dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
DND | Benchmarking Denoising Algorithms with Real Photographs
This dataset consists of 50 pairs of noisy and (nearly) noise-free images captured with four consumer cameras. Since the images are of very high-resolution, the providers extract 20 crops of size 512 × 512 from each image, thus yielding a total of 1000 patches. | Provide a detailed description of the following dataset: DND |
Cityscapes-VPS | Cityscapes-VPS is a video extension of the Cityscapes validation split. It provides 2500-frame panoptic labels that temporally extend the 500 Cityscapes image-panoptic labels. There are total 3000-frame panoptic labels which correspond to 5, 10, 15, 20, 25, and 30th frames of each 500 videos, where all instance ids are associated over time. It not only supports video panoptic segmentation (VPS) task, but also provides super-set annotations for video semantic segmentation (VSS) and video instance segmentation (VIS) tasks. | Provide a detailed description of the following dataset: Cityscapes-VPS |
METU Trademark | The METU Trademark Dataset is a large dataset (the largest publicly available logo dataset as of 2014, and the largest one not requiring any preprocessing as of 2017), which is composed of more than 900K real logos belonging to real companies worldwide. The dataset also includes query sets of varying difficulties, allowing Trademark Retrieval researchers to benchmark their methods against other methods to progress the field. | Provide a detailed description of the following dataset: METU Trademark |
PS-Plant dataset | Automated leaf segmentation is a challenging area in computer vision. Recent advances in machine learning approaches allowed to achieve better results than traditional image processing techniques; however, training such systems often require large annotated data sets. To contribute with annotated data sets and help to overcome this bottleneck in plant phenotyping research, here we provide a novel photometric stereo (PS) data set with annotated leaf masks. This data set forms part of the work done in the BBSRC Tools and Resources Development project BB/N02334X/1. | Provide a detailed description of the following dataset: PS-Plant dataset |
Real SVBRDF | A total of 80 real material samples were captured in a dark room. For each material, multiple captures were collected at different distances from the camera (between 250 and 650 mm) to observe both macro- and micro-level details. The dataset is mostly comprised of planar specimens but also includes non-planar objects such as mugs, globes, crumpled paper, etc. As shown above, it contains a rich diversity of materials, including diffuse or specular wrapping papers, fabrics, anisotropic metals, plastics, rugs, ceramic and wood flooring samples, etc. Each capture set includes 12 LDR (8 bpp) RGB-D images at 4K pixel resolution. Each set is captured at 50% and 100% of maximum light intensity. In total, we captured 462 such image sets (combinations of light intensities, distances to the camera, and material sample). | Provide a detailed description of the following dataset: Real SVBRDF |
MLPF | Dataset of 50,000 top quark-antiquark (ttbar) events produced in proton-proton collisions at 14 TeV, overlaid with minimum bias events corresponding to a pileup of 200 on average. The dataset consists of detector hits as the input, generator particles as the ground truth and reconstructed particles from DELPHES for additional validation.
The DELPHES model corresponds to a CMS-like detector with a multi-layered charged particle tracker, an electromagnetic and hadron calorimeter. Pythia8 and Delphes3 were used for the simulation.
Each file contains a bzip2-compressed python pickle with the following contents:
```
> data = pickle.load(bz2.BZ2File("out/pythia8_ttbar/tev14_pythia8_ttbar_0_0.pkl.bz2", "rb"))
# Each file contains lists of arrays X (detector elements), ygen (generator particles) and ycand (rule-based PF particles from Delphes) for 100 events
> len(data["ycand"]), len(data["ygen"]), len(data["X"])
100, 100, 100
#Each element in the list corresponds to an event. The first event in the file contains 5992 detector elements, ygen and ycand are 0-padded to the same length as X
> data["X"][0].shape, data["ygen"][0].shape, data["ycand"][0].shape,
((5992, 12), (5992, 7), (5992, 7))
# The X rows are detector elements: calorimeter towers and tracks with the following 12-features (0-padded)
# tower: [type==1, Et (GeV), eta, sin phi, cos phi, E (GeV), Eem (GeV), Ehad (GeV), 0, 0, 0, 0]
# track: [type==2, pt (GeV), eta, sin phi, cos phi, P (GeV), eta_outer, sin phi_outer, cos phi_outer, charge, is_gen_muon, is_gen_electron]
# The ygen (ycand) rows are generator-level truth particles (rule-based PF particles from Delphes) with the following features:
# [pid, charge, pt (GeV), eta, sin phi, cos phi, E (GeV)]
# pid==0: placeholder/padding entry
# pid==1: charged hadrons
# pid==2: neutral hadrons
# pid==3: photons
# pid==4: electrons
# pid==5: muons
``` | Provide a detailed description of the following dataset: MLPF |
EXPLICIT 3D CHANGE DETECTION USING RAY-TRACING IN SPHERICAL COORDINATES | Real and simulated lidar data of indoor and outdoor scenes, before and after geometric scene changes have occurred. Data include lidar scans from multiple viewpoints with provided coordinate transforms, and manually annotated ground-truth regarding which parts of the scene have changed between subsequent scans. | Provide a detailed description of the following dataset: EXPLICIT 3D CHANGE DETECTION USING RAY-TRACING IN SPHERICAL COORDINATES |
ACFR Orchard Fruit Dataset | ACFR Orchard Fruit Dataset is an agricultural dataset containing images and annotations for different fruits, collected at different farms across Australia. The dataset was gathered by the agriculture team at the Australian Centre for Field Robotics, The University of Sydney, Australia. | Provide a detailed description of the following dataset: ACFR Orchard Fruit Dataset |
University of Washington/Northwestern University (UW/NU) Corpus | The University of Washington/Northwestern University (UW/NU) Corpus contains recordings and textgrids of Pacific Northwest and Northern Cities speakers reading a subset of the IEEE "Harvard" sentences. The UW/NU Corpus Version 1.0 has been used to study the effects of dialectal variation on speech intelligibility, while version 2.0 is being used in ongoing research in speech intelligibility and gender interaction. Development is supported by the National Institutes of Health, National Institute on Deafness and Other Communication Disorders grant R01-DC006014. The PN/NC Corpus is well suited for both clinical and research studies where high-fidelity recordings and regional accent control are desirable. | Provide a detailed description of the following dataset: University of Washington/Northwestern University (UW/NU) Corpus |
UNITOPATHO | Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images. We introduce UniToPatho, an annotated dataset of 9536 hematoxylin and eosin stained patches extracted from 292 whole-slide images, meant for training deep neural networks for colorectal polyps classification and adenomas grading. The slides are acquired through a Hamamatsu Nanozoomer S210 scanner at 20× magnification (0.4415 μm/px) | Provide a detailed description of the following dataset: UNITOPATHO |
WiC-TSV | WiC-TSV is a new multi-domain evaluation benchmark for Word Sense Disambiguation. More specifically, it is a framework for Target Sense Verification of Words in Context which grounds its uniqueness in the formulation as a binary classification task thus being independent of external sense inventories, and the coverage of various domains. This makes the dataset highly flexible for the evaluation of a diverse set of models and systems in and across domains. WiC-TSV provides three different evaluation settings, depending on the input signals provided to the model. | Provide a detailed description of the following dataset: WiC-TSV |
Clinical Admission Notes from MIMIC-III | This dataset is created from **MIMIC-III** ([Medical Information Mart for Intensive Care III](https://paperswithcode.com/dataset/mimic-iii)) and contains simulated patient admission notes. The clinical notes contain information about a patient at **admission time** to the ICU and are labelled for four outcome prediction tasks: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay.
To obtain the data one first has to [gain access](https://mimic.physionet.org/gettingstarted/access/) to the MIMIC-III dataset and then run the scripts introduced in the linked repository. | Provide a detailed description of the following dataset: Clinical Admission Notes from MIMIC-III |
IBC | The Individual Brain Charting (IBC) project aims at providing a new generation of functional-brain atlases. To map cognitive mechanisms in a fine scale, task-fMRI data at high-spatial-resolution are being acquired on a fixed cohort of 12 participants, while performing many different tasks. These data—free from both inter-subject and inter-site variability—are publicly available as means to support the investigation of functional segregation and connectivity as well as individual variability with a view to establishing a better link between brain systems and behavior.
** What’s special about the IBC dataset?**
* Taskwise dataset: spanning the cognitive spectrum within subject
* Fixed cohort over a 10-year span to minimize inter-subject variability
* Fixed experimental setting to minimize inter-site variability
* Multimodal: fMRI (task-based and resting state), DWI, structural
** Main characteristics **
* 12 healthy participants (aged 27-40 at the time of recruitment)
* Spatial resolution: 1.5mm (isotropic); Temporal resolution: 2s
* Scanner: Siemens 3T Magnetom Prisma; Coil: 64-channel
* 50 acquisitions per participant upon completion of the dataset in 2022 | Provide a detailed description of the following dataset: IBC |
MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge | Under Institutional Review Board (IRB) supervision, 50 abdomen CT scans of were randomly selected from a combination of an ongoing colorectal cancer chemotherapy trial, and a retrospective ventral hernia study. The 50 scans were captured during portal venous contrast phase with variable volume sizes (512 x 512 x 85 - 512 x 512 x 198) and field of views (approx. 280 x 280 x 280 mm3 - 500 x 500 x 650 mm3). The in-plane resolution varies from 0.54 x 0.54 mm2 to 0.98 x 0.98 mm2, while the slice thickness ranges from 2.5 mm to 5.0 mm. The standard registration data was generated by NiftyReg. | Provide a detailed description of the following dataset: MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge |
Alchemy | The DeepMind Alchemy environment is a meta-reinforcement learning benchmark that presents tasks sampled from a task distribution with deep underlying structure. It was created to test for the ability of agents to reason and plan via latent state inference, as well as useful exploration and experimentation.
Alchemy is a single-player video game, implemented in Unity. The player sees a first-person view of a table with a number of objects on it, including a set of colored stones, a set of dishes containing colored potions, and a central cauldron. Stones have different point values, and points are collected when stones are added to the cauldron. By dipping stones into the potions, the player can transform the stones’ appearance, and thus their value, increasing the number of points that can be won. | Provide a detailed description of the following dataset: Alchemy |
Biase et al | Source: [Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell RNA sequencing](https://pubmed.ncbi.nlm.nih.gov/25096407/) | Provide a detailed description of the following dataset: Biase et al |
Goolam et al | Source: [Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4819611/) | Provide a detailed description of the following dataset: Goolam et al |
Yan et al | Source: [Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells](https://pubmed.ncbi.nlm.nih.gov/23934149/) | Provide a detailed description of the following dataset: Yan et al |
Deng et al | Source: [Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells](https://pubmed.ncbi.nlm.nih.gov/24408435/) | Provide a detailed description of the following dataset: Deng et al |
Pollen et al | TPM values together with cell type annotations that were obtained from Alex Pollen on 15/10/15 | Provide a detailed description of the following dataset: Pollen et al |
Treutlein et al | Source: [Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq](https://pubmed.ncbi.nlm.nih.gov/24739965/) | Provide a detailed description of the following dataset: Treutlein et al |
Synthetic Rain Datasets | The Synthetic Rain Datasets consists of 13,712 clean-rain image pairs gathered from multiple datasets (Rain14000, Rain1800, Rain800, Rain12). With a single trained model, evaluation could be performed on various test sets, including Rain100H, Rain100L, Test100, Test2800, and Test1200.
PSNR and SSIM are computed on Y-channel in YCbCr color space. | Provide a detailed description of the following dataset: Synthetic Rain Datasets |
EPIC-KITCHENS-100 | This paper introduces the pipeline to scale the largest dataset in egocentric vision EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version (EPIC-KITCHENS-55), EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection also enables evaluating the "test of time" - i.e. whether models trained on data collected in 2018 can generalise to new footage collected under the same hypotheses albeit "two years on".
The dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics. | Provide a detailed description of the following dataset: EPIC-KITCHENS-100 |
UAVDT | UAVDT is a large scale challenging UAV Detection and Tracking benchmark (i.e., about 80, 000 representative frames from 10 hours raw videos) for 3 important fundamental tasks, i.e., object DETection
(DET), Single Object Tracking (SOT) and Multiple Object Tracking (MOT).
The dataset is captured by UAVs in various complex scenarios. The objects of
interest in this benchmark are vehicles. The frames are manually annotated with bounding boxes and some useful attributes, e.g., vehicle category and occlusion.
The UAVDT benchmark consists of 100 video sequences, which are selected
from over 10 hours of videos taken with an UAV platform at a number of locations in urban areas, representing various common scenes including squares, arterial streets, toll stations, highways, crossings and T-junctions. The videos
are recorded at 30 frames per seconds (fps), with the JPEG image resolution of 1080 × 540 pixels. | Provide a detailed description of the following dataset: UAVDT |
The Contextual TV Dataset | Using the Experience-Sampling Method (ESM), participants are asked to report TV consumption multiple times each day for a five week period. Through self-reported data, authors decrease uncertainty of exposure to content, and allow collection of non-trivial information, such as how much attention is paid to the TV. The data is structured to accommodate quantitative analyses, e.g. in the CARS community, and is publicly available under the name **Contextual TV (CTV)** dataset. | Provide a detailed description of the following dataset: The Contextual TV Dataset |
twitter politicians data | Dataset based on Twitter usernames of American politicians. Data extracted from Wikidata.
