Neural Style Transfer Sample

This topic demonstrates how to build and run the Neural Style Transfer sample (NST sample) application, which does inference using models of style transfer topology.

Running

Running the application with the -h option yields the following usage message:

./style_transfer_sample --help
InferenceEngine:
    API version ............ <version>
    Build .................. <number>

style_transfer_sample [OPTION]
Options:

    -h                      Print a usage message.
    -i "<path>"             Required. Path to an .bmp image.
    -m "<path>"             Required. Path to an .xml file with a trained model.
    -pp "<path>"            Path to a plugin folder.
    -d "<device>"           Specify the target device to infer on; CPU, GPU, FPGA or MYRIAD is acceptable. Sample will look for a suitable plugin for device specified
    -ni "<integer>"         Number of iterations (default 1)
    -pc                     Enables per-layer performance report
    -mean_val_r,
    -mean_val_g,
    -mean_val_b             Mean values. Required if the model needs mean values for preprocessing and postprocessing

Running the application with the empty list of options yields the usage message given above and an error message.

You can do inference on an image using a trained model of NST network on Intel® Processors using the following command:

./style_transfer_sample -i <path_to_image>/cat.bmp -m <path_to_model>/1_decoder_FP32.xml

NOTE: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

Outputs

The application outputs an styled image(s) (out(1).bmp) which were redrawn in style of model which used for infer. Style of output images depend on models which use for sample.

See Also

• Using Inference Engine Samples