Omkar2118/datasets huggingface dataset:
Flickr Audio Caption Corpus
Full name: Flickr Audio Caption Corpus

The Flickr 8k Audio Caption Corpus contains 40,000 spoken captions of 8,000 natural images. It was collected in 2015 to investigate multimodal learning schemes for unsupervised speech pattern discovery. For a description of the corpus, see:

D. Harwath and J. Glass, "Deep Multimodal Semantic Embeddings for Speech and Images," 2015 IEEE Automatic Speech Recognition and Understanding Workshop, pp. 237-244, Scottsdale, Arizona, USA, December 2015
PCVC
Full name: Persian Consonant Vowel Combination

The **Persian Consonant Vowel Combination (PCVC)** dataset is a phoneme based speech dataset, and also the first free Persian speech dataset to help Persian speech researchers. This dataset contains of 23 Persian consonants and 6 vowels. The sound samples are all possible combinations of vowels and consonants (138 samples for each speaker) with a length of 30000 data samples. The sample rate of all speech samples is 48000 which means there are 48000 sound samples in every 1 second. In each sample, sound starts with consonant and then there is a vowel sound and at last there is silent. length of silence is dependent on length of combination of consonant and vowel. For example if combination ends in 20000th data sample, so the rest of 10000 sample (until 30000, the length of each sound sample) are silence.
DensePose-Track

DensePose-Track is a dataset of videos where selected frames are annotated in the traditional DensePose manner.

Source: [Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues](https://arxiv.org/pdf/1906.05706)

Introducing paper: Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues
2000 HUB5 English
Full name: 2000 HUB5 English

**2000 HUB5 English Evaluation Transcripts** was developed by the Linguistic Data Consortium (LDC)  and consists of transcripts of 40 English telephone conversations used in the 2000 HUB5 evaluation sponsored by NIST (National Institute of Standards and Technology). 

The Hub5 evaluation series focused on conversational speech over the telephone with the particular task of transcribing conversational speech into text. Its goals were to explore promising new areas in the recognition of conversational speech, to develop advanced technology incorporating those ideas and to measure the performance of new technology.
Parkinson Speech Dataset
Full name: Parkinson Speech Dataset

**Parkinson Speech Dataset** is an audio dataset consisting of recordings of 20 Parkinson's Disease (PD) patients and 20 healthy subjects. From all subjects, multiple types of sound recordings (26) are taken. The goal is to classify which patients have Parkinson's.
MDID
Full name: Multimodal Document Intent Dataset

The Multimodal Document Intent Dataset (MDID) is a dataset for computing author intent from multimodal data from Instagram. It contains 1,299 Instagram posts covering a variety of topics, annotated with labels from three taxonomies. The samples are labelled with 7 labels of intent: Provocative, Informative, Advocative, Entertainment, Expositive, Expressive, Promotive

Source: [Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts](https://arxiv.org/abs/1904.09073)

Introducing paper: Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts
Ciona17

Ciona17 is a semantic segmentation dataset with pixel-level annotations pertaining to invasive species in a marine environment. Diverse outdoor illumination, a range of object shapes, colour, and severe occlusion provide a significant real world challenge for the computer vision community. 

Source: [The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment](https://arxiv.org/pdf/1702.05564)
Image Source: [Galloway et al](https://arxiv.org/pdf/1702.05564)

Introducing paper: The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment
Arabic Speech Corpus
Full name: Arabic Speech Corpus

The **Arabic Speech Corpus** (1.5 GB) is a Modern Standard Arabic (MSA) speech corpus for speech synthesis. The corpus contains phonetic and orthographic transcriptions of more than 3.7 hours of MSA speech aligned with recorded speech on the phoneme level. The annotations include word stress marks on the individual phonemes The Speech corpus has been developed as part of PhD work carried out by Nawar Halabi at the University of Southampton. The corpus was recorded in south Levantine Arabic (Damascian accent) using a professional studio. Synthesized speech as an output using this corpus has produced a high quality, natural voice.
Mivia Audio Events Dataset
Full name: Mivia Audio Events Dataset

The **MIVIA audio events** data set is composed of a total of 6000 events for surveillance applications, namely glass breaking, gun shots and screams. The 6000 events are divided into a training set (composed of 4200 events) and a test set (composed of 1800 events).

