Synset Boulevard

A Synthetic Image Dataset for Vehicle Make and Model Recognition (VMMR) (Sielemann, Wolf et al., 2024)

43

makes

162

models

32400

independent images

259200

total images

The Synset Boulevard dataset is designed for the task of vehicle make and model recognition (VMMR), and is—to the best of our knowledge—the first entirely synthetically generated large-scale VMMR image dataset.

Through the simulation of image data rather than the manual annotation of real data, it is intended to mitigate common challenges in state-of-the-art VMMR datasets, namely bias, human error, privacy, and the challenge of providing systematic updates. On the other hand, the provision and use of synthetic data introduce individual challenges, such as potential domain gaps and a less pronounced intra-class variance.

The dataset was generated using path tracing and physically-based, data-driven models, and contains 32,400 independent images (each with different imaging simulations and with/without masked license plates, leading to a total of 259,200 images) from 162 different vehicle models of 43 makes depicted in front view. It is split into 8 sub-datasets to investigate the influence of optical/imaging effects on the classification ability.

Details

FieldTaskLicenseSamplesKey AnnotationsInstitutions
Computer VisionVMMR (vehicle make and model recognition)Creative Commons License
Creative Commons Attribution 4.0
32,400 (independent images)Per frame: Class (vehicle make, model), car paint (abstract color, RGB values, metallness), environment (dry/wet road, daytime, contrast), semantic segmentation.
Per class: Official make and model names, model years, number of doors.
Fraunhofer IOSB,
KIT-IES
Key properties of the dataset.

More examples for variations (expand)

Data details (expand)

Makes [43]

MakeNumber of models
Alfa Romeo1
Aston Martin1
Audi15
Buick2
Cadillac2
Chevrolet6
Chrysler2
Citroen6
Dacia1
Dodge2
Fiat2
Ford6
GMC1
Honda9
Hyundai8
MakeNumber of models
Infiniti3
Jaguar1
Jeep2
KIA3
Land Rover2
Lexus3
Lincoln2
Maserati1
Mazda3
Mercedes-Benz13
Mercury1
Mitsubishi2
Nissan4
Opel8
Peugeot7
MakeNumber of models
Porsche4
Renault7
SEAT3
Saab1
Saleen1
Skoda7
Subaru1
Suzuki4
Tesla1
Toyota8
VW3
VW Commercial Vehicles1
Volvo2

Models [162]

