This topic demonstrates how to run the Image Segmentation demo application, which does inference using image segmentation networks like FCN8.
Running
Running the application with the -h option yields the following usage message:
./segmentation_demo -h
InferenceEngine:
API version ............ <version>
Build .................. <number>
segmentation_demo [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.
-l "<absolute_path>" Required for MKLDNN (CPU)-targeted custom layers. Absolute path to a shared library with the kernels impl.
Or
-c "<absolute_path>" Required for clDNN (GPU)-targeted custom kernels. Absolute path to the xml file with the kernels desc.
-pp "<path>" Path to a plugin folder.
-d "<device>" Specify the target device to infer on: CPU, GPU, FPGA or MYRIAD is acceptable. The demo will look for a suitable plugin for a specified device (CPU by default).
-ni "<integer>" Number of iterations (default 1)
-pc Enables per-layer performance report
Running the application with the empty list of options yields the usage message given above and an error message.
You can use the following command to do inference on Intel® Processors on an image using a trained FCN8 network:
./segmentation_demo -i <path_to_image>/inputImage.bmp -m <path_to_model>/fcn8.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 are a segmented image (out.bmp).
How it works
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.
See Also