Update README.md

README.md

@@ -8,45 +8,36 @@ pinned: false
 ---
 
 
-# Random Baseline Model for Climate Disinformation Classification
+# Smoke fire detection
 
 ## 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 a yolo-based model for the Frugal AI Challenge 2025, specifically for the wildfire smoke detection
 ### Intended Use
 
-- **Primary intended uses**: Baseline comparison for climate disinformation classification models
+- **Primary intended uses**: Detect fire smoke on photos of forests, in different natural settings
 - **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
-- Split: 80% train, 20% test
-- 8 categories of climate disinformation claims
+The model uses the pyronear/pyro-sdis dataset:
+- Size: ~33 600 examples
+- Split: 88% train, 12% test
 
 ### 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
+0. Smoke
 
 ## Performance
 
 ### Metrics
-- **Accuracy**: ~12.5% (random chance with 8 classes)
+- **Accuracy**: ~92
 - **Environmental Impact**:
-  - Emissions tracked in gCO2eq
-  - Energy consumption tracked in Wh
+  - Emissions tracked in gCO2eq 0.23
+  - Energy consumption tracked in Wh 3.5
 
 ### Model Architecture
-The model implements a random choice between the 8 possible labels, serving as the simplest possible baseline.
+YOLO 11
 
 ## Environmental Impact
 
@@ -57,15 +48,8 @@ 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
+- May require GPU
 
 ## 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
 ```