Image Classification Async Python* Sample

This sample demonstrates how to do inference of image classification models using Asynchronous Inference Request API. Models with only 1 input and output are supported.

The following Python API is used in the application:




Asynchronous Infer

openvino.runtime.AsyncInferQueue , openvino.runtime.AsyncInferQueue.set_callback , openvino.runtime.AsyncInferQueue.start_async , openvino.runtime.AsyncInferQueue.wait_all , openvino.runtime.InferRequest.results

Do asynchronous inference

Basic OpenVINO™ Runtime API is covered by Hello Classification Python* Sample.



Validated Models


Model Format

OpenVINO™ toolkit 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 OpenVINO™ Runtime 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 OpenVINO™ Runtime with Your Application” guide.


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

python -h

Usage message:

usage: [-h] -m MODEL -i INPUT [INPUT ...]
                                      [-d DEVICE]

  -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).
  -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.

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


  • By default, OpenVINO™ Toolkit 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 Embedding Preprocessing Computation.

  • Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (*.xml + *.bin) using the Model Optimizer tool.

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


  1. Install the openvino-dev Python package to use Open Model Zoo Tools:

    python -m pip install openvino-dev[caffe]
  2. Download a pre-trained model:

    omz_downloader --name alexnet
  3. If a model is not in the IR or ONNX format, it must be converted. You can do this using the model converter:

    omz_converter --name alexnet
  4. Perform inference of banana.jpg and car.bmp using the alexnet model on a GPU, for example:

    python -m alexnet.xml -i banana.jpg car.bmp -d GPU

Sample Output

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

[ INFO ] Creating OpenVINO Runtime Core
[ INFO ] Reading the model: C:/test_data/models/alexnet.xml
[ INFO ] Loading the model to the plugin
[ INFO ] Starting inference in asynchronous mode
[ INFO ] Image path: /test_data/images/banana.jpg
[ INFO ] Top 10 results:
[ INFO ] class_id probability
[ INFO ] --------------------
[ INFO ] 954      0.9707602
[ INFO ] 666      0.0216788
[ INFO ] 659      0.0032558
[ INFO ] 435      0.0008082
[ INFO ] 809      0.0004359
[ INFO ] 502      0.0003860
[ INFO ] 618      0.0002867
[ INFO ] 910      0.0002866
[ INFO ] 951      0.0002410
[ INFO ] 961      0.0002193
[ INFO ]
[ INFO ] Image path: /test_data/images/car.bmp
[ INFO ] Top 10 results:
[ INFO ] class_id probability
[ INFO ] --------------------
[ INFO ] 656      0.5120340
[ INFO ] 874      0.1142275
[ INFO ] 654      0.0697167
[ INFO ] 436      0.0615163
[ INFO ] 581      0.0552262
[ INFO ] 705      0.0304179
[ INFO ] 675      0.0151660
[ INFO ] 734      0.0151582
[ INFO ] 627      0.0148493
[ INFO ] 757      0.0120964
[ INFO ]
[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool