Update README.md

README.md

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-title: Submission Template
+title: Image Classification with CNN
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-# Random Baseline Model for Climate Disinformation Classification
+# Convolutionnal Neural Network Model for Image CLassification Classification
 
 ## Model Description
 
-This is a random baseline model for the Frugal AI Challenge 2024, specifically for the text classification task of identifying climate disinformation. The model serves as a performance floor, randomly assigning labels to text inputs without any learning.
+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**: Baseline comparison for climate disinformation classification models
+- **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 QuotaClimat/frugalaichallenge-text-train dataset:
-- Size: ~6000 examples
+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
-- 8 categories of climate disinformation claims
-
-### Labels
-0. No relevant claim detected
-1. Global warming is not happening
-2. Not caused by humans
-3. Not bad or beneficial
-4. Solutions harmful/unnecessary
-5. Science is unreliable
-6. Proponents are biased
-7. Fossil fuels are needed
+
 
 ## Performance
 
 ### Metrics
-- **Accuracy**: ~12.5% (random chance with 8 classes)
+- **Accuracy**: ~83%
 - **Environmental Impact**:
   - Emissions tracked in gCO2eq
   - Energy consumption tracked in Wh
 
 ### Model Architecture
-The model implements a random choice between the 8 possible labels, serving as the simplest possible baseline.
+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
 
@@ -57,11 +49,8 @@ Environmental impact is tracked using CodeCarbon, measuring:
 This tracking helps establish a baseline for the environmental impact of model deployment and inference.
 
 ## Limitations
-- Makes completely random predictions
-- No learning or pattern recognition
-- No consideration of input text
-- Serves only as a baseline reference
-- Not suitable for any real-world applications
+- No object detection
+- 
 
 ## Ethical Considerations