The same politician can appear several times: if he has different pseudonyms on Twitter or Instagram, if he has been in several parties, or if several Twitter account IDs are associated with him. But the data is sorted in ascending order by name, so it is visible | Provide a detailed description of the following dataset: twitter politicians data |
CoNLL-2012 | The CoNLL-2012 shared task involved predicting coreference in English, Chinese, and Arabic, using the final version, v5.0, of the OntoNotes corpus. It was a follow-on to the English-only task organized in 2011. | Provide a detailed description of the following dataset: CoNLL-2012 |
3D Platelet EM | The platelet-em dataset contains two 3D scanning electron microscope (EM) images of human platelets, as well as instance and semantic segmentations of those two image volumes.
This data has been reviewed by NIBIB, contains no PII or PHI, and is cleared for public release. All files use a multipage uint16 TIF format. A 3D image with size [Z, X, Y] is saved as Z pages of size [X, Y]. Image voxels are approximately 40x10x10 nm | Provide a detailed description of the following dataset: 3D Platelet EM |
Sintel 4D LFV | A medium-scale synthetic 4D Light Field video dataset for depth (disparity) estimation. From the open-source movie Sintel. The dataset consists of 24 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20–50 frames, and has ground-truth disparity values, so that can be used for training deep learning-based methods. Each scene was rendered with a clean pass after modifying the production file of Sintel with reference to the MPI Sintel dataset. | Provide a detailed description of the following dataset: Sintel 4D LFV |
Bee4Exp Honeybee Detection | A dataset for flying honeybee detection introduced in ["A Method for Detection of Small Moving Objects in UAV Videos"](https://www.mdpi.com/2072-4292/13/4/653).
This dataset consists of three videos with flying honeybees in a natural environment. | Provide a detailed description of the following dataset: Bee4Exp Honeybee Detection |
MHSMA | The MHSMA dataset is a collection of human sperm images from 235 patients with male factor infertility. Each image is labeled by experts for normal or abnormal sperm acrosome, head, vacuole, and tail.
The training, validation, and test sets contain 1000, 240, and 300 images, respectively.
Images are available in two different crop sizes: 128x128- and 64x64-pixel.
Paper: [A novel deep learning method for automatic assessment of human sperm images](https://doi.org/10.1016/j.compbiomed.2019.04.030) | Provide a detailed description of the following dataset: MHSMA |
VITON | VITON was a dataset for virtual try-on of clothing items. It consisted of 16,253 pairs of images of a person and a clothing item .
The authors have removed the dataset and it is no longer publicly available due to copyright issues. | Provide a detailed description of the following dataset: VITON |
Metric-Type of Numerical Tables | **Metric-Type of Numerical Tables** is a dataset extracted from scientific papers (ACL anthology website) consisting of header tables, captions, and metric-types.
Image source: [Suadaa et al.](https://arxiv.org/pdf/2102.00819v1.pdf) | Provide a detailed description of the following dataset: Metric-Type of Numerical Tables |
Deeply vocal characterizer | Deeply vocal characterizer is a human nonverbal vocalization dataset. This sample dataset consists of about 0.6 hours(56.7 hours in the full set) of audio(16 kHz, 16-bit, mono) across 16 human nonverbal vocalization classes, including throat-clearing, coughing, laughing, panting, and etc. The audio contents are crowdsourced by the general public of South Korea.
The dataset is a subset(approximately 1%) of a much bigger dataset which were recorded under the same circumstances as these open-source datasets. Please contact us(contact@deeplyinc.com) for the full set with the research/commercial license. | Provide a detailed description of the following dataset: Deeply vocal characterizer |
Deeply Korean read speech | Deeply Korean read speech corpus contains pairs of Korean speakers reading a script with *__3 distinct text sentiments (negative, neutral, positive)__*, with *__3 distinct voice sentiments (negative, neutral, positive)__*, are recorded. The recordings took place in *__3 different types of places__*, which are *an anechoic chamber, studio apartment, and dance studio*, of which the level of reverberation differs. And in order to examine the effect of the distance of mic from the source and device, every experiment is recorded at *__3 distinct distances__* with *__2 types of smartphone__*, *iPhone X, and Galaxy S7*.
This sample dataset consists of about 3 hours(290 hours in the full set) of audio(16 kHz, 16-bit, mono), and one pair of speakers. The dataset is a subset(approximately 1%) of a much bigger dataset which were recorded under the same circumstances as these open-source datasets. Please contact us(contact@deeplyinc.com) for the full set with the research/commercial license. | Provide a detailed description of the following dataset: Deeply Korean read speech |
Deeply Parent-Child vocal interaction | Deeply Parent-Child Vocal Interaction contains the interaction of 24 pairs of parent and child(total 48 speakers), such as *__reading fairy tales, singing children’s songs, conversing, and others__*, is recorded. The recordings took place in *__3 different types of places__*, which are *an anechoic chamber, studio apartment, and dance studio*, of which the level of reverberation differs. And in order to examine the effect of the distance of mic from the source and device, every experiment is recorded at *__3 distinct distances)__* with *__2 types of smartphone__*, *iPhone X, and Galaxy S7*.
This sample dataset consists of about 3 hours(282 hours in the full set) of audio(16 kHz, 16-bit, mono), and one pair of speakers. The dataset is a subset(approximately 1%) of a much bigger dataset which were recorded under the same circumstances as these open-source datasets. Please contact us(contact@deeplyinc.com) for the full set with the research/commercial license. | Provide a detailed description of the following dataset: Deeply Parent-Child vocal interaction |
Lesion Boundary Segmentation Dataset | Lesion Boundary Segmentation Dataset is a dataset for lesion segmentation from the ISIC2018 challenge. The dataset contains skin lesions and their corresponding annotations.
Image source :[]() | Provide a detailed description of the following dataset: Lesion Boundary Segmentation Dataset |
HOC | The **Hallmarks of Cancer** (**HOC*) corpus consists of 1852 PubMed publication abstracts manually annotated by experts according to the Hallmarks of Cancer taxonomy. The taxonomy consists of 37 classes in a hierarchy. Zero or more class labels are assigned to each sentence in the corpus. | Provide a detailed description of the following dataset: HOC |
MIT-BIH AFDB | This database includes 25 long-term ECG recordings of human subjects with atrial fibrillation (mostly paroxysmal).
