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Course Outline
Introduction to Object Detection
- Foundations of object detection
- Practical applications of object detection
- Key performance metrics for evaluating detection models
Overview of YOLOv7
- Installation and initial setup of YOLOv7
- Architecture and core components of YOLOv7
- Benefits of YOLOv7 compared to other detection models
- Differences between various YOLOv7 variants
YOLOv7 Training Process
- Preparing and annotating data
- Training models using leading deep learning frameworks (such as TensorFlow and PyTorch)
- Fine-tuning pre-trained models for custom detection needs
- Evaluating and tuning for optimal performance
Implementing YOLOv7
- Implementing YOLOv7 using Python
- Integration with OpenCV and other vision libraries
- Deployment on edge devices and cloud platforms
Advanced Topics
- Multi-object tracking with YOLOv7
- Applying YOLOv7 to 3D object detection
- Video object detection using YOLOv7
- Optimising YOLOv7 for real-time performance
Requirements
- Proficiency in Python programming
- Foundational understanding of deep learning concepts
- Basic knowledge of computer vision
Target Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
21 Hours
Testimonials (1)
Hands on and the practical
