Updating documentation (cvat-ai#3800)

* update docs

* update README.md

* fix mistake

* update CHANGELOG.md and fix remark issues

* cancel change backup_guide.md

CHANGELOG.md

@@ -13,6 +13,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - interactor: add HRNet interactive segmentation serverless function (<https://github.com/openvinotoolkit/cvat/pull/3740>)
 - Added GPU implementation for SiamMask, reworked tracking approach (<https://github.com/openvinotoolkit/cvat/pull/3571>)
 - Progress bar for manifest creating (<https://github.com/openvinotoolkit/cvat/pull/3712>)
+- Add a tutorial on attaching cloud storage AWS-S3 (<https://github.com/openvinotoolkit/cvat/pull/3745>)
+  and Azure Blob Container (<https://github.com/openvinotoolkit/cvat/pull/3778>)
 
 ### Changed
 

README.md

@@ -86,7 +86,7 @@ For more information about supported formats look at the
 | [Object reidentification](/serverless/openvino/omz/intel/person-reidentification-retail-300/nuclio)     | reid       | OpenVINO   | X   |     |
 | [Semantic segmentation for ADAS](/serverless/openvino/omz/intel/semantic-segmentation-adas-0001/nuclio) | detector   | OpenVINO   | X   |     |
 | [Text detection v4](/serverless/openvino/omz/intel/text-detection-0004/nuclio)                          | detector   | OpenVINO   | X   |     |
-| [SiamMask](/serverless/pytorch/foolwood/siammask/nuclio)                                                | tracker    | PyTorch    | X   |     |
+| [SiamMask](/serverless/pytorch/foolwood/siammask/nuclio)                                                | tracker    | PyTorch    | X   | X   |
 | [f-BRS](/serverless/pytorch/saic-vul/fbrs/nuclio)                                                       | interactor | PyTorch    | X   |     |
 | [HRNet](/serverless/pytorch/saic-vul/hrnet/nuclio)                                                      | interactor | PyTorch    |     | X   |
 | [Inside-Outside Guidance](/serverless/pytorch/shiyinzhang/iog/nuclio)                                   | interactor | PyTorch    | X   |     |

site/content/en/docs/administration/advanced/installation_automatic_annotation.md

@@ -38,7 +38,7 @@ description: 'Information about the installation of components needed for semi-a
   wget https://github.com/nuclio/nuclio/releases/download/<version>/nuctl-<version>-linux-amd64
   ```
 
-  After downloading the nuclio, give it a proper permission and do a softlink
+  After downloading the nuclio, give it a proper permission and do a softlink.
 
   ```
   sudo chmod +x nuctl-<version>-linux-amd64
@@ -94,7 +94,7 @@ description: 'Information about the installation of components needed for semi-a
   - The number of GPU deployed functions will be limited to your GPU memory.
   - See [deploy_gpu.sh](https://github.com/openvinotoolkit/cvat/blob/develop/serverless/deploy_gpu.sh)
     script for more examples.
-  - For some models (namely [SiamMask](/docs/manual/advanced/ai-tools#trackers) you need an [Nvidia driver](https://www.nvidia.com/en-us/drivers/unix/)
+  - For some models (namely [SiamMask](/docs/manual/advanced/ai-tools#trackers)) you need an [Nvidia driver](https://www.nvidia.com/en-us/drivers/unix/)
     version greater than or equal to 450.80.02.
 
   **Note for Windows users:**

site/content/en/docs/administration/advanced/mounting_cloud_storages.md

@@ -205,9 +205,9 @@ Follow the first 7 mounting steps above.
 
 1. Edit `/etc/fstab` with the blobfuse script. Add the following line(replace paths):
 
-```bash
-/absolute/path/to/azure_fuse </path/to/desired/mountpoint> fuse allow_other,user,_netdev
-```
+   ```bash
+   /absolute/path/to/azure_fuse </path/to/desired/mountpoint> fuse allow_other,user,_netdev
+   ```
 
 ##### <a name="azure_using_systemd">Using systemd</a>
 

site/content/en/docs/getting_started.md

@@ -8,28 +8,34 @@ This section contains basic information and links to sections necessary for a qu
 
 ## Installation
 
-First step is to install CVAT on your system. Use the [Installation Guide](/docs/administration/basics/installation/).
+First step is to install CVAT on your system:
+- [Installation on Ubuntu](/docs/administration/basics/installation/#ubuntu-1804-x86_64amd64)
+- [Installation on Windows 10](/docs/administration/basics/installation/#windows-10)
+- [Installation on Mac OS](/docs/administration/basics/installation/#mac-os-mojave)
 
-## Getting started in CVAT
+To learn how to create a superuser and log in to CVAT,
+go to the [authorization](/docs/manual/basics/authorization/) section.
 
-To find out more, go to the [authorization](/docs/manual/basics/authorization/) section.
+## Getting started in CVAT
 
 To create a task, go to `Tasks` section. Click `Create new task` to go to the task creation page.
 
 Set the name of the future task.
 
-Set the label using the constructor: first click "add label", then enter the name of the label and choose the color.
+Set the label using the constructor: first click `Add label`, then enter the name of the label and choose the color.
 
 ![](/images/create_a_new_task.gif)
 
 You need to upload images or videos for your future annotation. To do so, simply drag and drop the files.
 
 To learn more, go to [creating an annotation task](/docs/manual/basics/creating_an_annotation_task/)
 
-## Basic annotation
+## Annotation
+
+### Basic
 
 When the task is created, you will see a corresponding message in the top right corner.
-Click the "Open task" button to go to the task page.
+Click the `Open task` button to go to the task page.
 
 Once on the task page, open a link to the job in the jobs list.
 
@@ -44,16 +50,24 @@ Choose a correct section for your type of the task and start annotation.
 | Cuboids   | [Annotation with cuboids](/docs/manual/advanced/annotation-with-cuboids/)                  | [Editing the cuboid](/docs/manual/advanced/annotation-with-cuboids/editing-the-cuboid/)              |
 | Tag       | [Annotation with tags](/docs/manual/advanced/annotation-with-tags/)                        |                                                                                                     |
 
+### Advanced
+
+In CVAT there is the possibility of using automatic and semi-automatic annotation what gives
+you the opportunity to speed up the execution of the annotation:
+- [OpenCV tools](/docs/manual/advanced/opencv-tools/) - tools included in CVAT by default.
+- [AI tools](/docs/manual/advanced/ai-tools/) - tools requiring installation.
+- [Automatic annotation](/docs/manual/advanced/automatic-annotation/) - automatic annotation with using DL models.
+
 ## Dump annotation
 
 ![](/images/image028.jpg)
 
 1. To download the annotations, first you have to save all changes.
-   Click the Save button or press `Ctrl+S`to save annotations quickly.
+   Click the `Save` button or press `Ctrl+S`to save annotations quickly.
 
-2. After you saved the changes, click the Menu button.
+2. After you saved the changes, click the `Menu` button.
 
-3. Then click the Dump Annotation button.
+3. Then click the `Dump Annotation` button.
 
 4. Lastly choose a format of the dump annotation file.
 

site/content/en/docs/manual/advanced/models.md

@@ -4,6 +4,10 @@ linkTitle: 'Models'
 weight: 13
 ---
 
+To deploy the models, you will need to install the necessary components using
+[Semi-automatic and Automatic Annotation guide](/docs/administration/advanced/installation_automatic_annotation/).
+To learn how to deploy the model, read [Serverless tutorial](/docs/manual/advanced/serverless-tutorial/).
+
 The Models page contains a list of deep learning (DL) models deployed for semi-automatic and automatic annotation.
 To open the Models page, click the Models button on the navigation bar.
 The list of models is presented in the form of a table. The parameters indicated for each model are the following:
@@ -20,5 +24,3 @@ The list of models is presented in the form of a table. The parameters indicated
 - `Labels` - list of the supported labels (only for the models of the `detectors` type)
 
 ![](/images/image099.jpg)
-
-Read how to install your model [here](/docs/administration/basics/installation/#semi-automatic-and-automatic-annotation).