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AI-Mobility-Research-Lab/centerpoint-nuscenes-roadside-8k
centerpoint-nuscenes-roadside-8k is a machine learning model from AI-Mobility-Research-Lab. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
This checkpoint was trained with CenterPoint (pillar-based) implemented in OpenPCDet on a custom roadside NuScenes-format dataset.
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Updated Mar 12, 2026
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From the Hugging Face model README
This checkpoint was trained with CenterPoint (pillar-based) implemented in OpenPCDet on a custom roadside NuScenes-format dataset.
| Property | Value |
|---|---|
| Architecture | CenterPoint (PillarVFE + PointPillarScatter + BaseBEVBackbone + CenterHead) |
| Classes | car, truck, pedestrian, bicycle |
| Point cloud range | [−51.2, −51.2, −5.0] → [51.2, 51.2, 3.0] m |
| Voxel size | 0.2 × 0.2 × 8.0 m (pillar) |
| Training epochs | 80 |
| Point features | x, y, z, intensity, timestamp (5-dim) |
| File | Description |
|---|---|
checkpoint_epoch_80.pth | Full model checkpoint (weights + optimizer state) |
centerpoint_8k_rebuilt_consecutive_val_6401_7200.yaml | Model + training config |
nuscenes_custom_8k_rebuilt_consecutive_val_6401_7200.yaml | Dataset config |
An interactive Gradio demo is available in the companion demo/ folder of the
OpenPCDet repository.
Run it with:
conda activate openpcdet_torch25
python demo/gradio_demo.py
If you use this model, please cite OpenPCDet:
@misc{openpcdet2020,
title={OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Clouds},
author={OpenPCDet Development Team},
howpublished={\url{https://github.com/open-mmlab/OpenPCDet}},
year={2020}
}