Omkar2118/datasets huggingface dataset:
ReDWeb-S

**ReDWeb-S** is a large-scale challenging dataset for Salient Object Detection. It has totally 3179 images with various real-world scenes and high-quality depth maps. The dataset is split into a training set with 2179 RGB-D image pairs and a testing set with the remaining 1000 image pairs.

Source: [https://github.com/nnizhang/SMAC](https://github.com/nnizhang/SMAC)
Image Source: [https://github.com/nnizhang/SMAC](https://github.com/nnizhang/SMAC)

Introducing paper: Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection
redwood-3dscan

A dataset of more than ten thousand 3D scans of real objects. 

Source: [A Large Dataset of Object Scans](/paper/a-large-dataset-of-object-scans)

Introducing paper: A Large Dataset of Object Scans
REFreSD
Full name: Rationalized English-French Semantic Divergences

Consists of English-French sentence-pairs annotated with semantic divergence classes and token-level rationales.

Source: [Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank](/paper/detecting-fine-grained-cross-lingual-semantic)

Introducing paper: Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank
ReINTEL

10,000 news collected from a social network in Vietnam.

Source: [ReINTEL: A Multimodal Data Challenge for Responsible Information Identification on Social Network Sites](/paper/reintel-a-multimodal-data-challenge-for)

Introducing paper: ReINTEL: A Multimodal Data Challenge for Responsible Information Identification on Social Network Sites
Relational Strategies in Customer Service (RSiCS) Dataset

Corpus for improving the quality and relational abilities of Intelligent Virtual Agents (IVAs).

Source: [An Annotated Corpus of Relational Strategies in Customer Service](/paper/an-annotated-corpus-of-relational-strategies)

Introducing paper: An Annotated Corpus of Relational Strategies in Customer Service
Relative Size

The Relative Size dataset contains 486 object pairs between 41 physical objects. Size comparisons are not available for all pairs of objects (e.g. bird and watermelon) because for some pairs humans cannot determine which object is bigger.

Dataset contains only object pairs that people have consistently agreed which one is bigger. Objects appear in 24 pairs on average, window with 13 pairs has the least, and eye with 35 pairs has the most number of comparisons.

Source: [Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects](/paper/are-elephants-bigger-than-butterflies)

Introducing paper: Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects
RELLIS-3D

**RELLIS-3D** is a multi-modal dataset for off-road robotics. It was collected in an off-road environment containing annotations for 13,556 LiDAR scans and 6,235 images. The data was collected on the Rellis Campus of Texas A&M University and presents challenges to existing algorithms related to class imbalance and environmental topography. The dataset also provides full-stack sensor data in ROS bag format, including RGB camera images, LiDAR point clouds, a pair of stereo images, high-precision GPS measurement, and IMU data.

Source: [https://github.com/unmannedlab/RELLIS-3D](https://github.com/unmannedlab/RELLIS-3D)
Image Source: [https://github.com/unmannedlab/RELLIS-3D](https://github.com/unmannedlab/RELLIS-3D)

Introducing paper: RELLIS-3D Dataset: Data, Benchmarks and Analysis
RELX

**RELX** is a benchmark dataset for cross-lingual relation classification in English, French, German, Spanish and Turkish.

Source: [https://github.com/boun-tabi/RELX](https://github.com/boun-tabi/RELX)

Introducing paper: The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification
Rendered Handpose Dataset

Rendered Handpose Dataset contains 41258 training and 2728 testing samples. Each sample provides:

-	RGB image (320x320 pixels)
-	Depth map (320x320 pixels)
-	Segmentation masks (320x320 pixels) for the classes: background, person, three classes for each finger and one for each palm
-	21 Keypoints for each hand with their uv coordinates in the image frame, xyz coordinates in the world frame and a visibility indicator
-	Intrinsic Camera Matrix K

Source: [Learning to Estimate 3D Hand Pose from Single RGB Images](/paper/learning-to-estimate-3d-hand-pose-from-single)
Image Source: [https://lmb.informatik.uni-freiburg.de/resources/datasets/RenderedHandposeDataset.en.html](https://lmb.informatik.uni-freiburg.de/resources/datasets/RenderedHandposeDataset.en.html)

Introducing paper: Learning to Estimate 3D Hand Pose from Single RGB Images
Rendered WB dataset
Full name: Rendered WB dataset

A dataset of over 65,000 pairs of incorrectly white-balanced images and their corresponding correctly white-balanced images.

Source: [When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images](/paper/when-color-constancy-goes-wrong-correcting)

Introducing paper: When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images
RESIDE

A new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. 

Source: [RESIDE](https://sites.google.com/view/reside-dehaze-datasets/reside-standard?authuser=3D0)
Retail50K

A dataset to encourage the community to adapt oriented bounding box (OBB) detectors for more complex environments.

Source: [PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments](/paper/piou-loss-towards-accurate-oriented-object)

Introducing paper: PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments
ReviewQA

ReviewQA is a question-answering dataset based on hotel reviews. The questions of this dataset are linked to a set of relational understanding competencies that a model is expected to master. Indeed, each question comes with an associated type that characterizes the required competency.

Source: [ReviewQA: a relational aspect-based opinion reading dataset](/paper/reviewqa-a-relational-aspect-based-opinion)

Introducing paper: ReviewQA: a relational aspect-based opinion reading dataset
RF signal

This dataset is used for **RF signal** recognition, used to recognize different RF devices based on the signals they transmitted.

