Computer Vision with SimpleCV Training Course

The CANN SDK (Compute Architecture for Neural Networks) delivers robust deployment and optimization capabilities for real-time AI solutions in computer vision and natural language processing, particularly on Huawei Ascend hardware.

This instructor-led live training, available online or onsite, targets intermediate-level AI professionals looking to construct, deploy, and refine vision and language models using the CANN SDK for real-world production scenarios.

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

• Deploy and optimize computer vision and NLP models utilizing CANN and AscendCL.
• Leverage CANN utilities to transform models and seamlessly integrate them into live processing pipelines.
• Enhance inference performance for applications such as detection, classification, and sentiment analysis.
• Construct real-time CV and NLP pipelines suitable for edge or cloud deployment environments.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical laboratory sessions focused on model deployment and performance profiling.
• Designing live pipelines through real-world CV and NLP use cases.

Course Customization Options

• For personalized training arrangements, please contact us directly.

This instructor-led, live training in Argentina (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 Argentina (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 Argentina (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.

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 Argentina (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 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 Argentina (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 Argentina (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 dedicated to the automatic extraction, analysis, and interpretation of valuable information from digital media. Python stands out as a high-level programming language renowned for its readable syntax and code clarity.

During this instructor-led live training, attendees will grasp the fundamentals of Computer Vision by developing a series of simple applications using Python.

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

• Grasping the core principles of Computer Vision
• Utilizing Python to execute Computer Vision tasks
• Developing their own systems for face, object, and motion detection

Audience

• Python developers with an interest in Computer Vision

Course Format

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

This instructor-led, live training in Argentina (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 Argentina (online or onsite) is designed for backend developers and data scientists seeking to integrate pre-trained YOLO models into enterprise applications and implement cost-effective object detection components.

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

• Install and configure the essential tools and libraries required for YOLO-based object detection.
• Customize Python command-line applications that leverage YOLO pre-trained models.
• Deploy pre-trained YOLO model frameworks for various computer vision initiatives.
• Transform existing datasets for object detection into the YOLO format.
• Grasp the fundamental concepts of the YOLO algorithm within the contexts of computer vision and deep learning.

This instructor-led, live training in Argentina (online or on-site) targets intermediate to advanced-level developers, researchers, and data scientists eager to learn how to implement real-time object detection using YOLOv7.

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

• Comprehend the fundamental concepts of object detection.
• Install and configure YOLOv7 for object detection tasks.
• Train and test custom object detection models utilizing YOLOv7.
• Integrate YOLOv7 with other computer vision frameworks and tools.
• Troubleshoot common issues related to YOLOv7 implementation.