Omkar2118/datasets huggingface dataset:
Visual Relationship Detection Dataset

A dataset containing 5000 images with 37,993 thousand relationships. The dataset contains 100 object categories and 70 predicate categories connecting those objects together.

Source: [Visual Relationship Detection with Language prior and Softmax](/paper/visual-relationship-detection-with-language-1)

Introducing paper: Visual Relationship Detection with Language prior and Softmax
ViText2SQL

**ViText2SQL** is a dataset for the Vietnamese Text-to-SQL semantic parsing task, consisting of about 10K question and SQL query pairs.

Source: [https://github.com/VinAIResearch/ViText2SQL](https://github.com/VinAIResearch/ViText2SQL)

Introducing paper: A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese
ViTT
Full name: Video Timeline Tags

The ViTT dataset consists of human produced segment-level annotations for 8,169 videos. Of these, 5,840 videos have been annotated once, and the rest of the videos have been annotated twice or more. A total of 12,461 sets of annotations are released. The videos in the dataset are from the [Youtube-8M dataset](https://paperswithcode.com/dataset/youtube-8m).

An annotation has the following format:
```
{
  "id": "FmTp",
  "annotations": [
    {
      "timestamp": 260,
      "tag": "Opening"
    },
    {
      "timestamp": 16000,
      "tag": "Displaying technique"
    },
    {
      "timestamp": 23990,
      "tag": "Showing foot positioning"
    },
    {
      "timestamp": 55530,
      "tag": "Demonstrating crossover"
    },
    {
      "timestamp": 114100,
      "tag": "Closing"
    }
  ]
}
```

Source: [Video Timeline Tags (ViTT)](https://github.com/google-research-datasets/Video-Timeline-Tags-ViTT)

Introducing paper: Multimodal Pretraining for Dense Video Captioning
VizWiz-Captions

Consists of over 39,000 images originating from people who are blind that are each paired with five captions.

Source: [Captioning Images Taken by People Who Are Blind](/paper/captioning-images-taken-by-people-who-are)

Introducing paper: Captioning Images Taken by People Who Are Blind
VizWiz-Priv
Full name: Visual Privacy dataset

VizWiz-Priv includes 8,862 regions showing private content across 5,537 images taken by blind people. Of these, 1,403 are paired with questions and 62% of those directly ask about the private content.

Source: [VizWiz-Priv: A Dataset for Recognizing the Presence and Purpose of Private Visual Information in Images Taken by Blind People](/paper/vizwiz-priv-a-dataset-for-recognizing-the)

Introducing paper: VizWiz-Priv: A Dataset for Recognizing the Presence and Purpose of Private Visual Information in Images Taken by Blind People
VizWiz-QualityIssues

A large-scale dataset that links the assessment of image quality issues to two practical vision tasks: image captioning and visual question answering.

Source: [Assessing Image Quality Issues for Real-World Problems](/paper/assessing-image-quality-issues-for-real-world)

Introducing paper: Assessing Image Quality Issues for Real-World Problems
VLEngagement

A novel dataset that consists of content-based and video-specific features extracted from publicly available scientific video lectures and several metrics related to user engagement. 

Source: [VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement](/paper/vlengagement-a-dataset-of-scientific-video)

Introducing paper: VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement
VMSMO

The Video-based Multimodal Summarization with Multimodal Output (**VMSMO**) corpus consists of 184,920 document-summary pairs, with 180,000 training pairs, 2,460 validation and test pairs. The task for this dataset is generating and appropriate textual summary of an article and choosing a proper cover frame from a video accompanying the article.

Source: [https://github.com/yingtaomj/VMSMO](https://github.com/yingtaomj/VMSMO)

Introducing paper: VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles
VocalFolds

The Vocal Folds dataset is a dataset for automatic segmentation of laryngeal endoscopic images.
The dataset consists of 8 sequences from 2 patients containing 536 hand segmented in vivo colour images of the larynx during two different resection interventions with a resolution of 512x512 pixels.

Source: [https://github.com/imesluh/vocalfolds](https://github.com/imesluh/vocalfolds)
Image Source: [https://github.com/imesluh/vocalfolds](https://github.com/imesluh/vocalfolds)

Introducing paper: A Dataset of Laryngeal Endoscopic Images with Comparative Study on Convolution Neural Network Based Semantic Segmentation
VoxClamantis

A large-scale corpus for phonetic typology, with aligned segments and estimated phoneme-level labels in 690 readings spanning 635 languages, along with acoustic-phonetic measures of vowels and sibilants. 

Source: [A Corpus for Large-Scale Phonetic Typology](/paper/a-corpus-for-large-scale-phonetic-typology)

Introducing paper: A Corpus for Large-Scale Phonetic Typology
VoxPopuli

VoxPopuli is a large-scale multilingual corpus providing 100K hours of unlabelled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning. VoxPopuli also contains 1.8K hours of transcribed speeches in 16 languages and their aligned oral interpretations into 5 other languages totaling 5.1K hours.

Source: [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](/paper/voxpopuli-a-large-scale-multilingual-speech)

Introducing paper: VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation
VQA-OV
Full name: Visual Quality Assessment of Omnidirectional Video

Collects 60 reference sequences and 540 impaired sequences. 

Source: [Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model](/paper/bridge-the-gap-between-vqa-and-human-behavior)

Introducing paper: Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model
VT5000

Includes 5000 spatially aligned RGBT image pairs with ground truth annotations. VT5000 has 11 challenges collected in different scenes and environments for exploring the robustness of algorithms.

Source: [RGBT Salient Object Detection: A Large-scale Dataset and Benchmark](/paper/rgbt-salient-object-detection-a-large-scale)

Introducing paper: RGBT Salient Object Detection: A Large-scale Dataset and Benchmark
Ward2ICU

**Ward2ICU** is a vital signs dataset of inpatients from the general ward. It contains vital signs with class labels indicating patient transitions from the ward to intensive care units

Source: [https://github.com/3778/Ward2ICU](https://github.com/3778/Ward2ICU)

Introducing paper: Ward2ICU: A Vital Signs Dataset of Inpatients from the General Ward
WeatherBench

A benchmark dataset for data-driven medium-range weather forecasting, a topic of high scientific interest for atmospheric and computer scientists alike. 

Source: [WeatherBench: A benchmark dataset for data-driven weather forecasting](/paper/weatherbench-a-benchmark-dataset-for-data)

Introducing paper: WeatherBench: A benchmark dataset for data-driven weather forecasting
WebCaricature Dataset

Aims to facilitate research in caricature recognition. All the caricatures and face images were collected from the Web. Compared with two existing datasets, this dataset is much more challenging, with a much greater number of available images, artistic styles and larger intra-personal variations. 

Source: [WebCaricature: a benchmark for caricature recognition](https://arxiv.org/pdf/1703.03230v4.pdf)

Introducing paper: WebCaricature: a benchmark for caricature recognition
WebChild

One of the largest commonsense knowledge bases available, describing over 2 million disambiguated concepts and activities, connected by over 18 million assertions.

Source: [WebChild 2.0 : Fine-Grained Commonsense Knowledge Distillation](/paper/webchild-20-fine-grained-commonsense)

Introducing paper: WebChild 2.0 : Fine-Grained Commonsense Knowledge Distillation
WOS
Full name: Web of Science Dataset

Web of Science (WOS) is a document classification dataset that contains 46,985 documents with 134 categories which include 7 parents categories.

Source: [HDLTex: Hierarchical Deep Learning for Text Classification](/paper/hdltex-hierarchical-deep-learning-for-text)

Introducing paper: HDLTex: Hierarchical Deep Learning for Text Classification
WGISD
Full name: Embrapa Wine Grape Instance Segmentation Dataset

Embrapa Wine Grape Instance Segmentation Dataset (WGISD) contains grape clusters properly annotated in 300 images and a novel annotation methodology for segmentation of complex objects in natural images.

Source: [Grape detection, segmentation and tracking using deep neural networks and three-dimensional association](/paper/grape-detection-segmentation-and-tracking)

Introducing paper: Grape detection, segmentation and tracking using deep neural networks and three-dimensional association
WHOI-Plankton

WHOI-Plankton is a collection of annotated plankton images. It contains > 3.5 million images of microscopic marine plankton, organized according to category labels provided by researchers at the Woods Hole Oceanographic Institution (WHOI). The images are currently placed into one of 103 categories.

Source: [WHOI-Plankton- A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification](/paper/whoi-plankton-a-large-scale-fine-grained)

Introducing paper: WHOI-Plankton- A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification
WHU

Created for MVS tasks and is a large-scale multi-view aerial dataset generated from a highly accurate 3D digital surface model produced from thousands of real aerial images with precise camera parameters.

Source: [A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-view Stereo Reconstruction from An Open Aerial Dataset](/paper/a-novel-recurrent-encoder-decoder-structure)

Introducing paper: A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-view Stereo Reconstruction from An Open Aerial Dataset
WIDER Attribute Dataset

The **WIDER Attribute** dataset is a human attribute recognition dataset with human attribute and image event annotations. Images are selected from the WIDER dataset. There are a total of 13,789 images. A bounding box is annotated for each person in these images, with no more than 20 people (with top resolutions) in a crowd image, resulting in 57,524 boxes in total and 4+ boxes per image on average. For each bounding box, 14 distinct human attributes are labelled. There are 805,336 labels in total.
WikiDataSets

Topical subsets of WikiData, assembled using the WikiDataSets python library. Extracted from Wikidata in April 2020.

Source: [WikiDataSets](https://graphs.telecom-paris.fr/Home_page.html#wikidatasets-section)

Introducing paper: WikiDataSets: Standardized sub-graphs from Wikidata
Wiki-40B

A new multilingual language model benchmark that is composed of 40+ languages spanning several scripts and linguistic families containing round 40 billion characters and aimed to accelerate the research of multilingual modeling.

Source: [Wiki-40B: Multilingual Language Model Dataset](https://www.shaip.com/blog/multimodal-large-language-models-mllms/)

Introducing paper: Wiki-40B: Multilingual Language Model Dataset
WikiANN
Full name: PAN-X

**WikiANN**, also known as **PAN-X**, is a multilingual named entity recognition dataset. It consists of Wikipedia articles that have been annotated with **LOC** (location), **PER** (person), and **ORG** (organization) tags in the **IOB2 format**¹². This dataset serves as a valuable resource for training and evaluating named entity recognition models across various languages.

For instance, it includes information about notable individuals, places, and organizations mentioned in Wikipedia articles. Researchers and practitioners can use WikiANN to develop and improve natural language processing systems that identify and classify named entities in text.

(1) wikiann · Datasets at Hugging Face. https://huggingface.co/datasets/wikiann.
(2) wikiann | TensorFlow Datasets. https://tensorflow.google.cn/datasets/catalog/wikiann.
(3) wikiann · Datasets at Hugging Face. https://huggingface.co/datasets/wikiann/viewer/en.
(4) WikiAnn Dataset | Papers With Code. https://paperswithcode.com/dataset/wikiann-1.

Introducing paper: Cross-lingual Name Tagging and Linking for 282 Languages
WikiAsp

A large-scale dataset for multi-domain aspect-based summarization that attempts to spur research in the direction of open-domain aspect-based summarization. 

Source: [WikiAsp: A Dataset for Multi-domain Aspect-based Summarization](/paper/wikiasp-a-dataset-for-multi-domain-aspect)

Introducing paper: WikiAsp: A Dataset for Multi-domain Aspect-based Summarization
WikiAtomicEdits

WikiAtomicEdits is a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. 

Source: [WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse](/paper/wikiatomicedits-a-multilingual-corpus-of)
Image Source: [https://arxiv.org/pdf/1808.09422v1.pdf](https://arxiv.org/pdf/1808.09422v1.pdf)

Introducing paper: WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse
WikiCatSum

**WikiCatSum** is a domain specific Multi-Document Summarisation (MDS) dataset. It assumes the summarisation task of generating Wikipedia lead sections for Wikipedia entities of a certain domain (e.g. Companies) from the set of documents cited in Wikipedia articles or returned by Google (using article titles as queries). The dataset includes three domains: Companies, Films, and Animals.

Source: [https://datashare.ed.ac.uk/handle/10283/3368](https://datashare.ed.ac.uk/handle/10283/3368)

Introducing paper: Generating Summaries with Topic Templates and Structured Convolutional Decoders
WikiConv

A corpus that encompasses the complete history of conversations between contributors to Wikipedia, one of the largest online collaborative communities. By recording the intermediate states of conversations---including not only comments and replies, but also their modifications, deletions and restorations---this data offers an unprecedented view of online conversation.

Source: [WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community](/paper/wikiconv-a-corpus-of-the-complete)

Introducing paper: WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community
WikiDocEdits

A dataset of single-sentence edits crawled from Wikipedia. 

Source: [Text Editing by Command](/paper/text-editing-by-command)

Introducing paper: Text Editing by Command
Wiki-en

**Wiki-en** is an annotated English dataset for domain detection extracted from Wikipedia. It includes texts from 7 different domains: “Business and Commerce” (BUS), “Government and Politics” (GOV), “Physical and Mental Health” (HEA), “Law and Order” (LAW), “Lifestyle” (LIF), “Military” (MIL), and “General Purpose” (GEN).

Source: [https://arxiv.org/pdf/1907.11499.pdf](https://arxiv.org/pdf/1907.11499.pdf)

Introducing paper: Weakly Supervised Domain Detection
Wiki-Flickr Event Dataset

The Wiki-Flick Event dataset for cross-modal event retrieval is a well-labelled but weakly-aligned dataset collected for cross-modality event retrieval. The dataset consists of 28,825 images on Flickr and 11,960 text articles from hundreds of social media, belonging to 82 categories of events.

Source: [https://github.com/zhengyang5/Wiki-Flickr-Event-Dataset](https://github.com/zhengyang5/Wiki-Flickr-Event-Dataset)

Introducing paper: Learning Shared Semantic Space with Correlation Alignment for Cross-modal Event Retrieval
WikiLingua

WikiLingua includes ~770k article and summary pairs in 18 languages from WikiHow. Gold-standard article-summary alignments across languages are extracted by aligning the images that are used to describe each how-to step in an article.

Source: [https://github.com/esdurmus/Wikilingua](https://github.com/esdurmus/Wikilingua)
Image Source: [Ladhak et al](https://arxiv.org/pdf/2010.03093v1.pdf)

Introducing paper: WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization
WikiLinks

A method for automatically gathering massive amounts of naturally-occurring cross-document reference data is used to create the Wikilinks dataset comprising of 40 million mentions over 3 million entities. 

Source: [WikiLinks](http://www.iesl.cs.umass.edu/data/data-wiki-links)