• views: 98
• date: 22 May 2025
• posted by: 0nelove

Build a real-world object detector for cattle ticks/flies using YOLO/PyTorch from scratch.
Collect, annotate & preprocess datasets for small object detection in agriculture.
Train, evaluate & optimize models using metrics like mAP and precision.
Deploy a user-friendly AI app (Flask/FastAPI) and hand over deliverables to clients.
Prepare the project report to the cusotmer
Requirements
•Basic Python knowledge (loops, functions). No prior AI experience needed—we'll start from the basics! A Google account (for free GPU via Colab).
Description
Transform raw images into a production-ready AI solution with this comprehensive project-based course. You'll develop a complete object detection system for identifying cattle parasites - a critical challenge in livestock management. Using industry-standard YOLO models with PyTorch, we'll guide you through the entire development lifecycle from initial data collection to final deployment.Course Value Proposition:Practical, hands-on approach focused on delivering a working solutionAgriculture-specific implementation addressing real-world problemsProfessional workflow covering both technical implementation and client deliveryDeployment-ready skills that go beyond academic exercisesKey Learning Components:Data Pipeline Development:Collecting and annotating agricultural image datasetsPreprocessing techniques for small object detectionDataset augmentation and balancing methodsModel Development:YOLO architecture fundamentalsTransfer learning with pretrained weightsPerformance evaluation using industry metricsProduction Implementation:Building a FastAPI web interfaceCloud deployment optionsCreating client documentationTarget Audience:This course is designed for Python developers seeking practical AI implementation skills, agriculture professionals exploring technology solutions, and students looking to build portfolio-worthy projects. Basic Python knowledge is recommended, but no prior AI experience is required as we cover all necessary fundamentals.Technical Stack:YOLOv5/YOLOv8 (Ultralytics implementation)PyTorch frameworkFastAPI for web servicesRoboflow for data annotationGoogle Colab for GPU accelerationBy course completion, you'll have a fully functional object detection system and the skills to adapt this solution to other agricultural or small-object detection use cases. The project-based approach ensures you gain practical experience that translates directly to professional applications.
Overview
Section 1: Dection Project Introduciton
Lecture 1 Lecture 1 Project Introuduction
Section 2: Data Capturing Equipment Introudction
Lecture 2 Lecture 2 Data Collection Device Selection
Section 3: Section 2 Data Set Prepration
Lecture 3 Lecture 3 Data Collection and Preprocessing for ML
Lecture 4 Lecture 4 Image Extraction Practical Code
Lecture 5 Lecture 5 Preparing Datasets for Cattle Tick DetectionTask
Lecture 6 Lecture 6 Hands-on Data preparation using RoboFlow
Section 4: Section 4 Detection Models
Lecture 7 Lecture 7: Detection Models Introudction
Lecture 8 Lecture 8: YOLOv8 With Goolge Colab: Proactical Object Dection
Lecture 9 Lecture 9 Hands on Proactical Code
Section 5: Section 4 : Model Deployment
Lecture 10 Lecture 10: Tick Detection Web App - Frontend Architecture
Lecture 11 Lecture 11 UI Design
Section 6: Pacakge Deliver to Customer
Lecture 12 Lecture 12 Package Introuduction
Lecture 13 Lecture 13 : echnical Report & Customer Guide: Livestock Tick Detection System
Python devs who want to break into AI Computer Vision with a hands-on project.,Agriculture professionals exploring AI solutions for pest/livestock monitoring.,AI students tired of theory—ready to build & deploy a real-world model.,Freelancers looking to add object detection skills .



   
   
   

Forex Trading

Crypto Trading. 20 USD Bonus after KYC

We need your support!
Make a donation to help us stay online
        
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
	
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem

Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u

USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm

0 1 2 3 4 5

| Print version |

	Information
		Users of GUESTS are not allowed to comment this publication.