dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
TCC | The largest and most realistic dataset available for TCC. It consists of 600 real-world videos recorded with a high-resolution mobile phone camera shooting 1824 x 1368 sized pictures. The length of these videos ranges from 3 to 17 frames (7.3 on average, the median is 7.0 and mode is 8.5). Ground truth information is present only for the last frame in each video (i.e., the shot frame), and was collected using a gray surface calibration target. | Provide a detailed description of the following dataset: TCC |
USPTO-50k | Subset and preprocessed version of Chemical reactions from US patents (1976-Sep2016) by Daniel Lowe.
It includes 50K randomly selected reactions that was later classified into 10 reaction classes by Nadine Schneider et al. | Provide a detailed description of the following dataset: USPTO-50k |
Eduge | Eduge news classification dataset provided by Bolorsoft LLC. Used to train the Eduge.mn production news classifier
75K news articles in 9 categories: урлаг соёл, эдийн засаг, эрүүл мэнд, хууль, улс төр, спорт, технологи, боловсрол and байгал орчин
Download train/test data via:
[train](https://storage.googleapis.com/eduge_dataset/eduge_train.csv)
[test](https://storage.googleapis.com/eduge_dataset/eduge_test.csv) | Provide a detailed description of the following dataset: Eduge |
Learning to Autofocus | This dataset contains 510 focal stacks (49 different focal distances) from in-the-wild scenes with calculated depth from SFM. This dataset was designed for research on Autofocus but can be used for any research which is interested in focal stacks, defocus cues, or depth signals (particularly for interest in close depth). | Provide a detailed description of the following dataset: Learning to Autofocus |
CMeEE | Chinese Medical Named Entity Recognition, a dataset first released in CHIP20204, is used for CMeEE task. Given a pre-defined schema, the task is to identify and extract entities from the given sentence and classify them into nine categories: disease, clinical manifestations, drugs, medical equipment, medical procedures, body, medical examinations, microorganisms, and department. | Provide a detailed description of the following dataset: CMeEE |
CMeIE | Chinese Medical Information Extraction, a dataset that is also released in CHIP2020, is used for CMeIE task. The task is aimed at identifying both entities and relations in a sentence following the schema constraints. There are 53 relations defined in the dataset, including 10 synonymous sub-relationships and 43 other sub-relationships. | Provide a detailed description of the following dataset: CMeIE |
CHIP-STS | CHIP Semantic Textual Similarity, a dataset for sentence similarity in the non-i.i.d.
(non-independent and identically distributed) setting, is used for the CHIP-STS task. Specifically, the
task aims to transfer learning between disease types on Chinese disease questions and answer data.
Given question pairs related to 5 different diseases (The disease types in the training and testing set
are different), the task intends to determine whether the semantics of the two sentences are similar. | Provide a detailed description of the following dataset: CHIP-STS |
CHIP-CDN | CHIP Clinical Diagnosis Normalization, a dataset that aims to standardize the terms
from the final diagnoses of Chinese electronic medical records, is used for the CHIP-CDN task.
Given the original phrase, the task is required to normalize it to standard terminology based on the
International Classification of Diseases (ICD-10) standard for Beijing Clinical Edition v601. | Provide a detailed description of the following dataset: CHIP-CDN |
CHIP-CTC | CHIP Clinical Trial Classification, a dataset aimed at classifying clinical trials eligibility criteria, which are fundamental guidelines of clinical trials defined to identify whether a subject
meets a clinical trial or not, is used for the CHIP-CTC task. All text data are collected from the
website of the Chinese Clinical Trial Registry (ChiCTR)
, and a total of 44 categories are defined.
The task is like text classification; although it is not a new task, studies and corpus for the Chinese
clinical trial criterion are still limited, and we hope to promote future researches for social benefits. | Provide a detailed description of the following dataset: CHIP-CTC |
KUAKE-QIC | KUAKE Query Intent Classification, a dataset for intent classification, is used for the
KUAKE-QIC task. Given the queries of search engines, the task requires to classify each of them into
one of 11 medical intent categories defined in KUAKE-QIC, including diagnosis, etiology analysis,
treatment plan, medical advice, test result analysis, disease description, consequence prediction,
precautions, intended effects, treatment fees, and others. | Provide a detailed description of the following dataset: KUAKE-QIC |
KUAKE-QTR | KUAKE Query Title Relevance, a dataset used to estimate the relevance of the title
of a query document, is used for the KUAKE-QTR task. Given a query (e.g., “Symptoms of vitamin
B deficiency”), the task aims to find the relevant title (e.g., “The main manifestations of vitamin B
deficiency”). | Provide a detailed description of the following dataset: KUAKE-QTR |
KUAKE-QQR | KUAKE Query-Query Relevance, a dataset used to evaluate the relevance of the
content expressed in two queries, is used for the KUAKE-QQR task. Similar to KUAKE-QTR, the
task aims to estimate query-query relevance, which is an essential and challenging task in real-world
search engines. | Provide a detailed description of the following dataset: KUAKE-QQR |
Oxford Road Boundaries | The **Oxford Road Boundaries** is a dataset designed for training and testing machine-learning-based road-boundary detection and inference approaches.
The authors have hand-annotated two of the 10 km-long forays from the [Oxford Robotcar Dataset](oxford-robotcar-dataset) and generated from other forays several thousand further examples with semi-annotated road-boundary masks. To boost the number of training samples in this way, the authors used a vision-based localiser to project labels from the annotated datasets to other traversals at different times and weather conditions.
As a result, the dataset consists of 62,605 labelled samples, of which 47,639 samples are curated. Each of these samples contains both raw and classified masks for left and right lenses. Our data contains images from a diverse set of scenarios such as straight roads, parked cars, junctions, etc. | Provide a detailed description of the following dataset: Oxford Road Boundaries |
JRDB-Act | **JRDB-Act** is an extension of the [JRDB](jrdb) dataset to create a large-scale multi-modal dataset for spatio-temporal action, social group and activity detection.
JRDB-Act has been densely annotated with atomic actions, comprises over 2.8M action labels, constituting a large-scale spatio-temporal action detection dataset. Each human bounding box is labelled with one pose-based action label and multiple (optional) interaction-based action labels. Moreover JRDB-Act comes with social group identification annotations conducive to the task of grouping individuals based on their interactions in the scene to infer their social activities (common activities in each social group). | Provide a detailed description of the following dataset: JRDB-Act |
RyanSpeech | **RyanSpeech** is a speech corpus for research on automated text-to-speech (TTS) systems. This dataset contains textual materials from real-world conversational settings. These materials contain over 10 hours of a professional male voice actor's speech recorded at 44.1 kHz. | Provide a detailed description of the following dataset: RyanSpeech |
LARC | **LARC** is a dataset built from ARC (Abstraction and Reasoning Corpus). ARC is a set of tasks that tests an agent's ability to flexibly solve novel problems. While most ARC tasks are easy for humans, they are challenging for state-of-the-art AI.
LARC or Language-annotated ARC, is a collection of natural language descriptions by a group of human participants, unfamiliar both with ARC and with each other, who instruct each other on how to solve ARC tasks. LARC contains successful instructions for 88% of the ARC tasks. | Provide a detailed description of the following dataset: LARC |
Physion | **Physion** is a visual and physical prediction benchmark to measure the performance of machine learning models on making predictions about commonplace real world physical events. In realistically simulating a wide variety of physical phenomena -- rigid and soft-body collisions, stable multi-object configurations, rolling and sliding, projectile motion -- this dataset presents a more comprehensive challenge than existing benchmarks. Moreover, the dataset also contains human responses for the stimuli so that model predictions can be directly compared to human judgments. | Provide a detailed description of the following dataset: Physion |
EuroCrops | EuroCrops is a dataset for automatic vegetation classification from multi-spectral and multi-temporal satellite data, annotated with official LIPS reporting data from countries of the European Union, curated by the Technical University of Munich and GAF AG. The project is managed by the DLR Space Administration and funded by BMWI (Federal Ministry for Economic Affairs and Energy). This dataset is publicly available for research causes with the idea in mind to assist in the subsidy control of agricultural self-declarations. | Provide a detailed description of the following dataset: EuroCrops |
GigaSpeech | GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised and unsupervised training. | Provide a detailed description of the following dataset: GigaSpeech |
Imgur5K | **Imgur5k** is a large-scale handwritten in-the-wild dataset, containing challenging real world handwritten samples from nearly 5K writers. It consists of ~135K handwritten English words from 5K different images. As opposed to existing dataests for OCR which have limited variability in their images, the images in Imgur5K contain a diverse set of styles. | Provide a detailed description of the following dataset: Imgur5K |
DisKnE | **DisKnE** is a benchmark for Disease Knowledge Evaluation built from MedNLI and MEDIQA-NLI. This benchmark is constructed to specifically test the medical reasoning capabilities of ML models, such as mapping symptoms to diseases.
