Computer Vision Training in Nepal

This instructor-led, live training in Nepal (online or onsite) is aimed at 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.

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 Nepal (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) delivers robust deployment and optimization capabilities for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.

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

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

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

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on labs focusing on model deployment and performance profiling.
• Live pipeline design exercises utilizing real-world CV and NLP use cases.

Customization Options

• To request a customized training session for this course, please contact us to make arrangements.

This instructor-led, live training in Nepal (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:

• 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 Nepal (offered online or on-site) is targeted at beginner-level law enforcement staff seeking to transition from manual facial sketching to utilizing AI tools for developing facial recognition systems.

By the conclusion 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.

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

Upon completing this training, participants will be able to:

• Navigate the Fiji interface and effectively use ImageJ’s core capabilities.
• Preprocess and enhance scientific images to improve analytical accuracy.
• Perform quantitative image analysis, including cell counting and area measurements.
• Automate routine tasks using macros and plugins.
• Tailor workflows to meet specific image analysis requirements in biological studies.

This instructor-led live training in Nepal (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.

Upon completing this training, participants will be equipped to:

• Set up and configure automated inspections using Vision Builder AI.
• Acquire and preprocess high-quality images for detailed analysis.
• Implement logic-driven decisions for defect detection and process validation.
• Generate inspection reports and fine-tune system performance.

This instructor-led, live training in Nepal (online or on-site) is designed for intermediate to advanced developers, researchers, and data scientists keen on implementing 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 a powerful open-source image processing package that bundles 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 leverage the Fiji distribution and its underlying ImageJ program to create robust 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 update of a Fiji installation on startup using the integrated update system
• Select from a broad selection of scripting languages to build custom image analysis solutions
• Utilize 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 a customized training for this course, please contact us to arrange.

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms.

Audience

This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects

This instructor-led live training in Nepal (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 method employed to identify specific patterns within an image. It helps ascertain whether particular features are present in a captured image, such as verifying the correct label on a defective product along a factory line or checking the specified dimensions of a component. This technique differs from "Pattern Recognition," which identifies broader patterns based on larger sets of related samples. Pattern Matching, by contrast, precisely defines what to look for and indicates whether the expected pattern is present.

Course Format

• This course covers the approaches, technologies, and algorithms used in pattern matching as applied to Machine Vision.

Computer Vision is a domain focused on the automatic extraction, analysis, and comprehension of valuable insights from digital media. Python is a high-level programming language renowned for its clean syntax and code readability.

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

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

• Grasp the fundamentals of Computer Vision
• Utilize Python to execute Computer Vision tasks
• Develop their own systems for face, object, and motion detection

Audience

• Python programmers interested in Computer Vision

Course Format

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

SimpleCV is an open-source framework, comprising a suite of libraries and software tools designed to help you build vision-based applications. It enables you to process images and video streams from a variety of sources, including webcams, Kinect sensors, FireWire and IP cameras, and mobile devices. The framework empowers developers to create software that not only captures visual data but also comprehends and interprets it.

Audience

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

This instructor-led live training in Nepal (online or on-site) is targeted at backend developers and data scientists who wish to incorporate pre-trained YOLO models into their enterprise-focused applications and implement cost-effective object detection components.

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

• Install and configure the necessary tools and libraries required for 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.