Download the SYNTHIA dataset

SYNTHIA-AL (ICCV Workshops 2019)

Description:

Dataset for active learning purposes. This is a video stream generated at 25 FPS. The classes considered in this dataset are void, sky, building, road, sidewalk, fence, vegetation, pole, car, traffic sign, pedestrian, bycicle, lanemarking, and traffic light. The provided ground truth includes instance segmentation, 2D bounding boxes, 3D bounding boxes and depth information!

For further details, please consult the following README

Data packages:
NamePackage
SYNTHIA-AL-Train SYNTHIA-AL-Train (12101 downloads)
SYNTHIA-AL-Test SYNTHIA-AL-Test (7621 downloads)
README SYNTHIA-AL-README (5477 downloads)

SYNTHIA-SF (BMVC 2017)

Description:

Video sequences subsets acquired at 5 FPS. There are 6 sequences featuring different scenarios and traffic conditions. There are 2224 images with associated ground truth used to check the accuracy of Slanted Stixels in our BMVC paper. For each sequence we provide useful information such as: left and right image, ground truth for semantic segmentation, instance segmentation, depth, and calibration parameters. The semantic classes are Cityscapes compatible, we consider: void, road, sidewalk, building, wall, fence, pole, traffic light, traffic sign, vegetation, terrain, sky, person, rider, car, truck, bus, train, motorcycle, bicycle, road lines, other, road works.

 
Related videos: slanted stixels, BMVC 2017 presentation.
Data packages:
NamePackage
SYNTHIA-SF-BMVC2017 SYNTHIA-SF-BMVC2017 (6423 downloads)

SYNTHIA-RAND (CVPR16)

Description:

This is the set containing the original 13,407 images used to perform training and domain adaptation of the models presented in our CVPR’16 paper. These images are generated as random perturbation of the world and therefore do not have temporal consistency (this is not a video stream). These images have annotations for 11 basic classes and do not have annotations for instances. The classes are: void, sky, building, road, sidewalk, fence, vegetation, pole, car, sign, pedestrian, cyclist.

 
 
Related videos: depth groundtruth, semantic segmentation groundtruth, RGB 360 deg.
 

SYNTHIA-RAND-CITYSCAPES (CVPR16)

Description:

It is a new set containing 9,000 random images with labels compatible with the CITYSCAPES test set. In addition to the CITYSCAPES test classes, we also provide other interesting ones such as lanemarking. The list of classes is: void, sky, building, road, sidewalk, fence, vegetation, pole, car, traffic sign, pedestrian, bicycle, motorcycle, parking-slot, road-work, traffic light, terrain, rider, truck, bus, train, wall, lanemarking. These images are generated as random perturbation of the virtual world, therefore no temporal consistency is provided (this is not a video stream). This set contains groundtruth for instances!

 

SYNTHIA VIDEO SEQUENCES (CVPR16)

Description:

Video subsets acquired at 5 FPS. There are 7 sequences featuring different scenarios and traffic conditions. Each of them is divided into different sub-sequences for commodity. Each sub-sequence consists of the same traffic situation but under a different weather/illumination/season condition. The current sub-sequences are: Spring, Summer, Fall, Winter, Rain, Soft-rain, Sunset, Fog, Night and Dawn. Each of these sub-sequences contains around 8,000 (1,000 x 8) images with associated ground truth. For each sub-sequence we provide useful information such as: 8 views, ground truth for semantic segmentation, instance segmentation, global camera poses, depth, and calibration parameters. In this case the semantic classes are 13: misc, sky, building, road, sidewalk, fence, vegetation, pole, car, sign, pedestrian, cyclist, lane-marking.

Data packages:
NamePakcage


Highway
SYNTHIA-SEQS-01-DAWN (7708 downloads)
SYNTHIA-SEQS-01-FALL (6268 downloads)
SYNTHIA-SEQS-01-FOG (7604 downloads)
SYNTHIA-SEQS-01-NIGHT (5520 downloads)
SYNTHIA-SEQS-01-SPRING (14134 downloads)
SYNTHIA-SEQS-01-SUMMER (4607 downloads)
SYNTHIA-SEQS-01-SUNSET (2195 downloads)
SYNTHIA-SEQS-01-WINTER (2799 downloads)
SYNTHIA-SEQS-01-WINTERNIGHT (10730 downloads)


