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Duration 21 hours
Course Outline
Foundations of Object Detection
- Basic principles of object detection
- Practical applications of detection models
- Key performance indicators for detection systems
Understanding YOLOv7
- Installation and initial setup of YOLOv7
- Architectural design and core components
- Benefits of YOLOv7 compared to alternative models
- Distinct features of various YOLOv7 variants
The YOLOv7 Training Workflow
- Preparation and annotation of datasets
- Training models using major deep learning frameworks (e.g., TensorFlow, PyTorch)
- Adapting pre-trained models for specialized detection tasks
- Performance evaluation and parameter tuning
Deployment of YOLOv7
- Implementing YOLOv7 logic in Python
- Combining with OpenCV and other vision libraries
- Deploying on edge devices and cloud infrastructure
Advanced Concepts
- Tracking multiple objects via YOLOv7
- Applying YOLOv7 to 3D detection scenarios
- Video stream object detection
- Optimizing for real-time efficiency
Requirements
- Proficiency in Python programming
- Basic comprehension of deep learning concepts
- Familiarity with fundamental computer vision principles
Target Audience
- Computer vision engineers
- Machine learning researchers
- Data scientists
- Software developers
Testimonials (1)
Hands on and the practical
