Implementation of SimMOD: A Simple Baseline for Multi-Camera 3D Object Detection. (AAAI 2023)
Check installation for installation.
Check data_preparation for preparing the nuScenes dataset.
To train SimMOD with 8 GPUs, run:
bash tools/dist_train.sh $CONFIG 8For evaluation, use:
bash tools/dist_test.sh $CONFIG $CKPT 8 --eval=bboxWe provide the pretrained models for SimMOD.
| Method | Pretrain | mAP | NDS | Log | Weights |
|---|---|---|---|---|---|
| SimMOD-r50 | ImageNet | 33.1 | 42.7 | log | model |
| SimMOD-r101 | ImageNet | 34.9 | 43.1 | log | model |
| SimMOD-r101 | FCOS3D | 37.0 | 45.4 | log | model |
| SimMOD-r101 | NuImg | 37.6 | 46.1 | log | model |
This project is mainly based on DETR3D. Thanks for their great work.
If you find this repo useful for your research, please consider citing the paper:
@article{zhang2022simple,
title={A Simple Baseline for Multi-Camera 3D Object Detection},
author={Zhang, Yunpeng and Zheng, Wenzhao and Zhu, Zheng and Huang, Guan and Zhou, Jie and Lu, Jiwen},
journal={arXiv preprint arXiv:2208.10035},
year={2022}
}