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Udemy - Real-Time Object Detection With Yolov11 - OneDDL - 05-28-2025 Free Download Udemy - Real-Time Object Detection With Yolov11 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 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 Homepage [To see links please register or login] Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [To see links please register or login] No Password - Links are Interchangeable