---
title: Image Classification with CNN
emoji: 🔥
colorFrom: yellow
colorTo: green
sdk: docker
pinned: false
---


# Convolutionnal Neural Network Model for Image CLassification Classification

## Model Description

This is aCNN model for the Frugal AI Challenge 2024, specifically for the image classification task of identifying smoke in images. The model contains 2 convolutionnal layers and one fully connected layer.

### Intended Use

- **Primary intended uses**: Test for image classification models
- **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 datase.
The Pyro-SDIS Subset contains 33,636 images, including:
- 28,103 images with smoke
- 31,975 smoke instances
- Split: 80% train, 20% test


## Performance

### Metrics
- **Accuracy**: ~83%
- **Environmental Impact**:
  - Emissions tracked in gCO2eq
  - Energy consumption tracked in Wh

### Model Architecture
The model implements a CNN model trained on augmented images (randomCrop, Horizontal and Vertical Flip, ColorJitters...). Only 2 convolutionnal layers and one fully connected layer was implemented in this model.

## 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
- No object detection
- 

## Ethical Considerations

- Dataset contains sensitive topics related to climate disinformation
- Model makes random predictions and should not be used for actual classification
- Environmental impact is tracked to promote awareness of AI's carbon footprint
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