问题
I am training an object detector and I ran the evaluation job. I see certain graphs in the tensorboard. What is DetectionBoxes_Recall/AR@10 vs AR@100 vs AR@100(medium) in the tensorflowboard as shown. And what is the difference between DetectionBoxes_Precision/mAP, mAP(large), mAP(medium), mAP(small), mAP(0.50IOU) and mAP(0.75IOU)? Please help I am very new to this thank you.
回答1:
'DetectionBoxes_Precision/mAP': mean average precision over classes averaged over IOU thresholds ranging from .5 to .95 with .05 increments.
'DetectionBoxes_Precision/mAP@.50IOU': mean average precision at 50% IOU
'DetectionBoxes_Precision/mAP@.75IOU': mean average precision at 75% IOU
'DetectionBoxes_Precision/mAP (small)': mean average precision for small objects (area < 32^2 pixels).
'DetectionBoxes_Precision/mAP (medium)': mean average precision for medium sized objects (32^2 pixels < area < 96^2 pixels).
'DetectionBoxes_Precision/mAP (large)': mean average precision for large objects (96^2 pixels < area < 10000^2 pixels).
'DetectionBoxes_Recall/AR@1': average recall with 1 detection.
'DetectionBoxes_Recall/AR@10': average recall with 10 detections.
'DetectionBoxes_Recall/AR@100': average recall with 100 detections.
'DetectionBoxes_Recall/AR@100 (small)': average recall for small objects with 100.
'DetectionBoxes_Recall/AR@100 (medium)': average recall for medium objects with 100.
'DetectionBoxes_Recall/AR@100 (large)': average recall for large objects with 100 detections.
来源：https://stackoverflow.com/questions/52068835/tensorboard-graph-recall