Fix documentation links (#972)

<!-- Contributing guide:
https://github.com/openvinotoolkit/datumaro/blob/develop/CONTRIBUTING.md
-->

### Summary
- Fix broken documentation links

<!--
Resolves #111 and #222.
Depends on #1000 (for series of dependent commits).

This PR introduces this capability to make the project better in this
and that.

- Added this feature
- Removed that feature
- Fixed the problem #1234
-->

### How to test
<!-- Describe the testing procedure for reviewers, if changes are
not fully covered by unit tests or manual testing can be complicated.
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### Checklist
<!-- Put an 'x' in all the boxes that apply -->
- [ ] I have added unit tests to cover my changes.​
- [ ] I have added integration tests to cover my changes.​
- [ ] I have added the description of my changes into
[CHANGELOG](https://github.com/openvinotoolkit/datumaro/blob/develop/CHANGELOG.md).​
- [ ] I have updated the
[documentation](https://github.com/openvinotoolkit/datumaro/tree/develop/docs)
accordingly

### License

- [ ] I submit _my code changes_ under the same [MIT
License](https://github.com/openvinotoolkit/datumaro/blob/develop/LICENSE)
that covers the project.
  Feel free to contact the maintainers if that's a concern.
- [ ] I have updated the license header for each file (see an example
below).

```python
# Copyright (C) 2023 Intel Corporation
#
# SPDX-License-Identifier: MIT
```

