dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
Hotels-50K | The Hotels-50K dataset consists of over 1 million images from 50,000 different hotels around the world. These images come from both travel websites, as well as the TraffickCam mobile application, which allows every day travelers to submit images of their hotel room in order to help combat trafficking. The TraffickCam images are more visually similar to images from trafficking investigations than the images from travel websites.
The training dataset includes 1,027,871 images from 50,000 hotels, and 92 major hotel chains. Of the 50,000 hotels, 13,900 include user contributed images from the TraffickCam application (a total of 55,061 TraffickCam images are included in the training set).
The test dataset includes 17,954 TraffickCam images from 5,000 different hotels (as well as versions of the test images that have medium and large occlusions to replicate the occlusions seen in real world trafficking victim photographs). | Provide a detailed description of the following dataset: Hotels-50K |
Houses Dataset | This dataset is used for predicting house prices from both images and textual information. It is composed of 535 sample houses from California, USA.
Source: [https://github.com/emanhamed/Houses-dataset](https://github.com/emanhamed/Houses-dataset) | Provide a detailed description of the following dataset: Houses Dataset |
House3D Environment | A rich, extensible and efficient environment that contains 45,622 human-designed 3D scenes of visually realistic houses, ranging from single-room studios to multi-storied houses, equipped with a diverse set of fully labeled 3D objects, textures and scene layouts, based on the SUNCG dataset (Song et.al.) | Provide a detailed description of the following dataset: House3D Environment |
HouseExpo | A large-scale indoor layout dataset containing 35,357 2D floor plans including 252,550 rooms in total. | Provide a detailed description of the following dataset: HouseExpo |
Houses3K | **Houses3K** is a dataset of 3000 textured 3D house models. Houses3K is divided into twelve batches, each containing 50 unique house geometries. For each batch, five different textures were applied forming the sets (A, B, C, D, E).
Source: [https://github.com/darylperalta/Houses3K](https://github.com/darylperalta/Houses3K)
Image Source: [https://github.com/darylperalta/Houses3K](https://github.com/darylperalta/Houses3K) | Provide a detailed description of the following dataset: Houses3K |
HoVer | Is a dataset for many-hop evidence extraction and fact verification. It challenges models to extract facts from several Wikipedia articles that are relevant to a claim and classify whether the claim is Supported or Not-Supported by the facts. In HoVer, the claims require evidence to be extracted from as many as four English Wikipedia articles and embody reasoning graphs of diverse shapes. | Provide a detailed description of the following dataset: HoVer |
HRA | A verified-by-experts repository of 3050 human rights violations photographs, labelled with human rights semantic categories, comprising a list of the types of human rights abuses encountered at present. | Provide a detailed description of the following dataset: HRA |
HSD | An annotated dataset is released to enable dynamic scene classification that includes 80 hours of diverse high quality driving video data clips collected in the San Francisco Bay area. The dataset includes temporal annotations for road places, road types, weather, and road surface conditions. | Provide a detailed description of the following dataset: HSD |
HS-SOD | HS-SOD is a hyperspectral salient object detection dataset with a collection of 60 hyperspectral images with their respective ground-truth binary images and representative rendered colour images (sRGB). | Provide a detailed description of the following dataset: HS-SOD |
Human-Parts | The **Human-Parts** dataset is a dataset for human body, face and hand detection with ~15k images. It contains ~106k different annotations, with multiple annotations per image.
Source: [https://github.com/xiaojie1017/Human-Parts](https://github.com/xiaojie1017/Human-Parts)
Image Source: [https://github.com/xiaojie1017/Human-Parts](https://github.com/xiaojie1017/Human-Parts) | Provide a detailed description of the following dataset: Human-Parts |
HUMBI | A new large multiview dataset for human body expressions with natural clothing. The goal of HUMBI is to facilitate modeling view-specific appearance and geometry of gaze, face, hand, body, and garment from assorted people. 107 synchronized HD cameras are used to capture 772 distinctive subjects across gender, ethnicity, age, and physical condition. | Provide a detailed description of the following dataset: HUMBI |
Humicroedit | Humicroedit is a humorous headline dataset. The data consists of regular English news headlines paired with versions of the same headlines that contain simple replacement edits designed to make them funny. The authors carefully curated crowdsourced editors to create funny headlines and judges to score a to a total of 15,095 edited headlines, with five judges per headline. | Provide a detailed description of the following dataset: Humicroedit |
HurricaneEmo | **HurricaneEmo** is an emotion dataset that contains 15,000 English tweets spanning three hurricanes: Harvey, Irma, and Maria.
