This topic demonstrates how to run the Neural Style Transfer sample application, which performs inference of style transfer models.
NOTE: The OpenVINO™ toolkit does not include a pre-trained model to run the Neural Style Transfer sample. A public model from the Zhaw's Neural Style Transfer repository can be used. Read the Converting a Style Transfer Model from MXNet* topic from the Model Optimizer Developer Guide to learn about how to get the trained model and how to convert it to the Inference Engine format (*.xml + *.bin).
NOTE: By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with
--reverse_input_channelsargument specified. For more information about the argument, refer to When to Specify Input Shapes section of Converting a Model Using General Conversion Parameters.
Running the application with the
-h option yields the following usage message:
Running the application with the empty list of options yields the usage message given above and an error message.
To perform inference on an image using a trained model of NST network on Intel® CPUs, use the following command:
The application outputs an image (
out1.bmp) or a sequence of images (
out<N>.bmp) which are redrawn in style of the style transfer model used for sample.