dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
Moments in Time | Moments in Time is a large-scale dataset for recognizing and understanding action in videos. The dataset includes a collection of one million labeled 3 second videos, involving people, animals, objects or natural phenomena, that capture the gist of a dynamic scene. | Provide a detailed description of the following dataset: Moments in Time |
VQA-CP | The **VQA-CP** dataset was constructed by reorganizing VQA v2 such that the correlation between the question type and correct answer differs in the training and test splits. For example, the most common answer to questions starting with What sport… is tennis in the training set, but skiing in the test set. A model that guesses an answer primarily from the question will perform poorly.
Source: [Unshuffling Data for Improved Generalization](https://arxiv.org/abs/2002.11894)
Image Source: [https://arxiv.org/pdf/1712.00377.pdf](https://arxiv.org/pdf/1712.00377.pdf) | Provide a detailed description of the following dataset: VQA-CP |
LJSpeech | This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours. The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain. | Provide a detailed description of the following dataset: LJSpeech |
QNLI | The **QNLI** (**Question-answering NLI**) dataset is a Natural Language Inference dataset automatically derived from the Stanford Question Answering Dataset v1.1 (SQuAD). SQuAD v1.1 consists of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The dataset was converted into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue. The QNLI dataset is part of GLUE benchmark. | Provide a detailed description of the following dataset: QNLI |
RTE | The **Recognizing Textual Entailment (RTE)** datasets come from a series of textual entailment challenges. Data from RTE1, RTE2, RTE3 and RTE5 is combined. Examples are constructed based on news and Wikipedia text. | Provide a detailed description of the following dataset: RTE |
MRPC | Microsoft Research Paraphrase Corpus (MRPC) is a corpus consists of 5,801 sentence pairs collected from newswire articles. Each pair is labelled if it is a paraphrase or not by human annotators. The whole set is divided into a training subset (4,076 sentence pairs of which 2,753 are paraphrases) and a test subset (1,725 pairs of which 1,147 are paraphrases). | Provide a detailed description of the following dataset: MRPC |
CODAH | The COmmonsense Dataset Adversarially-authored by Humans (**CODAH**) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions. It contains 2801 questions in total, and uses 5-fold cross validation for evaluation. | Provide a detailed description of the following dataset: CODAH |
CrowdPose | The **CrowdPose** dataset contains about 20,000 images and a total of 80,000 human poses with 14 labeled keypoints. The test set includes 8,000 images. The crowded images containing homes are extracted from MSCOCO, MPII and AI Challenger. | Provide a detailed description of the following dataset: CrowdPose |
LDC2017T10 | Abstract Meaning Representation (AMR) Annotation Release 2.0 was developed by the Linguistic Data Consortium (LDC), SDL/Language Weaver, Inc., the University of Colorado's Computational Language and Educational Research group and the Information Sciences Institute at the University of Southern California. It contains a sembank (semantic treebank) of over 39,260 English natural language sentences from broadcast conversations, newswire, weblogs and web discussion forums.
AMR captures “who is doing what to whom” in a sentence. Each sentence is paired with a graph that represents its whole-sentence meaning in a tree-structure. AMR utilizes PropBank frames, non-core semantic roles, within-sentence coreference, named entity annotation, modality, negation, questions, quantities, and so on to represent the semantic structure of a sentence largely independent of its syntax. | Provide a detailed description of the following dataset: LDC2017T10 |
MemexQA | A large, realistic multimodal dataset consisting of real personal photos and crowd-sourced questions/answers. | Provide a detailed description of the following dataset: MemexQA |
ECSSD | The **Extended Complex Scene Saliency Dataset** (**ECSSD**) is comprised of complex scenes, presenting textures and structures common to real-world images. ECSSD contains 1,000 intricate images and respective ground-truth saliency maps, created as an average of the labeling of five human participants. | Provide a detailed description of the following dataset: ECSSD |
HKU-IS | **HKU-IS** is a visual saliency prediction dataset which contains 4447 challenging images, most of which have either low contrast or multiple salient objects. | Provide a detailed description of the following dataset: HKU-IS |
PASCAL-S | **PASCAL-S** is a dataset for salient object detection consisting of a set of 850 images from PASCAL VOC 2010 validation set with multiple salient objects on the scenes. | Provide a detailed description of the following dataset: PASCAL-S |
DUT-OMRON | The **DUT-OMRON** dataset is used for evaluation of Salient Object Detection task and it contains 5,168 high quality images. The images have one or more salient objects and relatively cluttered background. | Provide a detailed description of the following dataset: DUT-OMRON |
MSMT17 | MSMT17 is a multi-scene multi-time person re-identification dataset. The dataset consists of 180 hours of videos, captured by 12 outdoor cameras, 3 indoor cameras, and during 12 time slots. The videos cover a long period of time and present complex lighting variations, and it contains a large number of annotated identities, i.e., 4,101 identities and 126,441 bounding boxes.
The dataset can be requested at http://www.pkuvmc.com/dataset.html | Provide a detailed description of the following dataset: MSMT17 |
USPS | **USPS** is a digit dataset automatically scanned from envelopes by the U.S. Postal Service containing a total of 9,298 16×16 pixel grayscale samples; the images are centered, normalized and show a broad range of font styles. | Provide a detailed description of the following dataset: USPS |
SIXray | The **SIXray** dataset is constructed by the Pattern Recognition and Intelligent System Development Laboratory, University of Chinese Academy of Sciences. It contains 1,059,231 X-ray images which are collected from some several subway stations. There are six common categories of prohibited items, namely, gun, knife, wrench, pliers, scissors and hammer. It has three subsets called SIXray10, SIXray100 and SIXray1000, There are image-level annotations provided by human security inspectors for the whole dataset. In addition the images in the test set are annotated with a bounding-box for each prohibited item to evaluate the performance of object localization.
