Omkar2118/datasets huggingface dataset:
MUStARD++

**MUStARD++** is a multimodal sarcasm detection dataset (MUStARD) pre-annotated with 9 emotions. It can be used for the task of detecting the emotion in a sarcastic statement.

Introducing paper: A Multimodal Corpus for Emotion Recognition in Sarcasm
Data for paper "Zone extrapolations in parametric timed automata"

Contains the current version of IMITATOR, all models and necessary scripts to reproduce all experiments on the benchmarks set.

Introducing paper: Zone extrapolations in parametric timed automata
Bike and Car Odometer Dataset ! Speedometer OCR
Full name: datacluster.ai

This dataset consists of odometer or speedometer images of bike and car vehicles.

### **Introduction**
This dataset can be used to detect or recognize odometer readings of the vehicles. Moreover, it can be used to classify the make of the car and bikes. The usecases can in the domain of insurance, repair and OCR.

### **Dataset Features**
- Captured by 4000+ unique users
- Rich in diversity
- Mobile phone view point
- Various lighting conditions
- Digital and Analog Categories
- Vehicle Model Types

### **Dataset Features**
- Classification and detection annotations available
- Multiple category annotations possible
- COCO, PASCAL VOC and YOLO formats

*To download full datasets or to submit a request for your dataset needs, please ping us on **sales@datacluster.ai***
Visit www.datacluster.in to know more.

**Note**:
All the images are manually verified and are contributed by the large contributor base on DataCluster platform
Mobile Phone Dataset | Smartphone & Feature Phone
Full name: datacluster.ai

### **This dataset is collected by DataCluster Labs, India. To download full dataset or to submit a request for your new data collection needs, please drop a mail to:&nbsp;[sales@datacluster.ai](mailto:sales@datacluster.ai)**

This dataset is an extremely challenging set of over 3000+ original Mobile Phone images captured and crowdsourced from over 1000+ urban and rural areas, where each image is **manually reviewed and verified** by computer vision professionals at ****DC Labs.

### **Dataset Features**

- Dataset size   : 3000+
- Captured by  : Over 1000+ crowdsource contributors
- Resolution     : 99% images HD and above (1920x1080 and above)
- Location        : Captured with 600+ cities accross India
- Diversity        : Various lighting conditions like day, night, varied distances, view points etc.
- Device used  : Captured using mobile phones in 2020-2021
- Applications  : Mobile Phone detection, cracked screen detection, etc.

### Available Annotation formats

COCO, YOLO, PASCAL-VOC, Tf-Record

**To download full datasets or to submit a request for your dataset needs, please ping us at [sales@datacluster.ai](sales@datacluster.ai) Visit [www.datacluster.ai](www.datacluster.ai) to know more.**  

**Note**:  
All the images are manually captured and verified by a large contributor base on DataCluster platform
Real world moire pattern classification
Full name: datacluster.ai

This dataset consists of two categories. Real world images and spoof images(Captured from Screen) which shows moire patterns.

### **Introduction**
This dataset can be used to classify real world images and images captured from laptop, mobile phones or TV. The spoof images shows the moire pattern which can help detect fake or spoof personality. This dataset can be used to enhance vision based security algorithms. 

### **Dataset Features**
- Captured by 3000+ unique users
- Rich in diversity
- Mobile phone view point
- Various lighting conditions
- Indoor and Outdoor scene

### **Dataset Features**
- Classification and detection annotations available
- Multiple category annotations possible
- COCO, PASCAL VOC and YOLO formats

*To download full datasets or to submit a request for your dataset needs, please ping us on **sales@datacluster.ai***
Visit www.datacluster.in to know more.

**Note**:
All the images are manually verified and are contributed by the large contributor base on DataCluster platform
Indian Food Image Dataset
Full name: datacluster.ai

This dataset is an extremely challenging set of over 5000+ original India food images captured and crowdsourced from over 800+ urban and rural areas, where each image is **manually reviewed and verified** by computer vision professionals at ****DC Labs.

### **Dataset Features**

- Dataset size   : 5000+
- Captured by  : Over 800+ crowdsource contributors
- Resolution     : 99% images HD and above (1920x1080 and above)
- Location        : Captured with 800+ cities accross India
- Diversity        : Various lighting conditions like day, night, varied distances, view points, dimlight etc.
- Device used  : Captured using mobile phones in 2020-2021
- Usage            : Indian food classification, Dish classification, Food plate detection, etc.

### Available Annotation formats

COCO, YOLO, PASCAL-VOC, Tf-Record

**To download full datasets or to submit a request for your dataset needs, please ping us at [sales@datacluster.ai](sales@datacluster.ai) Visit [www.datacluster.ai](www.datacluster.ai) to know more.**  

**Note**:  
All the images are manually captured and verified by a large contributor base on DataCluster platform
Suitcase/Luggage Dataset Indoor Object Image
Full name: datacluster.ai

This dataset is an extremely challenging set of over 7000+ original Suitcase/Luggage images captured and crowdsourced from over 800+ urban and rural areas, where each image is **manually reviewed and verified** by computer vision professionals at ****DC Labs.

### **Dataset Features**

- Dataset size   : 6000+
- Captured by  : Over 1000+ crowdsource contributors
- Resolution     : 99% images HD and above (1920x1080 and above)
- Location        : Captured with 800+ cities accross India
- Diversity        : Various lighting conditions like day, night, varied distances, view points etc.
- Device used  : Captured using mobile phones in 2021-2022
- Usage            : Luggage detection, suitcase detection, etc.
### Available Annotation formats

COCO, YOLO, PASCAL-VOC, Tf-Record

**To download full datasets or to submit a request for your dataset needs, please ping us at [sales@datacluster.ai](sales@datacluster.ai) Visit [www.datacluster.ai](www.datacluster.ai) to know more.**  

**Note**:  
All the images are manually captured and verified by a large contributor base on DataCluster platform
GlassTemp
Full name: Glass Transition Temperature

The GlassTemp dataset is collected from [Polyinfo](https://polymer.nims.go.jp/en/). It uses monomers as polymer graphs to predict the property of glass transition temperature. The glass transition temperature of the material itself denotes the temperature range over which this glass transition takes place.

