Any ideas on why my coreml model created with turicreate isn't working?

Pretty much brand new to ML here. I'm trying to create a hand-detection CoreML model using turicreate.

The dataset I'm using is from https://github.com/aurooj/Hand-Segmentation-in-the-Wild , which provides images of hands from an egocentric perspective, along with masks for the images. I'm following the steps in turicreate's "Data Preparation" (https://github.com/apple/turicreate/blob/master/userguide/object_detection/data-preparation.md) step-by-step to create the SFrame. Checking the contents of the variables throughout this process, there doesn't appear to be anything wrong.

Following data preparation, I follow the steps in the "Introductory Example" section of https://github.com/apple/turicreate/tree/master/userguide/object_detection

I get the hint of an error when turicreate is performing iterations to create the model. There doesn't appear to be any Loss at all, which doesn't seem right.

After the model is created, I try to test it with a test_data portion of the SFrame. The results of these predictions are just empty arrays though, which is obviously not right.

After exporting the model as a CoreML .mlmodel and trying it out in an app, it is unable to recognize anything (not surprisingly).

Me being completely new to model creation, I can't figure out what might be wrong. The dataset seems quite accurate to me. The only changes I made to the dataset were that some of the masks didn't have explicit file extensions (they are PNGs), so I added the .png extension. I also renamed the images to follow turicreate's tutorial formats (i.e. vid4frame025.image.png and vid4frame025.mask.0.png. Again, the SFrame creation process using this data seems correct at each step. I was able to follow the process with turicreate's tutorial dataset (bikes and cars) successfully. Any ideas on what might be going wrong?

I found the problem, and it basically stemmed from my unfamiliarity with Python.

In one part of the Data Preparation section, after creating bounding boxes out of the mask images, each annotation is assigned a 'label' indicating the type of object the annotation is meant to be. My data had a different name format than the tutorial's data, so rather than each annotation having 'label': 'bike', my annotations had 'label': 'vid4frame25`, 'label': 'vid4frame26', etc.

Correcting this such that each annotation has 'label': 'hand' seems to have corrected this (or at least it's creating a legitimate-seeming model so far).