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Course Outline
Introduction to Object Detection
- Foundations of object detection
- Practical applications of object detection
- Key performance metrics for evaluation
Overview of YOLOv7
- Installing and configuring YOLOv7
- Understanding YOLOv7 architecture and components
- Benefits of YOLOv7 compared to other models
- Differences between YOLOv7 variants
The YOLOv7 Training Process
- Data preparation and annotation techniques
- Training models using frameworks like TensorFlow and PyTorch
- Fine-tuning pre-trained models for custom use cases
- Performance evaluation and optimization
Implementing YOLOv7
- Writing YOLOv7 implementations in Python
- Integrating with OpenCV and other vision libraries
- Deploying YOLOv7 on edge devices and cloud infrastructure
Advanced Topics
- Multi-object tracking with YOLOv7
- Applying YOLOv7 to 3D object detection
- Video object detection using YOLOv7
- Optimizing YOLOv7 for real-time efficiency
Requirements
- Proficiency in Python programming
- Familiarity with deep learning fundamentals
- Basic knowledge of computer vision concepts
Target Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
21 Hours
Testimonials (1)
Hands on and the practical
