SA-Det3D
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Published
20 hours ago •
AutoVision-cloud
AutoVision-cloud
Small gap between the reproduced and the reported results
Dear authors,
Thanks for your good work. I have reproduced the FSA results on KITTI val split with 40 recall positions across PointPillars, SECOND, Point-RCNN, and PV-RCNN. I use 4 GPUs for training and use the training cfg files provided. Below is the results.
| --- | 3D | BEV |
|---|---|---|
| PointPillars (Report) | 79.04 | 88.47 |
| PointPillars (Reproduce) | 78.64 (-0.4) | 88.11 |
| SECOND (Report) | 81.86 | 90.01 |
| SECOND (Reproduce) | 81.48 (-0.38) | 90.20 |
| Point R-CNN (Report) | 82.10 | 88.37 |
| Point R-CNN (Reproduce) | 81.86 (-0.24) | 88.29 |
| PV-RCNN (Report) | 84.95 | 90.92 |
| PV-RCNN (Reproduce) | 84.68 (-0.27) | 91.01 |
Would you please provide some suggestions to remove the gap? Thank you very much in advance.
Regards, Yukang Chen
@yukang2017 @AutoVision-cloud hi, I want to train PV-RCNN_FSA model on v100 - 16gb on colab pro. But I can't train because of out of memory error even with batch size = 1. When I start training, memory keep increasing. How can I fix that problem ? Thank you so much.
