Omkar2118/datasets huggingface dataset:
EURLEX57K

EURLEX57K is a new publicly available legal LMTC dataset, dubbed EURLEX57K, containing 57k English EU legislative documents from the EUR-LEX portal, tagged with ∼4.3k labels (concepts) from the European Vocabulary (EUROVOC).

Source: [Large-Scale Multi-Label Text Classification on EU Legislation](https://www.aclweb.org/anthology/P19-1636.pdf)

Introducing paper: Large-Scale Multi-Label Text Classification on EU Legislation
Anonymized Keystrokes Dataset

Includes two datasets for this task, one for English-French (En-Fr) and another for English-German (En-De). For each dataset, the action sequences for full documents are provided, along with an editor identifier. The dataset contains document-level post-editing action sequences, including edit operations from keystrokes, mouse actions, and waiting times.

Source: [Translator2Vec: Understanding and Representing Human Post-Editors](https://arxiv.org/pdf/1907.10362.pdf)

Introducing paper: Translator2Vec: Understanding and Representing Human Post-Editors
METU-VIREF Dataset

**METU-VIREF** is a video referring expression dataset comprising of videos from VIRAT Ground and ILSVRC2015 VID datasets. VIRAT is a surveillance dataset and contains mainly people and vehicles. To line up with this and restrict the domain, only videos that contain vehicles from the ILSVRC dataset are used. The METU-VIREF dataset does not contain whole videos from these datasets (the videos need to be downloaded from the respective sources) but just referring expressions for video sequences containing an object pair. For this, object pairs are chosen which had a relation that a meaningful referring expression could be written for.

Source: [VIREF](https://github.com/hazananayurt/viref)

Introducing paper: Searching for Ambiguous Objects in Videos using Relational Referring Expressions
UNDD
Full name: Urban Night Driving Dataset

UNDD consists of 7125 unlabelled day and night images; additionally, it has 75 night images with pixel-level annotations having classes equivalent to Cityscapes dataset.

Source: [UNDD](https://github.com/sauradip/night_image_semantic_segmentation)
Image Source: [https://github.com/sauradip/night_image_semantic_segmentation](https://github.com/sauradip/night_image_semantic_segmentation)

Introducing paper: What's There in the Dark
Food.com Recipes and Interactions

Food.com Recipes and Interactions consists of 270K recipes and 1.4M user-recipe interactions (reviews) scraped from Food.com, covering a period of 18 years (January 2000 to December 2018).

Source: [Generating Personalized Recipes from Historical User Preferences](https://arxiv.org/pdf/1909.00105.pdf)

Introducing paper: Generating Personalized Recipes from Historical User Preferences
SYNTHIA-PANO

SYNTHIA-PANO is the panoramic version of SYNTHIA dataset. Five sequences are included: Seqs02-summer, Seqs02-fall, Seqs04-summer, Seqs04-fall and Seqs05-summer. Panomaramic images with fine annotation for semantic segmentation.

Source: [SYNTHIA-PANO](https://github.com/Francis515/SYNTHIA-PANO)
Image Source: [https://github.com/Francis515/SYNTHIA-PANO](https://github.com/Francis515/SYNTHIA-PANO)

Introducing paper: Semantic Segmentation of Panoramic Images Using a Synthetic Dataset
SUIM
Full name: Segmentation of Underwater IMagery

The Segmentation of Underwater IMagery (SUIM) dataset contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor. The images have been rigorously collected during oceanic explorations and human-robot collaborative experiments, and annotated by human participants.

Source: [Semantic Segmentation of Underwater Imagery: Dataset and Benchmark](https://arxiv.org/abs/2004.01241)
Image Source: [http://irvlab.cs.umn.edu/resources/suim-dataset](http://irvlab.cs.umn.edu/resources/suim-dataset)

Introducing paper: Semantic Segmentation of Underwater Imagery: Dataset and Benchmark
CSAbstruct Dataset

CSAbstruct is a new dataset of annotated computer science abstracts with sentence labels according to their rhetorical roles. The key difference between this dataset and PUBMED-RCT is that PubMed abstracts are written according to a predefined structure, whereas computer science papers are free-form. Therefore, there is more variety in writing styles in CSABSTRUCT. CSABSTRUCT is collected from the Semantic Scholar corpus (Ammar et al., 2018). Each sentence is annotated by 5 workers on the Figure-eight platform,6 with one of 5 categories {BACKGROUND, OBJECTIVE, METHOD, RESULT, OTHER}.

Source: [Pretrained Language Models for Sequential Sentence Classification](https://arxiv.org/pdf/1909.04054.pdf)

Introducing paper: Pretrained Language Models for Sequential Sentence Classification
Pesteh-Set

Pesteh-Set is made of two parts. The first part includes 423 images with ground truth. The pistachios are sorted into two classes: Open-mouth and closed-mouth. The ground truth of the images is a CSV file that consists of the bounding boxes of the two classes of pistachios in the images. There are between 1 to 27 pistachios in each image, and 3927 pistachios in total. The second part includes 6 videos with a total length of 167 seconds and 561 moving pistachios.

Source: [Pesteh-Set](https://github.com/mr7495/Pesteh-Set)

Introducing paper: Detecting and Counting Pistachios based on Deep Learning
CelebAGaze

CelebAGaze consists of 25283 high-resolution celebrity images that are collected from CelebA and the Internet. It consists of 21832 face images with eyes staring at the camera and 3451 face images with eyes staring somewhere else. All images (256 × 256) are cropped and the eye mask region by dlib is computed. Specifically, dlib is used to extract 68 facial landmarks and calculate the mean of 6 points near the eye region, which will be the center point of the mask. The size of the mask is fixed to 30×50. As described above, 300 samples from domain Y are randomly selected, 100 samples from domain X as the test set, the remaining as the training set. Note that this dataset is unpaired and it is not labeled with the specific eye angle or the head pose information.

Source: [Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild](https://arxiv.org/pdf/2008.03834.pdf)

Introducing paper: Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild
IQUAD
Full name: Interactive Question Answering Dataset

IQUAD is a dataset for Visual Question Answering in interactive environments. It is built upon AI2-THOR, a simulated photo-realistic environment of configurable indoor scenes with interactive object. IQUAD V1 has 75,000 questions, each paired with a unique scene configuration.

Source: [IQA: Visual Question Answering in Interactive Environments](https://arxiv.org/abs/1712.03316)

Introducing paper: IQA: Visual Question Answering in Interactive Environments
EVE
Full name: End-to-end Video-based Eye-tracking

EVE (End-to-end Video-based Eye-tracking) is a dataset for eye-tracking. It is collected from 54 participants and consists of 4 camera views, over 12 million frames and 1327 unique visual stimuli (images, video, text), adding up to approximately 105 hours of video data in total.

Official competition on Codalab: [https://competitions.codalab.org/competitions/28954](https://competitions.codalab.org/competitions/28954)

Introducing paper: Towards End-to-end Video-based Eye-Tracking
OpoSum

OPOSUM is a dataset for the training and evaluation of Opinion Summarization models which contains Amazon reviews from six product domains: Laptop Bags, Bluetooth Headsets, Boots, Keyboards, Televisions, and Vacuums.
The six training collections were created by downsampling from the Amazon Product Dataset introduced in McAuley et al. (2015) and contain reviews and their respective ratings. 

A subset of the dataset has been manually annotated, specifically, for each domain, 10 different products were uniformly sampled (across ratings) with 10 reviews each, amounting to a total of 600 reviews, to be used only for development (300) and testing (300).

Source: [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://arxiv.org/abs/1808.08858)

Introducing paper: Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised
ForecastQA

ForecastQA is a question-answering dataset consisting of 10,392 event forecasting questions, which have been collected and verified via crowdsourcing efforts. The forecasting problem for this dataset is formulated as a restricted-domain, multiple-choice, question-answering (QA) task that simulates the forecasting scenario.

Source: [ForecastQA: A Question Answering Challenge for Event Forecasting](https://arxiv.org/abs/2005.00792)

Introducing paper: ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data
TSU
Full name: Toyota Smarthome Untrimmed

Toyota Smarthome Untrimmed (TSU) is a dataset for activity detection in long untrimmed videos. The dataset contains 536 videos with an average duration of 21 mins. Since this dataset is based on the same footage video as Toyota Smarthome Trimmed version, it features the same challenges and introduces additional ones. The dataset is annotated with 51 activities.

