Fix wrong example of Datumaro dataset creation in document

CHANGELOG.md

@@ -12,6 +12,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Add ImageColorScale context manager
   (<https://github.com/openvinotoolkit/datumaro/pull/1194>)
 
+### Bug fixes
+- Fix wrong example of Datumaro dataset creation in document
+  (<https://github.com/openvinotoolkit/datumaro/pull/1195>)
+
 ## 16/11/2023 - Release 1.5.1
 ### Enhancements
 - Enhance Datumaro data format stream importer performance

docs/source/docs/command-reference/context_free/explorer.md

@@ -8,7 +8,7 @@ Explorer is a feature that operates on hash basis. Once you put dataset that use
 
 To explore similar data in dataset, you need to set query first. Query could be image, text, list of images, list of texts and list of images and texts. The query does not need to be an image that exists in the dataset. You can put in any data that you want to explore similar dataset. And you need to set top-k that how much you want to find similar data. The default value for top-k is 50, so if you hope to find more smaller results, you would set top-k. For single query, we computed hamming distance of hash between whole dataset and query. And we sorted those distance and select top-k data which have short distance. For list query, we repeated computing distance for each query and select top-k data based on distance among all dataset.
 
-The command can be applied to a dataset. And if you want to use multiple dataset as database, you could use merged dataset. The current project (`-p/--project`) is also used a context for plugins, so it can be useful for dataset paths having custom formats. When not specified, the current project's working tree is used. To save visualized result (`-s/--save`) is turned off as default. This visualized result is based on [Visualizer](../../jupyter_notebook_examples/visualizer).
+The command can be applied to a dataset. And if you want to use multiple dataset as database, you could use merged dataset. The current project (`-p/--project`) is also used a context for plugins, so it can be useful for dataset paths having custom formats. When not specified, the current project's working tree is used. To save visualized result (`-s/--save`) is turned off as default. This visualized result is based on [Visualizer](../../jupyter_notebook_examples/notebooks/03_visualize).
 
 Usage:
 ```console

docs/source/docs/data-formats/datumaro_format.md

@@ -4,8 +4,7 @@ So far, in the field of computer vision, there are various tasks such as classif
 and segmentation, as well as pose estimation and visual tracking, and public data is used by providing
 a format suitable for each task. Even within the same segmentation task, some data formats provide
 annotation information as polygons, while others provide mask form. In order to ensure compatibility
-with different tasks and formats, we provide a novel Datumaro format with `.json` ([Datumaro](../explanation/formats/datumaro.md)) or `.datum` ([DatumaroBinary](../explanation/formats/datumaro.md))
-extensions.
+with different tasks and formats, we provide a novel Datumaro format with `.json` ([Datumaro](./formats/datumaro)) or `.datum` ([DatumaroBinary](./formats/datumaro_binary)) extensions.
 
 A variety of metadata can be stored in the datumaro format. First of all, `dm_format_version` field
 is provided for backward compatibility to help with data version tracing and various metadata can be

docs/source/docs/get-started/quick-start-guide/examples.rst

@@ -85,9 +85,9 @@ Examples
     import numpy as np
     import datumaro as dm
 
-    dataset = dm.Dataset([
+    dataset = dm.Dataset.from_iterable([
       dm.DatasetItem(id='image1', subset='train',
-        image=np.ones((5, 5, 3)),
+        media=dm.Image.from_numpy(data=np.ones((5, 5, 3))),
         annotations=[
           dm.Bbox(1, 2, 3, 4, label=0),
         ]

docs/source/docs/level-up/intermediate_skills/08_data_validate.rst

@@ -10,7 +10,7 @@ be paid, and sometimes it may be necessary to filter or correct the data in adva
 data validation functionality for this purpose.
 
 More detailed descriptions about validation errors and warnings are given by :ref:`here <Validate>`.
-The Python example for the usage of validator is described in this `notebook <../../jupyter_notebook_examples/notebooks/11_validate>`_.
+The Python example for the usage of validator is described in this :doc:`notebook <../../jupyter_notebook_examples/notebooks/11_validate>`.
 
 
 .. tab-set::