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Course Outline
Foundations of Object Detection
- Core principles of object detection
- Practical applications of object detection
- Evaluation metrics for detection models
Understanding YOLOv7
- Installation and initial setup of YOLOv7
- Examining YOLOv7 architecture and key components
- Benefits of YOLOv7 compared to alternative models
- Exploring YOLOv7 variants and their distinct features
The YOLOv7 Training Workflow
- Preparing and annotating data
- Training models using leading deep learning frameworks like TensorFlow and PyTorch
- Adapting pre-trained models for custom detection needs
- Assessing and optimizing for peak performance
Deployment of YOLOv7
- Implementing YOLOv7 using Python
- Integration with OpenCV and other vision libraries
- Deploying YOLOv7 on edge devices and cloud infrastructure
Advanced Concepts
- Tracking multiple objects with YOLOv7
- Applying YOLOv7 to 3D object detection
- Video-based object detection with YOLOv7
- Optimizing YOLOv7 for real-time speed
Requirements
- Proficiency in Python programming
- Foundational knowledge of deep learning concepts
- Basic understanding of computer vision
Target Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
21 Hours
Testimonials (1)
Hands on and the practical
