Omkar2118/datasets huggingface dataset:
SynWoodScape
Full name: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving

**SynWoodScape** is a synthetic version of the surround-view dataset covering many of its weaknesses and extending it. WoodScape comprises four surround-view cameras and nine tasks, including segmentation, depth estimation, 3D bounding box detection, and a novel soiling detection. Semantic annotation of 40 classes at the instance level is provided for over 10,000 images. With WoodScape, we would like to encourage the community to adapt computer vision models for the fisheye camera instead of using naive rectification.

Introducing paper: SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving
NLU++
Full name: NLLU++ : A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue

**nlu++** is a dataset for natural language understanding (NLU) in task-oriented dialogue (ToD) systems, with the aim to provide a much more challenging evaluation environment for dialogue NLU models, up to date with the current application and industry requirements. nlu++ is divided into two domains (banking and hotels) and brings several crucial improvements over current commonly used NLU datasets. 1) Nlu++ provides fine-grained domain ontologies with a large set of challenging multi-intent sentences, introducing and validating the idea of intent modules that can be combined into complex intents that convey complex user goals, combined with finer-grained and thus more challenging slot sets. 2) The ontology is divided into domain-specific and generic (i.e., domain-universal) intent modules that overlap across domains, promoting cross-domain reusability of annotated examples. 3) The dataset design has been inspired by the problems observed in industrial ToD systems, and 4) it has been collected, filtered and carefully annotated by dialogue NLU experts, yielding high-quality annotated data.

List of datasets:

Banking: online banking queries annotated with their corresponding intents.
Span Extraction: the data used for the SpanConvert paper.
NLU++: a challenging evaluation environment for dialogue NLU models (multi-domain, multi-label intents and slots).
EVI: a challenging multilingual dataset for knowledge-based enrollment, identification, and identification in spoken dialogue systems.

Introducing paper: NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue
FREDo

FREDo is a Few-Shot Document-Level Relation Extraction Benchmark based on DocRED and SciERC. The dataset is divided into four subsets: training set (62 relations), validation set (16 relations), in-domain test set (16 relations), and cross-domain test set (7 relations).

Introducing paper: Few-Shot Document-Level Relation Extraction
SemEval-2022 Task-12

Symlink is a SemEval shared task of extracting mathematical symbols and their descriptions from LaTeX source of scientific documents. This is a new task in SemEval 2022, which attracted 180 individual registrations and 59 final submissions from 7 participant teams.

Introducing paper: SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions
OC-Cityscape
Full name: Out-of-Context Cityscapes

Out-of-Context Cityscapes (OC-Cityscapes) is a new dataset build 
 by replacing roads in the validation data of Cityscapes with various textures such as water, sand, grass, etc. 

https://drive.google.com/file/d/1pKdlglcvsGseLzS1MX8SdjzQO2o1KZm6/view?usp=sharing

Introducing paper: Robust Semantic Segmentation with Superpixel-Mix
ANUBIS
Full name: Skeleton-Based Action Recognition Dataset

**ANUBIS** is a large-scale human skeleton dataset containing 80 actions. Compared with previously collected datasets, ANUBIS is advantageous in the following four aspects: (1) employing more recently released sensors; (2) containing novel back view; (3) encouraging high enthusiasm of subjects; (4) including actions of the COVID pandemic era.

Introducing paper: ANUBIS: Skeleton Action Recognition Dataset, Review, and Benchmark
Kompetencer
Full name: Danish Job Postings Classification Dataset

Kompetencer (en: competences) is a Danish job posting dataset annotated for nested spans of competences.
ExVo2022
Full name: ICML ExVo 2022 Workshop & Competition Data

Baseline code for the three tracks of ExVo 2022 competition.

Consists of 59,201 recordings totaling more than 36 hours of audio data from 1 702 speakers. To our knowledge, this is substantially larger than than any previously available dataset of human vocal bursts.

Introducing paper: The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts
Cross-View Cross-Scene Multi-View Crowd Counting Dataset

A large synthetic multi-camera crowd counting dataset with a large number of scenes and camera views to capture many possible variations, which avoids the difficulty of collecting and annotating such a large real dataset. 

The dataset is generated using GCC-CL [50], which works as a
plug-in for the game “Grand Theft Auto V”. The generating
process consists of two parts: scene simulation and multiview recording. First, crowd scenes are simulated, through
the selection of the background selected, region of interest
(ROI), weather condition, human models and postures, etc.
Next, cameras are placed at various locations to record the crowd scene from various perspectives. Birds-eye views are also collected for visualization. Each person has a specific ID for mapping their coordinates in the world coordinate system and their locations in each camera-view image. The camera parameters, such as coordinates, deflection angles and fields-of-view, are also recorded.

In total, the whole synthetic MV counting dataset contains 31 scenes. For each scene, around 100 camera views re set for multi-view recording. The multi-view recording is performed 100 times with different crowd distributions in the scene, i.e., each scene contains 100 multi-view frames, with each frame comprising 60 to 120 camera-views. The image resolution is 1920×1080.
HaVG
Full name: Hausa Visual Genome Dataset

A dataset that contains the description of an image or a section within the image in Hausa and its equivalent in English.  Hausa, a Chadic language, is a member of the Afro-Asiatic language family. It is estimated that about 100 to 150 million people speak the language, with more than 80 million indigenous speakers.  The dataset comprises 32,923 images and their descriptions that are divided into training, development, test, and challenge test set. The Hausa Visual Genome is the first dataset of its kind and can be used for Hausa-English machine translation, multi-modal research, and image description, among various other natural language processing and generation tasks.

Introducing paper: Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation
Biographical
Full name: Biographical: A Semi-Supervised Relation Extraction Dataset

Biographical is a semi-supervised dataset for RE. The dataset, which is aimed towards digital humanities (DH) and historical research, is automatically compiled by aligning sentences from Wikipedia articles with matching structured data from sources including Pantheon and Wikidata.

Introducing paper: Biographical: A Semi-Supervised Relation Extraction Dataset
ComPhy
Full name: Compositional Physical Reasoning Dataset

****Compositional Physical Reasoning** is a dataset for understanding object-centric and relational physics properties hidden from visual appearances. For a given set of objects, the dataset includes few videos of them moving and interacting under different initial conditions. The model is evaluated based on its capability to unravel the compositional hidden properties, such as mass and charge, and use this knowledge to answer a set of questions posted on one of the videos.

Introducing paper: ComPhy: Compositional Physical Reasoning of Objects and Events from Videos
TuGebic
Full name: A Turkish-German Bilingual Code-Switching Corpus

**TuGebic** is a corpus of recordings of spontaneous speech samples from Turkish-German bilinguals, and the compilation of a corpus called TuGebic. Participants in the study were adult Turkish and German bilinguals living in Germany or Turkey at the time of recording in the first half of the 1990s. The data were manually tokenised and normalised, and all proper names (names of participants and places mentioned in the conversations) were replaced with pseudonyms. Token-level automatic language identification was performed, which made it possible to establish the proportions of words from each language.

Introducing paper: TuGeBiC: A Turkish German Bilingual Code-Switching Corpus
NHA12D
Full name: A New Pavement Crack Dataset

**NHA12D** is an annotated pavement crack dataset that contains images with different viewpoints and pavements types. This dataset is composed of 80 pavement images, including 40 concrete pavement images and 40 asphalt pavement images, captured by digital survey vehicles on the A12 network in the UK.

Introducing paper: NHA12D: A New Pavement Crack Dataset and a Comparison Study Of Crack Detection Algorithms
COUCH

**COUCH** is a large human-chair interaction dataset with clean annotations. The dataset consists of 3 hours and over 500 sequences of motion capture (MoCap) on human-chair interactions.

Introducing paper: COUCH: Towards Controllable Human-Chair Interactions
ONCE-3DLanes
Full name: Monocular 3D Lane Detection Dataset

ONCE-3DLanes is a real-world autonomous driving dataset with lane layout annotation in 3D space. A dataset annotation pipeline is designed to automatically generate high-quality 3D lane locations from 2D lane annotations by exploiting the explicit relationship between point clouds and image pixels in 211,000 road scenes.

Introducing paper: ONCE-3DLanes: Building Monocular 3D Lane Detection
VIS-TIR

A visible-light and thermal-infrared images dataset for dual-spectrum depth estimation.

Introducing paper: Unsupervised Visible-light Images Guided Cross-Spectrum Depth Estimation from Dual-Modality Cameras
TemporalWiki

TemporalWiki is a lifelong benchmark for ever-evolving LMs that utilizes the difference between consecutive snapshots of English Wikipedia and English Wikidata for training and evaluation, respectively. The benchmark hence allows researchers to periodically track an LM's ability to retain previous knowledge and acquire updated/new knowledge at each point in time.

Introducing paper: TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models
Charlotte-ThermalFace

**Charlotte-ThermalFace** is a thermal face dataset. The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image.

There are approximately 10,000 infrared thermal images from 10 subjects in varying thermal conditions, at several distances from the camera, and at changing head positions. We have also controlled the air temperature to change from 20.5°C ( 69°F) to 26.5 °C( 80°F). Images are available in four different temperatures, 10 relative distances from the camera, starting at 1m ( 3.3 ft) to 6.6m( 21.6 ft), and 25 head positions.

• The first public facial thermal dataset annotated with the environmental properties including air temperature, relative humidity, airspeed, distance from the camera, and subjective thermal sensation of each person at the time.

• All the images are manually annotated with 72 or 43 facial landmarks.

• We are publishing the data in the original 16-bit radiometric TemperatureLinear format, which has the thermal value of each pixel.

Introducing paper: A Novel Fully Annotated Thermal Infrared Face Dataset: Recorded in Various Environment Conditions and Distances From The Camera