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Course Outline
Foundations of Object Detection
- Basic principles of object detection
- Real-world applications of detection
- Key performance metrics for assessing models
Introducing YOLOv7
- Installation and environment setup
- Internal architecture and key components
- Benefits of YOLOv7 compared to other detection models
- Differences between various YOLOv7 variants
Training YOLOv7
- Data preparation and annotation techniques
- Training models using leading deep learning frameworks (TensorFlow, PyTorch, etc.)
- Adapting pre-trained models for custom detection needs
- Evaluation strategies and tuning for peak performance
Putting YOLOv7 into Practice
- Coding YOLOv7 implementations in Python
- Integrating with OpenCV and other vision libraries
- Deployment on edge devices and cloud infrastructure
Advanced Applications
- Multi-object tracking with YOLOv7
- Applying YOLOv7 to 3D object detection
- Object detection in video streams
- Optimizing YOLOv7 for real-time efficiency
Requirements
- Proficiency in Python programming
- Foundation in deep learning concepts
- Basic understanding of computer vision
Target Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
21 Hours
Testimonials (1)
Hands on and the practical
