Run Model Maker Object Detection TFLite model inference directly

[DoctorDinosaur]

I've trained a model in mediapipe model maker.

I want to run inference through tensorflow directly, on python, so I can use a Coral Edge TPU. Since it's a TFLite Model this should be possible.

But I'm struggling to get proper outputs.

For input, I resize to 256x256. I've tried normalisation in [0,255], [0,1] and [-1,1].

Running the signature function returns a dictionary of {detection_boxes, detection_scores} Where shape(detection_boxes) = (1, num_boxes, 4) and shape(detection_scores) = (1, num_boxes, num_classes)

However, the values I'm getting for detection_boxes are unnormalised and frequently negative. I've tried searching the repo for how decoding is done, and expected pre-processing on input, but its hard to traverse this repo.

Is there a minimal example of how to perform inference directly, and decode model output? Failing that, what model input is expected and what format are the output detection_boxes and detection_score?

(Code: https://gist.github.com/DoctorDinosaur/be495b6065fff29f79ec11306dd89c3b)