Demo Training Data Project: Difference between revisions

Revision as of 14:41, 31 January 2025

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.

Project's content

Demo Train Dataset is a project that contains shapes of foliage, drawn as areas on top of a Satellite Overlay. 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.

Individual foliage areas
Satellite Overlay serving as input for images

AI Training Data

In order to train a Mask R-CNN model, we need to obtain two datasets, one for training and one for testing. A dataset consists of a list of numbered files, with the number representing the data entry number. Each data entry consists of the following files:

An input image, as a PNG
A mask image, as a PNG, containing gray values, where each gray value represents a unique identifiable feature.
A label text file containing the classes of the identifiable features, where each class is identified with an integer number (larger than 0).
Optional: A text file containing the bounding-box pixel numbers; min x, min y, max x, max y, for each identifiable feature in the input image.

Input image, in this case an image exported using the Satellite Overlay
Mask image with each unique feature identified by a gray value.
Class labels
Bounding box coordinates

The Tygron Platform allows users to export datasets. The Demo Train Dataset Project is set up to contain two separate sets of Areas to export; One set of Areas with the attribute TRAIN_FOLIAGE and one with the attribute TEST_FOLIAGE. Each set can be exported with an Overlay. Since both sets are drawn based on a Satellite Overlay named Original Satellite, we will use that Overlay.

Export a dataset

This how-to explains how to generate an AI Training Data using the Tygron Platform. This data can be used to train your own AI model.

How to export AI Training Data:

Select current situation in the ribbon bar
Hover over Areas, and select Export Geo Data
Under Format, select the option AI Training Data
Select the filter option, and select the following attribute TRAIN_FOLIAGE
For the Overlay option, select the Satellite Overlay named Original Satellite.
Set the image size to 200 by 200. This ensures that the amount of areas per exported image does not exceed the maximum of 250.
The stride can be kept at 50%
Click on the Export Files button
Select a suitable folder for the generated dataset.
Wait until the dataset is fully generated.

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.

Anaconda Navigator will be used to manage a Conda environment and run Jupyter Notebooks with python.

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

How to train your own AI model for an Inference Overlay:

If you are not familiar with github, you can download the zip containing all the files that you need. Otherwise, clone the git repository
Optionally unzip and open the folder containing the downloaded files; the local repository.
Download and install Anaconda Navigator.
Open the Anaconda Navigator application
Select the tab Environments and click on the Import button. This will download all necessary dependencies and may take a while.
Select the file "tygronai.yml" from the local repository of the tygron-ai-suite. This file will automatically setup the environment needed to open, edit and execute the Jupyter Notebooks.
Once configured, select the Home tab
Open either the JupyterLab or Jupyter Notebook application. You might need to install it first by click on the install button.
A browser will open with the selected application.
Browse to the folder of the tygron-ai-suite repository and select the "example_config.ipynb".
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.
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.
Eventually an ONNX file will be created.

Training Considerations

There are some things to consider:

To train a better model, increase the number of trained epochs, for example to 10:
```
config.setEpochs(10)
```
Please note that training with too many epochs may not improve the model; it will become too strict.
If any dataset contains more label classes than you would like to recognize, you can adjust the autoLimitLabel configuration to:
```
config.setAutoLimitLabel(True)
```

Applying your own model

Once you have trained your own AI model and exported it to ONNX, you can import this model into your project and apply it using an Inference Overlay.

However, you will first need to create a new project on a different geographical location than this demo project is situated. Please follow the steps of the New Project Wizard.

Once you have created a new (non-empty) project opened in the editor, you can continue with the configuration of an Inference Overlay.

To do so, drag the ONNX file onto the Tygron Client Application
With the popup menu shown, select the option: Import as an Inference Overlay. This Inference Overlay should be configured almost automatically.
Next, add a new Satellite Overlay.
Next, select the Inference Overlay and for Input A, select the Satellite Overlay

ONNX file popup in the Tygron Client
Input Satellite Overlay
Inference Overlay using the imported ONNX file

Considerations

When exporting data and training your own AI model, there are a few considerations that require attention.

Application considerations

The input prequel overlay should match the data it was trained on. For example, if it was trained on a satellite overlay, you should not use a WMS-Overlay of an Infra Red service as input.
The grid cell size should match the precision it was trained on.

Demo Training Data Project: Difference between revisions

Revision as of 14:41, 31 January 2025

Contents

Project's content