Omkar2118/datasets huggingface dataset:
DDD20
Full name: DAVIS Driving Dataset 2020

The dataset was captured with a DAVIS camera that concurrently streams both dynamic vision sensor (DVS) brightness change events and active pixel sensor (APS) intensity frames. DDD20 is the longest event camera end-to-end driving dataset to date with 51h of DAVIS event+frame camera and vehicle human control data collected from 4000km of highway and urban driving under a variety of lighting conditions.

Source: [DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction](/paper/ddd20-end-to-end-event-camera-driving-dataset)

Introducing paper: DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction
DDI-100
Full name: Distorted Document Images

The DDI-100 dataset is a synthetic dataset for text detection and recognition based on 7000 real unique document pages and consists of more than 100000 augmented images. The ground truth comprises text and stamp masks, text and characters bounding boxes with relevant annotations.

Source: [DDI-100](https://arxiv.org/pdf/1912.11658v1.pdf)

Introducing paper: DDI-100: Dataset for Text Detection and Recognition
DECADE

DECADE is a large-scale dataset of ego-centric videos from a dog's perspective as well as her corresponding movements.

Source: [Who Let The Dogs Out? Modeling Dog Behavior From Visual Data](/paper/who-let-the-dogs-out-modeling-dog-behavior)

Introducing paper: Who Let The Dogs Out? Modeling Dog Behavior From Visual Data
Deep Fashion3D

A novel benchmark and dataset for the evaluation of image-based garment reconstruction systems. Deep Fashion3D contains 2078 models reconstructed from real garments, which covers 10 different categories and 563 garment instances. It provides rich annotations including 3D feature lines, 3D body pose and the corresponded multi-view real images. In addition, each garment is randomly posed to enhance the variety of real clothing deformations.

Source: [Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images](/paper/deep-fashion3d-a-dataset-and-benchmark-for-3d)

Introducing paper: Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images
DeepFish

**DeepFish** as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks. The dataset consists of approximately 40 thousand images collected underwater from 20 habitats in the marine environments of tropical Australia. It contains classification labels as well as point-level and segmentation labels to have a more comprehensive fish analysis benchmark. These labels enable models to learn to automatically monitor fish count, identify their locations, and estimate their sizes.

Source: [https://github.com/alzayats/DeepFish](https://github.com/alzayats/DeepFish)
Image Source: [https://github.com/alzayats/DeepFish](https://github.com/alzayats/DeepFish)

Introducing paper: A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis
OST
Full name: Egocentric Dataset

Is one of the largest egocentric datasets in the object search task with eyetracking information available

Source: [Deep Future Gaze: Gaze Anticipation on Egocentric Videos Using Adversarial Networks](https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Deep_Future_Gaze_CVPR_2017_paper.pdf)

Introducing paper: Deep Future Gaze: Gaze Anticipation on Egocentric Videos Using Adversarial Networks
DeepScores

DeepScores contains high quality images of musical scores, partitioned into 300,000 sheets of written music that contain symbols of different shapes and sizes. For advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding.

Source: [DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects](/paper/deepscores-a-dataset-for-segmentation)
Image Source: [https://tuggeluk.github.io/deepscores/](https://tuggeluk.github.io/deepscores/)

Introducing paper: DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects
DeepWeeds

The DeepWeeds dataset consists of 17,509 images capturing eight different weed species native to Australia in situ with neighbouring flora.

Source: [https://github.com/AlexOlsen/DeepWeeds](https://github.com/AlexOlsen/DeepWeeds)
Image Source: [https://github.com/AlexOlsen/DeepWeeds](https://github.com/AlexOlsen/DeepWeeds)

Introducing paper: DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning
DeepWriting

A new dataset of handwritten text with fine-grained annotations at the character level and report results from an initial user evaluation.

Source: [DeepWriting: Making Digital Ink Editable via Deep Generative Modeling](/paper/deepwriting-making-digital-ink-editable-via)

Introducing paper: DeepWriting: Making Digital Ink Editable via Deep Generative Modeling
Definite Pronoun Resolution Dataset

Composes sentence pairs (i.e., twin sentences).

Source: [Resolving Complex Cases of Definite Pronouns: The Winograd Schema Challenge](/paper/resolving-complex-cases-of-definite-pronouns)

Introducing paper: Resolving Complex Cases of Definite Pronouns: The Winograd Schema Challenge
DEFT Corpus

A SemEval shared task in which participants must extract definitions from free text using a term-definition pair corpus that reflects the complex reality of definitions in natural language. 

