End-to-End Small Object Detection Project
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 solution
Agriculture-specific implementation addressing real-world problems
Professional workflow covering both technical implementation and client delivery
Deployment-ready skills that go beyond academic exercises
Key Learning Components:
Data Pipeline Development:
Collecting and annotating agricultural image datasets
Preprocessing techniques for small object detection
Dataset augmentation and balancing methods
Model Development:
YOLO architecture fundamentals
Transfer learning with pretrained weights
Performance evaluation using industry metrics
Production Implementation:
Building a FastAPI web interface
Cloud deployment options
Creating client documentation
Target 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 framework
FastAPI for web services
Roboflow for data annotation
Google Colab for GPU acceleration
By 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.
Who this course is for:
- 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 .
Instructor
- 5.0 Instructor Rating
- 1 Review
- 5 Students
- 1 Course
Dedicated data scientist and PhD candidate within Griffith University's School of Information and Communication Technology, specialising in multi-source remote sensing data fusion and modelling, applied mathematics, and programming. Skilled in machine learning, deep learning, and multimodal data integration, with expertise in predictive modelling, synthetic data generation, and computational efficiency. Strong foundation in engineering mathematics and statistics, with a passion for leveraging remote sensing technologies to address challenges in health, agriculture, and environmental systems.