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The Tygron AI Suite | The Tygron AI Suite provides a comprehensive set of tools to assist you in all stages of creating, training, and utilizing your own [[Region-based Convolutional Neural Network (Inference Overlay)|RCNN]] within an [[Inference Overlay]] on our dedicated [[GPU]] cloud. | ||
[[File:aiflow.png|center|800px]] | |||
==Creating you own model== | ==Creating you own model== | ||
Follow these steps to create an AI model based on a [[Region-based Convolutional Neural Network (Inference Overlay)|RCNN]]. | |||
# 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 sub dived into palms, pines, etc. | # 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 sub dived into palms, pines, etc. | ||
# | # 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]] | ||
# Create two groups of objects a TRAIN and TEST dataset that can be exported. Follow: [[How to export AI Training Data]] | # Create two groups of objects a TRAIN and TEST dataset that can be exported. Follow: [[How to export AI Training Data]] | ||
# After exporting you can start training your [[Neural Network]] resulting in a [[ONNX]] file. Follow: [[How to train your own AI model for an Inference Overlay]]. | # After exporting you can start training your [[Region-based Convolutional Neural Network (Inference Overlay)|RCNN]] resulting in a [[ONNX]] file. Follow: [[How to train your own AI model for an Inference Overlay]]. | ||
# Then import the [[ONNX]] back into the {{software}} an run it in an Inference Overlay. For example: [[How to detect foliage using an Inference Overlay]] | # Then import the [[ONNX]] back into the {{software}} an run it in an Inference Overlay. For example: [[How to detect foliage using an Inference Overlay]] | ||
# Finally validate the results on a different project and iterate back to a previous step if needed. Follow: [[How to evaluate an AI model]] | # 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 model== | ||
* | * Apply a custom-created or existing [[ONNX]] model to other projects using the [[Inference Overlay]]. For example: [[How to detect foliage using an Inference Overlay]] | ||
{{article end | {{article end | ||
|seealso= | |seealso= | ||
* [[Model attributes (Inference Overlay)]] | * [[Model attributes (Inference Overlay)]] | ||
* [[Neural Network]] | * [[Region-based Convolutional Neural Network (Inference Overlay)|RCNN]] | ||
* [[ONNX]] | * [[ONNX]] | ||
* [[PyTorch]] | * [[PyTorch]] | ||
* [[Demo Training Data Project]] | * [[Demo Training Data Project]] | ||
}} | }} | ||
Latest revision as of 08:23, 3 August 2026
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 you own model
Follow these steps to create an AI model based on a RCNN.
- 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 sub dived into palms, pines, etc.
- 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
- Create two groups of objects a TRAIN and TEST dataset that can be exported. Follow: How to export AI Training Data
- 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.
- Then import the ONNX back into the Tygron Platform an run it in an Inference Overlay. For example: How to detect foliage using an Inference Overlay
- 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 model to other projects using the Inference Overlay. For example: How to detect foliage using an Inference Overlay