Computer Vision with SimpleCV Training Course

The CANN SDK (Compute Architecture for Neural Networks) equips developers with powerful deployment and optimization tools for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.

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

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

• Deploy and optimize CV and NLP models leveraging CANN and AscendCL.
• Utilise CANN tools to convert models and seamlessly integrate them into live pipelines.
• Optimise 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.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab sessions focused on model deployment and performance profiling.
• Live pipeline design incorporating real-world CV and NLP use cases.

Course Customization Options

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

This instructor-led, live training in Australia (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 Australia (online or on-site) is tailored for advanced-level professionals seeking to deepen their grasp of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models via Google Colab.

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

• Constructing and training convolutional neural networks (CNNs) using TensorFlow.
• Leveraging Google Colab for scalable and efficient cloud-based model development.
• Implementing image preprocessing techniques for computer vision tasks.
• Deploying computer vision models for real-world applications.
• Employing transfer learning to enhance the performance of CNN models.
• Visualising and interpreting the results of image classification models.

This instructor-led, live training in Australia (online or onsite) targets intermediate to advanced computer vision engineers, AI developers, and IoT professionals looking to implement and optimise computer vision models for real-time processing on edge devices.

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

• Grasp the fundamentals of Edge AI and its applications within 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 Australia (online or onsite) is designed for beginner-level law enforcement personnel who wish to transition from manual facial sketching to utilising AI tools for developing facial recognition systems.

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

• Comprehend the fundamentals of Artificial Intelligence and Machine Learning.
• Master the basics of digital image processing and its application in facial recognition.
• Develop proficiency in using AI tools and frameworks to build facial recognition models.
• Gain practical experience in creating, training, and testing facial recognition systems.
• Understand ethical considerations and best practices in the use of facial recognition technology.

Fiji is a powerful open-source image processing package that combines ImageJ (a program designed for scientific multidimensional images) 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 conclusion of this training, participants will be capable of:

• Extending ImageJ capabilities by using Fiji's advanced programming features and software components
• Stitching large 3D images from overlapping tiles
• Automating the update of a Fiji installation on startup using the integrated update system
• Selecting from a broad selection of scripting languages to build custom image analysis solutions
• Utilizing Fiji's powerful libraries, such as ImgLib, to process large bioimage datasets efficiently
• Deploying applications and collaborating 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 Australia (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 Australia (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 automatically extracting, analysing, and interpreting valuable information from digital media. Python, a high-level programming language, is renowned for its clean syntax and code readability.

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

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

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

Audience

• Python programmers keen on exploring Computer Vision

Course Format

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

This instructor-led, live training in Australia (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 Australia (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.
• Customise Python command-line applications that operate based on YOLO pre-trained models.
• Implement pre-trained YOLO model frameworks for various computer vision projects.
• Convert existing object detection datasets into YOLO format.
• Understand the fundamental concepts of the YOLO algorithm for computer vision and/or deep learning.

This instructor-led, live training in Australia (online or onsite) is tailored for intermediate to advanced-level developers, researchers, and data scientists keen on learning how to implement real-time object detection using YOLOv7.

Upon completing this training, participants will be capable of:

• Grasping the core concepts of object detection.
• Installing and configuring YOLOv7 for object detection tasks.
• Training and testing custom object detection models using YOLOv7.
• Integrating YOLOv7 with other computer vision frameworks and tools.
• Troubleshooting common issues related to YOLOv7 implementation.