The COCO Keypoints format is designed specifically for human pose estimation tasks, where the objective is to identify and localize body joints (keypoints) on a human figure within an image.

This specialized format is used with a variety of state-of-the-art models focused on pose estimation.

For more information, see:

COCO Keypoints export

For export of images:

  • Supported annotations: Skeletons
  • Attributes:
    • is_crowd This can either be a checkbox or an integer (with values of 0 or 1). It indicates that the instance (or group of objects) should include an RLE-encoded mask in the segmentation field. All shapes within the group coalesce into a single, overarching mask, with the largest shape setting the properties for the entire object group.
    • score: This numerical field represents the annotation score.
    • Arbitrary attributes: These will be stored within the attributes section of the annotation.
  • Tracks: Not supported.

Downloaded file is a .zip archive with the following structure:

archive.zip/
├── images/
│
│   ├── <image_name1.ext>
│   ├── <image_name2.ext>
│   └── ...
├──<annotations>.xml

COCO import

Uploaded file: a single unpacked *.json or a zip archive with the structure described here (without images).

  • supported annotations: Skeletons

person_keypoints,

Support for COCO tasks via Datumaro is described here For example, support for COCO keypoints over Datumaro:

  1. Install Datumaro pip install datumaro
  2. Export the task in the Datumaro format, unzip
  3. Export the Datumaro project in coco / coco_person_keypoints formats datum export -f coco -p path/to/project [-- --save-images]

This way, one can export CVAT points as single keypoints or keypoint lists (without the visibility COCO flag).