YOLO fastest输出日志的理解

在用自己的数据训练YOLO fastest的过程中，对保存的训练日志数据看着有点不理解，下面粘贴一段日志数据，然后稍微解释一下，以免理解不牢，容易搞混。

199996: 1.257923, 1.345533 avg loss, 0.000010 rate, 0.330016 seconds, 12799744 images, 0.008982 hours left
v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 115 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000001, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000000, iou_loss = 0.000000, total_loss = 0.000000 
v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 125 Avg (IOU: 0.336026, GIOU: 0.192184), Class: 0.995078, Obj: 0.929581, No Obj: 0.017476, .5R: 0.384359, .75R: 0.097060, count: 1803, class_loss = 2.072524, iou_loss = 870.181396, total_loss = 872.253906 
 total_bbox = 420148888, rewritten_bbox = 28.461716 % 
Loaded: 0.000028 seconds

Region XXX: cfg文件中YOLO-layer的索引。

Avg IOU（GIOU） : 当前迭代中，预测的box与标注的box的平均交并比，越大越好，期望数值为1.

class：标注物体的分类准确率，越大越好，期望数值为1.

obj：越大越好，期望数值为1.

no obj：越小越好

.5R：以IOU=0.5为阈值时的recall

.7R：以IOU=0.7为阈值时的recall

count：正样本的数目