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
is_crowdThis 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
segmentationfield. 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
- Arbitrary attributes: These will be stored within the
attributessection 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
Uploaded file: a single unpacked
*.json or a zip archive with the structure described
- supported annotations: Skeletons
- Install Datumaro
pip install datumaro
- Export the task in the
- Export the Datumaro project in
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).