Complete* list of autonomous driving related datasets

Overview

AD Datasets

Complete* and curated list of autonomous driving related datasets

Contributing

Contributions are very welcome! To add or update a dataset:

  • Update my-app/src/data.js: image

  • Make sure the dataset you add or edit has as many attributes as possible filled out:

    • Some attributes can only be found in associated papers
    • Some attributes can only be found in associated websites
    • Some attributes can only be found in the dataset itself
  • Send a pull request based on the created fork

Example Contribution

This is how the KITTI dataset is integrated into the website:

[...]
{
    id: "KITTI", //07.08. fertig
    href: "http://www.cvlibs.net/datasets/kitti/",
    size_hours: "6",
    size_storage: "180",
    frames: "",
    numberOfScenes: '50',
    samplingRate: "10",
    lengthOfScenes: "",
    sensors: "camera, lidar, gps/imu",
    sensorDetail: "2 greyscale cameras 1.4 MP, 2 color cameras 1.4 MP, 1 lidar 64 beams 360° 10Hz, 1 inertial and " +
        "GPS navigation system",
    benchmark: " stereo, optical flow, visual odometry, slam, 3d object detection, 3d object tracking",
    annotations: "3d bounding boxes",
    licensing: "Creative Commons Attribution-NonCommercial-ShareAlike 3.0",
    relatedDatasets: 'Semantic KITTI, KITTI-360',
    publishDate: new Date("2012-3").toISOString().split('T')[0],
    lastUpdate: new Date("2021-2").toISOString().split('T')[0],
    relatedPaper: "http://www.cvlibs.net/publications/Geiger2013IJRR.pdf",
    location: "Karlsruhe, Germany",
    rawData: "Yes"
},
[...]

* You're missing a dataset? Simply create a pull request ;)

Metadata

In the following, the scheme according to which the entries of the respective properties have resulted is illuminated.

Annotations

This property describes the types of annotations with which the data sets have been provided.

Benchmark

If benchmark challenges are explicitly listed with the data sets, they are specified here.

Frames

Frames states the number of frames in the data set. This includes training, test and validation data.

Last Update

If information has been provided on updates and their dates, they can be found in this category.

Licensing

In order to give the users an impression of the licenses of the data sets, information on them is already included in the tool. Location. This category lists the areas where the data sets have been recorded.

N° Scenes

N° Scenes shows the number of scenes contained in the data set and includes the training, testing and validation segments. In the case of video recordings, one recording corresponds to one scene. For data sets consisting of photos, a photo is the equivalent to a scene.

Publish Date

The initial publication date of the data set can be found under this category. If no explicit information on the date of publication of the data set could be found, the submission date of the paper related to the set was used at this point.

Related Data Sets

If data sets are related, the names of the related sets can be examined as well. Related data sets are, for example, those published by the same authors and building on one another.

Related Paper

This property solely consists of a link to the paper related to the data set. Sampling Rate [Hz]. The Sampling Rate [Hz] property specifies the sampling rate in Hertz at which the sensors in the data set work. However, this declaration is only made if all sensors are working at the same rate or, alternatively, if the sensors are being synchronized. Otherwise the field remains empty.

Scene Length [s]

This property describes the length of the scenes in seconds in the data set, provided all scenes have the same length. Otherwise no information is given. For example, if a data set has scenes with lengths between 30 and 60 seconds, no entry can be made. The background to this procedure is to maintain comparability and sortability.

Sensor Types

This category contains a rough description of the sensor types used. Sensor types are, for example, lidar or radar.

Sensors - Details

The Sensors - Detail category is an extension of the Sensor Types category. It includes a more detailed description of the sensors. The sensors are described in detail in terms of type and number, the frame rates they work with, the resolutions which sensors have and the horizontal field of view.

Size [GB]

The category Size [GB] describes the storage size of the data set in gigabytes.

Size [h]

The Size [h] property is the equivalent of the Size [GB] described above, but provides information on the size of the data set in hours.

Location

The place(s) the data was recorded at

rawData

Denotes if the dataset provides raw or processed data

Citation

If you find this code useful for your research, please cite our paper:

@article{Bogdoll_addatasets_2022_VEHITS,
    author    = {Bogdoll, Daniel and Schreyer, Felix, and Z\"{o}llner, J. Marius},
    title     = {{ad-datasets: a meta-collection of data sets for autonomous driving}},
    journal   = {arXiv preprint:2202.01909},
    year      = {2022},
}
Owner
Daniel Bogdoll
PhD student at FZI and KIT with a focus on deep learning and autonomous driving.
Daniel Bogdoll
Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection

SAGA Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection Please refer to the Jupyter notebook (Example.ipynb) for an example of using t

9 Dec 28, 2022
ICCV2021 Paper: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection

ICCV2021 Paper: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection

Zongdai 107 Dec 20, 2022
Array Camera Ptychography

Array Camera Ptychography This repository provides the code for the following papers: Schulz, Timothy J., David J. Brady, and Chengyu Wang. "Photon-li

Brady lab in Optical Sciences 1 Nov 15, 2021
Just Go with the Flow: Self-Supervised Scene Flow Estimation

Just Go with the Flow: Self-Supervised Scene Flow Estimation Code release for the paper Just Go with the Flow: Self-Supervised Scene Flow Estimation,

Himangi Mittal 50 Nov 22, 2022
Embeds a story into a music playlist by sorting the playlist so that the order of the music follows a narrative arc.

playlist-story-builder This project attempts to embed a story into a music playlist by sorting the playlist so that the order of the music follows a n

Dylan R. Ashley 0 Oct 28, 2021
Official PyTorch code for the paper: "Point-Based Modeling of Human Clothing" (ICCV 2021)

Point-Based Modeling of Human Clothing Paper | Project page | Video This is an official PyTorch code repository of the paper "Point-Based Modeling of

Visual Understanding Lab @ Samsung AI Center Moscow 64 Nov 22, 2022
PyTorch implementations of the NeRF model described in "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"

PyTorch NeRF and pixelNeRF NeRF: Tiny NeRF: pixelNeRF: This repository contains minimal PyTorch implementations of the NeRF model described in "NeRF:

Michael A. Alcorn 178 Dec 20, 2022
An efficient and effective learning to rank algorithm by mining information across ranking candidates. This repository contains the tensorflow implementation of SERank model. The code is developed based on TF-Ranking.

SERank An efficient and effective learning to rank algorithm by mining information across ranking candidates. This repository contains the tensorflow

Zhihu 44 Oct 20, 2022
Convnext-tf - Unofficial tensorflow keras implementation of ConvNeXt

ConvNeXt Tensorflow This is unofficial tensorflow keras implementation of ConvNe

29 Oct 06, 2022
CVPR2021: Temporal Context Aggregation Network for Temporal Action Proposal Refinement

Temporal Context Aggregation Network - Pytorch This repo holds the pytorch-version codes of paper: "Temporal Context Aggregation Network for Temporal

Zhiwu Qing 63 Sep 27, 2022
Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning.

Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning. Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive

<a href=[email protected](SZ)"> 7 Dec 16, 2021
COVID-Net Open Source Initiative

The COVID-Net models provided here are intended to be used as reference models that can be built upon and enhanced as new data becomes available

Linda Wang 1.1k Dec 26, 2022
Neural network chess engine trained on Gary Kasparov's games.

Neural Chess It's not the best chess engine, but it is a chess engine. Proof of concept neural network chess engine (feed-forward multi-layer perceptr

3 Jun 22, 2022
A highly modular PyTorch framework with a focus on Neural Architecture Search (NAS).

UniNAS A highly modular PyTorch framework with a focus on Neural Architecture Search (NAS). under development (which happens mostly on our internal Gi

Cognitive Systems Research Group 19 Nov 23, 2022
Learning Facial Representations from the Cycle-consistency of Face (ICCV 2021)

Learning Facial Representations from the Cycle-consistency of Face (ICCV 2021) This repository contains the code for our ICCV2021 paper by Jia-Ren Cha

Jia-Ren Chang 40 Dec 27, 2022
DUE: End-to-End Document Understanding Benchmark

This is the repository that provide tools to download data, reproduce the baseline results and evaluation. What can you achieve with this guide Based

21 Dec 29, 2022
Official code base for the poster "On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation" published in NeurIPS 2021 Workshop (SVRHM)

Self-Supervised Learning (SimCLR) with Biological Plausible Image Augmentations Official code base for the poster "On the use of Cortical Magnificatio

Binxu 8 Aug 17, 2022
Official project repository for 'Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination'

NCAE_UAD Official project repository of 'Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination' Abstract In this p

Jongmin Andrew Yu 2 Feb 10, 2022
Machine Learning Privacy Meter: A tool to quantify the privacy risks of machine learning models with respect to inference attacks, notably membership inference attacks

ML Privacy Meter Machine learning is playing a central role in automated decision making in a wide range of organization and service providers. The da

Data Privacy and Trustworthy Machine Learning Research Lab 357 Jan 06, 2023
Wandb-predictions - WANDB Predictions With Python

WANDB API CI/CD Below we capture the CI/CD scenarios that we would expect with o

Anish Shah 6 Oct 07, 2022