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Course Outline
Introduction to Object Detection
- Basics of object detection.
- Applications of object detection.
- Performance metrics for object detection models.
Overview of YOLOv7
- Installation and setup of YOLOv7.
- Architecture and components of YOLOv7.
- Advantages of YOLOv7 compared to other object detection models.
- Variants of YOLOv7 and their distinctions.
YOLOv7 Training Process
- Data preparation and annotation.
- Model training using popular deep learning frameworks (e.g., TensorFlow, PyTorch).
- Fine-tuning pre-trained models for custom object detection.
- Evaluation and tuning for optimal performance.
Implementing YOLOv7
- Implementing 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.
- YOLOv7 for 3D object detection.
- YOLOv7 for video object detection.
- Optimizing YOLOv7 for real-time performance.
Summary and Next Steps
Requirements
- Proficiency in Python programming.
- Foundational understanding of deep learning principles.
- Knowledge of computer vision basics.
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.
