Update model obtained from YOLOV8Detector to Apple MLPackage

[yageek]

Spoiler: I'm quite new in the area so if this question sounds too easy, I apologise 😅

Context

I trained a YOLOV8Detector model using a yolo_v8_xs_backbone backbone by following this tutorial on KerasCV Efficient Object Detection with YOLOV8 and KerasCV and training it on a different dataset

After some times, I was able to get predictions and visualise them as explained in the tutorial:

I would like to use this model inside an apple iOS application, so I used the coremltools package to convert it. However, it seems that the "outputs" produced by kerascv is not exactly the one expected by the apple world.

Keras prediction dictionary

Once the model is trained, I can ask for a prediction:

 images, y_true = next(iter(dataset.take(1)))
    y_pred = model.predict(images) // y_pred is a dictionary 

y_pred is a dictionary of with those keys ['boxes', 'confidence', 'classes', 'num_detections']

Valid Apple model

Using Netron, I can take a look at the shape of a model expected by the apple world

How can I modify/reshape the model generated from kerascv so instead of outputting dictionaries, I can have a model that outputs the confidence and coordinates answer as two separate outputs ?