diff --git a/docs/source/docs/data-formats/datumaro_format.md b/docs/source/docs/data-formats/datumaro_format.md
index abc68d4264..d16282eeab 100644
--- a/docs/source/docs/data-formats/datumaro_format.md
+++ b/docs/source/docs/data-formats/datumaro_format.md
@@ -8,7 +8,7 @@ with different tasks and formats, we provide a novel Datumaro format with `.json
 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
+is provided for backward compatibility to help with data version tracing and various metadata can be
 added to the `info` field. For example, you can record task types such as detection and segmentation,
 or record data creation time. Labels and attributes can be saved in the `categories` field, and mask
 colormap information can be saved. In addition, in the datumaro format, in order to respond to
diff --git a/docs/source/docs/data-formats/media_formats.md b/docs/source/docs/data-formats/media_formats.md
index d1aecb937b..c400eecaed 100644
--- a/docs/source/docs/data-formats/media_formats.md
+++ b/docs/source/docs/data-formats/media_formats.md
@@ -9,8 +9,9 @@ To create an unlabelled dataset from an arbitrary directory with images use
 `image_dir` and `image_zip` formats:
 
 ``` bash
-datum create -o <project/dir>
-datum import -p <project/dir> -f image_dir <directory/path/>
+cd </path/to/project>
+datum project create
+datum project import -f image_dir </path/to/directory/containing/images>
 ```
 
 or, if you work with Datumaro API:
@@ -20,8 +21,8 @@ or, if you work with Datumaro API:
   ```python
   from datumaro.project import Project
 
-  project = Project.init()
-  project.import_source('source1', format='image_dir', url='directory/path/')
+  project = Project.init('/path/to/project')
+  project.import_source('source1', format='image_dir', url='/path/to/directory/containing/images')
   dataset = project.working_tree.make_dataset()
   ```
 
@@ -30,7 +31,7 @@ or, if you work with Datumaro API:
   ```python
   from datumaro import Dataset
 
-  dataset = Dataset.import_from('directory/path/', 'image_dir')
+  dataset = Dataset.import_from('/path/to/directory/containing/images', 'image_dir')
   ```
 
 This will search for images in the directory recursively and add
@@ -57,14 +58,15 @@ Alternatively, you can use the `video_frames` format directly:
 > splitting the video into frames by any method.
 
 ``` bash
-datum create -o <project/dir>
-datum import -p <project/dir> -f video_frames <video/path.avi>
+cd </path/to/project>
+datum project create
+datum project import -f video_frames </path/to/video>
 ```
 
 ```python
 from datumaro import Dataset
 
-dataset = Dataset.import_from('video.mp4', 'video_frames')
+dataset = Dataset.import_from('/path/to/video', 'video_frames')
 ```
 
