| Name | vod-devkit JSON |
| Version |
1.1.2
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| download |
| home_page | None |
| Summary | The official devkit of the View-of-Delft Prediction dataset. |
| upload_time | 2024-11-21 14:56:10 |
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| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
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No requirements were recorded.
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No Travis.
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# The View-of-Delft Prediction devkit
Welcome to the View-of-Delft Prediction (VoD-P) development kit. This repository contains the code and documentation associated with the VoD-P dataset.
## Overview
- [Introduction](#introduction)
- [Changelog](#changelog)
- [Devkit setup](#devkit-setup)
- [VoD-P](#vod-p)
- [VoD-P setup](#vod-p-setup)
- [Getting started with VoD-P](#getting-started-with-vod-p)
- [Submitting to the VoD-P leaderboard](#submitting-to-the-vod-p-leaderboard)
- [Citation](#citation)
## Introduction
The View-of-Delft Prediction dataset is an extension of the [View-of-Delft dataset](https://intelligent-vehicles.org/datasets/view-of-delft/). It contains the 3D object annotations of the original dataset and additionally provides accurate 6-DoF global localisation and semantic map data.
The dataset is available in a format based on the [nuScenes dataset](https://www.nuscenes.org/), and hence this development kit is a modified version of the [nuScenes devkit](https://github.com/nutonomy/nuscenes-devkit).
## Changelog
- [2024-11-20] Launched the View-of-Delft Prediction leaderboard.
- [2024-11-15] Released a version of the development kit for Python 3.8.
- [2024-09-11] Released the View-of-Delft Prediction dataset and development kit.
## Devkit setup
The devkit is tested for Python 3.8. For a version of the devkit that is compatible with Python 3.6 and 3.7, see the v1.0.\* [PyPI releases](https://pypi.org/project/vod-devkit/) or [tags](https://github.com/tudelft-iv/view-of-delft-prediction-devkit/releases/).
To install Python, please check [here](https://github.com/tudelft-iv/vod-devkit/blob/master/docs/installation.md#install-python).
Our devkit is available and can be installed via [pip](https://pip.pypa.io/en/stable/installing/):
```
pip install vod-devkit
```
For an advanced installation, see [installation](docs/installation.md) for detailed instructions.
## VoD-P
### VoD-P setup
To download VoD-P, follow the instructions at the main [View-of-Delft dataset page](https://intelligent-vehicles.org/datasets/view-of-delft/).
Download the zipfile when you receive the access link.
Unzip the file and you should have the following folder structure:
```
/data/sets/vod
maps - Folder for all map files (vectorized .json files).
v1.0-* - JSON tables that include all the meta data and annotations. Each split (trainval, test) is provided in a separate folder.
```
### Getting started with VoD-P
Please follow these steps to make yourself familiar with the VoD dataset:
- Read the [main dataset page](https://intelligent-vehicles.org/datasets/view-of-delft/).
- [Request access](https://docs.google.com/forms/d/e/1FAIpQLSdKvkuKbzmJTn8raJBAWgekAJCpaQLS_ED63sUS89Ezo61RCQ/viewform) to the dataset.
- Download the dataset.
- Get the [vod-devkit code](https://github.com/tudelft-iv/vod-devkit/tree/main).
- Read the [tutorials](https://github.com/tudelft-iv/vod-devkit/tree/main/tutorials) or run one yourself using:
```
jupyter notebook $HOME/vod-devkit/tutorials/vod_tutorial.ipynb
```
- Read the View-of-Delft Prediction [paper](https://ieeexplore.ieee.org/document/10493110) for a closer look at the dataset.
- See the [FAQs](https://github.com/tudelft-iv/view-of-delft-prediction-devkit/blob/main/docs/faqs.md).
### Submitting to the VoD-P leaderboard
The VoD-P benchmark leaderboard can be found at https://eval.ai/web/challenges/challenge-page/2410/overview.
See the [benchmark instructions](https://github.com/tudelft-iv/view-of-delft-prediction-devkit/blob/main/docs/benchmark_instructions.md) for the submission format and rules.
## Citation
Please use the following citation when referencing the View-of-Delft (VoD-P) dataset:
```
@article{boekema2024vodp,
author={Boekema, Hidde J-H. and Martens, Bruno K.W. and Kooij, Julian F.P. and Gavrila, Dariu M.},
journal={IEEE Robotics and Automation Letters},
title={Multi-class Trajectory Prediction in Urban Traffic using the View-of-Delft Prediction Dataset},
year={2024},
volume={9},
number={5},
pages={4806-4813},
keywords={Trajectory;Roads;Annotations;Semantics;Pedestrians;Predictive models;History;Datasets for Human Motion;Data Sets for Robot Learning;Deep Learning Methods},
doi={10.1109/LRA.2024.3385693}}
```
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"description": "# The View-of-Delft Prediction devkit\nWelcome to the View-of-Delft Prediction (VoD-P) development kit. This repository contains the code and documentation associated with the VoD-P dataset.\n\n\n\n## Overview\n- [Introduction](#introduction)\n- [Changelog](#changelog)\n- [Devkit setup](#devkit-setup)\n- [VoD-P](#vod-p)\n - [VoD-P setup](#vod-p-setup)\n - [Getting started with VoD-P](#getting-started-with-vod-p)\n - [Submitting to the VoD-P leaderboard](#submitting-to-the-vod-p-leaderboard)\n- [Citation](#citation)\n\n## Introduction \nThe View-of-Delft Prediction dataset is an extension of the [View-of-Delft dataset](https://intelligent-vehicles.org/datasets/view-of-delft/). It contains the 3D object annotations of the original dataset and additionally provides accurate 6-DoF global localisation and semantic map data.\n\nThe dataset is available in a format based on the [nuScenes dataset](https://www.nuscenes.org/), and hence this development kit is a modified version of the [nuScenes devkit](https://github.com/nutonomy/nuscenes-devkit).\n\n\n## Changelog\n- [2024-11-20] Launched the View-of-Delft Prediction leaderboard.\n- [2024-11-15] Released a version of the development kit for Python 3.8.\n- [2024-09-11] Released the View-of-Delft Prediction dataset and development kit.\n\n## Devkit setup\nThe devkit is tested for Python 3.8. For a version of the devkit that is compatible with Python 3.6 and 3.7, see the v1.0.\\* [PyPI releases](https://pypi.org/project/vod-devkit/) or [tags](https://github.com/tudelft-iv/view-of-delft-prediction-devkit/releases/). \nTo install Python, please check [here](https://github.com/tudelft-iv/vod-devkit/blob/master/docs/installation.md#install-python).\n\nOur devkit is available and can be installed via [pip](https://pip.pypa.io/en/stable/installing/):\n```\npip install vod-devkit\n```\n\nFor an advanced installation, see [installation](docs/installation.md) for detailed instructions.\n\n\n## VoD-P\n\n### VoD-P setup\nTo download VoD-P, follow the instructions at the main [View-of-Delft dataset page](https://intelligent-vehicles.org/datasets/view-of-delft/).\nDownload the zipfile when you receive the access link. \nUnzip the file and you should have the following folder structure:\n```\n/data/sets/vod\n maps\t-\tFolder for all map files (vectorized .json files).\n v1.0-*\t-\tJSON tables that include all the meta data and annotations. Each split (trainval, test) is provided in a separate folder.\n```\n\n\n### Getting started with VoD-P\n\nPlease follow these steps to make yourself familiar with the VoD dataset:\n- Read the [main dataset page](https://intelligent-vehicles.org/datasets/view-of-delft/).\n- [Request access](https://docs.google.com/forms/d/e/1FAIpQLSdKvkuKbzmJTn8raJBAWgekAJCpaQLS_ED63sUS89Ezo61RCQ/viewform) to the dataset.\n- Download the dataset.\n- Get the [vod-devkit code](https://github.com/tudelft-iv/vod-devkit/tree/main).\n- Read the [tutorials](https://github.com/tudelft-iv/vod-devkit/tree/main/tutorials) or run one yourself using:\n```\njupyter notebook $HOME/vod-devkit/tutorials/vod_tutorial.ipynb\n```\n- Read the View-of-Delft Prediction [paper](https://ieeexplore.ieee.org/document/10493110) for a closer look at the dataset.\n- See the [FAQs](https://github.com/tudelft-iv/view-of-delft-prediction-devkit/blob/main/docs/faqs.md).\n\n\n### Submitting to the VoD-P leaderboard\n\nThe VoD-P benchmark leaderboard can be found at https://eval.ai/web/challenges/challenge-page/2410/overview.\n\nSee the [benchmark instructions](https://github.com/tudelft-iv/view-of-delft-prediction-devkit/blob/main/docs/benchmark_instructions.md) for the submission format and rules.\n\n\n## Citation\nPlease use the following citation when referencing the View-of-Delft (VoD-P) dataset:\n```\n@article{boekema2024vodp,\n author={Boekema, Hidde J-H. and Martens, Bruno K.W. and Kooij, Julian F.P. and Gavrila, Dariu M.},\n journal={IEEE Robotics and Automation Letters}, \n title={Multi-class Trajectory Prediction in Urban Traffic using the View-of-Delft Prediction Dataset}, \n year={2024},\n volume={9},\n number={5},\n pages={4806-4813},\n keywords={Trajectory;Roads;Annotations;Semantics;Pedestrians;Predictive models;History;Datasets for Human Motion;Data Sets for Robot Learning;Deep Learning Methods},\n doi={10.1109/LRA.2024.3385693}}\n\n```\n\n\n",
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