🗑️ Garbage Classifier

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📋 Overview

This project implements an image classification system to automatically categorize waste materials into six classes: cardboard, glass, metal, paper, plastic, and trash. Built with PyTorch Lightning and ResNet18, it provides a modular, scalable solution for waste management automation.

🎯 Key Features

• ✨ ResNet18 backbone fine-tuned for garbage classification
• ⚡ PyTorch Lightning framework for clean, scalable training
• 📊 Comprehensive EDA with visualization notebooks
• 🔄 Batch and single-image prediction support
• 📈 Automatic metrics tracking with loss curves and performance reports
• 📚 Auto-generated documentation using pdoc3
• 🎓 LaTeX report generation for academic documentation

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🚀 Quick Start

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Activate uv
source $HOME/.local/bin/env

Interactive demo

# Create Virtual Environment
uv venv garbage-env --python 3.12

# Install PyPI package
uv pip install -U -i https://pypi.org/simple/ garbage-classifier

# Launch demo
garbage-app

Full project

# Clone the repository
git clone https://github.com/NeoLafuente/garbage_classifier.git
cd garbage_classifier

# Sync dependencies
uv sync

# Train the model
uv run source/train.py

# Make a prediction
uv run source/predict.py path/to/image.jpg

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📦 Dataset

The project uses the Garbage Classification Dataset from Kaggle.

Dataset Preparation

The notebook notebooks/create_sample_dataset.ipynb automatically prepares the dataset:

• Downloads the Garbage Classification Dataset
• Creates the sample_dataset folder inside data/raw
• Reduces dataset size for lightweight experimentation

Dataset Structure:

• 6 classes: cardboard, glass, metal, paper, plastic, trash
• Location: data/raw/Garbage_Dataset_Classification/
• Metadata: Class distributions and image statistics in metadata.csv

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💻 Usage

Training

Train the GarbageClassifier model (ResNet18 with PyTorch Lightning):

uv run source/train.py

Output:

• Model checkpoint: models/weights/model_resnet18_garbage.ckpt
• Loss curves: models/performance/loss_curves/
• Training logs with metrics (accuracy, precision, recall, F1-score)

Configuration: Edit source/utils/config.py to customize:

• Batch size
• Learning rate
• Number of epochs
• Train/validation split ratio

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Prediction

Load the trained model and classify new images.

📸 Single Image Prediction

# Predict a specific image
uv run source/predict.py img.jpg

# Use default image from config
uv run source/predict.py

📁 Batch Folder Prediction

Process all images in a folder:

uv run source/predict.py data/test_images/
uv run source/predict.py ../new_samples/

Features:

• Auto-detects valid image files (.jpg, .jpeg, .png, .bmp, .gif, .tiff, .tif)
• Progress indicators for batch processing
• Summary table with all predictions

Example Output:

Predicting: cardboard_sample.jpg
Class: cardboard | Confidence: 98.5%

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🏗️ Model Architecture

GarbageClassifier

• Backbone: ResNet18 (pretrained on ImageNet)
• Framework: PyTorch Lightning
• Input: 224x224 RGB images
• Output: 6-class probability distribution

Custom Components

1. GarbageDataModule: PyTorch Lightning DataModule for efficient data loading
2. LossCurveCallback: Custom callback for tracking and saving loss curves
3. GarbageClassifier: Main model class with training/validation logic

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📚 Documentation

Auto-generated Documentation

HTML documentation is auto-generated from source code docstrings using pdoc3.

View Documentation:

• Open docs/index.html in your browser

Regenerate Documentation:

uv run scripts/generate_docs.py

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📓 Notebooks

Interactive Jupyter notebooks for exploration and analysis:

Notebook	Description
create_sample_dataset.ipynb	Dataset preparation and sampling
dataset_exploration.ipynb	EDA with class distribution and visualizations
performance_analysis.ipynb	Model evaluation, confusion matrices, error analysis

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📄 Reports

LaTeX Report

Academic-style report with methodology and results:

• Source: reports/main.tex
• Compiled PDF: reports/compiled/
• Figures:
  • reports/figures/EDA/ - Exploratory data analysis
  • reports/figures/performance/ - Model metrics and evaluation

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⚙️ Configuration

Central configuration in source/utils/config.py:

# Dataset configuration
CLASSES = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
DATA_PATH = 'data/raw/Garbage_Dataset_Classification'

# Model hyperparameters
BATCH_SIZE = 32
LEARNING_RATE = 0.001
NUM_EPOCHS = 50

# Data split
TRAIN_RATIO = 0.8
VAL_RATIO = 0.2

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📁 Project Organization

.
├── data
│   ├── interim              # Intermediate data transformations
│   ├── processed            # Final preprocessed data
│   └── raw                  # Original unprocessed datasets
│       └── Garbage_Dataset_Classification
│           ├── images       # Image files by class
│           └── metadata.csv # Dataset statistics
├── docs                     # Auto-generated HTML documentation
├── models
│   ├── performance          # Loss curves and metrics
│   │   └── loss_curves
│   └── weights              # Trained model checkpoints (.ckpt)
├── notebooks                # Jupyter notebooks for EDA and analysis
│   ├── create_sample_dataset.ipynb
│   ├── dataset_exploration.ipynb
│   └── performance_analysis.ipynb
├── reports                  # LaTeX reports and figures
│   ├── compiled             # PDF reports
│   ├── figures
│   │   ├── EDA              # Exploratory analysis plots
│   │   └── performance      # Model evaluation plots
│   └── main.tex             # Main report source
├── scripts                  # Utility scripts
│   └── generate_docs.py     # Documentation generator
├── source                   # Main source code
│   ├── predict.py           # Prediction script
│   ├── train.py             # Training script
│   └── utils
│       ├── config.py        # Configuration file
│       └── custom_classes   # Model implementations
│           ├── GarbageClassifier.py
│           ├── GarbageDataModule.py
│           └── LossCurveCallback.py
├── pyproject.toml           # Project dependencies (uv)
└── README.md

Note: dummy.txt files are placeholders to preserve empty folder structure in Git.

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• Dataset: Garbage Classification Dataset by zlatan599 on Kaggle
• Framework: PyTorch Lightning for the excellent deep learning framework
• Model: ResNet18 architecture from torchvision

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📧 Contact

Neo Lafuente - @NeoLafuente

Project Link: https://github.com/NeoLafuente/garbage_classifier

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