Real-Time Object Detection With Yolov11

From Annotation to Inference: A Complete YOLOv11 Workflow

What you'll learn

Understand the fundamentals of computer vision and object detection with YOLOv11.

Set up and train YOLOv11 models on custom datasets for real-time object detection.

Evaluate and fine-tune YOLOv11 performance using precision, recall, and mAP metrics.

Deploy YOLOv11 models for real-world applications using Python and OpenCV.

Requirements

Basic understanding of Python programming

Familiarity with machine learning or deep learning concepts is helpful but not mandatory

A computer with a stable internet connection and at least 8GB RAM (GPU recommended for training models)

Willingness to learn and experiment with computer vision tools and code

Description

Unlock the power of cutting-edge computer vision with YOLOv11, the latest and most advanced version of the "You Only Look Once" object detection architecture. This hands-on course will take you from the foundational concepts of object detection to building, training, and deploying your own YOLOv11 models in real-time.Whether you're a beginner in AI or an experienced developer looking to upgrade your skills, this course provides a complete, practical learning experience. You'll work with real datasets, learn how to annotate and prepare data, train models using the Ultralytics framework, evaluate performance using key metrics, and deploy your models using Python and OpenCV.You’ll also explore best practices for working with GPUs, optimizing model performance, and deploying solutions to edge devices. Each module includes code walkthroughs, assignments, and projects designed to reinforce key skills. No prior experience with YOLO is required—we’ll guide you through every step with the clear instructions and examples.In addition, you’ll gain insight into how object detection is used across industries, including autonomous driving, healthcare, retail analytics, and surveillance. You’ll finish the course with the confidence to apply your skills in both academic and professional settings. Join us and bring real-time computer vision into your projects today.

Overview

Section 1: Introduction to Computer Vision

Lecture 1 Applications of Computer Vision

Lecture 2 Introduction to YOLO algorithm

Lecture 3 Installing OpenCV library

Lecture 4 Setting up Python environment

Lecture 5 Computer vision Example - Demo

Lecture 6 Computer vision in Virtual mouse - Demo

Section 2: Image Processing Basics

Lecture 7 Image Loading and Displaying

Lecture 8 Image Transformation Techniques

Lecture 9 Image Filtering and Enhancemen

Lecture 10 Edge Detection Algorithms

Lecture 11 Overview of Computer Vision in YOLO - Demo

Lecture 12 Edge Dectections in open-cv - Demo

Section 3: Object Detection with YOLO

Lecture 13 Understanding Object Detection

Lecture 14 Object Detection with YOLO - Demo

Section 4: Roboflow Integrations

Lecture 15 Using Roboflow with popular deep learning frameworks

Lecture 16 Integrating Roboflow with cloud service

Lecture 17 Automating workflows with Roboflow APIs

Section 5: Training Models with Roboflow

Lecture 18 Choosing a model architecture

Lecture 19 Training and evaluating a model

Lecture 20 Roboflow-tutorial - Demo

Section 6: Deploying Models with Roboflow

Lecture 21 Exporting models from Roboflow

Lecture 22 Integrating models into applications

Lecture 23 Monitoring model performance

Section 7: Setting up Environment for YOLO-V11

Lecture 24 Installing necessary libraries

Lecture 25 Downloading pre-trained weights

Lecture 26 Configuring YOLO-V11

Section 8: Understanding YOLO-V11 Architecture

Lecture 27 Architecture overview

Lecture 28 Backbone network

Lecture 29 Detection layer

Lecture 30 Loss function

Section 9: Training YOLO-V11 on custom dataset

Lecture 31 Feature extraction in YOLO-V11

Lecture 32 Preparing custom dataset

Lecture 33 Annotating images for training

Lecture 34 Object Detection Yolo in Custom DATA - Demo

Lecture 35 Instance segmentation on custom Data - Demo

Lecture 36 Tracker with Bot Sort - Demo

Lecture 37 Tracker with Byte Track - Demo

Lecture 38 Example Project with Trackers - Demo

Developers, data scientists, and AI enthusiasts interested in computer vision,Students and beginners looking to learn real-time object detection with YOLOv11,Practitioners wanting to upgrade their skills using the latest YOLO version,Anyone seeking hands-on projects to apply computer vision in real-world scenarios