This topic demonstrates how to run the Image Classification sample application, which performs inference using image classification networks such as AlexNet and GoogLeNet.
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.
To run the sample you can use AlexNet and GoogLeNet models that can be downloaded with the OpenVINO Model Downloader or other image classification models.
IMPORTANT: To run the sample, the model should be first converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.
For example, to perform inference of an AlexNet model (previously converted to the Inference Engine format) on CPU, use the following command:
By default the application outputs top-10 inference results. Add the -nt
option to the previous command to modify the number of top output results.
For example, to get the top-5 results on Intel® HD Graphics, use the following commands:
Upon the start-up the sample 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 and outputs data to the standard output stream.