Omkar2118/datasets huggingface dataset:
TuSimple Lane

TuSimple Lane is an extension of the [TuSimple](/dataset/tusimple) dataset with 14,336 lane boundaries annotations. Each lane boundary in the dataset is annotated using 7 different classes such as “Single Dashed”, “Double Dashed” or “Single White Continuous”.

Source: [https://github.com/fabvio/TuSimple-lane-classes](https://github.com/fabvio/TuSimple-lane-classes)
Image Source: [Pizzati et al](https://arxiv.org/pdf/1907.01294v2.pdf)

Introducing paper: Lane Detection and Classification using Cascaded CNNs
TutorialBank

TutorialBank is a publicly available dataset which aims to facilitate NLP education and research. The dataset consists of links to over 6,300 high-quality resources on NLP and related fields. The corpus’s magnitude, manual collection and focus on annotation for education in addition to research differentiates it from other corpora.

Source: [TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation](/paper/tutorialbank-a-manually-collected-corpus-for)
Image Source: [http://aan.how/browse/resources/](http://aan.how/browse/resources/)

Introducing paper: TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation
TVC
Full name: TV show Captions

TV show Caption is a large-scale multimodal captioning dataset, containing 261,490 caption descriptions paired with 108,965 short video moments. **TVC** is unique as its captions may also describe dialogues/subtitles while the captions in the other datasets are only describing the visual content.

Source: [https://tvr.cs.unc.edu/tvc.html](https://tvr.cs.unc.edu/tvc.html)
Image Source: [https://github.com/jayleicn/TVCaption](https://github.com/jayleicn/TVCaption)

Introducing paper: TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval
TVPR
Full name: Top-View Person Re-Identification Dataset

The TVPR (Top View Person Re-identification) dataset stores depth frames (640x480) collected using Asus Xtion Pro Live in top-view configuration. This setup choice is primarily due to the reduction of occlusions and it has also the advantage of being privacy preserving, because faces are not recorded by the camera. The use of an RGB-D camera allows to extract anthropometric features for the recognition of people passing under the camera.

Source: [TVPR](http://vrai.dii.univpm.it/re-id-dataset)
TVSeries

A realistic dataset composed of 27 episodes from 6 popular TV series. The dataset spans over 16 hours of footage annotated with 30 action classes, totaling 6,231 action instances.

Source: [Online Action Detection](/paper/online-action-detection)

Introducing paper: Online Action Detection
TweetEval

TweetEval introduces an evaluation framework consisting of seven heterogeneous Twitter-specific classification tasks.

Source: [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](/paper/tweeteval-unified-benchmark-and-comparative)
Image Source: [https://arxiv.org/pdf/2010.12421v2.pdf](https://arxiv.org/pdf/2010.12421v2.pdf)

Introducing paper: TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification
Twitch-FIFA

**Twitch-FIFA** is video-context, many-speaker dialogue dataset based on live-broadcast soccer game videos and chats from Twitch.tv. This dataset can be used to train visually-grounded dialogue models that generate relevant temporal and spatial event language from the live video, while also being relevant to the chat history.

Source: [https://github.com/ramakanth-pasunuru/video-dialogue](https://github.com/ramakanth-pasunuru/video-dialogue)

Introducing paper: Game-Based Video-Context Dialogue
Twitter100k

Twitter100k is a large-scale dataset for weakly supervised cross-media retrieval.

Source: [Twitter100k: A Real-world Dataset for Weakly Supervised Cross-Media Retrieval](/paper/twitter100k-a-real-world-dataset-for-weakly)
Image Source: [https://arxiv.org/pdf/1703.06618v1.pdf](https://arxiv.org/pdf/1703.06618v1.pdf)

Introducing paper: Twitter100k: A Real-world Dataset for Weakly Supervised Cross-Media Retrieval
Twitter Conversations Dataset

This dataset is used for the task of conversational document prediction. The dataset includes conversations that occurred between users and customer care agents in 25 organizations on the Twitter platform. Each conversation ends with a customer care agent providing a URL to a document to resolve the issue the user is facing. The task is to predict the document given a dialog context.
The train, dev and test datasets include 10000, 525 and 500 conversations respectively.

Source: [https://github.com/IBM/twitter-customer-care-document-prediction](https://github.com/IBM/twitter-customer-care-document-prediction)
Image Source: [https://arxiv.org/pdf/2010.02305v1.pdf](https://arxiv.org/pdf/2010.02305v1.pdf)

Introducing paper: Conversational Document Prediction to Assist Customer Care Agents
Twitter Cyberthreat Detection Dataset

Twitter Cyberthreat Detection Dataset is a dataset that contains tweets from two sets of accounts related to cybersecurity. The tweets are annotated with different information such as whether they contain security-related information and named entities.

