Hello Classification Sample
===========================


.. meta::
   :description: Learn how to do inference of image classification
                 models using Synchronous Inference Request API (Python, C++, C).


This sample demonstrates how to do inference of image classification models using
Synchronous Inference Request API. Before using the sample, refer to the following requirements:

- Models with only one input and output are supported.
- The sample accepts any file format supported by ``core.read_model``.
- To build the sample, use instructions available at :ref:`Build the Sample Applications <build-samples>`
  section in "Get Started with Samples" guide.

How It Works
####################

At startup, the sample application sets log message capturing callback and reads command-line parameters.
Then it 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.

.. tab-set::

   .. tab-item:: Python
      :sync: python

      .. scrollbox::

         .. doxygensnippet:: samples/python/hello_classification/hello_classification.py
            :language: python

   .. tab-item:: C++
      :sync: cpp

      .. scrollbox::

         .. doxygensnippet:: samples/cpp/hello_classification/main.cpp
            :language: cpp

   .. tab-item:: C
      :sync: c

      .. scrollbox::

         .. doxygensnippet:: samples/c/hello_classification/main.c
            :language: c


You can see the explicit description of each sample step at
:doc:`Integration Steps <../../../openvino-workflow/running-inference>`
section of "Integrate OpenVINO™ Runtime with Your Application" guide.

Running
####################

.. tab-set::

   .. tab-item:: Python
      :sync: python

      .. code-block:: console

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

   .. tab-item:: C++
      :sync: cpp

      .. code-block:: console

         hello_classification <path_to_model> <path_to_image> <device_name>

   .. tab-item:: C
      :sync: c

      .. code-block:: console

         hello_classification_c <path_to_model> <path_to_image> <device_name>

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

- You can get a model specific for your inference task from one of model
  repositories, such as TensorFlow Zoo, HuggingFace, or TensorFlow Hub.
- You can use images from the media files collection available at
  `the storage <https://storage.openvinotoolkit.org/data/test_data>`__.

.. note::

   - 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 model conversion API with
     ``reverse_input_channels`` argument specified. For more information about
     the argument, refer to the **Color Conversion** section of
     :doc:`Preprocessing API <../../../openvino-workflow/running-inference/optimize-inference/optimize-preprocessing/preprocessing-api-details>`.
   - Before running the sample with a trained model, make sure the model is
     converted to the intermediate representation (IR) format (\*.xml + \*.bin)
     using the :doc:`model conversion API <../../../openvino-workflow/model-preparation/convert-model-to-ir>`.
   - The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
   - The sample supports NCHW model layout only.

Example
++++++++++++++++++++

1. Download a pre-trained model.
2. You can convert it by using:

   .. tab-set::

      .. tab-item:: Python
         :sync: python

         .. code-block:: python

            import openvino as ov

            ov_model = ov.convert_model('./models/alexnet')
            # or, when model is a Python model object
            ov_model = ov.convert_model(alexnet)

      .. tab-item:: CLI
         :sync: cli

         .. code-block:: console

            ovc ./models/alexnet

3. Perform inference of an image, using a model on a ``GPU``, for example:

   .. tab-set::

      .. tab-item:: Python
         :sync: python

         .. code-block:: console

            python hello_classification.py ./models/alexnet/alexnet.xml ./images/banana.jpg GPU

      .. tab-item:: C++
         :sync: cpp

         .. code-block:: console

            hello_classification ./models/googlenet-v1.xml ./images/car.bmp GPU

      .. tab-item:: C
         :sync: c

         .. code-block:: console

            hello_classification_c alexnet.xml ./opt/intel/openvino/samples/scripts/car.png GPU

Sample Output
#############

.. tab-set::

   .. tab-item:: Python
      :sync: python

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

      .. code-block:: console

         [ 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

   .. tab-item:: C++
      :sync: cpp

      The application outputs top-10 inference results.

      .. code-block:: console

         [ INFO ] OpenVINO Runtime version ......... <version>
         [ INFO ] Build ........... <build>
         [ INFO ]
         [ INFO ] Loading model files: /models/googlenet-v1.xml
         [ INFO ] model name: GoogleNet
         [ INFO ]     inputs
         [ INFO ]         input name: data
         [ INFO ]         input type: f32
         [ INFO ]         input shape: {1, 3, 224, 224}
         [ INFO ]     outputs
         [ INFO ]         output name: prob
         [ INFO ]         output type: f32
         [ INFO ]         output shape: {1, 1000}

         Top 10 results:

         Image /images/car.bmp

         classid probability
         ------- -----------
         656     0.8139648
         654     0.0550537
         468     0.0178375
         436     0.0165405
         705     0.0111694
         817     0.0105820
         581     0.0086823
         575     0.0077515
         734     0.0064468
         785     0.0043983

   .. tab-item:: C
      :sync: c

      The application outputs top-10 inference results.

      .. code-block:: console

         Top 10 results:

         Image /opt/intel/openvino/samples/scripts/car.png

         classid probability
         ------- -----------
         656       0.666479
         654       0.112940
         581       0.068487
         874       0.033385
         436       0.026132
         817       0.016731
         675       0.010980
         511       0.010592
         569       0.008178
         717       0.006336

         This sample is an API example, for any performance measurements use the dedicated benchmark_app tool.


Additional Resources
####################

- :doc:`Integrate the OpenVINO™ Runtime with Your Application <../../../openvino-workflow/running-inference>`
- :doc:`Get Started with Samples <get-started-demos>`
- :doc:`Using OpenVINO Samples <../openvino-samples>`
- :doc:`Convert a Model <../../../openvino-workflow/model-preparation/convert-model-to-ir>`
- `OpenVINO Runtime C API <https://docs.openvino.ai/2025/api/c_cpp_api/group__ov__c__api.html>`__
- `Hello Classification Python Sample on Github <https://github.com/openvinotoolkit/openvino/blob/master/samples/python/hello_classification/README.md>`__
- `Hello Classification C++ Sample on Github <https://github.com/openvinotoolkit/openvino/blob/master/samples/cpp/hello_classification/README.md>`__
- `Hello Classification C Sample on Github <https://github.com/openvinotoolkit/openvino/blob/master/samples/c/hello_classification/README.md>`__
