> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# ObjectDetectionColumnNames

<img className="inline m-0" src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/9e9834e4-image.jpeg" />[View source on GitHub](https://github.com/Arize-ai/client_python/blob/main/src/arize/utils/types.py#L225)

Arize class to map up to 3 columns (`bounding_boxes_coordinates_column_name`, `categories_column_name`, and `scores_column_name`) to a single object detection prediction or actual column.

```python theme={null}
class ObjectDetectionColumnNames(
    bounding_boxes_coordinates_column_name: str
    categories_column_name: str
    scores_column_name: Optional[str] = None
)
```

| Parameters                               | Data Type | Expected Type In Column                                 | Description                                                                                                                                                                                                                                                                                                                                                 |
| ---------------------------------------- | --------- | ------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `bounding_boxes_coordinates_column_name` | str       | The contents of this column must be List\[List\[float]] | (Required) Column name containing the coordinates of the rectangular outline that locates an object within an image or video. Pascal VOC format required.                                                                                                                                                                                                   |
| `categories_column_name`                 | str       | The contents of this column must be List\[str]          | (Required) Column name containing the predefined classes or labels used by the model to classify the detected objects.                                                                                                                                                                                                                                      |
| `scores_column_name`                     | str       | The contents of this column must be List\[float]        | (Optional\*) Column name containing the confidence scores that the model assigns to its predictions, indicating how certain the model is that the predicted class is contained within the bounding box. \* This parameter is not applicable to actual (ground truth) labels and is only applicable when defining `object_detection_prediction_column_names` |

### Code Example

| Index | prediction\_bboxes           | actual\_bboxes                                      | prediction\_categories | actual\_categories      | prediction\_scores |
| ----- | ---------------------------- | --------------------------------------------------- | ---------------------- | ----------------------- | ------------------ |
| `0`   | `[[50.43, 109.49, 538.21...` | `[[55.39, 107.72, 539.25, 362.9], [554.41, 194....` | `[bus]`                | `[bus, person, person]` | `[0.9997552]`      |

```javascript theme={null}
object_detection_prediction_column_names = ObjectDetectionColumnNames(
  (bounding_boxes_coordinates_column_name = "prediction_bboxes"),
  (categories_column_name = "prediction_categories"),
  (scores_column_name = "prediction_scores")
);
object_detection_actual_column_names = ObjectDetectionColumnNames(
  (bounding_boxes_coordinates_column_name = "actual_bboxes"),
  (categories_column_name = "actual_categories")
);
```