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Object detection has revolutionized the field of computer vision, security, autonomous driving, and more. Among the various algorithms available, YOLO stands out due to its efficiency and accuracy. This course dives deep into the YOLO architecture, highlighting its significance in object detection.

The course starts with the YOLO architecture, followed by tackling overfitting and mastering data augmentation. It delves into core YOLO concepts IoU and NMS, real-time object detection, data augmentation, and optimization strategies for different use cases. It concludes with a discussion on the role of hyperparameters in object detection.

After completing this course, you will have a comprehensive understanding of YOLO and its applications in object detection. Whether you aim to advance in a career in autonomous systems, security, or any domain requiring real-time object detection, this course will be a significant stepping stone in your professional journey.

Deep Dive into Object Detection with YOLO

The **mAP** is used in object detection to compare the performance of a model. It combines two scores, precision and recall, to calculate how our model performs with respect to FN and FP. 

Learn how can we evaluate performance of our model.

mAP Scores  as Performance Metrics

Introduction to Object Detection

Fundamentals for Understanding YOLO

YOLOv7 Architecture

Improving Model Performance: Handling Overfitting/Underfitting

Pre-Trained Models, Fine-Tuning, and Hyperparameters in OD

Sun Detection Using YOLOv8

Conclusion

mAP Scores as Performance Metrics