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Discarded Material Identification System

This project is an end-to-end Machine Learning solution designed to detect and classify waste objects in images/live video. It leverages the state-of-the-art object detection model to achieve high accuracy and real-time performance. The system is built with a production-ready mindset, featuring a modular architecture, automated pipelines, and CI/CD integration for cloud deployment.

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.
Deploy containerized applications to AWS EC2 using ECR.
Build an interactive web interface using Flask for model inference.

System Architecture

Discarded Material Identification System Architecture Diagram

High-level architecture overview of the Discarded Material Identification 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 YOLOv5 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.
Discarded Material Identification System
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