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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
- 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 the 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 Work?
- How Accurate is YOLO for Instance Segmentation?
- Parsing the Command-line Arguments
Extracting the YOLO Class Labels, Coordinates, and Dimensions
Displaying the Resulting Images
Detecting Objects in Video Streams with YOLO Implementations
- How is it Different from Basic Image Processing?
Training and Testing the YOLO Implementations on a Framework
Troubleshooting and Debugging
Summary and Conclusion
Requirements
- Python 3.x programming experience
- Basic knowledge of any Python IDEs
- Experience with Python argparse and command-line arguments
- Comprehension of computer vision and machine learning libraries
- An understanding of fundamental object detection algorithms
Audience
- Backend Developers
- Data Scientists
Testimonials
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
Apart from the content, I loved Abhi's flexibility to tweak the training based on our feedback
WesCEF
The second day going through feature extraction was great fun. Trainer was very knowledgeable and engaging.
WesCEF
Having some previous computer vision experience I found the second day covering feature extraction and CNNs most beneficial.
WesCEF
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