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Duration 21 hours
Course Outline
Foundations of Object Detection
- Core concepts in object detection
- Practical applications of object detection
- Key performance metrics for detection models
Introduction to YOLOv7
- Installation and initial setup of YOLOv7
- Understanding YOLOv7 architecture and key components
- Benefits of YOLOv7 compared to alternative detection models
- Different YOLOv7 variants and their distinct features
Training YOLOv7
- Preparing and annotating datasets
- Training models using major deep learning frameworks such as TensorFlow and PyTorch
- Fine-tuning pre-trained models for specific detection needs
- Optimizing evaluation and tuning for peak performance
Deploying YOLOv7
- Building YOLOv7 implementations in Python
- Integrating with OpenCV and other vision libraries
- Deploying YOLOv7 on edge devices and cloud infrastructures
Advanced Applications
- Tracking multiple objects with YOLOv7
- Applying YOLOv7 to 3D object detection
- Using YOLOv7 for video-based object detection
- Optimizing YOLOv7 for real-time processing
Requirements
- Proficiency in Python programming
- Familiarity with deep learning fundamentals
- Basic knowledge of computer vision
Target Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
Testimonials (1)
Hands on and the practical
