This topic demonstrates how to run the 3D Segmentation Demo, which demostrates how to set batch size dynamicly for certain infer request and check inference time difference.
Upon the start-up, the demo reads command-line parameters and loads a network and images to the Inference Engine plugin.
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_channels
argument specified. For more information about the argument, refer to When to Specify Input Shapes section of Converting a Model Using General Conversion Parameters.
Run the application with the -h
or --help
option to see the usage message:
The command yields the following usage message:
To run the sample, you can use public or pre-trained models. You can download the pre-trained models with the OpenVINO Model Downloader or from https://download.01.org/opencv/.
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.
For example, to do inference on images using a trained network with multiple outputs on CPU device with supported dynamic batch setting, run the following command:
The demo outputs a DOT file with a dumped graph.