In audio surveillance applications, the events of interest (for instance a scream) can occur at different distances from the microphone that correspond to different levels of the signal-to-noise ratio. Moreover, in these applications the events are generally mixed with a complex background, usually composed of several types of different sounds depending on the specific environments both indoor and outdoor (household appliances, cheering of crowds, talking people, traffic jam, passing cars or motorbikes etc.).

The data set is designed to provide each audio event at 6 different values of signal-to-noise ratio (namely 5dB, 10dB, 15dB, 20dB, 25dB and 30dB) and overimposed to different combinations of environmental sounds in order to simulate their occurrence in different ambiences.
VRAI
Full name: Vehicle Re-identification for Aerial Image

VRAI is a large-scale vehicle ReID dataset for UAV-based intelligent applications. The dataset consists of 137, 613 images of 13, 022 vehicle instances. The images of each vehicle instance are captured by cameras of two DJI consumer UAVs at different locations, with a variety of view angles and flight-altitudes (15m to 80m).

Source: [Vehicle Re-identification in Aerial Imagery: Dataset and Approach](https://arxiv.org/pdf/1904.01400)

Introducing paper: Vehicle Re-identification in Aerial Imagery: Dataset and Approach
RWCP Sound Scene Database
Full name: RWCP Sound Scene Database

The **RWCP Sound Scene Database** includes non-speech sounds recorded in an anechoic room, reconstructed signals in various rooms, impulse responses for a microphone array, speech data recorded with the same array, and recordings of background noises. It is intended for use when simulating sound scenes. It was developed by the Real Acoustic Environments Working Group of the Real World Computing Partnership (RWCP). The data was recorded from 1998 to 2000.
UIT-ViIC

UIT-ViIC contains manually written captions for images from Microsoft COCO dataset relating to sports played with ball. UIT-ViIC consists of 19,250 Vietnamese captions for 3,850 images.

Source: [UIT-ViIC: A Dataset for the First Evaluation on Vietnamese Image Captioning](https://arxiv.org/pdf/2002.00175)

Introducing paper: UIT-ViIC: A Dataset for the First Evaluation on Vietnamese Image Captioning
NAR
Full name: NAR

**NAR** is a dataset  of audio recordings made with the humanoid robot Nao in real world conditions for sound recognition benchmarking. All the recordings were collected using the robot’s microphone and thus have the following characteristics:
- recorded with low-quality sensors (300 Hz – 18 kHz bandpass)
- suffering from typical fan noise from the robot’s internal hardware
- recorded in mutiple real domestic environments (no special acoustic charateristics, reverberations, presence of multiple sound sources and unknown locations)
IISc VINE
Full name: Indian Institute of Science VIdeo Naturalness Evaluation

Indian Institute of Science VIdeo Naturalness Evaluation (IISc VINE) is a database consisting of 300 videos, obtained by applying different prediction models on different datasets, and accompanying human opinion scores. 

Source: [A Naturalness Evaluation Database for Video Prediction Models](https://arxiv.org/abs/2005.00356)

Introducing paper: Understanding the Perceived Quality of Video Predictions
MineNav

MinNav is a synthetic dataset based on the sandbox game Minecraft. The dataset uses several plug-in program to generate rendered image sequences with time-aligned depth maps, surface normal maps and camera poses. Thanks for the large game's community, there is an extremely large number of 3D open-world environment, users can find suitable scenes for shooting and build data sets through it and they can also build scenes in-game. 

Source: [MineNav: An Expandable Synthetic Dataset Based on Minecraft for Aircraft Visual Navigation](https://arxiv.org/pdf/2008.08454)

Introducing paper: Exploring the Impacts from Datasets to Monocular Depth Estimation (MDE) Models with MineNavi
Minecraft House
Full name: Minecraft House

**Minecraft House** is a crowd sourced dataset that collects examples of humans building houses in Minecraft.  Each user is asked to build a CraftAssist: A Framework for Dialogue-enabled Interactive Agents house on a fixed time budget (30 minutes), without any additional guidance or instructions. Every action of the user is recorded using the Cuberite server.

The data collection was performed in Minecraft’s creative mode, where the user is given unlimited resources, has access to all material block types and can freely move in
the game world. The action space of the environment is straight-forward: moving in x-y-z dimensions, choosing a block type, and placing or breaking a block.

There are 2586 houses in total.

Introducing paper: CraftAssist: A Framework for Dialogue-enabled Interactive Agents
ARVSU
Full name: Addressee Recognition in Visual Scenes with Utterances

ARVSU contains a vast body of image variations in visual scenes with an annotated utterance and a corresponding addressee for each scenario.

Source: [Deep Learning Based Multi-modal Addressee Recognition in Visual Scenes with Utterances](https://arxiv.org/pdf/1809.04288)

Introducing paper: Deep Learning Based Multi-modal Addressee Recognition in Visual Scenes with Utterances
m2cai16-tool-locations

The m2cai16-tool-locations dataset contains spatial tool annotations for 2,532 frames across the first 10 videos in the m2cai16-tool dataset, which includes 15 videos in total. The dataset consists of 3,141 annotations of 7 surgical instrument classes, with an average of 1.2 labels per frame and 7 instrument classes per video.

Source: [http://ai.stanford.edu/~syyeung/tooldetection.html](http://ai.stanford.edu/~syyeung/tooldetection.html)

Introducing paper: Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks
Minecraft Segmentation
Full name: Minecraft Segmentation

**Minecraft Segmentation** is a segmentation dataset for the [Minecraft House](https://www.paperswithcode.com/dataset/minecraft-house) that adds semantic
segmentation labels for sub-components of the house. There are 2050 houses in total and 1038 distinct labels of subcomponents.

Introducing paper: CraftAssist: A Framework for Dialogue-enabled Interactive Agents
CryoNuSeg

CryoNuSeg is a fully annotated FS-derived cryosectioned and H&E-stained nuclei instance segmentation dataset. The dataset contains images from 10 human organs that were not exploited in other publicly available datasets, and is provided with three manual mark-ups to allow measuring intra-observer and inter-observer variability.

Source: [CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images](https://arxiv.org/pdf/2101.00442.pdf)
Image Source: [https://github.com/masih4/CryoNuSeg](https://github.com/masih4/CryoNuSeg)

Introducing paper: CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images
Princeton Shape
Full name: Princeton Shape

The **Princeton Shape** dataset provides a repository of 3D models and software tools for evaluating shape-based retrieval and analysis algorithms.  The motivation is to promote the use of standardized data sets and evaluation methods for research in matching, classification, clustering, and recognition of 3D models.  Researchers are encouraged to use these resources to produce comparisons of competing algorithms in future publications. There are 1,814 models in total.
Opusparcus

Opusparcus is a paraphrase corpus for six European languages: German, English, Finnish, French, Russian, and Swedish. The paraphrases are extracted from the OpenSubtitles2016 corpus, which contains subtitles from movies and TV shows.

For each target language, the Opusparcus data have been partitioned into three types of data sets: training, development and test sets. The training sets are large, consisting of millions of sentence pairs, and have been compiled automatically, with the help of probabilistic ranking functions. The development and test sets consist of sentence pairs that have been annotated manually; each set contains approximately 1000 sentence pairs that have been verified to be acceptable paraphrases by two annotators.

Source: [Opusparcus](http://urn.fi/urn:nbn:fi:lb-2018021221)

Introducing paper: Open Subtitles Paraphrase Corpus for Six Languages
IKEA 3D
Full name: IKEA 3D

**IKEA 3D** is a dataset of IKEA 3D models and aligned images, which is suitable for pose estimation. There are 759 images and 219 models including Sketchup (skp) and Wavefront (obj) files.
RSOC
Full name: Remote Sensing Object Counting

RSOC is a large-scale object counting dataset with remote sensing images, which contains four important geographic objects: buildings, crowded ships in harbors, large-vehicles and small-vehicles in parking lots.

