This topic demonstrates how to run the Image Classification sample application, which performs inference using image classification networks such as AlexNet and GoogLeNet.
NOTE: This topic describes usage of C++ implementation of the Image Classification Sample. For the Python* implementation, refer to Image Classification Python* Sample.
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.
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_channelsargument specified. For more information about the argument, refer to When to Specify Input Shapes section of Converting a Model Using General Conversion Parameters.
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 or other public or pre-trained image classification models. To download the pre-trained models, use the OpenVINO Model Downloader or go to 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 perform inference of an AlexNet model 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 GPU, use the following commands: