[doc] fix spelling mistakes (#1665)

Fixes clear spelling mistakes in the documentation.

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

@@ -13,7 +13,7 @@ Prune supports various methodology.
 
 By default, datasets are updated in-place. The `-o/--output-dir` option can be used to specify another output directory. When updating in-place, use the `--overwirte` parameter (in-place updates fail by default to prevent data loss), unless a project target is modified.
 
-The current project (`-p/--project`) is also used as a context for plugins, so it can be useful for datasest paths having custom formats. When not specified, the current project's working tree is used.
+The current project (`-p/--project`) is also used as 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.
 
 The command can be applied to a dataset or a project build target, a stage or the combined `project` target, in which case all the project targets will be affected.
 

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

@@ -103,7 +103,7 @@ Basic dataset item manipulations:
 - [`remove_attributes`](#remove_attributes) - Removes attributes
 - [`astype_annotations`](#astype_annotations) - Transforms annotation types
 - [`pseudo_labeling`](#pseudo_labeling) - Generates pseudo labels for unlabeled data
-- [`correct`](#correct) - Corrects annotaiton types
+- [`correct`](#correct) - Corrects annotation types
 - [`clean`](#clean) - Removes noisy data for tabular dataset
 
 Subset manipulations:
@@ -177,7 +177,7 @@ Examples:
 #### `id_from_image_name`
 
 Renames items in the dataset based on the image file name, excluding the extension.
-When 'ensure_unique' is enabled, a random suffix is appened to ensure each identifier is unique
+When 'ensure_unique' is enabled, a random suffix is appended to ensure each identifier is unique
 in cases where the image name is not distinct. By default, the random suffix is three characters long,
 but this can be adjusted with the 'suffix_length' parameter.
 

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

@@ -58,7 +58,7 @@ Examples:
   datum validate -p <path/to/project/> -t classification -- -ir 40
   ```
 
-### List of validation items (annomaly types)
+### List of validation items (anomaly types)
 
 | Anomaly Type | Description | Task Type |
 | ------------ | ----------- | --------- |

docs/source/docs/command-reference/helper/format.md

@@ -13,7 +13,7 @@ usage: datum format [-h] [-li | -le] [-d DELIMITER]
 
 Parameters:
 - `-h, --help` - Print the help message and exit.
-- `-d DELIMITER, --delimiter DELIMITER` - Seperator used to list data format names (default: `\n`). For example, `datum format -d ','` command displays
+- `-d DELIMITER, --delimiter DELIMITER` - Separator used to list data format names (default: `\n`). For example, `datum format -d ','` command displays
   ```console
   Supported import formats:
   ade20k2017,ade20k2020,align_celeba,...

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

@@ -7,7 +7,7 @@ The AVA action format specification is available
 
 The dataset has annotations for recognizing an action per instance from video frames
 like visual tracking task. Specifically, the AVA action dataset contains frame indices,
-bounding box cooridnates, actions, and tracking ids in the annotation file. The action
+bounding box coordinates, actions, and tracking ids in the annotation file. The action
 categories are described in `ava_action_list_v2.2.pbtxt`. For the ease use for object
 detection, the AVA action dataset provides the bounding box proposals from `Faster R-CNN`.
 

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

@@ -137,7 +137,7 @@ Extra options for exporting to Cityscapes format:
 #...
 datum project export -f cityscapes -- --label-map mycolormap.txt
 ```
-or you can use original cityscapes colomap:
+or you can use original cityscapes colormap:
 ``` bash
 datum project export -f cityscapes -- --label-map cityscapes
 ```

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

@@ -122,7 +122,7 @@ For the panoptic task, a dataset directory should have the following structure:
 
 Annotation files must have the names like `<task_name>_<subset_name>.json`.
 The year is treated as a part of the subset name.
-If the annotation file name does't match this pattern, use one of the
+If the annotation file name doesn't match this pattern, use one of the
 task-specific formats instead of plain `coco`: `coco_captions`,
 `coco_image_info`, `coco_instances`, `coco_labels`, `coco_panoptic`,
 `coco_person_keypoints`, `coco_stuff`. In this case all items of the

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

@@ -46,7 +46,7 @@ At this time, it's essential to specify the column names for media and label suc
 
 ## Import Kaggle Image Txt dataset
 
-Another `kaggle_image_txt` format replaces only `columns` with an order of informations in `.txt`.
+Another `kaggle_image_txt` format replaces only `columns` with an order of information in `.txt`.
 For instance, dataset can be created by
 
 ```python

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

@@ -175,7 +175,7 @@ Extra options for exporting to KITTI format:
 datum project export -f kitti -- --label-map mycolormap.txt
 
 ```
-or you can use original kitti colomap:
+or you can use original kitti colormap:
 ``` bash
 datum project export -f kitti -- --label-map kitti
 ```

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

@@ -12,7 +12,7 @@ Supported annotation types:
     - `Mask` (class, instances, panoptic)
     - `Polygon`
 
-Supported atttibutes:
+Supported attributes:
     - `is_crowd`(boolean; on panoptic `mask`): Indicates that the annotation
     covers multiple instances of the same class.
 

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

@@ -18,7 +18,7 @@ Supported annotation attributes:
 
 You can download the MOT challenge dataset [here](https://motchallenge.net).
 
-A Datumaro project with the MOT challange source can be created in the following way:
+A Datumaro project with the MOT challenge source can be created in the following way:
 