Source: [https://arxiv.org/pdf/1904.01127.pdf](https://arxiv.org/pdf/1904.01127.pdf)

Introducing paper: Cyberthreat Detection from Twitter using Deep Neural Networks
Twitter Flood

This dataset contains two subsets of flood images from Twitter: The Harz17 dataset comprises images from tweets containing flood-related keywords during the occurrence of a flood in the Harz region in Germany in July 2017. Similarly, the Rhine18 dataset comprises images related to a flood of the river Rhine in January 2018.

Source: [https://github.com/cvjena/twitter-flood-dataset](https://github.com/cvjena/twitter-flood-dataset)

Introducing paper: Finding Relevant Flood Images on Twitter using Content-based Filters
TURL
Full name: Twitter News URL Corpus

Twitter News URL Corpus is a human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification.

Source: [A Continuously Growing Dataset of Sentential Paraphrases](/paper/a-continuously-growing-dataset-of-sentential)
Image Source: [https://arxiv.org/pdf/1708.00391v1.pdf](https://arxiv.org/pdf/1708.00391v1.pdf)

Introducing paper: A Continuously Growing Dataset of Sentential Paraphrases
TWT-16

The TWT16 dataset contains ~30k conversations in Twitter, collected from January to June 2016.

Source: [https://arxiv.org/pdf/1903.07319.pdf](https://arxiv.org/pdf/1903.07319.pdf)
Image Source: [https://github.com/zengjichuan/Topic_Disc](https://github.com/zengjichuan/Topic_Disc)

Introducing paper: What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
UAV-GESTURE

UAV-GESTURE is a dataset for UAV control and gesture recognition. It is an outdoor recorded video dataset for UAV commanding signals with 13 gestures suitable for basic UAV navigation and command from general aircraft handling and
helicopter handling signals. It contains 119 high-definition video clips
consisting of 37,151 frames. 

Source: [UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition](/paper/uav-gesture-a-dataset-for-uav-control-and)

Introducing paper: UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition
UAVid

UAVid is a high-resolution UAV semantic segmentation dataset as a complement, which brings new challenges, including large scale variation, moving object recognition and temporal consistency preservation. The UAV dataset consists of 30 video sequences capturing 4K high-resolution images in slanted views. In total, 300 images have been densely labeled with 8 classes for the semantic labeling task. 

Source: [UAVid: A Semantic Segmentation Dataset for UAV Imagery](/paper/the-uavid-dataset-for-video-semantic)
Image Source: [https://uavid.nl/](https://uavid.nl/)

Introducing paper: UAVid: A Semantic Segmentation Dataset for UAV Imagery
UBC3V Dataset

~6 million synthetic depth frames for pose estimation from multiple cameras.

Source: [Real-Time Human Motion Capture with Multiple Depth Cameras](/paper/real-time-human-motion-capture-with-multiple)

Introducing paper: Real-Time Human Motion Capture with Multiple Depth Cameras
UCFRep

The **UCFRep** dataset contains 526 annotated repetitive action videos. This dataset is built from the action recognition dataset UCF101.

Source: [https://github.com/Xiaodomgdomg/Deep-Temporal-Repetition-Counting](https://github.com/Xiaodomgdomg/Deep-Temporal-Repetition-Counting)

Introducing paper: Context-aware and Scale-insensitive Temporal Repetition Counting
UCLA Protest Image

40,764 images (11,659 protest images and hard negatives) with various annotations of visual attributes and sentiments.

Source: [Protest Activity Detection and Perceived Violence Estimation from Social Media Images](/paper/protest-activity-detection-and-perceived)

Introducing paper: Protest Activity Detection and Perceived Violence Estimation from Social Media Images
UC Merced Land Use Dataset

This is a 21 class land use image dataset meant for research purposes.

There are 100 images for each of the following classes:

- agricultural
- airplane
- baseballdiamond
- beach
- buildings
- chaparral
- denseresidential
- forest
- freeway
- golfcourse
- harbor
- intersection
- mediumresidential
- mobilehomepark
- overpass
- parkinglot
- river
- runway
- sparseresidential
- storagetanks
- tenniscourt
- Each image measures 256x256 pixels.

The images were manually extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the country. The pixel resolution of this public domain imagery is 1 foot.

Source: [UC Merced Land Use Dataset](http://weegee.vision.ucmerced.edu/datasets/landuse.html)
UFDD
Full name: Unconstrained Face Detection Dataset

Unconstrained Face Detection Dataset (UFDD) aims to fuel further research in unconstrained face detection.

