Computer Vision with SimpleCV Training Course

The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization solutions for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.

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

By the conclusion 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 seamlessly integrate them into live pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud deployment environments.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical labs focusing on model deployment and performance profiling.
• Live pipeline design exercises using real-world CV and NLP scenarios.

Course Customization Options

• For customized training requests, please contact us to arrange details.

This instructor-led, live training in Romania (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 Romania (online or onsite) is designed for advanced-level professionals who aim to deepen their knowledge of computer vision and investigate TensorFlow's capabilities for developing complex vision models using Google Colab.

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

• Construct and train convolutional neural networks (CNNs) using TensorFlow.
• Utilize Google Colab to facilitate scalable and efficient cloud-based model development.
• Apply image preprocessing techniques tailored for computer vision tasks.
• Deploy computer vision models for practical, real-world applications.
• Employ transfer learning to optimize the performance of CNN models.
• Visualize and analyze the outcomes of image classification models.

This instructor-led, live training in Romania (online or onsite) is designed for intermediate to advanced computer vision engineers, AI developers, and IoT professionals who aim 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 Romania (online or onsite) is designed for beginner-level law enforcement personnel looking to shift from manual facial sketching techniques to utilizing AI tools for building facial recognition systems.

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

• Grasping the core principles of Artificial Intelligence and Machine Learning.
• Learning the fundamentals of digital image processing and its role in facial recognition.
• Developing proficiency in employing AI tools and frameworks to construct facial recognition models.
• Acquiring practical experience in building, training, and testing facial recognition systems.
• Understanding the ethical implications and best practices associated with facial recognition technology.

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.

This instructor-led, live training in Romania (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 BSD, featuring several hundred computer vision algorithms.

Audience

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

This instructor-led, live training in Romania (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 automatically extracting, analyzing, and interpreting valuable information from digital media. Python, renowned for its clean syntax and high readability, serves as an ideal tool for this purpose.

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

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

• Grasp the core principles of Computer Vision
• Utilize Python to execute Computer Vision tasks
• Develop custom systems for face, object, and motion detection

Audience

• Python developers looking to specialize in Computer Vision

Course Format

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

This instructor-led, live training in Romania (online or onsite) targets 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 Romania (online or onsite) is designed for backend developers and data scientists who wish to integrate pre-trained YOLO models into their enterprise 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.

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

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

• Grasp the core principles of object detection.
• Install and set up YOLOv7 for object detection tasks.
• Train and evaluate custom object detection models utilizing YOLOv7.
• Integrate YOLOv7 alongside other computer vision frameworks and tools.
• Resolve typical challenges associated with YOLOv7 implementation.