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Duration 21 hours
Course Outline
Foundations of Object Detection
- Core principles of object detection
- Practical applications of object detection
- Key performance metrics for detection models
Introduction to YOLOv7
- YOLOv7 installation and environment setup
- Architecture and key components of YOLOv7
- Benefits of YOLOv7 compared to other detection models
- Differences between YOLOv7 variants
YOLOv7 Training Workflow
- Data preparation and annotation processes
- Training models using major deep learning frameworks (e.g., TensorFlow, PyTorch)
- Fine-tuning pre-trained models for specific detection needs
- Model evaluation and optimization for best results
Implementing YOLOv7
- Building YOLOv7 applications in Python
- Integrating with OpenCV and other vision libraries
- Deployment strategies for edge devices and cloud environments
Advanced Concepts
- Multi-object tracking techniques with YOLOv7
- Applying YOLOv7 to 3D object detection
- Video object detection using YOLOv7
- Optimizations for achieving real-time performance
Requirements
- Proficiency in Python programming
- Foundational understanding of deep learning
- Basic knowledge of computer vision
Target Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
Testimonials (1)
Hands on and the practical