Source: [https://github.com/shreydesai/hurricane](https://github.com/shreydesai/hurricane) | Provide a detailed description of the following dataset: HurricaneEmo |
HybridQA | A new large-scale question-answering dataset that requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table and multiple free-form corpora linked with the entities in the table. The questions are designed to aggregate both tabular information and text information, i.e., lack of either form would render the question unanswerable. | Provide a detailed description of the following dataset: HybridQA |
Hypersim | For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. **Hypersim** is a photorealistic synthetic dataset for holistic indoor scene understanding. It contains 77,400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry. | Provide a detailed description of the following dataset: Hypersim |
Hyperspectral City | Propose a dataset which adopts multi-channel visual input. | Provide a detailed description of the following dataset: Hyperspectral City |
iCartoonFace | The **iCartoonFace** dataset is a large-scale dataset that can be used for two different tasks: cartoon face detection and cartoon face recognition.
Source: [https://github.com/luxiangju-PersonAI/iCartoonFace](https://github.com/luxiangju-PersonAI/iCartoonFace)
Image Source: [https://github.com/luxiangju-PersonAI/iCartoonFace](https://github.com/luxiangju-PersonAI/iCartoonFace) | Provide a detailed description of the following dataset: iCartoonFace |
Ice Hockey News Dataset | Ice Hockey News Dataset is a corpus of Finnish ice hockey news, edited to be suitable for training of end-to-end news generation methods, as well as demonstrate generation of text, which was judged by journalists to be relatively close to a viable product. | Provide a detailed description of the following dataset: Ice Hockey News Dataset |
Icentia11K | Public ECG dataset of continuous raw signals for representation learning containing 11 thousand patients and 2 billion labelled beats. | Provide a detailed description of the following dataset: Icentia11K |
ICLabel | An Independent components (IC) dataset containing spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG recordings. | Provide a detailed description of the following dataset: ICLabel |
Icons-50 | Icons-50 is a dataset for studying surface variation robustness. | Provide a detailed description of the following dataset: Icons-50 |
ICubWorld | iCubWorld datasets are collections of images recording the visual experience of iCub while observing objects in its typical environment, a laboratory or an office. The acquisition setting is devised to allow a natural human-robot interaction, where a teacher verbally provides the label of the object of interest and shows it to the robot, by holding it in the hand; the iCub can either track the object while the teacher moves it, or take it in its hand. | Provide a detailed description of the following dataset: ICubWorld |
IDD | IDD is a dataset for road scene understanding in unstructured environments used for semantic segmentation and object detection for autonomous driving. It consists of 10,004 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads. | Provide a detailed description of the following dataset: IDD |
iFakeFaceDB | **iFakeFaceDB** is a face image dataset for the study of synthetic face manipulation detection, comprising about 87,000 synthetic face images generated by the Style-GAN model and transformed with the GANprintR approach. All images were aligned and resized to the size of 224 x 224.
