'precision-recall curve

I trained yolo for object detection with 2 class trying to plot precision vs recall curve given the following data

 detections_count = 761, unique_truth_count = 331  
   class_id = 0, name = with_mask, ap = 96.73%       (TP = 253, FP = 20) 
   class_id = 1, name = without_mask, ap = 94.02%        (TP = 62, FP = 14) 
   for conf_thresh = 0.25, precision = 0.90, recall = 0.95, F1-score = 0.93 
   for conf_thresh = 0.25, TP = 315, FP = 34, FN = 16, average IoU = 74.78 % 
   IoU threshold = 50 %, used Area-Under-Curve for each unique Recall 
   mean average precision ([email protected]) = 0.953771, or 95.38 % 
   Total Detection Time: 4 Seconds

python

Sources

This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.

Source: Stack Overflow

Solution	Source

'precision-recall curve

Sources

Related Questions