如何获得整个数据集的mAP？

问题描述

操作：我在Jupyter笔记本上运行了可可评估! python3 coco.py evaluate --dataset=/host/Downloads/coco_2017_dataset --model=last ，而不是直接在终端上运行它。这是Github repo的Mask R-CNN实现。

问题：此val2017评估/ dev COCO数据集中有5000张图像。为什么这里只显示6个AP和6个召回？

目标：

显示整个数据集的mAP。
对以上问题进行澄清/回答。

我当前的结果：

index created!
Running per image evaluation...
Evaluate annotation type *bBox*
DONE (t=2.59s).
Accumulating evaluation results...
DONE (t=0.87s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.286
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.473
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.317
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.119
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.337
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.443
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.242
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.332
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.340
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.133
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.397
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.514
Prediction time: 4761.41809463501. Average 9.52283618927002/image
Total time:  4820.066065311432

解决方法

暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！

如果你已经找到好的解决方法，欢迎将解决方案带上本链接一起发送给小编。

小编邮箱:dio#foxmail.com (将#修改为@）

deep-learning pycocotools