YOLOv9, YOLOv10 & YOLO11: Learn Object Detection & Web Apps

YOLOv9, YOLOv10 & YOLO11: Learn Object Detection & Web Apps

Object Detection, Object Tracking, WebApps using Flask, Object Detection on Custom Dataset, YOLO-World Object Detection

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What you'll learn

• Basics of Computer Vision
• Objects Detection using YOLOv9
• Training/ fine-tuning YOLOv9 on a Custom Dataset
• Object Tracking using YOLOv9 and DeepSORT Algorithm
• Object Tracking using YOLOv9 and SORT Algorithm
• Objects Detection using YOLO-World
• Integrating YOLOv9 with Flask and Creating Web Apps
• Personal Protective Equipment (PPE) detection using YOLOv9
• Person/Vehicles counting (entry and exit) using YOLOv9 and the DeepSORT algorithm.
• Object Detection in the Browser using YOLOv9 and Flask
• YOLOv10: Real-Time End-to-End Object Detection
• What is YOLOv10? An Architecture Deep Dive
• Object Detection in Images and Videos using YOLOv10
• Training/ fine-tuning the YOLOv10 model on a custom dataset

Welcome to the YOLOv9, YOLOv10 & YOLO11 Course, a 3-in-1 course. YOLO11, YOLOv10 & YOLOv9 represent the latest advancements 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 08, we will create web applications by integrating YOLOv9 with Flask.

Section 09, provides an introduction to YOLOv10, which includes what is YOLOv10, how YOLOv10 works, what architecture enhancements are made in YOLOv10, furthermore a performance comparison of YOLOv10 with other YOLO models is also presented in this section.

In Section 10, we demonstrate object detection in images and videos using YOLOv10. Subsequently, in Section 11, we train the YOLOv10 model on a custom dataset for Personal Protective Equipment (PPE) detection. 

In Section 12, we perform License Plate Detection and Recognition using YOLOv10 and PaddleOCR. Similarly, in Section 13, we showcase Real-Time Object Tracking using YOLOv10 and the DeepSORT algorithm.

Section 14 introduces YOLO11. In Section 15, we demonstrate object detection in images and videos using YOLO11. In Section 16, we perform object detection, instance segmentation, pose estimation, and image classification using YOLO11 on both Windows and Linux. Subsequently, in Section 17, we delve into testing and analyzing the performance of the YOLO11 model.

In Section 18, we explore training the YOLO11 object detection model on a custom dataset for PPE detection. In Section 19, we focus on training or fine-tuning the YOLO11 instance segmentation model on a custom dataset for pothole detection. 

In Section 20, we train or fine-tune the YOLO11 classification model on a custom dataset for plant classification. Finally, in Section 21, we fine-tune the YOLO11 pose estimation model for human activity recognition.