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The Spatial Match metric is part of the Deterministic Metric options in Galtea. It determines whether a predicted bounding box sufficiently overlaps with any of the reference boxes by computing the Intersection over Union (IOU) and applying a threshold. This binary variant of the IOU metric is particularly useful when an exact spatial match is not required, but a minimum quality of alignment must be enforced.

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Evaluation Parameters

To compute the spatial_match metric, the following parameters must be provided:
  • actual_output: A list of predicted bounding boxes. Each box must be in [x1, y1, x2, y2] format (coordinates of top-left and bottom-right corners). Accepts two formats:
    • JSON array: "[[10, 10, 50, 50], [100, 100, 120, 120]]"
    • JSON object with “bboxes” key: '{"bboxes": [[10, 10, 50, 50], [100, 100, 120, 120]]}'
  • expected_output: A single ground truth bounding box in [x1, y1, x2, y2] format. Accepts two formats:
    • JSON array: "[10, 10, 50, 50]"
    • JSON object with “bbox” key (singular): '{"bbox": [10, 10, 50, 50]}'
Important: Both actual_output and expected_output must be valid JSON. Truncated or malformed JSON will cause evaluation failures.

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How Is It Calculated?

  1. The predicted box is compared against each reference box.
  2. The IoU is calculated for each pair: IoU=Area of IntersectionArea of Union\text{IoU} = \frac{\text{Area of Intersection}}{\text{Area of Union}}
  3. The highest IoU score among all comparisons is selected as the final score.
  4. If this score is greater than or equal to a predefined threshold (0.5), the metric returns 100% (pass). Otherwise, it returns 0% (fail).
This approach accommodates situations where reference answers span multiple boxes (e.g., one box per word), and only the best alignment needs to be evaluated.

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Interpretation of Scores

  • 1.0 – Strong spatial match; high alignment.
  • 0.0 – Low spatial match; poor alignment.

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Suggested Test Case Types

Use Spatial Match when evaluating:
  • Document layout tasks requiring accurate box alignment.
  • OCR or form field extraction where exact box positioning matters.
  • Visual QA and multi-box alignment tasks that benefit from flexible but spatially aware comparisons.