Source: [Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results](/paper/pushing-the-limits-of-unconstrained-face)
Image Source: [https://ufdd.info/](https://ufdd.info/)

Introducing paper: Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results
UG^2

Contains three difficult real-world scenarios: uncontrolled videos taken by UAVs and manned gliders, as well as controlled videos taken on the ground. Over 160,000 annotated frames forhundreds of ImageNet classes are available, which are used for baseline experiments that assess the impact of known and unknown image artifacts and other conditions on common deep learning-based object classification approaches.

Source: [UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition](/paper/ug2-a-video-benchmark-for-assessing-the)

Introducing paper: UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition
UIT-ViNames

This dataset comprises over 26,000 full names annotated with genders. 

Source: [Gender Prediction Based on Vietnamese Names with Machine Learning Techniques](/paper/gender-prediction-based-on-vietnamese-names)

Introducing paper: Gender Prediction Based on Vietnamese Names with Machine Learning Techniques
UIT-ViNewsQA

UIT-ViNewsQA is a new corpus for the Vietnamese language to evaluate healthcare reading comprehension models. The corpus comprises 22,057 human-generated question-answer pairs. Crowd-workers create the questions and their answers based on a collection of over 4,416 online Vietnamese healthcare news articles, where the answers comprise spans extracted from the corresponding articles. 

Source: [New Vietnamese Corpus for Machine ReadingComprehension of Health News Articles](/paper/new-vietnamese-corpus-for-machine)

Introducing paper: New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles
UIT-ViQuAD

A new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. 

Source: [A Vietnamese Dataset for Evaluating Machine Reading Comprehension](/paper/a-vietnamese-dataset-for-evaluating-machine)

Introducing paper: A Vietnamese Dataset for Evaluating Machine Reading Comprehension
UMC005 English-Urdu

UMC005 English-Urdu is a parallel corpus of texts in English and Urdu language with sentence alignments. The corpus can be used for experiments with statistical machine translation.

The texts come from four different sources:

- Quran
- Bible
- Penn Treebank (Wall Street Journal)
- Emille corpus

Source: [UMC005 English-Urdu](http://ufal.ms.mff.cuni.cz/umc/005-en-ur/)
UIEB
Full name: Underwater Image Enhancement Benchmark Dataset

Includes 950 real-world underwater images, 890 of which have the corresponding reference images.

Source: [An Underwater Image Enhancement Benchmark Dataset and Beyond](/paper/an-underwater-image-enhancement-benchmark)

Introducing paper: An Underwater Image Enhancement Benchmark Dataset and Beyond
UniMiB SHAR

Includes 11,771 samples of both human activities and falls performed by 30 subjects of ages ranging from 18 to 60 years. Samples are divided in 17 fine grained classes grouped in two coarse grained classes: one containing samples of 9 types of activities of daily living (ADL) and the other containing samples of 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL, the age, the gender, and so on. 

Source: [UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones](/paper/unimib-shar-a-new-dataset-for-human-activity)

Introducing paper: UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones
MultiUN
Full name: Multilingual Corpus from United Nation Documents

The MultiUN parallel corpus is extracted from the United Nations Website , and then cleaned and converted to XML at Language Technology Lab in DFKI GmbH (LT-DFKI), Germany. The documents were published by UN from 2000 to 2009.

Source: [MultiUN](http://www.euromatrixplus.net/multi-un/)
UPIQ
Full name: Unified Photometric Image Quality

Contains over 4,000 images created by realigning and merging existing HDR and standard-dynamic-range (SDR) datasets.

Source: [Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality](/paper/consolidated-dataset-and-metrics-for-high)

Introducing paper: Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality
Urban Dict spelling variant

**Urban Dict spelling variant** is a variant spelling dataset for use of NLP research in the informal domain. It consists of around 25k variant spelling pairs form UrbanDictionary.

Source: [https://arxiv.org/abs/1911.04669](https://arxiv.org/abs/1911.04669)
Image Source: [https://arxiv.org/pdf/1911.04669.pdf](https://arxiv.org/pdf/1911.04669.pdf)

Introducing paper: How to Evaluate Word Representations of Informal Domain?
UrbanLoco

UrbanLoco is a mapping/localization dataset collected in highly-urbanized environments with a full sensor-suite. The dataset includes 13 trajectories collected in San Francisco and Hong Kong, covering a total length of over 40 kilometers.

Source: [UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes](/paper/urbanloco-a-full-sensor-suite-dataset-for)
Image Source: [https://advdataset2019.wixsite.com/urbanloco](https://advdataset2019.wixsite.com/urbanloco)

Introducing paper: UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes
Bend the Truth

"Bend the Truth" dataset contains news in six different domains: technology, education, business, sports, politics, and entertainment. The real news included in the dataset were collected from a variety of mainstream news websites predominantly in Pakistan, India, UK, and the USA. These news channels are BBC Urdu News, CNN Urdu, Express-News, Jung News, Noway Waqat, and many other reliable news websites. The fake news included in this dataset consist of fake versions of the real news in the dataset, written by professional journalists. 