Source: [https://github.com/MeioJane/SIXray](https://github.com/MeioJane/SIXray)
Image Source: [https://github.com/MeioJane/SIXray](https://github.com/MeioJane/SIXray) | Provide a detailed description of the following dataset: SIXray |
Django | The **Django** dataset is a dataset for code generation comprising of 16000 training, 1000 development and 1805 test annotations. Each data point consists of a line of Python code together with a manually created natural language description. | Provide a detailed description of the following dataset: Django |
PACS | **PACS** is an image dataset for domain generalization. It consists of four domains, namely Photo (1,670 images), Art Painting (2,048 images), Cartoon (2,344 images) and Sketch (3,929 images). Each domain contains seven categories. | Provide a detailed description of the following dataset: PACS |
BioGRID | **BioGRID** is a biomedical interaction repository with data compiled through comprehensive curation efforts. The current index is version 4.2.192 and searches 75,868 publications for 1,997,840 protein and genetic interactions, 29,093 chemical interactions and 959,750 post translational modifications from major model organism species. | Provide a detailed description of the following dataset: BioGRID |
Freiburg Forest | The **Freiburg Forest** dataset was collected using a Viona autonomous mobile robot platform equipped with cameras for capturing multi-spectral and multi-modal images. The dataset may be used for evaluation of different perception algorithms for segmentation, detection, classification, etc. All scenes were recorded at 20 Hz with a camera resolution of 1024x768 pixels. The data was collected on three different days to have enough variability in lighting conditions as shadows and sun angles play a crucial role in the quality of acquired images. The robot traversed about 4.7 km each day. The dataset creators provide manually annotated pixel-wise ground truth segmentation masks for 6 classes: Obstacle, Trail, Sky, Grass, Vegetation, and Void. | Provide a detailed description of the following dataset: Freiburg Forest |
SNIPS | The **SNIPS** Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:
* SearchCreativeWork (e.g. Find me the I, Robot television show),
* GetWeather (e.g. Is it windy in Boston, MA right now?),
* BookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night),
* PlayMusic (e.g. Play the last track from Beyoncé off Spotify),
* AddToPlaylist (e.g. Add Diamonds to my roadtrip playlist),
* RateBook (e.g. Give 6 stars to Of Mice and Men),
* SearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris).
The training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent. | Provide a detailed description of the following dataset: SNIPS |
Nottingham | The **Nottingham** Dataset is a collection of 1200 American and British folk songs.
Source: [Rethinking Recurrent Latent Variable Model for Music Composition](https://arxiv.org/abs/1810.03226)
Image Source: [https://highnoongmt.wordpress.com/2018/10/02/going-to-use-the-nottingham-music-database/](https://highnoongmt.wordpress.com/2018/10/02/going-to-use-the-nottingham-music-database/) | Provide a detailed description of the following dataset: Nottingham |
SOD | # Aiming
Detect small obstacles, like lost and found.
# frames
3000+ picture.
3000+ claimed labelled.
1600 actually labelled. | Provide a detailed description of the following dataset: SOD |
Cluttered Omniglot | Dataset for one-shot segmentation. | Provide a detailed description of the following dataset: Cluttered Omniglot |
PKU-MMD | The **PKU-MMD** dataset is a large skeleton-based action detection dataset. It contains 1076 long untrimmed video sequences performed by 66 subjects in three camera views. 51 action categories are annotated, resulting almost 20,000 action instances and 5.4 million frames in total. Similar to NTU RGB+D, there are also two recommended evaluate protocols, i.e. cross-subject and cross-view. | Provide a detailed description of the following dataset: PKU-MMD |
NTU RGB+D | **NTU RGB+D** is a large-scale dataset for RGB-D human action recognition. It involves 56,880 samples of 60 action classes collected from 40 subjects. The actions can be generally divided into three categories: 40 daily actions (e.g., drinking, eating, reading), nine health-related actions (e.g., sneezing, staggering, falling down), and 11 mutual actions (e.g., punching, kicking, hugging). These actions take place under 17 different scene conditions corresponding to 17 video sequences (i.e., S001–S017). The actions were captured using three cameras with different horizontal imaging viewpoints, namely, −45∘,0∘, and +45∘. Multi-modality information is provided for action characterization, including depth maps, 3D skeleton joint position, RGB frames, and infrared sequences. The performance evaluation is performed by a cross-subject test that split the 40 subjects into training and test groups, and by a cross-view test that employed one camera (+45∘) for testing, and the other two cameras for training. | Provide a detailed description of the following dataset: NTU RGB+D |
Birdsnap | **Birdsnap** is a large bird dataset consisting of 49,829 images from 500 bird species with 47,386 images used for training and 2,443 images used for testing. | Provide a detailed description of the following dataset: Birdsnap |
CoLA | The **Corpus of Linguistic Acceptability** (**CoLA**) consists of 10657 sentences from 23 linguistics publications, expertly annotated for acceptability (grammaticality) by their original authors. The public version contains 9594 sentences belonging to training and development sets, and excludes 1063 sentences belonging to a held out test set. | Provide a detailed description of the following dataset: CoLA |
ASTD | Arabic Sentiment Tweets Dataset (ASTD) is an Arabic social sentiment analysis dataset gathered from Twitter. It consists of about 10,000 tweets which are classified as objective, subjective positive, subjective negative, and subjective mixed. | Provide a detailed description of the following dataset: ASTD |
LSMDC | This dataset contains 118,081 short video clips extracted from 202 movies. Each video has a caption, either extracted from the movie script or from transcribed DVS (descriptive video services) for the visually impaired. The validation set contains 7408 clips and evaluation is performed on a test set of 1000 videos from movies disjoint from the training and val sets. | Provide a detailed description of the following dataset: LSMDC |
