March 19: QuickStart training Tygron Platform

Tygron AI Suite

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The Tygron Platform offers a comprehensive set of tools to assist you in all stages of creating, training, and utilizing your own neural networks on our dedicated GPU clusters.

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Inference

Using the Inference Overlay  (available in version 2025.0+) users can do machine learning on public and private data. For example detecting trees, bushes or objects from satellite imagery.

The Tygron GeoShare offers users an expanding collection of open-source pre-trained neural networks that can be directly applied to your own projects.

These neural network will be executed in the Tygron Engine’s GPU Supercomputer.

Open Neural Network Exchange

The Tygron Platform supports the Open Neural Network Exchange (ONNX) format.

This allow users to also upload their own trained models form other application like PyTorch and use them to run inference directly in the Tygron Engine.

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AI Training Data Exporter

The Tygron Platform can also help with creating your own Training Data for PyTorch.

First select objects that must be recognized form e.g.  trees in satellite imagery via QGIS connected with the Tygron Engine.

Then export the images, labels and mask directly from your projects as Training Data. Images can be satellite imagery, but also a many different types, like flow patterns or height maps.

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Training Script for Jupyter Notebook

We also maintain a public open-source repository on GitHub that can assist you in creating and training the appropriate neural networks.

By following these steps, you can train your own PyTorch neural network using Anaconda and Jupyter Notebooks.

The scripts also provide guidance on the correct metadata and ONNX formatting to ensure smooth execution in the Tygron Engine.

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