modify title line length

docs/source/docs/level-up/basic_skills/03_dataset_import_export.rst

@@ -1,22 +1,22 @@
-=============
+===============================
 Level 3: Data Import and Export
-=============
+===============================
 
 Datumaro is a tool that supports public data formats across a wide range of tasks such as
 classification, detection, segmentation, pose estimation, or visual tracking.
 To facilitate this, Datumaro provides assistance with data import and export via both Python API and CLI.
 This makes it easier for users to work with various data formats using Datumaro.
 
 Prepare dataset
-============
+===============
 
 For the segmentation task, we here introduce the Cityscapes, which collects road scenes from 50
 different cities and contains 5K fine-grained pixel-level annotations and 20K coarse annotations.
 More detailed description is given by :ref:`here <Cityscapes>`.
 The Cityscapes dataset is available for free `download <https://www.cityscapes-dataset.com/downloads/>`_.
 
 Convert data format
-============
+===================
 
 Users sometimes needs to compare, merge, or manage various kinds of public datasets in a unified
 system. To achieve this, Datumaro not only has `import` and `export` funcionalities, but also
@@ -59,32 +59,32 @@ We now convert the Cityscapes data into the MS-COCO format, which is described i
 
   .. code-block:: bash
 
-    datum create -o <path/to/project>
+    datum project create -o <path/to/project>
 
   We now import Cityscapes data into the project through
 
   .. code-block:: bash
 
-    datum import --format cityscapes -p <path/to/project> <path/to/cityscapes>
+    datum project import --format cityscapes -p <path/to/project> <path/to/cityscapes>
 
   (Optional) When we import a data, the change is automatically commited in the project.
   This can be shown through `log` as
 
   .. code-block:: bash
 
-    datum log -p <path/to/project>
+    datum project log -p <path/to/project>
 
   (Optional) We can check the imported dataset information such as subsets, number of data, or
   categories through `info`.
 
   .. code-block:: bash
 
-    datum info -p <path/to/project>
+    datum project info -p <path/to/project>
 
   Finally, we export the data within the project with MS-COCO format as
 
   .. code-block:: bash
 
-    datum export --format coco -p <path/to/project> -o <path/to/save> -- --save-media
+    datum project export --format coco -p <path/to/project> -o <path/to/save> -- --save-media
 
 For a data with an unknown format, we can detect the format in the :ref:`next level <Level 4: Detect Data Format from an Unknown Dataset>`!

docs/source/docs/level-up/basic_skills/04_detect_data_format.rst

@@ -1,14 +1,14 @@
-=============
+===================================================
 Level 4: Detect Data Format from an Unknown Dataset
-=============
+===================================================
 
 Datumaro provides a function to detect the format of a dataset before importing data. This can be
 useful in cases where information about the original format of the data has been lost or is unclear.
 With this function, users can easily identify the format and proceed with appropriate data
 handling processes.
 
 Detect data format
-============
+==================
 
 .. tabbed:: CLI
 

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

@@ -1,6 +1,6 @@
-=============
+===========================
 Level 8: Dataset Validation
-=============
+===========================
 
 
 When creating a dataset, it is natural for imbalances to occur between categories, and sometimes