Feature/azaytsev/accuracy checker doc fixes (openvinotoolkit#1276)

* Fixed links

* Added an anchor

* Fixed link

* Minor fixes

* Fixed links

* Fixed links

* Fixed Note

Author: andrew-zaytsev

tools/accuracy_checker/README.md

@@ -131,7 +131,7 @@ models:
     - name: dataset_name
 ```
 Optionally you can use global configuration. It can be useful for avoiding duplication if you have several models which should be run on the same dataset.
-Example of global definitions file can be found [here](dataset_definitions.yml). Global definitions will be merged with evaluation config in the runtime by dataset name.
+Example of global definitions file can be found <a href="https://github.com/opencv/open_model_zoo/blob/master/tools/accuracy_checker/dataset_definitions.yml">here</a>. Global definitions will be merged with evaluation config in the runtime by dataset name.
 Parameters of global configuration can be overwritten by local config (e.g. if in definitions specified resize with destination size 224 and in the local config used resize with size 227, the value in config - 227 will be used as resize parameter)
 You can use field `global_definitions` for specifying path to global definitions directly in the model config or via command line arguments (`-d`, `--definitions`).
 
@@ -141,26 +141,26 @@ Launcher is a description of how your model should be executed.
 Each launcher configuration starts with setting `framework` name. Currently *caffe*, *dlsdk*, *mxnet*, *tf*, *tf_lite*, *opencv*, *onnx_runtime* supported. Launcher description can have differences.
 Please view:
 
-- [how to configure Caffe launcher](accuracy_checker/launcher/caffe_launcher_readme.md).
-- [how to configure DLSDK launcher](accuracy_checker/launcher/dlsdk_launcher_readme.md).
-- [how to configure OpenCV launcher](accuracy_checker/launcher/opencv_launcher_readme.md).
-- [how to configure MXNet Launcher](accuracy_checker/launcher/mxnet_launcher_readme.md).
-- [how to configure TensorFlow Launcher](accuracy_checker/launcher/tf_launcher_readme.md).
-- [how to configure TensorFlow Lite Launcher](accuracy_checker/launcher/tf_lite_launcher_readme.md).
-- [how to configure ONNX Runtime Launcher](accuracy_checker/launcher/onnx_runtime_launcher_readme.md).
-- [how to configure PyTorch Launcher](accuracy_checker/launcher/pytorch_launcher_readme.md)
+- [How to configure Caffe launcher](accuracy_checker/launcher/caffe_launcher_readme.md)
+- [How to configure DLSDK launcher](accuracy_checker/launcher/dlsdk_launcher_readme.md)
+- [How to configure OpenCV launcher](accuracy_checker/launcher/opencv_launcher_readme.md)
+- [How to configure MXNet Launcher](accuracy_checker/launcher/mxnet_launcher_readme.md)
+- [How to configure TensorFlow Launcher](accuracy_checker/launcher/tf_launcher_readme.md)
+- [How to configure TensorFlow Lite Launcher](accuracy_checker/launcher/tf_lite_launcher_readme.md)
+- [How to configure ONNX Runtime Launcher](accuracy_checker/launcher/onnx_runtime_launcher_readme.md)
+- [How to configure PyTorch Launcher](accuracy_checker/launcher/pytorch_launcher_readme.md)
 
 ### Datasets
 
 Dataset entry describes data on which model should be evaluated,
 all required preprocessing and postprocessing/filtering steps,
 and metrics that will be used for evaluation.
 
-If your dataset data is a well-known competition problem (COCO, Pascal VOC, ...) and/or can be potentially reused for other models
+If your dataset data is a well-known competition problem (COCO, Pascal VOC, and others) and/or can be potentially reused for other models
 it is reasonable to declare it in some global configuration file (*definition* file). This way in your local configuration file you can provide only
 `name` and all required steps will be picked from global one. To pass path to this global configuration use `--definition` argument of CLI.
 
-If you want to evaluate models using prepared config files and well-known datasets, you need to organize folders with validation datasets in a certain way. More detailed information about dataset preparation you can find in [Dataset Preparation Guide](../../datasets.md).
