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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
- Key Differences Between YOLO and 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 Implementation
- Darknet Implementation
- PyTorch and Keras Implementations
- Utilizing OpenCV and NumPy
Overview of Object Detection Using YOLO Pre-trained Models
Building and Customizing Python Command-Line Applications
- Image Labeling Using the YOLO Framework
- Image Classification Based on a Dataset
Detecting Objects in Images with YOLO
- Understanding Bounding Boxes
- Evaluating YOLO Accuracy for Instance Segmentation
- Parsing Command-line Arguments
Extracting YOLO Class Labels, Coordinates, and Dimensions
Displaying Resulting Images
Detecting Objects in Video Streams with YOLO
- Differences from Basic Image Processing
Training and Testing YOLO Implementations
Troubleshooting and Debugging
Summary and Conclusion
Requirements
- Programming experience with Python 3.x
- Basic knowledge of Python IDEs
- Experience using Python argparse and command-line arguments
- Understanding of computer vision and machine learning libraries
- Familiarity with fundamental object detection algorithms
Target Audience
- Backend Developers
- Data Scientists
7 Hours
Testimonials (2)
Hands on and the practical
Keeren Bala Krishnan - PENGUIN SOLUTIONS (SMART MODULAR)
Course - Computer Vision with Python
I genuinely enjoyed the hands-on approach.
