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Course Outline
Introduction to Object Detection
- Foundations of object detection
- Real-world applications of object detection
- Key performance metrics for object detection models
Overview of YOLOv7
- YOLOv7 installation and configuration
- YOLOv7 architecture and core components
- Benefits of YOLOv7 compared to other detection models
- Differences between YOLOv7 variants
YOLOv7 Training Process
- Data preparation and annotation techniques
- Model training with leading deep learning frameworks (TensorFlow, PyTorch, etc.)
- Fine-tuning pre-trained models for specific detection needs
- Evaluation and optimization for peak performance
Implementing YOLOv7
- Building YOLOv7 applications in Python
- Integration with OpenCV and other computer vision libraries
- Deployment of YOLOv7 on edge devices and cloud infrastructure
Advanced Topics
- Multi-object tracking with YOLOv7
- Applying YOLOv7 to 3D object detection
- YOLOv7 for video-based object detection
- Optimizing YOLOv7 for real-time efficiency
Requirements
- Proficiency in Python programming
- Comprehension of deep learning fundamentals
- Familiarity with basic computer vision concepts
Target Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
21 Hours
Testimonials (1)
Hands on and the practical