Source: [https://github.com/socialabubi/iFakeFaceDB](https://github.com/socialabubi/iFakeFaceDB)
Image Source: [https://github.com/socialabubi/iFakeFaceDB](https://github.com/socialabubi/iFakeFaceDB) | Provide a detailed description of the following dataset: iFakeFaceDB |
IgboNLP Datasets | IgboNLP is a standard machine translation benchmark dataset for Igbo. It consists of 10,000 English-Igbo human-level quality sentence pairs mostly from the news domain. | Provide a detailed description of the following dataset: IgboNLP Datasets |
iHarmony4 | **iHarmony4** is a synthesized dataset for Image Harmonization. It contains 4 sub-datasets: HCOCO, HAdobe5k, HFlickr, and Hday2night (based on COCO, Adobe5k, Flickr, day2night datasets respectively), each of which contains synthesized composite images, foreground masks of composite images and corresponding real images. | Provide a detailed description of the following dataset: iHarmony4 |
IIIT-AR-13K | IIIT-AR-13K is created by manually annotating the bounding boxes of graphical or page objects in publicly available annual reports. This dataset contains a total of 13k annotated page images with objects in five different popular categories - table, figure, natural image, logo, and signature. It is the largest manually annotated dataset for graphical object detection. | Provide a detailed description of the following dataset: IIIT-AR-13K |
IIRC | Contains more than 13K questions over paragraphs from English Wikipedia that provide only partial information to answer them, with the missing information occurring in one or more linked documents. The questions were written by crowd workers who did not have access to any of the linked documents, leading to questions that have little lexical overlap with the contexts where the answers appear. | Provide a detailed description of the following dataset: IIRC |
IITB Corridor | An abnormal activity data-set for research use that contains 4,83,566 annotated frames. | Provide a detailed description of the following dataset: IITB Corridor |
IIW | Intrinsic Images in the Wild is a large scale, public dataset for intrinsic image decompositions of real-world scenes selected from the OpenSurfaces dataset. Each image is annotated with crowdsourced pairwise comparisons of material properties. | Provide a detailed description of the following dataset: IIW |
IKEA ASM | A three million frame, multi-view, furniture assembly video dataset that includes depth, atomic actions, object segmentation, and human pose. | Provide a detailed description of the following dataset: IKEA ASM |
iLur News Texts | iLur News Texts is a dataset of over 12000 news articles from iLur.am, categorized into 7 classes: sport, politics, weather, economy, accidents, art, society. The articles are split into train (2242k tokens) and test sets (425k tokens). | Provide a detailed description of the following dataset: iLur News Texts |
Image and Video Advertisements | The **Image and Video Advertisements** collection consists of an image dataset of 64,832 image ads, and a video dataset of 3,477 ads. The data contains rich annotations encompassing the topic and sentiment of the ads, questions and answers describing what actions the viewer is prompted to take and the reasoning that the ad presents to persuade the viewer ("What should I do according to this ad, and why should I do it? "), and symbolic references ads make (e.g. a dove symbolizes peace). | Provide a detailed description of the following dataset: Image and Video Advertisements |
Image Caption Quality Dataset | Image Caption Quality Dataset is a dataset of crowdsourced ratings for machine-generated image captions. It contains more than 600k ratings of image-caption pairs. | Provide a detailed description of the following dataset: Image Caption Quality Dataset |
Image Editing Request Dataset | A new language-guided image editing dataset that contains a large number of real image pairs with corresponding editing instructions. | Provide a detailed description of the following dataset: Image Editing Request Dataset |
Image Memorability | A database of images with measured probabilities that each picture will be remembered after a single view. | Provide a detailed description of the following dataset: Image Memorability |
IMDb-Face | IMDb-Face is large-scale noise-controlled dataset for face recognition research. The dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from 2.0 million raw images. All images are obtained from the IMDb website. | Provide a detailed description of the following dataset: IMDb-Face |
IMEMNET | The **Image-MusicEmotion-Matching-Net** (IMEMNet) dataset is a dataset for continuous emotion-based image and music matching. It has over 140K image-music pairs.
