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Course Outline
Introduction to Object Detection
- Fundamentals of object detection
- Practical applications of object detection
- Key performance metrics for object detection models
Overview of YOLOv7
- Installing and setting up YOLOv7
- Understanding YOLOv7 architecture and its components
- Comparing the advantages of YOLOv7 against other object detection models
- Exploring different YOLOv7 variants and their distinctions
The YOLOv7 Training Process
- Preparing and annotating data
- Training models using prominent deep learning frameworks (such as TensorFlow and PyTorch)
- Fine-tuning pre-trained models for specific custom object detection needs
- Evaluating and tuning models to achieve peak performance
Implementing YOLOv7
- Coding YOLOv7 implementations in Python
- Integrating YOLOv7 with OpenCV and other computer vision libraries
- Deploying YOLOv7 solutions on edge devices and cloud platforms
Advanced Topics
- Multi-object tracking techniques using YOLOv7
- Applying YOLOv7 for 3D object detection
- Utilizing YOLOv7 for video-based object detection
- Optimizing YOLOv7 to ensure real-time performance capabilities
Summary and Next Steps
Requirements
- Proficiency in Python programming
- A solid grasp of deep learning fundamentals
- Familiarity with the basics of computer vision
Target Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
21 Hours
Testimonials (1)
Hands on and the practical
