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