- The experiments are run with PyTorch 1.1, CUDA 10.0, and CUDNN 7.5.
- The training is conducted on 4 V100 GPUs in a DGX server.
- Testing times are measured on a TITAN RTX GPU with batch size 1.
We provide training / validation configurations, logs, pretrained models, and prediction files for all models in the paper
Model | Validation MAP | Validation NDS | Link |
---|---|---|---|
centerpoint_voxel_1440 | 59.6 | 66.8 | URL |
Please refer to LINK for centerpoint detection predicitons on nuScenes train/val/test sets.
These results are obtained before the sync bn bug fix + z axis augmentation .
Model | FPS | Validation MAP | Validation NDS | Link |
---|---|---|---|---|
centerpoint_voxel_1024 | 16 | 56.4 | 64.8 | URL |
Model | FPS | Validation MAP | Validation NDS | Link |
---|---|---|---|---|
centerpoint_pillar | 31 | 50.3 | 60.2 | URL |
Model | Tracking time | Total time | Validation AMOTA ↑ | Validation AMOTP ↓ | Link |
---|---|---|---|---|---|
centerpoint_voxel_1024 | 1ms | 64ms | 63.7* | 0.606 | URL |
*The numbers are from the centerpoint_voxel_1024 config (before the sync bn bug fix + z axis augmentation). Current detection models should perform slightly better.