How to use Module API for prediction?

How to use Module API for prediction?
all examples of Module API show usage over training and validation data iterators, which also happen to have a label. For genuine prediction, data doesn’t come with label - finding the label is actually the reason we do a model in the first place : )
How to run a prediction on a new sample?
SSD detection model instantiated with:

mod = mx.module.Module.load('model_algo_1', 0, False, label_names=['label'])
mod.bind(data_shapes=[( 'data', (1, 3, 500, 500))],
         label_shapes=[('label', (1, 1, 350))],
         for_training=False)   # no idea if this is correct. I guessed that part based on pieces of documentation here and there

How to run a prediction? mod.predict(...)?
predict seem to expect a DataIter. So should I do a DataIter without labels?

Here is some example code:

You have to define a metric and then you can call mod.score(). This function is expecting a DataIter. You can use this function to create an iterator from a rec-file:
DetRecordIter(path_imgrec, batch_size, data_shape, path_imglist=path_imglist, **cfg.valid)

The full code looks like this:

sym, arg_params, aux_params = mx.model.load_checkpoint('model_algo_1', 0)
mod = mx.mod.Module(symbol=sym, context=mx.cpu(), label_names=['label'])
mod.bind(for_training=False, data_shapes=[('data', (1,3,500,500))], label_shapes=[('label', (1, 1, 350))])
mod.set_params(arg_params, aux_params, allow_missing=True)

metric = MApMetric( )
eval_iter = DetRecordIter(path_imgrec, batch_size, data_shape,
                                     path_imglist=path_imglist, **cfg.valid)
results = mod.score(eval_iter, metric, num_batch=None,
                        batch_end_callback=mx.callback.Speedometer(batch_size, frequent=frequent, auto_reset=False))

mod.score() can get a metric result, like MAP. But if I want a boxes results, which I can use to plot the boxes in an image, how should I do ?