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I'm using the official example scripts/configs for the officially supported tasks/models/datasets.
main branch https://github.com/open-mmlab/mmdetection3d
all is ok
train
我参照s3dis数据集制作了自己的数据集,就是根据一个大场景的点云(xyzrgb),识别里面的电线杆和路灯。一开始我只标注了电线杆和路灯,训练和测试发现,背景也会被误识别成电线杆或者路灯。之后我增加了背景类(事实上我只想识别出电线杆和路灯,为了让背景不乱识别,所以单独给他一个分类),当再次训练时,我发现背景类的准确率很高,而电线杆和路灯准确率为0, +---------+-------------+--------+--------+--------+--------+---------+ | classes | dianxiangan | ludeng | bg | miou | acc | acc_cls | +---------+-------------+--------+--------+--------+--------+---------+ | results | 0.0000 | 0.0000 | 0.9899 | 0.3300 | 0.9899 | 0.3333 | +---------+-------------+--------+--------+--------+--------+---------+
我猜可能是背景类的点云数量占比太大造成的,怎么解决这类问题呢? 此外,我的数据是自己标的因此数据量较少,不确定这种情况和数据量是否也有关系
1
The text was updated successfully, but these errors were encountered:
No branches or pull requests
Prerequisite
Task
I'm using the official example scripts/configs for the officially supported tasks/models/datasets.
Branch
main branch https://github.com/open-mmlab/mmdetection3d
Environment
all is ok
Reproduces the problem - code sample
train
Reproduces the problem - command or script
我参照s3dis数据集制作了自己的数据集,就是根据一个大场景的点云(xyzrgb),识别里面的电线杆和路灯。一开始我只标注了电线杆和路灯,训练和测试发现,背景也会被误识别成电线杆或者路灯。之后我增加了背景类(事实上我只想识别出电线杆和路灯,为了让背景不乱识别,所以单独给他一个分类),当再次训练时,我发现背景类的准确率很高,而电线杆和路灯准确率为0,
+---------+-------------+--------+--------+--------+--------+---------+
| classes | dianxiangan | ludeng | bg | miou | acc | acc_cls |
+---------+-------------+--------+--------+--------+--------+---------+
| results | 0.0000 | 0.0000 | 0.9899 | 0.3300 | 0.9899 | 0.3333 |
+---------+-------------+--------+--------+--------+--------+---------+
我猜可能是背景类的点云数量占比太大造成的,怎么解决这类问题呢?
此外,我的数据是自己标的因此数据量较少,不确定这种情况和数据量是否也有关系
Reproduces the problem - error message
1
Additional information
1
The text was updated successfully, but these errors were encountered: