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Add docs to explain object assignment for classification
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@ -35,6 +35,15 @@ For object classification:
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- Ideal when multiple attributes can coexist independently.
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- Ideal when multiple attributes can coexist independently.
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- Example: Detecting if a `person` in a construction yard is wearing a helmet or not.
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- Example: Detecting if a `person` in a construction yard is wearing a helmet or not.
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## Assignment Requirements
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Sub labels and attributes are only assigned when both conditions are met:
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1. **Threshold**: Each classification attempt must have a confidence score that meets or exceeds the configured `threshold` (default: `0.8`).
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2. **Class Consensus**: After at least 3 classification attempts, 60% of attempts must agree on the same class label. If the consensus class is `none`, no assignment is made.
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This two-step verification prevents false positives by requiring consistent predictions across multiple frames before assigning a sub label or attribute.
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## Example use cases
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## Example use cases
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### Sub label
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### Sub label
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