Introducing paper: Graph Rationalization with Environment-based Augmentations
MeltingTemp
Full name: Melting Temperature

The MeltingTemp dataset is collected from [Polyinfo](https://polymer.nims.go.jp/en/). It uses monomers as polymer graphs to predict the property of polymer melting temperature.

Introducing paper: Graph Rationalization with Environment-based Augmentations
PolyDensity
Full name: Polymer Density

The PolyDensity is collected from [Polyinfo](https://polymer.nims.go.jp/en/). It uses monomers as polymer graphs to predict the property of polymer density.

Introducing paper: Graph Rationalization with Environment-based Augmentations
$O_2$Perm
Full name: Oxygen Permeability

The $O_2$Perm dataset is created from the [Membrane Society of Australasia portal](https://membrane-australasia.org/msa-activities/polymer-gas-separation-membrane-database/). It uses monomers as polymer graphs to predict the property of oxygen permeability. It has he limited size (595 polymers), which brings great challenges to the property prediction.

Introducing paper: Graph Rationalization with Environment-based Augmentations
Replication Data for: The elastic origins of tail asymmetry

Dataset and Stata codes for replicating Tables 1, 3 and Figures 1-4.

Introducing paper: The elastic origins of tail asymmetry
Motion Blurred and Defocused Dataset
Full name: datacluster.ai

This dataset consists of blurred, noisy and defocused images.

### **Introduction**
Dataset consists of blurred images captured using mobile phones in real-world scenario. Images were captured under wide variety of lighting conditions, weather, indoor and outdoor. This dataset can be used for Image De-noising, Deblurring and noise removal algorithms. This can be also work as robust test set for denoising algorithms.

### **Dataset Features**
- Captured by 3000+ unique users
- Rich in diversity
- Mobile phone view point
- HD Resolution
- Various lighting conditions
- Indoor and Outdoor scene

### **Dataset Features**
- Classification and detection annotations available
- Multiple category annotations possible
- COCO, PASCAL VOC and YOLO formats

*To download full datasets or to submit a request for your dataset needs, please ping us on **sales@datacluster.ai***
Visit www.datacluster.in to know more.

**Note**:
All the images are manually verified and are contributed by the large contributor base on DataCluster platform
Masks Dataset | Unattended Mask Images

This dataset is an extremely challenging set of over 7000+ original Masks images captured and crowdsourced from over 1200+ urban and rural areas, where each image is **manually reviewed and verified** by computer vision professionals at ****DC Labs.

### **Dataset Features**

- Dataset size   : 7000+
- Captured by  : Over 1200+ crowdsource contributors
- Resolution     : 99% images HD and above (1920x1080 and above)
- Location        : Captured with 900+ cities accross India
- Diversity        : Various lighting conditions like day, night, varied distances, view points etc.
- Device used  : Captured using mobile phones in 2020-2021
- Usage            : Mask detection, Mask segregation, Trash Mask detection, etc.
### Available Annotation formats

COCO, YOLO, PASCAL-VOC, Tf-Record

**To download full datasets or to submit a request for your dataset needs, please ping us at [sales@datacluster.ai](sales@datacluster.ai) Visit [www.datacluster.ai](www.datacluster.ai) to know more.**  

**Note**:  
All the images are manually captured and verified by a large contributor base on DataCluster platform
Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set

The PD database consists of training and test files. The training data belongs to 20 PWP (6 female, 14 male) and 20 healthy individuals (10 female, 10 male) who appealed at the Department of Neurology in Cerrahpasa Faculty of Medicine, Istanbul University. From all subjects, multiple types of sound recordings (26 voice samples including sustained vowels, numbers, words and short sentences) are taken. A group of 26 linear and time'“frequency based features are extracted from each voice sample. UPDRS ((Unified Parkinson's Disease Rating Scale) score of each patient which is determined by expert physician is also available in this dataset. Therefore, this dataset can also be used for regression.

After collecting the training dataset which consists of multiple types of sound recordings and performing our experiments, in line with the obtained findings we continued collecting an independent test set from PWP via the same physician's examination process under the same conditions. During the collection of this dataset, 28 PD patients are asked to say only the sustained vowels 'a' and 'o' three times respectively which makes a total of 168 recordings. The same 26 features are extracted from voice samples of this dataset. This dataset can be used as an independent test set to validate the results obtained on training set.
The manifest and store data of 870,515 Android mobile applications

Involves a crawler to collect data from the Google Play store including the application's metadata and APK files. The manifest files were extracted from the APK files and then processed to extract the features. The data set is composed of 870,515 records/apps, and for each app we produced 48 features. The data set was used to built and test two bootstrap aggregating of multiple XGBoost machine learning classifiers. The dataset were collected between April 2017 and November 2018. We then checked the status of these applications on three different occasions; December 2018, February 2019, and May-June 2019. (2022-06-03)

Introducing paper: apk2vec: Semi-supervised multi-view representation learning for profiling Android applications
2BallEllsbergData
Full name: Two Ball Ellsberg Paradox: Representative US Data

Two Ball Ellsberg Paradox: Representative US Data. You can find all the Stata code for data analysis, including commented lines, explanations and step-by-step implementation of ORIV.

Introducing paper: A Two-Ball Ellsberg Paradox: An Experiment