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
Low Complexity Channel estimation with Neural Network Solutions

Interpolation-ResNet Invited paper for WSA 2021, called 'Low Complexity Channel estimation with Neural Network Solutions'. Low complexity residual con

Dianxin 10 Dec 10, 2022
Generative Models as a Data Source for Multiview Representation Learning

GenRep Project Page | Paper Generative Models as a Data Source for Multiview Representation Learning Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip

Ali 81 Dec 03, 2022
Reproducible research and reusable acyclic workflows in Python. Execute code on HPC systems as if you executed them on your personal computer!

Reproducible research and reusable acyclic workflows in Python. Execute code on HPC systems as if you executed them on your machine! Motivation Would

Joeri Hermans 15 Sep 11, 2022
Underwater industrial application yolov5m6

This project wins the intelligent algorithm contest finalist award and stands out from over 2000teams in China Underwater Robot Professional Contest, entering the final of China Underwater Robot Prof

8 Nov 09, 2022
CLIPImageClassifier wraps clip image model from transformers

CLIPImageClassifier CLIPImageClassifier wraps clip image model from transformers. CLIPImageClassifier is initialized with the argument classes, these

Jina AI 6 Sep 12, 2022
The FIRST GANs-based omics-to-omics translation framework

OmiTrans Please also have a look at our multi-omics multi-task DL freamwork 👀 : OmiEmbed The FIRST GANs-based omics-to-omics translation framework Xi

Xiaoyu Zhang 6 Dec 14, 2022
Simultaneous Detection and Segmentation

Simultaneous Detection and Segmentation This is code for the ECCV Paper: Simultaneous Detection and Segmentation Bharath Hariharan, Pablo Arbelaez,

Bharath Hariharan 96 Jul 20, 2022
DiffQ performs differentiable quantization using pseudo quantization noise. It can automatically tune the number of bits used per weight or group of weights, in order to achieve a given trade-off between model size and accuracy.

Differentiable Model Compression via Pseudo Quantization Noise DiffQ performs differentiable quantization using pseudo quantization noise. It can auto

Facebook Research 145 Dec 30, 2022
CTRL-C: Camera calibration TRansformer with Line-Classification

CTRL-C: Camera calibration TRansformer with Line-Classification This repository contains the official code and pretrained models for CTRL-C (Camera ca

57 Nov 14, 2022
Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes

Naive-Bayes Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes Downloading Data Set Use our Breast Cancer Wisconsin Data Set Also you can

Faeze Habibi 0 Apr 06, 2022
Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)

Maximum Likelihood Training of Score-Based Diffusion Models This repo contains the official implementation for the paper Maximum Likelihood Training o

Yang Song 84 Dec 12, 2022
ELSED: Enhanced Line SEgment Drawing

ELSED: Enhanced Line SEgment Drawing This repository contains the source code of ELSED: Enhanced Line SEgment Drawing the fastest line segment detecto

Iago Suárez 125 Dec 31, 2022
Shuwa Gesture Toolkit is a framework that detects and classifies arbitrary gestures in short videos

Shuwa Gesture Toolkit is a framework that detects and classifies arbitrary gestures in short videos

Google 89 Dec 22, 2022
PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

Efficient Neural Architecture Search (ENAS) in PyTorch PyTorch implementation of Efficient Neural Architecture Search via Parameters Sharing. ENAS red

Taehoon Kim 2.6k Dec 31, 2022
Unofficial Implementation of MLP-Mixer in TensorFlow

mlp-mixer-tf Unofficial Implementation of MLP-Mixer [abs, pdf] in TensorFlow. Note: This project may have some bugs in it. I'm still learning how to i

Rishabh Anand 24 Mar 23, 2022
Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021

LoFTR: Detector-Free Local Feature Matching with Transformers Project Page | Paper LoFTR: Detector-Free Local Feature Matching with Transformers Jiami

ZJU3DV 1.4k Jan 04, 2023
Long Expressive Memory (LEM)

Long Expressive Memory for Sequence Modeling This repository contains the implementation to reproduce the numerical experiments of the paper Long Expr

Konstantin Rusch 47 Dec 17, 2022
An official source code for "Augmentation-Free Self-Supervised Learning on Graphs"

Augmentation-Free Self-Supervised Learning on Graphs An official source code for Augmentation-Free Self-Supervised Learning on Graphs paper, accepted

Namkyeong Lee 59 Dec 01, 2022
I decide to sync up this repo and self-critical.pytorch. (The old master is in old master branch for archive)

An Image Captioning codebase This is a codebase for image captioning research. It supports: Self critical training from Self-critical Sequence Trainin

Ruotian(RT) Luo 1.3k Dec 31, 2022
Code for the ECIR'22 paper "Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators"

Query Variation Generators This repository contains the code and annotation data for the ECIR'22 paper "Evaluating the Robustness of Retrieval Pipelin

Gustavo Penha 12 Nov 20, 2022