69 lines
2.8 KiB
Markdown
69 lines
2.8 KiB
Markdown
# Image Classification Sample
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This topic demonstrates how to build and run the Image Classification sample application, which does
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inference using image classification networks like AlexNet and GoogLeNet.
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## Running
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Running the application with the <code>-h</code> option yields the following usage message:
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```sh
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./classification_sample -h
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InferenceEngine:
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API version ............ <version>
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Build .................. <number>
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classification_sample [OPTION]
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Options:
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-h
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Print a usage message.
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-i "<path1>" "<path2>"
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Required. Path to a folder with images or path to an image files: a .ubyte file for LeNet
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and a .bmp file for the other networks.
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-m "<path>"
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Required. Path to an .xml file with a trained model.
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-l "<absolute_path>"
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Optional. Absolute path to library with MKL-DNN (CPU) custom layers (*.so).
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Or
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-c "<absolute_path>"
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Optional. Absolute path to clDNN (GPU) custom layers config (*.xml).
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-pp "<path>"
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Path to a plugin folder.
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-d "<device>"
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Specify the target device to infer on; CPU, GPU, FPGA or MYRIAD is acceptable. Sample will look for a suitable plugin for device specified
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-nt "<integer>"
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Number of top results (default 10)
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-ni "<integer>"
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Number of iterations (default 1)
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-pc
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Enables per-layer performance report
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-p_msg
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Enables messages from a plugin
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```
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Running the application with the empty list of options yields the usage message given above and an error message.
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You can do inference on an image using a trained AlexNet network on Intel® Processors using the following command:
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```sh
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./classification_sample -i <path_to_image>/cat.bmp -m <path_to_model>/alexnet_fp32.xml
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```
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### Outputs
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By default the application outputs top-10 inference results.
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Add the <code>-nt</code> option to the previous command to modify the number of top output results.
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<br>For example, to get the top-5 results on Intel® HD Graphics, use the following commands:
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```sh
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./classification_sample -i <path_to_image>/cat.bmp -m <path_to_model>/alexnet_fp32.xml -nt 5 -d GPU
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```
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### How it works
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Upon the start-up the sample application reads command line parameters and loads a network and an image to the Inference
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Engine plugin. When inference is done, the application creates an
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output image and outputs data to the standard output stream.
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## See Also
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* [Using Inference Engine Samples](./docs/Inference_Engine_Developer_Guide/Samples_Overview.md)
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