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Course Outline
Introduction to Object Detection
- Object detection basics
- Object detection applications
- Performance metrics for object detection models
Overview of YOLOv7
- YOLOv7 installation and setup
- YOLOv7 architecture and components
- Advantages of YOLOv7 over other object detection models
- YOLOv7 variants and their differences
YOLOv7 Training Process
- Data preparation and annotation
- Model training using popular deep learning frameworks (TensorFlow, PyTorch, etc.)
- Fine-tuning pre-trained models for custom object detection
- Evaluation and tuning for optimal performance
Implementing YOLOv7
- Implementing YOLOv7 in Python
- Integration with OpenCV and other computer vision libraries
- Deploying YOLOv7 on edge devices and cloud platforms
Advanced Topics
- Multi-object tracking using YOLOv7
- YOLOv7 for 3D object detection
- YOLOv7 for video object detection
- Optimizing YOLOv7 for real-time performance
Summary and Next Steps
Requirements
- Experience with Python programming
- Understanding of deep learning fundamentals
- Knowledge of computer vision basics
Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
21 Hours
Testimonials (1)
I genuinely enjoyed the hands-on approach.
