Omkar2118/datasets huggingface dataset:
SidechainNet

**SidechainNet** is a protein structure prediction dataset that directly extends ProteinNet. Specifically, SidechainNet adds measurements for protein angles and coordinates that describe the complete, all-atom protein structure (backbone and sidechain, excluding hydrogens) instead of the protein backbone alone.

Source: [https://github.com/jonathanking/sidechainnet](https://github.com/jonathanking/sidechainnet)

Introducing paper: SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning
SIDOD

SIDOD is a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications. This dataset contains 144k stereo image pairs that synthetically combine 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset) and flying distractors. 

Source: [SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognition with Distractors](/paper/sidod-a-synthetic-image-dataset-for-3d-object)
Image Source: [https://research.nvidia.com/publication/2019-06_SIDOD%3A-A-Synthetic](https://research.nvidia.com/publication/2019-06_SIDOD%3A-A-Synthetic)

Introducing paper: SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognition with Distractors
Simitate

**Simitate** is a hybrid benchmarking suite targeting the evaluation of approaches for imitation learning. It consists on a dataset containing 1938 sequences where humans perform daily activities in a realistic environment. The dataset is strongly coupled with an integration into a simulator. RGB and depth streams with a resolution of 960×540 at 30Hz and accurate ground truth poses for the demonstrator's hand, as well as the object in 6 DOF at 120Hz are provided. Along with the dataset the 3D model of the used environment and labelled object images are also provided.

Source: [https://arxiv.org/abs/1905.06002](https://arxiv.org/abs/1905.06002)
Image Source: [https://github.com/raphaelmemmesheimer/simitate](https://github.com/raphaelmemmesheimer/simitate)

Introducing paper: Simitate: A Hybrid Imitation Learning Benchmark
SIMMC
Full name: Situated and Interactive Multimodal Conversations

Situated Interactive MultiModal Conversations (**SIMMC**) is the task of taking multimodal actions grounded in a co-evolving multimodal input content in addition to the dialog history. This dataset contains two SIMMC datasets totalling ~13K human-human dialogs (~169K utterances) using a multimodal Wizard-of-Oz (WoZ) setup, on two shopping domains: (a) furniture (grounded in a shared virtual environment) and (b) fashion (grounded in an evolving set of images).

Source: [https://github.com/facebookresearch/simmc](https://github.com/facebookresearch/simmc)
Image Source: [https://github.com/facebookresearch/simmc](https://github.com/facebookresearch/simmc)

Introducing paper: Situated and Interactive Multimodal Conversations
simply-CLEVR

The **simply-CLEVR** dataset aims to provide a benchmark dataset that can be used for transparent quantitative evaluation of explanation methods (aka heatmaps/XAI methods).
It is made of simple Visual Question Answering (VQA) questions, which are derived from the original CLEVR task, and where each question is accompanied by two Ground Truth Masks that serve as a basis for evaluating explanations on the input image.

Source: [https://github.com/ahmedmagdiosman/simply-clevr-dataset](https://github.com/ahmedmagdiosman/simply-clevr-dataset)
Image Source: [https://github.com/ahmedmagdiosman/simply-clevr-dataset](https://github.com/ahmedmagdiosman/simply-clevr-dataset)

Introducing paper: Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI
SIS
Full name: Symmetry inference sentence

Comprises of 400 naturalistic usages of literature-informed verbs spanning the spectrum of symmetry-asymmetry.

Source: [Inferring symmetry in natural language](/paper/inferring-symmetry-in-natural-language)

Introducing paper: Inferring symmetry in natural language
SI-SCORE
Full name: Synthetic Interventions on Scenes for Robustness Evaluation

A synthetic dataset uses for a systematic analysis across common factors of variation.

Source: [On Robustness and Transferability of Convolutional Neural Networks](/paper/on-robustness-and-transferability-of)

Introducing paper: On Robustness and Transferability of Convolutional Neural Networks
SIZER

Dataset of clothing size variation which includes  different subjects wearing casual clothing items in various sizes, totaling to approximately 2000 scans. This dataset includes the scans, registrations to the SMPL model, scans segmented in clothing parts, garment category and size labels. 

Source: [SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing](/paper/sizer-a-dataset-and-model-for-parsing-3d)

Introducing paper: SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing
SketchGraphs

**SketchGraphs** is a dataset of 15 million sketches extracted from real-world CAD models intended to facilitate research in both ML-aided design and geometric program induction.
Each sketch is represented as a geometric constraint graph where edges denote designer-imposed geometric relationships between primitives, the nodes of the graph.

Source: [https://github.com/PrincetonLIPS/SketchGraphs](https://github.com/PrincetonLIPS/SketchGraphs)
Image Source: [https://github.com/PrincetonLIPS/SketchGraphs](https://github.com/PrincetonLIPS/SketchGraphs)

Introducing paper: SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design
ShoeV2
Full name: ShoeV2

**ShoeV2** is a dataset of 2,000 photos and 6648 sketches of shoes. The dataset is designed for fine-grained sketch-based image retrieval.

Source: [Sketch Me That Shoe](/paper/sketch-me-that-shoe)

Introducing paper: Sketch Me That Shoe
Skill2vec

Collects a huge number of job descriptions from Dice.com - one of the most popular career website about Tech jobs in USA. From these job descriptions, skills are extracted for each one by using skills dictionary. Now, the dataset is presented by a list of collections of skills based on job descriptions. After crawling, there are a total of 5GB with more than 1,400,000 job descriptions. From these data, skills are extracted and performed as a list of skills in the same context, the context here includes skills in the same job description.

