Demo Training Data Project

An overview of Demo Train Dataset.

The Demo Train Dataset project is available for all users and can be found in the main menu under Edit projects. This project does not count towards your license.

This project is intended for people who are working in fields such as AI, remote sensing, data analysis, and urban planning.

This project showcases a method in the Tygron Platform to export a dataset for training a Mask R-CNN model..

The demo is a working project in which a number of areas are drawn, using a satellite overlay as underlay. These areas are marked with specific attributes, making it easy to export them as a training or test set using an export option in the Tygron Platform.

Train Dataset

Demo Train Dataset is a project that contains shapes of foliage, drawn as areas on top of a satellite image. These areas, in combination with satellite images, can be exported as a dataset for training a Mask R-CNN model. Such a model can then be applied to a different project location to detect foliage. A specific use-case is detecting foliage on private property, such as gardens and private yards.

Train your own model

A Tygron AI Suite is available at github. This repository contains the necessary files to configure a Conda environment to train a new Mask R-CNN AI model.

Conda-forge's Miniforge will be used to manage a Conda environments and run Jupyter Notebooks with python.

Example notebooks provided in the repository can be used as a basis to train your own model.

TygronAI yml-file provided in the repository can be used initialize a Conda Environment. It includes Pytorch and Jupyter Notebook.

Demo Training Data Project:

1. If you are not familiar with github, you can download the zip containing all the files that you need. Otherwise, clone the git repository
2. Optionally unzip and open the folder containing the downloaded files; the local repository. Copy the path to this directory.
3. Download and install Miniforge.
4. Open the Miniforge Prompt application.
5. Change the directory to the path of the local repository. For example

   cd user/git/tygronai/

   This directory should contain the tygronai.yml file.
6. Create and initialize a new conda envirmonment with the tygronai.yml file using:

   conda env create -n tygronai -f tygronai.yml

   and press enter to confirm.
7. Wait for the downloads and unpacking to complete.
8. Activate the create tygronai environment using:

   conda activate tygronai

   The name (tygronai) should now have appeared in place of (base) in front of the prompt.
9. Start the Jupyter Notebook application by typing:

   jupyter notebook

10. A browser will open with the Jupyter Notebook application.
11. The application should open in the tygron ai repository folder. If not, browse to the folder of the tygron-ai-suite repository.
12. Once in the correct folder, select the "example_config.ipynb".
13. Adjust the following parameters:

    trainDirectory = "PATH TO TRAIN FILES"

    testDirectory = "PATH TO TEST FILES"

    to the folders containing the exported datasets. See how to export AI datasets for more information.
14. Press the double arrow button named Restart kernel and execute all cells to run the Jupyter Notebook. See the images below of what to expect.
15. Eventually an ONNX file will be created.

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How-to's

• How to export AI Training Data
• How to train your own AI model for an Inference Overlay