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End-to-End Small Object Detection Project
Rating: 5.0 out of 5(1 rating)
7 students

End-to-End Small Object Detection Project

(From Data Collection to Deployment & Customer Handover)
Created byJudy Yang
Last updated 5/2025
English

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

Course content

6 sections13 lectures1h 49m total length
  • Lecture 1 Project Introuduction10:58

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:

  1. Data Pipeline Development:

    • Collecting and annotating agricultural image datasets

    • Preprocessing techniques for small object detection

    • Dataset augmentation and balancing methods

  2. Model Development:

    • YOLO architecture fundamentals

    • Transfer learning with pretrained weights

    • Performance evaluation using industry metrics

  3. 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 .