Comments (4)
Thanks for the reply, I think I've made it work now. One more question, how to change the number of target classes for the network? Can I just change the CLASS_NAMES in the network YAML directly?
How did you managed to preprocess the kitti dataset without the calibration images? I also have a lidar only dataset
from centerpoint-kitti.
Since this is a Lidar-only object detection model, could we preprocess the data without the images? I think if we don't use the evaluation metric of 'bbox', we can ignore the infos from images, right?
I think so ?
Another question is, why do we only count the points inside the gt in the FOV of the image?
only these boxes are annotated
from centerpoint-kitti.
Thanks for the reply, I think I've made it work now. One more question, how to change the number of target classes for the network? Can I just change the CLASS_NAMES in the network YAML directly?
from centerpoint-kitti.
yeah, i think this should work
from centerpoint-kitti.
Related Issues (20)
- How to detect bounding boxes at the back of the vehicle? HOT 3
- How do I make the code run?Does this code actually run? HOT 1
- How can I get the kitti test set's labels? HOT 4
- Running the NN on Colab HOT 1
- help,KeyError: 'CenterPoint' HOT 10
- On model robustness HOT 3
- Scores on the demo, Voxelization HOT 5
- Question about the training HOT 1
- rank of centerpoint on kitti car hard benchmark HOT 1
- about multihead training HOT 1
- about the test on tracking in this project HOT 3
- RuntimeError: Expected object of backend CUDA but got backend CPU for argument #2 'other'
- RuntimeError: CUDA error: out of memory
- model loading for spconv 2.x
- RuntimeError: Error compiling objects for extension HOT 1
- multi gpu training result is incorrect , how to correct it ? HOT 1
- Details about the KITTI model
- dist.init_process_group hangs HOT 2
- The result of PointPillars Anchor-based and Center-based is different. HOT 2
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from centerpoint-kitti.