Omkar2118/datasets huggingface dataset:
ROSTD
Full name: Real Out-of-Domain Sentences From Task-oriented Dialog

A dataset of 4K out-of-domain (OOD) examples for the publicly available dataset from (Schuster et al. 2019). In contrast to existing settings which synthesize OOD examples by holding out a subset of classes, the examples were authored by annotators with apriori instructions to be out-of-domain with respect to the sentences in an existing dataset. 

Source: [Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented Dialog](/paper/likelihood-ratios-and-generative-classifiers)

Introducing paper: Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented Dialog
Rotowire-Modified

The RotoWire-Modified dataset is a cleaned extension of the RotoWire dataset, with writer information about each document. It contains 2705 samples for training, 532 for validation and 497 for testing.

Source: [https://github.com/aistairc/rotowire-modified](https://github.com/aistairc/rotowire-modified)

Introducing paper: Learning to Select, Track, and Generate for Data-to-Text
RP2K

A new large-scale retail product dataset for fine-grained image classification. Unlike previous datasets focusing on relatively few products, more than 500,000 images of retail products on shelves were collected, belonging to 2000 different products. The dataset aims to advance the research in retail object recognition, which has massive applications such as automatic shelf auditing and image-based product information retrieval.

Source: [RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification](/paper/rp2k-a-large-scale-retail-product-dataset)

Introducing paper: RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification
RPC
Full name: Retail Product Checkout

RPC is a large-scale retail product checkout dataset and collects 200 retail SKUs. The collected SKUs can be divided into 17 meta categories, i.e., puffed food, dried fruit, dried food, instant drink, instant noodles, dessert, drink, alcohol, milk, canned food, chocolate, gum, candy, seasoner, personal hygiene, tissue, stationery.

Source: [RPC: A Large-Scale Retail Product Checkout Dataset](/paper/rpc-a-large-scale-retail-product-checkout)
Image Source: [Wei et al](https://arxiv.org/pdf/1901.07249.pdf)

Introducing paper: RPC: A Large-Scale Retail Product Checkout Dataset
RSDD-Time

RSDD-Time is a dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Additionally, the dataset includes exact temporal spans that relate to the date of diagnosis. 

Source: [RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses](/paper/rsdd-time-temporal-annotation-of-self)

Introducing paper: RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses
RSICD
Full name: Remote Sensing Image Captioning Dataset

The **Remote Sensing Image Captioning Dataset** (**RSICD**) is a dataset for remote sensing image captioning task. It contains more than ten thousands remote sensing images which are collected from Google Earth, Baidu Map, MapABC and Tianditu. The images are fixed to 224X224 pixels with various resolutions. The total number of remote sensing images is 10921, with five sentences descriptions per image.

Source: [https://github.com/201528014227051/RSICD_optimal](https://github.com/201528014227051/RSICD_optimal)
Image Source: [https://github.com/201528014227051/RSICD_optimal](https://github.com/201528014227051/RSICD_optimal)

Introducing paper: Exploring Models and Data for Remote Sensing Image Caption Generation
RTN

A corpus of real-world spoken personal narratives comprising 10,296 narrative clauses from 594 video transcripts. 

Source: [Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types](/paper/exploring-aspects-of-similarity-between)

Introducing paper: Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types
RuBQ
Full name: Russian Knowledge Base Questions

The first Russian knowledge base question answering (KBQA) dataset. The high-quality dataset consists of 1,500 Russian questions of varying complexity, their English machine translations, SPARQL queries to Wikidata, reference answers, as well as a Wikidata sample of triples containing entities with Russian labels. The dataset creation started with a large collection of question-answer pairs from online quizzes. The data underwent automatic filtering, crowd-assisted entity linking, automatic generation of SPARQL queries, and their subsequent in-house verification.