Source: [https://github.com/linkAmy/IMEMNet](https://github.com/linkAmy/IMEMNet) | Provide a detailed description of the following dataset: IMEMNET |
Immediacy Dataset | Consists of 10,000 images is constructed, in which all the immediacy measures and the human poses are annotated. | Provide a detailed description of the following dataset: Immediacy Dataset |
iMoCap | A dataset that consists of 20 actions of various actors, such as tennis serves, yoga and Tai Chi. Take tennis serves as an example. The publicly available videos of some tennis players from YouTube are downloaded, and manually crop the videos roughly to obtain a set of video clips of serves for each player. | Provide a detailed description of the following dataset: iMoCap |
IMPPRES | An IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. | Provide a detailed description of the following dataset: IMPPRES |
iNaturalist Fine-Grained Geolocation | The **iNaturalist Fine-Grained Geolocation** dataset is an extension of the iNaturalist dataset with complementary geolocation information.
Source: [https://github.com/visipedia/fg_geo](https://github.com/visipedia/fg_geo)
Image Source: [https://github.com/visipedia/fg_geo](https://github.com/visipedia/fg_geo) | Provide a detailed description of the following dataset: iNaturalist Fine-Grained Geolocation |
Incidents | Contains 446,684 images annotated by humans that cover 43 incidents across a variety of scenes. | Provide a detailed description of the following dataset: Incidents |
Incremental Dialog Dataset | Simulates unanticipated user needs in the deployment stage. | Provide a detailed description of the following dataset: Incremental Dialog Dataset |
IndicNLP Corpus | The IndicNLP corpus is a large-scale, general-domain corpus containing 2.7 billion words for 10 Indian languages from two language families.
Source: [https://arxiv.org/abs/2005.00085](https://arxiv.org/abs/2005.00085) | Provide a detailed description of the following dataset: IndicNLP Corpus |
IndoNLU Benchmark | The IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia. It is a joint venture from many Indonesia NLP enthusiasts from different institutions such as Gojek, Institut Teknologi Bandung, HKUST, Universitas Multimedia Nusantara, Prosa.ai, and Universitas Indonesia. | Provide a detailed description of the following dataset: IndoNLU Benchmark |
IndoSum | The **IndoSum** dataset is a benchmark dataset for Indonesian text summarization. The dataset consists of news articles and manually constructed summaries.
Source: [https://github.com/kata-ai/indosum](https://github.com/kata-ai/indosum) | Provide a detailed description of the following dataset: IndoSum |
Industrial Benchmark | A benchmark which bridges the gap between freely available, documented, and motivated artificial benchmarks and properties of real industrial problems. The resulting industrial benchmark (IB) has been made publicly available to the RL community by publishing its Java and Python code, including an OpenAI Gym wrapper, on Github. | Provide a detailed description of the following dataset: Industrial Benchmark |
InfoTabS | InfoTabS comprises of human-written textual hypotheses based on premises that are tables extracted from Wikipedia info-boxes. | Provide a detailed description of the following dataset: InfoTabS |
InLoc | InLoc is a dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario. | Provide a detailed description of the following dataset: InLoc |
INQUISITIVE | A dataset of ~19K questions that are elicited while a person is reading through a document. | Provide a detailed description of the following dataset: INQUISITIVE |
INRIA Holidays Dataset | The Holidays dataset is a set of images which mainly contains some of the authors' personal holidays photos. The remaining ones were taken on purpose to test the robustness to various attacks: rotations, viewpoint and illumination changes, blurring, etc. The dataset includes a very large variety of scene types (natural, man-made, water and fire effects, etc) and images are in high resolution. The dataset contains 500 image groups, each of which represents a distinct scene or object. The first image of each group is the query image and the correct retrieval results are the other images of the group. | Provide a detailed description of the following dataset: INRIA Holidays Dataset |
Inspired | A new dataset of 1,001 human-human dialogs for movie recommendation with measures for successful recommendations. | Provide a detailed description of the following dataset: Inspired |
InstaFake | Includes two datasets published for the detection of fake and automated accounts. | Provide a detailed description of the following dataset: InstaFake |
InsuranceQA | **InsuranceQA** is a question answering dataset for the insurance domain, the data stemming from the website Insurance Library. There are 12,889 questions and 21,325 answers in the training set. There are 2,000 questions and 3,354 answers in the validation set. There are 2,000 questions and 3,308 answers in the test set. | Provide a detailed description of the following dataset: InsuranceQA |
INTEL-TAU | A new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high-resolution dataset for illumination estimation research. | Provide a detailed description of the following dataset: INTEL-TAU |
INTERACTION Dataset | The INTERACTION dataset contains naturalistic motions of various traffic participants in a variety of highly interactive driving scenarios from different countries. The dataset can serve for many behavior-related research areas, such as
- 1) intention/behavior/motion prediction,
- 2) behavior cloning and imitation learning,
- 3) behavior analysis and modeling,
- 4) motion pattern and representation learning,
- 5) interactive behavior extraction and categorization,
- 6) social and human-like behavior generation,
- 7) decision-making and planning algorithm development and verification,
- 8) driving scenario/case generation, etc. | Provide a detailed description of the following dataset: INTERACTION Dataset |
InteriorNet | **InteriorNet** is a RGB-D for large scale interior scene understanding and mapping. The dataset contains 20M images created by pipeline:
* (A) the authors collected around 1 million CAD models provided by world-leading furniture manufacturers.
