Owen-Liuyuxuan / visualDet3D

Official Repo for Ground-aware Monocular 3D Object Detection for Autonomous Driving / YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection
https://owen-liuyuxuan.github.io/papers_reading_sharing.github.io/3dDetection/GroundAwareConvultion/
Apache License 2.0
362 stars 77 forks source link

关于评价指标的疑问 #5

Closed cnexah closed 3 years ago

cnexah commented 3 years ago

您好!模型测试时会使用两种标准,例如对于3D easy case, 第一种是21.90,第二种是57.11。为什么第二种比第一种性能高这么多?对于easy case, overlap的阈值都是0.7吗? Car AP(Average Precision)@0.70, 0.70, 0.70: bbox AP:97.29, 84.61, 64.70 bev AP:29.40, 20.19, 15.59 3d AP:21.90, 14.54, 11.42 aos AP:96.11, 81.54, 62.28

Car AP(Average Precision)@0.70, 0.50, 0.50: bbox AP:97.29, 84.61, 64.70 bev AP:63.65, 44.70, 34.33 3d AP:57.11, 39.35, 30.90 aos AP:96.11, 81.54, 62.28

Owen-Liuyuxuan commented 3 years ago
Car AP(Average Precision)@0.70, 0.50, 0.50:

means 2D/BEV/3D Iou 0.7/0.5/0.5

cnexah commented 3 years ago

太感谢了!