yukang2017
commented
1 year ago https://github.com/dvlab-research/VoxelNeXt/issues/16
This is the result I get from this config.
I just wrote this config for an example on KITTI, without any hyper-parameters tuning. I think some hyper-parameters can be adjusted to get better performance.
Hi, I followed the released config file to train VoxelNeXt on the KITTI, but the detection results don't seem to be good?
Here is the epoch with the best results trained on my machine.
2023-05-30 13:25:51,687 INFO * Performance of EPOCH 72 *** 2023-05-30 13:25:51,688 INFO Generate label finished(sec_per_example: 0.0634 second). 2023-05-30 13:25:51,688 INFO recall_roi_0.3: 0.000000 2023-05-30 13:25:51,688 INFO recall_rcnn_0.3: 0.935357 2023-05-30 13:25:51,688 INFO recall_roi_0.5: 0.000000 2023-05-30 13:25:51,688 INFO recall_rcnn_0.5: 0.873562 2023-05-30 13:25:51,688 INFO recall_roi_0.7: 0.000000 2023-05-30 13:25:51,689 INFO recall_rcnn_0.7: 0.633500 2023-05-30 13:25:51,715 INFO Average predicted number of objects(3769 samples): 12.604 2023-05-30 13:26:09,365 INFO Car AP@0.70, 0.70, 0.70: bbox AP:90.4678, 89.2926, 88.3760 bev AP:89.0743, 86.4952, 83.4077 3d AP:86.5793, 76.6896, 74.6050 aos AP:90.45, 89.11, 88.13 Car AP_R40@0.70, 0.70, 0.70: bbox AP:95.5828, 91.9155, 90.6527 bev AP:91.6419, 87.5357, 85.0153 3d AP:87.9198, 77.8969, 75.1770 aos AP:95.56, 91.71, 90.39 Car AP@0.70, 0.50, 0.50: bbox AP:90.4678, 89.2926, 88.3760 bev AP:94.7389, 89.6084, 89.1113 3d AP:94.6626, 89.5168, 88.9023 aos AP:90.45, 89.11, 88.13 Car AP_R40@0.70, 0.50, 0.50: bbox AP:95.5828, 91.9155, 90.6527 bev AP:96.8324, 94.3452, 93.6283 3d AP:96.7766, 94.1481, 91.5978 aos AP:95.56, 91.71, 90.39 Pedestrian AP@0.50, 0.50, 0.50: bbox AP:76.9811, 73.3218, 69.6398 bev AP:65.7048, 61.0455, 56.5700 3d AP:62.2981, 56.9214, 52.1742 aos AP:75.42, 71.20, 67.12 Pedestrian AP_R40@0.50, 0.50, 0.50: bbox AP:77.9146, 73.9838, 70.5503 bev AP:66.2811, 60.9713, 55.7163 3d AP:61.5019, 56.5423, 50.7263 aos AP:76.24, 71.67, 67.70 Pedestrian AP@0.50, 0.25, 0.25: bbox AP:76.9811, 73.3218, 69.6398 bev AP:82.2534, 79.8235, 76.3670 3d AP:82.1528, 79.5656, 76.0224 aos AP:75.42, 71.20, 67.12 Pedestrian AP_R40@0.50, 0.25, 0.25: bbox AP:77.9146, 73.9838, 70.5503 bev AP:84.9853, 81.9345, 77.8001 3d AP:84.8836, 81.4974, 77.4063 aos AP:76.24, 71.67, 67.70 Cyclist AP@0.50, 0.50, 0.50: bbox AP:87.4056, 73.9096, 71.1011 bev AP:84.3527, 69.5915, 65.2032 3d AP:81.7647, 66.3772, 62.1164 aos AP:87.32, 73.52, 70.74 Cyclist AP_R40@0.50, 0.50, 0.50: bbox AP:89.9100, 75.6070, 71.9232 bev AP:86.1329, 70.2481, 66.1244 3d AP:82.1792, 65.8815, 61.9532 aos AP:89.81, 75.18, 71.51 Cyclist AP@0.50, 0.25, 0.25: bbox AP:87.4056, 73.9096, 71.1011 bev AP:86.7529, 71.9742, 69.1377 3d AP:86.7529, 71.9742, 69.1377 aos AP:87.32, 73.52, 70.74 Cyclist AP_R40@0.50, 0.25, 0.25: bbox AP:89.9100, 75.6070, 71.9232 bev AP:89.0928, 73.4686, 69.6789 3d AP:89.0928, 73.4686, 69.6789 aos AP:89.81, 75.18, 71.51