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Computer Visionadvanced

End to End Waste Detection System

This project highlights the creation and significance of an End to End Waste Detection System that leverages computer vision technology to accurately identify various waste materials. By enhancing waste management practices, this system not only promotes environmental sustainability but also delivers substantial societal benefits, showcasing the transformative power of technology in addressing pressing ecological challenges.

14 lectures

What You Will Learn

Master the end-to-end Machine Learning lifecycle for Object Detection tasks.
Learn to implement a custom YOLOv5 model for specific waste classification.
Understand modular code structure for scalable ML applications.
Gain hands-on experience with Data Ingestion, Validation, and Model Training pipelines.
Implement MLOps practices using GitHub Actions for CI/CD.
Build an interactive web interface using Flask for model inference.

System Architecture

End to End Waste Detection System Architecture Diagram

High-level architecture overview of the End to End Waste Detection System .

What You'll Build

  • A complete "End-to-End Waste Detection System" that classifies waste types from images.
  • A fully automated Training Pipeline that ingests data, validates it, and fine-tunes a CV model.
  • A Flask-based Web Application that allows users to upload images and view detection results with bounding boxes.
  • A CI/CD pipeline that automatically builds the application Docker image and deploys it to an AWS EC2 instance upon code changes.

Project Instructor

Boktiar Ahmed Bappy

Boktiar Ahmed Bappy

5+ years exp
LinkedIn
End to End Waste Detection System
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