YOLOv7: Real-time Object Detection with Computer Vision Training Course

Caffe is a deep learning framework designed 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 for government applications.

After completing this course, delegates will be able to:

• Understand Caffe’s structure and deployment mechanisms
• Perform installation, production environment setup, architecture tasks, and configuration
• Evaluate code quality, conduct debugging, and implement monitoring
• Implement advanced production techniques such as training models, implementing layers, and logging

This instructor-led, live training (conducted online or on-site) is designed for intermediate-level AI developers and computer vision engineers who are interested in building robust vision systems for autonomous driving applications. By the end of this training, participants will be able to: - Comprehend the foundational concepts of computer vision as applied in autonomous vehicles. - Implement algorithms for object detection, lane detection, and semantic segmentation. - Integrate vision systems with other subsystems within autonomous vehicles. - Apply deep learning techniques to enhance perception tasks. - Assess the performance of computer vision models in real-world conditions. This training aligns with public sector workflows, governance, and accountability standards, ensuring that participants are well-prepared to develop solutions for government use.

This instructor-led, live training (online or onsite) is designed for advanced-level professionals who aim 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 training is tailored to ensure that professionals can apply these skills effectively in their roles, particularly in projects for government.

This instructor-led, live training in [location] (online or onsite) is aimed at intermediate to advanced 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. - 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 training is designed to enhance the skills of professionals in the public sector, ensuring they are well-equipped to leverage advanced technologies for government applications.

This instructor-led, live training (online or onsite) is aimed at beginner-level law enforcement personnel who wish to transition from manual facial sketching to utilizing AI tools for developing facial recognition systems for government. 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 for government.

Fiji is an open-source image processing package that bundles ImageJ, a program designed for scientific multidimensional images, along with numerous plugins for advanced image analysis.

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

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

• Leverage Fiji's advanced programming features and software components to extend the functionality of ImageJ
• Stitch large 3D images from overlapping tiles efficiently
• Automatically update a Fiji installation on startup using the integrated update system for government applications
• Select from a wide range of scripting languages to build customized image analysis solutions
• Utilize powerful libraries, such as ImgLib, for processing large bioimage datasets in government projects
• Deploy their application and collaborate with other scientists on similar initiatives within the public sector

Format of the Course

• Interactive lecture and discussion
• Extensive exercises and practical activities
• Hands-on implementation in a live-lab environment

Course Customization Options

• To request a customized training for government agencies, please contact us to arrange.

This instructor-led, live training (online or onsite) is designed for government researchers and laboratory professionals at beginner to intermediate levels 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 effectively. - Preprocess and enhance scientific images for improved analysis. - Conduct quantitative image analysis, including cell counting and area measurement. - Automate repetitive tasks using macros and plugins. - Customize workflows to meet specific image analysis needs in biological research for government applications.

OpenCV (Open Source Computer Vision Library: ) is an open-source BSD-licensed library that includes several hundred computer vision algorithms.

Audience

This course is directed at engineers and architects seeking to utilize OpenCV for government projects involving computer vision.

This instructor-led, live training in US (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning applications. By the end of this training, participants will be able to: - View, load, and classify images and videos using OpenCV 4. - Implement deep learning techniques in OpenCV 4 with TensorFlow and Keras. - Run deep learning models and generate comprehensive reports from images and videos, enhancing capabilities for government projects.

Pattern Matching is a technique utilized to identify specified patterns within an image. It can be employed to determine the presence of specific characteristics within a captured image, such as the expected label on a defective product in a factory line or the precise dimensions of a component. This differs from "Pattern Recognition," which identifies general patterns based on larger collections of related samples, as Pattern Matching specifically defines what is being sought and confirms 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 for government applications.

Computer Vision is a field that involves automatically extracting, analyzing, and understanding useful information from digital media. Python, a high-level programming language renowned for its clear syntax and code readability, is widely used in this domain. In this instructor-led, live training, participants will gain an understanding of the fundamentals of Computer Vision as they work through the creation of several simple Computer Vision applications 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 for government projects **Format of the Course** - Part lecture, part discussion, with exercises and extensive hands-on practice

SimpleCV is an open-source framework designed for government and other sectors. It consists of a collection of libraries and software that enable the development of vision applications. This framework allows users to work with images or video streams from various sources, including webcams, Kinects, FireWire and IP cameras, as well as mobile phones. SimpleCV facilitates the creation of software that not only captures visual data but also processes and interprets it. ### Audience This course is intended for engineers and developers who are looking to build computer vision applications using SimpleCV.

This instructor-led, live training in [location] (online or onsite) is designed for intermediate-level professionals who wish to utilize Vision Builder AI to develop, implement, and enhance 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, ensuring alignment with standards and protocols for government.

This instructor-led, live training in US (online or onsite) is designed for back-end developers and data scientists who wish to integrate pre-trained YOLO models into their enterprise-driven programs and implement cost-effective solutions for object detection. 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 utilize YOLO pre-trained models. - Implement the framework of pre-trained YOLO models for a variety of computer vision projects. - Convert existing datasets for object detection into the YOLO format. - Understand the fundamental concepts of the YOLO algorithm in the context of computer vision and deep learning, ensuring alignment with public sector workflows for government.