Update COCO documentation (#4908)

CHANGELOG.md

@@ -26,7 +26,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 Skeleton (<https://github.com/cvat-ai/cvat/pull/1>), (<https://github.com/opencv/cvat/pull/4829>)
 - Added helm chart support for serverless functions and analytics (<https://github.com/cvat-ai/cvat/pull/110>)
 - Added confirmation when remove a track (<https://github.com/opencv/cvat/pull/4846>)
-- [COCO Keypoints](https://cocodataset.org/#keypoints-2020) format support (<https://github.com/opencv/cvat/pull/4821>)
+- [COCO Keypoints](https://cocodataset.org/#keypoints-2020) format support (<https://github.com/opencv/cvat/pull/4821>,
+  <https://github.com/opencv/cvat/pull/4908>)
 - Support for Oracle OCI Buckets (<https://github.com/opencv/cvat/pull/4876>)
 - `cvat-sdk` and `cvat-cli` packages on PyPI (<https://github.com/opencv/cvat/pull/4903>)
 

README.md

@@ -125,6 +125,7 @@ For more information about the supported formats, look at the
 | Segmentation masks from [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/)                             |   ✔️    |   ✔️    |
 | [YOLO](https://pjreddie.com/darknet/yolo/)                                                                |   ✔️    |   ✔️    |
 | [MS COCO Object Detection](http://cocodataset.org/#format-data)                                           |   ✔️    |   ✔️    |
+| [MS COCO Keypoints Detection](http://cocodataset.org/#format-data)                                        |   ✔️    |   ✔️    |
 | [TFrecord](https://www.tensorflow.org/tutorials/load_data/tfrecord)                                       |   ✔️    |   ✔️    |
 | [MOT](https://motchallenge.net/)                                                                          |   ✔️    |   ✔️    |
 | [LabelMe 3.0](http://labelme.csail.mit.edu/Release3.0)                                                    |   ✔️    |   ✔️    |

site/content/en/docs/manual/advanced/formats/format-coco.md

@@ -41,6 +41,32 @@ Uploaded file: a single unpacked `*.json` or a zip archive with the structure de
 
 - supported annotations: Polygons, Rectangles (if the `segmentation` field is empty)
 
+# [MS COCO Keypoint Detection](https://cocodataset.org/#keypoints-2020)
+
+- [Format specification](https://openvinotoolkit.github.io/datumaro/docs/formats/coco/)
+
+## COCO export
+
+Downloaded file: a zip archive with the structure described [here](https://openvinotoolkit.github.io/datumaro/docs/formats/coco/#import-coco-dataset)
+
+- supported annotations: Skeletons
+- supported attributes:
+  - `is_crowd` (checkbox or integer with values 0 and 1) -
+    specifies that the instance (an object group) should have an
+    RLE-encoded mask in the `segmentation` field. All the grouped shapes
+    are merged into a single mask, the largest one defines all
+    the object properties
+  - `score` (number) - the annotation `score` field
+  - arbitrary attributes - will be stored in the `attributes` annotation section
+
+## COCO import
+
+Uploaded file: a single unpacked `*.json` or a zip archive with the structure described
+[here](https://openvinotoolkit.github.io/datumaro/docs/formats/coco/#import-coco-dataset)
+(without images).
+
+- supported annotations: Skeletons
+
 ## How to create a task from MS COCO dataset
 
 1. Download the [MS COCO dataset](https://openvinotoolkit.github.io/datumaro/docs/formats/coco/#import-coco-dataset).