This sample demonstrates how to execute an inference of image classification networks like AlexNet and GoogLeNet using Asynchronous Inference Request API.
folder with images) to the Inference Engine plugin. The batch size of the network is set according to the number of read images. The batch mode is an independent attribute on the asynchronous mode. Asynchronous mode works efficiently with any batch size.
After that, the application starts inference for the first infer request and waits of 10th inference request execution being completed. The asynchronous mode might increase the throughput of the pictures.
When inference is done, the application outputs data to the standard output stream. You can place labels in .labels file near the model to get pretty output.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/IE_DG/Samples_Overview.md) section in Inference Engine Samples guide.
To run the sample, you need specify a model and image:
- you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.
> **NOTES**:
>
> - 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 Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md).
>
> - 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](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
>
> - The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
-c "<absolute_path>" Required for GPU, MYRIAD, HDDL custom kernels. Absolute path to the .xml config file with the kernels descriptions.
-d "<device>" Optional. Specify the target device to infer on (the list of available devices is shown below). Default value is CPU. Use "-d HETERO:<comma_separated_devices_list>" format to specify HETERO plugin. Sample will look for a suitable plugin for device specified.
You can do inference of an image using a trained [AlexNet network](https://docs.openvinotoolkit.org/latest/omz_models_model_alexnet.html) on GPU using the following command:
