Omkar2118/datasets huggingface dataset:
Slim
Full name: Slim

This dataset consists of virtual scenes rendered in MuJoCo with multiple views each presented in multiple modalities: image, and synthetic or natural language descriptions. Each scene consists of two or three objects placed on a square walled room, and for each of the 10 camera viewpoint the authors rendered a 3D view of the scene as seen from that viewpoint as well as a synthetically generated description of the scene.

Source: [GitHub](https://github.com/deepmind/slim-dataset)

Introducing paper: Encoding Spatial Relations from Natural Language
TableBank
Full name: TableBank

To address the need for a standard open domain table benchmark dataset, the author propose a novel weak supervision approach to automatically create the TableBank, which is orders of magnitude larger than existing human labeled datasets for table analysis. Distinct from traditional weakly supervised training set, our approach can obtain not only large scale but also high quality training data.

Nowadays, there are a great number of electronic documents on the web such as Microsoft Word (.docx) and Latex (.tex) files. These online documents contain mark-up tags for tables in their source code by nature. Intuitively, one can manipulate these source code by adding bounding box using the mark-up language within each document. For Word documents, the internal Office XML code can be modified where the borderline of each table is identified. For Latex documents, the tex code can be also modified where bounding boxes of tables are recognized. In this way, high-quality labeled data is created for a variety of domains such as business documents, official fillings, research papers etc, which is tremendously beneficial for large-scale table analysis tasks.

The TableBank dataset totally consists of 417,234 high quality labeled tables as well as their original documents in a variety of domains.

Source: [TableBank](https://github.com/doc-analysis/TableBank)

Introducing paper: TableBank: A Benchmark Dataset for Table Detection and Recognition
GitHub Typo Corpus
Full name: GitHub Typo Corpus

Are you the kind of person who makes a lot of typos when writing code? Or are you the one who fixes them by making "fix typo" commits? Either way, thank you—you contributed to the state-of-the-art in the NLP field.

GitHub Typo Corpus is a large-scale dataset of misspellings and grammatical errors along with their corrections harvested from GitHub. It contains more than 350k edits and 65M characters in more than 15 languages, making it the largest dataset of misspellings to date.

Source: [GitHub](https://github.com/mhagiwara/github-typo-corpus)

Introducing paper: GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors
word2word
Full name: word2word

word2word contains easy-to-use word translations for 3,564 language pairs.

- A large collection of freely & publicly available bilingual lexicons for 3,564 language pairs across 62 unique languages.
- Easy-to-use Python interface for accessing top-k word translations and for building a new bilingual lexicon from a custom parallel corpus.
- Constructed using a simple approach that yields bilingual lexicons with high coverage and competitive translation quality.

Source: [word2word](https://pypi.org/project/word2word/)

Introducing paper: word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs
Dakshina
Full name: Dakshina

The Dakshina dataset is a collection of text in both Latin and native scripts for 12 South Asian languages. For each language, the dataset includes a large collection of native script Wikipedia text, a romanization lexicon which consists of words in the native script with attested romanizations, and some full sentence parallel data in both a native script of the language and the basic Latin alphabet.

Source: [Google Research](https://github.com/google-research-datasets/dakshina)

Introducing paper: Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset
Dataset of Legal Documents

Dataset of Legal Documents consists of court decisions from 2017 and 2018 were selected for the dataset, published online by the Federal Ministry of Justice and Consumer Protection. The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG).

The dataset consists of 66,723 sentences with 2,157,048 tokens. The sizes of the seven court-specific datasets varies between 5,858 and 12,791 sentences, and 177,835 to 404,041 tokens. The distribution of annotations on a per-token basis corresponds to approx. 19-23 %.

Source: [GitHub](https://github.com/elenanereiss/Legal-Entity-Recognition)

Introducing paper: A Dataset of German Legal Documents for Named Entity Recognition
ChrEn
Full name: Cherokee-English Parallel Dataset

Cherokee-English Parallel Dataset is a low-resource dataset of 14,151 pairs of sentences with around
313K English tokens and 206K Cherokee tokens. The parallel corpus is accompanied by a monolingual Cherokee dataset of 5,120 sentences. Both datasets are mostly derived from Cherokee monolingual books.

Introducing paper: ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization
Image网

**Image网** (pronounced Imagewang; 网 means "net" in Chinese) is an image classification dataset combined from [Imagenette](/dataset/imagenette) and [Imagewoof](/dataset/imagewoof) datasets in a way to make it into a semi-supervised unbalanced classification problem:

* the validation set is the same as the validation set of Imagewoof; there are no Imagenette images in the validation set (they're all in the training set),

* only 10% of Imagewoof images are in the training set. The remaining images are in the "unsupervised" split.

Source: [fast.ai](https://github.com/fastai/imagenette)

Introducing paper: fastai: A Layered API for Deep Learning
CCAligned
Full name: CCAligned

**CCAligned** consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to multiple documents in different target language, it is possible to join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French).

Source: [CCAligned](http://www.statmt.org/cc-aligned/)

Introducing paper: CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs
WikiTableT
Full name: WikiTableT

WikiTableT contains Wikipedia article sections and their corresponding tabular data and various metadata. WikiTableT contains millions of instances while covering a broad range of topics and a variety of kinds of generation tasks.

Source: [WikiTableT](https://github.com/mingdachen/WikiTableT)

Introducing paper: WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections
AutoWeakS
Full name: AutoWeakS

Collects all the courses from XuetangX5, one of the largest MOOCs in China, and this results in 1951 courses. The collected courses involve seven areas: computer science, economics, engineering, foreign language, math, physics, and social science. Each course contains 131 words in its descriptions on average. Contains 706 job postings from the recruiting website operated by JD.com (JD) and 2,456 job postings from the website owned by Tencent corporation (Tencent). The collected job postings involve six areas: technical post, financial post, product post, design post, market post, supply chain and engineering post. 

Source: [AutoWeakS](https://github.com/jerryhao66/AutoWeakS)

Introducing paper: Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach
MMDB
Full name: Multimodal Dyadic Behavior

Multimodal Dyadic Behavior (MMDB) dataset is a unique collection of multimodal (video, audio, and physiological) recordings of the social and communicative behavior of toddlers. The MMDB contains 160 sessions of 3-5 minute semi-structured play interaction between a trained adult examiner and a child between the age of 15 and 30 months. The MMDB dataset supports a novel problem domain for activity recognition, which consists of the decoding of dyadic social interactions between adults and children in a developmental context.

Source: [MMDB](http://cbs.ic.gatech.edu/mmdb/)

Introducing paper: Decoding Children's Social Behavior
iQIYI-VID-2019
Full name: iQIYI-VID-2019

iQIYI-VID-2019 dataset is the first video dataset for multi-model person identification. This dataset aims to encourage the research of multi-modal based person identification. To get close to real applications, video clips are extracted from real online videos of extensive types. All the clips are labeled by human annotators, and use automatic algorithms to accelerate the collection and labeling process. The iQIYI-VID-2019 dataset is more challenging comparing to the iQIYI-VID-2018 dataset, since most hard examples are selected from iQIYI-VID-2018 while more person ids is added. The dataset contains 100K~200K video clips, divided into three parts, 40% for training, 30% for validation, and 30% for test. The dataset contains about 10, 000 identities, 5,000 of which come from the iQIYI celebrity database and mainly extracts from iQIYI-VID-2018.

Source: [2019 iQIYI Celebrity Video Identification Challenge](http://challenge.ai.iqiyi.com/detail?raceId=5c767dc41a6fa0ccf53922e7)
iQIYI-VID
Full name: iQIYI-VID

iQIYI-VID dataset, which comprises video clips from iQIYI variety shows, films, and television dramas. The whole dataset contains 500,000 videos clips of 5,000 celebrities. The length of each video is 1~30 seconds. 

Source: [iQIYI-VID](http://challenge.ai.iqiyi.com/detail?raceId=5afc36639689443e8f815f9e)

Introducing paper: iQIYI-VID: A Large Dataset for Multi-modal Person Identification
ELFW
Full name: Extended Labeled Faces in-the-Wild

Extended Labeled Faces in-the-Wild (ELFW) is a dataset supplementing with additional face-related categories —and also additional faces— the originally released semantic labels in the vastly used Labeled Faces in-the-Wild (LFW) dataset. Additionally, two object-based data augmentation techniques are deployed to synthetically enrich under-represented categories which, in benchmarking experiments, reveal that not only segmenting the augmented categories improves, but also the remaining ones benefit.

Source: [Extended Labeled Faces in-the-Wild ](https://multimedia-eurecat.github.io/2020/06/22/extended-faces-in-the-wild.html)
Image Source: [https://multimedia-eurecat.github.io/2020/06/22/extended-faces-in-the-wild.html](https://multimedia-eurecat.github.io/2020/06/22/extended-faces-in-the-wild.html)

Introducing paper: Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face Segmentation
KANFace
Full name: KANFace Dataset

KANFace consists of 40K still images and 44K sequences (14.5M video frames in total) captured in unconstrained, real-world conditions from 1,045 subjects. The dataset is manually annotated in terms of identity, exact age, gender and kinship.

Source: [KANFace Dataset](https://sites.google.com/view/kanface-dataset)

Introducing paper: Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study
BAVL
Full name: Blind Audio-Visual Localization (BAVL)

Blind Audio-Visual Localization (BAVL) Dataset consists of 20 audio-visual recordings of  sound sources, which could be talking faces or music instruments. Most audio-visual recordings (19) are videos from Youtube except V8, which is from [1]. Besides, the video V7 was also used in[2][3], and V16 used in [3]. All 20 videos are annotated by ourselves in a uniform manner. Details of the video sequences are listed in Table 1. 

The videos in the dataset have average duration of 10 seconds, and they are all recorded by one camera and one microphone. The audio files (.wav) was sampled at a 16 kHz for V7, V8, V16, and 44.1 kHz for the rest. The video frames contain the sound-making object (sound source) and distracting objects (e.g. pedestrian on the street), while the audio signals  consists of the sound produced by the sound source (human speech or instrumental music), environmental noise and sometimes other sounds. The distracting objects and other irrelevant noise/sounds do not exist in all videos. The primary usage of the dataset is to evaluate the performance of sound source localization method, in the presence of distracting motions and noise.

