Image Classification Async Python* Sample

This sample demonstrates how to do inference of image classification networks using Asynchronous Inference Request API.

Models with only 1 input and output are supported.

The following Inference Engine Python API is used in the application:




Asynchronous Infer

InferRequest.async_infer , InferRequest.wait , Blob.buffer

Do asynchronous inference

Custom Extension Kernels

IECore.add_extension , IECore.set_config

Load extension library and config to the device

Basic Inference Engine API is covered by Hello Classification Python* Sample.



Validated Models


Model Format

Inference Engine Intermediate Representation (.xml + .bin), ONNX (.onnx)

Supported devices


Other language realization


How It Works

At startup, the sample application reads command-line parameters, prepares input data, loads a specified model and image(s) to the Inference Engine plugin, performs synchronous inference, and processes output data, logging each step in a standard output stream.

You can see the explicit description of each sample step at Integration Steps section of “Integrate the Inference Engine with Your Application” guide.


Run the application with the -h option to see the usage message:

python <path_to_sample>/ -h

Usage message:

usage: [-h] -m MODEL -i INPUT [INPUT ...]
                                      [-l EXTENSION] [-c CONFIG] [-d DEVICE]
                                      [--labels LABELS] [-nt NUMBER_TOP]

  -h, --help            Show this help message and exit.
  -m MODEL, --model MODEL
                        Required. Path to an .xml or .onnx file with a trained
  -i INPUT [INPUT ...], --input INPUT [INPUT ...]
                        Required. Path to an image file(s).
  -l EXTENSION, --extension EXTENSION
                        Optional. Required by the CPU Plugin for executing the
                        custom operation on a CPU. Absolute path to a shared
                        library with the kernels implementations.
  -c CONFIG, --config CONFIG
                        Optional. Required by GPU or VPU Plugins for the
                        custom operation kernel. Absolute path to operation
                        description file (.xml).
  -d DEVICE, --device DEVICE
                        Optional. Specify the target device to infer on; CPU,
                        GPU, MYRIAD, HDDL or HETERO: is acceptable. The sample
                        will look for a suitable plugin for device specified.
                        Default value is CPU.
  --labels LABELS       Optional. Path to a labels mapping file.
  -nt NUMBER_TOP, --number_top NUMBER_TOP
                        Optional. Number of top results.

To run the sample, you need specify a model and image:


  • 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 ../../../../../docs/MO_DG/prepare_model/convert_model/ “Converting a Model Using General Conversion Parameters”.

  • 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.

  • The sample accepts models in ONNX format (.onnx) that do not require preprocessing.


  1. Download a pre-trained model using Model Downloader :

    python <path_to_omz_tools>/ --name alexnet
  2. If a model is not in the Inference Engine IR or ONNX format, it must be converted. You can do this using the model converter script:

python <path_to_omz_tools>/ --name alexnet
  1. Perform inference of car.bmp and cat.jpg using alexnet model on a GPU, for example:

python <path_to_sample>/ -m <path_to_model>/alexnet.xml -i <path_to_image>/car.bmp <path_to_image>/cat.jpg -d GPU

Sample Output

The sample application logs each step in a standard output stream and outputs top-10 inference results.

[ INFO ] Creating Inference Engine
[ INFO ] Reading the network: c:\openvino\deployment_tools\open_model_zoo\tools\downloader\public\alexnet\FP32\alexnet.xml
[ INFO ] Configuring input and output blobs
[ INFO ] Loading the model to the plugin
[ WARNING ] Image c:\images\car.bmp is resized from (637, 749) to (227, 227)
[ WARNING ] Image c:\images\cat.jpg is resized from (300, 300) to (227, 227)
[ INFO ] Starting inference in asynchronous mode
[ INFO ] Infer request 0 returned 0
[ INFO ] Image path: c:\images\car.bmp
[ INFO ] Top 10 results:
[ INFO ] classid probability
[ INFO ] -------------------
[ INFO ] 656     0.6645315
[ INFO ] 654     0.1121185
[ INFO ] 581     0.0698451
[ INFO ] 874     0.0334973
[ INFO ] 436     0.0259718
[ INFO ] 817     0.0173190
[ INFO ] 675     0.0109321
[ INFO ] 511     0.0109075
[ INFO ] 569     0.0083093
[ INFO ] 717     0.0063173
[ INFO ]
[ INFO ] Infer request 1 returned 0
[ INFO ] Image path: c:\images\cat.jpg
[ INFO ] Top 10 results:
[ INFO ] classid probability
[ INFO ] -------------------
[ INFO ] 876     0.1320105
[ INFO ] 435     0.1210389
[ INFO ] 285     0.0712640
[ INFO ] 282     0.0570528
[ INFO ] 281     0.0319335
[ INFO ] 999     0.0285931
[ INFO ] 94      0.0270323
[ INFO ] 36      0.0240510
[ INFO ] 335     0.0198461
[ INFO ] 186     0.0183939
[ INFO ]
[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool