BEVNeXt

This is the official repository of BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection. Paper link.

Installation & Dataset Preparation

Our repository is based on BEVDet. + Please follow docs/getting_started.md to prepare the Anaconda environment. + Prepare the nuScenes dataset based on instructions in docs/data_preparation.md. + Run the script to generate pkl: python tools/create_data_bevdet.py

Model Zoo

| Backbone | Pretrain | Method | NDS | mAP | Config | Download | |:---------------------------:|:----------:|:--------------:|:----:|-----:|:-----------------------------------------------------------:|:---------:| | R50 | ImageNet | BEVNeXt-Stage1 | - | - | config | model | | R50 | - | BEVNeXt-Stage2 | 54.8 | 43.7 | config | model | | R50 | Fcos3d | BEVNeXt-Stage1 | - | - | config | model | | R50 | - | BEVNeXt-Stage2 | 56.0 | 45.6 | config | model |

Training & Inference

• Training-Stage1: This stage uses no temporal information to warm the model up, as is done in SOLOFusion. # if R50 with perspective pretraining is to be used # remember to download the Fcos3d checkpoint and fill in the path in bevnext-pers-pretrained-stage1.py cfg="configs/bevnext/bevnext-stage1.py" work_dir="work_dirs/bevnext-stage1" bash tools/dist_train.sh $cfg 8 --work-dir $work_dir --seed 0
• Training-Stage2 (Single Node): This stage loads the weights from the previous stage and uses long-term temporal information for training. The BEV Encoder and Detection Heads from the previous stage are discarded. # remember to fill in the checkpoint path from the previous stage in bevnext-stage2.py cfg="configs/bevnext/bevnext-stage2.py" work_dir="work_dirs/bevnext-stage2" bash tools/dist_train.sh $cfg 8 --work-dir $work_dir --seed 0
• Training-Stage2 (Multi-Node): Obtaining historical features in a sliding window manner is generally slow. Using 16 gpus is recommended. cfg="configs/bevnext/bevnext-stage2.py" work_dir="work_dirs/bevnext-stage2" NNODES=2 NODE_RANK=your_node_rank MASTER_ADDR=your_master_node_ip \ bash tools/dist_train.sh $cfg 8 --work-dir $work_dir --seed 0
• Inference epoch_cnt=12 dir=your/path/to/ckpts bash tools/dist_test.sh $dir/*.py $dir/epoch_${epoch_cnt}_ema.pth 8 --eval mAP --no-aavt # Acknowledgements This codebase is largely based on the BEVDet Series. We also would like to thank the following repositories:
• BEVDet
• SOLOFusion
• FB-BEV
• mmdetection3d # Citation @inproceedings{li2024bevnext, title={BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection}, author={Li, Zhenxin and Lan, Shiyi and Alvarez, Jose M and Wu, Zuxuan}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={20113--20123}, year={2024} }