Retain previously trained weight while training new model Mask RCNN

Question

I previously have trained with Mask RCNN and generated crack1.h5. Now, with another dataset of crack images, I want to continue training the pre-trained crack1.h5 then generate crack_final.h5. In the code

model = modellib.MaskRCNN(mode="training", config=config,
                          model_dir=MODEL_DIR)

# Which weights to start with?
init_with = "coco"  # imagenet, coco, or last

if init_with == "imagenet":
    model.load_weights(model.get_imagenet_weights(), by_name=True)
elif init_with == "coco":
    # Load weights trained on MS COCO, but skip layers that
    # are different due to the different number of classesc
    # See README for instructions to download the COCO weights
    model.load_weights(COCO_MODEL_PATH, by_name=True,
                       exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",
                                "mrcnn_bbox", "mrcnn_mask"])
elif init_with == "last":
    # Load the last model you trained and continue training
    model.load_weights(model.find_last()[1], by_name=True)

I am confused as to which will I choose. Does the data in my crack1.h5 reset if I train with "coco"? I am afraid to lose the progress I have made. Thank you.