* (B) based on those models, around 1,100 professional designers create around 22 million interior layouts. Most of such layouts have been used in real-world decorations.
* (C) For each layout, authors generate a number of configurations to represent different random lightings and simulation of scene change over time in daily life.
* (D) Authors provide an interactive simulator (ViSim) to help for creating ground truth IMU, events, as well as monocular or stereo camera trajectories including hand-drawn, random walking and neural network based realistic trajectory.
* (E) All supported image sequences and ground truth. | Provide a detailed description of the following dataset: InteriorNet |
Interpretable STS | A dataset of sentence pairs annotated following the formalization. | Provide a detailed description of the following dataset: Interpretable STS |
Interview | A large-scale (105K conversations) media dialog dataset collected from news interview transcripts. | Provide a detailed description of the following dataset: Interview |
IntPhys 2019 | A benchmark for visual intuitive physics reasoning. | Provide a detailed description of the following dataset: IntPhys 2019 |
IntrA | **IntrA** is an open-access 3D intracranial aneurysm dataset that makes the application of points-based and mesh-based classification and segmentation models available. This dataset can be used to diagnose intracranial aneurysms and to extract the neck for a clipping operation in medicine and other areas of deep learning, such as normal estimation and surface reconstruction.
103 3D models of entire brain vessels are collected by reconstructing scanned 2D MRA images of patients (the raw 2D MRA images are not published due to medical ethics).
1909 blood vessel segments are generated automatically from the complete models, including 1694 healthy vessel segments and 215 aneurysm segments for diagnosis.
116 aneurysm segments are divided and annotated manually by medical experts; the scale of each aneurysm segment is based on the need for a preoperative examination.
Geodesic distance matrices are computed and included for each annotated 3D segment, because the expression of the geodesic distance is more accurate than Euclidean distance according to the shape of vessels. | Provide a detailed description of the following dataset: IntrA |
IP102 | IP102 contains more than 75,000 images belonging to 102 categories, which exhibit a natural long-tailed distribution. | Provide a detailed description of the following dataset: IP102 |
IPN Hand | The **IPN Hand** dataset is a benchmark video dataset with sufficient size, variation, and real-world elements able to train and evaluate deep neural networks for continuous Hand Gesture Recognition (HGR).
Source: [https://github.com/GibranBenitez/IPN-hand](https://github.com/GibranBenitez/IPN-hand) | Provide a detailed description of the following dataset: IPN Hand |
IPOD | Comprises 192k job titles belonging to 56k LinkedIn users. | Provide a detailed description of the following dataset: IPOD |
IPRE | A dataset for inter-personal relationship extraction which aims to facilitate information extraction and knowledge graph construction research. In total, IPRE has over 41,000 labeled sentences for 34 types of relations, including about 9,000 sentences annotated by workers. | Provide a detailed description of the following dataset: IPRE |
irc-disentanglement | This is a dataset for disentangling conversations on IRC, which is the task of identifying separate conversations in a single stream of messages. It contains disentanglement information for 77,563 messages or IRC.
