metadata

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