MakeModelDetailed Model Model Year Model End Doors
Alfa RomeoGiulietta 1.4 TB 16VGiulietta (940)2010/052013/035
Aston MartinRapideRapide2010/032013/075
AudiA1 Sportback 1.2 TFSIA1 (8X) Sportback2012/032014/115
AudiA3 1.2 TFSIA3 (8V)2013/022014/053
AudiA4 Allroad 45 TFSIA4 (B9) allroad2019/022019/055
AudiA4 Avant 1.8 TFSIA4 (B8) Avant2012/022015/045
AudiA6 2.0 TFSIA6 (C7) Limousine2011/102014/094
AudiA8 D4200920174
AudiQ2201620205
AudiQ3 1.4 TFSIQ3 (8U)2013/072014/105
AudiQ5 45 TFSIQ5 (FY)2019/012019/055
AudiS38V201320204
AudiS5 Sportback8T Facelift201120165
AudiS5F5201620192
AudiS7 Sportback codS7 (4G) Sportback2012/062014/055
AudiSQ7-201620185
AudiTT Coupé 1.8 TFSITT (8S) Coupé2015/082018/063
BuickEnclaveGMT967200720175
BuickLaCrosseGen. 2200920164
CadillacCTS-VCTS-V (II) Limousine2008/112010/124
CadillacSRX 3.0 V6SRX (II)2010/122012/015
ChevroletAveo 1.2Aveo2008/062011/013
ChevroletCamaro Cabriolet 6.2 V8Camaro (V) Coupé2011/092014/022
ChevroletCorvette CoupéCorvette (C6) Coupé2005/022008/033
ChevroletEquinoxGen. 2201520175
ChevroletMalibu 2.4Malibu2012/072014/084
ChevroletVoltVolt2011/112014/085
ChryslerPacifica-201620205
ChryslerGrand Voyager 3.8Grand Voyager2008/032010/125
CitroenBerlingo Kombi VTi 120Berlingo (II) Kombi2009/122012/035
CitroenC3 1.1C3 (II)2010/012012/035
CitroenC4 VTi 95C4 (II)2012/082013/065
CitroenC4 Cactus PureTech 75C4 Cactus2014/092015/035
CitroenGrand C4 Picasso VTi 120Grand C4 Picasso (II)2013/102015/025
CitroenDS 4 VTi 120DS 42011/052015/015
DaciaDuster dCi 110 FAPDuster (I)2010/062013/115
DodgeCaliber 1.8Caliber2006/062009/035
DodgeDurangoGen. 320105
Fiat500L 1.4 16V500L (199)2012/102017/055
FiatPanda 1.2 8VPanda (312)2013/022013/125
FordEdgeGen. 1 Facelift201020155
FordEscape Hybrid-200420125
FordExplorerGen. 5201020195
FordFiesta’09 Sedan (seventh series)201020174
FordFocus 1.6 Ti-VCTFocus (III)2011/092014/095
FordTaurusSHO201220194
GMCTerrainGen. 1200920175
HondaAccord Hybrid9. Generation201520194
HondaCR-VCR-V (III) (04/10 - 10/12)201020125
HondaCityGen. 6200820134
HondaCivic Limousine 1.8Civic (IX) Limousine2014/052017/064
HondaCrosstour-200920124
HondaJazz 1.2Jazz (III) (11/08 - 04/11)2008/112010/105
HondaPilotGen. 2 Facelift201220155
HondaPilot3201520225
HondaRidgelineGen. 2201620204
HyundaiEquusGen. 2 VI200920164
HyundaiIONIQ HybridIONIQ (AE) Hybrid2016/102018/085
HyundaiSanta Fe 2.4Santa Fe (CM)2010/012012/095
HyundaiSanta Fe 2.4 GDISanta Fe (DM)2017/082018/015
HyundaiSonataGen. 6 YF200920164
HyundaiSonataGen. 7201520174
Hyundaii10Gen. 2201320195
Hyundaii40 Kombi 1.6 GDIi40 (VF) Kombi2012/082013/095
InfinitiFX 37FX (S51)2012/072013/105
InfinitiQ60 2.0tQ60 (V37)2016/102018/082
InfinitiM 37M (Y51)2010/102013/114
JaguarXF 3.0 V6XF (X250) Limousine2010/022010/104
JeepCherokee 3.2 V6 PentastarCherokee (KL)2014/072015/075
JeepGrand CherokeeWK2201020215
KIARio 1.2Rio (UB)2011/092015/013
KIASorento 2.2 CRDiSorento (UM)2015/032017/105
KIASoul 1.6Soul (AM)2009/022009/115
Land RoverRange Rover Evoque 2.0 Si4Range Rover Evoque (I)2011/082013/115
Land RoverRange Rover Sport 5.0 V8 SCRange Rover Sport (II)2013/092015/075
LexusCT 200hCT (A10)2014/032017/105
LexusGS 250GS (L10)2012/062013/124
LexusNX 300hNX (AZ1)2017/112018/085
LincolnMKS-200820114
LincolnMKXGen. 1 CD3201020155
MaseratiLevante GT HybridLevante2021/072022/104
Mazda3 1.63 (BL)2009/052010/125
MazdaCX-3 SKYACTIV-G 120CX-3 (DK)2015/062018/065
MazdaCX-5 2.0 SKYACTIV-G 160CX-5 (GH)2012/042015/025
Mercedes-BenzA 180A-Class (176)2012/092015/075
Mercedes-BenzAMG GT CoupéAMG GT (190) Coupé2014/102017/012
Mercedes-BenzB 180 BlueEFFICIENCYB-Class (246)2011/112012/105