The dataset was built by annotating each positive MedNLI example with the types of medical reasoning that are needed. Negative examples were created by corrupting these positive examples in an adversarial way. Furthermore, the training-test splits are defined per disease, ensuring that no knowledge about test diseases can be learned from the training data. | Provide a detailed description of the following dataset: DisKnE |
PATTERN | PATTERN is a node classification tasks generated with [Stochastic Block Models](https://paperswithcode.com/paper/community-detection-and-stochastic-block), which is widely used to model communities in social networks by modulating the intra- and extra-communities connections, thereby controlling the difficulty of the task. PATTERN tests the fundamental graph task of recognizing specific predetermined subgraphs. | Provide a detailed description of the following dataset: PATTERN |
CLUSTER | CLUSTER is a node classification tasks generated with [Stochastic Block Models](https://paperswithcode.com/paper/community-detection-and-stochastic-block), which is widely used to model communities in social networks by modulating the intra- and extra-communities connections, thereby controlling the difficulty of the task. CLUSTER aims at identifying community clusters in a semi-supervised setting.
| Provide a detailed description of the following dataset: CLUSTER |
CSL | CSL is a synthetic dataset introduced in [Murphy et al. (2019)](https://paperswithcode.com/paper/relational-pooling-for-graph-representations) to test the expressivity of GNNs. In particular, graphs are isomorphic if they have the same degree and the task is to classify non-isomorphic graphs. | Provide a detailed description of the following dataset: CSL |
Large-scale Anomaly Detection | **Large-scale Anomaly Detection** (LAD) is a database to benchmark anomaly detection in video sequences, which is featured in two aspects. 1) It contains 2000 video sequences including normal and abnormal video clips with 14 anomaly categories including crash, fire, violence, etc. with large scene varieties, making it the largest anomaly analysis database to date. 2) It provides the annotation data, including video-level labels (abnormal/normal video, anomaly type) and frame-level labels (abnormal/normal video frame) to facilitate anomaly detection. | Provide a detailed description of the following dataset: Large-scale Anomaly Detection |
Counting Probe | Probing cross-modal capabilities of Vision & Language models with a counting task.
* binary classification
* following a FOIL setup (as introduced by Shekhar et al. 2017: https://www.aclweb.org/anthology/P17-1024/) | Provide a detailed description of the following dataset: Counting Probe |
BestRev | Survey instrument, analysis code, and anonymized responses for the paper on review practices in SE. | Provide a detailed description of the following dataset: BestRev |
COVID-19 Case Surveillance Public Use Data | This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data. | Provide a detailed description of the following dataset: COVID-19 Case Surveillance Public Use Data |
BABEL | BABEL is a large dataset with language labels describing the actions being performed in mocap sequences. BABEL consists of action labels for about 43 hours of mocap sequences from AMASS. Action labels are at two levels of abstraction -- sequence labels describe the overall action in the sequence, and frame labels describe all actions in every frame of the sequence. Each frame label is precisely aligned with the duration of the corresponding action in the mocap sequence, and multiple actions can overlap. There are over 28k sequence labels, and 63k frame labels in BABEL, which belong to over 250 unique action categories. Labels from BABEL can be leveraged for tasks like action recognition, temporal action localization, motion synthesis, etc. | Provide a detailed description of the following dataset: BABEL |
IFCNet | The full **IFCNet** dataset currently consists of 19,000 CAD models distributed over 65 classes according to the taxonomy of the Industry Foundation Classes (IFC) standard. The IFC standard provides an open data exchange format for projects in the Architecture, Engineering and Construction (AEC) domain. Due to high imbalances with respect to the number of objects in each class, a subset of 8,000 objects from 20 classes is selected to form the **IFCNetCore** dataset, providing a more balanced distribution. Apart from the geometric information of the CAD model, most objects also have semantic information in the form of key-value pairs, enums or lists, which are relevant to different stages of the construction process. | Provide a detailed description of the following dataset: IFCNet |
FIN | A dataset of financial agreements made public through U.S. Security and Exchange Commission (SEC) filings. Eight documents (totalling 54,256 words) were randomly selected for manual annotation, based on the four NE types provided in the CoNLL-2003 dataset: LOCATION (LOC), ORGANISATION (ORG), PERSON (PER), and MISCELLANEOUS (MISC). | Provide a detailed description of the following dataset: FIN |
WebVid | WebVid contains 10 million video clips with captions, sourced from the web. The videos are diverse and rich in their content.
Both the full 10M set and a 2.5M subset is available for download:
https://github.com/m-bain/webvid-dataset | Provide a detailed description of the following dataset: WebVid |
Kinships | The Kinships dataset describes relationships between members of the Australian tribe Alyawarra and consists of 10,686 triples. It contains 104 entities representing members of the tribe and 26 relationship types that represent kinship terms such as Adiadya or Umbaidya. | Provide a detailed description of the following dataset: Kinships |
CI-MNIST | **CI-MNIST** (Correlated and Imbalanced MNIST) is a variant of [MNIST](/dataset/mnist) dataset with introduced different types of correlations between attributes, dataset features, and an artificial eligibility criterion. For an input image $x$, the label $y \in \\{1, 0\\}$ indicates eligibility or ineligibility, respectively, given that $x$ is even or odd. The dataset defines the background colors as the protected or sensitive attribute $s \in \\{0, 1\\}$, where blue denotes the unprivileged group and red denotes the privileged group. The dataset was designed in order to evaluate bias-mitigation approaches in challenging setups and be capable of controlling different dataset configurations. | Provide a detailed description of the following dataset: CI-MNIST |
Box2D | Continuous control tasks in the Box2D simulator. | Provide a detailed description of the following dataset: Box2D |
JSRT | The standard digital image database with and without chest lung nodules (JSRT database) was created(*1) by the Japanese Society of Radiological Technology (JSRT) in cooperation with the Japanese Radiological Society (JRS) in 1998. Since then, the JSRT database has been used by a number of researchers in the world for various research purposes such as image processing, image compression, evaluation of image display, computer-aided diagnosis (CAD), picture archiving and communication system (PACS), and for training and testing.
・Useful for ROC analysis (154 nodule and 93 non-nodule images)
・ High resolution (2048 x 2048 matrix size, 0.175mm pixel size)
・ Wide density range (12bit, 4096 gray scale)
・ Universal image format (no header, big-endian raw data)
・ Useful for diagnostic training and testing
・ Additional information:
patient age, gender, diagnosis (malignant or benign), X and Y coordinates of nodule, simple diagram of nodule location, degree of subtlety in visual detection of nodules | Provide a detailed description of the following dataset: JSRT |
Sports10 | - Games dataset containing 100,000 Gameplay Images of 175 Video Games across 10 Sports Genres - AMERICAN FOOTBALL, BASKETBALL, BIKE RACING, CAR RACING, FIGHTING, HOCKEY, SOCCER, TABLE TENNIS, TENNIS.
- Hand-curated images to remove menu/transition frames and only include gameplay sequences.
- Games are divided into three visual styling categories:
RETRO (arcade-style, 1990s and earlier)
MODERN (roughly 2000s)
PHOTOREAL (roughly late 2010s). | Provide a detailed description of the following dataset: Sports10 |
Solar-Power | Solar Power Data for Integration Studies
NREL's Solar Power Data for Integration Studies are synthetic solar photovoltaic (PV) power plant data points for the United States representing the year 2006.
The data are intended for use by energy professionals—such as transmission planners, utility planners, project developers, and university researchers—who perform solar integration studies and need to estimate power production from hypothetical solar plants.
Data Methodologies
The Solar Power Data for Integration Studies consist of 1 year (2006) of 5-minute solar power and hourly day-ahead forecasts for approximately 6,000 simulated PV plants. Solar power plant locations were determined based on the capacity expansion plan for high-penetration renewables in Phase 2 of the Western Wind and Solar Integration Study and the Eastern Renewable Generation Integration Study.
NREL generated the 5-minute data set using the Sub-Hour Irradiance Algorithm. The day-ahead solar forecast data for locations in the western United States were generated by 3TIER based on numerical weather predication simulations for Phase 1 of the Western Wind and Solar Integration Study. NREL generated the day-ahead solar forecast data in eastern U.S. locations using the Weather Research and Forecasting model.
The data are for specific years and should not be assumed to be representative of typical radiation levels for a site. These data should not generally be used for site-specific project development work.
Naming Convention
The naming convention of the state-wise solar power data (.csv files) from the Solar Integration Studies is as follows.
Data Type_Latitude_Longitude_Weather Year_PV Type_CapacityMW_Time Interval _Min.csv
Data Type
Actual: Real power output
DA: Day ahead forecast
HA4: 4 hour ahead forecast
Weather Year: The PV data is based on the particular year's known weather condition.
PV Type
UPV: Utility scale PV
DPV: Distributed PV
Note: The practical difference between UPV and DPV is in the configurations (UPV has single axis tracking while DPV is fixed tilt equaling to latitude) and the smoothing (both are run through a low-pass filter, the DPV will have more of the high frequency variability smoothed out).
Capacity: Installed capacity in MW
Time Interval: PV generation data reading interval in minutes.