Source: [Skill2vec: Machine Learning Approach for Determining the Relevant Skills from Job Description](/paper/skill2vec-machine-learning-approach-for)

Introducing paper: Skill2vec: Machine Learning Approach for Determining the Relevant Skills from Job Description
SKU110K-R

**SKU110K-R** is a dataset relabeled with oriented bounding boxes based on SKU110K. It is focused on evaluating oriented and densely packed object detection.

Source: [https://github.com/Anymake/DRN_CVPR2020](https://github.com/Anymake/DRN_CVPR2020)
Image Source: [https://github.com/Anymake/DRN_CVPR2020](https://github.com/Anymake/DRN_CVPR2020)

Introducing paper: Dynamic Refinement Network for Oriented and Densely Packed Object Detection
SLURP
Full name: Spoken Language Understanding Resource Package

A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets.

Source: [SLURP: A Spoken Language Understanding Resource Package](/paper/slurp-a-spoken-language-understanding-1)

Introducing paper: SLURP: A Spoken Language Understanding Resource Package
SMHD
Full name: Self-reported Mental Health Diagnoses

A novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users.

Source: [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](/paper/smhd-a-large-scale-resource-for-exploring)

Introducing paper: SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions
SmokEng

SmokEng is a dataset of 3144 tweets, which are selected based on the presence of colloquial slang related to smoking and analyze it based on the semantics of the tweet. 

Source: [SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text](/paper/smokeng-towards-fine-grained-classification)

Introducing paper: SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text
SMS Spam Collection Data Set

This corpus has been collected from free or free for research sources at the Internet:

- A collection of 425 SMS spam messages was manually extracted from the Grumbletext Web site. This is a UK forum in which cell phone users make public claims about SMS spam messages, most of them without reporting the very spam message received. The identification of the text of spam messages in the claims is a very hard and time-consuming task, and it involved carefully scanning hundreds of web pages.
- A subset of 3,375 SMS randomly chosen ham messages of the NUS SMS Corpus (NSC), which is a dataset of about 10,000 legitimate messages collected for research at the Department of Computer Science at the National University of Singapore. The messages largely originate from Singaporeans and mostly from students attending the University. These messages were collected from volunteers who were made aware that their contributions were going to be made publicly available.
- A list of 450 SMS ham messages collected from Caroline Tag's PhD Thesis.
- the SMS Spam Corpus v.0.1 Big. It has 1,002 SMS ham messages and 322 spam messages.

Source: [SMS Spam Collection Data Set](http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection)
SMS-WSJ
Full name: Spatialized Multi-Speaker Wall Street Journal

Spatialized Multi-Speaker Wall Street Journal (SMS-WSJ) consists of artificially mixed speech taken from the WSJ database, but unlike earlier databases this one considers all WSJ0+1 utterances and takes care of strictly separating the speaker sets present in the training, validation and test sets. 

Source: [SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition](/paper/sms-wsj-database-performance-measures-and)

Introducing paper: SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition
SNLI-VE

Visual Entailment (VE) consists of image-sentence pairs whereby a premise is defined by an image, rather than a natural language sentence as in traditional Textual Entailment tasks. The goal of a trained VE model is to predict whether the image semantically entails the text. **SNLI-VE** is a dataset for VE which is based on the Stanford Natural Language Inference corpus and Flickr30k dataset.

Source: [https://github.com/necla-ml/SNLI-VE](https://github.com/necla-ml/SNLI-VE)

Introducing paper: Visual Entailment: A Novel Task for Fine-Grained Image Understanding
So2Sat LCZ42

So2Sat LCZ42 consists of local climate zone (LCZ) labels of about half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe. This dataset was labeled by 15 domain experts following a carefully designed labeling work flow and evaluation process over a period of six months. 

Source: [So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification](/paper/so2sat-lcz42-a-benchmark-dataset-for-global)
Image Source: [Zhu et al](https://paperswithcode.com/paper/so2sat-lcz42-a-benchmark-dataset-for-global)

Introducing paper: So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification
SOBA
Full name: Shadow-OBject Association

A new dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individual labeled masks.

Source: [Instance Shadow Detection](/paper/instance-shadow-detection)

Introducing paper: Instance Shadow Detection
SoccerData

A dataset of 4562 images of which 4152 images contain a soccer ball.

Source: [Utilizing Temporal Information in Deep Convolutional Network for Efficient Soccer Ball Detection and Tracking](/paper/utilizing-temporal-information-in)

Introducing paper: Utilizing Temporal Information in Deep Convolutional Network for Efficient Soccer Ball Detection and Tracking
SoccerDB

Comprises of 171,191 video segments from 346 high-quality soccer games. The database contains 702,096 bounding boxes, 37,709 essential event labels with time boundary and 17,115 highlight annotations for object detection, action recognition, temporal action localization, and highlight detection tasks. 

Source: [SoccerDB: A Large-Scale Database for Comprehensive Video Understanding](/paper/comprehensive-soccer-video-understanding)

Introducing paper: SoccerDB: A Large-Scale Database for Comprehensive Video Understanding
SoccerNet-v2

A novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production.

Source: [SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](/paper/soccernet-v2-a-dataset-and-benchmarks-for)

Introducing paper: SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
Social-IQ

Social-IQ is an unconstrained benchmark specifically designed to train and evaluate socially intelligent technologies. By providing a rich source of open-ended questions and answers, Social-IQ opens the door to explainable social intelligence. The dataset contains rigorously annotated and validated videos, questions and answers, as well as annotations for the complexity level of each question and answer. Social-IQ contains 1,250 natural in-the-wild social situations, 7,500 questions and 52,500 correct and incorrect answers. 

Source: [Social-IQ: A Question Answering Benchmark for Artificial Social Intelligence](/paper/social-iq-a-question-answering-benchmark-for)

Introducing paper: Social-IQ: A Question Answering Benchmark for Artificial Social Intelligence