Omkar2118/datasets huggingface dataset:
Box-Jenkins
Full name: Box-Jenkins Gas Furnace Problem

Box-Jenkins gas furnace, a well-known time series forecasting problem
Air Quality Index
Full name: Air Quality Index prediction probelm

The AQI dataset is collected from 12 observing stations around Beijing from year 2013 to 2017. The data is accessible at The University of California, Irvine (UCI) Machine Learning Repository.
32vis

Dataset for the 32 years of IEEE VIS

Introducing paper: Thirty-Two Years of IEEE VIS: Authors, Fields of Study and Citations
mini-Imagenet

mini-Imagenet is proposed by  **Matching Networks for One Shot Learning
. In NeurIPS, 2016**. This dataset consists of 50000 training images and 10000 testing images, evenly
distributed across 100 classes.

Introducing paper: Matching Networks for One Shot Learning
OASIS-1
Full name: Open Access Series of Imaging Studies

The Open Access Series of Imaging Studies (OASIS) is a project aimed at making neuroimaging data sets of the brain freely available to the scientific community. By compiling and freely distributing neuroimaging data sets, we hope to facilitate future discoveries in basic and clinical neuroscience.

OASIS-1 set consists of a cross-sectional collection of 416 subjects aged 18 to 96. For each subject, 3 or 4 individual T1-weighted MRI scans obtained in single scan sessions are included. The subjects are all right-handed and include both men and women. 100 of the included subjects over the age of 60 have been clinically diagnosed with very mild to moderate Alzheimer’s disease (AD). Additionally, a reliability data set is included containing 20 nondemented subjects imaged on a subsequent visit within 90 days of their initial session.
ZJU-MoCap
Full name: ZJU-MoCap Dataset

LightStage is a multi-view dataset, which is proposed in NeuralBody. This dataset captures multiple dynamic human videos using a multi-camera system that has 20+ synchronized cameras. The humans perform complex motions, including twirling, Taichi, arm swings, warmup, punching, and kicking. We provide the SMPL-X parameters recovered with EasyMocap, which contain the motions of body, hand, and face.
RSCD: Large-scale Road Surface Classification Dataset for Autonomous Vehicles

The preview of the road surface states is essential for improving the safety and the ride comfort of autonomous vehicles. This dataset consists of 1 million (240 x 360 pixels) road surface images captured under a wide range of road and weather conditions in China. The original pictures are acquired with a vehicle-mounted camera and then the patches containing only the road surface area are cropped. The images are classified into 27 categories, containing both the friction level, material, and unevenness properties.  The dataset is divided into train-set(~960k samples), validation-set(~20k samples), test-set(~50k samples) . This large-scale dataset is useful for developing vision-based road sensing modules to improve the performance of the driving assistance systems.

More details, please visit Github: https://github.com/ztsrxh/RSCD-Road_Surface_Classification_Dataset
An Extension of XNLI
Full name: An Extension of XNLI

https://github.com/salesforce/xnli_extension

Introducing paper: BERT is Not an Interlingua and the Bias of Tokenization
EBB!

This dataset contains around 5K pairs of aligned images captured using Canon 70D DSLR with low and high apertures, modeling normal photos and photos with bokeh (blur) effect. The height of each image in the dataset is 1024 pixels, the width varies over the images.
ImageNet ctest10k

Colorization validation set for unconditional/conditional colorization tasks. 
Subset of the ImageNet validation images and excludes andy grayscale single-channel images.

Introducing paper: Learning Representations for Automatic Colorization
Semeion
Full name: Semeion Handwritten Digit Data Set

1593 handwritten digits from around 80 persons were scanned, stretched in a rectangular box 16x16 in a gray scale of 256 values.

The dataset was created by Tactile Srl, Brescia, Italy (http://www.tattile.it) and donated in 1994 to Semeion Research Center of Sciences of Communication, Rome, Italy (http://www.semeion.it), for machine learning research.

For any questions, e-mail Massimo Buscema (m.buscema '@' semeion.it) or Stefano Terzi (s.terzi '@' semeion.it)

##Data Set Information:

1593 handwritten digits from around 80 persons were scanned, stretched in a rectangular box 16x16 in a gray scale of 256 values. Then each pixel of each image was scaled into a boolean (1/0) value using a fixed threshold.

Each person wrote on a paper all the digits from 0 to 9, twice. The commitment was to write the digit the first time in the normal way (trying to write each digit accurately) and the second time in a fast way (with no accuracy).

The best validation protocol for this dataset seems to be a 5x2CV, 50% Tune (Train +Test), and completely blind 50% Validation


##Attribute Information:
This dataset consists of 1593 records (rows) and 256 attributes (columns).
Each record represents a handwritten digit, originally scanned with a resolution of 256 grays scale (28).
Each pixel of each original scanned image was first stretched, and after scaled between 0 and 1 (setting to 0 for every pixel whose value was under the value 127 of the grey scale (127 included) and setting to 1 for each pixel whose original value in the grey scale was over 127).

Finally, each binary image was scaled again into a 16x16 square box (the final 256 binary attributes).