Metadata-Version: 2.1
Name: mean-average-precision
Version: 2021.4.26.0
Summary: Mean Average Precision evaluator for object detection.
Home-page: https://github.com/bes-dev/mean_average_precision
Author: Sergei Belousov aka BeS
Author-email: sergei.o.belousov@gmail.com
License: MIT
Description: # mAP: Mean Average Precision for Object Detection
        
        A simple library for the evaluation of object detectors.
        
        <p align="center">
          <img src="resources/img0.jpeg"/>
        </p>
        
        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']}")
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