MSR-VTT | **MSR-VTT** (Microsoft Research Video to Text) is a large-scale dataset for the open domain video captioning, which consists of 10,000 video clips from 20 categories, and each video clip is annotated with 20 English sentences by Amazon Mechanical Turks. There are about 29,000 unique words in all captions. The standard splits uses 6,513 clips for training, 497 clips for validation, and 2,990 clips for testing. | Provide a detailed description of the following dataset: MSR-VTT |
MSVD | The **Microsoft Research Video Description Corpus** (**MSVD**) dataset consists of about 120K sentences collected during the summer of 2010. Workers on Mechanical Turk were paid to watch a short video snippet and then summarize the action in a single sentence. The result is a set of roughly parallel descriptions of more than 2,000 video snippets. Because the workers were urged to complete the task in the language of their choice, both paraphrase and bilingual alternations are captured in the data. | Provide a detailed description of the following dataset: MSVD |
DiDeMo | The **Distinct Describable Moments** (**DiDeMo**) dataset is one of the largest and most diverse datasets for the temporal localization of events in videos given natural language descriptions. The videos are collected from Flickr and each video is trimmed to a maximum of 30 seconds. The videos in the dataset are divided into 5-second segments to reduce the complexity of annotation. The dataset is split into training, validation and test sets containing 8,395, 1,065 and 1,004 videos respectively. The dataset contains a total of 26,892 moments and one moment could be associated with descriptions from multiple annotators. The descriptions in DiDeMo dataset are detailed and contain camera movement, temporal transition indicators, and activities. Moreover, the descriptions in DiDeMo are verified so that each description refers to a single moment. | Provide a detailed description of the following dataset: DiDeMo |
MuPoTS-3D | MuPoTs-3D (Multi-person Pose estimation Test Set in 3D) is a dataset for pose estimation composed of more than 8,000 frames from 20 real-world scenes with up to three subjects. The poses are annotated with a 14-point skeleton model. | Provide a detailed description of the following dataset: MuPoTS-3D |
Helsinki Prosody Corpus | The Helsinki Prosody Corpus is a dataset for predicting prosodic prominence from written text. The prosodic annotations are automatically generated, high quality prosodic for the 'clean' subsets of LibriTTS corpus (Zen et al., 2019), comprising of 262.5 hours of read speech from 1230 speakers. The transcribed sentences were aligned and then prosodically annotated with word-level acoustic prominence labels. | Provide a detailed description of the following dataset: Helsinki Prosody Corpus |
WMCA | The Wide Multi Channel Presentation Attack (WMCA) database consists of 1941 short video recordings of both bonafide and presentation attacks from 72 different identities. The data is recorded from several channels including color, depth, infra-red, and thermal.
Additionally, the pulse reading data for bonafide recordings is also provided.
Preprocessed images for some of the channels are also provided for part of the data used in the reference publication.
The WMCA database is produced at Idiap within the framework of “IARPA BATL” and “H2020 TESLA” projects and it is intended for investigation of presentation attack detection (PAD) methods for face recognition systems. | Provide a detailed description of the following dataset: WMCA |
AQUAINT | The **AQUAINT** Corpus consists of newswire text data in English, drawn from three sources: the Xinhua News Service (People's Republic of China), the New York Times News Service, and the Associated Press Worldstream News Service. It was prepared by the LDC for the AQUAINT Project, and will be used in official benchmark evaluations conducted by National Institute of Standards and Technology (NIST).
Source: [Linguistic Data Consortium](https://catalog.ldc.upenn.edu/LDC2002T31)
Image Source: [https://catalog.ldc.upenn.edu/LDC2002T31](https://catalog.ldc.upenn.edu/LDC2002T31) | Provide a detailed description of the following dataset: AQUAINT |
MAFL | The **MAFL** dataset contains manually annotated facial landmark locations for 19,000 training and 1,000 test images. | Provide a detailed description of the following dataset: MAFL |
Species-800 | **Species-800** is a corpus for species entities, which is based on manually annotated abstracts. It comprises 800 PubMed abstracts that contain identified organism mentions. To increase the corpus taxonomic mention diversity the 800 abstracts were collected by selecting 100 abstracts from the following 8 categories: bacteriology, botany, entomology, medicine, mycology, protistology, virology and zoology. 800 has been annotated with a focus at the species level; however, higher taxa mentions (such as genera, families and orders) have also been considered. | Provide a detailed description of the following dataset: Species-800 |
LINNAEUS | LINNAEUS is a general-purpose dictionary matching software, capable of processing multiple types of document formats in the biomedical domain (MEDLINE, PMC, BMC, OTMI, text, etc.). It can produce multiple types of output (XML, HTML, tab-separated-value file, or save to a database). It also contains methods for acting as a server (including load balancing across several servers), allowing clients to request matching over a network. A package with files for recognizing and identifying species names is available for LINNAEUS, showing 94% recall and 97% precision compared to LINNAEUS-species-corpus. | Provide a detailed description of the following dataset: LINNAEUS |
NLVR | **NLVR** contains 92,244 pairs of human-written English sentences grounded in synthetic images. Because the images are synthetically generated, this dataset can be used for semantic parsing. | Provide a detailed description of the following dataset: NLVR |
ChestX-ray14 | **ChestX-ray14** is a medical imaging dataset which comprises 112,120 frontal-view X-ray images of 30,805 (collected from the year of 1992 to 2015) unique patients with the text-mined fourteen common disease labels, mined from the text radiological reports via NLP techniques. It expands on ChestX-ray8 by adding six additional thorax diseases: Edema, Emphysema, Fibrosis, Pleural Thickening and Hernia. | Provide a detailed description of the following dataset: ChestX-ray14 |
HICO | **HICO** is a benchmark for recognizing human-object interactions (HOI).
Key features:
- A diverse set of interactions with common object categories
- A list of well-defined, sense-based HOI categories
- An exhaustive labeling of co-occurring interactions with an object category in each image
- The annotation of each HOI instance (i.e. a human and an object bounding box with an interaction class label) in all images | Provide a detailed description of the following dataset: HICO |
Adverse Drug Events (ADE) Corpus | Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports.
A significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that can only be explored by human readers due to their unstructured nature. The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports. The documents are systematically double annotated in various rounds to ensure consistent annotations. The annotated documents are finally harmonized to generate representative consensus annotations. In order to demonstrate an example use case scenario, the corpus was employed to train and validate models for the classification of informative against the non-informative sentences. A Maximum Entropy classifier trained with simple features and evaluated by 10-fold cross-validation resulted in the F₁ score of 0.70 indicating a potential useful application of the corpus. | Provide a detailed description of the following dataset: Adverse Drug Events (ADE) Corpus |
Sports-1M | The **Sports-1M** dataset consists of over a million videos from YouTube. The videos in the dataset can be obtained through the YouTube URL specified by the authors. Approximately 7% (as of 2016) of the videos have been removed by the YouTube uploaders since the dataset was compiled. However, there are still over a million videos in the dataset with 487 sports-related categories with 1,000 to 3,000 videos per category. The videos are automatically labelled with 487 sports classes using the YouTube Topics API by analyzing the text metadata associated with the videos (e.g. tags, descriptions). Approximately 5% of the videos are annotated with more than one class. | Provide a detailed description of the following dataset: Sports-1M |
YouTube-8M | The **YouTube-8M** dataset is a large scale video dataset, which includes more than 7 million videos with 4716 classes labeled by the annotation system. The dataset consists of three parts: training set, validate set, and test set. In the training set, each class contains at least 100 training videos. Features of these videos are extracted by the state-of-the-art popular pre-trained models and released for public use. Each video contains audio and visual modality. Based on the visual information, videos are divided into 24 topics, such as sports, game, arts & entertainment, etc | Provide a detailed description of the following dataset: YouTube-8M |
Something-Something V2 | The 20BN-SOMETHING-SOMETHING V2 dataset is a large collection of labeled video clips that show humans performing pre-defined basic actions with everyday objects. The dataset was created by a large number of crowd workers. It allows machine learning models to develop fine-grained understanding of basic actions that occur in the physical world. It contains 220,847 videos, with 168,913 in the training set, 24,777 in the validation set and 27,157 in the test set. There are 174 labels.
[Source](https://developer.qualcomm.com/software/ai-datasets/something-something)
[Image Source](https://developer.qualcomm.com/software/ai-datasets/something-something) | Provide a detailed description of the following dataset: Something-Something V2 |
Jester (Jokes) | 6.5 million anonymous ratings of jokes by users of the **Jester** Joke Recommender System. | Provide a detailed description of the following dataset: Jester (Jokes) |
Something-Something V1 | The 20BN-SOMETHING-SOMETHING dataset is a large collection of labeled video clips that show humans performing pre-defined basic actions with everyday objects. The dataset was created by a large number of crowd workers. It allows machine learning models to develop fine-grained understanding of basic actions that occur in the physical world. It contains 108,499 videos, with 86,017 in the training set, 11,522 in the validation set and 10,960 in the test set. There are 174 labels.
⚠️ Attention: This is the outdated V1 of the dataset. V2 is available [here](https://paperswithcode.com/dataset/something-something-v2). | Provide a detailed description of the following dataset: Something-Something V1 |
HVU | HVU is organized hierarchically in a semantic taxonomy that focuses on multi-label and multi-task video understanding as a comprehensive problem that encompasses the recognition of multiple semantic aspects in the dynamic scene. HVU contains approx.~572k videos in total with 9 million annotations for training, validation, and test set spanning over 3142 labels. HVU encompasses semantic aspects defined on categories of scenes, objects, actions, events, attributes, and concepts which naturally captures the real-world scenarios. | Provide a detailed description of the following dataset: HVU |
PTC | **PTC** is a collection of 344 chemical compounds represented as graphs which report the carcinogenicity for rats. There are 19 node labels for each node. | Provide a detailed description of the following dataset: PTC |
UT-Kinect | The **UT-Kinect** dataset is a dataset for action recognition from depth sequences. The videos were captured using a single stationary Kinect. There are 10 action types: walk, sit down, stand up, pick up, carry, throw, push, pull, wave hands, clap hands. There are 10 subjects, Each subject performs each actions twice. Three channels were recorded: RGB, depth and skeleton joint locations. The three channel are synchronized. The framerate is 30f/s. | Provide a detailed description of the following dataset: UT-Kinect |
CAD-120 | The CAD-60 and **CAD-120** data sets comprise of RGB-D video sequences of humans performing activities which are recording using the Microsoft Kinect sensor. Being able to detect human activities is important for making personal assistant robots useful in performing assistive tasks. The CAD dataset comprises twelve different activities (composed of several sub-activities) performed by four people in different environments, such as a kitchen, a living room, and office, etc. | Provide a detailed description of the following dataset: CAD-120 |
NTU RGB+D 120 | NTU RGB+D 120 is a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. | Provide a detailed description of the following dataset: NTU RGB+D 120 |
N-UCLA | The Multiview 3D event dataset is capture by me and Xiaohan Nie in UCLA. it contains RGB, depth and human skeleton data captured simultaneously by three Kinect cameras. This dataset include 10 action categories: pick up with one hand, pick up with two hands, drop trash, walk around, sit down, stand up, donning, doffing, throw, carry. Each action is performed by 10 actors. This dataset contains data taken from a variety of viewpoints.
