Inference Module: Difference between revisions
Jump to navigation
Jump to search
No edit summary |
No edit summary |
||
| Line 4: | Line 4: | ||
* [[How to detect foliage using an Inference Overlay]] | * [[How to detect foliage using an Inference Overlay]] | ||
* [[How to import trees based on an Inference Overlay]] | * [[How to import trees based on an Inference Overlay]] | ||
* [[How to | * [[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 A.I. Inference]] | |||
}} | }} | ||
{{InferenceOverlay nav}} | {{InferenceOverlay nav}} | ||
Revision as of 14:36, 8 October 2025
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 A.I. Inference
