End-To-End Small Object Detection Project

(From Data Collection to Deployment & Customer Handover)

What you'll learn

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 .