Overview
A Vision Transformer (ViT) is a specialized machine learning model designed to process and classify image data. In mineral exploration, ViTs 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 ViT model and configuring the correct number of epochs (training iterations) is critical for accurate and reliable predictions.
How Vision Transformers Work
Vision Transformers follow these key steps when handling image data:
Processing Patches: The input image (e.g., geological data visualized as an image) is divided into smaller patches or grids of pixels.
Embedding: Each patch is transformed into a numerical representation, or "embedding," which captures essential information from that patch.
Encoding: The transformer encodes the patches using attention mechanisms. These mechanisms allow the model to focus on the most important parts of the data while minimizing distractions from irrelevant information.
Classification: After processing the patches, the model classifies the entire image, enabling predictions about geological or mineral features within the dataset.
Choosing the Right Vision Transformer Model
Selecting the correct ViT 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 Master Model. This model is designed to work across various systems and can deliver robust predictions even without specific training data.
Still have questions?
Reach out to your dedicated VRIFY AI Contact or email Support@VRIFY.com for more information.