+If you want to evaluate models using prepared config files and well-known datasets, you need to organize folders with validation datasets in a certain way. More detailed information about dataset preparation you can find in <a href="https://github.com/opencv/open_model_zoo/blob/develop/datasets.md">Dataset Preparation Guide</a>.
 
 Each dataset must have:
 
@@ -175,7 +175,7 @@ And optionally:
 - `segmentation_masks_source` - path to directory where gt masks for semantic segmentation task stored.
 
 Also it must contain data related to annotation.
-You can convert annotation inplace using:
+You can convert annotation in-place using:
 - `annotation_conversion`: parameters for annotation conversion
 
 
@@ -216,8 +216,8 @@ will be picked from *definitions* file.
 You can find useful following instructions:
 
 - [how to convert annotations](accuracy_checker/annotation_converters/README.md)
-- [how to use preprocessings](accuracy_checker/preprocessor/README.md).
-- [how to use postprocessings](accuracy_checker/postprocessor/README.md).
+- [how to use preprocessing](accuracy_checker/preprocessor/README.md).
+- [how to use postprocessing](accuracy_checker/postprocessor/README.md).
 - [how to use metrics](accuracy_checker/metrics/README.md).
 - [how to use readers](accuracy_checker/data_readers/README.md).
 
@@ -249,4 +249,4 @@ Typical workflow for testing new model include:
 
 Standard Accuracy Checker validation pipeline: Annotation Reading -> Data Reading -> Preprocessing -> Inference -> Postprocessing -> Metrics.
 In some cases it can be unsuitable (e.g. if you have sequence of models). You are able to customize validation pipeline using own evaluator.
-More details about custom evaluations can be found in [related section](accuracy_checker/evaluators/custom_evaluators/README.md).
+More details about custom evaluations can be found in the [related section](accuracy_checker/evaluators/custom_evaluators/README.md).

tools/accuracy_checker/accuracy_checker/annotation_converters/README.md

@@ -9,15 +9,15 @@ Process of conversion can be implemented in two ways:
 * via configuration file
 * via command line
 
-### Describing annotation conversion in configuration file.
+## Describing Annotation Conversion in Configuration File
 
-Annotation conversion can be provided in `dataset` section your configuration file to convert annotation inplace before every evaluation.
+Annotation conversion can be provided in `dataset` section your configuration file to convert annotation in-place before every evaluation.
 Each conversion configuration should contain `converter` field filled selected converter name and provide converter specific parameters (more details in supported converters section). All paths can be prefixed via command line with `-s, --source` argument.
 
 You can additionally use optional parameters like:
 * `subsample_size` - Dataset subsample size. You can specify the number of ground truth objects or dataset ratio in percentage. Please, be careful to use this option, some datasets does not support subsampling. You can also specify `subsample_seed` if you want to generate subsample with specific random seed.
 * `annotation` - path to store converted annotation pickle file. You can use this parameter if you need to reuse converted annotation to avoid subsequent conversions.
-* `dataset_meta` - path to store mata information about converted annotation if it is provided.
+* `dataset_meta` - path to store meta information about converted annotation if it is provided.
 * `analyze_dataset` - flag which allow to get statistics about converted dataset. Supported annotations: `ClassificationAnnotation`, `DetectionAnnotation`, `MultiLabelRecognitionAnnotation`, `RegressionAnnotation`. Default value is False.
 
 Example of usage:
@@ -35,7 +35,7 @@ Example of usage:
    dataset_meta: sample_dataset.json
 ```
 