Source: [https://arxiv.org/pdf/1908.09931.pdf](https://arxiv.org/pdf/1908.09931.pdf)

Introducing paper: Multi-stage Deep Classifier Cascades for Open World Recognition
DIML/CVl RGB-D Dataset

This dataset contains synchronized RGB-D frames from both Kinect v2 and Zed stereo camera. For the outdoor scene, the authors first generate disparity maps using an accurate stereo matching method and convert them using calibration parameters. A per-pixel confidence map of disparity is also provided. The scenes are captured at various places, e.g., offices, rooms, dormitory, exhibition center, street, road etc., from Yonsei University and Ewha University.

Source: [DIML/CVl RGB-D Dataset](https://dimlrgbd.github.io/)
RGB-DAVIS Dataset

Used to show systematic performance improvement in applications such as high frame-rate video synthesis, feature/corner detection and tracking, as well as high dynamic range image reconstruction.

Source: [Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging](/paper/joint-filtering-of-intensity-images-and)

Introducing paper: Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging
RGB-D Object dataset

The dataset contains 300 objects organized into 51 categories and has been made publicly available to the research community so as to enable rapid progress based on this promising technology.

Source: [RGB-D Object dataset](http://rgbd-dataset.cs.washington.edu/dataset.html)
RICE
Full name: Remote sensing Image Cloud rEmoving

**RICE** is a remote sensing image dataset for cloud removal. The proposed dataset consists of two parts: RICE1 contains 500 pairs of images, each pair has images with cloud and cloudless size of 512*512; RICE2 contains 450 sets of images, each set contains three 512*512 size images, respectively, the reference picture without clouds, the picture of the cloud and the mask of its cloud.

Source: [https://github.com/BUPTLdy/RICE_DATASET](https://github.com/BUPTLdy/RICE_DATASET)

Introducing paper: A Remote Sensing Image Dataset for Cloud Removal
Rijksmuseum Challenge 2014

Dataset used for the challenge to apply computer vision techniques on art objects (paintings, sculptures, drawings etc) from the Rijksmuseum (in Amsterdam, the Netherlands).

Source: [Rijksmuseum Challenge 2014](https://figshare.com/articles/Rijksmuseum_Challenge_2014/5660617)
RiSAWOZ

**RiSAWOZ** is a large-scale multi-domain Chinese Wizard-of-Oz dataset with Rich Semantic Annotations. RiSAWOZ contains 11.2K human-to-human (H2H) multi-turn semantically annotated dialogues, with more than 150K utterances spanning over 12 domains, which is larger than all previous annotated H2H conversational datasets. Both single- and multi-domain dialogues are constructed, accounting for 65% and 35%, respectively. Each dialogue is labelled with comprehensive dialogue annotations, including dialogue goal in the form of natural language description, domain, dialogue states and acts at both the user and system side. In addition to traditional dialogue annotations, it also includes linguistic annotations on discourse phenomena, e.g., ellipsis and coreference, in dialogues, which are useful for dialogue coreference and ellipsis resolution tasks.

Source: [https://github.com/terryqj0107/RiSAWOZ](https://github.com/terryqj0107/RiSAWOZ)

Introducing paper: RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling
RISE

**RISE** is a large-scale video dataset for Recognizing Industrial Smoke Emissions. A citizen science approach was adopted to collaborate with local community members to annotate whether a video clip has smoke emissions. The dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons.

Source: [https://arxiv.org/abs/2005.06111](https://arxiv.org/abs/2005.06111)
Image Source: [https://github.com/CMU-CREATE-Lab/deep-smoke-machine](https://github.com/CMU-CREATE-Lab/deep-smoke-machine)

Introducing paper: Project RISE: Recognizing Industrial Smoke Emissions
RIT-18

The RIT-18 dataset was built for the semantic segmentation of remote sensing imagery. It was collected with the Tetracam Micro-MCA6 multispectral imaging sensor flown on-board a DJI-1000 octocopter. 

The features this dataset include 1) very-high resolution multispectral imagery from a drone, 2) six-spectral VNIR bands, and 3) 18 object classes (plus background) with a severely unbalanced class distribution.

Source: [Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning](/paper/algorithms-for-semantic-segmentation-of)
Image Source: [https://github.com/rmkemker/RIT-18](https://github.com/rmkemker/RIT-18)

Introducing paper: Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning
RLLab Framework

A benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. 

Source: [Benchmarking Deep Reinforcement Learning for Continuous Control](/paper/benchmarking-deep-reinforcement-learning-for)

Introducing paper: Benchmarking Deep Reinforcement Learning for Continuous Control
RMFD
Full name: Real-World Masked Face Dataset

**Real-World Masked Face Dataset** (**RMFD**) is a large dataset for masked face detection.

Source: [https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset](https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset)
Image Source: [https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset](https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset)

Introducing paper: Masked Face Recognition Dataset and Application
Road Scene Graph

A special scene-graph for intelligent vehicles. Different to classical data representation, this graph provides not only object proposals but also their pair-wise relationships. By organizing them in a topological graph, these data are explainable, fully-connected, and could be easily processed by GCNs (Graph Convolutional Networks).

Source: [Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles](/paper/road-scene-graph-a-semantic-graph-based-scene)

Introducing paper: Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles
RoadText-1K

A dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. The dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. 

Source: [RoadText-1K: Text Detection & Recognition Dataset for Driving Videos](/paper/roadtext-1k-text-detection-recognition)

Introducing paper: RoadText-1K: Text Detection & Recognition Dataset for Driving Videos
RoboCupSimData

A large dataset from games of some of the top teams (from 2016 and 2017) in RoboCup Soccer Simulation League (2D), where teams of 11 robots (agents) compete against each other. 

Source: [RoboCupSimData: A RoboCup soccer research dataset](/paper/robocupsimdata-a-robocup-soccer-research)

Introducing paper: RoboCupSimData: A RoboCup soccer research dataset