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Course Outline
Foundations of Object Detection
- Core principles of object detection
- Practical applications of object detection
- Key performance metrics for evaluation models
Understanding YOLOv7
- Setting up and installing YOLOv7
- Examining YOLOv7 architecture and core components
- Benefits of YOLOv7 compared to other detection models
- Differences between various YOLOv7 variants
The YOLOv7 Training Workflow
- Preparing and annotating training data
- Training models with major deep learning frameworks (e.g., TensorFlow, PyTorch)
- Adapting pre-trained models for custom detection needs
- Assessing and tuning models for peak performance
Deploying YOLOv7
- Implementing YOLOv7 within Python scripts
- Integrating with OpenCV and other vision libraries
- Deployment strategies for edge devices and cloud platforms
Advanced Applications
- Tracking multiple objects using YOLOv7
- Applying YOLOv7 to 3D object detection
- Detecting objects in video streams
- Optimizing YOLOv7 for real-time efficiency
Requirements
- Proficiency in Python programming
- A solid understanding of 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
