WoodScape: A Multi-task, Multi-camera Fisheye Dataset for Autonomous Driving is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the automotive industry.

The dataset consists of 15000 images with 796503 labeled objects belonging to 46 different classes including road_surface, ego_vehicle, construction, and other: car, pole, curb, nature, green_strip, free_space, sky, person, lane_marking, other_ground_marking, bicycle, clean, opaque, other_wheeled_transport, parking_line, grouped_vehicles, void, semi transparent, trafficsign_indistingushable, rider, traffic_sign, transparent, motorcycle, van, zebra_crossing, fence, grouped_pedestrian_and_animals, unknown_traffic_light, truck, bus, traffic_light_green, movable_object, traffic_light_red, cats_eyes_and_botts_dots, parking_marking, train/tram, trailer, animal, traffic_light_yellow, grouped_botts_dots, caravan, grouped_animals, and structure.

Images in the WoodScape: RGB Fisheye dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. There are 1766 (12% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: train (12234 images) and test (2766 images). Additionally, every image contains information about its image source (rgb or source), while every instance of the object has vehicle info, depiction, glass, occlusion, and position tags. The dataset was released in 2021 by the Valeo, France.

Here is a visualized example for randomly selected sample classes:

To encourage the research community to improve AI-based systems, Valeo is making available to the public, free of charge, the Dataset subject to the terms and conditions below:

• Dataset means a collection of fisheye camera data and lidar data, collected in diverse conditions from moving vehicles, labelled by Mighty Ai and anonymized with brighter AI’s Deep Natural AnonymizationTM, available at woodscape.valeo.ai.
• Valeo means Valeo Comfort and Driving Assistance SAS.
• You means the individual or entity who accesses the Dataset subject to the present terms of use.

By downloading the Dataset You are agreeing to comply with the present terms and conditions.

You shall use the Dataset only (i) for research purposes in conjunction with internal non-published research projects, publicly available research, publications and studies, access to which is not subject to payment, (ii) teaching, (iii) to perform benchmarking for purposes of academic or applied research publication or (iv) for personal experimentation.

You shall not use the Dataset for commercial purposes i.e. primarily intended for or directed towards commercial advantage or monetary compensation, and notably not use or deploy the Dataset, any models trained on or refined using the Dataset including pre-training or augmentation, in operation of a vehicle or to assist in the operation of vehicle.

You shall cause to be conspicuously displayed in all public material and website referencing research conducted with the Dataset, a notice that the research was conducted using Valeo Woodscape Dataset by providing a link to the Dataset website. Valeo has taken reasonable care to remove any identifiable information from the Dataset including faces of people and license plates. Valeo prohibits You from using the Dataset in any manner to identify any person.

The Dataset may automatically update. Valeo is not obliged to make any updates available, to provide any technical support of any kind or to continue making the Dataset available for any period of time. Valeo may terminate Your access to the Dataset at any time, with or without cause, with or without notice.

These terms and conditions may be amended from time to time at Valeo’s sole discretion. Please periodically review the controlling version of these terms. By continuing to use the Dataset, you acknowledge and agree to such amendment.

YOU AGREE THAT THE DATASET IS PROVIDED “AS IS” WITHOUT ANY EXPRESS OR IMPLIED WARRANTY, INCLUDING WITHOUT LIMITATIONS WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON INFRINGEMENT. WITHOUT LIMITING TO THE FOREGOING, VALEO DOES NOT WARRANT THAT THE CONTENT OF THE DATASET IS COMPLETE, ACCURATE, RELIABLE OR CORRECT IN THEIR POSTED FORMS. IN NO EVENT SHALL VALEO BE LIABLE UNDER ANY LEGAL THEORY WITH RESPECT TO THE DATASET OR ANY WORK MADE OR DEVELOPPED USING THE DATASET, FOR ANY DIRECT DAMAGES OR FOR ANY INDIRECT, CONSEQUENTIAL, INCIDENTAL, PUNITIVE OR SPECIAL DAMAGES OR LOST PROFITS.

The entire risk arising out of use or performance of the Dataset remains with You. You agree that it is solely your responsibility to comply with all applicable laws with regard to the use of the Dataset. These terms and conditions shall be governed by and interpreted in accordance with the laws of France. All disputes about these terms will be presented to the applicable courts of Paris (France). You consent to the jurisdiction of such courts and waive any jurisdictional or venue defenses otherwise available.

Source

If you make use of the WoodScape data, please cite the following reference:

@InProceedings{Yogamani_2019_ICCV,
  author = {Yogamani, Senthil and Hughes, Ciaran and Horgan, Jonathan and Sistu, Ganesh and Varley, Padraig and O'Dea, Derek and Uricar, Michal and Milz, Stefan and Simon, Martin and Amende, Karl and Witt, Christian and Rashed, Hazem and Chennupati, Sumanth and Nayak, Sanjaya and Mansoor, Saquib and Perrotton, Xavier and Perez, Patrick},
  title = {WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  month = {October},
  year = {2019},
  doi = {10.1109/ICCV.2019.00940}
}

Source

Dataset WoodScape: RGB Fisheye can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='WoodScape: RGB Fisheye', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.