Source :[Counting from Sky: A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method](https://arxiv.org/pdf/2008.12470)

Introducing paper: Counting from Sky: A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method
RGRS
Full name: ResearchGate dataset for Recommending Systems

RGRS is a dataset for collaboratior recommendation on the ResearchGate academic social network. The data has been collected from Jan. 2019 to April 2019 and
includes raw data of 3980 RG users. 

Source: [Presenting a Dataset for Collaborator Recommending Systems in Academic Social Network: a Case Study on ReseatchGate](https://arxiv.org/abs/2101.01141)

Introducing paper: Presenting a Dataset for Collaborator Recommending Systems in Academic Social Network: a Case Study on ReseachGate
ObjectNet3D
Full name: ObjectNet3D

**ObjectNet3D** is a large scale database for 3D object recognition, named, that consists of 100 categories, 90,127 images, 201,888 objects in these images and 44,147 3D shapes. Objects in the images in the database are aligned with the 3D shapes, and the alignment provides both accurate 3D pose annotation and the closest 3D shape annotation for each 2D object. Consequently, the database is useful for recognizing the 3D pose and 3D shape of objects from 2D images. Authors also provide baseline experiments on four tasks: region proposal generation, 2D object detection, joint 2D detection and 3D object pose estimation, and image-based 3D shape retrieval, which can serve as baselines for future research.
Event-Stream Dataset

Event-Stream Dataset is a robotic grasping dataset with 91 objects.

Source: [Event-based Robotic Grasping Detection with Neuromorphic Vision Sensor and Event-Stream Dataset](https://arxiv.org/pdf/2004.13652)

Introducing paper: Event-based Robotic Grasping Detection with Neuromorphic Vision Sensor and Event-Stream Dataset
PersonalDialog

PersonalDialog is a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. 

Source: [Personalized Dialogue Generation with Diversified Traits](https://arxiv.org/abs/1901.09672)
Image Source: [https://arxiv.org/pdf/1901.09672v2.pdf](https://arxiv.org/pdf/1901.09672v2.pdf)

Introducing paper: Personalized Dialogue Generation with Diversified Traits
Thingi10K
Full name: Thingi10K

**Thingi10K** is a dataset of 3D-Printing Models. Specifically there are 10,000 models from featured “things” on thingiverse.com, suitable for testing 3D printing techniques such as structural analysis , shape optimization, or solid geometry operations.
CocoDoom

CocoDoom is a collection of pre-recorded data extracted from Doom gaming sessions along with annotations in the MS Coco format.

Source: [ResearchDoom and CocoDoom: Learning Computer Vision with Games](https://arxiv.org/abs/1610.02431)

Introducing paper: ResearchDoom and CocoDoom: Learning Computer Vision with Games
VOCASET
Full name: VOCASET

**VOCASET** is a 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio. The dataset has 12 subjects and 480 sequences of about 3-4 seconds each with sentences chosen from an array of standard protocols that maximize phonetic diversity.

Source: [timzhang642](https://github.com/timzhang642/3D-Machine-Learning#datasets)

Introducing paper: Capture, Learning, and Synthesis of 3D Speaking Styles
MSAW
Full name: Multi-Sensor All Weather Mapping

Multi-Sensor All Weather Mapping (MSAW) is a dataset and challenge, which features two collection modalities (both SAR and optical). The dataset and challenge focus on mapping and building footprint extraction using a combination of these data sources. MSAW covers 120 km^2 over multiple overlapping collects and is annotated with over 48,000 unique building footprints labels, enabling the creation and evaluation of mapping algorithms for multi-modal data. 

Source: [SpaceNet 6: Multi-Sensor All Weather Mapping Dataset](https://arxiv.org/pdf/2004.06500)

Introducing paper: SpaceNet 6: Multi-Sensor All Weather Mapping Dataset
ADE-Affordance

ADE-Affordance is a new dataset that builds upon ADE20k, which contains annotations enabling such rich visual reasoning. 

Source: [Learning to Act Properly: Predicting and Explaining Affordances from Images](https://arxiv.org/pdf/1712.07576)

Introducing paper: Learning to Act Properly: Predicting and Explaining Affordances from Images