https://github.com/Sense-GVT/Fast-BEV
Fast-BEV: A Fast and Strong Bird’s-Eye View Perception Baseline
https://github.com/Sense-GVT/Fast-BEV
2d-to-3d 3d autonomous autonomous-driving bird-eye-view detection multi-camera
Last synced: about 1 month ago
JSON representation
Fast-BEV: A Fast and Strong Bird’s-Eye View Perception Baseline
- Host: GitHub
- URL: https://github.com/Sense-GVT/Fast-BEV
- Owner: Sense-GVT
- License: other
- Created: 2022-10-19T13:21:52.000Z (over 2 years ago)
- Default Branch: dev
- Last Pushed: 2023-09-06T17:52:42.000Z (over 1 year ago)
- Last Synced: 2024-10-28T05:59:30.566Z (6 months ago)
- Topics: 2d-to-3d, 3d, autonomous, autonomous-driving, bird-eye-view, detection, multi-camera
- Language: Python
- Homepage:
- Size: 32.2 MB
- Stars: 620
- Watchers: 14
- Forks: 92
- Open Issues: 59
-
Metadata Files:
- Readme: README.md
- License: License
Awesome Lists containing this project
README
# Fast-BEV
[Fast-BEV: A Fast and Strong Bird’s-Eye View Perception Baseline](https://arxiv.org/abs/2301.12511)
## Better Inference Implementation
Thanks to the repository [CUDA-FastBEV](https://github.com/Mandylove1993/CUDA-FastBEV) inference using CUDA & TensorRT. And provide PTQ and QAT int8 quantization code.
You can refer to it to get faster speed.
## Usage
[usage](https://github.com/Sense-GVT/Fast-BEV/blob/dev/tools/fastbev_run.sh)
### Installation
* CUDA>=9.2
* GCC>=5.4
* Python>=3.6
* Pytorch>=1.8.1
* Torchvision>=0.9.1
* MMCV-full==1.4.0
* MMDetection==2.14.0
* MMSegmentation==0.14.1
### Dataset preparation
```
.
├── data
│ └── nuscenes
│ ├── maps
│ ├── maps_bev_seg_gt_2class
│ ├── nuscenes_infos_test_4d_interval3_max60.pkl
│ ├── nuscenes_infos_train_4d_interval3_max60.pkl
│ ├── nuscenes_infos_val_4d_interval3_max60.pkl
│ ├── v1.0-test
│ └── v1.0-trainval
```
[download](https://drive.google.com/drive/folders/10KyLm0xW3QiLhAefxBbXR-Hw_7nel_tm?usp=sharing)
### Pretraining
```
.
├── pretrained_models
│ ├── cascade_mask_rcnn_r18_fpn_coco-mstrain_3x_20e_nuim_bbox_mAP_0.5110_segm_mAP_0.4070.pth
│ ├── cascade_mask_rcnn_r34_fpn_coco-mstrain_3x_20e_nuim_bbox_mAP_0.5190_segm_mAP_0.4140.pth
│ └── cascade_mask_rcnn_r50_fpn_coco-mstrain_3x_20e_nuim_bbox_mAP_0.5400_segm_mAP_0.4300.pth
```
[download](https://drive.google.com/drive/folders/19BD4totDHtwnHtOqTdn0xYJh7stwYd9l?usp=sharing)
### Training
```
.
├── work_dirs
└── fastbev
└── exp
└── paper
└── fastbev_m0_r18_s256x704_v200x200x4_c192_d2_f4
│ ├── epoch_20.pth
│ ├── latest.pth -> epoch_20.pth
│ ├── log.eval.fastbev_m0_r18_s256x704_v200x200x4_c192_d2_f4.02062323.txt
│ └── log.test.fastbev_m0_r18_s256x704_v200x200x4_c192_d2_f4.02062309.txt
├── fastbev_m1_r18_s320x880_v200x200x4_c192_d2_f4
│ ├── epoch_20.pth
│ ├── latest.pth -> epoch_20.pth
│ ├── log.eval.fastbev_m1_r18_s320x880_v200x200x4_c192_d2_f4.02080000.txt
│ └── log.test.fastbev_m1_r18_s320x880_v200x200x4_c192_d2_f4.02072346.txt
├── fastbev_m2_r34_s256x704_v200x200x4_c224_d4_f4
│ ├── epoch_20.pth
│ ├── latest.pth -> epoch_20.pth
│ ├── log.eval.fastbev_m2_r34_s256x704_v200x200x4_c224_d4_f4.02080021.txt
│ └── log.test.fastbev_m2_r34_s256x704_v200x200x4_c224_d4_f4.02080005.txt
├── fastbev_m4_r50_s320x880_v250x250x6_c256_d6_f4
│ ├── epoch_20.pth
│ ├── latest.pth -> epoch_20.pth
│ ├── log.eval.fastbev_m4_r50_s320x880_v250x250x6_c256_d6_f4.02080021.txt
│ └── log.test.fastbev_m4_r50_s320x880_v250x250x6_c256_d6_f4.02080005.txt
└── fastbev_m5_r50_s512x1408_v250x250x6_c256_d6_f4
├── epoch_20.pth
├── latest.pth -> epoch_20.pth
├── log.eval.fastbev_m5_r50_s512x1408_v250x250x6_c256_d6_f4.02080021.txt
└── log.test.fastbev_m5_r50_s512x1408_v250x250x6_c256_d6_f4.02080001.txt
```
[download](https://drive.google.com/drive/folders/1Ja9mqOE0iGPysVxmLSrZyUoCEBYu5fMH?usp=sharing)
### Deployment
TODO
## View Transformation Latency on device
[2D-to-3D on CUDA & CPU](https://github.com/Sense-GVT/Fast-BEV/tree/dev/script/view_tranform_cuda)
## Citation
```
@article{li2023fast,
title={Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline},
author={Li, Yangguang and Huang, Bin and Chen, Zeren and Cui, Yufeng and Liang, Feng and Shen, Mingzhu and Liu, Fenggang and Xie, Enze and Sheng, Lu and Ouyang, Wanli and others},
journal={arXiv preprint arXiv:2301.12511},
year={2023}
}
```