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Object Detection And Tracking Using Yolov11 : Deep Learning
Rating: 4.2 out of 5(40 ratings)
3,788 students

Object Detection And Tracking Using Yolov11 : Deep Learning

Learn Complete Development of Object Detection And Tracking Using Yolov11 From Scratch
Created byARUNNACHALAM SHANMUGARAAJAN
Last updated 5/2025
English
English [Auto],

What you'll learn

  • Introduction to Object Detection & YOLOv11
  • Model Training and Optimization
  • Real-Time Application Deployment
  • fully functional object detection and tracking system using YOLOv11

Course content

2 sections • 9 lectures • 35m total length

  • Introduction To Object Detection And Tracking Using Yolov11 From Scratch0:48

    Master object detection and tracking with YOLO v11 from scratch by creating and annotating a dataset, training the model, and detecting and counting ghosts.

  • CLASS 1 : CREATE NEW PROJECT1:32

    Create a new Roboflow project for ghost detection and counting with YOLO v11, annotate data for the goat class, and prepare to train with Google Colab in object detection settings.

  • CLASS 2 : DATASET COLLECTION1:45

    Shows how to collect and import a dataset for object detection by downloading images with batch download tools and importing via Roboflow or Kaggle, aiming for 1000 images before annotation.

  • CLASS 3 : DATASET ANNOTATION10:00

    Annotate a goat dataset with bounding box and polygons in Roboflow to improve accuracy, using drag, polygon, smart polygon, label assist, box prompting, and train, validation, and test splits.

  • CLASS 4 : DATASET DOWNLOAD2:03

    Create a new dataset version, select train, test, and validation splits, download a YOLO 11 zip for goat annotations, then open and extract to reveal images and labels.

Requirements

  • Basic Python & ML Knowledge

Description

Object Detection and Tracking Using YOLOv11

Master the art of real-time object detection and tracking with YOLOv11! This course will guide you through the fundamentals of YOLO (You Only Look Once) and help you develop a robust system capable of detecting and tracking multiple objects in images and videos. Whether you're a beginner or an experienced AI enthusiast, this course will provide hands-on experience in training and deploying YOLOv11 models for real-world applications.


COURSE HIGHLIGHTS:


  • Understand YOLOv11’s architecture and its advantages in object detection tasks.

  • Learn how to collect, label, and preprocess data for training YOLOv11.

  • Train YOLOv11 models to detect and track object, fine-tuning parameters for accuracy.

  • Implement your trained model for real-time object detection and tracking in video feeds or IoT setups.

  • Analyze detection results, identify challenges, and refine your model for better performance.

This course is perfect for developers, AI enthusiasts, and anyone in the agriculture or livestock industry looking to integrate AI solutions into their workflows. By the end of the course, you’ll have built a fully functional object detection and tracking system and gained valuable machine learning expertise. Get ready to dive into hands-on projects, powerful AI techniques, and practical applications. Let’s start building intelligent computer vision systems together!

Who this course is for:

  • Computer Science Student
  • YOLO developers

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