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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.
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DetectionBoxes_Precision/mAP@. 50IOU: here, it specifies the IOU, so in this case it doesn't go over all IOU thresholds, only the specified one. This metric is the average precision using only IOU=0.5 (but still going over all images and classes).
The precision-recall curve shows the tradeoff between precision and recall for different threshold. A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate.
Average recall (AR) Average recall describes the area doubled under the Recall x IoU curve. The Recall x IoU curve plots recall results for each IoU threshold where IoU ∈ [0.5,1.0], with IoU thresholds on the x-axis and recall on the y-axis.
Mean Average Precision(mAP) is a metric used to evaluate object detection models such as Fast R-CNN, YOLO, Mask R-CNN, etc. The mean of average precision(AP) values are calculated over recall values from 0 to 1.
'DetectionBoxes_Precision/mAP': mean average precision over classes averaged over IOU thresholds ranging from .5 to .95 with .05 increments.
'DetectionBoxes_Precision/[email protected]': mean average precision at 50% IOU
'DetectionBoxes_Precision/[email protected]': 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.
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