Update README.md

README.md

@@ -8,45 +8,41 @@ pinned: false
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-# Random Baseline Model for Climate Disinformation Classification
+# MountAIn model for smoke 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 an evolution from YOLO baseline to focus on small-to-medium objects and integrated SAHI-like approach
 
 ### Intended Use
 
-- **Primary intended uses**: Baseline comparison for climate disinformation classification models
+- **Primary intended uses**: First submission of a novel class model
 - **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 the Pyro-SDIS Subset contains 33,636 images, including:
 
-### 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
+- 28,103 images with smoke
+- 31,975 smoke instances
 
+### Labels
+0. Smoke
 ## Performance
 
 ### Metrics
-- **Accuracy**: ~12.5% (random chance with 8 classes)
+- **Accuracy**: Still to be estimated but mAP:50 > 70%
 - **Environmental Impact**:
+
+  Emissions impact if inference is run on Cloud and/or on-premise gateways
   - Emissions tracked in gCO2eq
   - Energy consumption tracked in Wh
 
+  Emissions are null if run on MountAIn vision sensors since they are powered by renewable energy
+
 ### Model Architecture
-The model implements a random choice between the 8 possible labels, serving as the simplest possible baseline.
+Evolution from YOLO baseline
 
 ## Environmental Impact
 
@@ -54,18 +50,14 @@ 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.
+This tracking helps establish a baseline for the environmental impact of model deployment and inference while running in Cloud and/or on-premise gateways.
+The usage of MountAIn vision sensors enables no environmental impact thanks to the usage of renewable energy
 
 ## 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
+
+- Not suitable for any real-world applications as is without proper export to tiny MCUs
 
 ## 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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