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Course Outline
Introduction to Object Detection
- Fundamentals of object detection.
- Practical applications of object detection.
- Performance metrics for evaluating object detection models.
Overview of YOLOv7
- Installation and setup procedures for YOLOv7.
- Architecture and key components of YOLOv7.
- Advantages of YOLOv7 compared to other object detection models.
- Exploring YOLOv7 variants and their distinctions.
YOLOv7 Training Process
- Data preparation and annotation techniques.
- Model training using prominent deep learning frameworks (TensorFlow, PyTorch, etc.).
- Fine-tuning pre-trained models for specific object detection needs.
- Evaluation and tuning strategies for optimal performance.
Implementing YOLOv7
- Coding YOLOv7 in Python.
- Integration with OpenCV and other computer vision libraries.
- Deploying YOLOv7 on edge devices and cloud platforms.
Advanced Topics
- Multi-object tracking using YOLOv7.
- Applying YOLOv7 for 3D object detection.
- Utilizing YOLOv7 for video object detection.
- Optimizing YOLOv7 for real-time performance requirements.
Summary and Next Steps
Requirements
- Proficiency in Python programming.
- Solid understanding of deep learning principles.
- 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
