Computer Vision Training in Tashkent

This instructor-led, live training in Tashkent (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 Tashkent (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.

The CANN SDK (Compute Architecture for Neural Networks) offers powerful deployment and optimisation tools for real-time AI applications in computer vision and natural language processing (NLP), particularly on Huawei Ascend hardware.

This instructor-led, live training (available online or on-site) is designed for intermediate-level AI practitioners who aim to build, deploy, and optimise vision and language models using the CANN SDK for production-grade use cases.

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

• Deploy and optimise computer vision (CV) and NLP models using CANN and AscendCL.
• Leverage CANN tools to convert models and integrate them into live pipelines.
• Optimise inference performance for tasks such as object detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures and demonstrations.
• Practical labs covering model deployment and performance profiling.
• Live pipeline design using real-world CV and NLP use cases.

Course Customisation Options

• To request a customised version of this course, please contact us to arrange the details.

This guided, live training in Tashkent (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 Tashkent (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 instructor-led, live training in Tashkent (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 Tashkent (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.

This instructor-led, live training in Tashkent (available online or on-site) is tailored for intermediate to advanced-level developers, researchers, and data scientists who aim to master the implementation of real-time object detection using YOLOv7.

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

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

Fiji is a robust open-source image processing package that integrates ImageJ (a program designed for scientific multidimensional images) along with a comprehensive suite of plugins for scientific image analysis.

In this instructor-led, live training, participants will learn how to harness the Fiji distribution and its underlying ImageJ program to build effective image analysis applications.

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

• Use Fiji's advanced programming features and software components to extend ImageJ capabilities
• Stitch large 3D images from overlapping tiles
• Automate the updating of a Fiji installation at startup using the integrated update system
• Select from a broad selection of scripting languages to build custom image analysis solutions
• Leverage Fiji's powerful libraries, such as ImgLib, to process large bioimage datasets efficiently
• Deploy applications and collaborate effectively with other scientists on similar projects

Format of the Course

• Interactive lecture and discussion
• Extensive exercises and practical application
• Hands-on implementation in a live-lab environment

Course Customization Options

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

OpenCV (Open Source Computer Vision Library: http://opencv.org) is a BSD-licensed, open-source library containing hundreds of computer vision algorithms.

Target Audience

This course is designed for engineers and architects who aim to leverage OpenCV for computer vision initiatives.

This instructor-led, live training in Tashkent (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 domain focused on the automatic extraction, analysis, and comprehension of valuable information from digital media. Python, a high-level programming language, is renowned for its clear syntax and high readability.

During this instructor-led live training, participants will master the fundamentals of Computer Vision by progressively building a series of straightforward Computer Vision applications using Python.

Upon completing this training, participants will be equipped to:

• Grasp the fundamental concepts of Computer Vision
• Leverage Python to execute Computer Vision tasks
• Develop custom systems for face, object, and motion detection

Audience

• Python developers seeking to delve into Computer Vision

Course Format

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

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

Audience

This course is designed for engineers and developers who wish to create computer vision applications using SimpleCV.

This instructor-led, live training in Tashkent (online or on-site) is designed for backend developers and data scientists who wish to integrate pre-trained YOLO models into their enterprise applications and implement cost-effective components for object detection.

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

• Install and configure the essential tools and libraries required for object detection using YOLO.
• Customise 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 object detection datasets into YOLO format.
• Understand the fundamental concepts of the YOLO algorithm in the context of computer vision and/or deep learning.