Source: [https://github.com/jkkummerfeld/irc-disentanglement](https://github.com/jkkummerfeld/irc-disentanglement)
Image Source: [https://github.com/jkkummerfeld/irc-disentanglement](https://github.com/jkkummerfeld/irc-disentanglement) | Provide a detailed description of the following dataset: irc-disentanglement |
IRS | **IRS** is an open dataset for indoor robotics vision tasks, especially disparity and surface normal estimation. It contains totally 103,316 samples covering a wide range of indoor scenes, such as home, office, store and restaurant.
Source: [https://github.com/HKBU-HPML/IRS](https://github.com/HKBU-HPML/IRS)
Image Source: [https://github.com/HKBU-HPML/IRS](https://github.com/HKBU-HPML/IRS) | Provide a detailed description of the following dataset: IRS |
IS-A | The **IS-A** dataset is a dataset of relations extracted from a medical ontology. The different entities in the ontology are related by the “is a” relation. For example, ‘acute leukemia’ is a ‘leukemia’. The dataset has 294,693 nodes with 356,541 edges between them.
Source: [https://arxiv.org/pdf/1906.05939.pdf](https://arxiv.org/pdf/1906.05939.pdf) | Provide a detailed description of the following dataset: IS-A |
ISBDA | Consists of user-generated aerial videos from social media with annotations of instance-level building damage masks. This provides the first benchmark for quantitative evaluation of models to assess building damage using aerial videos. | Provide a detailed description of the following dataset: ISBDA |
ISIA Food-500 | Includes 500 categories from the list in the Wikipedia and 399,726 images, a more comprehensive food dataset that surpasses existing popular benchmark datasets by category coverage and data volume. | Provide a detailed description of the following dataset: ISIA Food-500 |
ISR-UoL 3D Social Activity Dataset | This is a social interaction dataset between two subjects. This dataset consists of RGB and depth images, and tracked skeleton data (i.e. joints 3D coordinates and rotations) acquired by an RGB-D sensor. It includes 8 social activities: {handshake, greeting hug, help walk, help stand-up, fight, push, conversation, call attention}. Each activity was recorded in a period around 40 to 60 seconds of repetitions within the same session at a frame rate of 30 frames per second. The only exceptions are help walking (at a short distance) and help stand-up, which were recorded 4 times to the same session, regardless of the time spent on it. | Provide a detailed description of the following dataset: ISR-UoL 3D Social Activity Dataset |
IStego100K | Contains 208,104 images with the same size of 1024*1024. Among them, 200,000 images (100,000 cover-stego image pairs) are divided as the training set and the remaining 8,104 as testing set. | Provide a detailed description of the following dataset: IStego100K |
IU ShareView | IU ShareView dataset consists of 9 sets of two 5-10 minute first-person videos. Each set contains 3-4 participants performing a variety of everyday activities (shaking hands, chatting, eating, etc.) in one of six indoor environments. | Provide a detailed description of the following dataset: IU ShareView |
IWSLT 2019 | The **IWSLT 2019** dataset contains source, Machine Translated, reference and Post-Edited text, which can be used to quantify and evaluate Post-editing effort after automatic MT.
Source: [https://arxiv.org/abs/1910.06204](https://arxiv.org/abs/1910.06204) | Provide a detailed description of the following dataset: IWSLT 2019 |
Jamendo Lyrics | Dataset for lyrics alignment and transcription evaluation. It contains 20 music pieces under CC license from the Jamendo website along with their lyrics, with:
* Manual annotations indicating the start time of each word in the audio file
* Predictions of start and end times for each word from both of the models presented in the paper | Provide a detailed description of the following dataset: Jamendo Lyrics |
JAMUL | A large-scale evaluation dataset for headlines of three different lengths composed by professional editors. | Provide a detailed description of the following dataset: JAMUL |
Japanese Word Similarity | This dataset contains information about Japanese word similarity including rare words. The dataset is constructed following the Stanford Rare Word Similarity Dataset. 10 annotators annotated word pairs with 11 levels of similarity.
