Publishing 2019 R1.1 content and Myriad plugin sources (#162)

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

diff --git a/inference-engine/samples/validation_app/README.md b/inference-engine/samples/validation_app/README.md
index 11c9ac7..042150b 100644 (file)
--- a/inference-engine/samples/validation_app/README.md
+++ b/inference-engine/samples/validation_app/README.md
@@ -15,7 +15,7 @@ Possible use cases of the tool:
 * Use Validation Application as another sample: although the code is much more complex than in classification and object
   detection samples, the source code is open and can be re-used.
 
-> **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).
+> **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 Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](./docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md).
 
 ## Validation Application Options
 
@@ -59,7 +59,7 @@ The tool options are divided into two categories:
 
 ## General Workflow
 
-> **NOTE**: By default, Inference Engine samples expect input images to have BGR channels order. If you trained you model to work with images in RGB order, you need to manually rearrange the default channels order in the sample 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](./docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md#when_to_reverse_input_channels).
+> **NOTE**: By default, Inference Engine samples expect input images to have BGR channels order. If you trained you model to work with images in RGB order, you need to manually rearrange the default channels order in the sample 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 Reverse Input Channels](./docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md#when_to_reverse_input_channels).
 
 When executed, the Validation Application perform the following steps:
 
@@ -157,7 +157,7 @@ The correct way to use such dataset is to specify the path as `-i <path>/dataset
 ### Dataset Format for Object Detection (VOC-like)
 
 Object Detection SSD models can be inferred on the original dataset that was used as a testing dataset during the model training.
-To prepare the VOC dataset, follow the steps below :
+To prepare the VOC dataset, follow the steps below:
 
 1. Download the pre-trained SSD-300 model from the SSD GitHub* repository at
    [https://github.com/weiliu89/caffe/tree/ssd](https://github.com/weiliu89/caffe/tree/ssd).
@@ -167,7 +167,7 @@ To prepare the VOC dataset, follow the steps below :
   $wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
   tar -xvf VOCtest_06-Nov-2007.tar
   ```
-3. Convert the model with the [Model Optimizer](docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md).
+3. Convert the model with the [Model Optimizer](./docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md).
 
 4. Create a proper `.txt` class file from the original `labelmap_voc.prototxt`. The new file must be in
 the following format: