Yolov9: Learn Object Detection, Tracking With Webapps

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

What you'll learn

Basics of Computer Vision

Objects Detection using YOLOv9

Training 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 a WebApp

Personal Protective Equipment (PPE) detection using YOLOv9

Person/Vehicles counting (entry and exit) using YOLOv9 and the DeepSORT algorithm.

Requirements

Laptop/PC

Description

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 a WebApp.

Overview

Section 1: Introduction to the Course

Lecture 1 Introduction

Section 2: Non Maximum Suppression & Mean Average Precision

Lecture 2 Non Maximum Suppression

Lecture 3 Mean Average Precision

Section 3: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

Lecture 4 What is YOLOv9

Section 4: Object Detection on Images, Videos & Live Webcam Feed using YOLOv9

Lecture 5 Object Detection in Images and Videos with YOLOv9 in Google Colab

Lecture 6 Testing YOLOv9 Model Performance: Image, Video, and Webcam Tutorial

Section 5: Training/ fine-tuning the YOLOv9 model on a custom dataset

Lecture 7 Personal Protective Equipment (PPE) Detection using YOLOv9

Section 6: Object Tracking using YOLOv9 and DeepSORT/ SORT Algorithm

Lecture 8 Real-Time Object Tracking using YOLOv9 and DeepSORT Algorithm

Lecture 9 Real-Time Object Tracking using YOLOv9 and SORT Algorithm

Lecture 10 Person / Vehicles Counting (Entry and Exit) using YOLOv9 and DeepSORT

Section 7: YOLO-World: Real-Time, Zero-Shot Object Detection

Lecture 11 YOLO-World: Real-Time, Zero-Shot Object Detection

Lecture 12 How to Detect Objects with YOLO-World

Section 8: YOLOv9 WebApps: Integrate YOLOv9 with Flask

Lecture 13 Introduction

Lecture 14 Object Detection on Images/ Videos/ Live Webcam Feed using YOLOv9

Lecture 15 Integrating YOLOv9 with Flask

Lecture 16 Integrating YOLOv9 with Flask and Creating a WebApp

Lecture 17 WebApp Layout Design

For Everyone who is interested in Computer Vision,For Everyone who wants to learn the latest YOLOv9 version,For Everyone who study Computer Vision and want to know how to use YOLO for Object Detection,For Everyone who aims to build Deep learning Apps with Computer Vision