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Course Outline
Introduction to Object Detection
- Basics of object detection
- Practical applications of object detection
- Key performance metrics for object detection models
YOLOv7 Overview
- Installation and setup procedures for YOLOv7
- YOLOv7 architecture and core components
- Benefits of YOLOv7 compared to other object detection models
- Different variants of YOLOv7 and their distinctions
The YOLOv7 Training Process
- Data preparation and annotation techniques
- Model training using major deep learning frameworks (such as TensorFlow and PyTorch)
- Fine-tuning pre-trained models for custom detection needs
- Evaluation strategies and tuning for optimal performance
Implementing YOLOv7
- Implementing YOLOv7 solutions in Python
- Integration with OpenCV and other computer vision libraries
- Deployment of YOLOv7 on edge devices and cloud platforms
Advanced Topics
- Multi-object tracking capabilities with YOLOv7
- Applying YOLOv7 to 3D object detection
- Video object detection using YOLOv7
- Optimizing YOLOv7 for enhanced real-time performance
Requirements
- Proficiency in Python programming
- Familiarity with deep learning fundamentals
- Basic understanding of computer vision principles
Target Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
21 Hours
Testimonials (1)
Hands on and the practical
