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https://github.com/Megvii-BaseDetection/BEVStereo
Official code for BEVStereo
https://github.com/Megvii-BaseDetection/BEVStereo
Last synced: 13 days ago
JSON representation
Official code for BEVStereo
- Host: GitHub
- URL: https://github.com/Megvii-BaseDetection/BEVStereo
- Owner: Megvii-BaseDetection
- License: mit
- Created: 2022-08-18T10:36:06.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2022-09-22T02:53:01.000Z (about 2 years ago)
- Last Synced: 2024-08-01T03:42:48.068Z (3 months ago)
- Language: Python
- Size: 1.13 MB
- Stars: 253
- Watchers: 13
- Forks: 14
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
## BEVStereo
BEVStereo is a new multi-view 3D object detector using temporal stereo to enhance depth estimation.
## Updates!!
* 【2022/09/22】 We released our paper on [Arxiv](https://arxiv.org/abs/2209.10248).
* 【2022/08/24】 We submitted our result on [nuScenes Detection Task](https://nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Camera) and achieved the SOTA.
## Quick Start
### Installation
**Step 0.** Install [pytorch](https://pytorch.org/)(v1.9.0).
**Step 1.** Install [MMDetection3D](https://github.com/open-mmlab/mmdetection3d)(v1.0.0rc4).
**Step 2.** Install requirements.
```shell
pip install -r requirements.txt
```
**Step 3.** Install BEVDepth(gpu required).
```shell
python setup.py develop
```
### Data preparation
We use the same data format as [BEVDepth](https://github.com/Megvii-BaseDetection/BEVDepth), if you have processed it before, please skip.
**Step 0.** Download nuScenes official dataset.
**Step 1.** Symlink the dataset root to `./data/`.
```
ln -s [nuscenes root] ./data/
```
The directory will be as follows.
```
BEVDepth
├── data
│ ├── nuScenes
│ │ ├── maps
│ │ ├── samples
│ │ ├── sweeps
│ │ ├── v1.0-test
| | ├── v1.0-trainval
```
**Step 2.** Prepare infos.
```
python scripts/gen_info.py
```
**Step 3.** Prepare depth gt.
```
python scripts/gen_depth_gt.py
```
### Tutorials
**Train.**
```
python [EXP_PATH] --amp_backend native -b 8 --gpus 8
```
**Eval.**
```
python [EXP_PATH] --ckpt_path [CKPT_PATH] -e -b 8 --gpus 8
```
### Benchmark
|Exp| Frames |EMA| CBGS |mAP |mATE| mASE | mAOE |mAVE| mAAE | NDS | weights |
| ------ | :---: | :---: | :---: | :---: |:---: |:---: | :---: | :----: | :----: | :----: | :----: |
|[R50](exps/bev_stereo_lss_r50_256x704_128x128_24e_key4.py)|key + sweep4| | |0.3427| 0.6560 | 0.2784| 0.5982| 0.5347| 0.2228| 0.4423|[github](https://github.com/Megvii-BaseDetection/BEVStereo/releases/download/v0.0.2/bev_stereo_lss_r50_256x704_128x128_24e_key4.pth)|
|[R50](exps/bev_stereo_lss_r50_256x704_128x128_24e_key4.py)|key + sweep4|√| |0.3435|0.6585|0.2757|0.5792|0.5034|0.2163|0.4485|[github](https://github.com/Megvii-BaseDetection/BEVStereo/releases/download/v0.0.2/bev_stereo_lss_r50_256x704_128x128_24e_key4_ema.pth)|
|[R50](exps/bev_stereo_lss_r50_256x704_128x128_24e_2key.py)|key + key|| | 0.3456 | 0.6589 | 0.2774 | 0.5500 | 0.4980 | 0.2278 | 0.4516 | [github](https://github.com/Megvii-BaseDetection/BEVStereo/releases/download/v0.0.2/bev_stereo_lss_r50_256x704_128x128_24e_2key.pth)|
|[R50](exps/bev_stereo_lss_r50_256x704_128x128_24e_2key_ema.py)|key + key|√| | 0.3494 | 0.6671 |0.2785 | 0.5606 | 0.4686 | 0.2295 | 0.4543 | [github](https://github.com/Megvii-BaseDetection/BEVStereo/releases/download/v0.0.2/bev_stereo_lss_r50_256x704_128x128_24e_2key_ema.pth)|
|[R50](exps/bev_stereo_lss_r50_256x704_128x128_20e_cbgs_2key_da.py)|key + key| |√| 0.3576 | 0.6071 | 0.2684 | 0.4157 | 0.3928 | 0.2021 | 0.4902 | [github](https://github.com/Megvii-BaseDetection/BEVStereo/releases/download/v0.0.2/bev_stereo_lss_r50_256x704_128x128_20e_cbgs_2key_da.pth)|
|[R50](exps/bev_stereo_lss_r50_256x704_128x128_20e_cbgs_2key_da_ema.py)|key + key|√|√| 0.3721 | 0.5980 | 0.2701 | 0.4381 | 0.3672 | 0.1898 | 0.4997 | [github](https://github.com/Megvii-BaseDetection/BEVStereo/releases/download/v0.0.2/bev_stereo_lss_r50_256x704_128x128_20e_cbgs_2key_da_ema.pth)|
## Acknowledgments
This project exists thanks to all the people who instruct.
[@Haotian-Zh](https://github.com/Tinyyyy) [@xavierwu95](https://github.com/xavierwu95) [@Tai-Wang](https://github.com/Tai-Wang)