 ``` bash
 datum project create
@@ -43,7 +43,7 @@ The MOT challenge dataset directory should have the following structure:
   └── seqinfo.ini (optional)
 ```
 
-`seqinfo.ini` is provided by the MOT challange dataset but it is optional in Datumaro.
+`seqinfo.ini` is provided by the MOT challenge dataset but it is optional in Datumaro.
 It includes `imdir` field which is the name of directory having image files.
 If this file is given, Datumaro will find the image files from the directory written in the `imdir` field.
 
@@ -52,7 +52,7 @@ run `datum project info`, which will display the project information.
 
 ## Export to other formats
 
-Datumaro can convert the MOT challange dataset into any other format [Datumaro supports](/docs/data-formats/formats/index.rst).
+Datumaro can convert the MOT challenge dataset into any other format [Datumaro supports](/docs/data-formats/formats/index.rst).
 
 Such conversion will only be successful if the output
 format can represent the type of dataset you want to convert,

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

@@ -16,9 +16,9 @@ Supported annotation attributes:
 
 ## Import MOTS dataset
 
-You can download the PNG format of MOTS challange dataset [here](https://www.vision.rwth-aachen.de/page/mots).
+You can download the PNG format of MOTS challenge dataset [here](https://www.vision.rwth-aachen.de/page/mots).
 
-A Datumaro project with the MOTS challange source can be created in the following way:
+A Datumaro project with the MOTS challenge source can be created in the following way:
 
 ``` bash
 datum project create
@@ -28,7 +28,7 @@ datum project import --format mots <path/to/dataset>
 It is possible to specify project name and project directory. Run
 `datum project create --help` for more information.
 
-The MOTS challange dataset directory should have the following structure:
+The MOTS challenge dataset directory should have the following structure:
 
 <!--lint disable fenced-code-flag-->
 ```
@@ -55,7 +55,7 @@ run `datum project info`, which will display the project information.
 
 ## Export to other formats
 
-Datumaro can convert the MOTS challange dataset into any other format [Datumaro supports](/docs/data-formats/formats/index.rst).
+Datumaro can convert the MOTS challenge dataset into any other format [Datumaro supports](/docs/data-formats/formats/index.rst).
 
 Such conversion will only be successful if the output
 format can represent the type of dataset you want to convert,

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

@@ -221,7 +221,7 @@ datum project export -f voc -- --tasks detection,classification
 # person:255,0,0:head:
 datum project export -f voc_segmentation -- --label-map mycolormap.txt
 ```
-or you can use original voc colomap:
+or you can use original voc colormap:
 ``` bash
 datum project export -f voc_segmentation -- --label-map voc
 ```

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

@@ -93,7 +93,7 @@ To add custom classes, you can use [`dataset_meta.json`](/docs/data-formats/form
 ## Export to YOLO-Ultralytics format
 
 Datumaro can convert [any other image dataset format](/docs/data-formats/formats/index.rst) which has bounding box annotations into YOLO-Ultralytics format.
-After the successful conversion, you can train your own detecter with the exported dataset and  [Ultralytics YOLOv8 trainer](https://github.com/ultralytics/ultralytics).
+After the successful conversion, you can train your own detector with the exported dataset and  [Ultralytics YOLOv8 trainer](https://github.com/ultralytics/ultralytics).
 
 > Note, if you want to see the end-to-end Jupyter-notebook example from the dataset conversion to the training, please see this [link](https://github.com/openvinotoolkit/datumaro/blob/develop/notebooks/08_e2e_example_yolo_ultralytics_trainer.ipynb).
 

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

@@ -19,7 +19,7 @@ Convert data format
 ===================
 
 Users sometimes need 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
+system. To achieve this, Datumaro not only has ``import`` and ``export`` functionalities, but also
 provides ``convert``, which shortens the import and export into a single command line.
 Let's convert the Cityscapes data into the MS-COCO format, which is described in :ref:`here <COCO>`.
 

docs/source/docs/level-up/intermediate_skills/11_data_generation.rst

@@ -10,7 +10,7 @@ since the manual annotations is quite expensive work.
 Base on the [FractalDB]_,
 Datumaro provides a fractal image dataset (FractalDB) generator that can be utilized to pre-train the vision models.
 Learning visual features of FractalDB is known to increase the performance of Vision Transformer (ViT) models.
-Note that a fractal patterns in FractalDB is calculated mathmatically using the interated function system (IFS) with random parameters.
+Note that a fractal patterns in FractalDB is calculated mathematically using the integrated function system (IFS) with random parameters.
 We thus don't need to concern about any privacy issues.