Real-Time Object Detection with YOLO Training Course
YOLO (You Only Look Once) is an algorithm that has been adapted into pre-trained models for object detection. Validated by the Darknet neural network framework, it is perfectly suited for building computer vision solutions based on the COCO (Common Objects in Context) dataset. The most recent iterations of the YOLO framework, specifically YOLOv3 through YOLOv4, enable programs to perform object localization and classification tasks efficiently and in real-time.
This instructor-led live training, available either online or onsite, is designed for backend developers and data scientists who aim to integrate pre-trained YOLO models into their enterprise applications and deploy cost-effective object-detection components.
Upon completing this training, participants will be able to:
- Install and set up the essential tools and libraries needed for object detection with YOLO.
- Tailor Python command-line applications that leverage YOLO pre-trained models.
- Deploy pre-trained YOLO model frameworks across various computer vision projects.
- Transform existing datasets for object detection into the YOLO format.
- Grasp the core concepts underlying the YOLO algorithm for computer vision and/or deep learning.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical sessions.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To request a tailored training session for this course, please get in touch with us to make arrangements.
Course Outline
Introduction
Overview of YOLO Pre-trained Models: Features and Architecture
- The YOLO Algorithm
- Regression-based Algorithms for Object Detection
- What Distinguishes YOLO from RCNN?
Selecting the Appropriate YOLO Variant
- Features and Architecture of YOLOv1-v2
- Features and Architecture of YOLOv3-v4
Installing and Configuring the IDE for YOLO Implementations
- The Darknet Implementation
- The PyTorch and Keras Implementations
- Executing OpenCV and NumPy
Overview of Object Detection Using YOLO Pre-trained Models
Building and Customizing Python Command-Line Applications
- Labeling Images Using the YOLO Framework
- Image Classification Based on a Dataset
Detecting Objects in Images with YOLO Implementations
- How Do Bounding Boxes Function?
- What is the Accuracy of YOLO for Instance Segmentation?
- Parsing Command-line Arguments
Extracting YOLO Class Labels, Coordinates, and Dimensions
Displaying Resulting Images
Detecting Objects in Video Streams with YOLO Implementations
- How Does This Differ from Basic Image Processing?
Training and Testing YOLO Implementations on a Framework
Troubleshooting and Debugging
Summary and Conclusion
Requirements
- Programming experience with Python 3.x
- Fundamental knowledge of any Python IDEs
- Practical experience with Python argparse and command-line arguments
- Understanding of computer vision and machine learning libraries
- Knowledge of fundamental object detection algorithms
Target Audience
- Backend Developers
- Data Scientists
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Real-Time Object Detection with YOLO Training Course - Enquiry
Testimonials (1)
Hands on and the practical
Keeren Bala Krishnan - PENGUIN SOLUTIONS (SMART MODULAR)
Course - Computer Vision with Python
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