Course Outline
Introduction
Overview of YOLO Pre-trained Models Features and Architecture
- The YOLO Algorithm
- Regression-based Algorithms for Object Detection
- How is YOLO Different from RCNN?
Utilizing the Appropriate YOLO Variant for Government
- Features and Architecture of YOLOv1-v2
- Features and Architecture of YOLOv3-v4
Installing and Configuring the IDE for YOLO Implementations in Government
- The Darknet Implementation
- The PyTorch and Keras Implementations
- Executing OpenCV and NumPy
Overview of Object Detection Using YOLO Pre-trained Models for Government
Building and Customizing Python Command-Line Applications for Government
- Labeling Images Using the YOLO Framework
- Image Classification Based on a Dataset
Detecting Objects in Images with YOLO Implementations for Government
- How do Bounding Boxes Work?
- How Accurate is YOLO for Instance Segmentation?
- Parsing the Command-line Arguments
Extracting the YOLO Class Labels, Coordinates, and Dimensions for Government
Displaying the Resulting Images for Government
Detecting Objects in Video Streams with YOLO Implementations for Government
- How is it Different from Basic Image Processing?
Training and Testing the YOLO Implementations on a Framework for Government
Troubleshooting and Debugging for Government
Summary and Conclusion for Government
Requirements
- Proficiency in Python 3.x programming for government applications
- Familiarity with any Python Integrated Development Environments (IDEs)
- Experience utilizing Python argparse and command-line arguments for efficient script execution
- Understanding of computer vision and machine learning libraries to enhance data analysis capabilities
- Knowledge of fundamental object detection algorithms to support advanced analytics
Audience
- Backend Developers for government systems
- Data Scientists working in public sector environments
Testimonials (2)
Trainer was very knowlegable and very open to feedback on what pace to go through the content and the topics we covered. I gained alot from the training and feel like I now have a good grasp of image manipulation and some techniques for building a good training set for an image classification problem.
Anthea King - WesCEF
Course - Computer Vision with Python
I genuinely enjoyed the hands-on approach.
