AI Suite

This functionality is currently in BETA.
Read more about Beta features.

The Tygron AI Suite provides a comprehensive set of tools to assist you in all stages of creating, training, and utilizing your own RCNN within an Inference Overlay on our dedicated GPU cloud.

Creating your own model

Follow these steps to create an AI model based on a RCNN.

1. Start with a definition of which objects you want to detect. For example trees, cars, solar panels, etc. These can also be subsets for example trees can also be subdivided into palms, pines, etc.
2. Create one or more varied projects and manually define training data by outlining the target objects. Follow: How to create AI training data with QGIS
3. Create two groups of objects a TRAIN and TEST dataset that can be exported. Follow: How to export AI Training Data
4. After exporting you can start training your RCNN resulting in a ONNX file. Follow: How to train your own AI model for an Inference Overlay.
5. Import the ONNX back into the Tygron Platform as a Region-based Convolutional Neural Network and assign it to an Inference Overlay. For example: How to detect foliage using an Inference Overlay
6. Finally validate the results on a different project and iterate back to a previous step if needed. Follow: How to evaluate an AI model

Apply a model

• Apply a custom-created or existing ONNX file as a Region-based Convolutional Neural Network to other projects using the Inference Overlay. For example: How to detect foliage using an Inference Overlay

See also

• Inference Overlay
• Region-based Convolutional Neural Network (Inference Overlay)
• Model attributes (Inference Overlay)
• RCNN
• ONNX
• PyTorch
• Demo Training Data Project

API Endpoints

• Api session event editor neuralnetwork add
• Api session event editor overlay set neural network