YOLOv9 represents the latest advancement in computer vision object detection models. This course begins by covering the fundamentals of computer vision, including Non-Maximum Suppression and Mean Average Precision. Moving forward, we delve deeply into YOLOv9, exploring its architecture and highlighting how it surpasses other object detection models. In Section 04, we demonstrate object detection on images and videos using YOLOv9, evaluating its performance across various parameters.

Subsequently, in Section 05, we train the YOLOv9 model on a custom dataset for Personal Protective Equipment (PPE) detection. Additionally, Section 06 focuses on object tracking, where we integrate YOLOv9 with the DeepSORT & SORT algorithms. Here, we also develop an application for person/vehicle counting (entry and exit) using YOLOv9 and the DeepSORT algorithm.

Section 07 provides a review of YOLO-World and a step by step guide to perform object detection using YOLO-World. Finally, in Section 09, we will create web applications by integrating YOLOv9 with Flask.

This comprehensive course covers a range of topics, including:

• Mean Average Precision (mAP).

• Non Maximum Suppression (NMS).

• What is YOLOv9 | Architecture of YOLOv9.

• Object Detection using YOLOv9.

• Testing YOLOv9 Model Performance on Images, Videos and on the Live Webcam Feed.

• Training YOLOv9 on a Custom Dataset.

• Personal Protective Equipment (PPE) Detection  using YOLOv9.

• Object Tracking using YOLOv9 and DeepSORT.

• Object Tracking using YOLOv9 and SORT.

• Person/ Vehicles Counting (Entering and Leaving) using YOLOv9 and DeepSORT algorithm.

• Introduction to YOLO-World.

• Object Detection on Images and Videos using YOLO-World.

• Integrating YOLOv9 with Flask and Creating Web Apps.

• Object Detection in the Browser using YOLOv9 and Flask