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--- |
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title: CEA List FrugalAI Challenge |
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emoji: 🔥 |
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colorFrom: red |
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colorTo: yellow |
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sdk: docker |
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pinned: false |
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--- |
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# YOLO for Early Fire Detection |
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## Team |
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- Renato Sortino |
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- Aboubacar Tuo |
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- Charles Villard |
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- Nicolas Allezard |
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- Nicolas Granger |
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- Angélique Loesch |
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- Quoc-Cuong Pham |
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## Model Description |
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YOLO model for early fire detection in forests, proposed as a solution for the Frugal AI Challenge 2025, image task. |
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### Intended Use |
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- **Primary intended uses**: |
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- **Primary intended users**: |
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- **Out-of-scope use cases**: |
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## Training Data |
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The model uses the pyronear/pyro-sdis dataset: |
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- Size: ~33000 examples |
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- Split: 80% train, 20% test |
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- Images annotated with bounding boxes in correspondence of wildfire instances |
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### Labels |
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0. Smoke |
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## Performance |
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### Metrics |
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- **Accuracy**: ~83% |
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- **Environmental Impact**: |
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- Emissions tracked in gCO2eq |
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- Energy consumption tracked in Wh |
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### Model Architecture |
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The model is a YOLO-based object detection model, that does not depend on NMS in inference. |
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Bypassing this operation allows for further optimization at inference time via tensor decomposition and quantization |
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## Environmental Impact |
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Environmental impact is tracked using CodeCarbon, measuring: |
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- Carbon emissions during inference |
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- Energy consumption during inference |
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This tracking helps establish a baseline for the environmental impact of model deployment and inference. |
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## Limitations |
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- It may fail to generalize to night scenes or foggy settings |
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- It is subject to false detections, especially at low confidence thresholds |
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``` |
