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Complete Goat Detection and Counting Using YOLOv11
What you'll learn
Data Annotation and Preparation For Goat
Model Training and Optimization
Real-Time Application Deployment
Complete YOLOv11 Project
Requirements
Basic Python & ML Knowledge
Description
Unlock the power of AI-driven object detection with the "Complete Goat Detection and Counting Using YOLOv11" course! Designed for beginners and intermediate learners, this comprehensive course focuses on using YOLOv11, the latest in object detection technology, to build a real-world project for detecting and counting goats in images or video feeds.The course begins with an introduction to YOLOv11 and its powerful architecture, setting the foundation for creating accurate detection systems. You'll learn how to prepare datasets, annotate images, and train YOLOv11 models tailored for goat detection. Dive into practical steps for improving model accuracy, optimizing performance, and deploying your model in real-time applications like farm management or animal tracking systems.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 count goats, fine-tuning parameters for accuracy.Implement your trained model for real-time goat detection and counting 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 goat detection and counting system and gained valuable machine learning expertise.
Who this course is for
Computer Science Student
YOLO developers
Homepage:
Download link
Say "Thank You"
rapidgator.net:
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Published 11/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 35m | Size: 383 MB
Learn Complete Development of Goat Detection and Counting Using YOLOv11
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 35m | Size: 383 MB
Learn Complete Development of Goat Detection and Counting Using YOLOv11
What you'll learn
Data Annotation and Preparation For Goat
Model Training and Optimization
Real-Time Application Deployment
Complete YOLOv11 Project
Requirements
Basic Python & ML Knowledge
Description
Unlock the power of AI-driven object detection with the "Complete Goat Detection and Counting Using YOLOv11" course! Designed for beginners and intermediate learners, this comprehensive course focuses on using YOLOv11, the latest in object detection technology, to build a real-world project for detecting and counting goats in images or video feeds.The course begins with an introduction to YOLOv11 and its powerful architecture, setting the foundation for creating accurate detection systems. You'll learn how to prepare datasets, annotate images, and train YOLOv11 models tailored for goat detection. Dive into practical steps for improving model accuracy, optimizing performance, and deploying your model in real-time applications like farm management or animal tracking systems.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 count goats, fine-tuning parameters for accuracy.Implement your trained model for real-time goat detection and counting 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 goat detection and counting system and gained valuable machine learning expertise.
Who this course is for
Computer Science Student
YOLO developers
Homepage:
Screenshots
Download link
Say "Thank You"
rapidgator.net:
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k2s.cc:
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