Of these, 23 records include the two ECG signals (in the .dat files); records 00735 and 03665 are represented only by the rhythm (.atr) and unaudited beat (.qrs annotation files.
The individual recordings are each 10 hours in duration, and contain two ECG signals each sampled at 250 samples per second with 12-bit resolution over a range of ±10 millivolts. The original analog recordings were made at Boston's Beth Israel Hospital (now the Beth Israel Deaconess Medical Center) using ambulatory ECG recorders with a typical recording bandwidth of approximately 0.1 Hz to 40 Hz. The rhythm annotation files (with the suffix .atr) were prepared manually; these contain rhythm annotations of types (AFIB (atrial fibrillation), (AFL (atrial flutter), (J (AV junctional rhythm), and (N (used to indicate all other rhythms). (The original rhythm annotation files, still available in the old directory, used AF, AFL, J, and N to mark these rhythms; the atr annotations in this directory have been revised for consistency with those used for the MIT-BIH Arrhythmia Database.) Beat annotation files (with the suffix .qrs) were prepared using an automated detector and have not been corrected manually. For some records, manually corrected beat annotation files (with the suffix .qrsc) are available. (The .qrs annotations may be useful for studies of methods for automated AF detection, where such methods must be robust with respect to typical QRS detection errors. The .qrsc annotations may be preferred for basic studies of AF itself, where QRS detection errors would be confounding.) Note that in both .qrs and .qrsc files, no distinction is made among beat types (all beats are labelled as if normal). | Provide a detailed description of the following dataset: MIT-BIH AFDB |
ARC-DA | **ARC Direct Answer Questions** (**ARC-DA**) dataset consists of 2,985 grade-school level, direct-answer ("open response", "free form") science questions derived from the ARC multiple-choice question set released as part of the AI2 Reasoning Challenge in 2018.
### How the dataset was built
These questions were derived from the ARC multiple-choice question set released as part of the AI2 Reasoning Challenge in 2018. The ARC Easy and ARC Challenge set questions in the original dataset were combined and then filtered/modified by the following process:
- Turking: Each of the multiple-choice questions was presented as a direct answer question to five crowdsourced workers to gather additional answers.
- Heuristic filtering: The questions were filtered based on the following heuristic filters:
- Questions having a threshold number of turker answers, as a proxy for concreteness of the question.
- Questions having at least two turker-provided answers with word overlap, as a measure of confidence in the correctness of the answers, and also straightforwardness of the question.
- Other heuristics to identify questions that only make sense as multiple-choice questions, such as, questions starting with the phrase “Which of the following”.
- Further manual vetting: We had volunteers in house do another pass of vetting where they:
- Marked highly open-ended questions with too many answer choices, such as “Name an insect”, or otherwise invalid questions, for removal. These are filtered out.
- Removed some of the bad answers gathered from turking.
- Reworded questions to make them more suited to direct answer question format, for e.g., a question such as “What element is contained in table salt?” which would make sense as a multiple-choice question, needs be reworded to something like “Name an element present in table salt”.
- Added any additional answers to the questions they could think of that were not present in the turker provided answers.
Image source: [ARC-DA dataset](https://allenai.org/data/arc-da) | Provide a detailed description of the following dataset: ARC-DA |
Switchboard-1 Corpus | The Switchboard-1 Telephone Speech Corpus (LDC97S62) consists of approximately 260 hours of speech and was originally collected by Texas Instruments in 1990-1, under DARPA sponsorship. The first release of the corpus was published by NIST and distributed by the LDC in 1992-3.
Switchboard is a collection of about 2,400 two-sided telephone conversations among 543 speakers (302 male, 241 female) from all areas of the United States. A computer-driven robot operator system handled the calls, giving the caller appropriate recorded prompts, selecting and dialing another person (the callee) to take part in a conversation, introducing a topic for discussion and recording the speech from the two subjects into separate channels until the conversation was finished. About 70 topics were provided, of which about 50 were used frequently. Selection of topics and callees was constrained so that: (1) no two speakers would converse together more than once and (2) no one spoke more than once on a given topic. | Provide a detailed description of the following dataset: Switchboard-1 Corpus |
MRDA | The **MRDA** corpus consists of about 75 hours of speech from 75 naturally-occurring meetings among 53 speakers. The tagset used for labeling is a modified version of the SWBD-DAMSL tagset. It is annotated with three types of information: marking of the dialogue act segment boundaries, marking of the dialogue acts and marking of correspondences between dialogue acts.
Description from [NLP Progress](http://nlpprogress.com/english/dialogue.html) | Provide a detailed description of the following dataset: MRDA |
CLEVR-Humans | We collect a new dataset of human-posed free-form natural language questions about CLEVR images. Many of these questions have out-of-vocabulary words and require reasoning skills that are absent from our model’s repertoire | Provide a detailed description of the following dataset: CLEVR-Humans |
Funcom | Funcom is a collection of ~2.1 million Java methods and their associated Javadoc comments. This data set was derived from a set of 51 million Java methods and only includes methods that have an associated comment, comments that are in the English language, and has had auto-generated files removed. Each method/comment pair also has an associated method_uid and project_uid so that it is easy to group methods by their parent project.
This dataset of function pairs is used for source code summarisation. | Provide a detailed description of the following dataset: Funcom |
Synthetic and Real Apache Log Records | Each file contains a specific dataset described in the [paper](https://arxiv.org/abs/2102.06320) "On Automatic Parsing of Log Records". For example, `T_E.txt` contains the data for the dataset $T_E$.
In a file, each log string resides on a separate line and contains a 2-tuple separated by tab (`\t`). The first element of the tuple is the actual log string that has to be parsed. The second element is the corresponding “translation” specifying the field name for each of the characters of the first element. | Provide a detailed description of the following dataset: Synthetic and Real Apache Log Records |
COVID-19 Fake News Dataset | Along with COVID-19 pandemic we are also fighting an `infodemic'. Fake news and rumors are rampant on social media. Believing in rumors can cause significant harm. This is further exacerbated at the time of a pandemic. To tackle this, we curate and release a manually annotated dataset of 10,700 social media posts and articles of real and fake news on COVID-19. We benchmark the annotated dataset with four machine learning baselines - Decision Tree, Logistic Regression , Gradient Boost , and Support Vector Machine (SVM). We obtain the best performance of 93.46\% F1-score with SVM. | Provide a detailed description of the following dataset: COVID-19 Fake News Dataset |
Real Blur Dataset | The dataset consists of 4,738 pairs of images of 232 different scenes including reference pairs. All images were captured both in the camera raw and JPEG formats, hence generating two datasets: RealBlur-R from the raw images, and RealBlur-J from the JPEG images. Each training set consists of 3,758 image pairs, while each test set consists of 980 image pairs.
The deblurring result is first aligned to its ground truth sharp image using a homography estimated by the enhanced correlation coefficients method, and PSNR or SSIM is computed in sRGB color space. | Provide a detailed description of the following dataset: Real Blur Dataset |
IG-3.5B-17k | **IG-3.5B-17k** is an internal Facebook AI Research dataset for training image classification models. It consists of hashtags for up to 3.5 billion public Instagram images. | Provide a detailed description of the following dataset: IG-3.5B-17k |
IG-1B-Targeted | **IG-1B-Targeted** is an internal Facebook AI Research dataset that consists of 940 million public images with 1.5K hashtags matching with 1000 ImageNet1K synsets. | Provide a detailed description of the following dataset: IG-1B-Targeted |
DAGM2007 | This is a synthetic dataset for defect detection on textured surfaces. It was originally created for a competition at the 2007 symposium of the DAGM (Deutsche Arbeitsgemeinschaft für Mustererkennung e.V., the German chapter of the International Association for Pattern Recognition). The competition was hosted together with the GNSS (German Chapter of the European Neural Network Society).
After the competition, the dataset has been used as a test dataset in multiple projects and research papers. It is publicly available from the University of Heidelberg website (Heidelberg Collaboratory for Image Processing).
The data is artificially generated, but similar to real world problems. The first six out of ten datasets, denoted as development datasets, are supposed to be used for algorithm development. The remaining four datasets, which are referred to as competition datasets, can be used to evaluate the performance. Researchers should consider not using or analyzing the competition datasets before the development is completed as a code of honour. | Provide a detailed description of the following dataset: DAGM2007 |
DSTC 8 Track 2 | Dialog System Technology Challenges 8 (DSTC) Track 2 builds on the success of DSTC 7 Track 1 (NOESIS: Noetic End-to-End Response Selection Challenge). It proposes an extension of the task, incorporating new elements that are vital for the creation of a deployed task-oriented dialogue system. Specifically, three new dimensions are added to the challenge:
- Conversations with more than 2 participants
- Predicting whether a dialogue has solved the problem yet,
- Handling multiple simultaneous conversations. Each of these adds an exciting new dimension and brings the task closer to the creation of systems able to handle the complexity of real-world conversation.
- This challenge is offered with two goal oriented dialog datasets, used in 4 subtasks. | Provide a detailed description of the following dataset: DSTC 8 Track 2 |
Ubuntu Chat Corpus | The **Ubuntu Chat Corpus** (**UCC**) is composed of archived chat logs from Ubuntu's Internet Relay Chat technical support channels. Ubuntu uses IRC as one of many modes of technical support -- it offers real-time problem solving. The authors have taken some of the archived messages (which are in the public domain), reorganized the file structure, removed some unnecessary system messages, and compressed them to make it easier to obtain. | Provide a detailed description of the following dataset: Ubuntu Chat Corpus |
Liu et al. Corpus | The **Liu et al. Corpus** is a pretraining dataset for large language models. It consists of 160Gb of news, books, stories, and web text. | Provide a detailed description of the following dataset: Liu et al. Corpus |
OSCAR | **OSCAR** or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. The dataset used for training multilingual models such as BART incorporates 138 GB of text. | Provide a detailed description of the following dataset: OSCAR |
S-SOD | To validate the generalization abilities of SOD models, we create a small-scale dataset by collecting the most challenging images with varying brightness and contrast, background and foreground colors overlap, among many others. We conclude that the current models, including ours, are not trust-worthy for real-world practice, demanding extensive future research for more efficient and generalized SOD models. | Provide a detailed description of the following dataset: S-SOD |
Reddit Corpus | **Reddit Corpus** is part of a repository of conversational datasets consisting of hundreds of millions of examples, and a standardised evaluation procedure for conversational response selection models using '1-of-100 accuracy'. The Reddit Corpus contains 726 million multi-turn dialogues from the Reddit board. | Provide a detailed description of the following dataset: Reddit Corpus |
Advising Corpus | Advising Corpus is a dataset based on an entirely new collection of dialogues in which university students are being advised which classes to take. These were collected at the University of Michigan with IRB approval. They were released as part of DSTC 7 track 1 and used again in DSTC 8 track 2. | Provide a detailed description of the following dataset: Advising Corpus |
CCNet | CCNet is a dataset extracted from Common Crawl with a different filtering process than for OSCAR. It was built using a language model trained on Wikipedia, in order to filter out bad quality texts such as code or tables. CCNet contains longer documents on average compared to OSCAR with smaller—and often noisier—documents weeded out. | Provide a detailed description of the following dataset: CCNet |
French Wikipedia | **French Wikipedia** is a dataset used for pretraining the CamemBERT French language model. It uses the official 2019 French Wikipedia dumps | Provide a detailed description of the following dataset: French Wikipedia |
CEDAR Signature | CEDAR Signature is a database of off-line signatures for signature verification. Each of 55 individuals contributed 24 signatures thereby creating 1,320 genuine signatures. Some were asked to forge three other writers’ signatures, eight times
per subject, thus creating 1,320 forgeries. Each signature was scanned at 300 dpi gray-scale and binarized using a gray-scale histogram. Salt pepper noise removal and slant normalization were two steps involved in image preprocessing. The database has 24 genuines and 24 forgeries available for each writer. | Provide a detailed description of the following dataset: CEDAR Signature |
BanglaLekhaImageCaptions | This dataset consists of images and annotations in Bengali. The images are human annotated in Bengali by two adult native Bengali speakers. All popular image captioning datasets have a predominant western cultural bias with the annotations done in English. Using such datasets to train an image captioning system assumes that a good English to target language translation system exists and that the original dataset had elements of the target culture. Both these assumptions are false, leading to the need of a culturally relevant dataset in Bengali, to generate appropriate image captions of images relevant to the Bangladeshi and wider subcontinental context. The dataset presented consists of 9,154 images. | Provide a detailed description of the following dataset: BanglaLekhaImageCaptions |
Multi-Domain Sentiment Dataset v2.0 | The Multi-Domain Sentiment Dataset contains product reviews taken from Amazon.com from many product types (domains). Some domains (books and dvds) have hundreds of thousands of reviews. Others (musical instruments) have only a few hundred. Reviews contain star ratings (1 to 5 stars) that can be converted into binary labels if needed. | Provide a detailed description of the following dataset: Multi-Domain Sentiment Dataset v2.0 |
PAQ | **Probably Asked Questions** (**PAQ**) is a very large resource of 65M automatically-generated QA-pairs. PAQ is a semi-structured Knowledge Base (KB) of 65M natural language QA-pairs, which models can memorise and/or learn to retrieve from. PAQ differs from traditional KBs in that questions and answers are stored in natural language, and that questions are generated such that they are likely to appear in ODQA datasets. PAQ is automatically constructed using a question generation model and Wikipedia. | Provide a detailed description of the following dataset: PAQ |
BABEL Project | **BABEL** is a multilingual corpus of conversational telephone speech from IARPA, which includes Asian and African languages. | Provide a detailed description of the following dataset: BABEL Project |
AIDA CoNLL-YAGO | **AIDA CoNLL-YAGO** contains assignments of entities to the mentions of named entities annotated for the original [CoNLL 2003 entity recognition task](https://www.clips.uantwerpen.be/conll2003/ner/). The entities are identified by YAGO2 entity name, by Wikipedia URL, or by Freebase mid. | Provide a detailed description of the following dataset: AIDA CoNLL-YAGO |
BTFDBB | Reflectance measurements of Bidirectional Texture Functions (BTFs)
Database contains both flat samples:
as well as 3D geometry with texture mapped BTFs:
furthermore, there are some multispectral BTFs:
 | Provide a detailed description of the following dataset: BTFDBB |
CoNLL-2014 Shared Task: Grammatical Error Correction | CoNLL-2014 will continue the CoNLL tradition of having a high profile shared task in natural language processing. This year's shared task will be grammatical error correction, a continuation of the CoNLL shared task in 2013. A participating system in this shared task is given short English texts written by non-native speakers of English. The system detects the grammatical errors present in the input texts, and returns the corrected essays. The shared task in 2014 will require a participating system to correct all errors present in an essay (i.e., not restricted to just five error types in 2013). Also, the evaluation metric will be changed to F0.5, weighting precision twice as much as recall.
The grammatical error correction task is impactful since it is estimated that hundreds of millions of people in the world are learning English and they benefit directly from an automated grammar checker. However, for many error types, current grammatical error correction methods do not achieve a high performance and thus more research is needed. | Provide a detailed description of the following dataset: CoNLL-2014 Shared Task: Grammatical Error Correction |
UBOFAB19 | A database of several hundred high quality fabric material measurements, provided as carefully calibrated rectified HDR images, together with SVBRDF fits.
Measurement HDR images are provided in OpenEXR format
SVBRDF fits are provided in X-Rite AxF format
Further geometric and radiometric calibration data is available as well. | Provide a detailed description of the following dataset: UBOFAB19 |
APPBENCH | A database of 56 high quality fabric material measurements, provided as carefully calibrated rectified HDR images, together with SVBRDF fits. Used in the [Fabric Appearance Challange](https://competitions.codalab.org/competitions/24979).
Measurement HDR images are provided in OpenEXR format
SVBRDF fits are provided in X-Rite AxF format
Further geometric and radiometric calibration data is available as well. | Provide a detailed description of the following dataset: APPBENCH |
Chickenpox Cases in Hungary | **Chickenpox Cases in Hungary** is a spatio-temporal dataset of weekly chickenpox (childhood disease) cases from Hungary. It can be used as a longitudinal dataset for benchmarking the predictive performance of spatiotemporal graph neural network architectures. The dataset consists of a county-level adjacency matrix and time series of the county-level reported cases between 2005 and 2015. There are 2 specific related tasks:
- County level case count prediction.
- National level case count prediction. | Provide a detailed description of the following dataset: Chickenpox Cases in Hungary |
Multimodal Opinionlevel Sentiment Intensity | Multimodal Opinionlevel Sentiment Intensity (MOSI) contains: (1) multimodal observations including transcribed speech and visual gestures as well as automatic audio and visual features, (2) opinion-level subjectivity segmentation, (3) sentiment intensity annotations with high coder agreement, and (4) alignment between words, visual and acoustic features. | Provide a detailed description of the following dataset: Multimodal Opinionlevel Sentiment Intensity |
WNUT 2017 | This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet “so.. kktny in 30 mins?” - even human experts find entity kktny hard to detect and resolve. This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text.
The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. | Provide a detailed description of the following dataset: WNUT 2017 |
RUSHOLD | RUHSOLD is hate speech and offensive language dataset in Roman Urdu. The dataset contains over 10 thousand tweets that are hand labelled into the following categories:
1) Abusive/Offensive
2) Untargeted
3) Sexism
4) Religious
5) Neutral | Provide a detailed description of the following dataset: RUSHOLD |
Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison | The Evaluation framework of Raganato et al. 2017 includes two training sets (SemCor-Miller et al., 1993- and OMSTI-Taghipour and Ng, 2015-) and five test sets from the Senseval/SemEval series (Edmonds and Cotton, 2001; Snyder and Palmer, 2004; Pradhan et al., 2007; Navigli et al., 2013; Moro and Navigli, 2015), standardized to the same format and sense inventory (i.e. WordNet 3.0).
Typically, there are two kinds of approach for WSD: supervised (which make use of sense-annotated training data) and knowledge-based (which make use of the properties of lexical resources).
**Supervised:** The most widely used training corpus used is SemCor, with 226,036 sense annotations from 352 documents manually annotated. All supervised systems in the evaluation table are trained on SemCor. Some supervised methods, particularly neural architectures, usually employ the SemEval 2007 dataset as development set (marked by *). The most usual baseline is the Most Frequent Sense (MFS) heuristic, which selects for each target word the most frequent sense in the training data.
**Knowledge-based:** Knowledge-based systems usually exploit WordNet or BabelNet as semantic network. The first sense given by the underlying sense inventory (i.e. WordNet 3.0) is included as a baseline.
Description from [NLP Progress](http://nlpprogress.com/english/word_sense_disambiguation.html) | Provide a detailed description of the following dataset: Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison |
PNT | **The Parsing Time Normalizations** (**PNT**) corpus in SCATE format allows the representation of a wider variety of time expressions than previous approaches. This corpus was release with SemEval 2018 Task 6. | Provide a detailed description of the following dataset: PNT |
SemEval-2018 Task 9: Hypernym Discovery | The SemEval-2018 hypernym discovery evaluation benchmark (Camacho-Collados et al. 2018) contains three domains (general, medical and music) and is also available in Italian and Spanish (not in this repository). For each domain a target corpus and vocabulary (i.e. hypernym search space) are provided. The dataset contains both concepts (e.g. dog) and entities (e.g. Manchester United) up to trigrams. | Provide a detailed description of the following dataset: SemEval-2018 Task 9: Hypernym Discovery |
WHU-Specular dataset | WHU-Specular is a large dataset of annotated specular highlight regions created from real-world images. It can be used for specular highlight detection task. It contains 4310 image pairs (specular images and corresponding highlight masks). We randomly selected 3,017 images as the training set, and other 1293 images as the testing set. | Provide a detailed description of the following dataset: WHU-Specular dataset |
NAB | **The First Temporal Benchmark Designed to Evaluate Real-time Anomaly Detectors Benchmark**
The growth of the Internet of Things has created an abundance of streaming data. Finding anomalies in this data can provide valuable insights into opportunities or failures. Yet it’s difficult to achieve, due to the need to process data in real time, continuously learn and make predictions. How do we evaluate and compare various real-time anomaly detection techniques?
