This topic demonstrates how to run the Segmentation demo application, which does inference using image segmentation networks created with Object Detection API. Note that batch size 1 is supported only.
The demo has a post-processing part that gathers masks arrays corresponding to bounding boxes with high probability taken from the Detection Output layer. Then the demo produces picture with identified masks.
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.
You can use the following command to do inference on Intel® Processors on an image using a trained network:
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.
The application output is a segmented image (out.png).
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.