YOLOv7: Real-time Object Detection with Computer Vision

• 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

YOLOv7 is a state-of-the-art real-time object detection model for computer vision tasks. 

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level developers, researchers, and data scientists who wish to learn how to implement real-time object detection using YOLOv7.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of object detection.
• Install and configure YOLOv7 for object detection tasks.
• Train and test custom object detection models using YOLOv7.
• Integrate YOLOv7 with other computer vision frameworks and tools.
• Troubleshoot common issues related to YOLOv7 implementation.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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