目标检测中的评价指标mAP以及coco评价标准

pycocotools安装
Linux: pip install pycocotools;
Windows: pip install pycocotools-windows

训练过程中输出值

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.534 
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.785
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.597
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.172
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.425
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.628
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.442
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.643
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.656
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.278
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.558
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.727

含义：
第一行：COCO数据集评价指标
第二行：PASVOC数据集评价指标
第三行：IoU=0.75 相比0.50是比较严格的一个指标
第四行：检测小目标的的指标
第五行：检测中等目标的
第六行：检测大目标的指标
第七行：设置目标检测框只有1个
第八行：设置目标检测框有10个
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