Proofreading pass 3

Author: sgolebiewski-intel

docs/articles_en/learn-openvino/openvino-samples/benchmark-tool.rst

@@ -213,9 +213,9 @@ For example, to run a benchmark on GPU, use:
          ./benchmark_app -m model.xml -d GPU
 
 
-You may also specify ``AUTO`` as the device, in which case the ``benchmark_app`` will
-automatically select the best device for benchmarking and support it with the
-CPU at the model loading stage. You can use ``AUTO`` when you aim for better performance.
+You may also specify ``AUTO`` as the device, to let ``benchmark_app``
+automatically select the best device for benchmarking and support it with
+CPU when loading the model. You can use ``AUTO`` when you aim for better performance.
 For more information, see the
 :doc:`Automatic device selection <../../openvino-workflow/running-inference/inference-devices-and-modes/auto-device-selection>` page.
 
@@ -279,8 +279,8 @@ the path to an image or a folder of images:
          ./benchmark_app -m model.xml -i test1.jpg
 
 
-The tool will repeatedly loop through the provided inputs and run inference on
-them for the specified amount of time or the number of iterations. If the ``-i``
+The tool will repeatedly loop through the provided inputs and run inference
+for the specified amount of time or the number of iterations. If the ``-i``
 flag is not used, the tool will automatically generate random data to fit the
 input shape of the model.
 
@@ -300,10 +300,9 @@ Advanced Usage
    By default, OpenVINO samples, tools 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 channel order in the sample or demo application or reconvert
-   your model using model conversion API with ``reverse_input_channels`` argument
-   specified. For more information about the argument, refer to When to Reverse
-   Input Channels section of Converting a Model to Intermediate Representation (IR).
-
+   your model.
+   For more information, refer to the **Color Conversion** section of
+   :doc:`Preprocessing API <../../openvino-workflow/running-inference/optimize-inference/optimize-preprocessing/preprocessing-api-details>`.
 
 Per-layer performance and logging
 +++++++++++++++++++++++++++++++++
@@ -312,27 +311,25 @@ The application also collects per-layer Performance Measurement (PM) counters fo
 each executed infer request if you enable statistics dumping by setting the
 ``-report_type`` parameter to one of the possible values:
 
-* ``no_counters`` report includes configuration options specified, resulting
-  FPS and latency.
-* ``average_counters`` report extends the ``no_counters`` report and additionally
-  includes average PM counters values for each layer from the network.
-* ``detailed_counters`` report extends the ``average_counters`` report and
+* ``no_counters`` - includes specified configuration options, resulting FPS and latency.
+* ``average_counters`` - extends the ``no_counters`` report and additionally
+  includes average PM counters values for each layer from the model.
+* ``detailed_counters`` - extends the ``average_counters`` report and
   additionally includes per-layer PM counters and latency for each executed infer request.
 
-Depending on the type, the report is stored in ``benchmark_no_counters_report.csv``,
+Depending on the type, the report is saved to the ``benchmark_no_counters_report.csv``,
 ``benchmark_average_counters_report.csv``, or ``benchmark_detailed_counters_report.csv``
-file located in the path specified in ``-report_folder``. The application also
-saves executable graph information serialized to an XML file if you specify a
-path to it with the ``-exec_graph_path`` parameter.
+file located in the path specified with ``-report_folder``. The application also
+saves executable graph information to an XML file, located in a folder
+specified with the ``-exec_graph_path`` parameter.
 
 .. _all-configuration-options-python-benchmark:
 
 All configuration options
 +++++++++++++++++++++++++
 
-Running the application with the ``-h`` or ``--help`` option yields the
-following usage message:
-
+Run the application with the ``-h`` or ``--help`` flags to get information on
+available options and parameters:
 
 .. tab-set::
 
@@ -604,8 +601,7 @@ following usage message:
                                              }
 
 
-
-Running the application with the empty list of options yields the usage message given above and an error message.
+The help information is also displayed when you run the application without any parameters.
 
 More information on inputs
 ++++++++++++++++++++++++++
@@ -625,18 +621,18 @@ Examples of Running the Tool
 ############################
 
 This section provides step-by-step instructions on how to run the Benchmark Tool
-with the ``asl-recognition`` Intel model on CPU or GPU devices. It uses random data as the input.
+with the ``asl-recognition`` Intel model on CPU or GPU devices. It uses random data as input.
 
 .. note::
 
    Internet access is required to execute the following steps successfully. If you
-   have access to the Internet through a proxy server only, please make sure that
-   it is configured in your OS environment.
+   have access to the Internet through a proxy server only, make sure
+   it is configured in your OS.
 
-Run the tool, specifying the location of the OpenVINO Intermediate Representation
-(IR) model ``.xml`` file, the device to perform inference on, and a performance hint.
-The following commands demonstrate examples of how to run the Benchmark Tool
-in latency mode on CPU and throughput mode on GPU devices:
+Run the tool, specifying the location of the ``.xml`` model file of OpenVINO Intermediate
+Representation (IR), the inference device and a performance hint.
+The following examples show how to run the Benchmark Tool
+on CPU and GPU in latency and throughput mode respectively:
 
 * On CPU (latency mode):
 
@@ -677,14 +673,15 @@ in latency mode on CPU and throughput mode on GPU devices:
 
 
 The application outputs the number of executed iterations, total duration of execution,
-latency, and throughput. Additionally, if you set the ``-report_type`` parameter,
-the application outputs a statistics report. If you set the ``-pc`` parameter,
-the application outputs performance counters. If you set ``-exec_graph_path``,
-the application reports executable graph information serialized. All measurements
-including per-layer PM counters are reported in milliseconds.
+latency, and throughput. Additionally, if you set the parameters:
+
+* ``-report_type`` - the application outputs a statistics report,
+* ``-pc`` - the application outputs performance counters,
+* ``-exec_graph_path`` - the application reports executable graph information serialized.
+
+All measurements including per-layer PM counters are reported in milliseconds.
 
-An example of the information output when running ``benchmark_app`` on CPU in
-latency mode is shown below:
+An example of running ``benchmark_app`` on CPU in latency mode and its output are shown below:
 
 .. tab-set::
 
@@ -826,11 +823,11 @@ latency mode is shown below:
          [ INFO ] Throughput:   91.12 FPS
 
 
-The Benchmark Tool can also be used with dynamically shaped networks to measure
+The Benchmark Tool can also be used with dynamically shaped models to measure
 expected inference time for various input data shapes. See the ``-shape`` and
 ``-data_shape`` argument descriptions in the :ref:`All configuration options <all-configuration-options-python-benchmark>`
-section to learn more about using dynamic shapes. Here is a command example for
-using ``benchmark_app`` with dynamic networks and a portion of the resulting output:
+section to learn more about using dynamic shapes. Below is an example of
+using ``benchmark_app`` with dynamic models and a portion of the resulting output:
 
 
 .. tab-set::