Source: [SemEval-2020 Task 6: Definition extraction from free text with the DEFT corpus](/paper/semeval-2020-task-6-definition-extraction)

Introducing paper: DEFT: A corpus for definition extraction in free- and semi-structured text
DemCare

Dem@Care is providing the following datasets, which are collected during lab and home experiments. The data collection took place in the Greek Alzheimer’s Association for Dementia and Related Disorders in Thessaloniki, Greece and in participants’ homes. The datasets include video and audio recordings as well as data from physiological sensors. Moreover, they include data from sleep, motion and plug sensors.

Source: [DemCare](http://www.demcare.eu/results/datasets)

Introducing paper: The Dem@Care Experiments and Datasets: a Technical Report
Dengue

Benchmark dataset for low-resource multiclass classification, with 4,015 training, 500 testing, and 500 validation examples, each labeled as part of five classes. Each sample can be a part of multiple classes. Collected as tweets and originally used in Livelo & Cheng (2018).

Source: [Dengue](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
DesireDB

Includes gold-standard labels for identifying statements of desire, textual evidence for desire fulfillment, and annotations for whether the stated desire is fulfilled given the evidence in the narrative context.

Source: [Modelling Protagonist Goals and Desires in First-Person Narrative](/paper/modelling-protagonist-goals-and-desires-in)

Introducing paper: Modelling Protagonist Goals and Desires in First-Person Narrative
DeSMOG

A dataset of stance-labeled GW sentences.

Source: [DeSMOG: Detecting Stance in Media On Global Warming](/paper/desmog-detecting-stance-in-media-on-global)

Introducing paper: Detecting Stance in Media on Global Warming
DET

DET is a lane detection dataset that consists of the raw event data, accumulated images over 30ms and corresponding lane labels. Contains 17,103 lane instances, each of which is labeled pixel by pixel manually. 

Source: [DET](https://spritea.github.io/DET/)
DFW
Full name: Disguised Faces in the Wild

Contains over 11000 images of 1000 identities with different types of disguise accessories. The dataset is collected from the Internet, resulting in unconstrained face images similar to real world settings. 

Source: [Recognizing Disguised Faces in the Wild](/paper/recognizing-disguised-faces-in-the-wild)

Introducing paper: Recognizing Disguised Faces in the Wild
DHP19
Full name: Dynamic Vision Sensor 3D Human Pose Dataset

DHP19 is the first human pose dataset with data collected from DVS event cameras. 

It has recordings from 4 synchronized 346x260 pixel DVS cameras and marker positions in 3D space from Vicon motion capture system. The files have event streams and 3D positions recorded from 17 subjects each performing 33 movements.

Source: [DHP19](https://sites.google.com/view/dhp19/home)
Diabetes60

RGB-D images of 60 western dishes, home made. Data was recorded using a Microsoft Kinect V2.

Source: [Diabetes60 Dataset](https://github.com/PatrickChrist/diabetes60)
Diabetic Foot Ulcers Classification Datasets
Full name: DTU

Contains Diabetic Foot Ulcers (DFU) from different patients.

Source: [DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification](/paper/dfunet-convolutional-neural-networks-for)

Introducing paper: DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification
Diabetic Retinopathy Detection Dataset

A large scale of retina image dataset.

Source: [Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation](/paper/diabetic-retinopathy-detection-via-deep)

Introducing paper: Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation
DiaBLa

A new English-French test set for the evaluation of Machine Translation (MT) for informal, written bilingual dialogue. The test set contains 144 spontaneous dialogues (5,700+ sentences) between native English and French speakers, mediated by one of two neural MT systems in a range of role-play settings. The dialogues are accompanied by fine-grained sentence-level judgments of MT quality, produced by the dialogue participants themselves, as well as by manually normalised versions and reference translations produced a posteriori. 

Source: [DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation](/paper/diabla-a-corpus-of-bilingual-spontaneous)

Introducing paper: DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation
DialoGLUE

DialoGLUE is a natural language understanding benchmark for task-oriented dialogue designed to encourage dialogue research in representation-based transfer, domain adaptation, and sample-efficient task learning. It consisting of 7 task-oriented dialogue datasets covering 4 distinct natural language understanding tasks.

Source: [DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue](/paper/dialoglue-a-natural-language-understanding)

Introducing paper: DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue
DialogueFairness

The Dialogue Fairness dataset is used to evaluate and understand fairness in dialogue models, focusing on gender and racial biases.

Source: [https://github.com/zgahhblhc/DialogueFairness](https://github.com/zgahhblhc/DialogueFairness)

Introducing paper: Does Gender Matter? Towards Fairness in Dialogue Systems
DIB-10K
Full name: DongNiao International Birds 10000

Is a challenging image dataset which has more than 10 thousand different types of birds. It was created to enable the study of machine learning and also ornithology research.

Source: [The DongNiao International Birds 10000 Dataset](/paper/the-dongniao-international-birds-10000)

Introducing paper: The DongNiao International Birds 10000 Dataset
DIPS

Contains biases but is two orders of magnitude larger than those used previously. 

Source: [End-to-End Learning on 3D Protein Structure for Interface Prediction](/paper/transferrable-end-to-end-learning-for-protein)

Introducing paper: End-to-End Learning on 3D Protein Structure for Interface Prediction
Diseases in Neurology Case Reports Dataset

Extracts diseases and syndromes (DsSs) from more than 65,000 neurology case reports from 66 journals in PubMed over the last six decades from 1955 to 2017.

Source: [Exploring Diseases and Syndromes in Neurology Case Reports from 1955 to 2017 with Text Mining](/paper/exploring-diseases-and-syndromes-in-neurology)

Introducing paper: Exploring Diseases and Syndromes in Neurology Case Reports from 1955 to 2017 with Text Mining
DiveFace

A new face annotation dataset with balanced distribution between genders and ethnic origins. 

Source: [SensitiveNets: Learning Agnostic Representations with Application to Face Images](/paper/sensitivenets-learning-agnostic)

Introducing paper: SensitiveNets: Learning Agnostic Representations with Application to Face Images
DLBCL-Morph

**DLBCL-Morph** is a dataset containing 42 digitally scanned high-resolution tissue microarray (TMA) slides accompanied by clinical, cytogenetic, and geometric features from 209 DLBCL cases.

Source: [https://github.com/stanfordmlgroup/DLBCL-Morph](https://github.com/stanfordmlgroup/DLBCL-Morph)

Introducing paper: DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set
dMelodies

**dMelodies** is dataset of simple 2-bar melodies generated using 9 independent latent factors of variation where each data point represents a unique melody based on the following constraints:
- Each melody will correspond to a unique scale (major, minor, blues, etc.).
- Each melody plays the arpeggios using the standard I-IV-V-I cadence chord pattern.
- Bar 1 plays the first 2 chords (6 notes), Bar 2 plays the second 2 chords (6 notes).
- Each played note is an 8th note.

Source: [https://github.com/ashispati/dmelodies_dataset](https://github.com/ashispati/dmelodies_dataset)

Introducing paper: dMelodies: A Music Dataset for Disentanglement Learning
DMQA
Full name: DeepMind Q&A

The DeepMind Q&A Dataset consists of two datasets for Question Answering, CNN and DailyMail. Each dataset contains many documents (90k and 197k each), and each document companies on average 4 questions approximately. Each question is a sentence with one missing word/phrase which can be found from the accompanying document/context.

Introducing paper: Teaching Machines to Read and Comprehend
doc2dial

A new dataset of goal-oriented dialogues that are grounded in the associated documents.

Source: [doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset](/paper/doc2dial-a-goal-oriented-document-grounded)

Introducing paper: doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset
DocBank

A benchmark dataset that contains 500K document pages with fine-grained token-level annotations for document layout analysis. DocBank is constructed using a simple yet effective way with weak supervision from the \LaTeX{} documents available on the arXiv.com.

Source: [DocBank: A Benchmark Dataset for Document Layout Analysis](/paper/docbank-a-benchmark-dataset-for-document)

Introducing paper: DocBank: A Benchmark Dataset for Document Layout Analysis
DocVQA

DocVQA consists of 50,000 questions defined on 12,000+ document images.

Source: [DocVQA: A Dataset for VQA on Document Images](/paper/docvqa-a-dataset-for-vqa-on-document-images)

Introducing paper: DocVQA: A Dataset for VQA on Document Images