Disabled generation of ilastik object model that receives segmentation masks from one that receives pixel probabilities; this was causing inaccurate results

Generate an InstanceSegmentationModel from an IlastikObjectClassifierFromPixelPredictionsModel given binarization parameters

Removed PatchStackObjectClassifier, merged this behavior into IlastikObjectClassifierFromSegmentationModel

Raise error if ilastik data not embedded in project file; but it appears that most test classifiers fail this

Added superclass for instance segmentation models and modified ilastik object classification to inherit from it

Added superclass for semantic segmentation models and modified ilastik pixel classification to inherit from it

Delegated export of patch products to ZMaskObjectTable class; consolidated both batch runner and API-facing workflows into infer_object_map_from_zstack(); consolidated parameterization of export products into pydantic model

Validated non-duplication when loading ilastik model with same project file name, irrespective of path formatting

Made the one implemented ilastik object classification model accomodate only the ilastik workflow that requires pixel prediction maps

Start online validation of autogenerated model; but need to make inference model that takes binary inputs