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Duration 21 hours
Course Outline
Foundations of Object Detection
- Essential principles of object detection
- Real-world applications of object detection
- Key performance metrics for evaluating detection models
Introduction to YOLOv7
- Installing and configuring YOLOv7
- Examining YOLOv7 architecture and components
- Benefits of YOLOv7 compared to other detection models
- Differentiating between YOLOv7 variants
The YOLOv7 Training Workflow
- Preparing and annotating datasets
- Training models using leading deep learning frameworks (e.g., TensorFlow, PyTorch)
- Adapting pre-trained models for specific object detection needs
- Assessing and optimizing for peak performance
Building with YOLOv7
- Implementing YOLOv7 using Python
- Connecting with OpenCV and other computer vision libraries
- Deploying YOLOv7 on edge devices and cloud infrastructure
Advanced Applications
- Performing multi-object tracking with YOLOv7
- Applying YOLOv7 to 3D object detection
- Detecting objects in video streams using YOLOv7
- Optimizing YOLOv7 for enhanced real-time efficiency
Requirements
- Proficiency in Python programming
- Solid grasp of deep learning fundamentals
- Familiarity with basic computer vision concepts
Target Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
Testimonials (1)
Hands on and the practical