Contact
Yingchen Zhang
Manager, Sensing, Measurement, and Forecasting Group
Yingchen.Zhang@nrel.gov
303-384-7090 | Provide a detailed description of the following dataset: Solar-Power |
ZhihuRec | ZhihuRec dataset is collected from a knowledge-sharing platform (Zhihu), which is composed of around 100M interactions collected within 10 days, 798K users, 165K questions, 554K answers, 240K authors, 70K topics, and more than 501K user query logs. There are also descriptions of users, answers, questions, authors, and topics, which are anonymous. To the best of our knowledge, this is the largest real-world interaction dataset for personalized recommendation. | Provide a detailed description of the following dataset: ZhihuRec |
Personal Events in Dialogue Corpus | The PEDC is a corpus of 14 episodes of This American Life podcast transcripts that have been annotated for events. The corpus contains excerpts from these episodes (listed in Tabe 1) that are dialogue. The granularity of annotation in this corpus is the token; each token is either annotated as an event, or a nonevent. For more information please download the corpus, and see the annotation guide for more specifics on how we define event, and the README for how the annotations are encoded. Also, much more information regarding the corpus, and its use is in the Automatic extraction of personal events from dialogue paper. | Provide a detailed description of the following dataset: Personal Events in Dialogue Corpus |
MeshRIR | MeshRIR is a dataset of acoustic room impulse responses (RIRs) at finely meshed grid points. Two subdatasets are currently available: one consists of IRs in a 3D cuboidal region from a single source, and the other consists of IRs in a 2D square region from an array of 32 sources. This dataset is suitable for evaluating sound field analysis and synthesis methods.
See the link below for the details.
- [https://sh01k.github.io/MeshRIR](https://sh01k.github.io/MeshRIR/) | Provide a detailed description of the following dataset: MeshRIR |
ONCE | ONCE (One millioN sCenEs) is a dataset for 3D object detection in the autonomous driving scenario. The ONCE dataset consists of 1 million LiDAR scenes and 7 million corresponding camera images. The data is selected from 144 driving hours, which is 20x longer than other 3D autonomous driving datasets available like [nuScenes](nuscenes) and [Waymo](waymo-open-dataset), and it is collected across a range of different areas, periods and weather conditions.
Consists of:
* 1 Million LiDAR frames, 7 Million camera images
* 200 km² driving regions, 144 driving hours
* 15k fully annotated scenes with 5 classes (Car, Bus, Truck, Pedestrian, Cyclist)
* Diverse environments (day/night, sunny/rainy, urban/suburban areas) | Provide a detailed description of the following dataset: ONCE |
SODA10M | **SODA10M** is a large-scale object detection benchmark for standardizing the evaluation of different self-supervised and semi-supervised approaches by learning from raw data. SODA10M contains 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected every ten seconds per frame within 32 different cities under different weather conditions, periods and location scenes. | Provide a detailed description of the following dataset: SODA10M |
Calliar | Calliar is a dataset for Arabic calligraphy. The dataset consists of 2500 json files that contain strokes manually annotated for Arabic calligraphy. | Provide a detailed description of the following dataset: Calliar |
HICRD | **HICRD** (Heron Island Coral Reef Dataset) is a large-scale real underwater image dataset for underwater image restoration. There are 2000 reference restored images and 6003 original underwater images in the unpaired training set. | Provide a detailed description of the following dataset: HICRD |
GOLOS | **Golos** is a Russian speech dataset suitable for speech research. The dataset mainly consists of recorded audio files manually annotated on the crowd-sourcing platform. The total duration of the audio is about 1240 hours.
## **Dataset structure**
| Domain | Train files | Train hours | Test files | Test hours |
|----------------|------------|--------|-------|------|
| Crowd | 979 796 | 1 095 | 9 994 | 11.2 |
| Farfield | 124 003 | 132.4| 1 916 | 1.4 |
| Total | 1 103 799 | 1 227.4|11 910 | 12.6 |
### **Audio files in opus format**
| Archive | Size | Link |
|------------------|------------|---------------------|
| golos_opus.tar | 20.5 GB | https://sc.link/JpD |
### **Audio files in wav format**
| Archives | Size | Links |
|-------------------|------------|---------------------|
| train_farfield.tar| 15.4 GB | https://sc.link/1Z3 |
| train_crowd0.tar | 11 GB | https://sc.link/Lrg |
| train_crowd1.tar | 14 GB | https://sc.link/MvQ |
| train_crowd2.tar | 13.2 GB | https://sc.link/NwL |
| train_crowd3.tar | 11.6 GB | https://sc.link/Oxg |
| train_crowd4.tar | 15.8 GB | https://sc.link/Pyz |
| train_crowd5.tar | 13.1 GB | https://sc.link/Qz7 |
| train_crowd6.tar | 15.7 GB | https://sc.link/RAL |
| train_crowd7.tar | 12.7 GB | https://sc.link/VG5 |
| train_crowd8.tar | 12.2 GB | https://sc.link/WJW |
| train_crowd9.tar | 8.08 GB | https://sc.link/XKk |
| test.tar | 1.3 GB | https://sc.link/Kqr |
## **Evaluation**
Percents of Word Error Rate for different test sets
| Decoder \ Test set | Crowd test | Farfield test | MCV<sup>1</sup> dev | MCV<sup>1</sup> test |
|-------------------------------------|-----------|----------|-----------|----------|
| Greedy decoder | 4.389 % | 14.949 % | 9.314 % | 11.278 % |
| Beam Search with Common Crawl LM | 4.709 % | 12.503 % | 6.341 % | 7.976 % |
| Beam Search with Golos train set LM | 3.548 % | 12.384 % | - | - |
| Beam Search with Common Crawl and Golos LM | 3.318 % | 11.488 % | 6.4 % | 8.06 % | | Provide a detailed description of the following dataset: GOLOS |
JerichoWorld | **JerichoWorld** is a dataset that enables the creation of learning agents that can build knowledge graph-based world models of interactive narratives. Interactive narratives -- or text-adventure games -- are partially observable environments structured as long puzzles or quests in which an agent perceives and interacts with the world purely through textual natural language. Each individual game typically contains hundreds of locations, characters, and objects -- each with their own unique descriptions -- providing an opportunity to study the problem of giving language-based agents the structured memory necessary to operate in such worlds.
JerichoWorld provides 24,198 mappings between rich natural language observations and: (1) knowledge graphs that reflect the world state in the form of a map; (2) natural language actions that are guaranteed to cause a change in that particular world state. The training data is collected across 27 games in multiple genres and contains a further 7,836 heldout instances over 9 additional games in the test set. | Provide a detailed description of the following dataset: JerichoWorld |
X-Fact | X-FACT is a large publicly available multilingual dataset for factual verification of naturally existing real-world claims. The dataset contains short statements in 25 languages and is labeled for veracity by expert fact-checkers. The dataset includes a multilingual evaluation benchmark that measures both out-of-domain generalization, and zero-shot capabilities of the multilingual models. | Provide a detailed description of the following dataset: X-Fact |
DocNLI | **DocNLI** is a large-scale dataset for document-level NLI. DocNLI is transformed from a broad range of NLP problems and covers multiple genres of text. The premises always stay in the document granularity, whereas the hypotheses vary in length from single sentences to passages with hundreds of words. Additionally, DocNLI has pretty limited artifacts which unfortunately widely exist in some popular sentence-level NLI datasets. | Provide a detailed description of the following dataset: DocNLI |
EMOVIE | **EMOVIE** is a Mandarin emotion speech dataset including 9,724 samples with audio files and its emotion human-labeled annotation. | Provide a detailed description of the following dataset: EMOVIE |
DISC21 | **DISC21** is a benchmark for large-scale image similarity detection. This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021). The goal is to determine whether a query image is a modified copy of any image in a reference corpus of size 1~million. The benchmark features a variety of image transformations such as automated transformations, hand-crafted image edits and machine-learning based manipulations. This mimics real-life cases appearing in social media, for example for integrity-related problems dealing with misinformation and objectionable content. The strength of the image manipulations, and therefore the difficulty of the benchmark, is calibrated according to the performance of a set of baseline approaches. Both the query and reference set contain a majority of ``distractor'' images that do not match, which corresponds to a real-life needle-in-haystack setting, and the evaluation metric reflects that. | Provide a detailed description of the following dataset: DISC21 |
Hi-Phy | **Hi-Phy** is a benchmark for physical reasoning that allows researchers to test individual physical reasoning capabilities. Inspired by how humans acquire these capabilities, the benchmark proposes a general hierarchy of physical reasoning capabilities with increasing complexity. this benchmark tests capabilities according to this hierarchy through generated physical reasoning tasks in the video game Angry Birds. | Provide a detailed description of the following dataset: Hi-Phy |
VAW | **VAW** is a large scale visual attributes dataset with explicitly labelled positive and negative attributes.
Details:
* 620 Unique Attributes including color, shape, texture, posture and many others
* 2,260 Unique Objects observed in the wild
* 72,274 Images from the Visual Genome Dataset
* 4 different evaluation metrics for measuring multi-faceted performance metrics | Provide a detailed description of the following dataset: VAW |
IMFW | Indian Masked faces in the wild Database is collected into three sets:(i) Indian Celebrity, (ii) Instagram and (iii) Indian Crowd. The Indian Celebrity contains 40 Indian celebrities with 435 images, including Bollywood actors/actresses, television stars, sports personalities, and politicians. The Instagram set contains 377 images of 40 subjects downloaded from Instagram. We collected masked and non-masked images of Indian people with a public profile. The Indian Crowd set is collected from the common people who volunteered to contribute to the dataset. This set contains 120 subjects with 562 images. All the Images are collected in both constrained and unconstrained environments with variation in pose, illumination, background and masks worn by the people. | Provide a detailed description of the following dataset: IMFW |
Fishnet Open Images | **Fishnet Open Images Database** is a large dataset of EM imagery for fish detection and fine-grained categorisation onboard commercial fishing vessels. The dataset consists of 86,029 images containing 34 object classes, making it the largest and most diverse public dataset of fisheries EM imagery to-date. It includes many of the characteristic challenges of EM data: visual similarity between species, skewed class distributions, harsh weather conditions, and chaotic crew activity. | Provide a detailed description of the following dataset: Fishnet Open Images |
Synthetic COVID-19 Chest X-ray | The **Synthetic COVID-19 Chest X-ray Dataset** consists of 21,295 synthetic COVID-19 chest X-ray images to be used for computer-aided diagnosis. These images, generated via an unsupervised domain adaptation approach, are of high quality. | Provide a detailed description of the following dataset: Synthetic COVID-19 Chest X-ray |
Dataset for methane combustion | The dataset contains 578,731 structures for methane combustion and their energies and forces under MN15/6-31G** level. | Provide a detailed description of the following dataset: Dataset for methane combustion |
CICIDS2017 | Intrusion Detection Evaluation Dataset (CIC-IDS2017)
Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) are the most important defense tools against the sophisticated and ever-growing network attacks. Due to the lack of reliable test and validation datasets, anomaly-based intrusion detection approaches are suffering from consistent and accurate performance evolutions.
Our evaluations of the existing eleven datasets since 1998 show that most are out of date and unreliable. Some of these datasets suffer from the lack of traffic diversity and volumes, some do not cover the variety of known attacks, while others anonymize packet payload data, which cannot reflect the current trends. Some are also lacking feature set and metadata.
CICIDS2017 dataset contains benign and the most up-to-date common attacks, which resembles the true real-world data (PCAPs). It also includes the results of the network traffic analysis using CICFlowMeter with labeled flows based on the time stamp, source, and destination IPs, source and destination ports, protocols and attack (CSV files). Also available is the extracted features definition.
Generating realistic background traffic was our top priority in building this dataset. We have used our proposed B-Profile system (Sharafaldin, et al. 2016) to profile the abstract behavior of human interactions and generates naturalistic benign background traffic. For this dataset, we built the abstract behaviour of 25 users based on the HTTP, HTTPS, FTP, SSH, and email protocols.
The data capturing period started at 9 a.m., Monday, July 3, 2017 and ended at 5 p.m. on Friday July 7, 2017, for a total of 5 days. Monday is the normal day and only includes the benign traffic. The implemented attacks include Brute Force FTP, Brute Force SSH, DoS, Heartbleed, Web Attack, Infiltration, Botnet and DDoS. They have been executed both morning and afternoon on Tuesday, Wednesday, Thursday and Friday.
In our recent dataset evaluation framework (Gharib et al., 2016), we have identified eleven criteria that are necessary for building a reliable benchmark dataset. None of the previous IDS datasets could cover all of the 11 criteria. In the following, we briefly outline these criteria:
Complete Network configuration: A complete network topology includes Modem, Firewall, Switches, Routers, and presence of a variety of operating systems such as Windows, Ubuntu and Mac OS X.
Complete Traffic: By having a user profiling agent and 12 different machines in Victim-Network and real attacks from the Attack-Network.
Labelled Dataset: Section 4 and Table 2 show the benign and attack labels for each day. Also, the details of the attack timing will be published on the dataset document.
Complete Interaction: As Figure 1 shows, we covered both within and between internal LAN by having two different networks and Internet communication as well.
Complete Capture: Because we used the mirror port, such as tapping system, all traffics have been captured and recorded on the storage server.
Available Protocols: Provided the presence of all common available protocols, such as HTTP, HTTPS, FTP, SSH and email protocols.
Attack Diversity: Included the most common attacks based on the 2016 McAfee report, such as Web based, Brute force, DoS, DDoS, Infiltration, Heart-bleed, Bot and Scan covered in this dataset.
Heterogeneity: Captured the network traffic from the main Switch and memory dump and system calls from all victim machines, during the attacks execution.
Feature Set: Extracted more than 80 network flow features from the generated network traffic using CICFlowMeter and delivered the network flow dataset as a CSV file. See our PCAP analyzer and CSV generator.
MetaData: Completely explained the dataset which includes the time, attacks, flows and labels in the published paper.
The full research paper outlining the details of the dataset and its underlying principles:
Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th International Conference on Information Systems Security and Privacy (ICISSP), Purtogal, January 2018
Day, Date, Description, Size (GB)
Monday, Normal Activity, 11.0G
Tuesday, attacks + Normal Activity, 11G
Wednesday, attacks + Normal Activity, 13G
Thursday, attacks + Normal Activity, 7.8G
Friday, attacks + Normal Activity, 8.3G
Victim and attacker networks information
Firewall: 205.174.165.80, 172.16.0.1
DNS+ DC Server: 192.168.10.3
Outsiders (Attackers network)
Kali: 205.174.165.73
Win: 205.174.165.69, 70, 71
Insiders (Victim network)
Web server 16 Public: 192.168.10.50, 205.174.165.68
Ubuntu server 12 Public: 192.168.10.51, 205.174.165.66
Ubuntu 14.4, 32B: 192.168.10.19
Ubuntu 14.4, 64B: 192.168.10.17
Ubuntu 16.4, 32B: 192.168.10.16
Ubuntu 16.4, 64B: 192.168.10.12
Win 7 Pro, 64B: 192.168.10.9
Win 8.1, 64B: 192.168.10.5
Win Vista, 64B: 192.168.10.8
Win 10, pro 32B: 192.168.10.14
Win 10, 64B: 192.168.10.15
MAC: 192.168.10.25
Monday, July 3, 2017
Benign (Normal human activities)
Tuesday, July 4, 2017
Brute Force
FTP-Patator (9:20 – 10:20 a.m.)
SSH-Patator (14:00 – 15:00 p.m.)
Attacker: Kali, 205.174.165.73
Victim: WebServer Ubuntu, 205.174.165.68 (Local IP: 192.168.10.50)
NAT Process on Firewall:
Attack: 205.174.165.73 -> 205.174.165.80 (IP Valid Firewall) -> 172.16.0.10 -> 192.168.10.50
Reply: 192.168.10.50 -> 172.16.0.1 -> 205.174.165.80 -> 205.174.165.73
Wednesday, July 5, 2017
DoS / DDoS
DoS slowloris (9:47 – 10:10 a.m.)
DoS Slowhttptest (10:14 – 10:35 a.m.)
DoS Hulk (10:43 – 11 a.m.)
DoS GoldenEye (11:10 – 11:23 a.m.)
Attacker: Kali, 205.174.165.73
Victim: WebServer Ubuntu, 205.174.165.68 (Local IP192.168.10.50)
NAT Process on Firewall:
Attack: 205.174.165.73 -> 205.174.165.80 (IP Valid Firewall) -> 172.16.0.10 -> 192.168.10.50
Reply: 192.168.10.50 -> 172.16.0.1 -> 205.174.165.80 -> 205.174.165.73
Heartbleed Port 444 (15:12 - 15:32)
Attacker: Kali, 205.174.165.73
Victim: Ubuntu12, 205.174.165.66 (Local IP192.168.10.51)
NAT Process on Firewall:
Attack: 205.174.165.73 -> 205.174.165.80 (IP Valid Firewall) -> 172.16.0.11 -> 192.168.10.51
Reply: 192.168.10.51 -> 172.16.0.1 -> 205.174.165.80 -> 205.174.165.73
Thursday, July 6, 2017
Morning
Web Attack – Brute Force (9:20 – 10 a.m.)
Web Attack – XSS (10:15 – 10:35 a.m.)
Web Attack – Sql Injection (10:40 – 10:42 a.m.)
Attacker: Kali, 205.174.165.73
Victim: WebServer Ubuntu, 205.174.165.68 (Local IP192.168.10.50)
NAT Process on Firewall:
Attack: 205.174.165.73 -> 205.174.165.80 (IP Valid Firewall) -> 172.16.0.10 -> 192.168.10.50
Reply: 192.168.10.50 -> 172.16.0.1 -> 205.174.165.80 -> 205.174.165.73
Afternoon
Infiltration – Dropbox download
Meta exploit Win Vista (14:19 and 14:20-14:21 p.m.) and (14:33 -14:35)
Attacker: Kali, 205.174.165.73
Victim: Windows Vista, 192.168.10.8
Infiltration – Cool disk – MAC (14:53 p.m. – 15:00 p.m.)
Attacker: Kali, 205.174.165.73
Victim: MAC, 192.168.10.25
Infiltration – Dropbox download
Win Vista (15:04 – 15:45 p.m.)
First Step:
Attacker: Kali, 205.174.165.73
Victim: Windows Vista, 192.168.10.8
Second Step (Portscan + Nmap):
Attacker:Vista, 192.168.10.8
Victim: All other clients
Friday, July 7, 2017
Morning
Botnet ARES (10:02 a.m. – 11:02 a.m.)