The dataset has been recorded in an apartment equipped with 7 Kinect v1 cameras. It contains common daily living activities of 18 subjects. The subjects are senior people in the age range 60-80 years old. The dataset has a resolution of 640×480 and offers 3 modalities: RGB + Depth + 3D Skeleton. The 3D skeleton joints were extracted from RGB. For privacy-preserving reasons, the face of the subjects is blurred.

Source: [Toyota Smarthome](https://project.inria.fr/toyotasmarthome/)

Introducing paper: Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection
AUTSL
Full name: Ankara University Turkish Sign Language Dataset

The Ankara University Turkish Sign Language Dataset (AUTSL) is a large-scale, multimode dataset that contains isolated Turkish sign videos. It contains 226 signs that are performed by 43 different signers. There are 38,336 video samples in total. The samples are recorded using Microsoft Kinect v2 in RGB, depth and skeleton formats. The videos are provided at a resolution of 512×512. The skeleton data contains spatial coordinates, i.e. (x, y), of the 25 junction points on the signer body that are aligned with 512×512 data.

Source: [AUTSL Dataset](http://cvml.ankara.edu.tr/datasets/)

Introducing paper: AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods
WikiHowQA

WikiHowQA is a Community-based Question Answering dataset, which can be used for both answer selection and abstractive summarization tasks. It contains 76,687 questions in the train set, 8,000 in the development set and 22,354 in the test set.

Source: [WikiHowQA](https://github.com/dengyang17/wikihowQA)

Introducing paper: Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering
DRealSR
Full name: Diverse Real-world image Super-Resolution

DRealSR establishes a Super Resolution (SR) benchmark with diverse real-world degradation processes, mitigating the limitations of conventional simulated image degradation. 

It has been collected from five DSLR cameras in natural scenes and cover indoor and outdoor scenes avoiding moving objects, e.g., advertising posters, plants, offices, buildings. The training images are cropped into 380×380, 272×272 and 192×192 patches, resulting in 31,970 patches.


Source: [Component Divide-and-Conquer for Real-World Image Super-Resolution](https://arxiv.org/abs/2008.01928)

Introducing paper: Component Divide-and-Conquer for Real-World Image Super-Resolution
EDEN

EDEN (Enclosed garDEN) is a multimodal synthetic dataset, a dataset for nature-oriented applications. The dataset features more than 300K images captured from more than 100 garden models. Each image is annotated with various low/high-level vision modalities, including semantic segmentation, depth, surface normals, intrinsic colors, and optical flow. 

Source: [EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes](https://arxiv.org/abs/2011.04389)
Image Source: [https://lhoangan.github.io/eden/](https://lhoangan.github.io/eden/)

Introducing paper: EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes
Wiki-CS

Wiki-CS is a Wikipedia-based dataset for benchmarking Graph Neural Networks. The dataset is constructed from Wikipedia categories, specifically 10 classes corresponding to branches of computer science, with very high connectivity. The node features are derived from the text of the corresponding articles.  They were calculated as the average of pretrained GloVe word embeddings (Pennington et al., 2014), resulting in 300-dimensional node features.

The dataset has 11,701 nodes and 216,123 edges.

Source: [Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks](https://arxiv.org/abs/2007.02901)
Image Source: [https://arxiv.org/pdf/2007.02901v1.pdf](https://arxiv.org/pdf/2007.02901v1.pdf)

Introducing paper: Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks
ChaosNLI

Chaos NLI is a Natural Language Inference (NLI) dataset with 100 annotations per example (for a total of 464,500 annotations) for some existing data points in the development sets of SNLI, MNLI, and Abductive NLI. The dataset provides additional labels for NLI annotations that reflect the distribution of human annotators, instead of picking the majority label as the gold standard label.

Source: [ChaosNLI Github Repository](https://github.com/easonnie/ChaosNLI)

Introducing paper: What Can We Learn from Collective Human Opinions on Natural Language Inference Data?
VideoForensicsHQ

VideoForensicsHQ is a benchmark dataset for face video forgery detection, providing high quality visual manipulations.  It is one of the first face video manipulation benchmark sets that also contains audio and thus complements existing datasets along a new challenging dimension. VideoForensicsHQ shows manipulations at much higher video quality and resolution, and shows manipulations that are provably much harder to detect by humans than videos in other datasets. 

VideoForensicsHQ contains 1,737 videos of speaking faces (44% male, 56% female), with 8 different emotions, most of them of “HD” resolution. The videos amount to 1,666,816 frames.

Source: [VideoForensicsHQ: Detecting High-quality Manipulated Face Videos](https://arxiv.org/abs/2005.10360)

Introducing paper: VideoForensicsHQ: Detecting High-quality Manipulated Face Videos
SOLO

The SOLO Corpus comprises over 4 million English tweets, each of which contains at least one of the following tokens: solitude, lonely, and loneliness. The corpus has been collected to analyze the language and emotions associated with the state of being alone in English tweets.

Tweets related to the state of being alone were collected by polling the Twitter API from August 28, 2018 to July 10, 2019 with the following query terms: loneliness, lonely, and solitude. Duplicate tweets, short tweets (containing less than three words), and tweets with external URLs were discarded. Further, only up to three tweets per user are kept. This minimizes the impact of prolific tweeters and bots on the corpus.

Source: [SOLO: A Corpus of Tweets for Examining the State of Being Alone](https://arxiv.org/abs/2006.03096)

Introducing paper: SOLO: A Corpus of Tweets for Examining the State of Being Alone
EXPO-HD

The EXPO-HD Dataset is a dataset of Expo whiteboard markers for the purpose of instance segmentation. The dataset contains two subsets (both include instances segmentation labels):

* Photorealistic synthetic image dataset with 5000 images.
* Real image dataset with 200 images (used for validation and test).

The dataset can be used for testing domain adaptation techniques, as the training set consists of only synthetic images, and the validation and test sets consist of real images.

Source: [EXPO-HD: Exact Object Perception using High Distraction Synthetic Data](https://arxiv.org/abs/2007.14354)

Introducing paper: Detection and Segmentation of Custom Objects using High Distraction Photorealistic Synthetic Data
Twitter Death Hoaxes

This is a dataset for detection fake death hoaxes. It consists of of death reports collected from Twitter between 1st January, 2012 and 31st December, 2014. It was collected by tracking the keyword 'RIP', and matching those tweets in which a name is mentioned next to RIP. Matching names were identified by using Wikidata as a database of names. 

The dataset contains 4,007 death reports, of which 2,301 are real deaths, 1,092 are commemorations and 614 are fake deaths.

Source: [Early Detection of Social Media Hoaxes at Scale](https://arxiv.org/abs/1801.07311)

Introducing paper: Early Detection of Social Media Hoaxes at Scale
KACC

The KACC benchmark consists of three subtasks that can be applied to knowledge graphs: knowledge abstraction, knowledge concretization and knowledge completion. 

- The **knowledge abstraction** subtask contains tasks of concept inference, schema prediction and concept graph completion on the two-view KG. 
- The **knowledge concretization** subtask requires models to do entity graph completion based on the two subgraphs. The concretization ability can be further examined by the results of long-tail entity link prediction. 
- The **knowledge completion** subtask consists of typical single-view knowledge graph completion tasks for each subgraph.

KACC contains 999,902 entities in the entity graph, with 691 types of relations . The concept graph contains 21,293 concepts with 198 types of meta-relations. There are 2,367,971 cross-links between the two.

Source: [KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion](https://arxiv.org/abs/2004.13631)

Introducing paper: KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion
MSSD
Full name: Music Streaming Sessions Dataset

The Spotify Music Streaming Sessions Dataset (MSSD) consists of 160 million streaming sessions with associated user interactions, audio features and metadata describing the tracks streamed during the sessions, and snapshots of the playlists listened to during the sessions. 

This dataset enables research on important problems including how to model user listening and interaction behaviour in streaming, as well as Music Information Retrieval (MIR), and session-based sequential recommendations.

Introducing paper: The Music Streaming Sessions Dataset
TaxiNLI

TaxiNLI is a dataset collected based on the principles and categorizations of the aforementioned taxonomy. A subset of examples are curated from MultiNLI (Williams et al., 2018) by sampling uniformly based on the entailment label and the domain. The dataset is annotated with finegrained category labels.

Source: [TaxiNLI: Taking a Ride up the NLU Hill](https://www.aclweb.org/anthology/2020.conll-1.4.pdf)

Introducing paper: TaxiNLI: Taking a Ride up the NLU Hill
AllMusic Mood Subset

The AllMusic Mood Subset (AMS) is a dataset for mood classification from songs. It is created by matching a subset of the Million Song Dataset (MSD), totalling 67k tracks, with expert annotations of 188 different moods collected from AllMusic.

