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
Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

Hrishikesh Kamath 31 Nov 20, 2022
Source code for ZePHyR: Zero-shot Pose Hypothesis Rating @ ICRA 2021

ZePHyR: Zero-shot Pose Hypothesis Rating ZePHyR is a zero-shot 6D object pose estimation pipeline. The core is a learned scoring function that compare

R-Pad - Robots Perceiving and Doing 18 Aug 22, 2022
Deep motion generator collections

GenMotion GenMotion (/gen’motion/) is a Python library for making skeletal animations. It enables easy dataset loading and experiment sharing for synt

23 May 24, 2022
Official code for the CVPR 2021 paper "How Well Do Self-Supervised Models Transfer?"

How Well Do Self-Supervised Models Transfer? This repository hosts the code for the experiments in the CVPR 2021 paper How Well Do Self-Supervised Mod

Linus Ericsson 157 Dec 16, 2022
Composable transformations of Python+NumPy programsComposable transformations of Python+NumPy programs

Chex Chex is a library of utilities for helping to write reliable JAX code. This includes utils to help: Instrument your code (e.g. assertions) Debug

DeepMind 506 Jan 08, 2023
BankNote-Net: Open dataset and encoder model for assistive currency recognition

BankNote-Net: Open Dataset for Assistive Currency Recognition Millions of people around the world have low or no vision. Assistive software applicatio

Microsoft 13 Oct 28, 2022
The first public PyTorch implementation of Attentive Recurrent Comparators

arc-pytorch PyTorch implementation of Attentive Recurrent Comparators by Shyam et al. A blog explaining Attentive Recurrent Comparators Visualizing At

Sanyam Agarwal 150 Oct 14, 2022
Open & Efficient for Framework for Aspect-based Sentiment Analysis

PyABSA - Open & Efficient for Framework for Aspect-based Sentiment Analysis Fast & Low Memory requirement & Enhanced implementation of Local Context F

YangHeng 567 Jan 07, 2023
Official repository for Jia, Raghunathan, Göksel, and Liang, "Certified Robustness to Adversarial Word Substitutions" (EMNLP 2019)

Certified Robustness to Adversarial Word Substitutions This is the official GitHub repository for the following paper: Certified Robustness to Adversa

Robin Jia 38 Oct 16, 2022
Re-implememtation of MAE (Masked Autoencoders Are Scalable Vision Learners) using PyTorch.

mae-repo PyTorch re-implememtation of "masked autoencoders are scalable vision learners". In this repo, it heavily borrows codes from codebase https:/

Peng Qiao 1 Dec 14, 2021
Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR)

This is the official implementation of our paper Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR), which has been accepted by WSDM2022.

Yongchun Zhu 81 Dec 29, 2022
Keras code and weights files for popular deep learning models.

Trained image classification models for Keras THIS REPOSITORY IS DEPRECATED. USE THE MODULE keras.applications INSTEAD. Pull requests will not be revi

François Chollet 7.2k Dec 29, 2022
Progressive Domain Adaptation for Object Detection

Progressive Domain Adaptation for Object Detection Implementation of our paper Progressive Domain Adaptation for Object Detection, based on pytorch-fa

96 Nov 25, 2022
git《Beta R-CNN: Looking into Pedestrian Detection from Another Perspective》(NeurIPS 2020) GitHub:[fig3]

Beta R-CNN: Looking into Pedestrian Detection from Another Perspective This is the pytorch implementation of our paper "[Beta R-CNN: Looking into Pede

35 Sep 08, 2021
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

Monitor deep learning model training and hardware usage from mobile. 🔥 Features Monitor running experiments from mobile phone (or laptop) Monitor har

labml.ai 1.2k Dec 25, 2022
Implementation of CVPR'2022:Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors

Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors (CVPR 2022) Personal Web Pages | Paper | Project Page This repository contains

151 Dec 26, 2022
[ICCV'21] Official implementation for the paper Social NCE: Contrastive Learning of Socially-aware Motion Representations

CrowdNav with Social-NCE This is an official implementation for the paper Social NCE: Contrastive Learning of Socially-aware Motion Representations by

VITA lab at EPFL 125 Dec 23, 2022
implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning"

MarginGAN This repository is the implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning". 1."preliminary" is the imp

Van 7 Dec 23, 2022
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation

AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation AniGAN: Style-Guided Generative Adversarial Networks for U

Bing Li 81 Dec 14, 2022
Scalable Multi-Agent Reinforcement Learning

Scalable Multi-Agent Reinforcement Learning 1. Featured algorithms: Value Function Factorization with Variable Agent Sub-Teams (VAST) [1] 2. Implement

3 Aug 02, 2022