openvino/samples/python/classification_sample_async/README.md

# Image Classification Async Python* Sample {#openvino_inference_engine_ie_bridges_python_sample_classification_sample_async_README}

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:

| Feature            | API                                                                                                                                                                                                                       | Description               |
| :----------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :------------------------ |
| 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](../hello_classification/README.md).

| Options                    | Values                                                                   |
| :------------------------- | :----------------------------------------------------------------------- |
| Validated Models           | [alexnet](@ref omz_models_model_alexnet)                                 |
| Model Format               | OpenVINO™ toolkit Intermediate Representation (.xml + .bin), ONNX (.onnx) |
| Supported devices          | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md)        |
| Other language realization | [C++](../../../samples/cpp/classification_sample_async/README.md)        |

## 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](../../../docs/OV_Runtime_UG/integrate_with_your_application.md) section of "Integrate OpenVINO™ Runtime with Your Application" guide.

## Running

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

```
python classification_sample_async.py -h
```

Usage message:

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

Options:
  -h, --help            Show this help message and exit.
  -m MODEL, --model MODEL
                        Required. Path to an .xml or .onnx file with a trained
                        model.
  -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:

- you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
- you can use images from the media files collection available at https://storage.openvinotoolkit.org/data/test_data.

> **NOTES**:
>
> - 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](../../../docs/MO_DG/prepare_model/convert_model/Converting_Model.md).
>
> - 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](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
>
> - The sample accepts models in ONNX format (.onnx) that do not require preprocessing.

### Example

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 classification_sample_async.py -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
```

## See Also

- [Integrate the OpenVINO™ Runtime with Your Application](../../../docs/OV_Runtime_UG/integrate_with_your_application.md)
- [Using OpenVINO™ Toolkit Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

[openvino.runtime.AsyncInferQueue]:https://docs.openvino.ai/2022.3/api/ie_python_api/_autosummary/openvino.runtime.AsyncInferQueue.html
[openvino.runtime.AsyncInferQueue.set_callback]:https://docs.openvino.ai/2022.3/api/ie_python_api/_autosummary/openvino.runtime.AsyncInferQueue.html#openvino.runtime.AsyncInferQueue.set_callback
[openvino.runtime.AsyncInferQueue.start_async]:https://docs.openvino.ai/2022.3/api/ie_python_api/_autosummary/openvino.runtime.AsyncInferQueue.html#openvino.runtime.AsyncInferQueue.start_async
[openvino.runtime.AsyncInferQueue.wait_all]:https://docs.openvino.ai/2022.3/api/ie_python_api/_autosummary/openvino.runtime.AsyncInferQueue.html#openvino.runtime.AsyncInferQueue.wait_all
[openvino.runtime.InferRequest.results]:https://docs.openvino.ai/2022.3/api/ie_python_api/_autosummary/openvino.runtime.InferRequest.html#openvino.runtime.InferRequest.results