Source: [RuBQ: A Russian Dataset for Question Answering over Wikidata](/paper/rubq-a-russian-dataset-for-question-answering)

Introducing paper: RuBQ: A Russian Dataset for Question Answering over Wikidata
Runway

A runway dataset, designing features suitable for capturing outfit appearance, collecting human judgments of outfit similarity, and learning similarity functions on the features to mimic those judgments.

Source: [Runway](http://tamaraberg.com/runway2realway/)
Ruralscapes

A dataset with high resolution (4K) images and manually-annotated dense labels every 50 frames.

Source: [Semantics through Time: Semi-supervised Segmentation of Aerial Videos with Iterative Label Propagation](/paper/semantics-through-time-semi-supervised)

Introducing paper: Semantics through Time: Semi-supervised Segmentation of Aerial Videos with Iterative Label Propagation
RuSentRel

**RuSentRel** is a corpus of analytical articles translated into Russian texts in the domain of international politics obtained from foreign authoritative sources. The collected articles contain both the author's opinion on the subject matter of the article and a large number of references mentioned between the participants of the described situations. In total, 73 large analytical texts were labeled with about 2000 relations.

Source: [RuSentRel](https://github.com/nicolay-r/RuSentRel)

Introducing paper: Extracting Sentiment Attitudes From Analytical Texts
RUSLAN

RUSLAN is a Russian spoken language corpus for text-to-speech task. RUSLAN contains 22,200 audio samples with text annotations – more than 31 hours of high-quality speech of one person – being one of the largest annotated Russian corpus in terms of speech duration for a single speaker. 

Source: [RUSLAN: Russian Spoken Language Corpus for Speech Synthesis](/paper/ruslan-russian-spoken-language-corpus-for)

Introducing paper: RUSLAN: Russian Spoken Language Corpus for Speech Synthesis
RuStance

Includes Russian tweets and news comments from multiple sources, covering multiple stories, as well as text classification approaches to stance detection as benchmarks over this data in this language.

Source: [Stance Prediction for Russian: Data and Analysis](/paper/stance-prediction-for-russian-data-and)

Introducing paper: Stance Prediction for Russian: Data and Analysis
RWF-2000

A database with 2,000 videos captured by surveillance cameras in real-world scenes. 

Source: [RWF-2000: An Open Large Scale Video Database for Violence Detection](/paper/rwf-2000-an-open-large-scale-video-database)

Introducing paper: RWF-2000: An Open Large Scale Video Database for Violence Detection
RxR
Full name: Room-across-Room

Room-Across-Room (RxR) is a multilingual dataset for Vision-and-Language Navigation (VLN) for Matterport3D environments. In contrast to related datasets such as Room-to-Room (R2R), RxR is 10x larger, multilingual (English, Hindi and Telugu), with longer and more variable paths, and it includes and fine-grained visual groundings that relate each word to pixels/surfaces in the environment.

Source: [Room-Across-Room (RxR) Dataset](https://github.com/google-research-datasets/RxR)

Introducing paper: Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding
S2TLD
Full name: SJTU Small Traffic Light Dataset

**S2TLD** is a traffic light dataset, which contains 5,786 images of approximately 1,080 * 1,920 pixels and 720 * 1,280 pixels. It also contains 5 categories (include red, yellow, green, off and wait on) of 1,4130 instances. The scenes cover a decent variety of road scenes and typical:
* Busy street scenes inner-city,
* Dense stop-and-go traffic
* Strong changes in illumination/exposure
* Flickering/Fluctuating traffic lights
* Multiple visible traffic lights
* Image parts that can be confused with traffic lights (e.g. large round tail lights)

Source: [https://github.com/Thinklab-SJTU/S2TLD](https://github.com/Thinklab-SJTU/S2TLD)
Image Source: [https://github.com/Thinklab-SJTU/S2TLD](https://github.com/Thinklab-SJTU/S2TLD)

Introducing paper: SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing
S3O4D
Full name: Stanford 3D Objects for Disentangling

The data consists of 100,000 renderings each of the Bunny and Dragon objects from the Stanford 3D Scanning Repository. More objects may be added in the future, but only the Bunny and Dragon are used in the paper. Each object is rendered with a uniformly sampled illumination from a point on the 2-sphere, and a uniformly sampled 3D rotation. The true latent states are provided as NumPy arrays along with the images. The lighting is given as a 3-vector with unit norm, while the rotation is provided both as a quaternion and a 3x3 orthogonal matrix.

