---
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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