[1] Kidron, Einat, Yoav Y. Schechner, and Michael Elad. "Pixels that sound."Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. Vol. 1. IEEE, 2005.

[2] Izadinia, Hamid, Imran Saleemi, and Mubarak Shah. "Multimodal analysis for identification and segmentation of moving-sounding objects."IEEE Transactions on Multimedia 15.2 (2013): 378-390.

[3] Li, Kai, Jun Ye, and Kien A. Hua. "What's making that sound?."Proceedings of the 22nd ACM international conference on Multimedia. ACM, 2014.

Source: [Sound of Pixels](https://ibug.doc.ic.ac.uk/resources/SOP/)
AV Digits Database
Full name: AV Digits Database

AV Digits Database is an audiovisual database which contains normal, whispered and silent speech. 53 participants were recorded from 3 different views (frontal, 45 and profile) pronouncing digits and phrases in three speech modes.

The database consists of two parts: digits and short phrases. In the first part, participants were asked to read 10 digits, from 0 to 9, in English in random order five times. In case of non-native English speakers this part was also repeated in the participant’s native language. In total, 53 participants (41 males and 12 females) from 16 nationalities, were recorded with a mean age and standard deviation of 26.7 and 4.3 years, respectively.

In the second part, participants were asked to read 10 short phrases. The phrases are the same as the ones used in the OuluVS2 database: “Excuse me”, “Goodbye”, “Hello”, “How are you”, “Nice to meet you”, “See you”, “I am sorry”,   “Thank you”, “Have a good time”, “You are welcome”. Again, each phrase was repeated five times in 3 different modes, neutral, whisper and silent speech. Thirty nine participants (32 males and 7 females) were recorded for this part with a mean age and standard deviation of 26.3 and 3.8 years, respectively.

Source: [AV Digits Database](https://ibug-avs.eu/)

Introducing paper: Visual-Only Recognition of Normal, Whispered and Silent Speech
Fabrics Dataset
Full name: The Fabrics Dataset

The Fabrics Dataset consists of about 2000 samples of garments and fabrics. A small patch of each surface has been captured under 4 different illumination conditions using a custom made, portable photometric stereo sensor. All images have been acquired "in the field" (at clothes shops) and the dataset reflects the distribution of fabrics in real world, hence it is not balanced. The majority of clothes are made of specific fabrics, such as cotton and polyester, while some other fabrics, such as silk and linen, are more rare. Also, a large number of clothes are not composed of a single fabric but two or more fabrics are used to give the garment the desired properties (blended fabrics). For every garment there is information (attributes) about its material composition from the manufacturer label and its type (pants, shirt, skirt etc.).

Source: [Fabrics Dataset](https://ibug.doc.ic.ac.uk/resources/fabrics/)
MobiFace
Full name: MobiFace

MobiFace is the first dataset for single face tracking in mobile situations. It consists of 80 unedited live-streaming mobile videos captured by 70 different smartphone users in fully unconstrained environments. Over 95K bounding boxes are manually labelled. The videos are carefully selected to cover typical smartphone usage. The videos are also annotated with 14 attributes, including 6 newly proposed attributes and 8 commonly seen in object tracking.

Source: [MobiFace](https://ibug.doc.ic.ac.uk/resources/mobiface/)

Introducing paper: MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild
LSFM
Full name: Large Scale Facial Model (LSFM)

The Large Scale Facial Model (LSFM) is a 3D statistical model of facial shape built from nearly 10,000 individuals.

Source: [LSFM](https://ibug.doc.ic.ac.uk/resources/lsfm/)
FaceScape
Full name: FaceScape

FaceScape dataset provides 3D face models, parametric models and multi-view images in large-scale and high-quality. The camera parameters, the age and gender of the subjects are also included. The data have been released to public for non-commercial research purpose.

Source: [FaceScape](https://facescape.nju.edu.cn/)

Introducing paper: FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction
AgeDB
Full name: AgeDB

AgeDB contains 16, 488 images of various famous people, such as actors/actresses, writers, scientists, politicians, etc. Every image is annotated with respect to the identity, age and gender attribute. There exist a total of 568 distinct subjects. The average number of images per subject is 29. The minimum and maximum age is 1 and 101, respectively. The average age range for each subject is 50.3 years.

Source: [AgeDB](https://ibug.doc.ic.ac.uk/resources/agedb/)
AFEW-VA
Full name: AFEW-VA Database for Valence and Arousal Estimation In-The-Wild

The AFEW-VA databaset is a collection of highly accurate per-frame annotations levels of valence and arousal, along with per-frame annotations of 68 facial landmarks for 600 challenging video clips. These clips are extracted from feature films and were also annotated in terms of discrete emotion categories in the form of the AFEW database (that can be obtained [there](https://cs.anu.edu.au/few/AFEW.html)).

Source: [AFEW-VA](https://ibug.doc.ic.ac.uk/resources/afew-va-database/)
KILT
Full name: KILT Benchmark

**KILT** (**Knowledge Intensive Language Tasks**) is a benchmark consisting of 11 datasets representing 5 types of tasks:

* Fact-checking (FEVER),
* Entity linking (AIDA CoNLL-YAGO, WNED-WIKI, WNED-CWEB),
* Slot filling (T-Rex, Zero Shot RE),
* Open domain QA (Natural Questions, HotpotQA, TriviaQA, ELI5),
* Dialog generation (Wizard of Wikipedia).

All these datasets have been grounded in a single pre-processed wikipedia snapshot, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning.

Source: [KILT Benchmarking](https://ai.facebook.com/tools/kilt/)

Introducing paper: KILT: a Benchmark for Knowledge Intensive Language Tasks
SOREL-20M
Full name: Sophos/ReversingLabs-20 Million

SOREL-20M is a large-scale dataset consisting of nearly 20 million files with pre-extracted features and metadata, high-quality labels derived from multiple sources, information about vendor detections of the malware samples at the time of collection, and additional “tags” related to each malware sample to serve as additional targets.

Source: [SOREL-20M](https://github.com/sophos-ai/SOREL-20M)

Introducing paper: SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection
Relational Pattern Similarity Dataset
Full name: Relational Pattern Similarity Dataset

The relational pattern similarity dataset is a new dataset upon the work of Zeichner et al. (2012), which consists of relational patterns with semantic inference labels annotated. The dataset includes 5,555 pairs extracted by Reverb (Fader et al., 2011), 2,447 pairs with inference relation and 3,108 pairs (the rest) without one.

Source: [Composing Distributed Representations of Relational Patterns](https://www.aclweb.org/anthology/P16-1215.pdf)

Introducing paper: Composing Distributed Representations of Relational Patterns
PHM2017
Full name: PHM2017

PHM2017 is a new dataset consisting of 7,192 English tweets across six diseases and conditions: Alzheimer’s Disease, heart attack (any severity), Parkinson’s disease, cancer (any type), Depression (any severity), and Stroke. The Twitter search API was used to retrieve the data using the colloquial disease names as search keywords, with the expectation of retrieving a high-recall, low precision dataset. After removing the re-tweets and replies, the tweets were manually annotated. The labels are:

- self-mention. The tweet contains a health mention with a health self-report of the Twitter account owner, e.g., "However, I worked hard and ran for Tokyo Mayer Election Campaign in January through February, 2014, without publicizing the cancer."
- other-mention. The tweet contains a health mention of a health report about someone other than the account owner, e.g., "Designer with Parkinson’s couldn’t work then engineer invents bracelet + changes her world"
- awareness. The tweet contains the disease name, but does not mention a specific person, e.g., "A Month Before a Heart Attack, Your Body Will Warn You With These 8 Signals"
- non-health. The tweet contains the disease name, but the tweet topic is not about health. "Now I can have cancer on my wall for all to see <3"

Source: [Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media](https://arxiv.org/pdf/1802.09130v2.pdf)

Introducing paper: Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
ORVS
Full name: Online Retinal image for Vessel Segmentation (ORVS)

The ORVS dataset has been newly established as a collaboration between the computer science and visual-science departments at the University of Calgary.

This dataset contains 49 images (42 training and seven testing images) collected from a clinic in Calgary-Canada. All images were acquired with a Zeiss Visucam 200 with 30 degrees field of view (FOV). The image size is 1444×1444 with 24 bits per pixel. Images and are stored in JPEG format with low compression, which is common in ophthalmology practice. All images were manually traced by an expert who a has been working in the field of retinal-image analysis and went through training. The expert was asked to label all pixels belonging to retinal vessels. The Windows Paint 3D tool was used to manually label the images.

Source: [Transfer Learning Through Weighted Loss Function and Group Normalization for Vessel Segmentation from Retinal Images](https://arxiv.org/pdf/2012.09250v1.pdf)

Introducing paper: Transfer Learning Through Weighted Loss Function and Group Normalization for Vessel Segmentation from Retinal Images
DR HAGIS
Full name: Diabetic Retinopathy, Hypertension, Age-related macular degeneration and Glacuoma ImageS

The DR HAGIS database has been created to aid the development of vessel extraction algorithms suitable for retinal screening programmes. Researchers are encouraged to test their segmentation algorithms using this database.

All thirty-nine fundus images were obtained from a diabetic retinopathy screening programme in the UK. Hence, all images were taken from diabetic patients. Since patients attending these screening programmes exhibit other co-morbidities, the DR HAGIS database consists of following four co-morbidity subgroups:

- Images 1-10: Glaucoma subgroup
- Images 11-20: Hypertension subgroup
- Images 21-30: Diabetic retinopathy subgroup
- Images 31-40: Age-related macular degeneration subgroup

Besides the fundus images, the manual segmentation of the retinal surface vessels is provided by an expert grader. These manually segmented images can be used as the ground truth to compare and assess the automatic vessel extraction algorithms. Masks of the FOV are provided as well to quantify the accuracy of vessel extraction within the FOV only.

The images were acquired in different screening centers, therefore reflecting the range of image resolutions, digital cameras and fundus cameras used in the clinic. The fundus images were captured using a Topcon TRC-NW6s, Topcon TRC-NW8 or a Canon CR DGi fundus camera with a horizontal 45 degree field-of-view (FOV). The images are 4752x3168 pixels, 3456x2304 pixels, 3126x2136 pixels, 2896x1944 pixels or 2816x1880 pixels in size.

The fundus images are saved as compressed JPEG files with 8 bits per colour plane. The ground truth and mask images are saved as binary PNG files.

Source: [DR HAGIS](https://personalpages.manchester.ac.uk/staff/niall.p.mcloughlin/)
ARIA
Full name: Automated Retinal Image Analysis (ARIA) Data Set

This data set was collected in 2004 to 2006 in the United Kingdom. Subjects were adult males and females, some of whom were healthy (control group), some with age-related macular degeneration (AMD group), and some were diabetic patients (diabetic group). Unfortunately, no other information from this time exists about this subjects.

Source: [ARIA](http://www.damianjjfarnell.com/?page_id=276)
VICAVR
Full name: VICAVR Database

The VICAVR database is a set of retinal images used for the computation of the A/V Ratio. The database currently includes 58 images. The images have been acquired with a TopCon non-mydriatic camera NW-100 model and are optic disc centered with a resolution of 768x584. The database includes the caliber of the vessels measured at different radii from the optic disc as well as the vessel type (artery/vein) labelled by three experts.

Source: [VICAVR](http://www.varpa.es/research/ophtalmology.html#vicavr)
OCTAGON
Full name: OCTAGON Dataset

The OCTAGON dataset is a set of Angiography by Octical Coherence Tomography images (OCT-A) used to the segmentation of the Foveal Avascular Zone (FAZ). The dataset includes 144 healthy OCT-A images and 69 diabetic OCT-A images, divided into four groups, each one with 36 and about 17 OCT-A images, respectively. These groups are: 3x3 superficial, 3x3 deep, 6x6 superficial and 6x6 deep, where 3x3 and 6x6 are the zoom of the image and superficial/deep are the depth level of the extracted image. The healthy dataset includes OCT-A images from people classified in 6 age ranges: 10-19 years, 20-29 years, 30-39 years, 40-49 years, 50-59 years and 60-69 years. Each age range includes 3 different patients with information of left and right eyes for each one. Finally, for each eye, there are four different images: one 3x3 superficial image, one 3x3 deep image, one 6x6 superficial image and one 6x6 deep image. Each image have two manual labelled of expert clinicians of the FAZ and their quantification in the healthy OCT-A images, and one manual labelled in the diabetic OCT-A images.

Source: [OCTAGON dataset](http://www.varpa.es/research/ophtalmology.html#octagon)

Introducing paper: Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images
CLOUD
Full name: CLOUD Dataset

The CLOUD dataset is a set of Optical Coherence Tomography of the Anterior Segment images (AS-OCT) used to the automatic identification and representation of the cornea-contact lens relationship. The dataset includes 112 AS-OCT images that were captured from 16 different patients. In particular, the images were obtained by an OCT Cirrus 500 scanner model of Carl Zeiss Meditec with an anterior segment module for users of scleral contact lens (SCL).

Source: [CLOUD Dataset](http://www.varpa.es/research/ophtalmology.html#cloud)
MESSIDOR
Full name: MESSIDOR DATABASE

The Messidor database has been established to facilitate studies on computer-assisted diagnoses of diabetic retinopathy. The research community is welcome to test its algorithms on this database. In this section, you will find instructions on how to download the database.

Source: [MESSIDOR](http://www.adcis.net/en/third-party/messidor/)
DIARETDB1
Full name: DIARETDB1

The database consists of 89 colour fundus images of which 84 contain at least mild non-proliferative signs (Microaneurysms) of the diabetic retinopathy, and 5 are considered as normal which do not contain any signs of the diabetic retinopathy according to all experts who participated in the evaluation. Images were captured using the same 50 degree field-of-view digital fundus camera with varying imaging settings. The data correspond to a good (not necessarily typical) practical situation, where the images are comparable, and can be used to evaluate the general performance of diagnostic methods. This data set is referred to as "calibration level 1 fundus images".

Source: [DIARETDB1](http://www2.it.lut.fi/project/imageret/diaretdb1/)
UDA-CH
Full name: Unsupervised Domain Adaptation on Cultural Heritage

UDA-CH contains 16 objects that cover a variety of artworks which can be found in a museum like sculptures, paintings and books. Specifically, the dataset has been collected inside the cultural site “Galleria Regionale di Palazzo Bellomo” located in Siracusa, Italy.

Source: [An Unsupervised Domain Adaptation Scheme for Single-Stage Artwork Recognition in Cultural Sites](https://arxiv.org/pdf/2008.01882.pdf)

Introducing paper: An Unsupervised Domain Adaptation Scheme for Single-Stage Artwork Recognition in Cultural Sites
EGO-CH
Full name: EGOcentric-Cultural Heritage

EGO-CH is a dataset of egocentric videos for visitors’ behavior understanding. The dataset has been collected in two different cultural sites and includes more than 27 hours of video acquired by 70 subjects, including volunteers and 60 real visitors. The overall dataset includes labels for 26 environments and over 200 Points of Interest (POIs). Specifically, each video of EGO-CH has been annotated with 1) temporal labels specifying the current location of the visitor and the observed POI, 2) bounding box annotations around POIs. A large subset of the dataset, consisting of 60 videos, is also associated with surveys filled out by the visitors at the end of each visit.

Source: [EGO-CH](https://iplab.dmi.unict.it/EGO-CH/)

Introducing paper: EGO-CH: Dataset and Fundamental Tasks for Visitors BehavioralUnderstanding using Egocentric Vision
MAP
Full name: Maybe Ambiguous Pronoun

**Maybe Ambiguous Pronoun** is a dataset similar to [GAP](/dataset/gap-coreference-dataset) dataset, but without binary gender constraints.

Source: [Toward Gender-Inclusive Coreference Resolution](https://www.aclweb.org/anthology/2020.acl-main.418.pdf)

Introducing paper: Toward Gender-Inclusive Coreference Resolution
GICoref
Full name: Gender Inclusive Coreference

GICoref is a fully annotated coreference resolution dataset written by and about trans people.

Source: [Toward Gender-Inclusive Coreference Resolution](https://www.aclweb.org/anthology/2020.acl-main.418.pdf)

Introducing paper: Toward Gender-Inclusive Coreference Resolution
NAF
Full name: National Archives Forms Dataset

This dataset was created with images provided by the United States National Archive and FamilySearch.

The goal of this data is to capture relationships between text/handwriting entities on form images. It will include transcriptions in the future, but doesn't currently.

The form images are organized into "groups", each group containing images of the same form type.

Source: [NAF Dataset](https://github.com/herobd/NAF_dataset)

Introducing paper: Deep Visual Template-Free Form Parsing
ImageNet-P

**ImageNet-P** consists of noise, blur, weather, and digital distortions. The dataset has validation perturbations; has difficulty levels; has CIFAR-10, Tiny ImageNet, ImageNet 64 × 64, standard, and Inception-sized editions; and has been designed for benchmarking not training networks. ImageNet-P departs from ImageNet-C by having perturbation sequences generated from each ImageNet validation image. Each sequence contains more than 30 frames, so to counteract an increase in dataset size and evaluation time only 10 common perturbations are used.

Source: [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/pdf/1903.12261.pdf)

Introducing paper: Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Combinatorial 3D Shape Dataset

The combinatorial 3D shape dataset is composed of 406 instances of 14 classes. Specifically, each object in the dataset is considered equivalent to a sequence of primitive placement.

Source: [Combinatorial 3D Shape Generation via Sequential Assembly](https://arxiv.org/pdf/2004.07414.pdf)

Introducing paper: Combinatorial 3D Shape Generation via Sequential Assembly
Chart2Text
Full name: Chart Summarization Dataset

Chart2Text is a dataset that was crawled from 23,382 freely accessible pages from statista.com in early March of 2020, yielding a total of 8,305 charts, and associated summaries. For each chart, the chart image, the underlying data table, the title, the axis labels, and a human-written summary describing the statistic was downloaded.

Source: [Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model](https://arxiv.org/pdf/2010.09142.pdf)

Introducing paper: Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model
DENSE
Full name: Depth Estimation oN Synthetic Events

DENSE (Depth Estimation oN Synthetic Events) is a new dataset with synthetic events and perfect ground truth.

Source: [Learning Monocular Dense Depth from Events](http://rpg.ifi.uzh.ch/docs/3DV20_Hidalgo.pdf)

Introducing paper: Learning Monocular Dense Depth from Events
PixelShift200

Advanced pixel shift technology is employed to perform a full color sampling of the image. Pixel shift technology takes four samples of the same image at nearly the same time, and physically controls the camera sensor to move one pixel horizontally or vertically at each sampling to capture all color information at each pixel. The pixel shift technology ensures that the sampled images follow the distribution of natural images sampled by the camera, and the full information of the color (R, Gr, Gb, B channel) is completely obtained without any need of interpolation. In this way, the collected RGB images are artifacts-free, which leads to better training results for demosaicing related tasks.

PixelShift200 Dataset contains 210 high quality 4K images.

- Training: 200 images
- Testing: 10 images
- Key Features: fully colored, demosiacing artifacts free
- Camera: SONY α7R III

Source: [PixelShift200](https://guochengqian.github.io/project/pixelshift200/#overview)

Introducing paper: Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline
VLEP
Full name: Video-and-Language Event Prediction

VLEP contains 28,726 future event prediction examples (along with their rationales) from 10,234 diverse TV Show and YouTube Lifestyle Vlog video clips. Each example (see Figure 1) consists of a Premise Event (a short video clip with dialogue), a Premise Summary (a text summary of the premise event), and two potential natural language Future Events (along with Rationales) written by people. These clips are on average 6.1 seconds long and are harvested from diverse event-rich sources, i.e., TV show and YouTube Lifestyle Vlog videos.

Source: [What is More Likely to Happen Next? Video-and-Language Future Event Prediction](https://arxiv.org/pdf/2010.07999.pdf)

Introducing paper: What is More Likely to Happen Next? Video-and-Language Future Event Prediction
Cata7

Cata7 is the first cataract surgical instrument dataset for semantic segmentation. The dataset consists of seven videos while each video records a complete cataract surgery. All videos are from Beijing Tongren Hospital. Each video is split into a sequence of images, where resolution is 1920×1080 pixels. To reduce redundancy, the videos are downsampled from 30 fps to 1 fps. Also, images without surgical instruments are manually removed. Each image is labeled with precise edges and types of surgical instruments. This dataset contains 2,500 images, which are divided into training and test sets. The training set consists of five video sequences and test set consists of two video sequence.

Source: [RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments](https://arxiv.org/pdf/1909.10360.pdf)
Image Source: [Ni et al](https://arxiv.org/pdf/1909.10360.pdf)

Introducing paper: RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments
UCC
Full name: Unhealthy Comments Corpus

The Unhealthy Comments Corpus (UCC) is corpus of 44355 comments intended to assist in research on identifying subtle attributes which contribute to unhealthy conversations online.

Each comment is labelled as either 'healthy' or 'unhealthy', in addition to binary labels for the presence of six potentially 'unhealthy' sub-attributes: (1) hostile; (2) antagonistic, insulting, provocative or trolling; (3) dismissive; (4) condescending or patronising; (5) sarcastic; and/or (6) an unfair generalisation. Each label also has an associated confidence score.

The UCC contributes further high quality data on attributes like sarcasm, hostility, and condescension, adding to existing datasets on these and related attributes, and provides the first dataset of this scale with labels for dismissiveness, unfair generalisations, antagonistic behavior, and overall assessments of whether those comments fall within 'healthy' conversation.

Source: [UCC ](https://github.com/conversationai/unhealthy-conversations)
Satire Dataset

The satire dataset is a new multi-modal dataset of satirical and regular news articles. The satirical news is collected from four websites that explicitly declare themselves to be satire, and the regular news is collected from six mainstream news websites. Specifically, the satirical news websites the articles were collected from are The Babylon Bee, Clickhole, Waterford Whisper News, and The DailyER. The regular news websites are Reuters, The Hill, Politico, New York Post, Huffington Post, and Vice News. The headlines
and the thumbnail images of the latest 1000 articles for each of the publications are collected. The dataset contains a total of 4000 satirical and 6000 regular news articles.

Source: [A Multi-Modal Method for Satire Detection using Textual and Visual Cues](https://www.aclweb.org/anthology/2020.nlp4if-1.4.pdf)

Introducing paper: A Multi-Modal Method for Satire Detection using Textual and Visual Cues
Headcam

This dataset contains panoramic video captured from a helmet-mounted camera while riding a bike through suburban Northern Virginia. 

Source: [Headcam](https://github.com/jonathanventura/cylindricalsfmlearner)

Introducing paper: Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video
OCNLI
Full name: Original Chinese Natural Language Inference

OCNLI stands for Original Chinese Natural Language Inference. It is corpus for Chinese Natural Language Inference, collected following closely the procedures of MNLI, but with enhanced strategies aiming for more challenging inference pairs. No human/machine translation is used in creating the dataset, and thus the Chinese texts are original and not translated.

OCNLI has roughly 50k pairs for training, 3k for development and 3k for test. Only the test data is released but not its labels.

OCNLI is part of the CLUE benchmark.

Source: [OCNLI](https://github.com/CLUEbenchmark/OCNLI)

Introducing paper: OCNLI: Original Chinese Natural Language Inference
QReCC

QReCC contains 14K conversations with 81K question-answer pairs. QReCC is built on questions from TREC CAsT, QuAC and Google Natural Questions. While TREC CAsT and QuAC datasets contain multi-turn conversations, Natural Questions is not a conversational dataset. Questions in NQ dataset were used as prompts to create conversations explicitly balancing types of context-dependent questions, such as anaphora (co-references) and ellipsis.

For each query the authors collect query rewrites by resolving references, the resulting query rewrite is a context-independent version of the original (context-dependent) question. The rewritten query is then used to with a search engine to answer the question. Each query is also annotated with answer, link to the web page that used to produce the answer.

Each conversation in the dataset contains a unique Conversation_no, Turn_no unique within a conversation, the original Question, Context, Rewrite and Answer with Answer_URL.

Source: [QReCC](https://github.com/apple/ml-qrecc)

Introducing paper: Open-Domain Question Answering Goes Conversational via Question Rewriting
PHD²
Full name: Personalized Highlight Detection Dataset

The dataset contains information on what video segments a specific user considers a highlight. Having this kind of data allows for strong personalization models, as specific examples of what a user is interested in help models obtain a fine-grained understanding of that specific user.

The data consists of YouTube videos, from which gifs.com users manually extracted their highlights, by creating GIFs from a segment of the full video. Thus, the dataset is similar to PHD-GIFS, with two major differences.

- Each selection is associated with a user, which is what allows personalization.
- instead of visual matching to find the position in the video from which a GIF was selected, PHD-GIFS uses the timestamps. Thus, the ground truth is free from any alignment errors.

The training set contains highlights from 12,972 users. The test set contains highlights from 850 users. 

Source: [Personalized Highlight Detection Dataset](https://github.com/gifs/personalized-highlights-dataset)

Introducing paper: PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation
VAST
Full name: VAried Stance Topics

VAST consists of a large range of topics covering broad themes, such as politics (e.g., ‘a Palestinian state’), education (e.g., ‘charter schools’), and public health (e.g., ‘childhood vaccination’). In addition, the data includes a wide range of similar expressions (e.g., ‘guns on campus’ versus ‘firearms on campus’). This variation captures how humans might realistically describe the same topic and contrasts with the lack of variation in existing datasets.

Source: [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://arxiv.org/pdf/2010.03640v1.pdf)

Introducing paper: Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations
Silent Speech EMG

Facial electromyography recordings during both silent and vocalized speech.

Source: [Silent Speech EMG](https://github.com/dgaddy/silent_speech)

Introducing paper: Digital Voicing of Silent Speech
SMOT
Full name: Single sequence-Multi Objects Training

The SMOT dataset, Single sequence-Multi Objects Training, is collected to represent a practical scenario of collecting training images of new objects in the real world, i.e. a mobile robot with an RGB-D camera collects a sequence of frames while driving around a table to learning multiple objects and tries to recognize objects in different locations.

Source: [SMOT](https://www.acin.tuwien.ac.at/en/vision-for-robotics/software-tools/smot/)

Introducing paper: Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images
3DNet

The 3DNet dataset is a free resource for object class recognition and 6DOF pose estimation from point cloud data. 3DNet provides a large-scale hierarchical CAD-model databases with increasing numbers of classes and difficulty with 10, 60 and 200 object classes together with evaluation datasets that contain thousands of scenes captured with an RGB-D sensor.

Source: [3DNet](https://www.acin.tuwien.ac.at/en/vision-for-robotics/software-tools/3dnet-dataset/)
ARID
Full name: Autonomous Robot Indoor Dataset

ARID is a large-scale, multi-view object dataset collected with an RGB-D camera mounted on a mobile robot.

Source: [ARID](https://www.acin.tuwien.ac.at/en/vision-for-robotics/software-tools/autonomous-robot-indoor-dataset/)

Introducing paper: Recognizing Objects In-the-wild: Where Do We Stand?
OCID
Full name: Object Clutter Indoor Dataset

Developing robot perception systems for handling objects in the real-world requires computer vision algorithms to be carefully scrutinized with respect to the expected operating domain. This demands large quantities of ground truth data to rigorously evaluate the performance of algorithms.

The Object Cluttered Indoor Dataset is an RGBD-dataset containing point-wise labeled point-clouds for each object. The data was captured using two ASUS-PRO Xtion cameras that are positioned at different heights. It captures diverse settings of objects, background, context, sensor to scene distance, viewpoint angle and lighting conditions. The main purpose of OCID is to allow systematic comparison of existing object segmentation methods in scenes with increasing amount of clutter. In addition OCID does also provide ground-truth data for other vision tasks like object-classification and recognition.

Source: [OCID](https://www.acin.tuwien.ac.at/en/vision-for-robotics/software-tools/object-clutter-indoor-dataset/)

Introducing paper: EasyLabel: A Semi-Automatic Pixel-wise Object Annotation Tool for Creating Robotic RGB-D Datasets
LfED-6D
Full name: Learning from Experience and Demonstration for 6-DOF Grasping Dataset

The LfED-6D dataset is a collection of 6D grasp annotations acquired through experience (with a robot platform) or by human demonstration. For known objects, the annotated grasps can be directly applied given the pose of the object model is correctly computed. For unknown objects, the grasps can be generalized using methods for shape matching, for example the Dense Geometrical Correspondence Network.

Source: [LfED-6D](https://www.acin.tuwien.ac.at/en/vision-for-robotics/software-tools/lfed-6d-dataset/)
NYU-VP

NYU-VP is a new dataset for multi-model fitting, vanishing point (VP) estimation in this case. Each image is annotated with up to eight vanishing points, and pre-extracted line segments are provided which act as data points for a robust estimator. Due to its size, the dataset is the first to allow for supervised learning of a multi-model fitting task.

Source: [CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus](https://arxiv.org/pdf/2001.02643.pdf)

Introducing paper: CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
YUD+
Full name: Additional Vanishing Point Labels for the York Urban Database

YUD+ is a dataset containing additional Vanishing Point Labels for the [York Urban Database](https://paperswithcode.com/dataset/york-urban-line-segment-database).

Source: [YUD+](https://github.com/fkluger/yud_plus)

Introducing paper: CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
NText

NText is an eight million words dataset extracted and preprocessed from nuclear research papers and thesis.

Source: [NukeBERT: A Pre-trained language model for Low Resource Nuclear Domain](https://arxiv.org/pdf/2003.13821.pdf)

Introducing paper: NukeBERT: A Pre-trained language model for Low Resource Nuclear Domain
NQuAD
Full name: Nuclear Question Answering Dataset

NQuAD is a Nuclear Question Answering Dataset, which contains 700+ nuclear Question Answer pairs developed and verified by expert nuclear researchers.

Source: [NukeBERT: A Pre-trained language model for Low Resource Nuclear Domain](https://arxiv.org/pdf/2003.13821.pdf)

Introducing paper: NukeBERT: A Pre-trained language model for Low Resource Nuclear Domain
Indoor and outdoor DFD dataset

The dfd_indoor dataset contains 110 images for training and 29 images for testing. The dfd_outdoor dataset contains 34 images for tests; no ground truth was given for this dataset, as the depth sensor only works on indoor scenes.

Source: [DFD dataset](https://github.com/marcelampc/d3net_depth_estimation)

Introducing paper: Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?
Lorenz Dataset

The Lorenz dataset contains 100000 time-series with length 24. The data has 5 modes and it is obtained using the Lorenz equation with 5 different seed values.

Source: [Lorenz Dataset](https://git.opendfki.de/koochali/forgan)

Introducing paper: Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN