Skip to main content

Mean Average Precision evaluator for object detection.

Project description

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.

Install package

pip install mean_average_precision

Install the latest version

pip install --upgrade git+https://github.com/bes-dev/mean_average_precision.git

Example

import numpy as np
from mean_average_precision import MetricBuilder

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

# print list of available metrics
print(MetricBuilder.get_metrics_list())

# create metric_fn
metric_fn = MetricBuilder.build_evaluation_metric("map_2d", async_mode=True, num_classes=1)

# add some samples to evaluation
for i in range(10):
    metric_fn.add(preds, gt)

# 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), mpolicy='soft')['mAP']}")

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for mean-average-precision, version 2021.4.26.0
Filename, size File type Python version Upload date Hashes
Filename, size mean_average_precision-2021.4.26.0.tar.gz (8.8 kB) File type Source Python version None Upload date Hashes View
Filename, size mean_average_precision-2021.4.26.0-py3-none-any.whl (14.2 kB) File type Wheel Python version py3 Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page