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Duration 21 hours
Course Outline
Introduction to Object Detection
- Foundations of object detection
- Practical applications of object detection
- Key performance metrics for object detection models
YOLOv7 Overview
- Installation and initial setup of YOLOv7
- Architecture and core components of YOLOv7
- Benefits of YOLOv7 compared to alternative detection models
- Distinct YOLOv7 variants and their specific differences
The YOLOv7 Training Process
- Data preparation and annotation strategies
- Training models using leading deep learning frameworks such as TensorFlow and PyTorch
- Fine-tuning pre-trained models for specialized object detection
- Evaluation and parameter tuning for peak performance
Implementation of YOLOv7
- Writing YOLOv7 implementations in Python
- Integrating with OpenCV and other computer vision libraries
- Deployment on edge devices and cloud infrastructure
Advanced Concepts
- Multi-object tracking with YOLOv7
- Applying YOLOv7 to 3D object detection
- Video-based object detection using YOLOv7
- Optimizing YOLOv7 for enhanced real-time speed
Requirements
- Proficiency in Python programming
- Foundational understanding of deep learning
- Basis knowledge in computer vision
Target Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
Testimonials (1)
Hands on and the practical
