COCO Keypoints

How to export and import data in COCO Keypoints format

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:
├── images/
│   ├── <image_name1.ext>
│   ├── <image_name2.ext>
│   └── ...

COCO import

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

  • supported annotations: Skeletons


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).