Hello NV12 Input Classification C++ Sample

This sample demonstrates how to execute an inference of image classification models with images in NV12 color format using Synchronous Inference Request API.

The following C++ API is used in the application:




Node Operations


Get a layer name

Infer Request Operations

ov::InferRequest::set_tensor , ov::InferRequest::get_tensor

Operate with tensors


ov::preprocess::InputTensorInfo::set_color_format , ov::preprocess::PreProcessSteps::convert_element_type , ov::preprocess::PreProcessSteps::convert_color

Change the color format of the input data

Basic OpenVINO™ Runtime API is described in Hello Classification C++ sample.



Validated Models


Model Format

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

Validated images

An uncompressed image in the NV12 color format - *.yuv

Supported devices


Other language realization


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 a synchronous inference request object. When inference is done, the application outputs data to the standard output stream. You can place labels in the .labels file near the model to make an output look cleaner.

For more information, refer to the explicit description of each sample Integration Steps in the Integrate OpenVINO Runtime with Your Application guide.


To build the sample, please use the instructions available at Build the Sample Applications section in OpenVINO™ Toolkit Samples guide.


Before running the sample, specify a model and an image:

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, use one of the widely available tools such as FFmpeg or GStreamer. The following command shows how to convert an ordinary image into an uncompressed NV12 image, using FFmpeg:

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

To run the sample, use the following script:

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


  • Because the sample reads raw image files, 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 order of channels. If you trained your model to work with RGB order, you need to reconvert your model, using Model Optimizer with --reverse_input_channels argument specified. For more information about the argument, refer to the When to Reverse Input Channels section of Embedding Preprocessing Computation.

  • Before running the sample with a trained model, make sure that the model is converted to the OpenVINO Intermediate Representation (OpenVINO IR) format (*.xml + *.bin) by using Model Optimizer.

  • The sample accepts models in the 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,onnx,tensorflow2,pytorch,mxnet]
  2. Download a pre-trained model:

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

    omz_converter --name alexnet
  4. Perform inference of the NV12 image, using the alexnet model on a CPU, for example:

    hello_nv12_input_classification alexnet.xml car.yuv 300x300 CPU

Sample Output

The application outputs top-10 inference results.

[ 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