GANet

This repo is the pytorch implementation for our paper:

A Keypoint-based Global Association Network for Lane Detection

Installation

1. Create a conda virtual environment and activate it.

   conda create -n ganet python=3.7 -y
   conda activate ganet
   conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch -y
   pip install -r requirements/build.txt

2. Clone this repository and enter it:

   git clone https://github.com/Wolfwjs/GANet.git
   cd GANet
   python setup.py develop

Dataset

Download datasets and put it into datasets/ folder. And edit the data_root in the config file to your dataset path.

CULane

[CULane website] [Download]

The directory structure should be like follows:

[data-path]/culane
├── driver_23_30frame
├── driver_37_30frame
├── driver_100_30frame
├── driver_161_90frame
├── driver_182_30frame
├── driver_193_90frame
└── list
    └── test_split
    |   ├── test0_normal.txt
    |   ├── test1_crowd.txt
    |   ├── test2_hlight.txt
    |   ├── test3_shadow.txt
    |   ├── test4_noline.txt
    |   ├── test5_arrow.txt
    |   ├── test6_curve.txt
    |   ├── test7_cross.txt
    |   └── test8_night.txt
    └── train.txt
    └── test.txt
    └── val.txt

TuSimple

[TuSimple website] [Download]

The directory structure should be like follows:

[data-path]/tusimple
├── clips
├── label_data_0313.json
├── label_data_0531.json
├── label_data_0601.json
├── label_data_0601.json
├── test_label.json
└── test_baseline.json

Evaluation

Here are our GANet models (released on April 24th, 2022):

CULane

Version	Backbone	FPS	F1	Weights
Small	ResNet18	153	78.79	ganet_culane_resnet18.pth
Medium	ResNet-34	127	79.39	ganet_culane_resnet34.pth
Large	ResNet-101	63	79.63	ganet_culane_resnet101.pth

TuSimple

Version	Backbone	FPS	F1	Weights
Small	ResNet18	153	97.71	ganet_tusimple_resnet18.pth
Medium	ResNet-34	127	97.68	ganet_tusimple_resnet34.pth
Large	ResNet-101	33	97.45	ganet_tusimple_resnet101.pth

To evalute the model, download the corresponding weights file into the [CHECKPOINT] directory and run the following commands.

# For example, model = ganet-small 
sh slurm_test [PARTITION] [JOB_NAME] tusimple final_exp_res18_s8 [CHECKPOINT]

We use the official evaluation tools of CULane and TuSimple to evaluate the results. And we include them in tools directory which may be helpful for you.

Training

# For example, model = ganet-small 
sh slurm_train.sh [PARTITION] [JOB_NAME] tusimple final_exp_res18_s8 ./output

Citation

If you find this repo useful for your research, please cite

@inproceedings{ganet-cvpr2022,
  author    = {Jinsheng Wang, Yinchao Ma, Shaofei Huang, Tianrui Hui, Fei Wang, Chen Qian, Tianzhu Zhang},
  title     = {A Keypoint-based Global Association Network for Lane Detection},
  booktitle = {CVPR},
  year      = {2022},
}

Contact

For questions about our paper or code, please contact Jinsheng Wang or Yinchao Ma