Mean Average Precision evaluator for object detection.

# mAP: Mean Average Precision for Object Detection

A simple library for the evaluation of object detectors.

In practice, a higher mAP value indicates a better performance of your detector, given your ground-truth and set of classes.

## Example

import numpy as np
from mean_average_precision import MeanAveragePrecision

# [xmin, ymin, xmax, ymax, class_id, difficult]
gt = np.array([
[439, 157, 556, 241, 0, 0],
[437, 246, 518, 351, 0, 0],
[515, 306, 595, 375, 0, 0],
[407, 386, 531, 476, 0, 0],
[544, 419, 621, 476, 0, 0],
[609, 297, 636, 392, 0, 0]
])

# [xmin, ymin, xmax, ymax, class_id, confidence]
preds = np.array([
[429, 219, 528, 247, 0, 0.460851],
[433, 260, 506, 336, 0, 0.269833],
[518, 314, 603, 369, 0, 0.462608],
[592, 310, 634, 388, 0, 0.298196],
[403, 384, 517, 461, 0, 0.382881],
[405, 429, 519, 470, 0, 0.369369],
[433, 272, 499, 341, 0, 0.272826],
[413, 390, 515, 459, 0, 0.619459]
])

# create metric_fn
metric_fn = MeanAveragePrecision(num_classes=1)

# add some samples to evaluation
for i in range(10):

# compute PASCAL VOC metric
print(f"VOC PASCAL mAP: {metric_fn.value(iou_thresholds=0.5, recall_thresholds=np.arange(0., 1.1, 0.1))['mAP']}")

# compute PASCAL VOC metric at the all points
print(f"VOC PASCAL mAP in all points: {metric_fn.value(iou_thresholds=0.5)['mAP']}")

# compute metric COCO metric
print(f"COCO mAP: {metric_fn.value(iou_thresholds=np.arange(0.5, 1.0, 0.05), recall_thresholds=np.arange(0., 1.01, 0.01))['mAP']}")