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.
pip install mean_average_precisionpip install --upgrade git+https://github.com/bes-dev/mean_average_precision.gitimport 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']}")