The Numenta Anomaly Benchmark (NAB) provides a standard, open source framework for evaluating real-time anomaly detection algorithms on streaming data. Through a controlled, repeatable environment of open-source tools, NAB rewards detectors that find anomalies as soon as possible, trigger no false alarms, and automatically adapt to any changing statistics.
NAB comprises two main components: a scoring system designed for streaming data and a dataset with labeled, real-world time-series data. | Provide a detailed description of the following dataset: NAB |
AVSpeech | **AVSpeech** is a large-scale audio-visual dataset comprising
speech clips with no interfering background signals. The segments
are of varying length, between 3 and 10 seconds long, and in each clip
the only visible face in the video and audible sound in the soundtrack
belong to a single speaking person. In total, the dataset contains
roughly 4700 hours of video segments with approximately 150,000
distinct speakers, spanning a wide variety of people, languages
and face poses. | Provide a detailed description of the following dataset: AVSpeech |
Kinect-WSJ | Kinect-WSJ is a multichannel, multispeaker, reverberated, noisy dataset which extends the [WSJ0-2mix](/dataset/wsj0-2mix-1) singlechannel, non-reverberated, noiseless dataset to the strong reverberation and noise conditions and the Kinect-like microphone array geometry used in [CHiME-5](/dataset/chime-5). | Provide a detailed description of the following dataset: Kinect-WSJ |
BiasBios | The purpose of this dataset was to study gender bias in occupations. Online biographies, written in English, were collected to find the names, pronouns, and occupations. Twenty-eight most frequent occupations were identified based on their appearances. The resulting dataset consists of 397,340 biographies spanning twenty-eight different occupations. Of these occupations, the professor is the most frequent, with 118,400 biographies, while the rapper is the least frequent, with 1,406 biographies.
Important information about the biographies:
1. The longest biography is 194 tokens, while the shortest is eighteen; the median biography length is seventy-two tokens.
2. It should be noted that the demographics of online biographies’ subjects differ from those of the overall workforce and that this dataset does not contain all biographies on the Internet. | Provide a detailed description of the following dataset: BiasBios |
BG-20k | BG-20k contains 20,000 high-resolution background images excluded salient objects, which can be used to help generate high quality synthetic data. | Provide a detailed description of the following dataset: BG-20k |
SPoC | Pseudocode-to-Code (SPoC) is a program synthesis dataset, containing 18,356 programs with human-authored pseudocode and test cases.
Image source: [https://sumith1896.github.io/spoc/](https://sumith1896.github.io/spoc/) | Provide a detailed description of the following dataset: SPoC |
OntoNotes 5.0 | **OntoNotes 5.0** is a large corpus comprising various genres of text (news, conversational telephone speech, weblogs, usenet newsgroups, broadcast, talk shows) in three languages (English, Chinese, and Arabic) with structural information (syntax and predicate argument structure) and shallow semantics (word sense linked to an ontology and coreference).
OntoNotes Release 5.0 contains the content of earlier releases - and adds source data from and/or additional annotations for, newswire, broadcast news, broadcast conversation, telephone conversation and web data in English and Chinese and newswire data in Arabic. | Provide a detailed description of the following dataset: OntoNotes 5.0 |
WebText | **WebText** is an internal OpenAI corpus created by scraping web pages with emphasis on
document quality. The authors scraped all outbound links from
Reddit which received at least 3
karma. The authors used the approach as a heuristic indicator for
whether other users found the link interesting, educational,
or just funny.
WebText contains the text subset of these 45 million links. It consists of over 8 million documents
for a total of 40 GB of text. All Wikipedia
documents were removed from WebText since it is a common data source
for other datasets. | Provide a detailed description of the following dataset: WebText |
MECCANO | The MECCANO dataset is the first dataset of egocentric videos to study human-object interactions in industrial-like settings.
The MECCANO dataset has been acquired in an industrial-like scenario in which subjects built a toy model of a motorbike. We considered 20 object classes which include the 16 classes categorizing the 49 components, the two tools (screwdriver and wrench), the instructions booklet and a partial_model class.
Additional details related to the MECCANO:
20 different subjects in 2 countries (IT, U.K.)
Video Acquisition: 1920x1080 at 12.00 fps
11 training videos and 9 validation/test videos
8857 video segments temporally annotated indicating the verbs which describe the actions performed
64349 active objects annotated with bounding boxes
12 verb classes, 20 objects classes and 61 action classes | Provide a detailed description of the following dataset: MECCANO |
ecoset | **Ecoset**, an ecologically motivated image dataset, is a large-scale image dataset designed for human visual neuroscience, which consists of over 1.5 million images from 565 basic-level categories. Category selection was based on English nouns that most frequently occur in spoken language (estimated on a set of 51 million words obtained from American television and film subtitles) and concreteness ratings from human observers. Ecoset consists of basic-level categories (including human categories man, woman, and child) that describe physical things in the world (rather than abstract concepts) that are important to humans. | Provide a detailed description of the following dataset: ecoset |
CoNLL 2003 | **CoNLL-2003** is a named entity recognition dataset released as a part of CoNLL-2003 shared task: language-independent named entity recognition.
The data consists of eight files covering two languages: English and German.
For each of the languages there is a training file, a development file, a test file and a large file with unannotated data.
The English data was taken from the Reuters Corpus. This corpus consists of Reuters news stories between August 1996 and August 1997.
For the training and development set, ten days worth of data were taken from the files representing the end of August 1996.
For the test set, the texts were from December 1996. The preprocessed raw data covers the month of September 1996.
The text for the German data was taken from the ECI Multilingual Text Corpus. This corpus consists of texts in many languages. The portion of data that
was used for this task, was extracted from the German newspaper Frankfurter Rundshau. All three of the training, development and test sets were taken
from articles written in one week at the end of August 1992.
