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The IOU (Intersection Over Union) metric is part of the Deterministic Metric options in Galtea. It evaluates the degree of spatial overlap between a single predicted bounding box and a set of ground truth boxes. This is especially useful for tasks involving layout analysis, bounding box prediction, or image-based text extraction, where location matters as much as content.

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Evaluation Parameters

To compute the iou 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 bounding box is compared against each ground truth box.
  2. For each comparison, the IoU is calculated as: IoU=Area of IntersectionArea of Union\text{IoU} = \frac{\text{Area of Intersection}}{\text{Area of Union}}
  3. The maximum IoU value across all comparisons is returned as the final score.
This method is particularly useful when the ground truth includes multiple bounding boxes for a single answer (e.g., one box per word in a multi-word phrase).

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Interpretation of Scores

  • ≥ 0.7 – Strong spatial alignment.
  • 0.4 – 0.7 – Moderate overlap; may require refinement.
  • < 0.4 – Poor alignment; predicted box diverges from reference.
Note: These thresholds may be adjusted based on task-specific precision requirements.

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Suggested Test Case Types

Use IoU when evaluating:
  • Layout-aware predictions, such as bounding boxes in OCR or form extraction.
  • Visual document understanding, where spatial positioning is essential.
  • Object or text region detection in image or PDF-based tasks.