Attacker: Kali, 205.174.165.73
Victims: Win 10, 192.168.10.15 + Win 7, 192.168.10.9 + Win 10, 192.168.10.14 + Win 8, 192.168.10.5 + Vista, 192.168.10.8
Afternoon
Port Scan:
Firewall Rule on (13:55 – 13:57, 13:58 – 14:00, 14:01 – 14:04, 14:05 – 14:07, 14:08 - 14:10, 14:11 – 14:13, 14:14 – 14:16, 14:17 – 14:19, 14:20 – 14:21, 14:22 – 14:24, 14:33 – 14:33, 14:35 - 14:35)
Firewall rules off (sS 14:51-14:53, sT 14:54-14:56, sF 14:57-14:59, sX 15:00-15:02, sN 15:03-15:05, sP 15:06-15:07, sV 15:08-15:10, sU 15:11-15:12, sO 15:13-15:15, sA 15:16-15:18, sW 15:19-15:21, sR 15:22-15:24, sL 15:25-15:25, sI 15:26-15:27, b 15:28-15:29)
Attacker: Kali, 205.174.165.73
Victim: Ubuntu16, 205.174.165.68 (Local IP: 192.168.10.50)
NAT Process on Firewall:
Attacker: 205.174.165.73 -> 205.174.165.80 (IP Valid Firewall) -> 172.16.0.1
Afternoon
DDoS LOIT (15:56 – 16:16)
Attackers: Three Win 8.1, 205.174.165.69 - 71
Victim: Ubuntu16, 205.174.165.68 (Local IP: 192.168.10.50)
NAT Process on Firewall:
Attackers: 205.174.165.69, 70, 71 -> 205.174.165.80 (IP Valid Firewall) -> 172.16.0.1
License
The CICIDS2017 dataset consists of labeled network flows, including full packet payloads in pcap format, the corresponding profiles and the labeled flows (GeneratedLabelledFlows.zip) and CSV files for machine and deep learning purpose (MachineLearningCSV.zip) are publicly available for researchers. If you are using our dataset, you should cite our related paper which outlining the details of the dataset and its underlying principles:
Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018 | Provide a detailed description of the following dataset: CICIDS2017 |
zbMATH Open dataset 2021 | zbMATH Open contains over 4 million bibliographic entries with reviews or abstracts drawn from more than 3.000 journals and book series and more than 190.000 books. | Provide a detailed description of the following dataset: zbMATH Open dataset 2021 |
Photozilla | **Photozilla** is a large-scale dataset which includes over 990k images belonging to 10 different photographic styles. The dataset can be used to train classification models to automatically classify the images into the relevant style. | Provide a detailed description of the following dataset: Photozilla |
Text-to-3D House Model | The dataset contains 2,000 houses, 13,478 rooms and 873 (some rooms have same textures so this number is smaller than the total number of rooms.) texture images with corresponding natural language descriptions. These descriptions are firstly generated from some pre-defined templates and then refined by human workers. The average length of the description is 173.73 and there are 193 unique words. In our experiments, we use 1,600 pairs for training while 400 for testing in the building layout generation. For texture synthesis, we use 503 data for training and 370 data for testing. | Provide a detailed description of the following dataset: Text-to-3D House Model |
EchoCP | **EchoCP** is an echocardiography dataset in cTTE targeting PFO (Patent foramen ovale) diagnosis. EchoCP consists of 30 patients with both rest and Valsalva maneuver videos which covers various PFO grades.
Patent foramen ovale (PFO) is a potential separation between the septum, primum and septum secundum located in the anterosuperior portion of the atrial septum. PFO is one of the main factors causing cryptogenic stroke which is the fifth leading cause of death in the United States. For PFO diagnosis, contrast transthoracic echocardiography (cTTE) is preferred as being a more robust method compared with others. | Provide a detailed description of the following dataset: EchoCP |
WikiPII | WikiPII, an automatically labeled dataset composed of Wikipedia biography pages, annotated for personal information extraction. | Provide a detailed description of the following dataset: WikiPII |
NVGaze | Quality, diversity, and size of training dataset are critical factors for learning-based gaze estimators. We create two datasets satisfying these criteria for near-eye gaze estimation under infrared illumination: a synthetic dataset using anatomically-informed eye and face models with variations in face shape, gaze direction, pupil and iris, skin tone, and external conditions (two million images at 1280x960), and a real-world dataset collected with 35 subjects (2.5 million images at 640x480). Using our datasets, we train a neural network for gaze estimation, achieving 2.06 (+/- 0.44) degrees of accuracy across a wide 30 x 40 degrees field of view on real subjects excluded from training and 0.5 degrees best-case accuracy (across the same field of view) when explicitly trained for one real subject. We also train a variant of our network to perform pupil estimation, showing higher robustness than previous methods. Our network requires fewer convolutional layers than previous networks, achieving sub-millisecond latency. | Provide a detailed description of the following dataset: NVGaze |
UA-GEC | UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language | Provide a detailed description of the following dataset: UA-GEC |
InFashAI | AI algorithms, and in particular Machine Learning (ML) algorithms, learn from data tasks that have been traditionally done by humans such as: image classification, facial recognition, linguistic translation etc. To have a good generalization capability, AI algorithms must learn from sufficiently representative data, which is unfortunately not often the case. This results in a hyper-specialization of AI and its inability to perform well on new data whose distribution is too far from the one of the training set. It raises ethical questions which will undoubtedly have direct or indirect consequences on society. However, and despite biases they can entail, AI technologies are revolutionizing virtually every industry, and are forcing players in those industries to reinvent their businesses.
Like many industries, the fashion industy is being hit hard by exponential advances in AI. AI technologies are now able to describe a dress model, extract its attributes (color, style, type of sleeve, etc.), predict future fashion trends and even offer personalized clothing styles. Several publicly large datasets of fashion images made it possible.
However, the lack of diversity in available datasets is palpable. For example, images of African fashion are almost absent from these datasets. It raises problems in terms of ability to generalize algorithms trained on those datasets to African styles for instance, and therefore it limits the adoption of AI technologies within the African fashion industry.
Therefore, for an inclusive AI in the field of fashion, and to ensure that African fashion can benefit from the potentials of AI, Ai4Innov initiated the Inclusive Fashion AI project (InFashAI) which aims to create datasets that are much more representative of the diversity that exists in the world of fashion. We will first focus on building up a large volume of data on African fashion. This dataset will be progressively open source and, we hope, will be the backbone for AI tools adapted to African fashion. | Provide a detailed description of the following dataset: InFashAI |
PNPB dataset | The dataset consists of a total of 20 videos, each of which is 5.5 minutes long in duration. The videos are captured at a resolution of 1024x1024 and at 30 frames per second. Each video contains only one pig performing the Novel Object Recognition task.
It contains annotations for the following tasks:
*Action Recognition*: Time intervals for object investigations made by the pig were manually annotated.
*Keypoint Detection*: The base of the tail, the tip of the nose, the crown of the head, and a bounding box for the whole pig were annotated for 668 frames. | Provide a detailed description of the following dataset: PNPB dataset |
Place Pulse 2.0 | Place Pulse is a crowdsourcing effort that aims to map which areas of a city are perceived as safer, livelier, wealthier, more active, beautiful and friendly. By asking users to select images from a pair, Place Pulse collected more than 1.5 million reports that evaluate more than 100,000 images from 56 cities. | Provide a detailed description of the following dataset: Place Pulse 2.0 |
Russian Event2Mind | The work provides a comprehensive overview of the corpus for the Russian language for the commonsense inference task. Namely, we construct event phrases, which cover a wide range of everyday situations with labelled intents and reactions of the event main participant and emotions of other people involved.
Example:
The main contribution of the paper is a text corpus suitable for event2mind training in Russian which consists of two parts:
1. 6,756 event phrases covering a diverse range of everyday events and situations in Russian,
2. a subset of 23,409 event phrases from English corpus translated via Google
translator.
 | Provide a detailed description of the following dataset: Russian Event2Mind |
Taiga Corpus | Taiga is a corpus, where text sources and their meta-information are collected according to popular ML tasks.
Each text in corpus is represented in plain text and with morphological and syntactic annotation (UDPipe, homonymy resolved automatically) + has metainformation - date, theme, authorship, text difficulcy…etc (depending on source)
By now, about 5 billions of words are 77% literary texts (33 literary magazines), 19% of naive poetry, 2% of news (4 popular sites) and 2% of other (popular science, culture mags, social networks, amateur poems and prose), with documentation available.
**Segments Info**
| Genres | Tokens, millions | % |
|------------------|------------------|-----|
| News | 92 | 1.5 |
| Literary Texts | 4605 | 76 |
| Special datasets | 2.5 | 0.5 |
| Social media | 80 | 1.5 |
| Subtitles | 101 | 1.5 |
| Poems | 1130 | 19 |
**Annotation Example **
(CONLL-u):
```
# newdoc
# newpar
# sent_id = 1
# 2003Armeniya.xml 1
# text = В советский период времени число ИТ- специалистов в Армении составляло около десяти тысяч.
# sent_id = 1
1 В в ADP _ _ 3 case 3:case _
2 советский советский ADJ _ Animacy=Inan|Case=Acc|Degree=Pos|Gender=Masc|Number=Sing 3 amod 3:amod _
3 период период NOUN _ Animacy=Inan|Case=Acc|Gender=Masc|Number=Sing 11 obl 11:obl _
4 времени время NOUN _ Animacy=Inan|Case=Gen|Gender=Neut|Number=Sing 3 nmod 3:nmod _
5 число число NOUN _ Animacy=Inan|Case=Acc|Gender=Neut|Number=Sing 11 obj 11:obj _
6 ИТ ит PROPN _ Animacy=Inan|Case=Nom|Gender=Neut|Number=Sing 8 compound 8:compound SpaceAfter=No
7 - - PUNCT _ _ 6 punct 6:punct _
8 специалистов специалист NOUN _ Animacy=Anim|Case=Gen|Gender=Masc|Number=Plur 5 nmod 5:nmod _
9 в в ADP _ _ 10 case 10:case _
10 Армении армения PROPN _ Animacy=Inan|Case=Loc|Gender=Fem|Number=Sing 5 nmod 5:nmod _
11 составляло составлять VERB _ Aspect=Imp|Gender=Neut|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Act 0 root 0:root _
12 около около ADP _ _ 14 case 14:case _
13 десяти десять NUM _ Case=Gen 14 nummod 14:nummod _
14 тысяч тысяча NOUN _ Animacy=Inan|Case=Gen|Gender=Fem|Number=Plur 11 nsubj 11:nsubj SpaceAfter=No
15 . . PUNCT _ _ 14 punct 14:punct _
``` | Provide a detailed description of the following dataset: Taiga Corpus |
Morph Call | Morph Call is a suite of 46 probing tasks for four Indo-European languages that fall under different morphology: Russian, French, English, and German. The tasks are designed to explore the morphosyntactic content of multilingual transformers which is a less studied aspect at the moment.
The tasks are divided into four groups:
* [Morphosyntactic Features](https://github.com/morphology-probing/morph-call/tree/main/data/morphosyntactic_features): probe the encoder for the occurrence of the morphosyntactic properties.
* Masked Token: analogous to [Morphosyntactic Features](https://github.com/morphology-probing/morph-call/tree/main/data/morphosyntactic_features) with the exception that the target word is replaced with a tokenizer-specific mask token.
* [Morphosyntactic Values](https://github.com/morphology-probing/morph-call/tree/main/data/morphosyntactic_values): is a group of k-way classification tasks for each feature where *k* is the number of values that the feature can take.
* [Perturbations](https://github.com/morphology-probing/morph-call/tree/main/data/perturbations): tasks test the encoder sensitivity to syntactic and inflectional sentence perturbations.
## Probing Methods
* [Supervised probing](https://github.com/morphology-probing/morph-call/tree/main/probing) involves training a Logistic Regression classifier to predict a property. The performance is used as a proxy to evaluate the model knowledge.
* [Neuron-level Analysis](https://github.com/fdalvi/NeuroX) [Durrani et al., 2020] allows retrieving a group of individual neurons that are most relevant to predict a linguistic property.
* [Contextual Correlation Analysis](https://github.com/johnmwu/contextual-corr-analysis/tree/master) [Wu et al., 2020] is a representation-level similarity measure that allows identifying pairs of layers of similar behavior.
## Usage
We provide an [example](https://github.com/morphology-probing/morph-call/blob/main/examples/case-category-masks-probing.ipynb) of the experiment on **Masked Token** task (Case, German).
```
bash
me@my-laptop:~$ python3 probe.py --help
INFO: Showing help with the command 'probe.py -- --help'.
NAME
probe.py - configure the experiment AND perform probing
SYNOPSIS
probe.py <flags>
DESCRIPTION
configure the experiment AND perform probing
FLAGS
--results_path=RESULTS_PATH
Type: Optional[str]
Default: None
path to a folder to store the probing results and the model intermediate activations
--model_architecture=MODEL_ARCHITECTURE
Type: typ...
Default: 'bert multilingual'
--model_is_finetuned=MODEL_IS_FINETUNED
Type: bool
Default: False
if to perform the experiment on the fine-tuned model
--model_finetuned_path=MODEL_FINETUNED_PATH
Type: Optional[str]
Default: None
(only if model_is_finetuned is True) path to store the fine-tuned model
--model_finetuned_config_google_url=MODEL_FINETUNED_CONFIG_GOOGLE_URL
Type: Optional[]
Default: None
(only if model_is_finetuned is True) the url of the fine-tuned model config if to be downloaded
--model_finetuned_model_google_url=MODEL_FINETUNED_MODEL_GOOGLE_URL
Type: Optional[]
Default: None
(only if model_is_finetuned is True) the url of the fine-tuned model weights if to be downloaded
--model_is_random=MODEL_IS_RANDOM
Type: bool
Default: False
if to perform the random initialization of the model
--layers_to_probe=LAYERS_TO_PROBE
Type: List
Default: 'all'
(either "all" or list w. possible numbers from 0 to 11) -- model layers to probe. e.g.: [1, 3, 11], or "all"
--train_n_sentences=TRAIN_N_SENTENCES
Type: int
Default: 1500
number of sentences used to train the probing classifier
--test_n_sentences=TEST_N_SENTENCES
Type: int
Default: 1000
number of sentences used to evaluate the probing classifier
--dev_n_sentences=DEV_N_SENTENCES
Type: int
Default: 0
DEPRECATED
``` | Provide a detailed description of the following dataset: Morph Call |
UDIS-D | UDIS-D is a large image dataset for image stitching or image registration. It contains different overlap rates, varying degrees of parallax, and variable scenes such as indoor, outdoor, night, dark, snow, and zooming. | Provide a detailed description of the following dataset: UDIS-D |
BCR dataset | Blender Cycles Ray-tracing (BCR) dataset contains 2449 high-quality images rendered from 1463 models. We render the images at a range of spp rates, including 1-8, 12, 16, 32, 64, 250, 1000, and 4000 spp. All the images are rendered at the resolution of 1080p. Each image contains not only the final rendered result but also the intermediate render layers, including albedo, normal, diffuse, glossy, and so on. | Provide a detailed description of the following dataset: BCR dataset |
RuCoS | Russian reading comprehension with Commonsense reasoning (RuCoS) is a large-scale reading comprehension dataset that requires commonsense reasoning. RuCoS 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 RuCoS is to evaluate a machine`s ability of commonsense reasoning in reading comprehension.
Example
```
{'source': 'Lenta',
'passage': {
'text':
'Мать двух мальчиков, брошенных отцом в московском аэропорту Шереметьево, забрала их. Об этом сообщили ТАСС в пресс-службе министерства образования и науки Хабаровского края. Сейчас младший ребенок посещает детский сад, а старший ходит в школу. В учебных заведениях с ними по необходимости работают штатные психологи. Также министерство социальной защиты населения рассматривает вопрос о бесплатном оздоровлении детей в летнее время. Через несколько дней после того, как Виктор Гаврилов бросил своих детей в аэропорту, он явился с повинной к следователям в городе Батайске Ростовской области.\n@context\nБросившего детей в Шереметьево отца задержали за насилие над женой\n@context\nРоссиянина заподозрили в истязании брошенных в Шереметьево детей\n@context\nОставивший двоих детей в Шереметьево россиянин сам пришел к следователям',
'entities': [
{'start': 60, 'end': 71, 'text': 'Шереметьево'},
{'start': 102, 'end': 106, 'text': 'ТАСС'},
{'start': 155, 'end': 172, 'text': 'Хабаровского края'},
{'start': 470, 'end': 485, 'text': 'Виктор Гаврилов'},
{'start': 563, 'end': 571, 'text': 'Батайске'},
{'start': 572, 'end': 590, 'text': 'Ростовской области'},
{'start': 620, 'end': 631, 'text': 'Шереметьево'},
{'start': 725, 'end': 736, 'text': 'Шереметьево'},
{'start': 778, 'end': 789, 'text': 'Шереметьево'}
]
},
'qas': [
{
'query': '26 января @placeholder бросил сыновей в возрасте пяти и семи лет в Шереметьево.',
'answers': [
{'start': 470, 'end': 485, 'text': 'Виктор Гаврилов'}
],
'idx': 0
}
],
'idx': 0
}
```
### How did we collect data?
All text examples were collected from open news sources, then automatically filtered with QA systems to prevent obvious questions to infiltrate the dataset. The texts were then filtered by IPM frequency of the contained words and, finally, manually reviewed. | Provide a detailed description of the following dataset: RuCoS |
DaNetQA | DaNetQA is a question answering dataset for yes/no questions. 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. The text-pair classification setup is similar to existing natural language inference tasks.
By sampling questions from a distribution of information-seeking queries (rather than prompting annotators for text pairs), we observe significantly more challenging examples compared to existing NLI datasets.
### Example
```
{
"text": "В период с 1969 по 1972 год по программе «Аполлон» было выполнено 6 полётов с посадкой на Луне. Всего на Луне высаживались 12 астронавтов США. Список космонавтов Список космонавтов — участников орбитальных космических полётов Список астронавтов США — участников орбитальных космических полётов Список космонавтов СССР и России — участников космических полётов Список женщин-космонавтов Список космонавтов, посещавших МКС Энциклопедия астронавтики.",
"question": "Был ли человек на луне?",
"label": true,
"idx": 5
}
```
### How did we collect data?
All text examples were collected in accordance with the methodology for collecting the original dataset. Answers to the questions were received with the help of assessors, and texts were also received automatically using ODQA systems on Wikipedia. Human assessment was carried out on Yandex.Toloka.
*Additionally, to increase number of samples and the distribution of yes/no answers, we added extra data in the same format (data were collected from Yandex.Toloka while generating MuSeRC dataset). | Provide a detailed description of the following dataset: DaNetQA |
RWSD | A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution. The schema takes its name from a well-known example by Terry Winograd.