Since the AMS is a subset of the MSD, the audio data is gathered by obtaining the 7-digital 30 second previews associated with all MSD tracks. These are 128kbps mp3 stereo
files sampled at 44.1kHz.

Source: [Mood Classification Using Listening Data](https://arxiv.org/abs/2010.11512)

Introducing paper: Mood Classification Using Listening Data
NISP
Full name: NITK-IISc Multilingual Multi-accent Speaker Profiling

This dataset contains speech recordings along with speaker physical parameters (height, weight, shoulder size, age ) as well as regional information and linguistic information.

There are a total of 345 speakers (219 male and 126 female). The dataset contains sentences that are taken out from newspapers. Each speaker has contributed about 4-5 minutes of data that includes recordings in both English and their mother tongue. The transcript for the text is provided in UTF-8 format.

Source: [NISP](https://github.com/iiscleap/NISP-Dataset)

Introducing paper: NISP: A Multi-lingual Multi-accent Dataset for Speaker Profiling
EDUVSUM
Full name: Educational Video Summarization

EDUVSUM contains educational videos with subtitles from three popular e-learning platforms: Edx,YouTube, and TIB AV-Portal that cover the following topics: crash course on history of science and engineering, computer science, python and web programming, machine learning and computer vision, Internet of things (IoT), and software engineering. In total, the current version of the dataset contains 98 videos with ground truth values annotated by a user with an academic background in computer science.

Source: [EDUVSUM](https://github.com/VideoAnalysis/EDUVSUM)

Introducing paper: Classification of Important Segments in Educational Videos using Multimodal Features
ADVANCE
Full name: AuDio Visual Aerial sceNe reCognition datasEt

The AuDio Visual Aerial sceNe reCognition datasEt (ADVANCE) is a brand-new multimodal learning dataset, which aims to explore the contribution of both audio and conventional visual messages to scene recognition. This dataset in summary contains 5075 pairs of geotagged aerial images and sounds, classified into 13 scene classes, i.e., airport, sports land, beach, bridge, farmland, forest, grassland, harbor, lake, orchard, residential area, shrub land, and train station.

Source: [](https://akchen.github.io/ADVANCE-DATASET/)

Introducing paper: Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition
Multi-Modal CelebA-HQ

Multi-Modal-CelebA-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA dataset by following CelebA-HQ. Each image has high-quality segmentation mask, sketch, descriptive text, and image with transparent background.

Multi-Modal-CelebA-HQ can be used to train and evaluate algorithms of text-to-image-generation, text-guided image manipulation, sketch-to-image generation, and GANs for face generation and editing.

Source: [Multi-Modal CelebA-HQ Dataset](https://github.com/weihaox/Multi-Modal-CelebA-HQ-Dataset)
Image Source: [Xia et al](https://arxiv.org/pdf/2012.03308.pdf)

Introducing paper: TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
Short Text Font Dataset

The proposed dataset includes 1,309 short text instances from Adobe Spark. The dataset is a collection of publicly available sample texts created by different designers. It covers a variety of topics found in posters, flyers, motivational quotes and advertisements.

Source: [Short Text Font Dataset](https://github.com/RiTUAL-UH/Font-prediction-dataset)

Introducing paper: Let Me Choose: From Verbal Context to Font Selection
SPHERE-calorie

The dataset contains both RGB and depth images, and the data from two accelerometers, together with ground truth calorie values from a calorimeter for calorie expenditure estimation in home environments. 

Source: [SPEHERE Calorie](https://research-information.bris.ac.uk/en/datasets/sphere-calorie)

Introducing paper: Calorie Counter: RGB-Depth Visual Estimation of Energy Expenditure at Home
SmartCity

SmartCity consists of 50 images in total collected from ten city scenes including office entrance, sidewalk, atrium, shopping mall etc.. Unlike the existing crowd counting datasets with images of hundreds/thousands of pedestrians and nearly all the images being taken outdoors, SmartCity has few pedestrians in images and consists of both outdoor and indoor scenes: the average number of pedestrians is only 7.4 with minimum being 1 and maximum being 14.

Source: [SmartCity](https://github.com/miao0913/SaCNN-CrowdCounting-Tencent_Youtu)

Introducing paper: Crowd counting via scale-adaptive convolutional neural network