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Getting Started

Download Data

The OpenLane-V2 dataset is a large-scale dataset for scene structure perception and reasoning in the field of autonomous driving. Following OpenLane-V1, the first 3D lane dataset, we provide lane annotations in 3D space. The difference is that instead of lane lines, we annotate lane centerlines, which can be served as the trajectory for autonomous vehicles. Besides, we provide annotations on traffic elements (traffic lights and road signs) and their attribute, and the topology relationships among lane centerlines and between lane centerlines and traffic elements.

The dataset is divided into two subsets.

  • The subset_A serves as the primary subset and is utilized for the coming challenges and leaderboard, in which no external data, including the other subset, is allowed.

  • The subset_B can be used to test the generalization ability of the model. For more details, please refer to the corresponding pages: use of data, notes of annotation, and dataset statistics.

Download now to discover our dataset!

Install Devkit

We provide a devkit for easy access to the OpenLane-V2 dataset. After installing the package, the use of the dataset, such as loading images, loading meta data, and evaluating results, can be accessed through the API of openlanv2. For more details on the API, please refer to devkit.

Prepare Dataset

Please follow the steps below to get familiar with the OpenLane-V2 dataset.

  1. Run the following commands to install the environment for setting up the dataset:

    git clone https://github.com/OpenDriveLab/OpenLane-V2.git
cd OpenLane-V2
conda create -n openlanev2 python=3.8 -y
conda activate openlanev2
pip install -r requirements.txt
python setup.py develop

  2. Use links to download data manually from

    • OpenDataLab OpenDataLab,
    • Google Drive Google Drive,
    • Baidu Yun Baidu Yun.

    Then put them into the data/OpenLane-V2/ folder and unzip them. The resulting folder hierarchy is described here. Or use the following commands to download example data for a quick glance at the dataset:

    cd data/OpenLane-V2
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1Ni-L6u1MGKJRAfUXm39PdBIxdk_ntdc6' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1Ni-L6u1MGKJRAfUXm39PdBIxdk_ntdc6" -O OpenLane-V2_sample.tar
md5sum -c openlanev2.md5
ls *.tar* | xargs -n1 tar -xvf
cd ../..

  3. Run the tutorials on jupyter notebook to get familiar with the dataset and devkit.

Train a Model

Plug-ins are provided to start training models on the OpenLane-V2 dataset. We appreciate your valuable feedback and contributions to plug-ins on different frameworks.

TODO

  • Plug-in for dataset v2.0
  • Plug-in for dataset v1.0

Baseline

The plug-in is built on top of v1.0.0rc6 and tested under:

  • Python 3.8.15
  • PyTorch 1.9.1
  • CUDA 11.1
  • GCC 5.4.0
  • mmcv-full==1.5.2
  • mmdet==2.26.0
  • mmsegmentation==0.29.1

Please follow the instruction to install the framework. Assuming OpenLane-V2 is installed under OpenLane-V2/ and the framework is built under framework/, create a soft link to the plug-in file:

└── framework
    └── projects
        ├── example_project
        └── openlanev2 -> OpenLane-V2/plugin/models

Then you can train or evaluate a model using the config framework/projects/openlanev2/configs/baseline.py, whose path is replaced accordingly. Options can be passed to enable supported functions during evaluation (--eval *), such as --eval-options dump=True dump_dir=/PATH/TO/DUMP to save pickle file for submission and --eval-options visualization=True visualization_dir=/PATH/TO/VIS for visualization.