Computer Vision with SimpleCV Training Course

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

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

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

• Deploy and optimize computer vision (CV) and natural language processing (NLP) models using CANN and AscendCL.
• Utilize CANN tools for model conversion and integration into live pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment scenarios.

Format of the Course

• Interactive lecture with demonstrations.
• Hands-on lab sessions focusing on model deployment and performance profiling.
• Live pipeline design using practical CV and NLP use cases.

Course Customization Options

• For a customized training session tailored to your specific needs, please contact us to arrange.

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

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

• Understand the fundamentals of Artificial Intelligence and Machine Learning.
• Learn the basics of digital image processing and its application in facial recognition.
• Develop skills in using AI tools and frameworks to create 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.

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

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

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

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

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Uzbekistan (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.

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source library licensed under the BSD license, which contains several hundred algorithms for computer vision.

Audience

This course is designed for engineers and architects who are looking to leverage OpenCV for their computer vision projects.

This instructor-led, live training in Uzbekistan (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 employed to identify specific patterns within an image. It helps determine whether certain characteristics are present in a captured image, such as the correct label on a defective product on a factory line or the precise dimensions of a component. Unlike "Pattern Recognition," which identifies general patterns based on large collections of related samples, Pattern Matching specifies what we are looking for and confirms whether the expected pattern is present.

Format of the Course

• This course provides an introduction to the approaches, technologies, and algorithms used in the field of pattern matching as it pertains to Machine Vision.

Computer Vision is a field that focuses on automatically extracting, analyzing, and understanding valuable information from digital media. Python is a high-level programming language renowned for its clear syntax and code readability.

In this instructor-led, live training, participants will learn the fundamentals of Computer Vision as they work through the process of creating simple Computer Vision applications using Python.

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

• Grasp the basics of Computer Vision
• Utilize Python to perform Computer Vision tasks
• Construct their own face, object, and motion detection systems

Audience

• Python programmers with an interest in Computer Vision

Format of the course

• Part lecture, part discussion, exercises, and extensive hands-on practice

This instructor-led, live training in Uzbekistan (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.

This instructor-led, live training in Uzbekistan (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.

This instructor-led, live training in Uzbekistan (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.