---
title: Submission Template
emoji: 🔥
colorFrom: yellow
colorTo: green
sdk: docker
pinned: false
---


# MountAIn model for smoke detection

## Model Description

This is an evolution from YOLO baseline to focus on small-to-medium objects and integrated SAHI-like approach

### Intended Use

- **Primary intended uses**: First submission of a novel class 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 the Pyro-SDIS Subset contains 33,636 images, including:

- 28,103 images with smoke
- 31,975 smoke instances

### Labels
0. Smoke
## Performance

### Metrics
- **Accuracy**: Still to be estimated but mAP:50 > 70%
- **Environmental Impact**:

  Emissions impact if inference is run on Cloud and/or on-premise gateways
  - Emissions tracked in gCO2eq
  - Energy consumption tracked in Wh

  Emissions are null if run on MountAIn vision sensors since they are powered by renewable energy

### Model Architecture
Evolution from YOLO baseline

## 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 while running in Cloud and/or on-premise gateways.
The usage of MountAIn vision sensors enables no environmental impact thanks to the usage of renewable energy

## Limitations

- Not suitable for any real-world applications as is without proper export to tiny MCUs

## Ethical Considerations

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