log average miss rate在yolov4中
时间: 2024-05-31 22:10:44 浏览: 16
Log average miss rate (LAMR)是一种计算目标检测算法性能的指标,它度量的是在不同的误检率下漏检率的均值的对数。在YOLOv4中,LAMR是作为一个评估指标来使用的,用于评估模型在COCO数据集上的性能。YOLOv4使用LAMR作为主要的评估指标,同时也使用了其他指标,例如AP、AP50、AP75等。LAMR越低,代表模型的性能越好,因为它表示模型在不同误检率下的平均漏检率较低。
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log average miss rate
Log average miss rate (LAMR) is a metric used to evaluate the performance of object detection models. It is calculated by taking the logarithm of the average miss rate (AMR) over different false positive rates (FPR) between 0 and 1. The AMR is the average number of missed detections per image, and the FPR is the fraction of false positive detections per image.
LAMR is a useful metric because it provides a single number that summarizes the overall performance of a detection model across all possible FPR values. It is commonly used in benchmark datasets such as PASCAL VOC and COCO to compare the performance of different object detection models.
A lower LAMR indicates better performance, as it means the model has fewer missed detections and false positives across a range of FPR values.
对数平均漏检率(Log Average Miss-rate,LAMR)
对数平均漏检率(Log Average Miss-rate,LAMR)是衡量目标检测算法性能的一项指标。它的计算方式是先计算出每个类别的漏检率(Miss-rate),再对这些漏检率取对数,最后求平均值。漏检率是指目标没有被检测到的比例,因此漏检率越低,模型性能越好。使用对数可以更好地反映漏检率的变化,例如将漏检率从 0.1 提高到 0.2 和将漏检率从 0.01 提高到 0.02 对应的对数差异是一样的。LAMR 越低,意味着模型性能越好。