Omkar2118/datasets huggingface dataset:
TREC-10
Full name: TREC-10 Question Classification

A question type classification dataset with 6 classes for questions about a person, location, numeric information, etc. The test split has 500 questions, and the training split has 5452 questions.

Paper: [Learning Question Classifiers](https://www.aclweb.org/anthology/C02-1150/)
Deep Thermal Imaging Dataset

The **Deep Thermal Imaging dataset** consists of two main datasets:

- **DeepTherm I** (Indoor materials) - 15 indoor materials were used to create the dataset DeepTherm I which consists of 14,860 processed thermal images (average count of data for each individual class: 990.7, SD=425.9; 400-600 images of each material per each variable). The dataset was created by recording thermal image sequences in a room with different lighting levels (bright / dark), with/without air-conditioning, different places (on a floor or a desk) and from different perspectives (Figure 5). The spatial temperature patterns were collected from different angles and different distances (between 10 and 50 cm, from the camera lens to the material). The data was collected five times in about 3 weeks. 

- **DeepTherm II** (Outdoor materials) - 17 outdoor materials were targeted. The data collection process produced the DeepTherm II dataset which includes 26,584 labelled thermal images. The average number of collected spatial thermal patterns from each material was 1563.8 (SD=295.3; about 300-500 images of each material per each condition).

Image source: [Cho et al.](https://arxiv.org/pdf/1803.02310v1.pdf)

Source: [Cho et al.](https://arxiv.org/pdf/1803.02310v1.pdf)

Introducing paper: Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
WIT
Full name: Wikipedia-based Image Text

**Wikipedia-based Image Text** (**WIT**) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.

**Key Advantages**

A few unique advantages of WIT:

- The largest multimodal dataset (time of this writing) by the number of image-text examples.
- A massively multilingual (first of its kind) with coverage for over 100+ languages.
- A collection of diverse set of concepts and real world entities.
- Brings forth challenging real-world test sets.

Introducing paper: WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
Unsplash Dataset

The Unsplash Dataset is created by over 200,000 contributing photographers and billions of searches across thousands of applications, uses, and contexts. It contains over 2M Unsplash images.
IDRiD
Full name: Indian Diabetic Retinopathy Image Dataset

Indian Diabetic Retinopathy Image Dataset (IDRiD) dataset consists of typical diabetic retinopathy lesions and normal retinal structures annotated at a pixel level. This dataset also provides information on the disease severity of diabetic retinopathy and diabetic macular edema for each image. This dataset is perfect for the development and evaluation of image analysis algorithms for early detection of diabetic retinopathy.
Fongbe  audio
Full name: Fongbe dataset

Fongbe Data collected by Fréjus A. A LALEYE

This dataset contains Fongbe speech corpus with audio data and transcriptions. 

Source: [Fongbe dataset](https://github.com/laleye/ALFFA_PUBLIC/tree/master/ASR/FONGBE)

Introducing paper: First Automatic Fongbe Continuous Speech Recognition System: Development of Acoustic Models and Language Models
DeepFluoroLabeling-IPCAI2020

This collection contains data and code associated with the IPCAI/IJCARS 2020 paper “Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration.” The data hosted here consists of annotated datasets of actual hip fluoroscopy, CT and derived data from six lower torso cadaveric specimens. Documentation and examples for using the dataset and Python code for training and testing the proposed models are also included. Higher-level information, including clinical motivations, prior works, algorithmic details, applications to 2D/3D registration, and experimental details, may be found in the companion paper which is available at [https://arxiv.org/abs/1911.07042](https://arxiv.org/abs/1911.07042) or [https://doi.org/10.1007/s11548-020-02162-7](https://doi.org/10.1007/s11548-020-02162-7). We hope that this code and data will be useful in the development of new computer-assisted capabilities that leverage fluoroscopy.

Introducing paper: Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration
Lens Flare Dataset

The Lens Flare dataset is an internal dataset for Flare Spot detection used in the paper "Automatic Flare Spot Artifact Detection and Removal in Photographs" by Patricia Vitoria and Coloma Ballester.

The dataset consists of 405 natural images in which a minimum of one flare spot artifact appears. The sources of light can be the sun, light bulbs or specular surfaces, among others. The images have been captured by different cameras with different technical specifications.

Introducing paper: Automatic Flare Spot Artifacts Detection and Removal in Photographs
SARA motion
Full name: Synthetic Actors and Real Actions

Sara motion is a 3D motion dataset, named Synthetic Actors and Real Actions (SARA), for training a model to produce motion embeddings suitable for reasoning about motion similarity. 

The motion sequence data for this dataset was generated by combining 18 different actors (i.e., action performing characters). The characters were rendered in a skeleton shape with Adobe Fuse software. Four action categories were selected (Combat, Adventure, Sport, and Dance) comprising a number of motion variations, where each action has a frame length of 32 or more. There are 4,428 base motions (e.g., dancing, jumping) in the SARA dataset.

Introducing paper: A Body Part Embedding Model With Datasets for Measuring 2D Human Motion Similarity
NTU RGB+D 120 motion similarity

Motion similarity annotations for [NTU RGB+D 120 dataset](https://paperswithcode.com/dataset/ntu-rgb-d-120) to evaluate motion similarity in the real world.

Introducing paper: A Body Part Embedding Model With Datasets for Measuring 2D Human Motion Similarity
BU-BIL
Full name: Boston University Biomedical Image Library

**BU-BIL** is an image library which includes six datasets that represent three imaging modalities and six object types. Providers of the datasets are instructed to choose images that capture the various environmental conditions and imaging noise that arose in their studies. These experts are asked to then select objects from those images that reflect the natural diversity of shape and appearances that these objects can exhibit. The image subregions containing the identified objects are cropped to create the image library. The outcome was a library with 305 objects from 235 images. Authors verify by visual inspection that the image library includes a variety of object appearances, backgrounds, and properties distinguishing objects from the background.

Paper: [How to Collect Segmentations for Biomedical Images? A Benchmark Evaluating the Performance of Experts, Crowdsourced Non-Experts, and Algorithms](https://www.cs.bu.edu/fac/betke/papers/Gurari-etal-WACV-2015.pdf)

Image source: [How to Collect Segmentations for Biomedical Images? A Benchmark Evaluating the Performance of Experts, Crowdsourced Non-Experts, and Algorithms](https://www.cs.bu.edu/fac/betke/papers/Gurari-etal-WACV-2015.pdf)
MTA-KDD'19
Full name: Malware Traffic Analysis Knowledge Dataset 2019

Malware Traffic Analysis Knowledge Dataset 2019 (MTA-KDD'19) is an updated and refined dataset specifically tailored to train and evaluate machine learning based malware traffic analysis algorithms. To generate it, that authors started from the largest databases of network traffic captures available online, deriving a dataset with a set of widely-applicable features and then cleaning and preprocessing it to remove noise, handle missing data and keep its size as small as possible. The resulting dataset is not biased by any specific application (although specifically addressed to machine learning algorithms), and the entire process can run automatically to keep it updated.

Source: [Letteri et al.](http://ceur-ws.org/Vol-2597/paper-14.pdf)

Introducing paper: A Novel Resampling Technique for Imbalanced Dataset Optimization
Cuff-Less Blood Pressure Estimation
Full name: Cuff-Less Blood Pressure Estimation. Pre-processed and cleaned vital signals for cuff-less BP estimation.

##Data Set Information:

The main goal of this data set is providing clean and valid signals for designing cuff-less blood pressure estimation algorithms. The raw electrocardiogram (ECG), photoplethysmograph (PPG), and arterial blood pressure (ABP) signals are originally collected from the physionet.org and then some preprocessing and validation performed on them. (For more information about the process please refer to our paper)

##Attribute Information:

This database consists of a cell array of matrices, each cell is one record part. 
In each matrix each row corresponds to one signal channel:

1: PPG signal, FS=125Hz; photoplethysmograph from fingertip

2: ABP signal, FS=125Hz; invasive arterial blood pressure (mmHg)

3: ECG signal, FS=125Hz; electrocardiogram from channel II

Note: dataset is splitted to multiple parts to make it easier to load on machines with low memory. Each cell is a record. There might be more than one record per patient (which is not possible to distinguish). However, records of the same patient appear next to each other. N-fold cross test and train is suggested to reduce the chance of trainset being contaminated by test patients.
POTUS Corpus

The **POTUS Corpus** is a Database of Weekly Addresses for the Study of Stance in Politics and Virtual Agents.