New York ish
SYNTHIA-SEQS-02-DAWN (2437 downloads)
SYNTHIA-SEQS-02-FALL (3772 downloads)
SYNTHIA-SEQS-02-FOG (6017 downloads)
SYNTHIA-SEQS-02-NIGHT (2973 downloads)
SYNTHIA-SEQS-02-RAINNIGHT (3412 downloads)
SYNTHIA-SEQS-02-SOFTRAIN (4067 downloads)
SYNTHIA-SEQS-02-SPRING (5770 downloads)
SYNTHIA-SEQS-02-SUMMER (3487 downloads)
SYNTHIA-SEQS-02-SUNSET (4251 downloads)
SYNTHIA-SEQS-02-WINTER (3540 downloads)


Old European Town
SYNTHIA-SEQS-04-DAWN (820043 downloads)
SYNTHIA-SEQS-04-FALL (6209 downloads)
SYNTHIA-SEQS-04-FOG (2412 downloads)
SYNTHIA-SEQS-04-NIGHT (4629 downloads)
SYNTHIA-SEQS-04-RAINNIGHT (1724 downloads)
SYNTHIA-SEQS-04-SOFTRAIN (1759 downloads)
SYNTHIA-SEQS-04-SPRING (4601 downloads)
SYNTHIA-SEQS-04-SUMMER (5263 downloads)
SYNTHIA-SEQS-04-SUNSET (1823 downloads)
SYNTHIA-SEQS-04-WINTER (4444 downloads)
SYNTHIA-SEQS-04-WINTERNIGHT (4298 downloads)


New York ish
SYNTHIA-SEQS-05-DAWN (4196 downloads)
SYNTHIA-SEQS-05-FALL (4943 downloads)
SYNTHIA-SEQS-05-FOG (1736 downloads)
SYNTHIA-SEQS-05-NIGHT (8270 downloads)
SYNTHIA-SEQS-05-RAIN (4344 downloads)
SYNTHIA-SEQS-05-RAINNIGHT (9474 downloads)
SYNTHIA-SEQS-05-SOFTRAIN (1763 downloads)
SYNTHIA-SEQS-05-SPRING (6257 downloads)
SYNTHIA-SEQS-05-SUMMER (2027 downloads)
SYNTHIA-SEQS-05-SUNSET (5707 downloads)
SYNTHIA-SEQS-05-WINTER (9631 downloads)
SYNTHIA-SEQS-05-WINTERNIGHT (2529 downloads)


Highway
SYNTHIA-SEQS-06-DAWN (1887 downloads)
SYNTHIA-SEQS-06-FOG (5381 downloads)
SYNTHIA-SEQS-06-NIGHT (1866 downloads)
SYNTHIA-SEQS-06-NIGHT (5865 downloads)
SYNTHIA-SEQS-06-SPRING (2886 downloads)
SYNTHIA-SEQS-06-SUMMER (4762 downloads)
SYNTHIA-SEQS-06-SUNSET (3131 downloads)
SYNTHIA-SEQS-06-WINTER (4083 downloads)
SYNTHIA-SEQS-06-WINTERNIGHT (2148 downloads)

Citation:

When using or referring to the SYNTHIA-CVPR’16 in your research, please cite our CVPR 2016 paper [ pdf ], please check our terms of use.

thumbnail of gros_cvpr16

 

@InProceedings{Ros_2016_CVPR,
author = {Ros, German and Sellart, Laura and Materzynska, Joanna and Vazquez, David and Lopez, Antonio M.},
title = {The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}

 
 
 
 
 

When using or referring to the SYNTHIA-SF in your research, please cite our BMVC 2017 paper [ pdf ], please check our terms of use.

 

@InProceedings{HernandezBMVC17,
author = {Hernandez-Juarez, Daniel and Schneider, Lukas and Espinosa, Antonio and Vazquez, David and Lopez, Antonio M. and Franke, Uwe and Pollefeys, Marc and Moure, Juan Carlos},
title = {Slanted Stixels: Representing San Francisco’s Steepest Streets},
booktitle = {British Machine Vision Conference (BMVC), 2017},
year = {2017}
}

 

When using or refferring to the SYNTHIA-AL in your research, please cite our ICCV Wokshops 2019 paper [ pdf ].

 

 

@InProceedings{bengarICCVW19,
author = {Zolfaghari Bengar, Javad and Gonzalez-Garcia, Abel and Villalonga, Gabriel and Raducanu, Bogdan and Aghdam, Hamed H and Mozerov, Mikhail and Lopez, Antonio M and van de Weijer, Joost},
title = {Temporal Coherence for Active Learning in Videos},
booktitle = {The IEEE International Conference in Computer Vision, Workshops (ICCV Workshops)},
year = {2019}
}