av2

Argoverse 2 API

Official GitHub repository for the Argoverse 2 family of datasets.

If you have any questions or run into any problems with either the data or API, please feel free to open a GitHub issue!

Announcements

Argoverse competitions are live!

• Argoverse 2
  • 3D Object Detection
    • Challenge Link: https://eval.ai/challenge/1710/overview
    • Baseline: https://github.com/benjaminrwilson/torchbox3d
  • Motion Forecasting
    • Challenge Link: https://eval.ai/challenge/1719/overview
• Argoverse 1
  • Stereo
    • Challenge Link: https://eval.ai/challenge/1704/overview

TL;DR

• Install the API: pip install av2
• Read the instructions to download the data.

Overview

• Setup
• Datasets
• Testing
• Contributing
• Citing
• License

Getting Started

Setup

The easiest way to install the API is via pip by running the following command:

pip install av2

Datasets

The Argoverse 2 family consists of four distinct datasets:

Dataset Name	Scenarios	Camera Imagery	Lidar	Maps	Additional Information
Sensor	1,000	:white_check_mark:	:white_check_mark:	:white_check_mark:	Sensor Dataset README
Lidar	20,000		:white_check_mark:	:white_check_mark:	Lidar Dataset README
Motion Forecasting	250,000			:white_check_mark:	Motion Forecasting Dataset README
Map Change (Trust, but Verify)	1,045	:white_check_mark:	:white_check_mark:	:white_check_mark:	Map Change Dataset README

Please see DOWNLOAD.md for detailed instructions on how to download each dataset.

Sensor Dataset

Lidar Dataset

Motion Forecasting Dataset

Map Change Dataset (Trust, but Verify)

Map API

Please refer to the map README for additional details about the common format for vector and raster maps that we employ across all AV2 datasets.

Compatibility Matrix

Python Version	linux	macOS	windows
3.8	:white_check_mark:	:white_check_mark:	:white_check_mark:
3.9	:white_check_mark:	:white_check_mark:	:white_check_mark:
3.10	:white_check_mark:	:white_check_mark:	:white_check_mark:

Testing

All incoming pull requests are tested using nox as part of the CI process. This ensures that the latest version of the API is always stable on all supported platforms. You can run the full suite of automated checks and tests locally using the following command:

nox -r

Contributing

Have a cool feature you'd like to add? Found an unhandled corner case? The Argoverse team welcomes contributions from the open source community - please open a PR using the following template!

Citing

Please use the following citation when referencing the Argoverse 2 Sensor, Lidar, or Motion Forecasting Datasets:

@INPROCEEDINGS { Argoverse2,
  author = {Benjamin Wilson and William Qi and Tanmay Agarwal and John Lambert and Jagjeet Singh and Siddhesh Khandelwal and Bowen Pan and Ratnesh Kumar and Andrew Hartnett and Jhony Kaesemodel Pontes and Deva Ramanan and Peter Carr and James Hays},
  title = {Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting},
  booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021)},
  year = {2021}
}

Use the following citation when referencing the Argoverse 2 Map Change Dataset:

@INPROCEEDINGS { TrustButVerify,
  author = {John Lambert and James Hays},
  title = {Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection},
  booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021)},
  year = {2021}
}

License

All code provided within this repository is released under the MIT license and bound by the Argoverse terms of use, please see LICENSE and NOTICE for additional details.