Created by: learning-boy

使用mask_rcnn_r50_fpn_1x.yml训练自定义数据集，每隔200次使用验证集进行评估一次，打印格式为： 2020-05-03 14:01:52,177-INFO: Save model to output/mask_rcnn_r50_fpn_1x/19400. 2020-05-03 14:01:56,182-INFO: Test iter 0 2020-05-03 14:01:56,926-INFO: Test finish iter 20 2020-05-03 14:01:56,927-INFO: Total number of images: 20, inference time: 24.955333250630904 fps. loading annotations into memory... Done (t=0.00s) creating index... index created! 2020-05-03 14:01:56,931-INFO: Start evaluate... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=0.02s). Accumulating evaluation results... DONE (t=0.01s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.707 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.894 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.832 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.140 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.727 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.767 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.741 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.741 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.300 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.754 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.800 loading annotations into memory... Done (t=0.00s) creating index... index created! 2020-05-03 14:01:56,983-INFO: Start evaluate... Loading and preparing results... DONE (t=0.00s) creating index... index created! Running per image evaluation... Evaluate annotation type segm DONE (t=0.02s). Accumulating evaluation results... DONE (t=0.01s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.708 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.894 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.852 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.101 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.705 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.818 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.222 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.733 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.733 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.300 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.731 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.833 2020-05-03 14:01:57,017-INFO: Best test box ap: 0.7444127249129933, in iter: 1800

想问一下，为什么平均精度和召回率会打印两次，就是这个--->Accumulating evaluation results...为什么会计算两次，而且结果是不一样的，map是根据哪一个进行计算，如何将每次评估的map打印出来，就像yolov3一样，打印本次map和最佳map 使用tensorboard进行可视化的时候，没有记录召回率，只有损失和map 谢谢您