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Hello Classification Python* Sample

This sample demonstrates how to do inference of image classification models using Synchronous Inference Request API.

Models with only 1 input and output are supported.

The following Python API is used in the application:

Feature

API

Description

Basic Infer Flow

[openvino.runtime.Core], openvino.runtime.Core.read_model , [openvino.runtime.Core.compile_model]

Common API to do inference

Synchronous Infer

openvino.runtime.CompiledModel.infer_new_request

Do synchronous inference

Model Operations

[openvino.runtime.Model.inputs], openvino.runtime.Model.outputs

Managing of model

Preprocessing

[openvino.preprocess.PrePostProcessor], openvino.preprocess.InputTensorInfo.set_element_type ,[openvino.preprocess.InputTensorInfo.set_layout], openvino.preprocess.InputTensorInfo.set_spatial_static_shape ,[openvino.preprocess.PreProcessSteps.resize], openvino.preprocess.InputModelInfo.set_layout ,[openvino.preprocess.OutputTensorInfo.set_element_type],[openvino.preprocess.PrePostProcessor.build]

Set image of the original size as input for a model with other input size. Resize and layout conversions will be performed automatically by the corresponding plugin just before inference

Options

Values

Validated Models

alexnet, googlenet-v1

Model Format

OpenVINO™ toolkit Intermediate Representation (.xml + .bin), ONNX (.onnx)

Supported devices

All

Other language realization

C++ , C

How It Works

At startup, the sample application reads command-line parameters, prepares input data, loads a specified model and image 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.

Running

python hello_classification.py <path_to_model> <path_to_image> <device_name>

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

  • you can use public or Intel’s pre-trained models from the Open Model Zoo. The models can be downloaded using the Model 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.

  • 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.

Example

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

python -m pip install openvino-dev[caffe,onnx,tensorflow2,pytorch,mxnet]

  1. Download a pre-trained model:

    omz_downloader --name alexnet

  2. 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

  1. Perform inference of banana.jpg using the alexnet model on a GPU, for example:

python hello_classification.py alexnet.xml banana.jpg 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: /models/alexnet/alexnet.xml
[ INFO ] Loading the model to the plugin
[ INFO ] Starting inference in synchronous mode
[ INFO ] Image path: /images/banana.jpg
[ INFO ] Top 10 results:
[ INFO ] class_id probability
[ INFO ] --------------------
[ INFO ] 954      0.9703885
[ INFO ] 666      0.0219518
[ INFO ] 659      0.0033120
[ INFO ] 435      0.0008246
[ INFO ] 809      0.0004433
[ INFO ] 502      0.0003852
[ INFO ] 618      0.0002906
[ INFO ] 910      0.0002848
[ INFO ] 951      0.0002427
[ INFO ] 961      0.0002213
[ INFO ]
[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool

See Also

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