https://github.com/bes-dev/mean_average_precision
Mean Average Precision for Object Detection
https://github.com/bes-dev/mean_average_precision
Last synced: about 1 year ago
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Mean Average Precision for Object Detection
- Host: GitHub
- URL: https://github.com/bes-dev/mean_average_precision
- Owner: bes-dev
- License: mit
- Created: 2020-05-30T13:56:29.000Z (about 6 years ago)
- Default Branch: develop
- Last Pushed: 2024-01-05T20:17:06.000Z (over 2 years ago)
- Last Synced: 2025-03-28T20:11:22.446Z (about 1 year ago)
- Language: Python
- Size: 126 KB
- Stars: 198
- Watchers: 2
- Forks: 31
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# mAP: Mean Average Precision for Object Detection
A simple library for the evaluation of object detectors.
[](https://pepy.tech/project/mean-average-precision)
[](https://pepy.tech/project/mean-average-precision)
[](https://pepy.tech/project/mean-average-precision)
In practice, a **higher mAP** value indicates a **better performance** of your detector, given your ground-truth and set of classes.
## Install package
```bash
pip install mean_average_precision
```
## Install the latest version
```bash
pip install --upgrade git+https://github.com/bes-dev/mean_average_precision.git
```
## Example
```python
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']}")
```