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Duration 21 hours
Course Outline
Introduction to Object Detection
- Foundations of object detection.
- Practical applications of detection systems.
- Key performance metrics for evaluating models.
Overview of YOLOv7
- Installation procedures and initial setup.
- In-depth look at YOLOv7 architecture and core components.
- Benefits of YOLOv7 compared to alternative detection models.
- Differences between various YOLOv7 variants.
YOLOv7 Training Process
- Preparing and annotating training data.
- Training models using major deep learning frameworks like TensorFlow and PyTorch.
- Adapting pre-trained models for specific detection needs.
- Assessing and tuning models for peak performance.
Implementing YOLOv7
- Building detection solutions with Python.
- Integrating with OpenCV and other vision libraries.
- Deployment strategies for edge devices and cloud environments.
Advanced Topics
- Tracking multiple objects with YOLOv7.
- Applying YOLOv7 to 3D detection scenarios.
- Detecting objects within video streams.
- Optimizing YOLOv7 for real-time efficiency.
Requirements
- Proficiency in Python programming.
- Solid understanding of deep learning fundamentals.
- Basic knowledge of computer vision concepts.
Audience
- Computer vision engineers.
- Machine learning researchers.
- Data scientists.
- Software developers.
Testimonials (1)
Hands on and the practical
