Publishing 2019 R1 content

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

diff --git a/inference-engine/samples/hello_shape_infer_ssd/README.md b/inference-engine/samples/hello_shape_infer_ssd/README.md
index f275abc..0f3846e 100644 (file)
--- a/inference-engine/samples/hello_shape_infer_ssd/README.md
+++ b/inference-engine/samples/hello_shape_infer_ssd/README.md
@@ -1,18 +1,22 @@
-# Hello Shape Infer Sample
+# Hello Shape Infer C++ Sample
 
 This topic demonstrates how to run the Hello Shape Infer SSD application, which does inference using object detection
 networks like SSD-VGG. The sample shows how to use [Shape Inference feature](./docs/IE_DG/ShapeInference.md).
 
+> **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 use the following command to do inference on Intel® Processors on an image using a trained SSD network:
+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).
+
+You can use the following command to do inference on CPU on an image using a trained SSD network:
 ```sh
 ./hello_shape_infer_ssd <path_to_model>/ssd_300.xml <path_to_image>/500x500.bmp CPU 3
 ```
 
-> **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
+## Sample Output
 
 The application renders an image with detected objects enclosed in rectangles. It outputs the list of classes
 of the detected objects along with the respective confidence values and the coordinates of the
@@ -20,3 +24,5 @@ rectangles to the standard output stream.
 
 ## See Also
 * [Using Inference Engine Samples](./docs/IE_DG/Samples_Overview.md)
+* [Model Downloader](https://github.com/opencv/open_model_zoo/tree/2018/model_downloader)
+* [Model Optimizer](./docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)