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Garbage Image

Garbage Classification using EfficientNet

This repository contains the implementation of a garbage classification system inspired by the research paper: Deep Learning-based Waste Detection in Natural and Urban Environments. The system achieves efficient and accurate classification of waste into predefined categories using a deep learning approach.

Overview

Garbage pollution is a significant environmental challenge, and efficient waste classification is crucial for effective recycling and waste management. This project implements a model based on EfficientNet architecture, which achieves 83% classification accuracy, making it a practical tool for real-world applications such as smart waste bins and urban waste monitoring.

Features

  • Deep Learning Backbone: Utilizes EfficientNet for image classification.
  • Dataset Integration: Prepares and trains on the Kaggle Garbage Classification dataset.
  • Accuracy: Achieves high accuracy with robust performance in diverse environmental conditions.
  • Customizable: Easy-to-use training and testing pipelines.

Getting Started

Prerequisites

  • Python 3.8 or higher
  • TensorFlow / PyTorch
  • NumPy
  • OpenCV (for image preprocessing)

Installation

  1. Clone this repository:

    git clone https://github.com/yourusername/garbage-classification
cd garbage-classification

  2. Install the dependencies:

    pip install -r requirements.txt

  3. Download the dataset:

    Download the Garbage Classification Dataset from Kaggle: https://www.kaggle.com/datasets/asdasdasasdas/garbage-classification/data. Extract the dataset and place it in the data/ directory.

  4. Preprocess the dataset:

    python preprocess_data.py --dataset-path data/Garbage\ Classification

Training the Model

Train the EfficientNet model using the provided dataset:

python train.py --dataset-path data/processed --epochs 50 --batch-size 32

Testing the Model

Evaluate the trained model on the test set:

python test.py --model-path models/efficientnet_best.pth --dataset-path data/processed

Implementation Details

Model Architecture

The project uses EfficientNet-B2, chosen for its balance of computational efficiency and accuracy. The architecture includes:

  1. Pre-trained EfficientNet backbone for feature extraction.
  2. Fully connected layers for waste category classification.

Waste Categories

The Kaggle dataset includes the following waste categories:

  • Cardboard
  • Glass
  • Metal
  • Paper
  • Plastic
  • Trash

These categories are used directly in the model.

Results

  • Classification Accuracy: 83% on the test set.
  • Dataset Size: The Kaggle dataset contains over 2,500 labeled images of waste.

Contributing

We welcome contributions! Please follow the steps below to contribute:

  1. Fork the repository.
  2. Create a feature branch (git checkout -b feature-name).
  3. Commit your changes (git commit -m 'Add feature').
  4. Push to the branch (git push origin feature-name).
  5. Open a pull request.

Acknowledgments