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Convert supervision/metrics/mean_average_precision.py docstrings to doctests

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Co-authored-by: copilot-swe-agent[bot] <198982749+copilot@users.noreply.github.com>
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jirkaandcopilot-swe-agent[bot] authored and Jirka Borovec committed 2026-02-02 18:10:37 +09:00
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supervision/metrics/mean_average_precision.py
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@@ -1139,38 +1139,30 @@ class MeanAveragePrecision(Metric):
Mean Average Precision (mAP) is a metric used to evaluate object detection models.
It is the average of the precision-recall curves at different IoU thresholds.
Example:
```python
import supervision as sv
from supervision.metrics import MeanAveragePrecision
predictions = sv.Detections(...)
targets = sv.Detections(...)
map_metric = MeanAveragePrecision()
map_result = map_metric.update(predictions, targets).compute()
print(map_result.map50_95)
# 0.4674
print(map_result)
# MeanAveragePrecisionResult:
# Metric target: MetricTarget.BOXES
# Class agnostic: False
# mAP @ 50:95: 0.4674
# mAP @ 50: 0.5048
# mAP @ 75: 0.4796
# mAP scores: [0.50485 0.50377 0.50377 ...]
# IoU thresh: [0.5 0.55 0.6 ...]
# AP per class:
# 0: [0.67699 0.67699 0.67699 ...]
# ...
# Small objects: ...
# Medium objects: ...
# Large objects: ...
map_result.plot()
```
Examples:
>>> import numpy as np
>>> import supervision as sv
>>> from supervision.metrics import MeanAveragePrecision
>>> predictions = sv.Detections(
... xyxy=np.array([[0, 0, 10, 10]]),
... class_id=np.array([0]),
... confidence=np.array([0.9])
... )
>>> targets = sv.Detections(
... xyxy=np.array([[0, 0, 10, 10]]),
... class_id=np.array([0])
... )
>>> map_metric = MeanAveragePrecision()
>>> map_result = map_metric.update(predictions, targets).compute()
>>> round(float(map_result.map50), 2)
1.0
>>> print(map_result)
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 1.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 1.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
![example_plot](\
https://media.roboflow.com/supervision-docs/metrics/mAP_plot_example.png\