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Published 5/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.04 GB | Duration: 3h 0m
From Annotation to Inference: A Complete YOLOv11 Workflow
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.04 GB | Duration: 3h 0m
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
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