Source: [S3O4D](https://github.com/deepmind/deepmind-research/tree/master/geomancer#stanford-3d-objects-for-disentangling-s3o4d)

Introducing paper: Disentangling by Subspace Diffusion
SailX

A dataset for grounded language learning that consists of navigational instructions and actions in a maze-like environment.

Source: [A new dataset and model for learning to understand navigational instructions](/paper/a-new-dataset-and-model-for-learning-to)

Introducing paper: A new dataset and model for learning to understand navigational instructions
Salient Closed Boundary Tracking

This dataset contains nine video sequences captured by a webcam for salient closed boundary tracking evaluation. Each sequence is about 30 sec (30 fps) and the frame size is 640×480 (width×height). There are 9598 frames in total. In each sequence, different motion styles such as translation, rotation and viewpoint changing are all performed.

Source: [https://github.com/NathanUA/SalientClosedBoundaryTrackingDataset](https://github.com/NathanUA/SalientClosedBoundaryTrackingDataset)
Image Source: [https://github.com/NathanUA/SalientClosedBoundaryTrackingDataset](https://github.com/NathanUA/SalientClosedBoundaryTrackingDataset)

Introducing paper: Real-Time Salient Closed Boundary Tracking via Line Segments Perceptual Grouping
Salient-KITTI

**Salient-KITTI** is a saliency map prediction dataset based on KITTI.

Source: [https://arxiv.org/pdf/2012.11863.pdf](https://arxiv.org/pdf/2012.11863.pdf)
Image Source: [https://github.com/Saixiaoma/SBA-SLAM](https://github.com/Saixiaoma/SBA-SLAM)

Introducing paper: Salient Bundle Adjustment for Visual SLAM
Salient Object Subitizing Dataset

A salient object subitizing image dataset of about 14K everyday images which are annotated using an online crowdsourcing marketplace. 

Source: [Salient Object Subitizing](/paper/salient-object-subitizing)

Introducing paper: Salient Object Subitizing
SAMM Long Videos

The **SAMM Long Videos** dataset consists of 147 long videos with 343 macro-expressions and 159 micro-expressions. The dataset is FACS-coded with detailed Action Units.

Source: [SAMM Long Videos: A Spontaneous Facial Micro- and Macro-Expressions Dataset](/paper/samm-long-videos-a-spontaneous-facial-micro)

Introducing paper: SAMM Long Videos: A Spontaneous Facial Micro- and Macro-Expressions Dataset
SAMSum

A new dataset with abstractive dialogue summaries.

Source: [SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization](/paper/samsum-corpus-a-human-annotated-dialogue-1)

Introducing paper: SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
San Francisco Landmark Dataset

The San Francisco Landmark Dataset contains a database of 1.7 million images of buildings in San Francisco with ground truth labels, geotags, and calibration data, as well as a difficult query set of 803 cell phone images taken with a variety of different camera phones. The data is originally acquired by vehicle-mounted cameras with wide-angle lenses capturing spherical panoramic images. For all visible buildings in each panorama, a set of overlapping perspective images is generated.

Paper: [D. Chen, G. Baatz, K. Koeser, S. Tsai, R. Vedantham, T. Pylvanainen, K. Roimela, X. Chen, J. Bach, M. Pollefeys, B. Girod, and R. Grzeszczuk, "City-scale landmark identification on mobile devices", IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2011.](https://ieeexplore.ieee.org/document/5995610)
SARA
Full name: StAtutory Reasoning Assessment

A dataset for statutory reasoning in tax law entailment and question answering.

