Omkar2118/datasets huggingface dataset:
Florence 3D Faces

This dataset is being constructed specifically to support research on techniques that bridge the gap between 2D, appearance-based recognition techniques, and fully 3D approaches. It is designed to simulate, in a controlled fashion, realistic surveillance conditions and to probe the efficacy of exploiting 3D models in real scenarios.

Source: [FLORENCE 3D FACES](https://www.micc.unifi.it/resources/datasets/florence-3d-faces/)
FMD
Full name: Fluorescence Microscopy Denoising

The **Fluorescence Microscopy Denoising** (**FMD**) dataset is dedicated to Poisson-Gaussian denoising. The dataset consists of 12,000 real fluorescence microscopy images obtained with commercial confocal, two-photon, and wide-field microscopes and representative biological samples such as cells, zebrafish, and mouse brain tissues. Image averaging is used to effectively obtain ground truth images and 60,000 noisy images with different noise levels.

Source: [https://arxiv.org/abs/1812.10366](https://arxiv.org/abs/1812.10366)
Image Source: [https://github.com/bmmi/denoising-fluorescence](https://github.com/bmmi/denoising-fluorescence)

Introducing paper: A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images
FocusPath

FocusPath is a dataset compiled from diverse Whole Slide Image (WSI) scans in different focus (z-) levels. Images are naturally blurred by out-of-focus lens provided with GT scores of focus levels. The dataset can be used for No-Reference Focus Quality assessment of Digital Pathology/Microscopy images.

Source: [FocusPath](https://github.com/mahdihosseini/FoucsPath)

Introducing paper: Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment
FollowUp

1000 query triples on 120 tables.

Source: [FANDA: A Novel Approach to Perform Follow-up Query Analysis](/paper/fanda-a-novel-approach-to-perform-follow-up)

Introducing paper: FANDA: A Novel Approach to Perform Follow-up Query Analysis
Fon-French Dataset

FFR Dataset is an ongoing project to collect, clean and store corpora of Fon and French sentences for machine translation from Fon-French. Fon (also called Fongbe) is an African-indigenous language spoken mostly in Benin, by about 1.7 million people. As training data is crucial to the high performance of a machine learning model, the aim of the project is to compile the largest set of training corpora for the research and design of translation and NLP models involving Fon. There are 117,029 parallel Fon-French sentences at the moment.

Source: [FFR Dataset](https://github.com/bonaventuredossou/ffr-v1/tree/master/FFR-Dataset)

Introducing paper: FFR v1.1: Fon-French Neural Machine Translation
FoodX-251

FoodX-251 is a dataset of 251 fine-grained classes with 118k training, 12k validation and 28k test images. Human verified labels are made available for the training and test images. The classes are fine-grained and visually similar, for example, different types of cakes, sandwiches, puddings, soups, and pastas.

Source: [FoodX-251: A Dataset for Fine-grained Food Classification](/paper/foodx-251-a-dataset-for-fine-grained-food)
Image Source: [Kaur et al](https://arxiv.org/pdf/1907.06167v1.pdf)

Introducing paper: FoodX-251: A Dataset for Fine-grained Food Classification
Ford AV Dataset

A challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18. 

Source: [Ford Multi-AV Seasonal Dataset](/paper/ford-multi-av-seasonal-dataset)

Introducing paper: Ford Multi-AV Seasonal Dataset
FPL
Full name: First-Person Locomotion

Supports new task that predicts future locations of people observed in first-person videos.

Source: [Future Person Localization in First-Person Videos](/paper/future-person-localization-in-first-person)

Introducing paper: Future Person Localization in First-Person Videos
Frames Dataset

This dataset is dialog dataset collected in a Wizard-of-Oz fashion. Two humans talked to each other via a chat interface. One was playing the role of the user and the other one was playing the role of the conversational agent. The latter is called a wizard as a reference to the Wizard of Oz, the man behind the curtain. The wizards had access to a database of 250+ packages, each composed of a hotel and round-trip flights. The users were asked to find the best deal. This resulted in complex dialogues where a user would often consider different options, compare packages, and progressively build the description of her ideal trip.

Source: [A Frame Tracking Model for Memory-Enhanced Dialogue Systems](/paper/a-frame-tracking-model-for-memory-enhanced)

Introducing paper: A Frame Tracking Model for Memory-Enhanced Dialogue Systems
Fraxtil

**Fraxtil** is an audio dataset where given a raw audio track, the goal is to produce a choreography step chart, similar to those used in the Dance Dance Revolution video game. It contains 90 songs choreographed by a single author, with 450 charts for the 90 songs.

Source: [https://arxiv.org/pdf/1703.06891.pdf](https://arxiv.org/pdf/1703.06891.pdf)
Image Source: [https://github.com/chrisdonahue/ddc](https://github.com/chrisdonahue/ddc)

Introducing paper: Dance Dance Convolution
FreebaseQA

FreebaseQA is a data set for open-domain QA over the Freebase knowledge graph. The question-answer pairs in this data set are collected from various sources, including the TriviaQA data set and other trivia websites (QuizBalls, QuizZone, KnowQuiz), and are matched against Freebase to generate relevant subject-predicate-object triples that were further verified by human annotators. As all questions in FreebaseQA are composed independently for human contestants in various trivia-like competitions, this data set shows richer linguistic variation and complexity than existing QA data sets, making it a good test-bed for emerging KB-QA systems.

Source: [FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with Freebase](/paper/freebaseqa-a-new-factoid-qa-data-set-matching)

Introducing paper: FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with Freebase
French CASS dataset

Composed of judgments from the French Court of cassation and their corresponding summaries.

Source: [STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings](/paper/strass-a-light-and-effective-method-for)

Introducing paper: STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings
FRSign

A large-scale and accurate dataset for vision-based railway traffic light detection and recognition.The recordings were made on selected running trains in France and benefited from carefully hand-labeled annotations.

Source: [FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains](/paper/frsign-a-large-scale-traffic-light-dataset)

Introducing paper: FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains
FSOCO

**FSOCO** is a collaborative dataset for vision-based cone detection systems in Formula Student Driverless competitions. It contains human annotated ground truth labels for both bounding boxes and instance-wise segmentation masks. The data buy-in philosophy of FSOCO asks student teams to contribute to the database first before being granted access ensuring continuous growth. By providing clear labeling guidelines and tools for a sophisticated raw image selection, new annotations are guaranteed to meet the desired quality.

Source: [https://github.com/fsoco/fsoco-dataset](https://github.com/fsoco/fsoco-dataset)
Image Source: [https://github.com/fsoco/fsoco-dataset](https://github.com/fsoco/fsoco-dataset)

Introducing paper: FSOCO: The Formula Student Objects in Context Dataset
FT Speech

FT Speech is a speech corpus created from the recorded meetings of the Danish Parliament, otherwise known as the Folketing (FT). The corpus contains over 1,800 hours of transcribed speech by a total of 434 speakers. It is significantly larger in duration, vocabulary, and amount of spontaneous speech than the existing public speech corpora for Danish, which are largely limited to read-aloud and dictation data. 

Source: [FT Speech: Danish Parliament Speech Corpus](/paper/ft-speech-danish-parliament-speech-corpus)

Introducing paper: FT Speech: Danish Parliament Speech Corpus
Fusion 360 Gallery

The **Fusion 360 Gallery** Dataset contains rich 2D and 3D geometry data derived from parametric CAD models. The dataset is produced from designs submitted by users of the CAD package Autodesk Fusion 360 to the Autodesk Online Gallery. The dataset provides valuable data for learning how people design, including sequential CAD design data, designs segmented by modelling operation, and design hierarchy and connectivity data.

Source: [https://github.com/AutodeskAILab/Fusion360GalleryDataset](https://github.com/AutodeskAILab/Fusion360GalleryDataset)
Image Source: [https://github.com/AutodeskAILab/Fusion360GalleryDataset](https://github.com/AutodeskAILab/Fusion360GalleryDataset)

Introducing paper: Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences
FVI
Full name: Free-form Video Inpainting

The Free-Form Video Inpainting dataset is a dataset used for training and evaluation video inpainting models. It consists of 1940 videos from the YouTube-VOS dataset and 12,600 videos from the YouTube-BoundingBoxes.

Source: [https://arxiv.org/abs/1904.10247](https://arxiv.org/abs/1904.10247)
Image Source: [https://github.com/amjltc295/Free-Form-Video-Inpainting](https://github.com/amjltc295/Free-Form-Video-Inpainting)

Introducing paper: Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGAN
G1020

A large publicly available retinal fundus image dataset for glaucoma classification called G1020. The dataset is curated by conforming to standard practices in routine ophthalmology and it is expected to serve as standard benchmark dataset for glaucoma detection. This database consists of 1020 high resolution colour fundus images and provides ground truth annotations for glaucoma diagnosis, optic disc and optic cup segmentation, vertical cup-to-disc ratio, size of neuroretinal rim in inferior, superior, nasal and temporal quadrants, and bounding box location for optic disc. 

Source: [G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection](/paper/g1020-a-benchmark-retinal-fundus-image)

Introducing paper: G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection
GamePad Environment

GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant.

Source: [GamePad: A Learning Environment for Theorem Proving](/paper/gamepad-a-learning-environment-for-theorem)

Introducing paper: GamePad: A Learning Environment for Theorem Proving
GameWikiSum

**GameWikiSum** is a domain-specific (video game) dataset for multi-document summarization, which is one hundred times larger than commonly used datasets, and in another domain than news. Input documents consist of long professional video game reviews as well as references of their gameplay sections in Wikipedia pages.

Source: [https://github.com/Diego999/GameWikiSum](https://github.com/Diego999/GameWikiSum)

Introducing paper: GameWikiSum: a Novel Large Multi-Document Summarization Dataset
GAP Coreference Dataset

GAP is a gender-balanced dataset containing 8,908 coreference-labeled pairs of (ambiguous pronoun, antecedent name), sampled from Wikipedia and released by Google AI Language for the evaluation of coreference resolution in practical applications.

Source: [GAP Coreference Dataset](https://github.com/google-research-datasets/gap-coreference)
Image Source: [https://arxiv.org/pdf/1810.05201.pdf](https://arxiv.org/pdf/1810.05201.pdf)

Introducing paper: Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns
GASP

**GASP** is a dataset composed by a list of cited abstracts associated with the corresponding source abstract. The dataset is composed by a training set of 100000 elements, a test set and a validation set of 10000 each.
The goal is to generate a paper abstract given cited paper's abstracts and model the human creativity behind the process.

Source: [https://github.com/ART-Group-it/GASP](https://github.com/ART-Group-it/GASP)

Introducing paper: GASP! Generating Abstracts of Scientific Papers from Abstracts of Cited Papers
Gazeta

**Gazeta** is a dataset for automatic summarization of Russian news. The dataset consists of 63,435 text-summary pairs. To form training, validation, and test datasets, these pairs were sorted by time and the first 52,400 pairs are used as the training dataset, the proceeding 5,265 pairs as the validation dataset, and the remaining 5,770 pairs as the test dataset.

Source: [https://github.com/IlyaGusev/gazeta](https://github.com/IlyaGusev/gazeta)

Introducing paper: Dataset for Automatic Summarization of Russian News
GCDC
Full name: Grammarly Corpus of Discourse Coherence

A corpus of real-world texts.

Source: [Discourse Coherence in the Wild: A Dataset, Evaluation and Methods](/paper/discourse-coherence-in-the-wild-a-dataset)

Introducing paper: Discourse Coherence in the Wild: A Dataset, Evaluation and Methods
GDXray+

GDXray+ is a collection of more than 21.100 X-ray images for the development, testing, and evaluation of image analysis and computer vision algorithms.

GDXray includes five groups of images:

* Castings,
* Welds,
* Baggage,
* Nature,
* Settings.

Each group has several series, and each series several X-ray images.

Source: [GDXray](https://domingomery.ing.puc.cl/material/gdxray/)
GeBioCorpus

A high-quality dataset for machine translation evaluation that aims at being one of the first non-synthetic gender-balanced test datasets.

Source: [GeBioToolkit: Automatic Extraction of Gender-Balanced Multilingual Corpus of Wikipedia Biographies](/paper/gebiotoolkit-automatic-extraction-of-gender)

Introducing paper: GeBioToolkit: Automatic Extraction of Gender-Balanced Multilingual Corpus of Wikipedia Biographies
Real Bacteria Dataset

A genomics dataset for OOD detection that allows other researchers to benchmark progress on this important problem.

Source: [Likelihood Ratios for Out-of-Distribution Detection](/paper/likelihood-ratios-for-out-of-distribution)

Introducing paper: Likelihood Ratios for Out-of-Distribution Detection
GeoCoV19

GeoCoV19 is a large-scale Twitter dataset containing more than 524 million multilingual tweets. The dataset contains around 378K geotagged tweets and 5.4 million tweets with Place information. The annotations include toponyms from the user location field and tweet content and resolve them to geolocations such as country, state, or city level. In this case, 297 million tweets are annotated with geolocation using the user location field and 452 million tweets using tweet content.

Introducing paper: GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information
GeoFaces

A large database of geotagged face images.

Source: [GeoFaces](http://geofaces.csr.uky.edu/)
GeoWebNews

GeoWebNews provides test/train examples and enable fine-grained Geotagging and Toponym Resolution (Geocoding). This dataset is also suitable for prototyping and evaluating machine learning NLP models.

Source: [A Pragmatic Guide to Geoparsing Evaluation](/paper/a-pragmatic-guide-to-geoparsing-evaluation)
Image Source: [https://arxiv.org/pdf/1810.12368.pdf](https://arxiv.org/pdf/1810.12368.pdf)

Introducing paper: A Pragmatic Guide to Geoparsing Evaluation
Get it #OffMyChest

Corpus and annotations for the CL-Aff Shared Task - Get it #OffMyChest - from Nanyang Technological University Singapore.

Source: [Get it #OffMyChest](https://github.com/kj2013/claff-offmychest)
GGPONC
Full name: German Guideline Program in Oncology NLP Corpus

German Guideline Program in Oncology NLP Corpus (GGPONC) is a German language corpus based on clinical practice guidelines for oncology. This corpus is one of the largest ever built from German medical documents. Unlike clinical documents, clinical guidelines do not contain any patient-related information and can therefore be used without data protection restrictions.

Source: [GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines](/paper/ggponc-a-corpus-of-german-medical-text-with)
Image Source: [https://arxiv.org/pdf/2007.06400.pdf](https://arxiv.org/pdf/2007.06400.pdf)

Introducing paper: GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines
GiantMIDI-Piano

**GiantMIDI-Piano** contains 10,854 unique piano solo pieces composed by 2,786 composers. GiantMIDI-Piano contains 34,504,873 transcribed notes, and contains metadata information of each music piece.

Source: [GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music](/paper/giantmidi-piano-a-large-scale-midi-dataset)

Introducing paper: GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music
Gigaword Entailment

The **Gigaword Entailment** dataset is a dataset for entailment prediction between an article and its headline. It is built from the Gigaword dataset.

Source: [https://github.com/nlp-titech/headline-entailment](https://github.com/nlp-titech/headline-entailment)

Introducing paper: Improving Truthfulness of Headline Generation
Global Voices

Global Voices is a multilingual dataset for evaluating cross-lingual summarization methods. It is extracted from social-network descriptions of Global Voices news articles to cheaply collect evaluation data for into-English and from-English summarization in 15 languages. 

Source: [Global Voices: Crossing Borders in Automatic News Summarization](/paper/global-voices-crossing-borders-in-automatic)

Introducing paper: Global Voices: Crossing Borders in Automatic News Summarization
GloREPlus

A distant supervision dataset by linking the entire English ClueWeb09 corpus to Freebase. 

Source: [Global Textual Relation Embedding for Relational Understanding](/paper/190600550)

Introducing paper: Global Textual Relation Embedding for Relational Understanding
Goldfinch
Full name: GOogLe image-search Dataset

Goldfinch is a dataset for fine-grained recognition challenges. It contains a list of bird, butterfly, aircraft, and dog categories with relevant Google image search and Flickr search URLs. In addition, it also includes a set of active learning annotations on dog categories.

Source: [The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition](/paper/the-unreasonable-effectiveness-of-noisy-data)

Introducing paper: The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
GolfDB

GolfDB is a high-quality video dataset created for general recognition applications in the sport of golf, and specifically for the task of golf swing sequencing.

Source: [GolfDB: A Video Database for Golf Swing Sequencing](/paper/golfdb-a-video-database-for-golf-swing)

Introducing paper: GolfDB: A Video Database for Golf Swing Sequencing
Google Landmarks Dataset v2

This is the second version of the Google Landmarks dataset (GLDv2), which contains images annotated with labels representing human-made and natural landmarks. The dataset can be used for landmark recognition and retrieval experiments. This version of the dataset contains approximately 5 million images, split into 3 sets of images: train, index and test

Source: [https://github.com/cvdfoundation/google-landmark](https://github.com/cvdfoundation/google-landmark)
Image Source: [https://github.com/cvdfoundation/google-landmark](https://github.com/cvdfoundation/google-landmark)

Introducing paper: Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval