> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/workflows/developer-guide/developer-guide/kinds/keypoint-detection-prediction.md).

# keypointdetectionprediction Kind

Prediction with detected bounding boxes and detected keypoints in form of sv.Detections(...) object.

Prediction with detected bounding boxes and detected keypoints in form of sv.Detections(...) object

## Data representation

{% hint style="warning" %}
**Data representation**

This kind has a different internal and external representation. **External** representation is relevant for integration with your workflow, whereas **internal** one is an implementation detail useful for Workflows blocks development.
{% endhint %}

### External

External data representation is relevant for Workflows clients - it dictates what is the input and output format of data.

Type: `dict`

### Internal

Internal data representation is relevant for Workflows blocks creators - this is the type that will be provided by Execution Engine in runtime to the block that consumes input of this kind.

Type: `sv.Detections`

## Details

This kind represents single keypoints prediction in form of [`sv.Detections(...)`](https://supervision.roboflow.com/latest/detection/core/) object.

Example:

```
sv.Detections(
    xyxy=array([[        127,         189,         322,         303]]), 
    mask=None, 
    confidence=array([    0.95898]), 
    class_id=array([6]), 
    tracker_id=None, 
    data={
        'class_name': array(['G'], dtype='<U1'),
        'detection_id': array(['51dfa8d5-261c-4dcb-ab30-9aafe9b52379'], dtype='<U36'),
        'parent_id': array(['image.[0]'], dtype='<U9'),
        'image_dimensions': array([[425, 640]]),
        'inference_id': array(['51dfa8d5-261c-4dcb-ab30-9aafe9b52379'], dtype='<U36'),
        'prediction_type': array(['instance-segmentation'], dtype='<U16'),
        'root_parent_id': array(['image.[0]'], dtype='<U9'),
        'root_parent_coordinates': array([[0, 0]]),
        'root_parent_dimensions': array([[425, 640]]),
        'parent_coordinates': array([[0, 0]]),
        'parent_dimensions': array([[425, 640]]),
        'scaling_relative_to_parent': array([1]),
        'scaling_relative_to_root_parent': array([1]),
        'keypoints_class_name': array(),  # variable length array of type object - one 1D array of str for each box
        'keypoints_class_id': array(),  # variable length array of type object - one 1D array of int for each box
        'keypoints_confidence': array(),  # variable length array of type object - one 1D array of float for each box
        'keypoints_xy': array(),  # variable length array of type object - one 2D array for bbox with (x, y) coords
    }
)
```

Prior to [sv.Keypoints(...)](https://supervision.roboflow.com/0.21.0/keypoint/core/) we introduced keypoints detection based on [`sv.Detections(...)`](https://supervision.roboflow.com/latest/detection/core/) object. The decision was suboptimal so we would need to revert in the future, but for now this is the format of data for keypoints detection.

The design of metadata is also suboptimal (as metadata regarding whole image is duplicated across all bounding boxes and there is no way on how to save metadata for empty predictions). We have [GH issue](https://github.com/roboflow/inference/issues/567) to communicate around this problem.

**Details of additional fields:**

* `detection_id` - unique identifier for each detection, to be used for when dependent elements are created based on specific detection (example: Dynamic Crop takes this value as parent id for new image)
* `parent_id` - identifier of image that generated prediction (to be fetched from `WorkflowImageData` object)
* `image_dimensions` - dimensions of image that was basis for prediction - format: `(height, width)`
* `inference_id` - identifier of inference request (optional, relevant for Roboflow models)
* `prediction_type` - type of prediction
* `root_parent_id` - identifier of primary Workflow input that was responsible for downstream prediction (to be fetched from `WorkflowImageData` object) - usually identifier of Workflow input placeholder
* `root_parent_coordinates` - offset regarding origin input - format (`offset_x`, `offset_y`)
* `root_parent_dimensions` - dimensions of origin input image `(height, width)`
* `parent_coordinates` - offset regarding parent - format (`offset_x`, `offset_y`)
* `parent_dimensions` - dimensions of parent image `(height, width)`
* `scaling_relative_to_parent` - scaling factor regarding parent image
* `scaling_relative_to_root_parent` - scaling factor regarding origin input image
* `keypoints_class_name` array of variable size 1D arrays of string with key points class names
* `keypoints_class_id` array of variable size 1D arrays of int with key points class ids
* `keypoints_confidence` array of variable size 1D arrays of float with key points confidence
* `keypoints_xy` array of variable size 2D arrays of coordinates of keypoints in `(x, y)` format

**SERIALISATION:**

Execution Engine behind API will serialise underlying data once selector of this kind is declared as Workflow output - serialisation will be executed such that `sv.Detections.from_inference(...)` can decode the output, but **loosing keypoints details** - which can be recovered if output JSON field is parsed. Entity details: [KeypointsDetectionInferenceResponse](https://detect.roboflow.com/docs)