The set would then be presented as a challenge for AI programs, along the lines of the Turing test. The strengths of the challenge are that it is clear-cut, in that the answer to each schema is a binary choice; vivid, in that it is obvious to non-experts that a program that fails to get the right answers clearly has serious gaps in its understanding; and difficult, in that it is far beyond the current state of the art.
### Task Type
Logic and Reasoning, World knowledge. Binary Classification: true/false
### Example
```
{
"text": "Кубок не помещается в коричневый чемодан, потому что он слишком большой."
"label": false,
"idx": 5,
"target": {
"span1_text": "чемодан",
"span2_text": "он слишком большой",
"span1_index": 5,
"span2_index": 8
},
}
```
### How did we collect data?
All text examples were collected manually translating and adapting original Winograd dataset for Russian. Human assessment was carried out on Yandex.Toloka. | Provide a detailed description of the following dataset: RWSD |
RUSSE | WiC: The Word-in-Context Dataset A reliable benchmark for the evaluation of context-sensitive word embeddings.
Depending on its context, an ambiguous word can refer to multiple, potentially unrelated, meanings. Mainstream static word embeddings, such as Word2vec and GloVe, are unable to reflect this dynamic semantic nature. Contextualised word embeddings are an attempt at addressing this limitation by computing dynamic representations for words which can adapt based on context.
Russian SuperGLUE task borrows original data from the Russe project, Word Sense Induction and Disambiguation shared task (2018)
### Task Type
Reading Comprehension. Binary Classification: true/false
### Example
```
{
"idx" : 8,
"word" : "дорожка",
"sentence1" : "Бурые ковровые дорожки заглушали шаги",
"sentence2" : "Приятели решили выпить на дорожку в местном баре",
"start1" : 15,
"end1" : 23,
"start2" : 26,
"end2" : 34,
"label" : false,
"gold_sense1" : 1,
"gold_sense2" : 2
}
```
### How did we collect data?
All text examples were collected from Russe original dataset, already collected by Russian Semantic Evaluation at ACL SIGSLAV. Human assessment was carried out on Yandex.Toloka.
In version 2, we have manually collected in the same format testset. | Provide a detailed description of the following dataset: RUSSE |
TERRa | Textual Entailment Recognition has been proposed recently as a generic task that captures major semantic inference needs across many NLP applications, such as Question Answering, Information Retrieval, Information Extraction, and Text Summarization. This task requires to recognize, given two text fragments, whether the meaning of one text is entailed (can be inferred) from the other text.
### Task Type
RTE (Recognizing Textual Entailment) Sentence Pair Classification - Entailment - Not Entailment
### Example
```
{
"premise": "Автор поста написал в комментарии, что прорвалась канализация.",
"hypothesis": "Автор поста написал про канализацию.",
"label": "entailment",
"idx": "6062"
}
```
### How did we collect data?
All text examples were collected from open news sources and literary magazines, then manually reviewed and supplemented by a human assessment on Yandex.Toloka | Provide a detailed description of the following dataset: TERRa |
MuSeRC | We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. The dataset is the first to study multi-sentence inference at scale, with an open-ended set of question types that requires reasoning skills.
### Task Type
Binary classification by each answer. True/False
### Example
```
{
"id": 397,
"text": "(1) Мужская сборная команда Норвегии по биатлону в рамках этапа Кубка мира в немецком Оберхофе выиграла эстафетную гонку. (2) Вторыми стали французы, а бронзу получила немецкая команда. (3) Российские биатлонисты не смогли побороться даже за четвертое место, отстав от норвежцев более чем на две минуты. (4) Это худший результат сборной России в текущем сезоне. (5) Четвёртыми в Оберхофе стали австрийцы. (6) В составе сборной Норвегии на четвёртый этап вышел легендарный Уле-Эйнар Бьорндален. (7) Впрочем, Норвегия с самого начала гонки была в числе лидеров, успешно проведя все четыре этапа. (8) За сборную России в Оберхофе выступали Иван Черезов, Антон Шипулин, Евгений Устюгов и Максим Чудов. (9) Гонка не задалась уже с самого начала: если на стрельбе из положения лежа Черезов был точен, то из положения стоя он допустил несколько промахов, в результате чего ему пришлось бежать один дополнительный круг. (10) После этого отставание российской команды от соперников только увеличивалось. (11) Напомним, что днем ранее российские биатлонистки выиграли свою эстафету. (12) В составе сборной России выступали Анна Богалий-Титовец, Анна Булыгина, Ольга Медведцева и Светлана Слепцова. (13) Они опередили своих основных соперниц - немок - всего на 0,3 секунды.",
"questions": [
{
"question": "На сколько секунд женская команда опередила своих соперниц?",
"answers": [
{
"text": "Всего на 0,3 секунды.",
"label": 1
},
{
"text": "На 0,3 секунды.",
"label": 1
},
{
"text": "На секунду.",
"label": 0
},
{
"text": "На 0.5 секунд.",
"label": 0
}
],
"idx": 0
}]
}
```
### How did we collect data?
Our challenge dataset contains ∼6k questions for +800 paragraphs across 5 different domains:
* elementary school texts
* news
* fiction stories
* fairy tales
* summary of series
First, we have collected all data from open sources and automatically preprocessed them, filtered only those paragraphs that corresponding to the following parameters: 1) paragraph length 2) number of NER entities 3) number of coreference relations. Afterwords we have check the correct splitting on sentences and numerate each of them.
Next, in Yandex.Toloka we have generated the crowdsource task to get from tolkers information: 1) generate questions 2) generate answers 3) check that to solve every question man need more than one sentence in the text.
### Principles
* We exclude any question that can be answered based on a single sentence from a paragraph.
* Answers are not written in the full match form in the text.
* Answers to the questions are independent from each other. Their number can distinguish. | Provide a detailed description of the following dataset: MuSeRC |
PARus | Choice of Plausible Alternatives for Russian language (PARus) evaluation provides researchers with a tool for assessing progress in open-domain commonsense causal reasoning. Each question in PARus 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%.
### Task Type
Evaluation of commonsense causal reasoning
Sentence Pair Classification: suitable - not suitable
### Example
```
{
"premise": "Гости вечеринки прятались за диваном.",
"choice1": "Это была вечеринка-сюрприз.",
"choice2":"Это был день рождения.",
"question": "cause",
"label": 0,
"idx": 4
}
```
### How did we collect data?
All text examples were collected from open news sources and literary magazines, then manually reviewed and supplemented by a human assessment on Yandex.Toloka | Provide a detailed description of the following dataset: PARus |
RCB | The Russian Commitment Bank is a corpus of naturally occurring discourses whose final sentence contains a clause-embedding predicate under an entailment cancelling operator (question, modal, negation, antecedent of conditional).
### Task Type
RTE (Recognizing Textual Entailment) Sentence Pair Classification - Entailment - Contradiction - Neutral
### Example
```
{
"premise": "Сумма ущерба составила одну тысячу рублей. Уточняется, что на место происшествия выехала следственная группа, которая установила личность злоумышленника. Им оказался местный житель, ранее судимый за подобное правонарушение.",
"hypothesis": "Ранее местный житель совершал подобное правонарушение.",
"verb": "судить",
"negation": "no_negation",
"label": "entailment",
"idx": 269
}
```
### How did we collect data?
All text examples were collected from open news sources and literary magazines, then manually reviewed and supplemented by a human assessment on Yandex.Toloka. | Provide a detailed description of the following dataset: RCB |
LiDiRus | LiDiRus is a diagnostic dataset that covers a large volume of linguistic phenomena, while allowing you to evaluate information systems on a simple test of textual entailment recognition. See more details diagnostics.
### Task Type
RTE (Recognizing Textual Entailment) Sentence Pair Classification - Entailment - Not Entailment
### Example
```
{
'sentence1': "Кошка сидела на коврике.",
'sentence2': "Кошка не сидела на коврике.",
'label': 'not_entailment',
'knowledge': '',
'lexical-semantics': '',
'logic': 'Negation',
'predicate-argument-structure': ''
}
```
### How did we collect data?
All text examples manually translated and adapted from English [SuperGLUE Diagnostics](https://super.gluebenchmark.com/diagnostics) | Provide a detailed description of the following dataset: LiDiRus |
AGORA | AGORA is a synthetic human dataset with high realism and accurate ground truth. It consists of around 14K training and 3K test images by rendering between 5 and 15 people per image using either image-based lighting or rendered 3D environments, taking care to make the images physically plausible and photoreal. In total, AGORA contains 173K individual person crops.
AGORA provides (1) SMPL/SMPL-X parameters and (2) segmentation masks for each subject in images. | Provide a detailed description of the following dataset: AGORA |
synthetic_dataset.h5 | The synethetic dataset (10000 pairs of images and region, 2.95GB) is shared with the code (hdf5 dataset format). | Provide a detailed description of the following dataset: synthetic_dataset.h5 |
Fast Linking Numbers of Loopy Structures Dataset | Copyright (C) 2021 Ante Qu <antequ@cs.stanford.edu>.
This is the dataset for this paper:
Ante Qu and Doug L. James. 2021. Fast Linking Numbers for Topology Verification
of Loopy Structures. ACM Trans. Graph. 40, 4, Article 106 (August 2021),
19 pages. https://doi.org/10.1145/3450626.3459778
In particular, these files correspond to all the closed-loop examples in
Table 1, with the exception that we do not release the chevron 3x3 stitch
pattern because it is not a collection of closed loops. Most files are in the
Binary Curve Collection (BCC) file format. The open-sourced fast linking
numbers [curve verification tool](https://github.com/antequ/fastlinkingnumbers/) released with the paper uses this file format.
