I made a workaround to allow resuming from a checkpoint saved in model_dir by manually calling tf.keras.models.load_weights({checkpoint_path}) on the model before starting to train again.
The quickest way if you want to try it is to install TFLiteModelMaker as source in pip and add:
model.load_weights({checkpoint_path}), in the train() function, just before the call to model.fit() in object_detector_spec.py