Computer Vision Training in Austin

This instructor-led, live training in Austin (online or onsite) is aimed at intermediate-level to advanced-level 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 Austin (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) provides powerful deployment and optimization tools for real-time AI applications in computer vision and NLP, especially on Huawei Ascend hardware.

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

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

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Use CANN tools to convert models and integrate them into live pipelines.
• Optimize inference performance for tasks like detection, classification, and sentiment analysis.
• Build real-time CV/NLP pipelines for edge or cloud-based deployment scenarios.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab with model deployment and performance profiling.
• Live pipeline design using real CV and NLP use cases.

Course Customization Options

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

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

This instructor-led, live training in Austin (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 Austin (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 Austin (online or onsite) is aimed at intermediate to advanced-level developers, researchers, and data scientists who wish to learn how to implement 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.

Caffe is a deep learning framework made with expression, speed, and modularity in mind.

This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example

Audience

This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.

After completing this course, delegates will be able to:

• understand Caffe’s structure and deployment mechanisms
• carry out installation / production environment / architecture tasks and configuration
• assess code quality, perform debugging, monitoring
• implement advanced production like training models, implementing layers and logging

Fiji is an open-source image processing package that bundles ImageJ (an image processing program for scientific multidimensional images) and a number of plugins for scientific image analysis.

In this instructor-led, live training, participants will learn how to use the Fiji distribution and its underlying ImageJ program to create an image analysis application.

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

• Use Fiji's advanced programming features and software components to extend ImageJ
• Stitch large 3d images from overlapping tiles
• Automatically update a Fiji installation on startup using the integrated update system
• Select from a broad selection of scripting languages to build custom image analysis solutions
• Use Fiji's powerful libraries, such as ImgLib on large bioimage datasets
• Deploy their application and collaborate with other scientists on similar projects

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• 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 Austin (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.

OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research.

In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.

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

• Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
• Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

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 field that involves automatically extracting, analyzing, and understanding useful information from digital media. Python is a high-level programming language famous for its clear syntax and code readibility.

In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of set of simple Computer Vision application using Python.

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

• Understand the basics of Computer Vision
• Use Python to implement Computer Vision tasks
• Build their own face, object, and motion detection systems

Audience

• Python programmers interested in Computer Vision

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training introduces the software, hardware, and step-by-step process needed to build a facial recognition system from scratch. Facial Recognition is also known as Face Recognition.

The hardware used in this lab includes Rasberry Pi, a camera module, servos (optional), etc. Participants are responsible for purchasing these components themselves. The software used includes OpenCV, Linux, Python, etc.

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

• Install Linux, OpenCV and other software utilities and libraries on a Rasberry Pi.
• Configure OpenCV to capture and detect facial images.
• Understand the various options for packaging a Rasberry Pi system for use in real-world environments.
• Adapt the system for a variety of use cases, including surveillance, identity verification, etc.

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• Other hardware and software options include: Arduino, OpenFace, Windows, etc. If you wish to use any of these, please contact us to arrange.

SimpleCV is an open source framework — meaning that it is a collection of libraries and software that you can use to develop vision applications. It lets you work with the images or video streams that come from webcams, Kinects, FireWire and IP cameras, or mobile phones. It’s helps you build software to make your various technologies not only see the world, but understand it too.

Audience

This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV.

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