The chainmail, rubber band, and woundball examples came originally from Houdini
OBJ files, and we have included their original files as well.
The reference certificate for each [name].bcc is in
reference_certificates/[name].txt. As described in the code release, the first
line indicates the number of curves in the model, and every subsequent line is
a triplet consisting of two curve indices (0-based) and the linking number
between the two curves. For example, "5,6,-2" indicates that curves 5 and 6
have a linking number of -2 between them. Any curve pairs not included in a
triplet have a linking number of zero. If the certificate file only has the
first row, then all its curves are unlinked.
We also included a script in scripts/dataset_referencegen.sh to generate these
certificates. Simply edit the "VERIFYCURVESPATH=" line to point to your
"verifycurves" executable.
Here is a list of the table entries and their corresponding BCC filenames:
1. Alien Sweater (Initial): alien_sweater_init.bcc
2. Alien Sweater (Final): alien_sweater_final.bcc
3. Sheep Sweater: sheep_sweater.bcc
4. Sweater: sweater.bcc
5. Glove: glove.bcc
6. Knit Tube (Initial): knittubeinit.bcc
7. Knit Tube (Final): knittubefinal.bcc
8. Chainmail (Initial): chainmail_init.bcc
9. Chainmail (Final): chainmail_final.bcc
10. Rubber Bands: rubber_bands_final.bcc
11. Double-Helix Ribbon λ=10, 200K Segs: double_helix_ribbon_lambda10_N200K.bcc
12. Double-Helix Ribbon λ=1K, 200K Segs: double_helix_ribbon_lambda1K_N200K.bcc
13. Double-Helix Ribbon λ=10, 20M Segs: double_helix_ribbon_lambda10_N20M.bcc
14. Double-Helix Ribbon λ=1K, 20M Segs: double_helix_ribbon_lambda1K_N20M.bcc
15. Thick Square Link, 500K Segs: thicksquarelink_N500K.bcc
16. Thick Square Link, 4M Segs: thicksquarelink_N4M.bcc
17. Torus λ=1M, 20M Segs: torus_lambda1M_N20M.bcc
18. Torus λ=0, 40M Segs: torus_lambda0_N40M.bcc
19. Woundball ν=1K, 1M Segs: woundball_nu1K_N1M.bcc
20. Woundball ν=10K, 2M Segs: woundball_nu10K_N2M.bcc
Note that some input files are B-Splines, some are uniform Catmull–Rom splines,
and some are polylines. This information is encoded in the BCC files.
knittubebroken.bcc: For the Knit Tube, knittubeinit.bcc comes from simulation
step 0 and knittubefinal.bcc comes from simulation step 13000, and both states
are unlinked and topologically valid. We also added knittubebroken.bcc, which
comes from simulation step 13080, when it has a linkage violation.
rubber_bands_init.bcc: For the Rubber Bands, we also added
rubber_bands_init.bcc with the initial state of the rubber bands.
Interestingly, this configuration has zero potentially linked loop pairs.
The knitted yarn models (1 through 7) are modified from Cem Yuksel's Yarn-level
Cloth Models located at http://www.cemyuksel.com/research/yarnmodels/.
Download the dataset zip [here](https://drive.google.com/file/d/1tTSrzwP92xYxmVXVc6bx3GGEJ0srukp9/view). | Provide a detailed description of the following dataset: Fast Linking Numbers of Loopy Structures Dataset |
AIT-QA | **AIT-QA** is a dataset for Table Question Answering (Table-QA) which is specific to the airline industry. The dataset consists of 515 questions authored by human annotators on 116 tables extracted from public U.S. SEC filings of major airline companies for the fiscal years 2017-2019. It also contains annotations pertaining to the nature of questions, marking those that require hierarchical headers, domain-specific terminology, and paraphrased forms.
Different from the Table QA dataset, the tables in this dataset have more complex layouts. | Provide a detailed description of the following dataset: AIT-QA |
Goal | **Goal** is a novel dataset of football (or 'soccer') highlights videos with transcribed live commentaries in English. As the course of a game is unpredictable, so are commentaries, which makes them a unique resource to investigate dynamic language grounding. | Provide a detailed description of the following dataset: Goal |
JetNet | JetNet is a particle cloud dataset, containing gluon, top quark, light quark jets saved in .csv format. | Provide a detailed description of the following dataset: JetNet |
GPLA-12 | **GPLA-12** is a new acoustic leakage dataset of gas pipelines involving 12 categories over 684 training/testing acoustic signals. The acoustic leakage signals were collected on the basis of an intact gas pipe system with external artificial leakages, and then preprocessed with structured tailoring which are turned into GPLA-12. GPLA-12 dedicates to serve as a feature learning dataset for time-series tasks and classifications. | Provide a detailed description of the following dataset: GPLA-12 |
XAI-Bench | **XAI-Bench** is a suite of synthetic datasets along with a library for benchmarking feature attribution algorithms. Unlike real-world datasets, synthetic datasets allow the efficient computation of conditional expected values that are needed to evaluate ground-truth Shapley values and other metrics. The synthetic datasets released offer a wide variety of parameters that can be configured to simulate real-world data. | Provide a detailed description of the following dataset: XAI-Bench |
riboflavin | The dataset contains 71 samples with (normalized) expression data for 4,088 genes. The response variable is the riboflavin production rate in Bacilluss subtilis. It may be used to construct a graphical model.
Introduced by Buhlmann, P., Kalisch, M., and Meier, L. (2014). High-dimensional statistics
with a view toward applications in biology. Annual Review of Statistics and Its
Application, 1(1), 255–278 | Provide a detailed description of the following dataset: riboflavin |
RuShiftEval | **RuShiftEval** is a manually annotated lexical semantic change dataset for Russian. Its novelty is ensured by a single set of target words annotated for their diachronic semantic shifts across three time periods, while the previous work either used only two time periods, or different sets of target words. | Provide a detailed description of the following dataset: RuShiftEval |
LIVE Livestream | **LIVE Livestream** is a database for Video Quality Assessment (VQA), specifically designed for live streaming VQA research. The dataset is called the Laboratory for Image and Video Engineering (LIVE) Live stream Database. The LIVE Livestream Database includes 315 videos of 45 contents impaired by 6 types of distortions. | Provide a detailed description of the following dataset: LIVE Livestream |
X-CSQA | **X-CSQA** is a multilingual dataset for Commonsense reasoning research, based on [CSQA](csqa). | Provide a detailed description of the following dataset: X-CSQA |
DDPM | The Deception Detection and Physiological Monitoring (**DDPM**) dataset captures an interview scenario in which the interviewee attempts to deceive the interviewer on selected responses. The interviewee is recorded in RGB, near-infrared, and long-wave infrared, along with cardiac pulse, blood oxygenation, and audio. After collection, data were annotated for interviewer/interviewee, curated, ground-truthed, and organized into train/test parts for a set of canonical deception detection experiments. The dataset contains almost 13 hours of recordings of 70 subjects, and over 8 million visible-light, near-infrared, and thermal video frames, along with appropriate meta, audio, and pulse oximeter data. | Provide a detailed description of the following dataset: DDPM |
rSoccer | **rSoccer** is an open-source simulator for the IEEE Very Small Size Soccer and the Small Size League optimized for reinforcement learning experiments. | Provide a detailed description of the following dataset: rSoccer |
VideoMatting108 | **VideoMatting108** is a large-scale video matting and trimap generation dataset with 80 training and 28 validation foreground video clips with ground-truth alpha mattes. | Provide a detailed description of the following dataset: VideoMatting108 |
Vāksañcayaḥ | This Sanskrit speech corpus has more than 78 hours of audio data and contains recordings of 45,953 sentences with a sampling rate of 22KHz. The content is mainly readings of texts spanning over various Śāstras of Saṃskṛtam literature and also includes contemporary stories, radio program, extempore discourse, etc. | Provide a detailed description of the following dataset: Vāksañcayaḥ |
ADNI | Alzheimer's Disease Neuroimaging Initiative (ADNI) is a multisite study that aims to improve clinical trials for the prevention and treatment of Alzheimer’s disease (AD).[1] This cooperative study combines expertise and funding from the private and public sector to study subjects with AD, as well as those who may develop AD and controls with no signs of cognitive impairment.[2] Researchers at 63 sites in the US and Canada track the progression of AD in the human brain with neuroimaging, biochemical, and genetic biological markers.[2][3] This knowledge helps to find better clinical trials for the prevention and treatment of AD. ADNI has made a global impact,[4]
firstly by developing a set of standardized protocols to allow the comparison of results from multiple centers,[4] and secondly by its data-sharing policy which makes available all at the data without embargo to qualified researchers worldwide.[5] To date, over 1000 scientific publications have used ADNI data.[6] A number of other initiatives related to AD and other diseases have been designed and implemented using ADNI as a model.[4] ADNI has been running since 2004 and is currently funded until 2021.[7] | Provide a detailed description of the following dataset: ADNI |
Ambiguous-HOI | Ambiguous-HOI is a challenging dataset containing ambiguous human-object interaction images for HOI detection based on HICO-DET. | Provide a detailed description of the following dataset: Ambiguous-HOI |
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