COCO数据集目标检测输出指标AP、AR、maxDets

最近在用retinanet进行一个实验，用的coco格式的数据集，输出的结果大致如下：

Loading and preparing results...
DONE (t=0.11s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=3.98s).
Accumulating evaluation results...
DONE (t=0.76s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.803
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.979
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.907
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.706
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.803
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.798
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.799
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.849
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.849
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.745
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.845
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.825

**

知识太贫乏了，看不懂
后查了资料，这是coco数据集输出的一个检测结果，解释如下：

1.第一行，是COCO的评价指标
2.第二行，是PASCAL VOC的评价指标
3.第三行，IoU=0.75 相比IOU=0.50是比较严格的一个指标
4.第四行，关注小目标的情况
5.第五行，检测中等目标的
6.第六行，检测大目标的指标
7.第七行，设置目标检测框只有1个
8.第八行，设置目标检测框有10个
9.第九行，设置目标检测框有100个 从图上看，10个和100个的指标值差不多

第八第九行对比看，是为了看目标数的大致情况

b站有个视频，讲的也是这个，有时间的也可以去看
链接: coco指标解读.

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