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TensorFlow* Object Detection Mask R-CNNs Segmentation Demo

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

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

./mask_rcnn_demo -h
InferenceEngine: 
    API version ............ <version>
    Build .................. <number>

mask_rcnn_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)
    -detection_output_name "<string>" The name of detection output layer (default: detection_output)
    -masks_name "<string>" The name of masks layer (default: masks)
    -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 network:

./mask_rcnn_demo -i <path_to_image>/inputImage.bmp -m <path_to_model>/faster_rcnn.xml

Outputs

The application output is a segmented image (out.png).

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