Hello Autoresize Classification C++ Sample

This topic describes how to run the Hello Autoresize Classification sample application. The sample is simplified version of Image Classification Sample. It demonstrates how to use the new input autoresize API of Inference Engine in applications. Refer to Integrate the Inference Engine New Request API with Your Application for details.

There is also new API introduced to crop a ROI object and set it as input without additional memory re-allocation. To properly demonstrate this new API, it is required to run several networks in pipeline which is out of scope of this sample. Please refer to Object Detection for SSD Demo, Security Barrier Camera Demo, or Crossroad Camera Demo with an example of using of new crop ROI API.

NOTE: By default, Inference Engine 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 Specify Input Shapes section of Converting a Model Using General Conversion Parameters.


To run the sample, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO Model Downloader or go to https://download.01.org/opencv/.

NOTE: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

You can do inference on an image using a trained AlexNet network on CPU using the following command:

./hello_autoresize_classification <path_to_model>/alexnet_fp32.xml <path_to_image>/cat.bmp CPU

Sample Output

The application outputs top-10 inference results.

See Also