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Track 1 - Robust BEV Detection

Preparation

We implemented BEVFormer as the baseline model for Track 1. The baseline model was trained on the official train split of the nuScenes dataset and evaluated on our robustness probing sets under different corruptions.

This codebase provides basic instructions for the reproduction of the baseline model in the RoboDrive Challenge.

⚙️ Installation

Kindly refer to GET_STARTED.md to set up environments and download necessary checkpoints.

♨️ Datasets

We use data under the nuScenes train split as the training set and the RoboDrive robustness probing data as the evaluation sets. For training data preparation, kindly refer to PREPARE_DATASET.md.

For evaluation data preparation, kindly download the dataset from the following resources:

Type Phase 1 Phase 2
Google Drive link1 or link2 link1 or link2

Uncompress the downloaded dataset and organize the folder structure as follows:

.
├── ckpts
│   ├── bevformer_r101_dcn_24ep.pth
│   └── bevformer_small_epoch_24.pth
├── data
│   ├── nuscenes
│   └── robodrive-release
├── projects
└── tools

Next, run the following command to generate the .pkl file for the evaluation sets:

bash tools/create_data.sh

🚙 Hint: You can download our generated robodrive_infos_temporal_test.pkl file from this Google Drive link.

The nuscenes folder should end up looking like this:

.
├── basemap
├── can_bus
├── can_bus.zip
├── expansion
├── lidarseg
├── maps
├── nuscenes_infos_temporal_train.pkl
├── nuscenes_infos_temporal_val.pkl
├── nuScenes-panoptic-v1.0-all
├── prediction
├── robodrive_infos_temporal_test.pkl
├── robodrive-v1.0-test
├── samples
├── sweeps
├── v1.0-mini
├── v1.0-test
└── v1.0-trainval

Getting Started

The training and evaluation instructions are summarized as follows.

🚀 Training

Kindly refer to GET_STARTED.md for the details regarding model training.

📊 Evaluation

Simply run the following command to evaluate the trained baseline model on the RoboDrive robustness probing sets:

cd BEVFormer
bash tools/dist_test_corruption.sh ./projects/configs/robodrive/bevformer_base.py ./ckpt/bevformer_r101_dcn_24ep.pth 1

The generated results will be saved in the folder like this:

.
├── brightness
│   └── results_nusc.json
├── color_quant
│   └── results_nusc.json
├── contrast
│   └── results_nusc.json
...
├── snow
└── zoom_blur

Next, kindly merge all the .json files into a single pred.json file and zip compress it.

You can merge the results using the following command:

python ./tools/convert_submit.py

⚠️ Note: The prediction file MUST be named as pred.json. The .zip file can be named as you like.

Finally, upload the compressed file to Track 1's evaluation server for model evaluation.

🚙 Hint: We provided the baseline submission file at this Google Drive link. Feel free to download and check it for reference and learn how to correctly submit the prediction files to the server.

Customized Dataset

To customize your own dataset, simply build your dataset based on NuScenesCorruptionDataset.

We mainly modified the data loading part. We only consider the subset of scenes for each corruption type, below is an example.

For more information, kindly refer to corruption_dataset.py.

data = mmcv.load(ann_file)

data_infos = data['infos']
sample_data_infos = []

for data_info in data_infos:
    if self.corruption is not None:
        if data_info['scene_token'] in self.sample_scenes_dict[self.corruption]:
            sample_data_infos.append(data_info)
    else:
        sample_data_infos.append(data_info)

Baseline Results

Phase 1

Corruption NDS mAP mATE mASE mAOE mAVE mAAE
Bright 0.4132 0.3157 0.8234 0.3738 0.5239 0.4568 0.2691
Dark 0.3712 0.2285 0.7797 0.3030 0.5334 0.4555 0.3586
Fog 0.5534 0.4519 0.6770 0.2665 0.3386 0.3069 0.1369
Frost 0.1718 0.0649 1.0171 0.4675 0.6829 1.0579 0.4562
Snow 0.2549 0.1402 0.8814 0.3362 0.7076 1.0516 0.2264
Contrast 0.2196 0.1216 0.9530 0.4754 1.0561 0.7240 0.2599
Defocus Blur 0.2762 0.1608 0.7869 0.4371 0.8307 0.6749 0.3125
Glass Blur 0.3327 0.1820 0.8334 0.3711 0.6560 0.4098 0.3124
Motion Blur 0.2760 0.1885 0.8369 0.3567 0.8282 0.7810 0.3798
Zoom Blur 0.1193 0.0082 1.1021 0.5698 0.8678 2.4270 0.4104
Elastic Transform 0.4997 0.3694 0.6417 0.2887 0.3154 0.4376 0.1664
Color Quant 0.3088 0.1878 0.8062 0.4045 0.9392 0.4420 0.2589
Gaussian Noise 0.2104 0.0833 0.8578 0.4349 0.6174 0.8689 0.5338
Impluse Noise 0.2411 0.1011 0.8660 0.4649 0.7304 0.6126 0.4205
Shot Noise 0.3441 0.1545 0.8484 0.2969 0.4450 0.5413 0.1995
ISO Noise 0.2505 0.1076 0.9036 0.3045 0.5644 1.5776 0.2607
Pixelate 0.3499 0.2668 0.8558 0.3047 0.4907 1.1205 0.1831
JPEG 0.4304 0.2552 0.7405 0.2798 0.2946 0.3910 0.2666

Phase 2

To be updated.

References

Kindly cite the corresponding paper(s) once you use the baseline model in this track.

@inproceedings{li2022bevformer,
    title = {BEVFormer: Learning Bird’s Eye View Representation from Multi-Camera Images via Spatiotemporal Transformers},
    author = {Li, Zhiqi and Wang, Wenhai and Li, Hongyang and Xie, Enze and Sima, Chonghao and Lu, Tong and Qiao, Yu and Dai, Jifeng},
    booktitle = {European Conference on Computer Vision},
    pages = {1-18},
    year = {2022},
}