View source for Demo Training Data Project

{{demo project summary
| title=Demo Train Dataset
| image=demo_train_dataset.jpg
| demographic=people who are working in fields such as AI, remote sensing, data analysis, and urban planning
| showcases=a method in the {{software}} to export a dataset for training a Mask R-CNN model.
| description=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 {{software}}.
}}

==Project's content==
Demo Train Dataset is a project that contains shapes of foliage, drawn as [[area]]s on top of a [[Satellite Overlay]]. These areas, in combination with satellite images, can be exported as a dataset for training a [https://github.com/matterport/Mask_RCNN 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.
<gallery>
File:Demo_train_dataset_foliage_areas.png|Individual foliage areas
File:Demo_train_dataset_satellite_overlay.png|Satellite Overlay serving as input for images 
</gallery>

==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. 

<gallery>
File:Demo_train_dataset_example_image.png|Input image, in this case an image exported using the [[Satellite Overlay]]
File:Demo_train_dataset_example_mask.png|Mask image with each unique feature identified by a gray value.
File:Demo_train_dataset_example_labels.png|Class labels
File:Demo_train_dataset_example_bbox.png|Bounding box coordinates
</gallery>

The {{software}} allows users to export datasets. The Demo Train Dataset Project is set up to contain two separate sets of [[Area]]s 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 export AI Training Data|how-to]] explains how to generate an AI Training Data using the {{software}}. This data can be used to train your [[How to train your own AI model for an Inference Overlay|own AI model]].
{{:How to export AI Training Data}}

===Export Considerations===
There are several considerations when exporting AI Training Data:
* The exported features ([[Building]]s, [[Area]]s, etc) should match the overlay that these are exported with. 
* A Larger image size leads to less inference calls by the Inference Model and therefore a faster calculation. However, the maximum amount of identifiable features within images of the train dataset may not exceed 250.
* The stride fraction can be configured to create more images and ensures features are fully inside certain images and not only on image borders.

== Train your own model ==
{{:How to train your own AI model for an Inference Overlay}}

There are some things to consider:
* To train a better model, increase the number of trained epochs:<pre>config.setEpochs(10)</pre> For example to 10. Please note that training with too many epochs may not improve the model; it will become too strict.

== 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 Wizard|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





{{article end
|seealso=
* [[Inference Overlay]]
* [[Neural Network]]
* [[ONNX]]
* [[PyTorch]]
* [[Anaconda Navigator]]
|howtos=
* [[How to export AI Training Data]]
* [[How to train your own AI model for an Inference Overlay]]
* [[How to detect foliage using an Inference Overlay]]
}}