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Duration 21 hours (3 days)
Course Outline
Introduction to Object Detection
- Basics of object detection
- Applications of object detection
- Performance metrics for object detection models
YOLOv7 Overview
- YOLOv7 installation and setup
- YOLOv7 architecture and components
- Benefits of YOLOv7 compared to other object detection models
- YOLOv7 variants and their distinctions
YOLOv7 Training Workflow
- Data preparation and annotation
- Model training using major deep learning frameworks (e.g., TensorFlow, PyTorch)
- Fine-tuning pre-trained models for custom object detection
- Evaluation and tuning for peak performance
Implementing YOLOv7
- Implementing YOLOv7 in Python
- Integration with OpenCV and other computer vision libraries
- Deployment of YOLOv7 on edge devices and cloud platforms
Advanced Topics
- Multi-object tracking with YOLOv7
- YOLOv7 for 3D object detection
- YOLOv7 for video object detection
- Optimizing YOLOv7 for real-time performance
Requirements
- Proficiency in Python programming
- Comprehension of deep learning fundamentals
- Familiarity with computer vision basics
Target Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
Testimonials (1)
Hands on and the practical