Mercedes-BenzC 160C-Class (205) Limousine2015/042018/044
Mercedes-BenzC 43 AMGC-Class (205) AMG Limousine2016/102018/044
Mercedes-BenzCLS Coupé 350CLS-Class (218) Coupé2011/012014/074
Mercedes-BenzE 200E-Class (212) Limousine2013/042015/104
Mercedes-BenzGLC 250GLC (253)2015/092018/055
Mercedes-BenzGLK 280GLK-Class (204)2008/102009/045
Mercedes-BenzML 350M-Class (166)2011/112014/105
Mercedes-BenzS 400 HYBRIDS-Class (222) Limousine2013/052015/044
Mercedes-BenzS 400 CoupéS-Class (217) Coupé2015/102017/092
Mercedes-BenzSLK 200SLK-Class (172)2011/032015/042
MercuryMilan-200920104
MitsubishiColt 1.1Colt (VI)2008/112010/103
MitsubishiOutlander 2.0 Plug-In HybridOutlander (III) Plug-In Hybrid2015/102018/085
Nissan370Z Coupé370Z (Z34) Coupé2009/122013/033
NissanMuranoGen. 3 Z5220155
NissanNote 1.2Note (E12) (10/13 - 12/16)2013/102015/095
NissanQashqai 1.6Qashqai (J10)2010/032010/085
OpelADAM 1.2ADAM2013/012014/113
OpelAstra 1.4 ecoFlexAstra (J)2009/122010/125
OpelCascada 1.4 TurboCascada2013/032015/012
OpelCombo Combi L1H1 1.4Combo (D) Combi2012/012013/115
OpelCorsa 1.2Corsa (D)2011/012014/083
OpelInsignia 1.6Insignia (A) Notch Back2008/112008/124
OpelMeriva 1.4Meriva (B)2010/052013/115
OpelZafira Tourer 1.8Zafira (C) Tourer2012/012015/065
Peugeot108 1.0 VTi 681082015/032016/103
Peugeot308 VTi 82308 (II)2013/092014/115
Peugeot5008 120 VTi5008 (I) Van2009/102011/075
Peugeot5008 1.2 PureTech 130 Active5008 (II) (03/17 - 09/20)2017/032018/075
Peugeot508 120 VTi508 (I) Limousine2011/032014/054
Peugeot508 SW 120 VTi508 (I) SW2011/032014/055
PeugeotiOniOn2010/122013/025
PorscheCayenneCayenne (958)2010/052014/075
PorscheCaymanCayman (981C)2013/032014/042
PorschePanameraPanamera (970)2010/052013/045
PorschePanameraPanamera (971)2016/112018/085
RenaultCaptur ENERGY TCe 90Captur (I)2013/062015/055
RenaultKadjar ENERGY TCe 130Kadjar2015/052018/085
RenaultLaguna Coupé TCe 170Laguna (III) Coupé2010/112011/052
RenaultMégane ENERGY TCe 100Mégane (IV)2016/032018/085
RenaultMégane Coupé-Cabriolet 1.6 16V 110Mégane (III) Coupé-Cabriolet2010/062013/032
RenaultScénic 1.6 16V 110Scénic (III)2009/062010/085
RenaultTwingo 1.2 LEV 16V 75Twingo (II)2012/012014/073
SEATAteca 1.0 TSI EcomotiveAteca (5FP)2016/082018/085
SEATExeo ST 1.6Exeo (3R) ST2010/052010/085
SEATLeon 1.4Leon (1P)2009/042010/085
Saab5-Sep9-5 1.6T2010/062011/124
SaleenS7-200020082
SkodaKodiaq 1.4 TSIKodiaq2017/032018/055
SkodaOctavia 1.2 TSIOctavia (III) Limousine2013/022015/045
SkodaRapidRapid 1.2 Active Limousine2012/102015/045
SkodaRapid Spaceback 1.2 TSIRapid Spaceback2013/102015/045
SkodaSuperb Combi 1.4 TSISuperb (II) Combi2010/012011/105
SkodaSuperb Combi 1.4 TSISuperb (III) Combi2015/092018/065
SkodaYeti 1.2 TSIYeti2009/112011/105
SubaruLegacy 2.0RLegacy (IV) Limousine2007/092008/104
SuzukiAlto 1.0Alto (V)2011/022011/035
SuzukiSX4 S-Cross 1.6SX4 (II) S-Cross2013/102015/085
SuzukiGrand VitaraGen. 1 JT200920125
SuzukiVitara 1.6Vitara (LY)2015/042018/085
TeslaModel S 60Model S2013/102014/105
ToyotaAvensis 1.6Avensis (T27) Notch Back2009/012010/084
ToyotaLand Cruiser V8 4.7Land Cruiser V8 (J20)2008/032009/115
ToyotaNoahVoxy R70G200720135
ToyotaPassoToyota Passo X Yururi201020165
ToyotaPrius 1.5 HybridPrius (HW2)2006/032009/065
ToyotaPrius 1.8 Plug-In HybridPrius (XW5) Plug-In2019/072020/075
ToyotaYaris 1.0Yaris (XP9)2009/012010/103
ToyotaYaris 1.0Yaris (XP13)2011/102014/083
VW Commercial Vehicles (Nutzfahrzeuge)T5 Multivan 2.0T5 Multivan2009/092013/064
VWGolf 1.2 TSI BMTGolf (VII)2012/112014/053
VWJetta 1.2 TSIJetta (IV)2011/012014/084
VWPassat Alltrack 1.8 TSIPassat (B7) Alltrack2012/032013/065
VolvoV90-201620205
VolvoXC90 B5XC90 (L)2019/082020/045

