A Deposit Type Model is a specialized machine learning model designed to process and classify image data. In mineral exploration, Deposit Type Models analyze geospatial data to help predict and assess the quality of mineral deposits. They work by embedding, encoding, and classifying input features (such as images or spatial data) to make predictions for each patch or grid of pixels.

Choosing the appropriate model and configuring the correct number of epochs (training iterations) is critical for accurate and reliable predictions.

Selecting the correct deposit type model is essential for optimizing your predictions. The model should be tailored to the specific mineral system you are targeting. For example, different models may be available for porphyry copper or gold deposits, each trained with data related to those types of mineral systems.

If a specialized model for your specific commodity or mineral system isn't available, you can use the Deposit Agnostic Model. This model is designed to work across various systems and can deliver robust predictions even without specific training data.

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