Source: [https://github.com/tmu-nlp/JapaneseWordSimilarityDataset](https://github.com/tmu-nlp/JapaneseWordSimilarityDataset) | Provide a detailed description of the following dataset: Japanese Word Similarity |
JESC | Japanese-English Subtitle Corpus is a large Japanese-English parallel corpus covering the underrepresented domain of conversational dialogue. It consists of more than 3.2 million examples, making it the largest freely available dataset of its kind. The corpus was assembled by crawling and aligning subtitles found on the web. | Provide a detailed description of the following dataset: JESC |
JHU CoSTAR Block Stacking Dataset | Involves data where a robot interacts with 5.1 cm colored blocks to complete an order-fulfillment style block stacking task. It contains dynamic scenes and real time-series data in a less constrained environment than comparable datasets. There are nearly 12,000 stacking attempts and over 2 million frames of real data. | Provide a detailed description of the following dataset: JHU CoSTAR Block Stacking Dataset |
JHU-CROWD | (JHU-CROWD) a crowd counting dataset that contains 4,250 images with 1.11 million annotations. This dataset is collected under a variety of diverse scenarios and environmental conditions. Specifically, the dataset includes several images with weather-based degradations and illumination variations in addition to many distractor images, making it a very challenging dataset. Additionally, the dataset consists of rich annotations at both image-level and head-level. | Provide a detailed description of the following dataset: JHU-CROWD |
JIT Dataset | The **Jejueo Interview Transcripts** (JIT) dataset is a parallel corpus containing 170k+ Jejueo-Korean sentences.
Source: [https://arxiv.org/abs/1911.12071](https://arxiv.org/abs/1911.12071) | Provide a detailed description of the following dataset: JIT Dataset |
JRDB | A novel egocentric dataset collected from social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360 degrees RGB video at 15 fps, 3D point clouds from two Velodyne 16 Lidars, line 3D point clouds from two Sick Lidars, audio signal, RGB-D video at 30 fps, 360 degrees spherical image from a fisheye camera and encoder values from the robot's wheels. | Provide a detailed description of the following dataset: JRDB |
JSUT Corpus | JSUT Corpus is a free large-scale speech corpus that can be shared between academic institutions and commercial companies has an important role. However, such a corpus for Japanese speech synthesis does not exist. | Provide a detailed description of the following dataset: JSUT Corpus |
JTA | JTA is a dataset for people tracking in urban scenarios by exploiting a photorealistic videogame. It is up to now the vastest dataset (about 500.000 frames, almost 10 million body poses) of human body parts for people tracking in urban scenarios. | Provide a detailed description of the following dataset: JTA |
JW300 | A parallel corpus of over 300 languages with around 100 thousand parallel sentences per language pair on average. | Provide a detailed description of the following dataset: JW300 |
KAIST Multispectral Pedestrian Detection Benchmark | KAIST Multispectral Pedestrian Dataset
The KAIST Multispectral Pedestrian Dataset is imaging hardware consisting of a color camera, a thermal camera and a beam splitter to capture the aligned multispectral (RGB color + Thermal) images. With this hardware, we captured various regular traffic scenes at day and night time to consider changes in light conditions. and, consists of 95k color-thermal pairs (640x480, 20Hz) taken from a vehicle. All the pairs are manually annotated (person, people, cyclist) for the total of 103,128 dense annotations and 1,182 unique pedestrians. The annotation includes temporal correspondence between bounding boxes like Caltech Pedestrian Dataset.
