Omkar2118/datasets huggingface dataset:
Da Vinci Dataset

A line drawing restoration dataset which consists of 71 line drawing sketches by Leonardo Da Vinci.

Source: [Da Vinci Dataset](http://hi.cs.waseda.ac.jp/~esimo/en/data/davincidataset/)
DAWN

DAWN emphasizes a diverse traffic environment (urban, highway and freeway) as well as a rich variety of traffic flow. The DAWN dataset comprises a collection of 1000 images from real-traffic environments, which are divided into four sets of weather conditions: fog, snow, rain and sandstorms. The dataset is annotated with object bounding boxes for autonomous driving and video surveillance scenarios. This data helps interpreting effects caused by the adverse weather conditions on the performance of vehicle detection systems.

Source: [DAWN: Vehicle Detection in Adverse Weather Nature Dataset](/paper/dawn-vehicle-detection-in-adverse-weather)
Image Source: [Kenk and Hassaballah](https://arxiv.org/pdf/2008.05402v1.pdf)

Introducing paper: DAWN: Vehicle Detection in Adverse Weather Nature Dataset
DAWT
Full name: Densely Annotated Wikipedia Texts

The DAWT dataset consists of Densely Annotated Wikipedia Texts across multiple languages. The annotations include labeled text mentions mapping to entities (represented by their Freebase machine ids) as well as the type of the entity. The data set contains total of 13.6M articles, 5.0B tokens, 13.8M mention entity co-occurrences. DAWT contains 4.8 times more anchor text to entity links than originally present in the Wikipedia markup. Moreover, it spans several languages including English, Spanish, Italian, German, French and Arabic. 

Source: [DAWT: Densely Annotated Wikipedia Texts across multiple languages](/paper/dawt-densely-annotated-wikipedia-texts-across)

Introducing paper: DAWT: Densely Annotated Wikipedia Texts across multiple languages
DBpedia NIF

The dataset provides the content of all articles for 128 Wikipedia languages. The dataset has been further enriched with about 25% more links and selected partitions published as Linked Data. 

Source: [DBpedia NIF: Open, Large-Scale and Multilingual Knowledge Extraction Corpus](/paper/dbpedia-nif-open-large-scale-and-multilingual)

Introducing paper: DBpedia NIF: Open, Large-Scale and Multilingual Knowledge Extraction Corpus
DDAD
Full name: Dense Depth for Autonomous Driving

**DDAD** 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).

Source: [https://github.com/TRI-ML/DDAD](https://github.com/TRI-ML/DDAD)
Image Source: [https://github.com/TRI-ML/DDAD](https://github.com/TRI-ML/DDAD)

Introducing paper: 3D Packing for Self-Supervised Monocular Depth Estimation