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title: Submission Template | |
emoji: 🔥 | |
colorFrom: yellow | |
colorTo: green | |
sdk: docker | |
pinned: false | |
# Smoke fire detection | |
## Model Description | |
This is a yolo-based model for the Frugal AI Challenge 2025, specifically for the wildfire smoke detection | |
### Intended Use | |
- **Primary intended uses**: Detect fire smoke on photos of forests, in different natural settings | |
- **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 pyronear/pyro-sdis dataset: | |
- Size: ~33 600 examples | |
- Split: 88% train, 12% test | |
### Labels | |
0. Smoke | |
## Performance | |
### Metrics | |
- **Accuracy**: ~92 | |
- **Environmental Impact**: | |
- Emissions tracked in gCO2eq 0.23 | |
- Energy consumption tracked in Wh 3.5 | |
### Model Architecture | |
YOLO 11 | |
## 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 | |
- May require GPU | |
## Ethical Considerations | |
- Environmental impact is tracked to promote awareness of AI's carbon footprint | |
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