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Data from: DDAD Dataset
- paperswithcode.com
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EmailClick to copy linkLink copiedCiteVitor Guizilini; Rares Ambrus; Sudeep Pillai; Allan Raventos; Adrien Gaidon, DDAD Dataset [Dataset]. https://paperswithcode.com/dataset/ddadAuthorsVitor Guizilini; Rares Ambrus; Sudeep Pillai; Allan Raventos; Adrien GaidonDescriptionDDAD is a new autonomous driving benchmark from TRI (Toyota Research Institute) for long range (up to 250m) and dense depth estimation in challenging and diverse urban conditions. It contains monocular videos and accurate ground-truth depth (across a full 360 degree field of view) generated from high-density LiDARs mounted on a fleet of self-driving cars operating in a cross-continental setting. DDAD contains scenes from urban settings in the United States (San Francisco, Bay Area, Cambridge, Detroit, Ann Arbor) and Japan (Tokyo, Odaiba).
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Data from: ddad
- huggingface.co
Updated Jul 3, 2025+ more versions DDAD (Company) - Reverse Whois Lookup
- whoisdatacenter.com
csv+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteAllHeart Web Inc, DDAD (Company) - Reverse Whois Lookup [Dataset]. https://whoisdatacenter.com/company/DDAD/csvAvailable download formatsDataset provided byAllHeart WebAuthorsAllHeart Web IncLicensehttps://whoisdatacenter.com/terms-of-use/https://whoisdatacenter.com/terms-of-use/
Time period coveredMar 15, 1985 - Jul 13, 2025DescriptionUncover historical ownership history and changes over time by performing a reverse Whois lookup for the company DDAD.
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DAD Dataset
- paperswithcode.com
Updated Feb 2, 2021+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteOkan Köpüklü; Jiapeng Zheng; Hang Xu; Gerhard Rigoll (2021). DAD Dataset [Dataset]. https://paperswithcode.com/dataset/dadDataset updatedFeb 2, 2021AuthorsOkan Köpüklü; Jiapeng Zheng; Hang Xu; Gerhard RigollDescriptionContains normal driving videos together with a set of anomalous actions in its training set. In the test set of the DAD dataset, there are unseen anomalous actions that still need to be winnowed out from normal driving.
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Project c66bdb4d-ddad-4684-8dc4-12cf225930c5.2
- pairedomicsdata.bioinformatics.nl
jsonUpdated Apr 11, 2020+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteLouis Felix Nothias (2020). Project c66bdb4d-ddad-4684-8dc4-12cf225930c5.2 [Dataset]. https://pairedomicsdata.bioinformatics.nl/projects/c66bdb4d-ddad-4684-8dc4-12cf225930c5.2jsonAvailable download formatsDataset updatedApr 11, 2020AuthorsLouis Felix NothiasLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionPaired Omics Data Platform project of MSV000079519 metabolome with 7 (Meta)Genome - Metabolome links and 5 BGC - MS/MS links
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FacebookTwitterEmailDDAD is a new autonomous driving benchmark from TRI (Toyota Research Institute) for long range (up to 250m) and dense depth estimation in challenging and diverse urban conditions. It contains monocular videos and accurate ground-truth depth (across a full 360 degree field of view) generated from high-density LiDARs mounted on a fleet of self-driving cars operating in a cross-continental setting. DDAD contains scenes from urban settings in the United States (San Francisco, Bay Area, Cambridge, Detroit, Ann Arbor) and Japan (Tokyo, Odaiba).