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 natural language processing, particularly on Huawei Ascend hardware.

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

Upon completion 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 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 scenarios.

Course Format

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

Customization Options

• To request a customized training session for this course, please get in touch with us to arrange details.

This instructor-led, live training in Greece (online or onsite) targets intermediate-level AI developers and computer vision engineers who aim to construct robust vision systems for autonomous driving applications.

Upon completing this training, participants will be able to:

• Grasp the fundamental concepts of computer vision within autonomous vehicles.
• Deploy 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 Greece (online or onsite) is designed for advanced professionals who wish to expand their expertise in computer vision and investigate TensorFlow's capabilities for developing advanced 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 for scalable and efficient cloud-based model development.
• Apply image preprocessing techniques specifically for computer vision applications.
• Deploy computer vision models for practical, real-world solutions.
• Employ transfer learning to optimize the performance of CNN models.
• Visualize and interpret outcomes from image classification models.

This instructor-led, live training in Greece (online or onsite) is designed for intermediate to advanced computer vision engineers, AI developers, and IoT professionals seeking to implement and optimize computer vision models for real-time processing on edge hardware.

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

• Grasp the core concepts of Edge AI and its role 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.
• Enhance AI models for performance, power efficiency, and low-latency inference.

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

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

• Grasp the core principles of Artificial Intelligence and Machine Learning.
• Acquire foundational knowledge of digital image processing and its role in facial recognition.
• Build proficiency in using AI tools and frameworks to construct facial recognition models.
• Gain practical experience in creating, training, and evaluating facial recognition systems.
• Comprehend the ethical implications and best practices associated with facial recognition technology.

Fiji is a robust open-source image processing package that bundles ImageJ (a tool 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 make arrangements.

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

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

• Navigating the Fiji interface and applying ImageJ’s core functionalities.
• Preprocessing and enhancing scientific images to improve analysis quality.
• Performing quantitative image analysis, such as cell counting and area measurement.
• Automating repetitive tasks through the use of macros and plugins.
• Tailoring workflows to meet specific image analysis requirements in biological research.

This instructor-led, live training in Greece (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.

Computer Vision is a discipline focused on the automatic extraction, analysis, and interpretation of valuable information from digital media. Python, a high-level programming language renowned for its clarity and code readability, serves as the primary tool for this training.

Through this instructor-led live session, participants will grasp the fundamentals of Computer Vision by developing a series of simple applications using Python.

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

• Grasp the core concepts of Computer Vision
• Utilize Python to execute Computer Vision tasks
• Develop custom systems for detecting faces, objects, and motion

Target Audience

• Python developers keen on exploring Computer Vision

Course Format

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

This instructor-led, live training in Greece (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 Greece (online or on-site) 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.

This guided, live training session in Greece (online or in person) is designed for developers, researchers, and data scientists at intermediate to advanced levels who aim to master the implementation of real-time object detection using YOLOv7.

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

• Grasp the core principles underlying object detection.
• Successfully install and configure YOLOv7 for various detection tasks.
• Train and evaluate custom object detection models utilizing YOLOv7.
• Seamlessly integrate YOLOv7 with existing computer vision frameworks and tools.
• Address and resolve common challenges associated with YOLOv7 implementation.