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title: Submission Template
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sdk: docker
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# Random Forest Model for Climate Disinformation Classification

## Model Description

This is a random forest model for the Frugal AI Challenge 2024, specifically for the audio classification task of identifying illegal deforestation.

### Intended Use

- **Primary intended uses**: Illegal deforestation classification model
- **Primary intended users**: Researchers and developers participating in the Frugal AI Challenge
- **Out-of-scope use cases**: Not intended for production use or real-world classification tasks

## Training Data

The model uses the rfcx/frugalai dataset:
- Size: ~50000 examples
- Split: 80% train, 20% test
- 2 categories of audio category

### Labels
0. chainsaw  (positively identifying a chainsaw)
1. environment (not containing a chainsaw).

## Performance

### Metrics
- **Accuracy**: ~89.3%
- **Environmental Impact**:
  - Emissions tracked in gCO2eq
  - Energy consumption tracked in Wh

### Model Architecture
The model implements a random forest model used on pre-processed data. The pre-processing consists in a resampling, a Fourier decomposition and a standard scaler. 

## Environmental Impact

Environmental impact is tracked using CodeCarbon, measuring:
- Carbon emissions during inference
- Energy consumption during inference

This tracking helps establish a baseline for the environmental impact of model deployment and inference.

## Limitations
- Takes some time to do the pre-processing.

## Ethical Considerations

- Environmental impact is tracked to promote awareness of AI's carbon footprint
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