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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
Testimonials
Trainer was very knowlegable and very open to feedback on what pace to go through the content and the topics we covered. I gained alot from the training and feel like I now have a good grasp of image manipulation and some techniques for building a good training set for an image classification problem.
Anthea King - WesCEF
Apart from the content, I loved Abhi's flexibility to tweak the training based on our feedback
WesCEF
The second day going through feature extraction was great fun. Trainer was very knowledgeable and engaging.
WesCEF
Having some previous computer vision experience I found the second day covering feature extraction and CNNs most beneficial.
WesCEF
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