Miscellaneous Fixes (0.17 beta) (#21336)

* fix coral docs

* add note about sub label object classification with person

* Catch OSError for deleting classification image

* add docs for dummy camera debugging

* add to sidebar

* fix formatting

* fix

* avx instructions are required for classification

* break text on classification card to prevent button overflow

* Ensure there is no NameError when processing

* Don't use region for state classification models

* fix spelling

* Handle attribute based models

* Catch case of non-trained model that doesn't add infinite number of classification images

* Actually train object classification models automatically

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

docs/docs/configuration/custom_classification/object_classification.md

@@ -11,6 +11,8 @@ Object classification models are lightweight and run very fast on CPU. Inference
 
 Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
 
+A CPU with AVX instructions is required for training and inference.
+
 ## Classes
 
 Classes are the categories your model will learn to distinguish between. Each class represents a distinct visual category that the model will predict.
@@ -35,6 +37,12 @@ For object classification:
   - Ideal when multiple attributes can coexist independently.
   - Example: Detecting if a `person` in a construction yard is wearing a helmet or not.
 
+:::note
+
+A tracked object can only have a single sub label. If you are using Face Recognition and you configure an object classification model for `person` using the sub label type, your sub label may not be assigned correctly as it depends on which enrichment completes its analysis first. Consider using the `attribute` type instead.
+
+:::
+
 ## Assignment Requirements
 
 Sub labels and attributes are only assigned when both conditions are met: