This topic demonstrates how to run the Image Segmentation demo application, which does inference using image segmentation networks like FCN8.
NOTE: This topic describes usage of C++ implementation of the Image Segmentation Demo. For the Python* implementation, refer to Image Segmentation Python* Demo.
Upon the start-up the demo application reads command line parameters and loads a network and an image to the Inference Engine plugin. When inference is done, the application creates an output image.
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.
NOTE: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.
You can use the following command to do inference on CPU on an image using a trained FCN8 network:
The application outputs are a segmented image (