-### Conversing process via command line.
+## Conversing Process via Command Line
 
 The command line for annotation conversion looks like:
 
@@ -49,7 +49,7 @@ You may refer to `-h, --help` to full list of command line options. Some optiona
 * `-a, --annotation_name` - annotation file name.
 * `-m, --meta_name` - meta info file name.
 
-### Supported converters
+## Supported Converters
 
 Accuracy Checker supports following list of annotation converters and specific for them parameters:
 * `cifar` - converts [CIFAR](https://www.cs.toronto.edu/~kriz/cifar.html) classification dataset to `ClassificationAnnotation`
@@ -86,7 +86,7 @@ Accuracy Checker supports following list of annotation converters and specific f
   * `images_dir` - path to directory with images related to devkit root (default JPEGImages).
   * `mask_dir` - path to directory with ground truth segmentation masks related to devkit root (default SegmentationClass).
   * `dataset_meta_file` - path path to json file with dataset meta (e.g. label_map, color_encoding).Optional, more details in [Customizing dataset meta](#customizing-dataset-meta) section.
-**Note: since OpenVINO 2020.4 converter behaviour changed. `data_source` parameter of dataset should contains directory for images only, if you have segmentation mask in separated location, please use `segmentation_masks_source` for specifying gt masks location.**
+**Note**: Since OpenVINO 2020.4 the converter behaviour changed. `data_source` parameter of dataset should contains directory for images only, if you have segmentation mask in separated location, please use `segmentation_masks_source` for specifying gt masks location.
 * `mscoco_detection` - converts MS COCO dataset for object detection task to `DetectionAnnotation`.
   * `annotation_file` - path ot annotation file in json format.
   * `has_background` - allows convert dataset with/without adding background_label. Accepted values are True or False. (default is False).
@@ -307,11 +307,11 @@ Accuracy Checker supports following list of annotation converters and specific f
   * `images_dir` - path to images directory.
   * `masks_dir` - path to mask dataset to be used for inpainting (Optional).
 * `aflw2000_3d` - converts [AFLW2000-3D](http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm) dataset for 3d facial landmarks regression task to `FacialLandmarks3DAnnotation`.
-   * `data_dir` - directory, where input images and annotation files in matlab format stored.
+   * `data_dir` - directory, where input images and annotation files in MATLAB format stored.
 * `style_transfer` - converts images to `StyleTransferAnnotation`.
   * `images_dir` - path to images directory.
 
-### Customizing dataset meta
+## <a name="customizing-dataset-meta"></a>Customizing Dataset Meta
 There are situations when we need customize some default dataset parameters (e.g. replace original dataset label map with own.)
 You are able to overload parameters such as `label_map`, `segmentation_colors`, `backgound_label` using `dataset_meta_file` argument.
 dataset meta file is JSON file, which can contains following parameters:

tools/accuracy_checker/accuracy_checker/evaluators/custom_evaluators/README.md

@@ -21,22 +21,21 @@ Each custom evaluation config should start with keyword `evaluation` and contain
 Before running, please make sure that prefix to module added to your python path or use `python_path` parameter in config for it specification.
 Optionally you can provide `module_config` section which contains config for custom evaluator (Depends from realization, it can contains evaluator specific parameters).
 
-
 ## Examples
 * **Sequential Action Recognition Evaluator** demonstrates how to run Action Recognition models with encoder + decoder architecture.
-  [Evaluator code](sequential_action_recognition_evaluator.py)
+  <a href="https://github.com/opencv/open_model_zoo/blob/develop/tools/accuracy_checker/accuracy_checker/evaluators/custom_evaluators/sequential_action_recognition_evaluator.py">Evaluator code</a>.
   Configuration file examples:
-    * [action-recognition-0001-encoder](../../../configs/action-recognition-0001-encoder.yml) - running full pipeline of action recognition model.
-    * [action-recognition-0001-decoder](../../../configs/action-recognition-0001-decoder.yml) - running only decoder stage with dumped embeddings of encoder.
+    * <a href="https://github.com/opencv/open_model_zoo/blob/master/tools/accuracy_checker/configs/action-recognition-0001-encoder.yml">action-recognition-0001-encoder</a> - Running full pipeline of action recognition model.
+    * <a href="https://github.com/opencv/open_model_zoo/blob/master/tools/accuracy_checker/configs/action-recognition-0001-decoder.yml">action-recognition-0001-decoder</a> - Running only decoder stage with dumped embeddings of encoder.
 
 * **MTCNN Evaluator** shows how to run MTCNN model.
-  [Evaluator code](mtcnn_evaluator.py)
+  <a href="https://github.com/opencv/open_model_zoo/blob/develop/tools/accuracy_checker/accuracy_checker/evaluators/custom_evaluators/mtcnn_evaluator.py">Evaluator code</a>.
   Configuration file examples:
-    * [mtcnn-p](../../../configs/mtcnn-p.yml) - running proposal stage of MTCNN as usual model.
-    * [mtcnn-r](../../../configs/mtcnn-r.yml) - running only refine stage of MTCNN using dumped proposal stage results.
-    * [mtcnn-o](../../../configs/mtcnn-o.yml) - running full MTCNN pipeline.
+    * <a href="https://github.com/opencv/open_model_zoo/blob/master/tools/accuracy_checker/configs/mtcnn-p.yml">mtcnn-p</a> - Running proposal stage of MTCNN as usual model.
+    * <a href="https://github.com/opencv/open_model_zoo/blob/master/tools/accuracy_checker/configs/mtcnn-r.yml">mtcnn-r</a> - Running only refine stage of MTCNN using dumped proposal stage results.
+    * <a href="https://github.com/opencv/open_model_zoo/blob/master/tools/accuracy_checker/configs/mtcnn-o.yml">mtcnn-o</a> - Running full MTCNN pipeline.
 