Source: [Bend the Truth](https://github.com/MaazAmjad/Datasets-for-Urdu-news)
Urdu Sentiment Corpus
Full name: Urdu Sentiment Corpus (v1.0): Linguistic Exploration and Visualization of Labeled Datasetfor Urdu Sentiment Analysis

Consists of Urdu tweets for the sentiment analysis and polarity detection. The dataset is consisting of tweets, such that it casts a political shadow and presents a competitive environment between two separate political parties versus the government of Pakistan. Overall, the dataset is comprising over 17, 185 tokens with 52% records as positive, and 48% records as negative. 
Source: [Urdu Sentiment Corpus (v1.0): Linguistic Exploration and Visualization of Labeled Dataset for Urdu Sentiment Analysis](https://ieeexplore.ieee.org/abstract/document/9080043)
UR-FUNNY

For understanding multimodal language used in expressing humor.

Source: [UR-FUNNY: A Multimodal Language Dataset for Understanding Humor](/paper/ur-funny-a-multimodal-language-dataset-for)

Introducing paper: UR-FUNNY: A Multimodal Language Dataset for Understanding Humor
US-4

The **US-4** is a dataset of Ultrasound (US) images. It is a video-based image dataset that contains over 23,000 high-resolution images from four US video sub-datasets, where two sub-datasets are newly collected by experienced doctors for this dataset.

Source: [https://github.com/983632847/USCL](https://github.com/983632847/USCL)
Image Source: [https://github.com/983632847/USCL](https://github.com/983632847/USCL)

Introducing paper: USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning
UTA-RLDD
Full name: University of Texas at Arlington Real-Life Drowsiness Dataset

Consists of around 30 hours of video, with contents ranging from subtle signs of drowsiness to more obvious ones.

Source: [A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection](/paper/a-realistic-dataset-and-baseline-temporal)

Introducing paper: A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection
UW IOM
Full name: University of Washington Indoor Object Manipulation

Comprises twenty individuals picking up and placing objects of varying weights to and from cabinet and table locations at various heights.

Source: [Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks](/paper/predicting-ergonomic-risks-during-indoor)

Introducing paper: Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks
V2C
Full name: Video-to-Commonsense

Contains ~9K videos of human agents performing various actions, annotated with 3 types of commonsense descriptions.

Introducing paper: Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning
VDQG
Full name: Visual Discriminative Question Generation

The **Visual Discriminative Question Generation (VDQG)** dataset contains 11202 ambiguous image pairs collected from Visual Genome. Each image pair is annotated with 4.6 discriminative questions and 5.9 non-discriminative questions on average.

Introducing paper: Learning to Disambiguate by Asking Discriminative Questions
VehicleX

**VehicleX** is a large-scale synthetic dataset. Created in Unity, it contains 1,362 vehicles of various 3D models with fully editable attributes.

Source: [https://github.com/yorkeyao/VehicleX](https://github.com/yorkeyao/VehicleX)
Image Source: [https://arxiv.org/pdf/1912.08855.pdf](https://arxiv.org/pdf/1912.08855.pdf)

Introducing paper: Simulating Content Consistent Vehicle Datasets with Attribute Descent
VeRi Dataset

To facilitate the research of vehicle re-identification (Re-Id), a large-scale benchmark dateset is built for vehicle Re-Id in the real-world urban surveillance scenario, named “VeRi”. The featured properties of VeRi include:

- It contains over 50,000 images of 776 vehicles captured by 20 cameras covering an 1.0 km^2 area in 24 hours, which makes the dataset scalable enough for vehicle Re-Id and other related research.
- The images are captured in a real-world unconstrained surveillance scene and labeled with varied attributes, e.g. BBoxes, types, colors, and brands. So complicated models can be learnt and evaluated for vehicle Re-Id.
- Each vehicle is captured by 2 ∼ 18 cameras in different viewpoints, illuminations, resolutions, and occlusions, which provides high recurrence rate for vehicle Re-Id in practical surveillance environment.
- It is also labeled with sufficient license plates and spatiotemporal information, such as the BBoxes of plates, plate strings, the timestamps of vehicles, and the distances between neighbouring cameras.

Source: [VeRi Dataset](https://vehiclereid.github.io/VeRi/)
VG-Depth

Enable visual relation detection and serves as an extension to Visual Genome (VG).

Source: [Improving Visual Relation Detection using Depth Maps](/paper/improving-visual-relation-detection-using)

Introducing paper: Improving Visual Relation Detection using Depth Maps