Course Outline

Introduction to Object Detection

  • Object detection basics
  • Object detection applications
  • Performance metrics for object detection models

Overview of YOLOv7

  • YOLOv7 installation and setup
  • YOLOv7 architecture and components
  • Advantages of YOLOv7 over other object detection models
  • YOLOv7 variants and their differences

YOLOv7 Training Process

  • Data preparation and annotation
  • Model training using popular deep learning frameworks (TensorFlow, PyTorch, etc.)
  • Fine-tuning pre-trained models for custom object detection
  • Evaluation and tuning for optimal performance

Implementing YOLOv7

  • Implementing YOLOv7 in Python
  • Integration with OpenCV and other computer vision libraries
  • Deploying YOLOv7 on edge devices and cloud platforms

Advanced Topics

  • Multi-object tracking using YOLOv7
  • YOLOv7 for 3D object detection
  • YOLOv7 for video object detection
  • Optimizing YOLOv7 for real-time performance

Summary and Next Steps

Requirements

  • Experience with Python programming
  • Understanding of deep learning fundamentals
  • Knowledge of computer vision basics

Audience

  • Computer vision engineers
  • Machine learning researchers
  • Data scientists
  • Software developers
 21 Hours

Testimonials (3)

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