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O2SFormer

Pytorch implementation of our paper End-to-End Lane detection with One to Several Transformer. We will merge the O2SFormer into PPLanedet, which is a lane detection toolbox based on PaddlePaddle.

News

[2024/2/28]: Lane2Seq is accepted by CVPR2024. Arxiv paper is here.

[2023/5/9]: We release the new version on arxiv.

[2023/5/2]: We update the arxiv paper.

[2023/5/1]: We release the code of O2SFormer, a SOTA lane detection method with DETR like architecture.

Overview

Abstract: Although lane detection methods have shown impressive performance in real-world scenarios, most of methods require post-processing which is not robust enough. Therefore, end-to-end detectors like DEtection TRansformer(DETR) have been introduced in lane detection. However, one-to-one label assignment in DETR can degrade the training efficiency due to label semantic conflicts. Besides, positional query in DETR is unable to provide explicit positional prior, making it difficult to be optimized. In this paper, we present the One-to-Several Transformer(O2SFormer). We first propose the one-to-several label assignment, which combines one-to-one and one- to-many label assignments to improve the training efficiency while keeping end-to-end detection. To overcome the difficulty in optimizing one-to-one assignment. We further propose the layer-wise soft label which adjusts the positive weight of positive lane anchors across different decoder layers. Finally, we design the dynamic anchor-based positional query to explore positional prior by incorporating lane anchors into positional query. Experimental results show that O2SFormer significantly speed up the convergence of DETR and outperforms Transformer-based and CNN-based detectors on CULane dataset.

Model Zoo

Results on CULane

Installation

We construct the code of O2SFormer based on mmdetection. We test our models under python=3.7.13,pytorch=1.12.1,cuda=10.2,mmdet=2.28.2,mmcv=1.7.1. It should be noted that mmdet<=2.28.x.

1. Clone this repo

git clone https://github.com/zkyseu/O2SFormer.git
cd O2SFormer

2. Install Pytorch and torchvision

Follow the instruction on https://pytorch.org/get-started/locally/.

# an example:
conda install -c pytorch pytorch torchvision

3. Install other needed packages

pip install -r requirement.txt
# Note: If you meet errors when install mmdetection or mmcv, we suggset you can refer to mmdetection repo for more details

4. Fix errors caused by PReLU in MMCV

vim /path/mmcv/cnn/bricks/transformer.py

Then, following pull request in MMCV to solve this problem.

Data

In our paper, we use CULane to evaluate the O2SFormer

Please download CULane dataset. Unzip data to $CULANEROOT and then create $data directory

cd $LANEDET_ROOT
mkdir -p data
ln -s $CULANEROOT data/CULane

Organize the CULane as following:

$CULANEROOT/driver_xx_xxframe    # data folders x6
$CULANEROOT/laneseg_label_w16    # lane segmentation labels
$CULANEROOT/list                 # data lists

Run

Download our O2SFormer model checkpoint with ResNet50 and perform the command below. You can expect to get the F1 score about 77.83.

bash eval.sh  /path/to/your/config /path/to/your/checkpoint

We use the O2SFormer trained for 20 epochs as an example to demonstrate how to train our model.

You can also train our model on a single process:

bash train.sh /path/config

You can run our model on multi-GPUs with following code:

bash dist_train.sh /path/config num_gpus

 python infer_img.py configs/resnet_34_culane.py --checkpoint model_res34.pth --img_path /path/img

Acknowledgement

• Our project is based on mmdetection and CLRNet. Thanks for their great work!
• We also thank the PaddleDetection for providing the code of HybridEncoder.

LICNESE

O2SFormer is released under the Apache 2.0 license. Please see the LICENSE file for more information.

Citation

If you find our work helpful for your research, please consider citing the following BibTeX entry.

@misc{zhou2023o2sformer,
      title={End to End Lane detection with One-to-Several Transformer}, 
      author={Kunyang Zhou and Rui Zhou},
      year={2023},
      eprint={2305.00675},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}