How to evaluate an AI model: Difference between revisions
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Created page with "{{editor steps | Create a validation project with a set of areas that can be used for validation. For example areas that define where trees are located with the attribute FOLIAGE_TYPE. | Create an Inference Overlay and run your model contained in the ONNX file. | Create a Combo Overlay that detects these validation areas and subtracts the results from the Inference Overlay. | The difference between these validation areas and Inference Overlay determine ho..." |
m Maxim@tygron.com moved page How to validate an AI model to How to evaluate an AI model without leaving a redirect |
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Revision as of 08:47, 15 October 2025
How to evaluate an AI model:
- Create a validation project with a set of areas that can be used for validation. For example areas that define where trees are located with the attribute FOLIAGE_TYPE.
- Create an Inference Overlay and run your model contained in the ONNX file.
- Create a Combo Overlay that detects these validation areas and subtracts the results from the Inference Overlay.
- The difference between these validation areas and Inference Overlay determine how good the AI model works.
- You can also use a TQL Queries to express this values in number, for example using SELECT_GRIDAREA_WHERE_GRID_IS_ID / SELECT_LANDPOLYGONS_WHERE_AREA_WITH_ATTRIBUTE_IS_FOLIAGE_TYPE.