MACHINE LEARNING MODEL TRAINING
Overview of our machine learning model training process using the dataset provided by Ciclope.
On March 10th, we have officially started training our machine learning model. This crucial phase is being powered by the extensive image dataset provided through our partnership with Ciclope.
Working with high-quality, real-world data is essential for building a robust early forest fire detection system. To ensure the best results, our work during this period focuses on three main pillars:
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Data Preprocessing: Organizing, filtering, and preparing the raw data from Ciclope so our algorithms can learn from the most relevant and accurate examples;
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Model Training: Feeding the visual data into our machine learning architecture, teaching the system to recognize the subtle, early visual patterns of smoke and fire;
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Validation and Fine-tuning: Continuously evaluating the model’s performance, tweaking parameters to increase accuracy, and making sure we minimize false positives as much as possible.