Inference Module: Difference between revisions
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* [[How to create the input overlay for leafless trees detection using the Inference Overlay]] | * [[How to create the input overlay for leafless trees detection using the Inference Overlay]] | ||
* [[How to train your own AI model for an Inference Overlay]] | * [[How to train your own AI model for an Inference Overlay]] | ||
* [[How to | * [[How to select specific input data for AI Inference]] | ||
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*[[Api session event editor overlay set neural network]] | |||
*[[Api session event editor overlay set tensor prequel]]. | |||
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{{InferenceOverlay nav}} | {{InferenceOverlay nav}} | ||
Latest revision as of 09:34, 9 October 2026
The Inference Module of the Inference Overlay is responsible for applying the convolution network on subsections of prequel grids configured for the Inference Overlay. The size of the subsection is defined by the input tensors of the neural network. The marching speed of this subsection is defined by the STRIDE_FRACTION attribute.
How-to's
- How to detect foliage using an Inference Overlay
- How to import trees based on an Inference Overlay
- How to create foliage height based on an Inference Overlay
- How to detect solar panels using an Inference Overlay
- How to create the input overlay for leafless trees detection using the Inference Overlay
- How to train your own AI model for an Inference Overlay
- How to select specific input data for AI Inference