-* **Text Spotting Evaluator** demonstrates how to evaluate text-spotting-0002 model via Accuracy Checker.
-  [Evaluator code](text_spotting_evaluator.py)
+* **Text Spotting Evaluator** demonstrates how to evaluate the `text-spotting-0002` model via Accuracy Checker.
+  <a href="https://github.com/opencv/open_model_zoo/blob/develop/tools/accuracy_checker/accuracy_checker/evaluators/custom_evaluators/text_spotting_evaluator.py">Evaluator code</a>.
   Configuration file examples:
-    * [text-spotting-0002](../../../configs/text-spotting-0002.yml)
+    * <a href="https://github.com/opencv/open_model_zoo/blob/master/tools/accuracy_checker/configs/text-spotting-0002.yml">text-spotting-0002</a>.

tools/accuracy_checker/sample/README.md

@@ -7,8 +7,8 @@ We will try to evaluate **SampLeNet** topology as an example.
 
 ### 1. Download and extract dataset
 
-In this sample we will use toy dataset which we refer to as *sample dataset*, which contains 10k images 
-of 10 different classes (classification problem), which is actually CIFAR10 dataset converted to png (image conversion will be done automatically in evaluation process)
+In this sample we will use toy dataset which we refer to as *sample dataset*, which contains 10K images 
+of 10 different classes (classification problem), which is actually CIFAR10 dataset converted to PNG (image conversion will be done automatically in evaluation process)
 
 You can download original CIFAR10 dataset from [official website](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz).
 
@@ -21,8 +21,8 @@ tar xvf cifar-10-python.tar.gz -C sample
 
 ### 2. Evaluate sample topology
 
-Typically you need to write configuration file, describing evaluation process of your topology.
-There is already config file for evaluating SampLeNet using OpenVINO framework, read it carefully. It runs Caffe model using Model Optimizer which requires installed Caffe. If you have not opportunity to use Caffe, please replace `caffe_model` and `caffe_weights` on
+Typically you need to write a configuration file describing evaluation process of your topology.
+There is already a config file for evaluating SampLeNet using OpenVINO framework, read it carefully. It runs Caffe model using Model Optimizer which requires installed Caffe. If you have not opportunity to use Caffe, please replace `caffe_model` and `caffe_weights` on
 
 ```yaml
 model: SampleNet.xml
@@ -39,9 +39,9 @@ If everything worked correctly, you should be able to get `75.02%` accuracy.
 
 Now try edit config, to run SampLeNet on other device or framework (e.g. Caffe, MXNet or OpenCV), or go directly to your topology!
 
-
 ###  Additional useful resources
 
-* [config](opencv_sample_config.yml) for running SampleNet via [OpenCV launcher](../accuracy_checker/launcher/opencv_launcher_readme.md)
-* [config](sample_blob_config.yml) for running SampleNet using compiled executable network blob.
-**Note: Not all OpenVINO plugins support compiled network blob execution**
+* <a href="https://github.com/opencv/open_model_zoo/blob/master/tools/accuracy_checker/sample/opencv_sample_config.yml">config</a>() for running SampleNet via [OpenCV launcher](../accuracy_checker/launcher/opencv_launcher_readme.md)
+* <a href="https://github.com/opencv/open_model_zoo/blob/master/tools/accuracy_checker/sample/sample_blob_config.yml">config</a> for running SampleNet using compiled executable network blob.
+
+>**NOTE**: Not all Inference Engine plugins support compiled network blob execution.

tools/downloader/README.md

@@ -307,13 +307,13 @@ To do this, use the `--dry_run` option:
 See the "Shared options" section for information on other options accepted by
 the script.
 
-Model quantizer usage
+Model Quantizer Usage
 ---------------------
 
 Before you run the model quantizer, you must prepare a directory with
 the datasets required for the quantization process. This directory will be
 referred to as `<DATASET_DIR>` below. You can find more detailed information
-about dataset preparation in the [Dataset Preparation Guide](../../datasets.md).
+about dataset preparation in the <a href="https://github.com/opencv/open_model_zoo/blob/develop/datasets.md">Dataset Preparation Guide</a>.
 
 The basic usage is to run the script like this: