Omkar2118/datasets huggingface dataset:
GOZ
Full name: Generic Object ZSL Dataset

The Generix Object Zero-shot Learning (**GOZ**) dataset is a benchmark dataset for zero-shot learning.

Source: [https://github.com/TristHas/GOZ](https://github.com/TristHas/GOZ)

Introducing paper: On zero-shot recognition of generic objects
GRAB

**GRAB** is a dataset of full-body motions interacting and grasping 3D objects. It contains accurate finger and facial motions as well as the contact between the objects and body. It contains 5 male and 5 female participants and 4 different motion intents.
The GRAB dataset also contains binary contact maps between the body and objects.

Source: [https://github.com/otaheri/GRAB](https://github.com/otaheri/GRAB)
Image Source: [https://github.com/otaheri/GRAB](https://github.com/otaheri/GRAB)

Introducing paper: GRAB: A Dataset of Whole-Body Human Grasping of Objects
GRAL

A new dataset containing over 550K pairs (covering 143 km^2 area) of RGB and aerial LIDAR depth images. 

Source: [RGB2LIDAR: Towards Solving Large-Scale Cross-Modal Visual Localization](/paper/rgb2lidar-towards-solving-large-scale-cross)

Introducing paper: RGB2LIDAR: Towards Solving Large-Scale Cross-Modal Visual Localization
GraspNet

A large-scale grasp pose detection dataset with a unified evaluation system. The dataset contains 87,040 RGBD images with over 370 million grasp poses. 

Source: [GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping](/paper/graspnet-a-large-scale-clustered-and-densely)

Introducing paper: GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping
Grocery Store

**Grocery Store** is a dataset of natural images of grocery items. All natural images were taken with a smartphone camera in different grocery stores. It contains 5,125 natural images from 81 different classes of fruits, vegetables, and carton items (e.g. juice, milk, yoghurt). The 81 classes are divided into 42 coarse-grained classes, where e.g. the fine-grained classes 'Royal Gala' and 'Granny Smith' belong to the same coarse-grained class 'Apple'. Additionally, each fine-grained class has an associated iconic image and a product description of the item.

Source: [https://github.com/marcusklasson/GroceryStoreDataset](https://github.com/marcusklasson/GroceryStoreDataset)
Image Source: [https://github.com/marcusklasson/GroceryStoreDataset](https://github.com/marcusklasson/GroceryStoreDataset)

Introducing paper: A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels
GTA-IM Dataset
Full name: GTA Indoor Motion

The **GTA Indoor Motion** dataset (GTA-IM) that emphasizes human-scene interactions in the indoor environments. It consists of HD RGB-D image sequences of 3D human motion from a realistic game engine. The dataset has clean 3D human pose and camera pose annotations, and large diversity in human appearances, indoor environments, camera views, and human activities.

Source: [https://github.com/ZheC/GTA-IM-Dataset](https://github.com/ZheC/GTA-IM-Dataset)
Image Source: [https://github.com/ZheC/GTA-IM-Dataset](https://github.com/ZheC/GTA-IM-Dataset)

Introducing paper: Long-term Human Motion Prediction with Scene Context
Gumar Corpus

A large-scale corpus of Gulf Arabic consisting of 110 million words from 1,200 forum novels.

Source: [A Large Scale Corpus of Gulf Arabic](/paper/a-large-scale-corpus-of-gulf-arabic)

Introducing paper: A Large Scale Corpus of Gulf Arabic
Gutenberg Dialog Dataset

This is a high-quality dataset consisting of 14.8M utterances in English, extracted from processed dialogues from publicly available online books.

Source: [https://github.com/ricsinaruto/gutenberg-dialog](https://github.com/ricsinaruto/gutenberg-dialog)

Introducing paper: The Gutenberg Dialogue Dataset
Gutenberg Time Dataset

A data set of hourly time phrases from 52,183 fictional books. 

Source: [What time is it? Temporal Analysis of Novels](/paper/what-time-is-it-temporal-analysis-of-novels)

Introducing paper: What time is it? Temporal Analysis of Novels
HAA500
Full name: Human-Centric Atomic Action Dataset

HAA500 is a manually annotated human-centric atomic action dataset for action recognition on 500 classes with over 591k labeled frames. Unlike existing atomic action datasets, where coarse-grained atomic actions were labeled with action-verbs, e.g., "Throw", HAA500 contains fine-grained atomic actions where only consistent actions fall under the same label, e.g., "Baseball Pitching" vs "Free Throw in Basketball", to minimize ambiguities in action classification. HAA500 has been carefully curated to capture the movement of human figures with less spatio-temporal label noises to greatly enhance the training of deep neural networks. 

Source: [HAA500: Human-Centric Atomic Action Dataset with Curated Videos](/paper/haa500-human-centric-atomic-action-dataset)

Introducing paper: HAA500: Human-Centric Atomic Action Dataset with Curated Videos
Habitat Platform

A platform for research in embodied artificial intelligence (AI).

Source: [Habitat: A Platform for Embodied AI Research](/paper/habitat-a-platform-for-embodied-ai-research)

Introducing paper: Habitat: A Platform for Embodied AI Research
HAM
Full name: Human-annotated Mappings

**HAM** is a dataset for molecular graph partitioning. This dataset contains coarse-grained (CG) mappings of 1206 organic molecules with less than 25 heavy atoms. Each molecule was downloaded from the PubChem database as SMILES. One molecule was assigned to two annotators to compare the human agreement between CG mappings. Downloaded SMILES were hand-mapped. The completed annotations were reviewed by a third person, to identify and remove unreasonable mappings (eg: one bead mappings) which did not agree with the given guidelines. Hence, there are 1.68 annotations per molecule in the current database (16% removed).

Source: [https://github.com/rochesterxugroup/HAM_dataset](https://github.com/rochesterxugroup/HAM_dataset)

Introducing paper: Graph Neural Network Based Coarse-Grained Mapping Prediction
HANS
Full name: Heuristic Analysis for NLI Systems

The HANS (Heuristic Analysis for NLI Systems) dataset which contains many examples where the heuristics fail. 

Source: [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://arxiv.org/abs/1902.01007)
Hanabi Learning Environment

A new challenge domain with novel problems that arise from its combination of purely cooperative gameplay with two to five players and imperfect information.

Source: [The Hanabi Challenge: A New Frontier for AI Research](/paper/the-hanabi-challenge-a-new-frontier-for-ai)

Introducing paper: The Hanabi Challenge: A New Frontier for AI Research
HandNet

The HandNet dataset contains depth images of 10 participants' hands non-rigidly deforming in front of a RealSense RGB-D camera. The annotations are generated by a magnetic annotation technique. 6D pose is available for the center of the hand as well as the five fingertips (i.e. position and orientation of each).

Source: [Rule Of Thumb: Deep derotation for improved fingertip detection](/paper/rule-of-thumb-deep-derotation-for-improved)

Introducing paper: Rule Of Thumb: Deep derotation for improved fingertip detection
HappyDB

**HappyDB** is a corpus of 100,000 crowdsourced happy moments.

Source: [HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments](/paper/happydb-a-corpus-of-100000-crowdsourced-happy)

Introducing paper: HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments
HarperValleyBank

The data simulate simple consumer banking interactions, containing about 23 hours of audio from 1,446 human-human conversations between 59 unique speakers.

Source: [HarperValleyBank: A Domain-Specific Spoken Dialog Corpus](/paper/harpervalleybank-a-domain-specific-spoken)

Introducing paper: HarperValleyBank: A Domain-Specific Spoken Dialog Corpus
HCU400

The dataset consists of the features associated with 402 5-second sound samples.
The 402 sounds range from easily identifiable everyday sounds to intentionally obscured artificial ones. The dataset aims to lower the barrier for the study of aural phenomenology as the largest available audio dataset to include an analysis of causal attribution. Each sample has been annotated with crowd-sourced descriptions, as well as familiarity, imageability, arousal, and valence ratings.

Source: [https://github.com/mitmedialab/HCU400](https://github.com/mitmedialab/HCU400)

Introducing paper: HCU400: An Annotated Dataset for Exploring Aural Phenomenology Through Causal Uncertainty
HDR+ Burst Photography Dataset

The dataset consists of 3640 bursts (made up of 28461 images in total), organized into subfolders, plus the results of an image processing pipeline. Each burst consists of the raw burst input (in DNG format) and certain metadata not present in the images, as sidecar files.

Source: [HDR+ Burst Photography Dataset](http://hdrplusdata.org/dataset.html)
Headlines dataset

The **Headlines dataset** for sarcasm detection is collected from two news website. TheOnion aims at producing sarcastic versions of current events. The dataset includes all the headlines from News in Brief and News in Photos categories (which are sarcastic) and real (and non-sarcastic) news headlines from HuffPost.
This dataset has following advantages over the existing Twitter datasets:

* Since news headlines are written by professionals in a formal manner, there are no spelling mistakes and informal usage. This reduces the sparsity and also increases the chance of finding pre-trained embeddings.
* Furthermore, since the sole purpose of TheOnion is to publish sarcastic news, the dataset has high-quality labels with much less noise as compared to Twitter datasets.
* Unlike tweets which are replies to other tweets, the obtained news headlines are self-contained.

Source: [https://github.com/rishabhmisra/News-Headlines-Dataset-For-Sarcasm-Detection](https://github.com/rishabhmisra/News-Headlines-Dataset-For-Sarcasm-Detection)
Image Source: [https://github.com/rishabhmisra/Headlines-Dataset-For-Sarcasm-Detection](https://github.com/rishabhmisra/Headlines-Dataset-For-Sarcasm-Detection)

Introducing paper: Sarcasm Detection using Hybrid Neural Network
HeadQA

HeadQA is a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. 

Source: [HEAD-QA: A Healthcare Dataset for Complex Reasoning](/paper/head-qa-a-healthcare-dataset-for-complex)
Image Source: [https://arxiv.org/pdf/1906.04701v1.pdf](https://arxiv.org/pdf/1906.04701v1.pdf)

Introducing paper: HEAD-QA: A Healthcare Dataset for Complex Reasoning
HHOI

A new RGB-D video dataset, i.e., UCLA Human-Human-Object Interaction (HHOI) dataset, which includes 3 types of human-human interactions, i.e., shake hands, high-five, pull up, and 2 types of human-object-human interactions, i.e., throw and catch, and hand over a cup. On average, there are 23.6 instances per interaction performed by totally 8 actors recorded from various views. Each interaction lasts 2-7 seconds presented at 10-15 fps.

Source: [Learning Social Affordance for Human-Robot Interaction](/paper/learning-social-affordance-for-human-robot)

Introducing paper: Learning Social Affordance for Human-Robot Interaction
HIDE

Consists of 8,422 blurry and sharp image pairs with 65,784 densely annotated FG human bounding boxes. 

Source: [Human-Aware Motion Deblurring](/paper/human-aware-motion-deblurring-1)

Introducing paper: Human-Aware Motion Deblurring
HiEve
Full name: Human-in-Events

A new large-scale dataset for understanding human motions, poses, and actions in a variety of realistic events, especially crowd & complex events. It contains a record number of poses (>1M), the largest number of action labels (>56k) for complex events, and one of the largest number of trajectories lasting for long terms (with average trajectory length >480). Besides, an online evaluation server is built for researchers to evaluate their approaches.

Source: [Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events](/paper/human-in-events-a-large-scale-benchmark-for)

Introducing paper: Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events
HIGGS Data Set

The data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate the need for physicists to manually develop such features. Benchmark results using Bayesian Decision Trees from a standard physics package and 5-layer neural networks are presented in the original paper. The last 500,000 examples are used as a test set.

Source: [HIGGS Data Set](https://archive.ics.uci.edu/ml/datasets/HIGGS)
Hindi Visual Genome

Hindi Visual Genome is a multimodal dataset consisting of text and images suitable for English-Hindi multimodal machine translation task and multimodal research.

Source: [Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation](/paper/hindi-visual-genome-a-dataset-for-multimodal)
Image Source: [https://ufal.mff.cuni.cz/hindi-visual-genome](https://ufal.mff.cuni.cz/hindi-visual-genome)

Introducing paper: Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation
HINT3

**HINT3** is a dataset for intent detection. It consists of 3 different datasets each containing a diverse set of intents in a single domain - mattress products retail, fitness supplements retail and online gaming named SOFMattress, Curekart and Powerplay11.

Source: [https://github.com/hellohaptik/HINT3](https://github.com/hellohaptik/HINT3)

Introducing paper: HINT3: Raising the bar for Intent Detection in the Wild
Bulgarian Reading Comprehension Dataset

A dataset containing 2,221 questions from matriculation exams for twelfth grade in various subjects -history, biology, geography and philosophy-, and 412 additional questions from online quizzes in history. 

Source: [Beyond English-Only Reading Comprehension: Experiments in Zero-Shot Multilingual Transfer for Bulgarian](/paper/beyond-english-only-reading-comprehension)

Introducing paper: Beyond English-Only Reading Comprehension: Experiments in Zero-Shot Multilingual Transfer for Bulgarian
HJDataset

HJDataset is a large dataset of Historical Japanese Documents with Complex Layouts. It contains over 250,000 layout element annotations of seven types. In addition to bounding boxes and masks of the content regions, it also includes the hierarchical structures and reading orders for layout elements. The dataset is constructed using a combination of human and machine efforts. 

Source: [A Large Dataset of Historical Japanese Documents with Complex Layouts](/paper/a-large-dataset-of-historical-japanese)
Image Source: [https://dell-research-harvard.github.io/HJDataset/](https://dell-research-harvard.github.io/HJDataset/)

Introducing paper: A Large Dataset of Historical Japanese Documents with Complex Layouts
Hong Kong Cantonese corpus

The Hong Kong Cantonese Corpus was collected from transcribed conversations that were recorded between March 1997 and August 1998. About 230,000 Chinese words were collected in the annotated corpus. It contains recordings of spontaneous speech (51 texts) and radio programmes (42 texts), which involve 2 to 4 speakers, with 1 text of monologue. The text were word-segmented, annotated with part-of-speech tagging and Cantonese pronunciation using the romanisation scheme of Linguistic Society of Hong Kong (LSHK).

Source: [Hong Kong Cantonese corpus](http://compling.hss.ntu.edu.sg/hkcancor/)
HLA-Chat

Models character profiles and gives dialogue agents the ability to learn characters' language styles through their HLAs.

Source: [ALOHA: Artificial Learning of Human Attributes for Dialogue Agents](/paper/follow-alice-into-the-rabbit-hole-giving)

Introducing paper: ALOHA: Artificial Learning of Human Attributes for Dialogue Agents
Hollywood 3D dataset

A dataset for benchmarking action recognition algorithms in natural environments, while making use of 3D information. The dataset contains around 650 video clips, across 14 classes. In addition, two state of the art action recognition algorithms are extended to make use of the 3D data, and five new interest point detection strategies are also proposed, that extend to the 3D data. 

Source: [Hollywood 3D: Recognizing Actions in 3D Natural Scenes](/paper/hollywood-3d-recognizing-actions-in-3d)

Introducing paper: Hollywood 3D: Recognizing Actions in 3D Natural Scenes