells, algae, and other biological samples.

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

• Navigate the Fiji interface and utilize ImageJ’s core functions.
• Preprocess and enhance scientific images for better analysis.
• Analyze images quantitatively, including cell counting and area measurement.
• Automate repetitive tasks using macros and plugins.
• Customize workflows for specific image analysis needs in biological research.

This instructor-led, live training in Belgrade (online or onsite) targets intermediate-level professionals aiming to utilize Vision Builder AI for the design, implementation, and optimization of automated inspection systems within SMT (Surface-Mount Technology) processes.

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

• Setting up and configuring automated inspections using Vision Builder AI.
• Acquiring and preprocessing high-quality images for analysis.
• Implementing logic-based decision-making for defect detection and process validation.
• Generating inspection reports and optimizing overall system performance.

This instructor-led, live training in Belgrade (online or on-site) is designed for intermediate to advanced developers, researchers, and data scientists seeking to implement real-time object detection using YOLOv7.

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

• Understand the fundamental concepts of object detection.
• Install and configure YOLOv7 for object detection tasks.
• Train and test custom object detection models using YOLOv7.
• Integrate YOLOv7 with other computer vision frameworks and tools.
• Troubleshoot common issues related to YOLOv7 implementation.

Fiji is an open-source image processing package that combines ImageJ (a program for processing scientific multidimensional images) and various plugins for scientific image analysis.

During this instructor-led live training, participants will learn how to utilize the Fiji distribution and its underlying ImageJ program to build an image analysis application.

Upon completing this training, participants will be able to:

• Leverage Fiji's advanced programming features and software components to extend ImageJ
• Stitch large 3D images from overlapping tiles
• Automatically update a Fiji installation on startup using the integrated update system
• Choose from a wide range of scripting languages to create custom image analysis solutions
• Apply Fiji's powerful libraries, such as ImgLib, to large bioimage datasets
• Deploy their application and collaborate with other scientists on similar projects

Format of the Course

• Interactive lecture and discussion.
• Numerous exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source library licensed under the BSD license, offering hundreds of computer vision algorithms.

Target Audience

This course is designed for engineers and architects who wish to apply OpenCV in computer vision projects.

This instructor-led, live training in Belgrade (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.

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

• View, load, and classify images and videos using OpenCV 4.
• Implement deep learning in OpenCV 4 with TensorFlow and Keras.
• Run deep learning models and generate impactful reports from images and videos.

Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.

Format of the Course

• This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.

Computer Vision is a discipline dedicated to the automatic extraction, analysis, and comprehension of valuable insights from digital media. Python stands out as a high-level programming language renowned for its clean syntax and high readability.

Through this instructor-led live training, participants will gain a foundational understanding of Computer Vision by progressively building a series of straightforward Computer Vision applications using Python.

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

• Gaining a clear understanding of Computer Vision fundamentals
• Leveraging Python to execute Computer Vision tasks
• Developing their own systems for face, object, and motion detection

Audience

• Python programmers with an interest in Computer Vision

Format of the course

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

SimpleCV is an open-source framework, meaning it is a collection of libraries and software that you can use to develop vision applications. It lets you work with the images or video streams that come from webcams, Kinects, FireWire and IP cameras, or mobile phones. It helps you build software to make your various technologies not only see the world, but understand it too.

Audience

This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV.

This instructor-led, live training in Belgrade (online or onsite) is aimed at backend developers and data scientists who wish to incorporate pre-trained YOLO models into their enterprise-driven programs and implement cost-effective components for object detection.

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

• Install and configure the necessary tools and libraries required in object detection using YOLO.
• Customize Python command-line applications that operate based on YOLO pre-trained models.
• Implement the framework of pre-trained YOLO models for various computer vision projects.
• Convert existing datasets for object detection into YOLO format.
• Understand the fundamental concepts of the YOLO algorithm for computer vision and/or deep learning.