One of the main challenges in the field of Embodied Conversational Agent (ECA) is to generate socially believable agents. The common strategy for agent behaviour synthesis is to rely on dedicated corpus analysis. Such a corpus is composed of multimedia files of socio-emotional behaviors which have been annotated by external observers. The underlying idea is to identify interaction information for the agent’s socio-emotional behavior by checking whether the intended socio-emotional behavior is actually perceived by humans. Then, the annotations can be used as learning classes for machine learning algorithms applied to the social signals. This paper introduces the POTUS Corpus composed of high-quality audio-video files of political addresses to the American people. Two protagonists are present in this database. First, it includes speeches of former president Barack Obama to the American people. Secondly, it provides videos of these same speeches given by a virtual agent named Rodrigue. The ECA reproduces the original address as closely as possible using social signals automatically extracted from the original one. Both are annotated for social attitudes, providing information about the stance observed in each file. It also provides the social signals automatically extracted from Obama’s addresses used to generate Rodrigue’s ones.

Introducing paper: The POTUS Corpus, a Database of Weekly Addresses for the Study of Stance in Politics and Virtual Agents
ImageNet VIPriors subset

The training and validation data are subsets of the training split of the Imagenet 2012. The test set is taken from the validation split of the Imagenet 2012 dataset. Each data set includes 50 images per class.

Introducing paper: VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges
BiRD
Full name: Bigram Relatedness Dataset

**Bigram Relatedness Dataset** (**BiRD**) is a large, fine-grained, bigram relatedness dataset, using a comparative annotation technique called Best Worst Scaling. Each of BiRD's 3,345 English term pairs involves at least one bigram. BiRD is made freely available to foster further research on how meaning can be represented and how meaning can be composed.

Image source: [http://saifmohammad.com/WebPages/BiRD.html](http://saifmohammad.com/WebPages/BiRD.html)

Introducing paper: Big BiRD: A Large, Fine-Grained, Bigram Relatedness Dataset for Examining Semantic Composition
Shiny dataset

The shiny folder contains 8 scenes with challenging view-dependent effects used in our paper. We also provide additional scenes in the shiny_extended folder. 
The test images for each scene used in our paper consist of one of every eight images in alphabetical order.

Each scene contains the following directory structure:
```
  scene/
    dense/
      cameras.bin
      images.bin
      points3D.bin
      project.ini
    images/
      image_name1.png
      image_name2.png
      ...
      image_nameN.png
    images_distort/
      image_name1.png
      image_name2.png
      ...
      image_nameN.png
    sparse/
      cameras.bin
      images.bin
      points3D.bin
      project.ini
    database.db
    hwf_cxcy.npy
    planes.txt
    poses_bounds.npy
```

- dense/ folder contains COLMAP's output [1] after the input images are undistorted.
- images/ folder contains undistorted images. (We use these images in our experiments.)
- images_distort/ folder contains raw images taken from a smartphone.
- sparse/ folder contains COLMAP's sparse reconstruction output [1].

Our poses_bounds.npy is similar to the LLFF[2] file format with a slight modification. This file stores a Nx14 numpy array, where N is the number of cameras. Each row in this array is split into two parts of sizes 12 and 2. The first part, when reshaped into 3x4, represents the camera extrinsic (camera-to-world transformation), and the second part with two dimensions stores the distances from that point of view to the first and last planes (near, far). These distances are computed automatically based on the scene’s statistics using LLFF’s code. (For details on how these are computed, see [this code](https://git.io/JqLKF)) 

hwf_cxcy.npy stores the camera intrinsic (height, width, focal length, principal point x, principal point y) in a 1x5 numpy array.

planes.txt stores information about the MPI planes. The first two numbers are the distances from a reference camera to the first and last planes (near, far). The third number tells whether the planes are placed equidistantly in the depth space (0) or inverse depth space (1). The last number is the padding size in pixels on all four sides of each of the MPI planes. I.e., the total dimension of each plane is (H + 2 * padding, W + 2 * padding).


References:

- [1]: [COLMAP structure from motion (Schönberger and Frahm, 2016)](https://colmap.github.io/).
- [2]: [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines (Mildenhall et al., 2019)](https://arxiv.org/abs/1905.00889).

Introducing paper: NeX: Real-time View Synthesis with Neural Basis Expansion