Metadata and label details (expand)

Labels

The label images mark the depicted vehicle, the visible road surface, and the road markings (by type). The labels are assigned as follows:

ClassRGB
Road0100125
Vehicle25150125
Straight Yellow Lane1252575
Dashed Yellow Lane150200200
Straight White Lane175075
Dashed White Lane20020075
Label Mapping

Straight yellow lane

Dashed yellow lane

Straight white lane

Dashed white lane

vehicle-metadata.csv

The vehicle-metadata.csv includes vehicle model specific metadata:

DirectoryMakeModelDetailed ModelModel YearModel EndDoors
DescriptionThe directory names match the pattern make_model (if unique, make_model_year otherwise). Each directory name can be applied to bayer_bad, bayer_good, bloom_bad as well as bloom_good.The vehicle’s make1The vehicle’s model name1Detailed model description1Vehicle model launch1 (with month if available)Last year of production1 (with month if available)Number of doors1
ExamplePorsche_Panamera_2010PorschePanameraPanamera (970)2010/052013/045
Description of the rows in vehicle-metadata.csv

1 The data stems from https://www.adac.de/rund-ums-fahrzeug/autokatalog/marken-modelle/

vehicle-colors.csv

This csv file includes framewise information about the depicted vehicle’s car paint material.

Image FileColor CategoryRGBMetalness
DescriptionGiven in the format directory/filename. This path end can be applied to bayer_bad, bayer_good, bloom_bad as well as bloom_good. For a mapping to the label images, the file ending must be replaced by “png”.white, black, gray, blue, green, red, orange, or yellow. More information about the color selection is given below.R value of the car paint’s RGB color value given in the range between 0.0 and 1.0.G value of the car paint’s RGB color value given in the range between 0.0 and 1.0.B value of the car paint’s RGB color value given in the range between 0.0 and 1.0.Metalness of the vehicle’s car paint material (0 or 1). A vehicle’s car paint is metalic with a probability of 0.9.
ExamplePorsche_Panamera_2010/0ig4di4gxb.jpgblue00.0070.0711
Description of the rows in vehicle-colors.csv

The color selection distribution is based on a statistic of the German Federal Motor Transport Authority2 (KBA) showing new vehicle registrations by color in Germany (2021). It is based on 2,622,132 newly registered cars.