The raw data were taken from the months of September to December 1992.
| English data | Articles | Sentences | Tokens | LOC | MISC | ORG | PER |
|-------------------|----------|-----------|---------|------|------|------|------|
| Training set | 946 | 14,987 | 203,621 | 7140 | 3438 | 6321 | 6600 |
| Development set | 216 | 3,466 | 51,362 | 1837 | 922 | 1341 | 1842 |
| Test set | 231 | 3,684 | 46,435 | 1668 | 702 | 1661 | 1617 |
Number of articles, sentences, tokens and entities (locations, miscellaneous, organizations, and persons) in English data files.
| German data | Articles | Sentences | Tokens | LOC | MISC | ORG | PER |
|-------------------|----------|-----------|---------|------|------|------|------|
| Training set | 553 | 12,705 | 206,931 | 4363 | 2288 | 2427 | 2773 |
| Development set | 201 | 3,068 | 51,444 | 1181 | 1010 | 1241 | 1401 |
| Test set | 155 | 3,160 | 51,943 | 1035 | 670 | 773 | 1195 |
Number of articles, sentences, tokens and entities (locations, miscellaneous, organizations, and persons) in German data files. | Provide a detailed description of the following dataset: CoNLL 2003 |
QAMR | **Question-Answer Meaning Representation** (**QAMR**) represents a predicate-argument structure of a sentence with a set of question-answer pairs, so that annotations can be easily provided by non-experts. QAMR is a dataset of over 5,000 sentences and 100,000 questions created by crowdsourcing workers. | Provide a detailed description of the following dataset: QAMR |
AW-OIE | **All Words Open IE** (**AW-OIE**) is an open information extraction dataset derived from [Question-Answer Meaning Representation (QAMR)](/dataset/qamr) dataset. | Provide a detailed description of the following dataset: AW-OIE |
MSU Deinterlacer Benchmark | This is a dataset for video deinterlacing problem. The dataset contains 40 video sequences. Each sequence's length is 1 second. Resolution of all video sequences is 1920x1080. FPS varies from 24 to 60. TFF interlacing was used to get interlaced data from GT. | Provide a detailed description of the following dataset: MSU Deinterlacer Benchmark |
StrategyQA | **StrategyQA** is a question answering benchmark where the required reasoning steps are implicit in the question, and should be inferred using a strategy.
It includes 2,780 examples, each consisting of a strategy question, its decomposition, and evidence paragraphs.
Questions in StrategyQA are short, topic-diverse, and cover a wide range of strategies. | Provide a detailed description of the following dataset: StrategyQA |
Oxford 102 Flower | **Oxford 102 Flower** is an image classification dataset consisting of 102 flower categories. The flowers chosen to be flower commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images.
The images have large scale, pose and light variations. In addition, there are categories that have large variations within the category and several very similar categories. | Provide a detailed description of the following dataset: Oxford 102 Flower |
ICB | A carefully chosen set of high-resolution high-precision natural images suited for compression algorithm evaluation.
The images historically used for compression research (lena, barbra, pepper etc...) have outlived their useful life and its about time they become a part of history only. They are too small, come from data sources too old and are available in only 8-bit precision.
These high-resolution high-precision images have been carefully selected to aid in image compression research and algorithm evaluation. These are photographic images chosen to come from a wide variety of sources and each one picked to stress different aspects of algorithms. Images are available in 8-bit, 16-bit and 16-bit linear variations, RGB and gray.
These Images are available without any prohibitive copyright restrictions.
These images are (c) there respective owners. You are granted full redistribution and publication rights on these images provided:
1. The origin of the pictures must not be misrepresented; you must not claim that you took the original pictures. If you use, publish or redistribute them, an acknowledgment would be appreciated but is not required.
2. Altered versions must be plainly marked as such, and must not be misinterpreted as being the originals.
3. No payment is required for distribution of this material, it must be available freely under the conditions stated here. That is, it is prohibited to sell the material.
4. This notice may not be removed or altered from any distribution.
*For grayscale evaluation, use the Grayscale 8 bit dataset, for color evaluation, use the Color 8 bit dataset.*
```
@online{icb,
author = {Rawzor},
title = {Image Compression Benchmark},
url = {http://imagecompression.info/}
}
``` | Provide a detailed description of the following dataset: ICB |
LIVE1 | Quality Assessment research strongly depends upon subjective experiments to provide calibration data as well as a testing mechanism. After all, the goal of all QA research is to make quality predictions that are in agreement with subjective opinion of human observers. In order to calibrate QA algorithms and test their performance, a data set of images and videos whose quality has been ranked by human subjects is required. The QA algorithm may be trained on part of this data set, and tested on the rest.
```
@article{sheikh2006statistical,
title={A statistical evaluation of recent full reference image quality assessment algorithms},
author={Sheikh, Hamid R and Sabir, Muhammad F and Bovik, Alan C},
journal={IEEE Transactions on image processing},
volume={15},
number={11},
pages={3440--3451},
year={2006},
publisher={IEEE}
}
@online{sheikh2006live,
title={LIVE image quality assessment database},
author={Sheikh, HR and Wang, Z and Cormack, L and Bovik, AC},
url={http://live.ece.utexas.edu/research/quality}
}
``` | Provide a detailed description of the following dataset: LIVE1 |
Classic5 | Five classic grayscale images commonly used for image quality assessment tasks. | Provide a detailed description of the following dataset: Classic5 |
hls4ml LHC Jet dataset | Dataset of high-pT jets from simulations of LHC proton-proton collisions
Prepared for FastML/HLS4ML studies: https://fastmachinelearning.org
Includes: High level features (see https://arxiv.org/abs/1804.06913)
Images: jet images with up to 100 particles/jet (see https://arxiv.org/abs/1908.05318)
List: list of jet features with up to 100 particles/jet (see https://arxiv.org/abs/1908.05318) | Provide a detailed description of the following dataset: hls4ml LHC Jet dataset |
RailEye3D Dataset | The RailEye3D dataset, a collection of train-platform scenarios for applications targeting passenger safety and automation of train dispatching, consists of 10 image sequences captured at 6 railway stations in Austria. Annotations for multi-object tracking are provided in both an unified format as well as the ground-truth format used in the MOTChallenge. | Provide a detailed description of the following dataset: RailEye3D Dataset |
GraspNet-1Billion | **GraspNet-1Billion** provides large-scale training data and a standard evaluation platform for the task of general robotic grasping. The dataset contains 97,280 RGB-D image with over one billion grasp poses. | Provide a detailed description of the following dataset: GraspNet-1Billion |
MAEC | **MAEC** is a new, large-scale multi-modal, text-audio paired, earnings-call dataset named MAEC, based on S&P 1500 companies. | Provide a detailed description of the following dataset: MAEC |
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