The dataset can be found in part-1, part-2 part-3, part-4, part-5, part-6, part-7, part-8, part-9, part-10, part-11, part-12, part-13, part-14, part-15, part-16,
We also created a version of the dataset that only contains RGB videos: RGB videos only. | Provide a detailed description of the following dataset: N-UCLA |
SBU | **SBU-Kinect-Interaction dataset version 2.0** comprises of RGB-D video sequences of humans performing interaction activities that are recording using the Microsoft Kinect sensor. This dataset was originally recorded for a class project, and it must be used only for the purposes of research. If you use this dataset in your work, please cite the following paper.
```
Kiwon Yun, Jean Honorio, Debaleena Chattopadhyay, Tamara L. Berg, and Dimitris Samaras, The 2nd International Workshop on Human Activity Understanding from 3D Data at Conference on Computer Vision and Pattern Recognition (HAU3D-CVPRW), CVPR 2012
``` | Provide a detailed description of the following dataset: SBU |
CK+ | The Extended Cohn-Kanade (**CK+**) dataset contains 593 video sequences from a total of 123 different subjects, ranging from 18 to 50 years of age with a variety of genders and heritage. Each video shows a facial shift from the neutral expression to a targeted peak expression, recorded at 30 frames per second (FPS) with a resolution of either 640x490 or 640x480 pixels. Out of these videos, 327 are labelled with one of seven expression classes: anger, contempt, disgust, fear, happiness, sadness, and surprise. The CK+ database is widely regarded as the most extensively used laboratory-controlled facial expression classification database available, and is used in the majority of facial expression classification methods. | Provide a detailed description of the following dataset: CK+ |
Acted Facial Expressions In The Wild (AFEW) | Acted Facial Expressions In The Wild (AFEW) is a dynamic temporal facial expressions data corpus consisting of close to real world environment extracted from movie | Provide a detailed description of the following dataset: Acted Facial Expressions In The Wild (AFEW) |
YouTube-VOS 2018 | Youtube-VOS is a Video Object Segmentation dataset that contains 4,453 videos - 3,471 for training, 474 for validation, and 508 for testing. The training and validation videos have pixel-level ground truth annotations for every 5th frame (6 fps). It also contains Instance Segmentation annotations. It has more than 7,800 unique objects, 190k high-quality manual annotations and more than 340 minutes in duration. | Provide a detailed description of the following dataset: YouTube-VOS 2018 |
TabFact | **TabFact** is a large-scale dataset which consists of 117,854 manually annotated statements with regard to 16,573 Wikipedia tables, their relations are classified as ENTAILED and REFUTED. TabFact is the first dataset to evaluate language inference on structured data, which involves mixed reasoning skills in both symbolic and linguistic aspects. | Provide a detailed description of the following dataset: TabFact |
MPI-INF-3DHP | **MPI-INF-3DHP** is a 3D human body pose estimation dataset consisting of both constrained indoor and complex outdoor scenes. It records 8 actors performing 8 activities from 14 camera views. It consists on >1.3M frames captured from the 14 cameras. | Provide a detailed description of the following dataset: MPI-INF-3DHP |
Beijing Multi-Site Air-Quality Dataset | This data set includes hourly air pollutants data from 12 nationally-controlled air-quality monitoring sites. The air-quality data are from the Beijing Municipal Environmental Monitoring Center. The meteorological data in each air-quality site are matched with the nearest weather station from the China Meteorological Administration. The time period is from March 1st, 2013 to February 28th, 2017. Missing data are denoted as NA. | Provide a detailed description of the following dataset: Beijing Multi-Site Air-Quality Dataset |
PhysioNet Challenge 2012 | The **PhysioNet Challenge 2012** dataset is publicly available and contains the de-identified records of 8000 patients in Intensive Care Units (ICU). Each record consists of roughly 48 hours of multivariate time series data with up to 37 features recorded at various times from the patients during their stay such as respiratory rate, glucose etc.
Source: [Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)](https://arxiv.org/abs/1905.00125)
Image Source: [https://physionet.org/content/challenge-2016/1.0.0/](https://physionet.org/content/challenge-2016/1.0.0/) | Provide a detailed description of the following dataset: PhysioNet Challenge 2012 |
MuJoCo | **MuJoCo** (multi-joint dynamics with contact) is a physics engine used to implement environments to benchmark Reinforcement Learning methods. | Provide a detailed description of the following dataset: MuJoCo |
WFLW | The **Wider Facial Landmarks in the Wild** or **WFLW** database contains 10000 faces (7500 for training and 2500 for testing) with 98 annotated landmarks. This database also features rich attribute annotations in terms of occlusion, head pose, make-up, illumination, blur and expressions. | Provide a detailed description of the following dataset: WFLW |
REDS | The realistic and dynamic scenes (**REDS**) dataset was proposed in the NTIRE19 Challenge. The dataset is composed of 300 video sequences with resolution of 720×1,280, and each video has 100 frames, where the training set, the validation set and the testing set have 240, 30 and 30 videos, respectively
Source: [Video Super Resolution Based on Deep Learning: A comprehensive survey](https://arxiv.org/abs/2007.12928)
Image Source: [https://seungjunnah.github.io/Datasets/reds.html](https://seungjunnah.github.io/Datasets/reds.html) | Provide a detailed description of the following dataset: REDS |
nuScenes | The **nuScenes** dataset is a large-scale autonomous driving dataset. The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore. Each scene is 20 seconds long and annotated at 2Hz. This results in a total of 28130 samples for training, 6019 samples for validation and 6008 samples for testing. The dataset has the full autonomous vehicle data suite: 32-beam LiDAR, 6 cameras and radars with complete 360° coverage. The 3D object detection challenge evaluates the performance on 10 classes: cars, trucks, buses, trailers, construction vehicles, pedestrians, motorcycles, bicycles, traffic cones and barriers. | Provide a detailed description of the following dataset: nuScenes |
Sleep-EDF | The sleep-edf database contains 197 whole-night PolySomnoGraphic sleep recordings, containing EEG, EOG, chin EMG, and event markers. Some records also contain respiration and body temperature. Corresponding hypnograms (sleep patterns) were manually scored by well-trained technicians according to the Rechtschaffen and Kales manual, and are also available. | Provide a detailed description of the following dataset: Sleep-EDF |
CommonsenseQA | The **CommonsenseQA** is a dataset for commonsense question answering task. The dataset consists of 12,247 questions with 5 choices each.
The dataset was generated by Amazon Mechanical Turk workers in the following process (an example is provided in parentheses):
1. a crowd worker observes a source concept from ConceptNet (“River”) and three target concepts (“Waterfall”, “Bridge”, “Valley”) that are all related by the same ConceptNet relation (“AtLocation”),
2. the worker authors three questions, one per target concept, such that only that particular target concept is the answer, while the other two distractor concepts are not, (“Where on a river can you hold a cup upright to catch water on a sunny day?”, “Where can I stand on a river to see water falling without getting wet?”, “I’m crossing the river, my feet are wet but my body is dry, where am I?”)
3. for each question, another worker chooses one additional distractor from Concept Net (“pebble”, “stream”, “bank”), and the author another distractor (“mountain”, “bottom”, “island”) manually. | Provide a detailed description of the following dataset: CommonsenseQA |
3DPW | The **3D Poses in the Wild dataset** is the first dataset in the wild with accurate 3D poses for evaluation. While other datasets outdoors exist, they are all restricted to a small recording volume. 3DPW is the first one that includes video footage taken from a moving phone camera.
The dataset includes:
* 60 video sequences.
* 2D pose annotations.
* 3D poses obtained with the method introduced in the paper.
* Camera poses for every frame in the sequences.
* 3D body scans and 3D people models (re-poseable and re-shapeable). Each sequence contains its corresponding models.
* 18 3D models in different clothing variations. | Provide a detailed description of the following dataset: 3DPW |
VOT2019 | **VOT2019** is a Visual Object Tracking benchmark for short-term tracking in RGB.
Source: [https://www.votchallenge.net/vot2019/dataset.html](https://www.votchallenge.net/vot2019/dataset.html)
Image Source: [https://www.votchallenge.net/vot2019/dataset.html](https://www.votchallenge.net/vot2019/dataset.html) | Provide a detailed description of the following dataset: VOT2019 |
MUSDB18 | The **MUSDB18** is a dataset of 150 full lengths music tracks (~10h duration) of different genres along with their isolated drums, bass, vocals and others stems.
The dataset is split into training and test sets with 100 and 50 songs, respectively. All signals are stereophonic and encoded at 44.1kHz. | Provide a detailed description of the following dataset: MUSDB18 |
PTB Diagnostic ECG Database | The ECGs in this collection were obtained using a non-commercial, PTB prototype recorder with the following specifications:
16 input channels, (14 for ECGs, 1 for respiration, 1 for line voltage)
Input voltage: ±16 mV, compensated offset voltage up to ± 300 mV
Input resistance: 100 Ω (DC)
Resolution: 16 bit with 0.5 μV/LSB (2000 A/D units per mV)
Bandwidth: 0 - 1 kHz (synchronous sampling of all channels)
Noise voltage: max. 10 μV (pp), respectively 3 μV (RMS) with input short circuit
Online recording of skin resistance
Noise level recording during signal collection
The database contains 549 records from 290 subjects (aged 17 to 87, mean 57.2; 209 men, mean age 55.5, and 81 women, mean age 61.6; ages were not recorded for 1 female and 14 male subjects). Each subject is represented by one to five records. There are no subjects numbered 124, 132, 134, or 161. Each record includes 15 simultaneously measured signals: the conventional 12 leads (i, ii, iii, avr, avl, avf, v1, v2, v3, v4, v5, v6) together with the 3 Frank lead ECGs (vx, vy, vz). Each signal is digitized at 1000 samples per second, with 16 bit resolution over a range of ± 16.384 mV. On special request to the contributors of the database, recordings may be available at sampling rates up to 10 KHz.
