Skip to content

Latest commit

 

History

History
187 lines (145 loc) · 10.4 KB

README.md

File metadata and controls

187 lines (145 loc) · 10.4 KB
Raw

BEVDet

News

  • 2024.07.01 DAL is accepted to ECCV24.

  • 2023.11.08 Support DAL for 3D object detection with LiDAR-camera fusion. [Arxiv]

  • History

Main Results

Nuscenes Detection

Config mAP NDS Latency(ms) FPS Model Log
BEVDet-R50 28.3 35.0 29.1/4.2/33.3 30.7 baidu baidu
BEVDet-R50-CBGS 31.3 39.8 28.9/4.3/33.2 30.1 baidu baidu
BEVDet-R50-4D-CBGS 31.4/35.4# 44.7/44.9# 29.1/4.3/33.4 30.0 baidu baidu
BEVDet-R50-4D-Depth-CBGS 36.1/36.2# 48.3/48.4# 35.7/4.0/39.7 25.2 baidu baidu
BEVDet-R50-4D-Stereo-CBGS 38.2/38.4# 49.9/50.0# - - baidu baidu
BEVDet-R50-4DLongterm-CBGS 34.8/35.4# 48.2/48.7# 30.8/4.2/35.0 28.6 baidu baidu
BEVDet-R50-4DLongterm-Depth-CBGS 39.4/39.9# 51.5/51.9# 38.4/4.0/42.4 23.6 baidu baidu
BEVDet-R50-4DLongterm-Stereo-CBGS 41.1/41.5# 52.3/52.7# - - baidu baidu
BEVDet-STBase-4D-Stereo-512x1408-CBGS 47.2# 57.6# - - baidu baidu
DAL-Tiny 67.4 71.3 - 16.6 baidu baidu
DAL-Base 70.0 73.4 - 10.7 baidu baidu
DAL-Large 71.5 74.0 - 6.10 baidu baidu

# align previous frame bev feature during the view transformation.

Depth: Depth supervised from Lidar as BEVDepth.

Longterm: cat 8 history frame in temporal modeling. 1 by default.

Stereo: A private implementation that concat cost-volumn with image feature before executing model.view_transformer.depth_net.

The latency includes Network/Post-Processing/Total. Training without CBGS is deprecated.

Nuscenes Occupancy

Config mIOU Model Log
BEVDet-Occ-R50-4D-Stereo-2x 36.1 baidu baidu
BEVDet-Occ-R50-4D-Stereo-2x-384x704 37.3 baidu baidu
BEVDet-Occ-R50-4DLongterm-Stereo-2x-384x704 39.3 baidu baidu
BEVDet-Occ-STBase-4D-Stereo-2x 42.0 baidu baidu

Inference latency with different backends

Backend 256x704 384x1056 512x1408 640x1760
PyTorch 28.9 49.7 78.7 113.4
TensorRT 14.0 22.8 36.5 53.0
TensorRT-FP16 4.94 7.96 12.4 17.9
TensorRT-INT8 2.93 4.41 6.58 9.19
TensorRT-INT8(Xavier) 25.0 - - -
  • Evaluate with BEVDet-R50-CBGS on a RTX 3090 GPU by default. We omit the postprocessing, which spends up to 5 ms with the PyTorch backend.

Get Started

Installation and Data Preparation

step 1. Please prepare environment as that in Docker.

step 2. Prepare bevdet repo by.

git clone https://github.com/HuangJunJie2017/BEVDet.git
cd BEVDet
pip install -v -e .

step 3. Prepare nuScenes dataset as introduced in nuscenes_det.md and create the pkl for BEVDet by running:

python tools/create_data_bevdet.py

step 4. For Occupancy Prediction task, download (only) the 'gts' from CVPR2023-3D-Occupancy-Prediction and arrange the folder as:

└── nuscenes
    ├── v1.0-trainval (existing)
    ├── sweeps  (existing)
    ├── samples (existing)
    └── gts (new)

Train model

# single gpu
python tools/train.py $config
# multiple gpu
./tools/dist_train.sh $config num_gpu

Test model

# single gpu
python tools/test.py $config $checkpoint --eval mAP
# multiple gpu
./tools/dist_test.sh $config $checkpoint num_gpu --eval mAP

Estimate the inference speed of BEVDet

# with pre-computation acceleration
python tools/analysis_tools/benchmark.py $config $checkpoint --fuse-conv-bn
# 4D with pre-computation acceleration
python tools/analysis_tools/benchmark_sequential.py $config $checkpoint --fuse-conv-bn
# view transformer only
python tools/analysis_tools/benchmark_view_transformer.py $config $checkpoint

Estimate the flops of BEVDet

python tools/analysis_tools/get_flops.py configs/bevdet/bevdet-r50.py --shape 256 704

Visualize the predicted result.

  • Private implementation. (Visualization remotely/locally)
python tools/test.py $config $checkpoint --format-only --eval-options jsonfile_prefix=$savepath
python tools/analysis_tools/vis.py $savepath/pts_bbox/results_nusc.json

Convert to TensorRT and test inference speed.

1. install mmdeploy from https://github.com/HuangJunJie2017/mmdeploy
2. convert to TensorRT
python tools/convert_bevdet_to_TRT.py $config $checkpoint $work_dir --fuse-conv-bn --fp16 --int8
3. test inference speed
python tools/analysis_tools/benchmark_trt.py $config $engine

Acknowledgement

This project is not possible without multiple great open-sourced code bases. We list some notable examples below.

Beside, there are some other attractive works extend the boundary of BEVDet.

Bibtex

If this work is helpful for your research, please consider citing the following BibTeX entries.

@article{huang2023dal,
  title={Detecting As Labeling: Rethinking LiDAR-camera Fusion in 3D Object Detection},
  author={Huang, Junjie and Ye, Yun and Liang, Zhujin and Shan, Yi and Du, Dalong},
  journal={arXiv preprint arXiv:2311.07152},
  year={2023}
}

@article{huang2022bevpoolv2,
  title={BEVPoolv2: A Cutting-edge Implementation of BEVDet Toward Deployment},
  author={Huang, Junjie and Huang, Guan},
  journal={arXiv preprint arXiv:2211.17111},
  year={2022}
}

@article{huang2022bevdet4d,
  title={BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection},
  author={Huang, Junjie and Huang, Guan},
  journal={arXiv preprint arXiv:2203.17054},
  year={2022}
}

@article{huang2021bevdet,
  title={BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View},
  author={Huang, Junjie and Huang, Guan and Zhu, Zheng and Yun, Ye and Du, Dalong},
  journal={arXiv preprint arXiv:2112.11790},
  year={2021}
}