2 Kraftfahrt-Bundesamt, Dezente Farben nach wie vor gefragt, https://www.kba.de/DE/Statistik/Fahrzeuge/Neuzulassungen/Farbe/2021/2021_n_farbe_kurzbericht_pdf.pdf, Accessed: 2023-02-20, 2021.

environment.csv

This csv file includes framewise information about the given environment. For the environment variation a total of 150 different road surfaces occur uniformly across the dataset, both as wet and dry surfaces. To light the environment, we uniformly sample from 183 environment maps from Poly Haven3. Their azimuth is varied uniformly.

Image FileRoad ConditionDaytimeContrast
DescriptionGiven in the format directory/filename. This path end can be applied to bayer_bad, bayer_good, bloom_bad as well as bloom_good. For a mapping to the label images, the file ending must be replaced by “png”.dry road or wet road. The approximate time of day of the lighting situation. Sunrise or sunset, morning or afternoon, midday, dawn, night, or not specified.high contrast, medium contrast, or low contrast.
ExamplePorsche_Panamera_2010/0ig4di4gxb.jpgdry roadsunrise or sunsethigh contrast
Description of the rows in environment.csv

The distribution of wet and dry roads across the entire Synset Boulevard dataset. Of the total 32,400 independent images, 29,098 show a dry road and 3,302 a wet road.

The distribution of environment contrast across the entire Synset Boulevard dataset. Of the total 32,400 independent images, 13,560 show a low constrast, 13,552 a high contrast, and 5,288 a medium contrast.

The distribution of environment daytime across the entire Synset Boulevard dataset. Of the total 32,400 independent images, 12,437 show sunrise or sunset, 12,242 morning or afternoon, 6,140 midday, 485 dawn, 939 night, and 157 not specified. It should be noted that the night images from Synset Boulevard are not comparable to night images from real data sets such as Compcars.

3 The used Environment Maps stem from Poly Haven https://polyhaven.com/

train.csv and test.csv

We provide an exemplary train and test split (3:1) given by train.csv and test.csv. Both csv files contain an image list given in the format directory/filename. This path end can be applied to bayer_bad, bayer_good, bloom_bad as well as bloom_good. For a mapping to the label images, the file ending must be replaced by “png”. The image lists are disjoint and add up to the entire Synset Boulevard data set. The test.csv includes 50 randomly chosen images per class, the train.csv the other 150 class images.

Paper and reference

Creative Commons License This work is licensed under Creative Commons Attribution 4.0.

To cite this dataset in your scientific work, please use the following bibliography entry:

Sielemann, A., Wolf, S., Roschani, M., Ziehn, J. and Beyerer, J. (2024). Synset Boulevard: A Synthetic Image Dataset for VMMR. In 2024 IEEE International Conference on Robotics and Automation (ICRA).

@inproceedings{synset_blvd_sielemann_2024,
  title={{Synset Boulevard: A Synthetic Image Dataset for VMMR}},
  author={Sielemann, Anne and Wolf, Stefan and Roschani, Masoud and Ziehn, Jens and Beyerer, Juergen},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2024}
}

In case of copy and redistribute or publishing an adapted version of our dataset, please provide the name of our dataset, the creator names, a copyright notice, a link to this website, a license notice with link to the license, and if changes were made, a disclaimer notice, and a short description of the applied changes. For example, as follows:

This work is based on the Synset Boulevard Dataset
by Anne Sielemann, Stefan Wolf, Jens Ziehn, Masoud Roschani, and Juergen Beyerer,
© 2024 Fraunhofer IOSB, All rights reserved.
Link: https://synset.de/datasets/synset-blvd/
Licence: CC BY 4.0
Disclaimer: The original authors are neither affiliated nor responsible for any applied changes.

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Credits

This work was supported by the Fraunhofer Internal Programs under Grant No. PREPARE 40-02702 within the ML4Safety project, and by the Ministry of Economic Affairs, Labour and Housing of the state of Baden-Wuerttemberg, Germany, as part of the FeinSyn research project.