Open ZZY816 opened 2 years ago
maybe try
voxel_size = [0.2, 0.2, 6]
tasks = [
dict(num_class=1, class_names=['Car']),
dict(num_class=1, class_names=['Pedestrian']),
],
train=dict(
type='RepeatDataset',
times=2)
Thank you very much and I will try your suggestion! It is very reasonable to reduce the voxel size and set two heads to improve performance. Meanwhile, I still wonder why my config leads to very poor performance (0.3-0.5 AP), which is far from the official performances. Note that my config is very similar with the official centerpoint config. Also, I can be sure that there is no problem with my data and evaluation. Because I successfully trained the pointpillars model on waymo and achieved the expected performance.
Hi @ZZY816,
Have you tried the new config and what are the new results? I would appreciate it if you could help answer this question! šš¼
@RunpeiDong After weeks of checking, I finally found out the reason. The poor model performance is mainly caused by the intensity of the waymo data. The intensity of waymo ranges from 0-40000 and it should be normalized. Adding the following codes to class ``LoadPointsFromFile(object)'' (line 425 in loading.py) can solve the problem.
points[:, 3] = np.tanh(points[:, 3])
Meanwhile, the output_shape
and grid size
in my config are not correct. They should be (468, 468) and [468, 468, 1] rather and (512, 512) and [512, 512, 1]
Hi @ZZY816, Thanks very much for your hard work and valuable answer. Great job!
the out_size_factor is need set 8, not 1?
@ZZY816 Hello! Thank you for your valuable comments. Could you tell me what the performance is after you normalize the intensity and correct the config?
does anyone have the model weights for the waymo dataset? Is it possible to share?
After referring to the official codes of OpenPCDet and CenterPoint, I wrote a CenterPoint model config trained on Waymo. But the strange thing is that the centerpoint-waymo model I trained on mmdet3D has poor performance. Can someone help me? Thanks!
Here is my config.
model config
dataset config
optimizer config
final config