Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] =

Average Precision (AP) @[ IoU=0.50:0.95 | area=all | maxDets=100 ] is a commonly used evaluation metric in object detection tasks. It measures the accuracy of an object detection model by calculating the precision at different levels of intersection over union (IoU) thresholds.

Here's how AP is calculated:

1. IoU Calculation: For each predicted bounding box, the IoU is calculated by dividing the area of overlap between the predicted box and the ground truth box by the area of union between them.

2. Precision-Recall Curve: The precision and recall values are calculated for each predicted bounding box, considering it as a true positive if its IoU with any ground truth box exceeds a certain threshold (e.g., 0.5). The precision is the ratio of true positives to the total number of predicted boxes, and the recall is the ratio of true positives to the total number of ground truth boxes.

3. Average Precision Calculation: The precision-recall curve is computed by varying the IoU threshold from 0.5 to 0.95. The average precision is then calculated as the area under this curve.

The specific parameters in the notation @[ IoU=0.50:0.95 | area=all | maxDets=100 ] indicate that the AP is calculated using IoU thresholds ranging from 0.50 to 0.95, considering all object sizes (area=all), and considering a maximum of 100 detections per image (maxDets=100).