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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
- Installation and setup procedures for YOLOv7.
- Detailed exploration of YOLOv7 architecture and components.
- Comparative advantages of YOLOv7 over other object detection models.
- Distinctions among various YOLOv7 variants.
YOLOv7 Training Process
- Data preparation and annotation techniques.
- Model training utilizing popular deep learning frameworks such as TensorFlow and PyTorch.
- Fine-tuning pre-trained models for custom object detection.
- Evaluation and tuning strategies for optimal performance.
Implementing YOLOv7
- Implementation of YOLOv7 using Python.
- Integration with OpenCV and other computer vision libraries.
- Deployment of YOLOv7 on edge devices and cloud platforms.
Advanced Topics
- Multi-object tracking utilizing YOLOv7.
- Application of YOLOv7 for 3D object detection.
- Utilization of YOLOv7 for video object detection.
- Optimization of YOLOv7 to achieve real-time performance.
Summary and Next Steps
Requirements
- Proficiency in Python programming.
- Fundamental knowledge of deep learning concepts.
- Basic understanding of computer vision principles.
Target Audience
- Computer vision engineers.
- Machine learning researchers.
- Data scientists.
- Software developers.
21 Hours
Testimonials (2)
Hands on and the practical
Keeren Bala Krishnan - PENGUIN SOLUTIONS (SMART MODULAR)
Course - Computer Vision with Python
I genuinely enjoyed the hands-on approach.
