Hello NV12 Input Classification Sample

.. meta:: :description: Learn how to do inference of image classification models with images in NV12 color format using Synchronous Inference Request (C++) API.

This sample demonstrates how to execute an inference of image classification models with images in NV12 color format using Synchronous Inference Request API. Before using the sample, refer to the following requirements:

The sample accepts any file format supported by ov::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 reads command line parameters, loads the specified model and an image in the NV12 color format to an OpenVINO™ Runtime plugin. Then, the sample creates an synchronous inference request object. When inference is done, the application outputs data to the standard output stream. You can place labels in .labels file near the model to get pretty output.

.. tab-set::

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

  .. scrollbox::

     .. doxygensnippet:: samples/cpp/hello_nv12_input_classification/main.cpp
        :language: cpp

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

  .. scrollbox::

     .. doxygensnippet:: samples/c/hello_nv12_input_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:: C++ :sync: cpp

  .. code-block:: console

     hello_nv12_input_classification <path_to_model> <path_to_image> <image_size> <device_name>

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

  .. code-block:: console

     hello_nv12_input_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>__.

The sample accepts an uncompressed image in the NV12 color format. To run the sample, you need to convert your BGR/RGB image to NV12. To do this, you can use one of the widely available tools such as FFmpeg or GStreamer. Using FFmpeg and the following command, you can convert an ordinary image to an uncompressed NV12 image:

.. code-block:: sh

ffmpeg -i cat.jpg -pix_fmt nv12 cat.yuv

.. note::

Because the sample reads raw image files, you should provide a correct image size along with the image path. The sample expects the logical size of the image, not the buffer size. For example, for 640x480 BGR/RGB image the corresponding NV12 logical image size is also 640x480, whereas the buffer size is 640x720.
By default, this sample expects that model input has BGR channels order. If you trained your model to work with RGB order, you need to 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.

Example +++++++

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

.. code-block:: console

ovc ./models/alexnet

Perform inference of an NV12 image, using a model on a CPU, for example:

.. tab-set::

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

  .. code-block:: console

     hello_nv12_input_classification ./models/alexnet.xml ./images/cat.yuv 300x300 CPU

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

  .. code-block:: console

     hello_nv12_input_classification_c ./models/alexnet.xml ./images/cat.yuv 300x300 CPU

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

.. tab-set::

.. 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\alexnet.xml
     [ INFO ] model name: AlexNet
     [ INFO ]     inputs
     [ INFO ]         input name: data
     [ INFO ]         input type: f32
     [ INFO ]         input shape: {1, 3, 227, 227}
     [ INFO ]     outputs
     [ INFO ]         output name: prob
     [ INFO ]         output type: f32
     [ INFO ]         output shape: {1, 1000}

     Top 10 results:

     Image \images\car.yuv

     classid probability
     ------- -----------
     656     0.6668988
     654     0.1125269
     581     0.0679280
     874     0.0340229
     436     0.0257744
     817     0.0169367
     675     0.0110199
     511     0.0106134
     569     0.0083373
     717     0.0061734

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

  The application outputs top-10 inference results.

  .. code-block:: console

     Top 10 results:

     Image ./cat.yuv

     classid probability
     ------- -----------
     435       0.091733
     876       0.081725
     999       0.069305
     587       0.043726
     666       0.038957
     419       0.032892
     285       0.030309
     700       0.029941
     696       0.021628
     855       0.020339

     This sample is an API example, for any performance measurements please 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>
API Reference <https://docs.openvino.ai/2026/api/api_reference.html>__
Hello NV12 Input Classification C++ Sample on Github <https://github.com/openvinotoolkit/openvino/blob/master/samples/cpp/hello_nv12_input_classification/README.md>__
Hello NV12 Input Classification C Sample on Github <https://github.com/openvinotoolkit/openvino/blob/master/samples/c/hello_nv12_input_classification/README.md>__