Publishing 2019 R1 content

[platform/upstream/dldt.git] / inference-engine / samples / hello_request_classification / README.md

diff --git a/inference-engine/samples/hello_request_classification/README.md b/inference-engine/samples/hello_request_classification/README.md
index 708fa81..fd8d35b 100644 (file)
--- a/inference-engine/samples/hello_request_classification/README.md
+++ b/inference-engine/samples/hello_request_classification/README.md
@@ -1,23 +1,26 @@
-# Hello Infer Request Classification Sample
+# Hello Infer Request Classification C++ Sample
 
 This topic describes how to run the Hello Infer Classification sample application.
-The sample is simplified version of [Image Classification Sample](./samples/classification_sample/README.md).
-It's intended to demonstrate using of new Infer Request API of Inference Engine in applications. Refer to 
-[Integrate with customer application New Request API](./docs/IE_DG/Integrate_with_customer_application_new_API.md) for details.
+The sample is simplified version of [Image Classification Sample](./inference-engine/samples/classification_sample/README.md).
+It demonstrates how to use the new Infer Request API of Inference Engine in applications. Refer to
+[Integrate the Inference Engine New Request API with Your Application](./docs/IE_DG/Integrate_with_customer_application_new_API.md) for details.
+
+> **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](./docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md).
 
 ## Running
 
-You can do inference on an image using a trained AlexNet network on Intel® Processors using the following command:
-```sh
-./hello_autoresize_classification <path_to_model>/alexnet_fp32.xml <path_to_image>/cat.bmp CPU
-```
+To run the sample, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](https://github.com/opencv/open_model_zoo/tree/2018/model_downloader) or go to [https://download.01.org/opencv/](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](./docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 
-### Outputs
+You can do inference on an image using a trained AlexNet network on CPU using the following command:
+```sh
+./hello_autoresize_classification <path_to_model>/alexnet_fp32.xml <path_to_image>/cat.bmp CPU
+```
 
-The application outputs top-10 inference results. 
+## Sample Output
 
+The application outputs top-10 inference results.
 
-## See Also 
+## See Also
 * [Using Inference Engine Samples](./docs/IE_DG/Samples_Overview.md)