Update README.md

README.md

@@ -1,41 +1,43 @@
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-title: Submission Template
+title: CEA List FrugalAI Challenge
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-colorTo: green
+colorFrom: red
+colorTo: yellow
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-# Random Baseline Model for Climate Disinformation Classification
+# YOLO for Early Fire Detection
+
+## Team
+- Renato Sortino
+- Aboubacar Tuo
+- Charles Villard
+- Nicolas Allezard
+- Nicolas Granger
+- Angélique Loesch
+- Quoc-Cuong Pham
 
 ## 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.
+YOLO model for early fire detection in forests, proposed as a solution for the Frugal AI Challenge 2025, image task. 
 
 ### Intended Use
 
-- **Primary intended uses**: Baseline comparison for climate disinformation 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
+- **Primary intended uses**: 
+- **Primary intended users**: 
+- **Out-of-scope use cases**: 
 
 ## Training Data
 
-The model uses the QuotaClimat/frugalaichallenge-text-train dataset:
-- Size: ~6000 examples
+The model uses the pyronear/pyro-sdis dataset:
+- Size: ~33000 examples
 - Split: 80% train, 20% test
-- 8 categories of climate disinformation claims
+- Images annotated with bounding boxes in correspondence of wildfire instances
 
 ### 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
 
@@ -46,7 +48,8 @@ The model uses the QuotaClimat/frugalaichallenge-text-train dataset:
   - 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 is a YOLO-based object detection model, that does not depend on NMS in inference. 
+Bypassing this operation allows for further optimization at inference time via tensor decomposition and quantization
 
 ## Environmental Impact
 
@@ -68,4 +71,4 @@ This tracking helps establish a baseline for the environmental impact of model d
 - 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
-```
+```