 Datumaro supports the following video formats:
diff --git a/docs/source/docs/data-formats/supported_formats.md b/docs/source/docs/data-formats/supported_formats.md
index 702ca8cff7..9059e45892 100644
--- a/docs/source/docs/data-formats/supported_formats.md
+++ b/docs/source/docs/data-formats/supported_formats.md
@@ -4,45 +4,45 @@ List of supported formats:
 - ADE20k (v2017) (import-only)
   - [Format specification](https://www.kaggle.com/soumikrakshit/ade20k)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/ade20k2017_dataset)
-  - [Format documentation](/docs/explanation/formats/ade20k2017)
+  - [Format documentation](../explanation/formats/ade20k2017)
 - ADE20k (v2020) (import-only)
   - [Format specification](https://groups.csail.mit.edu/vision/datasets/ADE20K/)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/ade20k2020_dataset)
-  - [Format documentation](/docs/explanation/formats/ade20k2020)
+  - [Format documentation](../explanation/formats/ade20k2020)
 - Align CelebA (`classification`, `landmarks`) (import-only)
   - [Format specification](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/align_celeba_dataset)
-  - [Format documentation](/docs/explanation/formats/align_celeba)
+  - [Format documentation](../explanation/formats/align_celeba)
 - BraTS (`segmentation`) (import-only)
   - [Format specification](https://www.med.upenn.edu/sbia/brats2018/data.html)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/brats_dataset)
-  - [Format documentation](/docs/explanation/formats/brats)
+  - [Format documentation](../explanation/formats/brats)
 - BraTS Numpy (`detection`, `segmentation`) (import-only)
   - [Format specification](https://www.med.upenn.edu/sbia/brats2018/data.html)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/brats_numpy_dataset)
-  - [Format documentation](/docs/explanation/formats/brats_numpy)
+  - [Format documentation](../explanation/formats/brats_numpy)
 - CamVid (`segmentation`)
   - [Format specification](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/camvid_dataset)
 - CelebA (`classification`, `detection`, `landmarks`) (import-only)
   - [Format specification](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/celeba_dataset)
-  - [Format documentation](/docs/explanation/formats/celeba)
+  - [Format documentation](../explanation/formats/celeba)
 - CIFAR-10/100 (`classification` (python version))
   - [Format specification](https://www.cs.toronto.edu/~kriz/cifar.html)
   - [Dataset example CIFAR-10](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/cifar10_dataset)
   - [Dataset example CIFAR-100](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/cifar100_dataset)
-  - [Format documentation](/docs/explanation/formats/cifar)
+  - [Format documentation](../explanation/formats/cifar)
 - Cityscapes (`segmentation`)
   - [Format specification](https://www.cityscapes-dataset.com/dataset-overview/)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/cityscapes_dataset)
-  - [Format documentation](/docs/explanation/formats/cityscapes)
+  - [Format documentation](../explanation/formats/cityscapes)
 - Common Semantic Segmentation (`segmentation`)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/common_semantic_segmentation_dataset)
-  - [Format documentation](/docs/explanation/formats/common_semantic_segmentation)
+  - [Format documentation](../explanation/formats/common_semantic_segmentation)
 - Common Super Resolution
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/common_super_resolution_dataset)
-  - [Format documentation](/docs/explanation/formats/common_super_resolution)
+  - [Format documentation](../explanation/formats/common_super_resolution)
 - CVAT (`for images`, `for video` (import-only))
   - [Format specification](https://opencv.github.io/cvat/docs/manual/advanced/xml_format)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/cvat_dataset)
@@ -53,45 +53,45 @@ List of supported formats:
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/imagenet_dataset)
   - [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](/docs/explanation/formats/imagenet)
+  - [Format documentation](../explanation/formats/imagenet)
 - 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)
-  - [Format documentation](/docs/explanation/formats/kitti)
+  - [Format documentation](../explanation/formats/kitti)
 - KITTI 3D (`raw`/`tracklets`/`velodyne points`)
   - [Format specification](http://www.cvlibs.net/datasets/kitti/raw_data.php)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/kitti_dataset/kitti_raw)
-  - [Format documentation](/docs/explanation/formats/kitti_raw)
+  - [Format documentation](../explanation/formats/kitti_raw)
 - Kinetics 400/600/700
   - [Format specification](https://www.deepmind.com/open-source/kinetics)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/kinetics_dataset)
-  - [Format documentation](/docs/explanation/formats/kinetics)
+  - [Format documentation](../explanation/formats/kinetics)
 - LabelMe (`labels`, `boxes`, `masks`)
   - [Format specification](http://labelme.csail.mit.edu/Release3.0)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/labelme_dataset)
 - LFW (`classification`, `person re-identification`, `landmarks`)
   - [Format specification](http://vis-www.cs.umass.edu/lfw/)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/lfw_dataset)
-  - [Format documentation](/docs/explanation/formats/lfw)
+  - [Format documentation](../explanation/formats/lfw)
 - Mapillary Vistas (import-only)
   - [Format specification](https://www.mapillary.com/dataset/vistas)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/mapillary_vistas_dataset)
-  - [Format documentation](/docs/explanation/formats/mapillary_vistas)
+  - [Format documentation](../explanation/formats/mapillary_vistas)
 - Market-1501 (`person re-identification`)
   - [Format specification](https://www.aitribune.com/dataset/2018051063)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/market1501_dataset)
 - MARS (import-only)
   - [Format specification](https://zheng-lab.cecs.anu.edu.au/Project/project_mars.html)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/mars_dataset)
-  - [Format documentation](/docs/explanation/formats/mars)
+  - [Format documentation](../explanation/formats/mars)
 - MNIST (`classification`)
