Fixed POT docs (openvinotoolkit#10574)

tools/pot/README.md

@@ -5,7 +5,7 @@
 .. toctree::
    :maxdepth: 1
    :hidden:
-   
+
    pot_InstallationGuide
    pot_docs_LowPrecisionOptimizationGuide
    pot_compression_algorithms_quantization_README
@@ -25,10 +25,10 @@ special methods without model retraining or fine-tuning, for example, post-train
 require a training dataset or a pipeline. To apply post-training algorithms from the POT, you need:
 * A floating-point precision model, FP32 or FP16, converted into the OpenVINO™ Intermediate Representation (IR) format
 and run on CPU with the OpenVINO™.
-* A representative calibration dataset representing a use case scenario, for example, 300 images. 
+* A representative calibration dataset representing a use case scenario, for example, 300 images.
 
 Figure below shows the optimization workflow:
-![](docs/images/workflow_simple.png) 
+![](docs/images/workflow_simple.png)
 
 ### Features
 
@@ -44,7 +44,7 @@ For benchmarking results collected for the models optimized with POT tool, see [
 POT is opensourced on GitHub as a part of OpenVINO and available at https://github.com/openvinotoolkit/openvino/tools/pot.
 
 Further documentation presumes that you are familiar with basic Deep Learning concepts, such as model inference,
-dataset preparation, model optimization, as well as with the OpenVINO™ toolkit and its components, such as  [Model Optimizer](@ref openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide) 
+dataset preparation, model optimization, as well as with the OpenVINO™ toolkit and its components, such as  [Model Optimizer](@ref openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide)
 and [Accuracy Checker Tool](@ref omz_tools_accuracy_checker).
 
 ## Get started
@@ -54,27 +54,27 @@ To install POT, follow the [Installation Guide](docs/InstallationGuide.md).
 
 ### Usage options
 
-![](docs/images/use_cases.png) 
+![](docs/images/use_cases.png)
 
 The POT provides three basic usage options:
 * **Command-line interface (CLI)**:
   * [**Simplified mode**](@ref pot_docs_simplified_mode):  use this option if the model belongs to the Computer Vision domain and you do have an unannotated dataset for optimization. Note that this optimization method can cause a deviation of model accuracy.
-  * [**Model Zoo flow**](@ref pot_compression_cli_README): this option is recommended if the model is imported from OpenVINO™ 
-[Model Zoo](https://github.com/openvinotoolkit/open_model_zoo) or there is a valid [Accuracy Checker Tool](@ref omz_tools_accuracy_checker_README)
-configuration file for the model that allows validating model accuracy using [Accuracy Checker Tool](@ref omz_tools_accuracy_checker_README).
+  * [**Model Zoo flow**](@ref pot_compression_cli_README): this option is recommended if the model is imported from OpenVINO™
+[Model Zoo](https://github.com/openvinotoolkit/open_model_zoo) or there is a valid [Accuracy Checker Tool](@ref omz_tools_accuracy_checker)
+configuration file for the model that allows validating model accuracy using [Accuracy Checker Tool](@ref omz_tools_accuracy_checker).
 * [**Python\* API**](@ref pot_compression_api_README): this option allows integrating the optimization methods implemented in POT into
-a Python* inference script that uses [OpenVINO Python* API](https://docs.openvino.ai/latest/openvino_inference_engine_ie_bridges_python_docs_api_overview.html). 
+a Python* inference script that uses [OpenVINO Python* API](https://docs.openvino.ai/latest/openvino_inference_engine_ie_bridges_python_docs_api_overview.html).
 
 
-POT is also integrated into [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench), a web-based graphical environment 
-that enables you to optimize, tune, analyze, visualize, and compare performance of deep learning models. 
+POT is also integrated into [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench), a web-based graphical environment
+that enables you to optimize, tune, analyze, visualize, and compare performance of deep learning models.
 
 ### Examples
 
 OpenVINO provides several examples to demonstrate the POT optimization workflow:
 
 * Command-line example:
-  * [Quantization of Image Classification model](https://docs.openvino.ai/latest/pot_configs_examples_README.html) 
+  * [Quantization of Image Classification model](https://docs.openvino.ai/latest/pot_configs_examples_README.html)
 * API tutorials:
   * [Quantization of Image Classification model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/301-tensorflow-training-openvino)
   * [Quantization of Object Detection model from Model Zoo](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/111-detection-quantization)
@@ -91,5 +91,5 @@ OpenVINO provides several examples to demonstrate the POT optimization workflow:
 
 * [Low Precision Optimization Guide](docs/LowPrecisionOptimizationGuide.md)
 * [Post-Training Optimization Best Practices](docs/BestPractices.md)
-* [POT Frequently Asked Questions](docs/FrequentlyAskedQuestions.md) 
+* [POT Frequently Asked Questions](docs/FrequentlyAskedQuestions.md)
 * [INT8 Quantization by Using Web-Based Interface of the DL Workbench](https://docs.openvino.ai/latest/workbench_docs_Workbench_DG_Int_8_Quantization.html)

tools/pot/configs/README.md

@@ -33,7 +33,7 @@ The main parameter is `"type"` which can take two possible options: `"accuracy_c
 - **Simplified mode** engines. These engines can be used only with `DefaultQuantization` algorithm to get a fully quantized model. They do not use the Accuracy Checker tool and annotation. In the case, of this mode the following parameters are applicable:
   - `"data_source"` Specifies the path to the directory​ where to calibration data is stored.
   - `"layout"` - (Optional) Layout of input data. Supported values: [`"NCHW"`, `"NHWC"`, `"CHW"`, `"CWH"`]​.
-- **Accuracy Checker** engine. It relies on the [Deep Learning Accuracy Validation Framework](@ref omz_tools_accuracy_checker_README) (Accuracy Checker) when inferencing DL models and working with datasets.
+- **Accuracy Checker** engine. It relies on the [Deep Learning Accuracy Validation Framework](@ref omz_tools_accuracy_checker) (Accuracy Checker) when inferencing DL models and working with datasets.
 The benefit of this mode is you can compute accuracy in case you have annotations.  When this mode is selected, you can use the accuracy aware algorithms family.
 There are two options to define engine parameters in this mode:
   - Refer to the existing Accuracy Checker configuration file which is represented by the YAML file. It can be a file used for full-precision model validation. In this case, you should define only the `"config"` parameter containing a path to the AccuracyChecker configuration file.
@@ -49,8 +49,8 @@ This section defines optimization algorithms and their parameters. For more deta
 
 ## Examples of the Configuration File
 
-For a quick start, many examples of configuration files are provided [here](https://github.com/openvinotoolkit/openvino/blob/master/tools/pot/configs/examples). There you can find ready-to-use configurations for the models from various domains: Computer Vision (Image 
- Classification, Object Detection, Segmentation), Natural Language Processing, Recommendation Systems. We basically 
+For a quick start, many examples of configuration files are provided [here](https://github.com/openvinotoolkit/openvino/blob/master/tools/pot/configs/examples). There you can find ready-to-use configurations for the models from various domains: Computer Vision (Image
+ Classification, Object Detection, Segmentation), Natural Language Processing, Recommendation Systems. We basically
  put configuration files for the models which require non-default configuration settings in order to get accurate results.
 For details on how to run the Post-Training Optimization Tool with a sample configuration file, see the [example](@ref pot_configs_examples_README).