Source: [A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering](/paper/a-dataset-for-statutory-reasoning-in-tax-law)

Introducing paper: A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering
SART

**SART** is a collection of three datasets for Similarity, Analogies and Relatedness for the Tatar language.
The three subsets are:
* Similarity dataset - 202 pairs of words along with averaged human scores of similarity degree between the words (in 0-to-10 scale). For example, "йорт, бина, 7.69".
* Relatedness dataset - 252 pairs of words along with averaged human scores of relatedness degree between the words. For example, "урам, балалар, 5.38".
* Analogies dataset - set of analytical questions of the form A:B::C:D, meaning A to B as C to D, and D is to be predicted. For example, "Әнкара Төркия Париж Франция". Contains 34 categories, and in total 30 144 questions.

Source: [https://github.com/tat-nlp/SART](https://github.com/tat-nlp/SART)

Introducing paper: SART - Similarity, Analogies, and Relatedness for Tatar Language: New Benchmark Datasets for Word Embeddings Evaluation
SatStereo

Provides a set of stereo-rectified images and the associated groundtruthed disparities for 10 AOIs (Area of Interest) drawn from two sources: 8 AOIs from IARPA's MVS Challenge dataset and 2 AOIs from the CORE3D-Public dataset.

Source: [A New Stereo Benchmarking Dataset for Satellite Images](/paper/a-new-stereo-benchmarking-dataset-for)

Introducing paper: A New Stereo Benchmarking Dataset for Satellite Images
SAVOIAS

A visual complexity dataset that compromises of more than 1,400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism. The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics. 

Source: [SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset](/paper/savoias-a-diverse-multi-category-visual)

Introducing paper: SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset
SberQuAD
Full name: Sberbank Question Answering Dataset

A large scale analogue of Stanford SQuAD in the Russian language - is a valuable resource that has not been properly presented to the scientific community. 

Source: [SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis](/paper/sberquad-russian-reading-comprehension)

See [DeepPavlov link](http://files.deeppavlov.ai/datasets/sber_squad-v1.1.tar.gz)

### Model results

| Model config                 | EM (dev)    | F-1 (dev)   |
|------------------------------|-------------|-------------|
|[DeepPavlov RuBERT ](https://github.com/deepmipt/DeepPavlov/blob/0.15.0/deeppavlov/configs/squad/squad_ru_rubert_infer.json)           | 66.30+-0.24 | 84.60+-0.11 |
| [DeepPavlov multilingual BERT](https://github.com/deepmipt/DeepPavlov/blob/0.15.0/deeppavlov/configs/squad/squad_ru_bert_infer.json) | 64.35+-0.39 | 83.39+-0.08 |
| [DeepPavlov R-Net](https://github.com/deepmipt/DeepPavlov/blob/0.15.0/deeppavlov/configs/squad/squad_ru.json)             | 60.62       | 80.04       |

Introducing paper: SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis
SBIC
Full name: Social Bias Inference Corpus

To support large-scale modelling and evaluation with 150k structured annotations of social media posts, covering over 34k implications about a thousand demographic groups.

Source: [Social Bias Frames: Reasoning about Social and Power Implications of Language](/paper/social-bias-frames-reasoning-about-social-and)

Introducing paper: Social Bias Frames: Reasoning about Social and Power Implications of Language
SBU Captions Dataset

A collection that allows researchers to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results.

Source: [Im2Text: Describing Images Using 1 Million Captioned Photographs](/paper/im2text-describing-images-using-1-million)

Introducing paper: Im2Text: Describing Images Using 1 Million Captioned Photographs
SBWCE
Full name: Spanish Billion Word Corpus and Embeddings

This resource consists of an unannotated corpus of the Spanish language of nearly 1.5 billion words, compiled from different corpora and resources from the web; and a set of word vectors (or embeddings), created from this corpus using the word2vec algorithm, provided by the gensim package. These embeddings were evaluated by translating to Spanish word2vec’s word relation test set.

Source: [SBWCE](https://crscardellino.github.io/SBWCE/)