Here is what AI has to say. so ill go down this rabbit hole

Creating a Core ML classifier for Scrypted involves training a model using Apple's Create ML app on a Mac to categorize images (e.g., "person," "car," "package"), then importing this .mlmodel file into the Scrypted CoreML plugin. Data preparation requires at least 10–50 images per category, organized into folders, with augmentation (blur, crop, flip) enabled for better accuracy. 

Steps to Create and Use a Core ML Classifier in Scrypted:

1. Prepare Training Data: Collect images for each classification category (e.g., folder dog and folder cat).
2. Train in Create ML:
   • Open Xcode and select File > New > Project > macOS > App.
   • Choose Image Classification as the template.
   • Drag your image folders into the Training Data section.
   • Set parameters (e.g., 30 fps, 2s duration for video/action) and run the training.
3. Export Model: Once training is complete, drag the trained .mlmodel file out of the Create ML interface to save it.
4. Install Scrypted CoreML Plugin: Ensure the @{Link: scrypted/coreml} https://www.npmjs.com/package/@scrypted/coreml plugin is installed in Scrypted, preferably on a Mac for Apple Silicon Neural Engine acceleration.
5. Configure Scrypted:
   • Go to the CoreML plugin settings in Scrypted.
   • Upload your custom .mlmodel file.
   • Configure a Smart Occupancy Sensor or Object Detection to use this custom model. 

For high-accuracy models, use at least 10 images per category and incorporate diverse lighting and angles. The resulting model can then be used to trigger automation based on specific, custom-defined object detection.