train.py --eval 算出的 val mAP 和 eval.py 算出来的 mAP 不一样
Created by: XiminLin
我用的是 yolov3 + resnet50 的模型, 用的是同一份 yolo_reader.yml 文件, 但是产生了不同的 val mAP:
- train 里面 --eval 产生的 log:
DONE (t=0.31s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.574 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.838 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.693 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.561 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.549 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.481 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.735 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.735 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.727 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.794 2020-08-20 00:21:17,973-INFO: Best test box ap: 0.5990483392386043, in iter: 57000
- eval.py 产生的 log:
DONE (t=0.19s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.536 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.809 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.650 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.522 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.510 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.463 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.697 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.697 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.694 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.738