Update README.md

README.md

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+# Wildfire Detection Task for the Frugal AI 2025 Challenge
+## Task Overview
+As part of the Frugal AI 2025 Challenge, I’m working on the wildfire detection task using the PyroNear/pyro-sdis dataset available on Hugging Face (https://huggingface.co/datasets/pyronear/pyro-sdis). This task aims to develop a model capable of detecting wildfires in images efficiently, contributing to early detection and mitigation of wildfire damage while minimizing environmental costs.
+
+## Dataset Overview
+This dataset is specifically designed for wildfire detection, containing labeled images with wildfire-related and non-wildfire-related scenes.
+- Key Features:
+Labels: Binary classification — wildfire present or not.  
+Images: Captured under real-world conditions, including diverse environments and challenging scenarios like smoke, clouds, and varying lighting.  
+Size: ~33 000 image labeled images, well-suited for training and validation of computer vision models. 
+- 28,103 images with smoke
+- 31,975 smoke instances
+This dataset is formatted to be compatible with the Ultralytics YOLO framework, enabling efficient training of object detection models.   
+Usage: Ideal for fine-tuning state-of-the-art models for wildfire detection tasks.  
+
+## Model Development Plan
+Model Choice: YOLOv11s  
+Why YOLOv11s?
+Efficiency: YOLO (You Only Look Once) models are known for their high-speed performance and accuracy, ideal for real-time applications.  
+Versatility: YOLOv11s builds upon prior versions, improving object detection, handling small objects, and performing well under challenging visual conditions.  
+Frugality: Optimized for computational efficiency, aligning with the sustainability goals of the Frugal AI Challenge.
+ 
+## Data Preprocessing:
+
+
+Fine-Tuning YOLOv11:
+Load a pre-trained YOLOv11s model as a starting point (transfer learning).
+Replace the output layer to align with the binary classification task (wildfire vs. no wildfire).
+Train the model using the PyroNear/pyro-sdis dataset.
+
+## Evaluation:
+
+Metrics:
+Precision and recall to assess detection accuracy.
+Inference time to evaluate real-time feasibility.
+
+Using CodeCarbon, the model's carbon footprint and energy consumption will be tracked, this information will help ensure the model's alignment with the sustainability objectives of the Frugal AI Challenge.
 
-
-# Random Baseline Model for Climate Disinformation 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.
-
-### 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
-
-## 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
-
-### 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)
-- **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.
-
-## 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
-- 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
-
-## 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
-```