   - [Format specification](http://yann.lecun.com/exdb/mnist/)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/mnist_dataset)
-  - [Format documentation](/docs/explanation/formats/mnist)
+  - [Format documentation](../explanation/formats/mnist)
 - MNIST in CSV (`classification`)
   - [Format specification](https://pjreddie.com/projects/mnist-in-csv/)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/mnist_csv_dataset)
-  - [Format documentation](/docs/explanation/formats/mnist)
+  - [Format documentation](../explanation/formats/mnist)
 - MOT sequences
   - [Format specification](https://arxiv.org/pdf/1906.04567.pdf)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/mot_dataset)
@@ -101,37 +101,37 @@ List of supported formats:
 - MPII Human Pose Dataset (`detection`, `pose estimation`) (import-only)
   - [Format specification](http://human-pose.mpi-inf.mpg.de)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/mpii_dataset)
-  - [Format documentation](/docs/explanation/formats/mpii)
+  - [Format documentation](../explanation/formats/mpii)
 - MPII Human Pose Dataset (JSON) (`detection`, `pose estimation`) (import-only)
   - [Format specification](http://human-pose.mpi-inf.mpg.de)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/mpii_json_dataset)
-  - [Format documentation](/docs/explanation/formats/mpii_json)
+  - [Format documentation](../explanation/formats/mpii_json)
 - MS COCO (`image_info`, `instances`, `person_keypoints`, `captions`, `labels`,`panoptic`, `stuff`)
   - [Format specification](http://cocodataset.org/#format-data)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/coco_dataset)
   - `labels` are our extension - like `instances` with only `category_id`
-  - [Format documentation](/docs/explanation/formats/coco)
+  - [Format documentation](../explanation/formats/coco)
 - NYU Depth Dataset V2 (`depth estimation`) (import-only)
   - [Format specification](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/nyu_depth_v2_dataset)
-  - [Format documentation](/docs/explanation/formats/nyu_depth_v2)
+  - [Format documentation](../explanation/formats/nyu_depth_v2)
 - Open Images (`classification`, `detection`, `segmentation`)
   - [Format specification](https://storage.googleapis.com/openimages/web/download.html)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/open_images_dataset)
-  - [Format documentation](/docs/explanation/formats/open_images)
+  - [Format documentation](../explanation/formats/open_images)
 - PASCAL VOC (`classification`, `detection`, `segmentation` (class, instances),
   `action_classification`, `person_layout`)
   - [Format specification](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/index.html)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/voc_dataset)
-  - [Format documentation](/docs/explanation/formats/pascal_voc)
+  - [Format documentation](../explanation/formats/pascal_voc)
 - Supervisely (`pointcloud`)
   - [Format specification](https://docs.supervise.ly/data-organization/00_ann_format_navi)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/sly_pointcloud_dataset)
-  - [Format documentation](/docs/explanation/formats/sly_pointcloud)
+  - [Format documentation](../explanation/formats/sly_pointcloud)
 - SYNTHIA (`segmentation`) (import-only)
   - [Format specification](https://synthia-dataset.net/)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/synthia_dataset)
-  - [Format documentation](/docs/explanation/formats/synthia)
+  - [Format documentation](../explanation/formats/synthia)
 - TF Detection API (`bboxes`, `masks`)
   - Format specifications: [bboxes](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md),
     [masks](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/instance_segmentation.md)
@@ -139,27 +139,27 @@ List of supported formats:
 - VGGFace2 (`landmarks`, `bboxes`)
   - [Format specification](https://github.com/ox-vgg/vgg_face2)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/vgg_face2_dataset)
-  - [Format documentation](/docs/explanation/formats/vgg_face2)
+  - [Format documentation](../explanation/formats/vgg_face2)
 - VoTT CSV (`detection`) (import-only)
   - [Format specification](https://github.com/microsoft/VoTT)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/vott_csv_dataset)
-  - [Format documentation](/docs/explanation/formats/vott_csv)
+  - [Format documentation](../explanation/formats/vott_csv)
 - VoTT JSON (`detection`) (import-only)
   - [Format specification](https://github.com/microsoft/VoTT)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/vott_json_dataset)
-  - [Format documentation](/docs/explanation/formats/vott_json)
+  - [Format documentation](../explanation/formats/vott_json)
 - WIDER Face (`bboxes`)
   - [Format specification](http://shuoyang1213.me/WIDERFACE/)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/widerface_dataset)
-  - [Format documentation](/docs/explanation/formats/wider_face)
+  - [Format documentation](../explanation/formats/wider_face)
 - YOLO (`bboxes`)
   - [Format specification](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data)
   - [Dataset example](https://github.com/openvinotoolkit/datumaro/tree/develop/tests/assets/yolo_dataset)
-  - [Format documentation](/docs/explanation/formats/yolo)
+  - [Format documentation](../explanation/formats/yolo)
 - YOLO-Ultralytics (`bboxes`)
   - [Format specification](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml)
   - [Dataset example](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml)
-  - [Format documentation](/docs/explanation/formats/yolo)
+  - [Format documentation](../explanation/formats/yolo_ultralytics)
 
 ## Supported annotation types
 
@@ -173,6 +173,8 @@ List of supported formats:
 - 3D cuboids
 - Super Resolution Annotation
 - Depth Annotation
+- Ellipses
+- Hash Keys
 
 Datumaro does not separate datasets by tasks like classification, detection
 etc. Instead, datasets can have any annotations. When a dataset is exported
@@ -185,9 +187,9 @@ To do this, use `dataset_meta.json`.
 
 ```json
 {
-"label_map": {"0": "background", "1": "car", "2": "person"},
-"segmentation_colors": [[0, 0, 0], [255, 0, 0], [0, 0, 255]],
-"background_label": "0"
+    "label_map": {"0": "background", "1": "car", "2": "person"},
+    "segmentation_colors": [[0, 0, 0], [255, 0, 0], [0, 0, 255]],
+    "background_label": "0"
 }
 ```