Author: JihwanEom

README.md

@@ -15,10 +15,11 @@ CVAT annotations                             ---> Publication, statistics etc.
 ```
 <!--lint enable fenced-code-flag-->
 
-- [Getting started](https://openvinotoolkit.github.io/datumaro/docs/getting_started)
+- [Getting started](https://openvinotoolkit.github.io/datumaro/latest/docs/get-started/quick-start-guide)
+- [Level Up](https://openvinotoolkit.github.io/datumaro/latest/docs/level-up/basic_skills)
 - [Features](#features)
-- [User manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual)
-- [Developer manual](https://openvinotoolkit.github.io/datumaro/api)
+- [User manual](https://openvinotoolkit.github.io/datumaro/latest/docs/user-manual/how_to_use_datumaro)
+- [Developer manual](https://openvinotoolkit.github.io/datumaro/latest/docs/reference/datumaro/datumaro)
 - [Contributing](#contributing)
 
 ## Features
@@ -45,7 +46,7 @@ CVAT annotations                             ---> Publication, statistics etc.
     (`bboxes`, `masks`)
   - [YOLO](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data) (`bboxes`)
 
-  Other formats and documentation for them can be found [here](https://openvinotoolkit.github.io/datumaro/docs/user-manual/supported_formats).
+  Other formats and documentation for them can be found [here](https://openvinotoolkit.github.io/datumaro/latest/docs/data-formats/supported_formats).
 - Dataset building
   - Merging multiple datasets into one
   - Dataset filtering by a custom criteria:
@@ -85,10 +86,10 @@ CVAT annotations                             ---> Publication, statistics etc.
     - RISE for object detection
 
 > Check
-  [the design document](https://openvinotoolkit.github.io/datumaro/docs/design)
+  [the design document](https://openvinotoolkit.github.io/datumaro/latest/docs/explanation/architecture)
   for a full list of features.
 > Check
-  [the user manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual)
+  [the user manual](https://openvinotoolkit.github.io/datumaro/latest/docs/user-manual/how_to_use_datumaro)
   for usage instructions.
 
 ## Contributing
@@ -99,12 +100,12 @@ Feel free to
 [open an Issue](https://github.com/openvinotoolkit/datumaro/issues/new), if you
 think something needs to be changed. You are welcome to participate in
 development, instructions are available in our
-[contribution guide](https://openvinotoolkit.github.io/datumaro/docs/contributing).
+[contribution guide](https://github.com/openvinotoolkit/datumaro/blob/develop/contributing.md).
 
 ## Telemetry data collection note
 
 The [OpenVINO™ telemetry library](https://github.com/openvinotoolkit/telemetry/)
 is used to collect basic information about Datumaro usage.
 
 To enable/disable telemetry data collection please see the
-[guide](https://openvinotoolkit.github.io/datumaro/docs/user-manual/how_to_control_tm_data_collection/).
+[guide](https://openvinotoolkit.github.io/datumaro/latest/docs/user-manual/how_to_control_tm_data_collection).

datumaro/cli/util/__init__.py

@@ -107,7 +107,7 @@ def show_video_import_warning():
         "system setup changes (library version, OS, etc.). "
         "If you need stable results, consider splitting the video "
         "manually using instructions at: "
-        "https://openvinotoolkit.github.io/datumaro/docs/media_formats/"
+        "https://openvinotoolkit.github.io/datumaro/latest/docs/data-formats/media_formats"
     )
 
 

docs/source/docs/get-started/introduction.rst

@@ -43,7 +43,7 @@ Datumaro supports the following features:
     (`bboxes`, `masks`)
   - `YOLO <https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data>`_ (`bboxes`)
 
-  Other formats and documentation for them can be found `here <https://openvinotoolkit.github.io/datumaro/docs/user-manual/supported_formats>`_.
+  Other formats and documentation for them can be found `here <https://openvinotoolkit.github.io/datumaro/latest/docs/data-formats/supported_formats>`_.
 - Dataset building
 
   - Merging multiple datasets into one

notebooks/06_tiling.ipynb

@@ -32,7 +32,7 @@
     "    ├── ...\n",
     "```\n",
     "\n",
-    "However, for import compatibility, `obj.names` file must be added to `d6-dice/obj.names` filepath for import compatibility. This `obj.names` file includes the label names of the dataset, e.g., `[dice1, ..., dice6]`. Therefore, you can write it with the following simple code. Please see [Yolo Loose format](https://openvinotoolkit.github.io/datumaro/docs/formats/yolo/) for more details."
+    "However, for import compatibility, `obj.names` file must be added to `d6-dice/obj.names` filepath for import compatibility. This `obj.names` file includes the label names of the dataset, e.g., `[dice1, ..., dice6]`. Therefore, you can write it with the following simple code. Please see [Yolo Loose format](https://openvinotoolkit.github.io/datumaro/latest/docs/explanation/formats/yolo) for more details."
    ]
   },
   {

notebooks/08_e2e_example_yolo_ultralytics_trainer.ipynb

@@ -23,7 +23,7 @@
     "    ├── ...\n",
     "```\n",
     "\n",
-    "However, for import compatibility, `obj.names` file must be added to `d6-dice/obj.names` filepath for import compatibility. This `obj.names` file includes the label names of the dataset, e.g., `[dice1, ..., dice6]`. Therefore, you can write it with the following simple code. Please see [Yolo Loose format](https://openvinotoolkit.github.io/datumaro/docs/formats/yolo/) for more details."
+    "However, for import compatibility, `obj.names` file must be added to `d6-dice/obj.names` filepath for import compatibility. This `obj.names` file includes the label names of the dataset, e.g., `[dice1, ..., dice6]`. Therefore, you can write it with the following simple code. Please see [Yolo Loose format](https://openvinotoolkit.github.io/datumaro/latest/docs/explanation/formats/yolo) for more details."
    ]
   },
   {

notebooks/09_encrypt_dataset.ipynb

@@ -23,7 +23,7 @@
     "    ├── ...\n",
     "```\n",
     "\n",
-    "However, for import compatibility, `obj.names` file must be added to `d6-dice/obj.names` filepath for import compatibility. This `obj.names` file includes the label names of the dataset, e.g., `[dice1, ..., dice6]`. Therefore, you can write it with the following simple code. Please see [Yolo Loose format](https://openvinotoolkit.github.io/datumaro/docs/formats/yolo/) for more details."
+    "However, for import compatibility, `obj.names` file must be added to `d6-dice/obj.names` filepath for import compatibility. This `obj.names` file includes the label names of the dataset, e.g., `[dice1, ..., dice6]`. Therefore, you can write it with the following simple code. Please see [Yolo Loose format](https://openvinotoolkit.github.io/datumaro/latest/docs/explanation/formats/yolo) for more details."
    ]
   },
   {

notebooks/10_noisy_label_detection.ipynb

@@ -213,7 +213,7 @@
    "source": [
     "## Import statistics and analyze them to find the noisy label samples\n",
     "\n",
-    "In this step, we import the statistics obtained during the model training to find the noisy label samples. Because we set the output directory as `outputs` in the previous step, we you can find the dataset including statistics which is exported to [Datumaro format](https://openvinotoolkit.github.io/datumaro/docs/formats/datumaro/) at `outputs/logs/noisy_label_detection`. To make training loss dynamics analysis easier, we provide an interface called `dm.LossDynamicsAnalyzer`. You can get the top-k candidates for noisy label samples using it. Additionally, we can also plot the exponential moving average (EMA) training loss curves of top-k candidates and compare them with the average of samples in the dataset. The EMA training loss is a very strong criterion for noisy label samples. For more detail, please see [1].\n",
+    "In this step, we import the statistics obtained during the model training to find the noisy label samples. Because we set the output directory as `outputs` in the previous step, we you can find the dataset including statistics which is exported to [Datumaro format](https://openvinotoolkit.github.io/datumaro/latest/docs/explanation/formats/datumaro) at `outputs/logs/noisy_label_detection`. To make training loss dynamics analysis easier, we provide an interface called `dm.LossDynamicsAnalyzer`. You can get the top-k candidates for noisy label samples using it. Additionally, we can also plot the exponential moving average (EMA) training loss curves of top-k candidates and compare them with the average of samples in the dataset. The EMA training loss is a very strong criterion for noisy label samples. For more detail, please see [1].\n",
     "\n",
     "[1]: Zhou, Tianyi, Shengjie Wang, and Jeff Bilmes. \"Robust curriculum learning: from clean label detection to noisy label self-correction.\" International Conference on Learning Representations. 2021."
    ]

notebooks/11_validate.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Validate datasets\n",
     "\n",
-    "In this notebook example, we are going to generate the validation report, which contains types of anomaly data regarding to https://openvinotoolkit.github.io/datumaro/docs/user-manual/command-reference/validate. Through this, we can identify which data was inapplicable for DL workflow because it is broken or undefined. Moreover, we detect imbalanced, out-of-distributed, or rare samples together.\n",
+    "In this notebook example, we are going to generate the validation report, which contains types of anomaly data regarding to https://openvinotoolkit.github.io/datumaro/latest/docs/command-reference/context_free/validate. Through this, we can identify which data was inapplicable for DL workflow because it is broken or undefined. Moreover, we detect imbalanced, out-of-distributed, or rare samples together.\n",
     "\n",
     "## Prerequisite \n",
     "### Download COCO 2017 validation dataset \n",