Add notebook for Kaggle importers (#1254)

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docs/source/docs/data-formats/formats/index.rst

@@ -24,6 +24,7 @@ Supported Data Formats
    icdar
    image_zip
    imagenet
+   kaggle
    kinetics
    kitti
    kitti_raw
@@ -111,6 +112,9 @@ Supported Data Formats
    * `Dataset example (txt for classification) <https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/imagenet_txt_dataset>`_
    * Detection format is the same as in PASCAL VOC
    * `Format documentation <imagenet.md>`_
+* Kaggle (``classification``, ``detection``, ``segmentation``) (import-only)
+   * `Dataset examples <https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/kaggle_datasets>`_
+   * `Format documentation <kaggle.md>`_
 * KITTI (``segmentation``, ``detection``)
    * `Format specification <http://www.cvlibs.net/datasets/kitti/index.php>`_
    * `Dataset example <https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/kitti_dataset>`_

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

@@ -0,0 +1,77 @@
+# Kaggle
+
+## Format specification
+
+[Kaggle](https://www.kaggle.com/) provides more than 2800 computer vision datasets for the public good and fair competition.
+All datasets are available for downloading at [here](https://www.kaggle.com/datasets?tags=13207-Computer+Vision).
+However, since Kaggle doesn't enforce community to follow specific rule for dataset uploads, it is more natural to explore a dataset directoy structure by manual.
+So it eventually requires to take some time for importing those datasets into their machine learning codes.
+Therefore, Datumaro is providing an ability to import them through Datumaro Python APIs.
+
+Supported type of annotations:
+- `Label` (classification)
+- `Bbox` (object detection)
+- `Mask` (segmentation)
+
+## Import Kaggle Image CSV dataset
+
+Indeed, Kaggle doesn't have any specific directory structure, and Datumaro hence requires more user-aided arguments for importing.
+For `kaggle_image_csv` format, we want to have one `csv` file and `image_directory` as shown below.
+
+```
+├── <annotation_file>.csv
+└── <image_directory>
+    ├── <name_of_image_1>.jpg # extension of video could be other
+    ├── <name_of_image_2>.jpg
+    └── ...
+```
+
+Here `<annotation_file>.csv` contains media name and annotation information such as label or box coordinates as
+
+```
+media_name_in_image_directory,label,...
+<name_of_image_1>,<category_1>,...
+<name_of_image_2>,<category_2>,...
+...
+```
+
+A Datumaro dataset with a Kaggle dataset can be created in the following way in Python API:
+
+```python
+import datumaro as dm
+
+dataset = dm.Dataset.import_from('<path_to_image_directory>', format='kaggle_image_csv', ann_file='<path_to_csv_file>', columns={"media": "column_name_of_media", "label": "column_name_of_label"})
+```
+At this time, it's essential to specify the column names for media and label such as `dm.Dataset.import_from(..., columns={"media": "column_name_of_media", "label": "column_name_of_label"})`
+
+## Import Kaggle Image Txt dataset
+
+Another `kaggle_image_txt` format replaces only `columns` with an order of informations in `.txt`.
+For instance, dataset can be created by
+
+```python
+dataset = dm.Dataset.import_from('<path_to_image_directory>', format='kaggle_image_txt', ann_file='<path_to_txt_file>', columns={"media": 0, "label": 1})
+```
+
+## Import Kaggle Image Mask dataset
+
+For segmentation tasks, `kaggle_image_mask` requires to have a directory for mask images as following Python API.
+```python
+dataset = dm.Dataset.import_from('<path_to_image_directory>', format='kaggle_image_mask', mask_path='<path_to_mask_directory>')
+```
+
+## Import Kaggle VOC and Kaggle YOLO datasets
+
+Sometimes, communities upload their annotation files for each images with VOC (`xml`) and YOLO (`txt`) formats thanks to its popularity.
+But, they violate the directory sturcture of the original Pascal-VOC and YOLO described in [VOC](./pascal_voc.md) and [YOLO](./yolo.md), respectively.
+For these cases, we provide `kaggle_voc` and `kaggle_yolo` formats by specifying the path to annotation files as below.
+
+```python
+dataset = dm.Dataset.import_from('<path_to_image_directory>', format='kaggle_voc', ann_path='<path_to_annotation_directory>')
+```
+or
+```python
+dataset = dm.Dataset.import_from('<path_to_image_directory>', format='kaggle_yolo', ann_path='<path_to_annotation_directory>')
+```
+
+Please refer to [here](https://github.com/openvinotoolkit/datumaro/blob/develop/notebooks/20_kaggle_data_import.ipynb) for various practices.