Refactor docs design & Add contents

docs/source/_static/css/custom.css

@@ -68,6 +68,71 @@
   --pst-icon-admonition-important: var(--pst-icon-exclamation-circle);
 }
 
+/* Main Block Width to 90% */
+.bd-page-width {
+  width: 90%;
+}
+
+@media (min-width: 960px) {
+  .bd-page-width {
+    max-width: 90%;
+  }
+}
+
+/* Reduce Sidebar Width to 20% */
+.bd-sidebar-primary {
+  background-color: var(--pst-color-background);
+  border-right: 1px solid var(--pst-color-border);
+  /* display:flex; */
+  flex: 0 0 20%;
+  flex-direction: column;
+  gap: 1rem;
+  max-height: calc(100vh - var(--pst-header-height));
+  max-width: 20%;
+  overflow-y: auto;
+  padding: 2rem 1rem 1rem;
+  position: sticky;
+  top: var(--pst-header-height);
+}
+
+/* Main Width to 100% */
+.bd-main .bd-content .bd-article-container {
+  display: flex;
+  flex-direction: column;
+  justify-content: start;
+  max-width: 100%;
+  overflow-x: auto;
+  padding: 1rem;
+  width: 100%;
+}
+
+/* Hide Section Navigation Title */
+nav.bd-links p.bd-links__title {
+  display: none;
+}
+
+/* Smaller current page side bar */
+.bd-sidebar-secondary {
+  background-color: var(--pst-color-background);
+  display: flex;
+  flex-direction: column;
+  flex-shrink: 0;
+  max-height: calc(100vh - var(--pst-header-height));
+  order: 2;
+  overflow-y: auto;
+  padding: 2rem 1rem 1rem;
+  position: sticky;
+  top: var(--pst-header-height);
+  /* width:var(--pst-sidebar-secondary); */
+}
+
+/* Hide Search Button */
+@media (min-width: 960px) {
+  .navbar-persistent--container {
+    display: none;
+  }
+}
+
 .navbar {
   background: #0095ca !important;
 }

docs/source/conf.py

@@ -71,6 +71,8 @@
 
 html_theme_options = {
     "navbar_center": [],
+    "navbar_end": ["search-field.html", "theme-switcher.html", "navbar-icon-links.html"],
+    "search_bar_text": "Search",
     "logo": {
         "image_light": "logos/otx-logo.png",
         "image_dark": "logos/otx-logo.png",

docs/source/guide/explanation/additional_features/auto_configuration.rst

@@ -1,6 +1,14 @@
 Auto-configuration
 ==================
 
+|
+
+.. figure:: ../../../../utils/images/auto_config.png
+   :align: center
+   :width: 100%
+
+|
+
 Auto-configuration for a deep learning framework means the automatic finding of the most appropriate settings for the training parameters, based on the dataset and the specific task at hand.
 Auto-configuration can help to save time, it eases the process of interaction with OpenVINO™ Training Extensions and gives a better baseline for the given dataset.
 

docs/source/guide/get_started/cli_commands.rst

@@ -9,6 +9,14 @@ All possible OpenVINO™ Training Extensions CLI commands are presented below al
     Also, by default, the OpenVINO™ Training Extensions CLI is written using jsonargparse, see jsonargparse or LightningCLI.
     Please refer `Jsonargparse Documentation <https://jsonargparse.readthedocs.io/en/v4.27.4/#configuration-files>`_
 
+|
+
+.. figure:: ../../../utils/images/cli.png
+   :align: center
+   :width: 100%
+
+|
+
 *****
 Help
 *****

docs/source/guide/get_started/introduction.rst

@@ -9,12 +9,20 @@ Introduction
 
 **OpenVINO™ Training Extensions** is a low-code transfer learning framework for Computer Vision.
 
-The CLI commands of the framework allows users to train, infer, optimize and deploy models easily and quickly even with low expertise in the deep learning field. OpenVINO™ Training Extensions offers diverse combinations of model architectures, learning methods, and task types based on `PyTorch <https://pytorch.org/>`_ , `Lightning <https://lightning.ai/>`_ and `OpenVINO™ toolkit <https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html>`_.
+The CLI commands of the framework or API allows users to train, infer, optimize and deploy models easily and quickly even with low expertise in the deep learning field. OpenVINO™ Training Extensions offers diverse combinations of model architectures, learning methods, and task types based on `PyTorch <https://pytorch.org/>`_ , `Lightning <https://lightning.ai/>`_ and `OpenVINO™ toolkit <https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html>`_.
 
 OpenVINO™ Training Extensions provide `recipe <https://github.com/openvinotoolkit/training_extensions/tree/develop/src/otx/recipe>`_ for every supported task type, which consolidates necessary information to build a model. Model templates are validated on various datasets and serve one-stop shop for obtaining the best models in general. If you are an experienced user, you can configure your own model based on `torchvision <https://pytorch.org/vision/stable/index.html>`_, `mmcv <https://github.com/open-mmlab/mmcv>`_ and `OpenVINO Model Zoo (OMZ) <https://github.com/openvinotoolkit/open_model_zoo>`_ frameworks.
 
 Furthermore, OpenVINO™ Training Extensions provides :doc:`automatic configuration <../explanation/additional_features/auto_configuration>` of task types and hyperparameters. The framework will identify the most suitable recipe based on your dataset, and choose the best hyperparameter configuration. The development team is continuously extending functionalities to make training as simple as possible so that single CLI command can obtain accurate, efficient and robust models ready to be integrated into your project.
 
+|
+
+.. figure:: ../../../utils/images/diagram_otx.png
+   :align: center
+   :width: 100%
+
+|
+
 ************
 Key Features
 ************