🟢 FrugalAI

Answers to the challenge FrugalAI task — Deforestation Detection This repository contains code, notebooks, and results related to the Frugal AI Challenge — an ML competition encouraging efficient and environmentally sustainable AI models (e.g., minimizing energy, CO₂ impact) while maintaining strong performance. ([GitHub][2])

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📌 Project Overview

The goal of this repository is to build a model to detect illegal deforestation (e.g., chainsaw sounds) in audio recordings while prioritizing frugality: low compute, low energy, and high accuracy.

This includes:

• A Jupyter notebook pipeline for model development
• A RandomForest.ipynb showing a lightweight classical model
• Data preprocessing artifacts in spectrograms/
• Quantified results (accuracy and environmental metrics)

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📁 Repository Structure

FrugalAI/
├── spectrograms/            # (likely) audio preprocessing outputs or utilities
├── FrugalAI.ipynb           # Main notebook: model development & evaluation
├── RandomForest.ipynb       # Notebook exploring Random Forest model baseline
├── README.md                # This comprehensive README

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🧠 Challenge Context

This project was built as part of the Frugal AI Challenge — an initiative promoting efficient AI solutions that balance performance with environmental sustainability. Participants were evaluated not only on accuracy but also on energy consumption during training & inference. ([GitHub][2])

Your repository’s FrugalAI.ipynb likely includes loading audio data, transforming it into spectrograms/features, training a model, validating results, and reporting the energy footprint.

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🚀 Quick Start

🛠️ Requirements

General tools you’ll need:

• Python 3.8+
• Jupyter Notebook or Jupyter Lab
• ML packages (e.g., scikit‑learn, librosa, numpy, pandas)
• Optional: visualization libraries (matplotlib, seaborn)

Install dependencies (example):

pip install numpy scipy pandas scikit-learn librosa matplotlib

You may need to add additional packages based on the notebooks’ imports.

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📊 Running the Notebook

1. Open FrugalAI.ipynb in Jupyter

   • Follow the cells: load data, preprocess audio, extract features.
   • Train and evaluate your model.
   • Record metrics such as accuracy, energy consumption, and CO₂ impact (if measured).

2. Explore RandomForest.ipynb

   • Compares a Random Forest baseline — often efficient and interpretable.
   • Typically useful for tabular or engineered audio feature data.

3. Usage of spectrograms/

   • Contains outputs from audio → spectrogram conversion for model input.
   • You can visualize, augment, or use these features for ML pipelines.

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📈 Evaluation & Results

According to the repository summary:

• Your Random Forest baseline achieved ~80% accuracy
• With extremely low energy usage: ≈ 0.00017 kg CO₂ and ≈ 0.003002 kWh of electricity (measured or estimated) — demonstrating frugality. ([GitHub][3])

This aligns with the “frugal” criteria of the challenge: strong performance with minimal environmental and computational cost.

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🧩 How It Works (High Level)

1. Data Input

   • Audio recordings (e.g., short clips of chainsaw vs. environment)
   • Converted to spectrograms (time‑frequency representations)

2. Feature Extraction

   • Extract spectral features or embed audio for classification

3. Model Training

   • Lightweight models (e.g., Random Forest, small neural networks)
   • Emphasis on low training and inference cost

4. Evaluation

   • Measure accuracy on validation/test sets
   • Optionally log energy impact (e.g., using packages like codecarbon)

5. Reporting

   • Document results, charts, and insights in notebooks

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🧪 Example Code Snippet (Audio to Spectrogram)

Here’s a typical snippet you might see in the notebooks (example for feature extraction):

import librosa
import numpy as np

def extract_spectrogram(audio_path, sr=12000, n_mels=128):
    y, sr = librosa.load(audio_path, sr=sr)
    spect = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels)
    spect_db = librosa.power_to_db(spect, ref=np.max)
    return spect_db

spectrogram = extract_spectrogram("sample_audio.wav")

Use these spectrograms as features in any ML classifier.

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📦 Dependencies (suggested)

Typical Python packages include:

• numpy
• pandas
• scikit-learn
• librosa
• matplotlib / seaborn
• notebook

Add these to a requirements.txt or Conda environment for reproducibility.

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🏆 Challenge Impact

Frugal AI encourages responsible and efficient AI — prioritizing both accuracy and sustainability. Models like yours demonstrate how lightweight approaches like Random Forests or simple feature‑based classifiers can be competitive while drastically reducing energy consumption. ([GitHub][2])

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❤️ Contributing

If this evolves further, consider adding:

• Dataset loader scripts
• A CLI or Python module for reproducible runs
• Scripts to measure and record environmental metrics
• A Dockerfile or Binder config for easy sharing
• More model baselines (light neural nets, boosted trees)

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📄 License

If not already specified, consider adding an MIT License or another permissive open‑source license.