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Course Outline
Introduction to Object Detection
- Fundamentals of object detection
- Practical applications of object detection
- Key performance metrics for detection models
Understanding YOLOv7
- Installing and setting up YOLOv7
- Architectural components of YOLOv7
- Benefits of YOLOv7 compared to other models
- Differences between various YOLOv7 variants
The YOLOv7 Training Workflow
- Data preparation and annotation strategies
- Training models with deep learning frameworks like TensorFlow and PyTorch
- Adapting pre-trained models for custom detection tasks
- Evaluating and tuning models for peak performance
Putting YOLOv7 into Practice
- Implementing YOLOv7 using Python
- Working with OpenCV and other vision libraries
- Deploying YOLOv7 on edge devices and cloud infrastructure
Advanced Applications
- Tracking multiple objects with 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 grasp 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
