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♻️ Waste Classification using CNN

📌 Project Overview

Waste Classification using CNN is a deep learning project built on Google Colab that classifies waste into different categories using Convolutional Neural Networks (CNNs).
The goal is to promote efficient waste segregation, which is crucial for sustainable development and smart waste management systems.

This project demonstrates how AI can help build a cleaner and greener environment by automating the waste classification process.

✨ Features

  • ✅ Implemented and trained on Google Colab
  • ✅ Image preprocessing and augmentation for better performance
  • ✅ CNN-based waste image classification
  • ✅ Supports multiple categories (Organic, Recyclable, Hazardous, etc.)
  • ✅ Easy to use and scalable for real-time applications

🛠️ Tech Stack

  • Platform: Google Colab
  • Programming Language: Python
  • Framework: TensorFlow / Keras
  • Libraries: NumPy, Pandas, Matplotlib, OpenCV, Scikit-learn

🚀 How to Run in Google Colab

  1. Open the Colab Notebook:

    • Upload the .ipynb file to Google Drive or open it directly in Colab.

  2. Install required libraries (if not already available in Colab):

    !pip install tensorflow numpy pandas matplotlib scikit-learn opencv-python

📊 Dataset

We used a Waste Classification Dataset containing labeled images of different types of waste. Categories include:

-🥗 Organic
-🧴 Recyclable (Plastic, Paper, Glass, Metal, etc.)

📈 Results
-Achieved high accuracy on validation data
-Plotted training vs validation curves

🌍 Future Scope
-Deploy as a Web/Mobile Application
-Integrate with IoT Smart Bins
-Improve the dataset with more real-world waste images