Within the header (.hea) file of most of these ECG records is a detailed clinical summary, including age, gender, diagnosis, and where applicable, data on medical history, medication and interventions, coronary artery pathology, ventriculography, echocardiography, and hemodynamics. The clinical summary is not available for 22 subjects. | Provide a detailed description of the following dataset: PTB Diagnostic ECG Database |
BoolQ | **BoolQ** is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally occurring – they are generated in unprompted and unconstrained settings.
Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
Questions are gathered from anonymized, aggregated queries to the Google search engine. Queries that are likely to be yes/no questions are heuristically identified and questions are only kept if a Wikipedia page is returned as one of the first five results, in which case the question and Wikipedia page are given to a human annotator for further processing. Annotators label question/article pairs in a three-step process. First, they decide if the question is good, meaning it is comprehensible, unambiguous, and requesting factual information. This judgment is made before the annotator sees the Wikipedia page. Next, for good questions, annotators find a passage within the document that contains enough information to answer the question. Annotators can mark questions as “not answerable” if the Wikipedia article does not contain the requested information. Finally, annotators mark whether the question’s answer is “yes” or “no”. Only questions that were marked as having a yes/no answer are used, and each question is paired with the selected passage instead of the entire document. | Provide a detailed description of the following dataset: BoolQ |
COPA | The Choice Of Plausible Alternatives (**COPA**) evaluation provides researchers with a tool for assessing progress in open-domain commonsense causal reasoning. COPA consists of 1000 questions, split equally into development and test sets of 500 questions each. Each question is composed of a premise and two alternatives, where the task is to select the alternative that more plausibly has a causal relation with the premise. The correct alternative is randomized so that the expected performance of randomly guessing is 50%. | Provide a detailed description of the following dataset: COPA |
ReCoRD | **Reading Comprehension with Commonsense Reasoning Dataset** (ReCoRD) is a large-scale reading comprehension dataset which requires commonsense reasoning. ReCoRD consists of queries automatically generated from CNN/Daily Mail news articles; the answer to each query is a text span from a summarizing passage of the corresponding news. The goal of ReCoRD is to evaluate a machine's ability of commonsense reasoning in reading comprehension. ReCoRD is pronounced as [ˈrɛkərd].
Image Source: [Zhang et al](https://arxiv.org/pdf/1810.12885v1.pdf) | Provide a detailed description of the following dataset: ReCoRD |
LIDC-IDRI | The **LIDC-IDRI** dataset contains lesion annotations from four experienced thoracic radiologists. LIDC-IDRI contains 1,018 low-dose lung CTs from 1010 lung patients. | Provide a detailed description of the following dataset: LIDC-IDRI |
ORL | The **ORL** Database of Faces contains 400 images from 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement). The size of each image is 92x112 pixels, with 256 grey levels per pixel. | Provide a detailed description of the following dataset: ORL |
EgoGesture | The **EgoGesture** dataset contains 2,081 RGB-D videos, 24,161 gesture samples and 2,953,224 frames from 50 distinct subjects. | Provide a detailed description of the following dataset: EgoGesture |
Street Scene | **Street Scene** is a dataset for video anomaly detection. Street Scene consists of 46 training and 35 testing high resolution 1280×720 video sequences taken from a USB camera overlooking a scene of a two-lane street with bike lanes and pedestrian sidewalks during daytime. The dataset is challenging because of the variety of activity taking place such as cars driving, turning, stopping and parking; pedestrians walking, jogging and pushing strollers; and bikers riding in bike lanes. In addition the videos contain changing shadows, moving background such as a flag and trees blowing in the wind, and occlusions caused by trees and large vehicles. There are a total of 56,847 frames for training and 146,410 frames for testing, extracted from the original videos at 15 frames per second. The dataset contains a total of 205 naturally occurring anomalous events ranging from illegal activities such as jaywalking and illegal U-turns to simply those that do not occur in the training set such as pets being walked and a metermaid ticketing a car. | Provide a detailed description of the following dataset: Street Scene |
PH2 | The increasing incidence of melanoma has recently promoted the development of computer-aided diagnosis systems for the classification of dermoscopic images. The PH² dataset has been developed for research and benchmarking purposes, in order to facilitate comparative studies on both segmentation and classification algorithms of dermoscopic images. PH² is a dermoscopic image database acquired at the Dermatology Service of Hospital Pedro Hispano, Matosinhos, Portugal. | Provide a detailed description of the following dataset: PH2 |
FSNS - Test | Arabic handwriting dataset. | Provide a detailed description of the following dataset: FSNS - Test |
Materials Project | The **Materials Project** is a collection of chemical compounds labelled with different attributes. The labelling is performed by different simulations, most of them at DFT level of theory.
The dataset links:
* [MP 2018.6.1](https://github.com/materialsvirtuallab/megnet/tree/master/mvl_models/mp-2018.6.1) (69,239 materials)
* [MP 2019.4.1](https://github.com/materialsvirtuallab/megnet/tree/master/mvl_models/mp-2019.4.1) (133,420 materials) | Provide a detailed description of the following dataset: Materials Project |
Semantic3D | **Semantic3D** is a point cloud dataset of scanned outdoor scenes with over 3 billion points. It contains 15 training and 15 test scenes annotated with 8 class labels. This large labelled 3D point cloud data set of natural covers a range of diverse urban scenes: churches, streets, railroad tracks, squares, villages, soccer fields, castles to name just a few. The point clouds provided are scanned statically with state-of-the-art equipment and contain very fine details. | Provide a detailed description of the following dataset: Semantic3D |
SemanticKITTI | **SemanticKITTI** is a large-scale outdoor-scene dataset for point cloud semantic segmentation. It is derived from the KITTI Vision Odometry Benchmark which it extends with dense point-wise annotations for the complete 360 field-of-view of the employed automotive LiDAR. The dataset consists of 22 sequences. Overall, the dataset provides 23201 point clouds for training and 20351 for testing. | Provide a detailed description of the following dataset: SemanticKITTI |
Wine | These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.
Source: [UCI Machine Learning Repository Wine Dataset](https://archive.ics.uci.edu/ml/datasets/Wine)
Image Source: [https://archive.ics.uci.edu/ml/datasets/Wine](https://archive.ics.uci.edu/ml/datasets/Wine) | Provide a detailed description of the following dataset: Wine |
JSB Chorales | The **JSB** chorales are a set of short, four-voice pieces of music well-noted for their stylistic homogeneity. The chorales were originally composed by Johann Sebastian Bach in the
18th century. He wrote them by first taking pre-existing melodies from contemporary Lutheran hymns and then harmonising them to create the parts for the remaining
three voices. The version of the dataset used canonically in representation learning contexts consists of 382 such chorales, with a train/validation/test split of 229, 76 and 77 samples respectively. | Provide a detailed description of the following dataset: JSB Chorales |
Tiny ImageNet | **Tiny ImageNet** contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images and 50 test images. | Provide a detailed description of the following dataset: Tiny ImageNet |
AFHQ | Animal FacesHQ (AFHQ) is a dataset of animal faces consisting of 15,000 high-quality images at 512 × 512 resolution. The dataset includes three domains of cat, dog, and wildlife, each providing 5000 images. By having multiple (three) domains and diverse images of various
breeds (≥ eight) per each domain, AFHQ sets a more challenging image-to-image translation problem.
All images are vertically and horizontally aligned to have the eyes at the center. The low-quality images were discarded by human effort. | Provide a detailed description of the following dataset: AFHQ |
FSS-1000 | **FSS-1000** is a 1000 class dataset for few-shot segmentation. The dataset contains significant number of objects that have never been seen or annotated in previous datasets, such as tiny daily objects, merchandise, cartoon characters, logos, etc.
Source: [https://github.com/HKUSTCV/FSS-1000](https://github.com/HKUSTCV/FSS-1000)
Image Source: [https://github.com/HKUSTCV/FSS-1000](https://github.com/HKUSTCV/FSS-1000) | Provide a detailed description of the following dataset: FSS-1000 |
Reddit | The **Reddit** dataset is a graph dataset from Reddit posts made in the month of September, 2014. The node label in this case is the community, or “subreddit”, that a post belongs to. 50 large communities have been sampled to build a post-to-post graph, connecting posts if the same user comments on both. In total this dataset contains 232,965 posts with an average degree of 492. The first 20 days are used for training and the remaining days for testing (with 30% used for validation). For features, off-the-shelf 300-dimensional GloVe CommonCrawl word vectors are used. | Provide a detailed description of the following dataset: Reddit |
DeepFashion | **DeepFashion** is a dataset containing around 800K diverse fashion images with their rich annotations (46 categories, 1,000 descriptive attributes, bounding boxes and landmark information) ranging from well-posed product images to real-world-like consumer photos. | Provide a detailed description of the following dataset: DeepFashion |
FER2013 | Fer2013 contains approximately 30,000 facial RGB images of different expressions with size restricted to 48×48, and the main labels of it can be divided into 7 types: 0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral. The Disgust expression has the minimal number of images – 600, while other labels have nearly 5,000 samples each. | Provide a detailed description of the following dataset: FER2013 |
Pinterest | The **Pinterest** dataset contains more than 1 million images associated to Pinterest users’ who have “pinned” them.
Source: [https://openaccess.thecvf.com/content_iccv_2015/papers/Geng_Learning_Image_and_ICCV_2015_paper.pdf](https://openaccess.thecvf.com/content_iccv_2015/papers/Geng_Learning_Image_and_ICCV_2015_paper.pdf) | Provide a detailed description of the following dataset: Pinterest |
LOL | The **LOL** dataset is composed of 500 low-light and normal-light image pairs and divided into 485 training pairs and 15 testing pairs. The low-light images contain noise produced during the photo capture process. Most of the images are indoor scenes. All the images have a resolution of 400×600. | Provide a detailed description of the following dataset: LOL |
HRF | The **HRF** dataset is a dataset for retinal vessel segmentation which comprises 45 images and is organized as 15 subsets. Each subset contains one healthy fundus image, one image of patient with diabetic retinopathy and one glaucoma image. The image sizes are 3,304 x 2,336, with a training/testing image split of 22/23. | Provide a detailed description of the following dataset: HRF |
DBRD | The DBRD (pronounced dee-bird) dataset contains over 110k book reviews along with associated binary sentiment polarity labels. It is greatly influenced by the Large Movie Review Dataset and intended as a benchmark for sentiment classification in Dutch. | Provide a detailed description of the following dataset: DBRD |
Kaggle-Credit Card Fraud Dataset | The dataset contains transactions made by credit cards in September 2013 by European cardholders.
This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependent cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification. | Provide a detailed description of the following dataset: Kaggle-Credit Card Fraud Dataset |
Thyroid | **Thyroid** is a dataset for detection of thyroid diseases, in which patients diagnosed with hypothyroid or subnormal are anomalies against normal patients. It contains 2800 training data instance and 972 test instances, with 29 or so attributes. | Provide a detailed description of the following dataset: Thyroid |
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