Omkar2118/datasets huggingface dataset:
QuickDraw-Extended

Consists of 330,000 sketches and 204,000 photos spanning across 110 categories.

Source: [Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval](/paper/doodle-to-search-practical-zero-shot-sketch)

Introducing paper: Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval
Quizbowl

Consists of multiple sentences whose clues are arranged by difficulty (from obscure to obvious) and uniquely identify a well-known entity such as those found on Wikipedia.

Source: [Quizbowl: The Case for Incremental Question Answering](/paper/quizbowl-the-case-for-incremental-question)

Introducing paper: Quizbowl: The Case for Incremental Question Answering
Qulac

A dataset on asking Questions for Lack of Clarity in open-domain information-seeking conversations. **Qulac** presents the first dataset and offline evaluation framework for studying clarifying questions in open-domain information-seeking conversational search systems.

Source: [https://github.com/aliannejadi/qulac](https://github.com/aliannejadi/qulac)
Image Source: [https://github.com/aliannejadi/qulac](https://github.com/aliannejadi/qulac)

Introducing paper: Asking Clarifying Questions in Open-Domain Information-Seeking Conversations
RAD
Full name: RELEVANCE AND DIVERSITY DATASET

The dataset is useful for query-adaptive video summarization and annotated with diversity and query-specific relevance labels. 

Source: [Query-adaptive Video Summarization via Quality-aware Relevance Estimation](/paper/query-adaptive-video-summarization-via)

Introducing paper: Query-adaptive Video Summarization via Quality-aware Relevance Estimation
RADIATE
Full name: RAdar Dataset In Adverse weaThEr

**RADIATE** (**RAdar Dataset In Adverse weaThEr**) is new automotive dataset created by Heriot-Watt University which includes Radar, Lidar, Stereo Camera and GPS/IMU.
The data is collected in different weather scenarios (sunny, overcast, night, fog, rain and snow) to help the research community to develop new methods of vehicle perception.
The radar images are annotated in 7 different scenarios: Sunny (Parked), Sunny/Overcast (Urban), Overcast (Motorway), Night (Motorway), Rain (Suburban), Fog (Suburban) and Snow (Suburban). The dataset contains 8 different types of objects (car, van, truck, bus, motorbike, bicycle, pedestrian and group of pedestrians).

Source: [https://github.com/marcelsheeny/radiate_sdk](https://github.com/marcelsheeny/radiate_sdk)
Image Source: [https://github.com/marcelsheeny/radiate_sdk](https://github.com/marcelsheeny/radiate_sdk)

Introducing paper: RADIATE: A Radar Dataset for Automotive Perception in Bad Weather
RadioTalk

**RadioTalk** is a corpus of speech recognition transcripts sampled from talk radio broadcasts in the United States between October of 2018 and March of 2019. The corpus is intended for use by researchers in the fields of natural language processing, conversational analysis, and the social sciences. The corpus encompasses approximately 2.8 billion words of automatically transcribed speech from 284,000 hours of radio, together with metadata about the speech, such as geographical location, speaker turn boundaries, gender, and radio program information.

Source: [https://github.com/social-machines/RadioTalk](https://github.com/social-machines/RadioTalk)

Introducing paper: RadioTalk: a large-scale corpus of talk radio transcripts
RAF-ML
Full name: Real-world Affective Faces Multi Label

Real-world Affective Faces Multi Label (RAF-ML) is a multi-label facial expression dataset with around 5K great-diverse facial images downloaded from the Internet with blended emotions and variability in subjects' identity, head poses, lighting conditions and occlusions. During annotation, 315 well-trained annotators are employed to ensure each image can be annotated enough independent times. And images with multi-peak label distribution are selected out to constitute the RAF-ML.

RAF-ML provides 4908 number of real-world images with blended emotions, 6-dimensional expression distribution vector for each image, 5 accurate landmark locations and 37 automatic landmark locations, and baseline classifier outputs for multi-label emotion recognition.

Source: [Real-world Affective Faces Multi Label](http://whdeng.cn/RAF/model2.html)
RainNet

**RainNet** is a real (non-simuated) large-scale spatial precipitation downscaling dataset that contains 62,424 pairs of low-resolution and high-resolution precipitation maps for 17 years. Contrary to simulated data, this real dataset covers various types of real meteorological phenomena (e.g., Hurricane, Squall, etc.), and shows the physical characters - Temporal Misalignment, Temporal Sparse and Fluid Properties - that challenge the downscaling algorithms.

Source: [https://github.com/neuralchen/RainNet](https://github.com/neuralchen/RainNet)
Image Source: [https://github.com/neuralchen/RainNet](https://github.com/neuralchen/RainNet)

Introducing paper: RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling
RareAct

**RareAct** is a video dataset of unusual actions, including actions like “blend phone”, “cut keyboard” and “microwave shoes”. It aims at evaluating the zero-shot and few-shot compositionality of action recognition models for unlikely compositions of common action verbs and object nouns. It contains 122 different actions which were obtained by combining verbs and nouns rarely co-occurring together in the large-scale textual corpus from HowTo100M, but that frequently appear separately.

Source: [https://github.com/antoine77340/RareAct](https://github.com/antoine77340/RareAct)
Image Source: [https://github.com/antoine77340/RareAct](https://github.com/antoine77340/RareAct)

Introducing paper: RareAct: A video dataset of unusual interactions
RarePlanes Dataset

The dataset specifically focuses on the value of synthetic data to aid computer vision algorithms in their ability to automatically detect aircraft and their attributes in satellite imagery. Although other synthetic/real combination datasets exist, RarePlanes is the largest openly-available very-high resolution dataset built to test the value of synthetic data from an overhead perspective. Previous research has shown that synthetic data can reduce the amount of real training data needed and potentially improve performance for many tasks in the computer vision domain. The real portion of the dataset consists of 253 Maxar WorldView-3 satellite scenes spanning 112 locations and 2,142 km^2 with 14,700 hand-annotated aircraft. 

Source: [RarePlanes: Synthetic Data Takes Flight](/paper/rareplanes-synthetic-data-takes-flight)

Introducing paper: RarePlanes: Synthetic Data Takes Flight
RAVEN-FAIR

**RAVEN-FAIR** is a modified version of the RAVEN dataset.

Source: [https://github.com/yanivbenny/RAVEN_FAIR](https://github.com/yanivbenny/RAVEN_FAIR)

Introducing paper: Scale-Localized Abstract Reasoning
ReCO

A human-curated ChineseReading Comprehension dataset on Opinion. The questions in ReCO are opinion based queries issued to the commercial search engine. The passages are provided by the crowdworkers who extract the support snippet from the retrieved documents. 

Source: [ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion](/paper/reco-a-large-scale-chinese-reading)

Introducing paper: ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion
RED
Full name: Real Embodied Dataset

The **Real Embodied Dataset** (**RED**) is a computer vision large-scale dataset for grasping in cluttered scenes. It contains complete segmentation masks for partially occluded objects, with their order of occlusion.

Source: [https://arxiv.org/pdf/2004.13358.pdf](https://arxiv.org/pdf/2004.13358.pdf)
Image Source: [https://arxiv.org/pdf/2004.13358.pdf](https://arxiv.org/pdf/2004.13358.pdf)

Introducing paper: Transferable Active Grasping and Real Embodied Dataset