For more information, read [Multispectral Pedestrian Detection: Benchmark Dataset and Baseline (CVPR 2015)](https://openaccess.thecvf.com/content_cvpr_2015/papers/Hwang_Multispectral_Pedestrian_Detection_2015_CVPR_paper.pdf) or visit [this website](https://soonminhwang.github.io/rgbt-ped-detection/) | Provide a detailed description of the following dataset: KAIST Multispectral Pedestrian Detection Benchmark |
Kannada-MNIST | The **Kannada-MNIST** dataset is a drop-in substitute for the standard MNIST dataset for the Kannada language. | Provide a detailed description of the following dataset: Kannada-MNIST |
KaoKore | Consists of faces extracted from pre-modern Japanese artwork. | Provide a detailed description of the following dataset: KaoKore |
KaWAT | A new word analogy task dataset for Indonesian. | Provide a detailed description of the following dataset: KaWAT |
KELM | KELM is a large-scale synthetic corpus of Wikidata KG as natural text. | Provide a detailed description of the following dataset: KELM |
K-EmoCon | A multimodal dataset with comprehensive annotations of continuous emotions during naturalistic conversations. The dataset contains multimodal measurements, including audiovisual recordings, EEG, and peripheral physiological signals, acquired with off-the-shelf devices from 16 sessions of approximately 10-minute long paired debates on a social issue. | Provide a detailed description of the following dataset: K-EmoCon |
KenyanFood13 | The Kenyan Food Type Dataset (**KenyanFood13**) is an image classification dataset for Kenyan food. The images are categorized into 13 different labels.
Source: [https://github.com/monajalal/Kenyan-Food](https://github.com/monajalal/Kenyan-Food)
Image Source: [https://github.com/monajalal/Kenyan-Food](https://github.com/monajalal/Kenyan-Food) | Provide a detailed description of the following dataset: KenyanFood13 |
KeypointNet | **KeypointNet** is a large-scale and diverse 3D keypoint dataset that contains 83,231 keypoints and 8,329 3D models from 16 object categories, by leveraging numerous human annotations, based on ShapeNet models.
Source: [https://github.com/qq456cvb/KeypointNet](https://github.com/qq456cvb/KeypointNet)
Image Source: [https://github.com/qq456cvb/KeypointNet](https://github.com/qq456cvb/KeypointNet) | Provide a detailed description of the following dataset: KeypointNet |
KinectFaceDB | The Dataset consists of the multimodal facial images of 52 people (14 females, 38 males) obtained by Kinect. The data is captured in two sessions happened at different time period (about half month). In each session, the dataset provides the facial images of each person in 9 states of different facial expressions, different lighting and occlusion conditions: neutral, smile, open mouth, left profile, right profile, occlusion eyes, occlusion mouth, occlusion paper and light on [Figure 1]. All the images are provided in three sources of information: the RGB color image, the depth map (provided in two forms of the bitmap depth image and the text file containing the original depth levels sensed by Kinect) as well as 3D. In addition, the dataset comes with the manual landmarks of 6 positions in the face: left eye, right eye, the tip of nose, left side of mouth, right side of mouth and the chin [Figure 2]. Other information of the person such as gender, year of birth, glasses (this person wears the glasses or not), capture time of each session are also available. | Provide a detailed description of the following dataset: KinectFaceDB |
KINNEWS and KIRNEWS | Two news datasets (KINNEWS and KIRNEWS) for multi-class classification of news articles in Kinyarwanda and Kirundi, two low-resource African languages. The two languages are mutually intelligible. | Provide a detailed description of the following dataset: KINNEWS and KIRNEWS |
KINS | Augments the KITTI with more instance pixel-level annotation for 8 categories. | Provide a detailed description of the following dataset: KINS |
Kinteract | Explicitly created for Human Computer Interaction (HCI). | Provide a detailed description of the following dataset: Kinteract |
Kitchen Scenes | Kitchen Scenes is a multi-view RGB-D dataset of nine kitchen scenes, each containing several objects in realistic cluttered environments including a subset of objects from the BigBird dataset. The viewpoints of the scenes are densely sampled and objects in the scenes are annotated with bounding boxes and in the 3D point cloud. | Provide a detailed description of the following dataset: Kitchen Scenes |
KIT Motion-Language | The KIT Motion-Language is a dataset linking human motion and natural language. | Provide a detailed description of the following dataset: KIT Motion-Language |
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